Advances in Agronomy, Volume 67

Advisory Board Martin Alexander

Ronald L. Phillips

Cornell University

University of Minnesota

Kenneth J. Frey

Larry P. wilding

Iowa State University

Texas A&M University

Prepared in cooperation with the

American Society of Agronomy Monograpbs Committee Jon Bartels Jerry M. Bigham Jerry L. Hatfield David M. Kral

Diane E. Stott, Chair Linda S. Lee David M. Miller Matthew J. Mom Donald C. Reicosky John H. Rechcigl

Wayne I? Robarge Dennis E. Rolston Richard Shbles JeffreyJ. Volenec

Edited by

Donald L. Sparks Department of Plant and Soil Sciences University of Delaware Newark, Delaware

ACADEMIC PRESS SanDiego London Boston NewYork Sydney Tokyo Toronto

This book is printed on acid-free paper. 63 Copyright 0 1999 by ACADEMIC PRESS All Rights Reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the Publisher. The appearance of the code at the bottom of the first page of a chapter in this book indicates the Publisher’s consent that copies of the chapter may be made for personal or internal use of specific clients. This consent is given on the condition, however, that the copier pay the stated per copy fee through the Copyright Clearance Center, Inc. (222 Rosewood Drive, Danvers, Massachusetts 01923), for copying beyond that permitted by Sections 107 or 108 of the U.S.Copyright Law. This consent does not extend to other kinds of copying, such as copying for general distribution, for advertising or promotional purposes, for creating new collective works, or for resale. Copy fees for pre-1999 chapters are as shown on the title pages. If no fee code appears on the title page, the copy fee is the same as for current chapters. 0065-2113/99 $30.00

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Contents CONTRIBUTORS ........................................... PREFACE .................................................

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ASPECTS OF PRECISION AGRICULTURE Francis J. Pierce and Peter Nowak I. Introduction.. ............................................

II. Overview of the Basic Components of Precison Farming. ......... III. Conclusions .............................................. References ...............................................

2 5 65 67

SURFACE CHARGE AND SOLUTE INTERACTIONSINSOILS N. S. Bolan, R. Naidu, J. K. Syers, and R. W. Tillman I. Introduction.. ............................................ II. Types of Electrical Surface Charge. ........................... III. Development of Surface Charge. ............................. N. Components of Surface Charge .............................. V. Solution-Surface Interface .................................. VI. Concepts of Point of Zero Charge ............................ VII. Measurement of Surface Charge. ............................. W I . Factors Affecting Surface Charge ............................. M. Effect of Surface Charge on Soil Properties. .................... X. Manipulation of Surface Charge to Control Solute Interactions. . . . . XI. Conclusions and Future Research Needs ....................... References ...............................................

88 90 90 96 97 104 107 112 120 126 130 131

ALLELOPATHY: PRINCIPLES, PROCEDURES, PROCESSES, AND PROMISES FOR BIOLOGICAL CONTROL Inderjit and K. Irwin Keating I. Introduction. ............................................. 11. SomeConcerns.. ......................................... III. Allelopathy in Agroecosystems ...............................

142 145 148

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CONTENTS

W. Factors Influencing Allelopathy .............................. v. Secondary Metabolites with Allelopathic Potential . . . . . . . . . . . . . . . VI. Mechanisms of Action of Allelopathic Chemicals . . . . . . . . . . . . . . . . w. Allelopathic Growth Stimulation ............................. vm. Roles for Allelopathy in Biocontrol Programs ................... Ix. Additional Comments ...................................... X . Concluding Remarks ....................................... References ...............................................

166 181 184 189 190 195 206 207

TURFGRASS MOLECULAR GENETICIMPROVEMENT FOR ABIOTI~/EDAPHIC STRESS RESISTANCE

R . R. Duncan and R. N. Carrow I. Introduction ..............................................

TI. Molecular Genetic Improvement ............................. III. Enhancement Strategy for Multiple-Stress Resistance ............ W. Summary ................................................ References ...............................................

INDEX...................................................

233 235 275 282 283

307

Contributors

Numbers in p n t h e s z s indicate the pages on which the authors' conbibutions begin.

N. S. BOLAN (87), Department of Soil Science, Massq University, Palmerston North $301, New Zealand R. N . CARROW (233), Department of Crop and Soil Sciences, University of Georgia at Gnfin, &fin, Georgia 30223 R. R. DUNCAN (23 3), Department of Crop and Soil Sciences, Universityof Georgia at &fin, &fin, Georgia 30223 INDERJIT (141),Department ofAgriCultzlra1Sciences (weed Science), The Royal Veterinury and Agricultral Univenity, DK-1871 Freabiksberg C., Copenhagen, Denmark K. IRWIN KEATING (141),Department ofEnvironmenta1Science, Rutgers, The State University $New Jm-ey,New Brunswick, New 'jersey 08903 R. NAIDU (87), CSIRO Land and Water, Glen Omond 5064, South Australia, Australia PETER NOWAK (l), Department of Rural Sociology, University of Wisconsin, Madison, Wisconsin 53706 FRANCISJ . PIERCE (I),Department of Crop and Soil Scimes, Michigan State University, East Lansing, Michigan 48824 J. K. SYERS (87), Department of Agrinrltzlral and Environmental Science, University of New Castle upon Tpe, New Castle upon Tpe NE1 7RU, United Kingdom R. W. TILLMAN (87), Department ofsoil Science, Massq University, Palmnston North 5301, New Zealand

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Preface

Volume 67 contains four comprehensiveand timely reviews of topics that should be of great interest to professionals and students in crop and soil sciences. Chapter 1 addresses one of the most active areas in agronomic research-precision agriculture. All aspects of the topic, including technologies, management, and economic and environmental impacts, are discussed. Chapter 2 is a thoughtful review of surface charge and solute interactions in soils. In addition to a theoretical treatment of the topic, practical applications,including surface charge effects on solute interactions and dispersiodflocculation and manipulation of surface charge by amendment additions, are included. Chapter 3 is a useful review of a topic of great interest to agronomists-allelopathy. Principles, procedures, processes, and promises for biological control are discussed. Chapter 4 thoroughly covers advances in the use of molecular genetics to enhance abioticledaphic stress resistance in turfgrass. Many thanks to the authors for their first-rate reviews.

DONALD L. SPARKS

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ASPECTS OF PRECISION AGRICULTURE Francis J. Pierce' and Peter Nowak* 'Department of Crop and Soil Sciences Michigan State University East Lansing, Michigan 48824 2Department of Rural Sociology University of Wisconsin Madison, Wisconsin 53706

I. Introduction A. Definition of Precision Agriculture B. Intuitive Appeal 11. Overview of the Basic Components of Precision Fanning A. The Enabling Technologies B. Steps in Precision Agriculture HI. Conclusions References

Precision agriculture is the application of technologies and principles to manage spatial and temporal variabilityassociated with all aspects of agricultural production for the purpose of improvingcrop performance and environmental quality. Success in precision agriculture is related to how well it can be applied to assess, manage, and evaluate the space-time continuum in crop production. This theme is used here to assess the current and potential capabilities of precision agriculture. Precision agriculture is technology enabled. It is through the integration of specific technologies that the potential is created to assess and manage variability at levels of detail never before obtainable and, when done correctly,at levels of quality never before achieved.The agronomic feasibility of precision agriculture has been intuitive, depending largely on the application of traditional management recommendations at finer scales, although new approaches are appearing. The agronomic success of precision agriculture has been limited and inconsistent although quite convincing in some cases, such as N management in sugar beet (Bera vulgaris L.).Our analysis suggests prospects for current precision management increase as the degree of spatial dependence increases, but the degree of difficulty in achieving precision management increases with temporal variance. Thus, management parameters with high spatial dependence and low temporal variance (e.g.,

I A&mctx in Agronomy, Volume 67

Copyright8 1999 by Academic Prea. All rights of reproduction in m y farm reserved. 0065-21 13/99 $30.00

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FRANCIS J. PIERCE AND PETER NOWAK liming, P, and K)will be more easily managed precisely than those with large temporal variance (e.g.. mobile insects). The potential for economic, environmental, and social benefits of precision agriculture is complex and largely unrealized because the spacetime continuum of crop production has not been adequately addressed. 0 1999 Academic Press

I. INTRODUCTION It would be a simple matter to describe the earth’s surface if it were the same everywhere. The environment,however, is not like that: there is almost endless variety. -Webster and Oliver ( I 990) The quote by Webster and Oliver (1990) is particularly applicable because precision agriculture is concerned with the management of variability in the dimensions of both space and time. Without variability, the concept of precision agriculture has little meaning (Mulla and Schepers, 1997) and would never have evolved. It appears that any component of production agriculture-from natural resources to plants, production inputs, farm machinery, and farm operators-that is variable in some way is included in the realm of precision agriculture. Aspects of precision agriculture, therefore, encompass a broad array of topics, including variability of the soil resource base, weather, plant genetics, crop diversity, machinery performance, and most physical, chemical, and biological inputs used in the production of a crop, whether natural or synthetic. By necessity, these aspects are all framed within the context of the socioeconomicaspects of production agriculture because to be successful on the farm, precision agriculture must fit the needs and capabilities of the farmer (Nowak, 1997) and must be profitable (Lowenberg-DeBoer and Swinton, 1997). Bell et al. (1995) state correctly that efforts toward precision agricultural management should recognize that the factors affecting crop yields and environmental sensitivity vary in both space and time. Managing soils and crops in space and time is the sustainable management principle for the twenty-first century, a principle exemplified by farming by soilscapes, managing zones within the field, and managing the noncrop period (Pierce and Lal, 1991). The unifying theme of this chapter is that success in precision agriculture is directly related to how well it can be applied to manage the space-time continuum in crop production. We postulate that prospects for precision management increase as the degree of spatial dependence increases, but the degree of difficulty in achieving precision management increases with temporal variance. Thus, for management parameters that vary spatially, those with high temporal correlations (e.g., liming) will be more easily man-

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aged with precision agriculture than those with large temporal variance (e.g., mobile insects). Within a given management parameter, the success to date of precision management is to a large extent determined by the degree to which the spatial variability is temporally stable. This chapter provides an overview of precision agriculture and an assessment of its current state and its potential to improve crop performance and environmental quality in production agriculture. In this chapter, we define precision agriculture, explore the technological capabilities that enable it, assess its agronomic feasibility and environmental efficacy, and evaluate its performance to date relative to economic and social impacts. The chapter concludes with an analysis to identify needed developments in precision agriculture and we provide some thoughts for a future research agenda. Given the expansive nature of precision agriculture coupled with space constraints, we attempt to synthesize the important aspects of precision agriculture while guiding the reader to the growing volume of literature on the subject. Readers seeking more detail are referred to the following major publications related to precision agriculture: Auernhammer ( 1994),American Society of Agricultural Engineers (ASAE) (1991), BIOS (1997), Lake et al. (1997), National Research Council (NRC) (1997), Pierce and Sadler (1997), Robert et al. (1993, 1995, 1996), Sawyer (1994, Schueller (1992), Stafford (1996b), and Sudduth (1998). We are aware of the rapid rate of change in precision agriculture and the inadequacies this causes in an overview of this nature.

A. DEFINITION OF PRECISION AGRICULTURE Currently, no precision agricultural systems exist; rather, various components of traditional crop management systems have been addressed separately regarding their potential for site-specific management, perhaps most notably soil fertility (Lowenberg-DeBoer and Swinton, 1997).The state of precision agriculture from a systems perspective is analogous to the early days of no-tillage crop production. Technology became available in the 1960sto plant seeds in untilled soil, but it was not until the many aspects of crop production were adequately addressed under lack of tillage and crop residue management, including the management of fertility and pests, that successful no-tillage systems were developed and implemented (Blevins et al., 1998). The adoption of no-tillage did not proceed at a significant rate until the 1980s when the integration of appropriate technologies and public policies supported its dissemination to farmers (Allmaras et al., 1998; Larson et al., 1998;Now& and Korsching, 1998).In a similar fashion, although certain technologies in the early days of precision agriculture allowed for the variable application of nutrients and pesticides, there did not exist a thorough understanding of how soil fertility and pests varied in space and time. Most important, explanations were lacking on what specifically caused the variability so that appropriate inputs

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could be matched to site-specific conditions. Today, farmers are adopting individual components of precision agriculture on the farm but a distinctive precision farming system has not yet evolved. Technological developments continue to occur and as a result of ongoing research a better understanding of underlying processes is being developed but a true system has not emerged. Therefore, any definition of precision agriculture can at best be considered only operational. Since the mid-l980s, a host of terms have been used to describe the concept of precision agriculture, including farming by the foot (Reichenberger and Russnogle, 1989), farming by soil (Carr et al., 1991; Larson and Robert, 1991). variable rate technology (VRT) (Sawyer, 1994), spatially variable, precision, prescription, or site-specific crop production (Schueller, 1991), and site-specific management (Pierce and Sadler, 1997). All these terms, however, have in common the concept of managing variability at scales that are within fields. As Stafford (1996b; p. 595) states, precision agriculture involves “the targeting of inputs to arable crop production according to crop requirements on a localized basis.” Thus, the intent of precision agriculture is to match agricultural inputs and practices to localized conditions within a field to do the right thing, in the right place, at the right time, and in the right way (Pierce ef al., 1994). A recent report of a National Research Council, Board on Agriculture Committee defined precision agriculture as “a management strategy that uses information technologies to bring data from multiple sources to bear on decisions associated with crop production” (NRC, 1997; p. 17). While the NRC definition raises important informational dimensions of precision agriculture, it fails to emphasize the basic premise of precision agriculture-the management of spatial and temporal variability. In this chapter, we use the following definition of precision agriculture as the basis of our discussions: Precision agriculture is the application of technologies and principles to manage spatial and temporal variability associated with all aspects of agricultural production for the purpose of improving crop performance and environmental quality. We provide a final note on the word precision because there is sure to be confusion regarding its meaning in precision agriculture versus its use in statistics. The term precision refers to the quality or state of being precise, where precise means minutely exact, a term synonymous with correct. Precision agriculturerefers to exactness and implies correctness or accuracy in any aspect of production. In statistics, however, precision is the closeness of repeated measurements of the same quantity to each other, whereas accuracy is the closeness of a measured or computed value to its true value (Sokal and Rohlf, 1995). In measurements, accuracy is synonymous with correctness (i.e., validity), whereas precision refers to reproducibility (i.e., reliability). Thus, something can be precise but not accurate. Another matter is measurement precision implied by number of digits reported for a given measurement. The nature of computers makes it easy to imply more precision than was possible in various aspects of data collection, analysis, and compu-

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tation in precision agriculture. Precision here refers to the limits on the measurement scale between which the true measurement is believed to lie, implied by the number of digits reported for a measurement (Sokal and Rohlf, 1995). The more digits reported for a measurement, the higher the precision implied. A pH of 5.44 implies more precision than a pH of 5.4. The appropriate precision with which to report a number is to include one additional digit beyond the last significant one measured by the observer. Statistics plays an important role in the application of precision agriculture and care should be taken in dealing with accuracy, precision, and implied precision in the reporting data.

B. INTUITIVEAPPEAL Precision agriculture is intuitively appealing because it is closely aligned with the scientific principles of management of soils, crops, and pests. Few would argue against a management philosophy that espouses matching inputs to the exact needs everywhere. Precision agriculture is intuitively appealing because it offers a means to improve crop performance and environmental quality in production agriculture (Wolf and Nowak, 1995). While the intuitive appeal creates high expectations for precision agriculture, the physical evidence supporting the agronomic (Lowenberg-DeBoer and Swinton, 1997; Sawyer, 1994) and environmental (Larson et al., 1997) benefits of precision agriculture is limited in part because it is still in its infancy. As we will demonstrate in our discussion, successful implementation of precision agriculture depends on numerous factors, including (i) the extent to which conditions within a field are known and manageable, (ii) the adequacy of input recommendations, (iii) the degree of application control, and (iv) the degree of support through private and public infrastructures. Individual success also depends on the expectationsplaced on precision agriculture which represent the difference between promotional and educational efforts versus the actual experience of farmers.

II. OVERVIEW OF THE BASIC COMPONENTS OF PRECISION FARMING The main componentsof any precision agriculture system that may emerge must first address the measurement and understanding of variability. Next, this system must use information to manage this variability by matching inputs to conditions within fields using site-specific management recommendations and mechanisms to control the accuracy of site-specific inputs. Finally, and most important, this system must provide for the measurement and recording of the efficiency and effica-

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cy of these site-specific practices in order to assess value on and off the farm. Thus, precision agriculture is technology enabled, information based, and decision focused (Pierce, 1997a).

A. THEENABLING TECHNOLOGIES While the concept of matching inputs to site-specific conditions is not new, as just discussed, there is little doubt that important advances in technology continue to enable precision agriculture. The enabling technologies of precision agriculture can be grouped into five major categories: computers, global position system (GPS), geographic information systems (GIS), sensors, and application control. Few of the enabling technologies were developed specifically for agriculture and their origins date back more than 20 years, as illustrated in the time chart in Figure 1. It is the integration of these technologies that has enabled farmers and their service providers to do things not previously possible, at levels of detail never before obtainable, and, when done correctly, at levels of quality never before achieved (Fortin and Pierce, 1998). 1. Computers

Many technologies support precision agriculture, but none is more important than computers in making precision agriculture possible. Also, it is not computers alone that are important but their ability to communicate that is so powerful for agriculture.As Taylor and Wacker (1997) suggest, it is the fusion of computers and communication that gave birth to connectivity, and it is connectivity that is driving the access of everyone to everyone, everything to everything, and everything to everyone. This electronic linkage and communication define the age of access (Taylor and Wacker, 1997). It is this notion that may have prompted the NRC (1997) to define precision agriculture in terms of a management strategy that uses information technologies for decision making. Precision agriculture requires the acquisition, management, analysis, and output of large amounts of spatial and temporal data. Mobile computing systems were needed to function on the go in farming operations because desktop systems in the farm office were not sufficient. These mobile systems needed microprocessorsthat could operate at speeds of millions of instructions per second (MIPS), had expansive memory, and could store massive amounts of data. The first microchip created by Intel in 1971 (Intel 4044 processor) contained a mere 2300 transistors and performed about 60,000instructions per second. Since 1971, the number of transistors per chip has doubled every 18 months (Fig. 2) affirming Gordon Moore’s observation in 1965 that a doubling of transistor density on a manufactured die was occurring every year, a concept referred to as “Moore’s law” (Moore, 1997).

Date

Event

1840s

Aerial photography emerges; pictures taken from balloons First image sensors incorporated in satellites; low-resolution black and white TV First commercial CIS First chlorophyll sensor (Benedict and Swidler, 1961) First multispectral photography done from space Apollo 9 manned mission Baumgardener et al. (1970) related soil organic matter to multispectral data Intel 4040 processor Launch of Earth Resources Technology Satellite-1 later renamed Landsat; permitted continuous coverage of most of the earth's surface Soil organic matter sensor (Page, 1974) Apple computer commercialized (http://www.apple.com)

1960s 1961 1968 1970 1971 1972 1974 1977 1978 1980 1981 1982

1983 1984

1985 1986

1987

1988

I990 1991 1992 1993 I994 1996 1997

Launch of first NAVSTAR GPS satellite First IBM PC Intel 80286 processor Launch of Landsat "Thematic Mapper ("h4) added The Jet Propulsion Lab produces hyperspectral sensors for use from a high-altitude aircraft platforms known as AIS (Airborne Imaging Spectrometer) GPS available for civilian use Ortlip patent issued to SoilTEQ Launch of Landsat 5 286 Intel processor Grain flow monitoring on combines @e Baerdwmeker et al., 1985) French launch an operational series of earth-observingsatellites called SPOT (SysPme Probatoire d'observation de la Terre); first offering of multispectral data to world users on a commercial basis The Jet Propulsion Lab produces a second hyperspectral sensor known as AVIRIS (Airborne VisibldnfraRed Imaging Spectrometer) Yield mapping in Texas (Bae et al.. 1987) India launches earth resources satellite (IRS-IA) that gathers data in the visible and near IR with the Linear Imaging Self-scanning sensor (LISS) Intel 40486 processor Canadian Radarsat, ERS-I, and ERS-2 managed by the European Space Agency A class of satellite remote sensors using radar systems Japan launches JERS- 1 and JERS-2 that include both optical and radar sensors Selective availability (SA) imposed on GPS signal First symposium on site-specific crop production (ASAE, 1991) Commercial yield monitors appear in the United States First international conference on soil specific crop management (Robert et al., 1993) Pentium processor Full constellation of 24 GPS satellites in NAVSTAR system complete Earth System Science Pathfinder launched by NASA Pentium I1 processor India launches the latest in the series. IRS-ID, on September 29, 1997 First European conference on precision agriculture (BIOS, 1997) Board of Agriculture, National Research Council report on precision agriculture (NRC,1997)

Figure 1 Historical developments in the technologies that enabled precision agriculture.

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Yorr

Figure 2 Illustration of Moore’s law showing the doubling of computer speed and capacity every year [Source:Intel Corporation (www.intel.com)].

As Fig. 2 indicates, Moore’s law is expected to hold until 2017 (according to Moore) and appears to hold for memory and storage capacity. Data storage capacity will need to increase rapidly as sensor technology and digital geospatial data become increasingly available to agriculture. Moore notes, By the Year 2012, Intel should have the ability to integrate 1 billion transistors onto a production die that will be operating at 10 GHz. This could result in a performance of 100,OOOMIPS, the same increase over the currently cutting edge Pentium I1 processor as the Pentium 11processor was to the 386! We see no fundamental barriers in our path to Micro 2012, and it’s not until the Year 2017 that we see the physical limitations of wafer fabrication technology being reached. We can expect, therefore, that computers will drive significant technological development to enable precision agriculture for the foreseeable future. The extent to which agriculture can utilize computer technology is important to the success of agriculture in general (Holt, 1985; Ortmann et al., 1994). However, the agricultural sector is lagging in the adoption of computer technologies on the farm relative to other business sectors. According to the 1997 annual survey of the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS, 1997), of 2,053,800 farms in the United States, only 38% had computer access, 31% owned or leased a computer, and 13% had Internet access. Part of this computer lag in agriculture is due to the lack of access or connectivity in rural areas, lack of training, and little perceived utility in available software (Nowak, 1997;

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Peterson and Beck, 1997).In any event, farmers will have to become as comfortable working with computers and their data as they are working with their farm machinery (Klein, personal communication, 1997). While it appears that computer hardware will be more than adequate for precision agriculture, the same cannot be said for the software. Advances in software logically lag behind the hardware technology. However, software for precision agriculture has been more an experience than an application. Berry (1999, in discussing the human factor in GIS, describes experience as “what you get when you don’t get what you want.” Computer software in precision agriculture has become better with time, but precision agriculture is loaded with Berry’s type of experience. Software will be adequate for precision agriculture when it becomes, as Berry (1 995) suggests, second nature to the user for assessing information and translating it into knowledge. For precision agriculture, the knowledge needed is that for managing variability on the farm, knowledge that is requisite for decision making. Computers and salient, usable software are going to play a critical role in the emergence of a precision agriculture system in the near future.

2. Geographic Information Systems Formally, GIS is an organized collection of computer hardware, software, geographic data, and personnel designed to efficiently capture, store, update, manipulate, analyze, and display all forms of geographicallyreferenced information [Environmental Systems Research Institute (ESRI), 19971. The GIS concept dates back to the 1960s when computersbecame available for use in spatial analysis and quantitative thematic mapping (Burrough, 1986).The science of GIS has evolved since the 1960s to include data management and modeling, enabling a shift from mapping to spatial reasoning (Berry, 1993, 1995). The ability to perform spatial operations on the data distinguishes a true GIS from the many software programs that do thematic mapping and database management. During the past few years, mapping software programs have been adding spatial operations, workstation GIS software programs have spawned microcomputer versions with more limited GIS capabilities to fit desktop computer technologies, and new microcomputer-based GIS systems have emerged. There are many different mapping and GIS software programs that offer different GIS features. None, however, have captured the market for application in precision agriculture. Because precision agriculture is concerned with spatial and temporal variability and because it is information based and decision focused (Pierce, 1997a), it is the spatial analysis capabilitiesof GIS that enable precision agriculture. This statement is true because the value of precision agriculture is derived only when resulting information is turned into a management decision that increases profitability, benefits the environment, or provides some other value to the farm. AGIS, in the full sense of its formal definition given previously, is key to extracting val-

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ue from information on variability. Clark and McGucken (1996) refer to GIS as the brain of a precision farming system. However, available GIS software packages are complex and difficult to learn for nonspecialists. Some GIS lack data management and spatial analysis tools needed to understand the variability observed on the farm and needed to derive site-specific management recommendations. More functional, easy to use interfaces are needed in order to fully utilize this technology in production agriculture (Berry, 1995; NRC, 1997).Computer simulation modeling can help derive the needed understanding of variability (Sadler and Russell, 1997; Verhagen and Bouma, 1997) and linking GIS to models (Goodchild et al., 1993) will be important to precision agriculture.

3. Global Positioning Systems Location control (Schueller, 1992) is essential to precision agriculture for assessing spatial variability and for site-specific application control (Auernhammer, 1994; Tyler et al., 1997). In the early days of precision agriculture, relative position within a field was determined by dead reckoning. This was a simple method in which position was measured relative to a known point determined by measuring distance using radar, ultrasound, and wheel shaft counters. Direction was determined either by using a steering-angle sensor or a gyroscope from the known point or by direction only if a field had linearized tramlines of known fixed location (Auernhammer and Muhr, 1991). Triangulation methods, in which position is determined relative to two or more known locations using, for example, radio signals transmitted from reference stations to mobile units (Palmer, 1991, 1995; Scorer, 1991), improved position accuracy to as low as 15 cm (95%probability) but such systems were time-consuming and expensive. By the early 1990s. however, the GPS known as NAVSTAR (NAVigationSystem with Time And Ranging) was becoming available for general civilian use including agriculture.This system was based on 18 satellites that were in orbit by early 1990 (Hoffmann-Wellenhofet al., 1994; Kaplan, 1996; Kennedy, 1996; Leick, 1995; NAPA, 1995). The United States NAVSTAR GPS system consists of a constellation of 24 satellites, including 3 spares. The first satellite was launched in 1978 but it was not until the Soviet downing of a Korean airliner in 1983 that the decision was made to make GPS available for civilian use [National Academy of Public Administration (NAPA), 19951. The NAVSTAR GPS system was fully deployed by 1994 and declared fully operational in 1995. The Russians also deployed a GPS system called GLONASS (Global Navigation Satellite System) consisting of 24 satellites completed in 1995. Although there are differences in time standards and coordinate systems between GLONASS and NAVSTAR, higher end GPS receivers currently available accommodate the combined use of both GPS systems resulting in increased reliability and accuracy. Although the Russian GLONASS policy called for ensured availability for 15 years, no charge on a constant global basis, and no selective availability, the system was degraded to only 14 or 15 active spacecraft

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during the fall of 1997 (Perry, 1998). Therefore, changes in GPS technology are to be expected. The GPS technology enables precision agriculture because all phases of precision agriculture require positioning information. GPS is able to provide the positioning in a practical and efficient manner for a few thousand dollars ( v l e r et al., 1997). Expensive, high-precision differential GPS (DGPS) systems are available that achieve centimeter accuracies (Lange, 1996), allow for automated machinery guidance (O’Conner et al., 1996; 51er et al., 1997) and kinematic mapping of topography (Clark, 1996), and are useful in the creation of digital elevation models needed for terrain analysis (Bell et al., 1995; Moore et al., 1993). While the GPS signal is ubiquitous, there have been problems in making available GPS at the needed precision for agriculture (Saunders et al., 1996). The U.S.Department of Defense implemented selective availability (SA) on March 25, 1990, which limited accuracy of GPS to civilians from about 8-10 m without SA to about 100 m with SA. This was done by varying the reported precise time of clocks on board the satellites and by providing incorrect orbital positioning data (NAPA, 1995). The SA has been overcome by the use of differential corrections transmitted to GPS receivers (rovers) from GPS receivers at known fixed locations (base). DGPS involves the transmission of a differential correction, that is, the difference between actual and predicted position at the base GPS receiver, to rover GPS receivers, which then apply the corrections to received GPS signals to solve for a more accurate position (Qler et al., 1997). There are four general ways of providing a differential correction: a private local GPS base receiver with a radio modem to transmit to a mobile receiver, a commercial GPS base station at which differential corrections are transmitted on FM subcarrier frequencies, a public GPS base station at which differential corrections are transmitted on AM frequencies from radio beacons with up to a 250-mile radius [U.S. Coast Guard (USCG) beacon system), and a wide area differential GPS (WADGPS) network in which differential corrections from a network of base stations are used by the roving GPS receiver to correct its position (vier et al., 1997). In all cases, DGPS requires additional receivers and antennas and is fee based for commercial correction providers. A differentialcorrection is desirable even without SA because it is needed to achieve the accuracies needed in some aspects of precision agriculture, including navigation and guidance. Currently, only WADGPS provides national coverage, whereas all others are dependent on whether the rover is close enough to a base station to receive the signal consistently. However, this is changing because FM providers are planning to offer national coverage in the near future and there are plans for completion of the USCG beacon system nationally (Divis, 1998). There is currently a debate as to whether the public sector should provide a national DGPS (NDGPS) to agriculture (NAPA, 1995;Pointon, 1997). Other sectors of the U.S. economy also need a national NDGPS, so the discussion of who benefits from a publicly supported NDGPS should not be focused on agriculture alone. The Office of Management and Budget did not support expansion of the USCG

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radio beacon system for public NDGPS in Ey98. However, some believe that a government-provided NDGPS system is so important to critical activities that it is best for the government to provide it (Divis, 1998). Certainly, precision agriculture needs DGPS and will require increased position accuracy as new technologies for navigation and guidance require higher precision, which may require DGPS accuracies not available from a government NDGPS. The prophecy of Auernhammer and Muhr (1991; p. 395) that “their use will also be without costs in the future” will probably never be realized because DGPS is big business. Regardless of who provides all aspects of DGPS, farmers and their service providers need reliable DGPS to achieve the desired positioning for precision farming operations. Farmers still experience interruptions and interferences in the GPS and/or differential correction signals, creating gaps in data collection or loss of application or guidance control. In activities at higher speeds, such as aerial applications (Kirk and Tom, 1996), time delays in differential corrections may limit positional accuracy in kinematic mode (NRC, 1997). The specified availability (four satellites in view at any location) of the NAVSTAR system is 99.85%, with a reliability (system is in service when it needs to be) of 99.97% (NAPA, 1995). However, the suitability of the satellite geometry for calculating a solution, referred to as dilution of precision (DOP), is a problem in farming in which natural or man-made structures obstruct the receivers’ view of some satellites or interfere with differential correction reception. There are also geographic locations at which DOP has been inadequate for needed location precision at certain times during the day. Additionally,some GPS receivers are susceptible to unwanted interfering signals from a variety of sources, including farm machinery, making the receiver useless in navigation or positioning. Some interferences can be overcome in the design of the GPS receiver. Regardless of problems, DGPS has greatly enabled precision agriculture. Of great importance for precision agriculture, particularly for guidance and for digital elevation modeling, position accuracies at the centimeter level are possible in DGPS receivers that use carrier phase in combination with DGPS (Lange, 1996; Tyler er al., 1997).Accurate guidance and navigation systems will allow for farming operations not currently in use, including field operations at night when wind speeds are low and more suitable for spraying and the use of night tillage to reduce the light-induced germination of certain weeds (Hartmann and Nezadal, 1990). DGPS technology changes continually and can be followed on the internet (e.g., Peter Dana’s web site hrtp://wwwhost.cc.utexus.edu/Stp/pub/grg/gcrafr/ notes/gps/gps.html or www.gpsworld.com).

4. Sensors Sensors are devices that transmit an impulse in response to a physical stimulus such as heat, light, magnetism, motion, pressure, and sound. With computers to

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record the sensor impulse, a GPS to measure position, and a GIS to map and analyze the sensor data, any sensor output can be mapped at very fine scales. Sensor technology currently lags behind other enabling technologies (Sudduth et al., 1997) and the availability of sensors has been cited as the most critical factor preventing the wider implementation of precision agriculture (Stafford, 1996b). Sensors are critical to success in the development of a precision agricultural system for three important reasons: Sensors have fixed costs, sensors can sample at very small scales of space and time, and sensors facilitate repeated measures. This means that the cost per sample is determined by the extent of sensor use, sample intensity is determined by the capability of the sensor and not the cost or difficulty in sampling associated with traditional physical sampling schemes, and sampling frequency is determined by accessibility of the target and not costs. The value of sensors and their potential for the future of precision agriculture are illustrated by yield monitoring.Yield monitoring systems, which use sensorsto measure crop flow, allow the creation of yield maps with detail not practical with other measurementtechniques (Pierce er al., 1997).Yield mapping technology may be the major factor responsible for the growing interest in precision agriculture observed since its commercial introduction in 1992 (Stafford, 1996b). Prior to 1992, the focus was on VRT, which would not in itself have sustained precision agriculture. Yield mapping bolstered precision agriculture and is currently the major precision agriculture technology in U.S. agriculture. However, the promise of sensing technologies may make yield mapping technology unnecessary in the future if high-resolution remote sensing of the growing crop leads to quantitative prediction of crop yield prior to harvest. Yield mapping will serve to validate sensor-based predictive technology, but once operational, yield monitors may not be needed. The use of remote sensing to forecastcrop yields is in use worldwide, and forecastingoffers farmers the ability to market their crops prior to harvest when prices are more favorable. Sensors are very desirable for use in precision agriculture. Every effort should be made to promote the application and adaptation of sensors developed in other industrial sectors, especially the space and defense industries, as well as to promote the development of new sensor technologies for use in assessing and managing variability in soils, plants, pests, and machinery. Sensors can be contact or remote, ground based or space based, and direct or indirect. Sensors have been developed to measure machinery, soil, plants, pests, atmosphericproperties, and water by sensing motion, sound, pressure, strain, heat, light, and magnetism and relating these to properties such as reflectance, resistance, absorbance, capacitance, and conductance. Sensors are needed in precision agriculture because such a system requires the collection,coordination, and analysis of massive quantities of data (Sudduth et al., 1997),some for strategic surveys and inventories and some for use in real-time applications. Remote sensing involves the detection and measurement of photons of differing energies emanating from distant materials. These photons may be identified

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and categorized by classhype, substance, and spatial distribution, with most designed to monitor reflected radiation (Frazier et al., 1997). Satellite remote sensing dates back to the first aerial photographs taken from balloons in the 1840s.The first satellite imagery was obtained from TV cameras mounted in satellites in the early 1960s. Since the U.S.Landsat program launched the first observation satellite in 1972, earth observation has increased and currently India, France, Russia, Japan, and the European Space Agency also operate earth observation satellites (Figure 1). Many companies now offer commercial products to agriculture from images obtained by these satellites or enhanced digital products derived from them. Remote-sensing satellites collect image data actively by sending a known signal from the satellite to the earth and measuring the portion of the signal that is returned. Passive data collection occurs by measuring the incoming energy from the sun reflected by an object or heat energy emanated from an object. The electromagnetic energy emanated from an object varies in wavelengths as determined by the object’s physical and chemical structure. Different images of an object can be constructed by combining different wavelengths, creating images far more revealing than images obtained from visible light alone. Remote-sensing systems vary in spatial resolution (meters to kilometers), spectral coverage (portion of the light spectrum covered), and temporal frequency (days to months). Different applications in agriculture will require different spatial resolutions, spectral coverages, or temporal frequencies. NASA (1998) provides an online tutorial on remote sensing and its applications. Moran et al. (1997) provide a comprehensive review of image based remote sensing for precision agriculture. Remote sensing holds great promise for precision agriculture because of its potential for monitoring spatial variability over time at high resolution (Hatfield and Pinter, 1993; Moran e? al., 1997; Stevens, 1993). For example, monitoring of a growing crop using remote sensing is critical because yield maps document yield variability but do not provide information on the cause of observed variability. However, the promise of remote sensing for agriculture has not been realized for many reasons, including costs, timeliness, and availability (Frazier et al., 1997; Stafford, 1996b).

5. Application Control Control is that portion of an automated system in which sensed information is used to influence the system’s state in order to meet an objective (Stone, 1991). For precision agriculture, control must be achieved in space and time for varying single or multiple inputs at different rates, at varying soil depths, and in a uniform and location-specific manner within fields. Because it is a required component, control technology has been a strength of precision agriculture since its inception and the state of application control was recently reviewed by Anderson and Humburg (1 997). Simply stated, if the needed accuracy cannot be achieved at the point

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of application of inputs, then precision agriculture cannot be successful (Anderson and Humburg, 1997; Schueller, 1992; Stafford, 1996b). Application control completes the precision agriculture loop. Control systems are currently available at varying degrees of precision for variable seed and metering granular fertilizers and pesticides, changing varieties on the go, anhydrous N application, sprayers, imgation, manure application, and various tillage implements (Anderson and Humburg, 1997; ASAE, 1991; Robert el al., 1993, 1995, 1996). The first patented technology for variable rate application of fertilizers was the Ortlip patent awarded in 1985 to Soil-TEQ, Inc. (now owned by AgChem), although the earliest references to precision application of fertilizers appear to be Luellen ( 1985) and Elliot ( 1987). All issues relating to the accuracy of application equipment are important to precision agriculture but not all accuracy issues are unique to precision agriculture (Anderson and Humburg, 1997). General sources of variability in application of inputs include driving precision, uniformity of distribution, topography, field surface conditions, wind conditions, and metering efficiency. Specific to precision agriculture are the transition time for changes in rate or product and positioning or location control and those aspects of application in which changing rates or products affect variability in performance. A high precision, absolute reliable DGPS will offer the positioning precision required for various tasks in precision agriculture. Some argue for a backup system, such as dead reckoning, to avoid loss of control if DGPS fails (Auernhammer and Muhr,1991). While very high position accuracy is available using DGPS, currently the major consideration is cost. Human driving precision has an expected coefficient of variation of 10% for moderate skill levels (Chaplin et al., 1995) but should be greatly improved with DGPSbased guidance systems, depending on the accuracy of the DGPS system in use (51er el al., 1997). O'Conner et al. (1996) report the use of a carrier phase differential GPS for automatic vehicle control to achieve a vehicle position accuracy within a few centimeters and heading to within 0.1". The issue of transition time is illustrated by the V-shaped spray pattern resulting from a transport delay incurred between the injection point and the nozzle discharge for a simple chemical injection system (Steward, 1994, as cited by Anderson and Humburg, 1997). Transition times of 3-9 s were reported by Bahri et al. (1996) when changing seeding rates in grain drills, with transition time depending on the magnitude of the application rate change. Their data indicated that small rate changes in seeding rate of 10 kg ha-' did not provide a real rate change, illustrating potential step size rate limitations for some inputs. A transition phase may limit the spatial resolution of variable rate application of inputs if the target area is small (Stafford, 1996b)or may cause applicationerrors if the transition time is greater than the time between detection of the need for change and the equip ment arrival at the detected position as would occur in real-time application. Current equipment may not be suitable for precision agriculture. Bashford

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(1993) and Bashford et al. (1996) report outlet CVs for grain drills ranging from 12 to 22.5%for wheat (Triticumaestivuum L.) and from 16 to 42%for soybean [Glycine 1 7 1 ~(L). Merr.]. They suggest that external fluted metering devices are not suitable for precision agriculture. Bahri et al. (1996) measured down-the-row CVs ranging from 10 to 19%.In general, variation in grain drills has been considered acceptable if the variability in grain and fertilizer delivery among row units is below a CV of 15%(Prairie Agricultural Machinery Institute, 1987). This variance, however, may exceed the desired accuracy in precision seeding systems. Centrifugal fertilizer spreaders are known to have high sensitivity of the spread pattern to flow rate variations and efforts are under way to design centrifugal spreads for precision agriculture (Olieslagers et al., 1996; Kaplan and Chaplin, 1996). Rate changes also affect nozzle performance relative to drop size and flow rate for the given nozzle design (Anderson and Humburg, 1997) and these issues are currently being addressed (Giles et al., 1996; Nuspl et al., 1996). The major issues for precision application of inputs remain transition time for changes in product or rate, uniformity of application, and rate increment control. There are other issues affecting the availability or performance of application control, including the development of standards for communication and connectivity among manufacturers, a topic being addressed by many organizations including an association of industry and the public sector called the Ag Electronics Association, ASAE, and the International Standards Organization. There are laws regulating fertilizer quality that in some states (e.g., Arkansas and Michigan) limit the blending of fertilizers on the go because of the need for a guaranteed chemical analysis, and such laws will have to be properly addressed. There are issues related to equipment wear (Ballal et al., 1996) and to weather conditions at the time of application. Kirk and Tom (1996) report that up to 13%of the variability in their tests for spray aircraft was due to wind conditions. Heterogeneity in the composition of some materials affects the flow or spreading properties (e.g., manures; Ess et al., 1996).Topography and field surface conditions also affect accuracy, in part due to their effect on flow of materials in the hoppers or tanks. Application control, including navigation and guidance, has been enabling precision agriculture since its inception and continues to improve. Farmers and their service providers have the capability to apply very precise applications of inputs site specifically. Application control technology will continue to improve and support the needs of precision agriculture. What is needed is knowledge of what inputs are required where and when.

B. STEPSINPRECISIONAGRICULTURE The basic steps in precision agriculture are assessing variation, managing variation, and evaluation. While the enabling technologies facilitate precision agri-

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culture, it is the knowledge and understanding of variability and the extent that site-specific agronomic recommendations are available to manage this variability that make precision agriculture viable. Also, because nothing is known with certainty and many factors affect crop production, evaluation must be an integral part of any precision agriculture system. These are interdependent and necessary components of a precision agriculture system yet to emerge. As will be seen, there is significant variation in the progress and research underlying each of these components. Assessing variability, precision management, and evaluation are the focus of the remainder of this chapter. 1. Assessing Variability

Assessing variability is the critical first step in precision agriculture since it is clear that one cannot manage what one does not know. The processes and properties that regulate crop performance and yield vary in space and time. Adequately quantifyingthe variability of these processes and properties and determining when and where different combinations are responsible for the spatial and temporal variation in crop yield is the challenge facing precision agriculture (Mulla and Schepers, 1997). Techniques for assessing spatial variability are readily available (Beckettand Webster, 1971; Cressie, 1991; Goovaerts, 1997,1999; Isaaks and Srivastava, 1989; Mausbach and Wilding, 1991; Mulla, 1997; Mulla et al., 1990; Rossi et al., 1992; Trangmar et al., 1985; Warrick et al., 1986; Webster and Oliver, 1990; Wollenhaupt et al., 1997) and have been applied extensively in precision agriculture.The bulk of the literature on precision management relates to some aspect of assessing spatial variability. Techniques for assessing temporal variation also exist (Shumway, 1988) but the simultaneous reporting on spatial and temporal variation is rare and the theory of these types of processes is still in its infancy (McBratney et al., 1997). Such space-time statistical applications are important to precision agriculture because many phenomena exhibit spatial patterns that develop over the course of time. They are also important because a cause-effect relationship may exist in time but not in space (Stein et al., 1997). A good example of the latter point is a yield map. A yield map defines the spatial distribution of crop yield but does not explain the observed variability. Imagery of crop growth and development over the growing season can uncover the cause-effect relationship that explains not only the yield variation within a field but also the magnitude of yield observed in a particular growing environment (Schepers et al., 1996). As we will discuss later, some variables exhibit strong spatial dependence but low temporal dependence (high temporal correlation), making them more conducive to current forms of precision management. To repeat our premise, structured spatial dependence is needed for precision management, whereas temporal variability increases the difficulty with which it can be implemented. This will become more clear in our discussion of managing variability.

’

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For precision agricultureto be useful, variation must be known, of sufficient magnitude, spatially structured (nonrandom), and manageable (Pierce, 1995). Knowing variation implies a measure of accuracy, either in measurement or in prediction. An accurate assessment of variability is essential, but the prolific use of maps without measures of accuracy indicates that this important aspect is often neglected. Knowledge also implies a sense of understanding. It is not uncommon to have detailed measures of variation within a field with little understanding of the causes of the observed variability, as suggested in our yield map example. The magnitude of both the mean and the variation determines the potential for benefits from precision agriculture.For a given parameter, a threshold variance must be exceeded for precision management to be an improvement over whole-field management for crop production.Also, the absolute magnitude of a parameter must be in a manageable range. For example, field soil fertility is already in the high range and no fertilizer would be recommended anywhere. Thus, a parameter can vary spatially but not deviate sufficiently from the mean field value or values do not fall within a manageable range to justify precision management. A high degree of spatial dependence is needed for current applications of precision agriculture. Spatial dependence drives precision agriculture because parameters with high random variation (low spatial dependence) will not be conducive to site-specific management and will be best managed on the average. Finally, accurate knowledge of large, spatially dependent variation is not sufficient for precision agriculture if the variation is not manageable. Scale is very important in this regard. For example, even with accurate maps, nutrient deficiencies or pests may be difficult to manage precisely if their areal expression is considerably smaller than the minimum area treatable by available application equipment. Biological processes that vary on very small scales are difficult if not impossible to exploit with current precision management (Groffman, 1997). Drainage ways within fields may be difficult to manage because they vary from high to low yields across years depending on seasonal precipitation patterns. While variation in drainage is known, spatially structured, and of sufficient magnitude, how to precisely manage time-dependent entities such as drainage is uncertain. Management decisions in these situations are more likely to be driven by risk assessment strategies. Ultimately, farmers must be able to delineate areas that will respond similarly to inputs that optimize crop performance (yield, quality, and environment). Maps form one basis for precision management; real-time management forms the other basis. Use of management maps is more common and these can be categorized as condition maps, prescription maps, and performance maps. In real-time precision management, maps are not necessary because inputs are triggered by real-time measurements of soil, crop, or pest condition. Condition maps are measured and/ or predicted using a broad array of technologies and techniques for estimating the spatial distribution of one or more properties or processes. Measurementsobtained in real-time precision management can be mapped as condition maps for later use.

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Prescription maps are derived from one or more condition maps and form the basis for VRT (Sawyer, 1994). Performance maps record either inputs (fertilizers, pesticides, seeds, energy, etc.) or outputs (crop yield and quality) and include derivatives of performance maps, such as profit maps (outputs-inputs). Performance maps are possible for any part of a farming enterprise that can be sensed in real time and a location recorded. Performance maps can ultimately serve as condition maps. For example, multiple-year yield maps can be used to estimate yield goal maps often needed for precision prescriptions or to estimate soil test levels using mass balance approaches. Condition maps are a critical component of precision agriculture and can be generated in four major ways: (i) surveys, (ii) interpolation of a network of point Samples, (iii) high-resolution sensing, and (iv) modeling to estimate spatial patterns. All these methods are scale dependent and have limitations to their use in precision management. We briefly discuss these here but refer the reader to more detailed references. a. Surveys Surveys are purposeful inventories of specific quantities and have been particularly useful in natural resource management. Surveys are designed with specific purposes in mind and, in general, have limitations when used for other purposes or intense applications such as precision agriculture. The National Cooperative Soil Survey of the USDA-NRCS is an extensive inventory of soil resources that includes soil data and maps needed for crop production (Soil Survey Division Staff, 1993). Surveys of varying age and scale of measurement are available for most of the United States. At first glance, the soil survey should be an important asset to the principles of precision agriculture. Farming by soils was initially thought to be a reasonable basis for precision management (Cam et al., 1991; Larson and Robert, 1991).However, existing soil surveys have proved of limited value in explaining spatial variability observed within fields. Mausbach et al. (1993) state that “Kellogg (1961; p. 58) was very clear that the soil survey and its interpretations are not site specific.” They concluded that while soil surveys are useful for planning on-farm resource management systems and for highway, urban, and other planning activities, they are not designed for specific applications such as soil-specific farming. Are soil surveys important to precision agriculture? We should not expect that variation in crop performance, either agronomic or environmental, will be explained by soil and landscape properties alone. The crop production process ia more complex than this deterministic assertion would suggest. However, within a given climate regime, we should expect crop performance to generally correspond to differences among soils and landscapes. Yield mapping has supported this general relationship.The value of the soil survey to precision agriculture could be improved by intensifying map scales to fine scale resolutions needed in detailed en-

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vironmental modeling applications or site-specific management (Moore et al., 1993). Accomplishing this is no small task. Nonetheless, new data sources (e.g., digital ortho photos, airborne and satellite imagery, and yield maps) and analysis techniques (e.g., terrain analysis) make it possible to map soils at needed resolutions (Bell et al., 1995; Moore ef al., 1993). Mausbach el al. (1993) suggest that such data collection activities should be a private-sector activity and not performed by the NRCS. However, there are two areas in which a private and public-sector cooperative relation could be developed. First, the NCSS program should complete the current soil survey in a manner that embraces available technologies for collecting data, generating finer resolution soil surveys, and publishing surveys in digital formats. Second, as will be noted later, the actual implementation of precision agriculture is spatially “lumpy” or unequal in its distribution across agricultural areas. Not all agricultural areas are of equal agronomic importance to the United States. Certain critical areas from either an agronomic or an environmental perspective could be designated for enhanced NCSS activities. The soil survey can be important to precision agricultureif the same logic (i.e., what is needed and where it is needed) is applied to defining public and private-sector roles in the allocation of scarce fiscal resources. b. Interpolation of Point Samples Another technique for assessing spatial variability involves sampling processes. A network of points in some spatial arrangement is sampled and then interpolated to produce a spatial estimation (usually a map) of the whole area using a range of statisticalprocedures. Readers are referred to references previously cited for details on the use of these procedures. These spatial statistical techniques can also be repeated over time to estimate the temporal variability (McBratney ef al., 1997; Stein et al.. 1997). Network or spatial sampling of points and interpolation into maps is useful when it is feasible to directly measure only a small number of points due to economic or temporal constraints. To a large extent, sampling depends on the nature of the entity of interest. Soil sampling for soil survey, for example, is used to determine how much of the land is of a particular type or what proportion possesses some soil attribute (Webster and Oliver, 1990). For pest management, interest may be in obtaining insect pest density maps either within a field or within a region and over time (Fleischer er al., 1997). Regardless of entity, the nature of spatial and temporal variation of that entity should affect sampling and statistical estimation procedures. The goal of network or spatial sampling for precision agriculture is to provide an accurate and affordable map of the occurrence of a specific parameter to be managed. What this parameter is will depend on the nature of the cropping system and its biophysical context. Three important issues need to be addressed regarding spatial sampling for assessing variability: sample unit, sample design (arrangement and intensity), and map accuracy. The first important question to address is what must be included in the entity un-

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der investigation and how it is defined operationally-the sample unit definition. If soil is the entity of interest, then it is important to define what constitutes a soil, for example, whether a soil is defined in terms of a map unit, a pedon, or a sample depth and if the sample is cornposited (Webster and Oliver, 1990; Wollenhaupt et al., 1997). For insects, Fleischer et al. (1997) cite Pedigo’s (1994) definition of sampling unit as the proportion of habitable space from which insect counts are taken. Here, sample unit includes unit areas, plant parts such as leaf surfaces,and time trap ping (Fleischer et al., 1997).There is little standardizationof current applicationsof precision agriculture in defining the sample unit when engaging in spatial sampling. Sample design refers to the spatial and temporal arrangement of samples and the number of samples needed to accurately estimate the spatial distribution of the parameter of interest. Sample designs will vary for different parameters and for different biophysical environmentsas parameters vary at different spatial and temporal scales and may vary in different environments.There must be a link between the capability of the enabling technologies with the underlying agronomic rules for precision management to the level of precision (scale) needed in variation assessment. Little progress in precision management will be made if something varies at the submeter level but can only be managed at scales compatible with large field application equipment (Groffman, 1997). The scale of spatial and temporal assessment depends on the spatial and temporal heterogeneity in the biophysical environment and cropping system to be managed. For example, some fields contain dissimilar soil types, whereas others are more homogeneous. They need not be sampled with the same design, nor do they have the same potential for precision management. Insects vary spatially but may require different sampling designs for each progressive generation during a growing season (Fleischer et al., 1997).Again, there has been little discussion in the precision agriculture literature of the congruency between the sample design with the enabling technologies, agronomic principles, and biophysical diversity. The objective of a specified sample unit and congruent sample design is to produce a quality map that has value for management decisions. There is no guarantee that a given sampling scheme will produce an accurate map or that an interpolation method is optimal (Gotway et al., 1996b). Techniques to evaluate map quality include cross-validationand mean square estimates from the regression of estimated versus measured values obtained from validation sets. Management decisions based on inaccurate maps appear to increase variability rather than manage it. More consideration needs to be given to map accuracy and its implication for precision management. c. High-Resolution Sensing The importance of sensing technology to precision agriculture was discussed earlier. The improvement of high-resolution sensing over interpolation of sampling points is evident when yield mapping is considered. For example, sampling

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intensity in a yield map is orders of magnitude higher than that used in grid sampling. The exercise of grid sampling a yield map would reveal the weaknesses inherent in interpolation of spatial sample points. Yield maps do not indicate the causes of the yield magnitude or its variability. However, it is the causes of variability that need to be quantified if farmers are to adjust their management practices to specific conditions within a field at appropriate times during the growing season. High-resolution remote sensing of the growing crop will reveal stresses that impact the crop during the growing season (Scheper et al., 1996).Additionally, it is not physically or economically possible to accurately map certain soil properties, crop condition, or pest status without the use of high-resolution sensing. The lower cost and ease of measure of high-resolution sensors will be critical to the future success of precision agriculture. d. Modeling Modeling is proposed as an important tool in precision agriculture to stimulate spatial and temporal variation in soil properties (Verhagen and Bouma, 1997), pests (Kropff et aL, 1997), crop yield (Bamett et al., 1997; Sadler and Russell, 1997). and environmental performance of cropping systems (Verhagen et al., 1995a).Models have been developed and calibrated for specific purposes but have not been used extensively in spatial prediction. A major problem of models is the availability of inputs needed to run them. A major advantage of models is their ability, once calibrated, to simulate the temporal component of crop production. This capability should allow models designed to account for spatial variability to evaluate different precision management scenarios that would otherwise be prohibited by time and cost considerations. The application of models to the simulation of the space-time continuum of crop production is a critical research need (Sadler and Russell, 1997).

2. ManagingVariability Once variation is adequatelyassessed, farmers must match agronomic inputs to known conditions employing management recommendationsthat are site specific and use accurate application control equipment. As already discussed, the potential for accurate application control on the farm exists but the extent to which it has reached the farm is limited. High-precision application control technology is available in many areas commercially,but it is not generally installed on farmerowned equipment. Application technology is not the only factor that can limit precision management, however. Our discussion now focuses on the agronomic feasibility of precision agriculture, i.e., whether precision agriculture improves crop performance. Precision agriculture has operated for some time on the assumption that best managementpractices developedfrom decades of agronomicresearch are applicable at any scale of management. This assumption can and will be tested as

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precision agriculture evolves on the farm. What follows is a discussion of the state of precision agriculture relative to precision management of inputs. a. Precision Soil Fertility Management Nutrient input to crop production is important because soils naturally do not supply nutrients in sufficient quantities to meet nutrient demands of commercial crops. Approximately 2 1.3 million tons of commercial N, P,O,, 60(1 1.7,4.4, and 5.1 million tons, respectively)fertilizers were applied and 1.23, 1.32, and 1.44 million tons respectively of economically recoverable nutrients in manure were available for application in the United States in 1995 [USDA-Economic Research Service (ERS), 19971. While important inputs to crop production, fertilizers and manures are also identified as major sources of nutrient contamination of surface and groundwater in agricultural areas of the United States (Mueller etal., 1995). Soils vary in their ability to supply nutrients to plants, and crops vary in their demand for nutrients. The fact that soil supply and plant demand vary in space and time and nutrient losses through leaching, erosion, and runoff also vary temporally and spatially (Sharpley, 1997)indicates that significantopportunitiesmay exist for precision management of soil fertility. In support of precision nutrient management is the fact that soil testing has been the basis for fertilizer recommendations since the late 1940s and recently has been important in determining where nutrients should not be applied as well as where they should be applied (Hergert etal., 1997). The potential for improved precision in soil fertility management combined with increased precision in application control (Anderson and Humburg, 1997) make precision soil fertility management an attractive, but largely unproven, alternative to uniform field management (Sawyer, 1994). For successful implementation, the concept of precision soil fertility management requires that within-field variability exists and is accurately identified and reliably interpreted (fertilizer recommendations are site specific), that the variability influences crop yield, crop quality, and/or the environment, and that inputs can be applied accurately (Sawyer, 1994; Pierce, 1995). We hypothesize that the ease with which precision management is accomplishedand its value vary with specific nutrients or lime. The higher the spatial dependence of a manageable soil property, the higher the potential for precision management and the greater its potential value. The degree of difficulty, however, increases as the temporal component of spatial variability increases. Applying this hypothesis to soil fertility would suggest that liming and P and K fertility are very conducive to precision management because temporal variability is low. For N, the temporal component of variability can be larger than its spatial component (Pan et al., 1997), making precision N management much more difficult in some cases. We will discuss precision management for lime, P and K, and N separately. While there is little work published on precision management of micronutrients,we expect that concepts discussed for lime and the macronutrients should apply.

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i. LIME. The value of liming acid soils is well known (Adams, 1984)and techniques to measure lime requirement are well established (McLean, 1982). Overapplication of lime is costly and may have detrimental effects on factors affecting crop yield, particularly nutrient availability (McLean and Brown, 1984). Spatial variation in soil pH is to be expected (Linsley and Bauer, 1929; Peck and Melsted, 1973). Thus, the importance of pH management in soils through liming and the expectation that pH varies spatially make the prospects of variable liming good where soil acidification is expected. Acidity can have considerable spatial dependence but the spatial dependence has a low temporal dependence (Franzen and Peck, 1995; Hergert et al., 1997). That is, exchangeable acidity varies with time but areas of acidity will remain acid until limed. This fact makes variable lime management relatively easy once the spatial variability is accurately established. Through grid soil sampling, the spatial dependence of soil pH has been verified (Peck and Melsted, 1973; Laslett et al., 1987; Tevis et al., 1991). Peck and Melsted (1973) sampled soils from two 16.2-ha fields in Illinois in 1961 on a systematic grid spacing of 25.2 m and found that pH averaged 6.55 and 6.21 for the two fields but ranged from 5.5 to 8.0, with little correlation to soil map unit. Over time, the Mansfield field was sampled and limed periodically, but the spatial pattern of soil pH remained similar between 1961 and 1991 (Franzen and Peck, 1995; Hergert et d., 1997). Pierce et al. (1995) reported that soil pH ranged from 2.0 to 3.1 pH units in three fields in Michigan, whereas mean pH values ranged from 6.0 to 6.7, with a strong spatial dependence at each site. There are, however, surprisingly few published studies that evaluate variable liming even though this practice is often identified as a major benefit of precision agriculture. Borgelt et al. (1994) showed that for an 8.8-ha field, which on average required a 3.4- to 4.5-mg ha-' lime application, 9-12% of the field would have been overlimed by a uniform application of lime and 37-41% of the field would have been underlimed, with different lime application rates depending on the method of lime determination used. Crop response was not evaluated in this study since variable lime applicationswere not made. Also, they did not evaluate map accuracy, although they used a modified composite design sampling pattern (sampling density of approximately 7.7 samples per acre) that gave a diversity of sampling points at varying distances to more precisely measure the semivariogram.Franzen and Peck (1995) applied lime to the Mansfield field originally sampled by Peck and Melsted (1973) at a uniform rate of 4.48 mg ha-' and evaluated the change in soil pH and Ca and Mg concentrations in leaf tissue of corn (&a mays L.) and soybean. Hergert el al. (1997) reported that grain yields were positively correlated to soil pH in 1991 before liming but soybean yields were unrelated to soil pH in 1992 after lime application. What is the potential for variable lime in the United States? It must first be determined whether variable liming will pay. The expectation is that soils requiring lime will benefit from increased yields and that overliming is costly and potentially yield reducing. The field in the Borgelt et al. (1994) study on average need-

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ed lime so it should be determined whether the more precise lime application improved the benefits over a uniform application. From this study, it is not possible to tell if the average application might have been sufficient to alleviate plant-limiting effects of low pH. About 12% of the Borgelt et al. (1994) field (1.1 ha) required no lime and not applying lime would have resulted in a cost saving. What effect overliming would have on crop productivity is uncertain. Another issue is missing liming opportunities by average field testing. In the fields evaluated by Pierce ef al. (1999, the average pH was adequate for crop growth and no lime would have been applied to any of the three fields. In such cases, only underliming effects would occur, assuming the lime application map was correct in the first place. Very rarely is the quality or accuracy of the map assessed and its importance can be easily demonstrated (Laslett et al., 1987). Figure 3 presents three maps of soil pH for a field in central Michigan generated from three grid sample spacings (F. Pierce, unpublished data). The average pH for this field is 6.5. Note that the area needing lime varies considerably with grid scale. In fact, grid intensity and sampling design as well as the map interpolation method all impact map accuracy (Gotway er al., 1996b). Figure 3 clearly demonstrates that the amount of overlimed or underlimed area varies with grid scale. The field average pH, however, results in considerable underliming in this field. Because lime requirement can have high spatial dependence but low temporal variability, spending the time and

Figure 3 Lime recommendation maps interpolated using inverse distance squared for 30-, 61-, and 91-m grid soil samples obtained from a central Michigan field.

26

FRANCISJ. PIERCE AND PETER NOWAK

resources to obtain quality lime application maps makes sense. This notion is further supported by the possibility that soil property, fertilizer application, and crop yield maps could be used to predict the temporal variation of soil pH, making extensive future sampling unnecessary once the spatial variability is known (Hergert et d.,1997). Real-time sensing of soil pH could also refine our ability to make accurate and cost-effective lime requirement maps and, over time, predict the need for additional variable lime applications. The caveats to variable liming are clear. For variable liming to be profitable, increased yields or lime application savings are needed to compensate for the cost of variable liming. However, acid soils in need of lime do not necessarily reduce crop yields because grain crops may not necessarily be affected by acid soil conditions (Black, 1993). McLean and Brown's (1984) summary of crop response to soil pH in the Midwest showed that corn frequently did not respond to soil pH of 5 or 6, whereas alfalfa was strongly affected by this pH range, with soybean intermediate in response. It was the beneficial effects of lime on legumes that formed the basis for lime applications prior to the 1950s, after which the need for lime was based on neutralizing the soil acidity resulting from additions of large quantities of residually acid fertilizers (McLean and Brown, 1984). Farmers may or may not experience yield changes due to liming. This may also be the case for overliming because liming soils of high pH may or may not have detrimental effects on crop yield. Negative effects of overliming are usually tied to decreased nutrient availability at high pH (Adams, 1984). Christensen et al. (1998), however, reported that applications of sugar beet lime to the high pH lakebed soils of the thumb region of Michigan increased soil pH by 0.3-0.5pH units but had no detrimental effects on crop yield and improved sucrose content in the first 2 years of the 5-year study, Where will variable liming work? The potential for variable liming is related in part to how much lime is applied and where it is applied. Lime was applied to <6% of acreage of any major crop in the United States in 1995 with an average application rate of < 2 Mg ha-' (USDA-ERS, 1997). The amount of lime applied could increase with variable liming if there are sufficient fields with an average pH near optimum but with a broad range in pH such as the fields evaluated by Franzen and Peck (1995) and Pierce et al. (1995). To some extent, the need for variable lime can be anticipated by identifying where soil acidification is a recumng problem in production agriculture. There may be equipment limitations to variable lime application because agricultural lime is difficult to apply uniformly with conventional spreaders. The availability of appropriate precision equipment may be limited due to capital equipment costs and lack of sufficient acreage for lime application services to pay for such equipment. Variable lime applications are often made by applying uniform rates to areas within fields delineated as needing lime, but the extent to which this is occurring is difficult to assess. The potential for variable lime exists where soil acidification is recurring. Accurate lime application maps are needed to ensure precision applications and avoid

ASPECTS OF PRECISION AGRICULTURE

27

over- and underliming. Expectations for crop improvement from liming should be lowered to reflect the lack of response to low pH by some crops at least in some years. In summary, the character of high spatial variability with a low temporal component for soil pH makes it ideal for precision agriculture if this management decision can be shown to be profitable. AND POTASSIUM.Precision management of P and K was an ii. PHOSPHORUS early focus of precision agriculture because there was an established basis for fertilizer recommendations in soil testing that could theoretically be applied at any scale. Moreover, the technology to variably apply fertilizers became available in the mid-1980s. Spatial variability in P and K was already known (Peck and Melsted, 1973) and was not difficult to measure within agricultural fields. The concept of VRT was very intuitive and easy to understand and implement (Luellen, 1985). The temporal component of the spatial variability of P and K is low, making it easy to soil test at a convenient time and requiring only periodic (every few years is often the recommendation) repeated sampling. Further support for precision management derives from the fact that P soil tests are increasing (Hergert er al., 1997) and P in runoff and sediment is an increasing environmental problem (Sharpley, 1997), thereby increasing the importance of soil testing and matching P inputs with crop need. The current basis for precision management of P and K, therefore, is fertilizer recommendations based on traditional soil fertility tests using various sampling schemes to assess within-field variability (Hergert et al., 1997). There are some interesting issues in this regard. While traditional soil testing and fertilizer recommendations are currently used, there are questions as to whether these are appropriate for site-specific management (Hergert er al., 1997). The improvement or verification of soil test and interpretations is considered by Fixen (1998) to be a major research need for site-specific nutrient management. Interestingly, there is a countertrend in fertilizer recommendations toward increasing the scale of soil test recommendations rather than making them more site specific. Vitosh er al. (1 996), for example, aggregated soil test recommendations from Indiana, Michigan, and Ohio into a single tristate set of recommendations. We suspect a similar change in scale of underlying agronomic recommendations is occurring elsewhere in the United States. Another complicating issue is that for a given soil test result, there are many different rate recommendations available to a farmer depending on the source of the recommendation (Cox, 1994; Olson er al., 1987). There are no standards for soil test sampling designs, sampling intensity, or methods of interpolation used in creating nutrient management maps, although some offer suggestions (Gotway et al., 1996a,b; McBratney et al., 1996; Wollenhaupt er al., 1997). In short, there appear to be no standards regarding the underlying agronomic principles that should be guiding the development and application of precision agriculture.

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FRANCIS J. PIERCE AND PETER NOWAK

Commercially, grid sampling is frequently done using a grid sampling intensity of one sample per hectare or less. Since the cost of grid soil sampling ($ ha-') is inversely proportional to the grid spacing squared, it is very easy for sampling costs to exceed the value in fertilizer savings as sampling intensity increases.There is no guarantee that a specific grid sampling intensity will result in an accurate nutrient or fertilizer recommendation map (Birrell et af., 1996). It is rare to find verification of the accuracy of nutrient or fertilizer recommendation maps either in practice (farmers)or in the literature. Long er af. (1999, however, provided an exception to this generalization because they evaluated the quality of management maps by comparing simple measures of accuracy and precision. The need for accurate identification and reliable interpretation of within-field variation was concluded by Sawyer (1994) to be the basis for VRT and is prerequisite for its success. Also, it is also not uncommon to find little correlation between grid P or K values and crop yield (Pierce et af., 1995; Smith er af., 1998). It is clear that more complex analyzes are needed to assess these relationships (Mallarino er af.,1996). Lowenberg-DeBoer and Swinton (1997) reported inconsistent results and generally low profitability of precision P and K management for the studies they reviewed, suggesting that management of one or two nutrients will not form the basis for profitability of precision agriculture. These are current or potential difficulties with precision management of nutrients such as P and K. However, the low temporal component of variability for these nutrients suggests that precision management will be of increasing value as spatial dependence increases. Studies assessing the management of P and K nationwide are being performed, although the literature is lagging behind the activity. Also, not all reports are new. Peck and Melsted (1973) reported that P soil tests for two fields in Illinois sampled on a 25.2-111 systematic grid in 1961 had means of 32.9 and 30.7 kg ha-' and ranges of 10-165 and 10-100 kg ha-', with CVs of 76 and 49%. Pierce et af. ( 1995) reported that spatial variability of P and K for three fields in Michigan sampled on a 30.5-m regular grid had means (mg kg-I) and CVs (%) of 23 (34%), 50 (50%),and 124mgkg-' (26%)forPand210(39%), 173 (50%),and 121 mgkg-' (297%) for K with strong spatial dependence for each nutrient. Han et af. (1996) reported spatial variability of P and K for two adjacent center pivots in Washington on a 6 1-mregular grid that had means (mg kg- *) and CVs (%) of 24 mg kg(24%) for P and 183 mg kg-' (21%) for K but very weak spatial structure even after data detrending.Walker ef af. (1996) examined the effects of 80 years of cultivation and slope position on soil test K levels. For the crest, midslope, and depression slope positions, the mean (mg kg-') and CVs (%) for soil test K were 283 (23%), 450 (41%), and 753 mg kg- (16%) for cultivated sites and 625 (18%), 509 (22%), and 695 mg kg-' (21%) for native sites in an east-central Alberta, Canada, location. Mallarino er al. (1996) reported variability of soil tests for three Iowa fields of 3-6 ha in size that were sampled on 15-m grid spacing. Soil test means (mg kg-I) and CVs (%) were 88 (13%), 20 (25%), and 45 mg kg-' (16%)

'

'

ASPECTS OF PRECISION AGRICULTURE

29

for P and 243 (1 3%), 107 (1 7%), and 2 13 mg kg- (28%) for K. Response to variable P and K management has been mixed (Lowenberg-DeBoer and Swinton, 1997). Carr et al. (1991) and Wibawa et al. (1993) did not find profitable returns for variable management, whereas Mulla et al. (1992) and Wollenhaupt et al. (1994) did. When and where will precision P and K management be profitable?The answer may be found in soil testing philosophies that form the basis for fertilizer recommendations. There are two fundamental fertility management philosophies-sufficiency versus buildup maintenance (Dahnke and Olson, 1990). The buildup maintenance concept promotes the application of sufficiently high rates of P and K to raise soil test levels in 1 or 2 years, followed thereafter by an annual application equivalent to the amount to be removed by the crop to be grown. This is a preventative approach to protect against yield loss because of nutrient deficiency and focuses on building soil fertility. Once the initial buildup is achieved, then the need for further soil testing is eliminated if fertilizer application adheres to the maintenance application (Dahnke and Olson, 1990).Applying this soil testing and fertilizer management philosophy to precision management, the goal would be to either increase the soil test levels of a field through fertilization or decrease the soil test levels through crop removal to the maintenance level and then fertilize based on crop removal as indicated by yield maps. Where nutrients are high within a field, no fertilizers would be applied until soil test levels were depleted to the maintenance levels, and where nutrients are low fertilizers would be applied based on a buildup program. The long-term impact of this management philosophy is to homogenize soil test levels and then rely on yield maps for fertilizer recommendations, thereby eliminating the need for grid soil sampling (Goedeken et al., 1998). The sufficiency approach, preferred by universities, is similar to the rapid buildup and maintenance except the rate of buildup is much slower and the buildup level is much lower (Dahnke and Olson, 1990). Fertilizers are applied based on probabilities of yield response to applied fertilizer at soil test classes of low, medium, and high. No fertilizers are recommended for high testing soils, a maintenance level is recommended for medium testing soils, and a larger amount than the maintenance level is applied on low testing soils. The sufficiency approach is crop specific, whereas the buildup maintenance approach builds soil fertility. Because the sufficiency approach is more conservative, less fertilizer will be recommended. Applying this approach to precision management, soil testing is still required because soil test category is required to make fertilizer recommendations.Ultimately, however, the sufficiency approach will approach the maintenance plateau as the fertility of low testing soils increases and that of high testing soils decreases over time. It appears that the only differencebetween the two management philosophies is how much and how long it will take to reach the maintenance plateau. To some extent this is related to the critical level of soil tests proposed by the different management philosophies.

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FRANCIS J. PIERCE AND PETER NOWAK

Ultimately, if precision nutrient management is based on soil testing and fertilizer management philosophies, over time variable rate applications should create soil test levels that are optimal throughout the field and would require future variable rate applicationsbased on crop removal and nutrient fixation in soils for maintenance. One might call this precision agriculture on the maintenance plateau. Goedeken et al. (1998) suggest that over time variable rate P fertilization should reduce the need for variable rate applications because soil tests will become spatially uniform. However, even if spatially uniform soil tests resulted from variable rate applications, unless crop nutrient removal was uniform there would always be a need to variably apply nutrients because the fertilizer management philosophy of maintenance requires fertilizers to match crop removal. If accurate nutrient maps were obtained for a field, then spatial mass balance calculations should preclude the need for further soil testing, except for intermittent monitoring of control points to check predictions. This also assumes that none of the nutrients are being lost to the larger environment (discussed later). The key to precision management of P and K is first and foremost to obtain accurate maps. Sawyer (1994) made this perfectly clear in his review of precision nutrient management. Accurate fertility maps require appropriate sampling designs and adequate sampling densities (Gotway er al., 1996b; Laslett er al., 1987; Wollenhaupter al., 1997) in addition to well-calibrated soil test procedures and associated interpretations (Hergert er al., 1997). Rarely is the accuracy of P and K maps evaluated. For one Michigan field, interpolation of a 91-m grid predicted only 3 1% of the variation in soil test P levels obtained from a 30.5-m independent sampling grid, whereas the 30.5-m grid interpolation predicted 73% of the variation in measured soil test P levels at the 91-m grid (Pierce, unpublished data). While the increased precision was evident from the improved regression, a significant deviation of the regression line from the 1:1 line indicated considerable inaccuracy at high P levels even with the 30.5-m grid. Smith er al. (1998) suggest that more intense grid sampling is needed to assess soil fertility in the cotton (Gossypiurn hirsuturn) fields they studied. Once an accurate map of P and K levels is created, then expectations about the relationship between yield and soil fertility need to be kept in perspective. A low or lack of correlation between soil fertility and yield in a given year means that other factors regulated yield. This outcome does not diminish the value of following a proven soil fertility management philosophy. The low temporal variability in soil tests suggests that precision P and K management will work where fields vary spatially in their need for added inputs. Since precision P and K management will ultimately lead to variable rate nutrient management based on replacing nutrients removed by crops, a precision management program based on a proven soil fertility management philosophy will be successful where crop yields vary in response to other unmanageable yield-limiting factors.

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iii. NITROGEN.The precision management of N will be applicable to situations in which the factors that control total N in soils and N availability to plants vary spatially (Pan er al., 1997). However, precision N management will be increasingly more difficult but may in fact have increasingly more environmental benefits as the temporal component of spatial variability of N availability increases. For this reason, precision N management is more complex than precision management of lime, P,and K but may have significantly more value. The difficulty presented by a large temporal component of N availability is clearly illustrated by the data of Cahn et al. (1994). From May 13 to June 15, 1992, in a 1.1ha field, soil concentrations of nitrates in the 0 to 15-cm depth decreased on the average from 8.3 to 1.6 mg kg-I and the spatial dependence observed in May was no longer present in June (Fig. 4). Conversely, early success in precision N management has occurred where the temporal variability of a significant aspect of N availability was low, a case exemplified by the success of precision N management in sugar beets in the Red River Valley (Cattanach er al., 1996; Lilleboe, 1996) and in irrigated corn in Nebraska (Hergert et al., 1996), where N leaching overwinter was low and residual nitrates were high. Total N in soils is primarily in the organic form (about 90%;Stevenson, 1982), and most of the inorganic N is mineral-fixed N ammonium (Young and Aldag, 1982). Since total N in soil varies with soil organic matter, clay mineralogy, and clay content of soils, and since N mineralization is the major natural source of inorganic N in soil, then N in soil will vary spatially with soil texture and organic matter content. Inorganic N, as nitrate and ammonium, is required by plants in large quantities and its content in soil is intricately woven into the complex soil N cycle processes of mineralization-immobilization, leaching, and denitrification (Meisinger et al., 1992).In crop production, net mineralization rates from soil organic matter are generally 1 or 2%of total N (Meisinger and Randall, 1991), which is often insufficient to supply the N requirements of crops that do not fix atmospheric N. Therefore, N in the form of inorganic fertilizers or waste materials containing N are applied to soil or a crop to meet crop N needs. More than 11.7 million metric tons of commercial N fertilizers and more than l million metric tons of N in animal manures were applied in the United States in 1995 (USDA-ERS, 1997). Both N deficiency and excess N availabilitycreate problems for production agriculture. Most fertility concerns are focused on deficiencies in N availability to plants because they reduce yield and/or quality of crops (Olson and Kurtz, 1982). Excess nitrate-N in soil can lead to N losses to the environment that reduce water quality (Mueller et al., 1995) and can reduce yield and/or quality for some crops. Excess N increases protein content but reduces sugar content in sugar beet (Hills and Ulrich, 1971) and oil content in canola (Brassica napus L.; Henry and MacDonald, 1978), whereas both yield and crop quality can be reduced in wheat (Rasmussen and Rhode, 1991). Leaching, runoff, and denitrificationare processes that

32

FRANCIS J. PIERCE AND PETER NOWAK

May 1992

a $ , h a 2 o ~ m o i m

c n

I

0,

15

P

lo

Y

v

z

5

'

0

0" 2 110

0 n

June 1992

c

I

EI,

1s

F

lo

Y

U

z 1

mw data

5 0

a

20

40

60

w1100

Lag diatanco (m)

0 "

z

Figure 4 Spatial distributions and semivariograms of raw and residual NO,-N data (0-15 cm) from the 1.1-ha area in May and June 1992 (reprinted with permission from Cahn era[., 1994).

result in loss of N from the soil-plant system creating the potential for N deficiency in crops and degradation of water and air quality. The environmental threat of N losses from soil is further compounded by the application of manures at rates in excess of crop nutrient needs (Nowak et al., 1998). This problem is exacerbated by the concentration of livestock and the high costs of transporting wastes resulting in excess animal manure being applied to limited areas (Hatfield and Stewart, 1997). Deficiencies and excesses of N can occur within the same field sometime during the year. Thus, it is the variability in space and time of the processes that

ASPECTS OF PRECISION AGRICULTURE

33

regulate the availability of N to plants and the fate of N in soil that make precision N management attractive. Nitrogen management for crop production is to varying degrees prescriptive by nature because crop yield is to a large extent determined by growing conditions that occur after the N uptake phase in plants. Nitrogen management strategies are approached in two basic ways: (i) prevention strategies, whereby prescriptive applications of N inputs are made prior to or early in the N uptake phase of plant growth to avoid nutrient deficiencies, and (ii) intervention strategies, whereby N inputs are applied to meet N requirements as determined by the nutrient status of soils or plants during the rapid N uptake phase of growing plants. Hybrid strategies supply a portion of crop needs early and intervene later. For example, the early presidedress nitrate tests used in the northeast United States were designed to adjust N fertilizer recommendations based on critical nitrate concentrations in soil following preplant applications of fertilizer N (Magdoff et al., 1990). Prevention is the most common N management strategy and is usually based on a mass balance approach in which fertilizer recommendations are based on some combination of yield goal, N requirements of the crop, residual soil N in the soil profile, and N mineralized from soil or plant residues (with some using soil organic matter as a proxy for N mineralization). Intervention strategies include foliar fertilization (Stone et al., 1996), delayed N applications based on N content in plants (Schepers et al., 1992), and chemigation (King et al., 1996) strategies that assess crop needs based on tissue sampling, sensing plant reflectance, or crop simulation. From the current literature, it appears that precision N management will be feasible using prevention strategies based on N balance approaches that rely on soil testing for residual soil profile nitrates where temporal variation in soil profile nitrates is low. Intervention strategies will be more applicable to precision N management where the temporal component of spatial variability is moderate to high. Intervention strategies that sense the occurrence of N deficiencies in plants during the uptake phase (Schepers et al., 1996) may be better adapted to precision N management than strategies that use mass balance approaches that are to a large extent soil sampling based, such as the presidressed soil nitrogen test (PSNT), even where they may be useful on a field level basis (Bock et al., 1992). The success of precision N management will depend on how well we can predict and control the dynamic soil and crop processes that regulate N availability in soils and plant N requirements (Pan et al., 1997). Research on precision N management to date has taken three basic approaches. The first is based on the application of current N recommendations to site-specific within-field scales where some form of grid soil sampling is employed. Residual soil nitrate-N values are interpolated to generate N availability maps forming the basis for N fertilizer recommendation maps (Cattanach et al., 1996; Ferguson

34

FRANCIS J. PIERCE AND PETER NOWAK

et al., 1996; Fiez et al., 1994a; Franzen et al., 1996; Hergert et al., 1996; Kitchen et al., 1995; Redulla e l al., 1996). This approach is based on the prediction of N requirement of crops using a balance sheet approach that is the basis for current N recommendations for crop production (Meisinger et al., 1992) such as that described by Eq. (1) as presented by Pan et al. (1997):

Nf = [ G ,

X

1I(NfINaVX NavlNs X Gw/Nt)]- (Nmin+ Nin)

(1)

where Nf is the fertilizer N requirement, G, is the yield goal, Nav is the available soil N in the root zone, N, is the plant N, N, is the predicted requirement for N supply to crop, Nminis the soil N supplied from net mineralization, and Ninis the preplant inorganic N. In practice, university recommendations for fertilizer N are usually much simpler than that given in Eq. (l), basically a function of the product of yield goal times an N factor adjusted for N credits and in some cases residual nitrates. The tristate N fertilizer recommendation for corn in Indiana, Michigan, and Ohio is based on yield goal and N credits from manure or legumes in rotation (Vitosh et al., 1996), whereas Nebraska’s N recommendation for corn relies on three variables-yield goal, residual soil nitrate, and percentage organic matter as a proxy for N mineralization (Hergert et al., 1995a). The second approach is to develop site-specific optimal N rate recommendations based on condition-specific N response curves (Blackmer and White, 1996; Kachanoski etal., 1996; Malzer et al., 1996) andlor related to landscape attributes (Hollands, 1996; Solohub et al., 1996; Vetsch et al., 1995).A common experimental procedure is to apply a range of N rates in replicated strips across a field, including a no N treatment, to obtain an optimal N rate by condition within a field. Ultimately, the derived site-specific optimum N rates must be related to soil or landscape properties if they are to be useful elsewhere. The third approach is to develop site-specific intervention N management based on crop monitoring of N status (Bausch et al., 1996; King et al., 1996; Schepers et al., 1992, 1996; Stone et al., 1996; Vetsch et al., 1996).The idea is to monitor plant N concentration by monitoring plant or canopy reflectance of light or some measure of plant N content such as chlorophyll content (Schepers et al., 1992), estimate N fertilizer requirement using established relationships between reflectance and N content, and fertilize the crop to the optimal N content for maximum economic yield. In some cases, a portion of a crop is fertilized to optimal levels and used as the standard for adjusting the N recommendationsfor the remainder of a field (Schepers et al., 1996) or plant N is estimated using a previously developed index calculated from measured canopy reflectance (Bausch et al., 1996). Some success has been attained with each of these approaches but none are broadly applicable. Where and when will precision N management pay off in terms of either profitability or environmental benefits? Since the key to precision N management is that the factors that regulate these processes vary in space and time we expect pre-

ASPECTS OF PRECISION AGRICULTURE

35

cision N management to be profitable and/or beneficial to the environment to varying degrees under the following conditions: Where N inputs are high: Dryland wheat would be marginal due to low inputs of N and low yields (Cam et al., 1991 ; Solohub et al., 1996), whereas high-yield wheat production in the Pacific Northwest would have potential because of high N inputs, high residual N, and high yields (Fiez et al., 1994a,b, 1995). Corn accounts for the major input of fertilizer N and use of manure, whereas potatoes (Solanurn tuberosurn L.) receive the highest average N application rate of all crops (248 kg N ha-'; USDA-ERS, 1997). These crops are the focus of much of the current research on precision N management. Where residual N is temporally stable and/or high residual N is predictable from the yield of the previous crop, for example, low yields the previous year: Residual N is reported to be spatially variable and stable from fall to spring in irrigated corn production in Nebraska (Hergert et al., 1995b) and in soil profiles prior to sugar beet production in the Red River Valley of eastern North Dakota and western Minnesota (Cattanach et al., 1996). Residual profile N is frequently leached in the humid regions over winter (Everett and Pierce, 1996; Hoeft et al., 1992), except where climatic factors, such as low annual precipitation and temperatures that keep soils frozen for extended periods overwinter, favor retention of residual profile nitrates (Bundy et al., 1992). Residual profile nitrates are not considered in nitrogen recommendations for corn or wheat in certain areas such as the tristate area of Indiana, Michigan, and Ohio (Vitosh et al., 1996).The PSNT, which is primarily an index of mineralization in the surface 30 cm of soil, may be relevant in some humid regions but not others (Bock et al., 1992). Where crop quality is affected by excess N is soil: A major benefit of grid soil testing and variable rate N application is a significant increase in sucrose content in sugar beet (Cattanach et al., 1996; Lenz, 1996; Lilleboe, 1996). Where crop yield spatial variability is high and predictable: Nitrogen recommendations are based primarily on crop yield. If yield is variable, then precision N management based on yield goal makes sense if the yield variation is predictable from year to year (low temporal variability). If yield variability is high but not predictable, then precision N management will be difficult because it is generally prescriptive. At this time, it is common to use a uniform yield goal in site-specific N fertilizer predictions while varying N recommendationsbased on residual N (Long et al., 1996) and in some cases organic carbon (Hergert et al., 1996).There have been some attempts at using previous-year yield maps to predict N requirements, although previous yield has not been very useful (Kitchen et al., 1995) and some suggest previous yield maps have no relevance (Vetsch et al., 1995).Some suggest that previous-year yield maps do not reflect N requirements because other factors besides N availability regulate yields (Kachanoski

36

FRANCIS J. PIERCE AND PETER NOWAK

et al., 1996; Malzer et al., 1996). Yields have been related to spatial variability of water availability associated with landscape position (Mulla et al., 1992).The use of digital terrain models to predict water availability in the landscape is promising (Bell et al., 1995). Where net mineralization is high and consistently related to soil and landscape properties: Soils with low residual nitrates from the previous crop can provide a significant portion of the N requirements of the succeeding crop through mineralization of soil organic N and crop residues. Pan et al. (1997) calculated that mineralization of N from the surface 30 cm ranges from 39 to 224 kg N ha-'. Vetsch et al. (1995) report that the inability of current fertilizer recommendations to predict soil N supply and fertilizer N use efficiency in the field makes them inadequate to capture the benefits of site-specific N management. Walters et al. (1996) predicted N requirements from soil organic matter maps. Stevenson and van Kessel (1996) showed that following pea in rotation, the depressional areas had higher soil N and a reduction in fertilizer N would be needed in wheat to reduce the negative effect of excessive soil N supply, but this was not the case following wheat in rotation. Corn following legumes in rotation can have little response to fertilizer N (Frye et al., 1988; Hesterman, 1988). However, spatial variation might be expected in legume contributions within a field. Where N application is not restricted in time: Sprinkler-irrigatedlands have unrestricted accessibility to the field to apply N based on crop needs using chemigation (Evans et al., 1996; King et al., 1996). Stone et al. (1996) used spectral radiance measurements for correcting in-season wheat N deficiencies at Feekes physiological stages 4 and 6. Where leaching potential is high and spatially variable prior to or during the crop N uptake period of plant growth: Within-season leaching events result in losses of nitrate either through leaching itself or due to denitrification if soils remain saturated over extended periods. Because loss of N may vary with landscape position and soil variability, intervention applications of N inputs to reduce N deficiencies could be varied spatially. On the preventative side, anticipation of areas where such losses might be expected is the basis for variable rate application of nitrification inhibitors (Malzer et al., 1995). Where variation in topographic position regulates N availability or yield: Certainly yield is very much dependent on water availability-whether drought or in excessive amounts. Effects of landscape position on wheat yields have been reported by Fiez et al. (1994a,b, 1995) and Halvorson and Doll (1991). In the Red River Valley, N availability is higher in knolls than in depressions and is key to managing variable rate N in sugar beet (Holland, 1996; Franzen et al., 1996). Techniques to predict water in the landscape have been enhanced by recent advances in digital terrain analysis (Bell et d., 1995; Moore et d., 1993). Thus,

ASPECTS OF PRECISION AGRICULTURE

37

where soils or landscapes consistently regulate water availability, precision N management would have potential. On the negative side, precision N management will not work well under the following conditions: Where the previous crop exceeds yield goal: When yield exceeds yield goal as it did in 1994 (Hergert et al., 1995b), residual nitrates were reduced. Since the benefits of grid sampling appear to increase with increasing residual N levels and increasing spatial variability (Lenz, 1996),higher than expected crop yields may diminish the prospects for variable rate N success the following year. Where large-scale leaching events occur prior to the growing season: In the areas flooded in 1993, additional fertilizers, including N, were applied to replenish flood-damaged soils (USDA-ERS, 1997). Since nitrates are soluble, large leaching events should minimize residual nitrates within the root zone. Where precision N management has successfully reduced spatial variation in yield and residual N: The short-term financial benefit of precision N management is an increase in N use efficiency by accounting for residual N in the N fertilizer recommendation, thereby reducing N fertilizer as well as reducing residual N available for leaching (Hergert et al., 1995b).Therefore, the benefits from precision N management are a result of accounting for residual N that is spatially variable. As farmers become skilled in this technique, the long-term effect of precision N management is a reduction in residual N, making precision N management less profitable although N leaching is continually reduced. Farmers do not get paid for environmental benefits so good precision management will lose its potential profit benefit to farmers. It appears that when residual N is low, grid sampling and VRT will be less profitable. On the other hand, if precision N management is to increase soil N to maximize economic yield where inadequate amounts of N would be applied under uniform management, then residual N will be higher than it would with uniform N management, unless N use efficiencies increase proportionately. Actually, residual N will be an issue when yields from the previous crop are less than yield goals and/or nitrogen use efficiencies are reduced. Therefore, yield maps combined with grain protein content and with N application maps should be a guide to precision N management but only where overwinter leaching is low. Where there is a strong temporal component of spatial variability: Where residual N is low due to high leaching potential, precision N management must rely on prediction of crop yield, net N mineralization, N losses during N uptake period, and where amendments such as nitrification inhibitors will synchronize nitrate availability with crop uptake demand. Not only does the prediction of the spatial variability of inorganic N over time make precision N management much more difficult but also we believe the potential to the farmer and the environ-

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FRANCIS J. PIERCE AND PETER NOWAK

ment are larger than when temporal variation is higher. The ability to use intervention N management strategies may allow fanners to overcome the management difficulties when the availability of N encountered has a large temporal dependence. The potential for precision N management can also be considered in terms of the steady-state and non-steady-state approximations to whole-crop N balance in the soil-crop system (Table I) discussed by Meisinger and Randall (1991). Soilcrop systems that are steady state will have fertilizer N needs that are directly proportional to crop removals and indirectly proportional to fertilizer efficiency because no soil N availability terms appear in the steady-state case. Situations conducive to steady state, such as those listed in Table I, are common in humid regions (Meisinger et al., 1992). Precision N management for the steady-state case would be based on crop yield variability and fertilizer use efficiency, both related to soil and landscape properties. Fields with dissimilar soils and/or variable landscapes would be well suited to precision N management if crop yield variability were known or predictable, whereas fields with similar soils and uniform landscape would not. Soil-crop systems that are non-steady state have high temporal variability associated with management, soil, and climatic factors. Such conditions are encountered with crop rotations involving forages, organic inputs, changes in drainage, or tillage or with large climatic fluctuations. Precision N management may be easiest when climatic factors favor N accumulation in soil profiles (low precipitation) but more difficult when management or climatic factors result in N transformation processes that are dynamic. An evaluation of the steady- versus non-steady-state approach to precision N management needs to be performed. In summary, the potential for precision N management is directly related to the extent of spatial variability in the factors that regulate N availability in soils, whereas the difficulty in precision N management is related to the degree of temporal variability. Where spatial variability is high and temporal variability is low, precision N management appears to be profitable and to reduce N available for leaching. Intervention N management strategies will be needed where the temporal component of N availability is high but access to the crop will be required for delayed N fertilizer applications. Finally, residual nitrates are human induced. As precision N management begins to control the anthropogenic sources of variation, only the natural sources will remain to be dealt with. b. Precision Pest Management Weeds, insects, and diseases are an ever-present and costly management problem to crop production because significant infestations reduce crop yield and/or quality and, if severe, can limit crop production options. The USDA-ERS (1997) reports annual expenditures of $7.5 billion on agricultural pesticides in the United States, of which two-thirds is spent on herbicides and about one-third on in-

ASPECTS OF PRECISION AGRICULTURE

39

'hble I

Characteristics of Steady-Statevs Non-Steady-StateMi-Crop Systemtf ~~

Circumstances conducive to steady-state approximation

~~

~~

Circumstances conducive to non-steady-state approximation

Goal or objective Objectives of N budget lie in making estimates Objective of N budget is for a short-term period, e.g., the next crop (1-year period) over a long-term period (10 year average) Management factors Arecent (within 1-5 years) change in the soil The same soil and water management system in and water management system, e.g., new place for an extended period (5-20 years), tillage system and new drainage or irrigation e.g., same tillage system, same soil drainage system, and same irrigation system system The same crop management practices used for A recent change in crop management, e.g., new an extended period, e.g., same rotation and rotation system and recent use of cover crops long-term use of cover crops The same N management system practiced for Arecent change in N management practices, e.g., recent use of manure to replace fertilizer N an extended period, e.g., same N source (manure or fertilizers)and same timing and placement practices Soil and climatic factors Soil total N content approximately constant over Soil total N content changing systematically a long period (10-20 years), e.g., implies that over time, e.g., decline in soil N when grassN mineralization approximates N in residues land is plowed and increase in soil N with plus immobilization continued manure applications Soil inorganic N content approximatelyconstant Soil inorganic N content changes widely from year to year, e.g.. periodic drought and over time step, e.g., soil leached out each year, irregular irrigation highly permeable soil in humid region, and excess irrigation Climate (precipitation and temperature) not Climate highly variable and slows organic N highly variable and is conducive to organic N turnover, i.e., cool and marked cold season or dry season and unpredictable hydrologic turnover, i.e., warm and humid and predictable yearly hydrologic cycle cycle Soil type has low total N content, low NO, Soil type has high total N content, readily retains content, shallow root zone, well-drained, high NO,, deep root zone, poorly drained, low infilinfiltration,deep water table, low clay content, tration, shallow water table, high clay content, e.g., a clay loam soil in a cool semiarid climate e g , sandy loam soil in warm humid climate "Reproduced with permission from Meisinger and Randall (1991).

secticides.More important, public concerns regarding the impacts of pesticide use include health risks related to food safety, water quality, and worker safety and concerns over wildlife and ecosystem health. Therefore, agricultural management practices that reduce pesticide use, improve pest management, or reduce risks of pesticides to human and ecosystem health are very desirable. The intuitive appeal of precision agriculture is that it offers the potential for such

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FRANCIS J. PIERCE AND PETER NOWAK

benefits. The potential direct economic benefit of precision pest management to farmers is a reduction in chemical/nonchemicalpest management costs, crop damage, or both due to more efficacious or efficient application of pest control measures. A reduction in pesticide use, however, does not translate into profit if the cost of obtaining information about pest populations and distribution exceeds the savings (Forcella, 1993; NRC, 1997).Environmental benefits are presumed to result from a reduction in pesticide usage, particularly in sensitive environments, although the results of the few studies on the potential of precision farming to provide environmental benefits have been inconclusiveregarding its effect on pesticide use (USDA-ERS, 1997). There are two basic questions in precision pest management: Is it possible to effectively manage pests spatially? and Will it pay? Here, we assume that spatial precision pest management is doable so that we can consider the potential benefits of it. If the main benefit of precision pest management is a reduction in input costs and applications and avoidance of unneeded applications for environmentalbenefit (NRC, 1997),then the task at hand is to determine where pesticides are used and then assess the potential for precision management. A summary of pesticide use in the United States by selected crop is given in Table 11. Four crops account for 78% of pesticide use on major crops in the United States [corn (35.6%), potatoes (15.4%), cotton (14.8%), and soybean (12.1%)]. Vegetable crops account for approximately 12%. Corn, soybean, and cotton account for 89% of the herbicide use in the United States. Cotton, corn, and potatoes account for 69% of the insecticide use, and fruits and vegetables account for 29%. Potatoes and vegetables account for 66% of the fungicide use, with fruits accounting for 30%. Potatoes and cotton account for 73% of other pesticide usage, a category which includes soil fumigants, growth regulators, desiccants, and harvest aides; vegetables account for 26%. If savings in chemical uses is sought, then we need to examine where the chemical use is taking place. Usage does not necessarily imply potential benefit from precision management. The average annual pesticide costs for corn, soybean, and cotton are 62, 59, and 124 $ ha-' compared to 15 $ ha-' for wheat (USDA-ERS, 1997). Consider a spraying operation costs about $5 ha-'. If a farmer wants input cost and applications savings to generate profits from precision management, then the fraction of input and application costs saved by precision management must exceed the cost of information gathering and variable input application costs. Crop improvement by improved pest management would add to the profit, and crop damage and/or intervention costs for pest escapes from inadequate management would add to the cost. The risks and associated codbenefit for either scenario have not been assessed. For crops that have low annual pesticide costs, precision pest management will be hard to justify economically solely on pesticide savings given the potential cost of precision management. Crop improvement or returns for environmental benefits from precision management will be needed. Currently, farmers are not compensated for environmental benefits accrued from improved pest management.

Table II Pesticide Use in 1995 in Selected U.S. Crops by Pesticide kg ai crop Corn Cotton wheat Sorghum Rice soybeans

Peanuts Potatoes Other Vegetables Citrus Apples Other Fruit Total

%

Herbicide

Insecticide

Fungicide

Other

Total

Total

Herbicide

Insecticide

Fungicide

Other

84,587 14,924 9,105 na na 30,929 na 1,314 2,778 2,118 348 898 147,001

6,790 13,638 413 na na 234 na 1,411 2,530 2,335 1,618 2,628 3 1,598

9 474 227 na na 6 na 3,620 9,902 1,825 2,125 2,154 20,341

0 8,959

91,385 37,995 9,745 0 0 31,169 0 39,454 30,325 6,359 4,133 6,235 256,800

35.6 14.8 3.8 0.0 0.0 12.1 0.0 15.4 11.8 2.5 1.6 2.4

57.5 10.2 6.2 na na 21.0 na 0.9 1.9 1.4 0.2 0.6

21.5 43.2 1.3 na na 0.7 na 4.5 8.0 7.4 5.1 8.3

0.0 2.3 1.1 na na 0.0 na 17.8 48.7 9.0 10.4 10.6

0.0 15.5 0.0 na na 0.0 na 57.2 26.1 0.1 0.1 1.o

0 na na

0 na 33,109 15,115 81 42 554 57,860

"Adapted from ESRI (1997). includes soil fumigants, growth regulators, desiccants, and harvest aides.

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FRANCISJ. PIERCE AND PETER NOWAK

Therefore, it appears that the potential for benefits from precision pest management, assuming it is doable, depends on the dependence on a particular crop for costly pest management practices (e.g., pesticide use) and actual improvementsto crop yield, crop quality, and/or to the environment from improved pest management practices. There are risks associated with being wrong, and these should enter into the discussion because farmers understand that they are managing risks in producing a crop. Next, we examine the potential for precision management for weeds and insects. There is little work on precision management of diseases reported in the literature. Diseases vary spatially within a field but are difficult to predict since they are host specific and vary temporally. i. WEEDMANAGEMENT. The application of precision agricultureto weed management is potentially beneficial to agriculture because (i) it offers an opportunity to reduce chemicalhonchemical inputs into crop production through sitespecific weed control and the use of precise application techniques and (ii) the acquisition of spatial and temporal information on weed occurrence and distribution made possible with precision agriculture technologies will lead to an improved understanding of weed biology and ecology needed to develop more effective weed management strategies (Johnson et al., 1997). Precision weed management is possible because weeds are spatially aggregated and not randomly distributed within most agronomic fields (Dessaint et al., 1991; Johnson et al., 1995; Mortensen et al., 1993, 1995, 1998; Von Groenendael, 1988) and because the efficacy, efficiency, and fate of weed control inputs vary with weed and crop conditions and with soil physical and chemical properties, all of which can vary spatially and, to a varying extent, temporally (Johnson et al., 1997). It is important to understand that precision weed management is much more than variable rate application, although this aspect receives the bulk of attention. Precision weed management also involves an understanding of the spatial and temporal interactions between landscape characteristics, pest populations, and weed management strategies; this understanding is currently lacking (Johnson et al., 1997). Consider that technologically it will be possible to perform field operations very precisely at night when environmental conditions may be more conducive to weed management operations, such as spraying in the dark to take advantage of cooler temperatures and reduced winds (Holmberg, 1998). Research has already shown that night tillage can be effective in control of the germination of weed species for which germination is light induced (Asgard, 1994; Hartmann and Nezadal, 1990; Scope1 et al., 1994). Thus, a very important dimension of precision weed management is that new weed management strategies will evolve because of it. While not the only aspect of precision weed management, its potential for reduction in herbicide use may be most important in its adoption because of public concerns regarding the environment and food safety. Management systems and inputs that foster a reduction in herbicide use in agriculture are very desirable for

ASPECTS OF PRECISION AGRICULTURE

43

economic and environmental reasons, and these aspects have been an important focus in crop protection research. Pesticide product formulations have changed to lessen environmental and human health effects, to reduce the development of pesticide-resistant pests, and to provide more cost-effective pest controls (USDAERS, 1997). Application rates have been lowered for new herbicides, which require a fraction of the previous rates. There has been a shift from preemergence to postemergence herbicides, using chemicals that have greatly reduced soil residual activities, that foster integrated pest management (IPM), that require lower rates when applied to small weeds, and that when combined with herbicide-resistant crops lower herbicide costs and amounts. These newer herbicides and crop protection technologies will benefit from precision application techniques, further supporting the case for precision weed management. However, the results of the few studies on the potential of precision farming to provide environmental benefits have been inconclusive regarding its effect on pesticide use (NRC, 1997; USDA-ERS, 1997). A common approach to precision weed management is site-specific weed control achieved by (i) applying herbicides only where weeds are present or above economic threshold levels, termed intermittent herbicide application (Mortensen et al., 1995) or patch spraying (Stafford and Miller, 1993, 1996); (ii) varying herbicide application (type, formulation, or rate) according to soil physical and chemical properties or weed characteristics (species, growth stage, and density); or (iii) some combination of the two approaches (Johnson et al., 1997). For prevention or preemergence weed control, site-specific application requires prior knowledge of historical weed distributions since no weeds are visible at the time of application. This knowledge can be obtained by mapping weed aggregation in previous years (Brown et al., 1990; Lass and Callihan, 1993).Weed control treatment or intensity can also be varied based on soil properties according to label or other recommendations if knowledge of the spatial variation of these properties is adequately known. Clearly, however, the county soil survey is generally not sufficient in describing the variability in surface soil properties affecting herbicide performance (Mausbach et al., 1993). Therefore, this precision weed management strategy requires intensive and expensive soil variability assessment. Intervention or postemergence weed control treats emerged weeds and intermittent or patch-spraying weed control systems require either a map of weeds present just prior to a weed control application or a real-time sensing of existing weeds at the time of application.The key is that weeds are aggregated into patches rather than randomly distributed. Weed mapping has been performed using remote sensing (Hanson et al., 1995). real-time ground-based detection using light reflectance (Duff, 1993; Felton et al., 1991; Felton and McCloy, 1992; Haggar et al., 1983; Nelson, 1993; Shearer and Jones, 1991; Shropshire et al., 1990) or digital image processing (Guyer et al., 1986; Sadjadi, 1996; Woebbecke et al., 1995), and GPSassisted scouting (Colliver et al., 1996; Stafford et al., 1996; Stafford and Miller,

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1996).There are limitationsto real-time systems in weed detection, in sprayer control, and in controlling herbicide type or concentration in real time. These include difficulty in separation of weeds from the crop, the time lag between weed detection and weed control application, spray pattern effects caused by the transport delay between change in herbicide type or rate and chemical discharge at a given nozzle, and nozzle performance as affected by herbicide type and rate change alterations of droplet size and flow rate for a given nozzle design or nozzle wear (Anderson and Humburg, 1997). Additionally, equipment size (boom width and nozzle spacing) will determine the scale of weed management zones, with larger equipment spraying larger areas relative to the nonweed areas (Johnson et al., 1997).Extensive research on sprayer control, weed detection, and spot spraying is under way (Ballal et al., 1996; Giles et al., 1996; Kirk and Tom, 1996; Nuspl et al., 1996). Regardless of the approach, the economic and environmental benefits of sitespecific herbicide application are derived from applying herbicides only to areas occupied by weeds at rates adequate for weed control and using environmentally safe herbicides or rates. The smaller the area occupied by weeds, the larger the potential benefits. The key, therefore, is to know the spatial distribution of weed populations. Lack of knowledge of weed distributions results in over- or underapplication of herbicides or other weed control measures-errors which have associated costs. The extent of overapplication(application to areas that did not require it or overapplication for the conditions present) is difficult to measure unless overapplication caused measurable injury to the crop. Underapplication, on the other hand, results in weed escapes that are quite visible and often lead to yield reductions and weed-induced harvest problems. The risk of underapplication of weed control practices, because it is most visible, will drive farmers’decisions more than potential environmental concerns of overapplication. The potential for weed escapes associated with inadequate knowledge of weed distributions or inability to control inputs sufficiently probably limits the adoption of precision weed management techniques. Johnson et al. (1997) provide some generalities with regard to precision weed management and we summarize them here. Weeds are aggregated within fields but are more aggregated at low populations than at high ones. However, the best economic returns to simulated site-specific weed management were obtained when weed pressure and aggregation were high. Weeds more costly to control and herbicides with high costs or with high environmental sensitivity favor the use of precision weed management. Therefore, inexpensive, low-hazard chemicals are less economical for precision weed management than expensive, hazardous chemicals. Use of economic threshold values increases the amount of area within a field that does not require herbicide. Economic thresholds were developed for uniform herbicide management and do not include the effects of aggregated weed distributions. Site-specific weed management may be more important for early emerging

ASPECTS OF PRECISION AGRICULTURE

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weeds than later emerging weeds since crops are more competitive later in the season. Stability in weed patches will affect the ability to predict weed distributions that do occur in localized patches, with weeds in no-tillage more stable than those in tilled systems. In the future, site-specific weed management may shift weed species by selecting for weeds with long seed dispersal mechanisms (nonpatchy distributions),with light reflectance properties or morphologies similar to those of crops that escape preemergence herbicides or that build resistance to herbicides in programs that use pre- and postemergence herbicides with the same mode of action. On the other hand, improved knowledge of the factors driving weed presence or absence, whether they be management, biological, or environmental, should lead to better understanding of weed biology and ecology which should lead to better weed management strategies. This aspect of precision weed management is yet to be fully explored. Ultimately, the extent of herbicide reduction in precision weed management is contingent on the weed infestation level, spatial distribution, soil heterogeneity, and performance of the application equipment. Forcella (1993) suggests that management of spatial variability is worthwhile as long as the degree of variability is large enough to justify the cost of obtaining the information and managing the differences accordingly. The growing body of evidence from research studies on intermittent or patch weed control suggests that considerable areas within fields are weed free or have weed densities below economic threshold levels (Colliver et al., 1996; Gerhards et al., 1996; Heisel et al., 1996; Johnson et al., 1995; Mortensen et al., 1995; Sadjadi, 1996; Stafford and Miller, 1996), but a thorough assessment of the economic and environmental benefits from these practices has not yet been done. The risk of weed escapes may also play a role in adoption patterns but this has not been addressed in most studies. Stafford and Miller (1996) conclude that methods for automatic detection of weeds have yet to be developed to the point where they can be used in production agriculture. Therefore, research should focus on the acquisition of knowledge and understanding of the spatial distribution of weeds and the benefits to sustainable weed management that can be derived from an increased understanding of the distribution. ii. INSECTMANAGEMENT. Precision insect management has potential because distributions of insect populations are spatially variable, in part because insects are mobile during at least part of their life cycle and in part because during the relatively nonmobile stages insects cluster in response to environmental (e.g., temperature and moisture) and behavioral responses (Heischer ef al., 1997; Taylor, 1984).Therefore, precision insect management has the potential to reduce insecticide applications and improve the efficacy of both prevention and intervention insect management strategies. A major difficulty with the management of insects is that their populations are highly dynamic and prediction of insect density is difficult or uncertain, both of which make it necessary to collect field esti-

46

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mates of pest density to monitor density over time (Fleischer et al., 1997).The difficulty and costs associated with repeated sampling of the spatial distribution of insect densities are considered by some to be an insurmountable barrier to the use of precision insect management in IPM programs (Fleischer et al., 1997). This difficulty is reflected in the limited research on the spatial management of insects. At the precision agriculture conference held in Minnesota in 1996 (Robert et al., 1996). only 1 of the 147 papers included in the proceedings dealt with insects, a spatial characterizationof corn rootworm (Diabrotica sp.) populations in corn by Ellsbury et al. (1996). The review of Fleischer et al. (1997) represents the major synthesis on this topic and is recommended reading. Beyond recognizing the sampling difficulties, they propose many important benefits of precision insect management worth repeating here. First, precision insect management is an enhancement of IPM in that it is a continuation along the path of using knowledge to replacing chemical inputs. A map of insect density is a more realistic model than a mean estimate of pest density currently used in IPM and may stimulate the use of IPM by farmers. Second, the alarming increase in resistance requires management strategies that maintain susceptiblephenotypes and ensure gene flow among susceptible and resistant subpopulations.This need, coupled with the need to maintain predator and parasitoid populations, supports the notion of leaving habitats untreated within fields, areas referred to as refugia. The creation of temporally dynamic refugia within fields is a new development in insect IPM and is a capability of precision agriculture.Third, the improved knowledge provided by precision insect management regarding insect populations across a landscapecan be used to enhance areawide and landscape pest management programs. Currently, the sampling problem inhibits progress in precision insect management. Commercially acceptable sampling schemes are not available and it is unknown which insects and which crops will respond to precision insect management (Fleischer et d., 1997). Like others dealing with other aspects of precision agriculture, Ellsbury et al. (1996) conclude their paper with the caveat about the need to know variability in that precision insect management should lead to reduced pesticide inputs “provided the spatial variation of rootworm populations can be economically and reliably monitored and predicted.” Despite sampling difficulties, positive effects of site-specific management have been reported. The development of new sampling schemes and technologies for rapid scouting of fields should create opportunities to develop new insect management interventions that are economical and environmentally friendly. c. Crop Management The potential for precision crop management derives from the genotype X environment interaction (G X E). The G X E refers to changes in the relative performance of cultivars across different environments. where environment includes

ASPECTS OF PRECISION AGRICULTURE

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all variables encountered in producing a crop, including soil type, soil fertility, moisture, temperature, and cultural practices (Fehr, 1987). Precision crop management could be achieved by varying cultivars and/or by manipulating planting geometry (populations, spacing, and seeding depth) according to variation in environments within fields. Varying cultivars or planting geomebies within fields has potential if the G X E is such that the rank among genotypes changes across environments but not if the differenceamong genotypes varies without any alteration in rank. The success or precision crop management, therefore, depends on whether cultivars are available that have been bred for adaptation to specific environments rather than adaptation over multiple environments. The technologies needed to vary cultivar or planting geometry are already available (Anderson and Humburg, 1997), but the agronomic basis for varying them is not clear. Farmers often plant more than one variety within a field and yield maps have revealed that a G X E interaction can frequently exist within fields. The value of varying cultivars within fields is illustrated by comparing the performance of two cultivars along parallel swaths within a field (Fig. 5). At some locations along the transect, the two cultivars do not vary, whereas at some locations cultivar A performs better than cultivar B or B outperforms A. The obvious management strategy is to plant each cultivar where it performs best and either cultivar where there are no performance differences. While intuitively appealing, there are three major limitations to variable cultivar management. First, of critical importance is the existence of cultivars that are responsive to specific environments. Many cultivars are bred to perform best over a range of environments, although yield mapping has revealed that this may not be as true for current cultivars as was previously believed. Criteria for variable cultivar selection may be based on risk management rather than overall performance. For example, cultivars with higher resistance to a specific stress may be preferred over less resistant cultivars even if under low stress the latter cultivar performs considerably better. Second, cultivar selection is based on expectations of past performance and there may be few data available to

Means A = B

Distance Figure 5 Illustration comparing the performance of two varieties dong parallel swaths across the length of a field.

48

FRANCIS J. PIERCE AND PETER NOWAJC

quantify the environmentunder which cultivar performance was evaluated. Therefore, there may be a limited basis on which to base within-field cultivar selection decisions. Finally, there may be limited knowledge about the spatial variation of environments within fields or the temporal variability of within-field environments is sufficient to make prediction difficult. Precision farming technologies do make it possible to evaluate cultivar performance by environment through precision planting and yield mapping. By collecting and analyzing yield maps documenting cultivar performance by environment across a region, the agronomic basis for varying cultivars within fields could be developed (Peterson, 1997). One problem may be that the life span of a commercial cultivar may be too short to develop site-specific recommendations. Conversely, the intensive on-farm evaluation of cultivars may accelerate the collection of cultivar performance data allowing for earlier release of improved cultivars or improved screening of poor performing cultivars. Varying planting geometry within fields has potential because plants respond to competition for light, water, and nutrients, and competition is reduced when spatial arrangement of plants is improved (Sojka et al., 1988). For plant distribution to be a limiting factor to crop yield, other limiting factors need to be eliminated (Porter er al., 1997). Sojka et al. conclude that a yield advantage may exist for a wide range of species if established in dense, uniformly spaced canopies than for more open canopies, provided early weed management, water availability,and fertility are adequate. The caveat suggests that within-field variability of environments might support variable planting geometries. The evidence specifically supporting variable seeding rates is limited and inconsistent. Bullock et al. (1998) reported on an extensive variable seeding rate study in corn at 170 individual locations across the corn belt from 1987 to 1996. They found that economically optimal plant densities and field quality were correlated. However, they concluded that variable rate seeding will only be profitable to the farmer who has sufficient knowledge about the relationship between yield and plant density for each section of his fields, far more knowledge than any farmer currently possesses. Others have shown positive responses to variable seeding corn. Barnhisel et al. (1996) reported higher corn yields from variable seeding rates based on topsoil depth, which greatly influenced yields at this location. They cite yield increases from variable seeding corn in Indiana of 0.125-0.878 mg ha-' as reported by Reichenberger (1996). Fiez and Miller (1995) evaluated seeding rates for winter wheat by landscapeposition in the Palouse region of eastern Washington State. They reported yield averaged over two site years increased 10.3% when seeding rates were doubled on the north backslope position but not on other landscape positions. Increased seeding costs were more than compensated by increased yields. Since the yield increases were attributed to increased spike density, they suggest that cultivars with greater tillering potential and adaptation to cooler, lower light conditions on north backslopes might result in similar yield in-

ASPECTS OF PRECISION AGRICULTURE

49

creases without increasing seeding rate. They refer to the results reported by Ciha (1984) that indicate soft white winter wheat cultivars significantly vary by landscape position and there was a cultivar-landscape position interaction for spikelets per plant and 1000-kernel weight. While not well documented, a potential for variable seeding rates is suggested by research on planting geometry. Interest in increasing plant populations in corn is fostered by the fact that modern corn hybrids tolerate higher plant density stress more than older hybrids (Tollenaar, 1991).This may be due in part to the fact that newer hybrids appear to have improved ability to resist barrenness and other types of injury associated with above optimum plant populations (Nafziger, 1994). Cox (1996) found a hybrid-plant density interaction for dry matter and grain yield in corn, suggesting that modern commercial corn hybrids interact with plant density, regardless of growing conditions. They state, however, that some modem hybrids do not tolerate high or even moderate plant density stress in dry years. A hybriddensity interaction would mean that some varieties would respond to variable seeding rates while others might not, depending on the stress conditions within a field. Thomison and Jordan (1 995) evaluated the effect of hybrid differences in ear growth habit and prolificacy and concluded they are of limited importance in determining optimum plant populations compared to environment, hybrid, and plant population main effects. Nafziger (1996) reported the net effect of doubles (two plants growing in the same space) was to increase corn yield, whereas the net effect of skips (missing plants) was to decrease corn yield, both resulting primarily through their effects on plant population. Plant spacing variability may increase with planting speed (Nielsen, 1995) or with soil conditions that affect plant stands. Therefore, where planting operations or soil conditions increase the stand loss (more skips) through poor germination or emergence, increasing the plant population in those locations in anticipation of reduced stands may increase yields over uniform seeding rates. This is consistent with Nafziger’s (1996) suggestion that the primary strategy to overcome the effects of skips in the row may be to increase the anticipated plant population. Gaps within rows can be more important in determining yield in soybeans than plant population differences when stand densities are moderate to high (Hicks et al., 1990). Devlin et al. (1995) evaluated the influence of environment on the optimum row spacing and seeding rate for soybean in Kansas from 1991 to 1993. At all 11 locations, soil fertility was adequate and rainfall levels and planting dates covered the range of conditions expected in Kansas soybean production. They concluded that narrow rows should be recommended for locations where adequate moisture and high yields (>50 bushels per acre) are expected and wide rows recommended where soil moisture is expected to limit grain yields. When soil moisture was limiting, grain yields were not affected by increased seeding rates, whereas at high yields, grain yields varied in response to changes in seeding rate, with grain yields more responsive to seeding rates in 18-cm row spacings than in 76-cm row spacings. Therefore, environment

50

FRANCIS J. PIERCE AND PETER NOWAK

and row spacing interact with seeding rate in determining the success of variable seeding rate for soybeans. Pioneer Hi-Bred (1997) reported that narrow rows for corn production increased yields but increased plant populations were not necessary for increased yields at narrow row spacings. However, increasing plant populations beyond 3 1,OOO plants per acre continued to increase yields in narrow rows but not in wide rows. Sojka ef al. (1988) identified the manipulation of row spacing as the most practical means of optimizing canopy geometry and this was supported by the data of Porter et al. (1997) for corn. Theoretically, opportunities for precision crop management appear to exist, but early field evidence does not support it. To some extent, a lack of response may result from plant breeding programs developing cultivars that tend to perform well over a range of environmental conditions rather than to optimize for specific environments. Comparison of cultivars under varied management developed under this scenario is a self-fulfilling prophecy in the sense that cultivars created to perform on the average will on average outperform those that do not. Whether breeding will focus on improvements in traits other than yields or on yielding ability when nutrients and water are suboptimal or continue to focus on yield ability in normal or highly favored environments is under discussion (Duvick, 1996). Regardless, a glance at the plethora of yield maps revealing spatial variability in crop yield is sufficient to raise expectations about what might have happened if a different cultivar had been planted or something different had been done with respect to planting geometry. Certainly, something as simple as varying planting depth based on soil moisture in the seed zone (Price and Gaultney, 1993; Weatherly and Bowers, 1997) should find some utility in production agriculture. We will not know what precision crop management can or will bring to production agriculture until it becomes a research priority in the crop science community. d. Precision Water Management

In most crops, growth can proceed unimpaired and crop yield can be maximized only when the soil moisture potential remains high (and water remains readily available) continuously throughout the growing season. -Hillel (1990) Water is critical to crop productivity since crop yields generally increase linearly with water transpired by a crop (Howell, 1990).Excess water (waterlogging) can induce nutrient and aeration stresses and encourage pests that reduce yield and quality (Wesseling, 1974; USDA-ERS, 1987). Water management is also critical to water quality because techniques to optimize water relations for plants can also impact fate and transport of pollutants to surface water and groundwater. Naturally, the adequacy of water for plant growth is primarily related to the amount, frequency, and distribution of rainfall, soil properties as they affect processes that regulate soil water availability to plants, and landscape properties that regulate the

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hydrologic cycle within a watershed. Three approaches to precision water management are therefore apparent: (i) variable rate irrigation, (ii) matching agronomic inputs to water availability defined by soil and/or landscape properties, and (iii) drainage. i. VARIABLERATEIRRIGATION.Hillel (1990) defines a well-managed irrigation system as one that optimizes the spatial and temporal distribution of water so as to promote crop growth and yield and to enhance the economic efficiency of crop production (maximum net return). He further states that since the physical circumstances and the socioeconomic conditions for irrigation are site specific (and often season specific) in each case, there can be no single solution to the problem of how best to develop and manage an irrigation project. Hillel unknowingly defined the rationale for variable rate irrigation management. Considerable progress has been made with variable rate irrigation systems primarily with sprinkler irrigation provided by center-pivot and linear-move machines (Camp and Sadler, 1994; Evans et ul., 1996; King et ul., 1995, 1996; McCann and Stark, 1993).These site-specific irrigation systems require high spatial resolution (currently 10-30 m) achieved by adding more discrete control between contiguous elements of the machine, all at higher costs than those of current systems (Sadler et al., 1998).Variable irrigation is coupled with precision nutrient and pest management via chemigation, in part because variable irrigation facilitates increased management precision in space and time (King et al., 1995) and in part because it may not be economically feasible to site-specifically manage only for water (Evans et al., 1996).The uniformity of chemical application depends on the uniformity of water application (King et al., 1995), requiring injection equipment that can vary the amount of chemical injected into the boom in proportion to the flow rate of water in order to achieve the desired chemical application rate (Sadler et al., 1996). Success of precision irrigation management has been achieved with regard to application control (Camp etal., 1996;Wall et al., 1996).The key to the agronomic success of precision irrigation management depends to a large extent on how well the water needs of the soil-plant system can be measured or predicted and the accuracy of water application (and agrichemical) prescriptions. The value of precision irrigation management depends on whether increased profits and the reduction in pollutants more than offset the cost of increased resolution needed in irrigation systems to apply irrigation and chemigate site specifically. Evans et al. (1996) concluded that the hardware, software, and communication systems to deliver a prescription work well but a major limitation lies in the ability to interpret spatially variable data and develop rational and coherent site-specific prescriptions. They further conclude that Due to the random variability in water application distributions due to wind, start-stop operations of the self-propelled machines, and sprinkler pattern vari-

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ations combined with the low cost of water and N fertilizers, it is probably not economically feasible to site-specifically manage only for water and/or nitrogen. King et al. (1995) agree that the lack of a service infrastructure to generate and deliver the maps needed to control and manage the irrigation system throughout the season remains a major limitation to variable water application. They identified the lack of variable rate sprinklers as the other major limitation, although they overcame this limitation to some extent by the use of multiple sprinklers and sizing the sprinkler nozzles to provide a stepwise variable application rate. Because of the large temporal variability in water availability, sensing plant stresses to determine water application rates may be useful (Stone et al., 1996), but its efficacy is not fully determined. Like other factors, variable imgation management requires sufficient knowledge about the spatial and temporal variability of the factors that regulate water availability to plants and water use in plants in order to achieve precision management. Thus, its potential will vary by soils, landscape, and climate. ii. SOIL-LANDSCAPE WATER MANAGEMENT. The potential for precision management of agronomic inputs increases with spatial variability in water availability within a field. Differences in water availability within a field are governed by (i) the Occurrence of dissimilar soil types; (ii) the presence of soil degradation processes (e.g., erosion, compaction, and salinization); and (iii) variation in landscapes. The evidence for spatial variation in water availability is clear. Hanna et al. (1982) reported that north-facing slopes had 20% more available water in soils than south-facing slopes throughout the year, whereas soils on east-facing slopes were the driest. Crop yields are often highest in the lower slope positions where soil water and nutrient contents are higher (Fiez et al., 1994a,b; Halvorson and Doll, 1991; Jones et al., 1989; Mulla et al., 1992; Spomer and Piest, 1982). Eroded soils often have lower infiltration rates and lower available water than their noneroded counterparts (Daniels et al., 1985; Langdale et al., 1979). Some portion of landscape variability can be attributed to the variation of soil properties with landscape position (Brubaker et al., 1993; Khakural et al., 1996; Kreznor et al., 1989; Walker et al., 1968), whereas some is attributable to redistribution of water within a landscape due to either runoff or subsurface horizontal flow of water (Miller et al., 1988; Stone et al., 1985). Compacted soils reduce infiltration or restrict plant roots, thereby limiting water availability to plants (Lindstrom and Voorhees, 1994). Areas of high salinity are known to reduce yields (McKenzie et al., 1983). Nonuniformity is easily demonstrated in dryland areas of the United States where crops generally use all the water that is available each year and stored soil water is an essential source of water for crop production (Hanna et al., 1982). Additionally, topographic trends in soil water storage or crop growth can be important even where it is not expected, such as the sand-plain landscape in central Minnesota (Tomer et al., 1995).

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Knowledge of the spatial distribution of water availability can be used as a basis for site-specific input recommendations. There are three approaches for mapping soil water variability (Bell et al., 1995): (i) county soil surveys; (ii) interpolation of a network, usually a grid, of point samples to estimate spatial distribution of soil properties or water content; and (iii) soil-landscape models to estimate spatial patterns of soil water availability. The internal variability of soil map unit delineations in Order II county soil surveys may limit their use in site-specific management (Kellogg, 1961; Mausbach et al., 1993), although where soil map units are dissimilar, map units may correspond to crop yield variability. Interpolation techniques are used extensively in precision agriculture and their use and limitations have been discussed previously. The presence of small-scale spatial variability in soil physical properties (Peck, 1983) and the high cost of network sampling may limit its use in mapping water availability. Site-specific soil water monitoring is used to some extent as a basis for variable rate irrigation (Evans er al., 1996) and in landscape studies (Khakural et al., 1996). Statistical models of soil-landscape relationships offer opportunities to map spatial patterns of soil properties where relief or some landscape attribute is a primary factor contributing to soil variability (Bell et al., 1992). Soil-landscape models are important because terrain modifiesthe distributionof hydrologic and erosional processes (i.e., soil water content, runoff, and sedimentation)and soil temperature in fields (Moore et ul., 1993), all important in regulating crop productivity and off-site movement of agrichemicals. The depiction of the spatial variability of topography with a regular grid of elevation observations is referred to as a digital terrain model (DTM) when attributes of a landscape are of interest and a digital elevation model (DEM) when merely relief is represented (Weibel and Heller, 1991). A DTM allows the estimation of derivatives of elevation including slope, curvature, aspect, catchment area, and surface drainage proximity variables that correlate to soil and land qualities (Bell et al., 1992, 1994; Moore et al., 1993; Odeh et al., 1995). A comprehensive review of relevant techniques and applicationsof DTM is presented by Weibel and Heller (1991). The value of DTM is that it increases the resolution of soil maps for use in site-specific management and in environmental modeling by using terrain attributes to spatially distribute estimated soil attribute data (Moore et al., 1993). Therefore, terrain modeling efforts have focused on its application to soil survey to model and depict the spatial variability of soil horizons in reference to the topographic surface (Bell et al., 1995) and spatial application of simulation models to evaluate current and potential management practices regarding their effects on crop production and the environment in space and time (Verhagen et al., 1995a,b). The extent of use of soil-landscape models is currently limited in field applications of precision agriculture. However, high-resolution DEMs can easily be created using DGPs and laser-based systems with high vertical accuracies (Clark, 1996; Lange, 1996). As elevation maps become available, soil-landscape modeling techniques such as DTM will be increasingly used in precision agriculture.

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iii. DRAINAGE.Poor drainage is often cited by farmers as a source of yield variability within fields. Many options for drainage currently exist and can be applied site specifically.Therefore, there is little need to design site-specificdrainage practices. The decision to install drainage is economically, not technically, limited. Regardless of scale, the decision to drain hinges on the expectation of returns on investment that exceed costs of installation. Site specifically, the cost of draining portions of fields or small isolated areas may be higher because costs to connect to drain outlets are proportional to the distance to the outlet. Yield mapping and GIS have made the cost/benefit calculation easier because the yield depression due to poor drainage can be accurately assessed if sufficient years are included in the calculation. Drainage, therefore, is a site-specific, economic decision based on the conditions at each site and cannot be generalized.

3. Evaluation of Precision Agriculture We have probed the technological capabilities and agronomic feasibility of precision agriculture, i.e., the technologies and techniques for assessing and managing spatial and temporal variation. Essentially, we have argued that initial forms of a precision agriculture system are technologically feasible and based on credible agronomic principles. However, being technologically possible while being based on sound scientific principles does not necessarily establish utility or value in the process. An evaluation of precision agriculture is also required. Three important evaluation issues surrounding precision agriculture remain unresolved: economics, environment, and technology transfer. The economic evaluation focuses on whether the documented agronomic benefits-translated into value through market mechanisms-exceed the technological and service costs. Environmental evaluation focuses on whether precision agriculture can improve soil, water, and the general ecological sustainability of our agricultural systems. Finally, and perhaps most important, is the question of whether this bundle of enabling technologies and agronomic principles will work on individual farms. Being technologically feasible and at least economically neutral are necessary conditions but, as will be shown, may not be sufficient conditions for transfer to farms. Examining precision agriculture from the perspective of the technology transfer provides a context for the ongoing debate over the scale neutrality of this technology. a. Economics The most important fact regarding the analysis of the profitability of precision agriculture is that the value comes from the application of the data and not from the use of the technology. This can be contrasted with traditional agricultural innovations in which the value is derived from the use of the new technology, e.g., new seed genetics that increase yield or a new herbicide that reduces yield loss. However, the enabling technologies discussed earlier only generate data that have

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to be analyzed into practical information (Boehjle, 1994) so that it can influence management decisions. If any value is to be derived from precision agriculture, it will come from the resulting management decisions and not through the use of the enabling technologies. The data have no value. It is only through the interpretation and application of data that value may be derived (Lowenberg-DeBoer and Boehjle, 1996).This is a critical distinction that needs to be emphasized and reemphasized because it dictates two postulates that should drive any analysis of the economics of precision agriculture. First, a theoretical or modeled analysis of the economics of precision agriculture will not necessarily have the same result as an actual application on a farm. One can model the costs of the enabling technologies (Kohls, 1996) and, based on agronomic principles, determine the scope and extent of differences in production efficiency (i.e., increases in yield per unit of input). However, an actual application on a farm may have different results because of variation in the managerial capacity of the operator relative to responding to temporal events such as climate and pest or market cycles. The profitability will differ if a piece of equipment was not properly calibrated or erroneous agronomic decision rules were used. In summary,real-world profitability of precision agriculture is highly dependent on the human capability to manage dynamic forms of spatial and temporal variability. Second, the potential profitability of precision agriculture is directly related to the nature and extent of variability in the biophysical setting in which it is applied. If this biophysical setting was homogeneous, then there would be no difference in profitability between a precision and a conventional (“one rate fits all”) agricultural system. As the heterogeneity in this biophysical system increases, especially in those salient situations or processes in which valid agronomic principles exist, then the potential for profitability will increase. Of course, at some point in this implied linear relationship the extent of heterogeneity will overwhelm the current capability of the enabling technologies and the agronomic principles. Nonetheless, the potential profitability of precision agriculture is strongly correlated with the biophysical heterogeneity of the setting to which it is applied. At issue is the extent that these two postulates have influenced research on the profitability of precision agriculture. Evidence of the economics driving the adoption of precision agriculture is fragmentary and incomplete relative to these postulatesfor several reasons. First, as has been noted (Lowenberg-DeBoer and Swinton, 1997; Nowak, 1997), there is no precision agriculture system. Rather, there are different clusters of technologies and tools that may be adopted in different sequences and combinations,each of which may be used with varying degrees of efficiency under different cropping system conditions. Moreover, the impacts of precision agriculture applications may be masked by the interaction with other new technologies or techniques simultaneouslyintroduced into the operation. New herbicides, crop genetics, or tillage tools may influence the overall profitability of a production system. In summary, it is difficult to assess the profitability of a system

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when there is little consistency in the nature of the system between comparative sites. At the other end of the spectrum is what may be called a partial analysis of the profitability of precision agriculture. To date, the profitability of precision agriculture has only been addressed by a limited number of case studies, focus groups, antidotal stories, or modeling efforts (Denton, 1996; Holmes, 1993; Hornbaker, 1996; Olson, 1995; Schnitkey er al., 1996; Swinton and Ahmad, 1996). Much of this effort is focused on examining the profitability of a specific aspect of precision agriculturefor a specific producer or a specific cropping practice such as fertility. No research studies have examined the economics of precision agriculture for an agroecological region, the type of production system, or across all dimensions of a production system. Many different factors and methods may be employed in an analysis of the profitability of precision agriculture (Fairchild, 1993). Lowenberg-DeBoer and Swinton (1997) analyzed many studies that used partial budget analysis but were characterized by little consistency in exactly what costs were considered. They suggest a broader investment analysis to capture the flow of costs and benefits across time. A related problem is the relative ease of capturing costs (Kohls, 1996), whereas benefits are more difficult to conceptualize and measure. Besides yield differences or reduction in input costs (i.e., efficiency), precision agriculture may also have benefits with regard to managing differentiated products, food safety, increasing the span of control, and environmentalprotection (Lowenberg-DeBoer and Boehjle, 1996; Nowak,1997). Future research on the economics and adoption of precision agriculture need to carefully consider the earlier postulates on how precision agriculture differs from traditional agricultural technologies. While there is consensus that profitability will dictate the adoption rate of precision agriculture technologies, care needs to be exercised in the dominanceattributed to economic rationality in these decisions. This comment is based on the apparent lack of economic rationality driving many of the adoption decisions reported in the media and case studies. That is, the adoption decision does not appear to be driven by proven short-term profits based on a partial budget analysis or long-term gains substantiated by an investment analysis. Rather, one reads in this collection of adoption stones a strong underlying belief that the information-especially that associated with spatially referenced yield data-will be valuable even if that value does not manifest itself in the short term. The spatial and temporal dimensions of precision agriculture may represent a new type of information whose value has not yet been established under traditional accounting procedures or existing market mechanisms. Profitability remains important, but current adopters of precision agriculture also report a belief that the information collected under this method will somehow, sometime pay for itself. For example, in addition to the potential to enhance production efficiency, this new type of information has been associated with on-farm experimentation, thus reducing dependence on external parties (Fortin and Pierce, 1998). It can also pro-

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vide value to the farmer in evaluating the worth of products and services offered by vendors. Furthermore, aggregated data from local firms offering mapping or analysis services may acquire additional value for private-sector vendors who develop marketing or promotional strategies based on product efficacy across a local area or region (Nowak, 1997). Finally, there is the potential to quantify environmental benefits for either self-satisfaction or regulatory avoidance (Larson et al., 1997; NRC, 1997).Consequently, rather than strict economic rationality, many current adoption decisions appear to be driven by a future and unknown potential value where the adopter is waiting for the science to “catch up” with technological applications. A more traditional economics-oriented analysis of the potential profitability of precision agriculture is found in the NRC (1997) report. This discussion, however, results in three paradoxical conclusions. First, the potential profitability of precision agriculture is directly related to the extent of natural and anthropogenic sources of manageable variance. Producers who have effectively used analytical tools (e.g., soil testing and scouting) to manage these sources in the past actually have less potential for profit versus producers who have used a “one-size-fits-all” farming approach. At the same time, the prediction of the traditional adoption model (Rogers, 1995)that the more sophisticatedor better managers will be among the first to adopt appears to be holding relative to precision agriculture. This leads us to the conclusion that those who will benefit the least economically are among the first to use precision agriculture. The second paradox is related to the sources of variation and the potential profit to be derived from the process of applying precision agricultural technologies. While indicators of the adoption of precision agriculture appear to be following the traditional logistic rate of adoption (Rogers, 1995), the greatest sources of potential profit have yet to be developed. Potential profits from managing nutrients that are relatively stable in space and time (P and K) are significantly less than profits from managing weeds, insects, disease, water, and genetics across space and time. These latter factors are much more spatially and temporally diverse, represent a greater proportion of production expenses, and generally have a greater impact on production outcomes. They will also require further development of the enabling technologies and agronomic decision rules if these larger profits are to be realized. Consequently, the adoption and diffusion of precision agriculture is continuing at expected rates, whereas the greatest benefits from this technology are yet to be realized. One might paraphrase the English poet John Donne by stating that “no man is an island onto himself’ when it comes to the process of managing precision agriculture for profit. While we have constantly alluded to the farmer or producer adopting and managing these enabling technologies, the simple fact of the matter is that the outcome of this process is highly dependent on the support of suppliers of products and services. This infrastructure, a concept discussed more fully later,

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has a major influence on the ability of any producer to achieve profits with precision agriculture. Thus, the third paradox surrounding the economics of precision agriculture is the fact that the extent of profitability for any producer will be strongly influenced by the quality of the local infrastructure. Precision agriculture is a “team” activity, and although there are a few individuals who can make it on their own, the vast majority of farmers will require quality support products and services if they are to achieve a profit. b. Environment In our opinion, one of the greatest constraints to managing for improving water quality is the inability of agricultural producers to control inputs in ways that account for the positional and temporal variability in growing conditions across a field. -Evans et al. ( 1996) Potential improvements in environmental quality are often cited as a reason for using precision agriculture (NRC, 1997). Reduced agrochemical use, higher nutrient use efficiencies, increased efficacy of managed inputs, and increased protection of soils from degradation (erosion) are frequently cited as potential benefits to the environment (Engel and Gaultney, 1990; Larson et al., 1997). Some pose precision agriculture as a “win-win for the environment” (Farm Industry News, 1994). While the impacts of precision agriculture on the environment are assumed positive, proof that it is even benign in its environmental impacts is generally undocumented. Studies evaluating the environmental benefits of precision agriculture are limited (Khakural ef al., 1995; Mulla et al., 1996; Verhagen et al., 1995a). Negative impacts of precision agriculture could derive from increased aggression by farmers on site-specific potentials or problems (Pierce, I997b). Furthermore, many factors may limit reductions in chemical applications in some situations and encourage increased use in others (NRC, 1997). Some doubt that precision agriculture can materially improve the environmental performance of agricultural production systems because current systems are fundamentally flawed compared to natural ecosystems and precision agriculture cannot fix these flaws. Groffman ( 1997) argues that precision agriculture techniques do not address many of the key factors that cause poor environmental performance in agricultural systems, that agricultural inputs are high relative to ecological processes we hope to manage, and that these processes vary on scales that are incongruent with precision management techniques. Specifically, he discusses four key factors that constrain the environmental performance of agricultural production systems: (i) temporal discontinuities in nutrient cycling processes (a non-crop period that provides no plant nutrient sink to prevent losses); (ii) high levels of soil disturbance (mainly through tillage) that exacerbates temporal discontinuities in nutrient cycling processes, creates small-scale spatial discontinuities in this

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process, and increases the susceptibility of agricultural systems to erosion; (iii) high levels of nutrient enrichment that foster high losses unless fertilizer use is reduced dramatically; and (iv) lack of resistance to the disturbance of extreme climatic events (i.e., high rainfall) that cause a major portion of nutrient and sediment losses. The uncertainty about environmental benefits of precision agriculture is underscored by the fact that environmental improvements by themselves offer little incentive for farmers to adopt precision agriculture and will be an incentive only where producers bear at least a share of the cost of agricultural pollution (NRC, 1997). The notion that agriculture is inherently leaky is reason to pursue the potential of new technologies such as precision agriculture. That soils vary considerably in their potential to pollute the environment further supports this view. For example, soil erosion (Larson er al., 1983)and nitrate leaching (Mueller er al., 1995)are major problems on only a portion of cropland in the United States and can vary within fields (Larson et al., 1997). We started this discussion with the notion that the temporal component of spatial variability is what makes precision agriculture difficult but perhaps more rewarding. While it is probably not possible for precision agriculture to make agricultural production systems as tightly coupled to ecological processes as Groffman (1997) would like, there are many opportunities to reduce inputs, increase use efficiencies, and protect the soil using precision agriculture technologies than there are without using them. We will briefly discuss the potential opportunities for precision agriculture to improve environmental quality. However, keep in mind that evaluating the environmental performance of precision agriculture is problematic since all aspects of precision agriculture to date are merely components of a system rather than a system in itself (i.e., no precision agricultural systems exist). Pollution of the environment by agriculture requires the presence of a pollutant in a form that is suitable for transport in air or water by suspension, volatilization, or dissolution. Precision agriculture can be of benefit to the environment by reducing the application of a given input, affecting its mobility or persistence, and/ or regulating the mechanisms responsible for its transport. Farmers must manipulate these aspects not discretely but over the continuum of space and time represented by the nature of inputs (type, quantity, volatility, sorptivity, solubility, and persistence) and the heterogeneity of the biophysical environment where inputs are introduced. Environmental benefits of precision agriculture could include ecologically based management practices such as the introduction of refugia within fields or landscapes to preserve or enhance biological and genetic diversity (Fleischer et al., 1997) and precision management of the non-crop period (Pierce and Lal, 1991). The environmental impacts of precision agriculture will be difficult and costly to quantify, particularly as the temporal component of variability increases. What follows is a brief listing of the areas in which potential environmental benefits of

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precision agricultureexist (We recognize that these are not well documented in the literature): Reduction in nutrient inputs where nutrient levels or supply capacity are sufficient to meet the nutritional requirements of crops: This may have short-term benefits in areas in which residual nitrates can be utilized by the crop and thereby prevented from leaching (Hergert er af., 1996) but may not have short-term benefits, as in the case of P, in which it may take decades for soil test levels to be depleted from high levels (McCollum, 1991). Addition of nutrients where needed to meet crop nutritional requirements should have minimal impact on the environment.Thus, redistribution of nutrients through precision agriculture may or may not decrease total fertilizer use but may have environmental benefits within fields. Reduction in pesticide inputs through variable rate applications:This can be particularly useful in patch spraying where significant portions of a field may not receive any pesticide treatment (Johnson et af., 1997). Reduction in irrigation water inputs in areas subject to leaching using variable rate imgation. Minimizing or avoiding nutrient and pesticide additions where the potential for significant losses exist: This can be accomplished by varying nutrient or pesticide type, formulation, and rate according to soil conditions for erosion, leaching, runoff, and volatilization. For example, Sharpley (1997) recommends the use of environmental soil tests for P and the delineation of transport zones within a field to minimize the losses of P to surface waters. Increased erosion control or a reduction in runoff achieved through site-specific tillage and residue management: Different parts of a field erode at different rates, making precision tillage and residue management very desirable (Voorhees ef al., 1993). Pierce and Gilliland (1997) suggest that precision agriculture provides the necessary level of design and control needed to achieve soil quality control on the land. It is quite remarkable that an analysis of yield maps has not been performed to demonstrate the effects of erosion on productivity, thereby quantifying the importance of erosion control on the farm. Management of field and landscape buffer zones not possible without precision management systems. Because of spatially variable control, buffer zones and refugia can be managed without interruption of farming operations creating possibilitiesfor increased biological diversity and interception of pollutant transport to surface water. Although precision agriculture is intuitively appealing, environmental benefits are not necessarily forthcoming. However, there is equally little evidence to suggest that precision agriculture will increase environmental pollution or that it cannot, as Groffman (1997) suggests, address the fundamental environmental problems facing agriculture.The best interest of agriculture is served by documenting

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the environmental performance of precision agriculture over the space-time continuum. c. Technology Transfer The third critical dimension of evaluating precision agriculture involves examining the technology transfer process. Enabling technologies can make precision agriculture feasible, agronomic principles or decision rules can make it applicable, and enhanced production efficiency or other forms of value can make it profitable. However, will precision agriculture be used on a farm? The reality check for this scientifically feasible and potentially economically viable bundle of technology comes from an assessment of what must happen on a farm to make it work. We know that producers have three basic questions relative to the adoption of precision agriculture: Will it work? Will it pay? and Can I make it work in my operation? While basic and applied research may answer the first two questions, the third question is highly dependent on farmers and the context in which they operate. Research of a different sort is required to answer this question-research that has been subnamed under the label of technology transfer. The term technology transfer could imply that precision agriculture occurs when individuals or firms simply acquire and use the enabling technologies (i.e., it is transferred to them). While precision agriculture does involve the application of enabling technologies and agronomic principles to manage spatial and temporal variability, the key term is manage. Much of the attention in what is called technology transfer has focused on how to communicate with the farmer (Crookston, 1996; Holt and Sonka, 1995; Peterson and Beck, 1997). However, communicating the answers to the three basic questions of a farmer listed previously is different than the substantive nature of those answers. If farmers are going to manage precision agriculture, then we need to understand what will influence this management process. It is here, and not in communicationtechniques, that we will begin to understand what drives the technology transfer process. Three factors will influence the transfer or diffusion of precision agriculture among a population of farmers: distributions of human capital, spatial locations and the nature of infrastructure support, and the compatibility of the enabling technologies to the needs and capabilities of producers. According to Bouma (1997), farmers make strategic, tactical, and operational decisions relative to agriculture in general and precision agriculture in particular. Strategic decisions surround the nature of the farm such as the use of crop rotations, tactical decisions focus on things such as crop varieties, and operational decisions are those focused on mounts and timing of inputs for the selected crop varieties. Bouma (1997) observes that most precision agriculture research has focused on improving the efficiency of operational decisions. However, the decision to adopt precision agriculture clearly occurs at the strategic level. Therefore, technology transfer is the process of getting farmers to make a strategic decision

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on how operational decisions will be made in the future. How do we explain why some farmers make this decision whereas others remain skeptical, uninformed, or negative regarding precision agriculture? On an individual level, perhaps the best predictor of the adoption and diffusion of precision agriculture will be based on the classic adoption model (Rogers, 1995), which places a heavy emphasis on the social psychological or human capital attributes of the potential adopter. Others have already hypothesized that the adoption of precision agriculture will follow the pattern predicted by this model (Lowenberg-DeBoer, 1997; NRC, 1997). Individuals who have greater risk propensity and who are younger, better educated, able to manage transition costs, and are well integrated into diverse information networks will be among the first to adopt precision agriculture. Individuals who are among the first to adopt are called “innovators” in the model and comprise approximately 2.5% of the population. These innovators then influence the early adopters in local areas or neighborhoods who comprise the next 13.5%who adopt the innovation. They are followed by the early majority and late majority, each comprising 34% of the population. The final group is composed of those who are either unable or unwilling to adopt for a variety of reasons. This “trickle down” process through social contagion and market processes has been well documented (Rogers, 1995). However, an individual’s social-psychological, economic, and demographic profile may be necessary, but it is not sufficient for the adoption of precision agriculture. Access to resources and support through local infrastructure is also critical for adoption. That is, one may be an innovator, but without access to dealerships, consultants, experienced farmers, manufacturing representatives, or other experts, adoption of precision agriculture is more difficult. A parallel can be found in the computer industry. There is a well-developed computer industry infrastructure in the “Silicon Valley” of California.The entrepreneur in this area is supported by the levels of innovation, adaptation, and competition for success, whereas entrepreneurs in other regions are penalized because they do not have access to similar supporting resources. It is relatively easy for the hardware or software designer to find supporting parts, services, and ideas that may not be available to the entrepreneur in the midcontinent or other regions. The same situation also applies to agriculture in general and precision agriculture in particular. Since private and public supporting resources are not equally distributed across the agricultural production landscape, some producers are going to have a greater opportunity to adopt precision agriculture than others regardless of individual managerial or economic attributes. Related to this notion of a lumpy distribution of supporting resources is the relative role of the private and public sectors. The role of infrastructure in diffusing precision agriculture is contrary to many claims of the emerging information age. Here, information technologies are supposed to create equal access to ideas, data, and management information. This “leveling of the playing field” due to information technologies, however, is not occurring with precision agriculture,

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due largely to the complexity and fragmentation of current tools and analytical processes. Making precision agriculture work on a farm is a very difficult process involving contradictory agronomic recommendations, incompatible hardware, and high learning costs with few specific objectives (other than profitability). All this creates a situation in which contracted expertise, products, and services from off the farm become the only viable strategy. This situation will change as more protocols, standards, and a uniform set of expectations are developed, at which point precision agriculture will help agriculture move into the information age. What is the importance of the private sector relative to the public sector in influencing the strategic decisions of farmers? As Wolf (1998) notes, After more than 100 years of public-sector leadership in agriculture research and extension in the United States, we appear to be entering a new era in which private and corporate interests have significantly increased authority and responsibility as information providers. In essence, the private sector largely dictates the production decisions of commercial farmers in the United States today. Since precision agriculture is being largely designed for commercial applications, we would expect the private sector to have an inordinate influence on current technology transfer efforts for precision agriculture, affecting the geographical areas where it is available, the extent to which the various products and services are accessible to the producers in these areas, and the marketing strategies used to promote precision agriculture. Dealerships, consultants, or manufacturing representatives,like the customers they serve, can be aggressive or conservative relative to new technologies. Furthermore, the geographic concentration of customers largely dictates the location of these supporting facilities due to basic market mechanisms.Agricultural technologies, precision or otherwise, are designed based on potential market demand and technological feasibility. Factors such as the number of potential customers, their ability to invest in new technology, and the geographic concentration of these customers all help to determine the spatial market demand for precision agriculture. Publicsector organizations, such as the land grant university or the USDA, will focus their precision agriculture efforts on problem solving, efficacy testing, and basic research. In essence, however, it needs to be emphasized that most of the technology transfer surrounding precision agriculture will be market driven from the private sector, commodity specific, and concentrated in certain agricultural areas. The final point about technology transfer concerns the compatibility of the enabling technologies to the needs and capabilities of the farmers. Much could be said about this issue, but attention has focused on whether precision agriculture is a scale-neutral technology.A pure economics approach to this issue would address whether there are equivalent proportionate investments across scales of operations. This approach, however, ignores several spatial dimensions associated with access to this technology. First, precision agriculture technologies have not been

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developed and adapted to work with all agricultural commodities. There was a clear sequence of developmentfrom certain cash grains to some horticulture crops to crops such as cotton. Producers of some agricultural commodities,regardless of scale of operations, cannot adopt this technology because it has not been developed for their commodity. Second, as discussed earlier, the support infrastructure has a lumpy spatial distribution. Producers in some areas will either not be able to adopt or have a very difficult time adopting because of the lack of these supporting resources. Finally, farmers are not equivalent relative to issues such as computer literacy, technical competence, or abstract reasoning. To the extent that current forms of precision agriculture call for these skills, it is not compatible with a certain proportion of these farmers. Consequently, while the scale neutrality of precision agriculture has been a contentious issue, understanding what influences the potential management of precision agriculture provides more insight than semantical debates. Precision agriculture is not scale neutral for the reasons discussed as part of the technology transfer process. Nor is there any inherent reason why it should be other than a social value related to equity. Understanding why this is the case should move the debate out of the normative and political arena and allow future research to focus on how to accelerate technology transfer for targeted regions of the country. What happens as new products or management techniques are designed specifically to work under precision agriculture?Consider the commercial release of the Windows 98 operating system. There may be little interest in moving to this new operating system as users perceive little value in that decision. The utility of a new operating system will emerge as developers create useful products that will only operate efficiently under Windows 98. The same was true of the transition from Windows 3.1 1 to Windows 95. An analogous situation exists relative to precision agriculture, a new operating system for crop production. Precision agriculture presents a new set of diagnostic, analytical, and management tools oriented toward spatial and temporal variation that must produce utility with old products and production models. Much of the current agricultural machinery, nutrients, pesticides, and genetics were designed to work with the old one-size-fits-all agricultural system that has evolved since the 1950s (Nowak, 1997). Should new products and techniques specifically designed to work in the new operating system of precision agriculture emerge, the paradigm for technology transfer changes dramatically to one in which to reap the benefits of new products and practices farmers must be equipped for precision agriculture. Consider, for example, current genotypes that optimize performance across a wide range of microenvironmentsmay not be able to compete against a suite of new genotypesthat maximize performancein selected microenvironments. It is conceivable that a series of these new genotypes developed just for precision applications will significantly outperform the old one-sizefits-all genotype currently available. Few have yet to realize how emerging biotechnology applications will also support precision agriculture and vise versa.

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Imagine nutrients whose formulation is designed for microenvironmentsbased on reducing mobility while also being in synchronization with the needs of the plant across the growth cycle. Similar “designer” approaches can be developed relative to pesticides and water management. In short, we have been evaluating the utility of a new production system under the playing rules of the old system. The technology transfer issues associated with the managerial capability of the operator, the spatial distribution of infrastructure,and the compatibility of the technology to individual farms will change radically as precision agriculture continues to develop. Anticipating these changes is important for evaluation. It is also important for understanding how the assessment and management of variation in precision agriculture must be performed. Researchers need to examine current developments. However, we also need to give attention to the “what could be” futures for precision agriculture.

III. CONCLUSIONS The value of the emerging system of precision agriculture will ultimately be measured by its success in managing the space-time continuum of all aspects of crop production. This, in turn, will have implications for environmentalprotection and farm profitability. Early successes in precision management have come where spatial dependence is moderate to high and temporal dependence is low, e.g., certain aspects of soil fertility and weed patch management. We believe that the longterm application of precision management where temporal dependence is low will reduce spatial dependenceand increase predictability. This should make precision management easier and more efficient as a system but potentially less profitable with respect to a given component. For example, through precision management, soil test P levels within a field will evolve to the maintenance plateau levels, thereby linking P fertilizer rates to crop removal rates estimated from yield maps. For spatially dependent entities, high temporal dependence makes precision management more difficult and requires more of an intervention rather than a prevention management strategy. Both IPM in general and N management in humid regions exemplify this situation. Managing entities with a high space-time dependence, however, may be potentially more profitable and environmentally beneficial than managing high spatial-low temporal-dependententities. These hypotheses remain to be tested but appear in principle to be supported by existing studies. Perhaps most problematic is the notion that the more complex the space-time situation, the more difficult it may be for farmers to manage without sophisticated decision aides. Technology transfer may be increasingly difficult as the complexity of precision agriculture increases. One conclusion is clear: To achieve success, precision agriculture must evolve

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from a collection of partially compatible technologies into an integrated management system able to accommodate the diverse biophysical settings and cropping enterprises characteristic of today’s agriculture. Precision agriculture must be structured in ways that enable farmers to complete the basic steps of assessing variability, managing variability, and evaluating the outcome of these processes. This enabling has not happened to the extent desired or possible and important issues remain in this regard. With respect to assessing variability, there is insufficient scientific guidance regarding exactly what properties or processes need to be measured. Although the tools exist to measure a given entity, this does not mean that the entity needs to be measured in assessing variability. This will be especially important as we shift the focus to the more temporally dynamic components of crop production because it will become as important to know when and where to look as it is to know what to look for. For these reasons there needs to emerge a clear description of the salient properties and processes that affect crop performance and these need to be assessed for precision agriculture by biophysical settings and cropping systems. Lack of these standard protocols has lead to situations in which interpretation of measured variability is often clouded by inaccurate assessment (due to poor measurement techniques or inadequate sampling designs) or lack of scientific understanding of underlying causal processes. Therefore, more emphasis must be placed on both the validity and the accuracy in variability assessment. Tools that increase assessment accuracy of valid properties and processes while keeping costs affordable should be a high priority for research. Sensors, both ground based and remote, should increase the detail needed for precision agriculture at reduced costs, but these will require significant improvements in software, particularly user interfaces, to fully utilize precise data. However, sensor data also need to be integrated with advances in software that will improve our understanding of the spatial and temporal variation in the properties and processes that regulate crop production as well as the fate and transport of pollutants to the environment. With regard to managing variability, the agronomic basis for most precision management techniques is not well established due to scale incongruities. Current management recommendations rely on traditional best management practices and concepts such as whole-field IPM, which may or may not have site-specific correlates at a finer resolution. A concerted effort should be made to design products and management tools based on the emerging science of precision agriculture while not forcing new enabling technologies to operate under traditional crop management strategies and guidelines. Precise management recommendations will need to be based on measurable or predictable site-specific conditions that are consistent with the capabilities of the enabling technologies. On-farm research, which is increasing with the adoption of certain enabling technologies, offers a great opportunity to evaluate current and future management practices over a broad spacetime continuum not possible under traditional small-plot research.Traditional pub-

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lic-sector research needs to integrate with the on-farm research network that is rapidly emerging in the realm of precision agriculture. With regard to evaluation, it is essential that evaluation procedures be developed that are consistent with the emerging features of precision agriculture while not relying solely on the traditional approach used for agricultural machinery, genetics, or chemicals. These evaluation procedures must involve the farmer. The farmer is the integral part of evaluating precision agriculture because the assessed variability must ultimately be managed on the farm. The enabling technologies, the agronomic rules, and even the data have no value in themselves. Value is obtained through the management of data on the farm. It is the data and the decisions they lead to on the farm that are the essence of precision agriculture. Our evaluation efforts need to begin with this farm management process. The impacts of precision agricultural systems will extend beyond crop production to the environment and to the very structure of our agriculture system. Consequently, evaluation needs to involve all sectors of agriculture. We have argued that the essence of precision agriculture is associated with spatial and temporal variability. Evaluation also needs to address this dimension. The critical impacts of precision agriculture will not be found by only asking the traditional question of what is occurring; rather, these insights will emerge when we focus on where and when it is occurring. Precision agriculture is intuitively appealing, has captured the interest of the agricultural sector, and offers many exciting and challenging research questions. Lacking in this interchange, however, has been sufficient attention to underlying scientific principles and standards. In our overview of precision agriculture, we have attempted to point out what we believe are some unifying principles and analytical deficiencies. We hope this discussion will focus future work on precision agriculture so that both scientific and practical (agronomic, economic, and environmental) objectives can be realized.

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agement. In “The State of Site-Specific Management for Agriculture” (F. J. Pierce and E. J. Sadler, Eds.), ASAMiscellaneous Publication, pp. 161-181. ASA, CSSA, and SSSA, Madison, WI. U S . Department of Agriculture, Economic Research Service (USDA-ERS) (1987). “Farm Drainage in the United States: History, Status, and Prospects” (George A. Pavelis, Ed.), Publ. No. 1455. USDA-ERS, Washington, DC. U.S. Department of Agriculture, Economic Research Service (USDA-ERS) (1997). “Agricultural Resources and Environmental Indicators, 1996-97,”Agricultural Handbook No. 712. USDA-ERS, Natural Resources and Environment Division, Washington, DC. Verhagen, J., and Bouma, J. (1997).Modeling soil variability. In “The State of Site-Specific Management for Agriculture” (F. J. Pierce and E. J. Sadler, Eds.), ASA Miscellaneous Publication, pp. 55-67. ASA, CSSA, and SSSA, Madison, WI. Verhagen, A.. Booltink, H. W. G., and Bouma, J. (1995a). Site-specific management: Balancing production and environmental requirements at farm level. Agric. Sysrems 49,369-384. Verhagen, A., Verburg, P., Sybesma, M., and Bouma, J. (1995b).Terrain modeling as a basis for optimal agroecological land management using dynamic simulation. In “Proceedings of the Second International Conference on Site Specific Management for Agricultural Systems, Bloomington/ Minneapolis, MN, 27-30 March 1994” (P. C. Robert, R. H. Rust, and W. E. Larson, Eds.), ASA Miscellaneous Publication, pp. 229-250. ASA, CSSA, and SSSA, Madison, WI. Vetsch, J. A., Malzer, G. L., Robert, P. C., and Huggins, D. R. (1995). Nitrogen specific management by soil condition: Managing fertilizer nitrogen in corn. In “Proceedings of the Second International Conference on Site Specific Management for Agricultural Systems, Bloomington/Minneapolis, MN, 27-30 March 1994” (P. C. Robert. R. H. Rust, and W. E. Larson, Eds.),ASA Miscellaneous Publication, pp. 465-473. ASA, CSSA, and SSSA, Madison, WI. Vetsch, W. C., Duke, H. R., and Iremonger, C. J. (1996).Assessment of plant nitrogen in imgated corn. In “Proceedings of the Third International Conference on Precision Agriculture, Minneapolis, MN, 23-26 June 1996” (R C. Robert, R. H. Rust, and W. E. Larson. Eds.), ASA Miscellaneous Publication, pp. 23-32. ASA, CSSA, and SSSA, Madison, WI. Vitosh, M. L., Johnson, J. W., and Mengel, D. B. (1996).Tri-state fertilizer recommendations for corn, soybeans, wheat and alfalfa, Extension Bull. No. E-2567.Michigan State University, East Lansing. Von Groenendael, J. M. (1988).Patchy distribution of weeds and some implications for modeling population dynamics: A short literature review. Weed Res. 28,437-441. Voorhees, W. B., Allmaras, R. R., and Lindstrom, M. J. (1993).Tillage considerations in managing soil variability. In “Proceedings of Soil Specific Crop Management: A Workshop on Research and Development Issues, 14-16 April 1992, Minneapolis, MN” (P. C. Robert, R. H.Rust, and w. E. Larson, Eds.),pp. 95-111. ASA, CSSA. and SSSA, Madison, WI. Walker, B. D., Haugen-Kozyra, K.,and Wang. C. (1996).Effects of long-term cultivation on a morainal landscape in Alberta, Canada. In “Proceedings of the Third International Conference on Precision Agriculture, Minneapolis, MN, 23-26 June 1996” (P. C. Robert, R. H. Rust, and W. E. Larson. Eds.), ASA Miscellaneous Publication, pp. 107-116. ASA, CSSA, and SSSA, Madison, WI. Walker, P. H., Hall, F. F., and Protz, R. (1968). Relation between landform parameters and soil properties. Soil Sci. SOC.Am. Pmc. 32, 101-104. Wall, R. W., King, B. A., and McCann, I. R. (1996). Center-pivot irrigation system control and data communications network for real-time variable water application. In “Proceedings of the Third International Conference on Precision Agriculture, Minneapolis, MN, 23-26 June 1996” (P. C. Robert, R. H.Rust, and W. E. Larson, Eds.), ASAMiscellaneous Publication, pp. 757-766. ASA, CSSA, and SSSA. Madison, WI. Walters, C. S., Frazier, B. E., Miller, B. C., and Pan, W. L. (1996). Remotely sensed organic matter mapping for site-specific N management in dryland winter wheat. In “Proceedings of the Third International Conference on Precision Agriculture, Minneapolis, MN, 23-26 June 1996 (P. C.

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Robert, R. H. Rust, and W. E. Larson, Eds.), ASAMiscellaneousPublication, p. 579. ASA, CSSA, and SSSA, Madison, WI. Wanick, A. W., Myers, D. E., and Nielsen, D. R. (1986). “GeostatisticalMethods Applied to Soil Science. Methods of Soil Analysis. Part I,” 2nd ed., Agronomy Monograph No. 9. ASA, CSSA, and SSSA, Madison, WI. Weatherly, E. T., and Bowers, C. G.,Jr. (1997). Automatic depth control of a seed planter based on soil drying front sensing. Trans. Am. SOC.Agric. Eng. 40,295-305. Webster, R., and Oliver, M. A. (1990). “Statistical Methods in Soil and Land Resource Survey.” Oxford Univ. Press, New York. Weibel, R., and Heller, M. (1991). Digital terrain modeling. In “GeographicInformation Systems, Principles and Applications” (M. F. Goodchild and D. Rhines, Eds.), pp. 269-297. Taylor & Francis, New York. Wesseling, J. (1974). Drainage and crop production. In “Drainage for Agriculture” (J. Van Schilfgaarde, Ed.), pp. 7-37. Agronomy Vol. 17. ASA, Madison, WI. Wibawa, W. D., Dludlu, D. L., Swenson, L. J., Hopkins, D. G., and Dahnke, W. C. (1993). Variable fertilizer application based on yield goal, soil fertility, and soil map unit. J. Prod. Agric. 6,255261. Woebbecke, D. M., Meyer, G. E., Von Bargen, K.,and Mortensen, D. A. (1995). Shape features for identifying young weeds using image analysis. Trans. Am SOC.Agric. Eng. 38,271-281. Wolf, S. A. (1998). Introduction. In “Privatization of Information and Agricultural Industrialization” (S. A. Wolf, Ed.), pp. xvii-xxv. CRC Press, Boca Raton, FL. Wolf, S. A., and Nowak, P. (1995). Site-specificity in agriculture: Hard or soft paths for Wisconsin’s agrichemical dealers. In “Proceedings of the 1995 Fertilizer,Aglime, and Pest Management Conference, University of Wisconsin-Madison.” pp. 121-129. Department of Soil Science, University of Wisconsin-Madison. Wollenhaupt, N. C.. Wolkowski, R. P., and Clayton, M. K. (1994). Mapping soil test phosphorus and potassium for variable-rate fertilizer application. J. Prod. Agric. 7,441-448. Wollenhaupt, N. C., Mulla. D. J., Gotway, C. A., and Crawford, L. A. (1997). Soil sampling and interpolation techniques for mapping spatial variability of soil properties. In ‘The State of Site-Specific Management for Agriculture” (p. J. Pierce and E.J. Sadler, Eds.), ASA Miscellaneous Publication, pp. 19-53. ASA, CSSA, and SSSA, Madison, WI. Young, J. L., and Aldag, R. W. (1982). Inorganic forms of nitrogen in soil. In “Nitrogen in Agricultural Soils’’ (F. J. Stevenson, Ed.), Agronomy Monograph No. 22, pp. 43-66. ASA, CSSA, and SSSA, Madison, WI.

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SURFACECHARGE AND SOLUTE INTERACTIONS INSOILS N. S. Bolan,' R. Naidu,*J. K. S y e r ~and , ~ R. W. Tillman' 'Department of Soil Science, Massey University, Palmerston North 5301, New Zealand ZCSIRO Land and Water, Glen Osmond 5064,South Australia, Australia 'Department of Agricultural and Environmental Science, University of Newcastle upon T p e , Newcastle upon Tyne NE17RU, United Kingdom

I. Introduction

II. Types of Electrical Surface Charge III. Development of Surface Charge A. Isomorphous Substitution B. Protonation/Deprotonation C. Specific Adsorption of Anions and Cations W. Components of Surface Charge V. Solution-Surface Interface A. Diffuse Double Layer B. Diffuse Double-Layer Model of Variable-ChargeMinerals VI. Concepts of Point of Zero Charge W.Measurements of Surface Charge A. Potentiomemc Titration B. Ion Retention C. ElecuokineticTechniques VIZI. Factors Affecting Surface Charge A. Soil Components B. Ionic Strength of Soil Solution C. Soil Solution Composition D. pH of Soil Solution E. Specific Adsorption of Anions and Cations M. Effect of Surface Charge on Soil Properties A. Solute Interactions B. Anion- and Cation-Induced Adsorption C. Dispersion and Flocculation X. Manipulation of Surhce Charge to Control Solute Interactions A. Liming 87 Adumm in Agrmay, VDkanr 67 Copyright 0 1999 by Academic Press. AU righa of reproduction in my form resewed. 0065-2 I 13/99 $30.00

88

N. S. BOLAN ETAL. B. Organic Matter Addition C. Phosphate and Silicate Addition XI. Conclusions and Future Research Needs References

Many soil physical and chemical properties are controlled by the nature and the amount of surface charge and the variation of surface charge with soil solution characteristics. These properties include dispersion and flocculation. electrophoretic mobility, solubility,and the adsorption and movement of solutes. The surfacereactions of charged particles are essential to the biogeochemicalcycling of nutrients and pollutants and the pathway of detoxification of the latter when present at hazardous concentrations. Surface charge can be manipulatedto take advantage of solid phase interactions relating to the movement of nutrient and pollutant ions in soils, the degradation of pesticides, and the decontaminationof soils. This chapter brings together fundamental aspects of surface charge and recent developmentson the implicationsof surface charge in relation to other soil properties, particularly solute interactions in soils. We first outline the development of charge on both permanent- and variable-charge surfaces. Then we discuss the various methods used to measure surface charge and factors affecting this charge. An attempt has been made to compare current theories on the nature of the charged solid surface-solution interface. The manipulation of surface charge can be achieved through liming and the addition of fertilizers containing specifically adsorbed ions. The practical implications of surface charge to soil properties have been discussed in relation to the dispersion and the flocculation of soils and the adsorption and leaching of inorganic cations and anions. Future research should focus on the development of methods to measure surface charge under in situ conditions and to explore further the role of surface charge in remediating 8 1999 Academic Ress contaminated soils.

I. INTRODUCTION Traditionally, soil scientists have employed the principles of physical, chemical, and biological sciences to understand the properties of soils. In recent times these basic sciences have contributed much more, especially to an understanding of the physical, chemical, and biological fertility of soils for plant growth and the long-term impact of intensive and prolonged agricultural production on soil properties. With increasing awareness of the impact of agricultural activities on the wider environment, soil is not only considered as a “source” of nutrients for plant growth but also as a “sink” for the removal of contaminants from industrial and agricultural waste materials. The “filtering” action of soil is controlled largely by the reactions of pollutants with soil components carrying surface charge. The discovery of surface charge on soil components revolutionized both fun-

SURFACE CHARGE AND SOLUTE INTERACTIONS

89

damental and applied research in soil science (Sposito, 1984; Sparks, 1986).Many soil physical and chemical properties are directly or indirectly controlled by the nature and the amount of surface charge and the variation of surface charge with soil solution characteristics. These properties include dispersion (Sumner, 1993; Quirk, 1994),flocculation/deflocculation(Suarez et al., 1984;Gregory, 1989;Bolan et al., 1996b),coagulation (Hohl et al., 1980; Heil and Sposito, 1993),electrophor-etic mobility (Schulthess and Sparks, 1988), solubility (White and Zealazny, 1986; Carroll-Webb and Walther, 1988; Polubesova et al., 1995), and the adsorption and movement of nutrient ions, toxic heavy metals, and pesticides (Barrow, 1989; Evans, 1989; Bolan et al., 1997; Naidu et al., 1996, 1997). The colloidal behavior of soil particles has been attributed to high surface charge density resulting from a combination of high charge and very small particle size (Mattson, 1931a,b). Parks and de Bruyn (1962) reported that a suspension is stable because of the repulsive effects of the like charges on particles. At the point of zero charge (PZC; see Section 111) the charge is neutralized, enabling closer approach of the particles due to van der Waals attraction resulting in coagulation or flocculation and settling. PZC is the point of minimum solubility of a solid phase in equilibrium with the solution and thus the coagulation and sedimentation rates are also maximum at the PZC (Parks, 1967; Shanmuganathan and Oades, 1982). Sposito (1992) indicated that the surface reactions of charged particles are essential to the biogeochemical cycles of trace elements and the pathway of detoxification of these elements when present in aqueous environments at hazardous concentrations. Surface charge also plays a major role in the interactions of solutes with soils (Greenland and Hayes, 1981; Banow, 1987, 1996). It can be manipulated to take advantage of solute interactions relating to the movement of pollutants and nutrient ions in soils, the degradation of pesticides, and the decontamination of soils. With the greater public awareness of contamination of soil environment there has been increasing interest among the scientific community in the interactions of pollutants with soils. In this chapter, we will first outline the development of charge on both permanent- and variable-charge surfaces. Then, we will discuss the various methods used to measure surface charge. An attempt will be made to compare current theories on charge surface and the solution interface. The practical implications of surface charge to solute interaction will be discussed in relation to adsorption and leaching of inorganic cations and anions and organic compounds in soil. The review was undertaken primarily to bring together the fundamental aspects of surface charge and recent developments on the implications of surface charge in relation to solute interactions in soils in a comprehensive manner for readers, other than surface chemists, including soil scientists, agronomists, and environmental scientists.

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N. S. BOLAN ETAL.

II. TYPES OF ELECTRICAL SURFACE CHARGE The electrical charge properties of soil colloids cover a spectrum of behavior (Arnold, 1978; Barrow, 1983,1985; Barrow etal. 1993).For one end-member, the charge on the surface is fixed or permanent and remains independent of the solution composition, but the electric potential is sensitive to indifferent electrolyte concentration.This member is usually termed the constant charge surface or permanent surface charge. For the other end-member of the constant potential surface, the charge varies with changing concentration of indifferent electrolyte. This charge is known as variable surface charge. The development of these two types of surface charge is discussed in Section III. Briefly, permanent charges are developed by isomorphous substitution of ions in the lattice structures of silicate clay minerals, whereas variable charges are developed through the dissociationof functionalgroups. Organic surfaces, such as humified organic matter, acquire their charge through the ionization of carboxylic (COOH), phenolic (OH), and possibly other functional groups. In the presence of various functional groups it is difficultto specify single acidic dissociation constants because the loss of protons (H+)tends to become increasinglydifficultas more functional groups become negatively charged. Thus, in the dissociation of COOH groups, R-COOH R-COO(1-f) f The dissociation constant (pKa) is given by:

+

H+

PKa = PH - 1% (fl(1 - f 1)

(1)

(2)

wheref is the fraction of COOH groups dissociated. The dissociation constant for organic compounds is expected to increase with increasingf (Momson and Boyd, 1973).In many soils between 10 and 90% of the total negative charges are derived from the functional groups of the organic matter.

III. DEVELOPMENT OF SURFACE CHARGE Soil constituents usually carry both positive and negative charges. Soil contains both permanent- and variable-charge surfaces. The permanent charges are fixed and are not altered by soil conditions. The permanent- or fixed-charge constituents develop surface charge as a result of substitution of metal ions in the lattice. In most silicate clay minerals, the majority of surface charges are fixed and are developed through substitution. Barrow (1985) suggested that the term “variable charge” be used to describe those constituents whose charge varies mainly with the pH of the

SURFACE CHARGE AND SOLUTE INTERACTIONS

91

soil solution. The variable charge of soils is also affected by other factors, such as ionic strength (I)of the soil solution and reactions with anions and cations. Important variable-chargeconstituents in soils include oxides and hydroxides of iron (Fe), aluminum (Al), titanium (Ti), manganese (Mn), and organic matter. In Table I the nature of charge development in various soil constituents is presented (Gillman and Uehara, 1980). Although clay minerals are considered either permanently or variably charged, these classifications are idealized end points (Lewis-Russ, 1991).Isomorphous substitution can occur in oxides and hydrous oxides, thus imparting some permanent charge to these predominantly variable-charge minerals. Single minerals may exhibit both permanent and variable charge (Schulthess and Huang, 1990). For example, the broken edges of permanently charged clay minerals, such as kaolinite and halloysite, are sites of variable charge (Bolland et al., 1976;Chorover and Sposito, 1995;Schroth and Sposito, 1997).Similarly, the variable-chargedchloritic group of minerals consists of montmorillonite and vermiculite which formed under acidic conditions and incorporated dissolved A1 in the interlayers. The interlayers compensate for any permanent charge substitution so the charge character of these clays is variable (Uehara and Gillman, 1980) Permanent and variable surface charges are developed by three processes (Stumm and Morgan, 1981; Sposito, 1992): 1. Isomorphous substitution 2. Dissociation and association of protons (H+) (protonation/deprotonation) 3. Specific adsorption of anions and cations While permanent charge is developed by the first process, variable charge is developed by the last two processes.

Table I Permanent- and Variable-Charge Constituentsin Soils Component

Permanent charge

Variable charge

Kaolinite Halloysi te Chloritic Smectite Illite Micaceous Montmorillonite Vermiculite Allophane Fe and Al oxides Organic matter

J J J J J J J J

J J J

-

-

-

J J J

92

N. S. BOLAN ETAL. Table I1 Ionic Size of Various Cations, Substitution in the Clay Layer, and the Development of Surface Charge

Ion Si4+ ~13+ Fe3+ Mg2+ Fe2+ Ca2+ Na+

Radius (pm)

Substitution by

Substitution layer

4.2 5.1 6.4 6.6

~ 1 3 +

Tetrahedral Dioctahedral Trioctahedral Trioctahedral Trioctahedral Trioctahedral No substitution No substitution Interlayer Interlayer

7.0 9.9 9.7 13.3

K+ NH:

Mg2+ Mg2+ ~ 1 3 +

Fe3+ ~ 1 3 +

NH: K+

Charge Negative Negative Negative Positive Positive Positive

No charge No charge

A. ISOMORPHOUS SUBSTTIWTON Permanent charge is developed by substitution of ions of similar sizes but with different charges in the lattice structure of clay minerals (Table 11). Permanent negative charge is developed by substitution of higher valence cations with lower valence cations. For example, if in an array of solid SiO, tetrahedra a Si4+is replaced by an A13+,a negatively charged framework is established. Similarly, isomorphous replacement of the A13+by a Mg2+in networks of A1,0, dioctahedral sheets of silicate clay minerals leads to a negatively charged lattice (Fig. l). On the other hand, permanent positive charge is developed by substitution of lower valence cations with higher valence cations. For example, substitution of a Mg2+by an A13+in the trioctahedral layer of silicate clay minerals results in net positive charge (Fig. 2) Isomorphous substitution is more common in 2: 1-type silicate minerals than in Si4+substituted by A13+

SizOI

SiAlO*-

Figure 1 Developmentof surface negative charge through the substitution of Si4+by A13+ in the tetrahedral layer of a silicate clay mineral.

SURFACE CHARGE AND SOLUTE INTERACTIONS

OR

93

OR

0

I MC I

A$+ I

OH

OK

Figure 2 Development of surface positive charge through the substitution of MgZ+by A13+in the trioctahedral layer of a silicate clay mineral.

other minerals (Table III). For example, in the common micas, muscovite and biotite, every fourth Si4+is replaced by an A13+, and the resultant charge deficit is balanced internally by K+ in the interlayer positions. While the permanent negative charge is developed in both tetrahedral and dioctahedral sheets of silicate clays, the permanent positive charges are developed only in trioctahedral sheets. Sparingly soluble salts also cany a surface charge because of lattice imperfections.

B. PROTONATION/DEPROTONA~ON When metal oxides are suspended in water, the metal ions near the surface or broken edges tend to coordinate with water molecules rather than with the hydroxyl (OH-) groups which make up the bulk of the material. These surface water molecules then tend to lose or gain protons (H+) depending on the H+ concentration in solution.When the pH is low (with a higher H+concentration)the surface gains a H+ resulting in an excess of positive charge. Similarly, when the pH is high

lbble III Examples of Isomorphow Substitutionin Clay Layers and the Amount of Charge Developed Substitutionand net charge Mineral

Vermiculite Muscovite Vermiculite

Octahedral sheet

Tetrahedral sheet

2: 1-type dioctahedral minerals AI,,,M&.3 (-0.3) Siz.6Al,, (-0.4) Si,AI (- 1.00) A4 2: I-type trioctahedral minerals Mg,,Fe,,; (+0.3) Siz,5(Al.Fe),,5(- 1.00)

Charge per unit formula

-0.70 - 1.00 -0.70

94

N. S. BOLAN ETAL. AI(OH);

-

AlOH'

A10

High pH (net negative)

Low pH (net positive)

Figure 3 Development of variable surface positive charge through the dissociation or association of H+on a mineral surface.

the water molecules tend to lose H+,resulting in an excess of negative charge (Fig. 3). Alternatively,depending on the pH, dissociation of H+ (deprotonation)or OH-, (protonation) from the exposed surfaces of minerals and organic matter results in the the creation of surface charge (Fig. 4). Most oxides and hydrous oxides exhibit such amphoteric behavior. Thus, the charge is strongly pH dependent; at low pH a positively charged surface prevails, and at high pH a negatively charged surface. The mechanism by which H+ is lost or gained by soil organic matter differs in that it involves functional groups, such as COOH and OH groups. Again, the process of charge development by organic matter through dissociation of H+ depends on the pH. The pH at which the dissociation of H+ occurs varies between the different functional groups on the organic matter (Morrison and Boyd, 1973). The development of net negative or net positive charge on organic matter depends on the relative distribution of the various functional groups (Harter and Naidu, 1995) and this can influence the metal-binding capacity of soils. Dissociation of H+ from COOH or OH functional groups in soil organic matter results in net negative charge (Fig. 4). Similarly,on a bacterium surface the charge results from protolysis of functional amino (NH,) and COOH groups (Fig. 5 ) (Stumm and Morgan, 1981;Huysman and Verstraete, 1993).

C. SPECIFICADSORPTION OF ANIONSAND CATIONS Charge can also originate by a process in which a solute becomes coordinately bound to solid surface. For example, the enrichment of iron hydrous oxides with phosphate (HPOi-) results in net negative charge: FeOOH,,,

+ HPOi- @ FeOHPO& + OH-

(3)

This phenomenon, which is frequently referred to in the literature as specific adsorption, causes charge reversal (Parfitt, 1978; Barrow, 1985). Specific adsorption

COOH

COO

+

OH

0

+

H' H+

(pH>3) @H>9)

Figure 4 Development of variable surface positive charge through the dissociation or association of H+on an organic matter surface.

SURFACE CHARGE AND SOLUTE INTERACTIONS

95

Figure 5 Developmentof variable surface positive charge through the dissociation or association of H+on a bacterium surface.

of anions and cations conveys negative (Hingston et al., 1972; Ryden and Syers, 1975; Wann and Uehara, 1978a; Naiduef al., 1990a) and positive charge (Parks, 1967; Bolan et al., 1993), respectively, to the surface. Many studies have shown that specific adsorption of anions and cations increases the net surface charge of variable-charge surfaces (Table IV). The amount and the nature of surface charge acquired through specific adsorption depends on the nature of anion and cation adsorbed and the pH and electrolyte concentration of the solute (Bowden et al., 1980; Yu, 1997). Addition of HPOi- and silicate (SO:-) to soils has been done in an attempt to increase the negative charge [or cation exchange capacity (CEC) of variable-charge soils (Wann and Uehara, 1978a,b;Naidu et al., 1990b).The mechanisms involved in the development of surface charge through specific adsorption will be discussed in detail in Section IX. In summary, permanent charge in soils originates from isomorphous substitution of metal ions in layer silicate minerals. This charge develops over a long period during pedogenic weathering and cannot easily be altered by soil management Table IV Increase in Surface Charges Due to SpecificAdsorption of Anions and Cations Soil constituent

Solute

pH

Iron hydrous oxide Allophane Soil

Phosphate Phosphate Phosphate Sulfate Phosphate

6.5 5.1 6.5 6.5

Soil

Soil Soil Aluminum oxide Soil Soil Soil

Phosphate Phosphate Sulfate Sulfate Calcium Calcium

5.0 6.5 7.5 7.0 5.8

5.0 5.6 5.8 5.8

Charge added (mol mol-' ion) 1.25

0.5 0.65 0.26 0.38 0.47 0.77 0.35-0.7

0.52 1.06 0.25 0.35-0.58 0.52

Reference Bolan et al. (1985) Rajan et al. (1974) Bolan et al. (1986b) Sawhney ( 1974)

Schalscha et al. (1974) Naidu et al. (199Ob) Rajan (1978) Curtin and Syers ( 1990) Bolan et a[. (1993) Ryden and Syers (1975)

96

N. S. BOLAN ETAL.

practices. Variable charge in soils originates from the dissociation of H+or OHfrom functional groups of soil organic matter and from the specific adsorption of anions and cations. This charge can be altered through soil management practices. It is important to note that most soils contain both permanent- and variable-charge components.

Iv. COMPONENTS OF SURFACE CHARGE Based on the structure of the solid soil components and their reactions with aqueous species, five principal charge components have been identified (Breeuwsma and Lyklema, 1971, 1973; Sposito, 1981): structural (uo),net proton (aH), inner-sphere complex (uis),outer-sphere complex (uos),and diffuse layer (ud).The first four charge components are related to the solid component and the last component is related to the aqueous suspension. The first four surface charge components are grouped into intrinsic surface charge density (structural and net proton) and Stem layer surface charge density (inner-sphere and outer-sphere or specific and nonspecific). Although solid particles may carry electrical charge, aqueous suspensions of particles are always electrically neutral. To maintain the surface charge balance, the sum of the previously mentioned five charge components must be equal to zero (Sposito, 1984): uo + U H + UiS + uos + Ud = 0

(4)

All the charge components are measured either in Coulombs or moles charge and are expressed either on a unit mass (C or mol kg- ') or surface area (C or mol mP2) basis. When surface charge is expressed per unit surface area, it gives the surface charge density, which is the measure of the resultant surface charge within the heterogeneous mixture of reactive solid surfaces of soils (Sposito, 1984). Structural surface charge density is created by isomorphous substitution in the crystal structures of clay minerals. The structural surface charge is constant and is not affected by the solid-solution interface. Although isomorphous substitution results in both positive and negative surface charge, invariably the net uo in silicate minerals is negative (see Section 111). This is mainly due to the more extensive substitution of Si4+by A13+in the tetrahedral layers of silicate clay minerals, resulting in net negative charge. Net proton surface charge density is proportional to the difference between the amounts of H+and OH- adsorbed or complexed by surface functional groups: uH = F(q,+

- qOH-)/as

(5)

where qH+or qoH- is the specific adsorbed charge (mol kg-') of H+or OH- cornplexed by surface groups, F is Faraday's constant, and as is the specific surface

SURFACE CHARGE AND SOLUTE INTERACTIONS

97

area. Conceptually, diffuse-layer H+ is not included in the definition of uH The values of uH can be negative, zero, or positive, depending on pH, ionic strength, etc. Inner-sphere complex surface charge density is contributed by the net total charge of the ions, other than the potential determining ions (PDIs), such as H+or OH-, which are bound into inner-sphere surface complexes. Outer-sphere surface complexes are contributed by the net total charge of the ions, other than the PDIs, which are bound into outer-sphere surface complexes. The inner-sphere and outer-spherecomplex charge components are also known as specifically adsorbed and nonspecifically adsorbed charge components, respectively (Bowden et al., 1980).

V. SOLUTION-SURFACE INTERFACE A. DIFFUSE DOUBLE LAYER Most particles in aqueous media are charged for various reasons, such as the ionization of surface groups and specific adsorption of ions. In a solution, the distribution of ions around a charged particle is not uniform and gives rise to an electric double layer (Hunter, 1981).The behavior of charged soil and colloidal particles in soil water suspension is similar to that of charged particles in an electric field (Lee,similar to that of electrophoresis).During electrophoresisthe charge distribution in soil solution relative to the immobile capillary surface leads to the formation of an electrical double layer (Li, 1992). Similarly, in a soil with predominantly negatively charged particles there is an accumulation of cations and a deficit of anions in the vicinity of the solid surfaces relative to the equilibrium solution. The thermal motion of the ions counteracts the electrostatic interaction. Thus, as cations are being attracted and anions repelled, the cation concentration increases as the surface is approached, whereas the anion concentration decreases (Fig. 6). The concentration of ions near the soil particle surface is high and it decreases with increasing distance from the surface. This diffuse character of the counter ion “atmosphere” was first noticed by Gouy ( 1910, 1917) and Chapman ( 1913), who presented a theoretical relationship describing the diffuse layer. According to the Gouy model, if the double layer is created by the adsorption of PDIs such as H+or OH-, the electric potential at the double-layer surface is solely determined by the concentration (or activity) of these ions in solution since the particles act as a reversible electrode toward these ions. If this was so then the potential is given by the Nernst equation: Qo = (kT/ve)Zn(c/co)

(6)

where Q is the electric potential at the surface, k is the Boltzman constant, Tis the absolute temperature, e is the electric charge, v is the valence of the PDIs, c is the

98

N. S. BOLAN ETAL.

High electrolyte concentration

Distance,d Figure 6 The Gouy double-layer model.

concentration of these ions in solution, and co is the concentration at the point of zero charge when a0= 0. Despite the initial success of the Gouy model, deviations from the general conclusions of the theory were frequently encountered with specific colloidal systems. Some of the difficulties with the model were attributed to the assumptions which consider ions as point charges and because specific effects related to ion size are neglected. Given these deficiencies, Stem (1924) proposed a double-layer model which, unlike the Gouy model, considers that the closest approach of a counter ion to the charge surface is limited by the size of these ions. Stem suggested that the ion is separated from the surface charge by a layer of thickness 6 in which there is no charge; this separation was later described as “Stern layer.” In this layer the electric potential drops linearly with distance from a value a0at the surface to a value which is called the Stem potential (Fig. 7). Beyond this point the electric potential decreases approximately exponentially with distance similar to the Gouy model. Unlike the Gouy model, however, the interaction between charged particles is governed predominantly by the overlap of diffuse layers, so the potential most relevant to the interaction is that at the boundary between the Stem and diffuse layers (the Stem potential) rather than the potential at the particle surface. As discussed previously, this boundary (the Stem plane) is generally considered to be at a distance of 0.3-0.5 nm from the particle surface and corresponds to the diameter of a hydrated counter ion. The Stern potential cannot be measured directly, although the electrokinetic or zeta potential is often used as the closest approximation. The structural charge, discussed previously, is balanced by cations or anions at

SURFACE CHARGE AND SOLUTE INTERACTIONS

6

99

DiffuseLayer

Charge reversal due to specifically adsorbed counterions

Figure 7 The Stem model.

or near the mineral surface. The spatial distribution of these ions, often described as counter ions, controls the colloidal behavior of clay minerals. Mattson (193 la,b) was one of the first scientists to study the role of such charges in the colloidal behavior of clay minerals. Since then, a great deal of effort has been directed toward the development of models which describe the interfacial chemistry of the colloidal particles; the best known of these models is the diffuse double-layer theory (DDL). van Olphen (1977) suggested that the equilibrium distribution of exchangeable cations can quantitatively be described by the Boltzmann equation: n+(x) = n: exp[-z+e+(x)/kr]

(7)

where n+ (x) is the concentration of cations, +(x) is the electric potential at a distance x from the surface, n: is the concentration of cations in the bulk solution, z+ is the cation charge, and e, k, and Tare as defined earlier. Although Eq. (7) does not account for the effect of entropy on the spatial distribution of ions, the model illustrates that the electric potential develops as a result of the thermal energy of the counter ions which diffuse away from the surface to an extent limited by the higher energy states of ions further from the surface. For real systems, such as colloid-water suspensions, the charge distribution on colloidal surfaces should also include the hydration energy because the hydration water of the counter ions affects the distribution of ions at the surface (Shainberg and Kemper, 1966).Shainberg and Kemper calculated cation distribution near a mineral surface, demonstrating that the presence of cations in the Stem layer was dependent on their degree of hydration. For example, relatively

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N. S. BOLAN ETAL.

few of the strongly hydrated lithium ions (Li+) are strongly adsorbed in the Stern layer; most Li+ are in the diffuse layer. The opposite trend was evident for the weakly hydrated potassium ions (K+). However, the nature of interaction between the ions in solution and the colloidal particle surface is dependent on the origin and distribution of particle surface charge and potential. In the constant surface potential systems, maintenance of surface chemical equilibrium is assumed during particle interactions but this may not be a realistic assumption because of the very short time of the encounter between colloidal particles (typically about 10 ps for a Brownian collision; Verwey and Overbeek, 1948). Constant charge interactions may be expected when the particles have a fixed surface charge density, such as with latex particles with bound ionic groups or clays with certain ionexchange capacity. Although much effort has been directed toward the development of theories of DDL interactions, the results are of limited use due to the lack of information on real systems encountered in field environments. Moreover, the dynamics of double-layer interactions in complex systems, such as soil colloids, are not fully understood and it is for this reason that most calculations are based on constant potential or constant charge models, neither of which apply directly to real situations. The net surface charge density (0) at any point near the charged surface is a result of the distribution of positive and negative charges over a unit surface area. Such a distribution varies with the distance from the colloidal particle surface:

where e, z,, and n+ are as defined previously and z- and n- are the anion charge and concentration, respectively, at distance x. Gast (1977) provided a detailed mathematical treatment of the distribution of electrical potential from the clay surface. According to his model, the total excess charge of the diffuse double layer per unit surface is obtained by integration from the surface (x = 0) into the bulk solution (x = 03):

eo = - 6 e a x

(9)

The double-layer model prediction of surface electric potential for permanentcharge and variable-charge surfaces varies substantially.For permanent-charge surfaces and for small surface potentials there is an exponential decrease in potential with distance from the surface (van Olphen, 1977): = $0 exp( - a)

(10)

According to Figs. 6 and 7 and Eqs. (8) and (9), high counter ion charge (z), high electrolyte concentration, or low dielectric constant of the solvent (E) should reduce the thickness of the double layer, thereby flocculating colloidal suspensions

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101

by reducing interparticle electrostatic repulsions. Flocculation occurs only if particles (i) collide with each other and (ii) can adhere when brought together by collision (Gregory, 1989). These predictions are well illustrated by Norrish (1954), who demonstrated a reduction of interlayer spacings of montmorillonite at higher electrolyte concentrations consistent with the double-layer model which predicts a reduction in the thickness of the double layer with increasing electrolyte concentration. Other examples include the studies by Schofield and Samson (1953, 1954) and Quirk and Schofield (1955), who showed coagulation of soil colloidal particles above a critical electrolyte concentration at which the thickness of the double layer is reduced beyond the critical level, leading to destabilization of the suspended material. There are, however, many examples that show flocculationof colloidal particles in electrolytes of weakly hydrating cations which is not predicted by doublelayer theory. One example of such a phenomenon includes the flocculation of clays in the presence of quartenary ammonium (NH,+)salts, implying that in the absence of strong cation hydration forces the Boltzmann distribution of cations near the surface of layer silicates is nondiffuse (McBride, 1989). Such difficulties with the DDL model led to the development of the Stem model (Stem, 1924), which reduces the predicted electric potentials near the surface to realistic values by recognizing that the closest approach of an ion to the surface is limited by the hydrated ionic radius (Shainberg and Kemper, 1966).

B. DIFFUSEDOUBLE-LAYER MODEL OF VARIABLE-CHARGE MINERALS As discussed previously, regardless of the origin of the charge, electrical neutrality in the system must be maintained. This requires ions of charge opposite to the surface (counter ions) to accumulate in a diffuse cloud around the particle. The diffuse cloud is composed of an excess of ions of opposite charge to the surface and a deficit of ions of similar charge (co-ions).This diffuse cloud of ions, together with the charged surface, constitutes the electrical double layer. The behavior of the variable-charge surfaces can be described qualitatively by the Gouy-Chapman equation (van Olphen, 1977): uo = ( ~ ~ & T / I T sinh(ze/2kT)+, )"~

where uo, n, E, k, T z, e, and Jl0 are as defined earlier. The variables which can easily be controlled in Eq. (1 1) to determine the variable surface charge and n and z of the counter ion. Because in variable-charge systems +o is constant, Jlo is a direct function of these variables. Thus, if the electrolyte concentration or valence of counter ions is increased, surface charge increases. Because the surface area of the system remains constant, increasing the

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N. S. BOLAN E T A .

charge on the surfaces increases the surface charge density. Because H+ and OHare the PDIs, surface charge is governed by pH and hence the frequently used but technically incorrect term “pH-dependent charge.” From the Nernst equation, which relates +o to pH: +o = (kT/e) In (aH+/&+) = (2.303kT/e)@H0 - pH)

(12)

where pHo is the pH at which IJ.I~= 0, indicating that the surface has an equal number of positive and negative charges. pH, is identical to the PZC of the variablecharge surfaces. Combining Eqs. (11) and (12), one obtains

uo = (2nekT/n)”* sinh [1.15 z(pHo - pH)]

(13)

which shows that at some point on the pH scale, uo = 0, indicating that the variable-charge surface has a net zero charge. According to Eq.(13),one can readily manipulate the surface charge by changing the values of (pH, - pH), n, and z. When the relationship in Eq.(13) is plotted (Fig. 7), charge varies with pH, n, and z. The latter two parameters define ionic strength (0in the relationship

I = 1/2 xtcizi’

(14)

where ci = (ni X 10-3)lN, and N is Avogadro’s number (6.02 X Increasing n, z, and pH and decreasing pHo increases net negative charge in the system and vice versa. In systems with mixed permanent- and variable-charge surfaces, the charge distribution takes the shape as shown in Fig. 7 as a result of the addition of a constant value for the permanent negative charge (a,). In such cases, one must redefine the point of zero net charge (PZNC), at which the entire system (permanent plus variable charge) contains equal numbers of positive and negative charges. Thus, the more permanent-charge minerals are present in a soil, the lower the pH at which the soil reaches an overall net zero charge. For those readers interested in a more complete description of these phenomena, see Uehara and Gillman (1980). The effect of concentration and valence of solutes on the thickness of the diffuse double layer is well known. An increase in the concentration of soil solution (electrolyte) reduces the thickness of the electric diffuse double layer, enhancing neutralization of negatively charged surfaces. Under this condition, electrostatic repulsive forces between soil colloids are reduced. Consequently, soil humic polyanions interact with soil inorganic colloids and organomineral complexes are formed. Polyvalent cations may act as bridges between the inorganic surface and organics to form such complexes (Theng, 1982; Oades, 1989). It is possible that with decreasing ionic strength changes in surface-bound organic matter can complex more cations. Diffuse layer surface charge density arises from the ions in the diffuse layer which may move about freely in aqueous solutions while remaining near enough to particle surfaces to create an effective surface charge density. The diffuse layer

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103

surface charge density develops mainly to balance the charge developed on the particle. Intrinsic surface charge density reflects particle or structural charge developed developed from the adfrom isomorphic substitution (a,) and proton charge (aH) sorption of H+or OH-: Ui"

= a,

+ UH

(15)

The Stem layer surface charge density reflects particle charge developed from the specifically (inner-sphere complex) and nonspecifically (outer-sphere complex) adsorbed counter ions:

+

as= ais aos

(16)

The net total particle surface charge density (a,) then can be defined by aP = (Ti"

+ a, = a, + U H + ais+ aos

(17)

Thus, the total particle charge density (a,) is balanced by the diffuse layer charge (ad):

a,

+ OH + ais+ aos= -ad

(18)

This equation represents the balance of surface charge and can be applied both to individual particles in suspension and to an entire suspension (Sposito, 1984). Soils with active surface constituents, dominated by oxides and hydrous oxides, do in fact behave differently from those dominated by clays where charges arise from isomorphous lattice substitution. The majority of hydrophobic colloids acquire their surface charge by the adsorption of PDIs, which by strict definition are ions common to the colloid and to the aqueous medium. Operationally, PDIs are defined as those which leave the solution, cross over the real solid-solution boundary, and become part of the solid surface (Sposito, 1992). On this basis any ion which is associated with Si4+or other metal ions as a ligand may be capable of altering the surface potential and hence fall into the category of PDIs. In constant surface potential colloids the surface electrical potential is only constant as long as the activity of the PDIs remains constant. Surface PDIs should not, after adsorption, be chemically distinguishable from ions already present in the lattice, and so PDIs should not alter the chemical potential of the surface of the solid. For many oxides surfaces, H+and OH- behave as PDIs although they are not always constituents of the lattice. It is therefore debatable whether H+ and OH- can, without qualification, be termed PDIs on the hydroxylated surfaces of insoluble oxides and hydrous oxides and on edges of clay mineral lattice. It can be concluded that for oxides and hydrous oxides in aqueous media, the most important pair of PDIs is H+and OH- and the development of surface charge is measured by their adsorption. In the presence of other PDIs in an aqueous oxide system, the specific adsorption causes the PZC to move to a lower

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N. S. BOLAN ETAL.

pH in the case of anions and to a higher pH in the case of cations. Clearly, the PDIs are adsorbed into (or lost from) the surface and hence fall into the category of chemisorbed ions. Most soils contain both variable-charge surface functional groups (ionizable groups bound to inorganic or organic adsorbents) and permanent-charge surface groups (created by ionic substitution within the crystal structure of minerals). The net charge on colloidal particles bearing both variable and structural charges is the sum of constituent charge densities (Eq. 18) (Madrid el al., 1984). In general, when the charge densities are expressed on a unit mass basis the sum of inner sphere, outer sphere, and dissociated surface charge is equal to the net adsorbed charge: (Tis

+ uos+ ad= (n+ - n - )

uo + U H = - (n+ - n - )

(19) (20)

where n+ and n- are the adsorbed positive and negative charges, respectively. This provides a basis for relating ion adsorption measurements to structural and variable surface charge densities. Equation (20) shows that the net adsorbed ion charge may be pH dependent in soils which contain a mixture of permanent-charge clay minerals and variable-charge adsorbents, such as organic matter and oxides and hydrous oxides of Al, Fe, Mn, and Si. In summary, diffuse double-layer models are capable of explaining the effects of solution composition (e.g., pH, ionic strength, and valence of ions in solution) on the nature and the amount of surface charge of homogeneous materials, such as metal oxides and hydrous oxides. In heterogeneous media, such as soils which contain both permanent- and variable-charge components, the double-layer models are less effective in predicting net surface charge. Nevertheless, these models are useful in explaining the effects of surface charge on some soil properties, such as flocculation and deflocculation, and the adsorption and desorption of inorganic cations and anions (discussed in Section IX).

VI. CONCEPTS OF POINT OF ZERO CHARGE The concepts of point of zero charge are only concerned with minerals exhibiting variable-charge behavior. Such minerals include the oxides and hydrous oxides of Al, Fe, Mn,and Si. These minerals generally do not exhibit permanentcharge behavior. As discussed previously, the ion-exchange capacity of these minerals results from the adsorption of PDIs (H+ and OH-). These mineral surfaces exhibit amphoteric behavior and their surface charge varies with both pH and electrolyte concentration. When the pH charge curves are plotted in suspensions

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105

lsble V Definitions of Some Point of Zero Charges“ Symbol

Name

Definition

PZC or ISP PZNPC

Point of zero charge or isoelectric point Point of zero net proton charge Point of zero salt effect Point of zero net charge

pH at which the total net particle charge vanishes pH at which the net proton charge is equal to zero The pH value that shows no change with ionic strength pH at which the total of dissociated and the outer surface complex charges is zero

PZSE PZNC

Defining equation ud= 0

u,=o (aU,/al), = 0

+

uos ud= 0

From “The Surface Chemistry of Soils” by Garrison Sposito. Copyright 0 1984 by Garrison Sposito. Used by permission of Oxford University Press, Inc.

of varying electrolyte concentrations,the curves intersect at a common pH value. This pH value is often defined as the PZC. However, since the introduction of this concept, many PZCs have been identified and defined for variable-charge surfaces (Table V), including PZC or zero point of charge (ZPC), PZNC, point of zero net pristine charge (PZNPC), isoelectric point (IEP), and point of zero salt effect (PZSE). According to Polubesova et al. (1993, one of the challenges for soil and colloid chemists is to understand and apply these myriad “zero point” terminologies. Parker et al. (1979) insisted that terms such as ZPC and IEP were too vague and preferred terms such as PZSE and PZNC. Sposito (1981) also suggested the term PZNPC. Bowden et al. (1977) used the term isoelectric point of the solid and pristine point of zero charge, and Hendershot (1978) used the zero point of titration. Furthermore, the abbreviations ZPC and PZC are used interchangeably. Parfitt (1980) observed that “isoelectric weathering” (Mattson, 1932) may also take place in that ZPC approaches the soil pH with time. Sposito (1 984) indicated that PZCs are pH values at which one or more of the individual components of the surface charge density specified are equal to zero (Table V). The PZNPC, which is a pH value at which the net proton surface charge density is zero (aH= 0), depends on the concentration of the ionizable surface functional groups and on the composition of the solution phase. The PZNPC is the most important PZC for soils which contain both permanent and variable charge because it is the only PZC in which the contribution of aH is considered separately from that of cro. Many others have simply assumed that PZNPC is equal to PZSE (Bolan er al., 1986b). Equality between PZPNC and PZSE requires the special condition that the net adsorbed ion charge at the PZNPC is independent of ionic strength and that a0 is equal to zero. Clearly, there is a need for chemists to iden-

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N. S. BOLAN ETAL.

tify the precise nature of their study and the charge measurement conditions prior to defining the PZC. In order to estimate either PZPNC or uH in the presence of both variable-charge and permanent-charge adsorbents, it is necessary to first measure the permanentcharge density accessible to adsorptive ions (effective a,) by an independent method. One such method is the cesium (Cs+)adsorption method in which the Cs+saturated adsorbent is dried to promote the formation of inner-sphere surface complexes, and then it is washed once with a dilute solution of LiCl. Lithium preferentially displaces Cs+ from variable-charge sites and leaves Cs+ adsorbed to structural charge sites. Cesium from the latter sites is extracted with ammonium acetate. The PZPNC of organic matter may be well below 3 because the COOH groups on organic matter are more strongly acidic than simple carboxylic acids (Tipping and Cooke, 1982); the PZPNC for A1 and Fe oxides is >7 (Parks 1967; Schwartz et al., 1984), and the PZPNC for kaolinite is between 4 and 5 (Ferries and Jepson, 1975).Both cationic charge and ionic radii may influence the PZNPC values. This may be attributed to the greater screening effect of the ions with increasing charge and decreasing hydrated ionic radii. The PZC values for minerals reported in the literature often vary significantly with the method used for their estimation. This may be attributed to the nature of the surfaces and the chemistry at the soil-particle interface which can also be influenced by the method used for the estimation of charge density and the PZC. Adsorbing solids in soils are inorganic and organic polymers bearing surface functional groups whose reactivity determines the operational meaning of surface area and surface charge. Even in the most oxidic soils, normally particles will be present with appreciable permanent negative charge. Hence, the PZC derived from the point of intersection of potentiometric titration curves of soil, obtained with different concentrations of electrolyte, is not always the same as the PZC measured using the direct measurement of ion retention. As a general rule, the PZC measured using the ion-retention method is lower than that obtained from a potentiometric titration. This difference is attributed to the fact that some of the negative charge on the original soil is balanced by strongly adsorbed A13+.Pretreatment of soils in the ion-retention method will remove most of these A13+ and results in a close estimate of the PZC. In soil systems, the PZC is rarely equal to the PZSE; various reason have been advanced for this. First, the presence of permanent negative charge should always result in an increase in PZSE over PZC (Gillman and Uehara, 1980). Second, the H+ or OH- added during the potentiometric measurements are sometimes consumed in reactions other than charge balancing and cause a deviation in PZSE from the PZC (Parker et al., 1979).Third, the selective adsorption of index ions during the charge measurements by the ion-retention method can displace the PZC from PZSE (Sposito, 1981).

SURFACE CHARGE AND SOLUTE INTERACTIONS

107

If there is no variation among the reactive surfaces of the soil in relation to anion adsorption, the PZSE for adsorption should coincide with the PZC of the anionated surface.This has been observed for uniform surfaces such as goethite (Barrow et af., 1980). For heterogeneous surfaces, such as soils, there are many surfaces that can adsorb anions and many that cannot. The proportion of the surfaces that are reactive in adsorption will vary with the anion and the soil. While the PZC is the pH at which positive and negative charges of the soil as a whole are in balance, the PZSE for adsorption is the pH at which the positive and negative potentials of the surface, which are reactive to that particular anion, are in balance. The higher value of PZSE for sulfate (SO:-) adsorption than for HPOi- adsorption obtained by Bolan et af. (1986b) in variable-charge soils indicate that the surfaces which are reactive to SO:- have a more positive potential than the surfaces that are reactive to HPOi-. Bolan et al. (1986b) examined the effect of HPOi- and SO:- adsorption on PZSE of allophanic (Patua) and nonallophanic (Tokomaru) soils which vary in their anion adsorption characteristics. They observed that anion adsorption shifted the PZSE to lower pH values and the extent of shift varied between the soils and the anions species adsorbed. The commonly observed increase in negative charge with HPOi- adsorption may explain the movement of the PZSE for adsorption to lower pH values. However, Rajan (1976) suggested that a large amount of HPOi- must be adsorbed before any addition of negative charge to the surface occurs. This may explain why for the Tokomaru soil, which adsorbed one-tenth as much HPOi- as the Patua soil, the PZSE for adsorption was little affected by increasing adsorption. Sulfate, however, is adsorbed onto positive sites, which may explain why there was little observable effect of increasing SO:- adsorption on the PZSE for adsorption. In summary, many PZCs, which give the pH value at which one or more of the individual components of the surface charge density are equal to zero, have been proposed for colloidal systems. The definition of a particular PZC depends mainly on the conditions used for charge measurements. Although in soil systems these PZCs have been measured extensively and often been used interchangeably, their practical importance in controlling some soil properties has not been well examined.

VII. MEASUREMJZNT OF SURFACE CHARGE The characterization and modeling of the surface charge behavior of soil and colloid systems depends on the technique used to measure surface charge. The method used to measure surface charge enables identification of the specific component of the surface charge, and unless the method of measurement is the same,

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N. S. BOLAN ETAL.

it is often difficult to compare the surface charge behavior between different studies. Many techniques have been developed to measure the net total particle surface charge density, intrinsic surface charge density, net structural surface charge and net proton surface charge in soils, and other geological materials, including potentiometric titration, ion retention, electrophoretic mobility, salt titration, and mineral addition (Sposito, 1983; Lewis-Russ, 1991).

A. POTENTIOMETRIC TITRATION Electrometric titration or potentiometric titration methods are used to measure Qpically, uH is determined for aqueous partithe net proton surface charge (aH). cle suspensions by electrometric titration as a function of pH for specific conditions of the particle and the aqueous systems. Potentiometric titrations are reserved for PZC analysis only (Schulthess and Sparks, 1986) in which the pH of a suspension is modified in small steps by adding known concentrations of dilute acid or base. At each step, the pH is measured to determine the quantity of H+ and OHremaining in solution. These amounts are subtracted from the total H+ and OHadded, and the reminder is assumed to be adsorbed onto the solid particles. In potentiometric titration electroneutrality is maintained at every point of the titration: Znegative charges = Zpositive charges

(21)

Positive charges are due to the positive surface sites, H+, and other cations in solution. Negative charges are due to the negative surfaces sites, OH-, and other anions in solution. The net surface charge is the sum of both surface negative and positive charges: uo = positive surface - negative surface

(22)

u0 = (CA - CB)- (H+ - OH-)

(23)

When the surface charge is neutral, the negative and positive charges are equal and the surface is said to have zero charge. However, the charge distribution is such that the positive and negative charges do not stereochemically cancel each other (van Raij and Peech, 1972).At the pH of PZC, Eq.(23) simplifies to

(C,

-

CB)- (H+ - OH-)

=0

(24)

Combining the Gouy-Chapman equation with Eq.(24) gives Eq.(13). This equa= pH of the suspension, then uo = 0 and (negation indicates that when pH, tive charge) = (positive charge) for any ionic strength (no).If pH > pH, then uo < 0 and (negative charge) > (positive charge). Conversely, if pH C pH, then uo > 0 and (negative charge) C (positive charge). At a fixed pH value >

SURFACE CHARGE AND SOLUTE INTERACTIONS

109

pH, an increase in electrolyte concentration will increase both uo (making it more negative) and C, (more base is needed to attain the pH value desired). At a fixed pH value < pH, an increase in electrolyte concentration will increase both uo (making it more positive) and CA (more acid is needed to attain the pH value desired). Thus, if several charge curves (a, vs pH) at various ionic strengths are superimposed, the pH value where the curves intersect is the PZC. The pH at which titration curves for solutions of different ionic strengths intersect is also known as the PZSE. At a pH above the PZSE, the surface is negative (excess OH-) and for that below the PZSE the surface charge is positive. If the electrolyte has a net effect on the surface charge, as in the case of electrolytes containing indifferent ions, the PZSE will not occur at the same pH as PZC. Therefore, PZSE is an operational term, referring to a measurement technique, and PZC is a conceptual term. Sposito (1992) considered that side reactions resulting from the aqueous phase and solid phase are likely to interfere with the proton surface charge measurements. The interference results both from the analytical system (electrometric interferences) and from the solid and aqueous systems. Electrometric interference depends on the relative response of the pH electrode to both the bulk and the diffuse layer H+. Because diffuse layer H+ are relatively mobile in an aqueous medium, electrometric measurement of H+ using a glass electrodeheference electrode pair will include H+ in the diffuse layer. Because uH conventionally should include only H+ or OH- bound in the surface complexes, contribution to proton surface excess (an,) from H+ or OH- adsorbed in the diffuse layer will produce interpretive errors. In potentiometric titrations it is assumed that only the H+ consumed in a suspension are those which have reacted with solid particles to form surface complexes. This assumption cannot be true unless the change in H+ in solution is corrected for the side reactions of the added H+ or OH- with dissolved chemical species. Side reactions due to solid phases include dissolution-precipitation reactions, H+ or OH- adsorption in the diffuse layer, and flocculation and dispersion processes. Furthermore, if the surface functional groups are resistant to protonation within the titration period, charge is underestimated. The range of pH values over which uHcan be measured accurately should be kept between the highest and lowest pH beyond which dissolution is likely to occur (Charlet and Sposito, 1987). Lewis-Russ ( 1 99 1) identified three major problems with titration techniques: the selection of the appropriate “blank” for the correction of acid and base consumption by the electrolyte, the dissolution of the solid surface during titration, and the coagulation or aggregation of solid during the titration. In many cases the solubility of the solids increases at pH values away from the PZC values, and in such situations the solubility of the particles and the speciation of the dissolved species need to be known. Murray (1974), Huang (1981), and Schulthess and Sparks (1986) achieved this by titrating the supernatant solution collected soon af-

110

N. S. BOLAN ETAL.

ter the addition of the electrolyte to the solids. Although this technique is superior to the titration of the electrolyte solution, it does not take into account the changes in solubilities of the solid with pH. One other factor which needs to be considered during the potentiometric titration is the changes in solution ionic strength during dissolution or precipitation at low and high pH values, respectively. Schulthess and Sparks (1986) observed that the dissolution of adsorbents varied with pH and developed a back-titration technique which involves titration of the supernatant to pH 7. The volume of acid or base used for back-titration is subtracted from the original volume added to the suspension to obtain the quantity of H+ adsorbed or desorbed. Dissolution of solid surfaces is promoted by adsorption of H+ and OH-. The slowest rate of dissolution occurs in the regions of the PZC where the predominant surface sites are neutral. Parker et al. (1979) indicated that in a potentiometric titration some of the H+ and OH- added can be consumed in reactions other than charge balancing and cause a deviation in PZSE from the PZC. Coagulation or aggregation is a serious problem, especially close to PZC and at high ionic strength of the background electrolyte. The aggregation of colloids may decrease the readily accessible surface area, resulting in diffusion-controlled adsorption kinetics (Zeltner, 1986). Generally in a potentiometric titration the acid or base is added to the suspension. An alternative technique is the titration of a sample suspension of known pH with an indifferent salt solution (Gillman and Uehara, 1980). When the solid is added to the electrolyte solution pretreated with the acid or base, this technique is known as the mineral addition technique. Samples are equilibrated at various pH values in a solution of weak ionic strength. The pH is recorded after equilibration and the ionic strength is increased. The new pH is compared with the initial pH and the pH which does not change is independentof salt concentration and is therefore the PZSE.

The ion-retention technique was developed by Schofield (1949). It is based on changes in ion retention with varying surfacecharge. This technique is widely used to measure surface charges of pure systems and soils. The technique involves saturation of soil surface sites with a nonspecifically adsorbed cation and anion by equilibrating with an index-indifferent electrolyte solution. The amount of adsorbed cation and anion can be calculated either directly by subsequent displacement with an electrolyte containing specifically adsorbed ions or indirectly by measuring the concentration remaining in the solution. For the displacement procedure, the concentration of cation and anion in the entrained solution must be known before the introduction of the displacing electrolyte. For this purpose, the soil is usually washed with the same electrolyte at a lower concentration at the last

SURFACE CHARGE AND SOLUTE INTERACTIONS

111

step before the displacement. Although this treatment will reduce the concentration of the saturated ions in the entrained solution, it may cause some variation in charge measurement (Naidu ef al., 1990a). In order to measure the PZC the soil sample is equilibrated with the index electrolyte at a range of pH values (Anderson and Sposito, 1991, 1992). The concentration of the cation and anion remaining on the soil surface is measured for an estimate of the surface charge. The pH at which the cation and anion adsorption is equal is the PZNC. One of the major problems with this technique is that, depending on the PZC, cation and anion retention becomes very low at extreme pH values and the measurement of the cation and anion at these pH values becomes difficult due to the low detection limits of the instrumental techniques, especially against other ions present in the solution. Murray (1974) and Espinoza ef al. (1975) equated PZC values to the pH values at which negligible adsorption of cation and anion occurred on oxides and soils, respectively, whereas van Raij and Peech (1972) and Naidu ef al. (1990a) reported PZC as the pH at which net charge (cations adsorbed-anions adsorbed) was zero. A major problem with this technique is that charge balances by H+ and OH- are not considered.

C. ELECTROKINETIC TECHNIQUES Electrokinetic techniques are based on the measurement of charge-induced particle or liquid movement. These techniques directly measure the electric potential developed when the liquid and solid phases move in relation to each other and include electroosmosis, streaming potential, and electrophoretic mobility. In electroosmosis,the application of an electric field to the solution causes the liquid phase to move along a stationary-charged solid surface. The measurement of the liquid velocity of the volume of liquid transported per unit of current flow provides information about charge on the solid surface. In streaming potential measurement, the solution is forced to flow along a charged surface, creating an electric potential. In electrophoresis, an electric field is applied to suspension, which causes charged particles to migrate to oppositely charged electrodes. Average particle velocity is used to calculate the charge per particle. Electrophoresis has been used to determine IEP for colloids that have low PZC values not amenable to potentiometric titration measurements. In summary, although many methods have been developed to measure surface charge, currently no method of quantifying natural particle surface charge has universal applicability. Most experimental methods involve the reaction of charged particles with aqueous species whose adsorption reactions can be used to help quantify one or more components of the particle surface charge. The separate rneasurement of different components of surface charge density is a difficult issue on which research must continue, especially for heterogeneous natural particles (Sposito, 1992).

112

N. S. BOLAN ETAL.

Vm. FACTORS AFFECTING SURFACE CHARGE The net surface charge density on soil colloid particles arises from the complex interaction between the nature and the amount of soil constituents and the soil solution composition (Ilton-Morias et ul., 1976; Singh and Uehara, 1986).Although both the nature and the quantity of soil constituents affect the permanent and variable charge on soil particles, the soil solution composition affects mainly the variable-charge component. In most studies the effect of various factors on surface charge is examined in relation to the adsorption of anions and cations. The effect of various soil and experimental variables on solute adsorption is attributed through their effect on surface charge and no attempt has been made here to separate the effects of these factors on adsorption and surface charge.

k

soILcoMPoNEN-rs

Sources of surface charge in soils include both inorganic and organic components. Inorganic sources of charge in soils include all silicate clay minerals and noncrystalline inorganic materials in soils. The nature and the amount of surface charge in various soil components is presented in Table VI. Silicate clay minerals carry mainly the permanent charge. With the phyllosilicate minerals common in soils, the 2: 1 members provide significantly higher permanent charge, whereas the 1:1 members provide very low quantities of permanent charge Wte and Zelazny, 1986). Metal oxides and hydrous oxides, such as those of Al, Fe, and Mn, carry both permanent and variable charges, and the nature and the extent of charge in soils dominated by these materials is strongly influenced by the soil solution composition, particularly soil solution pH. Although rarely above 5% by weight in soils, the high surface area of these components, combined with their frequent occurrence as coatings on mineral grains, results in a relatively high surface charge density (Hendershot and Lavkulich, 1983). Organic matter carries only variable charge and the various functional groups in the humic substances of soil organic matter are the major sources of pH-dependent charge in most soils.

B. IONICSTRENGTH OF SOILSOLUTION It has generally been observed that increasing ionic strength increases the net surface charge by increasing the positive charge below the PZC and by increasing the negative charge above the PZC.If only Coulombic forces are involved in anion adsorption, increasing ionic strength should decrease anion adsorption above the PZC and increase adsorption below the PZC (Hingston, 1981). For example,

SURFACE CHARGE AND SOLUTE INTERACTIONS

113

'Lgble VI Surface Charges of Various Soil Components Soil components

Surface charge (C mol kg-I)

Kaolinite

-0.63 to - 1.36

Kaolinite Kaolinite Kaolinite

+0.75 (pH 3.0) to -0.3 (PH 5.5) -1.7 0 - 13 (pH 7.0)

Origin

Reference

Permanent or structural

Schroth and Sposito (1997) Proton or variable

Permanent Net Net

Cowan et al. (1992)

Mite Illite

-2.0 to -5.0 +4.0 (pH 3) to -6.0 (PH 9) -0.6 (pH 6.2) to - 1.8 (pH 7.7) -26 -21.0 (pH 7.0)

Bolland et al. (1976) Ferris and Jepson (1975) Hendershot and Lavkulich (1983) Wieland and Stumm (1992) Motta and Miranda (1989)

Permanent Net

Illite Illite

-8.Oto -120 -21.0 (pH 7.0)

Permanent Net

Illite

-69.8

Permanent

Smectite

-72 (pH 4.0) to 80 (pH 9.4) -98 -90.4 to -127.6 (pH 7.0) - 10.4

Net

Greenland and Mott (1978) Hendershot and Lavkulich (1983) Madrid er al. (1984) Hendershot and Lavkulich (1983) Anderson and Sposito (1991) Cowan er al. (1992)

Permanent Net

Greenland and Mott (1978) Bouabid er al. (1991)

Permanent

- 124 - 195.3 (pH 7.0)

Permanent Net

Muscovite

-80.4t0 -117.1 (pH 7.0) -22 (pH 7.0)

Anderson and Sposito (1991) Greenland and Mott (1978) Bouabid etal. (1991) Bouabid er al. (1991)

Net

Microcline

-2 (pH 7.0)

Net

Quartz

-2 (pH7.0)

Net

Allophane (natural) Allophane (synthetic) Al hydroxide

+3.3 (pH 5.0) +3.0 (pH 5.0) +50.6 (pH 5.0)

Net Net Net

Kaolinite Kaolinite Kaolini te

Montmorillonite Montmorillonite Montmorillonite Vermiculite Vermiculite Smectite

Permanent Permanent Net

Hendershot and Lavkulich (1983) Hendershot and Lavkulich (1983) Hendershot and Lavkulich (1983) Rajan (1979) Rajan (1979) continues

114

N. S. BOLAN ETAL. Table VI-Continued

Soil component

Al hydroxide Al hydroxide Al oxide

Gi bbsite Gibbsite Gibbsite Gibbsite Si oxide Fe hydroxide

Fe hydroxide Lepidocrocite Goethite Goethite Goethite Goethite Goethite Goethite Humic acid

Surface charge (C mol kg-I) +16.0 (pH 3.6) to +10.0 (pH 6.0) +56.0 (pH 5.8) +24 (pH 2.0) to -8.0 (pH 11.0) +7.2 (pH 6.0) +1.8 (pH 9.0) -0.88 (pH 9.0) -0.5 to +0.9 (pH 8.0) +20 (pH 2) to -220 (PH 10) +72 (pH 3.8) to -6.0 (pH 6.5) +34 (pH 5.8) +80 (pH 4) to -40 (pH 9.5) +4.0 (pH 6.0) 0.0 (pH 9.0) +18.3 (pH 4.5) -0.38 (pH 9.0) -0.9 to 4 . 1 (pH 8.0) +19.0 (pH 3.5) to -5.8 (pH 10.5) -330 to -340

Origin Variable Variable Proton Net Net Net Variable Net Variable Variable Variable Net Net Net Net Variable Variable Variable

Reference Hendershot and Lavkulich (1983) Bolan er al. (1985) Schulthess and Sparks ( 1987) Hingston et al. (1974) Hingston et al. (1974) Hingston et al. (1974) Mashali (1977) Schulthess and Sparks (1989) Hendershot and Lavkulich (1983) Bolan et al. (1985) Madrid er al. ( 1984) Hingston et al. (1974) Hingston er al. (1974) Hingston et al. (1974) Hingston et al. (1974) Mashali (1977) Madrid and De Arambarri (1978) Posner (1 964)

Courchesne (1991) attributed a decrease in SO:- adsorption above PZNC with increasing ionic strength to an increase in negative charge with increasing ionic strength. However, in most studies, increasing ionic strength increases anion adsorption above PZC and decreases adsorption below PZC (Ryden et d., 1977; Barrow er al., 1980; Karen and O’Connor, 1982). This suggests that the effect of ionic strength on adsorption operates through its effect on electrostatic potential in the plane of adsorption rather than through its effect on surface charge. Barrow et al. (1980) suggested that if increasing ionic strength decreases anion adsorption, then this implies that the potential in the plane of adsorption must be positive. In most studies it has often been observed, depending on the pH, that increasing ionic strength can either increase or decrease HP0;- adsorption. This suggests that HP0;- can be adsorbed when the potential in the plane of adsorption is either positive or negative. In contrast, SO:- adsorption always decreased with increasing ionic strength indicating that SO:- is always adsorbed only when the potential is

SURFACE CHARGE AND SOLUTE INTERACTIONS

11s

positive. Black and Waring (1979) obtained a strong positive correlation between the adsorption of SO:- and nitrate (NO;) for a range of Australian soils and concluded that both NO; and SO:- are adsorbed only onto positive sites. These observations are supported by the work of Marsh et al. (1 987), who obtained a linear relationship between the amount of SO:- adsorbed and positive charge in a range of some new Zealand soils. Bolan et al. (l986b) compared the PZSE on pH and the adsorption of HP0,'and SO:- for two soils which varied in their charge components. The effect of ionic strength of the supporting medium on adsorption varied between HP0,'- and SO:-. For HPOi-, there was a pH at which adsorption increased with increasing ionic strength and below which adsorption decreased with increasing ionic strength. The pH at which ionic strength had no effect on HP0:- adsorption (PZSE on adsorption) decreased with increasing HPOi- adsorption. In contrast to HPO:-, SO:- adsorption decreased with increasing ionic strength throughout the pH range and the adsorption curves converged at above pH 7. At this point the amount of SO:- adsorbed was very close to zero for both soils. It was thus not possible to establish the effect of SO:- adsorption on the PZSE for adsorption. The PZSE for both HP0:- and SO:- adsorption occurred at pH values higher than that for the PZC for both soils.

C. Son. SOLUTIONCOMPOSITION Changes in ionic strength indirectly affect the charge distribution on a soil component surface and the surrounding aqueous solution, thereby altering the attractive or repulsive interaction between adsorbing anions and the surface (Hingston, 1981). Changes in ionic strength could also be considered strictly as an increase in the concentration of competing anions. For example, Neal et al. (1987) observed that in the case of chloride (Cl-) competition with selenite (SeO?) may be minimal but with other anions, such as SO:- and HPOi-, competition for Se0;- in aqueous solution may be considerable. Rajan (1979) indicated that SeOg- was retained by soils mostly through mechanisms similar to those for HP0:- and SO:-. A twofold increase in C1- concentration had no effect on S e q - adsorption. Chloride is considered to be adsorbed by outer-sphere surface complexation and should therefore have little effect on Se0;- adsorption if it involves inner-sphere surface complexes such as HPOi-. There was no effect of SO:- on SeOZ- adsorption but the addition of HP0:- substantially decreased SeOi- adsorption. Adsorption of Se0:- was found to be higher in the presence of calcium (Ca2+) than sodium (Na+), and the effect of Ca2+on Se0;- adsorption was slight at pH values where Ca adsorption is expected to be least (Neal er al., 1987). At higher pH values, Ca2+adsorption increases, and this is where there is increased SeOiadsorption by the solid phases. Bolland er al. (1977) and Curtin er al. (1992) ob-

116

N. S. BOLAN ETAL.

served an increase in the adsorption of HPOi- in the presence of divalent cations such as CaZ+.An increase in surface charge by Ca2+adsorption could enhance the adsorption of Se0;- and HP0:- as observed by Bolan er al. (1993) for SO:-. Ryden and Syers (1976) have also observed a similar effect of Ca2+adsorption on HPO$- adsorption. Naidu er al. (1995) and Naidu and Harter (1998) investigated the effects of organic ligands on adsorption of Cd2+by soils at varying pH values. They found that at low pH values changes in solution composition controlled Cd2+ adsorption through changes in the surface charge of soils. At low pH values, adsorption of organic ligands by soils enhanced Cd2+retention, whereas nonspecifically sorbing anions such as NO; had little effect on Cd2+adsorption. The effect of specifically sorbing anions on electrophoretic mobility and the surface charge density of soil colloids was also discussed by Harter and Naidu (1995).

D. PH OF SOILSOLUTION Soil solution pH is one of the major factors controlling surface properties of variable-charge components (Barrow, 1984; Mora and Barrow, 1996). pH affects the surface charge through the supply of H+ for adsorption onto the metal oxides and the dissociationof the functional groups in the soil organic matter. An increase in pH increases the net negative charge and a decrease in pH increases the net positive charge. Thus, change in surface charge has been considered as one of the reasons for the effect of pH on anion and cation adsorption. Studying anion adsorption at a range of pH values, Hingston (198 1) obtained a relationshipbetween the apparent Langmuir maxima for a range of anions and pH. This was termed the “adsorption envelope” and an attempt was made to relate the characteristics of the envelope to properties of the adsorbent and the adsorbate. Apparent maxima in the envelope were found at the pK, values for anions with monoprotic conjugate acids and the breaks of slope were found at pKu values for anions of polyprotic conjugate acids. A good correlation was found between points of inflection in the adsorption envelope and pKa values for conjugate acids. The correlation between the tendency for anions to react at oxide surfaces through ligand exchange and the pKu of their conjugate acids is analogous to the correlation between the log of stability constants for complex formation with metal ions and pKu value of the acids correspondingwith various ligands. Indeed, chemical bonding for anions specifically adsorbed on oxides would be expected to be similar to the bonding in complexes and crystalline compounds. The characteristic used to distinguish between adsorption and compound formation is that adsorption occurs only at the interfaces. Bolan er al. (1 997) observed that the adsorption of Cd2+increased with an increase in pH, consistent with the findings of Tiller er al. (1979), Basta and Tabatabai ( 1992), and Naidu er al. ( 1994a). Three possible reasons have been advanced for

SURFACE CHARGE AND SOLUTE INTERACTIONS

117

the increase in Cd2+adsorption with increasing pH (Naidu et al., 1994a). First, in variable-charge soils, an increase in pH causes an increase in surface negative charge resulting in an increase in cation adsorption. Second, an increase in soil pH is likely to result in the formation of hydroxy species of metal cations which are adsorbed preferentially over the metal cation (Hodgson et al., 1964). Naidu et al. (1994a) observed that CdOH+species are formed above pH 8 which have a greater affinity for adsorption sites than do Cd2+species. Third, precipitation of Cd2+as Cd(OH), is likely to result in greater retention at pH values above 10. Bolan et al. (1997) attempted to relate the pH-induced increases in surface charge to an increase in Cd2+adsorption. Approximately 50% of the increase in surface negative charge was found to be occupied by Cd2+.The remaining surface negative charge was expected to be occupied by the H+ and K+added in acid and alkali to alter the soil pH. This indicates that the increase in Cd2+adsorption with an increase in pH is attributable to an increase in negative charge (Fig. 8). Simi-

0

0 0

A

A A

0

50

0

I

I

I

I

100

150

200

250

increase in negative charge (mmoi kg-')

Figure 8 Relationshipbetween the increase in Cd2+adsorption and the increase in negative charge for the Egmont ( 0 )and the Manawatu (A) soils. The dotted line indicates the stoichiometric relationship between the amount of Cd2+ adsorption and the amount of negative charge required (2 mol of charge per mole of CdZ+).The increase in negative charge was achieved by increasing the pH of the soils (Bolan et al., 1997).

118

N. S. BOLAN ETAL.

larly, Naidu et al. (1994a) demonstratedthat the ionic strength effects on Cd2+adsorption operate through the effect of Cd2+on surface charge.

E. SPECIFICADSORPTIONOF ANIONS AND CATIONS It has often been observed that the specific adsorption of anions and cations contributes charge to the surface (TableW,Fig. 9). The increase in surface charge with specific adsorption of anions and cations can be explained both by double-layer theory and by empirical reaction equations. Using empirical equations (White, 1980) it has been demonstratedthat, in the case of HF'Oi- adsorption, the charge conveyed to the surface is large when the HPOz- displaces the water molecules coordinated with the surface and small when it displaces the OH-. The same description is achieved by focusing attention on the way the charge on the HPOa- is balanced. In the reaction involving displacement of water molecules, the charge on the €PO:- must be balanced by changes in the electrolyte ions associated with the surface,either an increasedsurface concentrationof cations or a decreasedconcentration of anions. That is, charge balance is outside the boundary of the surface and the net change in the charge on the surface would be equal to the charge on the anion. In the second reaction the charge balance occurs by displacement of OH-. That is, the charge balance is inside the boundary of the surface and the net change in charge of the surface would be zero. However, neither of these equations by itself an adequate representation of the behavior. There is always some negative charge conveyed to the surface and the amount is always less than the charge on the anion (Hingston 1970; Bowden et al., 1980; Naidu et al., 1990b). Similarly, the output of Bowden's variable-charge model also shows that the charge added to the surface per unit molecule of HPOZ- adsorbed (5) is always less than 2 (Bowden et al., 1980). The amount of charge added to the surface through anion adsorption varies depending on the net charge on the surface. At pH values below the PZC, the surface is positively charged. In the absence of HF'Oa- adsorption, this positive charge on the surface is balanced by the electrolyte anions (e.g., C1-). At low adsorption, the negative charge on the adsorbed HF'0:- is largely balanced by displacement of the C1-. Therefore, the decrease in positive charge on the surface would be rapid initially and produce large 5 values at low surface coverage. With increased HF'Oi- adsorption there will be fewer C1- to be displaced and there will be a greater tendency for the negative charge on the adsorbed €€PO:- to be balanced by adsorption of H+ onto (or release of OH- from) the surface plane. The net charge conveyed to the surface is therefore smaller and 5 is decreased. When the surface becomes negative (at high pH or at high levels of adsorption) the charge on the adsorbed €€PO:- is balanced by the adsorption of electrolyte cations (e.g., Na+).Thus, the charge balance moves increasinglyto the region outside the bound-

SURFACE CHARGE AND SOLUTE INTERACTIONS

119

\ n

a

1 I 0

50

'

I 100

'

I 160

'

I 200

'

I 260

Phosphats adsorbsd (mmol P kg-')

0.40

0.00

I 0

60

'

I 100

'

I 150

'

I 200

'

I 250

Phosphats sdsorbod (mmol P kg-')

Figure 9 Relationship between (left) the total negative surface charge and the amount of phosphate adsorbed (0,Egmont soil; A, Manawatu soil) and (right)the negative charge added per unit phosphate adsorbed (0,Egmont soil; A, Manawatu soil) and the amount of phosphate adsorbed (Bolan et al., 1997).

ary of the surface and the value for 5 increases when the surface charge becomes net negative. The negative charge added to the surface by adsorption of H P O has been found to increase with the ionic strength of the background electrolyte (Atkinson eral., 1967; Hingston el al., 1972; Ryden and Syers, 1975) because with increasing ionic strength the increased availability of electrolyte ions causes the charge balance to increasingly move to the outside boundary of the surface. The charge added to the surface during anion adsorption depends on the charge on the anion adsorbed (Hingston, 1970), but it also differs between the anions of the same charge. The 5 values for SO:- adsorption on gibbsite were found to be larger than those for HPO:-, with both anions having the same charge (Hingston, 1970).This difference can be explained by the position of the plane of adsorption. If the mean plane of adsorption is further away from the solid surface the charge balance is mainly outside the boundary of the surface. Hence, when the surface is positive the charge on the adsorbed anion is more likely to be balanced by the displacement of electrolyte anions; when the surface is negative, it is likely to be balanced by the adsorption of electrolyte cations. As a result, the net charge conveyed to the surface will be larger. This may suggest that the mean plane of adsorption of SO:- is further away from the surface than that of HPO:-. On the other hand, if the mean plane of adsorption is close to the surface the charge on the adsorbed

120

N. S. BOLAN ETAL.

anions would be mainly balanced inside the boundary of the surface by uptake of H+ (or release of OH-). As a result, the net charge conveyed to the surface would be small. This is probably the situation for fluoride (F-), for which the value of 5 has been found to be very small (Hingston, 1970). It is reasonable to assume that the small F- is adsorbed very close to the surface. In summary, while the nature and quantity of soil components affect the permanent and variable charge, soil solution composition affects mainly the variable charge. In general, soils containing noncrystalline materials, such as organic matter and short-range order Fe and A1 oxides and hydrous oxides, carry large net surface charge. The nature and the concentration of cations and anions in the soil solution affect variable charge through specific adsorption and electrostatic charge balance mechanisms. In general, an increase in pH increases the net negative charge and a decrease in pH increases net positive charge, resulting in increased adsorption of anions and cations at high and low pH values, respectively.

IX. EFFECT OF SURFACE CHARGE ON SOIL PROPERTIES A. SOLUTEINTERACTIONS Charged solute species (ions) are attracted to the charged soil surface by electrostatic attraction and/or through the formation of specific bonds (Mott,1981). Retention of charged solutes by charged surfaces is broadly grouped into specific and nonspecific retention. In general, nonspecific anion adsorption is a process in which the negatively charged anions balance the positive charges on the soil particles through electrostaticattractions, whereas specific adsorption involves chemical bond formation between the anions and the ions in the soil surface. Nitrate and Cl- are considered to be adsorbed by a nonspecific process and the adsorption of HPOZ- and SO:- involves both specific and nonspecific adsorption processes. The main differences between the specific and nonspecific adsorption processes are presented in Table VII. Marsh et al. (1988) suggested that SO:- was adsorbed electrostatically, although the amounts of SO:- adsorbed exceeded the measured positive charge. Cation adsorption is largely determined by the amount of surface negative charge (Bouabid et al., 1991; Kookana et al., 1994). It is well established that the mechanism of K+ fixation in 2: 1 phyllosilicates is the entrapment of K+ in the interlayer space. Entrapment is due to the collapse of adjacent silicate layers and the associated dehydration of the interlayer cations (Grim, 1968). The low hydration energy of K+and its size, similar to that of ditrigonal holes in the tetrahedral sheets, explain why K+ is preferentially fixed by 2: 1 phyllosilicates. Total charge density

SURFACE CHARGE AND SOLUTE INTERACTIONS

121

Table VII Comparison between Nonspecific and Specific Anion Adsorption Procesws Nonspecificadsorption

Specific adsorption

Electrostatic attraction between the negatively charged anions and the positive sites on the soil particles Balances the positive charges on the surface and hence no new charges are added to the surface

Chemical bond formation between the anions and the ions on the soil surface

Significant adsorption occurs only when the soil is net positively charged Adsorption depends on the number of positive charges (anion exchange capacity) on the surface In variable-charge soils, the adsorption is high at low pH and decreases with an increase in soil pH Adsorption is weak and reversible

Add negative charge to the surface and the number of negative charges added is generally less than the anion charge Adsorption occurs even when the surface is net negatively charged Adsorption exceeds the anion exchange capacity of the soils Adsorption occurs over a wide range of soil pH values Adsorption is strong and less reversible

and distribution of the charge between the tetrahedral sheets are also important characteristics which influence the extent of K+ fixation by phyllosilicates (Goulding, 1983; Inoue, 1983). Horvath and Novak (1975) and Ruhlicke (1985) found that the amount of K+ fixed by vermiculite and smectites is related to the total charge density. Weir and White (195 1) and van Olphen (1966) stated that when the charge is concentrated in the tetrahedral sheets, K+ is bound by stronger electrostatic forces because of the proximity of charge to the interlayer K+. Barshad and Kishk (1970) and Ristori (1979) found that for smectite with similar total charge densities, those with higher tetrahedral charge fixed more K+ than those with higher octahedral charge. A good correlation is generally observed between K+ fixation and total CEC and tetrahedral CEC. Although octahedral CEC is not correlated with K+ fixation, it does contribute to total interlayer charge density and thus to K+ fixation. Bouabid et al. (1991) observed that tetrahedral and octahedral charges contribute to 64 and 36% of K' fixation, respectively. This effect of the former charge is due mainly to the proximity of tetrahedral charge to the interlayer space in 2:l phyllosilicate clays. The fact that the intercept of the relationship between K+ fixation and total CEC is close to zero indicates that total CEC accounts for most of the K+ fixed. The effect of pH values >6 in lowering free metal ion activities in soils has been attributed to the increase in pH-dependent surface charge on oxides of Fe, Al, and Mn (Stahl and James, 1991), chelation by organic matter, or precipitation of metal hydroxides (Lindsay, 1971). The larger the CEC of the soil, the lower the satu-

122

N. S. BOLAN ETAL.

ration of the exchange sites by a given Zn2+concentration.The effect of pH on the activity of Zn2+ in solution in naturally acidic soils is found to decrease with increasing pH. The gradual decrease in Zn2+activity with increasing pH is attributed to increasing CEC (Shuman, 1986). Similarly, Stahl and James (1991) observed that with an increase in surface charge there was an increase in Zn2+ retention but nonexchangeable Zn2+sorption was favored over exchangeable Zn2+ retention. In general, both the CEC and the total amount of Zn2+ removed from soil solution increased with an increase in soil pH. Thus, the extent of nonspecific adsorption of cations and anions by soils depends largely on the amount of negative and positive charge, respectively, whereas the specific adsorption of cations and anions generally exceeds the amounts of charges in soils. The effect of soil solution composition (ionic strength, pH, etc.) on the adsorption of cations and anions operates through its effect on surface charge.

B. ANION- AND CATION-INDUCED ADSORPTION The increase in surface charge due to the specific adsorption of anions and cations (see Section VIII) induces the adsorption of other ions. Anion-induced cation adsorption has been reported for many cations (Ryden and Syers, 19676; Bolland et al., 1977; Wann and Uehara, 1978a; Shuman, 1986; Kamewada and Takahashi, 1996). Specific adsorption of inorganic anions onto variable-charge components has often been shown to increase the surface negative charge (Bolan and Barrow, 1984; Barrow, 1987). Ryden and Syers ( 1976) concluded that the retention of Ca2+in response to HP0;- sorption by soils results from the increase in negative charge induced by HPOi- sorption. Similarly, Bolland er al. (1977) and Shuman (1986) observed that the specific adsorption of anions, such as HP0:- and SO:-, increases the adsorptionof Zn2+by variable-chargesoils. Wann and Uehara (1978b) observed that K+ added in the presence of HP0:- is less susceptible to leaching than that added in the presence of other anions. Kuo and McNeal(l984) reported that HPOi- adsorption increases the adsorption of Cd2+ by hydrous ferric oxide. Anion-induced cation adsorption depends on the variablecharge components of the soils. Addition of SO:- has been shown to increase A13+ adsorption possibly due to a SO:--induced increase in negative surface charge (Gibson et al., 1992). Naidu ef al. (1994b) studied the effect of inorganic ligands on Cd2+adsorption by an Oxisol and a Xeralf. They found that adsorption increased markedly in the presence of SO:- and HPO:-. A subsequent detailed study using two soils which varied in variable-charge components showed that there was only a small effect of increasing HP0:- adsorption on Cd2+adsorption by a soil dominated by permanent-charge silicate clay minerals (Bolan er al., 1997). However, increasing

SURFACE CHARGE AND SOLUTE INTERACTIONS

123

adsorption of HPOi- caused a significant increase in the adsorption of Cd2+ by a soil dominated by variable-charge components. The increase in Cd2+ adsorption per unit increase in HPOi- adsorption decreased with increasing HPOi- adsorption. Several mechanisms can be advanced for the positive effect of HPOi- on Cd2+ adsorption: (i) precipitation of Cd2+as CdJPO,),, (ii) coadsorption of HPOi- and Cd2+ as an ion pair, (iii) surface complex formation of Cd2+ onto the adsorbed HPOi-, and (iv) HPOi--induced Cd2+adsorption. Precipitation of Cd,(PO,), was ruled out because the concentrations of HPOiand Cd2+were deliberately kept below 0.1 M and the soil systems were unsaturated with respect to Cd3(P0,),. Furthermore, the Cd2+adsorbed with an increasing concentration of HPOi- was completely recovered in MgSO,. Similarly, many authors have shown that the solubility of Cd3(P0,)2 is too high to control the concentration of Cd2+ in suspensions involving Fe and A1 oxides and soils (Bolland et al., 1977; Street et al., 1978; Soon, 1981; Kuo and McNeal, 1984; Naidu et al., 1994b). Marcano-Martinezand McBride (1989) observed an increase in the adsorption of Ca2+in the presence of SO;- which was attributed to cooperative adsorption of Ca2+and SO;- as an ion pair. Equimolar adsorption of Ca2+and SO:- at a high concentration of CaSO, has been taken as evidence for coadsorption of Ca2+and SO:- as an ion pair (Aha et al., 1990). It has been suggested that specifically adsorbed anions such as HPOi- form complexes with the soil surface so that cations are adsorbed on the adsorbed anion. Helyar et al. ( 1976)and Bolland er al. ( 1977) proposed a similar complex formation for the increased sorption of Ca2+onto HPOi--enriched gibbsite and Zn2+ onto HPOi--enriched goethite. It has been shown that Zn2+,Cd2+,or Cu2+sorption by A1 and Fe oxides can be increased by low or moderate enrichment of oxides with HPOi- (Bolland et al., 1977; Kuo, 1986).This may be due to the increased surface negative charge or potential (or reduced surface positive charge) after HPOi- adsorption. Similarly,the increases in the adsorption of Ca2+(Ryden and Syers, 1976) and Cd2+(Bolan et al., 1997; Fig. 10) by soils with HPOi- adsorption have been attributed to an increase in surface negative charge. Cation-induced anion adsorption by variable-charge soils has also been reported (Katou er al., 1996). For example, Bolan er al. (1993) observed that the adsorption of SO;- ions by variable-charge soils was higher in the presence of Ca2+ than K+. Various mechanisms have been suggested for the increase in SO;- adsorption in the presence of the Ca2+.First, the increase in the adsorption of anions such as H2PO; and SO:- in the presence of Ca2+has been related to the formation of a surface complex between the anion and Ca2+(Helyar et al., 1976). This involves coordination of one Ca2+to two adsorbed anion groups, reducing the repulsive force between two adjacent anion groups and thereby enhancing further

N. S. BOLAN ETAL.

124

0

20

40

80

80

100

Increase In negatlve charge (mmol (4kg-I)

Figure 10 Relationship between the increase in Cd2+ adsorption and the increase in negative charge. The increase in negative charge was achieved through phosphate adsorption when phosphate was added as calcium phosphate (0) or potassium phosphate ( 0 ) (Bolan et al., 1997).

adsorption. Second, increased adsorption of SO$- and H2PO; at higher levels of Ca2+addition has been attributed to precipitation reactions occurring at high pH (>7.0) (Adams and Rawajfih, 1977; Freeman and Rowell, 1982). Third, specific adsorption of Ca2+ by hydrous oxides has been shown to increase the positive charge on the surface (Kinniburgh et al., 1975; Kinniburgh, 1983) and thereby increase the adsorption of anions. Marcano-Martinez and McBride (1989) proposed a mechanism involving CaSOt ion pair adsorption on mineral surface in which the presence of one ion of high concentration facilitates the formation of an ion pair and increases the adsorption of the other ion. Bolan et al. (1993) observed that the increase in positive charge with Ca2+adsorption accounted for most of the increase in SO:- adsorption at low levels of Ca2+(<0.002 M) in solution. The role of positive charge in SO:- adsorption by soils has been documentedwell (Marsh etal., 1987,1988; Curtin and Syers, 1990). Thus, the specific adsorption of anions and cations increases the net negative and positive charge, respectively,and thereby increases the adsorption of ionic solutes. Addition of specifically adsorbed anions has been attempted to increase the CEC of variable-charge soils (see Section X).

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125

C. DISPERSION AND FLOCCULATION Dispersion and flocculation of colloid particles are often manifested through changes in surface potential and charge densities. As discussed earlier, when two colloid particles approach each other, their diffuse layers overlap and repulsion is experienced between them. This interaction of the diffuse layer and the surface potential of colloid particles contributes to the overall stability of colloids. Thus, manipulation of particle charge densities assists management of dispersive soils. Such charge manipulation has often been obtained by the use of inorganic salts. Added salts can reduce both the effective surface potential and the extent of the diffuse layer, giving a lower colloid stability. Generally, indifferent electrolytes are not used as flocculating agents, probably because they do not allow the formation of strong aggregates which can withstand the shear forces encountered in most flocculation studies. Salts with specifically adsorbing counter ions are much more effective. Dispersion is caused by mutual repulsion of soil particles because of surface charge. If the repulsive forces are dominant, soil becomes dispersed and virtually unmanageable in an agronomic sense. It is the balance of attractive and repulsive forces which determines whether a soil is flocculated or dispersed. Double-layer theory is remarkably successful in explaining the flocculation and deflocculation behavior of soils. Factors which affect the surface charge of soil particles determine the extent of dispersion, including electrolyte concentration of the soil solution, the valence of the dominant cation occupying the exchange sites, and pH (Arora and Coleman, 1979; Shainberg et al., 1989; Itami and Kyuma, 1995). Suarez et al. (1984) and Chiang er al. (1987) indicated that pH is one of the important factors affecting dispersion, and the sensitivity of hydraulic conductivity to pH changes depends on the quantity of variable-charge minerals and organic matter present in the soil. Soils with large amounts of variable charge are likely to be most susceptible to pH effects. Arora and Coleman (1979) found that increasing the pH of a Georgia kaolinite, which showed variable-charge properties, increased dispersion more than in any of the permanent-charge clays, including smectites, illites, and vermiculites. Also, Chiang et al. (1987) observed that whereas an increase in the pH of Cecil soil, which contained a significant amount of variable-charge components, caused a decrease in K,,a similar increase in the pH of Davidson and Iredell soils, which contained no variable-charge components, resulted in no change in the K,. Bolan et al. (1 996b) examined the effect of pH on dispersion and saturated hydraulic conductivity (K,)in two soils with different variable-charge components. Dispersion remained constant between pH 4.4 and 6.4 and increased on either side of these pH values. The relationship between pH and K, was the mirror image of that between pH and dispersion.At the lowest pH value (pH 2 or 3), there was ev-

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N. S. BOLAN ETAL.

idence for the dissolution of Fe and A1 compounds. Such compounds can act as binding agents (Deshpande et aZ., 1964; El-Swaify, 1976) and their dissolution may be one of the reasons for the sharp increase in dispersion and consequent decrease in K,.However, the main effect of pH on dispersion and Ks is generally attributed to its effect on charge (Rengasamy and Naidu, 1994). The effect of pH on dispersion was investigated by obtaining a relationship between dispersion and pH relative to PZNC. Except for the lowest pH values, at which dissolution of the soil surface occurred, dispersion increased as the difference between the PZNC and pH increased. In other words, when the pH of the soil is close to PZNC, the net charge is less and hence there is less repulsion between the particles, resulting in flocculation. When the pH is further away from the PZNC, the net charge increases and hence particles repel each other, causing dispersion. Gillman (1974) also observed that when the pH of a soil is close to its PZNC, the amount of water-dispersibleclay becomes small. Shanmuganathanand Oades (1982) reported that the addition of Fe polycations increased the PZNC of soil and complete flocculation occurred when a sufficient amount of polycation had been added to increase its PZNC to the pH of the soil. Bolan et al. (1996b) observed that the effect of pH on dispersion varied between the Na+- and the Ca2+-saturatedsoils. At the same value of net charge, the Ca2+saturated soils exhibited less dispersion than the Na+-saturated soils. This can partly be explained by the increased surface charge screening mechanism of Ca2+compared to Na+, developed from the Derjaguin, Landau, Verwey, and Overbeek theory (Greene et aZ., 1973). It has often been observed that the dispersion of clay decreases as the percentage of Ca2+-saturation increases (Rengasamy, 1983), which has been related to the decrease in charge density (van Olphen, 1977).The thickness of the DDL of the Ca2+-saturatedsoil samples is likely to be smaller than that of the Na+-saturated soils. As the DDL becomes smaller, the soil particles are attracted to each other resulting in increased flocculation and greater Ks. In summary, dispersion is caused by mutual repulsion of soil particles because of surface charge and dispersion can be reduced by reducing the surface charge and/or by decreasing the thickness of the DDL. When the pH of a soil is brought close to the PZNC, the net surface charge will decrease, resulting in flocculation. Similarly, when the soil is saturated with polyvalent cations, such as Ca2+,the thickness of the DDL will decrease, resulting in flocculation.

X. MANIPULATION OF SURFACE CHARGE TO CONTROL SOLUTE INTERACTIONS Various attempts have been made to control the retention of anions and cations by manipulating the surface charge of variable-charge soils. Wann and Uehara

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(1978a) suggested that, unlike soils with constant surface charge mineralogy, the surface charge density of soils with variable surface charge (or constant surface potential) mineralogy should be treated as a management variable. Because variable charge largely depends on the pH of the soil solution, treatment of soils with pH amendments has frequently been used to control the reactions of nutrient ions and toxic heavy metals. Similarly, addition of specifically adsorbed anions can increase the CEC of soils. Addition of electrically charged porous materials, such as exchange resins, bark materials, and other organic materials, enhances the retention of cations and anions in soils.

A. LIMING Liming of soils has often been shown to decrease the retention of anions, such as SO:- (Marsh er al., 1987; Bolan et al., 1988b) and HPO$- (Naidu et al., 1990b), and increase the retention of cations, such as nutrient ions (Adams, 1984) and toxic heavy metals (Alloway and Jackson, 1991; Helmke and Naidu, 1996). Bolan et al. (1988b) and Naidu ef al. (1 990b) observed that the addition of liming materials increases soil pH and thereby decreases the positive charge and the adsorption of SO:- (Table VIII) and HPOi-, respectively. The decrease in adsorption of anions increases their uptake by plants and their loss by leaching (Bolan et al., 1986a;Motavalli et al., 1993).Addition of lime has often been observed to increase the concentration of anions such as SO:- in soil solution (Probert, 1976; David et al., 1982; Bolan et al., 1988b), and several reasons have been proposed to explain this (Freney and Stevenson, 1966; Korentager

Table VIII The Effect of Liming on Surface Charge and the Adsorption of Sulfate" Surface charge (mmol kg-') Soil Patua

Tokomaru

Lime added (mmol kg-I)

PH

+ve

0 160 320 600 0 40 80 160

4.7 5.6 6.4 7.0 4.9 5.9 6.5 6.8

11.9 8.0 3.9 3.0 3.9 2.0 I .o 0

"After Bolan eta!. (1988b).

-ve 53.9 157.5

236.2 277.6 74.6 83.9 96.3 109.8

Sulfate adsorbed (mmol kg-I) 4.67 2.52 1.88 1.44 0.66 0.43 0.38 0.37

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N. S. BOLAN ETAL.

al., 1983): (i) SO:- mineralized from soil organic matter by microorganisms growing in a more favorable pH environment; (ii) SO:- released from organic matter by chemical hydrolysis; (iii) adsorbed SO:- released from the soil surface; or (iv) SO:- release from sparingly soluble Fe and A1 hydroxy sulfates, which become more soluble at higher pH values. During liming, both the pH of the soil and the concentration of Ca2+in the soil solution increase. Whereas an increase in soil pH can decrease the adsorption of anions, such as SO:- and HPOi-, and increase the adsorption of cations such as K+, an increase in Ca2+concentration has the opposite effect on the adsorption of both anions and cations. Bolan et al. (1988a) examined the effect of liming on the adsorption of HPOi- and K+ using both batch and column experiments. In the case of column experiments, an increase in pH through liming decreased the adsorption of HPOi- but increased the adsorption of K+. This resulted in increased leaching of added HPOi- but decreased leaching of K+. In batch experiments, however, an increase in pH through liming increased the adsorption of HPOi- but decreased that of K+. Whereas a decrease in HPO,*- adsorption with increasing pH can be attributed to the decrease in electrostatic potential in the plane of adsorption (Barrow, 1984), the increase in HPOi- adsorption with increasing Ca2+concentration has been attributed to many mechanisms,including precipitation of calcium phosphate (Freeman and Rowell, 1982), surface complex formation between the sorbed HP0:and solution Ca2+(Helyar ef al., 1976), an increase in ionic strength of the soil solution (Haynes, 1982),the specific effect of Ca2+on electrostatic potential (Barrow et al. 1980; Curtin et al., 1992) and the adsorption of HP0:- by freshly precipitated Fe and A1 hydroxides following liming (Amarasiri and Olsen, 1973). In the case of cations such as K+, the concentration of Ca2+in the soil solution largely influences adsorption. In batch experiments, the decrease in K+adsorption with liming is mainly due to an increase in the concentration of Ca2+in soil solution (Galindo and Bingham, 1977) and to a decrease in charge density (Goedert et al., 1975) which results in an increase in selectivity of Ca2+over K+. In column experiments, however, the Ca2+concentration in soil solution was decreased by the percolating solution. Thus, in the absence of competition from Ca2+,the increased negative charge at higher pH through liming resulted in an increase in K+ retention. Thus, an increase in pH through liming increases the net negative charge and thereby increases the adsorption of cations and decreases the adsorption of anions, whereas an increase in Ca2+in soil solution through liming is likely to increase the adsorption of anions and decrease the adsorption of cations. Therefore, the resultant effect of liming on the adsorption of cations and anions depends largely on the concentration of Ca2+in soil solution. Under natural leaching conditions in which most of Ca2+is lost from soil solution, liming of soils may not necessarily cause increased leaching of subsequently added K+ (Goedert et al., 1975) or Mg2+ feret

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tilizers (Grove et al., 1981). It is possible, however, that liming a soil may lead to displacement of other cations already present in the soil and hence induce leaching if there is a water flux (Edmeades, 1982).

B. ORGANIC MATTERADDITION Porous, electrically charged materials can be used to adsorb nutrients and pollutants from effluents. This can most likely be achieved on farms by infiltration through constructed soil, sand, or bark filters. Recent research has established that finely ground, composted Pinus radiata bark is an efficient cation exchanger (Mahimairaja et af.,1993) which has the potential to trap and remove the bulk of the NHf and K+from dairy- and piggery-shed effluents and heavy metals from industrial effluents. Bolan et af. (1996a) examined the potential of I! radiata bark in the retention and release of various nutrient ions (NH:, HPOi-, and K+) from dairy-shed effluents using batch and column experiments. Bark materials with a size fraction of 1 or 2 mm were treated with Fe and A1 hydroxides and an industrial waste product, fluidized bed boiler ash (FBA), to enhance the cation and anion retention capacity of the original bark. Greater retention of HPOi- was obtained for the Fe and A1 hydroxides- and FEiA-treated bark than for the untreated bark. The retention of NH:, however, increased only for the FBA-treated bark. Fe and A1 hydroxides increased the positive charge of the bark material and thereby increased the retention of HPOi-. FEiAcontains slacked lime [Ca(OH),] which is likely to precipitate HPOi- as calcium phosphate and increase the pH of the bark materials. The increase in pH caused an increase in the negative charge (CEC) of the bark and thereby increased the retention of NH,+and K+ in the dairy-shed effluent.

C. PHOSPHATE AND SILICATE ADDITION Addition of specifically adsorbed anions, such as HPOi- and SiOi-, has been attempted to increase the CEC of soils (Blair et al., 1990). Fox (1978) showed that the addition of SiOi- reduced HPOi- sorption in a Typic Gibbsihumox soil in Hawai. Application of HPOi- has often been shown to increase the retention of specifically adsorbed cations, such as Zn2+ and Ca2+(see Section IX),through an increase in the surface negative charge. Ayers and Hagihara (1953) and Wann and Uehara (1 978b) showed that leaching losses of K+ in variable-charge soils could be reduced by prior application of P fertilizer to the soil. Wann and Uehara (1978a) suggested that HPO$- fertilizers added to soils not only serve as a nutrient but also as an amendment to increase CEC of the soil. The most frequently cited causes for

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N. S. BOLAN ETAL.

HPO:--induced CEC include (i) a shift in the ZPC to lower pH values (Hingston et al., 1972; Breeuwsma and Lyklema, 1973; Wann and Uehara, 1978a) (ii) neutralization of positive charge (Hingston et al., 1972; Schalscha et al., 1974), (iii) and electrolyte inhibition (Thomas, 1960). Although P fertilizer application has been considered a management tool to increase the CEC of variable-charge soils, large quantities of fertilizer are required to cause a significant increase in CEC. At a maintenance application rate of 40 kg P ha-' it can be estimated that the increase in CEC ranges from 0.07 to 0.18 C mol kg-' soil (assuming a bulk density of soil = 1.0 Mg m-3, depth of incorporation of fertilizer = 50 mm, and the increase in surface charge due to HPOi- adsorption = 0.31-0.70 mol(-) mol P-I).

XI. CONCLUSIONS AND FUTURERESEARCH NEEDS Soils carry both permanent- and variable-charge surfaces. The permanent charge is developed through isomorphous substitution of cations with similar size but different valencies. The variable charge is developed through dissociation/ association of H+from mineral surfaces and the functional groups of organic matter. Specific adsorption of anions and cations also results in surface charge. While specific adsorption of anions increases the negative charge, the specific adsorption of cations increases the positive charge. Surface charge in soils is measured mainly by potentiometric and ion-retention methods. Potentiometric methods are suitable for the measurement of PZC and the ion-retention method is suitable for the measurement of both variable and permanent charges. Improved ion-retention methods, involving ions which are accessible to permanent-charge sites, have been developed to differentiate between permanent and variable charges in soils carrying both these surface charges. Based on the structure of the soil solid components and their reactions with aqueous species, five charge components have been identified: structural, proton, inner-sphere complex, outer-sphere complex, and diffuse-layer charges. Many PZCs have been identified which measure the pH values at which one or more of the individual components of the surface charge density are equal to zero. In an aqueous solution containing soil particles, the distribution of ions around a charged particle is not uniform and gives rise to an electric double layer. Many models have been developed to describe the relative distribution of ions close to the soil surface and in the soil solution. In these models the ions are assumed to distribute at different distances (planes) from the charged surfaces and such models have been used successfully to describe the adsorption of anions and cations by charged surfaces. Although both the nature and the quantity of soil constituents affect the perma-

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nent and variable charge on soil particles, the soil solution composition affects mainly the variable-charge component. Generally, soils containing the silicate clay minerals carry mostly permanent charge and in soils containing Fe, Al, and Mn oxides and organic matter they carry mostly variable charge. Soil pH is considered to be the most important property which influences variable charge in soils. An increase in pH increases the net negative charge and a decrease in pH increases the net positive charge. Surface charge is involved in the retention and movement of cations and anions and in flocculation and dispersion in soils. Surface charges in soils can be manipulated to enhance the retention of solutes and to improve the hydraulic conductivity of soil. Liming has often been shown to increase the negative surface charge and thereby increase the retention of nutrient ions and toxic heavy metals. This is likely to result in reduced leaching of these ions and thereby minimize the risk of contamination of groundwater. Although much work has been done on the assessment of surface charge characteristics of soils there remains a need to develop techniques which enable quantification of permanent- and variable-charge components and in siru measurement of charge. Almost all techniques currently used expose soil surfaces to solutions of varying composition and concentration. Such solutions invariably interact with the surfaces of colloids, thereby altering their charge characteristics. Thus, much of the published information provides at best estimates rather than real charge values as exhibited by colloid particles under field conditions. The effect of particle charge density on contaminant interactions in soils and the implications to contaminant transport and remediation are areas which lack detailed research. As we increasingly protect the natural resource base there is a need to study the role of surface charge on solute-colloid interactions at the soilparticle interface in relation to both nutrient dynamics and remediation. The role of charge and surfactants in remediation studies is just beginning to be realized by soil scientists, and to gain a better understanding of these phenomena soil scientists must interact with scientists from other disciplines.

REFERENCES Adams, E. (1984). “Soil Acidity and Liming.” Soil Sci. Soc.Am., Madison, WI. Adams, F., and Rawajfih. Z. (1977). Basaluminite and alunite: A possible cause of sulfate retention by acid soils. Soil Sci. Soc. Am. J. 41,686-692. Alloway, B. J., and Jackson, A. P. (1991). The behavior of heavy metals in sludge amended soils. Sci. Total Environ. 100,151-176. Aha. A. K., Sumner, M. E., and Miller, W. P.(1990). Reactions of gypsum and phosphogypsum in highly weathered acid subsoils. Soil Sci. SOC.Am. J. 54,993-998. Arnarasiri, S. I., and Olsen, S. R. (1973). Liming as related to solubility of P and plant growth in an acid tropical soil. SoilSci. Soc. Am. Proc. 37,716-721.

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Anderson, S. J., and Sposito, G. (1991). Cesium-adsorptionmethod for measuring accessible structural surface charge. Soil Sci. SOC.Am. J. 55,1569-1576. Anderson, S. J., and Sposito, G. (1992). Proton surface-charge density in soils with structural and pH-dependent charge. Soil Sci. SOC.Am. J. 56,1437-1443. Arnold, P.W. (1978). Surface-electrolyte interactions. In “The Chemistry of Soil Constituents” (D. J. Greenland and M. H. B. Haynes, Eds.), pp. 355-448. Wiley, New York. Arora, H. S., and Coleman, N. T. (1979). The influence of electrolyte concentration on flocculation of clay suspensions. Soil Sci. 127, 134-139. Atkinson, R. J., Posner, A. M., and Quirk, J. P. (1967). Adsorption of potential determining ions at the ferric oxide aqueous electrolyte interface. J. Phys. Chem. 71,550-558. Ayers, A. S., and Hagihara, H. H. (1953). Effect of anion on the sorption of potassium by some humic and hydro1 humic Latosols. Soil Sci. 75,l-17. Barrow, N. J. (1983). A mechanistic model for describing the sorption and desorption of phosphate by soil. J. Soil Sci. 34,733-750. Barrow, N. J. (1984). Modeling the effects of pH on phosphate sorption by soils. J. Soil Sci. 35,283297. Barrow, N. J. (1985). Reactions of anions and cations with variable-charge soils. Adv. Agron. 38, 183230. Barrow, N. J. (1987). “Reactions with Variable Charge Soils.” Martinus Nijhoff/Junk, Dordrecht. Barrow, N. J. (1989).The reaction of plant nutrients and pollutants with soil. Aust. J. Soil Res. 27,475492. Barrow, N. J. (1996). The reaction of anions and cations with metal oxides as models for their reaction with soil. In “Adsorptionon New and Modified Inorganic Sorbents” (A. Dabrowski and V. A. Tertykh. Us.),Studies in Surface Science and Catalysis Vol. 99, pp. 829-856. Elseiver,Amsterdam. Barrow, N. J., Bowden, J. W., Posner. A. M., and Quirk, J. P. (1980). Describing the effects of electrolyte on adsorption of phosphate by a variable charge surface. Aust. J. Soil Res. 18,395-404. Barrow, N. J., Briimmer. G. W.,and Strauss, R. (1993). Effects of surface heterogeneity on ion adsorption by metal oxides and by soils. Am. Chem SOC. 9,2606-261 I. Barshad, I., and Kishk, F. M. (1970). Factors affecting potassium fixation and cation exchange capacities of soil vermiculite clays. Cluys Clay Mines 18, 127-131. Basta, N. T., and Tabatabai,M. A. (1992). Effect of cropping systems on adsorption of metals by soils. II. Effect of pH. Soil Sci. 153,195-204. B1ack.A. S., and Waring, S. A. (1979).Adsorptionof nitrate, chloride and sulfate by some highly weathered soils from South-East Queensland.Ausr. J. Soil Res. 17,271-282. Blair, G. J., Freney, J. R., and Park, J. K. (1990). Effect of sulfur, silicon, and trace metal interactions in determiningthe dynamics of phosphorus in agricultural systems. In “PhosphorusRequirements for Sustainable Agriculture in Asia and Oceania,” pp. 269-280. International Rice Research Institute, the Philippines. Bolan, N. S., and Barrow, N. J. (1984). Modeling the effect of adsorption of phosphate and other anions on the surface charge of variable charge oxides. J. Soil. Sci. 35,273-281. Bolan, N. S., Barrow, N. J., and Posner, A. M. (1985). Describing the effect of time on the sorption of phosphate by iron and aluminum hydroxides. J. Soil Sci. 36, 187-196. Bolan. N. S., Scotter, D. R., Syers, J. K., and Tillman, R. W. (1986a). The effect of adsorption on sulfate leaching. Soil Sci. SOC.Am. J. 50, 1419-1424. Bolan, N. S., Syers, J. K.,and Tillman, R. W. (1986b). ionic strength effects on surface charge and adsorption of phosphate and sulfate by soils. J. Soil Sci. 37,379-388. Bolan, N. S., Syers, J. K., and Tillman, R. W. (1988a). Effect of pH on adsorption of phosphate and potassium in batch and in column experiments. Aust. J. Soil Res. 26, 165-170. Bolan, N. S., Syers, J. K., Tillman, R. W., and Scotter, D. R. (1988b). Effect of liming and phosphate additions on sulfate leaching in soils J. Soil Sci. 39,493-504.

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Thomas, G . W. (1960). Effect of electrolyte imbibition upon cation exchange behavior of soils. Soil Sci. SOC.Am. J. 24,329-332. Tiller, K. G., Nayyar. V. K., and Clayton, P. M. (1979). Specific and non-specific adsorption of Cd by soil clays as influenced by zinc and calcium. Ausr. J. Soil Res. 17,17-28. Tipping, E., and Cooke, D. (1982). The effects of adsorbed substances on surface charge of goethite (a-FeOOH) in freshwaters. Ceochem. Cosmochim. Acra 46,75-80. Uehara, G., and Gillman, G. P. (1980). Charge characteristics of soils with variable and permanent charge minerals. I. Theory. Soil Sci. SOC.Am. J. 44,250-252. van Olphen. H. (1966).Collapse of potassium montmorilloniteclays upon hearing. Potassium fixation. Clays Clay Miner: 14,393-405. van Olphen, H. (1977). “An Introduction to Clay Colloid Chemistry,” 2nd ed. Wiley, New York. van Raij, B., and Peech, M. (1972).Electrochemicalproperties of some oxisols and alfisols of the tropics. Soil Sci. SOC.Am, Proc. 36,587-593. Verwey, E. J. W., and Overbeek, J. Th. G. (1948). ‘Theory of the Stability of Lyophobic Colloids.” Elsevier, Amsterdam. Wann, S. S., and Uehara, G. (1978a). Surface charge manipulation in constant surface potential soil colloids: I. Relation to sorbed phosphorus. Soil Sci. SOC.Am. J. 42,565-570. Wann. S. S., and Uehara, G. (1978b). Surface charge manipulation in constant surface potential soil colloids: 11. Effect on solute transport. Soil Sci. SOC.Am. J. 42,886-888. Weiland, E., and Stumm, W. (1992). Dissolution kinetics of kaolinite in acidic aqueous solutions at 25°C. Ceochim. Cosmochim. Acia 56,3357-3363. Weir, J. I., and White, J. L. (I95 1). Potassium fixation in clay minerals related to crystal structure. Soil Sci. 71, 1-14. White, G . N.. and Zelazny, L. W. (1986). Charge properties of soil colloids. In “Soil Physical Chemistry” (D. L. Sparks, Ed.), pp. 39-81. CRC Press, Boca Raton, FL.

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White, R. E. (1980). Retention and release of phosphate by soil and soil constituents. In “Soils and Agriculture” (P.B. Tinker, Ed.), Critical Reports on Applied Chemistry Vol. 2, SOC. Chem. Ind., pp. 71-114. Blackwell, Oxford. Yu,T. R. (1997). “Chemistry of Variable Charge Soils” Oxford Univ. Press, Oxford. Zeltner, W. A. (1986). Charge development at the goethitelwater interface: Effects of aggregation and carbonate adsorption. PhD Thesis, University of Wisconsin, Madison.

ALLELOPATHY: PRINCIPLES, AND PROCEDURES, PROCESSES, PROMISES FOR BIOLOGICAL CONTROL Inderjit' and K. Irwin Keating* 'Department of Agricultural Sciences (Weed Science), The Royal Veterinq and Agricultural University, DK-187 1 Frederiksberg C, Copenhagen, Denmark *Departmentof Environmental Science, Rutgers, The State University of New Jersey, New Brunswick, New Jersey 08903

I. Introduction

II. Some Concerns III. Allelopathy in Agroecosystems A. Weed Allelopathy B. Crop Allelopathy C. Allelopathic Potential of Crop and Weed Residues D. AllelopathicPotential of Cyanobacteria E. Pollen Allelopathy n! Factors Influencing Allelopathy A. Morphological, Physiological, and Ecological Characteristics B. Habitat and Climatic Factors C. SoilFactors D. Abiotic and Biotic Stress Factors V SecondaryMetabolites with AllelopathicPotential A. Plant Phenolics B. Plant Terpenoids C. Other Classes of SecondaryMetabolites Known to Possess Allelopathic Activities VI. Mechanisms of Action of AllelopathicChemicals A. Interference with Cell Elongation B. Interferencewith Photosynthesis C. Interferencewith Respiration D. Interference with Mineral Ion Uptake E. Interference with Protein and Nucleic Acid Metabolism VII. AllelopathicGrowth Stimulation VIII. Roles for Allelopathy in Biocontrol Programs A. Cover Crops B. AllelopathicChemicals as Natural Herbicides 141 Adzanm in Agr~nmny,Volumc 67 Copyright 0 199Y by Academic Press. All rights of reproduction in my form reserved 0065-2113/99 $30.00

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INDERJIT AND K. IRWIN KEATING M. Additional Comments A. Additive Activities of Allelopathic Chemicals B. Autotoxicity C . Practical Considerations D. Rhizosphere Ecology E. Multifaceted Approach E Statistical Analysis X. Concluding Remarks References

Allelopathy can be defined as chemical interactions between and among both plants and microorganismsvia releases of biologically active chemical compounds into the environment. During the past three decades this scientific field has received growing attention from soil scientists, microbiologists, ecologists, plant physiologists, biochemists, botanists, weed scientists, agronomists, and natural product chemists. Although a few studies are. acknowledged to have demonstrated probable allelopathy in nature, many appeared limited to in vifro circumstances.This difficultyreflects the complexity of allelopathic interactions.Allelopathic effects are often modified by additional biotic and abiotic stress factors, uncertain meteorological events, or physical, chemical, and biological soil factors. all of which can influence the residence time, persistence,concentration, and fate of allelopathic compounds in the environment. Special emphasis is given to an overview of the allelopathic activities of weed and crop species, especially via crop residues in the agroecosystem. and to consideration of the biotic and abiotic factors which influence the expression of allelopathy.A brief discussion of secondary metabolites with allelopathic activities and of the mechanisms of action of allelopathic compounds is also provided. The allelopathic potential of certain weed and crop species can influence the growth and distribution of associated weed species and the yield of desired plants, and allelopathy has been employed successfully in biocontrol programs focusing on control of problematic weeds and plant diseases. Thus, it plays an important role in an agroecosystem and it is clear that a better understanding of allelopathy can help both in crop improvement and in developing more sustainable agriculture. 0 1999 Academic Press

I. INTRODUCTION The term allelopathy (allelon, to each other; pathos, to suffer) was coined by German scientist Hans Molisch in 1937. Observations on allelopathy, however, were recorded 2000 years ago (Putnam and Tang, 1986; Rice, 1984, 1995; Willis, 1997a,b), and modern scientists described the phenomenon in the 1920s. Massey (1925) reported that black walnut (Juglans nigru) and butternut walnut (J. cinerea) caused wilting and dying of alfalfa, tomato, and potato, and Davis (1928) associciated the toxicity of black walnut with synthetic juglone (5-hydroxy-a-

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napthaquinone)and reported its toxic effects on alfalfa and tomato. It was not until 1974, however, that Elroy L. Rice’s English text focused attention on the phenomenon. Rice (1984) offered the definition of allelopathy as the effect(s) of one plant (including microorganisms) on another plant(s) through the release of a chemical compound(s) in the environment. The effects could be either inhibitory or stimulatory, depending on the concentration of the compounds. The compounds involved in allelopathic interference are often termed allelopathic compounds, allelochemicals, or phytotoxins. We prefer the terms allelopathic compound or allelopathic chemical rather than allelochemical or phytotoxin because the traditional use of the term allelopathy includes both inhibitory and stimulatory activities. While traditional use must be respected, this traditional use of the pathy suffix is awkwardly broad for most current scientific discussion. Also, currently the term allelochemical is used in a wider context in the field of chemical ecology in which it includes, but is not limited to, plant and microbial interactions. We identify the plant that releases allelopathic compounds as the producer, or the donor plant, and the plant that is affected as the target, or afflicted, plant. Many researchers (Willis, 1985; Putnam and Tang, 1986; Horsley, 1991; Inderjit and Dakshini, 1995a) have suggested protocols suitable for certain demonstration of allelopathy. These can be summarized as follows: 1. Consistent demonstration of quantitative effects on the growth of the target species due to chemicals released in the donor plant extract, leachate, or exudatewith appropriate controls. 2. Isolation, purification, and characterization of allelopathic compounds, followed by assay of these chemicals against species that are associated with the donor plant in natural systems. 3. Induction under field conditions of responses similar to those observed in the laboratory by the addition to the substratum of the compounds identified from the producer plant. 4. Verification of the in siru release of allelopathic chemicals from the donor plant and of their bioactive concentration in the vicinity of the target plant in nature. 5 . Demonstration that the afflicted plant is sensitive to the allelopathic compound and that it has some means of contact with, or uptake of, the compound. Because target species may be indirectly affected by the nutrient imbalances, microbial ecology shift, and/or microbial nutrient immobilization due to addition of allelopathic compounds in the substratum, this direct contact and sensitivity may not be a valid criterion for all cases of allelopathy. 6. Exclusion of resource competition, herbivory, disease, or other biotic interferences as the basic cause of a pattern of growth inhibition. The allelopathic effects of compoundscould be due to (i) direct release of chemical compounds from the donor plant; (ii) degraded or transformed products of re-

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leased compounds resulting from abiotic and biotic soil or water influences; (iii) effects of released compounds on physical, chemical, and biological soil or water characteristics;or (iv) induction of release of biologically active compounds by a third species. Various ecological and agroecological factors, such as soil and water properties (physical, chemical, and biological), climate, and agricultural practices, greatly influence allelopathy. This review discusses especially the allelopathic interferences of weeds, crop species, and crop residues in agroecosystems. The importance of various biotic factors such as age, density, life cycle pattern, and morphological characteristicsof donor plant in influencing allelopathy will be considered. Alper (1998) reported that long-chain sugars secreted by certain cyanobacteria and bacteria helped bind soil into a black crust which protects these microorganisms from heat. While this is not an example of allelopathy, it illustrates the significance of compounds in shaping habitat for species. In India and elsewhere, cyanobacterial inoculum is used to increase the N content of paddy soils. However, recent laboratory studies suggest that cyanobacterial inoculum, when mixed in high amounts into soils, can adversely affect paddy growth (Inderjit and Dakshini, 1997). Keating (1987) has clearly shown that different species of cyanobacteria have quite distinct allelopathic potentials. In fact, it is most reasonable to consider a strain of the same species, isolated from a different locale, to be of uncertain value when used to replace the original isolate in a system which might benefit by N fixation. The difficulty here is not just that biologically active compounds might be idiosyncratic products of a given isolate of the same species but also that the taxonomy of the cyanobacteria, unlike that of the other prokaryotes, is based on morphology and not biochemistry. This is a remnant of the past association of the blue-green prokaryotic algae with the eukaryotic algae. The prokaryotic algae, much like the other prokaryotes,do not exhibit sufficient morphological difference to support taxonomic distinctions and many cyanobacterial taxons are poorly placed in the phylogeny of prokaryotes. In addition to a discussion of allelopathy among annual and perennial weed and crop plant species, the allelopathic potential of the cyanobacteria and its relevance to agriculture is also discussed. Allelopathic effects can be stimulatory or inhibitory depending on the identity of the active compound on the static and dynamic availability,persistence, and fate of organics in the environment, and on the particular target species. While water is a more efficient dispersing agent, soil especially has its own mechanisms of detoxification. Processes such as sorption, degradation, retention, and transformation greatly influence the quantitative and qualitative availability of organic molecules. The importance of phenolic compounds and terpenoids in the inhibition of nitrification has been questioned. For example, it has been reported that phenolic compounds, such as caffeic and ferulic acids, myricetin, tannins, and tannin derivative compounds, can inhibit the oxidation of NH,+to NO; by Nitmsomonus (Rice, 1984).However, some workers (McCarty and Bremner, 1986)disagree with

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this viewpoint and report that terpenoids and phenolics enhance the immobilization of ammonium N by soil organisms rather than the inhibition of nitrification. These controversial aspects will be explored to gain better insight. The allelopathic potential of certain weeds and cover crops has been exploited in biocontrol programs. Convincing evidence has been presented concerning ways to exploit allelopathy both for crop improvement and for development of a more sustainable agriculture, including weed control, cover crops, pest management through crop rotation, nutrient enrichment, and residue management. Because of an increased understanding of allelopathy, it is clear that allelopathy can help in the progress toward a more sustainable agriculture worldwide. The objective of this review is to discuss (i) the allelopathic potential of certain noxious weeds, crops, and their residues; (ii) the concerns of some ecologists regarding the way in which allelopathic research is done; (iii) the significance of well-replicated field studies; (iv) what characteristics of donor plants favor their allelopathic potential; (v) how stress (herbicide, disease, moisture, nutrient, and light), site, climatic (temperature, growing seasons, etc.), habitat, and physical, chemical, and biological environmental factors influence expression of allelopathy; (vi) how the allelopathicpotential of certain weeds and cover crops can be exploited in biocontrol programs; (vii) the significance of additive effects of allelopathic compounds and other organics at low concentrations;and (viii) the practical difficulties in studying allelopathy that must be considered.

II. SOME CONCERNS Ecologists often express concerns regarding the conclusive demonstration of allelopathy in natural system (Harper, 1975; Keeley et al., 1985; Connell, 1990; Williamson, 1990; Thijs et al., 1994; Inderjit and Del Moral, 1997; Inderjit and Dakshini, 1998a).According to Harper (1977, p. 372), “It is an extraordinarily difficult task to design an experiment that conclusively tests the toxin hypothesis of plant interaction.” Lewis (1986) commented that many laboratory bioassays have been conducted without much consideration of the evolutionary context of the organism, and it is difficult to conclude through laboratory bioassays alone that allelopathy is a main force influencing the competition of species in natural systems. Griimmer (1961) reported on several phenolic compounds, including 4-hydroxybenzoic and vanillic acids from roots and rhizomes of couch grass (Agropyron repens). He states p.221, “It is difficult to believe that an effect specific to couch grass should depend on substances so common in the plant kingdom.” However, many workers conclusively and convincingly demonstrated allelopathy in terrestrial and aquatic ecosystems (Rice, 1964,1968,1971; Muller, 1965,1966; Aubert, 197I ; Del Moral and Cates, 1971 ; Del Moral et al., 1978; Keating, 1977, 1978).

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Wardle and coworkers (1996) investigated the allelopathic potential of six grasses-cocksfoot (Ducryfis gfornerata), phalaris (Phafaris aquatica), prairie grass (Brornus wifdenowii),perennial ryegrass (Lolium perenne), tall fascue (Festuca arundinacea), and Yorkshire fog (Hofcus lanata)-and four legumes-lucerne (Medicago sativa), red clover (Trifoliurnpratense), subterranean clover (T subterraneurn), and white clover (T. repens). The test species used was Carduus nutans. Wardle and coworkers (1996) concluded that the allelopathic effects of the 10 grassland forage species on C. nufans in field plots were significantly correlated with the results of bioassays of these 10 species. Like allelopathy, competition is difficult to demonstrate. Nonetheless, probably reflecting the long use and interpretation of the term “competition,” this term continues to be accepted and used. It appears that the bar for proof of allelopathy in nature is set higher than that for proof of competition because, although it is true that not many studies on competition have investigated the possibility of an allelopathic component, studies of allelopathy are expected to eliminate all possibility of competition.This represents a need to include proof of the negative, which is not a usual scientific practice. It also suggests that allelopathy and competition for specific resources are mutually exclusive. This is faulty logic since natural selection would favor any and all of the traits which provide selective advantage to an organism and that would ensure that multiple competitive tactics would be simultaneously in use. To date, we have found no published work which both eliminates allelopathy and could truly be said to demonstrate competition, and we do not anticipate such material in the future. Some ecologists choose not to invoke allelopathy as a mechanism explaining plant interference unless there is specific evidence to support it and all other mechanisms have been eliminated. The most desired evidence involves isolation, identification, and characterizationof a bioactive compound@)which can be shown to induce the allelopathy. Other desired evidence includes the demonstration that the allelopathic effects on other plants are a primary function of the bioactive compound(s).The need for the primary function must be challenged. An evolutionary explanation for the maintenance, in the genetic pharmacopoeia of a plant, of a compound which functions as an allelopathic substance would not require an exclusive, primary, or even significant allelopathically generated advantage for the producing plant. In terms of natural selection, selection for continued maintenance of the compound would require only a competitively advantageous use and not an allelochemical use. The allelopathic activity could be serendipitous.The allelopathic events may favor the producer but need not do so. Keating (1987) suggests the term “secondary allelochemistry” to categorize the set of circumstances she repeatedly observed during her Linsley Lake study. In that aquatic community several bloom-dominant algal forms (types of cyanobacteria) leave behind bioactive materials when they are no longer able to maintain numerical dominance of the community. These materials commonly favor the succeeding dominant form providing some of the complex of advantageous factors which select it over all other co-occurring species. In a 5-

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year study, no contradictory events occurred, i.e., no producer left bioactive substances behind that negatively affected a successor. The positive allelopathy was readily demonstrated in vitro via cultures established in lake waters freshly collected before and after a bloom and was similarly demonstrated with cell-free filtrates of pure (some axenic) cultures of the producing and affected cyanobacteria (Fig. 1). Since the active material could be concentrated via ultrafilters, and nutritional limitations were excluded by complex nutrient supplementation,the demonstrations of allelopathy were generally accepted. Although no competitive advantage could accrue to the producing organism by virtue of enhancing the growth of its successor, the genetic capacity to produce the bioactive compounds was maintained in the genome of the producer. Thus, some other, nonallelopathic advantage was required to maintain the selective value of this production, and this positive allelopathy could not be the “primary” function of the bioactive material. Demonstrating allelopathy in natural systems is difficult because different mechanisms of interference (resource competition, allelopathy, microbial nutrient immobilization, etc.) can not be separated under field conditions.Also, allelopathic compounds are interwoven with environmental stresses, and separating allelopathy and other mechanisms of interference such as resource competition is not realistic in nature (Inderjit and Del Moral, 1997). n o - w a y analysis of plant interference (i.e., studying competition and allelopathy simultaneously)is important to the generation of more ecologically relevant data (Inderjit and Del Moral, 1997; Inderjit, 1998). It is important to take into account that any adverse effect on the growth and distribution of a plant could be due to organic molecules leached into the soil by donor plants and passed to afflicted plants. Also, it could be due to effects of organic molecules on microbial ecology, microbial nutrient immobiliza-

%

I

100

75 50

25 0

Figure 1 Allelochemical activity of nine cell-free filtrates of cyanobacteria tested against diatoms (D), cyanobacteria (C) chlorophytes ( G ) , and motile forms (M)(identical, autoclaved, cell-free filtrates-the combination of heat, pressure, and pH associated with autoclaving eliminates the activity); light gray. growth promoted; dark gray, growth inhibited; dotted, neutral effect on growth.

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tion, or soil nutrient availability. Furthermore, organic and inorganic soil components may modify the expression of allelopathy (Blum et al., 1992).In no case can we prove unequivocally that allelopathy is the only factor responsible for the observed pattern-only that allelopathy offers the most reasonable explanation. There are many problems with bioassays employed to demonstrate allelopathy (Inderjit and Dakshini, 1995a; Inderjit, 1996; Maestrini and Bonin, 1981). In general, broad criteria to demonstrate allelopathy include (i) identificationof an allelopathic donor plant with reduced growth of other plants in its vicinity; (ii) the capacity of a donor plant to produce bioactive chemical compounds and to release them into the environment; (iii) isolation, identification, and characterization of biologically active chemical compounds; and (iv) observation of the effects of isolated chemical compounds and their mixtures on seed germination andor growth of certain plant species. However, there are many other aspects of this phenomenon which need to be considered. For example, during phytochemical analysis, attention is often paid only to biologically active compounds with “appropriate” concentrations, and compounds with low concentrationremain neglected. Furthermore, bioassays are often performed only with individual compounds, and insufficientattention is paid to the roles of compounds in mixtures of allelopathic chemicals. The significance of soil texture, microorganisms, and associated species in relation to laboratory bioassays is discussed by Inderjit and Dakshini (1995a). It is important to study at what concentration, and in what form, a chemical is available to the target species and to consider how the qualitative and quantitative concentration aspects of a given compound are influenced by habitat, by physical, chemical, and biological soil factors, by climatic factors, and by many other characteristics of the habitat. Finally, it is not always true that a chemical is present in the environment when allelopathic symptoms are observed (Cheng, 1989). It is also likely that by the time allelopathic symptoms are observed, the allelopathic compound has undergone degradation/transformation to other more or less active compounds. These aspects are discussed in the following sections.

III. WLELOPATHY IN AGROECOSYSTEMS A. WEEDALLELOPATHY Many weeds are considered troublesome in cropping systems (Zimdahl, 1993; Aldrich and Kremer, 1997; Holm et al., 1997), and approximately 250 weed species are known to be problematic in agriculture (Worsham, 1989). Allelopathy has been suggested as a likely mechanism of interference in many weed species (Rice, 1984, 1995; h t n a m and Weston, 1986; Waller, 1987; Inderjit et al., 1995) (Table I). Many studies have been conducted under laboratory conditions to eval-

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lsble I Common Agroecosystem Weeds with Potential AUelopathic Activitiw Weed species

Common name

Abutilon theophrasti Agropyron r e p e d Amaranthus dubius Amaranthus palmeri

Velvetleaf Quackgrass Amaranth Palmer amaranth

Amaranthus retrofiexus Ambrosia artemisiifolia Ambrosia cumanensis Ambrosia psilostachya Ambrosia trifida Antennaria microphylla Argemone maxicana Artemisia annw Artemisia vulgaris Asclepias syriaca Avena f a t w Berteroa incana Bidens bipinnata Bidens pilosa Brachiaria mutica Brassica nigra Bromus japonicus Calluna vulgaris Camelina alyssum Camelina sativa Celosia argentea Cenchrus pauciJorus Centaurea diffusa Centaurea maculosa Centaurea repens Centaurea solstitialis Chenopodium album Chenopodium ambrosioides Chromolaena odorata Cirsium arvense Cirsium discolor Citrullis colocynthis Citrullis lanatus Convolvulus sepium Cynodon dactylon Cyperus breifolius Cyperus esculentus Cyperus kyllingia

Redroot pigweed Common ragweed Western ragweed Giant ragweed Small everlasting Annual wormwood Mugwort Common milkweed Wild oat Hoary alyssum Beggar-ticks

-

Black mustard Japanese brome

-

Flax weed Large-seed falseflax

-

Field sandbur Diffuse knapweed Spotted knapweed Russian knapweed Yellow star thistle Common lamb’s-quarter

Siam weed Canada thistle Tall thistle

Hedge bindweed Bermuda grass

-

Yellow nutsedge

-

Reference Dekker et al. (1983) Weston and Putnam (1985,1986) Altieri and Doll (1978) Bradow and Connick (1987), Menges (1988) Bhowmik and Doll (1983) Jackson and Willemsen (1976) Anaya and De.1 Amo (1978) Neil1 and Rice (1971) Le Tourneau et al. (1956) Manners and Galitz (1986) Sharma and Nathawat (1987) Lydon et al. (1997) Mann and Barnes (1945) Rasmussen and Einhellig (1975) Schumacher et al. (1983) Bhowmik and Doll (1979) Meissner et al. (1986) Stevens and Tang (1985) Chou (1989) Muller (1969) Rice (1 964) Jalal and Read (1983) Grummer and Beyer (1960) Lovett and Duffield (1981) Pandya (1975) Rice (1964) Muir and Majak (1983) Locken and Kelsey (1987) Fletcher and Renney (1963) Memll(l989) Qasem and Hill (1989) Jimhez-Osornio et al. (1996) Sahid and Sugau (1993) Stachon and Zimdahl(l980) Le Tourneau el al. (1956) Bhandari and Sen (1971) Bhandari and Sen (1972) @inn (1974) Meissner er al. (1989) Komai and Tang (1989) Tames er al. (1973) Komai and Tang (1989) continues

150

INDERJITAND K. IRWIN KEATING Table I-Continued ~~

Weed species

Cyperus rotundus Digera altemifolia Digitaria sanguinalis Echinochloa crus-galli Echinops echinatus Eleusine indica Eragrostis pweoides Erica australis Erica scoparia Eupatorium adenophorum Euphorbia corollata Euphorbia esula Euphorbia granulaf a Euphorbia prostrata Euphorbia supina Galium aparine Gomphmna decumbens Helenium amanun Helianthus annuus Helianthus mollis Hemarthria altissima Holcus lanatus Imperata cylindrica Iva xanthifolia Kochia scoparia Lactuca scariola kersia hexundra Lepidium virginicum Leptochloa filifonnis Lolium multiflonun Lychnis alba Matricaria inodora Nepera cataria Oenothera biennis Oryza perennis Panicum dichotorn#lonun Parthenium hystemphorus

Common name Purple nutsedge Large crabgrass Barnyard grass

Goosegrass

-

Heath Flowering spurge Leafy spurge -

Prostrate spurge Catchweed Bitter sneezeweed Sunflower

Bigalta limpograss Yorkshire fog Cogon grass Marshelder Kochia Prickly lettuce

-

Plantago purshii Pluchea lanceolata

Virginia pepperweed Red sprangletop Italian ryegrass White cockle Mayweed Catnip Evening primrose Wild rice Fall panicum Ragweed Parthenium Wooly plantain -

Polygonum aviculare Polygonum orientale

Prostrate knotweed Prince’s-feather

Reference Tang et al. (1995) Ashraf and Sen (1980) Parenti and Rice ( 1969) Bhowmik and Doll (1979), Li et al. ( 1992b) Jha and Sen (1981) Altieri and Doll (1978) Hussain et al. (1984) Carballeira (1980) Ballester er al. (1977) Baruah et al. (1994) Rice (1964) Manners (1987) Hussain (1980) Alsaadawi et al. (1990) Rice (1969) Komai er al. (1983) Solomon and Bhandari (1981) Smith (1989) Spring and Hager (1982). Leather (1987) Anderson ef al. (1978) Tang and Young (1982) Wardle et al. (1992) Indejit and Dakshini (1991a) Le Tourneau et al. (1956) Karachi and Pieper (1987) Rice (1964) Chou et al. (1984) Bieber and Hoveland (1968) Altieri and Doll (1978) Naqvi and Muller (1975) Bhowmik and Doll (1979) Mann and Barnes (1945) Le Tourneau et al. (1956) Bieber and Hoveland (1968) Chouetal. (1991) Bhowmik and Doll (1979) Patil and Hedge (1988). Megharaj er al. (1987) Rice (1964) Indejit (1998), Indejit and Dakshini (1994a,b, 1996a,b) h a a d a w i and Rice (1982) Datta and Chatterjee (1978)

continues

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151

Table I- Continued Weed species

Common name

Polygonum pensylvanicum Polygonum sachalinese Portulaca oleracea Polypogon monspeliensis Proboscidea louisianica Rorippa sylvestris Rorippa indica Rwnex crispus Saccharum spontaneum Salsola kali Salvadora oleoides Salvia syriaca Sasa cernua Setariafaberi Setaria glauca Setaria viridis Solanum suranense Solidago sp. Sorghum halepense Stellaria media

Pennsylvania smartweed Sakhalim knotweed Common purslane Unicorn (devil’s claw) plant Yellow fieldcress Dock Wild cane Russian thistle Syrian sage Sasa Giant foxtail Yellow foxtail Green foxtail Goldenrod Johnsongrass Chickweed

Stevia eupatoria Striga densgora Tagetes minuta Tagetespatula Tephmsia purpurea Trianthema portulacastrum Urgenia indica Xanthium pensylvanicum Xanthium strumarium

Kempton’s weed Wild marigold Common cocklebur -

Reference

Le Toumeau et al. (1956) Inoue et al. (1992) Le Tourneau et al. (1956) Inderjit and Dakshini (1995b) Mercer et al. (1987), Rime et al. (1990) Mizutani and Yamane (1991) Yamane et al. (1992a) Einhellig and Rasmussen (1973) Amritphale and Mall (1978)

Lodhi (1979) Mohnot and SON (1976) Abu-haileh and Qasem (1986) Li et al. (199%) Gilmore (1985) Bhowmik and Doll (1983) Rice (1964) Sharma and Sen (1971) Le Tourneau et al. (1956) Abdul-Wahab and Rice (1967) Mann and Barnes (1950). Inderjit and Dakshini (1998) Lovett ( 1982) Zuberi et al. (1989) Meissner et al. (1986) Altieri and Doll (1978) Sundaramoorthy and Sen (1990) Sethi and Mohnot (1988)

Khare ( 1980) Rice ( 1964) Inam et al. (1987)

Note. From A. R. F’utnam and L. A. Weston (1986). Adverse impacts of allelopathy in agricultural systems, In ‘The Science of Allelopathy” (A. R. Pumam and C. S. Tang, Eds.), pp. 43-56. Copyright Q 1986 by John Wiley & Sons, Inc.; adapted with permission of John Wiley & Sons, Inc. “Many of the weed species have been tested for their allelpathic potential using leachate or extract bioassays and do not demonstrate allelopathy conclusively. bNew Weed Science Society of America approved name is Elytrigia repens.

uate the effects of weed species either (i) directly through aqueous or organic leachates or extracts of donor plants (Bhandari and Sen, 1971; Friedman and Horowitz, 1971; Sharma and Sen, 1971; Srivastva and Das, 1974; Chou and Young, 1975; Turner and Quarterman, 1975; Datta and Chattejee, 1978; Sugha, 1978; Hussain, 1980; Murthy and Zakharia, 1980; Dam and Chakrabarti, 1982;

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INDERJIT AND K. IRWIN KEATING

Biswas and Chakraborti, 1984; Manners and Galitz, 1986; Konar and Kushari, 1989;Perez and Ormeno-Nunez, 1991) or (ii) indirectly through extraction of the leachate after addition to the sand, agar, or soil supporting growth of donor plants (Mullerand Muller, 1964;Selleck, 1972;Lodhi and Nickell, 1973;Colton and Einhellig, 1980;Bhowmik and Doll, 1983;Alsaadawi et al., 1990;Anaya et al., 1990; Inderjit and Dakshini, 1991a, 1992a, 1994a,b;Sahidand Sugau, 1993).Many studies on weed allelopathy (Table I) were conducted using an aqueous extract or leachate and examined only seedling growth of afflicted plants as test growth parameters. It may be that allelopathy would not exist in many weed species if allelopathic studies are repeated under more natural conditions (Putnam, 1985). There is a need for well-replicated and repeated field studies. Nevertheless, there are some interesting studies which convincingly prove allelopathy. Weston and Putnam (1985, 1986) demonstrated quackgrass (Elytrigia repens, formerly A. repens) allelopathy in nature. They reported that living and herbicide (glyphosate, N-(phosphonomethy1)glycine)-killed quackgrass significantly inhibited nodulation of snap bean (Phuseolus vulgaris). Figure 2 shows the allelopathic inhibition of quackgrass residues on nodulation of snap bean. Figure 3 shows that snap bean roots lack hairs when treated with quackgrass shoot extracts. Since the untreated snap bean roots have root hairs, quackgrass may have eliminated the physiological sites (i.e., root hairs) for Rhizobium infection. Weidenhamerand Romeo (1989) reported that soils infested with Polygonella myriphylla significantly suppressed the seed germination and growth of Bahia grass (Paspalum notatum). Figure 4 shows patches of P. myriophylla, bordering a citrus field dominated by bahiagrass, which convincingly suggested the operation of allelopathy in nature. Many studies have detected potential allelopathic chemicals from soil infested with allelopathic plants in nature (Levitt and Lovett, 1984; Lovett and Potts, 1987; Oleszek and Jurzysta, 1987; Li et al., 1992a; Inderjit and Dakshini, 1991b, 1992b; Rice, 1995).Lovett and Potts (1987) showed the rapid release into soils of two alkaloids, scopolamine and hyoscyamine, by Datura stramonium seeds. Levitt and Lovett (1984)suggested that the fate and activity of scopolamineand hyoscyaminein natural soil depend on both physical and biological factors. Observations (Oleszek and Jurzysta, 1987) on the fate of medicagenic acid glycoside released in the soil by alfalfa root, as well as data (Li et al., 1992a)on the presence ofp-coumaric, ferulic, vanillic, andp-hydroxybenzoicacids in the rhizosphere soil of the weed Sasa cernua, suggest the operation of allelopathic interference in the field. Table I lists weed species that have been investigated for their allelopathic potential. Many of these weed species were tested for their allelopathic interference using leachate or extract bioassays, and allelopathy is not convincingly proven. Holm (1969) lists the 10 worst weeds: Bermuda grass (Cynodon dactylon), purple nutsedge (Cyperus rotundus), barnyard grass (Echinochloa crus-galli),jungle rice (Echinochloa colona), goosegrass (Eleusine indica), water hyacinth (Eichhomia crassipes), cogon grass (Imperata cylindrica), lantana (Lantana camara), john-

figure 2 Nodulation of snap bean (Phaseolus vulgaris)roots in the presence of dead quackgrassresidues (left) and in the absence of quackgrass residues (right) R. Putnam and L. A. Weston (1986). Adverse impacts of allelopathy in agricultural systems, In “The Science of Allelopathy” (A. R. P u m a and C. S. Tang, Eds.), pp. 43-56. Copyright Q 1986 by John Wiley & Sons, Inc.; reprinted with permission of John Wiley & Sons, Inc.]. [source: A.

Figure 3 Scanning electron micrograph ( X 100 magnification) of a typical untreated, aseptically grown, control snap bean (Phaselous vulgaris) root (top) and treated snap bean root grown in extract of quackgrass shoots (bottom). Root segment was taken 1.5 cm from root tip. Note the presence of numerous root hairs in the control and the absence of root hairs and flaking of epidermal tissue in the treated snap bean [source: Weston, L. A,, and Putnam, A. R. (1986).Inhibition of legume seedling growth by residues and extracts of quackgrass (Agropyron repens). Weed Sci. 34,366-372. Reproduced with permission of the Weed Science Society of America].

ALLELOPATHY

15s

Figure 4 Patches of Polygonella myriophylla (left) dominate portions of a scrub near Sun Ray, Florida. bordering an abandoned citrus field that has been invaded by Bahia grass, Paspnlum norarum, and other ruderal species. The bare zone is approximately I m wide [source: Weidenhamer and Romeo ( 1989).Reproduced with permission of Plenum Publishing Corporation].

songrass (Sorghum halepense),and guinea grass (Panicum maximum). Except for barnyard grass, jungle rice, and goosegrass, all of these are perennial. Additionally, allelopathy has been demonstrated in cogon grass (Chou, 1989; Inderjit and Dakshini, 1991a), Bermuda grass (Chou and Young, 1975; Meissner et al., 1989), purple nutsedge (Friedman and Horowitz, 1971; Komai et al., 1977, 1991; Tang et al., 1995), barnyard grass (Gressel and Holm, 1964; Li et al., 1992b), goosegrass (altieri and Doll, 1978). water hyacinth (Ahmed et al., 1982; Sun et al., 1988), lantana (Archhireddy and Singh, 1984; Mersie and Singh, 1987; Singh et ul., 1989), johnsongrass (Abdul-Wahab and Rice, 1967; Lolas and Coble, 1982), and guinea grass (Chou and Young, 1975). In addition to the previously mentioned noxious weeds, there are many other allelopathic weeds with restricted distribution. Li et al. (1992a) reported Sasa (Sasa cernua) as a serious weed in Japan and some parts of Asia and Europe and detected phenolic acids, such as p-coumaric, ferulic, vanillic, p-hydroxybenzoic acids, and p-hydroxybenzaldehyde, from Sasa-infested soils. Another weed which has naturalized in Hokkaido, Japan, is yellow fieldcress (Rorippa sylvestris). While investigating the allelopathic potential of yellow fieldcress, Yamane et al. (1992b) reported hirustin and pyrocatechol as major allelopathic chemicals released by the weed into the soil. Isothiocyanates were reported as the major allelopathic chemicals in R. sylvestris (Mizutani and Yamane, 1991; Kawabata et al., 1989) and R. indica (Yamane et al., 1992a). Rice (1995) reviewed the allelopathic research on

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INDERJIT AND K. IRWIN KEATING

most of the problematic perennial weeds. Discussing every weed with demonstrated allelopathicpotential is beyond the scope of this article, and our discussion is limited to cropland weed species.

1. Plucbea lanceolata: A Case Study Pluchea lanceolata (hereafter referred to as Pluchea) is an aggressive, noxious, rhizomatous, evergreen, asteracean weed which has become common in sandy and saline tracts of the dry plains of the northwestern parts of India (Inderjit, 1993; Rice, 1995). It also occurs in North Africa, Afghanistan, and Pakistan (Inderjit et al., 1998). In India, it occurs in both cultivated and uncultivated areas and causes considerable damage to winter and summer season crops. Indejit and Dakshini (1990) performed preliminary studies on the interference potential of Pluchea. They reported that leachate from Pluchea leaves was more inhibitory than soil suspension, prepared after amending soils with Pluchea leaves, to the growth of certain crop species. Of 10spots (unidentified phenolic fractions), only 4 were detected from soils amended with leaves of Pluchea. These results supported the need for involving soils in allelopathic bioassays. They did not identify allelopathic chemicals in their study. Thijs and coworkers (1994) suggested that prior to isolation, purification, and characterization of chemical compounds, it is important to demonstrate allelopathy in nature. Many farmers in India plow aboveground parts of the weed into soil; this is followed by irrigation. This practice makes water-soluble compounds available in the soil. To simulate these conditions, Inderjit and Dakshini (1992a) amended Pluchea-free soil with Pluchea leaves. They studied the effects of water-soluble compounds from Pluchea leaves on growth, partitioning and accumulation of nutrient ions, and other physiological parameters of the asparagus bean (Kgna unguiculara var. sesquipedalis).They reported that amending soils with Pluchea leaves influences (i) the quantitative and qualitative increase in total phenolics; (ii) the pattern and accumulation of nutrient ions in the shoot and root of asparagus beans; (iii) physiological parameters such as chlorophyll a and b, net photosynthetic rate, stomata1 conductance; and (iv) the nodule number and weight of the asparagus bean (Table 11). These results clearly demonstrated that Pluchea has the potential to leach water-soluble phenolics into the substratum and that this may allelopathically influence growth and physiological parameters of crops under experimental conditions. Phenolic compounds such as hesperidin, taxifolin 3-arabinoside, formononetin 7O-glucoside, phenol, phloroglucinol,and quercitrin have been detected in the soils associated with this weed (Indejit and Dakshini, 1991b, 1992b, 1994a, 199%). It has been reported that the allelopathic potential of Pluchea increases with cultivation (Inderjit and Dakshini, 1994c, 1996a). In addition to demonstrating the allelopathic potential of a plant under controlled conditions and identifying alleged allelopathic compounds, it is important to investigate the allelopathic potential of

ALLELOPATHY

157

lsble U Growth and Physiological Characteristics of Asparagus Bean Plants with Control and 'hat ed Soil" Soil

Growthlphysiologicalcharacteristics

Control (SD)

Treated (SD)

Seed germination (a) Node (No.) Internode length (cm) Leaf area (cm2) Root length (cm) Root phytomass (g) Shoot length (cm) Shoot phytomass (g) Nodule (No.) Nodule weight6 (g) Chl a (mg g fresh wt-') (pg cm-? Chl b (mg g fresh wt-l) (pg cm-3 Chl a:b (ratio) Net photosynthetic rate (pmol mg chl-I h-l) (pmol g fresh wt h-I) Stomatal conductance (mol m-* S-I) Substomatal cavity CO, conc. (pbar)

83.30 (4.70) 4.21 (0.36) 4.64 (0.75) 23.42 (3.42) 34.40 (7.89) 0.31 (0.02) 96.53 (10.10) 1.92 (0.10) 27.66 (2.90) 0.21 (0.05) 0.140 (0.021) 2.157 (0.064) 0.262 (0.018) 5.72 (0.110) 0.515 (0.05) 41.28 (14.62) 17.01 (6.03) 0.338 (0.015) 285.24 (20.58)

66.66 (7.34)* 3.38 (0.58)* 2.61 (0.79)* 22.53 (6.57) 23.33 (3.18)* 0.32 (0.04) 73.54 ( I O N ) * 1.66 (0.69) 8.64 (3.60)** 0.059 (0.006)** 0.068 (0.021)** 1.42 (0.35)* 0.202 (0.045)* 4.23 (0.77)* 0.339 (0.70)* 96.55 (6.52)** 26.07 (1.26)* 0.668 (OM)** 318.74 (10.95)

"Source: Inderjit and Dakshini (1992a). Reproduced with permission of the Botanical Society of America. 6Weight of 10 nodules per replicate. * p < 0.05. **p < 0.005.

the plant in natural systems. Yields of crops continue to decrease even after Pluchea is physically removed from the field because (i) Pluchea has a deep subterranean root and rhizome system (up to 25 m depth; Inderjit and Dakshini, 1996b) and (ii) Pluchea influences certain soil properties (Inderjit, 1998). Inderjit (1998) compared soils infested with Pluchea and soils 10,20,30, and 40 m away from the weed for various chemical characteristics. In addition to the higher phenolic content of soils in the vicinity of Pluchea, pH, electrical conductivity,potassium, and soluble chloride were also influenced in soils in contact with Pluchea compared to soils at some distance away from Pluchea. This study further showed that soil phenolic content increases after invasion of Pluchea. This aggressive allelopathic potential of Pluchea has been shown under field conditions. It is of importance to design strategies to check or contain Pluchea and to lim-

INDERJIT AND K. IRWIN KEATING

158

it or prevent its deleterious soil-mediated effects in order to save valuable arable land of semiarid regions of India. It is equally desirable to investigate soil-mediated allelopathy of this nature in terms of the generally desirable practice of plowing aboveground remnants of both weeds and postharvest crops into the soil to enhance both the nutrient store and the texture of agricultural soils. Without careful evaluation of the allelopathicpossibilities in a given situation, it remains unknown whether this apparently desirable agriculturalpractice is ecologically sound. Many instances might exist wherein unsuspected allelopathic materials are conserved, or their negative activities are enhanced, by this assumed to be desirable practice. The following sections highlight many factors which suggest a need for reconsideration of the custom.

B. CROPALLELOPATHY The allelopathic effects of many crop species have been observed on other crop and weed species (Rice, 1984, 1995; Table 111).It is well known that crops cultivated in rotation produce higher yields than those grown in monoculture. Various workers have investigated the allelopathic potential of alfalfa (M. sariva) and have reported saponins from alfalfa rhizosphere soil implicating their role as allelopathic chemicals (Kehr et af.,1983; Ellis and McSay, 1984; Oleszek et al., 1990; Wyman-Simpson et af., 1991; Waller et af., 1995; Miller, 1996). Read and Jensen (1989) reported that allelochemicals from alfalfa soil inhibit growth of barley, wheat, radish, and alfalfa. However, red clover (T prutense) growth was not affected. Hegde and Miller (1990) discussed various situations with alfalfa allelopathy and autotoxicity (Fig. 5). They stressed the importance of distinguishing between growth suppression due to resource competition and that due to chemical

lsible III Crop Species with Potential AUelopathic Activities" Crop species Allium fistulosum Allium sativum Apium graveolens Asparagus officinalis Avena sariva Brassica compestris

Common name

Garlic Celery Asparagus Oat

Leafy turnip

Reference Machatia and Peffley ( 1995) Singh et al. (1992) Bewick et al. (1994) Hazebroek er al. (1989), Hartung et al. (1990) Martin et al. (1990) Vaughn and Boydston (1997), Mason-Sedun and Jessop (1988) continues

159

ALLELOPATHY lsble HI- Continued Crop species

Common name

Brassica hirra

White mustard

Brassica juncea

Brown mustard

Brassica napus

Rapeseed

Brassica nigra

Black mustard

Brassica oleracea Cajanus cajan Carthamus tincrorius Colocasia esculenta Cucurbita mosctiata Cucumis sativis Fagopyrum sagirtarum Fesruca arundinacea Glycine max Hemanhria alrissima Hordeum vulgare lpomoea bararas Ipomoea tricolor Lepidium sarivum Linum usirarissimum Medicago sativa

Chinese cabbage Pigeon pea Safflower Taro Squash Cucumber Buckwheat Tall fescue Soybean Limpograss Barley Sweet potato Garden cress Flax Alfalfa

Nicoriana rusrica 0ryz.a sativa Phaseolus vulgaris Pisum sarivum Secale cereale

Tobacco Rice Bean Pea Rye

Sorghum bicolor Sorghum bicolor X Sorghum sudanense Trifolium alexandrium Trifolium incanatum Trifolium prarense Trifolium repens Triticum aestivum

Sorghum Sudex Barseem clover Crimson clover Red clover White clover Wheat

Viciafaba Vicia villosa Zea mays

Broad bean Hairy vetch Corn

Reference Vaughn and Boydston (1997), Mason-Sedun and Jessop (1988) Vaughn and Boydston (1997), Mason-Sedun and Jessop (1988) Vaughn and Boydston (1997). Mason-Sedun and Jessop (1988) Vaughn and Boydston (1997), Mason-Sedun and Jessop (1988) Akram and Hussain (1987) Hepperly and Diaz (1983) Lovett and Jessop ( 1982) Pardales and Dingal (1988) Anaya er al. (1 987) Putnam and Duke ( 1974) Eskelsen and Crabrree (1995) Stephenson and Posler (1988) Martin er al. (1990) Young and Bartholomew (1981) Read and Jensen (1989) Harrison and Peterson (1991) Anaya er a/. (1990) Vaughn and Boydston (1997) Gubbels and Kenaaschuk (1989) Hegde and Miller (1990), Waller er al. (1995) Kennedy et al. ( 1992) Wang and Olofsdotter (1996) Anaya er al. (1987) Schenk and Werner (1991) Chou and Patrick (1976); Wojcik-Wojtkowiak er al. (1990) Lehle and Putnam (1983) Weston et al. (1989) Bradow and Connick (1990) White er al. (1989) Oleszek and Jurzysta (1987) MacFarlane er al. (1982) Steinnsiek et al. (1982), Thorne er al. (1990), Blum er al. (1992) Lovett and Jessop (1982) White et af. (1 989) Ortega et al. ( 1988)

"Many of the crop species have been tested for their allelopathic potential using leachate or extract bioassays and do not demonstrate allelopathy conclusively.

LNDERJIT AND K. IRWIN KFATING

160

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Figure 5 A flow diagram for the study of allelopathy andlor autotoxicityin crops [source:Hegde and Miller (1990). Reproduced with permission of the American Society of Agronomy].

interference (allelopathy or autotoxicity). In Fig. 5, specific effects due to allelopathic chemicals (allelopathy or autotoxicity) in a cropping system are shown by broken arrows and boxed text (Hegde and Miller, 1990). Hegde and Miller suggested that allelopathy and autotoxicity are dependent on the type and duration of interaction between crops. They termed chemical interactionsbetween crops with-

ALLELOPATHY

161

in a season as “short-term” allelopathy (or short-term autotoxicity) and chemical interference beyond one season as “long-term” allelopathy (or long-term autotoxicity). Schreiber (1967) reported that cultivating alfalfa in plots infested with Canada thistle (Cirsium urvense) (33 plant/m2) reduced the population of Canada thistle to 11 plants/m2. Employing buckwheat (Fugopyron sugittutum), an annual crop cultivated as a grain and/or as a cover crop, Eskelsen and Crabtree (1995) carried out a replacement series experiment to investigate the role of the allelopathic interference of buckwheat with Canada thistle. They reported a reduced growth of Canada thistle with increasing buckwheat density (Fig. 6). Canada thistle, however, did not affect the growth of buckwheat. Many members of the Brassicacae have been investigated for their allelopathic activities (Bell and Muller, 1973; Lovett and Duffield, 1981;Jimbnez-Osornio and Gliessman, 1987; Oleszek, 1987; Mason-Sedun and Jessop, 1988; Gubbels and Kenaaschuk, 1989; Gliessman, 1989; Bialy e f ul., 1990; Kluson, 1995). Vaughn and Boydston (1997) reported the allelopathic potential of several crucifers: white mustard (Brussicu hirtu), brown mustard (B. junceu), black mustard (B. nigru), leaf turnip (B. compestris), rapeseed (B. nupus), and garden cress (Lepidium sutivum). It was found that seed germination and fresh weight of hemp sesbania (Sesbuniu exultutu) was suppressed when grown with chopped leaf tissues from these crucifers. Choesin and Boerner (1991) investigated the probable allelopath-

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Figure 6 Effect of buckwheat density on Canada thistle shoot biomass [source: Eskelsen, S. R., and Crabtree, G . D. (1995).The role of allelopathy in buckwheat (Fagopymn sagittarurn) inhibition of Canada thistle (Cirsium arvense). Weed Sci. 43,70-74. Reproduced with permission of the Weed Science Society of America].

INDERJIT AND K. IRWIN KEATmG

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ic potential of B. napus and its glycoside derivative allyl isothiocyanate.From their results, they concluded that B. nupus did not exhibit allelopathy. They reported that the concentration of allyl isothiocyanate in B. napus rhizosphere soil did not suppress alfalfa growth, and that the recovery of allyl isothiocyanate in soil decreases with time (Fig. 7).

C. ALLELOPATHIC POTENTIAL OF CROPAND WEEDRESIDUES Crop and weed residues have been demonstrated to interfere allelopathically with succeeding crop species (Patrick and Koch, 1958; Rice, 1984; Barnes er al., 1986; Putnam and Weston, 1986; Bradow and Connick, 1990). Anderson and Cruce (1995) suggested that residue age, previous weathering history, and residue placement with respect to seed are important factors in residue allelopathy. Yakle and Cruce (1983) observed significant root inhibition of corn when fresh residues were placed at the corn planting depth. Controls included no residue application or residue placement above the seed. They also reported significant root inhibition, compared to placing residues above the seed, when corn residues were placed 5 cm below the seed. While investigating the peach (Prunus persica) replant problem in Ontario, Canada, Patrick ( 1955) reported toxic substances from microbial decomposition of peach residues. He found that substances inhibiting the respiration of excised

ALLELOPATHY

163

peach root tips are produced after microbial action on peach root residues. Patrick and Koch (1958) reported that decomposing rye residues inhibited respiration in tobacco (Nicotiana tabacum) seedlings. Patrick (1971) added that extracts of decomposing rye residues were responsible for delayed seed germination and reduced root growth of tobacco and lettuce (Lactuca saliva). Barnes ef al. (1987) reported that allelopathic activity of rye mulch was largely due to 2,4-dihydroxy1,4(2H)-benzoxazin-3-0ne(DIBOA) and to its degradation product 2(3H)-benzoxazolone (BOA). One degradation product of BOA from soil was identified as 2,2’-0xo-l,1 ’-azobenzene,a chemical involved in rye residue allelopathy (Nair et al., 1990). Wojcik-Wojtkowiak ef al. (1990) reported seven phenolic acids from decomposing rye residues: p-hydroxybenzoic, protocatechuic, gallic, vanillic, syringic, p-coumaric, and ferulic acids. They reported that the concentrations of the individual phenolic acid and their total amounts do not correlate with the phytotoxicity observed in bioassays. They concluded that in addition to the phenolic acids, some other water-soluble compounds are also important in rye residue allelopathy. Yenish et al. (1995) investigated the decomposition rate of rye residues and the disappearing rates of DIBOA-glucoside and related compounds from the rye residue in natural systems. They reported that 50% of the aerial part of rye residues disappear by 105 days after clipping, and that all residue might have disappeared by 200 days after rye clipping. They found that total mounts of DIBOAglucoside, DIBOA, and BOA declined to 50% approximately 10 and 12 days after planting. Weed suppression by rye cover crop has been shown to last through 4 weeks of cover crop kill (Barnes and Putnam, 1986, 1987). Rye residues in the study by Yenish’s group (1995) took 15 weeks to decompose to 50% of starting day levels. Allelopathic effects of wheat straw have been reported (Guenzi and McCalla, 1962; Guenzi et al., 1967; Lynch, 1977, 1978; Chapman and Lynch, 1983; Hicks el al., 1989). Hicks and coworkers (1989) reported allelopathic effects of wheat straw on germination,emergence, and yield of cotton (Gossypium hirsutum). They found that the maximum inhibition in cotton germination and emergence occurred when wheat straw was mixed throughout the soil. Additionally, Shilling et al. (1992) reported that celery residues reduced the emergence and early growth of lettuce.

D. ALLELOPATHIC POTENTIAL OF CYANOBACTEIUA Biological nitrogen fixation, via rotation of legume crops or inoculation of appropriate cyanobacterial strains, plays an important role in maintaining available N for many crops (Huang, 1978; Rodgers ef al., 1979; Roger, 1991; Roger and Ladha, 1992).Cyanobacteria have the potential to contribute up to 20-30 kg N/ha during one cropping season (Inderjit and Dakshini, 1997). Cyanobacterial inocu-

164

INDERJIT AND K. IRWIN KEATING

lum has been used in the paddy field to improve N in the soil. Various workers, however, have reported a significant negative allelopathic potential for cyanobacteria (Keating, 1977, 1978; Rice, 1984; Inderjit and Dakshini, 1994d) and/or the effects of secondary metabolites from cyanobacteria on the growth of algae and higher plants (Gleason and Chase, 1986; Gross e f al., 1991; Chauhan et al., 1992; Bagchi et al., 1993). In 1987, Pedurand and Reynaud reported negative effects of 133 unialgal cyanobacterial strains on seed germination and seedling growth of rice. In 1997, Inderjit and Dakshini investigated the probable allelopathic potential of cyanobacterial inoculum on soil characteristics and seedling growth of rice and wheat. They found that soil inoculated with a cyanobacterial inoculum had different values for total phenolics and for other soil chemical properties (Table IV). Significant increases in phenolic levels were observed with increasing doses of the cyanobacterial inoculum. They concluded that in order to employ cyanobacterial inocula as biofertilizers, a soil to algae ratio with minimum altered chemical characteristicsafter amendment should be formulated. Also, owing to the variety of effects produced by different cyanobacterial species (Fig. l), care must be taken to avoid significant inclusion of cyanobacteria that produce idiosyncratic, exceptionally damaging, allelopathic chemicals.

E. POLLEN ALLELOPATHY While investigating the allelopathy of parthenium (Parthenium hysterophorus), Kanchan and Jayachandra (1980) reported that pollens from Parthenium ragweed, dusted on the stigmatic surfaces of crop plants, inhibit the fruit development. Jimknez-Osornio et al. (1983) investigated the allelopathic effects of corn pollen on radicle growth of Bidens pilosa, Cassiajalapensis, and Rumex crispus. They reported that corn pollen allelopathically interferes with the growth of C.jalapensis. Thereafter, Ortega’s group (1988) reported that the radicle growth of watermelon (Citrullus lanatus) was inhibited when dusted with corn pollen. Anaya and coworkers (1992) reported that radicle growth of Amaranthus leucocurpus and E. crusgalli is inhibited in the presence of sprinkled corn pollen. They found that most of allelopathic activity of corn pollen can be attributed to phenyl acetic acid. Murphy and Aarssen (1989) investigated the allelopathic activities of pollen extracts from five locally common grassland species in eastern Canada: Agrostis stolonifera, Erigeron annuus, Melilotus alba, Phleum pratense, and Vicia cracca. They found that pollen extracts from f! pratense allelopathically influenced the pollen germination of 38 target species: Achillea millefolium, Agropyron repens, Agrostis stolonifera, Ambrosia artemisiifolia, Asclepis syriaca, Aster dumosus, A. ericoides, A. laterijlorus, A. novae-angliae, A. vimineus, Chicorium intybus, Chrysanthemum leucanthemum, Cirsium arvense, Convolvulus arvensis, Daucus carota, Dianthus armeria, Echium vulgare, Euphorbia ofJicinalis, Galium palustre, Hypericum per-

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166

INDERJIT AND K. IRWIN KEATING

foratum, Iris versicolor;Medicago lupulina, Melilotus alba, Phleum pratense, Poa compressa, 19 pratensis, Potentilla recta, Prunella vulgaris, Ranunculus acris, Sisyrinchium montanum,Solidago c a d e n s i s , S. graminifolia, S. nemoralis,S. rugosa, Stellaria graminea, Trifolium pratense, T. repens, and Kcia cracca. They also found that pollen extracts of A. stolonifera, M. alba, and K cracca inhibited pollen germination of certain species; however, the effects were more pH mediated. In their later studies, Murphy and Aarsson (1995a,b) further demonstrated the allelopathic potential of pollen extracts of Z? pratense. In the event that I? pratense was not allowed to flower, seed set in Elytrigia repens (Murphy and Aarsson, 199%) and in Danthonia compressa (Murphy and Aarsson, 1996) increased. Murphy and Aarsson also reported (1995d) that pollen allelopathy in Hierucium aurantiacum, H. jloribundum, and H. pratense is not likely to influence the reproductive success in Lotus corniculatum, Medicago sativa, Trifolium hybridum, T repens, and Kcia cracca. Clearly, pollen allelopathy may be a probable cause of poor seed setting in certain species. This aspect needs greater attention.

Iv. FACTORS INFLUENCING ALLELOPATHY A. MORPHOLOGICAL, PHYSIOLOGICAL, AND ECOLOGICAL CHARACTERISTICS 1. Plant Density Plant density of target species greatly influences the responses to allelopathic compounds (Weidenhamer et al., 1989; Thijs et al., 1994; Weidenhamer, 1996). While investigating the effects of weed density on herbicide absorption and bioactivity, Winkle and coworkers (1981) report that activity of two herbicides, alachlor [2-chloro-N-(2,6-diethylphenyl)-N-(methyloxymethyl)acetaamide]and atrazine [6-chloro-N-ethyl-N’-(1-methylethyl)-1,3,5-triazine-2,4-diamine],decreases with increased weed populations. Hoffman and Lavy (1978) found that plants growing in atrazine-treated soil compete for available atrazine, and with increasing weed populations the herbicide rate for weed control must be increased. Weidenhamer and coworkers (1989) investigated density-dependent allelopathhic effects by growing different densities of Bahia grass ( R notatum) and tomato (Lycopersicon esculentum) in soils amended with varying amounts of gallic acid and hydroquinone. They concluded that the amount of allelopathic chemicals available to each target species plant decreases with the increase in density of the target species. There is a need to investigate the influence of target plant density against the allelopathic effects of perennial weeds. Perennial weeds such as johnsongrass, purple nutsedge, cogon grass, and Pluchea are present throughout the year, and they

ALLELOPATHY

167

are likely to add allelopathic compounds to the soil on a continuing basis. In such instances, both originally contributed compounds and their degradation products would be available in the rhizosphere. Whether a higher density of target species could result in better yield, or whether competition among target species due to their increased density would negate the protective advantage of increased density, is of interest. It is unlikely that a higher density of target species would pose a competitive threat to the competing perennial allelopathic weeds because of the long-term occurrence of the weed species in that particular habitat. It is therefore important to examine more thoroughly the possibility of density-dependent allelopathic effects in natural systems. 2. Life Cycle

Life cycle plays an important role in the expression of allelopathy by weeds (Inderjit and Dakshini, 1995a),and the relative timing of crop sowing and weed emergence is an important factor in the determination of crop losses due to weeds (Forcella, 1993). It has been suggested that crop yield losses are small if a weed emerges after the crop is sown and completes its life cycle before the crop does (Blackshaw et ul., 1981). Many have expressed their concern about the allelopathic potential of annual weeds in nature (Bhowmik and Doll, 1984; Indejit and Dakshini, 1995b). To demonstrate an allelopathic potential in annual weeds, the life cycle of the weed species should be considered when designing bioassays and should be examined in well-replicated fields trials. The static and dynamic availability of possible allelopathiccompounds and their persistence and fate in the rhizosphere should be considered. Jimhez-Osornio and Gliessman (1987) reported that water extracts of both wild mustard (B. compestris) and broccoli (B. oleruceu var. itulicu) have allelopathic activities dependent on the stage of the life cycle of the plants used to prepare the water extracts. Inderjit and Dakshini (1995b) investigated the allelopathic potential of an annual weed, Polypogon monspeliensis, to test the hypothesis that if annual weeds appear either at the time of or after crop seed sowing the chances of annual weed allelopathy are remote. They compared I? monspeliensis-infestedand I? monspeliensis-free soils for total phenolics content and certain inorganic ions and reported that the two soils were not different for phenolic content (Table V). However, quantitative and qualitative increases in phenolic content in soils amended with F! monspeliensis straw were observed. From these results, Inderjit and Dakshini (1995b) concluded that it is unlikely that I? monspeliensis interferes allelopathically with associated crops of the same season, but it is likely that this weed expresses its allelopathic potential through release of phenolic compounds from straw and thus interferes with the next season’s crop. Life cycle is not fixed-it is significantly influenced by environmental factors. For example, the life cycle of biennial wild carrot can be extended beyond 2 years.

168

INDERJIT AND K. IRWIN KEATING 'LBble V

Mean f SD of 12 Variables oP Soils Assodated with (SI) and without (SN)the Weed Polypgon monspeZiens9 Variable

SI

SN

8.06 t 0.30 0.203 t 0.062 12.5 t 4.3 1.14 2 0.89 0.67 t 0.30 3.15 t 0.87 173.7 t 113.7 59.1 2 15.2 49.9 t 20.1 709.1 2 205.7 38.46 t 7.8 2.32 2 1.74

8.08 t 0.34 0.164 t 0.040** 17.6 t 3.5** 1.83 t O M * 0.79 2 0.21 2.92 t 1.19 264.4 t 60.5* 97.1 t 23.3** 66.3 t 50.8 764.5 t 457.1 34.78 2 4.4 2.44 t 1.24

Nore. Values are significantly different at level of *p < 0.05 and **p C 0.005. EC, electrical conductivity; OM, organic matter; TP, total phenolics.

"Source: Inderjit and Dakshini (1995b). Reproduced with permission of Kluwer Academic Publishers.

Plants of species commonly classified as annuals can be made to live for more than 1 year by providing conditions that encourage vegetative growth and discourage flowering and fruiting (Young, 1961).This factor must also be considered and may play an important role in some cases.

3. Plant Age and Habit Plant age and habit influence the expression of allelopathy (Rice, 1984). Inderjit and Dakshini (1995a) discussed the importance of a critical age for donor plant, i.e., the particular age attained after which release of allelopathic compounds starts. Guenzi and coworkers (1967) reported that changes in the toxicity of crop residues during decomposition in the fields varied considerably depending on the type of residues. They reported that the toxicity of wheat straw remains for 4 weeks and decomposes after 8 weeks. The highest toxicity of oat straw was observed at harvest stage. Norstadt and McCalla (1963) suggested that the effect of crop residues might be due to the combination of toxins from residues and microbes. For both tobacco and sunflower, the amounts of phenolic compounds such as scopolin and chlorogenic acid were reported to be influenced by the age of leaves in tobacco and sunflower (Koeppe etal., 1969,1970). Kimber (1973) reported that

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slightly green straw was more inhibitory than fully mature residues. Bell and Koeppe (1 972) reported that interference with corn growth by Seturiufuberi was brought about only after the weed attained a significantgrowth advantage over the crop. In this way, a sufficient pool of allelopathic chemicals became available to the germinating corn seeds to cause interference. Similarly, the observation of Schumacher and coworkers (1983) that wild oats become allelopathic to the growth of spring wheat at the four-leaf stage suggests the importance of the plant age at which release of allelochemicalsstarts. Kanchan and Jaychandra (1979a,b) reported that maximum release of allelopathic compounds from roots of Parthenium ragweed occurred at the rosette and flowering stage. Woodhead (1981) found that levels of phenolic compounds in sorghum leaves decreased with age followed by an increase at heading time. With increasing age of sudex (Sorghum bicolor X S.sudunese), Weston and coworkers (1989) observed a decrease in concentrations of p-hydroxybenzoic acid and p-hydroxybenzenaldehyde and in the allelopathic potential of the crop. Ponder and Tadros (1985) reported that black walnut accumulates allelopathic chemicals in bioactive concentrations after the tree reaches 15-25 years of age. All these studies demonstrate the significanceof age of donor plant in allelopathic expression. Plant habit is an important factor in the determination of the allelopathic potential of a species. For example, perennial weeds often present allelopathic interference to associated plants by their continuous presence and cyclic replenishment of allelopathic chemicals in the rhizosphere (Inderjit and Dakshini, 1994b). Weeds with dense rhizomatous subterranean parts (e.g., Plucheu lunceolutu and Cyprus rotundus) have more chances to build up the bioactive concentration of allelopathic compounds in soil. Indejit and Dakshini (1996b) reported the presence of phenol and phloroglucinol in the water from wells installed in Plucheu-infested cultivated fields. They suggested that a deep and dense spread of roots and rhizomes (up to 25-m depth) results in the leaching of these phenolic compounds into the groundwater. This indicates that the use of Plucheu-tainted well-water for irrigating cultivated fields enhances the allelopathicinterference of Plucheu on crop species. While preparing their fields for cultivation, farmers damage subterranean parts of Plucheu, and due to the high vegetative growth capacity of Plucheu this practice is likely to result in more vigorous growth of the weed.

B. HABITATAND CLZMATIC FACTORS Habitat may have significant influence on the expression of allelopathy. Plucheu lunceolutu is reported from both cultivated and uncultivated areas (Indejit et ul., 1998). Compared to uncultivated soil, cultivated soil had higher values for total phenolics (Indejit and Dakshini, 1994~).Indejit and Dakshini (1996a) found that when compared soils cultivated once a year, the phenolic content of Plucheu soils

INDERJIT AND K. IRWIN KEATING

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cultivated twice a year is higher. The allelopathic potential, in terms of total phenolic content of soils, increases with the presence of the weeds and cultivation. This can be summarized as follows: soil without Pluchea (not allelopathic) -P uncultivated soils infested with Pluchea -P Pluchea soils cultivated once a year Pluchea soils cultivated twice a year. Inderjit and Dakshini (199%) identified flavonoids, quercetin and quercitrin, from leaves of Pluchea. They reported that the amount of quercitrin from soil infested with Pluchea increases with cultivation; however, quercetin was not detected in Pluchea-infested soils. It is important to carry out allelopathic studies at more than one location. Inderjit and coworkers (1996) reported that the amounts of phenolic compounds chlorogenic acid, phenol, phloroglucinol, hesperidin, taxifolin, quercetin, quercitrin, and other unidentified compounds vary with site (Table VI). In nature the allelopathic potential of a plant is likely to vary with site due to its climatic and edaphic conditions. Allelopathy is modified by seasonal conditions such as air and soil temperature and soil moisture (Lodhi, 1978; Weston, 1996; Inderjit, 1998). Luu and coworkers (1982) investigated the influence of N fertilization and seasonal changes on allelopathic activities of tall fescue on bird's-foot trefoil (Lotus comiculatus). They reported that, when treated with the June through September extracts of tall fescue, the percentage germination and the root and hypocotyl length of bird's-foot trefoil were respectively 10, 13, and 7 times higher compared to those of extracts prepared from herbage collected during January through May. Wyman-Simpson +

'IBble VI High-Performance Liquid Chromatography Analysis of Pluchea l a n c e o k Soils from Four Sites" Site Compound Quercitrin Quercetin glycoside Chlorogenic acid Phloroglucinol Hespendin Ononin Unidentified compound

SI 1,050 4,548 9,072 7,076 1,684 1,269

s2 676

-

3,778 8,545 10,862 1,908 1,467

s3

s4

73 73 789 2,873 1.852 274

917 6,880 8,413 9,33 I 3,303 1,263

Note. Concentration was determined by total peak area: -, compound not detected. Total phenolics content of soils from the four sites was not significantly different at p < 0.05. "Source: Indejit et al. (1996). Reproduced with permission of the National Research Council of Canada.

ALLELOPATHY

171

and coworkers ( 1991) investigated the biological activity of alfalfa root saponins using wheat, cheat (Bromus secalinus), and Trichodema viride bioassays. They reported that the biological activity of alfalfa root saponins was higher during periods of rapid growth and high rainfall. With more rainfall the increased amounts of saponins leached into the soil and inhibited the growth of T. viride. Dolling and coworkers (1994) found that allelopathic inhibition of germination and growth of aspen (Populus tremula) was most significant in May, June, and September due to volatile compounds from braken fern (Pteridium aquilinum). Cistus ladanifer is a Mediterranean species with known allelopathic activities (Chaves and Escudero, 1997). Chaves and coworkers (1993) identified several flavonoids: kaempferol-3-(O)-methyl, kaempferol-3,7-di(O)methyl, and apigenin-4‘-(O)-methyl were identified as major components, and apigenin, apiand genin-7-(O)methyl, apigenin-7,4’-di(O)methyl,kaempferol-3,4’-(O)methyl, kaempferol-3,7,4’-tri(O)methyl were identified as minor components of labdanum exudate by C. ladanifel: Chaves er al. (1997a) quantified the variation of flavonoids from 24 individuals of C. ladanijierpopulationsand pointed out the possibility of the existence of two chemotypes.The kaempferokapigeninratio exhibits seasonal variations, i.e., minimum in spring, and a two- to fourfold increase in summer (Chaves er al., 1997b). Inderjit (1998) investigated the impact of different seasons on biotic and chemical characteristics of Pluchea lanceolara and total phenolic content of Pluchea-infested soils. He found that Pluchea-infested soils sampled in February (postwinter) had significantly higher phenolic content compared to those sampled in October (postrain).

C. SOILFACTORS The isolation and identification of certain chemicals from donor plants with biological activity does not demonstrate that these compounds actually interfere allelopathically in nature. Cheng (1989, 1995) offered a conceptual model to understand the allelopathic mechanism in the soil rhizosphere. He suggested that retention, transformation, and transport (i.e., flow of allelopathic chemicals from roots of host plant to roots of target plants) of allelopathic chemicals in soil and physicochemical and biological components of soil influence the fate of allelopathic chemicals, and thus allelopathy, in soil. Lehmann and coworkers (1987) investigated the recovery of p-hydroxybenzoic, vanillic, syringic, p-coumaric, ferulic, and sinapic acids 0, 5, 15, and 30 min and 1,4, and 72 h after their addition into Palouse soil. The recovery of p-hydroxybenzoic, vanillic, and p-coumaric acids after 72 h was 85,49, and 62%, respectively. In contrast, the ferulic, sinapic, and syringic acids were not detected after 72 h. It is therefore important tc understand the fate of allelopathic compounds in soil and how abiotic and biotic soil components influence their persistence and fate in the rhizosphere. Henn and

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coworkers (1988) studied the interference between Hieracium pilosella and Arrhenatherum elatius in colliery spoils of northern France. Hieracium pilosella was reported to have allelopathic activity, and compounds such as umbelliferon, apigenin glucoside, and skimin were identified from H. pilosella roots. However, no allelopathic effects were observed in soils underneath H. pilosella, and none of these compounds were detected from its rhizosphere. Again, physical, chemical, and biological soil factors must be taken into account when interpreting allelopathy in natural systems. 1. Physicochemical Factors

Physicochemical soil factors affect the quantity and quality of allelopathic compounds and thus allelopathy (Haider and Martin, 1975; Wang et al., 1978; Rice, 1984; Dalton, 1989; Dalton et al., 1989a,b; Cheng, 1995; Blum, 1996; Inderjit, 1996). In particular, soil texture significantly influences the expression of allelopathy in natural systems (Muller and Del Moral, 1966; Del Moral and Muller, 1969; Del Moral and Cates, 1971; Rice, 1984; Oleszek and Jurzysta, 1987). Del Moral and Muller (1970) found that, compared to fine-textured soils, sandy loam soils sorb lesser amounts of phenolic compounds. Kuiters and Denneman (1987) reported large amounts of mild, alkaline-extractablephenolics in sandy loam soil when compared to loamy soil, and Oleszek and Jurzysta (1987) reported a higher degree of inhibition when alfalfa roots were amended with loose sand rather than with heavy soil because, compared to heavy soils, essentially no sorption occurs in loose soil. Inderjit and Dakshini (1994b) added different amounts of leaf leachate of Pluchea to sandy loam, clay loam, sand, and silty loam soils. Not only the texture but also the chemical characteristics of these soils differed, and these differences significantly influenced the chemical characteristics and phenolic content of soils (Table VII). Omay and coworkers (1997) reported that changes in soil microbial and chemical properties and long-term crop rotation and fertilization are related to the amount of crop residues and to soil texture (clay and sand content). Reflecting these findings, soil texture should be considered in all bioassays for allelopathy. Soil factors such as pH, organic carbon, and available nitrogen influence allelopathic expression (Dalton et al., 1983; Blum et al., 1987; Dalton, 1989; Blum, 1996). Soil pH plays an important role in the uptake and immobilization of inorganic ions (Nilsson et al., 1982; Brand etal., 1986; Blum, 1996) and on the resultant accumulation of nutrients (Alban, 1982; Facelli and Pickett, 1991; Young et al., 1991), and higher pH can stimulate microbial activity (Aarino and Martikainen, 1994). Ocio and coworkers (1991) investigated the effects of winter wheat straw on microbial biomass and inorganic N. They found that values for microbial biomass (measured as biomass C, N, and nin-hydrin-reactive N) doubled

Table W Chemical Characteristics of Subsamples (Control and TSl-TSJ)” of Sandy Loam, Clay Loam,Sand, and Silty Loam Chemical characteristics ~~~

~

8

EC Texture Sandy loam

Clay loam

Dilution level

cs TS 1 TS2 TS3 TS4 Ts5

cs

TS 1 TS2 TS3 TS4 TS5 Sand

cs

TS 1 TS2 TS3 TS4 TS5 Silty loam

cs

TS 1 TS2 TS3 Ts4 TS5

pH

(pnholcm)

7.75 6.73* 7.85 7.88 7.94 6.68* 7.40 6.40 6.67 6.76 6.75 6.60 8.01 7.54 7.78 7.86 7.78 7.60 6.96 7.40 7.23 7.21 7.22 6.77

155.7 1273.4* 591.2* 414.4* 350.8* 1414.8* 162.7 1354.6* 523.3* 365.7* 294.4* 1653.3* 137.4 1505.4* 552.5* 365.6* 286.7 976.8* 5074.3 5759.3 5522.9 5016.4 5460.8 5279.1

OM

1.98 2.19 2.10 1.85 1.84

2.52 0.481 1.01 0.667 0.619 0.573 2.32* 0.309 0.654 0.309 0.183 0.298 0.522 0.653 1.090 0.963 0.699 0.688 2.m*

rng/lOO g C1-

0.0083 0.1033* 0.0333 0.0225 0.0 183 0.1250* 0.0092 0.1067* 0.0308* 0.0208* 0.0183 0.18 10* 0.0067 0.1380* 0.0333* 0.0225 0.0175 0.0767* 0.8310 0.9950 0.9710 0.9020 1.w*

0.966

TP

Po;

cu

zn

Na

K

Mg

Ca

6.07 435.8* 56.3* 20.2* 15.5 484.8* 2.30 385.8* 48.6* 21.7* 11.9* 674.5* 2.59 794.7* 153.4* 92.9* 47.2 181.7* 2.79 155.3* 24.9* 10.9* 7.38* 392.10*

32.42 42.3* 39.3 38.8 38.1 31.4 6.6 7.29 6.01 5.29 6.13 6.35 4.47 6.49 4.76 4.47 4.72 6.63 5.86 9.66 6.87 5.72 7.07 8.61

0.140 0.191* 0.155 0.131 0.119 0.182 0.030 0.0633 0.0227 0.0280 0.0233

2.72 2.50 2.50 2.30* 2.28* 2.43 0.247 0.310* 0.97 1* 0.237 0.232 0.395* 0.107 0.289* 0.262* 0.133 0.256* 0.216 0.418 0.547 0.414 0.447 0.674 0.770*

20.08 105.66 42.0* 38.1* 30.5* 133.6* 16.6 119.6* 5 1.2* 39.1* 38.5* 142.5* 31.58 145.70* 46.4* 22.8* 19.7* 59.1* 369.9 471.7* 427.8* 383.2 421.6* 434.3*

78.4 79.9 59.3* 79.1 85.1 117.3* 50.6 104.2* 74.8*

16.7 36.8* 26.5* 21.6* 20.6 54.7* 55.6 74.8* 58.7* 51.2* 5 1.5* 95.7* 3.08 37.5* 9.90* 6.90* 5.40* 26.9* 103.O 131.2* 123.1 105.6* 124.1* 124.6*

883.7 814.5 788.5 749.1* 801.7 633.6* 203.7 1%.1 189.5 171.8 168.4* 235.5* 573.7 521.8 544.8 569.5 566.8 422.2* 794.2 744.3* 802.3 758.5* 811.1 696.7*

0.0980*

0.0150

0.0660* 0.0220 0.0193 0.0387 0.0627* 0.165 0.297 0.170 0.182 0.198 0.268*

80.8*

84.3* 92.5* 105.7 118.1* 85.0*

65.4 75.7 93.5* 88.8 87.8 94.7 88.7 1OO.9* 104.9*

Nore. From Indeqit and Dakshini (1994b). Reproduced with petmission of the Botanical Society of America. EC,electrical conductivity; OM, organic matte6 TP,total phenolics; CS, control soil. “Different dilution levels. *Significance level with reference to control: p < 0.001.

174

INDERJIT AND K IRWIN KEATING

within 7 days of straw amendments. After remaining constant for the next 7 days, values dropped. Soil nutrients are known to influence allelopathic activities significantly (Putnam, 1985; Rice, 1984). Lehman and Rice (1972) reported increased amounts of caffeoylquinnic acids in sunflower due to deficiency of N, K, and S and more phenolics were reported to leach down from different parts of phosphate-deficient plants when compared to phosphate-sufficient plants (Koeppe et al., 1976). Stowe and Osborn (1980) reported that phenolic compounds were inhibitory at low nutrient levels. Hall and colleagues (1982) reported that the total phenolics of the sunflower, expressed as chlorogenic acid equivalent, increase with increased nutrient stress and significantly inhibit the seed germination of redroot pigweed. Mwaja’s group (1995) investigated the effects of fertility regimes (low, medium, and high) on phytotoxicity and allelochemicalcontent of cereal rye and found that the shoot biomass of rye was higher when grown either under a high-fertility regime in monoculture or with hairy vetch (vicia vellosa) in polyculture. They found that moistened rye residues grown under medium- and high-fertility regimes, when compared to those grown under low-fertility regimes, evidenced more inhibition to radicle elongation of cress. However, extracts of dried rye shoots grown under a high-fertility regime were less inhibitory when compared to those grown under low- and medium-fertility regimes. The concentrations of allelopathic compounds, DIBOA and BOA, were lowest in rye shoots when grown under high-fertility regimes. These authors concluded that, despite high amounts of rye biomass, under high-fertility regimes the allelopathic content and biological activity were not increased. A concern regarding allelopathy is that addition of plant material results in enhanced microbial activity which further results in depletion of nitrogen and phosphorus (Broadbent and Tyler, 1962; Harper, 1977). Therefore, any growth response after addition of plant debris may be due to nitrogen and phosphorus depletion in the soil and not due to organic molecules. Soil amendment with Cbased compounds such as plant phenolics, however, increases microbial respiration significantly to a much higher degree than microbial biomass (Wardle and Ghani, 1995;Wardle and Nilsson, 1997). It is also reported that microbial biomass and activity often do not linearly respond to substrate amendments (Anderson and Domsch, 1985; Wolters, 1991; Wardle and Ghani, 1995). Lower nitrate concentrations in soil could be due to nitrogen immobilization,inhibition of nitrification, and/or preferential microbial acquisition of NO,-N (Blum er al., 1993; Eviner and Chapin, 1997; Stark and Hart, 1997). Rice and Pancholy (1973, 1974) determined the concentrations of NH,-N and NO,-N, the number of Nitrosomonas and Nitrobactol;and the amounts of phenolic compounds and tannins in two oldfield successional stages and concluded that climax vegetation inhibits soil nitrification due to the production of phenolic compounds by climax species. However, McCarty and Bremner (1986) found no support for the conclusions drawn by Rice and Pan-

ALLELOPATHY

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choly and, in contrast, reported that phenolic compounds and tannins do not inhibit soil nitrification. Northup and coworkers (1995) reported that the polyphenol content of decomposing litter of Pinus muricata controlled the proportion of nitrogen released as dissolved organic nitrogen versus mineral forms (NH: + NO;) (Fig. 8). The chemical characteristics of soil (e.g., pH, organic carbon, inorganic ions, and solute potential) often change after addition of plant debris, roots, leaves, or leachate of donor plants (Inderjit and Dakshini, 1994a,b, 1997;Inderjit and Mallik, 1996a, 1997a) (Tables IV and VI), and such changes have been shown to modify the action of allelopathic chemicals (Blum et al., 1992). Quantitative changes can also be anticipated since large amounts of phenolics were recovered from soils with increased acidity of soils during extraction (Dalton et al., 1983; Blum et al., 1992).

F 0

a

i

i

c

a

a

ii

Y

0

i 8 3r

f 2

Figure 8 Pinus muricafa Oa (moderately to highly decomposed) litter nitrogen release versus concentration of total phenolics. a, dissolved organic nitrogen; b, mineral nitrogen (NH: + NO;). Litter under monospeciesclusters of pine was sampled in three contrasting soil acidity/fertilityconditions on the Ecological Staircase, near Mendwino, California. Values represent nitrogen release rates per gram of litter nitrogen during a 3-week aerobic incubation under laboratory conditions at 25°C (reproduced by permission from Nature 377,227-229, 1995, copyright Macmillan Magazines Ltd.).

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INDERJIT AND K. IRWIN KEATING

Inderjit and Mallik (1997b) reported that phenolic compounds such as ferulic, p-coumaric, p-hydroxybenzoic, cinnamic, and chlorogenic acids influence soil properties. For example, certain phenolics have been shown to affect the accumulation of soil nutrients by forming complexes with nutrient ions (Appel, 1993;Inderjit, 1996). Phenolics affect phosphate availability in a variety of ways, such as by binding to iron, manganese, and aluminum, which would otherwise bind to phosphate. This results in increased availability of phosphate in the environment (Tan and Binger, 1986; Kafkafi et al., 1988). Sorption plays an important role in the movement of allelopathic compounds and thus in allelopathy. Dao (1987) suggested that adsorption reduces the solute concentration in the medium and lowers the redistribution of compounds in soil. Phenolic sorption by soil organic carbon is influenced by pH (Dao, 1987; Blum et al., 1987), and concentrations of water-soluble phenolics are largely correlated with total soil carbon (Kuiters and Denneman, 1987;Blum etal., 1987).It is therefore not surprising that Lehmann and Cheng (1988) reported that phenolic acids were more stable in forest soils with high organic matter than in cultivated agricultural soils. Huang and coworkers (1977) reported that in soil organic matter retained approximately half (40-50%) of total sorbed phenolics. They suggested that soils be pretreated with sodium acetate and hydrogen peroxide, which removes organic matter, and that this would greatly reduce the sorption capacity of the soil. Soil under high cultivation may result in low organic matter content (Burle et al., 1997). Inderjit and Dakshini (1996a) found higher values of phenolics in soils cultivated year-round when compared to soil cultivated once per year. Since phenolics commonlyplay a role in allelochemicalactivity, studies conducted without involving natural soils or studies which disregard physicochemical soil components, especially organics, cannot provide conclusive proof of allelopathy.

2. Biological Factors Soil microorganismsplay an important role in allelopathy because they have the potential to modify its effects (Muller and Muller, 1956; Turner and Rice, 1975; Kaminsky, 1981; Sparling et al., 1981; Vaughan et al., 1983; Rice, 1984, 1995; Blum et al., 1987;Blum and Shafer, 1988;Ruixia et al., 1997). Soil organisms can degrade allelopathiccompoundsproducingeither less toxic or more toxic by-products. They can influence the availability of soil nutrients, which may further influence the fate of allelopathic chemicals, and they can influence the release of chemical compounds bound (sorbed) to soil particles. Fungal species Aspergillus niger, A. fivus, Cephalosporium curtipies, C. furcatium, Pullularia fennentens, Penicillium adametri, Rhodotorula rubra, and Venturia inaequalis have been reported to degrade different phenolic acids and flavonoids (Rice, 1984). Many bacterial species have also been reported to degrade allelopathic compounds (Rice, 1984). Lovett and Jackson (1980) found an exponential increase after 12 h of in-

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cubation in a bacterial population of aqueous washings from foliage of Camefina sativa which, in turn, induce allelopathicactivities. Benzyl isothiocyanate was isolated from aqueous extracts of C. sativa (Lovett and Duffield, 1981). The bacterium Pseudomonaspurida has been isolated from German soils beneath walnut trees and this bacterium can convert juglone (5-hydroxy-1,Cnapthoquinone) to 2hydroxymuconic acid (Rettenmaier et af., 1983). Schmidt (1988) isolated the bacterium from U.S. soils beneath walnut trees and reported that the bacterium could easily use juglone as a carbon source. He concluded that the chances of allelopathic interference of walnut due to juglone are very remote because of its rapid degradation in soil. Williamson and Weidenhamer (1990), however, disagreed with the conclusions drawn by Schmidt. They argued that the allelopathic potential of juglone is due to both its static (i.e., existing concentration in the soil suspension) and its dynamic (i.e., renewal rate) availability. Therefore, even if juglone is degraded over a period of time, its bioactive concentration in the soil is maintained due to its cyclic replenishment. Schmidt (1990), however, did not agree with the justification made by Williamson and Weidenhamer, and in 1990 he suggested the need for controlled experiments to demonstrate actual forms in which juglone is available in the soil. He further stressed that organic solvents should not be used to estimate the availability of juglone. Blum (1998) reported that, due to microbial degradation, reversibly bound phenolic acids (ferulic and p-coumaric) could not accumulate to phytotoxic levels in Cecil horizon soils. These studies demonstrate the significance of soil microorganisms in the expression of allelopathy and the difficulty in determining precise mechanisms (Inderjit and Dakshini, 1995a). Plant leachates have the potential to stimulate soil actinomycetes (Kaminsky, 1981; Katz et af., 1987), and soil-borne actinomycetes may enhance allelopathic activities (DeFrank and Putnam, 1985). Tanrisever and coworkers (1987) reported that Ceratiofa ericoides produces an inactive dihydrochalcone, ceratiolin, which undergoes transformation to produce the toxic compound hydrocinnamic acid under certain light, heat, and acidic soil conditions. Hydrocinnamic acid further undergoes microbial degradation to form acetophenone (Fischer et af., 1994). The germination and growth of Schizachyrium scoparium, a fire-fuel grass of sand pine scrub, is inhibited due to both transformed (hydrocinnamic acid) and degraded (acetophenone)products but not to the compound, ceratiolin, which is directly contributed by the plant. In many instances, therefore, degraded products present increased allelopathic activity. Allelopathic compounds may be present in free, reversibly bound, or irreversibly bound forms. Generally, the first two forms are considered important from the standpoint of allelopathy. Bound forms, however, can also be important (Rice, 1984).Allelopathic compounds bound to soil particles can be released into the soil solution by the actions of fungal populations. Novak and coworkers (1995) suggested that fungal hyphe can penetrate the organic matrix or inner clay layer, and that this results in the release of bound forms into soil solution. Based on plant up-

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take studies (Duke et al., 1991), <3% of the added amounts of herbicides are present in available forms. Approximately, 20-70% of the applied residues are in bound forms. It is therefore important to degrade such bound organic forms to reclaim valuable cultivated land. Organic residues and inorganic N and P are added at land forming sites and during composting to stimulate microbial activity (Ziegenfuss et al., 1991; Novak et al., 1995).

D. ABIOTICAND BIOTICSTRESSFACTORS Various abiotic and biotic stress factors influence the expression of allelopathy (Einhellig, 1989, 1995a).Some have suggested that there is an increased production of allelopathic chemicals with environmental stress (Einhellig, 1989, 1996; Oleszek er al., 1988a,b; Inderjit and Del Moral, 1997). Weidenhamer (1996) discussed the impact of biotic and abiotic factors on plant responses to allelopathic chemicals (Fig. 9). He suggested that environmental stresses due to such factors as moisture, nutrient, and temperature stresses, as well as pathogens, plant density, light, and organics, influence the leaching of allelopathic compounds. After entering into the soil environment, the availability, persistence, and fate of allelopathic compounds are influenced especially by physicochemical soil factors and soil microorganisms.

NTAL FACTORs -Moisture ond Nutrient Stress Temperolure

-

- Plont

Density

PLANT UPTAKE

PHYSICAL lCHEMICAL

COMPOUNDS

BY

MICROORGANISMS

(LITTER, RESIDUE

Figure 9 Environmental biotic and abiotic factors influencing plant response to allelochemicals [source:Weidenhamer (1996). Reproduced with permission of the Agronomy Society of America].

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In 1980, allelopathic effects of yellow nutsedge on corn and soybean were reported by Drost and Doll. In 1986, during a study of the interactions between sweet corn (Zea mays) and purple nutsedge at different irrigation levels, Ardi determined that inhibitory effects of purple nutsedge on sweet corn yield were reduced at the highest water stress imposed. Later, on the basis of a study of sesquiterpene compounds, four chemotypes (H, M, 0, and K types) of purple nutsedge were identified (Komai and Tang, 1989; Tang et af., 1995). H chemotypes were most inhibitory, and 0 types least inhibitory, to the seedling growth of lettuce and oat. Tang and coworkers, while studying water-deficit stress on the allelopathhic potential of purple nutsedge (Kohl, 1993; Tang et al., 1995), collected root exudates of purple nutsedge grown under different conditions of water stress. They found that both fresh and dry weights of shoots and roots decrease with increasing water stress. From lettuce bioassays, Tang’s group also concluded that the inhibitory activities of purple nutsedge were mostly controlled by relatively polar, methanolsoluble compounds. In recent studies of the effects of shading on the growth of yellow and purple nutsedge, Santos and coworkers ( 1997) found that 80% of shading from incident sunlight (compared to full-sunlight controls) reduced dry matter partitioning to tubers and increased dry matter partitioning to shoots of yellow nutsedge. In purple nutsedge, however, they found that while partitioning to tubers was reduced, no increase in partitioning to shoots was evident. In a study of ecological affectors of seasonal variation of flavonoid content of C. ladnijer exudates, a two to fourfold increase in content in summer compared to spring was observed by Chaves and coworkers (1997b). They suggested that during the summer plants were subjected to UV irradiation, high temperature, and hydric stress. They found that UV radiation was the main stress factor responsible for the higher production of flavonoid in the summer. Del Moral (1972) reported increased amounts of total chlorogenic and total iso-chlorogenic acids in sunflower supplemented with UV light. Generally, both abiotic and biotic stresses tend to increase the levels of secondary metabolites (Gershenzon, 1984; Einhellig, 1996). In particular, Inderjit and Del Moral (1997) discussed the significance of stress factors, such as drought, shade, and nutrients, in enhancing levels of allelopathic chemicals. To interpret allelopathy in nature, it is important to determine how these stress factors influence the release of allelopathic compounds. Koeppe’s group (1976) reported that the higher amounts of phenolics were released from sunflower grown under phosphorus-deficient conditions. Alsaadawi’s group (1985) reported the exposure of grain sorghum seeds to gamma irradiation significantly increased the allelopathic activity of aqueous extracts, root exudates, and decomposing residues, and Sterling’s group (1987) reported that exudates from velvetleaf glandular trichomes have higher allelopathic activities when plants are subjected to high-temperature stress compared to those grown under low-temperature conditions. The influence of particular nutrients on allelopathic activities is difficult to gen-

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eralize. For example, Stowe and Osborn (1980) reported that vanillic and pcoumaric acids in N- or P-deficient barley seedling have high allelopathic activity. Williamson and coworkers (1992) reported that low N and low K conditions favor higher allelopathic activities associated with hydrocinnamic acid to little bluestem grass (Schizachyrium scoparium);however, no enhanced allelopathiceffects were recorded with low Pconditions. In some situations it is difficult to identify specific stress factors in natural systems.The weed Pfuchea fanceofata, for example, occurs in both uncultivated and cultivated areas. Compared to Pfuchea growing in uncultivated areas, Pfuchea plants growing in cultivated soils have higher values for height, density, leaf area, and leaf weight and lower values for total phenolics (Inderjit, 1993). It is hypothesized that the higher phenolic content in Pfuchea plants growing in uncultivated areas is due to the fact that these plants were subjected to nutrient and moisture stresses. Pfuchea-infested cultivated soils, however, have higher values for total phenolics when compared to those of Pfuchea-infested uncultivated soils. Higher injury to Pfuchea plants, during plowing, is a likely explanation for the higher phenolic content of Pluchea-infested cultivated soils. This may also be considered as a form of physical stress. It is therefore important to study the interactions of various stress factors and the impacts of such interactions on allelopathy. The following biotic factors may influence allelopathy; herbivory, disease, interaction with herbicides, and insect damage (Einhellig, 1996).Plant residues have been shown to enhance pathogenesis or pathogenic fungi (Patrick et af., 1964; Lynch, 1977). Hartung and Stephens (1983) reported that toxic activities of asparagus residues on asparagus plants are positively correlated to the degree of infection by Fusarium species. Many tall fescue plants are reported to be infected by fungal endophytes (Acremonium coenophifufurn) (Shelby and Dalrymple, 1987). Springer (1996) investigated the allelopathic effects of endophyte-freeand -infected tall fescue on the germination and seedling growth of five species of clover: red clover, subterranean clover, crimson clover, white clover (Zrepens), and ball clover (T nigrescens). He found no significant influence of tall fescue endophytes on seed germination or seedling growth. The length and density of clover root hairs, however, was influenced by the presence of endophytes. Dieteman and coworkers (1964) reported that levels of scopolin increased 30fold after spraying with 2,4-D (2,4-dichlorophenoxyaceticacid). In most of the cases studied, herbicides in combination with allelopathic chemicals have additive activities when compared to either alone (Einhellig, 1987). Einhellig (1996) tested the validity of the hypothesis that allelopathic chemicals and herbicides have additive activities. He reported that when treated alone, ferulic acid (250 pm)and atrazine (10 pg/liter) inhibited the growth of oat plants by 31 and 9%, respectively. The reduction in oat growth was 47% when ferulic acid and atrazine were combined at these concentrations, hinting at a synergistic effect. The previous discussion illustratesthe complex nature of allelopathy in nature. Under field conditions,

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various factors, such as moisture, shade, nutrients, plant diseases, and herbicides, can influence allelopathy. Although it is impossible to exactly mimic field conditions in laboratory bioassays, researchers should try to design realistic bioassay conditions to lessen the gap between laboratory and field. Field studies are essential to unequivocal demonstration of the phenomena.

V. SECONDARY METABOLITES WITH ALLELOPATHIC POTENTIAL Most of the allelopathic compounds can be grouped under “secondary metabolites” (Whittaker and Feeny, 1971). This reflects the absence of an obvious role in primary metabolism (Rice, 1984). Nonetheless, these secondary metabolites are of the utmost importance in plant defense. Certain primary metabolites, however, also play a significant role in allelopathy. For instance, several free amino acids from seeds of velvetleaf are reported to possess allelopathic activities (Gressel and Holm, 1964). Waller and coworkers (1987) isolated primary metabolites such as palmitic and stearic acids from soil and demonstrated their allelopathicpotential. Lynch (1987) reported that acetic acid is produced in sufficient amounts in decomposing residues; however, no correlation with biological activity has been proven. Janovicek and coworkers (1997a) investigated the effects of acetic, propionic, and butyric acids on early corn seedling growth and did note effects. Although seed germination of corn was not affected, all three acids reduced radicle growth of corn. These authors concluded that reduction in early corn growth due to these acids depends on the particular acid type, its concentration, the soil pH, and the corn hybrid variety. These observations are important because these acids are known to be produced during anaerobic decomposition of plant residues. Many secondary metabolites, e.g., phenolic compounds, terpenoids, alkaloids, steroids, polyacetylenes, and essential oils, have been reported to possess allelopathic activities. Listing every allelopathic compounds from each class is beyond the scope of this article. We will discuss the allelopathic potential of important classes known to have compounds with allelopathic activities.

k

PLANTPHENOLICS

There are many review articles on the ecological, physiological, and biochemical aspects of plant phenolics (Levin, 1971; Harborne, 1989; Kuiters, 1990; Siqueira et al., 1991;Appel, 1993; Inderjit, 1996; Stafford, 1997). Total phenolics is the sum of soluble polymers (tannins) and monomers (phenolic acid and flavonoids).Harborne (1989) discussed the biosynthetic origin of plant phenolics

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INDERJIT AND K. IRWIN KEATING HO

Shikimic acid

-1

Some quinoner

Flavonolds and + a n

lroflavonoidr

Fiavolanr

Phenylalanine

p-Hydroxycinnamio acid

+

3malonrte thonu and Hydroxyrtilbsn-

\HO pSlydroxydnr\.myl alcohol

A I--.-I

Phenyiproponu

Llgnans

UgniM

Figure 10 Biosynthetic origin of plant phenolics from shikimate and phenylalanine pathways [reprinted from Harbome, J. B., 1989, General procedures and measurement of total phenolics, In “Methods in Plant Biochemistry: Plant Phenolics” (J. B. Harborne, Ed.), Vol. I, pp. 1-28, by permission of the publisher Academic Press Limited, London].

from shikimate and phenylalanine pathways (Fig. 10). The major classes of phenolics are (i) simple phenols and benzoquinones (C,) and (ii) phenolic acids (C6C acetophenones and phenylacetic acids (C6-C2); hydroxycinnamic acids, phenylpropanes, coumarins, isocoumarins, and chromones (C,-C,); naphthoquinones (C6-C4); xanthones (C,-C, -C6); stilbenes and anthraquinones (C6C,-C,); flavonoids and isoflavonoids (C6-C3-C6); lignans and neolignins [(C,C,),]; biflavonoids [(C,-C,-C,),]; lignins [(C,-C,),]; catechol melanins [(C,),]; and flavolans, i.e., condensed tannins [(C,-C,-C,),J (Harborne, 1989). The allelopathic potential of simple phenols, benzoic and cinnamic acid derivatives, flavonoids, and tannins is well demonstrated in the literature (Rice, 1984, 1995; Indejit et al., 1995). Fisher (1979, p. 323) stated that, “Phenolics comprise the largest group of secondary compounds in plants and are more often identified as allelopathic agents than all other compounds put together.” Furthermore, phenolic compounds are water soluble and could easily be leached by rain, whereas leaves are still attached to the plant or, thereafter, from leaf litter (Alsaadawi et al., 1985). Water-soluble compounds are of even more ecological relevance in situations in which irrigation is frequent (Del Moral and Muller, 1970); however, all water-soluble compounds are not always allelopathic in nature. Many highly

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water-soluble compounds have low biological activity, whereas many slightly water-soluble compounds have high biological activity (J. D. Weidenhamer, personal communication). From the standpoint of allelopathy, as long as the solubility exceeds concentrations required for biological activity, the compound should be regarded as potentially able to exert allelopathic effects (Weidenhamer et al., 1993).

B. PLANTTERPENOIDS The different classes of terpenoids are mono (Clo), sesqui- (CIS),di- (C,,), tri(Gershenzon, 1994). There are several reviews on the ecological, physiological, and biochemical aspects of terpenoids in the Journal of Chemical Ecology (Fischer et al., 1994; Gershenzon, 1994; Langenhein, 1994; Takabayashi et al., 1994; White, 1994). Certain terpenoids are produced solely for defense purposes, i.e., in response to herbivory or to pathogen attack (Takabayashi et al., 1994; Gershenzon, 1994). The enzymes responsible for induction of such terpenoids are not detected in healthy plants or in plants not subjected to herbivory but are known to occur from infected plants or plants under herbivory stress (Gershenzon, 1994). For example, Gershenzon and Croteau (199 1) reported that grand fir (Abies grandis) produces large amounts of monoterpenes after being wounded, and these monotepnoids serve as defense against bark beetles and fungi. Terpenoids are the second largest group (after phenolics) of secondary metabolites implicated in allelopathy.The allelopathhic potential of monoterpenoids (e.g., camphene, 1,&cineole, a-pinene, P-pinene, dipentene, a-phellandrene, pcymene, piquerol A, piquerol B, limonene, borneol, and pulegone) is well reported (Muller and Chou, 1972; Gant and Clesbsh, 1975; Nishimura et al., 1982; Fischer, 1986; Weidenhamer ef al., 1993). Weidenhamer and coworkers (1993) suggested that unsaturated solutions of monoterpenoids in a natural system may possess significant allelopathic activities. Fischer (1986) discussed the allelopathic potential of several sesquiterpenoids, e.g., P-bisaabolene, P-caryophyllene, bergaamotene, a-guayene, a-bulnosene. P-patchoutin, (E,E)farnesol, p-selinene, vitrenal, phomenone, metabpodin B, and cinerenin. Fischer and coworkers (1 994) demonstrated that allelopathy is a mechanism restricting the fire-prone grasses and pines from invading scrub communities in Florida and implicated terpenoids as probable allelopathic candidates. Finally, aqueous leachates of Conradim canescens significantly inhibited the sandhill grasses, e.g., Schizachyrium scoparium, due to the presence of monoterpenes (1,8-cineole, carveol, carvone, a-terpineol, camphor, bomeol, myrtenal, and myrtenol) and some triterpenoids (ursolic acid and betulin). (C3J, and tetra terpenoids (C,)

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c. OTHER CLASSES OF SECONDARYMETABOLITES &OWN TO

POSSESS ALLELOPATHIC ACTMTIES

Alkaloids have received considerable attention for their allelopathic activities. Alkaloids possess nitrogen in a heterocyclic ring or side chain and generally occur in plants as salts of organic acids (Wink, 1983; Rice, 1984; Levitt and Lovett, 1985; Waller, 1989). Lovett and coworkers (1987) reported the allelopathic potential of hyoscyamine and scopolamine from thornapple. Thereafter, Lovett (1989) showed that the alkaloids gramine and hordenine, produced by grain barley, interfere allelopathically with seedling growth of white mustard. Many other alkaloids (e.g., scopolamine, hyoscyamine, caffeine, theophylline, theobromine, paraxantheine, colchicine, podophyllotoxin, and vinblastine) have been suggested to possess allelopathicactivities (Worsham, 1989;Wink and Twardowski, 1992; Wink and Latz-Briining, 1995). Waller and Burstom (1969) reported that diterpenoid alkaloids, delcosine and ajacocnine, from Delphinium ajacis had allelopathic effects on cambium growth of the pea. Stevens (1986a) discussed the allelopathic potential of polyacetylenes known to possess allelopathic activities. He (1986b) reported polyacetylenes from Russian knapweed and demonstrated their allelopathic potential. Griimmer (1961) reported antimicrobial activities of agropyrene, a polyacetylene produced from quackgrass. Also, the polyacetylene cis-dehydromatricaria ester from Solidago altissmia and cis- and trans-matricaria and cis-lachnophyllum from Erigeron annuus have been reported to possess allelopathic activities (Rice, 1984). However, little information is available on the allelopathic potential of polyacetynes, and it is important to demonstrate the allelopathic activities of the polyacetylenes in nature. Although the allelopathic potential of one class of secondary metabolites may be demonstrated, the possible involvement of compounds from another class cannot automatically be ruled out. To date, the determination of such allelopathic activity has been serendipitous, focusing on a particular class of compounds depending on the amount of compound of a particular class detected, on its biological activity, and on the personal research interests, expertise, and facilities available in a given laboratory.

VI. MECHANISMS OF ACTION OF ALLELOPATHIC CHEMICM,S Allelopathy is often categorized under ecological chemistry/chemical ecology or physiological ecology. In 1969, while discussing chemical interactions among organisms, Hegnauer suggested the term ecological chemistry. Ecological chem-

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istry involves using chemistry and biochemistry to explain ecologically significant interactions among organisms (Towers er al., 1989), as distinguished from the more general term physiological ecology, in which physiology is used to explain ecological interactions. When we identify some plant to plant interference in nature, we first need to identify an ecological interplay, i.e., whether the observed pattern is best explained by allelopathy, resource competition, microbial nutrient immobilization,etc. Once we identify the problem, and demonstrate that allelopathy best explains the observed growth pattern, we need to study the physiological/ biochemical mechanisms of action of allelopathic chemicals. In this section, we will discuss some of the important physiological/biochemicalmechanisms of action of allelopathic chemicals in allelopathy. Various workers discussed the mechanisms of action of allelopathic chemicals in allelopathy (Rice, 1984;’Muller,1986; Einhellig, 1986, 1995b; Waller, 1989). We will discuss how allelopathic chemicals interfere with various physiological, biochemical, and molecular processes of target plant species.

A. INTERFERENCE WITH CELL ELONGATION Allelopathic chemicals play an important role in the regulation of plant cell growth, and there are many reports on the interference of allelopathic chemicals with cell elongation and cell division (Muller, 1965; Jankay and Muller, 1976; Rice, 1984; Ortega et al., 1988).Many bioassays for allelopathhy employ seed germination, seedling lengths, or fresh seedling weight, to quantify allelopathic effects. Wink and Latz-Briining (1995) reported that many salts, amino acids, sugars, phenolic compounds, organic acids, terpenoids, and alkaloids influence the hypocotyl elongation and root growth of garden cress (Lepidiurn sativurn).Aliotta and coworkers ( 1993)investigated the interference of several phenylpropanoids and coumarins with germination and subsequentroot growth of radish. They found that coumarins inhibited cell elongation of the differentiating zone of the root. They also noted an apical shift of root hair differentiation to form tufts not observed in the control. Li and coworkers (1993) reported that juglone, at concentrations of lop4 and M, inhibited cell elongation in the epicotyl sections of etiolated bean (F! sativurn) seedlings.

B. INTERFERENCE w r r PHOTOSYNTHESIS ~ Several studies have shown adverse effects of allelopathic compounds on photosynthesis (Rice, 1984).Einhellig’sgroup ( 1970) reported a significantreduction in photosynthesis of tobacco plants when treated with lop3 and lop4M concentrations of scopoletin. Several workers reported a reduction in photosynthesis in

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intact plants due to individual compounds (Patterson, 1981; Stiles et al., 1994). In contrast to these observations, Inderjit and Dakshini (1992a) observed a significant increase in net photosynthetic rate, stornatal conductance, and stomatal cavity CO, concentrations in asparagus bean grown with Pluchea-amended soil compared to those grown with Pluchea-free soils (Table 11). This might be due to quantitative and qualitative differences in allelopathic compounds or to speciesspecific responses. Many studies (Einhellig etal., 1970; Einhellig and Kuan, 197 1; Stiles et al., 1994) have investigated the effects of a single compound on photosynthesis, whereas Inderjit and Dakshini (1992a) studied the effects of total phenolics equivalence on net photosynthetic rate. Einhellig and Kuan (1971) and Turner (1972) observed enhanced stornatal conductance/opening with allelopathic chemicals. It has often been argued that reduced growth is due to reduced chlorophyll content (Colton and Einhellig, 1980; Alssadawi er al., 1986b; Einhellig, 1995b).However, Indejit and Dakshini (1992a) observed a reduction in Chl a and b and Chl a:b ratio with an enhanced net photosynthetic rate in asparagus bean plants grown with Pluchea-amended soil compared to those grown with Plucheafree soils (Table 11). Lee (1986, p. 109) states that “lower chl a:b ratios provide more effective absorbances across the visible spectrum,” which may explain the enhanced net photosynthetic rate in asparagus bean plants grown with Plucheaamended soil compared to those grown with Pluchea-free soils reported by Inderjit and Dakshini (1992a). While vanillic, ferulic, and p-coumaric acids inhibited chlorophyll content in soybean (Glycine mar) leaves, no influence on the chlorophyll content of grain sorghum was recorded (Einhellig and Rasmussen, 1979). Since more sophisticated instrumentation to determine photosynthetic rate, stornatal conductance, and other related physiological parameters is now available, more research on the measurement of various photosynthetic parameters of intact plants should be done at the field level. Furthermore, it would be of interest to know how photosynthetic parameters are influenced by variation in soil phenolic content due to season, sites, and agricultural practices. ’z)-8’,1l’, 14’-pentadecatrienelSorgoleone (2-hydroxy-5-methoxy-3-[(8’~,11 p-benzoquinone) has been reported to possess allelopathic activities (Chang et al., 1986; Netzly et al., 1988; Einhellig and Souza, 1992; Einhellig er al., 1993; Einhellig, 1995a). Nimbal’s group (1996a) found that sorgoleone inhibited the evolution of 0, during photosynthesis in the potato (Solonurn tubersum) and in common groundsel (Senecio vulgaris). After their studies on triazine-susceptible potato and redroot pigweed thylakoids, Nimbal et al. (1996b) reported sorgoleone to be a competitive inhibitor of atrazine binding sites. Gonzalez and coworkers (1997) found that sorgoleone inhibited the photosystem I1 electron transport reactions. They considered sorgoleone as an efficient herbicide because of its potential to inhibit electron transfer between QA and Q, at the reducing site of photosystem 11.

ALLELOPATHY

C. INTERFERENCEWITH

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RESPIRATION

Interference of allelopathic chemicals with different processes of respiration, such as 0, uptake, oxidation of NADH, ATP production, electron transport, and dark CO, production, is well documented (Muller, 1969; Stenlid, 1970; Lang and Racker, 1974; Patterson et al., 1979; Ortega et al., 1988; Li et al., 1993; Stiles et al., 1994). While most of the processes of respiration are inhibited by allelopathic chemicals, stimulation was observed in dark CO, production by Dedonder and van Sumere (197 I ) and Lodhi and Nickell (1973). There is no doubt that future research will continue to broaden our knowledge of how allelopathic chemicals influence different respiration processes; more research is needed to understand the interference of mixtures of allelopathic chemicals on different respiration and other related physiological processes. Research into how the interference of allelopathic chemicals on respiration processes varies with species, and into the qualitative and quantitative variation in the pool of allelopathic chemicals in the rhizosphere, is needed.

D. INTERFERENCE WITH MINERALION UPTAKE Several workers discussed the interference of allelopathic chemicals on mineral ion uptake by affected plants (Glass, 1973, 1974; Rice, 1984; Lyu and Blum, 1990; Booker et al., 1992; Einhellig, 1995b; Indejit and Mallik, 1996b; Baziramakenga et al., 1997). Such interference is reported to be consistently concentration dependent and ion and species specific. Alsaadawi and coworkers (1986b) reported that syringic, caffeic, and protocatechuic acids lower the uptake of nitrogen, potassium, phosphorus, iron, and molybdenum in cowpea (Kgna sinensis); however, magnesium uptake was not influenced. Booker et al. (1992) observed a shortterm influence of ferulic acid on the inhibition of phosphate, nitrate, and sulfate uptake. Lyu and coworkers (1990) reported additive inhibitory activities on phosphate uptake by cucumber seedlings due to a mixture of ferulic, p-coumaric, and vanillic acids. This is an important study because the influence of individual phenolic acids in a mixture was studied and this is a more natural situation (Einhellig, 1995a; Blum, 1996, 1997). While the previously discussed studies were conducted with individual or a mixture of identified compounds, some workers investigated the effects of root exudates and leaf leachates on ion uptake. Newman and Miller (1977) reported that root exudates of Anthoxanthum odoratum, Lolium perenne, Plantago lanceolata, and Trifolium repens influenced phosphorus uptake. Inderjit and Dakshini ( 1992a) found that concentrations of K+ were higher and those of Zn2+,PO:-, and Mg2+ were lower in shoots of asparagus bean plants grown with Pluchea-amended soils compared to those grown with Pluchea-free soils. Likewise, roots of asparagus

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bean plants grown with Pluchea-amended soil showed higher Mg2+and NO; uptake when compared to those grown with Pluchea-free soils. They reported higher shoot:root ratios for Zn2+,Na2+,Ca2+,Mg2+,and NO; in asparagus bean plants grown with Pluchea-free soils and higher shoot:root ratios for PO:- in asparagus bean plants grown with Pluchea-amended soil. Yu and Matsui (1997) investigated the effects of root exudates of cucumber with its allelopathic chemicals on uptake of NO;, H,PO,, SO:-, K+, Ca2+,Mg2+,and Fez+by cucumber seedlings. They reported inhibition of the uptake of all except H2PO;. Inhibition of these ions by cinnamic acid, a major allelopathic compound, was concentration and pH dependent. Baziramakenga’s group (1997) reported that benzoic, vanillic, cinnamic, and ferulis acids inhibited the uptake of 32Pby soybean seedlings; however, p-hydroxybenzoic and p-coumaric acids increased slightly the absorption of 32Pby soybean seedlings.

E. INTERFERENCE WITHPROTEIN AND NUCLEIC ACIDMETABOLISM Many allelopathic compounds have been reported to interfere with protein synthesis and RNA and DNA metabolism (Svensson, 1972; Cameron and Julian, 1980; Wink, 1987; Wink and Twardowski, 1992; Einhellig, 1995b), and various workers observed the influence of allelopathic chemicals on enzyme and amino acid metabolism (Van Sumere efal.,1971;Rice, 1984).Cameron and Julian (1980) reported that cinnamic and ferulic acids, at 50 pMconcentrations,reduced protein synthesis in lettuce seedlings. Mersie and Singh (1993) reported that 1 pM concentrations of ferulic acid reduced the incorporation of the amino acid leucine, into protein in a cell suspension. Wink and Latz-Briining ( 1995) reported that many alkaloids (aconitine, berberine, caffeine, cinachonine, harmaline, lobeline, quinidine, quinine, and songuinarine) affected DNA interactions, DNA polymerase I, reverse transcription, protein synthesis, and membrane stability. Baziramakenga and coworkers ( 1997) reported that benzoic, p-hydroxybenzoic, vanillic, ferulic, cinnamic, and p-coumaric acids, at 250 pM concentrations, reduced the incorporation of 32Pinto DNA and RNA. They concluded that interference of allelopathic chemicals with nucleic acids and protein metabolism is one of the important mechanisms in the action of allelopathic chemicals in allelopathy. These authors also observed a reduced incorporation of the amino acid methionine into protein in response to allelopathic chemicals. Further study of the molecular aspects of allelopathy is needed. This may improve our understanding of the genetic aspects of allelopathhy, which in turn may have important implications when attempts are made to incorporate allelopathic traits into crop plants in association with biocontrol programs. The uptake of allelopathic chemicals by the roots of afflicted plants has been

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suggested (Glass and Bohm, 1971; Shann and Blum, 1987), and it is important to demonstrate uptake of allelopathic chemicals through labeled studies. However, it should not be presumed that the uptake of allelopathic compounds is a must for allelopathic activities.Allelopathic activities can also occur due to external injury to roots of afflicted plants, to a shift in microbial ecology, or to nutrient immobilization, changed soil characteristics,or other environmental factors if they are caused by allelopathic chemicals. Another important aspect that needs attention is the cause of higher or lower than expected levels of allelopathic chemicals in afflicted/receptor plants. Inderjit and Dakshini (1992a) reported a higher phenolic content in asparagus bean plant grown in soils amended with Pluchea leaves compared to Pluchea-free soils. This could be due to (i) the uptake of phenolics or (ii) the enhanced synthesis of phenolics in treated asparagus bean plants due to chemical stress created by Pluchea leaves via the release of phenolic compounds into soil. Detoxification of allelopathic chemicals has also been suggested (Balke et af., 1987).Allelopathic chemicals may become inactive after entering into target plants through enzymatic glucosylation (Cosio and McClure, 1984; Kisiel, 1984). Balke and coworkers (1987) suggested glycosylation as one of the common modes of detoxification of phenolic compounds. They suggested that glycosylation results in the detoxification of phenolic compounds. They suggested that glycosylation results in the detoxification of allelopathicchemicals because (i) the reactive groups of allelopathic chemicals are blocked and (ii) there is an increase in the water solubility of the conjugate. They reported the enzymatic glycosylation of salicylic acid by oat. It is therefore important to determine in what state allelopathic chemicals are present in the receptor plants. Studying mechanisms with only synthetic chemicals may be misleading.

VII. ALLELOPATHIC GROWTH STIMULATION Those who study allelopathy generally highlight the growth reductions in their bioassays and growth stimulation receives less attention (Rice, 1986).Allelopathy is a chemically mediated phenomenon, and growth responses are concentration dependent. A compound may be inhibitory at high concentration stimulatory at low concentration, or have no effect at other concentrations. It is unwise to conclude that a particular compound will always produce either inhibitory or stimulatory effects, and it is clear from aquatic community data (Fig. 1) that a single species will, at times, produce inhibitory, stimulatory, and no-effect responses from different targets. Neil1 and Rice (197 1) reported that rhizosphere soil from western ragweed significantly stimulated the growth of Amranthus retrojexus,Andropogon femur-

190

INDERJIT AND K. IRWIN K E A m G

ius, Bromus japonicus, Digitaria sanguinulis, Leptoloma coynatum, Rubeckia hirta, and Tridens Jlavus. Jimbnez-Osornio and Gliessman (1987) reported a 50% stimulation in the yield of broccoli grown with wild mustard in controlled densities compared to broccoli plantation alone. It0 and coworkers (198 1) reported that, when used as mulches, purple nutsedge, wormwood (Arfemisiaprinceps), crabgrass (Digitaria sanguinulis), Rumexjaponicus, Polygonum longisetum, and Lolium multijlorum significantly enhanced the shoot:root ratio of peach seedlings. They also reported that shoot growth of lettuce was promoted by aqueous extracts of these species. While studying the causes for poor growth of new grass species into old swards, Gussin and Lynch (1981) fermented fresh leaves of Agrostis stolonifera, Alopecorus pratensis, A. odoratum, Festuca rubra, H. lanatus, L. perenne, and Poa trivialis aerobically and anaerobically. After 10 days of aerobic fermentation, it was found that the residues stimulated the seedling growth of Alopecurus myossuridu, A. stoloniferaa, E rubra, L. perenne, Poa annua, f? trivialis, H. lanutus, and T. repens. Ground ivy (Glechoma hederacea) has the potential to stimulate the growth of radish (Raphanus sativus) and downy brome (Bromus rectorum) (Rice, 1986). There are many other convincing examples of allelopathic growth stimulation available in the literature. Rice (1986) discussed (i) microbial growth stimulation by microorganisms: bacterial stimulation by bacteria, fungal stimulation of bacteria, algal stimulation of bacteria, bacterial stimulation of algae, fungal stimulation of fungi, algal stimulation of algae; (ii) growth stimulation of plants by microorganisms; (iii) growth stimulation of microorganisms by plants; and (iv) growth stimulation of plants by plants.

Vm. ROLES FOR ALLELOPATHY IN BIOCONTROL PROGRAMS

k COVERCROPS While discussing sustainable pest management, Lewis’s group (1997) stressed the need for a total system approach for crop production to solve the economic and environmental consequences of agricultural pests. The use of allelopathic potentials of cover crops and crop and weed residues for weed management in agroecosystems has been widely discussed (Newman, 1982; Einhellig and Leather, 1988; Worsham, 1991; Rice, 1995; Miller, 1996; Weston, 1996). Allelopathy also plays an important role associated with the beneficial effects of crop rotation, especially those involving cereals, legumes, and crucifers (Anderson and Cruse, 1995; Rice, 1995), and allelopathic suppression of weeds by crops such as sweet potatoes (Zpomoea baratas), sorghum, and sunflower has been suggested (Harri-

ALLELOPATHY

191

son and Peterson, 1986; Forney et al., 1985; Leather, 1983; Einhellig and Leather, 1988). Many have considered the allelopathic suppression of weeds by various cover crops-buckwheat (Eskelsen and Crabtree, 1995; Rice, 1995), black mustard (Bell and Muller, 1973; Rice, 1984; Jimhez-Osornio and Gliessman, 1987), sorghum (Forney and Foy, 1985; Weston et al., 1989; Alsaadawi et al., 1986a), wheat (Shilling et al., 1985), and rye (Barnes and Putnam, 1983; 1987; Yenish et al., 1995).Residues of several cover crops, such as winter wheat, barley, oats, rye, grain sorghum, and Sudan grass, have demonstrated an allelopathic potential to suppress weeds (Barnes and Potnam, 1983; Einhellig and Leather, 1988; Rice, 1995; Weston, 1996; Miller, 1996; Teasdale, 1998). Cereal crops such as wheat, maize, and rye have the potential to produce hydroxamic acids, which are important in Poaceae to different kinds of pests and diseases (Niemeyer, 1988; Niemeyer and Perez, 1995). Hydroxamic acids are released into the soils through root exudation. Various workers suggest that detoxification of triazine herbicides is associated with hydroxamic acids (Niemeyer and Perez, 1995). Velvet bean (Mucunu pruriens var. utilis), a legume cultivated for green manure, has a beneficial impact on the yield of graminaceous crops and has the ability to smother noxious weeds such as purple nutsedge and cogon grass (Fujii et al., 1992). Fujii and coworkers screened 70 plant species for their ability to smother weeds. They found that velvet bean has significant potential to smother noxious weeds. It was found that L-DOPA (~-3,4-dihydroxyphenylalanine) is mainly responsible for this allelopathic activity. Worsham and Blum (1992) reported that weeds, such as species of amaranth (A. retrojexus, A. spinosus, and A. hybridus) and common lamb’squarter, can be controlled when planted into killed cover crops of rye and subterranean clover. Macharia and Peffley (1995) reported the inhibitory effects of Alliumjstulosum and A. cepa genotypes on seed germination and plant growth of spiny amaranth (A. spinosus) and kochia (Kochiu scopariu). The allelopathic effects of a cover crop may vary with different plant parts. While separating the allelopathic effects of root and shoot residues of rye and sorghum, Hoffman’s group (1996a)found that cover crop root residues suppressed the growth of weeds but that the shoot residues of cover crops, in general, do not suppress the growth of weeds. Hoffman et al. (1996b) also investigated the allelopathic interference of germinating seedling of different cover crops on seedling growth of weeds (Table VIII), and found that while germinating sorghum suppressed the radicle growth of weeds, the germinating rye enhanced the weed radicle length. Increasing soil erosion from cropland has stimulated interest in using surface soil residues for the control of soil erosion. Tillage systems which leave crop residues in the field are effective at reducing soil erosion (Phillips et al., 1980). However, conservation tillage systems may have poorer crop stands and more uneven growth rates than conventional tillage systems (Griffith el ul., 1973). In cultivated fields, the growth reduction which accompanies surface mulcheshesidues

INDERJIT AND K. IRWIN KEATING

192

'hble WI Gmwtb of 6-Day-OldWeeds Germinatedwith Cover Crop Species Compared to "hat of Weeds Germinated Alone" % of control

Weed species Germinated with sorghum Velvetleaf Smooth pigweed Large crabgrass Green foxtail Germinated with annual white sweet clover Velvetleaf Smooth pigweed Large crabgrass Green foxtail Germinated with rye Cheat Shepherd's purse Hairy vetch Green foxtail Germinated with crimson clover Cheat Shepherd's purse Hairy vetch Green foxtail

Radicle length

Shoot length

96 106 100 52*

67* 71* 93 74*

102 86* 99 101

86 110 85 96

77* 90

94 103

Germination

101

104

89

103

111 100 111 105

123* 94 128* 108*

121* 85 133 94

106 76 115 96

129* 78 109 108

135* 86 111 106

"Source: Hoffman, M. L., Weston, L. A,, Snyder, J. C., and Regnier. E. E.(1996b). Allelopathic influence of germinating seeds and seedlings of cover crops on weed species. Weed Sci. 44,579-584. Reproduced with permission from the Weed Science Society of America. *The parameter measured differed from the control according to Fisher's LSD (p = 0.05) test.

is evident at early growth stages (Willis et al.. 1957). Residues of many crops have been known to influence the growth and yield of crops (Rice, 1984; Yakle and Cruse, 1983;Guenzi and McCalla, 1966; Patrick and Koch, 1958;Abdul-Baki and Teasdale, 1997). The type of mulches should be carefully monitored in order to avoid allelopathic growth suppression in the following years (e.g., sorghum residues have been reported to be phytotoxic even to next year's crop; Worsham, 1991). Bewick and coworkers (1994) investigated the allelopathic interference of celery root residues on the growth of certain weed species [bamyard grass, common purslane, green foxtail, large crabgrass, spiny amaranths, wild mustard, rice flatsedge (Cyperus iria), and black nightshade (Solanum nigrum)] and crops [celery, carrot, lettuce, radish, and escarole (Cichorium endiva)].The relative sensi-

ALLELOPATHY

193

tivity of the different weed species to celery root residues was spiny amaranths > barnyard grass > wild mustard > black nightshade > large crabgrass > green foxtail = rice flatsedge > common purslane. The relative sensitivity of the crop species was radish escarole + lettuce + carrot + celery. Downum and coworkers (1989) screened 115 species from 57 genera and eight plant families for their phototoxic activities using standard antimicrobial bioassays. They found the presence of many phototoxic compounds in the family Asteraceae, particularly in the subtribe Pectidinae and the tribe Heliantheae. These compounds are generally reported from species evolved under high light conditions and provided with efficient plant defense mechanisms. Przepiorkowski and Gorski (1 994) carried out greenhouse and laboratory studies to determine the influence of rye on germination and growth of three triazineresistant weed species: barnyard grass, willow herb (Epilobiurn ciliaturn), and horseweed (Conyza canadensis).They found that willow herb and horseweed germination was suppressed with aqueous extracts of rye shoot tissues and soil containing rye seeds. However,barnyard grass germination was not influenced. Growth suppression was observed in both biotypes (resistant and susceptible)of three weed species in soil containing rye roots. It has been shown that weed suppression due to rye cover crop will last 4 weeks after killing of the cover crop (Barnes and Putnam, 1986, 1987).Yenish and coworkers (1995) reported that rye residues take 15 weeks to decompose to 50% of first-day level. Their studies show that the duration of weed suppression due to rye cover crop is related more to the disappearance of rye allelochemicals from rye residues than to the disappearance of rye residues. Worsham (1991) discusses other beneficial aspects of cover crops, including (i) conserving soil moisture, (ii) increasing soil organic matter, (iii) controlling wind and water erosion, and (iv) maintaining soil fertility of recycled nutrients. The beneficial effects of crop rotation, especially for cereals and legumes, have been established (Rice, 1995; Weston, 1996).Rotational crops such as tall red fescue (Festuca arundinacea), creeping red fescue ( E rubra), asparagus, sorghum, alfalfa, black mustard, and oats are used for weed suppression (Weston, 1996). Leguminous crop plants may benefit cereals in crop rotation by (i) providing nitrogen compounds; (ii) improving soil physical properties; (iii) reducing soil erosion, and (iv) suppressing weeds, insects, and diseases (Sarobol and Anderson, 1992). Various crops, such as corn, soybean, wheat, cotton, sorghum, and barley, have been reported to benefit from being preceded by different crops in the previous year (Crookston, 1984). However, to date the phenomenon of yield improvement in crop rotation is not well understood. Allelopathy may be involved in the residual effects of crop rotation. Sarobol and Anderson (1992) reviewed the cornsoybean rotation and found that increased yield of corn following soybean, in comparison to yield of continuous corn, was due to the combination of the adverse effects of corn on corn and beneficial effects of soybean on subsequent corn. Allelopathic effects of cruciferous crop plants in crop rotation have also been well +

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INDERJIT AND K. IRWIN KEATING

documented (Grodzinsky, 1992).The yield of cruciferous plants, when introduced to crop rotation, was reported to be higher (17-20%) than that in monoculture (Grodzinsky, 1992). The important question is whether we can achieve near 100% weed suppression by using cover crops, crop residues, or rotational crops. While investigating weed suppression by hairy vetch, Teasdale (1988) found that other methods of weed control are still needed. Worsham (1991) suggested that most of the many herbicides, particularly postemergence herbicides, are still needed because allelopathic suppression is generally adequate for the first few weeks only. Worsham suggested that herbicides are needed to kill cover crops since, if not killed, cover crops would be likely to compete with planted crops for essential resources. He provided some approached to avoid using herbicides for killing cover crops: (i) mowing and sweeping to mechanically kill grain cover crops and legumes, respectively; (ii) planting crops such as soybean and grain sorghum after cover crops mature naturally; and (iii) killing cover crops such as sorghum and spring oat, which can be planted for 1 year and killed during the winter, resulting in dead mulch for next spring. Indejit and Olofsdotter (1998) discussed the allelopathic potential of rice. Olofsdotter and Navarez (1996) investigated the allelopathic potential of 111 rice cultivars against problematic weeds. They reported that 10 cultivars had allelopathic effects on the growth of barnyard grass and argued the need for introducing weed suppressing ability into rice. Dilday’s group (1991) evaluated 10,000rice accessions for allelopathic activities of ducksalad (Heteranthea limosa). They reported that 3.5% of accessions possess allelopathic activities. Dilday and coworkers (1998) reported that 412 rice accessions were allelopathic to ducksalad, 145 to red stem (Ammannia coccinea), and 16 to both species. Hassan and coworkers (1997) reported that 30 rice accessions had allelopathic activities against barnyard grass, 15 against Cyperus difformis, and 5 against both species. Olofsdotter and Navarez (1996) reported that 1 rice cultivar (Taichung native I ) had allelopathic activities against the growth of barnyard grass, Trianthemmu portulacastrium, H. limosa, and A. coccinea. Mattice and coworkers (1998) identified the phenolic compounds 4-hydroxybenzoic, 4-hydroxyhydrocinnamic, and 3,4-dihydroxyhydrocinnamic acids in water from allelopathic rice cultivars. None of these compounds were detected in water from nonallelopathicrice cultivars. Olofsdotter and coworkers (1997) stressed the need for formulating breeding strategies to exploit allelopathic rice cultivars in biocontrol programs.

B. ALLELOPATHIC CHEMICALS AS NATURAL HERBICIDES Herbicidescontinue to be a key component in most integrated weed management systems. Nevertheless, extensive use of synthetic herbicides poses serious threats to both the environment and public health (Macias, 1995).From both public health and environmental perspectives there is great incentive to discover biologically ac-

ALLELOPATHY

195

tive natural products from higher plants that are as good as or better than synthetic agrochemicals and that are likely to be much safer. The development of natural products as herbicides, fungicides, and pesticides and their role in biocontrol of plant disease promise to reduce environmental and health hazards (Rice, 1995). Furthermore, in comparison to long-persistence, nontarget toxicity, polluting, carcinogenic, and mutagenic activities of synthetic agrochemicals, natural plant products are biodegradable, somewhat specific, and likely to be recycled through nature (Epstein et al., 1967; Matsunaka and Kwatsuka, 1975; Duke, 1988). Allelopathic chemicals show selectivity (Weston, 1996). Some allelochemicals have already been investigated as possible inclusions in alternative weed management strategies (Macias, 1995). Biologically active natural products (allelochemicals) isolated from higher plants and microbes are now being employed as herbicides and fungicides and in the biocontrol of plant diseases (Rice, 1985; Einhellig, 1984). Many weeds have become resistant to important herbicides classes such as s-triazines (Gressel, 1985) and dinitroanilines (Mudge et al., 1984). It is difficult to establish a direct relationship between the structuralcomplexity and the activity of a compound (Macias, 1995).Tahara and coworkers (1994) reported that mucondialdehyde (trans-2,trans-4-hexadienedial),an antifungal compound from leaves of common lamb’s-quarter, was induced in response to cupric chloride stress. Table IX lists the allelopathic chemicals from different classes of compounds with known potential use as natural herbicides. Macias ( 1995) suggested that the normal range of concentrationstested for allelopathic chemical is between and lo-’ M. According to him, good candidates for natural herbicide should have activity between lop5 and lo-’ M. Many phenolic compounds, alkaloids, and quinones, however, have an activity range of 10-2-10-5 M and thus are poor candidates for natural herbicides (Macias, 1995).Heisey (1996) isolated a quassinoid compound, ailanthone, from root bark of tree-of-heaven (Ailanthus altissima). He reported the pre- and postemergence herbicidal activity of this compound in greenhouse trials. The previous discussion suggests the potential of several allelopathic compounds as natural herbicides. Because demonstrating potential herbicidal activity for a particular compound under laboratory or controlled conditions may not guarantee its success in natural systems, more research is needed to test the suitability of these potential herbicides under field conditions.

M.ADDITIONAL COMMENTS A. ADDITIVEACTIVITIES OF ALLELOPATHIC CHEMICALS Plant species may respond differently to mixtures of two or more compounds and this may not be predictable from their growth responses to individual allelo-

Table IX

Selected AUelopathic Cornponds with Potential Use as Natural Herbicides" Allelopathic compound

~~

L-p-Hydroxybutyric acid ( 1 Ethyl propionate (1)

-

Target species

Activity range (ppb)

~~

Ethyl 2-methylbutyrate (1)

8.9

trans-DME ( 2 ) cis-Dihydro-ME ( 2 ) Arachidic, khenic, and myristic acids (3) 2(3H)-benzoxazolinone(4)

103 5 x 103-5 x 104 5 x 103

2,2-Oxo-l.l-azoknzene (4)

5 x 104

Caffeine (4) pHydroxyknzoic acid (5)

105-4 x 105 (6.9 x 104 inactive

Vanillic acid (5)

e . 6 x 104 inactive

p-Coumaric acid (6)

a . 2 x 104 inactive

X

102-4.4 X 103

105

m \o

of activityb

~

Chenopodiumalbum, Amaranthus retrofkxus Allium cepa, Lycopersicon esculentum Daucus carota Allium cepa Daucus camta, Lycopersicon esculentum Echinochloa crus-galli Oryur sativa Cynodon dactylon

Growth (-) Growth met.) Growth (-) Growth (-) Growth (Ret.)

Lepidium sativm, Cucwnis sativus, Phaseolus vulgaris Echinochloa crus-galli Echinochloa crus-galli,Lepidium sativm, Cucumis sativus, Phaseolus vulgaris Lactuca sativa Lactuca sativa, Deschampsiajlexuosa. Chamaenerion angustifolium Chamaenerion angustifolium Lolium mulh~onun Saccharum oficinarum, Lactuca sativa. Lolium multiporum Senecio sylvaticus Scrophularia nodosa Deschampsiajkxuosa, Chamaenerion angustifolium Senecio sylvaticus, Scrophularia nodosa Lactuca sativa, Lolium multiporum Raphanus sativum, Chamaenerionangustifolium, Deschampsiajlexuosa Scrophularia nodosa Senecio sylvaticus

Growth (-) NA Growth (-)

Growth (-) Growth (-) Germination (-)

Growth (-) Growth (-) Germination (-) Growth (-) Growth (-) Growth (+, -) NA Germination (-) NA Growth (-) Growth (-)and germination (-) NA Growth (+, -)

Ferulic acid (6)

9.6 x 104

Xanthotoxin (7)

<104 inactive

Senecio sylvaticus Amtatica hierochuntica Lwtuca sativa

Bergapten (7)


Lactuca sativa

Xanthotoxin (7)

<1.3 X 104

Lactuca sativa

Tujone (8) Carvone (8) Camphor (8)

3.3 x 103 8 X 103 2.7 x 104

Lactuca sativa Lwtuca sativa, Leptochloa dubia, Schizochyrium scopariwn Lactuca sativa kptochloa dubia kptochloa dubia, Schizochyrium scoparium

1,8-Cineol(8)

2 x 106

Schizochyrium scoparium

Emodin (9) Physcion (9)

104-105 104-105

Juglone (9)

<9 x lo3 inactive

Guayulin A (10)

3.5 x 1 6

Heliannuol(10)

2.6 X 102-2.6 X l@

Lactuca sativa, Amaranthus viridis, Phleum pratense Lactuca sativa, Amaranthus viridis Phleum pratense Alnus glutinosa Rudbeckia h i m , Lactuca sativa Amaranthw palmeri, A. retrofixus Daucus carota Lactuca sativa Lactuca sativa

Lepidium sativum, Lolium multiporum, Chumaenerion angustifolium Scrophulariaa nodosa Deschampiaafelxuosa

Hordeum vulgare

Growth (-) Germination (-) Growth (-) and germination (-) Growth (+, -) Growth (-) Growth (-) and germination (Growth (-) and germination (Growth (-) and germination (Germination (-) Germination (-) Germination (-) NA Growth (-) and germination (-) Growth (-) and germination (-) Growth (-) Growth (+, -) Growth (- ) Growth (-) Germination (-) Germination (-) NA Germination (+) Growth (-) and gemination (+) NA continues

'LgbleIX-Continued Allelopathic compound

Activity range (ppb)

Target species

Strigol ( 10) SoulangianolideA (11) Melampomagnolide A (11) Annuolide A-E (11)

0.3

11,13-Dihydroburrodin(11) Parthenolide (11)

2.5 0.25-2.5 X 103

a-Santonin (1 1) Hirsutin (12)

0.25-2.5 X lo4 2.2 x 103-4.4 x 104

Striga lutea Lacruca sativa Luctuca sativa Lactuca sativa Hordeum vulgam Striga Iutea StTiga lwea Lactuca sativa Phaseolus vulgaris Loctuca sativa

Camelinin ( 12)

5 X lo4-2.6 X 1 P

Lactuca sativa

Betulinaldehyde (13)

0.5-5 x 104

h t u c a sativa Honieum vulgare

Messagenin (13)

0.5-5 x 104

Soyasapogenol(l3)

10

Lactuca sativa Lepidium sativum Hordeum vulgare Triticum aestivum Agalinis purpura

0.25-2.5 x 104 0.25-2.5 X lo4 0.25-2.5 X 103

of activiv Germination (+) Germination (+) Germination (+) Germination (-) NA Germination (+) Germination (+) Growth (+I

Growth (+) Growth (-) and germination (-) Growth (-) and germination (-) Germination (+) Growth (+)and germination (+) Germination (+) Growth (+) Germination (+) NA Germination (+)

"Modified after Macias (1995).Copyright 0 1995 American Chemical Society. bResults: +, stimulation; -, inhibition; Ret., retardation, NA, not active; +, -, stimulation or inhibition depending on the concentration. 'Chemical classes: 1, simple acids and esters; 2, polyacetylenes; 3, long-chain fatty acids; 4, alkaloids; 5, benzoic acid derivatives; 6, cinnamic acid derivatives; 7, coumarins; 8, monoterpenese; 9,quinones; 10, sesquiterpenes; 11, sesquiterpene lactones; 12, sulfured compounds; 13, triterpenes.

ALLELOPATHY

199

pathic compounds. In natural systems, allelopathic growth responses are the result of either additive or partial antagonistic activities of allelopathic chemicals (Einhellig et al., 1982; Williams and Hoagland, 1982; Einhellig, 1995a; Blum, 1996; Inderjit, 1996). Einhellig (1989) reported that a 50 pA4 mixture of p-hydroxybenzoic, protocatechuic, vanillic, gentisic, gallic, caffeic, p-coumaric, syringic, ferulic, and o-methoxybenzoic acids and 500 pA4 concentrations of individual compounds were equally inhibitory to the growth of velvetleaf. Blum (1996) suggested that allelopathic activities are due to mixtures of allelopathic compounds (e.g., phenolic acids) and other organic compounds, and that the concentration of each compound in a mixture might be significantly less than the concentration of individual compounds required to induce an allelopathic effect. Many recent studies suggest the significance of a mixture of allelopathicchemicals in predicting growth responses (Inderjit et al., 1997; VBronneau et al., 1997). The literature deals with numerous mixture models, in which additivity of allelochemical or herbicide effects and additivity of doses are confused. Traditionally, some mixture research is based on empirical studies at some preset dose rates in factorial designs and sometimes analyzed with polynomial regressions. In factorial designs with mixtures of, for example, biologically active compounds, the interaction is based on the effects of the allelochemicals or herbicides and merely tells us whether an effect of a allelochemical or herbicide remains unchanged in mixture with another allelochemical or herbicide. Interaction will inevitably occur if the dose range is wide enough because at very low and very high doses the responses approach the upper and lower limits of the dose-response curve. Consequently, such interactions are of little biological relevance but have been extensively used to claim antagonism and/or synergism (Nash, 1981). A more general way to describe the joint action of allelochemical or herbicide mixtures is to use the response curves of the allelochemicalsor herbicides applied alone and in mixtures and incorporate various joint action reference models, for example, the additive dose model and the multiplicativesurvival model outlined by Morse (1978). Unfortunately, very few investigations have explicitly defined the mixture model used (Streibig, 1992; Streibig et al., 1998).

B. AUTOTOXICITY Autotoxic effects have often been discussed in allelopathic research. Autotoxicity, however, technically differs from allelopathy because (i) autotoxicity means self-toxicity, and allelopathiceffects refer to the effects of one plant on another (including microorganisms) through the release of chemical compounds into the environment, and (ii) allelopathic effects can be both stimulatory and inhibitory, whereas autotoxic effects are only inhibitory effects. While allelopathy is generally considered as a mechanism of interference, allelopathic compounds are also

200

INDERJIT AND K. IRWIN KEATING

considered as defense compounds. It is not certain why an organism would produce autotoxic compounds. The ecological role of autotoxic compounds may be an open question, but unanticipated trace element deficiencies must be ruled out before autotoxicity is assumed. It has been shown that some grassland forms grow better when grown with leachates from their own species (Newman and Miller, 1977). These effects of beneficial growth response to self-produced allelopathic chemicals cannot be considered a result of autotoxicity. Lodhi (1979) reported autotoxic activities of phytotoxins from kochia. He concluded that a drastic reduction in growth of kochia during its second year of revegetation was due to its autotoxic properties. Autotoxicity in coffee (Cofleu urubicu), due to an alkaloid caffeine and theophylline, is reported by Waller (1989). Autotoxicity has been well documented in asparagus plants (Friedman and Waller, 1985; Young and Chou, 1985; Hegde and Miller, 1990; Friedman, 1995).Asparagus replant problems and reduction in yield of asparagus for old plantations have been reported (Klein and Miller, 1980; Kehr et al., 1983; Young, 1986). In a discussion of asparagus replant problems in tropical Taiwan, Young (1986) suggests an interval period of 2 or 3 years between asparagus plantings. Asparagus is native to seacoast, riverside, and semidesert areas from southern Europe to southern Russia. These areas have well-drained sandy and sandy-loam soils (Young, 1986). The better growth of asparagus in its native soils could be attributed to (i) better leaching of chemicals in sandy soils and (ii) better aeration for degradation of chemicals leached down by the asparagus plants. Miller (1983) reported that significantly lower seedling populations and second-year yield of asparagus cannot be improved by eliminating N and P deficiencies; however, unrecognized trace nutrient limitations may be involved. Read and Jensen (1989) reported that certain water-soluble compounds released from decomposing residues of asparagus may have autotoxic effects. While investigating the autotoxic potential of mesquite (Prosopisjulijbru),Warrag (1995) found that aqueous extracts of mesquite foliage had autotoxic effects on its seed germination and early growth. However, it is important that the autotoxic potential of mesquite be tested under more natural conditions involving soils in bioassay experiments. Waller (1989) suggested that autotoxic compounds are present in the outer part of seeds and diaspores of certain plants. Such seeds can germinate only after these compounds are washed away with rainwater or metabolized by microorganisms. He applied the term “natural protectants” to those compounds which are difficult to leach out from seeds and which have inhibitory effects when applied exogenously. Friedman and coworkers (1983) reported that an autotoxic compound, 8methoxypsorlaen, is stored in the outer dead layer of fruit of bishop’s weed (Arnrni rnujus). This compound can only be released if the outer shell is broken; thus, the embryo remains unaffected. Aert’s group (1991a,b) reported that mixtures of alkaloids, cinchoncine, dihydrocinchonine, and quinamine (2:7:1) are strongly inhibitory to the seed germina-

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tion of Cinchona ledgerianu seeds. The main alkaloids synthesized during germination were cinchonine and its dihydro derivatives, and minor amounts of one indole alkaloid quinamine were synthesized. These authors suggested that balancing and compartmentalization of alkaloids was probably the mechanism for avoiding autotoxicity. Production of nonprotein amino acids is a well-reported mechanism for avoiding autotoxic effects (McKey, 1979). Many nonprotein amino acids are toxic to plants but do not show autotoxic activities (Friedman, 1995). Fowden and Lea (1979) reported that proline is stored in shoots of Convulluriu mujulis and has no toxic effects on the producer. However, it resulted in toxocity when applied to mung bean (Phaselous uureus). Friedman (1995) explained the mechanism which producers use to avoid autotoxicity due to nonprotein amino acids. The activation of the amino acids by aminoacyl-tRNAsynthetaseoccurs prior to mRNA and chain initiation and termination factors determine the nature of the protein. This results in activation of analogs instead of common protein amino acids. However, in plants that produce nonprotein amino acids, the aminoacyl-tRNA synthetase differentiates between the analog and protein amino acid. These mechanisms can explain the method of avoiding autotoxicity from chemicals present inside the plant cell. Research is needed to investigate how plants avoid autotoxicity from chemicals present in their rhizospheres. Williamson (1990) discussed different mechanisms by which plants might avoid autotoxicity: (i) Allelopathic compounds are produced after their removal from the donor plant; (ii) microorganisms present in the rhizosphere produce allelopathic compounds; and (iii) less toxic compounds, produced by the donor plant, may be degraded into more toxic compounds.

C. PRACTICAL CONSIDERATIONS Laboratory bioassays have certain limitations; however, they are an important integral part of allelopathic research (Leather and Einhellig, 1986; Inderjit and Dakshini, 1995a). It is true that laboratory experimental conditions exactly simulating those in the field represents an impossible goal, but one can and should avoid steps which widen the gap between laboratory bioassays and field interactions. Laboratory study can best be applied in situations which allow close examination of carefully isolated components of the complex natural system. Often, to assess allelopathic potential, preliminary bioassays are performed with leachate or extracts of allelopathic plants or artificial soil or natural soil is amended with debris (Rice, 1984, 1995). The mere presence of allelopathic compounds in plant parts does not demonstrate allelopathy (Heisey, 1990). Fisher (1979, p. 327) states that “it seems unlikely that the allelochemicalsthat may be extracted from plant material are actually those that reach the host plant, yet all our information on allelopathic compounds is derived from extracts that have never been exposed to soil.”

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Stowe (1979) argued that leachates of whole plant, foliar parts, residues, or root exudates may cause inhibition of seed germination and seedling growth even when a plant has not demonstrated allelopathy in natural systems. Thus, any conclusion on the allelopathic potential of a plant made only on the basis of leachate may not be of much significance and should be avoided (Inderjit and Dakshini, 1999).The problem with attributingallelopathiceffects to plant extracts is that compounds affecting plant growth may not even leach out, or exude, from plants in nature and therefore might not be significant physiologically even though they are significant in laboratory bioassays. The bioactive concentration of compounds, and hence allelopathic effects in the soils, may be affected by processes such as retention, transformation, and transport (Cheng, 1995). When designing amending experiments to study allelopathy, the following points should be considered in order to improve the chances of obtaining ecologically relevant results: 1. Avoid using organic solvents to prepare leachates or extracts for allelopathy bioassays (Schmidt, 1988; Inderjit and Dakshini, 1995a). 2. Avoid the use of artificially sensitive species as test species. Many bioassays used lettuce as test species because of its sensitivity to allelopathic compounds. The use of plant species naturally associated with the allelopathic plant would give more ecologically relevant results. 3. Use appropriate soil and plant debris ratios, reflecting reasonable natural conditions. While studying the allelopathic potential of Pluchea lunceoluru, Inderjit and Dakshini (1994b) amended natural soils with different amounts of Plucheu leaves and leaf leachate. It was concluded that soil should be amended with plant debris in different amounts followed by chemical analysis for inorganic and organic molecules. The statistical comparison of amended soils with soils naturally infested with the allelopathic plant should be made, and the soil-plant amending ratio that has the fewest differences in chemical characteristics from those of natural soils should be selected for bioassay. 4. Avoid grinding plant materials. The natural state of plant material should be maintained. 5 . Generate data with more than one test species. It is well known that some species are more sensitive to certain allelopathic chemicals and others respond to significantly different levels. While investigating the influence of vanillic and ferulic acids on early corn seedling growth, Janovicek and coworkers (1997b) found that corn sensitivity to these phenolic acids is likely to be hybrid dependent, and Keating (1977, 1978) found that cultures of the same target algal species, isolated from different ecosystems, responded differently to biologically active filtrates of a single producing species. Sensitivity was consistently higher when producer and target were from the same ecosystem. 6. Do not underestimate the significance of details in controls in allelopathic

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bioassays (Williamson and Richardson, 1988). In some studies peat moss (inert organic matter) is added to the control soil to equalize the organic matter to that of amended soils (Rice, 1984). However, peat is not always an appropriate control, and in some studies no peat moss is added (Inderjit and Dakshini, 1992a, 1994a,b). While studying the allelopathic potential of Ledum groenlundicum, Inderjit and Mallik (1997a) found that peat moss was significantly different from natural unamended organic soils for various chemical characteristics. This point should be kept in mind while amending control soil with peat moss to equalize organic matter content. 7. Do not rely solely on seed germination as an indicator of allelopathic potential. It would be difficult to assess germination promotion in a treatment if there is 100% germination in the control (Inderjit and Dakshini, 1995a). Sometimes allelopathic chemicals do not inhibit seed germination but do significantly delay the onset of germination (Chaves er ul., 1997a). Generally, root and shoot seedling growth of 1 or 2 week’s duration is tested to investigate allelopathic activities. Low inhibition during early seedling stage, however, may have significant long-term impacts. Putnam and Tang (1986) suggested that allelopathy may also have startling effects. They cited an example of tomatoes which suddenly die when growing in the close vicinity of black walnut (J. nigru). Sometimes, studies such as those using transmission and scanning electron microscopy provide interesting evidence for allelopathy (Fig. 3). 8. Do not ignore distances between producer and target species when evaluating the effects of an allelopathicchemical. While discussing the importance of soil sampling in allelopathy research, Cheng (1995) suggested that transport of allelopathic compounds may only be over short distances. Moreover, while collecting soil samples, most of the time the investigator does not have data on the transport of allelopathic compounds. Also, collection of soil samples depends on the morphology of the donor plant and its subterranean parts. Cheng suggested that researchers should decide at what distance soil should be sampled because physicochemical and biological components of rhizosphere soil differ from those of soil away from the rhizosphere. Care should be taken while collecting a rhizosphere soil sample because it may include small fragments of roots and root hairs. After collecting soil samples, handling is an important factor in allelopathic studies. Under aerobic and anaerobic conditions, allelopathic chemicals may undergo microbial and/or chemical degradation (Lehmann er al., 1987; Lehmann and Cheng, 1988; Cheng, 1989, 1995). An important aspect of allelopathic research is the extraction of allelopathic chemicals from soils. While many allelopathic compounds are soluble in soil suspensions, many are difficult to extract with water. Allelopathic compounds may be present in free, reversibly bound, or irreversibly bound forms in soil. Kaminsky and Muller (1 977) reported a technique for extracting allelopathic chemicals from

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soils using water and a reducing agent. Cheng and coworkers (1983) found that many allelopathic chemicals may undergo oxidation during extraction, and that this can be avoided by adding reducing agent to the extraction solution. Kaminsky and Muller (1978) cautioned against using alkaline extractants such as NaOH for extracting allelopathic compounds from soils. Whitehead and coworkers (1981) considered phenolic acids extracted with water, rather than soil extractants Ca(OH), and 2M NaOH, to be more important allelopathically. Udo Blum (personal communications) believes that there are no satisfactory extraction procedures for soil total phenolics. He and coworkers (1994) reported that free and reversibly bound phenolic acids can be extracted with water and EDTA (at pH 7.0), respectively. In 1997, he reported the benefits of citrate extractions over EDTA extractions for extracting phenolic acids from soils and plant debris. This is because EDTA-extracted phenolic acids are analyzed quantitatively with Folin Ciocalteu phenol’s reagent using high-performance liquid chromatography (HPLC). Citrate extractions, however, did not interfere with Folin Ciocalteu phenol’s reagent and are considered better extractants for estimating total phenolics (Blum, 1997). The extraction of allelopathic chemicals can be the most difficult part of allelopathic research.

D. RHIZOSPHERE ECOLOGY The rhizosphere (i.e., root-soil interface) is a zone of higher microbial activity that is due to the presence of plant roots, and it is of great significancewith regard to plant-plant and plant-microbe interactions (Gray and Parkinson, 1968; Campbell, 1985; Curl and Truelove, 1986; Cunningham et al., 1996). Rhizosphere microorganisms may also induce the release of organic molecules from plant roots. There are various factors that influence microbial populations in the rhizosphere. Various kinds of carbohydrates, amino acids, and organic molecules from root exudates serve as carbon sources for soil microorganisms. Plant roots provide additional surface area for microorganisms to colonize. For example, compared to tap roots, fibrous roots have a large surface area (Atlas and Bartha, 1993; Cunningham et al., 1996). Microbial components are significantly influenced by plant age and species and by soil type (Campbell, 1985). Rhizosphere soils are different from bulk soils in microbial ecology, carbon dioxide and oxygen concentrations, osmotic and redox potentials, moisture content, and pH (Cunninghamet al., 1996). Due to these differences in biotic and abiotic components between the rhizosphere and bulk soil, quantitative and qualitative differences in organic molecules cannot be ruled out. Furthermore, increased microbial degradation of allelopathic chemicals is expected in the rhizosphere (McFarlane et al., 1981; Rice, 1984). Allelopathic chemicals released from the donor plants pass through the rhizosphere zone.

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It would be of interest to investigate the persistence and fate of allelopathic chemicals as they move from the rhizosphere to bulk soil.

E. MULTIFACETED APPROACH Many allelopathic studies are designed from the population level standpoint.As a result of the progress in chemical ecology during the past three decades, it is clear that plants (including microorganisms) produce many secondary metabolites which may act as allelopathic chemicals, insect repellents, allomones or phytoalexins, etc. It is extraordinarily difficult to distinguish the effects of mycorrhizae, nutrient limitations, root competition, microbial activity, litter-quality allelopathy, and other biotic (herbivory, diseases, etc.) and abiotic (drought, shade, injury, nutrients, etc.) stress factors (Einhellig, 1995a;Kimmins, 1997;Inderjit and Del Moral, 1997). Although allelopathy deals with plant-plant (including microorganisms) chemical communications, the allelopathic chemicals have other vital functions. For example, wild mustard has been reported to possess allelopathic activities. It is also reported to produce chemicals that reduce herbivory from the crop (Gliessman, 1989). When wild mustard is intercropped in different densities with broccoli, there is a significant reduction in herbivory (Gliessman, 1989).Gliessman and Altieri (1982) reported that allelochemicals from wild mustard are also important in attracting insect pests that would otherwise damage nearby crops of economic importance. It is difficult to conclude that a particular mechanism is solely responsible for the observed growth responses in the given ecosystem or that only plants (and microbes) are affected by biologically active products. Reflecting the latter point, some workers prefer the term allelochemistry (Keating, 1987, 1999; Butler, 1995).

F. STATISTICAL ANALYSIS Statistical analysis plays an important role in interpreting data. First, there is need to replicate and repeat studies in space and time, and field and laboratory trials must be compared. After completing experimental work, data must be subjected to relevant statistical analysis. Finney (1989) discussed the need for selecting appropriate statistical techniques, i.e., ones that are relevant to the specific problem. He argued that a statistician plays an important role in interpreting data from the statistical standpoint. Dyke (1997) advocates the need for more consideration of the statistical aspects of a study at the level of design and analysis. He very accurately pointed out the drawbacks of using popular statistical software. Often, workers rely completely on computer output and use “default

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options” which may not always be the best options (Dyke, 1997). To design a field study, a statistician and biometrician can be of great help. Using inappropriate statistics may lead to a wrong interpretation of the results. Dyke (1997) discussed “data distribution,” commenting that sometimes it is difficult to relate data to normal distribution. Inderjit and Dakshini (1 996a) compared cultivated fields and found a wide range of total phenolics from the same field. In the topsoil from group I, values of total phenolics varied from 1.51 to 75.3 mg/100 g, (17.0 ? 18.8 mg/100 g), and values for total phenolics in subsoil varied from 0.90 to 73.1 mg/100 g, (10.23 2 17.2 mg/100 g) in field conditions. It is sometimes difficult to avoid such large variations, and statistical procedures developed to evaluate quite different kinds of data can produce misleading analyses. Researchers must consider what type of statistics is most appropriate to such data, e.g., to compare group means. Palmquist (1997) discusses the significance of the appropriate use of analysis of variance (ANOVA). He stresses the significance of testing the homogeneity of variance between treatments prior to conducting ANOVA. Palmquist suggests that an investigator must be sure of the homogeneity of variance before running an ANOVA. No doubt, statistical help while designing a field study can make the study more statistically sound and the results more valid in nature. Maurer (1998) discusses some aspects of ecological science and statistical paradigms and stresses that both theoretical and empirical ecologists should be up to date on more sophisticated statistical techniques.

X. CONCLUDING REMARKS The wealth of information on the processes, procedures, and practices of allelopathy has contributed to our understanding of some perplexing mechanisms related to this field of science. Allelopathy should not be studied in isolation, and the need for a multifaceted approach, encompassing different ecological mechanisms such as resource competition, allelopathy, soil nutrient dynamics, and microbial ecology, must be acknowledged. Laboratory, greenhouse, and field trials should be designed from this multifaceted standpoint. Molecular and biotechnological techniques should be employed in allelopathy to better understand the phenomenon of allelopathy at the molecular level. More research is needed conceming (i) the relationships between allelopathic and ecological events, such as ecological succession, community structure, and biodiversity; (ii) the use of allelopathic compounds as herbicides/pesticides; (iii) the use of cover crops in the biological control of weeds and plant diseases; and (iv) the possibilities of using plant breeding or genetic manipulation to enhance allelopathic varieties for use in biological control schema.

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ACKNOWLEDGMENTS We sincerely thank Professors Jeff Weidenhamer and Leslie Weston for providing photographs and Ralph E. J. Boerner, Garvin D. Crabtree, Randy A. Dahlgren, Jeffrey B. Harborne, Melinda L. Hoffman, Francisco A. Macias, Darrell A. Miller, Jeff Weidenhamer, and Leslie Weston for granting permission to include their work in this chapter. We appreciate the help of Dr.Surinder Kaur for proofreading the manuscript.

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Waller, G. R. (Ed.) (1987). “Allelochemicals: Role in Agriculture and Forestry.” American Chemical Society, Washington, DC. Waller, G. R. (1989). Allelochemical action of some natural products. In “Phytochemical Ecology: Allelochemicals, Mycotoxins and Insect Pheromones and Allomones” (C. S. Chou and G.R. Waller, Eds.), Academia Sinica Monograph Series No. 9, pp. 129-154. Institute of Botany, Taipei. Waller, G. R., and Burstrom, H. (1969). Diterpenoid alkaloids as plant growth inhibitors. Nature 222, 576-578.

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TURFGRASS MOLECULAR GENETIC IMPROVEMENT FOR ABIOTI~EDAPHIC STRESSRESISTANCE R. R. Duncan and R. N. Carrow Department of Crop and Soil Sciences, University of Georgia at Griffin, Griffin, Georgia 30223

I. Introduction 11. Molecular Genetic Improvement k Stress Adaptation B. Moisture Deficiency C. Temperature Extremes D. Salinities E. Acidities E Alkalinities G. High Soil Strength H. Low Soil Oxygen I. Low Light Intensity J. Low Nutrient Availability K. Multiple Stress Interactions L. Comparative Mapping M. Endophyte Enhancement 111. Enhancement Strategy for Multiple-Stress Resistance A. Components B. AbiotidEdaphic Stress and the Rhizosphere C. Role of Turfgrass Management IV. Summary References

I. INTRODUCTION Turfgrasses are subject to many climatic, soil, pest, and use-related stresses (Fig. 1). The frequency, duration, and intensity of stresses are increasing in response to such factors as reduced water availability, more frequent use of effluent water and irrigation water of lower quality, installation of turfgrass facilities on 233 Advances m Apnmny, Volume 67 Copyright 0 1999 by Academic Press. AU rights of reproduction in any form reserved. 0065-2 I 1 3/99$30.00

R. R. DUNCAN AND R. N. CARROW

234

7 Pesb (above ground)

* discasea

* tmpmture (low, high)

use (wear, compaction)

* mowing

light (radiation;~ow,high) * moisture (deficient, excess) * wind atmospheric pollutants

cultural practices

* Bases (02,COJ

* low moisture * excess moisture macrdmicro pores

o2and co*

* other p w

soil strength low tempcratun

high temperature soil stability

Chemical P r o d @ * PH

Al. Mn toxicities

nutrient defiaenaca nutrient imbalances dts low CEC heavy metals toxic substances calureous soils

microorganism types microorganism population levels microorganism population balances insscts nematodes *'Weeds

animals

Figure 1 The turfgrass system: plant-soil- -atmosphere-man.

sites with marginal soil quality, and increasing usage of recreational sites. These factors, when coupled with steady environmental pressure toward reduced water, nutrient, and pesticide inputs, mandate the development of more stress-resistant grasses. Research efforts are being channeled into genetic improvement of overall stress tolerance in grasses, but enhanced efficiency in utilizing gene technology is needed. Conventional breeding techniques can address individual and multiple stresstolerance traits, but advancementsin gene biotechnology can provide new and in-

TURFGRASS MOLECULAR GENETIC IMPROVEMENT

23 5

novative methods for enhancement of individual and more complex, multiple stress tolerances (Brilman, 1997, Lee et al., 1996). The primary objective of this review is to address some of the biotechnology methods that could lead turf enhancement programs into the twenty-first century. However, biotechnology is not an end in itself. As with any scientific methodology, it must fit within a system or framework with other components if the end result is to significantly improve turfgrasses. Thus, a secondary objective is to present a strategic framework for turfgrass improvement that incorporates gene technology.

II. MOLECULAR GENETIC IMPROVEMENT A. STRESSADAPTATION Unpredictable and irregular cyclic environmentalextremes create variable turfgrass performance responses. These responses may be in the form of (i) susceptibility to the stress and diminished turf quality, ultimately leading to death of the plant, with the duration of exposure and degree of stress dictating the time frame; (ii) acclimation, which is governed by an increase in turfgrass resistance (or preadaptation) to harsh environments after exposure to periods of sublethal stress (i.e., a phenotypic adjustment to gradual seasonal climatic changes) (Hallgren and Oquist, 1990; Hoffman and Parsons, 1991); or (iii) adaptation, which implies short- and long-term responses involving adjustments of physiological (Amzallag and Lerner, 1995) and genetic (Allard, 1988; Orr and Coyne, 1992; Perez de la Vega, 1996)parameters to environmental stress conditions. Plasticity in response to stress is a buffering mechanism whereby turf genotypes respond to environmental changes involving genetically based natural selection (Via, 1994). Phenotypic plasticity in unpredictable environments is the variability in phenotypic expression of individual turf traits within a species gene pool in response to changeable or varying environments (Bradshaw, 1965; Counts, 1993; Via, 1994). Quantitative traits that genetically control many of the stress responses exhibit high plasticity (Perez de la Vega, 1996). Adaptation mechanisms are not well understood since environmental stress factors are usually complex, several genetic and biochemical/physiological systems control the turfgrass response, developmental stages are not equally sensitive or responsive to variable environmental stress factors, and the same genes and associated response mechanisms can function in multiple stress environments (Perez de la Vega, 1996). Genotypic differences are maximized under extremely stressful environmental conditions (Parsons, 1987); consequently, stress adaptation is more easily accomplished in less favorable environments in which selection pressure reduces genetic variability for the stress response traits by eliminating less

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R. R. DUNCAN AND R. N. CARROW

adapted genes (Perez de la Vega, 1996). Thus, poor-performing genotypes can be excluded from the pool. Multiple severe abiotic and edaphic field stress environments can be utilized in turf breeding programs to enhance turf survivability and persistence (Duncan and Carrow, 1997) in less favorable environments. However, these same stress-adapted turf genotypes are capable of performing quite well in more favorable environments,with better buffering (plasticity) against rapid shifts in environmental extremes compared to their counterparts. Genetic variation is a key determinant in successful adaptation to environmental stresses (Stanca et al., 1992). Selection in stress environments may not necessarily target specific individual genes governing a single component of the stress response mechanism but may act on multiple loci (Allard, 1988; Allard et al., 1993). If major (qualitative) genes are functioning in adaptation to rapid environmental changes (Macnair, 1991), the turf population must adapt rapidly or become locally extinct (Perez de la Vega, 1996). If stress adaptation results from multiple alleles, each having small effects on various components of the response mechanism (polygenes or quantitative genes), the breeding program can utilize sequential exposure to increasing levels of stress and multiple cycles of pollination to enhance adaptation (Duncan and Carrow, 1997) without radically eliminating variability or positive turf traits. Selection indices of 1-5% can be used in this population breeding program for successful advancement. The key components are a diverse gene pool, one or more discriminatingly severe field stress environments, high plant numbers in the breeding program, and the requirement for surviving turf plants to go through entire life cycles including the reproductive stage. Biotechnology can be used to supplement and enhance this strategy. Gene technology addressing specific abiotic/edaphic stresses is available (Owens, 1995) and its possible application to turfgrass stress-resistance enhancement will be presented. Integration of biotechnology in improvementof turf environmental stress adaptation is dependent on 1. Enhancement of genetic-based root plasticity (sufficient root volume to maintain the plant under cyclic stresses, i.e., rooting depth, secondary and tertiary roots, and root viability and functionality) 2. Discernment of multiple stress-tolerance mechanisms in highly tolerant genetic resources 3. Mapping and cloning of stress response genes 4. Utilization of marker-assisted selection (MAS) techniques and transformationhegeneration methods to manipulate specific mechanisms across/within species 5 . Field evaluation in severe multiple stress environments 6. Refinement of management strategies to ensure maximum expression of stress resistances

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B. MOISTUREDEFICIENCY 1. Drought Resistance

Turfgrass drought resistance encompasses both drought avoidance and tolerance mechanisms. Drought resistance is exhibited through such strategies as developing deep root systems and shoot morphological/biochemical/physiological traits that minimize evapotranspiration (ET) losses (Beard, 1989); enhanced root viability, functionality, and plasticity (Huang et al., 1997); and greater recoverability from transient drought stress following soil rewetting (BassiriRad and Caldwell, 1992; Brady ef al., 1995; Nobel and Huang, 1992; Wraith ef al., 1995). a. Drought Avoidance The ideal drought-avoidingturfgrass needs to have low ET during both high and low soil moisture availability; a viable, functional, and plastic root system through cyclic stresses; and seasonality (Perez de la Vega, 1996), which is the avoidance of stress periods by completing the life cycle during favorable conditions (annual species, e.g., many grassy weeds in turf) or the capacity to go dormant during severe stress periods (perennials, e.g., centipede grass). Extensive research has been conducted on turfgrass ET rates (Table I). However, this trait is only one component (Nguyen ef al., 1997) of the overall droughtavoidance mechanism and is sensitive to climatic variables (humid vs arid, wellwatered vs limited soil moisture, and greenhouse vs field). These factors challenge data interpretation and development of useful management strategies (Canow, 1995b). Under dry environmental conditions, turfgrass stomatal aspects (density, location, and interaction with leaf water potential that influences rate of closure), delayed or postponed dehydration, and rooting characteristics strongly influence ET (Carrow, 1995a,b, 1996a,b). Drought avoidance in tall fescue is associated with the development of high root length density in the deeper root zone (20-60 cm), maintenance and functionalityhiability of deep roots over the summer months (combination of genetic tolerance to edaphic stresses and indirect high temperature stress), and development of cultivars with inherently low ET and the ability to maintain low ET during soil drydown (Carrow, 1996a). b. Drought Tolerance A turf plant can possess greater drought tolerance if it has these components: osmotic adjustment, maintenance of positive turgor pressure, and delayed leaf rolling (White ef al., 1992, 1993). However, both drought-avoidance and -resistance components contribute to wilt and leaf-firing expression in turf plants under field conditions (Carrow, 1996a;Nguyen etal., 1997). Some turfcultivars may ex-

'hble I EvapotranspirptionRanges for Variaus Mgrasses Species

N w

m

Tall fescue Rebel (6.27) Kenhy (7.09) Shortstop (8.6) Alta (10.0) Rebel II KY-3 1 KY-3 1 Murieta Apache St. Augustine

TX common Raleigh Centipede grass GA common Common Buffalo grass TX common

Location

Range (&day)

or mean

Reference

Arid, field, nonlimiting soil moisture Field, arid, nonlimiting soil moisture

7.2-12.6 6.3-7.1

Beard (1985) Kopec et al. (1988)

Greenhouse,arid, nonlimiting soil moisture

Field Field Controlled environment Field Field Field Field Field

7.1- 10.0 2.7-3.4 3.57 3.69 5.1-7.1 6.6 7.7 6.3-9.6 4.5-5.1 6.7-8.1 4.8-6.3 3.28 4.0-8.7 4.7-5.5 3.8

Bowman and Macaulay (1991) C m w (1996a) carrow (199%) carrow (199%) Kim and Beard (1988) Fernandez and Love (1993) Femandez and Love (1993) Beard (1985) Atkins eral. (1991) Atkins eral. (1991) Kim and Beard (1988) C m w (1995b) Beard (1985) Kim and Beard (1988) C m w (1995b)

Field

4.4-5.3

Kim and Beard (1988)

Field, humid, limited soil moisture Field, humid, limited soil moisture Field, humid, limited soil moisture Field, humid, nonlimited soil moisture

Greenhouse Greenhouse

Zoysia grass

Meyer Emerald Meyer Bermuda grass Common Tifway AZ common Tifgreen Tifway Kentucky bluegrass

Bristol N W

\o

Wabash Perennial ryegrass Saturn

Accolade Seashore paspalum Adalayd

Field Field Controlled Field Field Field Field Field Field Field Field Field Field

4.8-7.6 3.8-4.7 7.6-10.0 4.1-5.8 4.9-6.5 3.54 4.0-8.7 3.03 4.2-5.2 3.11 4.2-5.8 4.6-5.4 4.1-5.9

Beard (1985) Green et al. (1991) Greenetal. (1991) Kim and Beard (1988) Kim and Beard (1988) Carrow (1995b) Beard (1985) Carrow (1995b) Beard et al. (1992) C m w (1995b) Kim and Beard (1988) Kim and Beard (1988) Kim and Beard (1988)

Greenhouse Greenhouse

5.5 6.0

Fernandez and Love (1993) Fernandez and Love (1993)

Greenhouse Greenhouse

6.2 6.3

Fernandez and Love (1993) Fernandez and Love (1993)

Field

4.7-6.1

Kim and Beard (1988)

240

R. R. DUNCAN AND R. N. CARROW

hibit rapid leaf firing due to a restricted root system and not because they lack drought tolerance. Components of drought tolerance in turfgrasses include hardiness, or greater membrane stability under dehydration; tolerance of protoplasmic constituents to dehydration;binding of cell water to protoplasmic proteins and carbohydrates;loss of excess water that contributes to tissue succulence; and accumulation of certain metabolites (i.e., proline, betaine, and abscisic acid). Genetic engineering of drought (and salt)-tolerance components is possible (Table 11). The quantitative trait loci (QTLs) controlling leaf rolling (Champoux et aL, 1995) and osmotic adjustment/dehydrationtolerance (Lilley etal., 1996;Lilley and Ludlow, 1996)have been located. lsble II

QTLs or Other Genes Linked to Drought-Tolerance' h i t s Category

Qn. QTL

Trait

Reference

Visual leaf rolling during three growth stages Osmotic adjustment and dehydration tolerance

Champoux er al. (1995) Lilley er al. (1996) Lilley and Ludlow (1996)

Mannitol Glycine betaine Proline torn PRO1 (encodes y-glutamyl kinase and y-glutamyl phosphate reductase) torn PRO2 (encodes Dl-pyrroline-5-carboxylate synthetase with GGK and GGPR activity) Trehalose

Tarczynski et al. (1992, 1993) Rathinasabapathier al. (1994) Kishor er al. (1995) Maggio et al. ( 1996)

Osmolytes Gene@) Gene(s) Gene(s)

Gene(s) proteins Gene(s) Gene(s) Genes Genes GsPM 112 Ose 730 Other Gene cDNA cDNA cDNA Gene Gene

LEA families: D-19, D-113, D-11, D-7 HVAl (LEA or dehydrin) Rehydrins (Tr 155) Imm (dehydrin) LEA D- 113 family LEA D-1 1 family cdr 29 (cDNA clone coding for acyl-coenzyme A oxidase) Mn-SOD (provides oxidative stress and water-deficit tolerance) pCrnPMI/9 (LEA dessication tolerance-soybean) LEA (dessication tolerance-soybean) Asr I (tomato) Rd17 (Arabidopsis-LEA dehydration stress tolerance)

Maggio er al. (1996) Holmstrom et al. (1996) Dure (1993)

Xu et ul. (1996) Oliver (1996) Close (1997) Chiang er al. (1996) Chen and Chen (1996) Stanca et al. (1996) McKersie er al. (1996) Hsing et al. (1995) Burns et al. (1997) Giad er al. (1997) Iwasaki er al. (1997)

TURFGRASSMOLECULAR GENETIC IMPROVEMENT

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Genetic manipulation of low-molecular-weight osmolytes [polyols, proline, betaines, and 3-dimethylsulfoniopropionate(DMSP)] can potentially confer osmotolerance to water-deficit (or freezing and salt stress) conditions (Yancey, 1994). Transgenes controlling production and accumulation of mannitol (Tarczynski et al., 1993), glycine betaine (Rathinasabapathi et al., 1994), proline (Kishor et al., 1995),and trehalose (Carninci et al., 1998; Holmstrom et al., 1996) have been engineered into various plant species (Table 11). These osmolytes are functional in stabilizing dehydrated enzymes and lipid membrane structure/integrity. Water-deficit (cold and salt) stress-responsive cDNAs encoding different protein classes-late embryogenesis abundant (LEA) dehydrins (Close, 1997), responsive to abscisic acid, and ion channel proteins-have been isolated and characterized, but their role in stress physiology and their physiological functions were initially not well understood (Bray, 1993). Dessication (caused by drought, salinity, or extracellular freezing) tolerance incorporatestwo mechanisms: cellular protection and cellular repair/recovery (Oliver, 1996).Cellular protection processes/componentsinclude decreased rate of water loss, stabilization of membranes, water replacement, lipid modifications, compartmental stabilization,structural modifications, antioxidants, decreased water loss-induced damage, reduced UV light-induced damage, osmotic adjustment, mRNA conservation, chromatin condensation, greater cell wall elasticity, cell wall-membrane interactions, and stabilization of sugar, protein, dehydrin, and polyamine synthesis. Cellular repair processes include compartmental integrity, membrane reassembly, cytoskeletal reassembly, pH and ion balance maintenance, consistent electron transport, adequate energy supply, reestablishment of chromatin, DNA repair, lipid synthesis, protein synthesis, RNA synthesis, nutrient uptake, structural integrity, and metabolic reestablishment. Modified dessication-tolerantplants rely on drying-induced and abscisic acidcontrolled cellular protection strategies for survival. Dessication-tolerant species utilize a combination of a constitutive protection strategy and a rehydrationinducible recovery mechanism (Oliver, 1996). Transcription and mRNA stability are essential for activating rehydrins or proteins specific to the rehydration phase and essential for manifestation of dessication tolerance. c. Biotechnology Stress-responsive genes are not necessarily adaptive (may not contribute to overall adaptation/persistence when transformed) and stress-adaptive genes are not necessarily responsive (may be nonfunctional when transformed because of signaling or other problems) (Nguyen et al., 1997). Several QTLs governing visual leaf rolling and osmotic adjustment/dehydration tolerance have been isolated and characterized (Table 11). Genes controlling the production of osmolytes, such as mannitol, glycine betaine, proline, and trehalose, in response to stress have been identified and sequenced. Rutgers University (Belanger er al., 1997) is using

242

R. R.DUNCAN AND R. N. CARROW

the gene encoding the enzyme mannitol- 1-phosphate dehydrogenase in creeping bentgrass (AgrostispafustrisHuds.) transformation studies to accumulate the sugar alcohol mannitol and enhance cell protection from drought stress (Tarczynski et af., 1992, 1993). In vitro selection with mannitol and subsequent regeneration of enhanced osmoregulation has been accomplished in Brassica plants (Gangopadhyay et al., 1997). Recently, a LEA protein gene from barley (HVA1) conferred water deficit and salt stress tolerance in a transgenic rice (Xuet af., 1996) line that was previously highly sensitive to both stresses. Additionally, a Mn-superoxide dismutase cDNA from Nicotiana pfumbaginifoliawas successfully introduced into alfalfa (Medicago sativa L.) to provide oxidative stress tolerance and reduce injury from water-deficit stress. Consequently, alien gene expression for some components of drought tolerance has been demonstrated. Another strategy could involve low-molecular-weight antioxidants such as ascorbate, a-tocopherol, carotenoids, or glutathione and antioxidantenzymes such as glutathione reductase, ascorbate peroxidase, and superoxide dismutases (Ye et af., 1997), which have higher activity levels in drought-tolerant plants. There are also increased amounts of putrescine-generatingenzymes (arginine decarboxylase and ornathine decarboxylase) in drought-resistantplants, and these enzymes have an antioxidant defense function in plants. Transformation with myoinositol 0methyltransferase (IMT1) can result in D-ononitol concentrations that might provide better drought tolerance than the use of osmolyte adjustment (Sheveleva et af., 1997).

C. TEMPERATUREEXTREMES 1. High-Temperature Stress

' h o stress response mechanisms function in turfgrasses, particularly in cool season species. Direct high-temperature stress occurs when temperatures are sufficiently high to cause immediate death of the plant. Both protein degradation and membrane deactivation result in catastrophic effects. Indirect high-temperature stress results in impaired turf growth (with injury levels ranging from slight to extensive), depletion of carbohydrate reserves in the crown region, and progressive reduction in turf quality and performance. Both protein and membrane deactivation are initiated. Photosynthesis is inhibited (Giardi et af., 1997) due to degradation of D, protein synthesis, which is initiated by an increase in oxygen radicals, followed by modification and de novo synthesis of proteinases, an ATP/ADP ratio change, modification of phosphorylation levels, and a redox potential change. Carbohydrate accumulation positively affects D, protein turnover and subsequently photosystem II function and stability (Kilb et

TURFGRASS MOLECULAR GENETIC IMPROVEMENT

243

al., 1996).Consequently, maintaining adequate carbohydrate loads in the turf plant is essential to the heat stress-tolerance response. The expression of the rbcS genes (encoding a small subunit of Rubisco) and the expression of the cab genes (encoding the polypeptides of the light-harvesting system) are also controlled by carbohydrate metabolism (Sheen, 1990). Turf management strategies can have significant impacts on heat (and also drought and cold) acclimation or hardiness. Poor water drainage, wet or compacted soils, excess N applications, K deficiency, shade, and close mowing can enhance succulence in turf plants and escalate heat stress problems. High N applications, close mowing, shade, mechanical injury, or deficiencies in Fe, Mg, Mn, S , and N can also decrease carbohydrates and, coupled with prolonged heat stress, decrease turf quality and persistence. For high-temperature stress protection, heat-tolerant cultivars need more heatstable photosynthetic systems; greater thermostability of heat-sensitive enzymes, proteins, and membranes; morphological features (pubescent leaves, waxy leaves, lighter green color, and vertically oriented leaves) that reduce heat absorption;heat shock proteins for protection; and high total nonstructural carbohydrates coupled with an efficient partitioning system for shoot and root growthhiability. A disaccharide such as trehalose provides thermostabilization and thermoactivation of enzymes involved in heat stress protection and cell homeostasis (Caminciet al., 1998). a. Biotechnology Genes controlling photosynthetic processes under stress have been identified (Table 111). Numerous heat shock protein families have been characterized (Neumann et al., 1984), including several turfgrass species (Park et al., 1996). One genetic engineering strategy to increase thermotolerance could involve the biogenic nonmethane hydrocarbon isoprene, which stabilizes and enhances hydrophobic interactions (lipid-lipid, lipid-protein, and protein-protein) in thykaloid (chloroplast) membranes (Singsaas et al., 1997). Another strategy could utilize high catalytic activity controlled by the catalase gene (CAT-I)to decrease photorespiratory CO, losses at high (38°C)temperatures while increasing net photosynthesis (Brisson et al., 1998; Schultes et al., 1994). A third strategy could involve heat (cold, salinity, anoxia, or hypoosmotic) stress-induced changes in cytosolic Ca2+ signal transduction (Gong et al., 1998).A fourth strategy could include low-molecularweight heat shock proteins that provide photosynthetic and photosystem U (H,Ooxidizing, quinone-reducing complex) thermotolerance and are a major adaptive mechanism to acute heat stress tolerance in plants (Downs et al., 1998; Heckathorn et al., 1998). Breeding tactics such as utilizing high bulk density soils, minimal water, and severe scalping during high heat stress periods can be used in the breeding program to improve root plasticity, carbohydrate load in the crown and subsequent partitioning to roots and shoots, and heat stress recoverability in coolseason grasses under field conditions (Duncan and Carrow, 1997).

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244

Table IlI Genes Linked to Temperature Stress Tolerance Stress

Category

High temperature Photosynthesis

Calmodulin Soybean Potato Arabidopsis

Gene psb A (core D , protein for photosystem II) rbc S (encode Rubisco) cab (encode polypeptides for light harvesting) HaCaM (sunflower) Polyubiquitin ubi3-2 (ubiquitin)

AfJlO (molecular chaperone and specific regulator of HSPs) Yeast CAJl (molecular chaperone, binds calmodulin) Heat shock HSP 110 (CL,) protein families HSP 90 (80-95 kDa) HSP 70 (63-79 kDa) HSP 60 (53-62 kDa) HSP 20 (10-30 kDa) HSP 8.5 (ubiquitin) Low temperature Winter hardiness QTL: field survival, LT,,, growth habit, crown fructan content Antifreeze proteins (AFPs) Arctic fish Antifreeze glycoprotein (ice-binding gene) Six AFPs (16-35 kDa) (accumulate. during acclimation) Cold-induced proteins Barley COR 14 Arabidopsis COR 15 Bermuda grass COR 27 (chitinase protein) Arubidopsis COR 47/COR 6.6 Alfalfa CAS 18 (cold acclimationspecific gene encodes LEAlABA-responsive/ dehydrin proteins) Spinach 70-kDa heat shock protein (metabolic adjustment during acclimation)

Reference Giardi et al. (1997) Sheen (1990) Sheen (1990) Courbou e f al. (1997) Huq ef al. (1997) Roy0 and SanchezSerrano (1997) Lin and Lin (1997)

Mukai ef al. (1994)

Neumann et al. (1984)

Hays et al. (1993)

Chen er al. (1997a.b)

Griffith ef al. (1997)

Stanca ef al. (1996) Lin and Thomashow (1992) Gatschet et al. (1994, 1996) Gilmour et al. (1992) Wolfraim er al. (1993)

Neven ef al. (1992)

continues

245

TURFGRASS MOLECULAR GENETIC IMPROVEMENT lsble IU-Continued Stress Low temperature

Category Barley Tobacco, potato Wheat

Gene Acyl-coenzymeA oxidase Bacterial ice nucleation gene ( i d WCS 120 family (WCS

Reference Grossi et al. (1995) Baertlein et al. (1992) Sarhan et al. (1997)

2001180/66/ 120/40/ 726/80 cold induced,

Barley

Alfalfa Rapeseed

soybean Wheat cDNA clones Barley

Wheat

Alfalfa

Bromegrass

Rice

ABA independent) COR 75 (ABA regulated, induced by low temperature. drought) CDPK (cold-regulated protein kinases) B,PEPCK (coldacclimatedATPdependent phosphoenolpyruvate carboxykinase) LEA pMA2005 (ABAdehydration regulated) pt 59 (encodes coldinduced COR 14) paf 93 (induced by dehydration,ABA independent) cdr 29 (encodes for a acyl-coenzymeA oxidase) SODI.1/1.2 (chloroplast CulZn superoxide dismutates; protection from superoxide radicals) pSM 1409 (ABA and cold-acclimated gene expression)-alfalfa pBCA 61 (NADPHdependent aldose reductase; genes associated with sugar metabolism, osmotic stress, pmtease activity) HvMyb4 (ABAindependentresponse)

Stanca et al. (1996)

MONOYet al. ( 1993) Saez-Vasquez et al. (1995)

Burns et al. ( 1997) Cuny et al. (1991)

Stanca et al. (1996)

G. Wu etal. (1996)

Mohapatra et al. (1988)

Lee and Chen (1993)

Pandolfi e f al. (1997) continues

R.R.DUNCAN AND R.N. CARROW

246

lbble III-Continued stress

LoIRtemperature

Category Barley

mRNAs Brassica Wheat

Tobacco

Gene HvCR21 (low temperature regulation; not dehydration regulated) BN IJS, BN 19, BN 26 (cold accliition) pKABA1 (ABAresponsive protein kinase; upregulation by osmotic stress, dehydration, cold temperature) rlt 1412 (low temperature expression in roots) rlt 142J (low temperature expression in mature leaves) car 333/30/757 (chilling acclimation responsive) fod 7 de~aturase(coldacclimation fatty acid

Reference Chang et al. (1996)

Weretilnyk et al. (1993) Holappa and Walker-Simmons (1995)

Zhang ef al. (1993)

Anderson et al. (1994)

Kodama et al. (1994)

increase)

Barley

HvELTll (cold induction)

Dunn er al. (1990)

2. Low-Temperature Stress

Freezing stress resistance encompasses the ability of the turf plant to maintain functionality while surviving freezing temperatures (Palta and Simon, 1993). Warm-season grasses become dormantwith temperaturesnear 10°Cand go off color. Cool-season grasses maintain green color and functionality at much lower tempera-~. lbrf plants have evolved survival mechanisms to cold temperatures based on tolerating extracellular ice or by avoiding extracellularand intracellular ice (Palta and Simon, 1993). Turf plants have the ability to increase both freezing tolerance and avoidancethrough acclimation or hardening. Seasonal environmental changes in photoperiod, temperature, and water status regulate the increase/ decrease of tolerance/avoidance components, the rate of acclimation, the maintenance of resistance over long periods, and the deacclimation and resumption of growth with the onset of favorable environmental conditions. All three components of resistance (tolerance, avoidance, acclimation) are heritable traits (Palta and Simon, 1993). In addition, considerable genetic variation has been found for rate or speed of deacclimation (Sutinen et aL, 1992). Turf breeding programs can select for various individual resistance components and then recombine them to

TURFGRASS MOLECULAR GENETIC IMPROVEMENT

247

incorporate all components into one plant (Palta and Simon, 1993). Examples include characteristics that avoid ice in the crown and root tissues (i.e., crown position) and characteristics that reduce ice injury in shoots and roots (i.e., root tissue water content, stress protein accumulation, and membrane stability). In vitro selection for improved frost tolerance is also feasible (Dorffling er al., 1997; Galiba and Sutka, 1989; Kendall et al., 1990; Lazaret al., 1988;Lee and Chen, 1993;Van Swaaij et al., 1986). Turf plants can be categorized as follows (Palta and Simon 1993): Freezing tolerant but able to cold acclimate (harden) Freezing tolerant but unable to acclimate Freezing sensitive but able to acclimate Freezing sensitive but unable to acclimate Freezing tolerance and acclimation ability are independent traits and quantitatively inherited, and few major genes control freezing tolerance and cold acclimation. The capacity for acclimation is under additive-dominance control and lack of tolerance and lack of acclimation apparently involve some form of dominance (Palta and Simon, 1993). Molecular markers are available to select for freezing tolerance in grasses (Houde et al., 1992). With subzero temperatures, ice forms in intercellular spaces in the mesophyll and xylem vessels of turfgrass leaves (Pearce, 1988). Both the rate of ice propagation through the tissues and the ice crystal size influence the degree of injury. Survival of frozen turf tissues is dependent on prevention of cellular damage caused by growth of intercellular (apoplast) ice crystals. Three mechanisms may influence the increase of ice in the apoplast:

1. Structural segmentation of xylem vessels by zones of tracheids (Aloni and Griffith, 1991): These tracheids slow ice propagation through the xylem (Zamecnik et al., 1994). 2. Accumulation of arabinoxylans in cell walls (Kindel et al., 1989): These large polysaccharides inhibit the rate of ice crystal growth and elevate its melting point (Olien and Lester, 1985; Williams, 1992). Crown apoplastic invertase and fructan exohydrolase may provide specific adaptive responses to freezing temperature survival (Livingston and Henson, 1998). 3. Accumulation of antifreeze proteins (AFPs) in the apoplast of grass leaves during cold acclimation (Griffith et al., 1992; Hon et al., 1994): Some AFPs modify ice crystal growth (Marentes et al., 1993; Hon et al., 1994). inhibit ice recrystallization (Griffith and Antikainen, 1996), accumulate during cold acclimation and are correlated with greater leaf freezing tolerance (Marentes et al., 1993). and are associated with the epidermis and mesophyll cells that surround intercellular spaces of cold-acclimated grass leaves (Antikainen et al., 1996).These are known sites for ice formation on leaf surfaces and in intercellular spaces within the

248

R. R. DUNCAN AND R. N. CARROW

mesophyll (Pearce and Ashworth, 1992),suggesting that AFPs prevent inoculative freezing of cells (Antikainen et al., 1996; Antikainen and Griffith, 1997). Six 16- to 35-kDa AFPs have been isolated from the apoplast of cold-acclimated winter rye leaves (Hon et al., 1994) (see Table IV). These AFPs, resembling pathogenesis-relatedproteins, can be grouped into three classes (Hon er al., 1995): Chitinase-like proteins (CLPs) p- 1,3-Glucanase-likeproteins (GLPs) Thaumatin-like proteins (TLPs) One chitinase protein is encoded by an upregulated COR gene in response to cold acclimation in Bermuda grass (Gatschet et al., 1996).CLPs, GLPs, and TLPs are found in the leaf epidermis, leafsheath, and crown regions, whereas only CLPs are located in the root epidennis of cold-acclimated grasses (Griffith et al., 1997). Several CLPs and GLPs are found in intercellular spaces of leaf mesophyll cells and in the cortex of the crown region (Antikainen et al., 1996). a. Biotechnology Genes associated with metabolic adjustmentto low-temperature acclimation are different from genes encoding responses to cold acclimation and freezing tolerance (Sarhan et al., 1997). Genes such as dehydrins are induced only after a significant increase in abscisic acid (ABA) content, but genes rapidly induced by dehydration are eitherABAindependentor triggered by very small variations in ABA content (Stanca et al., 1996).This diverse gene activity is shown in Table III, which summarizes some of the QTLs, antifreeze protein genes, COR protein genes, and cDNA clones activated by low temperature, drought, ABA, or combinations of these stresses. Cryoselected wheat plants have been regenerated from calli (Kendall et al., 1990). The COR genes, upregulated in association with cold acclimation (Cattivelli and Bartels, 1992; Thomashow, 1993), encode COR proteins, which are synthesized as long as cold exposure persists. Some COR proteins (sucrose synthase) modify plant metabolism to enhance survival at low winter temperatures (Crespi et al., 1991). Other COR (LEA-like) proteins function in physiological adjustments that increase freezing stress tolerance by counteracting dessication stresses (Gilmour et al., 1992; Guo ef al., 1992; Houde et al., 1992). One strategy to modify osmoprotectant (cryoprotectant)levels in leaves would be to engineer a more balanced pro1ine:proline betaine ratio (Nolte et al., 1997) since proline betaine is a more potent protectant than proline (Amin et al., 1995). Genes must be found for the enzymes that catalyze methylation of proline to proline betaine (Essery et d., 1962).Another strategy might involve the introduction of genes that encode separate enzymes involved in the two-step oxidation of choline (Rhodes and Hanson, 1993). A gene for the first enzyme (proline methy-

TURFGRASS MOLECULAR GENETIC IMPROVEMENT

249

lation) has been recently cloned (Nolte et al., 1997) and the gene for the enzyme mediating the second step (choline oxidation) is also available (Ishitani et al., 1995). Additionally, single bacterial genes encoding the enzymes that catalyze both steps in glycine betaine synthesis have been identified (Deshnium et al., 1995; Lilius et al., 1996). Since the sulfonium betaine DMSP has exceptionally strong cryoprotectant properties (Karsten et ul., 1996), enzymes and genes involved in the two-step conversion from S-methylmethionine (Hanson and Gage, 1996) would offer another genetic engineering strategy for osmoprotection (Nolte et al., 1997; Trossat et al., 1998). Since plants do not have internal ice-nucleating agents that are active at temperatures higher than -5°C (Levitt, 1980), engineering ice nucleation genes that encode proteins to prevent supercooling would allow plants to survive at lower temperatures (Baertlein er al., 1992).Nucleation at high subfreezing temperatures facilitates slow dehydration of cells in equilibrium with extracellular ice. Supercooling followed by spontaneous ice nucleation at colder temperatures causes plant tissues to freeze too rapidly for cell dehydration to occur. Ice penetrates cells, ruptures the membranes, and ultimately causes cell death (Mazur, 1977). The efficient expression of ice nuclei in plant cells reduces the degree of supercooling (Baertlein et al., 1992). Additionally, the presence of extracellular ice nuclei in plant cells increases the ability of freeze-tolerant plants to tolerate freezing processes. Transformation and gene expression has been accomplished in tobacco and potato (Baertlein et al., 1992). Introduction of thefud 7 chloroplast desaturase gene can increase the amounts of trienoic fatty acids [hexadecatrienoic (16:3) and linolenic (18:3)] and enhance cold tolerance (Kodama et al., 1994). Ca2+ functions in signal transduction in response to cold acclimation (Monroy et al., 1993) by inducing cas 15/18 genes (Monroy et al., 1993; Wolfraim et al., 1993), with low temperatures (<15"C) required to stabilize the cas transcripts (Monroy and Dhiindsa, 1995).Amplification of the Ca2+ signal along the pathway to cold acclimation is genotype dependent and involves specific protein kinases/phosphatases and their substrates, which leads to cas gene expression and enhanced freezing tolerance (Monroy and Dhindsa, 1995). Genotype-dependent signaling pathways provide an additional strategy for bioengineering plants for frost tolerance. Low-temperature-induced rapid root hydraulic conductivity acclimation is another strategy (Fennel1 and Markhart, 1998).

D. SALINITIES Salt-affected environments impact both turf plant and edaphic (soil) factors. Plant resistance responses include both salt avoidance (selective uptake or exclusion mechanisms and salt secretion, such as through salt glands) and salt tolerance

250

R. R. DUNCAN AND R. N. CARROW

(osmotic adjustment to maintain turgor pressure; tissue tolerance to specific toxic ions, e.g., Na and C1; and tissue dehydration tolerance). Salinity effects on turf plants directly affect physiological/biochemical activities governing growth and developmental processes: Roots: Slow or stop cell elongation, hasten maturity and suberization, reduce permeability due to dehydration, and enhance casparian strip formation Shoots: Acute leaf tip or leaf margin burnhecrosis Roorshhoots: Specific toxic (B, Na, and C1) and deficient (K, Ca, and Mg) ion effects on morphology/physiology, hormonal imbalances, reduced metabolic activity, and disrupted protoplasm due to dehydration

Salinity effects in soils are manifested by reduced water availability due to high osmotic water potential from excess salt (saline and saline/sodic soils), deterioration of soil structure due to Na-induced deflocculation of soil colloids (sodic and saline/sodic soils), toxic ion availability (Na, C1, and B), and nutritional imbalances of macro- and microelements (Carrow and Duncan, 1998). Salinity-related problems on turf are not limited to coastal areas or areas experiencing saltwater intrusion. Poor water quality resulting from effluent/reclaimed/ recycled/gray irrigation sources, brackish water sources, or combinations of these when used on recreational turf (golf courses, sports complexes, and landscapes/ parkdroadsides) will also increase salinity-responseproblems with turf. Because potable water use is increasingly excluded for turf and nonpotable water use is mandated, salt resistance and cultivar within-species selection will become key long-term management strategies for maintenance of quality turf in the twentyfirst century.

1. Turfgrass Diversity Turf genotypes vary greatly in their response to salinity (Table IV). Seashore paspalum has the highest overall general salinity tolerance among warm-season species, whereas alkalai grass is the top cool-season species (Carrow and Duncan, 1998). 2. Gene ExpressiodBiotechnology Salt tolerance is considered a multigenic or quantitative trait (Winicov, 1994). Since root morphology, salt (Na/CI) uptake and transport, as well as physiological/metabolic/regulatory processes at the cellular level are involved in the salttolerance complex, genetic enhancement on a whole plant basis has been a challenge. Even within a species, mechanisms contributing to salt tolerance of ecotypes can differ significantly (Alfacea et al., 1993; Marschner, 1995). Gene technology should be guided by the mechanisms that function in the most salt-

TURFGRASS MOLECULAR GENETIC IMPROVEMENT

25 1

tolerant ecotypes within and among turf species. Molecular biology studies have revealed numerous genes controlling various components of salinity tolerance (Winicov, 1994; Zhu et al., 1997; Yeo, 1998). Several QTLs (Foolad et af., 1997; Yeo, 1998) have been identified, as have specific genes controlling osmoprotectants (glycine betaine), dehydrins, early salt stress induction, K-Na selectivity, and chloride channels (Table V). Transgene expression with enhanced salt tolerance has been demonstrated (Dvorak ef af., 1994; Monforte et af., 1996; Winicov and Shirzadegan, 1997). Plant transformation resulting in stable solute (i.e., D-ononitol) accumulation may provide better salt tolerance than strategies using osmotic adjustment by metabolites (e.g., mannitol, glycine betaine, proline, trehalose, and fructan) that are constitutively present (Sheveleva et af., 1997). Gene expression involved in stress signaling pathways provides another strategy for engineering enhanced osmotic stress tolerance (Quintero et af.,1996; Zhu et af., 1997). Another engineering strategy could involve S-methylmethionine (SMM) synthesis in the cytosol and conversion to DMSPin the chloroplast (Trossat el af., 1998).Salinization increases the capacity or affinity of an SMM transporter in the chloroplast envelop, thereby enhancing osmoregulation and protection of phyotosynthetic processes from stress injury. DMSP is a sulfur analog of the osmolyte betaine. An additional strategy could involve gene coding for synthesis of novel and specific carbohydrates, such as mannitol (Tarczynski et af., 1993) and raffinose oligosaccharide (Gilbert et af., 1997), and membrane enzymes such as plasma membrane H+-ATPase (Wu and Seliskar, 1998). Subtle changes in carbohydrate types in source and sink tissues may serve as signals for new gene expression, trigger new enzyme activities when induced by salinity stress, and allow redirection of growth responses for long-term plant survival (Gilbert et af., 1997). Inducing expression of IMT and increasing myoinositol synthesis facilitates Na sequestration and protects photosynthesis (Nelson et af.,1998). In v i m selection and plant regeneration of salt-adapted lines have been demonstrated in rice (S. Basu et af., 1997), Brassica (Gangopadhyay ef af., 1997), and Medicago media (Chaudhaq et al., 1997). A high-boron-requiring gene (bor 1-1) has also been isolated and characterized in Arabidopsis, which provides B toxicity tolerance (Noguchi et af., 1997).

E. ACIDITIES Soil acidity problems can become limiting on highly sensitive turf species when the pH (water) decreases below 5.5. For most species and cultivars within species, quality and persistence traits are not severely affected until pH decreases below 5.0. Excessive acidity (pH 6 0 ) is a worldwide stress problem in humid tropical/ subtropical and some temperate climates. The most direct and immediate impact on the turf plant occurs in the rhizosphere. Root growth and functionality are im-

lsble N Salinity Tolerance Ranking of Warm- and CodseaSon Mgrass Species"

Decisiemens (d S m-l) N N VI

General

Threshold ECec

50% growth reduction ECe'

Salinity

Common name

Scientific name

toleranceb

Average

Range

Average

Range

Paspalwn vaginatum Distichlis stricta Pennisetum clandestinum Stemtaphnun secundnturn Buchloe dactyloides Bouteloua gracilis Cynodon dactylon Cynodon spp. Zoysia spp. Axonopus spp. Eremochloa ophiumides

T T T T MT MT

8.6 8.0 8.0 6.5 5.3 5.2 4.3 3.7 2.4 1.5 1.5

0-20 6-10 6-10 0-18 0-10 2-10 0-12 0-10 0-11

31

18-49

29 13 21 22 16

22-44 13 12-32 11-33 4-40

8

8-9

Warmseason Seashore paspalurn saltgrass KikllyU

St. Augustine grass Buffalo grass Blue grama Bermuda grass, common Bermuda grass, hybrids Zoysia grass Carpet grass Centipede grass

MT

MT MS

vs vs

0-1

0-3

Cool season Alkali grass Fairway wheatgrass Western wheatgrass Tall fescue Perennial ryegrass Slender creeping red fescue Hard fescue Creeping red fescue Creeping bentgrass Kentucky bluegrass Annual bluegrass Colonial bentgrass Rough bluegrass N v, W

Puccinella spp. Agropyron cristatum Agropyron smithii Festuca arundnnacea Lolium perenne Festuca tuba L. spp. trichopylla Festuca longifolia Festuca tuba L. spp. tuba Agrostis palushis Poa pratensis Poa annua Agrostis tenuis Poa trivialis

MT MT MT

MS

vs vs vs

8.5 8.0 8.0 6.5 6.5 6.3

6-12 6-10 6-10 5-10 3-10 3-10

4.5 4.5 3.7 3.0 1.5 1.5 1.5

3-6 3-6 0-10 0-6 0-3 0-3 0-3

25

20-30

14 11 9 10

12-16 8-12 8-10 8-12

10 8 14

8-12 8 3-30

-

-

"Reproduced with permission from Carrow and Duncan (1998). bVS (very sensitive) < 1.5 d S m-I Ece;MS (moderately sensitive), 1.6-3.0 MT (moderately tolerant), 3.1-6.0 T (tolerant), 6.1-10.0; and VT (very tolerant), > 10.1. "Averages and ranges are based on a composite of all values reported in the literature.

R. R. DUNCAN AND R.N. CARROW

2 54

'hble V Genetic Engineering for Salinity Tolerance Category Trait

Reference

Qm

Monforte et al. (1996) Monforte et al. (1997) Gulick and Dvorak (1992). Dubcovsky er al. (l994), Zhong and Dvorak (1995) Claes et al. (l990), Garcia et al. (1995) Dvorak et al. (1994) Xu et al. (1996) Quintero et al. (1996)

QTL Gene(s)

cDNA

cDNA

Total fruit weight (tomato) Earliness, fruit yield (tomato) Esi (early salt stress induced 2,3,4, 14, 15, 18, 28.32, 35,47,48--Lophopyrurn) sal T (active in leaf sheaths and roots: responsive to salts, PEG, ABA, and dought-rice) Kna I (K+/Na+discrimination and production-wheat) HVAl (LEA) SALl (sulfur assimilation and phosphoinositide signaling pathways; Na+/Li+ fluxes-Arabidopsis) SOSl (high-affinity K+ uptakeltransport systemA rabidopsis) HKTl (K+/Na+selectivity/transport-wheat) HAL1 (osmotic tolerance/K+accumulation- yeast) pA9, pA18, MsPRP2, Alfn I (tissue specific expressionalfalfa) Asrl (tomato) CIC-Nt 1 (voltage-dependent CI- channel: membrane depolarization, nutrient transport, cell turgor regulation) TBP (regulates transcriptional activation events induced by salinity or drought) Encodes osmotin-like protein

DSCLP (salt stress signal transduction, Ca-calmodulin enzyme regulation-Dunaliella) pDZ6.2 PRP (proline-rich protein involved in cell wall modification-saltgrass) Summary of additional salt-tolerance genes Gene expression in response to osmotic stress (cos, 10s. hos)

S . J . Wu er al. (1996) Rubio et al. (1995) Gaxiola er al. (1992) Winicov and Shirzadegan ( 1997) Gilad et al. (1997) Lurin et al. ( 1996) Schaeffer er al. (1995) Loulakakis ( 1997). Medina and Quatrano ( 1996) KO and Lee (1 996) Furniss er al. (1995) Winicov (1994) Zhu et al. (1997)

paired-the roots become brittle and their ability to absorb water and nutrients is diminished. The predominant constraint resulting from increasing soil acidity is a severe chemical imbalance caused by toxic levels of Al, Mn, and H ions coupled with a parallel critical deficiency in available P,K, Ca, Mg, and Mo. Ion toxicities and Ca deficiency are not necessarily mutually exclusive injuries. Excessively high levels of A13+ and Mn2+ may cause toxicity responses by replacing Ca2+ in root cell walls and plasma membranes, thereby causing an adverse reaction. Additional micro- and macronutrient imbalances result, causing impaired physiological/ biochemical/metabolicprocesses that detrimentally affect long-term growth, de-

TURFGRASS MOLECULAR GENETIC IMPROVEMENT

25 5

velopment, and maintenance of turf. Excess Mn inhibits chlorophyll synthesis and decreases photosynthetic rate (Clijsters and Van Assche, 1985). Short-term management strategies to alleviate low pH problems include surface application of lime, but subsoil acidity problems are difficult to alleviate, particularly with perennial turf species. Excess application of acid-forming fertilizers and use of acid irrigation water or acid rainfall will enhance acidity problems. Additional environmental stresses, such as compaction, low organic matter (<5%) soils, high and low temperature extremes, sandy or high bulk density soils, and slopping topography, will escalate the acid soil stress response in turf. Drought significantly enhances acid soil stress due to concentrations of toxic ions and associated root/rhizosphere problems. For recreational/landscapeturf grown in acid soil stress-prone regions with high rainfall (tropical/subtropical ecozones), selection of acid soil-tolerant species and cultivars within species should be the first management strategy. In general, acid soil-tolerance rankings among turf species has been difficult to assess due to significant diversity in response among cultivars within a species. Annual bluegrass (Poa annual L.) and creeping bentgrass (Agrosrispalustris Hud.) growth on acidic soils of variable A1 stress has been compared (Kuo, 1993a,b), with annual bluegrass being more P responsive and creeping bentgrass more Ca responsive. Additional field studies on tall fescue (Miles and Manson, 1995)and Zoysia spp. (Duncan and Shuman, 1993)and greenhouse studies on Kentucky bluegrass (Liu ef al., 1995a) have been conducted. Twenty-four Zoysia japonica Steud. cultivars were compared at pH (water) 14.0 (53-83% Al saturation at 0-30 cm depth) and >5.2 (4-6% Al saturation) with significant differential responses. Greenhouse screening of 58 fine fescues (Liu er al., 1996) revealed intraspecies diversity in A1 tolerance among chewings fescue, strong creeping red fescue, slender creeping red fescue, and sheep fescue cultivars but no diversity among hard fescue cultivars. Hard fescue and chewings fescues were more A1 tolerant than strong creeping red fescue. Fine fescues are more Al tolerant than Poa prafensis, Lolium perenne L., and Festuca arundinacea Schreb. (Liu et al., 1997a).Agrostis tenuis was more A1 tolerant than Agrostis stolonifera (Liu et al., 1997b). Additional comparisons of cultivars within the fine fescues, Bermuda grasses, bent grasses, and Kentucky bluegrasses have been published (H. Liu et al., 1998).

1. Genetic Control The acid soil complex is quantitatively controlled, with A1 toxicity tolerance varying from single to multiple dominant gene control depending on species and with Mn toxicity tolerance being multigenic and additive (Duncan, 1994). Acid soil tolerance generally involves more than one mechanism. For example, plasma membrane/cell wall exclusionary responses via selective permeability/polymer-

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R. R. DUNCAN AND R. N. CARROW

ization, pH barrier (chelating ligands or mucilage) formation at the root-soil interface, internal chelation by organic acids (carboxylic, citric, malic, and transaconitic) or metal-binding proteins, and enzymes and vacuolar comparbnentation of Al or Mn are involved (Duncan, 1994). Specific turfgrass A1 toxicity tolerance mechanistic associations include rhizosphere pH changes, organic acid production, root cation exchange capacity (CEC) changes, nonmetabolic site compartmentation, root phosphatase activity, P-Fe use efficiency, Si concentration, and NH:-N0;-Ca2+-Mg2+ uptake and transport (Duncan, 1994). Manganese toxicity tolerance is linked to root oxidizing capability, silicon suppression of Mn toxicity, Mn absorption/translocationrates, nonmetabolic site entrapment, and high internal tolerance (Duncan, 1994). Chlorophyll content and leaf elongation rate can be used as Mn toxicity selection criteria (Moroni et al., 1991).

2. Biotechnology Several genes encoding organic acid production and chelation of Al in the rhizosphere are available (Table VI). Genes induced by Ca deficiency and/or A1 toxicity (and wounding) that encode various proteins have been cloned. Additional root membrane proteins that bind A1 or enzymes that trigger tolerance responses have been located. Somaclonal variation can also be used to improve A1 toxicity tolerance (Duncan, 1997; Moon et al., 1997; Rao et al., 1992; Van Sint Jan et ai., 1997) and Mn toxicity tolerance (Santandrea et al., 1997). Manganese toxicity tolerance could be engineered by (i) detoxification mechanisms associated with high CEC pectic polysaccharide and glycoprotein fractions in root cell walls (Wang et al., 1992), (ii) deaccleration or blocking of the enzymatic (IAA-oxidase) destruction of auxin (Morgan et al., 1966), or (iii) alteration of isoprenoid pathway biosynthetic reactions (Wilkinson, 1991).

F. ALKALINITIES One of the major constraints on neutral and alkaline, calcareous soils is iron (Fe) deficiency. “Iron-efficient” plants have the ability to respond to Fe-deficiency stress by activating biochemical reactions that release compounds (phytosiderophores) to enhance Fe uptake (Bienfait, 1988). Both inter- and intraspecies diversity exists for Fe-deficiency response among turfgrasses (Clark and Goss, 1986). In general, C, species are more productive under Fe-deficiency stress than C, species. Grasses release Fe-solubilizing substances (biosynthetic chelates or phytosiderophores) that are composed of low-molecular-weight nonproteinaceous amino acids (particularly mugeneic and avenic acids), reimport the phytosiderophores, and release and reduce Fe3+ inside the plant (Romheld, 199 1; Scholz et al., 1992). Mugeneic acid-family phytosiderophores (MAS) are

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Table VI Genetic Engineering of SpecificAcid Soil Tolerance Complex Genes

Gene Citric acidlAlt I locus (wheat) Malic acid (carrot) wali 1-5 (wheat) wali 6-7 (wheat) SaliS-4dSali3-2 (soybean)

pAL.564 (tobacco) ALR (Arabidopsis) Protein 51-kDa (wheat)

Trait

Reference

A1 chelation (roots) A1 chelation (roots) Al toxicitylca deficiencylwound induction (roots) Al toxicity induction (roots) Al tolerance (mot)

Delhaize et al. (1993) Ojima and Ohira (1988) Snowden and Gardner

Early response to Al stress: signal transduction pathway Organic acid release and increased rhizosphere pH

(1993)

Richards et al. (1994) Ragland and Soliman ( 1997) Ezakietal. (1997)

Degenhardt et al. (1998). Larsen et al. (1998)

Microsomal root membrane protein (A1 stress inducible and maintained) Al binding (roots)

A. Basu et al. (1997)

A1 binding (roots)

Campbell and Jackson

TAL- 18 (wheat)

Al binding (roots)

Cruz-Ortega and Ownby

Endo- 1.3-P-glucanase

Catalyzes the hydrolysis of 1.3-P-glucosidic carbohydrate linkages in cell wall wali 1 : Ca deficiencylAl toxicity induction wali 2/7: Al toxicity induction wali 3/5/6: Ca deficiency andlor Al toxicity induction wali 4: Ca deficiencylAl toxicity induction

Cruz-Ortega et al. (1995)

23-kDa exudate polypeptide (wheat) 18.7-kDa (alfalfa)

Basu et al. (1994)

( 1994)

(1993)

Metallothionein-like Unknown function Putative Bowman-Birk proteinase inhibitors Phenylalanine ammonia lyase (PAL) Enzymes NAD+ kinase (wheatlrye) 1-3-P-Glucanase (wheat)

Catalyzes NADP+ production-root Al tolerance Root A1 toxicity tolerance/ callose synthesis

Snowden et al. (1995) Snowden et al. (1995) Snowden et al. (1995) Snowden et al. (1995)

Slaski (1995) Cruz-Ortega et al. (1995)

biosynthesized from nicotianamine in gramineaceous roots (Scholtz et af., 1992; Shojima et af.,1990;Stephan and Scholz, 1993).Iron-efficientcultivars have high substance release that makes Fe readily available, high Fe uptake affinity, highly effective phytosiderophore structures for sequestering Fe, and an affinity for

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R. R. DUNCAN AND R. N. CARROW

ferrated microbial siderophores, whereas Fe-"inefficient" species lack one or more of these traits.

1. Genetic Control Iron efficiency can range from monogenic to polygenic control, depending on species (Duncan, 1994). Both additive and dominant gene actions may be involved.

2. Biotechnology A few genes governing Fe-deficiency responses in grasses have been identified and cloned (Table VII). These genes are involved in the biosynthetic pathways for mugeneic acid and in Fe (111)-MAtransport. Additionally, strategy I (dicots) genes involving ferric-reduction and NADP-oxidationheduction activities have been isolated. One QTL governing Fe efficiency has also been identified. Additional molecular engineering for Fe-deficiency response could involve femoxamine B, rhodotorubic acid, peroxidiase, or 0-phenanthroline (Duncan, 1994). Photochemical capacity (the number of photosyntheticunits, i.e., chlorophyll content per unit leaf) could be another genetic engineering target (Terry, 1979).The MAScan also be produced in plant cell culture (Kawai ef al., 1992).

Table VII Genetic Enhancementof Alkaline Soil-Based Iron-DeficiencyResponse Mechanisms in Grasses (Strategy 11) Gene

Trait

Reference ~~~~

NAATI (barley)

Encodes enzyme involved in biosynthesis of phytosiderophores (nicotianamine aminotransferase) Ids 1 (barley) Regulatory mugeneic acid biosynthesis andlor Fe(III)-mugeneic acid transport in plasmamembrane rbohA (rice) NAD(P)H oxidationlfemc to ferrous Fe reduction Strategy I: Dicots and nongraminaceous monocots (Bienfait, 1988) froh C (Arabidopsis) Root surface iron chelate reductase activity

Kanazawa er al. (1995)

Okumura et al. (1992)

Groom er al. (1996)

Robinson and Groom (1997)

FREl/FRPl (yeast) PAL 201 (tobacco) QTL. (soybean)

Encode ferric-reductase Anionic peroxidase isozymelinduced by Al toxicitylP starvation Fe efficiency

Roman etal. (1993) Ezaki er al. (1996) Diers er al. (1992)

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2 59

G. HIGHSOILSTRENGTH High bulk density (>1 S O g/cm3) soils directly impede root growth, particularly in nonexpanding heavy clay (i.e., kaolinitic) soils. The inability of root systems to penetrate these soils can reduce their stress-resistance capabilities. Plow layers and compaction zones in sandy or loamy soils can create similar problems. Soil compaction and moisture stress are major problems for recreational turfgrass sites involving perennial species. Vehicular and foot traffic increases bulk density, reduces aeration, increases soil strength, and alters pore size distribution (Agnew and Carrow, 1985a; Daniel et al., 1993; Wiecko et al., 1993). The resulting impact on turf performance is negative, with an escalation in multiple stresses and subsequent decreases in quality (Agnew and Carrow, 1985b; Carrow and Petrovic, 1992; Minner et al., 1993; Wiecko et al., 1993). Relative tolerance to compaction among cool-season species may be summarized as follows: perennial ryegrass = Kentucky bluegrass > tall fescue > colonial bentgrass > red fescue (Carrow and Petrovic, 1992). Among warm-season species, buffalo grass has high tolerance, whereas Bermuda and zoysia grass have good tolerance.

1. GeneticdBiotechnology Refer to Section III,B and Table I11 for discussion of genetics and biotechnology. Breeding programs to indirectly select turfgrasses with root systems capable of adaptation to soil physical constraints have been developed (Duncan and Carrow, 1997).

H. Low SOILOXYGEN Oxygen movement through the soil profile is essential to maintaining highquality turf and for turf persistence. Inter- and intraspecies diversity exists relative to tolerance to low soil oxygen among turfgrasses. "bo different forms of stress occur: 1 . Reduced oxygen levels caused by high bulk density soils or compaction layers/zones caused by traffic on the turf: This is usually a secondary component of stress complexes associated with summer decline of bentgrass greens (Carrow, 1996b). Indirect high-temperature stress and carbohydrate depletion are primary components of this system; however, deterioration of roots in the surface zone can cause oxygen stress. 2. Hypoxia caused by waterlogged soils occurs when rainfall exceeds evaporative demand for extended periods, when soil drainage is poor, and when low oxy-

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R.R. DUNCAN AND R. N. CARROW

gen concentration in the rhizosphere alters turf carbohydrate metabolism and respiratory activity (Huang and Johnson, 1995). Grasses vary in tolerance to hypoxic conditions, with Bermuda grass and centipede grass being quite sensitive to this problem whereas seashore paspalum (Paspalum vaginatum) and Paspalum distichum thrive in saltwater and freshwater ecosystems, respectively. 1. Genetic Tolerance

Grass tolerance to hypoxia is apparently associated with low root respiration, oxygen consumption rate, high sugar accumulation (Huang and Johnson, 1995), aerenchyma (air-filled passage cells) formation, and adventitious root development (Huang et al., 1994a; Justin and Armstrong, 1987). Selection for aerenchymous crown roots and genotypes that maintain stornatal openings under hypoxic conditions and resume seminal root growth following termination of hypoxia are additional criteria for low-oxygen genetic enhancement (Huang et al., 1994b). Aerenchyma in roots allow growth in saturated soils, penetration into compacted layers, and better tolerance to both drought and floods (Clark et al., 1998; Comis, 1997).

2. Genetic Engineering Genes regulated by anoxia have been cloned and sequenced in rice (OsMyb7 and OsMyb4) and barley (HvMyb.4)(Menguzzato et al., 1995;Pandolfi etal., 1997; Gubler et al., 1997). Two gibberellin (GA)-induced receptor-like kinase genes (OsTMK from rice and TMKl from Arabidopsis) that signal enhanced growth in submerged internodes (intercalary meristem cell production and elongation) have been identified (Chang et al., 1992; Van der Knaap et al., 1996). Additional loci governing submergence tolerance have been identified (Xuand Mackill, 1996).

I. Low LIGHTINTENSITY Plants are categorized as sun or shade plants based on their adaptability to specific light intensities (Bjorkman, 1968). Light intensity adaptation is a heritable trait (Patterson, 1980). Turfgrass species vary considerably with regard to shade tolerance (Bar et al., 1995; Barrios er al., 1986; Harivandi et al., 1984; Wood, 1969). The most shadetolerant warm-season turfgrass is St. Augustine grass, although <45% light intensity (compared to full sun) caused diversity responses within the species (Peacock and Dudeck, 1993). A relative ranking of turf species based on shade tolerance level and percentage of relative illumination is provided in Table VIII. The C, grasses are more commonly found in shaded forest habitats than are C,

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261

'hble VIM Relative Shade Tolerance of Various lbrfgrass Species

Light intensityldegree of shading Very low intensitylhigh shade

Relative shade tolerance level" (3' % relative illumination) Category V (1-15) V

ws

CSb

Red fescue St. Augustine grass

N (15-25) N IV

ws ws ws

Zoysia grass Seashore paspalurn Kikuyu grass

III (25-50)

cs cs cs cs

Creeping bentgrass Colonial bentgrass Tall fescue Rough bluegrass

D(50-90)

ws ws cs ws

Bahia grass Centipede grass Perennial ryegrass carpet grass

cs cs ws ws

Kentucky bluegrass Weeping alkali grass Hybrid Bermuda grass Common Bermuda grass

m m m n n LI

I(90-loo)

High intensitylvery low shade (full sun)

Turfgrass

I I I

"Modified from Harivandi er al. (1984), Busey and Davis (1991). and Dudeck and Peacock (1992). bCS, cool season; ws, warm season.

grasses (Wilson, 1997). For example, the C, Punicum luxurn is significantly more shade tolerant than other species in the predominantly C, Punicum genus (Wilson, 1997). 1. Reduced-Light Environments

Turf responses to variable light intensity are governed by both quality and quantity of photosynthetically active radiation or photon irradiance (PI). The proportion of direct to diffuse radiation will be critical for selection of improved shadetolerant ecotypes and at least four options are available: tree canopy, shade cloth (artificial), roof structure/proximity to tall buildings or wall structuredspectral color of wall finish or soil, and tropical areas with seasonal monsoonal or prolonged cloudy days. Turf species will perform and persist differently when sub-

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R. R.DUNCAN AND R. N. CARROW

jected to shade evaluations under the four different screening options. For example, Bermuda grass does not normally perform well in monsoonal-proneareas with prolonged cloudy, wet, cooler conditions, but seashore paspalum thrives under reduced light intensity, wetlhumid environments (R. R. Duncan and R. N. Carrow, personal observations). However, paspalum and Bermuda grass have difficulty persisting under full tree shade canopies. Shady areas adjacent to tall walls or buildings that receive no direct sunlight impose multiple stresses, such as cooler soils, reduced or escalated air movement, and often more persistently wetter soils with moss or algae growth. Turfgrasses subjected to these environments must tolerate these stresses as well as adapt to the reduced light intensity (Wilson, 1997).

2. Shade Adaptation Tolerance to low light intensity involves physiological, pathogenesis-related, and climatological responses in turfgrasses (Beard, 1965). In addition to PI reduction problems, ideal microclimates for diseases, competition for nutrients/ water under tree canopies, and succulent growth habits interactively impact turf quality and persistence. Photosynthetic-respiratory balance (Wilkinson er al., 1975), maintenance of carbohydrate reserves (Wilson, 1997), and root plasticity are critical factors in shade-tolerance mechanisms. Photosynthetic adaptation to different light intensities can be influenced by synthesis or breakdown of the carboxylation enzymes (RuDP carboxylase in C, plants and PEP carboxylase in C, plants) and by increased light-use efficiency (Boardman, 1977; Wilson, 1997).

3. Breeding The complex nature of shade tolerance in turf species presents a challenge in developmental programs. Multiple abiotic (drought, cold, and heat stress) and biotic (insect and disease resistance) factors must be included in the selection criteria. Morphological attributes such as increased stem elongation, increased leaf to stem ratio, increased specific leaf weight, decreased leaf thickness/root mass, erect shoots, longer leaf sheaths, and reduced tillering maximize light capture and help in repartitioning carbohydratereserves for enhancing tolerance to reduced light intensity. These morphological shifts are generally contrary to more desirable shortstatured, dense-canopied, and highly tillered turf systems. When selection programs for shade tolerance are being designed, a compromise between these two strategies should probably be used. Traits such as erect stature with mowing heights >50 mm to maximize light capture, intermediate leaf widths with a focus on root system enhancement for abiotidedaphic stresses, and highly efficient crown regions for maintaining carbohydrate reserves should be considered. Selection of parent clones that cope with both full sun and shade conditions should incorporate critical seed production attributes (Wilson, 1997).

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263

The University of Georgia-Griffin tall fescue breeding program (Duncan and Carrow, 1997) has adopted this overall strategy. Parent clones from KY-31 origin were selected from surviving "adapted" plants located on the north side of evergreen Magnolia grand$ora L. trees since these tall fescue plants received no direct sunlight during the year. The clones were screened in space-planted nursery plots with approximately70% light reduction under a mature Quercus spp. canopy. Survivors must polycross and produce seed. Turf plot evaluations from the progeny are currently conducted in shade environments exceeding 70% light reduction and with multiple abiotic/biotic/edaphicstresses. Persistence has been good. Seed production in Oregon has consistently exceeded 2700 kg ha-' in preliminary trials. 4. Biotechnology

Modification of the red (R):far red (FR) ratio to increase light interception (McConnac et al., 199I ) has been attempted (Table IX).Expression of oat transgene(s) has been successful in tomatoes (Boylan and Quail, 1989) and rice transgenes in tobacco (Kay et al., 1989; Keller et al., 1989). Other quantum efficiency modifications could include increased chlorophyll content, decreased chlorophyll a:b ratio, increased photosystem 11: I ratio, changed pigment composition (decreasing blue wavelength and increasing RF receptors),increasing granal stacking (Wilson, 1997), and altering Rubisco activity (Sage and Seemann. 1993). Mutations can be induced that alter metabolic traits governing light perception and chloroplast differentiation (Redei and Koncz, 1992).Analysis of phyB andphyD null mutations in Arabidopsis indicated that these phytochrome genes had highly overlapping functions in controlling R:FR shade-avoidance responses (Goosey er al., 1997). Genetic engineering in flower color traits could provide genes involved in anthocyanin biosynthesis for alteration of pigment composition (Oud et al., 1995). Somaclonal variation offers another strategy for enhancement of shade tolerance in turf species. lsble M

Genetic Engineering for Shade Tolerance Gene phy A (oat) phy A, B, C (Arahidopsis) dfr (maize)

Trait Phytochrome sensation of RlFR Differential regulation of metabolism and development Accumulation of pelqonidinderived pigments

Reference McCormac et al. ( 1991) Sharmck and Quail (1989)

Oud et aL (1995)

2 64

R. R. DUNCAN AND R. N. CARROW

J. Low NUTRIENT AVAILABILITY Genotypic components in plant nutrient efficiency include root system uptake efficiency, transport across membranes and distribution within the plant, and utilization (metabolic and growth efficiency) (Duncan, 1994; Duncan and Baligar, 1990). Nutrient uptake efficiency and nutrient utilization efficiency are governed by different physiological mechanisms. Plants continuously respond to three different levels of nutrient availability: deficiency, tolerance, and toxicity. Genetic control is generally complex (polygenic) for the macronutrients (N-PK) and relatively simple (monogenic in many cases) for micronutrient efficiency factors, depending on species (Graham, 1984). Additional reviews on the effects of genetic factors on plant nutritional requirements are available (Baligar and Duncan, 1990; Clark, 1982; Gabelman and Gerloff, 1983; Vose, 1963, 1982). Soil properties that determine nutrient availability include pH, base saturation, CEC, organic matter content, permeability, sesquioxide content, moisture retention capacity, and physical components (soil strength, oxygen levels, and temperature). Nutrient availability, toxicity or deficiency levels, and genotype interact with climatic and edaphic factors to govern turf quality and persistence.

1. Nitrogen Variables involved in genetically controlled N use efficiency (NUE)include NO; and/or NHZ uptake, translocation of nitrogeneous compounds, reduction of NO;, and assimilation of reduced N into organic compounds. Physiological factors associated with NUE include (i) root proliferation (absorption efficiency, selective ion absorption,and tolerance to NH;); (ii) nitrate uptake efficiency (uptake induction, stimulation/inhibition, and growth-stage partitioning); (iii) N translocation efficiency (nitrate reductase regulation, remobilization efficiency, and protein concentration); (iv) nitrate reductase enzyme efficiency (induction, rate of activity, accumulation, and distribution); and (v) NHZ tolerance (NHZ root assimilation,enzyme metabolism, and K accumulation) (Duncan, 1994). Each stage of N assimilation is under genetic control. Nitrate is reduced to ammonia by two soluble enzymes: nitrate reductase (NR) and nitrite reductase (NiR). These metalloenzymes require cofactors: molybdenum-pterin for NR and Fe-containing hydrochlonn (siroheme) for NiR. The NR enzyme is localized in the cytosol, whereas NiR is found in leaf and root tissue plastids. The molybdenum cofactor may be related to plant stress adaptation mechanisms (soil acidity, salinity, drought, and highllow temperatures) since it is integrated into aldehyde oxidase, which catalyzes the first step in ABA synthesis (Sagi et al., 1997). Molybdenum cofactor NR-regulatory gene mutants (cnx) from tobacco have been used to study genetic control of nitrate assimilation (Pouteau et al., 1989). Genes encoding these enzymes have been cloned (Table X). The genes encoding proteins involved in ni-

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Table X Genes Involved in Nitrate Metabolism Gene

Organism

Qms

Barley

areA

Aspergillus

nit-2

Neurospora

nit-2

Chlamydomonas Chlamydomonas Asperg illus

nirA

Regulatory function N content in straw, harvest index, total yield, straw yield, 8 N in grain NH,+ repression of NR, expression of 100 structural genes in N metabolism NH,+ repression of NR, expression of 100 structural genes in N metabolism Nitrate regulation High-affinity N transport Nitrate reduction

Reference Kjaer and Jensen (1995)

Fu and Marzluf (1990) Kudla et al. ( 1990), Fu and Marzluf (1990) Fernandez et al. (1989) Quesada et al. ( 1994) Crawford and Campbell ( 1990)

nar7

Neurospora Barley Barley

nar2-6, -8

Barley

chl-3 nia-1 chll

Arabidopsis Arabidopsis Arabidopsis

nrtA

Synechococcus

Nitrate reduction Encodes NAPH:NR apoenzyme Encodes NAP(P)H bispecific N R apoenzyme Molybdenum cofactor synthesis or assembly Encodes 95% NR activity Encodes 5% NR activity Encodes protein involved in lowaffinity nitrate uptakeltransport Encodes protein for nitrate transport

CmA rch2

Aspergillus Brassica

Nitrate transport Nitrate transport

nial, nia2 niaD

A rabidopsis Asperg illus

niiA psbA

Aspergillus Cyanothece

pertfnrl

Pea

Cytokinin-induced NR activity Nitrate reductase structural gene, mediates autoregulation Nitrate reductase structural gene Cyclic expression of nitrogenasel oxygenic photosynthesis Root ferrodoxin-NADP oxidoreductase, coinduced with nitrate assimilation enzymes Nonleaf or mot, tissue-specific ferrodoxin-NADPC-oxidoreductase Ferrodoxin, principal redox carrier during photosynthetic growth

nit-4 narl

Fu et al. (1989) Kleinhofs et al. ( 1989) Kleinhofs et al. (1989) Warner and Kleinhofs (1992)

Crawford et al. (1988) Cheng et al. (1988) Huang et al. ( 1996). Tsay et al. (1993) Omata (1991). Omata et al. (1993) Unklesetal. (1991) Muldin and Ingemarsson ( 1995)

FNR

Rice

PetFl

Plectonema

Yu et al. (1998) Johnstone et al. (1990) Johnstone et al. (1990) Adamowicz and Sherman (1996) Bowsher and Knight (1996)

Aoki et al. (1996) Cassing et al. (1995)

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R. R. DUNCAN AND R. N. CARROW

trate uptake and transport have been isolated (Huang et al., 1996;Tsay et al., 1993). Selection for mutants with low nitrate uptake ability has been demonstrated in rice (Hasegawa, 1996) using chlorate resistance as a selective marker for NR deficiency. Nitrate uptake was coupled to H+-ATPase in the plasma membrane, indicating a protonhitrate symport mechanism (Lu and Briskin, 1993). Nitrite reductase genes have been cloned from spinach (Back et al., 1988), rice (Matsui et al., 1990), and maize (Lahners et al., 1988). To date, transgenes controlling NiR from spinach and NR from tobacco have been expressed in Escherichia coli (Crawford and Campbell, 1990). A NiR promoter region has been introduced into tobacco and was expressed in cortex and vascular cylinder cells when NO; was added (Rastogi et al., 1993). Other enzymes involved in N assimilation include glutamine synthetase, glutamate synthase, aspartate amino transferase, asparagine synthetase, glutamate dehydrogenase, glutamate decarboxylase, ferredoxin, methionine sulfoximine, azaserine, and aminooxyacetate (Oaks, 1994a; Sechley et al., 1992). Two glutamine-requiring asparagine synthetase genes have been isolated in pea (Tsai and Coruzzi, 1990). n o glutamine synthetase genes from bean (Forde et al., 1989) and one from soybean (Miao et al., 1991) were expressed differentially in leguminous and nonleguminous plants. In grasses, much of the nitrate reduction occurs in leaves, whereas in nodulated and dinitrogen fixing legumes, this reduction occurs in roots (Oaks, 1994a).Additionally, any genetic alteration of other enzyme systems (e.g., Rubisco) in plants can alter partitioning of carbon and N metabolites (Quick et al., 1991; Sonnewald and Willmitzer, 1992). One example of this is cytosolic pyruvate kinase that generates carbon skeletons required for anabolic processes such as N assimilation (Knowles et al., 1998). Amino acids or peptides circulating in the phloem may control the rate of root nitrate uptake (Imsande and Touraine, 1994; Muller and Touraine, 1992). Intracellular pH control by organic acids and polyamines is another possible mechanism for nitrate uptake regulation (Altman and Levin, 1993; Andersen et al., 1998; Carroll et al., 1994; Imsande and Touraine, 1994).Regulation of the oxidative pentose phosphate pathway can alter nitrite reduction (Kammerer et al., 1998), providing another strategy for enhancing N nutrition. The C, and C, grasses differ in their efficiency of N utilization (Oaks, 1994b), which can complicate transgene expression. Another area of potential enhancement of N nutrition in turfgrasses and stress environmental adaptation involves associative N, fixation by bacteria (Alexander and Zuberer, 1988;Pacovsky, 1990) and mycorrhizal fungi (Clark and Zeto, 1996). Several genes have been isolated and characterized (Bali et al., 1992; Blanco et al., 1993; Hawkins et al., 1991; Kennedy and Dean, 1992; Kennedy et al., 1994; Walmslely et al., 1994), including 16 nifgenes (Dixon et al., 1997). Genes identified for the structural subunits of nitrogenase include nifH, nifD, and nifK; genes required for protochlorophyllide reductase activity include chl L (frx C or bck L), chl N (bch N), and chl B (bch B); and genes for chlorin reductase activity include bch X, bch I: and bch Z (Dixon et al., 1997). A gene regulating the ability of

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267

Azospirillum to flocculate and colonize (flcA) wheat roots has been identified and sequenced (Katupitiya et al., 1995; Pereg et al., 1996). The long-term strategy to improve N fixation and NUE in turfgrasses involves a four-pronged approach (Ladha et al., 1997; Kennedy and Tchan, 1992; Shantharam and Mattoo, 1997; Swensen and Mullin, 1997):

I . Improve the associations between specific turf species and N-fixing soil bacteria and fungi, including colonization enhancement and invasion of turf roots by compatible diazotrophs. 2. Engineer turf plants capable of nodular associations (legume-like symbiosis) through identification of compatible rhizobia and species-specificcultivars and by investigation of turfgrass defense responses that avoid or inhibit symbiosis or the overall N fixation process. 3. Transform turf plants with N, fixation (nz8genes to enhance expression of nitrogenase, to protect nitrogenase from oxygen inactivation, and to ensure an energy supply for N, fixation without compromising turf growth, development, and quality traits. 4. Research the impact of N, fixation on the carbon and energy source-sink budget adjustments in turf plants to better understand overall N metabolism. Genetic engineering of cytokinin levels using the ipt gene could enhance the sourcesink relations (Ladha et al., 1997). 2. Phosphorus Phosphorus efficiency in turfgrasses includes absorption (uptake) efficiency, translocation (partitioning), and internal utilization (redistribution) efficiency. Intraspecies differences in P efficiency among turfgrasses are attributed to root properties/morphology (Johnson et al., 1994); distribution (extensiveness) in the soil profile and hydraulic conductivity (Skinner and Radin, 1994); size, length, proliferation, type, and mycorrhizal associations; P uptake/translocation/accumulation (compartmentation)-metabolic use interactions (Clark, 1990;Clark and Duncan, 1991 ); carbohydrate load to provide energy for maintenance of membrane systems (Duncan, 1994); and the mesophyll resistance, CO, exchange rate, sucrose, and P-synthase complex (Qiu and Israel, 1992). Turfgrasses vary inter- and intraspecifically in P absorption (Liu et al., 1995b). Mechanisms associated with mycorrhizal-enhancedP uptake include extension/increase of root absorption surface areas, reduction of P diffusion distance, chemical modification of P sources that increases P uptakehransfer, and modification of root properties (Clark, 1997). The P content in the plant and/or soil may govern mycorrhizal responses in plants (Menge et al., 1978). Phosphate acquisition in plants involves an energy-mediated,proteon gradientgenerated cotransport process by plasma membrane H+-ATPases (Epstein, 1976). A high-affinity transport system operates at low concentrations and is regulated by

268

R. R. DUNCAN AND R. N. CARROW

P availability, whereas the low-affinity transport system operates at higher concentrations and is expressed constitutively. Enhanced uptake apparently occurs in response to the increased synthesis of a carrier system as a result of inorganic phosphate starvation (C. Liu ef al., 1998).

a. Breeding Phosphate uptake is multigenic; P use efficiency is multigenic with additive, dominance, and epistatic effects; and P assimilation may involve a single, partially dominant gene (Duncan, 1994). In some grass species, tolerance to P deficiency may involve multiple recessive genes, but both additive and dominant gene effects have been involved in inheritance (Chaubey et al., 1994). b. Genetic Engineering The QTLs governing yield performance (maize) under low P input (Reiter et al., 1991) and P concentration in barley strawlgrain (Kjaer and Jensen, 1995) have been identified. Additional genes governing phosphate transport and sucrose phosphate synthase have been cloned (Table XI). Additional strategies to enhance P uptake and use efficiency could involve proteoid root (cluster root) morphology and citric acid production (Johnson et al., 1994),total chlorophyll concentration (Lopez-Cantarero ef al., 1994),phosphorylase stimulation to degrade starch (Qiu and Israel, 1992), and partitioning of C between glycolytic and pentose phosphate biosynthetic pathways (Blakeley and Dennis, 1993). Genes encoding pyruvate kinase in potato (Cole et al., 1992) and castor bean (Blakeley et al., 1991) have been identified, but transgenic expression has been difficult (Gottlob-Mchugh ef al., 1992) when trying to modify flux through the glycolytic pathway. Genes encoding the a (regulatory) and p (catalytic) subunits of pyrophosphate-dependent phosphofructokinase (Blakeley et al., 1992; Carlisle ef al., 1990)that are involved in the interconversion of fructose6-phosphate to fructose- 1,6-biphosphate have been isolated and sequenced. Expression of an E. coli pyrophophatase in plants altered photosynthate partitioning (Sonnewald, 1992).Additional genes encoding glycolytic enzymes have been isolated, but transgenic expression has been difficult: 1. Aldose (Chopra ef al., 1990; Kelley and Tolan, 1986) 2. Glyceraldehyde-3-phosphate dehydrogenase (Brinkman et al., 1989; Martin and Cerff, 1986; Shih et al., 1991) 3. Phosphoglyceratekinase (Longstaff ef al., 1989) 4. Phosphoglyceromutase(Botha and Dennis, 1986; Grana et al., 1992)

Genes controlling other enzymes have been successfully expressed in transgenic plants: 1. Sucrose phosphate synthase (Worrell ef al., 1991)-regulation of sucrose synthesis

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269

'Igble XI Genetic Engineering for Phosphorus Efficiency Gene

Organism

QTL QTL AtPTl AtPZ' AtPT4 cDNA

Maize Barley Arabidopsis Arabidopsis Arabidopsis Soybean

kPTIR

Tomato

PH084

Sacchuromyces

pho-5+ APTI/2 LePTI/2 Pit STPTlR phoBR

Neumspora Arabidopsis Tomato Cathuranrhus roseus Potato Synechococcus

s

synechococcus

Ptr A

Synechococcus

ORFl-7 psr3. I

Synechococcus Brassica

pTPSIl

Tomato

PSI

Regulatory function Yield at low P

P concentration in strawlgrain High-affinity phosphate transporter Phosphate transporter Phosphate transporter Root sucrose synthase (regulation of sucrose synthesis) High-affinity inorganic phosphate transporters, root expressed, induced by P starvation High-affinity inorganic phosphate transporter High-affinityphosphate transport Phosphate transporters Phosphate transporters Phosphate transporter Phosphate transporters Response regulator (B) and histidine protein kinase (R) regulation of P S I S Periplasmic phosphate-binding protein, induced by phosphate depletion Putative transcriptional activator in Psi S regulation Phosphate transportlbinding P-Glucosidase induced by phosphate starvation, sugar metabolism function Induced by phosphate starvation

Reference Reiter er al. (1991) Kjaer and Jensen (1 995) Muchhal et al. (1996) Mukatira et al. (1997) Lu et al. ( 1997) Zhang et al. (1997) C. Liu et al. (1998)

Bun-ya et al. (1991) Versaw (1995) Smith et al. (1997) Mukatira et al. (1996) Kai et al. ( 1997) Leggewie et al. (1997) Watson et al. ( 1996)

Scanlan et al. ( 1993)

Scanlan et al. (1996) Scanlan and Mann (1996) Malboobi et al. (1996)

Liu and Raghothama ( 1995)

2. ADP-glucose pyrophosphorylase (Stark et al., 1992)-starch biosynthesis 3. Invertase (Sonnewald et al., 1991; Von Schaewan et al., 1990)-enhanced phloem unloading of photoassimilate/source-sink regulation A phosphate translocator has also been cloned and sequenced (Emes and Traska, 1987; Willey et al., 1991). Three classes of phosphate translocator genes (triose phosphate/phosphate translocator, phosphoenolpyruvate/phosphatetranslocator, and glucose 6-phosphate/phosphate translocator) show differential expression patterns (Kammerer et al., 1998) with partially overlapping substrate specificities.

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3. Potassium Potassium is an essential nutrient in turfgrasses that functions physiologically in enzyme regulation; osmoregulationand movement; carbohydrate, nucleic acid, and protein formation; photosynthesis; enhancement of rooting/early establishment; heat/cold/drought tolerance; wear resistance; and maintenance of turf quality (Turner and Hummel, 1992). Potassium efficiency in turfgrasses is linked to root growth/morphology,uptake (influx) efficiency, efflux, translocation, and utilization efficiency (Pettersson and Jensen, 1983).Gene-controlled K transport carrier synthesis has been responsible for variation in maximum K uptake efficiencies among turf species (Jensen and Pettersson, 1978). Potassium uptake by turfgrass roots may be mediated by two mechanisms: high-affinity and low-affinity transport systems (Maathuis and Sanders, 1996).High-affinity K uptake mechanisms include K+ pumps, K+-H+ antiporters, and K+-H+symporters, whereas low-affinitymechanisms involve inward-rectifying (transport) Kf channels and provide regulation of membrane potential (Gassmann and Schroeder, 1994). Xylem loading of K was regulated separately from K uptake, and adaptation of K translocation to shoot demand is coupled with an altered capacity in the root for xylem loading (Engles and Marschner, 1992). The xylem parenchyma cells surrounding the xylem vessels are important in salt transport, long-distance signaling, and ascent of the transpiration stream (Wegner and De Boer, 1997).Two types of outward-rectifying channels have been identified: K+ outward rectifying conductance (KORC), which mediates K+ efflux from xylem parenchyma cells into the xylem vessels and functions at low Ca2+ levels, and nonselectiveoutward-rectifying conductance (NORC), which operates at high Ca2+ levels. A Ca2+-dependent kinase or phosphatase regulates the activity of KORC and NORC channels (Wegner and De Boer, 1997). The existence of multiple K+ channel genes, their tissue (roots and leaves) specificity, and their expression-dependentmodulation contribute to the functional diversity of K+ channels (Cao etal., 1995).The expression level can affect electrophysiological properties of the channel, thereby providing an additional strategy for diversifying plant K+ channel function. a. Breeding Genetic control of “K efficiency” for individual mechanisms ranges from multigenic with dominance, additivity, and epistasis involved in the inheritance to single recessive genes (Duncan, 1994). b. Genetic Engineering Several genes have been cloned from bacteria, fungi, yeast, and higher plants (Table XII). Both high-affinity and low-affinity genes are available and transgenic expression has been demonstrated (Lagarde et al., 1996; Sentenac et al., 1992).

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Table XI1 Genetic Engineering for Potassium Efficiency Gene

Organism

Regulatory function

EcKup

Escherichia coli

K+ transporter

AtKup l/2/3

A ra bidopsis

So HAKI

Schwanniomyces occidentalis Sacchammyces cerevisiae Sacchammyces uvarum Schizosacchammyces pombe Barley

High-affinity K uptake/ transport High-affinity K + transporter High-affinity K+ transporter High-affinity K+

Sc TRKI/TRK2

SuTRK Sp TRK

H v HAKI/ Hv HAK2 Kna I

Wheat

HKTl

Wheat

AKTl

Arabidopsis

AKR

Arabidopsis

KCOl

-

KATl

Arabidopsis

KSTl

Potato

SOS3

Arabidopsis

HvCBTl

Barley

SKTl

Potato

High-affinity K+ transporter High-affinity K+ uptake, low-affinity Na+ uptake K+/Na+ discrimination High-affinity K+/Na+ symporter, root expressed Low-affinity inwardrectifying K+ channel, expressed in roots Leaf-specific expression of K+ channel Low affinity outwardrectifying K+ channel Inward-rectifying K+ channel Low-affinity inwardrectifying K+ channel K+ nutrition, K+/Na+ selectivity, salt tolerance, Ca2+ regulation K+ influxlaccumulation/ transport, Ca2+ signaling K+ inward-rectifying channel protein

Reference Schleyer and Bakker ( 1993) Kim et al. ( 1998). Fu and Luan (1998) Banuelos et al. (1995) Gaber et al. (1988). KO and Gaber (1991) Anderson et al. (1991) Soldatenkov et al. (1995) Santa-Maria et al. (1997) Dvorak et al. (1994). Dubcovsky et al. (1996) Rubio et al. (1993, Schachtman and Schroeder (1994) Sentenac et al. (1992), Lagarde et al. (1996). Gaymard et al. (1996) Cao et al. (1995) Czempinski et al. ( 1997) Schachtman et al. (1992) Muller-Rober et al. (1995)

Liu and Zhu (1997)

Schuurink et al. (1998) Zimmerman et al. (1998)

Functional expression of AtKATl in Saccharomyces has been demonstrated (Anderson et al., 1992). Calcium trigger protein signal transduction and plasma membrane-localized ion transport alteration offer additional genetic engineering strategies (Schuurink et al., 1998).

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R. R. DUNCAN AND R. N. W R O W

K. MULTIPLESTRESSINTERACTIONS Acascade of events occur in turfgrass plants when environmental stresses are imposed. A series of genetic, physiological, and biochemical events are activated, beginning with stress signal perception (via an His osmosensor) and continuing through signal transduction (via Ca2+, ABA, or protein kinase-phosphorylation processes) and initial gene expression. The gene products that result in response to stress induction can be categorized as functional proteins involved in cellular adaptation and specific stress tolerance and as regulatory proteins with gene expression and signal transduction roles (Shinozaki and Yamaguchi-Shinozaki, 1997).An example of the drought/salt/coldtemperature stress response complex is presented in Fig. 2. Dehydration or osmotic stress activates the sensing mechanisms, triggers the production of ABA or the Ca2+/proteinkinase signal transduction pathways, modulates the stress signals to cellular signals, transduces the cellular signals to the nucleus, causes transcriptional control of stress-inducible genes followed by gene expression and biochemical/physiologicalresponses. Phosphoinositide signaling may mobilize Ca2+ in the cytoplasm. At least four signal transduction pathways may be operating in response to water stress (Shinozaki and Yamaguchi-Shinozaki, 1997). At least four different transcription factors may function in the regulation of dehydration-inducible genes (two are ABA responsive and two are ABA independent). Many genes encode factors involved in the signal transduction cascades and these signaling factors either amplify stress signals or function in adaptation of plant cells to osmotic stress. A two-component osmosensor system involves EnzV (a His kinase) that monitors plasma membrane mechanical changes, Sholp (another transmembrane osmosensor), ETRl (ethylene signal transduction receptor), PBS2/ HOG 1 (activates glycerol biosynthesis genes for osmoprotection), and/or SlnlpYpdlp-Ssklp (sensor proteins involved in the early phase hyperosmolarity stress response). Other cellular triggers related to osmotic stress include detoxification enzymes (ascorbateperoxidase, superoxidedismutase, glutathione-S-transferases,and epoxide hydrolase), “touch” genes (calmodulins, Ca2+-bindingproteins, xyloglucan, endotransglycosylase,and protein kinases), and oxidative burst/physical tension of membrane cytoskeletons (Shinozaki and Yamaguchi-Shinozaki, 1997). The exposure of turf plants to environmental stresses alters the accumulation of mRNAs of stress-induced genes (Dubcovsky et al., 1995). Linkage relationships among drought/salt/heat shock-responsivegenes in wheat revealed a tightly conserved distribution of these stress-related genes on one chromosome. The QTL analysis in conjunction with bulk segregant populations can be used to assess allelic variation in stress-induced genes (Prioul er al., 1997). The synergistic genetic relationships of multiple stresses are well documented: high temperature-salinity (Kuznetsov and Shevyakova, 1997), hyperthermiaheavy metal toxicity (Wollgiehn and Neumann, 1995), aluminum toxicity-oxidative stress (Richards et al., 1998),the polyamine link to oxidant stress-acidity-0s-

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273

'I

WATE STRESS

S I G N 7 PER!XPTION SIGNAL r T C T I O N

I

GENE PRODUCTS

-7

FUNCTIONAL PROTEINS

REGULATORYPROTEINS

Water channel proteins

Transcription fktom MYB. MYC. Bzip

Key enzymcs for osmolyte biosyathesis (proline. bctaine, sugars)

I

RoteinLinases mMAspKIM,

S6Y CDPK SNFL

Chaperones

Phospholipasc C LEA proteins

14-3-3 prolcin

Roteinascs

I

tktoxlcatmgenzymcs

I

1 I

I

STRESS TOLERANCE :STRESS RESPONSE

Figure 2 Gene and gene product induction governing droughtlsaltlcold stress tolerance and plant stress response (reproduced with permission from Shinozaki and Yamaguchi-Shinozaki, 1997).

motic stress (Ye et al., 1997), polyamines-nitrogen metabolism (Altman and Levin, 1993), proline link to heat/cold/salt stress (Iyer and Caplan, 1998), D-ononitol link to salt-drought stress (Sheveleva e? al., 1997), UV radiation-heat stress (Jenkins et al., 1997), dehydrin link to cold-salt stress (Close, 1997; Xu et al., 1996), cold-salinity-anoxia (Gong et al., 1998), drought-low temperature-salt stress (Yamaguchi-Shinozakiand Shinozaki, 1994), as well as other associations.

Extensive similarity among genomes of related Poaceae in the Graminease family has revealed (i) a similarity in the conservation of gene content and gene order

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R. R. DUNCAN AND R. N. CARROW

(constitution of the chromosomes) or synteny and possible linkage relationships (Dubcovsky et al., 1995) and (ii) a similarity in the linear arrangement within these chromosomes or collinearity (Avramova et al., 1998; Bennetzen and Freeling, 1997; Glaszmann et al., 1997). Genome size differences among species are attributable to amounts of repetitive DNA (Flavell et al., 1974). Retrotransposons inserted between genes account for a majority of the repetitive DNA in some large genome grasses (San Miguel et al., 1996). Microsynteny can be used as a vehicle for the map-based identification, isolation, and cloning of genes involved in complex (abiotidedaphic) physiological processes (Bennetzen, 1996; Bennetzen and Freeling, 1993; Devos et al., 1995). Comparative mapping of stress-tolerance traits across grass species can be accomplished even for species lacking detailed genetic maps (Bennetzen and Freeling, 1997). Information gained from the study of a specific candidate gene in one species can be applied to the study of the same gene in other species (panspecies use of information and genes) and can be applied to both simple and quantitative traits such as drought, salinity, acidity, or other environmental stresses. The synteny of grass genomes will allow synergistic reengineering and enhancement of species with common gene content and physiological/biochemical mechanisms/processes (Bennetzen and Freeling, 1997). In the turfgrass arena, comparative genetics/mapping efforts have been initiated in Loliurn (Barker and Warnke, 1998) and Cynodon (A. H. Paterson and W. W. Hanna, personal communication). As more genes are isolated and their functions are determined, genomic technology, such as specialized gene chips and DNA microarrays, will provide the capability for simultaneous monitoring of thousands of genes and genetic diversity assessment (Lipshutz et al., 1995; Schena et al., 1995). Additional references on this topic include Moore et al. (1995) and Paterson et al. (1 995).

M. ENDOPHYTEENHANCEMENT A grass-fungus symbiotic association or defensive mutualism (Clay, 1988) can enhance abiotic stress tolerance in cool-season grasses (Bacon, 1993; Cheplick, 1997; Schardl and Phillips, 1997). Endophyte-infected grasses acclimate to severe environmental stress conditions quickly, are more aggressive, recover from stress rapidly, and resist biotic constraints better than noninfected grasses (Richardson et al., 1998). Endophyte-mediated enhancement of persistence under drought has been demonstrated in tall fescue and perennial ryegrass (Elbersen and West, 1996; Read and Camp, 1986; Funk et al., 1994; Arechavaleta et al., 1989), as has tolerance to soil acidity in tall fescue (Belesky and Fedders, 1995) and enhanced Puptake (Malinowski et al., 1998), Al toxicity tolerance in fine fescues (Liu et al., 1996), and efficient response to low soil N under deficiency conditions in tall fescue (Lyons et al., 1990;Arechavaleta et al., 1989) due to enhanced glutamine syn-

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275

thase activity (the primary enzyme involved in NH; assimilation). Endophyte stimulation of osmotic adjustment under heat, cold, and salt-stressed conditions has not been verified but may be possible (Bacon, 1993; Bacon et al., 1997). Genetic engineering will involve improved endophyte-surrogate host fitness attributes and identification of desirable enhancement genes contributed by the fungus that will prove beneficial in abiotidedaphic stress responses (Bacon et al., 1997; Richardson et al., 1998).

III. ENHANCEMENT STRATEGY FOR MULTIPLE-STRESS RESISTANCE A. COMPONENTS Utilization of biotechnology to improve crop performancecan be successful, ineffective, or inefficient. A review of tissue culture-induced variation for crop improvement during the past 20 years was summarized by the following statement: “For the major worldwide effort incorporating in vitro technology in breeding research programs since the 1970s, relatively few agronomically useful cultivars or germplasm sources have been released for utilization in production programs” (Duncan, 1997, p. 217). Gene technology (or any other tool of biotechnology) must be carefully integrated into a strategic framework that encompasses several essential components to contribute significant progress toward developmentof more stress-resistantturfgrasses. Biotechnological approaches applied to turfgrasses (Sticklen and Kenna, 1998) have implemented gene technology minimally to improve specific climatic, biotic, edaphic. or use-related stresses. The first component of a strategic framework is to identify and understand the primary stresses limiting turfgrass performance for regional utilization of grasses. This aspect appears to be simple but the recognition of primary versus secondary stresses can be easily misunderstood, as will be shown in the following section. If biotechnology methods (or traditional breeding and genetics methods) are not focused on high-priority primary stresses that limit field performance, then all other components that follow will be misdirected. Second, understanding and utilizing existing genetic-based stress-resistance mechanisms within a species for the stresses targeted in enhancement program are essential components. This requires identification of the most stress-resistant ecotypes within a species in order to take advantage of the genetic diversity already inherent within a species. Additionally, elicitation of the specific mechanism(s) in the most stress-resistant ecotypes is essential to account for the level of resistance. The fact that stress resistance varies within ecotypes of a species in-

276

R.R.DUNCAN AND R. N. CARROW

dicates that different mechanisms or degrees of expression in each mechanism are functioning. Specific resistance mechanisms must be associated with adaptive genes. Once the gene-mechanism relationship is established within a species, this can be used to identify ecotypes with this resistance mechanism. Gene expression of the mechanism often requires that the abiotidedaphic stresses be present. Also, once a specific stress mechanism is clarified, a search of the scientific literature may reveal that the associated genes have been identified in other plant species-one of the objectives of this review article is to assess the current status of gene identification for various abiotic and edaphic stresses. Thus, this component of an overall strategic framework aids in focusing biotechnology methods and manipulaton on high-priority genes that are responsible for the specific mechanism@)that causes a high level of stress resistance. A third component is to integrate laboratory-generated biotechnology stressenhanced germplasm back into the traditional breeding and genetics programs for turfgrass improvement. This includes evaluation of germplasm performance under severely stressed and nonstressed field conditions. The following are reasons for using this approach: (i) Unless the “enhanced germplasm” is placed under the stress in question, success cannot be determined; (ii) germplasm that has been enhanced for a specific stress mechanism must still perform under multiple stresses; and (iii) the germplasm can be incorporated into other germplasm by conventional breeding to enhance a broader germplasm pool within a species. Therefore, this third component integrates biotechnology methods with traditional breeding and genetic protocols for the purpose of maximizing the efficiency and magnitude of progress toward greater turfgrass stress performance.

B. ABIOTIC/EDAPHIC STRESSAND

THE RHIZOSPHERE

The common abiotic and edaphic stresses that affect turfgrass stress response plasticity and therefore persistence include Moisture deficiency/excess problems Extreme soil/air temperatures Salinities/poor water quality Acidity/alkalinity High soil strength/traffic/compaction Low soil oxygen Reduced light intensity/shade Low nutrient availability Many of these constraints interact to create significantgenotype-environment interactions and directly influence turf quality and performance. Turfgrass stress re-

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277

sistance can involve several mechanismsthat may be exhibited at the whole plant to subcellular level. Each mechanism is genetic based. Most turfgrasses are grown in multiple-stress (drought-high temperature and soil acidity-drought) environments. The common plant feature that links all these constraints together is the root system. The first line of defense in adaptation to abiotic and edaphic stresses is the root system, which provides essential nutrients and water (i.e., drought resistance) for critical turfgrass functions. Unfortunately, most turf breeding programs do not address root plasticity (functional root volume and viability under cyclic stresses) either directly or indirectly. Root system improvement should be the first step in a comprehensive abiotic/edaphic stress-resistancebreeding program because this strategy addresses primary components of stress response that directly in fluence the turf plant capability to acquire essential nutrients and water and to ultimately persist. Many diverse field situations limit turfgrass rooting, but only six primary soil chemical and physical constraints account for restricted rooting in turfgrasses in these field stress situations (Table XIII). These primary stresses can be incorporated into breeding programs as either single- or multiple-stress screening protocols (Carrow and Duncan, 1996; Duncan and Carrow, 1997; Maranville, 1993). Gene technology can be integrated with this traditional breeding strategy to enhance genetic-based root plasticity; discern multiple stress tolerance mechanisms; locate, sequence,clone, and map stress-responsivegenes; and utilize marker-assisted selection techniques.

1. Genetic Potential for Rooting Turf species vary in their genetic potential for rooting depth. Table XIV compares several cool- and warm-season grasses for general root depth potential. Genotypes within a species can also exhibit inherent differences in rooting depth under nonlimiting soil conditions (Lehman and Engelke, 1991), but rooting depth potential is only one component of the overall rhizosphere stress adaptation response mechanism because multiple abiotic and edaphic stresses often limit maximum rooting depth. While root morphology is governed by genetics (Aeschbacher et al., 1994), developmental plasticity in response to environmental stimuli (light, nutrients, temperature, aeration, water, physical barriers, microorganisms, gravity, competition from adjacent roots, and chemical barriers) (Schiefelbein and Benfey, 1991) will ultimately determine the final configuration of the root system (Fitter and Stickland, 1992;Lynch, 1995; Schiefelbein et al., 1997).Developmental alterations occur in the form of changes in the direction of growth after perception of an external signal, transduction of the signal, alteration in gene regulation and protein activity, and modification of cell division-expansion-differentiation (Aeschbacher et al., 1994). Turf species differ in their capacity for enhanced root growth and rapid root water uptake at deeper soil layers, maintenance of root viability at the

R. R.DUNCAN AND R. N. CARROW

278

Table XIII Six Primary Soil Physical and Chemical Constraintsof Rooting, Associated Field Situations in Which the StressesAre Expressed, and Relative Importance of Stress on 'hrfgrasses Relative importance of stress Root stress

Associated field problems

Fine-textured soil Compaction Layers with few macropores Sodic soil Soil drought Low soil 0, Fine-textured soils Compaction High water table Poor surface drainage Layer impeding percolation Sodic soil Acid soil root toxicities (T)/ Acid soil complex (T, D) deficiencies (D) AllMnlH toxicities Nutrient deficiencies (Ca, Mg, K) Usually low organic matter Usually high soil strength Moderately acid soil (D) Acid sulfate soil (T.D) Acid mine spoils (T,D) Sodic soil (T, D) Salt root toxicities/ Na, CI, B, OH toxicities deficiencies K, Mg, Ca deficiencies High soil strength Low soil 0, Saline soil (T, D) Saline-sodic soil (T, D) Soil drying Desiccation Direct high-temperature root injury High soil temperature Indirect high-temperatureb stress limits root development, maintenance, viability

High soil strength

Warm season

Cool season

xxxxx

XXxxx"

xx

xxxx

xxxx

xxxx

xxxx

xxxx

xxx

XxXxX

X

xxxxx

"High organic matter alleviates the Al toxicity factor. "Indirect high-temperature stress is the major factor limiting cool-season grass adaptation into warmer temperature climatic zones because it determines carbohydrate status for maintaining root viability. It becomes a site-specific problem when site conditions inhibit canopy cooling. High root temperatures enhance indirect high-temperature stress just as high aerial temperatures will do so. "The more Xs, the greater the importance.

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2 79

lsble XIV Genetic Potential for Rooting Depth among lbrfgrasses" Q p e of grass Root depth

Shallow

Cool season Creeping bentgrass Kentucky bluegrass Perennial ryegrass Tall fescue

Warm season Buffalo grass Meyer zoysia, Common centipede Argentine Bahia grass Seashore paspalurn (Adalayd) Emerald zoysia Tifway Bermuda Common St. Augustine Texturf 10 Bermuda

"Reproduced with permission from C m o w (1989).

surface drying layer, and rapid root regeneration after rewatering under drought conditions (Huang et af., 1997). Roots vary morphologically and physiologically in response to variable soil nutrient distributions (Robinson, 1996) and to mechanical impedants such as high soil bulk densities or compacted layers (Bengough and Young, 1993; Carrow and Petrovic, 1992; Materechera et af., 1992; Wiecko et al., 1993). Heritability estimates are quite variable, depending on growing conditions during evaluation and species differences (Browning er af., 1994; Lehman and Engelke, 1991).

2. Biotechnology Many of the edaphic and abiotic stress-responsive genes involve the root system. QTLs linked to root morphological characters (Champoux et af., 1995) and root penetration ability into compacted soils (Ray et af., 1996) have been identified, with potential application in turfgrass transformation studies (Table XV). Increased root density and depth provide an avoidance mechanism in response to abiotic stress, particularly drought (O'Toole and De Datta, 1986).Compacted soil layers can impede depth of rooting and negatively enhance the overall stress response in turf. Root penetration ability varies both interspecifically (Assaeed ef al., 1990; Materechera et af., 1992) and intraspecifically (Kasperbauer and Busscher, 199I ; Masle, 1992) in plants. Screening systems are available to effectively measure root penetration variability among genotypes (Huang et al., 1997; Yu ef aZ., 1995) and select in the field for root plasticity under stress (Duncan and Carrow, 1997; Erb, 1993; Montpetit and Coulman, 1991).Transgenes governing root gen-

280

R. R. DUNCAN AND R. N. CARROW Table XV QTLs L i e d to Root System Enhancement in Stressed Environments Root trait

Reference

Root thickness, rootshoot ratio, root dry weight per tiller, deep root dry weight per tiller, maximum root length Root penetration ability into compacted soil layers Other root-inducing genes (plasmid root-inducing) rol (root loci: A, B, C, D) am (auxin synthetic: 1,2)

Champoux ef al. (1995)

Ray et al. ( 1996) Chriqui er al. (1996)

eration and growth have been expressed in plants (Chriqui et al., 1996). Several RFLP probes are available to screen for root elongation growth and drought tolerance (Price and Tomos, 1994).Alteration of leaf cytosolic pyruvate kinase can affect source-sink relationships as well as root biomass (Knowles et al., 1998).

C. ROLEOF TURFGRASS MANAGEMENT While various abiotic and edaphic stresses and their related mechanisms are genetically controlled, long-term management strategies and variable climatic/ growth conditions will govern turfgrass quality, performance, and persistence. Most management practices are conducted to alleviate or prevent specific stresses or constraints. Because of the three-way interactions between turf species and cultivar, specific multiple stresses, and the environment, management strategies must be adjusted to site-specific situations. Managing the turfgrass root system for maximum development (depth, volume, and plasticity) and viability/functionality is the key to maintaining high-quality turf in stress environments.

1. Root Management Root systems in perennial turfgrasses are dynamic or ever-changing (Fig. 3). Seasonal weather patterns govern growth cycles and affect root topology (branching capacity), root distribution (total biomass, which includes root length and depth of penetration into the soil), and functionality of roots (root dieback). Most turf roots survive from 6 months to 2 years, depending on species, management conditions, and environmental constraints (Carrow, 1989). Duration of exposure and severity of a stress or multiple stresses have profound influences on turfgrass persistence mainly because carbohydrates produced in green shoot tissues by pho-

TURFGRASS MOLECULAR GENETIC IMPROVEMENT

-

28 1

....._....Wpm%8#Oll

Figure 3 Seasonal root growth rates of turfgrasses.

tosynthesis are usually utilized first for shoot growth and maintenance and secondarily for root growth and maintenance. Severe environmental stress will create unbalanced carbohydrate demands in turf plants that can enhance root mortality or decrease root functionality and ultimately diminish turf quality. Proper root management to minimize stress is essential to turf quality longevity (Carrow, 1989, 1995a):

1. Select species and cultivars within species with the best root plasticity capability. 2. Promote maximum net carbohydrate production by a. Optimizing leaf area, which ensures maximum photosynthesis, by increasing mowing height, decreasing wear damage, and controlling biotic constraints. b. Optimizing leaf chlorophyll content by avoiding (i) Fe, Mn, Mg, S, and M deficiencies, (ii) low soil oxygen or waterlogged conditions, and (iii) prolonged water-deficit conditions. c. Promoting good light capture conditions by (i) pruning trees and removing excess grass clippings and (ii) selecting appropriate cultivars. 3. Avoid depletion of carbohydrate reserves in the crown region by minimizing excessive and frequent N applications (especially fast-release N sources), overwatering, and close mowing. Modify high soil temperatures that contribute to the depletion of carbohydrates with imgation, drainage, cultivation, or by increasing mowing height.

2 82

R. R. DUNCAN AND R. N. CARROW

4. Correct soil physical problems as follows: Correct high soil strength (i.e., high bulk density and heavy clay soils) and low soil oxygen with cultivation (aeration) and additions of peat or gypsum; excessively dry soils with irrigation and additions of organic matter to increase water-holding capacity; low soil oxygen with cultivation and surface/subsurface drainage; soil layering with cultivation; and cold soils in the spring with cultivation and proper drainage. 5. Correct poor soil chemical conditions as follows: Correct acid/high Al soils with lime; very alkaline soils with S, H,SO,, or acidic N carriers; infertile soils with fertilizers or microbial amendments; and salt-affected soils with cultivation, gypsum, or sulfur amendments, drainage, and use of alternative water sources. Avoid toxins by limiting excessive use of herbicides or other chemicals, limiting heavy metal-containing soil amendments, and judicious application of macro- and micronutrients. 6. Correct soil biotic problems as follows: Correct root-feeding insects, diseases, and nematodes with preventive, cultural, or chemical control treatments; thatch by mechanical removal, cultivation, and promotion of microbial degradation.

Iv. SUMMARY Perennial grasses will always be subjected to fluctuating multiple stresses. Traditional breeding programs can address specific environmental constraints and, as mechanisms governing stress response become better understood, these programs can focus on specific components of these mechanisms. Gene technology provides an enhancement strategy for these traditional breeding approaches. An increasing number of genes are being identified, sequenced, and cloned. Transformation and regeneration technology is available for implementation into turfgrass stressresistance programs. With the release of new “biotech” turf cultivars in the twenty-first century, management strategies will have to be adjusted to maximize performance and persistence. Enhanced abiotidedaphic stress tolerance in turf will provide 1. Improvements in performance under environmental extremes 2. Functional root systems that perform equally well in stressed and nonstressed environments 3. Improved water use efficiency 4. Improved nutrient uptake/utilization efficiency 5. Better adapted cultivars for niche environments 6. More high-quality and environmentally compatible turfgrasses under abiotic/edaphic stressed conditions

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A Accuracy in assessing variability, 18 concepts of, 4 in management maps, 21 Acetic acid, allelopathy and, 181 Acetophenone, 177 Acid soil stress, see Soil acidity stress Actinomycetes,see Soil fungi ADP-glucose pyrophosphorylase,269 Adsorption, see also Nonspecific adsorption; Phosphate adsorption; Specific adsorption; Sulfate adsorption allelopathic compounds and, 176 effects of pH on, 1 16-1 I8 effects of soil liming on, 127-129 induced, 122-124, 122-125 point of zero salt effect and, 107 soil solution anion competition and, 115-1 16 soil solution ionic strength and, 112, 114-115 solute-surface charge interactions and, 120-1 22 Aerenchyma, 260 Agriculture, see also Precision agriculture computer utilization and, 8-9 Agropyrene, 184 Agrosris palusfris, acid soil tolerance, 255 Ailanthone, 195 Aircraft, see Spray aircraft Ajacocnine, 184 Aldose, 268 Alfalfa, allelopathic activity of, 158, 160-161, 171 Alkaloids activity ranges of, 195 in allelopathy, 152, 184, 188 as autotoxic compounds, 200-201 Allelopathic compounds, see also Alkaloids; Phenolics; Terpenoids additive activities of, 195, 199 biotic stress factors and, 180-181 in Cisrus allelopathy, 170

307

detoxification of, 189 effects of, 143-144, 184-190 environmental stress factors and, 178-180 evolution and, 146 extraction from soil, 203-204 forms of, 177 herbicides and, 180, 199 as natural herbicides, 194-195, 196-198(table) from Pluchea, 156, 157, 170, 171 primary metabolites as, I8 1 rhizosphere and, 169,204-295 secondary metabolites as, 181-184 in soil, factors affecting, 171-178 in target plants, concentrations, 189 uptake of, 188-189 Allelopathy autoxicity and, 199-201 bioassays and, 148, 185,201-204 biocontrol programs and, 145, 190-195 biotic stress and, 180-181 climate factors and, 170-171 competition and, 146 of crop species, 158-162 of cyanobacteria, 144,146-147,163-164, 165(table) defined, 143 demonstrating, issues in, 143, 145-148, 201-204 detoxification and, 189 donor plant age and, 168-169 donor plant habit and, 169 donor plant life cycle and, 167-168 effects on plant systems and growth, 185-190 environmental stress and, 178-180 habitat factors and, 169-170 herbicides and, 180, 194. 199 multifaceted approach to, 205 natural selection and, 146 overview of, 142-145 of pollen, 164. 166 rhizosphere ecology and, 204-295 soil factors and, 171-178

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INDEX

Allelopathy (continued) statistical analysis and, 205-206 target plant density and, 166-167 of weed species, 148-158 Allophanic soils, point of zero salt effect and, 107 Ally1 isothiocyanates, 162 Aluminum anion-induced adsorption of, 122 toxicity, tolerance to, 255-256 Aluminum hydroxides, in cation exchange material, 129 Amaranth, allelopathic control of, 191 Amino acids, nonprotein, autotoxicity and, 201

Aminoacyl-tRNA-synthetase,201 Ammonium, cation exchange and, 129 Analysis of variance, applied to allelopathic studies, 206 Anion adsorption cation-induced, 123-1 24 effects of soil liming on, 127-128 point of zero salt effect and, 107 soil solution composition and, 115-116 soil solution ionic strength and, 114-1 15 soil solution pH and, 116 specific, 95, 118-120 Anion-induced adsorption, 122-1 23 Annual bluegrass, acid soil tolerance, 255 ANOVA, applied to allelopathic studies, 206 Antifreeze proteins, 247-248 Antioxidants, 242 Application control, 14-16.22 Arabinoxylans, 247 Arginine decarboxylase, 242 Asparagus, autoxicity of, 200 Asparagus bean, Pluchea allelopathy and, 156, 186, 187-188, 189

Atrazine, 166, 180 Autotoxicity, 158, 160-161, 199-201

B Bacteria, variable surface charges on, 94 Bahia grass, Polygonella allelopathy and, 152, 155(figure) Barnyard grass, 193, 194 Beans, quackgrass allelopathy and, 152, l53(figure), 154(figure) Bermuda grass antifreeze proteins in, 248 shade tolerance in, 262

Bioassays, demonstration of allelopathy and, 148,185,201-204

Biotechnology, see also Genetic engineering precision agriculture and, 64 Biotite, isomorphous substitution in, 93 Black walnut, allelopathy and, 169 BOA, 163, 174 Boltzmann equation, in diffuse double-layer theo'y. 99 bor 1-1 gene, 25 1 Boron toxicity tolerance, 251 Brassicaceae, allelopathic activity of, 161-162. 167

Brassica compestris, 167 Brassica napus, 161. 162 Brassica oleracea var. italica, 167 Broccoli, 167 Buckwheat, allelopathic activity of, 161 Buffer zones, precision agriculture and, 60 Butyric acid, allelopathy and, 181

C C, grasses iron-deficiency stress and, 256 shade tolerance in, 260-26 1 C, grasses iron-deficiency stress and, 256 shade tolerance in, 260-261 Cadmium adsorption anion-induced, 122-1 23 effects of organic ligands on, 116 effects of pH on, 116-1 18 Caffeoylquinnic acids, 174 Calcium anion-induced adsorption of, 122, 123 in cation-induced adsorption, 123-1 24 in freezing stress tolerance, 249 soil concentration of, effects on anion retention, 128 soil retention of, phosphate and, 129 in soil solution dynamics, 115, 116 Calcium-saturated soils, dispersion in, 126 Camelina sativa, 177 Canada thistle, crop allelopathy and, 161 Carbohydrate production, net, in turfgrass root management, 28 I Carboxylic group, variable surface charges and, 90.94

cas genes, 249

INDEX Cation adsorption anion-induced, 122-123 effects of soil liming on, 127-129 nonspecific, 120-1 22 soil solution pH and, 116 specific, 95, I18 Cation exchange, with Pinus radiara bark, 129 Cation exchange capacity (CEC), 95, 121-122 methods of increasing, 129-130 CEC, see Cation exchange capacity Celery root residues, allelopathic activity of, 192- 193 Cell elongation, allelopathic interference with,

I85 Cerariola ericoides, I77 Ceratiolin, 177 Cesium adsorption method, 106 Chemigation, precision water management and, 51

Chitinase-like proteins, 248 Chloride in phosphate adsorption, 118 in soil solution, I15 Chloritic group, variable surface charges and, 91 Chlorophyll content, allelopathic interference with, 186 Cinchone, 200-201 Cinnamic acid, 188 Cirsiurn arvense, 1 6 1 Cisrus ladanifer: 17 I , 179 Clay minerals, see also Silicate clays development of permanent surface charges in, 90.92-93 diffuse double-layer theory and, 99-101 Climate, effects on allelopathy, 170-171 Coffee, autoxicity, 200 Cold resistance, in turfgrass, 246-248 genetic engineering for, 244-246(table), 248-249 Colloids dispersion and flocculationin, 125-126 hydrophobic, diffuse double-layer model and, 103-104 solution-surface interface, 97-104 Compacted soils, turfgrass and, 259,279 Competition, allelopathy and, 146 Computers agriculture and, 6.8-9 geographic information systems and, 9 microprocessor development and, 6-8

3 09

Condition maps, 18 issues of accuracy in, 2 1 methods of generating, 19-22 Connectivity, 6 Conradina canescens, 183 Constant charge surface, see Permanent surface charge Control technology, 14-16 Conyza canadensis, 193 COR genes, 244(table),245(table), 248 Corn allelopathy and, 162, 164, 169, 181 precision nitrogen management and, 35 variable planting geometry and, 49,50 variable seeding rates and, 48 Corn pollen, allelopathy of, 164 Corn residues, allelopathic activity of, 162 Cotton, wheat straw allelopathy and, 163 Coumarins, 171, 185, 187, 188, 196(table) Counter ions, in diffuse double-layertheory, 99, 101

Cover crops, allelopathic weed management and, 190-194 Creeping bentgrass, acid soil tolerance and, 255 Crop residues allelopathic activity of, 162-163, 168, 174-175,180 allelopathic weed management and, 190, 191-193 soil erosion and, 191 Crop rotation, allelopathic weed management and, 193-194 Crops allelopathic activity of, 158-162, 190, 193-1 94 monitoring for nitrogen status, 34 pesticide use on, 40,4l(table) precision management of, 46-50 Crop yield, see also Yield maps forecasting, 13 precision nitrogen management and, 35-36, 31 soil and landscape effects, 19,36 water availability and, 36.50 Cruciferouscrops, allelopathic activity of, 193-194 Cultivars, variable management, 47-48 Cultivation,weed allelopathy and, 158, 169-170

310

INDEX

Cyanobacteria allelopathic activity of, 144, 146-147. 163-164,165(table) nitrogen fixation and, 163-164 Cynodon, 274

D Datura stramoniwn, 152 Defensive mutualism, grassendophyte, 274-275 Dehydrins, late embryogenesis abundant, 24 1 Delcosine, 184 DEMs, see Digital elevation models Deprotonation,variable surface charges and, 93-94 Desiccation tolerance, in turfgrass, 241 Detoxification,of allelopathic compounds, 189 DJBOA, 163,174 Differential global positioning systems (DGPS), 11-12 in application control, 15 Diffuse double layer, 97-101 in dispersion and flocculation. 125-126 of variable-chargeminerals, 101-101 Diffuse layer surface charge density, 96,97, 102-1 03 Digital elevation models (DEMs), 53 Digital terrain models (DTMs),53 Dilution of precision, 12 Dispersion, 125-126 DMSP, 251 DNA metabolism, allelopathic interference with, 188 DPGS, see Differential global positioning systems Drainage, 54 precision management and, 18 Drought avoidance, 237 Drought resistance, in turfgrass as drought avoidance, 237 as drought tolerance, 237,240-241 evapotranspirationloss, 237,238-239(table) genetic engineering for, 241-242 Drought tolerance, 237,240-241 Dryland wheat, precision nitrogen management and, 35 DTMs, see Digital terrain models Ducksalad, 194

E Ecological chemistry, 184-185 Economics, of precision agriculture, 54-58 Effluents cation exchange and, 129 salt stress and, 250 Electrokinetic potential, 98.11 1 Electrometric titration, 108 Electroosmosis. 111 Electrophoresis, 111 electrical double layer in, 97 Elytrigia repens, allelopathic activity of, 152, 153(figure), 154(figure) Endophytes, turfgrass and, 274-275 Environment, see also Genotype x environment interaction; Microenvironments effects of precision agriculture on, 58-61 in nitrogen management, 37 in pest management, 39,40,42-43 effects on application control, 16 nitrogen leaching and, 32 Environmental stress, effects on allelopathy, 178-1 80 Epilobium ciliaturn, 193 Equipment application control technology and, 14-16 effects on precision weed management, 44 Erosion, see Soil erosion Evapotranspiration loss, in turfgrass. 237, 238-239(table) Evolution, allelopathic compounds and, 146

F fad 7 gene, 249 Fagopymn sagirratum, allelopathic activity of, 161 Farmers, precision agriculture and, 54-58.6165.67 Fertility maps, 28.30 Fertilizer management, precision environmental effects, 58.59.60 nitrogen, 30-38,39(table) phosphorus, 27-30 potassium, 27-30 Fertilizers mixing on the go, issues in, 16 soil cation exchange capacity and, 129-130 Fertilizer spreaders, variable rate application, 15, 16

311

INDEX Ferulic acid, 171, 180, 187, 188, 197(tabIe) Fescues acid soil tolerance and, 255 shade tolerance breeding in, 263 Flavonoids in Cistus allelopathy, 170 in fluchea allelopathy, 170 W stress and, 179 Floculation, 125, 126 diffuse double-layer theory and, 100-101 Fluidized bed boiler ash, 129 Fluoride adsorption, 120 Forecasting, of crop yields, 12 Freezing stress resistance genetic engineering for, 244-246(table), 248-249 in turfgrass, 246-248 Functional groups, variable surface charges and, 90.94 Fungi, see also Soil fungi pathogenic, allelopathy and, 180 Fungicides, use of, by selected crops, 40.41 (table)

G Gene mapping, in turfgrass, 273-274 Genetic diversity, stress-resistance enhancement strategies and, 236,275-276 Genetic engineering, see also Biotechnology in strategies for plant enhancement, 236. 275-276 in turfgrass, 234-235.236 acid soil tolerance, 256,257(table) drought resistance, 240(table), 241242 endophyte enhancement, 275 high-temperaturetolerance, 243, 244(table) low soil oxygen tolerance, 260 low-temperaturetolerance, 244-246(table), 248-249 nitrogen efficiency, 264-267 phosphorus efficiency,268-269 potassium efficiency,270-271 salt tolerance, 250-25 1 shade tolerance, 263 Genotype x environment interaction, precision crop management and, 46-47 Geographic information systems (GIS), 9-10, 15

CIS, see Geographic information systems Global Navigation Satellite System (GLONASS), 10-11 Global positioning systems (GPS), 10-12 GLONASS, see Global Navigation Satellite System ~-1.3-Glucanase-Iikeproteins, 248 Gl yceraldehyde-3-phosphate dehydrogenase, 268 Glycolytic enzymes. turfgrass phosphorus efficiency and, 268 Glycosylation, in detoxification of allelopathic compounds, 189 Gossypiwn hirsutum, wheat straw allelopathy and, 163 Gouy-Chapman equation, 101 Gouy double layer model. 97-98 GPS, see Global positioning systems Grain crops, soil acidity and, 26 Grain drills, application control technology and, 15, 16 Gramine, 184 Grand fir, monoterpenoids, 183 Grid sampling mapping soil water availability and, 53 in precision fertility management, 28-29 Growth, allelopathic stimulation, 189-190

H Habitat, effects on allelopathy, 169-170 Heat shock protein families, 243,244(table) Heat stress, see Temperature stress Heavy metals, cation exchange and, 129 Herbicide residues, 178 Herbicides allelopathic compounds as, 194-195, 196- 198(table) allelopathy and, 180, 194, 199 costs Of, 38-39 precision weed management and, 42-45 sorgoleone as, 186 use of, by selected crops, 40,4l(table) weed density and, 166 Heteranthea limosa, 194 Hieracium pilosella, 172 High-resolution sensing, 21-22 Hordenine, 184 Horseweed, 193 Humic acid, surface charges and, 112, 114(table)

312

INDEX

Hydraulic conductivity, saturated, 125-126 Hydrocinnamic acid, 177 Hydrophobic colloids, diffuse double-layer model and, 103-104 Hydrous oxides diffuse double-layer model and, 103-104 point of zero charge concepts and, 104-107 surface charges of, 112, 113-114(table) Hydroxamic acids, 191 Hydroxybenzoic acid, 171, 196(table) Hyoscyamine, 152,184 Hypoxia, turfgrass and, 259-260

I Ice crystals, in low-temperature stress, 247 Ice nucleation genes, 249 Information, economics of precision agriculture and, 54-55,56-57 Inner-sphere complex surface charge density, 96.97 Insecticides precision insect management and, 45-46 use of, by selected crops, 40,4l(table) Insect management, precision, 45-46 Integrated pest management (TPM), 46 Internet remote sensing tutorial, 14 sites on global positioning systems, 12 Intrinsic surface charge density, 96, 103 Invertase, 269 Ion(s) nonspecific adsorption and, 120-122 toxicity, in soil acidity problems, 254-255 uptake, allelopathic interference with, 187 Ion retention technique, 106, 110-111 IPM, see Integrated pest management Iron-deficiency response, 256-258 Iron hydroxides, in cation exchange material, 129 Irrigation with nonpotable water, salt stress and, 250 precision management of, 36.51-52.60 Isoelectric point, 105, 111 Isomorphous substitution, 90,9 1,92-93 Isoprene, 243

J Juglone, 142-143, 177. 185, 197(table)

K Kochia, 200 KORC channels, 270

L Landsat program, 14 Landscape, see also Soil-landscape water management crop yield variability and, 19.36 Late embryogenesis abundant dehydrins, 241 Laruca sariva, rye allelopathy and, 163 Leachates, using to demonstrate allelopathy, 201-202 (see also Bioassays) Leaching, see also Nitrogen leaching effects of precision agriculture on, 59.60 Leaf area, in turfgrass root management, 28 1 Leaf chlorophyll content, in turfgrass root management, 28 1 Legume crops, liming and, 26 Lettuce, rye allelopathy and, 163 Liming effects on anion and cation retention, 127 precision management of, 24-27 Lolium, 274

M Manganese toxicity tolerance, 255-256 Mannitol, 242 Manure, 3 1,32 Maps, see Precision management maps; Yield maps Medicago sativa, allelopathic activity of, 158, 160-1 6 1 Mesophyll cells, freezing tolerance and, 247-248 Mesquite, autoxicity and, 200 Metal oxides, surface charges of, 112, 113-1 lytable) Methionine, allelopathy and, 188 S-Methylmethionine, 25 1 Mica, isomorphous substitution in, 93 Microenvironments, precision agriculture and, 64-65 Microprocessors, 6-8 Microsynteny, turfgrass genomes and, 274 Mineral addition technique, 110 Mineral ion uptake, allelopathic interference with, 187

INDEX Mineralization rates, see Nitrogen mineralization rates Mobile computing systems, 6 Molybdenum cofactor, 264 Monoterpenoids. 183 Moore, George, 6 Moore’s law, 6, 8 Mucondialdehyde, 195 Mucuna pruriens var. utilis, 191 Mugeneic acid, 256, 258 Muscovite, isomorphous substitution in, 93 Mycorrhizae, turfgrass and, 266,267 Myoinositol 0-methyltransferase,242

N

313

environmental problems and, 32 precision management of, 31-38.39 (table) Nitrogen fixation cyanobacteria and, 163-164 turfgrass and, 266-267 Nitrogen leaching effects of precision agriculture on, 59.60 environmental effects, 32 precision nitrogen management and, 36, 37 Nitrogen mineralization rates in nitrogen dynamics, 3 1 precision nitrogen management and, 36 Nitrogen use efficiency precision nitrogen management and, 37 in turfgrass, 264, 266 Nonallophanic soils, point of zero salt effect and, 107 Nonselective outward-rectifying channels (NORC), 270 Nonspecific adsorption, 120-122 outer-sphere complex surface charge density and, 97 Non-steady-state crop-nitrogen model, 38, 39(table) NORC channels, 270 No-tillage, development of, 3 Nozzles application control technology and, 16 effects on precision weed management,

National Aeronautics and Space Administration, remote sensing tutorial, 14 National Cooperative Soil Survey, 19, 20 National differential global positioning system, 11-12 National Resource Council, on precision agriculture, 4.6 Natural selection, allelopathic compounds and, 146 Navigation System with Time and Ranging, 10, 12 Nernst equation, Gouy double layer model and, 97-98 Net carbohydrate production, in turfgrass root management, 281 Net proton surface charge, 108 44 Net proton surface charge density, 96-97, 105 Nucleic acid metabolism, allelopathic interferNet surface charge density, 100 ence with, 188 Nicoriunu lubucum, rye residue allelopathy and, Nutrient deficiency, in soil acidity problems, 163 254-255 nifgenes, 266 Nutrient stress, effects on allelopathy, 174, Nitrate adsorption, soil solution ionic strength 179-180 and, 115 Nutrient uptake Nitrate assimilation, in turfgrass, 264,266 nitrate, 266 Nitrate reductase, 264, 266 phosphate, 267-268 Nitrification, allelopathic inhibition of, potassium, 270 144-145. 174-175 Nitrite reductase, 264, 266 0 Nitrogen excess, problems of, 31, 32 Oat straw, allelopathic activity of, 168 precision management of, 31-38.39 (table) Organic matter Nitrogen deficiency, problems of, 3 1-32 cation exchange, 129 Nitrogen fertilizers surface charges of, 112, 114 (table) current use of, 3 I Ornathine decarboxylase. 242

3 14

INDEX

Osmolytes. in turfgrass drought resistance, 24 1 Osmoprotectants,in engineering freezing stress tolerance, 248-249 Osmosensorsystems, 272 Osmotic stress, see also Salt stress in multiple stress interactions, 272 Outer-spherecomplex charges, 96,97 Overliming, consequences of, 26 Oxides diffuse double-layer model and, 103-104 point of zero charge concepts and, 104-107

P Parthenium hysterophorus. allelopathic activity of, 164, 169 Paspalum, shade tolerance in, 262 Paspalum notatum, Polygonella allelopathy and, 152, 155(figure) Patch spraying, 60,see also Pest management, precision Pathogenic fungi, allelopathy and, 180 Patua soils effects of liming on, 127(table) point of zero salt effect and, 107 PDIs, see Potential determining ions Peach residues, allelopathic activity of, 162-163 Peat moss, in allelopathy bioassays, 203 Performance maps, 19 Permanent surface charges developmentof, 90.9 I , 92-93.95 diffusedouble-layer theory and, 100-101, 104 Pesticides COStS Of, 38-39,40 precision management and, 45-46 use of, by selected crops, 40,4l(table) Pest management, precision, 38-46 pH, see also Soil pH effects on anion and cation retention, 128 effects on dispersion and flocculation, 125-126 in ion-retentiontechniques, 11 1 nonspecific adsorption and, 121-122 point of zero charge concepts and, 104-107 in potentiometrictitration. 108-1 10 Phaseolus vulgaris, quackgrass allelopathy and, 152.153(figure), l54(figure)

pH electrodes, in potentiometric titration, 109 Phelewn pratense, 164. 166 Phenolics, see also Allelopathic compounds activity ranges of, 195 allelopathic inhibition of nitrification and, 144-145 in allelopathic rice, 194 in cyanobacterial allelopathy, 164 detoxification of, I89 effects on soil microorganisms, 174-175 extracting from soil, 204 nutrient stress and, 174 overview of, 181-183 in Pluchea allelopathy, 156, 157, 170, 171 in rye residues, 163 soil chemical characteristics and, 175-176 Phenyl acetic acid, in corn pollen allelopathy, 164 Phosphate in soil, effects of phenolics on, 176 soil cation exchange capacity and, 129-130 in soil solution dynamics, 115, 116 Phosphate adsorption, 120 in anion-induced cation adsorption, 122-123 cation-induced, 123-124 effects of silicate on, 129 effects of soil liming on, 128 point of zero salt effect and, 107 soil solution ionic strength and, 114, I15 specific, 95, 118-119 Phosphate translocator genes, 269 Phosphate uptake, 267-268 Phosphoglycerate, 268 Phosphoglyceromutase,268 Phosphorus, precision management of, 27-30,

60 Phosphorus efficiency genetic engineering for, 268-269 in turfgrass, 267-268 Photons, in remote sensing, 13-14 Photosynthesis allelopathic interference with, 185-186 high-temperature stress and, 242 pH stress, see Soil acidity stress Phyllosilicates nonspecific adsorption, 121 surface charges of, 112, 113(table) Physiological ecology, 185 Phytotoxins, see Allelopathic compounds

INDEX Pinus radiara bark, 129

Plant breeding, in turfgrass high-temperature tolerance, 243 phosphorus efficiency, 268 potassium efficiency, 270 root system improvements, 277 shade tolerance, 262-263 Plant density allelopathy effects and, 166-167 precision crop management and, 48,49-50 Planting geometry, precision crop management and, 48.49-50 Plant life cycle, allelopathic activity and, 167-1 68 Plowing, weed allelopathy and, 158, 169-170 Pluchea lanceolata allelopathy, 156-158, 169-170, 171, 180, 186,187-188, 189, 202 Poa annua, acid soil tolerance and, 255 Poaceae, comparative mapping in, 273-274 Point of zero charge (PZC) concepts of, 104-107 described, 89 diffuse double-layer model and, 102, 103-104 measurement with ion retention, 111 measurement with potentiometric titration, 108-109, 110 Point of zero net charge (PZNC), 102, 105, 111, 126 Point of zero net pristine charge (PZNPC), 105-106 Point of zero salt effect (PZSE),105, 106-107, 109,110,115 Point sampling, 20-21 Pollen allelopathy, 164, 166 Pollution, reducing, precision agriculture and, 59,60 Polyacetylenes, 184 Polygonella myriphyllu. allelopathic activity of, 152, 155(figure) Polypogon monspeliensis, 167 Potassium cation exchange and, 129 precision management of, 27-30 Potassium adsorption effects of soil liming on, 128 nonspecific, 120-121 Potassium channels, 270 Potassium efficiency, in turfgrass, 270-271

315

Potatoes, precision nitrogen management and, 35 Potential determining ions (PDIs) in diffuse double-layer model, 103, 104 in Gouy double layer model, 97 Potentiometric titration, 106, 108-1 10 Precision concepts of, 4-5 dilution of, 12 Precision agriculture basic steps in, 16-17 definition of, 3-5 enabling technologies, 3-16 application control and, 14-16 computers, 6-9 geographic information systems, 9-10 global positioning systems, 10-12 sensors, 12-14,66 evaluations, 67 economic, 54-58 environmental, 58-61 technology transfer perspective, 61-65 intuitive appeal of, 5 issues in, 64-67 on-farm research and, 66-67 real-time management, 18 space-time variables and, 2-3. 17.22.65 variability assessments, 17-22, 66 high-resolution sensing, 21-22 modeling, 22 point sampling, 20-21 surveys, 19-20 variability management, 22-54.66-67 crops, 46-50 lime, 24-27 nitrogen, 31-38,39(table) pests, 38-46 phosphorus, 27-30 potassium, 27-30 soil fertility, 23 water, 50-54 Precision management maps, 18-19 fertilizer management, 28,30 issues of accuracy in, 21.28 lime management, 25-26 methods of generating, 19-22 nitrogen management, 33-34 water management, 53 weed control, 43 Prescription maps, I9

3 16

INDEX

Presidedressed soil nitrogen test (PSNT), 33, 35 Primary metabolites, as allelopathic compounds, 181 Private sector effects on strategic farming decisions, 63 National Cooperative Soil Survey and, 20 Profit, precision agriculture and, 54-58 Profit maps, 19 Proline autotoxicity and, 201 in engineering freezing stress tolerance, 248 Proline betaine, 248 Propionic acid, allelopathy and, 181 Pmsopis julifora, 200 Protein metabolism, allelopathic interference with, 188 Protonation, variable surface charges and, 93-94 Prunus persica. allelopathic residues of. 162-163 Pseudomonas putida, I 77 PSNT, see Presidedressed soil nitrogen test Public sector effects on strategic farming decisions, 63 National Cooperative Soil Survey and, 20 Purple nutsedge, 179 Putrescine-generatingenzymes, 242 PZC,see Point of zero charge PZNC, see Point of zero net charge PZNPC, see Point of zero net pristine charge PZSE, see Point of zero salt effect

Q Quackgrass,allelopathic activity of, 152, 153(figure),154(figure) Quantitative trait loci (QTL) leaf rolling, 240.241 osmotic tolerance, 240.24 1 phosphorusefficiency, 268 salt tolerance, 25 1 Quercetin, 170 Quercitrin, 170 Quinones, activity ranges of, 195

R Rainfall, effects on allelopathic activity. 171 Real-time precision management, 18

Refugia, precision agriculture and, 46, 59, 60 Remote sensing, 13-14,22 Repetitive DNA, in turfgrass, 274 Residual nitrogen, precision nitrogen management and, 33-34,35,37,38.60 Residues, see Crop residues; Herbicide residues Respiration, allelopathic interference with, 187 Rhizosphere, allelopathic activity and, 169, 171, 204-205 Rice allelopathic activity of, 194 allelopathic activity of cyanobacteria and, 164 cyanobacterial inoculum and, 144. 164 Root depth, in turfgrass, 277 Root exudates, allelopathic activity of, 187-1 88 Root management, in turfgrass, 280-282 Root plasticity, in turfgrass, 277, 279 Root residues, in allelopathic weed rnanagement, 191, 192-193 Roots of turfgrass acid soil stress and, 25 1,254 genetic potential of, 277, 279 low oxygen tolerance and, 260 management for, 280-282 potassium uptake and, 270 primary stresses on, 277,278(table), 280-28 1 salt stress and, 250 significance to comprehensive plant enhancement, 277 soil compaction and, 259 uptake of allelopathic compounds, 188-189 Rorippa sylvestris, 155 Row spacings, variable, 49-50 Runoff, reduction, precision agriculture and, 60 Russia, GLONASS system, 10-11 Rye, allelopathic activity of, 163, 174, 193

S Salinity, see Salt tolerance Salt stress, in turfgrass, 249-250 Salt tolerance genetic engineering for, 250-25 1 in turfgrass, 250-25 I , 252-253(table) Sample design, 21 Sample units, 21 Sampling processes, precision agriculture and, 20-2 I, 46

INDEX Saponins. in alfalfa allelopathy, 158, 171 Sasa cemua. 155 Satellites, see also Global positioning systems remote-sensing, 14 Saturated hydraulic conductivity, 125-126 Scopolamine, 152, 184 Secondary metabolites, as allelopathic compounds, 181-184 (see also Alkaloids; Phenolics; Terpenoids) Seed germination allelopathic interference with, 185 in allelopathy bioassays, 203 Seeding rates, variable, 48-50 Seedling growth, allelopathic interference with, 185 Seeds, autotoxic compounds and, 200-20 1 Selenite, in soil solution dynamics, 115-1 16 Sensors, precision agriculture and, 12-14, 21-22 Sesquiterpenoids. 183 Setariafaberi, 169 Shade tolerance, in turfgrass, 260-263 Shoots, in turfgrass, salt stress and, 250 Signal transduction, in multiple stress interactions, 272 Silicate clays, see also Clay minerals; Phyllosilicates soil cation exchange capacity and, 129 surface charges and, 90.92-93, 112, 1 13 (table) Sinapic acid. 17 1 Site-specific weed control, 43-45 Slacked lime, in cation exchange material, I29 Smectites, nonspecific adsorption, 121 Snapbean, quackgrass allelopathy and, 152, 153(figure). 154(figure) Sodium-saturated soils, dispersion in, 126 Software geographic information systems and, 9, 10 precision agriculture and, 9 Soil compacted, turfgrass and, 259,279 concepts of, 88 effects on allelopathy, 171-178 extraction of allelopathic compounds from, 203-204 fluchea allelopathy and, 157. 158 in turfgrass root management, 282 Soil acidity stress, in turfgrass, 251,254-255

317

Soil acidity tolerance genetic engineering for, 256,257(table) in turfgrass, 255-256 Soil alkalinity, plant iron-deficiency responses and, 256-258 Soil bulk density, turfgrass and, 259,279 Soil components, surface charges of, 112, 113-1 14(table) Soil erosion crop residues and, 191 precision agriculture and, 59.60 Soil fertility management, 23 buildup maintenance concept, 29 lime, 24-27 nitrogen, 31-38, 39(table) phosphorus, 27-30 potassium, 27-30 sufficiency concept, 29 Soil fungi, allelopathic compounds and, 176, 177-178 Soil-landscape water management, 52-53 Soil microorganisms allelopathic compounds and, 174-175, 176-178 wheat straw amendments and, 172, 174 Soil nitrification, effects of phenolics on, 174-175 Soil organic matter allelopathic compounds and, 176 point of zero net pristine charge and, 106 surface charge and, 94, 112, Il4(table) Soil oxygen, turfgrass and, 259-260 Soil particles, see also Soil surface charges colloidal behavior, 89 dispersion and flocculation in, 125-126 solution-surface interface, 97-104 Soil pH, see also pH diffuse double-layer model and, 102 effects on allelopathy, 172 effects on anion and cation retention, 128 effects on dispersion and flocculation, 125-126 effects on surface charge, 110-1 18 nonspecific adsorption and, I2 1-1 22 precision lime management and, 24-27 spatial dependence of, 24 variable surface charges and, 90-91, 93-94 Soil solutes, surface charges and, 89, 120-122, 127-130

318

INDEX

Soil solution qualities affecting surface charge composition, 115-116 ionic strength, 112, 114-115 pH, 116-118 solution-surface interactions,97-104 Soil strength, see Soil bulk density Soil surface charges components of, 96-97 development of, 90-96 effects on soil properties dispersion and flocculation, 125-126 induced adsorption, 122-124 solute interactions, 120-122 factors affecting soil components, 112, 113-114(table) soil solution composition, 115-1 16 soil solution ionic strength, 112, 114-115 soil solution pH, 116-118 specific adsorption, 95, 118-120 measurement of, 107-1 11 overview of, 88-89, 130-131 point of zero charge concepts and, 89, 104-107 solute interactions and, methods of controlling, 127-130 in solution-surfaceinteractions, 97-104 types of, 90 Soil surveys, precision agriculture and, 19-20, 43,53 Soil testing, in precision fertility management, 27-30 Soil texture, effects on allelopathy, 172, 173(table) Solutes, see Soil solutes Sorgoleone, 186 Soybeans, variable seeding rates and planting geometry,49-50 Specific adsorption, see also Adsorption; Phosphate adsorption; Sulfate adsorption compared to nonspecific adsorption, 120, 12l(table) described, 95,118-120 in induced-adsorptiondynamics, 122-124 inner-spherecomplex surface charge density and, 97 Spray aircraft, wind conditions and, 16 Sprinkler-irrigation precision nitrogen management and, 36

precision water management and, 51 Statistics, applied to allelopathic studies, 205-206 Steady-state crop-nitrogen model, 38,39(table) Stem double layer model, 98, 101 Stem layer, 98.99-100 Stem layer surface charge density, 96, 103 Stem potential, 98 Straw allelopathic activity of, 163, 167, 168 soil microorganisms and, 172, 174 Streaming potential measurement, 11 I Stress-induced genes, in multiple stress interactions, 272-273 Stress resistance, see also individual stresses mechanisms of, 235-236 for multiple stresses, enhancement strategies for, 275-276 Structural surface charge density. 96 Sucrose phosphate synthase, 268 Sudex, 169 Sugar beets, precision nitrogen management and, 35 Sulfate, in soil solution dynamics, 115, I16 Sulfate adsorption, 119-120 in anion-induced cation adsorption, 122-123 cation-induced, 123-124 effects of soil liming on, 127-128 point of zero salt effect and, 107 soil solution ionic strength and, 114-115 Sunflower,phenolics and nutrient stress in, 174 Surface charge density, point of zero concepts and, 105 Surveys, 19-20 Synteny, turfgrass genomes and, 274 Syringic acid, 171

T Tall fescue allelopathic activity of, 170, 180 drought avoidance in, 237 shade tolerance breeding in, 263 Technology precision agriculture and, 3-4, 6-16,61-65 recent history of, 7(table) Technologytransfer, 61-65 Temperature stress, in turfgrass high-temperature, 242-243 low-temperature, 246-248

3 19

INDEX Temperaturetolerance, genetic engineering for, 243,244-246(table), 248-249 Terpenoids allelopathic inhibition of nitrificationand, 144, 145 in allelopathy, 183 Terrain modeling, in precision water management, 53 Thaumatin-like proteins, 248 Tillage in precision weed management and, 42 weed allelopathy and, 158, 169-170 Tobacco. rye residue allelopathy and, 163 Tokomaru soils effects of liming on, 127(table) point of zero salt effect and, 107 Tracheids, freezing tolerance and, 247 Transition time, in application control, 15, 16 Trehalose, 243 Triangulation, 10 Turfgrass acid soil stress in, 251,254-255 acid soil tolerance in, 255-256 comparative mapping in, 273-274 drought resistance in, 237-241 endophytes and, 274-275 genetic engineering in, 234-235 acid soil tolerance, 256,257(table) comprehensive strategy for, 236, 275-276 drought resistance, 240(table), 241-242 high-temperaturetolerance, 243,244 (table) iron-deficiencyresponse, 258 low soil oxygen tolerance, 260 low-temperaturetolerance, 244-246(table), 248-249 nitrogen efficiency, 264-267 phosphorus efficiency,268-269 potassium efficiency,270-271 root plasticity, 279-280 salt tolerance, 250-251 shade tolerance, 263 high soil strength tolerance in, 259 high-temperature stress in, 242-243 iron-deficiency response in, 256-258 low soil oxygen stress in, 259-260 low soil oxygen tolerance in, 260 low-temperaturestress in, 246-248 multiple stress interactions and, 272-273

multiple stress resistance, enhancement strategy for, 275-282 nitrogen fixation and, 266-267 nitrogen use efficiency in, 264,266 phosphorus efficiency in, 267-268 potassium efficiency in, 270 root management in, 280-282 root system enhancement and, 276-280 salt stress in, 249-250 salt tolerance in, 250-251,252-253(table) shade tolerance in, 260-263 stress adaptation in, 235-236 stress and, 233-234

U Ultraviolet radiation stress, allelopathy and, 179 United States Coast Guard, beacon system, 11-12 W stress, see Ultraviolet radiation stress

V Vanillic acid, 171, 187. 188, 196(table) Variable rate application, see Soil fertility management Variable surface charges development of, 90,91,93-95,96 diffuse double-layer model of, 101-104 factors affecting, 112, 131 point of zero charge concepts and, 104-107 Vehicle control, 15 Velvet bean, 191 Kgna unguiculata var. sesquipedalis, Plucka allelopathy and, 156

W Walnut trees allelopathy and, 169 juglone and, 177 Water crop yield and, 36,50 nonpotable, salt stress and, 250 precision managementof, 50-54 Waterlogged soil, see Hypoxia Water quality, effects of water management on, 50

Water stress effects on allelopathy, 179

320

INDEX

Water stress (conrinued) in multiple stress interactions, 272 Weather, effects on application control, 16 Weed allelopathy, 148-158 from residues, 162 target plant density and, 166-167 weed life cycle and, 167-168 Weed density, herbicides and, 166 Weed management allelopathic, 190-195, 196-198(table) in precision agriculture, 42-45 Weed mapping, 43 Wheat precision nitrogen management and, 35 variable seeding rates and, 48-49 Wheat straw allelopathic activity of, 163, 168 soil microorganismsand, 172, 174 Wild mustard, 167 Wild oats, allelopathic activity of, 169 Willow herb, 193 Wind, effect on aircraft spray control, 16 Winter wheat, variable seeding rates and, 48-49

X Xylem freezing tolerance and, 247 root, potassium uptake and, 270

Y Yellow fieldcress, 155 Yield maps, 13 assessing poor drainage with, 54 high-resolution sensing and, 21-22 measures of temporal variability and, 17 in precision fertilizer management, 29 in precision nitrogen management, 35.37 in variable cultivar management,48 Yield monitoring systems, 13

Z

Zero point of charge, 105; see also Point of zero charge

Zeta potential, 98 Zinc anion-induced adsorption, 122 nonspecific adsorption, 122 soil retention of, phosphate and, 129

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