ACKNOWLEDGMENTS We should like to convey our thanks to B.P. Nikol'skii, E.A. Materova, P.A. Kriukov, V.A. Kovda, A.N. Tyuryukanov, V.M. Leontiev, L.O. Karpatchevsky, A.P. Travleyev, A.D. Khlystovsky, E.M. Samojlova, T.A. Sokolova, whose works, comments and advice played a significant role in our investigations. One could hardly overestimate the influence of the scientists who worked with us for a number of years in the lab, in the field, and during expeditions. These are T.L. Bystritskaya, R. MeszarosDraskovits, V.V. Volkova, K. Fiala, J. Jakrlova, V. Zelena, A.G. Dubinin, A.E. Andreyeva, M.L. Ena, P.P. Kretchetov, E.N. Kesov, S.V. Mergel, and O.V. Rukhovich. This book is also a result of the efforts of our assistants, who shared with us the hardships of field works and laboratory experiments, lent us support when things went wrong, and, last but not the least, performed most of the technical work on the manuscript. They are N.F. Pochueva, T.G. Ospennikova, E.V. Danilina, T.D. Demidova, E.R. Gruzdeva, V.R. Khrisanov, T. Horvath and other colleagues from the Institute of Basic Biological Problems of Russian Academy of Science (former Listitute of Soil Science and Photosynthesis), Institute of Ecology and Botany of the Hungarian Academy of Sciences, Department of Plant Taxonomy and Ecology of Eotvos Lorand University Budapest. We appreciate the kind support of Dr. J. Japenga (The Netherlands) and Dr. I. PoUak (Hungary). We are much indebted to Dr. A.E. Hartemink from ISRIC in Wageningen (The Netherlands) for scientific and editorial assistance. Special thanks to the Intemational Scientific Fund (grant JHEIOO), Russian Fundamental Research Fund (grants 95-04-28659 and 95-07-19223), Hungarian National Science Fund (OTICA grants 2049, T5340, T 021166, F 6434), and grant of histitute of Agronomy and Soil Science (NWO, AB-DLO, Haren, The Netherlands) for financial support of the given investigation.
311 AUTHOR INDEX
A Abiad M.N. - 86 Adams F . - 1 8 , 19,20,24,58 Adams P . - 1 8 Afanasieva E.A. - 221 AlekhinO.A.-175 Alexandrova V. - 90 AllmarrasR.R.-215 Alloway B.J. - 224 Andersson A. - 220 AndreevA.G.-177 Andreeva A.E. - 8, 98, 104, 164, 168, 207,209,210 Andrianov P.I. - 110 AskinaziD.L.-213 AtanasovI.S. - 16 AvakyanN.O.-24, 31
B Baas-Becking L.G.M. - 129, 192, 252 Bailey L.D. - 54 Balatova-Tulackova E. - 76 BalazsA.-lOO, 101 BaldovinosF.-19 Bashkin V.N. - 252 BatesR.G.-25,28,37 Beauchamp E.G. - 54 BekarevichN.E.-120 BemiettA.C.-18 BeusA.A.-224 BeveridgeA.-223 Bezel V . S . - 2 5 2 Black C.A.-221 Bloomfield C. - 222 BohnH.L.-132 Bolt G.H. - 86,239 Bonyoncos G.J. - 24 Bound G. - 37 Bower C . A . - l 1,29 Boyarovich N. M. - 211 BotticherR.-211,245 Bradford G . R . - 2 2 0 BrechtelH.M.-102
Briggs L. - 6 Brown T.N.-214 Bruggenwert M.G.M. - 86 Bujtas K. - 257 BulatkinG.A.-96, 118 Bulla B . - 7 2 Butler J . N . - 2 7 Buyanovsky G.A. - 184 Bystritskaya T.L. - 7, 8, 40, 61, 88, 145,190,209,210,212,213,214, 215,216,218
Cachioni-Walter L.S. - 211 Cammann K. - 25, 41, 57, 93, 263 Carlisle A . - 1 0 2 Cataldo D.A. - 224 Cheng B.T.-186 Chemoberezhsky Yu.M. - 30, 35 Christ Ch.L.-132, 175 ChurilinaYu.G.-173 Clark W . M . - 5 5 , 56, 191,203 ClineM.G.-214 Coldewey-Zum Eschenhoff H. - 97 CottenieA.-221 Covington A.K. - 43 CrowtheJ.-lOl Csillag J. - 90
D Danilina E.V. - 8 DanilovaN.S.-211 DarrachP.R.-110 Debye-27, 28, 51, 54, 177,264 DefayR.-200,202 Demetra- 7, 66, 170, 232, 235 Demidova T.D. - 8 DemkinV.A.-16, 58 DergachovaM.I.-218 Dethier V. - 203 Dmitrienko O.I. - 53 DmitrievE.A.-147 Donnan - 29
312 DonskikhI.N.-210,211 Doyarenko A.G. - 24 Drachev S.M. - 90, 96 DreverJ.-183 Dubinin A.G. - 8, 162, 200, 203, 252 Dumanskaya A.P. - 10 Dumansky A.V. - 10 Durst R.A. - 25 Dzuin G.P. - 85
E EdmeadesP.C.-110,210 Efremova T.T. - 94 Egorov V.V. - 76, 79, 195, 305 Eisenman G. - 24 El-BassamN.-231 Elgawhary S.M. - 213 Endovitsky A.N. - 175 Ena M.L. - 8 Epstein E. - 20 Ermolaev A.M. - 199 Euclid-253, 255 Evdokimova T.I. - 16, 216 EvstropievK.S.-24
F FallerN.-212 Faraday M. - 25, 42 Fedorovsky D.V. - 24 Femandes Marcos M.L. - 210 FialaK.-8 Fischer W.R. - 86, 96, 110,189,190 Fleet B. - 37 Freundlich - 85, 86, 224, 232, 233, 236, 240, 262
G Gaines G.J. - 85, 232, 233, 238, 240 Gantimurov I.I. - 54 Gapon E.N. - 85, 90, 232, 233, 238, 240 GarrelsR.M.-132, 175 GellerI.A.-61,95,96 GertsygV.V.-173 GlansdorffP.-200
Glazovskaya M.A. - 191, 305 GlazovskyN.F.-lOO GodunovI.B.-173 Goertzen J.O. - 11 Goncharov V.V. - 37, 88, 89, 90 Gonchar-Zaikin P.P. - 53, 88 GorbatovV.S.-225 Gorbunov N.I. - 86 Gorbunova R.G. - 24 GorshkovaE.I.-132, 183, 195 GrechinI.P.-183 Greenland D.J. - 54 Grieve I . e . - 1 3 8 , 210 GrishinaL.A.-118 Grodzinsky A.M. - 59 Grodzinsky D.M. - 59 Gruzdeva E.R. - 8 Gubin S.V. - 79 GuliakinI.V.-220 GunarI.L-90,211 GurovA.F.-192
H Hagen C.E. - 20 HanewaldK.-lOl HantschelR.-16 Harris V.E. - 55, 56 Hayes M.H.B. - 54 Hartemink A.E. - 8 HedroitzK.K.-6,22,86,251 HeinrichsH. - 2 2 1 HemJ.A.-212 HingstonF.J.-llO HirataShigeru-30 Hitoshi Fukuda - 93 Hodgson J.F. - 220 Horvath T. - 8 Howard D.D.-19, 20 Huckel - 27, 28, 51, 54, 177, 264
I IimuraK.-230 IlerR.K.-213,215 InishevaL.I.-189 lonue A. - 204
313 Isaeva G.S. - 55 Ishcherekov V.P. - 6, 22, 23 ItohS.-220 Ivakhnenko N.N. - 166, 188
J Jakrlova J. - 8 Japenga J. - 8 JindilA.R.-189 Jones M.S.-110 Juzefaciuk G. - 30
K Kabata-Pendias A. - 220, 221, 222 Karpachevsky L.O. - 7, 99, 148, 169 KarpenchukG.K.-llO Kaurichev I.S. - 54, 58, 214, 242 Keller W.D.-214 Kerzum P.A. - 24, 37, 39, 53, 58, 88, 89 KesovE.N.-8,52, 143, 145 KeszeiE.-lOO KhasawnehF.E.-18, 19 KhitrovN.B.-30,31 KholopovaL.B.-104 Khromchenko N.Y. - 88 KimE.L.-139 Kirsanov A.T. - 138 Kiselev G.G. - 30, 37, 88, 89, 90 Khlystovsky A.D. - 7 Khrisanov V.R. - 8 KitagishiK.-231 KleckaW.R.-253 Kleopov Yu.D. - 75 KlokeA.-231 Knyazeva N.V. - 24 KobozevN.I.-201 Kochergin A.E. - 259 Komarova N.A. - 11, 15, 22, 23, 24, 31,32,90 Komissarova N.F. - 67, 265 KondratievaM.P.-118 Kononenko A.D. - 175 KopeikinV.A.-213 Kostenkov N.M. - 57, 96, 196
Kovacs-Lang E. - 7, 145, 197 KovalskiyV.V.-231 Kovda V.A. - 7, 9, 22, 40, 54, 86, 88, 110,213,214,218,241 KovriginS.A.-168,209 Kovrigo V.P. - 85, 88 Kozak J. - 86 KrauskopfK.B.-213,214 Kravtsov V.P. - 204 Kravtsova A.V. - 204 Krechetov P.P. - 8 KreyerK.G.-59 Kriukov P.A. - 7, 11, 15, 22, 23, 24, 31,32 Krupennikov LA. - 212 KrupskyN.K.-88,90 Kudeyarov V.N. - 211 KulikovaV.K.-173 KurlikovaM.V.-183 Kurovskaya O.V. - 57 Kuzakhmetov G.G. - 97 KylliR.K.-200
Laitinen G.A. - 55, 56 Langmuir I. - 85, 86, 224, 232, 233, 236, 237, 240, 262 Lamm C.C. - 40 Lavrenko E.M. - 75 Leninger A . - 2 0 1 Leontiev V. M. - 7 Lewis G.-25,26,263 LiethH.-73 Light T.S.-41 Lindsay W.L. - 213,214, 221, 231 LinzonS.N.-231 LundZ.F.-18, 19 Lurje Yu.Yu.-42 LuttkusK.-211,245 LyakhinYu.L-175
M MaciasF.-210 Mahalanobis - 256 Mahler R.L.-118, 214
314 Maiboroda N.M. - 91 Maimusov D.F. - 88 Makarov B.N. - 96 MaksimovG.B.-211 Manderscheid B. - 138 Marschner H.A. - 110 Marshall C.E. - 24, 29 MaslovaA.L.-144,250 Materova E.A. - 7, 24, 25 MatskevichV.V.-176 MatznerE.-138 Mayer R. - 220 Mazsa K. - 97 McCoolM.M.-24 McGeorge V. - 39 McKeaque J.A.-214 Means T.H. - 6, 24 MeleshkoD.P.-37,38 Mergel S.V. - 8 Meszaros-Draskovits R. - 8, 197 MikhaelisL.-55,203,204 MikhailovA.S.-213 Miller R.B.-102 MinashinaN.G.-88 Minczewski J. - 220 Minina E.G. - 96 MinkinM.B.-175, 177 Miragaya Y.G. - 224 Mitzkevich B.F. - 223 MorfW.E.-25,263 Mubarak A. - 24
N NairP.K.-40, 53, 109, 117 NazarovA.G.-214 Negus L.E.-41 NekrasovN.I.-55 NemethT.-221 N e m s t - 2 5 , 41 Neunylov B.A. - 96 NihlgardB.-103 Nikol'skii B.P. - 6, 7, 24, 25, 29, 85, 90, 232, 233, 238, 240 Norov Sh.K. - 90 NyeP.H.-12
o 01senR.A.-24, 103 01senS.R.-19 Orlov D.S. - 9 , 54, 81, 86, 132, 183, 189,195,222,268 Ospennikova T.G. - 8 Overbeeck J.Th.G. - 29 PachepskyYa.A.-37,38 PallmannH.-29,269 PanovN.P.-213,214 Parfenova O.M. - 55 Parfitt R. - 86 Peczely Gy. - 73 Pendias H. - 222 Pereverzev V.N. - 215 PervovaN.E.-16, 216 PesantA.R.-186 Peterburgsky A.V. - 1 7 3 Pickering W.F.-223 PochuevaN.F. - 8 PoddubnyN.N.-189, 196 Pollak I. - 8 Pollard J.H.-232, 238 Polubesova T.A. - 11, 12, 88, 138 Ponizovsky A.A. - 1 1 , 1 2 , 30, 88, 138 Ponnamperuma F.N. - 87, 132, 196 Porter W.M.-118 Prigogine I. - 200, 202 Prisyaznaya A.A. - 7, 252; see also Zavizion A.A. Prokhorova Z.A. - 91 Prosyannikov E.V. - 110 Pryanishnikov D.N. - 70, 111
R Rabinovich V.A. - 26, 57, 263 RappM.-102 Razumova N.A. - 67, 265 ReintamL.Yu.-16,200 Remezov N.P. - 24, 39, 54, 85, 111, 200 ReshetnikovS.I.-257 Richards L . A . - 2 3 , 24
315 RihaSusanJ.-170 Romanova T.A. - 168, 188 RomheldV.-llO RozanovB.G.-218 Ruellan Alain - 7 Rukhovich O.V. - 7, 8 RusselE.W.-213 Rybnicek K. - 76 Rybnickova E. - 76 Ryklan L.R. - 55, 56
SaarmanT. - 16 Samoilova E.M. - 7, 16, 58, 213 SavichV.I.-llO Schaller G. - 86, 96, 110,189,190 SchlesingerW.H.-lOO Schloesing Th. - 6, 22 Schmidt C.L.A. - 55, 56 Semagina R.N. - 99 Serdobolsky P.P. - 39, 59, 138, 185, 192,196,242 Shaimukhametova A.A. - 49 ShilovaE.I.-22,59 ShirshovaL.T.-199,218 Shiryaev A.D. - 203 Shmuk A.A. - 24 SinkevichZ.A.-110,212 Sinyagina M.T. - 242 Skryrmikova I.N. - 22 SmithJ.-188 Snakin V.V. - 7, 18, 36, 40, 43, 52, 54, 57,59,61,86,101,119,145,158, 161, 162, 163, 175, 178, 185, 191, 193, 197, 200, 212, 230, 244, 252, 258, 268 Snyder W.S.-203 Sobolev F.S. - 96 SoderliindR.-102 Sokolenko E.A. - 87 SokolovA.V.-167 SokolovI.A.-149 SokolovM.S.-190 Sokolova T.A. - 7 Sokolovsky O.M. - 252 Sposito G. - 26
StashchukM.F.-196 StenlidG.-103 Stepanov N.N. - 96 Stepanova M.D. - 223 Stepniewska Z. - 189 StrekozovB.P.-190 StrekozovaV.L-211 Strida M. - 73 SzaboM.-lOO, 102, 103
TabatabaiM.A.-118 Talibudeen O. - 40, 53, 109, 117 TararinaL.F.-196,242 TargulianV.O.-149 Thomas G.W.-19 Thomas H.C. - 85, 232, 233, 238, 240 TietjenC.-231 TikhonenkoD.G.-188 Tiller K.G.-220 Tills A . - 2 2 4 Tinker P . B . - 1 2 TiurinI.V.-221 TovbinM.B.-175 Travleev A.P. - 7, 13 TravleevL.P. - 13 Trofimov A.V. - 10, 11, 12, 24, 34, 37, 38, 88, 89 TrubetskovaO.M.-211 Truitt R.E. - 223 TschapekH.-29,34 TserlingV.V.-lll Tuyruykanov A.N. - 7
u UchvatovV.P.-lOO
VakulovaV.I.-148 Vazhenin I.G. - 148 VemadskyV.I.-6, 17 Villee C. - 203 Volkova V.V. - 7, 8, 91, 168, 209, 210,213,216,238,252 Volobuev V.P. - 200
316 Volokh P.V. - 8 Vozbudskaya A.E. - 132, 189, 2U
w Walter H . - 7 3 Waring R.H.-100 WatanabeF.S.-19 Weber J.H. - 223 Webster R. - 256 Wehrmann J. - 97 Whitehead D.C.-103 Whitney M. - 6, 24 Wiegner G. - 29, 269 WildungR.E.-223,223 WoltJ.D.-17
X XiemingBao-185
YakovlevA.S.-257 Yamane I . - 2 3 1
Yamasaki S. - 2 2 0 YamnovaLYa.-31,40, 59 YastrebovM.T.-61,96 Yoshida Minora - 30 YuT.R.-38,40, 196 YudinaL.P.-31,40, 59 Yumura Y. - 220
Zakharievsky M.S. - 54 Zakharov S.A. - 257 Zavizion A.A. - 5 9 , 87, 164, 175, 179, 192; see also Prisyazhnaya A.A. Zavodnov S.S. - 176 ZborishukN.G.-184 Zelena V. - 8, 76 Zelichenko E.N. - 87 Zhupakhina E.S. - 53 Zmijewska W. - 220 Zsolnay A. - 2 1 8 Zykina O.K. - 31, 40, 58, 118, 194 ZyrinN.G.-132,222
INTRODUCTION The liquid phase of soil (soil solution) is a very thin, penetrating and all-embracing water layer. It has the most extensive surface among the biosphere components and interacts with all these components, hivestigation of the soil liquid phase can of great significance in environmental research. Soil water is one of the most important natural water category in the biosphere (Vemadsky, 1960). V.I.Vemadsky considered it "the basic element of the biospheric mechanism" and "the basic life substratum". According to K.K.Hedroitz (1975a), "to move on in solving some theoretical as well as practical issues of agronomy we have to find another approach to solve the problem of soil solution; we should study the composition of the solution and its temporal changeability as depending on external conditions. It will not be an exaggeration to say that further achievements of agronomy depend on the solving of this problem". Soil liquid phase investigations have not become an efficient instrument in ecology or applied soil science, despite extensive soil solution data. This is due to the difficulties in studying soil solutions in unchanged state, spatial heterogeneity of soil properties (including soil liquid phase) and dynamic composition of soil solutions responding to environmental changes. The more difficult the problem, the more interesting it is to fathom its depths. Soil liquid phase investigation dates back to the start of experimental environmental research. Two trends have emerged from the very beginning: (i) attempts to separate soil solution from soil in order to analyze its composition (Schloesing, 1866; Ishcherekov, 1910), and (ii) experiments on soil liquid phase carrying out immediate investigation in soil, without preliminary extraction, through electrometric methods (Whitney &, Means, 1897; Briggs, 1899). The first trend was used for a long time, though it was noted that "all the attempts at extracting soil solution from soil at a low moisture content are bound to fail" (Hedroits 1975a). Development of the second trend was drawn back by the imperfection of electrometric techniques. It was not until the ion-selective electrodes technology (ISE) was introduced that progress was made and the first ISE (glass H^-electrode) was used in soil investigations (Nikol'skii, 1930). Development of different ISE technology and field ionometers allowed to expand the circle of determinable ions in water (liquid) phase of different soils, and to investigate natural soil liquid phase
under field conditions without breaking their internal physico-chemical balances (the so-called in situ measurements). A brand-new class of data is the case, which enables us to assess parameters of physico-chemical and biological processes in soil under natural conditions. It is often that analysis of soil samples resuhs in unreliable data, especially at the preliminary stage of investigations. Soil sample properties reflect the stages of selection and preservation, and its redox, gas-exchange and microbiological processes are different from soils in the field. Livestigation of soil as a component of natural and cultivated ecosystems should be dynamic and should reveal its nature and the links within the solid, liquid and gas phases. We agree with Ruellan (1983), that to study recent soil processes the newest technical means should be used in order tofindout the structure and composition of soil components in situ. This study is devoted to search and back-up of new approaches to soil liquid phase analysis and aims to fmd out, the role of soil liquid phase in thefimctioningof natural and agricultural ecosystems in recent soil-formation, formation of primary biological production, and in bio-geochemical turnover of elements. Direct investigation of soil liquid phase is the determination of the concentration (activity) of ions or redox potential in situ; while the analysis of soil solution implies that the soil solution is extractedfromsoil. The authors have aspired to give insight into the development of ideas and theories as well as certain results of Russian schools of soil science and ecology on problem of studying of soil liquid phase. The references therefore contain mainly articles in Russian. As compared with earlier publications on soil liquid phase investigation (Bystritskaya, Volkova, Snakin, 1981; Snakin, 1989; Snakin, Kovacs-Lang, Bystritskaya et al., 1991; Snakin, Prisyazhnaya, Rukhovich, 1997) this work is substantially expanded. It includes new field investigation data as well as all data generalization carried out by the means of a special complex database «Demetra» (developed by the authors of this work) on soil liquid phase composition and other soil-ecological properties in various ecosystems in Central and Eastem Europe.
CHAPTER 1. SOIL LIQUID PHASE AS A STRUCTURAL ELEMENT OF AN ECOSYSTEM The subject of this work is the liquid phase of the soil. Normally, soil liquid phase is considered a part of soil and is not distinguished as an independent component of ecosystem. In this respect, soil samples analysis used to be the approach to investigate the soil liquid phase (water extracts, suspensions, and insulation of soil solution, non-destructive methods). This approach is characteristic for the soil investigations and the relation between the composition of soil solution and solid soil phase can be elucidated only. However, soil liquid phase is an element of an ecosystem, situated at the boundary between the living matter, solid soil part, the atmosphere (soil air) and sometimes ground water. The properties of soil liquid phase reflect the overall impact of all these components and a range of environmental factors, which determine the chemical conditions in ecosystem and plant nutrition. Therefore, as far as the properties of soil liquid phase are concerned, it should be recognised as a separate structural element of ecosystem. The presented results provide support for this approach (see part 4.6). Traditionally, soil science has viewed soil as a three-phase system (soHd, gaseous, liquid phases) and organic (including living) matter. The notion of "phase" is only used in a conventional sense, and in the strict sense, and does not correlate with the thermodynamic definition. According to this definition, a phase is a sum of system components, identical by their chemical composition and thermodynamic properties in the state of thermodynamic equilibrium (Chemical Encyclopaedic Dictionary, 1983). Although it has been suggested to use more precise notions of "solid, liquid and gaseous parts" instead of "phase" notion (Orlov, 1985), the conventional terminology has sustained, so we prefer to use the term "soil liquid phase".
l.L TYPES OF SOIL WATER
Soil liquid phase is a complicated subject, this may be explained by the diversity of water forms in soil and the characteristics of water itself, in which we ofl;en come across the term "anomalous". Kovda (1973) distinguished a range of basic water forms in soil: vaporous, chemically hard bonded, crystallizational water, physically bonded (hygroscopic) and slightly
10 bonded (pellicular), capillary, gravitational, ground, surface and that in form of ice. He also said that the nature of the boundaries between them is conventional. At present many scientists think that these soil water forms differ in their energetic status (water potential). Three forms have a bearing on soil liquid phase: layer, capillary and gravitational (Fig. 1). We shall consider their properties and formation later.
Soil water
Chemically bonded (crystallised)
Pellicular (adsorbed) NcDn- solvent ^Olljme
Liquid
Capillary (porous) Soil solution
Gaseous
Gravitational Lysimetric water
Fig. 1. Water forms in the soil
1 1 1 PELLICULAR WATER
Pellicular water in soil is usually associated with the concept of'non-solvent volume' (NV) and 'negative ion adsorption', elaborated by the works of A.V. Trofimov (1925, 1927 a, b). It is based on the fact that water surrounding solid soil particles is under the direct impact of surface charge and adsorption force. This results in differences between the energetic status and properties of water contained in soil liquid phase and those of water at standard conditions (potential = 0). Experiments have shown that within this water-layer the anion concentration is lower than in the other of soil moisture; this was the basis for the experimental techniques of non-solvent volume measurement (Trofimov, 1925; Dumansky & Dumanskaya, 1934). With this technique a solution of known concentration is added to an air-dry or fresh soil sample, and via concentration increase
11 in supernatant after thorough mixing and centriftigation, non-solvent volume is estimated according to the: C -C where X - non-solvent volume value, Cj - initial reagent (tested substance) concentration, V solution volume, C2 - reagent concentration after mixing with soil. In the estimation of non-solvent volume it is necessary to remember that this is an abstract concept. It is difficult to imagine the well-defined moisture boundaries, close to solid phase surface, not containing dissolved substances. This is proved by the absence of specific points in the curves of negative suction pressure dependence of residual soil moisture (Kriukov & Komarova, 1956). However, it has been experimentally proven that the concentration of salts close to the surface of soil particles is lower than at a distance from it (Bower & Goertzen, 1955). The actual amount of water under the impact of soil solid phase is much greater than the estimate of nonsolvent volume. The non-solvent volume (NV) value is not a precise physical constant of soil - it depends on the specific surface of soil solid phase and its condition, temperature, moisture and some other factors. The higher the sah concentration in the soil liquid phase, the lower the NV value (Fig. 2). This relation described by the Freundlich adsorption equation, takes the following form: X = 4.95 C'^' - for chernozem, X-^ 1.45 C ' ' ' - f o r loam, X = 0.75 C'-'' - for podzolic soil, where X- non-solvent volume value, C - concentration of reagent. As the alkaUnity increases, so does the value of the non-solvent volume (Trofimov, 1925; Polubesova & Ponizovsky, 1987) and this is probably due to soil colloid peptization; and decreases with the reduction in moisture content (Polubesova & Ponizovsky, 1987). Soil drying decreases the non-solvent volume, whereas this decrease is inversely on soil solution concentration, and directly on initial soil moisture (Trofimov, 1927b).
12
JO^8. ^ 6"o 0 4 .
1
0
-
1
-
2
IgC
Fig. 2. Non-solvent volume in chernozem at different concentration of chlorides (according to Trofimov, 1925)
The NV value is a dynamic variable and may have a significant impact on soil liquid phase composition. Pellicular water plays the role of a buffer: as the concentration of salts in the soil solution increases, a part of pellicular water is being transferred to capillary water, thus it counteracts concentration increases. Leaving the soil fallow leads to a sharp decrease in the amount of bonded water, i. e. to its dehydration, which is not found under vegetation (Trofimov, 1927b). At the end of the vegetation period an increase in NV was observed, which was particularly evident in the shallow horizon of grey forest soil (Polubesova & Ponizovsky, 1987). The values of non-solvent volume depend on the composition of solutions used (Table 1). The 0.01 M CaCli solution technique has gained the widest acceptance, since the CI" ion reacts to the smallest degree with the soil adsorbing complex (SAC) and the NV value may be determined in a most pure state. The values of NV close to CI" solutions are given by solutions of NO3", while sugar solutions give half as much the value. For chernozems, 864^" solutions show no negative adsorption, i. e. S04^" ions are partially adsorbed by the soil surface, resulting from the interaction with the adsorbed two-valency SAC cations (Trofimov, 1925). The fact that only anions (CI", NO3", HCO3", partially S04^") negative adsorption has been discovered, while soil colloids bear a negative net charge, allow to consider the electrostatic interaction between anions and the surface of soil solid phase (Nye & Tinker, 1977) as a cause for negative adsorption. However, this is an incomplete interpretation, for the absence of negative cation adsorption would mean that the concept of non-solvent volume is significant for anions only, while there is a sphere of increased cation content around soil colloids. This makes the
13 concept of non-solvent volume pointless, as if it really becomes non-solvent for some of the anions and supersolvent for cations. At the same time, this interpretation of the non-solvent volume concept does not correspond to soil solution homogeneity investigations, presented in the next section: at high pressures the replaced soil solution has an increasingly smaller content of soluble salts, particularly Na^ cation.
Table 1 The value of non-solvent volume (NV) for various soils Soil*
Depth
Wh**
Wmax**
Humus
Tested
(%)
(%)
substance
1.0-1.8
(%) _*** -
7.13
6.66
-
NV (%)
(cm)
6.4
5.9-6.0
Grey forest
: -
2.6-3.0
2.6-2.8
Slight sod-podzolic
0-10
2.59
2.55
20-30
3.30
2.70
-
Chernozem arable
Loam Sod-podzolic (ashy horizon) Southern chernozem Ordinary chernozem Leached chernozem Greatly leached chernozem Solonetze
Grey forest arable
9.7 7.4 11.4 1.4-3.6
7.1-8.8
6.6-7.8
6.8-6.9
5.8-6.0
3.2-3.8
3.2-3.7
0-20
5.6-11
20-30
6.2-10
30-50
9.3-10.7
10.5 10.5 10.5 3.36 1.45
_
CaCl2
Reference
Trofimov, 1925
-
HCl
6.72
Sugar
Dumansky &
7.8-8.1
Sugar
Dumanskaya, 1934
5.6-7.4
Sugar
5.5-6.2
Sugar
4.1-6.1
Sugar
1.9-4.7
Sugar
CaCl2+NaOH CaCl2 CaCl2
4.37
Sugar
0.97
Sugar
-
CaCl2
Polubesova &
CaCl2
Ponizovsky, 1987
CaCl2
* The soil type by FAO UNESCO - see "Correlation between soil names ** Wh - hygroscopic moisture; Wmax - maximum hygroscopic moisture; *** "-"-no data available.
In our opinion, it is more correct to explain the non-solvent volume phenomenon rather by the fact that the water around the surface of solid particles has a different structure and, consequently, different dissolving capacity (Travleev & Travleev, 1979), than by electrostatic repulsion of ions. Against this background take place the various processes of sorption and ion exchange between SAC and soil solution. Resulting from the superposition of these processes, the conventional non-solvent volume estimated value may vary for different ions.
14 The negative ion adsorption phenomenon seems to be affected by the following three processes: (i) the electrostatic interaction between the anions and solid phase surface, (ii) the lower solvent capacity of layer moisture, and (iii) positive anion adsorption. From this point of view, an experiment with a non-dissociating substance, like sugar, is the most accurate approach to estimate non-solvent volume. For different soil types bonded water varies between single and double maximal water hygroscopicity. Such range makes it difficult to express the concentration in units per 100 g of dry soil. Since precise experimental data on the value are hard to obtain, given the conventional estimation techniques, we take soil hygroscopic moisture as adsorbed moisture volume, i. e. the moisture of an air-dry basis. The latter somewhat corresponds to NV value estimated in a sugar solution (see Table 1).
1 1 2 CAPILLARY WATER
Capillary water also called free or pore moisture, is a major part of the soil liquid phase. It is the most available to plants. The concept of soil solution in the wide sense is associated with capillary water. It is the part of soil moisture that is investigated mainly in in situ measurements by ion-selective electrodes, it can be extracted by pressure, centrifugation, liquid substitutes, etc. Here we shall focus on the heterogeneity of capillary moisture, for this problem also deals with the relation between the replaced and non-replaced soil solution and the one between the results of in situ measurements and results of analysis of replaced soil solution. There are two reasons for capillary water to be heterogeneous: •
Firstly, the soil liquid phase is heterogeneous as a result of non-equilibrium processes,
uncompensated difRision, and possible emissions of substances by plants and micro-organisms. Such heterogeneity corresponds to the nature of soil with its biota. •
Secondly, the different properties of water on different distance from solid surface.
Moreover, the presence of capillary water in pores of different size, intra-aggregate and interaggregate pores may influence water properties, what closely connected with energetic status.
15
a
C (meq/L)
ae (n- 1 0 Ohm') 89 (n -lO'^Ohm'^)
SB
inn-
Na"
_
100
150 a mixture of solution and dispacement agent
cr 80 •
80
60 -
60
40 •
40
Mg^-
heterogeneous solution
50
sof
20 •
100
20
Cat_ Time
. homogeneous solution
Time
Fig. S. The change in bentonite-replaced sohition content (a), and the conductivity of NaCl solution, replaced from silicagel by ethanol (b) in the course of time (according to Kriukov, Komarova, 1956)
A detailed investigation of soil liquid phase heterogeneity was presented by P. A. Kriukov and N.A. Komarova (1954, 1956), who used the methods of soil solution replacement under pressure and by ethanol displacement. It was demonstrated that the heterogeneity of replaced solutions depends on solid phase surface properties, on its hydrophilic properties and electrolyte concentration. Thus, in a series of experiments with clay substances (bentonite, ascangel, caolin), pre-purified, dried and carefully mixed with NaCl solution, the concentration of CI' ions in solution fractions consecutively isolated by pressing, initially it was constant and then it decreased (Fig. 3). The more hydrophilic the clay, the higher the heterogeneity of the solution. At the same time, the less electrolyte concentration was used, the faster it changed at pressing. Low hydrophilic substances, such as montmorillonite clays, demonstrated no heterogeneity of replaced solution at high electrolyte concentrations. But the degree of heterogeneity varied for different ions (Fig. 3 a). The heterogeneity was not related to the solution replacement technique, because similar results were obtained with ethanol substitution of solutions (Fig. 3b). Heterogeneity was higher at larger soil moisture. Replacement of soils solutions from salt affected soils revealed no heterogeneity in solution fractions (Komarova, 1939). The above results correlate well with the concept of non-solvent volume, considering capillary moisture a more or less homogeneous substance. The portion of pelHcular or bonded water under the direct impact of surface forces of soil particles may be greater in soil liquid phase
16 compared to the measurement, based on the non-solvent volume value. This is particularly important in hydrophilic soils with low concentration of salts in their liquid phase.
1 1 3 GRAVITATIONAL WATER
The gravitational moisture of a soil is slightly susceptible to the influence of soil solid phase. It moves in the soil under the effect of gravity and is of temporary nature for soils of normal water regime (resulting from spring thawing, heavy rains or irrigation of agricultural lands). There is a relation between this form of moisture and the lysimetric water. This may be more relevant for research of substances migration in the soil profile than for plant nutrition. Since both capillary and gravitational moisture is under the influence of the soil, the chemical composition of soil solutions and lysimetric water is similar. Differences are determined by the lack of equilibrium between lysimetric water and soil solid phase. On the other hand, lysimetric water reflects the composition of snow, rain or irrigation water, and the interactions between them and aboveground plant parts and litter, the atmospheric air, which differs from soil air. The soils of light mechanical composition and low adsorption capacity (podzol) have similar soil solution and lysimetric water properties (Evdokimova & Pervova, 1977). Lysimetric water is less minerahsed, what is true for most components although there are exceptions. The soil solutions replaced from a chernozem monolith with high Ca^^, Mg^^, SO4 ^' ion concentrations were an order poorer in HCO3" ions than lysimetric water (Samoilova & Demkin, 1976). This was also observed for leached chernozem samples, which showed increased pH in lysimetric water in all cases especially in summer and autumn (Atanasov et al., 1981). Compared to soil solutions, the K^ and C content was higher in the lysimetric water of podzolic soils (Evdokimova & Pervova, 1977). The NH4" turned out to be higher in penetrating solutions compared to soil solution, replaced by centrifugation from sandy brown podzolic soils. In this case, the environmentally important relation between the proportions of Ca/Al and Mg/Al concentrations was more narrow in the penetrating solutions, which is explained by the mobility of colloidal Al in the undisturbed soil pores (Hantschel et al., 1986). Reintam and Saarman (1973) found high amounts of suspended material in lysimetric water of brown soil and they demonstrated clay transport in soil profile and the presence of lessivage in soils of Estonia.
17 The material showed that among the components of soil liquid phase, the central place is occupied by capillary moisture, considering either its proportion to other water forms, its salt content, or its effects on soil processes. Pellicular water has a part of a buffer for external impact, while gravitational water is predominantly concerned with the redistribution of substances in the soil profile, thus forming the genetic horizons. Capillary water on the other hand, affects most processes such as soil formation as well as plant nutrition processes.
1.2. SOIL LIQUID PHASE IN ENVIRONMENTAL RESEARCH According to Vernadsky (1960), most water on of Earth is represented in form of soil solutions and ground water exceeded in weight only by the water of oceans. The study of this important water category often integrates different, sometimes independent branches of science, such as soil science, agronomy, biogeochemistry, plant physiology, physico-chemistry, ecology and others. Perhaps, the liquid phase of soil has been investigated in the deepest detail in soil chemistry. This is because the composition and the dynamics of soil solutions are the indicators of important physico-chemical processes in soils. The composition of soil liquid phase is also important for the identification of soil forming processes, as well as for plant nutrition. The liquid phase has been considered to a lesser degree with regard to environmental factors. In this field, it occurs to be of great interest because it is a habitat for plants and many micro-organisms, and a chain between living and dead matter, and between ecosystem components. The ever increasing anthropogenic pressure on natural ecosystems is accompanied by changes in composition and other properties of soil liquid phase, which can affect living organisms. In this respect, the solutions of soil are indicators of environmental change, and prospective means of environmental norming of anthropogenic impact (see Section 7.5). The following chapters give a more detailed view on these subjects. We shall focus on the question of significance of soil liquid phase to plant nutrition. The liquid phase of soil is the direct substrate for uptake of nutrients by plants. "Soil solution composition proves to be the most directly correlated soil index of absolute bioavailability" (Wolt, 1994). It would be a simplification to state that the composition of soil solution fully determines the process of plant nutrition because the soil solution contains only a small portion of the plant nutrients (see Table 37). From the chemical point of view one of the most important is the intensity factor - ion concentration or activity in soil liquid phase (Fig. 4). The level of ion uptake is determined by ion
18 activity instead of concentration (Khasawneh, 1971). The intensity factor is determined by ion chemical potential (jij) according to the following equation: |Lii=m^ + RTlnai,
where |ii^- standard potential, T - temperature (K), ai - ion activity, R - universal gas constant.
Ion uptake by plants
1
Intensity factor- ion activity in soil liquid phase
Replenishment factor
Relative intensity factor the dependence of ion uptake on ion interaction
1 Nutrition supply (reserve) gross exchange forms of elements
Buffer capacity the resistance of the system towards changes
Fig. 4. Factors determining ion uptake by plants from soils (according to Khasawneh, 1971)
An example, illustrating the influence of intensity factor on plant, is presented by Adams and Lund (1966). They showed that the relative root length in acid soils of different chemical, physical and mineralogical properties is explained exclusively by the amount of dissolved aluminium in replaced soil solutions (Fig. 5). Root growth was correlated to Al content in the soils (soil solutions) and to CaS04 solution. No relation was observed between root growth and the amount of exchangeable Al for any soils studied. The growth of Sudan grass and cotton-plant roots was inhibited at equal NH3 concentration in both solutions of different soils and nutrient solutions (Bennett & Adams, 1970). The replenishment factor plays a decisive role in the sustainability of plant nutrition. The ability of a soil to support ion activity in a certain range depends on soil buffer capacity, nutrient reserve in solid phase and the speed of mobile pool and long pool transition of reserves into solution. This speed is regulated by plants themselves, releasing various substances into the liquid phase (Snakin, 1980).
19
AI(mol/L-10"')
Fig. 5. The influence of Al concentration in soil solution on growth of cotton-plant roots (Adams & Lund, 1966)
There is no multi-purpose equation for the description of replenishment factor, several attempts of qualitative approaches have been made in this field. For a range of calcareous (Olsen & Watanabe, 1963) and acid soils (Baldovinos & Thomas, 1967) it has been shown that the amount of P, diffusing from soil particles surface to the roots, may be described by the following equation: M^a-c-yJD'b, where c - P concentration in soil solution, h - buffer capacity, D - porous system diffijsion coefficient, a - constant that lamps other factors in the original equations which would remain constant for a given soil of a given moisture content. If we express a-^D
by y^ and introduce the
concept of nutrient pools of q = c-h, then the following expression:
describes the relation between ion uptake, intensity and buffer capacity (Khasawneh, 1971). The relative factor (ion interaction factor) leflects the influence of other ions in the course of uptake of a given ion. The fact that for a given plant ion activity in the soil liquid phase may be insufficient or excessive is explained by ion interaction. An interesting example is given in the works by Howard and Adams (1965). They showed that cotton root growth decrease sharply, if Ca is less than 20% of total ion concentration in the soil solution (Fig. 6). In the general, the factor may be expressed as:
K= ^^+^'+S^-^;
20
where V, - uptake rate of/-th ion with activity a,; K„ax - maximum uptake rate when activity is not a limiting factor; QJ - activity of other ions; k, and k, - Michaelis constants. Such equation describes cases of competitive inhibition (Epstein & Hagen, 1952).
^ 1.0 5 0.6 ^ 0.4 Q:
0.2 L ,
0.2
0.4
0.6
0.8
1.0
Fig. 6. The relationship between Ca concentralion and total ion concentration on growth of cotton roots (Howard & Adams, 1965)
The material showed that the composition of the soil solution is a satisfactory indicator of plant nutrition conditions. Nevertheless, the problem of soil liquid phase in the functioning of ecosystem has received little attention. This is explained by the relatively poor development of study methodologies.
21 CHAPTER 2. SOIL LIQUID PHASE INVESTIGATION
In order to study the role of soil liquid phase (SLP) in the functioning of a ecosystem, it is necessary to find more advanced methodologies. In our opinion, it should consist of developing methods of in situ analysis of the composition of soil liquid phase without violating the interactions within the ecosystem. This approach does, however, not exclude the use of traditional methods. The investigation of SLP has a history of more than 100 years. During this period, numerous analytical techniques were proposed (Fig. 7). There are two different methodologies: (i) application of various types extractions of soil liquid phase such as soil solution in order to fiirther determination of their composition; (ii) attempts to analyze soil liquid phase composition in situ.
Soil liquid phase
Field measurements in situ (ionometry, conductometry)
Laboratory analyses of soil samples
Direct analysis with the help ISE
Non-solving volume measurement
Lysimetric water investigation
Preparation of water extract
Extraction of soil solution (by liquid displacement, pressurization, centrifuging, combined methods)
Fig. 7. Scheme of investigations on the soil Uquidphase
The first approach allowed wide range of analytical techniques for determining the numerous components of soil solutions. Concerns have however been expressed that the
22
composition of extracted soil solutions differs from that of the SLP (Shilova, 1964; Kriukov, 1971; Kovda, 1973; Hedroitz, 1975a, etc.). The second approach could start only with the development of potentiometric (ionometry, determination of soil redox potential) and conductometric (determination of the overall salt concentration) techniques. It is characterized by a limited range of determined parameters due to the lack of ion-selective electrodes (ISE) suitable for soil-chemical investigations. This enabled us to begin the in situ measurements in SLP investigations. The method of water extracts that have been named as soil solutions before, should be considered a method of SLP analysis. Water extracts are widely used in chemical analysis for determination of the amount of readily soluble salts in soils and their temporal changes. They provide only indirect information about the SLP composition, and in our investigations, they were only used for comparison. Below are brief descriptions of the methods of soil solutions extraction. More detailed information can be found in reviews by Komarova (1968); Kriukov (1971); Skrynnikova (1977).
2.L METHODS OF SOIL SOLUTIONS EXTRACTION
The displacing liquid method was first introduced by T.Schloesing and water was used as the displacer (Schloesing, 1866). The insufficient degree of displacement, the impossibility of precise determination of the borders of out-flowing soil solution in this variant prompted the idea to search for some new displacers. Ischerekov (1910) chose ethanol and Komarova (1956) brought improvements into the technique (Table 2). The most suitable for this process was to use 100-150 cm long plastic or glass tubes with the inside diameter of 4 cm filled with a mixture of the investigated soil with purified quartz sand. On the top 10-20 ml of ethanol was poured every 2 hours. The degree of extraction is indicated by the appearance of ethanol in the outflowing solution. The testing of the solution for alcohol is made organoleptically.
Table 2 The percentage of soil solutions displacement by various displacers (Komarova, 1956) Displacing liquid
Displaced soil solution (%)
1,4-dioxane
61
Ethanol
57
Methanol
45
Acetone
58
23
The Ishcherekov-Komarova method proved to be convenient, which accounts for its wide usage. The possibility to automatize the feeding of displacer (5-10 ml/hr) by the use of peristaltic pump or the appropriate capillaries (Fig. 8) enabled to increase the degree of soil solution displacement and to standardize the displacement conditions. In the course of the investigations we did not face the need to mix soil samples with sand for better displacement. Automatic inflow of alcohol made it impossible for ethanol to go stagnant except for soil samples of heavy mechanical composition with high moisture content. This method allows to obtain soil solution even at low moisture condition of the soil samples.
Fig. 8. The unit for ethanol displacement of soil solutions operating with capillaries for maintaining automatic inflow of ethanol Suction or press out soil solutions became widely used after publication of works by Richards (1941) and Kriukov (1947). The high pressure equipment (up to 10,000 kg/cm^) was used. This allowed to obtain soil solution and to extract part of the soil bound water. The method of pressing, despite the complicated equipment it requires, proved to be efficient to investigate the soil liquid phase. As a rule, a pressure of 50-200 atm. is used. Instead of excessive pressure, suction is often used, but soil solution can only be extracted in case the soil is moist.
24
Centrifugation can also be used to extract soil solutions, but it lacks the benefits of the two previous methods. Usually, a speed of 5000-9000 rpm is used, and the remaining moisture corresponds approximately to one third of the hygroscopic moisture (Komarova & Knyazeva, 1967). The willingness to obtain soil solutions under minimal change as compared to their natural state has resulted in a number of combined methods. Doyarenko (1924) noted, that displacement by ethanol does not extract a true solution in terms of its physical properties as ethanol changes the osmotic pressure as well as the conductivity. The use of high pressures leads to additional dissolving of components. Depression causes an increase in evaporation which leads to a higher concentration. That is why Doyarenko suggested to extract soil solution using oils that do not mix with water like purified linseed or vaseline oil. Fresh soil should be mixed with oil during which soil solution turns into an emulsion and can be further extracted at a low pressure and the oil is extracted after centriftiging. Shmuk (1923) used a similar method. It is also possible to combine the methods of displacement by ethanol and pressing (Kriukov & Komarova, 1956), centriftiging and displacement by a liquid that does not mix with water, e.g., e c u (Mubarak & Olsen, 1976; Adams et al., 1980), extraction of soil solution by pressure of compressed gas with the use of Richards' press (Fedorovsky, 1964).
2.2. lONOMETRIC ANALYSIS OF SOIL SAMPLES
Analysis of soil liquid phase without extraction of soil solution was carried out for the first time in the 19* century (Whitney & Means, 1897). However, the electrometric method for determination of the presence of readily soluble salts in soil on the basis of impedance measurements never became popular mainly because of the necessity to correlate the obtained data with the type of soil and moisture content. Later the conductomentric method turned out to be rather useftil, for example, in studies of salt affected soils (Gorbunova, 1977). The method of direct determination of the concentrations of salts in soil solution by the freezing temperature of the soil also never came to be widely used (Bonyoncos, McCool, 1915). Potentiometric technique allowed direct measurement of the soil liquid phase composition. Long before the first ion-selective electrode (glass hydrogenous) was designed, works on pH determination in soil and redox potential in soil suspensions and pastes had been published (Remezov, 1929; Trofimov, 1931). As various new ion-selective electrodes were designed, the number of researchers applying ionometry in soil investigations grewV The new method eliminated
^ See: Nikol'skii (1930); Nikol'skii, Evstropiev (1930); Marshall (1942); Avakyan (1953); Eisenman et al. (1957); Kerzum, Gorbunova (1973); Materova (1969).
25 what was seemingly impossible to cope with - the problem of extraction of soil solution in an unchanged form. At the same, time ionometry arouse a number of problems connected primarily with the notions of separate ions' activity and the possibility of distortion induced by the charged suspensed particles and the gas phase while carrying out measurements in suspensions and soils ("the suspension effecf). Excessive exaggeration of these problems often makes some scientists reject the possibility to interpret the data obtained by ISE in colloidal systems. Let us consider these troublesome issues without detailing the general problems of ISE usage which have been discussed by other^. 2 2 1. ACTIVITY AND CONCENTRATION OF IONS The potential (E, mV) of a system of electrodes (ISE - reference electrode) registered by a measuring unit (pH-meter, millivoltmeter, ionometer) in general correlates with Nernst's equation: n 2,3'RT E = E'±-^-^^\ga^
(1)
where E^ - the system's standard potential, mV; R - the universal gas constant; T - the absolute temperature, ^K; z - the measured x ion's charge; F - Faraday constant; GZ - the measured ion's activity. The symbol "+" is valid when measuring the cation activity; the symbol "-" - when measuring the activity of anions. Under room temperature (+25''C), if the ionic activity changes 10 fold, the system's potential should change by 59 mV for monovalent ion, or by 29.5 mV for bivalent ion. Thus, the electrode system's potential is the function of the activity, not the concentration of ions in the solution. The concentration of ions in the solution signifies its quality in a volume unit (or mass) of the solvent. A common way to express the concentration is molarity, i.e., the amount of matter expressed in gram-moles per 1 litre of the solvent, mol/L. In soil investigations, due to small concentrations of soil solutions, the mmol/L values are often used"^. To perform precise thermodynamic calculations it is necessary to express the concentration in molar parts (the number of moles of the matter per 100 moles of the solvent) or molality values (for water solutions - mol/kg H2O), which are very close to molarity for low concentration solutions. Most of the thermodynamic relationships expressed through concentration are applicable only to ideal systems, i.e., such systems where the ionic and molecular interactions are absent. To characterize the real ecosystems' behavior, G. Lewis introduced in 1907 the notion of ion activity - See: Durst (1969); Bates (1973); Nikol'skii, Materova (1980); Cammann (1973); Morf (1981); Handbook of Electrode Technology (1982). The data quoted below uses millimole-equivalent per liter, meq/L to express ion concentration (activity).
26 instead of concentration so that its use should allow applying the laws of ideal systems to real systems. Thus, activity is a function of concentration, differing from the latter by a certain factor, which was called by Lewis the activity coefficient:
where a, - activity, C, - concentration and y!^ - ionic activity coefficient. "Different methods to express the concentration correspond to different chemical potential values in standard hypothetical solutions of unit concentration, and, therefore, to different values of a, in one solution" (Chemical Encyclopedic Dictionary, 1983). Therefore, activity has the same dimensionality through which the concentration is expressed. It is said that the activity is a non-dimensional value (Sposito, 1984), because in a number of thermodynamic equations, activity is expressed through a ratio of fugitivity. The activity coefficient is described by the dimensionality opposite to the concentration. Naturally, activity is an artificial notion and therefore its dimensionality (or nondimensionality) can be made conditional. However, we think that the mentioned approach disagrees with the basic idea of introduction of activity instead of concentration. Special mention should be made of the fact that in thermodynamic calculations the concentration is immeasurable, since the molal part - moles ratio; molality - the ratio of the substance mass to the solvent's mass are nondimentional themselves. We assigned to activity values the same mode of expression as to concentration. An important obstacle in using ionometry is the thermodynamic uncertainty of the notions of activity and the activity coefficient of a certain ion. This is a topical problem in physical chemistry and has been discussed for a long time (Rabinovich, 1985). The contemporary techniques to determine the activity can only be used for electroneutral component, and the activity coefficient determined this way is applied for an average ionic one. The real coefficients of the anion and cation activity of a salt may vary. To proceed with an individual activity coefficient from the average ionic ones, various suppositions are used, which give close resuhs with weak ionic strength in the investigated solution. One of these suppositions holds that the anion and cation activity coefficients are equal in water solutions of KCl:
An practical issue is standardization of the activity scales of individual ions. At present, a standardization has been performed only in the field ionometry. Due to the absence of accepted standard solutions with a known value of activity for various ions, there inevitably arises the
27 problem of ionic activity calculation for standard calibration solutions in the working area of the electrodes/^ Table 3 shows the activity coefficients of some ions in water solutions with various degrees of ionic strength. For diluted solutions the activity coefficient can be calculated through the DebyeHuckel equation:
where z - the ion's charge, / - the ionic strength in the solution: I = —'^c.z^
(Cj - the ion's
concentration); b - the ion's size parameter, with the same exponent as the diameter of the hydrated ion (Table 4); A and B are the constants depending on the temperature and the solvent's dielectric properties (Table 5).
Table 3 Some ion activity coefficients (Butler, 1964) Ions
Ionic strength of the solution (/) 0.0005
0.001
0.0025
0.005
OOl
0.025
005
01 0.750
K^, r , NO3", Cr, N H / , Ag^ 0.975
0.964
0.945
0.924
0.899
0.850
0.800
OH', F '
0.975
0.964
0.946
0.926
0.900
0.855
0.810
0.760
Na^ HC03^
0.975
0.964
0.947
0.928
0.902
0.860
0.820
0.775
Pb'^ CO3''
0.903
0.868
0.805
0.742
0.665
0.550
0.455
0.370
Ca"^^, Fe-^
0.905
0.870
0.809
0.749
0.675
0.570
0.485
0.405
Mg^^
0.906
0.872
0.813
0.755
0.690
0.595
0.520
0.450
Table 4 The meaning o f ' b ' index (Butler, 1964) Ions
b
IT
9 4 3 8 6 5
Na", HC03~ H2P04~ OH", ¥\ K^, c r , Br", F, HS , NO3", NH/, Ag"^ Mg'" Ca'", Cu'^ Zn'\ FQ^\ Mn^^ B a ' \ S ' , P b ' \ C03^' SO4"", HP04^ A\'\ Fe'" PO4''
4 9 4
^ These calculations are not necessary if the task is to directly determine the ion's concentration in the investigated substrate. However, in this case the standard solutions should be prepared so that ionic strength of the standard solution and that under investigation be the same. The latter cannot be carried out to a full extent due to the unknown composition of the soil liquid phase under investigation.
28 Table 5 Parameters for the Debye-Huckel equation (Bates, 1973)
Parameters
-em
Temperatui
30
0
5
10
15
20
25
A
0.4918
0.4952
0.4989
0.5028
0.5070
0.5115
0.5161
B
0.3248
0.3256
0.3264
0.3273
0.3282
0.3291
0.3301
For ionic strength over 0.1 the hypothesis of different degree of preciseness is used. Such is the hypothesis of ionic couples in solution which concentration is subtracted from the total concentration of the given ion determined by conventional analytical methods.
2 2 2 ISE SELECTIVITY COEFFICIENTS
Since there are no absolutely selective electrodes, the potential at the ISE surface depends on the presence of various ions in addition to the measured ones. If the electrode's potential is influenced by both the A''a.nd the ^"'ions, the system's potential cannot be calculated by the equation (1). Here, the following equation is applicable: 0
2,3RT
r
. 1
^ = E +VT7^'4^^+^^/^'<'^J^
(2)
where QA and QA are the activity of ions A and B, KA/B is the ratio of an ^-selective electrode selectivity coefficient to ion B. I^KA/B = 1, then the electrode is not selective to any of the two ions and can be only used to measure the sum of their activity; if A^AB^I, the electrode is selective to the A ions; and in case of the opposite value, it is selective to the B ions. Many ISE are highly selective. For example, a film K^-electrode made of polyvinilchloride using of valinomycin has the coefficient of selectivity to the Na^ ions of lO'^-lO""^, i.e., the ions of Na* can essentially interfere with K" measurements only, when their concentration is thousand fold that of the K" ions. In practice, before starting the analysis, it is important to check the calibration curve, and the electrode's selectivity to the components supposedly existing in the substrate to be measured, since different consignments of ISE are often characterized by varying properties.
29 2.2.3. THE INFLUENCE OF SOLID PHASE ON lONOMETRIC MEASUREMENTS IN SOIL lonometric measurements in a heterogeneous system are often linked with a number of obstacles and with a number of possible errors. The following items should be considered as problems related to the presence of soil solid phase: • to judge whether the composition of the solution extracted from a heterogeneous system is adequate to the composition of the intermicellar liquid within the system; • the possibility of influence of the charged colloidal particles on the potential of the electrode system; • the possible influence of soil moisture on the measurements; • the problems of electrodes calibration in the soil and the problems of qualitative assessment of the effects of the previous factors. The first investigations of the pH in colloidal systems revealed the suspension effect (SE) described by Wiegner and Pallmann (1930). This effect implies that the concentrations (activities) of ions measured in suspension are different from the soil solution extracted by centrifugation or some other method. The nature of SE is still under discussion. We think that there are three reasons for SE: 1. Inadequacy in the composition of the intermicellar liquid in colloidal system to that of the solution extracted from this system. This has been theoretically substantiated by NikoFskii (1939) in the very first theoretical analysis of SE on the basis of the classical ideas about Donnan's equilibrium between the disperse system and the solution. It was proved in a number of investigations and lead to the conclusion that the supernatant's pH cannot characterize that of the whole system (Tschapek et al, 1966). Consequently, the soil solution extracted can only approximately characterize the composition of the soil liquid phase. 2. The existence of a liquid junction potential on the boundary between the electrolyte of reference electrode and the investigated colloidal system. The potential is caused by changes in the transport numbers of the cation and the anion of the reference electrode electrolyte's salt (usually KCl) under the influence of the colloidal particles' charge^. Marshall (1964) and Tschapek et al. (1966) showed that the value of this potential is almost insignificant in case of essential prevalence of the solutions' concentration in the reference electrode over the gross salt concentration in the substrate under investigation, that is if the saturated Silver/silver chloride or Calomel reference electrodes are used. ^ There is another point of view, whose proponents consider the main cause of SE to be the difference in diffiision potentials appearing on tlie boundary of the connection of the reference electrode's salt bridge witli tlie dispersion system and the balanced solution (Overbeeck, 1953; Bower, 1961, etc.)
30
3. Changes in the composition of the dispersion medium in the process of its extraction from the colloidal system, which can vary when different extraction methods are used, especially in such multiphase system as the soil. The first cause has its roots in reality; the second is an error of ionometric method of measurement and this is true suspension effect; the third cause is an error of the method of the solution's extraction from the colloidal system. It is the combination of these causes into the phenomenon named "the suspension effect" that accounts for the controversial character of the conclusions about the SE value, and, accordingly, for some mistrust (Ponizovsky & Kiselev, 1989; Khitrov, 1987) direct ionometry in undisturbed. Let us try to analyze the three causes and the three parts of the supposed suspension effect. Investigation of the SE value can be interesting since it provides information about the disperse systems' properties: determination of the positiveness or negativeness of the particles charge, the position of the isoelectric point and determination of the specific surface. Nevertheless, SE is a disturbing factor in soil liquid phase investigations. The regularities of the SE value have been studied mostly for diluted disperse systems (suspensions, pastes). Only a few investigations of SE in soils with natural moisture content have been carried out, probably due to the difficulty of separating the SE value from the whole combination of effects of the possible interfering factors. It is known that charge of SE depends on the dispersed particles. If the particles are charged negatively, which is often the case in a soil, the value Apx (Apx = pXsuspension - pXbaianced solution) is negative. This is called the "acid" SE for pH measurements. It has been shown for a large number of disperse systems (Chernoberezhsky, 1978) that the growth of the electrolyte's concentration in balanced solution causes a decrease in the SE value. SE dependence on the electrolyte's concentration in the salt bridge moves in the opposite direction, and if the concentrations are equal, SE equals zero. If the particles have charged surfaces, SE decreases as the size of the particles increases. If the proportion of solid phase grows, the SE value grows, reaches a plateau and then decreases to zero, and can even change its charge. There is another, so-called noncontact, method of SE exclusion (Juzefaciuk, 1987). Contact between the salt bridge and the disperse system is provided by a thin water film on the electrode's surface. The set-up time of the equilibrium potential in such a system is tremendous and for that research it is unsuitable for use in practice. Analysis of literature on SE values for soil systems shows that values decrease in the following direction: suspensions - pastes - natural moisture soils. The analysis of pH measurement in 32 types of suspensions showed a difference of 0.1-0.6 pH units compared to the supernatants (Yoshida &, Hirata, 1975). For pastes of saline soils this difference rarely exceeded to 0.5 pH (both
31 positive and negative) and was not found for Na^ (Khitrov, 1987). Yudina and Yamnova (1979) did not observe any difference in the activity of ions of Na"^, K^ and CI' in the paste and the soil solution extracted from Sierozem (Xerosol by FAO UNESCO). Avakyan (1953) showed that for a series soils the following values occurred: ApH=l.1-0.14; ApNa=0.46-0.07. Higher ApH is characteristic for peat soils, whereas the soils of natural moisturization are of lower ApH values. Our measurements carried out using electrodes of six types in eight soils (Zykina et al., 1977) showed that SE decreased with the decrease of water/soil proportion. This can possibly be explained by the increasing degree of peptization under dilution (Table 6). The lowest deviations in the electrodes' reading caused by the suspension effect were observed in soils with field moisture and in pastes 1:1 water/soil proportion, and the highest values (> 20%) - in suspension of 10:1. The difference in the value of potential in suspension and filtrate varies depending on the type and the texture of the soil. In a suspension of 10:1 of sandy gray-brown soil (silt 1%) it is 13%, while in clay meadow soil (sih 20-40%)) and in meadow-steppe solonetze it is up to 60%o, increasing with the depth (Table 7). Table 6 Average ion activity deviation values for 8 soil types (see Table 7) in suspensions and filtrates for some ISE (%) The Ion Determined
soil: water 1 : 10
1 :5
1: 1
Ca^^
23.3
9.0
1.2
5.2
Field Moisture
Ca'" + Mg'^
20.0
7.0
0.1
2.9
K^
-
18.2
4.2
2.0
Na""
22.0
14.5
6.2
-
NO3"
22.2
8.3
2.1
4.0
cr
17.5
21.7
1.7
1.7
Kriukov and Komarova (1956), comparing pNa values measured in pastes and in centrifuged or pressed solutions in a number of soils, silts and clays, observed significant differences only for hydrophilic askangel. In most cases the pNa value in pastes and solutions were equal (Table 8).
32
Table 7 Average ion activity deviation values for 6 ISE (see Table 6) in suspensions and filtrates for various soils (%) Soil*
Depth, cm
Field moisture
Silt
Soil: water
fraction, %
1 : 10
1 :5
1: 1
Grey-brown
0-10
1.0
13.6
8.5
2.7
Grey forest
0-10
5.5
23.1
11.1
2.4
11.8
2.4
Sod-podzolic
0-20
6.3
25.5
Ordinary chernozem
0-10
9.0
20.7
8.8
1.5
Dark chestnut
0-25
9.5
23.8
10.8
1.8
Tundra soil
55 - 65
-
27.3
10.0
2.8
Meadow solonchakous
30-80
24.1
42.0
55.4
57.7
80 - 190
37.3
41.0
43.7
56.2
0-30
15.6
36.4
26.3
5.1
30-80
39.6
68.4
32.9
29.9
80 - 160
41.1
42.9
61.0
26.5
Meadow steppe solonetze
2.1
* The soil type by FAO UNESCO - see Section ^Correlation between soil names"
Table 8 Comparison of pNa values in pastes and solutions (Kriukov & Komarova, 1956) Object
pNa in paste
pNa in solution
Na-kaolin in 0.0In NaCl
1.91
1.91
Na - kaolin in 0.05n NaCl
1.35
1.35
Na-kaolin in O.lOn NaCl
1.07
1.07
Na-kaolin in 0.50n NaCl
0.37
0.37
Na - askangel in 0.0 In NaCl
1.65
1.87
Na - askangel in 0.05n NaCl
1.29
1.29
Na - askangel in 0.1 On NaCl
1.07
1.07
Na - askangel in 0.50n NaCl
0.49
0.49
0-12 CM
2.47
2.47
12 -26 CM
1.11
1.11
26 - 32 CM
0.99
0.99
210-240 CM
0.94
0.94
Silt Karachailag
0.94
0.94
Silt. Pacific
0.31
0.31
Bentonite. Oglanlinsk
1.16
1.16
Askangel
1.18
1.68
Solonetz. medium-columnar:
It might be interesting to evaluate the obtained data in the light of "non-solving volume" concept (see Section 1.1.1). In the mentioned case with askangel, a maximum value of the non-solving
33
volume was achieved, and it is possible that in the given case and otherwise the deviations of pX values in the paste and the extracted solutions were caused rather by emission of some pellicular water than by suspension effect. As a result, the solution obtained was less concentrated than in the soil liquid phase measured directly in the paste. Let us have a look at another possible aspect. The data for soils of natural moisture condition prove that the suspension effect is within the error of ionometric analysis or can be absent (see Tables 6 and 7). It seems that in undisturbed soils of natural humidity the colloidal particles are coagulated (^ - potential close to zero, the size of the aggregates is larger), which also suggests an insignificant suspension effect. The following experiment provided important information. If the displacement tube was filled with ordinary chernozem soil and, instead of a filter, a net with cell diameter 1mm to mechanically support the soil was inserted at the bottom, the ethanol displaced soil solution was almost always transparent. Except when soils were collected immediately after rain or in winter: in this case soil solutions were turbid even if filters were inserted into the tubes. In order to assess the contribution of various phenomena to the SE, we have carried out a number of experiments within the soil-solution system to measure the ion-selective system's potential in the supernatant and in the pellet after centrifugation (20 min, 2000 rpm) in accordance with Fig. 9. The difference in the potential Ei - E2 or E2 - E3 reflects the actual differences between the properties of the intermicellar liquid and the extracted equilibrium solution. This difference is maximal for the given suspension since the difference between the supernatant and the sediment, and not the initial suspension is the subject of research. The difference between the potential E3-E4 or E2-E1 reflects the change in the liquid junction potential's value, and the maximum value for the given suspension. To analyze the third cause, let us consider the differences in the results of the in situ measurements in soil and in the ethanol-displaced soil.
r'l
r'n
r'n ill
^ ' ^
^2|j
Fig. 9. Position of the ion-selective (1) and reference electrode (2) while measuring the suspension effect (3 - supernatant; 4 - sediment)
34 The ionic activity in the macrophases of the heterogeneous system (suspension, sediment, supernatant) is different, therefore the ionic activity in the supernatant cannot be characteristic of the system on the whole (Tschapek et al., 1966). In this sense, the in situ ionometry is the only method to determine the real activity of the ion in the soil liquid phase, while all the methods of extraction of the natural solution from the colloidal substrate give distorted information. Table 9 shows the possible value of such distortion.
Table 9 The difference between potential (mV) of ion-selective electrodes in an equilibrium solution and sediment (Ei - E4 and E2 -E3; see Fig. 9) of grey forest soil suspension, for different ions _
\t
Na"
NO3
cr
(ESL-43-07)*
(ESL- 51-07)
(EM-NO3-OI)
OP-Cl
17±2
1+3
1±2
-4,0 + 1,3
14 ± 4
2±2
-2 ± 4
-7,0 + 1,2
10 ± 5
2±2
-2+4
-11+3
*777£? type of sensing electrode, reference electrode is EVL-IM (silver/silver chloride) - see Table 15
The influence of the solid phase on the activity of various ions differs and is higher for H^ ions with 10-17 mV, which is about 0.3 pH unit. For cations (H^, Na^), there was an increase in the potential when the electrode was moved from the supernatant to the sediment, while for anions (Cf, NO3'), a decrease was observed. Since for cations and anions the calibration curves are opposite directed, the presence of the solid phase the ionic activity decreases. A possible reason for this might be the presence of the non-solving volume, i. e. the soil water around the solid particles with no dissolved salts. This also explains the so-called negative absorption phenomenon described by A.V. Trofimov (1925)). While centriftiging, the volume of the film liquid decreases, and the supernatant comes out more diluted than the intermicellar liquid. The issue of the liquid junction potential is interesting in terms of the ionometry method, since it brings controversy into measurement results. Let us try to assess the value of this uncertainty. Table 10 shows that the difference in the potential value on the boundary between the reference electrode and the medium in case of sediment and the supernatant is about 8 mV. As expected, this difference does not depend on the type of the indicating electrode and the ratio of S:L. Such liquid junction potential value can cause uncertainty in estimation of the activity value of about 0.15 pX for monovalent ions and 0.30 pX for bivalent ions. When the potential of ion-selective system is measured separately in the supernatant and the sediment only the first and the second reasons for SE are relevant. Both the addition (cations) and
35 the partial compensation (anions) of the SE potentials are possible. Our investigations of various soil suspensions also showed an insignificant change in the liquid junction potential (Table 11). Table 10 The difference in the potential (mV) of an ion-selective pair when the electrode is placed into the sediment and supernatant (E2 -Ei and E3 -E4; see Fig. 9) of grey forest soil suspension S: L
Ion-selective pair ESL-43-07/EVL-1M*
ESL-51-07/EVL-1M
EM-NO3-OI/EVL-IM
1 :1
7±2
4±2
7+6
1:2,5
6+3
4+2
8 ±4
1:5
-1+5
4±2
4±4
"^The type of electrode - see Table 15
Table 11 The value of the suspension effect (mV) for various soils (average for suspensions 1:2.5; 1:5; 1:10 by 36 measurements) Type of soil*
Type of the electrode PN03
pK
pCa
Grey forest
-1.5+0.8
-3.9 + 4.9
4.8+1.7
Typical chernozem
-1.2 + 2.0
-11+6.2
5.0 + 2.2
Sod-calcareous
-2.8 + 1.5
0.9 + 4.0
5.0+1.7
Chestnut
-1.7+1.6 -3.2 + 1.8
-
8.8 + 5.4
Sod-podzolic
4.5+1.4
Sierozem
-3.1+3.0
-0.4 + 2.7
2.5 + 2.5
Solonetz
-2.2 + 2.9
2.4 ±3.2
-3.7 ±6.3
* The soil type by FAO UNESCO - see Section " Correlation between soil names "
It is worth mentioning that measuring such insignificant differences in potentials is difficult in terms of fixing their values on the existing equipment, especially on the field ionometers in the presence of the electrode potential drift. The scale point value for field ionometers, made in Russia, is 1-5 mV, for field ionometer 407A made by "Orion" - 10 mV. There is information that as the soil suspension is diluted, the SE can increase (see Table 7). This does not contradict the data of the increase in the suspension effect with the increase in the solid phase concentrafion (Chernoberezhsky, 1978). The latter was observed under the constant concentration of soil liquid phase, while if the soil suspension is diluted with water, the electrolyte concentrafion decreases, as a result the process of peptisation of colloids can occur, which can make SE grow. Our investigation on the SE linked ^-potential showed, that ^-potential of soil colloids is
36 lower when soil interacts with the equilibrium soil solutions than when the solution is diluted with distilled water (Snakin et al, 1989). Therefore we could assume that under natural moisture condition in soils, SE is not be larger than in the pastes and suspensions. The third reason for SE exists irrespective of ionometry. In the process of extraction of the solution from its original colloidal system, various changes can occur in the composition of this solution. Most of the changes take place while collecting soil samples, transporting them and in the course of their further processing while extracting the soil solution. As a multiphase, the soil undergoes critical changes in its regime during this procedure. This affects the soil liquid phase composition, e.g., on its pH value (Table 12).
Table 12 The value of pH in grey forest soil under corn measured in situ (1) and in ethanol displaced soil solution (2) Fertilizer
Date of measurement 29.04.1989
08.06.1989
24.08.1989
Reference
6.6 + 0.7
6.6
6.4 + 0.2
6.7
N.0P6..K,,
6.9 + 0.4
7.1
6.6 + 0.3
7.1
N.0P60K90
7.4 ±0.2
7.1
6.4 + 0.2
7.2
i>0()Pf5<)K9O "*" manure
7.0 + 0.3
7.4
6.1+0.2
7.1
6.3+0.1
7.5
We have shown that the difference in the pH value measured in situ and in the soil solution, is closely linked to the activity of the soil living components (Snakin, 1989). This causes changes in concentration to a maximum of pH 1.5 units. The considered material allows us to conclude that the methodological error of ionometry while analyzing the soil colloidal systems is insignificant, the uncertainty in the obtained resuUs does not exceed 0.15 pX for monovalent and 0.3 pX for bivalent ions and remains within the range of the field method error.
2 2 4 INFLUENCE OF SOIL MOISTURE ON THE lONOMETRIC MEASUREMENTS
Here we shall discuss the reliability of the electrodes' data in soils of various moisture content. The very first ionometric investigations faced the problem of the moisture level dependence of measurement results reliability. The natural threshold of soil moisture, above which the measurements could be possible, is determined by the measurement chain resistance including soil. Taking into consideration the high
37
Ohm impedance of the pH-meters such a threshold is low. Using a quinhydrone electrode to measure pH in sand is not complicated even when its moisture is 1%, though the accuracy of such measurements is lower under lower moisture levels (Trofimov, 1931). The experiments with the non-salty sierozems with various moisture contents led to the conclusion that there are no reasons to deny the reliability of the meter's data within the moisture range of 1-10% (Kerzum et al., 1970). At low soil moistures, another problem can arise: the influence of the suction force of the dry soil on the speed of the electrolyte flowing out of the reference electrode. Apart form that, on the boundary between the reference electrode and the investigated substrate, there can arise an additional out-flow potential which adds uncertainty to the results. The value of this potential (EQF) depends on the speed of the outflow of the solution from reference electrode (Bates, 1973). One could assume that the value EQF is important when the soil moisture is low and the electrode's electrolytic bridge contacts with small capillaries, high suction pressure of which is characteristic under low moistures. If the moisture exceeds the wilting point of plants, the role of this factor should decrease but it is hard to perform calculations and assessment for this factor separately. It was determined that the overall systematic error of pH measurements in soil solution under low moisture levels in the soil samples under investigation amounted to 0.2-0.3 pH units (Meleshko & Pachepsky, 1981). The insignificant impact of out-flow potential on measurement error under sufficient moistures seems to have made Kerzum et al. (1970) confident of the fact that the glass pH electrodes' data are quite reliable when soil moisture exceeds 10%. Such confidence is proved by an earlier work by Trofimov (1931) in which soil samples were used buffered by the saturated solution of magnesium biphthalate and the phthalic acid. For soil moisture of 15% or higher the deviations between the parallel determinations under similar experimental conditions did not normally exceed 0.05 pH for both the buffered and the untreated soils. We should note here that the liquid junction potential's value can be excluded by using a solid reference electrode (Bound & Fleet, 1977) which has no leak of the solution. When measuring in chains without transfer, that is, in the absence of the liquid junction potential, the ion-selective electrodes sensitive to H^, Na^ and Cf ions give correct data even under moisture close to that of air-dry soil (Goncharov & Kiselev, 1987). 2 2 5 INFLUENCE OF THE GAS PHASE ON THE lONOMETRIC MEASUREMENTS IN SOIL (INCOMPLETE CONTACT BETWEEN THE ELECTRODE AND SOIL) In addition to the influence of the solid phase on ionometric measurements in soil with natural moisture, the gas phase can also have its own impact. This is of special importance while
38
determining pH because of the components soil gas phase, primarily CO2, can influence parts of the electrode that do not touch the soil particles and as a result the electrode potential can change. When using the quinhydrone electrode to measure pH by an electrode partially immersed in liquid Trofimov (1931), who was the first to investigate this phenomenon, found that the fact of partial immersion had nothing to do with the deviation in electrode potential. The possibility of distortion of the results of pH measurement in soils by a glass electrode because of the incomplete contact with soil liquid phase has been considered in detail by (Meleshko & Pachepsky, 1981). The possibility of such distortion has been shown when the ball of the glass electrode was only partially plunged into solution. The presence of isopotential point can be observed here, where the potential is independent of the degree of submergence the electrode; and the coordinates of this point are close to the pH value of distilled water, which is in equilibrium with CO2 of the air. It is assumed that on the part of the electrode that is not in contact with soil, there is an absorbed liquid or condensate film. Though interaction with atmospheric CO2 this contributes to the change in electrode potential. The experiments with sand mixed with buffer solutions showed that if the sand moisture is low (2%), the electrode's calibration is close to the scale of the half-sub merged one, and under high moisture (20%) to that of an electrode fully submerged into the buffer solution. The error is calculated by the following equation: A p H = pHmea.surement " pHtrue = ( t t - 1) ' (pHtrue " p H j ) ,
(3)
where pHi is the coordinate of the isopotenial point, a is a coefficient reflecting the degree of the electrode ball's contact with soil; its value for the soils under investigation when the moisture exceeds 5% makes up 0.90 - 0.97. If the soil solution and the film on the electrode's surface are influenced by the same soil air (pHtrue and pHj are very close), then the error of pH determination due to the soil air influence becomes insignificant (<0.1pH). According to T.R. Yu (1985), the influence of the gas phase on the glass electrode potential is insignificant since the water film on the surface of the electrodes acquires the value of pH in soil liquid phase due to the diffusion processes. If glass electrodes are placed into the soil for a long period of time (1-2 days), the effect is of no importance. This chapter on the error of the direct ionometric measurements in soil samples with natural moisture allows to conclude that such measurements are possible and useful since they open up new perspectives in of investigating soil properties without disturbance. The equipment error^ is not large and about 2% for monovalent and 4% for bivalent ions. The result error caused by the various interfering effects can be estimated at 0.1-0.2 pX units. ^' The equipment error is normally estimated at 1/2 of units of the scale of the meter used.
39 The above discussed problems of factors interfering with measurements in soil are important when precise measurements of ionic activity are the case. However, the accuracy of measurements is often hard to achieve due to the heterogeneity of soil properties and low reliability of existing ISE. The number of electrodes available for in situ measurements is limited indeed (H^, K^, Na^, NH4^ Ca^^ Cr, NO3) and this leaves out such ions as Mg^^, AP^, 804^", P04^", HCO3", etc.
2.3. /iY.9/rt/MEASUREMENTS OF IONIC ACTIVITY IN SOIL
Elaboration of methods to investigate the composition of SLP without extraction of the soil solution serves as methodological ground for further advance in this direction: the transition to studying "living" soil in its undisturbed natural state. The choice between the field and laboratory measurements of the study should be made primarily focusing on the goal. However, we should bear in mind that the field equipment (ionometers) is less precise and reliable. Under field conditions, mechanic safety of electrodes is put under great risks; there exists large temperature gradient and as a sequence the reference electrode and the ion-selective electrode can be under different temperatures, and temperature compensation can cause difficulties. It often happens that under field conditions the micro-heterogeneity of soil demands more electrodes or secondary measurements than in laboratory conditions. The weather conditions such as precipitation, temperature can also influence the accuracy of measurements . It is interesting to carry out laboratory analysis of soil samples, which have preserved the natural field moisture, and the basic properties of the undisturbed soil. To settle this problem, we have attempted to compare the results of measurements performed in situ in gray forest soil with planted corn, then in freshly collected soil samples in laboratory with the same electrodes and equipment 24 hours and 5 days later, while the samples were kept in plastic bags in the refrigerator. The data in Table 13 show that the laboratory and in situ results differ, especially for dynamic parameters as such pH and pNOs. These parameters in the collected soil samples vary in time, which must be due to the changes in the gas regime like diffusion of the carbon dioxide when the samples are collected, and then its accumulation as a result of the microorganisms' activity, as well as the increasing rate of denitrification in tight plastic bags. Despite the inconveniences of the field variant and the lower precision of the measurements while studying soil liquid phase, we prefer the in situ measurements. The field redox (Eh) measurements in soils by Remezov (1929) and Serdobolsky (1953), pH measurements in undisturbed soils by McGeorg (1937) and Kerzum et al. (1970), application of various electrodes to analyze the soils of agrocenoses and natural communities undertaken by a
40
number of researchers (Lamm et al., 1972; Nair & Talibudeen, 1973; Yudina & Yamnova, 1979; Yu, 1986) created the basis for the in situ measurements methodology. Table 13 Redox potential and ion activity in the liquid phase of grey forest soil Fertihzer
Time* Eh, mV
PH
NO3
1
617+10**
6.35+0T8
3.5+0.8
r0.11+0.04
NH4^
Reference
0.02±0.01
16.9+6.1
2
607+7
5.93+0.11
4.4+1.3
0.11±0.04
0.02+0.01
1.7+0.5
3
619+33
6.13+0.13
7.2+2.4
0.31±0.2
0.04+0.01
2.4+0.4
Ion activity, meq/L Ca^^
N.oP6..K6n
N.oP6<)K,„
1
564±31
6.61+0.25
4.5+3.9
0.23+0.08
0.05+0.03
56.0+19.8
2
588+15
5.97+0.28
5.5+1.7
0.13+0.03
0.03+0.01
7.1+2.3
3
603±9
6.02+0.23
5.9+2.7
0.32±0.15
0.1010.03
10.3+3.4
1
590+24
6.44+0.20
7.8+2.8
0.32+0.10
0.30+0.24
60.3+23.0
2
588+20
6.10+0.23
10.1+3.7
0.21+0.11
0.04+0.01
7.7+2.1
3
594±36
6.55±0.30
7.6+2.9
0.28+0.15
0.05±0.02
9.2+5.0
6.06±0.17
5.6+1.9
0.31+0.15
0.10+0.07
48.3+28.4
0.05+0.04
8.613.4
0.0810.06
7.3+2.7
N90P60K90
1
595+24
+ manure
2
615+10
5.73+0.13
13.0±5.8
0.32+0.15
3
612+37
6.16+0.27
8.8+3.3
0.30+0.06
* The data, measured on June 7, 1989 in situ at the depth of 7 cm (I) and under laboratory conditions on June 8, J 989 (2) and on June 12, 1989 (3) in the samples collectedfrom the layer 0-20 cm (A ploughed) ** standard deviation
In our previously published works steps have been made to develop this methodology (Kovda et al., 1977; Zykina et al., 1978; Bystritskaya et al, 1981; Snakin, 1989; Snakin, et al., 1991). We shall first have to solve problems with the in situ measurements, such as the compensation of temperature dependence of ion-selective electrodes, and the possible thermodynamic interpretation of the redox potential values obtained by such measurements. 2 3 1 COMPENSATION OF TEMPERATURE DEPENDENCE IN ION-SELECTIVE SYSTEMS During in situ measurements, especially when studying soil processes, situations emerge when the temperature of the standard solutions used for ISE calibration and that of the analyzed objects differ significant. Thus, one of the basic rules of the methodologies is violated.
41 The temperature dependence of ISE, more precisely that of the electrode system: ISE + the reference electrode can be compensated by the temperature resistor which is in almost all ionometers and pH meters. It is incorrect for two reasons: 1. The temperature compensation in the registering equipment does not work in the millivoltmeter mode of measurement, which is used when the calibration curves are employed; 2.
In the "pX" mode the ionometers (pH-meters in the "pH" mode) only count the temperature coefficient of the electrode ionic function's gradient in the Nernst's equation^. Temperature compensation is mostly based on investigations of the glass pH-electrode's properties, and many temperature compensators recommended by producers of electrodes prove to be absolutely useless (Cammann, 1973). It is known that different ISE have different temperature properties (Negus & Light, 1972;
Snakin et al, 1987b) and therefore there is a basic constraint in creating a universal temperature compensator. Let us try to calculate the necessary value of the ion's concentration (activity) if caUbration is made at different temperature than the analyzed sample (soil, soil suspension, paste, natural water). Fig. 10 shows a typical temperature dependence of the calibration curve of the ionselective electrode. Many ion-selective systems have the so-called isopotential point (pXi) in the working area, where their potential is almost independent of the temperature. The higher the concentration difference, the bigger the temperature influence on the appropriate electrode potential. In this case, determining pX through the potential of electrode system's (Ex) by the calibration curve, determined under the temperature Tk (K), we obtain value pXk, which is different from the real (pX).
pXk pX pXj
pX
Fig. 10. Calihration curves of the cation-selective electrode at temperatures Tk and T
' Apart from the temperature coefficient of the Nernst's ionic function, the standard potential and temperature changes also contribute to the ion-selective system's temperature dependency.
42
Let us use the Nernst's equation (1) to describe this case. For one thing , E,=El-h-T,-pX,,
(4)
for another E,=E^-h
TpX,
(5)
It follows from equations (4) and (5) that: ^
E'-Ei^b-T,^pX, b-T
(6)
In the latter, the standard potentials values also depend on the temperature. Within the temperature range of 0-50''C this dependency is about linearly (Lurie, 1971), i.e., E';. =nT^^m^
(7)
E' =n-T + m^
(8)
where n and m are certain coefficients. If we substitute (7) and (8) in (6), we arrive at: ^
n-T + m-n-T^ -m + h-T^ -pX^ ^n(T-T^) h-T b'T
^ T^ ^ t^ p '
(9)
If we mark value nih with coefficient C, we have the following equation: pX = pX,.^-C-(^-l).
(10)
If there is an isopotential point pX = PXK = pXi: pX.=px,.^-r.(^-7),
(11)
therefore C = pXi, and
It can be easily proven that when we determine the anions, the equation looks as follows:
p,--px/f.c-i^-n^
03)
Thus, it is possible to reduce the evaluation of temperature dependence of the ion-selective system to determining coefficient C, which coincides with the isopotential point of the system. Knowing C by the calibration curve built for temperature TK(K) we can calculate activity of an ion to be measured at another temperature T(K).
^ Coefficient b in tliis case marks, for our convenience, tlie value 2.3 R/nF, where: R - the universal gas constant, n- the ion's charge, F - Faraday number; pX = -Igax, where ax - activity of ion x.
43 Let us see specific examples of temperature dependence of ion-selective electrodes within a climate chamber i.e., when the object's temperature and that of the electrodes were kept equal (Snakinet. al, 1987b). Determination of pH by means of a glass electrode is best studied in various aspects. The industrially produced electrodes ESL-43-07 and ESP-04-14 (See Table 15) together with an AgCl saturated reference electrodes EVL-IM have the isopotential point pHi = 7.2±0.5. Therefore, the temperature dependence of this system is: (14) Equation (14) differs from the previously presented equation for glass pH electrodes (Covington, 1969), which took into account only the temperature coefficient of the electrode's ionic function gradient in the Nemst equation: pH = pH,-pH,(^-l).
(15)
Determination of pK is the most widespread because of the availability of film electrodes type EM-K-01 based on valinomycin with an internal solution of O.IN KCl+AgCW. The presence of an isopotential point^ pKi = 2.3±0.3 (Fig. 11) is also characteristic of the EM-K-01 - EVL-IM system, and, therefore, the compensation should be carried out in accordance with the following equation: pK = pK,^-2,3-(^-l).
(16)
E(mV) 70 50 30
Ex,
10
-10
•
•
-30 -50
-
vN 10°
-70 -90 I
. L_. 2pKi 3
\ \ \
20° 30°
pK
Fig. J J. Temperature dependence of the potential of the measurement chain with pK-electrodes
'^ Detennining the isopotential point graphically by crossing calibration curves taken at various temperatures resuks rather in a range of values than in a point.
44
Determination of pCa in soi analyses is also done using film electrodes of the EM-Ca-01 type on the basis of tenoiltriftoracetone filled with 0.05N KCl +0.05N Ca(N03)2 + AgCU- The system's isopotential point is within the pCai = 2.0±0.4 area and the compensation equation is as follows: pCa = pCa^
T
—-2,0-{—-l). ^T
(17)
Determination of pNOs was carried out by film electrodes of the EM-NO3-OI type based on tetradecylammonium salt with an internal fill of 0.05N KC1+0.05N KNO3 + AgClsat. The absence of isopotential point in the working range is characteristic of the ion-selective system of such an electrode (Fig. 12). Since, the graphic position of isopotential point is out of the working range of the electrodes, the C coefficient in equation (13) was analytically determined as follows: C =
3,3±0J. The corresponding equation is:
pNO,=p(NO,),^
+
3,3-{^-l).
(18)
E(mV)
Fig. 12. Temperature dependence of the potential of the measurement chain withpNOs
electrodes
It is worthwhile to emphasize that the above-mentioned coefficients are valid only when the AgCl saturated reference electrode is used. When other reference electrodes with different temperature dependencies are used, the coefficient's value are different. For example, for the system of the EM-NO3-OI electrode coupled with a saturated Calomel reference electrode, the C coefficient's values are equal to 1.5±0.5. Such ion-selective systems are not subject to temperature
45 changes. For such measurements it is not recommended to use calomel electrodes since the equilibrium potentials on the boundaries between the half-element and the electrolyte appear much slower at temperature changes than when the Ag/AgCl electrodes are used (Handbook of Electrode Technology, 1982). Let us try to assess the accuracy of the suggested equations. We calibrated the ISE at lO'^C and at the object's (soil solution) temperature. As seen in Table 14, the use of equations 14, 16 & 18 gives same values as determined by calibration curves drawn for the respective temperatures. Table 14 provides clear evidence that it is necessary to take temperature into account when analyzing soil solution. Table 14 Comparison of pX values in soil solutions at different temperatures Ion
Temperature
determined
of the object (^C)
1*
pH
10
4.36
4.26
10
8.74
8.79
8.80
25
4.36
4.41
4.43
10
3.86
3.92
3.92
10
2.98
3.00
3.03
pK
pNOs
pX
4.27
25
1.34
1.36
1.40
25
3.86
3.76
3.74
25
2.98
2.97
2.95
10
1.34
1.31
1.27
10
2.86
3.08
3.06
25
3.76
3.64
3.64
25
1.95
1.86
1.85
25
2.86
2.76
2.75
10
3.76
4.01
4.02
10
1.95
2.13
2.11
* 7 - calculated by the calibration curve at 20^C, 2 - calculated by the relevant equations, 3 - calculated by the calibration curve built at the temperature of the object of investigation
Measurements of objects of various temperature or of temperature different from that of the standard calibration solutions equations 14, 16-18 may be used for ion-selective systems. In general, it is necessary to determine the C coefficient's value in equation 10 or 13 possessing the numeral value of the relevant isopotential point.
46 2 3 2 THE SELECTION OF ELECTRODES
The rapid development of ionometry in the past 30 years made it possible to analyze more than 30 ions. But most of the electrodes cannot be used for in situ measurements in soil. Electrodes should meet the following requirements for in situ measurements: • sufficient sensitivity for measuring the expected range of the activity of the ion to be measured; • sufficiently high selectiveness to the ion under investigation in the presence of other ions in the SLP; • independence of the electrode's potential of the pH value within the range of its possible values in the analyzed soils; • mechanical durability enabling to place the body of the electrode into the soil without causing demage of the sensitive membrane or other parts. If the membrane electrodes are coated with plastic, it is advisable to place bars of inert material inside the membrane to increase its durability; • the membrane's construction that would ensure the maximum contact of the electrode's sensitive element with the soil; • reliable isolation between the electrode and the contacts to avoid short circuit through the soil while performing the measurements. According to our 20 years experience in this area, we have the possibility to measure the activity of ions H^, Na^, K^, NR^^, Ca^^, NO3", CI' in the liquid phase of some types of soils using industrially manufactured electrodes. Table 15 gives some electrode types and electrode producers. Prior to utilization it is worthwhile to analyze the documentation of the electrode, and, naturally, to test a number of their properties (sensitivity, selectivity, stability).
Table 15 Properties of ion-selective electrodes T\pe of electrode
Range of
Selectivity
pH range
1
2
Temperature
Manufacturer*
range ("C)
measurements 3
4
5
6
H^ ESL-43-07
0-12 pH
0-40
1
ESL-63-07
0-14 pH
25-100
1
ESL-41G
0-12.6 pH
0-40
1
ESL-UG
-0.5-12 pH
20-100
1
OP-0718R
0-14 pH
5-60
3
OP-0808R
0-14 pH
5-60
3
47
Table 15 (continued) 1
ESL-51-07
-0.5-4 pNa
OP-Na
1-6 pNa
941100
0-6 pNa
EM-K-01
1-4 pK
K^-3-10"^**
>pNa+4
0-100
1
>pNa+3
0-80
3
0-80
4
0-50
2
0-60
3
NH/-2-10"^
Na^-5.10-^ NH/-5-10'^
OP-K
0-6 pK
ir-6-10"^
3-10
Na'^-3-Ur' NH/-MO"^ 931900
0-6 pK
19-15
1-6 pK
0-40
4
0-40
5
0-50
2
0-6
0-60
3
4.5-10
0-50
2
0-50
3
0-40
4
4-8
0-40
5
2-9
0-50
Na'^-MO"' ir-4.10-'
EM-NH4-OI
l-4pNH4
OP-NH4
I-6PNH4
EM-Ca-01
0.6-4 pCa
Na'^-S-Ur^
r-1
Mg'^-2-10-' Ba^^-2-10-^ Na^-2-Ur^ K'^-2-10"^ NH4^-3-10~-'
OP-Ca
0-5 pCa
Na^-S-lO"^ K'^-5-10^ Mg^^-MO'^ H^-2-10'*
932000
0-6 pCa
20-15
1-5 pCa
Ba'^-5-10-^ Mg^^-9.10-^ K'^-MO' NaM-lO--'
N03 EM-NO3-01
0.3-- 4 PNO3
SO4^~-M0"^
cr-MO"^ HCO3-2 -10
Br, r -no
48 Table 15 (continued)
OP-NO3
1-5 PNO3
Cr-10"'
0-50
Br-lC' 930700
1-5 PNO3
07-25
1- 6 PNO3
r-12
0-40
4-10
0-40
cr-5-10-^ HC03'-4 10^
cr EM-ci-01
0.2-3.5 pCI
5-50
In absence of S'~,Br,J,CN
OP-Cl
1-5 pCI
3-10
0-80
Br"-3 10^ J -2-10^ S~" - traces 931700
0-5.3 pCI
17-17
1-5 pCI
0-50 Br -110^
1-13
0-80
r-MO' OH"-8-10' In absence of S^
r OP-F
1-6 pF
940900
0-6 pF
OH -10"^
5-5.5
0-80 0-80
J OP-J
1-7 pJ
OH" - 10^
3-12
0-80
cr-io' Br - 2 - 1 0 ' S^~ - traces 945300
0-7.3 pJ
OP-S
0-6 pS
0-80
In absence of
13-14
0-80
0-60
interfering ions AgVS'EAL-01
1-8.5 pS
2-14
1-4 pAg
0-5
941600
0-7 pS
0-80
0-7 pAg * 7 - CwmeI Measurement Equipment Factory (Belorus); 2- NPO ^'Analitpribor" (Georgia); 3 - "Radelkis" (Hungary); 4 - "ORION Research Inc. " (USA); 5 - "Crytur" (the Czech Republic) ** " coefficient of selectivity
49 Measurement of pH seems to be the most well-developed one. Many pH-electrodes are available and usable such as ESL-llG, ESL-41G, ESL-43, ESL-63. In order to avoid destruction of the glass ball avoid excessive pressure when installing the electrodes in the soil. Gravelly soils are the most dangerous, and attempts to establish a maximum contact of the electrode with soil may damage electrodes. Measurement of Ca^^ ions activity can be carried out by means of the EM-Ca-01 type electrodes. These electrodes are sensitive and selective for measurement of Ca ^ in most soils. There is evidence that electrodes EM-Ca-01 and 20-15 type are less selective in comparison with EIM-3 electrodes (Shaimukhametova et al., 1987), which is of importance when analyzing saline soils. Measurement of K"^ ions activity can be best performed by EM-K-01 electrodes based on valinomycin. Glass electrodes of the ESL-91 type are significantly less sensitive and less selective to Na^ and N H / ions, which makes them unsuitable for soil research. In saline soils where Na activity exceeds that of K by a factor of 200 the use of valinomycin electrodes is hardly possible. N H / ions activity can be measured by EM-NH4-OI electrodes. There are significant limits due to the relative low sensitivity which is 10'"^^ mol/L, while for a number of soils ammonium ion concentration is below 10"^ mol/L. Selectivity almost totally blocks excessive concentration of K ions. It is not suitable for saline soils. Na^ ions activity can be measured by means of glass electrodes of the ESL-51 type. But their sensitivity (lO'"^ mol/L) is not always sufficient for non-saline soils. In order to decrease H ions influence on the electrode, the pH value of soils should exceed the relevant pNa value by about 4. Cr ions activity could be measured by EM-Cl-01 electrode. These electrodes are sensitive and selective in most soils. But according to our experience, the drift of potential is significant in CI electrodes. NO3' ions activity can be measured by means of a plastified electrode type EM-NO3-OI. The only exclusion would be highly saline soils where concentration of CI' ions is usually 100 times higher than that of NO3'. There is no experience about the use of ion-selective electrodes in situ for measurement of other ions. The reasons for this might be in the insufficient sensitivity of electrodes (heavy metals, pj), low selectiveness to other ions (pMg) and the necessity to buffer the solution for pH (pF) etc. As new electrodes are developed and their quality improves, the number of ions measured in situ will increase. Selecting the reference electrode. Ag/AgCl and Calomel electrodes are commonly used. The latter are more stable, but they are toxic and more sensitive to temperature changes then the Ag/AgCl ones. The Ag/AgCl electrodes of the EVL-IM type have a conic electrolyte key that is useful when placing the electrode in the soil. For the filling of the reference electrodes KCl
50 solutions with various concentrations (O.OIM; O.IM; IM; 3.5M and saturated are used). To stabilize the diffusion potential, we recommend that more concentrated solutions be used (see Section 2.2). If the electrode is filled with saturated KCl solution in the presence of its solid phase, one can also visually monitor the constant concentration of the reference electrode's internal fill. While carrying out the measurements, it is necessary to balance the internal pressure within the reference electrode by periodic opening the hermetic seal. Choosing the pH/mV meter. For reading the ion-selective system (sensing electrode + reference electrode), almost any field pH-meter or millivoltmeter with a high impedance (10^^-10 Ohm) will do. For example, ionometers 1-102 and pH-150 (Gomel MEF, Belorus), or 407 model pH-meters produced by ''Orion" (USA) could be applied. If the work is carried out with several electrodes simultaneously, then readings should be done on a millivolt scale. 2 3 3 GETTING THE ELECTRODES SET FOR WORK This procedure is prescribed in electrode manual. Usually this implies filling the internal reference electrode if no solid contact exists. Since the internal reference electrode is silver chloride type, the internal solution should contain ions of CI determinable in the concentrations of 10" -10" mol/L This solution should be saturated for AgCl and a few salt crystals should be added. For example, the internal solution of electrode EM-NO3-OI is lO'^M KCl+lO'^M KNOs+AgCl. Some of the electrodes (glass, solid plastic membrane) should be soaked in the solutions containing the ion being determined in concentrations of 0.1-0.01 mol/L during 24 hours. It is recommended to place them with the solution into the thermostat at 40'^C for 6-8 hours. The calibration of electrodes. Ion-selective electrodes are calibrated in a series of standard solutions with the known level of activity of the ion being determined. For preparation of solutions, fixative solution or certain amounts of analytically pure salts are used. The amounts of salts for preparation of 1 liter O.IM solution are given in Table 16. A series of standard solutions within the range of expected concentrations (10" to 10" mol/L) is prepared through gradual dilution of O.IM solution. The appropriate values for various ions activity (pX) are shown in Table 17. It is important to keep in mind that one should not prepare a series of solutions with a wider range than that of the expected activity values in soil. In case there are significant changes in concentration of standard solutions (by an order of 3 or more), the dynamic properties of the electrodes worsen, and it takes more time for calibration.
51 Table 16 Amount of salts necessary for preparation of 1 liter O.IM solution Type of salt
NaCl
NaN03
NaF
KCl
KNO3
KF
NH4CI
Amount (g)
5.85
8.50
4.20
7.46
10.10
5.80
5.35
Type of salt
NH4NO3
NH4F
CaCl2-6H20'
Ca(N03)2-4H20
MgCl2-6H20
Mg(N03)2-6H20
Amount (g)
8.00
3.70
21.90
23.60
20.33
19.43
Solutions ofCa andMg chlorides should be made ofCaCO^
andMgCOs, neutralized by HCl To get 0.1 M ofCaC^
and
MgCh 10 g CaCOs and 8.43 g MgCO^ should be put into 1 L bottles and 0.2 MHCI solution prepared out from standardizing solution should be added. More carbonate should be taken and then the solution is filtered.
Table 17 The values of pX in standard calibration solutions, calculated on the basis of individual coefficients of ion activities using the Debye-Huckel equation The ion
Type of the
determined
salt
~ ^
1:1 (NaCl,
Standard solution concentrations, m/1 ~W
5-10"^
W
5-10"^
1(P
5-10"*
IF
sIP
lO"^
Tm
1.389
2.045
2.334
3.016
3.312
4.005
4.305
5.002 5.302
5-10"^ 10"'
1.123
1.395
2.047
2.335
3.016
3.312
4.005
4.305
5.002 5.302 6.001
1-460
1.694
2.244
2.491
3.098
3.373
4.034
4.325
5.011
5.309
6.004
1-538
1.750
2.264
2.503
3.101
3.375
4.034
4.325
5.011
5.309
6.004
1.123
1.395
2.047
2.335
3.016
3.312
4.005
4.305
5.002 5.302
6.001
0.881
1.143
1.775
2.056
2.726
3.019
3.708
4.006
4.702
6.001
NaN03...) K\NH4"
1:1 (KCl, KNO3...)
Mg^^
l:2(MgCl2, Mg(N03)2)
Ca-"
1.2(CaCl2, Ca(N03)2)
Cr,N03",F- 1:1 (NaCl, KNO3...) l:2(CaCl2,
5.002 5.702
Mg(N03)2)
While carrying out calibration, the temperature of the standard solutions is recorded in order to adjust the results if the actual soil temperature and standard solution temperature differ from each other (see Section 2.3.). To calibrate pH electrodes, special buffer solutions are used. Since their pH values depend on the temperature, it is appropriate to calibrate them according to the values given in Table 18. Measurements in standard solutions should be performed at least twice to ensure that the values of the electrodes' potentials are correct. Prior to each shift to the next concentration of the solution, the electrode system should be rinsed in distilled water and dabbed with soft tissue paper. After soil measurements, calibration should be repeated to avoid the influence of drift in the electrode potential. If there is a coincidence between calibration values before and after soil
52
measurements whereas the difference should be less than 5 mV, calibration data are entered into PC or calibration charts are built (shown in Fig. 10-12).
Table 18 Changes in the nominal values of pH buffer solutions depending on the temperature t ("C)
pH 4.01
6^86
TOO
_
_
9718
_
0
4.00
10
4.00
6.92
7.06
9.33
20
4.00
6.87
7.01
9.23
30
4.02
6.85
6.98
9.14
40
4.03
6.84
6.97
9.07
50
4.06
6.83
6.97
9.01
70
4.13
6.85
-
8.92
90
4.21
6.88
-
8.85
Note: for precise standardization of the pH-meter it is necessary to measure the temperature of the buffer solution and use the true pH values of the solution (Handbook of Electrode Technology, 1982)
Electrodes should be calibrated immediately prior to measurements. Since the in situ measurements are time consuming, it is important that the electrodes' calibration curves remain stable during the 1-2 weeks before going to the field.
2 3 4 THE PROCESS OF MEASUREMENTS IN SOIL
First of all one should decide about the number of electrodes for
simultaneous
measurements parallels, based on the properties of the electrodes, heterogeneity of the soil and preciseness required for the measurements. According to our experience, within the normal ionometric analysis, the error is about 1020% for the majority of soils. We can judge with 90% of certainty about the activity of ions in soil liquid phase based on 10-20 electrodes' data, and for pH and Eh values 5 electrodes are sufficient (Snakin & Kesov, 1984). The electrodes are usually installed in the field at follow. First, the measurement site is selected in accordance with the goals of the measurement. Then, after a previously marked pattern, holes of diameter equals that of the electrodes and 5-7 cm deep are made, after which the calibrated electrodes are carefiilly placed within these holes and tightly pressed. If it is necessary to perform measurements at the depth of 15-50 cm, one should carefiiUy remove the upper layer of soil (-10
53 cm), place the electrode at the appropriate depth in the undisturbed layer of soil and then replace the removed soil layer. When carrying out repeated measurements on the same site, some authors recommend to place the electrodes in the holes for the measurements period, and then replace them with plastic tubes of the same diameter with sealed bottom (Nair & Talibudeen, 1973). To avoid interference of spatial heterogeneity, these authors carried out their measurements in the same holes later, after moisturizing the soil with distilled water in the place of contact with the electrode. However, one cannot rule out the possibility that something affects those holes. There can be only one reference electrode for several sensing electrodes and it should be plunged into soil for the period of data recording to avoid soil contamination by the leaking KCl solution, and no fiirther than 40 cm away from the sensing electrodes. During long-term field investigations of the soil processes it is desirable to place control electrodes in soil. These electrodes will be regularly checked by means of standard solutions and replaced into soil. The electrodes will show stable readings 10-15 minutes after being placed in soil (Gohcnar-Zaikin, 1974; Dmitrienko & Zhupakhina, 1957; Kerzum et al., 1970). While investigating the dynamics of soil processes it is worthwhile to perform readings several hours later or even the following day so that the soil processes recover after having been disturbed by the installation of the electrode in soil. Measurements of the potential are carried by a field ionometer in the mV regime. The reading should be made 2-3 times to make sure that the data are stable and to avoid mistakes during the process of measurement (short circuit of the contacts with each other or with the soil, errors while plugging the electrodes in, etc.). It is also necessary to check the contacts of the sensing electrodes that are not used for measurements to avoid short circuit with each other or with the soil. Otherwise it will take longer to reach the equilibrium value of the electrode's potential while plugging it into the meter. Normally, the time of reading does not exceed 1 min, except for glass pH electrodes, whose potential sometimes stabilizes within 3-5 minutes. It is important to determine the temperature field and hygroscopic moisture of soil and possibly to extract soil solution. Electrodes with the homogeneous membrane (pCl, pJ) can be transported and kept dry, previously rinsed with distilled water. The soaked glass pH-electrodes should be kept in distilled water. While transporting them, to avoid mechanical damage, it is advisable to put a plastic or silicon cap with 1 ml distilled water onto the sensitive part.
54
Those plastified and glass electrodes requiring preliminary soaking should be kept in proper salt solutions. To avoid the spill of the solution during transportation, a piece of cotton soaked in the solution at the bottom of the electrode container. The ion activity value is calculated by a calibration curve or special software. The calculations can only be done for the electrodes that were calibrated before and after the field investigations and the permitted differences depend on the required preciseness of the analysis and usually remain within the range of 2-5 mV. When the temperatures of the soil and calibration solutions differ, temperature compensation is carried out as described in 2.3.1. The temperature compensation can be programmed by computer. If necessary one could convert ion activity into concentration. It is necessary to divide the estimated activity by the activity coefficient calculated after Debye-Huckel equation, or in another way. After this one could convert the obtained concentration values (mmol/L) of soil liquid phase into units mg/100 g of soil more accepted in the soil science literature. Here one should subtract the hygroscopic moisture from the soil moisture (since not all of the soil moisture participates in solving the substances). The conversion is done by: C = C, E(W-WH)J(r',
(19)
where C is the value to be found (mg/100 g of soil); Cx - the ion concentration in soil liquid phase (mmol/L); E - the ionic mass; W- the field moisture (%); Wh - the hygroscopic moisture of soil (%).
2.4. MEASUREMENT OF THE SOIL REDOX POTENTIAL The problem of the soil redox potential has always attracted research attention and has been developed in soil science since Remezov (1929) measured Eh in soil for the first time. However, literature on the subject is not very extensive and sometimes controversial, as can be seen from reviews made on the problem soil redox potential (Zakharievsky, 1967; Gantimurov, 1969; Kovda, 1973; Greenland & Hayes, 1981; Kaurichev & Orlov, 1982). Possible reasons for this are: the insufficient attention paid to the methodology of the work, to the selection and preparation of indifferent electrodes and reference electrodes, which led to incorrect measurements and differences in results; the difficulties in comparing authors' data from different soil types as a result of their usage of different electrodes^^ and methods of measurement. ' The data of various types of indifferent electrodes, being the same in standard ferro- and ferri-solutions, can differ essentially in soil (by 100 mV and more) (Snakin et al., 1977). A grave attention should also be paid to the cleanness of the electrodes, especially the platinmn ones, whose working surface under low Eh can be contaminated by the sulfides. To pmfy file electrodes, they recommend the cycles of heating - cooling, rinsing in mixes of 10% HCl + the detergent, then in 10% H2O2 and distilled water (Bailey & Beauchamp, 1971).
55 Some authors made their measurements directly in non-disturbed soil, others - in soil in laboratory conditions; some of them in spring, other late in autumn, and so on. It is said that thermodynamic interpretation of redox potential value measured in biological systems is impossible (Mikhaelis, 1936; Nekrasov, 1938). This is due to the fact that the nature of the processes in the living systems and particularly in soils is complex, irreversible and non-equilibrium, and that the methodical difficulties lead to poor reproducibility of the measurement data. While discussing the obtained results in Eh for any complex redox system we should consider two questions: 1. Is the potential adequate to that of the system on the whole? 2. Does it fully reflect the state of the redox system? To prove adequacy of the potential measured by an indifferent electrode to the given system as a whole, a technique is used that was suggested by Nekrasov and Parfenova (1938) and was later used for microbiological investigations (Isaeva et al., 1975). The experiment consisted of shifting the value by ±200mV as compared to the primary value by applying polarization current of 2 mA/cm . The authors think that when the polarization circuit is disconnected the electrode potential returns to the original value, and this reflects the value of the system potential. The adequacy condition was observed in the soils we investigated, and the time period of the return to the primary Eh value after cathode and anode polarization made up 35-60 min (fig. 13). When studying the complex redox system, one can introduce the notion of potential mediators, which gives the answer the second question and to prove the right to use Eh values in electrochemical and thermodynamic calculations. If the redox potential is measured in a nonequilibrium system in reactions with composition compounds the metallic indifferent electrode may lose its ability to measure Eh in contact with these components. The measured potential can hardly be reproduced and is subject to change (Laitinen & Harris, 1979). In such cases to measure Eh one should use potential mediators. These are secondary redox pairs representing a simple system, such as Fe^^/Fe^^, Ce'^VCe^^, flY, Tl^VTf etc. in small concentrations. And the system is so simple that it cannot not influence the redox potential of the system as a whole (Ryklan, Schmidt, 1944; Clark, 1960). It is supposed that in the simple mediator system which is balanced with a complex system, the ratio of the reduced form concentration to that of the oxidized one is settled, and this corresponds to the potential of the complex system. A balance is achieved between the mediator system and the indifferent (Pt) electrode. This is explained by the large value of its current exchange.
56 (pirvM) 800-
2000
Time (sec)
Figure 15. Changes in the indifferent electrode potential in an ordinary chernozem after cathode and anode polarization Earlier works showed that the potential of mediators can be used to determine Eh in irreversible non-organic systems: Cr^VCr"^^,
AS^VAS^^
(Laitinen & Harris, 1979), and complex
organic systems: B.coli and B.typhosiim (Clark, 1960), cyctine-cystein (Ryklan & Schmidt, 1944). Moreover, there is evidence that except for the endemic iodine region iodine of various degrees of oxidation is always contained in soils. It is also known that underground waters genetically linked to soil and soil solutions contain iodine compounds in such concentrations (-10* g/L) that they serve as mediators to the potential. In soil solutions there are also iron compounds with various degree of oxidization and these can also serve as mediators. The method of electrode polarization and the concept "potential's mediators" allow us to assert that values of potential in soils obtained by platinumized electrodes reflect their redox regime. For determining Eh value, the same reference electrodes and meters are used as for ionselective electrodes. At present various indifferent electrodes recommended for Eh measurements are industrially produced: • platinum electrodes - made out of a platinum plate or platinum-point electrode (EPV-1 type); • platinumized electrodes made of mat glass coated with a thin platinum layer (ETPL-01 or ETP02 type); • glass electrodes - electrodes with a sensitive element in the form of a ball made of a special kind of glass containing iron oxidized to various degrees (EO-01 type). There are indifferent electrodes made out of graphite, gold, etc.
57 All these electrodes according to our data, can read similarly in the control ferro- and ferrisolutions but differ essentially by the value of potential in soil and give different temporal dynamics of the potential's value (Snakin et al, 1977). In our opinion, while measuring Eh in soil preference should be made for thin platinumized electrodes of the ETP-02 type, which due to their big specific surface can give information on the average Eh value of 1 cm layer of soil. Some authors (Kostenkov, 1987) recommend to avoid serial electrodes because of unstable readings, but make electrodes according to the method of Rabinovich and Kurovskaya (1953). ETP-02 electrodes should be it checked for their potential in a control solution containing 13.5 g of K3[Fe(CN)6] and 3.8 g of K4[Fe(CN)6] in 1 L of distilled water. In a platinum electrode system the potential should be 0±3 mV at 25^C. Unfortunately, there is no standard solution for redoximetry at present, which does not allow calibration of indifferent electrodes. Eh measurements in situ are carried out in the same way as with ion-selective electrodes. Since the redox potential value is normally expressed in reference to the normal hydrogen electrode, it is necessary to add the reference electrode's potential adjusted to soil temperature according to Table 19 to the mean values of the electrode system. Table 19 Temperature dependence of the most frequently used reference electrodes (Cammann, 1973) Reference electrodes potential (mV) t ('C)
Ag/AgCl
5
220
-
-
-
10
214
215
254
256
15
209
212
251
254
20
204
208
248
252
25
199
205
244
250
30
194
201
241
248
35
189
197
238
246
40
184
193
234
244
Saturated
Calomel 3.5MKa
Saturated
3.5MKC1
58
2.5. COMPARISON OF DIFFERENT METHODS OF SOIL LIQUID PHASE INVESTIGATION
2 5 1 LABORATORY METHODS
Fresh soil samples with natural moisture can be analyzed directly by ISE, or used for extraction of soil solution. The composition of soil solutions displaced by ethanol or extracted by pressure is identical regarding their dry residue and the content of HCO3', CI', S04^', Ca^^, Mg^^ ions (Kaurichev et al., 1963). Soil solutions displaced by the saturated water solution of CaS04 as well as extracted by plain centrifuging or centrifliging with CCU as the displacer, had about the same composition (Adams et al., 1980). Similar results were achieved by Zykina et al. (1975) measuring ionic activity in soil solutions displaced by ethanol or extracted by pressure, or in immediate ionometric measurement. The only difference was found in the pH. Some discrepancy in measuring the pH value in soil solutions displaced by ethanol and pressure-suction were described by Samoilova and Demkin(1976). It can be concluded that the ion selective electrodes will give information about that part of soil moisture which is extracted from soil by the displacing liquid or under pressure. Some discrepancy occurs due to insufficient measurement repetitions or by equipment error. The discrepancy in pH values in soil solutions displaced by ethanol and extracted under pressure-suction is largely due to high pressure causing dissolution of CO2 from soil air. This results in greater acidity than in case of ethanol displaced solutions.
2 5 2 W.S7rf/MEASUREMENTS AND DISPLACED SOIL SOLUTIONS
When studying the peculiarities of functioning of soil as a component of ecosystems it is needed to compare physical and chemical soil properties in situ with the analysis of soil samples in laboratory, apart from information on the methodical aspect of this issue. Differences in the data of laboratory analysis of fresh samples compared to field measurements were pointed out in the very first works on in situ measurements. For sierozem soils, differences between field pH measurements and that of water suspensions and pastes in laboratory were 0.2-2.4 units (Kerzum et al., 1970). The authors explain the discrepancies by hydrolysis of carbonic sahs in diluted suspensions leading to an increase in pH, and to CO2 in soil air which brings down the pH in the rooting zone. When a sample of alkaline soil taken immediately from the auger was put into the pH-meter's glass (1:2.5), the meter read pH 7.5. Within 10-20 minutes, the
59 pH increased to 9 or more. Frequently in soil samples taken in a cotton field, laboratory analyses showed very unfavourable Na2C03 content and pH values for cotton. An increase of the pH value in soil solutions displaced by ethanol as compared to in situ measurements was pointed out by Yudina and Yamnova (1979) and by Snakin and Zavizion (1979). The authors explain this by volatilization of CO2 from soil air while the samples are collected and the corresponding shift in the carbonic balance. The changes in the redox potential can also be explained by the changes in the soil air composition. In 10-20 minutes after the sample was collected, the Eh was different from the Eh measured in situ (Serdobolsky, 1953). Table 20 shows our data on pH measurements by SLP, ethanol displaced soil solutions, water (1:5) and salt (1:2.5 in IM. KCl) extracts from air-dry samples. Analysis of the data shows significant differences between the various methods. Special mention should be made of the fact that higher pH values of soil solutions were achieved as compared to the in situ measurements. The latter was found in all soil types except the strongly acid humus horizon of the podzol. There was an artificial increase of pH when analyzing the soil solutions. One can hardly accept that the most fertile soil - chernozem of virgin state - has a pH of 8.0-8.3 in the liquid phase, while the optimum pH for most plants is 6.0-7.0 (Grozdinsky & Grozdinsky, 1964). The main reason for the artificial increase is the CO2 volatilization from soil air while the samples are collected for soil solution displacement^\ Alkalization of lysimetric waters was also observed by Shilova and Kreyer (1957). The above is also proven by an experiment, in which the soil solution was displaced by ethanol and showed a pH equal to 8.03±0.12, but when the tubes were filled with soil under continuous CO2, flow the pH value of extracted solution decreased to 7.76+0.12. It is difficult to explain the difference by a change in the soil air carbon dioxide gas content. In arable lands, CO2 content in the soil air is similar to that in virgin soils (Snakin & Zavizion, 1979), while the difference in pH is about 0.2-0.3 pH. This might be due to the fact that when measurements were carried agrolandscapes contained little phytomass (and often no phytomass at all) compared to the virgin site. Analysis of other results (Table 20) also showed that when collecting soil samples the more phytomass with more active biological component in soil, the larger the distortion of pH. An insignificant difference was observed in sandy weak humus calcareous soil under fragmentary steppe vegetation with below ground phytomass of 10-150 g/m^, which was at its minimum in the site not covered by vegetation (0.11 pH).
It is possible (e.g., at a strongly alkaline reaction) that tlie content of CO2 in tlie soil air is lower tlian that in tlie atmosphere, and then no alkaUzation occurs.
60 Table 20 H ion activity in liquid phase of various soils (average of the data of 10-30 electrodes) Depth
pH
pH
pH
pH
Soil
tCC)
CO2 of soil air
in
of soil
of water
of salt
moisture
situ
solution
extract
extract
(%)
0-10
7.76
8.00
8.18
7.80
3.12
~^2
chernozem
0-10
6.52
7.01
7.30
-
31.4
12
-
Southern chernozem virgin
0-10
6.36
7.25
6.41
5.40
25.6
15
0.10
Southern chernozem arable
0-10
6.91
7.20
7.16
6.46
23.0
13
0.09
0-10
6.03
6.19
6.80
5.79
24.5
13
0.11
0-10
6.86
8.35
7.34
6.67
25.5
12
0.11
0-10
6.86
7.58
6.71
5.97
24.5
12
0.07
45-55
7.47
7.66
8.09
7.15
24.5
9
0.16
0-10
7.36
7.67
7.39
6.46
22.1
15
0.14
0-10
7.08
7.82
6.69
5.86
27.3
14
0.04
0-10
6.27
7.22
6.49
5.65
37.0
14
0.08
Typical chernozem arable
0-10
6.67
7.10
6.65
5.55
21.3
15
0.04
Gray forest virgin soil
0-10
6.08
7.06
6.04
5.12
40.5
9
0.09
18-22
5.82
5.90
5.74
4.32
24.1
9
0.09
Gray forest arable soil
0-10
5.56
5.61
5.64
4.53
16.8
14
0.11
Podzolic virgin soil
0-10
3.87
3.48
3.57
2.85
22.3
13 10
Soil type* Sandy low humus calcareous
(cm)
(%) -
virgin Meliorated solonetzic compact
irrigated Southern chernozem arable dr> Ordinary chernozem in Priazovie*' Ordinary chernozem in Priazovie " ' Ordinary chernozem arable in Priazovie Typical chernozem virgin (steppe) Typical chernozem virgin (forest)
10-15
3.73
4.90
4.38
3.25
22.7
40-50
3.80
5.90
4.33
3.63
22.0
10
Podzolic arable soil
0-10
6.10
5.72
5.45
4.40
22.8
20
-
Meadow-boggy with
0-10
5.21
5.80
4.39
3.58
24.8
22
0.16
0-10
5.35
5.60
4.72
3.66
37.6
19
0.24
permafrost virgin Meadow-boggy with permafrost arable * The soil type by FAO UNESCO - see Section "Correlation between soil names " ** Virgin soil under the cover of mixed grass-fescue-feather grass association. ** Virgin soil under the cover of creeping Agropyron association
For chernozem under a mixed grass-fescue-feather association with below ground phytomass of 600 g/m^ the difference in pH can reach 1.49. For the soil of creeping Agropyron
61 association, the difference is 0.72 pH, possibly because the larger total belowground phytomass (650 g/m^). Annual production of creeping Agropyron association is less than that of the mixed grass-fescue-feather association: 1570 and 2050 g W respectively (Snakin & Bystritskaya, 1984). The processes of phytomass dynamics in the second association is more intensive. The influence of production process on the pH deviation in ethanol displaced soil solution is illustrated by the data in Table 21.
Table 21 H^ ion activity in soil solution displaced by ethanol and in the liquid phase of sod-podzolic heavy loam soil during various observation periods (barley crops) Periods of observation
pH in situ
pH of the soil
W (%)
tCC)
Aboveground phytomass (g/m^)
solution 27.04-4.05.1984
6.7610.25
6.65±0.23
18.7
13
20
29.06-30.06.1984
6.77±0.22
7.20±0.14
21.3
15
585
12.10-13.10.1984
6.76+0.14
6.8510.09
26.6
14
0
In spring, when the sprouts just appear and in autumn, when the agricultural crops have already been harvested, the difference between the pH values of the soil liquid phase and soil solution is minimal. On the other hand, when the barley is at the milk-wax stage of ripeness, the pH difference is 0.46. The influence of the vegetation and the relevant micro-organisms can be indicated as acidification due to CO2 release in the process of respiration^^. The various root excrements, most of which consist of organic acids are among the sources of acidification^^. The growing seeds acidify the distilled water (Geller, 1948). Our pH measurements in the liquid phase of chernozem under mixed grass-fescue-feather grass association showed that vegetation removal of an area of about 2 m^ results in gradual alkalization (Table 22). The change in the pH has complex reasons. The pH increase after vegetation removal in the steppe area may be partly due to changes in the hydrothermal regime, as a result of which a more alkaline (see Table 20) liquid phase of the deeper horizons is drawn to the surface.
" Compared to soil with no plants soil air under plants contains 30-50% more CO2 (Yastrebov, 1963). In a cultivated sod-podzolic soil (Table 21) acidification must have been compensated alkalization as a result of the plants extracting N from the NaNOs used as fertilizer. This leads to a higher pH value in the soil solution.
62 Table 22 pH in the liquid phase of a chernozem (0-10 cm) during various periods of observation in the virgin and fallow site. Vegetation eliminated in June 1976. Site
Time of observation 3-7.4.1977
10-14.5.1977
25-28.6.1977
20-24.4.1978
Virgin
6.80+0.26
6.86±0.18
6.6610.22
6.84±0.14
Fallow
7.06+0.16
7.20±0.10
7.24±0.11
7.40±0.15
CO2 content in soil air of virgin soils and that of the arable soils are almost the same (see Table 20). However, the extraction of a sample from virgin soil leads to deeper changes of SLP due to loss of acidifying effect of biota than in arable soil. Similarly to the changes in pH of the liquid phase when collecting the sample other components can also change. For example, displaced soil solutions have lower Ca^^ ions activity. Table 23 illustrate this phenomenon and it is necessary also to take into account that Ca^^ activity in the soil solution is lower than the given concentrations measured by Na-EDTA titration. According to our data, Ca^^ ions' activity coefficient in the soil solutions varies from 0.4 to 0.8. Table 23 Ca^' (meq/L) in the liquid phase of various soils (0-10 cm) Soil*
Ca^^ activity in 5/Yw-measurements 10.4
Meliorated solonetzic compact chernozem Ordinary chernozem in Priazovie: mixed grass-fescue-feather grass association 20.0 creeping Agropyron association 14.1 arable 21.1 Southern chernozem: virgin 9.2 arable 8.2 T>qDical chernozem: steppe 5.2 forrest 3.2 arable 5.1 fallow site 7.7 Sod-podzolic arable 5.2 * The soil type by FAO UNESCO - see Section "Correlation between soil names"
Ca^^ concentration in soil solution 2.6 7.6 4.9 5.6 2.7 12.0 7.9 5.1 4.0 4.6 2.5
The biological component of a ecosystem exerts a permanent influence on the SLP composition. Studying soil solution extracted from the soil, there is the risk of obtaining values
63 different to those of the real soil in ecosystem. This is also true for pH values of water and salt extracts (Table 20). The use of such results can lead to an incorrect assessment of the state and dynamics of soil processes. Carbonate balance analysis (Section 6.1) can serve an example to indirectly prove the correctness of ionometry. The thermodynamic calculations of the degree of saturation of the liquid phase with CaCOs in the chernozem by water extract analysis showed a large degree of undersaturation. The calculations by the displaced soil solution, on the other hand, showed significant over saturation. lonometric data of the in situ measurements showed that the carbonate horizons were saturated, and that the top horizon (0-10 cm) was under saturated. Table 24 Potassium in the liquid phase of various soils (meq/L) Object of investigation*
Depth (cm)
aK+ (in situ)
CK+ in soil solution
Southern virgin chernozem
7
0.37
0.36
22
0.08
0.11
Southern arable chernozem dry
0.51
0.50
Southern arable chernozem irrigated
0.87
0.50
7
1.58
0.52
35
<0.02
0.01
7
1.52
0.64
50
<0.02
0.02
0.45
0.09
Ordinary chernozem: mixed grass-fescue-feather grass association creeping Agropyron association arable Typical Chernozem: steppe
0.06
0.05
forest
0.48
0.21
fallow site since 1947
<0.02
0.04
arable
0.07
0.03
5.97
0.05
potatoes, V. 1984
3.71
0.81
barley, VIII. 1983
0.22
0.11
barley, X. 1983
0.13
0.11
barley. V. 1984
7.41
0.31
barley, V. 1985
0.11
0.35
Meliorated solonetzic compact chernozem Sod-podzolic arable:
* The soil type by FAO UNESCO - see Section ''Correlation between soil names"
pH values obtained by various methods (see Table 20) are closely correlated with coefficients of 0.90-0.96 for all soil types. However, because the coefficient values are so close it seemed impossible to make a reliable conclusion about difference in the strength of connection
64 between the different values. There is a weak correlation between the water extract pH value and liquid phase pH, also for the displaced soil solution for arable lands as compared to the virgin soils. The correlation coefficients are 0.84-0.83 and 0.96 respectively. Analyzing K^ ion activity data obtained in situ with the K^ concentration in the displaced soil solution measured by spectrophotometer yielded similar results (Table 24), with the exception of the in situ measurement in meliorated solonetzic-compact chernozem where the enormous prevalence of Na over K led to incorrect readings of ion-selective electrodes. Table 25 Nitrate (meq/L) in the liquid phase of sod-podzolic soil (fertilizer dosage- see Table 56) Treatment
Date of
^NOS
measurements
in situ
Control
V.1984 VI. 1984 X.1984
4.211.9
V.1985 NPKCa
Lime + NPK
Lime
l/2manure+l/2NPK
Manure
Aboveground soil solution
phytomass (g/m^)
11±4
1.6 + 0.3
8.7 ±1.6
2.1 ±0.4
-
1.7 + 0.6
1.8 + 0.2
28
V.1984
16 + 3
2.1+0.8
VI. 1984
9.2+1.7
-
X.1984
66 ± 10
15±3
-
V.1985
13 + 10
2.3
56
V.1984
6.5 + 5.3
6.6 ±0.8
VI. 1984
10.3+2.6
-
X.1984
65 ± 30
20
-
V.1985
8.7 + 2.2
3.0
51
V.1984
-
3.2
VI. 1984
11+6
-
X.1984
17+6
2.5
-
V.1985
1.7±1.1
2.4
35
V.1984
10+6
1.1 ±0.6
VI. 1984
14+4
-
X.1984
15 + 8
12
-
V.1985
8+5
2.6
60
V.1984
19+20
2.3 ±0.4
VI. 1984
9.4 + 3.8
-
X.1984
7.8 + 5.7
6.7
-
V.1985
4.0+1.3
3.2
63
A large difference was found in sod-podzolic soil in second day after application of mineral fertilizers (May 1984 - barley and potatoes), and the time of measurements in situ and displaced soil
65 solution are was unequal (see Table 24). Usually, we get soil solution two and more days after the displacement had began. Soil samples for displacement and in situ measurements carried out were collected simultaneously. With regard to heterogeneity of soil properties, coefficient of variations of in situ measurements being as large as 50-70%, one may come to the conclusion that the use of ethanol displaced soil solution allows to measure K^ ions activity in SLP correctly. The situation with NOa' is more complex (Table 25). NO3' activity in situ and in soil solution can only coincide if activity levels are low. The reason for lower quantities of NO3' in the ethanol displaced soil solution is denitrification in the soil sample, which occurs under anaerobic conditions in the plastic bag while transporting the sample and in the displacement tube. 2.6. SOIL SOLUTION, SOIL AND PLANT ANALYTICAL METHODS The methods for the analysis of soil liquid phase composition in situ were presented in 2.3. Here we present chemical methods, which we used for the analysis of replaced soil solutions, soils and plants. The composition of replaced soil solution was measured as follows: • pH - by ion-selective electrode; • Cr concentration - by argenometric technique; • NO'3 activity - by ion-selective electrode; • Ca^^ and Mg^^ concentrations - by trilonometric technique; • K^, Na^ concentrations - by flame photometer; • S04^" concentration - by turbidimetric technique; • "C" concentration - by bichromatic technique with preliminary evaporation of solution; • Si02 concentration - by photocolorimetric technique. Chemical soil analysis: CEC was estimated as the sum of exchangeable cations and hydrolytic acidity. The measurement of exchangeable cations was performed through treatment of soil samples by ammonium acetate solution buffered at pH 7.0. Ca and Mg were measured by atomic absorption spectrophotometer. The measurement of Na and K cations was performed by flame photometer. Hydrolytic acidity measurement was performed by mixing soil samples with solution of sodium acetate and filtering after 5 minutes, the filtrate was titrated by a O.IM alkaline solution with phenolphthalein.
66 Carbon content was measured by bichromate oxidisation. Humus content was estimated by multiplying of C content by 1.724. The measurement of acid-soluble heavy metal forms was conducted by an atomic-absorption spectrophotometer in the filtrate, obtained after treatment of the soil sample by IM HNO3 solution (1 : 10). The measurement of mobile heavy metals forms was performed by an atomic-absorption spectrophotometer in the filtrate, obtained after treatment of the soil samples by ammonium acetate solution buffered at pH 4.8 (1 : 10). To measure CO2 in soil air a certain volume (200-500 cm"^) of soil air was exhausted by syringe, and titration was performed by Ba(0H)2 solution with subsequent titration by 0.0IN H2SO4 in the presence of phenolphthalein. Field moisture measurement was carried out by gravimetric technique. The in situ measurement of temperature was performed by a mercury thermometer.
Plant analysis: NO3' content was measured by potentiometric technique in plant living matter, homogenized using 1% KA1(S04)212H2O solution. Wet combustion of plant material. The dry plant sample was homogenized to powder-like condition. A 1 g air-dry material was placed in a 50 ml retort and 3 ml of concentrated HCIO4 and 7 ml of concentrated HNO3 was added. It was left at room temperature for an hour. Then it was heated until the solution cleared up, and evaporated to wet salt state. A 0.5N HNO3 solution was added, heated and transferred the solution to a 50 ml measuring bottle and diluted with bidistilled water. After wet combustion of plant material the heavy metals obtained in the solution were measured by the method of atomic-absorption spectrophotometry.
2.7. DATA BASE
The data base (DDB^"^) on composition of liquid phase of various soil types and the characteristics of related solid soil phase, soil air and vegetation was developed to store, results and literature data, obtained in Central and Eastern Europe, in order to ensure greater availability to data and to raise interest of other researchers. The database is a unique source of information on the composition of soil liquid phase in the ecosystems of various management regimes, and a useful tool in the analysis of various components and mechanisms of ecosystem functioning. It allows
DDB - DEMETRA data base named in honour of the Greek goddess of fertility.
67 tracing of ecosystems dynamics under the increasing anthropogenic load, for the characterisation of habitats, and is a basis for biodiversity conservation. The database is of interest to researchers in the field of experimental ecology and soil science in Russia and abroad. The basis for the data base are the results of field investigations (see chapter 3), under different management. There are more than 600 records in the data base. Each record includes the information on the site and time of investigation, type of ecosystem, type of soil, granulometric composition, hydrothermal characteristics of soils, redox and pH equilibrium in soil, data on soil liquid phase composition (the in ^////-measured ion activity) and ethanol-replaced soil solutions, data on main soil characteristics (humus, cation exchange capacity, exchangeable cations, total content of some elements, including heavy metals) and soil air (carbon dioxide content), as well as characteristics of production (phytomass, productivity). The data on soil liquid phase composition contain the results of both the conventional and newly developed methods for field measurements of ion activity in undisturbed systems {in situ measurements). The input language of the Clipper compiler was used for the creation of the data base management system (DBMS). DBMS is based on a user menu, containing all system functions. DDB has allowed us to summarize research results and to elucidate the principles of SLP formation, explained in the following chapters.
2.8. CONCLUSIONS
Real characteristics of ecosystem can be achieved by field in situ measurements of separate ions' activity by ion-selective electrodes and soil redox potential using indifferent electrodes. Measurement of CO2 content in the soil air by potentiometric C02-meters can also be attributed to this group of methods (Komissarova & Razumova, 1987). These C02-meters can provide valuable information. However, this does not downgrade the useflilness of other methods of soil liquid phase investigation as each method has a specific goal and has shortcomings. The field variant of ionometry can give unique information about the activity of ions in processes in the "living", nondisturbed soil. At the same time it is less precise compared to the laboratory measurements. Both the field and laboratory ionometry are limited by the number of the electrodes designed. Important ions like the phosphate and bicarbonate ions, which play important an role in the soil formation processes and microorganisms cannot be measured. The methods of extraction of soil solution give information about these ions, as well as on organic and silicon compounds, reflecting the heterogeneity of the soil and its dynamics. The data on the composition of soil solutions are more difficult to reproduce and can hardly be standardized. At the same time, analyses of pastes and
68 various soil extracts give an incomplete impression of the natural conditions, reflect the conservative properties of soils and allow to compare various soil types with greater precision. The fact that many methodological issues are unsettled limits the wide use of ionometry in situ. The analysis of experiments and results that we have carried out show that the in situ measurements are valuable and reliable, provided certain requirements are met. The error in measurement of the ion's activity in soil liquid phase is within the limit of 0.1-0.3 pX. Elaboration of the temperature compensation method for ion-selective electrodes, enables us to decrease the error of field measurements. Therefore we suggest this method of ion activity and soil redox potential measurements in soil liquid phase in situ to researchers in the field of ecology and soil science. Various methods of soil liquid phase investigation complement each other, giving important information about soil processes, which is why we also make use of the data obtained through other methods, such as displacement of soil solutions by ethanol and water extracts. Comparing various methods of soil liquid phase investigation showed that the in situ measurements in natural soils give realistic information on ions' activities. The development of this method in comparison with the other ones has been a benchmark in studying soil-environmental processes. The carbonic balance and redox regime of soils are the most difficult to study because they change when the samples are being collected. This depends not on the volatilisation of CO2 and an increase in oxygenium concentration but it also occurs because the link: soil liquid phase - soil living matter is broken. The more influence of living matter on soil, the bigger changes in soil physical and chemical parameters when collecting samples. If the study of soil as a separate object is the objective one can make use of all the methods ever known. But if the study of environmental processes and soil as a component of ecosystem is the objective, one should choose prefer in situ measurements.
69 CHAPTER 3. STUDY AREAS Results cited in the previous sections are based on investigations in various geographic zones of Central and Eastern Europe during the period 1976-1990 in natural reserves and adjacent agricultural lands (Fig. 14). Various ecosystems served as objects of investigations:
^ /S) K/ e^^ r^^^ Fig. 14. Location of study areas (1-20 - corresponds to the numbers of objects in text)
1. Mixed grass-fescue-feather grass and creeping Agropyron community in the Ukranian steppe, the branch of the Khomutovskaya Steppe Reserve (Ukraine, Donetsk region, ordinary chernozem^ ^ of the Priazov region); ' The soil type by FAO UNESCO - see Section "Correlation between soil names"
70
2. Mixed fescue grass open dry community on the sand of the Bugac site Kiskunsag National Park, (Hungary, weakly developed sandy sod-calcareous soil); 3. Mixed grass-fescue-feather grass steppe in the Danube-Tisza Interfluve, Hungary, at Csaszartoltes site Kiskunsag National Park (shallow low humus calcareous southern chernozem); 4. Secondary meadow community in forest cut at the Kamenicky site of the Institute for Landscape Ecology of the Czech Academy of Sciences (Czech RepubUc, gleysolic acidic brown soil); 5 Mixed grass-fescue steppe of the Askania-Nova Reserve (Ukraine, Kherson region, virgin southern chernozem); 6. Recultivated plots and grassland communities on loess sites in the region of Verkhnednepr Metallurgical Combine (Ukraine, Dnepropetrovsk region); 7 Mixed grass-feather grass community and oakwood in the Centralnochemozemny Reserve named after Alekhin (Russia, Kursk region, typical chernozem); 8. Fallow plot since 1948 (under special management, i.e. all plants are removed each year to keep the surface bare) in the Centralnochemozemny Reserve named after Alekhin (Russia, Kursk region, typical chernozem); 9. Oak grove in a forest estate of Malino (Russia, Tula region, grey forest soil); 10. Oak, birch and Hme mixed forest of the Zaokskoe forestry, The «Russian forest» association (Russia, Moscow region, grey forest soil); 11 Pine forest with green moss and dry litter cover in the Prioksko-terrace State Reserve (Russia, Moscow region, sandy weak podzolic soil); 12. Agricultural lands and sowed meadow of the Experimental Field Station of the Institute of Soil Science and Photosynthesis of RAS (Russia, Moscow region, grey forest soil); 13. Meadow in the basin of the Oka river (Russia, Kashira district of the Moscow region, alluvial sod-meadow calcareous soil); 14. Plots of Agrochemical Field Station named after Pryanishnikov (Russia, Moscow, sod-podzoHc soil); 15. Spruce forest with green moss and understorey in the Central-forest State Reserve (Russia, Tver region, podzolic soil); 16. Mixed broad-leaved boxwood forest in the Yew-Box grove of the Caucasus State Reserve (Russia, Krasnodar region, cinnamonic soil);
71 17. Hornbeam-oak forest in the Yew-Box grove of the Caucasus State Reserve (Russia, Krasnodar region, brown forest soil); 18. Laurel-cherry yew woodland in the Yew-Box grove of the Caucasus State Reserve (Russia, Krasnodar region, compact cinnamonic soil); 19. Agricultural plots in the experimental fields of the Stavropol Institute for Agricultural Research in the Vodorazdelny Farm (Russia, Stavropol region, ameliorated solonetzic compact chernozem); 20. Tallgrass meadow community in the Tungiro-Neniuginsk area (Russia, Chita region, meadowboggy with permafrost soil). Work was also carried out in agricultural plots, adjacent to Askania-Nova, Khomutovskaya steppe, Centralnochernozemny (Central chernozem), Centralnolesnoy (Central-forest) reserves, Malino forest estate, and some other ecosystems. Since detailed investigations were carried out in the ecosystems of the Khomutovskaya Steppe Reserve and the sites of Bugac, Csaszartoltes and Kamenicky (Snakin et al, 1991), their characteristics are given in further detail. All the four objects of investigation are herbaceous ecosystems with strict seasonal dynamics. The sites are of great area and are situated far from each other. This implies that there climatic conditions may differ. Due to their geographic position, their natural environments are somewhat different, however in the continental climate xerophytes are predominant. The materials below present the ecosystems in accordance with their complexity of their structure. First of all is given the data on perennial community Festucetum vaginatae, the Bugac site. A virgin steppe ecosystem, Salvio-Festucetum rupicolae stipetosum pannonicum community at the Danube terrace and the ecosystems of the Priazov steppe, Salvio-Festucetum rupicolae stipetosum ponticum community are also elucidated. The study areas can be ordered into a sequence based on the species richness and complexity of their ecosystems, i.e. from the most simple open sand grassland to the complex Pontic steppe. The secondary meadow of Kamenicky is out of the above range.
3.1. THE ENVIRONMENT
The Bugac and Csaszartoltes sites belong to the Kiskunsag National Park in Hungary. The Kiskunsag area is located in the Carpathian Basin of Central Europe, between the rivers Danube and Tisza (Danube-Tisza Interfluve) and is a part of the Great Hungarian Plain.
72
The Danube-Tisza Interfluve area is a large Quaternary alluvial fan of the river Danube, where sand, sandy loess and in some places aeolian loess are mixed to the alluvium. Wind erosion led to the formation of a low hilly sandy plain (Table 26). The National Park was founded in 1975 for the protection, management and research of the following ecosystems: oak forests, JuniperPoplar woodlands xeric and salt affected grasslands, Pinus and Robinia plantations, wineyards, orchards and agricultural fields. The Bugac site is situated close to the West from village Bugac.
Table 26 Description of the main research sites Vegetetion
Investigation
Fes Tuce turn
Hungary:
47°00^ N.
vaginatae
Bugac
20°00' E.
Co-ordinates
site
Landform
Parent material
Sandy rolling
Sandy hills.
Ancient alluvial
plain, the
10-20 m high
carbonate sand
Height above Location sea level (m) 100-120
Interfluve of the Danube and the Tisza rivers Salvio-Festucetum
Hungary:
46°40' N.
nipicolae
Csaszartoltes
19°10^E.
110
Flood-plain terrace Rolling plain of the letVhand
stipetosum
bank of the
pannonicum
Danube
Salvio-Festucetum
Ukraine:
47°20^ N.
rupicolae
Khomutovskaya
38°10' E.
stipetosum
steppe
Sandy loess
50-70
Ancient flood-plain Rolling plain
Silty loess type
terrace of the G.
clay
Elanchik river
ponticum Polygalo-
Czech Rep.:
49°30^ N.
Nardetum strictae
Kamenicky
15°40'E.
625
Products of
The Czech-
A steep slope
Moravian Upland
of the
orthogneiss
northern-
weathering
eastern basin
The area is hilly and the height of hills is about 3-15 m. They have been formed by alluvial calcareous sand which was shaped in the Holocene by wind (Bulla, 1962). The Csaszartoltes site is situated in the middle part of the delta of the Danube-Tisza Interfluve. This area is characterised by a gradual southward transition from sand to loess deposits. The Csaszartoltes site is a loess steppe of 100 x 200 m eastwards from the Danube. The Khomutovskaya steppe reserve occupies an area of about 1,000 ha, including 975 ha virgin lands. It was declared a reserve in 1926. In the central part of the reserve, there is a strictly protected plot of 105 ha. The rest of the territory is periodically mowed to preserve plant diversity. The reserve is situated within the boundaries of the low undulating Priazovskaya coastal plain, in
73
the East bordered by the Gruzskoy Elanchik river. Covered by loam and clay loess, sarmatic limestones are the main parent rock of the soils. The Kamenicky site is situated in the Kamenitskaya Basin Geomorphologically it belongs to the territory of the Zhdyarsky hills on sandy-silt (Strida, 1980). The landscape has been strongly modified by human activity and has a polyfunctional character and forests, large secondary grasslands, and pastures are common.
3.2. CLIMATE
Bugac and Csaszartoltes. The climate of the Danube-Tisza Interfluve region is temperate with continental and submediterranean features. Strong seasonal and daily fluctuations of temperature and air humidity, uneven spatial and temporal distribution of precipitation are characteristic. Average annual rainfall (500-600 mm) is lower than the potential evapotranspiration (680-700 mm) and especially in the summer months the climate is strongly semi-arid. Since the winds reach the area in a descending stream, a so-called basin effect in the spatial distribution of precipitation can be detected (Peczely, 1979). The number of warm days is about 200, and there are some 25-50 days with snow (Table 27).
Table 27. Climate of research sites Duration
Global radiation
Precipitation (mm)
Temperature ( C)
Climate
of vegetation
during vegetation
average
total during
average
max
mm
type*
period (april-
period
annual
vegetation
annual
(July)
(January)
10.6
21
-1.5
10.7
21.8
-1.7
9.2
23
-4.5
5.6
15.1
-4.4
october), days*' Bugac
1
244
period 3895
250
Khomutovskaya
3
516
575
215-220
441
380
417
254
steppe Kamenicky
4
25 (January)
66
33
(July)
(January)
56
20
(June)
(October)
554
103
40
(May-
(April-
(July)
(February)
September)
October)
2300
755
60 (June)
* 7 - temperate with continental and sab mediterranean character; 2 - temperate with continental and stronger sabmediterranean
character;
3 - continental; 4 - subatalntic under continental influence (according to Walter, Lieth, 1960) ** With vegetation period taken as the period of average temperature above 5^C.
The climate of the Khomutovskaya steppe reserve is continental. Winter is moderately cold with average winter temperature of -3.4 *^C and alternation of frosts and thaws. The spring is
74 usually cold. Frosts are typical for April and they are common in the first part of May. The average summer temperature is 22 ^C. Autumn is warmer then spring and the decrease in temperature is gradual. The average annual precipitation is 440 mm, varying from 320 to 678 mm. Eastward winds are characteristic for the region of the Khomutovskaya steppe and they are most frequent in spring. The climate of the Kamenicky site may be characterised as subatlantic under continental influence. According to a Czech classification, the area is mildly warm and a very humid. The duration of the vegetation period with average temperatures above 5 ^C, is about 193 days. There are 89 days with snow and the average height of snow cover is 40 cm.
3.3. VEGETATION
Wooded steppe, the zonal vegetation of the Great Hungarian Plain (Alfold) is a continuation of the Southern Russian steppe zone forming its Western border. The vegetation of the Bugac site has a mosaic pattern. The mosaic is formed of forest patches of Populus canescens, sparse woodland patches of Juniperus communus - Populus alba, mainly on tops of hills enclosing patches of denser grassland formed by Calamagrostis
epigeios
and the open perennial sand grassland of Festuca vaginata typical for the place. Bare patches with open sand surface nudum or with dense moss and Hchen cover also occur. The mosaic pattern of the vegetation is controlled by herbivores (deer, rabbit, etc). Our investigation was predominantly concerned with the widespread and characteristic community of Festucetum vaginatae danubiale (Table 28). The canopy cover equals 40%. The number of species is rather low, but these are adapted to the water limited habitat. The dominating species is the subendemic Festuca vaginata W. et K., the condominating species are Koeleria glauca (Schk.) DC, Carex liparicarpos widespread are Stipa
borysthenica
Klokov,
Alkanna
Gaud. The most typical and
tinctoria
(L),
Tausch.,
Euphorbia
seguieriana Necker, Fumana procumbens (Dun.) Gr. et Godc, Ephedra distachya Z., Fotentilla arenaria Berkh., Colchicum arenarium W. et K., Linum hirsutum L.. The abundance of over 32 Mediterranean and continental species may found there is abundance. The moss-Hchen synusia are well developed. The grass cover of the Csaszartoltes site is represented by a Salvio-Festucetum
rupicolae
stipetosum pannonicum community. The loess patches have preserved fragments of this primary steppe vegetation and these relic plots are the rephugiums for the steppe flora. As a result of
75 agricultural activities, the area of these steppes drastically decreased and now there are only a few patches with an area of 100-500 m^ each.
Table 28 The vegetation of the research sites Site
Vegetation type
Ecosystem type
Phytomass * (g/m^) aboveground
Bugac
Csaszartoltes
Primary edaphophytic
perennial sandy open community
semi desert vegetation
Festucetum vaginatae danubiale
A fragment of relic
loess steppe community Salvio-
primary steppe
Festucetum rupicolae stipetosum
vegetation
pannonicum
Khomutovskaya
A xerophytic version of
Mixed fescue-stipa steppe
steppe
primary steppe
community
vegetation
rupicolae stipetosum ponticum
Kamenicky
roots
Projective
Number of
cover (%)
species / m^
' (0-20 cm)
living
total
59
294
1050
60
32
273
420
1500
100
44
350
500
1800
100
82
242
990
2200
100
43
Salvio-Festucetum
Secondary meadow
mesophytic grassland community
vegetation
Polygalo-Nardetum
strictae
* In time of maximum standing crop
A characteristic community of the Great Hungarian Plain is the mixed grass-fescue-stipa steppe, with Salvia nutanti-nemorosae-Festucetum rupicolae. It is rich in species of Pontic (Black Sea) and continental origin. This colorful community is characterized by high density sward and the canopy cover is 100% with a high biodiversity. The dominant species are fescue {Festuca rupicola Heuff)., feather grass {Stipa capillata L), bluestem (Andropogon ischaemum L). In addition Astragalus onobrychis L, Medicfgo falcata L, Seseli varium Trev., Centaurea sadleriana Janka and some others are widespread. The number of species is 44/m^ and there is almost no moss-lichen cover. The grass cover of the Khomutovskaya steppe is as a xeric version of mixed sheep's fescue stipa steppes (Kleopov & Lavrenko, 1933). The dominant species are sheep's grass and feather grass, Festuca rupicola Heuff., Stipa lessingiana Trin. etRupr., Stipa capillata L We have studied the mixed community Salvio-Festucetum rupicolae stipetosum ponticum at strict conservation and periodical mowing regime. It partly found spread on the plain and steep slopes. The projective canopy cover is 95-100%, including: Stipa lessingiana (30-45%)), Festuca rupicola (15-25%)), Stipa capillata (10%)), Salvia nutans L (5-10%o) and Achillea setacea W. et K.
76 (3%). The characteristic species of the community are Phlomis tuberosa L., Crambe tataria Sebeok, Medicago romanica Prod., Otites chersonensis Cleop. and Lirmm austriacum L. Some 82 flowering plant species have been registered. The biodiversity is high, with 24-27 species per m^. No moss-lichen cover has been found. In the grass cover of the Khomutovskaya steppe, the creeping Agropyron community can be found under the same conditions as the Festuca grassland. The predominance of a certain species in the grass cover is controlled by the land use regime. In the territories of strict protection, which rules out mowing and grazing, the species of Agropyron {Agropyron repens P. B., Agropyron trychophora (Link.) Nevski), bluegrass (Poa angustifolia L), bromus {Bromus inermis Leyss) are widespread. They have a high density of predominantly monodominant sward of poor species composition and a rich layer of litter (up to 15 cm) which is preserved during the whole vegetation period. Periodic mowing or grassing favours the development of species rich bunchgrass communities and in the Khomutovskaya steppe they are Salvio-Festucetum
rupicolae
stipe tosum ponticum. The mowed and unmowed plots of the Polygalo-Nardetum
community in the Kamenicky
site is a typical secondary community emerged from the wet submontaneous alder and spruce stands and is described as Piceo-Alnetum (Rybnickova & Rybnicek, 1979, 1988). The species composition of the studied community (Polygalo-Nardetum
strictae) is
characterized by the predominance of Nardus striata L, covering almost the whole area (80 - 100 %). Festuca capillata L. (cover 60-80%) and Sanguisorba officinalis L. (cover 60-80%) are spread as codominants. From the total amount of 43 higher plants species, the most frequent are Potentilla
erecta (I.) Raeu., Luzula capestris (I.) Dc,
Carex pilulifera
L,
Anthoxanthum
odoratum L, Ranunculus Acer L, Bryza media L. Mosses are: Climacium dendroides (Hedw.), Web. et Mohr. and Aulacomnium palustre (Hedw.) Schwaegr (Balatova-Tulackova et al., 1977; Zelena, 1979). Shrubs also frequently occur. The gradual accumulation of dead wood and increase in the amount of Nardus stricta, Sanguisorba officinalis, Deschampsia caespitosa and Avenula flexuosa takes place in the unmowed plots.
3.4. SOILS
Table 29 gives general characteristic of the soils of the research sites. The soils are classified in accordance with (Egorov et al., 1977); the international classification corresponds to FAO UNESCO (1990).
77
Table 29 Soils of the study sites Site
Soil*
Topsoil
Depth of
weakly developed sandy
10(12)
Texture
pHwater
(1:2.5)
effervescence
(cm)
Bugac
'--org- ( % )
with 10% HCl
in layer 0 - 20 cm
from the surface
0.2-0.5
7.4
fine sand
from the surface;
2.6
7.8
loamy sand
heavy silty
sod-calcareous Csaszartoltes
shallow low humus
30
rapid - 2-5 cm
calcareous southern chernozem Khomutovskaya
low humus deep ordinary
steppe
chernozem
Kamenicky
gleysolic acid brown
80
70
3.1
7.1
20
no effervescence
10-13
4.6-4.8
loam sandy clay soil
* The soil type by FAO UNESCO - see Section "Correlation between soil names "
The soil of the Bugac site is a undeveloped low humus sandy calcareous soil on wind-blow sand. The soil profile has not been formed, and we only distinguished an indistinctly shaped low humified (humus content of 0.5-0.9%) horizon with a light grey colour. The upper part of this sod horizon (~3 cm) is represented by wind-blown sand, and below this horizon there is a homogeneous non-banding mass of well sorted calcareous fine sand (Table 30, 31). The cation exchange capacity in the sod horizon is very low and about 1.5-1.8 meq/100 g of soil. Calcium predominates the exchangeable cations, and the share of exchangeable mangesium is also significant (Table 31). There are almost no highly soluble salts and the dry residue makes up only 0.03%.
Table 30 Physical characteristics of a sandy calcareous soil of the Bugac site Depth
Moisture,
Volume
(cm)
W(%)
——2.0
10-15 30-35
2.9
CaCOs
Particle size distribution (%)
weight (g/cm^)
content (%)
0.25 mm
0.25-0.05
0.05-0.02
0.02-0.01
1.47
3.89
34.7
62.1
1.6
-
1.42
6.55
31.4
65.7
1.7
0.01-0.005
0.005-0.002
-
0.002
_ 1.2
43-48
3.5
1.34
6.21
34.3
63.7
1.2
0.8
70-75
3.3
1.47
7.04
28.2
71.0
0.8
105-110
6.7
1.45
4.31
20.2
76.6
125-130
3.2
1.46
5.55
38.3
60.9
.
1.6
-
-
1.6 0.8
78 Table 31 Physico-chemical properties of a sandy calcareous soil (5-15 cm) of the Bugac site. Samples were taken from the rhizosphere of various plants Plant species
Hygroscopic
PHH20
moisture
(1:2.5)
(%) Carex
Corg
Ntotal
CO^of
K2O
carbonate
P2O5
(mg/100 g)
CEC
Exchangeable cations
(meq/100 g)
Ca^"
Mg^"
r
Na^
% of CEC
(%)
0.18
7.30
0.53
0.021
3.70
2.89
0.62
1.56
0.21
7.45
0.28
0.013
3.37
2.25
0.53
1.68
0.18
7.45
0.24
0.017
4.26
2.41
0.45
1.81
0.18
7.65
0.19
0.012
2.18
lipancarpos Festuca vaginata Koelena
64
32
61
36
glauca Bare surface
Since the grass cover of the plots was not continuous, we collected samples from the rhizosphere of individual bunches of the grass species and from the same depth at bare patches. Samples taken from the surface horizon were used for soil solutions replacement. The obtained solutions (see Table 30) demonstrate higher pH in the surface horizon of the bare patches and a lower organic C and CO2 content. Mobile K in a 1% extract of (NH4)2C03 was higher and mobile P was lower as measured in the rhizosphere. From this follows that the mobile K in these soils is predominantly of biological origin, and P comes from the parent material.
Table 32. Physico-chemical properties of the soils of the Khomutovskaya steppe reserve and the Csaszartoltes site Horizon,
PHH20
Corg.
CEC,
Exchangeable (nations
depth (cm)
(1:2.5)
(%)
(meq/100 g)
Ca'^
Mg'"
K^
Na^
(%)
•
Available to plants Hygros-
Dry
Carbonate
Gypsum
residue
CO2
S04^"
K2O
(%)
(%)
(mg/100 g of soil) isture, %
( % )
P2O5
copic mo-
Khomutovskaya steppe 0-20
7.1
3.06
41.3
81
14
4.2
0.5
0.216
5.2
0.66
3.52
7.4
1.96
35.2
77
20
2.2
0.6
0.120
8.1
-
53.9
.A,. 40-50
23.4
0.20
-
.A£, 80-90
7.6
0.66
28.2
74
23
1.4
1.2
0.110
9.2
0.002
16.4
0.45
Be 110-120
7.7
0.49
33.5
60
34
1.5
4.0
0.104
11.3
0.015
16.6
0.55
BcC\ 140-150
7.8
-
33.0
59
33
1.5
6.0
0.096
11.2
0.019
19.5
1.45
28.7
70
19
2.5
8.6
0.152
10.2
0.007
22.9
0.75
16.6
1.85
1.65
10.8
0.65
-
•\o^.
Cioess, 220-240 7.8
-
Csaszartoltes .Asod.0-16
7.6
2.57
28.7
83
15
1.8
0.2
0.140
1.0
Au 16-30
8.0
1.23
19.6
77
21
1.2
0.2
0.066
1.4
.\B, 30-42
8.0
0.74
16.1
76
22
1.1
0.3
0.078
5.8
-
7.7
0.65
BcC, 42-52
8.15
0.59
6.4
78
21
1.1
0.4
0.036
6.4
0.017
5.3
0.40
52-70
8.0
5.8
79
20
0.9
0.3
0.020
5.5
0.019
4.3
0.55
C, 80-100
8.1
-
3.0
83
16
1.1
0.4
3.0
0.039
4.6
0.50
-
79 For the Csaszartoltes site with a Ught textured soil, a relatively thin humus horizon and low humus content are characteristic. The transition between the horizons is gradual and the horizons are more or less parallel with visible carbonate excretion is absent (Table 32, 33). In accordance with Egorov et al. (1977) it may be referred to a shallow calcareous southern chernozem.
Table 33 Soil texture of the Khomutovskaya steppe and the Csaszartoltes site based on pyrophosphate method (%) Horizon,
Particle size (mm)
depth (cm)
1-0.25
0.25-0.05
0.05-0.01
0.01-0.005
Texture
A.od, 0-20
2.83
11.73
36.88
4.48
17.40
26.68
48.56
heavy silty loam
Al, 40-50
2.72
8.88
33.88
7.32
17.04
30.16
54.52
fine silty clay
B, 80-90
0.82
0.46
33.68
10.92
19.60
34.52
65.04
silty heavy clay
Be, 110-120
0.78
1.98
31.24
9.68
19.84
36.48
66.00
medium silty clay
BcC, 140-150
0.61
3.03
26.04
7.76
22.88
39.68
70.32
medium silty clay
C,, 220-240
1.82
8.18
23.32
7.04
18.60
41.04
66.68
medium silty clay
C,, 580-600
14.51
41.37
10.56
4.28
5.88
23.40
33.56
medium sandy clay loam medium sandy silty loam
0.005-0.001
0.001
<0.0I
Khomutovskaya steppe
Stipa*. 0-\0
22.44
20.92
21.32
6.48
0.72
19.32
35.52
Festuca*. 0-10
18.23
16.81
23.84
5.20
12.08
23.84
41.12
heavy sandy silty loam
Vicia*, 0-10
14.18
17.38
22.40
8.12
11.72
26.20
46.04
heavy sandy loam
-Asod. Ai,0-20
1.02
58.30
28.96
5.20
2.88
3.64
11.72
loamy sand
AB, 16-30
0.56
39.32
41.12
7.36
4.44
7.20
19.00
coarse silty loamy sand
B, 30-42
0.67
35.37
44.96
2.40
9.84
6.76
19.00
coarse silty loamy sand
BcC 42-52
0.80
28.56
54.96
2.04
6.96
6.68
15.68
coarse silty loamy sand
BC, 52-92
0.54
27.10
56.32
4.04
6.44
5.56
16.04
coarse silty loamy sand
C, 90-110
0.59
31.29
54.36
1.76
5.08
6.92
13.76
coarse silty loamy sand
Loess, 500-600
0.20
12.72
66.64
3.44
3.48
13.52
20.44
fine silty loam coarse loamy sand
Csaszartoltes
Stipa*. 0-10
1.22
46.38
40.88
2.88
3.96
4.68
11.52
Festuca*, 0-10
0.71
51.65
40.16
1.92
1.08
4.48
7.48
coarse silty unfixed sand
Astragalus*,0-10
1.22
51.78
38.60
2.76
1.60
4.04
8.40
heavy silty fixed sand
* Samples were taken from the rhizosphere of the mem ioned plants.
According to macromorphological analysis of our samples carried out by S. V. Gubin, the soil profile to a depth of about 90 cm has derived from alluvial deposits with a high mollusc shell content. The humus forms indicate that the transformation of plant remains tend to be mineralized. The microaggregation of the soil is low and the pore space is dominated by non-ramified pores. In the lower horizon of soil profile to a depth down to 60-70 cm, i. e. in the illuvial horizon contains microzones of sand and dust material are predominating and carbonate material is predominant at greater depths.
80 Textural analysis of the soil at Csaszartoltes shows that in the surface soddy-humic horizon (0-16 cm) fine sand is predominant (58%). At greater depth the content steadily decreases to 30% and the coarse dust fraction content increases from 29 to 60%. The Si content of the Csaszartoltes soil is at a maximum of 30% in the surface horizon and decreases to 22% with depth. Aluminium content is also high and in the middle part of the profile it reaches 16-17%. The Si02 : R2O3 and SiOi : AI2O3 ratios are 2.5-2.4. The soil contains a lot of calcium especially in the layer of 60-110 cm. The soil of Csaszartoltes has a alkaline reaction
(PHH2O
= 8 to 8.4). Humus content is
moderate in the surface soddy horizon but at a depth of 20-30 cm it is very low (Table 32). The degree of humification is low, and humus is hymic-phulvate type. The soil has a high proportion of fixed humic acids and calcium-bound humic acids are absent. The cation exchange capacity (CEC) is 30-20 meq/lOOg of soil in the upper 30 cm and at a depth of 40 cm it decreases to 6-4 meq/lOOg. Calcium is predominant and amounts to 70-80% of the total exchange cations. In the middle part of the profile at a depth of 20-50 cm, exchangeable Mg content is high (20-22%). The soil contains almost no soluble salts and gypsum, while carbonate content is high throughout the soil profile especially at a depth of 50-100 cm, up to 25%. The soil of the Khomutovskaya Steppe Reserve is referred to as a deep low-humus heavy loamy ordinary chernozem. The soil has a sod cover 20 cm deep, interlaced with roots, homogeneous dark-grey colour, and a strong granular-crumb structure (see Table 32, 33). Effervescence with HCl were observed in the lower part of the humus horizon. The transitional horizon B has a depth of about 20 cm and uneven colour. Dim greyish spots and dark vertical channels of earth-worms can be seen against a brown background. There is no visible excretion of carbonates and the thin pored transition to the carbonate horizon is well seen when white soft spots appear. The deep carbonate horizon is of pale yellow colour, white when dry, dense, with distinct powder-like soft spots. Their number decreases with depth and the size increases up to 2-3 cm in diameter. At a depth of 2 m the horizon is homogeneously brown, compact clay, witch is typical for the Priazov region. These clays are embedded in the ancient alluvial sandy silt sediment, characteristic for terraced chernozems. Mechanical analysis (Table 33) shows that the upper soddy rich in root mass horizon of the chernozem has a heavy loamy texture, whereas the rest of the profile is clayey. Clay content is within the range of 65-70%, and fractions smaller than 0.001 mm is about 35-41%. The change in texture takes place below 2.5 m depth, where we can observe embedding of loess in the ancient alluvial sandy sediments.
81 The Khomutovskaya steppe chernozem is a characteristic soil profile for chernozem formation on heavy loess sediments in the southern part of the Russian plain. Si content is in the range of 28-34%, Al 8-13%, and Fe about 2.5%. The maximum Si and minimum Fe and Al content are in the soddy surface horizon, and argillization (Si02:R203 = 3.4) coincides with the Be horizon to a depth of 110-130 cm. These soils have a low alkalinity (Table 32). Humus content and the degree of humification in the 0-50 cm layer are moderate (Orlov et al, 1986). The type of humus is humatic and sometimes phulvate-humatic, the content of free humic acids is very low whereas the content of fixed humic acids is high. The maximum CEC is found in the soddy surface layer and gradually decreasing with depth to 30 meq/100 g. Ca is predominant, and it is worth to note the increasing amount of Mg and Na in the carbonate horizon. The profile below 2.5 m contains no soluble salts and gypsum (Table 31). Carbonate content is at its maximum at the depth of 1.2 m.
Table 34 Some hydrophysical properties of a gleysolic acid brown soil of the Kamenicky site Depth
Particle content (%)
(cm) <0.01 mm
Specific
Hygroscopic
Maximum
weight
moisture
hygroscopic moisture
< 0.001 mm " (g/cm')
(%)
Porosity
Capillary moisture capacity
5-10
41.8
12.3
2.23
4.74
12.7
70.0
51.4
10-15
56.8
32.2
2.43
3.92
12.4
39.5
32.3
20-30
18.8
8.0
2.61
0.58
1.7 32.3
50-60
30.4
17.0
2.68
1.46
4.4
41.4
75-85
31.7
18.7
2.69
1.18
4.2
41.3
32.2
95-105
26.5
12.1
2.69
1.04
3.3
42.7
33.3
The soils of the Kamenicky site developed under the secondary grassland community Polygalo-Nardetum. They are formed out from the weathering products of orthogneiss and may be defined as sandy-clay gleysohc acid brown soil. Tables 34 and 35 show the physical and chemical properties of an unfertilized gleysolic brown soil under an unmowed community of PolygaloNardetum. The topsoil contains up to 40-50% of clay and has a low specific weight and porosity. These have been marked by maximum hygroscopic moisture and capillary moisture capacity. The porosity is caused by the high organic matter content. The degree of SAC base saturation indicates the predominance of H"^ ions, which is in agreement with low pH values of these horizons, and the pH gradually increases with depth. The soddy horizon has a high organic carbon and total nitrogen content, which decreases with depth. This may be traced in the content and distribution of CaO
82 and P2O5, partially K2O and Na20 in the soil profile. Two high values for Fe content have been observed at 5-20 cm and a 70-130 cm depth.
Table 35 Some chemical properties of gleysolic acid brown soil of the Kamenicky site Depth
PH
Ntoul
Crcsave
(cm)
H2O
KCl
(%)
5-10
4.8
3.9
6.4
Ca
104
V*,
K
Na
Fe
Mg
7.1
5.8
52
70
19
52.5
11.2
17.8 30.3
(meq/100 g)
(mg/100 g) 0.87
H*
s'
P
(%)
10-15
4.7
3.9
6.0
0.96
96
5.8
3.3
63
79
7.8
30.5
13.2
20-30
4.9
4.1
7.0
0.06
59
0
2.5
37
28
13
5.5
1.2
17.9
50-60
4.9
4.0
0.12
0.07
45
0
3.3
33
28
11
6.0
5.5
48.0
75-85
4.9
4.0
0.06
0.05
50
0
4.1
33
102
13
-
-
95-105
5.2
3.7
0.06
0.05
34
0
3.3
41
70
11
4.5
4.5
* H - hydrolytic acidity; S - sum of exchangeable
50.0
cations: V- - degree of base unsaturation
The content of soluble nutrients in the soddy horizon is given in Table 36. Ca, Mg and S are predominant, whereas P contents are low.
Table 36 Soluble amounts of nutrients in water extract of soils of Polygalo-Nardetum strictae community at the Kamenicky site Depth
Ca
(cm)
mg/100 g
Mg
0-10
6.43
2.89
10-20
4.00
1.69
S
K
Na
N
P
CI
0.91
0.45
0.70
0.01
0.57
1.92
0.45
1.64
0.42
0.22
0.91
0.05
When comparing the investigated soils, differences in their genesis become especially evident. Properties of the Bugac sandy soils reflect the initial stage of soil formation with sparse open grass cover. The profile is not formed yet, and soil formation has resulted in a shallow low humic layer (10-12 cm), over a well sorted ancient alluvial sands. The soils of the Csaszartoltes are typical for the soddy soil formation on the Danube terraces. The light texture and the recent hydrothermal regime caused by submediterranean effects determine the dominance of mineralization over humification. The soils of the Khomutovskaya steppe reserve are a nice example of the so-called Tsar of soils, a nickname for the chernozems of the southern part of the Russian plain. The morphological characteristics (colour, structure, sequence of genetic horizons, etc.) and the physico-chemical
83 properties reflect the results of chernozem formation under the cover of mixed-grass sheep's fescue feather-grass steppe. The soils of the Kamenicky site are typical for the meadow and forest landscape and they are gleying and have a surface horizon of peaty character. The genetic horizons was formed in a forest ecosystem.
84
CHAPTER 4. ENVIRONMENTAL IMPACT ON THE SOIL LIQUID PHASE
The liquid phase of the soil is one of its most variable components due to a variety of factors The lack of inertness in its composition is caused by the activity of dissolved elements and the greater contact surface of liquid phase with other soil components. The composition of the soil liquid phase is determined by the multicomponent nature of soil and external impacts (Fig. 15).
Fig. 15. The relation between the soil liquid phase and other components of ecosystem
4. L SOIL SOLID PHASE The solid phase of a soil is the basic pools of chemical elements. Soil liquid phase contains only a small part even of ions, especially in adsorbing complex of a chernozem (Table 37). For sod-calcareous and sod-podzolic soils, it has been established that NFL^-N makes up 6-10% of the exchangeable amount, K - 2-5%, P - only fractions of percent, measurable in an acidic-salty
85 extract (Dzuin & Kovrigo, 1972). This is important as it provides buffer properties to soil liquid phase composition
Table 37 The content of some elements in different components of steppe ecosystem (g/m ) Indicator
Total N
Soil (chernozem) Phytomass aboveground (0-10 cm) living dead 370 5.8 2.3
NO3-N
-
-
-
-
0.12
Total K
1550
4.9
4.4
6.2
0.16
Exchangeable K
54
_
_
.
.
Total Ca
3000
1.8
2.0
14.1
3
_Soil solution (0-10 cm) ' (0-10 cm) 18.2
roots
Exchangeable Ca
900
-
-
_
Total Na
500
0.10
0.23
0.72
0.12
Exchangeable Na
5
.
.
_
.
The main processes regulating the exchange between soil solid and liquid phases are the adsorption-desorption, ion exchange, solution and sedimentation. The solubility of soil soUd phase components determines mainly the composition of soil liquid phase. Most of CI" and NO3' ions are found in dissolved state. The activity of low-soluble components in solution can be determined by their solubility multiplication product. The quantitative estimations are difficult to carry out due to the absence of real data in soil liquid phase for activity coefficients. This has caused literature with 'oversaturation' of soil solutions which may be the result of errors in calculations of activity coefficients. On theoretical grounds it is impossible in the presence of the surface of soUd particles (see Section 6.1.1). Saturation in soil Uquid phase implies that both the process of solution of component at its excess in solid phase and its sedimentation into solid phase with simuhaneous neo-formation in soil may take place. The major processes, determining the impact of soil soHd phase on liquid phase composition, are the adsorption-desorption and ion exchange processes. The use of equations (Freundlich, Langmuir, Nikol'skii, Gapon and Gaines-Thomas equations) to describe these processes is considered in section 6. 8. Cation adsorption is characteristic for soils. Cation exchange capacity of different soils varies from 3-7 meq/lOOg in podzolic soil to 70 meq/lOOg and higher in a topsoil of a typical chernozem (Remezov, 1957).
86 For equal-valency cations a sequence of selectivity in adsorption is proposed as follows (Orlov, 1985): H" > Cs" > Rb' > K" « NH4" > Na^ > Li^; Ba''> Ca'" > Mg'"; Fe'^ > Al'^. The adsorption order of different cations is determined by the specificity of soil adsorbing complex which depends on the soil type. Anions are also adsorbed on the colloid surface, but to a lesser extent. The sequence for anions adsorption in the soil is as follows (Kovda, 1973; Bolt & Bruggenwert, 1978):
PO4' »so/">N03 >cr. The presence of non-solvent volume (see Section 1.1.1) often leads to the so-called negative adsorption, and sometimes the result of the process is negative. Thus, S04^' and CI" ions are almost not adsorbed by the soil, especially when there are phosphate-ions present in the liquid phase. In the P04^"-free systems, a CI" ion adsorption can be observed, especially at pH below 6. The presence of A1(0H)3 and Fe203 • nH20 increases anion adsorption. Processes of anion adsorption are complicated by the competition of carboxyl ions and the impact of such cations, as Ca and Al (Partiff, 1978). Nitrate adsorption can be described by the Freundlich and Langmuir equations (Kozak & Abiad, 1985). The time of equilibrium establishment between the soil and liquid phase is essential for liquid phase equilibrium. Early experimental investigations by Hedroitz (1975b) have shown that cation exchange between the solution and SAC is quick, and within 1-5 min after mixing with a 10.5 N salt solution the equilibrium can be established. Experiments on 60 soil samples have shown that from 80 to 100% of input H^ ions from a HCl solution was adsorbed by highly acidic soils within seconds in exchange for Ca, Mg and Al ions (Schaller & Fisher, 1985b). The exchange on the surface of highly dispersed soil particles explains such high speed. In case of adsorption by total soil volume, the speed of reaction may decrease significantly and be limited by diffusion processes. The decrease of soil moisture also causes the decrease of the speed of exchange reaction. Thus, at minimum soil moisture capacity it has been proven that the speed of exchange reaction decreases to 10-14 days (Gorbunov, 1948). Our experiments on addition of salts simulating the effects of fertilisers under field moisture conditions (Table 38), have shown that within 30 minutes after salt solutions were added to soils, the ion-selective electrodes indicated stabilisation of ion activity.
87 Table 38 Ion activity in liquid phase of grey forest soil after addition of KNO3 solution (mmol/1) Ion
NO3
K^
Ca^^
pH
Version of
Time from the beginning of experiment ( h )
experiment*
0,5
4
6
24
29
48
120
168
1
36+9**
46±4
48±8
52±6
52±6
42±2
41±6
37±5
2
16.1±1.5
17.4±1.1
16.6+1.8
19.7±2.9
17.4+0.7
18.2±2.8
18.4±3.3
16.2±2.2
1
6.0±0.5
5.0±1.6
5.2±1.3
5.3±0.8
5.7±0.9
5.5+1.7
5.9±1.9
6.0±1.9
2
0.41±0.03
0.49±0.07
0.41±0.08
0.48±0.04
0.48±0.12
0.69±0.25
0.61dh0.20
0.47±0.11
1
24±4
16±1
14±2
15±3
12±3
12±3
11±2
11±3
2
7.2±0.2
6.1±0.9
6.2±1.1
6.6±0.7
5.5±1.2
6.4±0.6
6.1±0.3
5.5±1.4
1
-
-
-
4.57±0.01
-
4.61±0.02
4.64±0.05
4.63±0.04
4.83±0.03
4.88±0.05
4.82±0.03
2
4.84±0.01
* 1- experiment (100 ml, O.IMKNO3); 2 - control (100 mL ofHiO) mean square deviation at 5 series of measurements (an-i)
Under natural conditions, it is almost impossible to achieve the equilibrium because many processes such as uptake and excretion by the living compartment are superimposed on the physical and chemical processes. Even under laboratory conditions, the activity of micro-organisms in soil samples may lead to deviations within hours. In experiments with watersaturated soils, redox processes with micro-organisms, organic substances and compounds of nitrogen, iron, manganese and a number of other substances, are stabiHsed in a period of 8-12 weeks (Ponnamperuma et al, 1972).
4.2. ATMOSPHERE AND SOIL AIR
There are many relations between soil air and soil liquid phase. The adsorption of different gases from soil air by liquid phase and condensation of water vapour takes place simultaneously with gas release from the liquid phase. Changes in soil air composition lead to changes in the soil liquid phase composition and this has been analysed in extracted soil samples. With changes in the temperature and partial pressure of CO2, which leads to the redistribution of carbonate groups, pH change and in some cases to sedimentation of carbonate (Kerzum et al, 1970; Snakin & Zavizion, 1979; ZeUchenko & Sokolenko, 1985). At the same time, the increase in CO2 concentration increases the concentration of H^ in the soil liquid phase and their fixrther exchange with SAC cations, and it increases the concentration of different elements in the soil solution (Khromchenko & Kovrigo, 1974).
Oxygen content in the soil air has also a significant impact on soil hquid phase composition, changing the ratio of oxidised and reduced forms of elements and immediately affecting soil living matter. A detailed analysis of the impact of CO2 on liquid phase composition is given in Section 6.1.
4.3. HYDROLOGICAL REGIME
Soil moisture and temperature are hard to predict, and their impact on the Hquid phase is accompanied by an indirect, often reverse influence through other factors, such as vegetation, micro-organisms and soil air. The increase in soil moisture results in dilution of soil liquid phase. The differences in solubility explain the variability in the concentration change of particular ions. If we consider the ion exchange equation (Section 6.8) of K"^ and Ca^^, a small moisture change leads to no significant change in the composition of SAC content, and the following equation may be proposed: j ; ^ = const^^
^
(20)
where QK and aca - ion activity before moisture increase; GK and QCM - after moisture increase. Let us assume that Ca activity after dilution decreased two-fold, i. e. aca = 2aca • Then: '^A-V«r.
a^ = -
2a,.
O
^ = 0.1 a. .
4i
(21)
This is a two-fold decrease of a two-valency ion activity and the one-valency ion activity decreases by a factor of 1.4. A simple dilution of soil liquid phase leads to increase in the share of monovalent cations while drying of the soil results in the increase of polyvalent ions. This conclusion proves the practical results of investigation of soil solution composition dynamics, determined by soil drying and moistening processes (Maimusov, 1975; Bystritskaya et al, 1981). Numerous investigations^^ have shown that the pH increases with the increase in soil moisture. Dilution occurs and hydrolysis are likely to take place leading to a sharp increase in the pH, especially in salinized soils. When measuring the alkalinity of extracted soil solutions, Kovda " See the works by Trofimov (1931), Kovda (1946), Kovda and Minashina (1967), Krupsky et al. (1969), Kerzum et al. (1970), Gonchar-Zaikin (1974), Goncharov and Kiselev (1987) etc. At the same time, there are data (Ponizovsky & Polubesova, 1986) on pH decrease in ethanol-displaced arable grey forest soil solutions at moisture content below 20% and pH increase at moisture content above 20%.
89 (1946) came to the concluded that hydrolysis of carbonates the increasing soil alkalinity is fatal for juvenile cotton-plants. Experiments have shown that the dilution of extracted soil solution with initial concentration of 326 g/L by 1.5 times, causes an increase of pH from 7.98 to 9.18 . Such drastic changes were not observed in non-saline soils (Fig. 16).
0
10
20
30
40 50 Moisture (%)
Fig. 16. The moisture dependence of soil pH value chernozem (Kerzum etal,
in humiferous horizon of non-saline
1970)
Table 39 Ion activity measurements in southern chernozem of different moisture status (Goncharov & Kiselev, 1987) Soil moisture (%)
pH
pNa
pH-pNa
6
5.6
1.85
3.75
8
6.3
1.4
4.9
10.5
6.5
2.68
3.82
13
6.75
2.72
4.03
18
7.05
2.85
4.20
23
6.67
3.0
3.67
33
7.45
3.05
4.4
43
6.95
3.3
3.65
53
7.2
3.25
3.95
'^ The FT activity coefficient sharply decreases at high concentrations, which leads to a disproportional pH increase with dilution. This was already mentioned in the work of Trofimov (1931), who considered ion concentration of solution a major regulative factor in soil reactions. In his opinion, moisture had only an indirect influence on the pH.
90 With the dilution of the soil liquid phase a decrease in Na activity takes place (Krupsky et al., 1968; Goncharov & Kiselev, 1987; Csillag et al., 1995). The data in Table 39 show that the difference between pH-pNa values does not depend on soil moisture and fluctuates around some average value, probably due to measurement error. In this case, despite a significant difference in activity values, Na and H ions are subject to the same regularity. This has been proven by the Gapon and Nikol'skii equations (see Section 6.8), according to which the exchange of equal valency cations adsorption does not depend on dilution. This has been proven for chernozems (Gunar, 1937), but when one of the cations involved into the exchange is H^, the equations of exchange adsorption are not valid. In the presence of ion in soil solid phase, its concentration in the liquid phase remains constant with water dilution provided that there is enough time to reach equilibrium. The nonsolvent volume may influence ion concentration change. Perhaps, these two reasons may explain the conclusion of Drachev & Alexandrova (1932) on reverse dependence between moistening and soil solution concentration with regard to NO3' and Ca^^. A proportional decrease in concentration with simultaneous soil moisture increase has only been observed for CI ions (Table 40). Komarova (1939) observed that the amount of CI in soil solution extracted from agricultural soil at the Vakhshkaya valley was almost the same at different moisture values as calculated per lOOg of soil. Table 40 The dilution dependence of K^, Ca^^, CI' and NO3" ions activity (Norov et al., 1978) Soil
^?^
cr
4^90
3.62
46^8
155
3.02
3.80
20.1
2.51
5.60
8.32
1.78
7.59
4.27
1.51 4.79
Soil: water
K^
ratio
meq/L
High-loamy meadow
1:0.5
gypsic sierozem
1:1
0-20 cm
1:2.5
1,76
1:5
1.26
NO3-
Low-loamv sierozem.
1:0.5
5.25
2.81
7.76
0-20 cm
1:1
4.37
2.62
5.01
3.55
1:2.5
2.88
2.39
1.05
2.34
1:5
1.82
1.99
0.68
1.91
Loamy light sierozem.
1:0.5
1.51
1.65
6.03
0.90
0-20 cm
1:1
0.56
2.00
1.20
0.79
1:2.5
0.32
2.40
0.48
0.58
1:5
0.21
3.01
0.23
0.46
91 Under natural conditions the increase in soil moisture by precipitation or irrigation sometimes leads to different results. Acid rains cause soil acidification and precipitation falling through the canopy enriches with leached nutrients may significantly increase the concentration of soil liquid phase. Pre- and post-rainfall (32.5 mm) observations on the composition of ordinary chernozem liquid phase under virgin steppe vegetation, have shown (Fig. 17) that at a depth of 7 cm the decreased redox potential quickly returned to its initial state. After precipitation the pH tended to decrease and not to alkalinise, as was expected according to theory. This was probably because of the acidity of the rain. Ca and NO3 activity considerably decreased. On the contrary, K activity increased, which was probably due to leaching of K from aboveground plant parts, which was pointed out in the works by Volkova (1978) and Maiboroda (1971). It is noteworthy, with an increase in soil moisture exchangeable K increases (Prokhorova, 1957). Eh, mV
Rainstorm (32,5 mm)
W, %
^w^ 620 -
30
600
25
"7
~~~ ^^^^'•'^
Eh
pH 7.0 - ^Ca^\ "^eq/L
#
6.8 75
—•
2+
Ca
•
3l^*, meq/L 50 10
-
8
.
."''"'
• * ,
/
Z""*^'"'---*
-•-
.^^
K" •,.-«—•^"•
6
/
4 - ^NO, • ^^^^ 10 • 6 2
\ \
1
•
•
29
\
NO3 *•* " » . . .T ^
" "»„«.„.A... * r " — . ' » » . " ^«' Y ' " "^' ^. "«'.^,y„„„^„»M,^.»»..^
30 July
31
1
2
.,
3
August 1977
Fig. J 7. Moisture (W), redox and ion activity dynamics in the liquid phase of chernozem in the Priazov region under virgin steppe vegetation before and after rain
92 4. 4. TEMPERATURE
The influence of temperature on soil liquid phase composition is of interest for the analysis of ecosystems. There have been few studies in this field and in many cases the influence of temperature on solubility of components and on the speed of chemical reactions is determined only hypothetically. Using the advantages of direct measurements in soils by ion-selective electrodes, we have investigated the change of H^, K^, Ca^^ and NO3' ion activity depending on the changes of temperature in a climate chamber using three types of soils: virgin ordinary chernozem of the Priazov region (0-10 cm), arable high-loamy sod-podzolic soil (unfertilised, 0-10 cm) and arable grey forest soil (unfertiHsed, 0-10 cm).
-^-» ^
55|-
-•
46|-
_M
45I-
•
2 •
_,
3
"^
40h>^-*-. 39 38 •
^
-
•
.
33 3,2
^ •^• ^- -3
J
31
f "^ 1
""'
26 2.5
24 c
2.2L
^
2 1[-
2»
T •_
16|. 2.1
1*
.•,
^-^
20 O z "
1.9
1.811.7
1"*""'
^--^' • — '
^.
•
• 30 t (°C)
Fig. 18. The influence of temperature on H^, IC, Cc^^ and NO3 ions activity in liquid phase of a chernozem (1), grey forest (2) and sod-podzolic (3) soils
93 The activity of potassium ions in the liquid phase of the 3 soils studied significantly increased with an increase in temperature (Fig. 18). The increase was about the same for the 3 soils and comprised 0.1 pK per 7-8 ^C. Since the influence of biological factor on physico-chemical processes is minimised, such growth may be explained by the release of K from the adsorbing complex, as it is known that soil solution contains only a small amount of K compared to its supplies in SAC (see Table 37). A different pattern has been observed for nitrate-ions, of which the greater part is in soluble form in soil. With an increase in temperature their activity decreased approximately to the same degree as the activity of K^ ions increases. This is about 0.1 pNOs per 7-11^. It may be influenced by a decrease in the activity coefficient which in a heterogeneous system is greater than in standard solutions, and the uncompensated release of NO3" from the soil. Temperature dependence of exchange and equilibrium constants the brings a change in the behaviour of different ions. A likely reason for the decrease in NO3 activity with an increase in temperature may be the decrease in nonsolvent volume. This was proven by moisture equilibrium investigations on Richards press with the increase in temperature residual moisture showed a downfall trend (Hitoshi 1958). A significant change in the composition of soil liquid phase especially with such biophylic ions as K" and NO3" may be caused by changes in activity of various groups of micro-organisms depending on the temperature. We can only hypothesize on the change in hydrogen and calcium ions activity with a change in temperature. With an increase in temperature Ca^"^ activity increases and H^ activity decrease as was observed in chernozem. For the acid grey forest and sod-podzolic soils with calcium-deficit, a different pattern was observed. As the temperature increased, the Ca^^ ions activity decreased, whereas acidity tended to increase. The influence of temperature on redox processes has been investigated in ordinary chernozem of the Priazov region using containers with a mixture of fescue grass, timothy grass and clover sowing. The response of a chloride-silver saturated reference electrode to temperature change was estimated by the equation (Cammann, 1973), Eh = Ei + E2, where Ei is instrumental readings (mV), and E2 is standard electrode potential a given temperature (see Section 2.4.1.). Cooling the containers at 1-1.5^C per hour was accompanied by an increase in redox potential at a rate of 2.4 mV/^C. At temperatures below zero a sharp increase in the potential was detected (Fig. 19a). Soil warming led to a potenfial decrease of 1.4 mV/^C. In this case, we observed hysteresis possibly caused by a slower restoration of the redox conditions to the initial status compared to the temperature increase. The fiinctioning of ETP-02 - EVL-IM electrodes
94 pair in a standard ferro/ferri-system was the same as in soil (Fig. 19b), but no hysteresis was observed at the temperature change of Eh at 2.5 mV/'C.
Eh (mV)
Eh mV) 660
a 520
640
510 500
>\1
^
620
490 480
600
470 460
580
1
1
0
1 1 L—1
1 1 1 1 12
17
1 1 ^ 20
-I
t(t:)
i
I
10
L_JL
14
J 18
L. 22
t('fe)
Fig. 19. The influence of temperature on soil redox potential in ordinary chernozem: 1 - cooling, 2 - warming (a) and on the potential in standard ferroferri-system
(b)
In conclusion, there are two ways the temperature affects the soil: 1) directly, similar to that in a standard ferro/ferri-system; 2) indirectly, determined by changes in the activities of microorganisms and vegetation as compared with the first one. The temperature impact on soil physico-chemical processes is complicated. The use of regression analysis for processing the results of seasonal Eh and temperature dynamics, has shown a reverse relation between these parameters. For peat and swampy-peat soils, the linear regression showed a decrease in Eh value by 11.5 mV per degree of soil temperature (Efremova, 1978). Such temperature dependency of Eh in the seasonal cycle, may be explained by active decomposition of organic matter of peat soils after their drying out in the warm period. Our observations on the dependence between the amplitude of fluctuations of NO3' ions activity and the soil temperature in sandy low humus calcareous soil liquid phase under open steppe vegetation have demonstrated that at soil temperature up to 18 ^C, the diurnal dynamics of the PNO3 value lacks reliability (Fig. 20). The maximum amplitude of pNOs value was marked in the range from 20 to 24 ^C, and with increasing temperature a decrease in pNOs value was observed. This shows the indirect influence of soil temperature on the daily activity dynamics of NO3' ions in the soil liquid phase, which highly coincides with the conditions for optimal
95 functioning of the grass cover and the microbiota. In the first section of the graph, the direct influence of temperature is in a reverse direction. ApNOg
0.5 A
0.4 H
0.3
0.2
0.1
10
20
30 tsoil (°C)
Fig. 20. Relation between daily fluctuation of NO3 ion activity (ApNOs) in the soil liquid phase and the daily average soil temperature of a sandy semi-desert steppe
Therefore, the influence of temperature on soil liquid phase is dual-natured. It is both immediate and indirect. It often happens that the impact of plants and of microbiota have opposite directions, whereas the biological factor may predominate.
4.5. VEGETATION
Many studies have focused on the influence of plants on soil characteristics. Rhizosphere micro-organisms have been given special attention and have been viewed as complemented partners of higher plants. The role of plants in the ecosystem can be observed through changes in soil liquid phase composition. Growing seeds in a reduced suspension increases its redox potential via acidification, while in an oxidized suspension redox shows a downward trend (Geller, 1948). Acidification of
96 soil solutions is especially evident through the excretion of organic acids by plant roots in soils with low buffer capacity. The investigated plants (leguminous plants, buckwheat and different grass species) acidified the soil during the first part of the vegetation period, then they somewhat alkalinised the soil, and at the end of the vegetation period the pH lowered again (Minina, 1927). Compared to steppe vegetation, forest vegetation leads to significant soil acidification (Stepanov, 1932). This has been proven by pH measurements in the liquid phase of a typical chernozem under broad-leaved forest and in virgin steppe (see Table 20). Change in soil solution pH was large near the tips of roots and in the root hair zone. Special pH-microelectrodes showed that in the rhizosphere ofArachis
at a distance of 2.5 mm from the root surface, the pH tended to decrease
with no fertiHsers from 5.5 to 4.0, while in the corn rhizosphere it increased to 6.5 (Schaller & Fisher, 1985a). A typical example of the impact of lower plants on liquid phase pH is presented in the results of our research carried out on calcareous sands which was densely populated by moss and lichen synusia (Table 41). With the growing abundance of lichen and mosses the alkalinity of the soil liquid phase tends to decrease. At the same time, vegetation may have a buffer effect in relation to pH value, for example, during acid rains. It has been shown that acid rain tend to be alkalinised by the winter wheat cover (Bulatkin, 1980). The degree of alkalinization increases with the increase of fertilisation, which is determined by the larger biomass of the fertilised plots. Plants and bacteria largely determine the ratio of oxidized and reduced compounds in soil solutions. As a rule, the redox potential in the rhizosphere is less than the background values by about 50-90 mV (Geller, 1952). In rice fields with reduction processes predominating, high Eh values have been observed in separate areas around rice roots with a potential difference up to 600 mV as compared to the soil mass (Neunylov, 1961; Kostenkov, 1976). The impact of plants and bacteria on soil liquid phase is effected through absorption and emission. Release of CO2 by respiration and root excretion of organic substances cause soil acidification and increase the ion concentration of the soil liquid phase. Mineralization and nitrification result in the increase of soil solutions concentration, but nutrient uptake by plants and micro-organisms stabilises soil liquid phase composition. The absence of plants disturbs this balance. For example, fallowing is accompanied by an increase in total concentration of soil solution, NO3 content and the appropriate amount of Ca, Mg and K (Sobolev, Drachev, 1926), a decrease in CO2 and an increase O2 in soil air (Makarov, 1952; Yastrebov, 1963). However, continuous fallowing leads to a substantial decrease of organic C and N (Drachev, 1927). Our
97 analysis of the soil solution in soils of virgin lands and an arable field of the Centralnochernozemny reserve territory left fallow since 1947 has not indicated an increase in total soil solution concentration (Table 42). The content of all nutrients decreased, except for NO3 and Si.
Table 41. Changes in species composition, cover and soil liquid phase pH in different zones of moss and Hchen synusia, Bugac site (Hungary), July 5-6, 1985 (Mazsa et al., 1987) Stage of
Species
Projective
Succession
composition
over (%)
0
No vegetation (carbonate sand)
I
n
Tortella inclinata
50
Diploschistes muscorum
7
Cladonia magyarica
5
Tortella inclinata
60
Cladonia magyfrica
70
Diploschistes muscorum
15
Cladonia convoluta
5
Toninia coeruleo-
2
pH value at a depth of 5 cm (2% moisture) 1 cm(6% moisture)
-
7.9610.70*
8.04±0.20
8.0010.16
7.90±0.09
7.8210.25
7.69±0.41
7.1910.23
7.3310.29
7.0710.32
nigricans
m
IV
Tortella inclinata
40
Diploschistes muscorum
30
Cladonia magyarica
20
Cladonia convoluta
40
Parmelia pokomyi
1
Hupnum cupressiforme
10
Thuidium abietinum
5
Tortula ruralis
1
Thuidium abietinum
60
Hupnum cupressiforme
40
Cladonia furcata
20
Cladonia convoluta
10
* mean square deviation at 6 series of measurements
The uptake-release activity of plants is main cause for heterogeneity of the soil liquid phase. Field experiments have shown that NO3 and exchangeable K in the root zone decrease towards the roots surface (Wehrmann & Coldewey, 1986). The presence of Caragana shrubs in steppe vegetation leads to an increase in spatial heterogeneity (Kuzakhmetov, 1986).
98 Table 42 The composition of soil solutions of typical chernozem (0-10 cm) Plot
W (%) PH
HCOj'
cr
NO3
Ca^^
Mg^^
Y:
Na^
Corg
Si02
mg/L
meq/L Virgin land, steppe
25.9
7.82
5.4
0.72
0.14
7.95
1.49
0.049
0.16
42
19.4
Virgin land, forest
33.4
7.22
3.6
0.69
0.45
5.09
0.80
0.21
0.19
32
9.5
Fallow land since 1947
22.9
6.86
0.5
0.33
2.69
4.59
0.40
0.036
0.11
16
38.5
19.8
7.57
2.6
0.54
0.58
3.98
0.83
0.031
0.19
27
20.8
Arable field for winter wheat. adjacent to reserve territory
Note: the data is of May 29, .]982; average value of 3-5 samples
The following chapters consider the role of plants in the formation of soil hquid phase and in developing spatial and temporal heterogeneity of soil properties. We give due consideration to the role of plant cover in transformation of atmospheric precipitation, since this important in the process of soil Hquid phase formation (see Fig. 15).
4 5 1 ATMOSPHERIC PRECIPITATION AND FOREST VEGETATION We carried out our research in the relic Colchid forest where the age of dominant tree species reaches hundreds and even thousands of years. The amount of precipitation is high (12401660 mm/year), and stemflow and throughfall are important. The amount of nutrients washed out from aboveground plant parts and litter is far larger than nutrients returned by the decomposition of dead plant parts. Special precipitation collectors consisting of kapron grid-covered plastic cuvettes, were installed on metal platforms at 1.5 m above the soil surface (Fig. 21). These collectors were installed to measure a range of elements, and atmospheric precipitation transformed by the canopy of tree species in various stands of the yew-box Taxus haccata-Buxus colchica grove at the Caucasus State Biosphere Reserve. For the description of precipitation collectors see Andreeva et al. (1990) and Andreeva (1990). In each forest (mixed broad-leaved boxwood forest, hornbeamoak forest and laurel-cherry yew woodland) stand seven collectors were installed under the forest canopy and in open fields (gaps). Precipitation ran through siHcon tubes into 1-litre polyethylene vessels. Special attention is given to the amount of interception of precipitation by tree crowns. According to Table 43, communities dominated by evergreen species intercepted half of atmospheric precipitation by canopy. Yew forest was the thickest and thus got the most of the
precipitation (Semagina, 1990). Our data show the high-trunked laurel-cherry yew woodland (from 20 to 25 m), with its well-developed undergrowth had an average interception of 67%. Hornbeamoak forest with predominant deciduous species intercepted about one-third of precipitation. The high variability of interception is characteristic for the given community during vegetation period and the variation coefficient Cv more than twice exceeds the one in boxwood and yew forest.
Fig, 21. A precipitation collector for the investigation of transformation of atmospheric precipitation under the cover of the Colchidforest
Table 43 The interception of atmospheric precipitation by crown of various trees (in 1988) Date of precipitation
Precipitation
Interception (%)
collection
(mm)
boxwood
laurel-cherry yew
hornbeam-oak
March, 21
47
48
18
14
March, 24
24
54
60
7
June, 30
1.2
100
100
100
July, 1
14
36
67
15
July, 7
8.8
41
83
29
July, 8
3.6
90
91
14
October, 12
52
34
53
28
Average
22
58±27
67±28
30±32
Cv, %
95
47
42
107
The observed interception values were high compared to other forests. Karpachevsky (1977) gives the following data on interception values: oak woods - 13-14%, birch and aspen -
100 35%, fir woods - 42-69%. For the various US forests, interception was in the range of 10 to 35%) (Waring & Schlesinger, 1985). For deciduous species in the forests of Germany interception ranged from 26 to 28%), for fir trees: 33-37%) (Balazs, 1983). The average interception rate of juniper was estimated at 36%) and at 32% for poplars (Szabo & Keszei, 1985). It is obvious that in the Colchid forest ecosystems interception is determined by climatic conditions and by the high density and mass of vegetation. According to the data Table 43, the maximum interception may be estimated at 20-21 mm for mixed broad-leaved boxwood forest and laurel-cherry yew woodland and 11 mm for hornbeam-oak forest. The transformation of the composition of atmospheric precipitation by tree crowns depends on many factors: precipitation intensity and regularity, season, type of community, etc. Average data from March to October, 1988 shows significant differences in composition of throughfall under various communities (Table 44). The increase of concentration in precipitation has been observed for almost all nutrients except HCO3 ions. This may have been caused by the following: 1) leaching from leaves and branches of trees; 2) adsorption of substances from atmospheric precipitation by leaves, including water; 3) wash away with partial dissolution of dust accumulated on the surface of leaves and tree branches; 4) concentration of precipitation, resulting from water evaporation from leaves surface.
Table 44 Average ion composition of throughfall under different communities in 1988 Species
j^H
C^
Mg^
K^
N7
HCCV
CV
NO?
SO?^
mg-eqv/1
SiO^^
"C"
mg/1
Boxwood
6.0±0.2*
0.68±0.35
0.14±0.06
0.13±0.08
0.0410.02
001+008
0.27+0.05
012+020
oTs
133
14.4
Vevv
5.6±0.4
0.52±0.30
0.27±0.12
0.29±0.26
0.05±0.01
O09±0.09
0.38±0.16
O08±0.90
0.11
3.34
23.0
Hornbeam-oak
6.0+0.4
0.47±0.34
0.13±0.10
0.10±0.08
0.05±0.02
0.09±0.09
0.27±0.08
O.lliO.ll
0.12
2.20
17.5
Olade
6.0±0.2
0.26±0.17
0.12±O08
0.0210.01
00310.01
0.0910.08
0.2610.07
00710.07
0.09
093
11.2
The analysis of atmospheric precipitation collected in the glades^^ showed that important factor is the closeness to the Black sea. An increase in CI", Mg^^, Ca^^ and SO/" concentration has been observed. The influence of other factors is also evident, which has been proven by increased '^ Atmospheric precipitation, collected in the glade, may partially be under the influence of surrounding forest massifs (Uchvatov & Glazovsky, 1982).
101 NOs and very low Na^ concentration compared to CI", Ca^^, Mg^^. Moreover, the ratio of the ions in atmospheric precipitation and seawater is completely different. One also has to pay attention to anthropogenic factors such as the influence of local industries and intensive traffic along the sea coast, resulting in NOx SO2 and dust concentration increase in air, and of Ca^^ SO/", NO3" in precipitation. Pollution by local industries is not as great, and rain is not acidic. pH of rain water range from 5.8 to 6.5 For comparison, in Germany the average annual pH is about 3.8-4.5 (Balazs & Hanewald, 1986). To trace different ions in the atmospheric precipitation during their interception by tree crowns, we shall use the results in Table 45, which gives estimated inputs into the soil. We took into account the interception and transforming effects of canopy and the total annual amount of precipitation (1450 mm).
Table 45 Average yearly flow of elements in soluble forms throughfall (kg/ha) Species
Ca
Mg
K
Na
CI
S-SO4
N-NO3
Boxwood
83
10
31
6
58
17
10
Yew
50
16
54
5
64
8
5
Hombeam-oak
95
16
40
12
97
19
16
Glade
75
21
11
10
134
21
14
Note: as of 1998, stem/low is not included
The atmospheric has a pH of 6.0 with variation coefficient of 3.3%. It changes significantly under the cover of laurel-cherry yew woodland and on average, it is acidified by 0.4 pH units. This implies leaching of organic acids from plant leaves, the concentration of organic matter in throughfall being at its maximum in this woodland, and compensatory release of H"^ as a result of possible absorption of Ca^^ and Mg^^ by leaves. Calcium is dominant in the atmospheric precipitation, its input is about 75 kg/ha per year. This is much greater compared to other observations (Snakin et al., 1990) - 3.5-37.9 kg/ha per year. The high Ca content may be explained by presence of dust including dust of anthropogenic origin. It contains Ca and Mg carbonates, since local rocks belong to an carbonate-composed area. This was also observed in carbonate rock areas in Western Malaysia, where rainfall adsorbed from the atmosphere and leached from vegetation about 104 kg/ha of Ca and 68 kg/ha of Mg per year (Crowthe, 1987). A significant part of it came from the dust of local quarries.
102 Dust may also alkalinise precipitation of the Yew-Box grove, and this moves the pH close to the neutral (6.9+0.2). Under the canopy of the boxwood forest there is less Ca in the precipitation than that in the glades. Such significant difference (25 kg/ha per year) suggests a Ca adsorption from atmospheric precipitation by leaves of the evergreen trees. Magnesium is also found in atmospheric precipitation in significant amounts and its input is about 21 kg/ha per year. The value closest to that was found in New Zealand, where precipitation was under the impact of the sea. The ratio of Ca to Mg in initial precipitation in the Colchid region equals 3.6:1. In Central Europe this is about 7.6:1 (Szabo, 1977), in New Zealand 0.65:1 (Miller, 1963), and in sea water 0.32:1. The study of Mg in the throughfall and in the glades proves the possible absorption by tree leaves. The region of investigation receives potassium approximately in the same amount as in other regions. Its wash-off from tree crowns was large. Common K input was about 54 kg/ha per year for laurel-cherry yew woodland, followed by hornbeam-oak forest (40 kg/ha) and mixed broad-leaved boxwood forest (31 kg/ha). Atmospheric precipitation contains really small amounts of sodium and it is precipitated in insignificant amounts both under the canopy and in the glades. The supply of Na is somewhat greater than in the Central Europe (1.5 kg/ha per year) but much less than in New Zealand (55.2 kg/ha per year). Chlorine is predominant in the atmospheric precipitation and the yearly input to the glade is 134 kg/ha. It originated from the sea and for comparison the amount of precipitation CI in New Zealand 102-140 kg/ha. The balance of CI shows that it is partially intercepted by tree crowns and this is especially evident in the evergreen communities. An equal amount of sulphates sulphur is precipitated in the region of investigation compered to Central Europe (18-20 kg/ha per year) (Brechtel et al., 1986; Szabo, 1977). Taking into account the minimum industrial pollution of the research area as compared to Central Europe, the high SO4 content of the precipitation is explained by the proximity to the sea. We observed SO4 absorption by tree crowns in the evergreen communities, which was also observed by other researchers (Olsen, 1957; Whitehead, 1964). According to the results of the 1988 experiment, NO3 was precipitated in the amount of 14 kg/ha, which significantly exceeded the data of the North Caucasus (1-0.5 kg/ha per year), as cited in (Soderlund, 1981). Our results are comparable to that on total N supply data (Rapp, 1969;
103 Szabo, 1977). Many researchers have paid attention to the fact that the amount of total N in the throughfall is less than that in the open fields (Carlisle et al, 1967; Nihlgard, 1970; Szabo, 1977). Such decrease takes place mainly due to absorption by the leaves (StenUd, 1958). According to our data, NO3 in atmospheric precipitation are absorbed by laurel-cherry yew woodland and to a lesser extent by mixed broad-leaved boxwood forest. Therefore, when atmospheric precipitation passes through the canopy of the Colchid forest, the observed interception values were high (30-67%) and as a result, the concentration in atmospheric precipitation increases practically for all components studied (K, Na, Ca, Mg, CI, SSO4, N-NO3), expect for bicarbonate-ions. Thus, the increase in K concentration occured from 5 to 14 times. To a large extent the components of atmospheric precipitation were intercepted by vegetation at their passage through crones of trees. The highest absorption was observed for Ca ^, Mg^^, S04^', NO3" and CI" ions. A significant change of precipitation pH was detected for laurelcherry yew woodland which was on average more than 0.4 pH units. The influence of vegetation on soil liquid phase is multi-faceted. On one hand, it is represented by absorption of various substances during growth, on the other hand, by enrichment of soil liquid phase composition in the process of root excretion and in washing off elements by atmospheric precipitation. This is given fiirther consideration in Chapter 7.
4.6. ECOSYSTEMS AND SOIL TYPES As noted previously, soil Uquid phase is usually considered as part of soil and it is not indicated as an element of ecosystem. The analysis of extracted soil samples (water extracts, suspensions, and soil solutions, gained by various techniques) became the main approach of soil liquid phase investigation. Such approach is characteristic for separated soil investigations. In natural conditions soil liquid phase is an element of the ecosystem, making connection between living matter, soUd phase and soil air (see Fig. 15). Its properties reflect the influence of these components and a range of environmental factors. It also demonstrates the migration of chemical substances in ecosystem and plant nutrition. Considerable study has been devoted to the influence of plants on soil Hquid phase, while little attention has been paid to the influence of vegetation types or ecosystems. We have carried out a analysis of variance of physical and chemical properties of the soil liquid phase in relation to the ecosystem type, soil type and vegetation period. The analysis involved data for coniferous and broad-leaved forests, meadow, meadow-steppe and steppe
104 communities including podzolic, grey forest soils, chernozem and chestnut soil under natural vegetation and agricultural lands. To estimate the seasonal changes in the soil liquid phase of agricultural lands, five stages were identified: 1) before vegetation; 2) the beginning of vegetation; 3) the peak of vegetation; 4) the end of vegetation; 5) post-vegetation period. In the natural communities, three stages of vegetation were distinguished: spring, summer and autumn. The impact assessment of ecosystem type, soil type and period of vegetation on a physical and chemical parameters of the soil liquid phase was performed according to the respective determination coefficient. Estimations have shown that redox potential, soil pH, Ca^^ and K^ ion activity depended on ecosystem type (Table 46). Andreeva (1990) obtained similar results except for the Ca^^ activity and in the informative and logical analytical techniques, Kholopova (1977) observed the ecosystem type dependence of pH value. Table 46 Determination coefficients and effect of ecosystem type, soil type and vegetation period on soil liquid phase composition (%) Factors
Soil liquid phase parameters
Eh
pH
a<:a *
aK^
aN03
Natural communities
Ecosystem type
20
13
31
15
4*
Soil type
6
5
11
5*
4*
Vegetation period
6
3
13
16
8*
Joint impact
61
17
58
31
19*
Soil type
4*
9
17
6*
12
Vegetation period
10
6
7*
6*
5*
Joint impact
18
15
24
12*
17
Agricultural lands
* Coefficients are non-•significant
(P<0.05)
Eh, pH and Ca^^ ion activity values reliably depend on soil type, but the dependence of Ca ^ activity is larger than that of Eh and pH (see Table 46). The influence of ecosystem type on the parameters studied is much more significant than that of soil type. A reliable impact of seasonal variability on Eh, pH, Ca^^ and K^ ion activity has been found, but the impact was more significant for K" and Ca'".
105 The influence of soil types on the composition of the soil liquid phase in agricultural lands has been assessed for pH, NO3' and Ca^^ ion activity. The impact of seasonal variability is significant only for the Eh and pH. For natural ecosystems the following tendencies have been traced in the seasonal variability of physico-chemical parameters in the soil liquid phase: •
decrease in Eh value by autumn
•
acidification in summer and alkalinization in autumn
•
increases in activity of Ca^^ and K^ ion from spring to autumn
•
activity of NO3' is minimal in summer. Comparing the seasonal dynamics of the composition of soil liquid phase in agricultural and
the natural communities, the following similarities can be observed: 1) Eh value tends to decrease by autumn; 2) soil liquid phase is acidified in summer and alkalinised by autumn; 3) Ca ^ activity is minimal in spring and increases by autumn. At the same time, K^ activity is minimal at the peak of vegetation period. Seasonal variability of parameters of soil liquid phase in agricultural lands is lower than in soils with natural vegetation. Special attention must be given to individual properties of the soil liquid phase in each ecosystem type. Multifactor analysis of variance allows to assess the influence of a factor on each of the investigated parameters of the soil liquid phase. To assess the influence of a given factor on all parameters of the soil liquid phase we have carried out a analysis of discriminance. The data on soil liquid phase composition for a complex of investigated parameters was divided into separate groups: • by ecosystem type: agricultural, grassland and forest communities; • by vegetation type: coniferous, broad-leaved, meadow, meadow-steppe and steppe vegetation; • by soil type: podzolic, grey forest, chernozem and chestnut soils (separately for agricultural and natural communities); • by vegetation periods (separately for agricultural and natural communities). We classified the data according to initial groups. The better the data was grouped, the more was the influence of a particular factor on soil liquid phase. On the contrary, the more the data from a particular group corresponded to the other one, the closer were the characteristics of these groups as to the properties of soil liquid phase. The data for ecosystem types is shown in Table 47. The specific properties of soil liquid phase in forest ecosystems defines the grouping of data. Thus, 86% of the data on 5 above
106 mentioned parameters correspond to a certain hypothetical area designated as forest communities group. The data on agricultural ecosystems in the least degree is grouped and their soil liquid phase has much in common with grassland ecosystems. Some 14% of data of grassland communities correspond to group of forest ecosystems, and this may be explained by inclusion of the forest glades communities into the herbaceous communities group.
Table 47 Classification of ecosystems based on complexes of physico-chemical parameters of soil liquid phase (%) Proposed groups Initial groups
Agricultural
Grassland
Forest
lands
coiimiunities
communities 10
Agricultural lands
55
35
Grassland communities
19
67
14
Forest communities
5
9
86
Table 48 Classification of natural ecosystems based on complexes of physico-chemical parameters of soil liquid phase (%) Proposed groups Initial groups
steppe
meadow
meadow
broad-leaved
coniferous
communities
steppes
communities
forests
forests
communities
45
22
20
13
0
Meadow steppes
50
50
0
0
0
18
0
55
27
0
forests
0
12
0
88
0
Coniferous forests
0
0
0
0
100
Steppe
Meadow coimnunities Broad-leaved
Among the natural ecosystems, the parameters of soil liquid phase in coniferous forests are the most well grouped. All the data obtained correspond to the proposed group of coniferous forests (Table 48). The soil liquid phase in broad-leaved forests is also well grouped. About 88% of experimental data correspond to the proposed group. The soil liquid phase composition of
107 grassland ecosystems are quite similar to other ecosystems. About 50% of the data correspond to the rest of the groups. Classification according soil type showed that the soil of natural communities are clearly grouped (Table 49). Podzolic soils are the most separated from chernozem and chestnut soils and not a single value of the soil Uquid phase composition belongs to these groups and vice versa. The Uquid phase of grey soils is very similar to chernozems and 20% of the data fall within this group. It has little similarity to podzolic and chestnut soils. Chernozems resemble grey forest soils. Table 49 Classification of different soil types in the natural ecosystems based on a complex of physicochemical parameters of soil liquid phase (%) Initial groups
Proposed soil groups
of soils
Podzolic
Grey forest
Chernozems
Chestnut
Podzolic
88
12
0
0
Grey forest
7
64
21
8
Chernozems
0
28
67
5
Chestnut
0
22
22
56
The soils of agricultural lands are more alike and the classification of data is less distinct (Table 50). The Uquid phase of podzoHc soils resembles the other soil types. A large number of grey forest soils and chernozems values fall within the podzolic soils groups. Therefore, we can conclude that the liquid phase of the investigated soil types is grouped to a lesser degree than that of the natural vegetation and shows a greater degree of similarity. The anthropogenic load surpasses the natural impact and levels even zonal differences of soils. Table 50 Classification of agricultural soils based on a complex of physico-chemical parameters of soil liquid phase (%) hiitial groups
Proposed soil groups
of soils
Podzolic
Grey forest
Chernozems
Chestnut 17
Podzolic
59
17
7
Grey forest
27
45
9
19
Chernozems
31
4
55
10
Chestnut
0
14
20
66
108 The vegetation period has a great influence on the dynamics of the soil liquid phase parameters (Table 51). Spring data group particularly well since in spring the Hquid phase of the soil is distinctly different from its summer-autumn values. Autumn values are well grouped by a complex of parameters and they show similarity to summer measurements. In summer, the soil liquid phase of the natural ecosystems has only a negligible similarity to the spring values, some more similarity to the autumn values, but is quite well grouped. Table 51 Grouping of soil liquid phase parameters in the natural communities according to vegetation period Initial groups
Spring
Proposed groups Spring
Summer
Autumn
100
0
0
Summer
8
68
23
Autuimi
0
20
80
We have not observed distinct distribution according to the
vegetation period in
agricultural systems (Table 52). The influence of anthropogenic factors such as the use of fertilisers and different soil treatment technologies has a great influence on the ecosystem components. But autumn soil liquid phase values are better grouped and show close similarity to early spring values.
Table 52 Grouping of soil hquid phase parameters in cultivated soils according to the vegetation period Initial groups
Before vegetation
Proposed groups Before
The beginning
At the peak of
The end of
After
vegetation
of\ vegetation
vegetation
vegetation
vegetation
38
8
23
15
15
34
13
16
18 21
Tlie begiiming of vegetation
18
At the peak of vegetation
15
16
32
16
The end of vegetation
29
0
29
43
0
At\er vegetation
29
0
0
29
43
Based on the results of multifactor variance analysis, we conclude that ecosystem type has a larger influence on the soil liquid phase composition than soil type. The investigation of seasonal dynamics of soil liquid phase has shown that the dynamics of its composition is in agreement with
109 the activity of plants, which again supports the importance of living components for the physicochemical properties of the soil liquid phase.
4.7. ANTHROPOGENIC FACTORS
Human activities is accompanied by an ever-increasing pressure on the soil. The impact of various field management and agrochemical techniques, such as recultivation of disturbed lands and industrial soil pollution on the soil composition, especially soil liquid phase, is so strong that it exceeds the influence of natural factors and levels out climatic differences.
4 7 1 FIELD MANAGEMENT AND SOIL LIQUID PHASE COMPOSITION
Mineral fertilisers have increase the ion concentration in the soil liquid phase, and changes in the content and the ratio of all components of soil solutions take place, resulting from the interaction with SAC. Our laboratory experiments simulating the addition of fertilisers have shown that the increase in K activity is disproportional to the added amount and constitutes only 2-20% of the respective increase in NO3 activity (Table 53). Similar results were obtained from experiments on brown forest soil in the Rothamsted experimental station (Nair & Talibudeen, 1973), where only 7-8% of added K was found by ion-selective electrodes and chemical techniques, while the respective NO3 content amounted at 87-95%).
Table 53 Soil liquid phase composition after addition of salt solutions and water
Soil type
Treatment
Moisture (%)
In situ measurements
initial
PH
after addition
Grey forest
aK
aca
Ethanol-displaced soil solution aN03
pH
Oca
CMg
Cwa
Ca
aN03
-
0.75
45
0.75
14
1.44 0.81
-
-
0.45
1.23
30
0.22
1.75
1.6
meq/L
meq/L
0. IN KNO3 100 ml/kg
36.6
43.3
4.61
5.57
14.3
44
5.36
7.7
4.1
H2O 100 ml/kg
36.6
43.3
4.84
0.51
6.2
17
5.87
6.5
1.1
Heavy loamy
O.INKNO3 100 ml/kg
2.05
15.5
4.87
0.38
2.1
12.5
5.76
10.8
sod-podzolic
H2O 100 ml/kg
2.05
15.5
5.20
0.11
1.9
1.2
6.45
1.9
-
Sandy sod-
O.INKNO3 100 ml/kg
20.1
35.2
4.76
4.62
11.2
33
6.70
24.7
calcareous
H2O 100 ml/kg
20.1
35.2
5.14
0.92
6.0
1.1
6.85
3.3
Grey forest
O.lNCaClz 200 ml/kg
8.1
27.4
4.56
1.00
18.2
60.1
5.6
0.82
57.1
0.52
8.1
27.4
5.01
0.36
4.2
-
6.25
H2O 200 ml/kg
6.85
4.6
2.2
0.55
0.95
0.32
Note: measurements were carried out after seven days of solutions addition
no The increase in the activity of two-valent ions in soil solution as a result of exchange in SAC moves them to lower horizons, which leads to a decrease in soil productivity. It has been noted that the use of fertilisers decreases the amount of exchangeable Ca and Mg (Hinqston & Jones, 1985). For acidic soils calcium nitrate or calcium cyanamid fertilisation was proposed by (Kovda, 1985). Acidification observed in the laboratory did not always take place in the fields, due to the mineral nutrition of plants. While NH4 forms of fertilisers and carbonyl diamide lead to soil acidification, the use of NaNO? and KNO3 results in soil alkalinization (Andrianov, 1926; Schaller & Fischer, 1985a; Hinqston & Jones, 1985). During the application of (NH4)2S04 the pH value in the rhizosphere decreased by 2-3 units, while adding Ca(N03)2 the pH was higher 0.7 units than in the reference samples with no fertilisers added (Schaller & Fischer, 1985a; Romheld & Marschner, 1986). After application of phosphoric fertiliser there was an insignificant increase in the amount of P in chernozem soil solutions (Sinkevich, 1973). The use of fertilisers increases salt concentration in lysimetric waters. The soil solution of the upper horizons in fertihsed chernozem contained NO3 twicefold than the non-fertilised variant. The soil solutions of lower horizons differed by a ratio of 8 as a resuh of NO3 losses (Sinkevich, 1973). In a number of cases an increase in Na and CI and high amounts of another components in soil solutions were observed, resultant from the unwarrantable use of fertiHsers (Savich et al., 1987). This may lead to negative processes such as inhibition of nitrification (Darrach et al., 1987). Lime application and manure fertilisation decrease soil acidification. Lime application was not accompanied by significant increase in Ca ions activity in the liquid phase of a sod-podzolic soil There was no correlation between Ca ions activity and carbonate reserve in the soil of the Pridnestrov region (Prosyannikov, Karpenchuk, 1982). However, Edmeades et al, (1985) showed an increase in Ca^^, Mg^^, HCO'3 content and a decrease in Al content as a result of liming, while ionic strength of solutions increased twicefold. Fertiliser application to the same soils lead to an insignificant decrease in ionic strength and ion concentration in soil solution except for K. Mineral fertilisers have a dual impact on soil: 1) they interact with SAC directly which results in SAC cations, mainly protons, replacement by cations of fertilisers. The pH decreases with the growth in concentration of almost all ions; 2) their influence is indirect through plants and micro-organisms according to the specificity of uptake of various nutrients. Fertilisers are a powerful tool which may be used to influence the soil liquid phase composition. The existing practice fertihser use is accompanied by some negative consequences.
Ill To investigate the impact of various field management techniques on soil liquid phase composition, experiments have been carried out of the D. N. Pryanishnikov field station. Investigations included an experiment on increasing doses of mineral fertilisers against manure application and without manure (intensive three-field crop-rotation since 1937) and an experiment on the relative efficiency of the application of organic and mineral fertilisers (extensive four-field crop-rotation since 1931) on heavy loamy sod-podzolic soil. Table 54 shows some properties of intensive crop-rotation soils. The application of mineral and organic fertilisers in a period of 46 years had impact on exchangeable Na and K almost proportional to the amounts of fertiliser applied. The mineral fertilisers had no notable impact on total N, while organic manure increased it insignificantly. At the same time, the use of fertilizers has increased a yield. This is an additional evidence to the fact that conventional techniques do not admit the measurement of soil N with the necessary accuracy (Tserling, 1978).
Table Table 54 54 Chemica properties of a heavy loamy sod-podzolic soil under intensive crop-rotation (data from Chemical June, 1983) Indicator
Treatment
P2O5,mg/100g
control
manure*
NPK**
2NPK
3NPK
3NPK + manure
3.58
8.13
3.75
12.8
16.9
25.0
K2O,mg/100g
3.86
8.22
4.47
8.0
13.6
30.8
Exchangeable K^, meq/100 g
0.12
0.16
0.15
0.18
0.42
0.48
0.08
0.10
0.19
0.20
0.23
92
113
2.90
3.05
Exchangeable Na"^, meq/100 g
0.07
Total N,mg/100g
81
93
78
80
Yield of barley, t/ha
1.12
1.58
1.33
2.58
* 20 t/ha ** Base dose for barley N30P30K30
Tables 55 and 56 summarise the data on redox potential and pH of soil Uquid phase from direct field measurements. The influence of experimental conditions on Eh is often levelled by the heterogeneity of the soil and in separate plots the variation coefficient of Eh value varies from 10 to 20%. Nevertheless, when manure was used under both intensive and extensive crop-rotation, higher redox potential was observed. This was first noted by Remezov (1929), and it may be linked to improvement in soil physical properties, since the direct impact of organic matter on soil results in Eh decrease.
112 Table 55 The redox and pH conditions in soil under intensive crop-rotation Treatment*
Eh (mV)
pH
Barley
Potatoes
Potatoes
Barley 1983
1984
1984
522
6.59
-
6.28
558
6.71
6.49
6.37
542
531
6.69
6.34
6.48
481
510
6.82
6.94
6.37
543
473
522
6.95
6.94
6.23
549
479
553
6.96
6.96
6.32
1983**
1984
1984
Control
603
508
Manure
580
528
NPK
616
2NPK
604
3NT'K 3NI^K + manure
* Base dose - see Table 54 **Aaverage data of triple measurements during the year
Table 56 The redox and pH values in soil under extensive crop-rotation
Treatment
Eh (mV) 5.V. 1984
pH 29.VI.1984
1985
1984
1985
fallow
1985 winter wheat
Control
701122*
NPKCa(N,2oP6oK72Ca24o)
710136
551185
500172
Lime + NPK
664154
495160
469168
Lime, 6 t/ha
638177
528163
448140
6.22
6.29
5.6910.19
1/2 manure + 1/2 NPK
715141
581144
556194
5.50
5.52
5.5210.49
Manure, 24 t/ha
648184
579156
631137
5.27
5.44
5.4810.57
591152
589160
5.01
5.5910.48
5.55
5.65
4.9710.48
6.02
6.36
5.5910.27
5.38
* X ±c
The variation coefficient of pH is also high (2-6%). However, in 1983 and 1984 with the growing rate of fertiliser use on barley, an increase in alkalinity of the soil liquid phase (see Table 55, Fig 22) took place, which is probably due to the form of fertiliser used. This is a temporary phenomenon because in the next year with a change in mineral fertilisers (ammonium nitrate for barley, ammonium sulphate for potatoes, sodium nitrate for beet at intensive crop-rotation), the alkalisation was levelled out. It is likely that such changes in pH may result from the alkalinising effect of nitrate.
113
Barley yield, t/ha
Fig. 22. Changes of barley yields and redox andpH values of the soil liquid phase in heavy loamy sod-podzolic soil Average data of spring, summer and autumn measurements of 1983 and 1984
In the extensive crop-rotation, only two variants of liming differ reliably according to their pH values, whereas in the fallow the difference is more distinct, perhaps as result of vegetation. Analysis of ethanol-displaced soil solutions composition shows that the use of fertilisers is accompanied by a significant increase in soil liquid phase concentration, and Na and CI appear in large amounts (Table 57 and 58). Calcium concentration increases as a result of Ca displacement from the soil adsorbing complex by monovalent ions, predominantly by K, of fertilisers. Soil liming is accompanied by insignificant increase of Ca in the soil solution.
Table 57 The composition of soil solutions replaced by ethanol from sod-podzolic soil under intensive croprotation (meq/L) Treatment*
K^
Na^
Ca^^
Mg^^
cr
1
2
3
4
5
6
Control
0.02
0.47
2.35
0.85
Manure
0.04
0.70
3.01
1.43
NPK
0.02
1.09
2.74
0.93
2NPK
0.06
1.89
2.39
0.65
3NPK
0.17
3.28
3.45
0.83
3NPK + manure
0.29
2.74
3.53
1.00
Barley, 1983**
114 Table 57 (continued) 1
3
2
4
5
6
1.64
Barley, 1984** Control
0.03
0.14
5.73
0.58
Manure
0.06
0.30
2.81
0.84
1.94
NPK
0.07
1.05
2.54
1.21
2.12
2NPK
0.20
2.28
4.10
0.23
2.64
3.91
5.02
0.44
3.25
3.04
6.34
0.53
3.03
3NPK
0.34
3NPK + manure
0.49
Barley, May, 14, 1985 Control
0.05
0.35
3.56
0.62
0.64
Manure
0.03
0.11
4.08
0.52
0.47
NPK
0.09
1.22
4.80
0.92
1.22
2NT^K
0.47
4.12
6.21
0.89
3.34
3NI>K
0.58
5.61
7.08
0.48
3.57
3NPK + manure
0.90
5.28
10.00
1.00
3.29
Potatoes, 1984 ** Control
0.02
0.16
2.23
0.64
1.25
Manure
0.04
0.17
4.02
1.16
2.04
NPK
0.11
1.70
15.52
1.44
6.91
2NT^K
0.35
5.54
14.46
1.05
11.81
3NT>K
1.8
10.55
6.69
2.13
17.46
3NT'K + manure
1.42
6.31
8.83
2.79
10.30
* Base dose - see Table 54. ** Average ,results of triple measurements during the year.
Table 58 The composition of soil solutions replaced by ethanol from sod-pozolic soil under extensive croprotation (meq/L) Treatment*
K"
Na"
Ca^^
Mg'"
cr
1
2
3
4
5
6
1.47
Fallow plot. May, 4, 1984 Control
0.038
0.21
1.69
0.70
NPKCa
0.044
0.22
2.54
0.51
1.38
Lnne + NPK
0.062
0.24
3.66
0.52
1.35
Lime
0.010
0.15
2.77
0.68
1.35
1/2 manure + 1/2 NPK
0.082
0.30
1.93
0.66
1.21
Manure
0.14
0.31
2.24
0.98
141
115 Table 58 (continued) 1
3
2
4
5
6
Fallow plot, September, 13, 1984 Control
0.041
0.25
3.20
0.98
1.32
NPKCa
0.81
1.00
18.78
3.04
14.65
Lime + NPK
0.59
0.96
18.75
3.80
18.85
Lime
0.026
0.26
3.85
0.85
1.47
1/2 manure + 1/2 NPK
0.046
1.48
17.00
4.15
10.4
Manure
0.50
1.52
7.95
3.10
3.47
0.47
Fallow plot. May 5, 1985 Control
0.02
0.14
1.65
0.55
NPKCa
0.05
0.26
3.20
0.60
1.03
Lime + NPK
0.03
0.21
5.40
0.41
0.56
Lime
0.01
0.12
3.28
0.72
0.47
1/2 manure + 1/2 NPK
0.10
0.28
2.68
0.72
0.66
Manure
0.08
0.27
2.84
0.86
0.47
* Base dose - see Table 56
In the agricultural lands the influence of fertilisers on soil solutions prevails upon other affecting factors and CI is a perfect indicator of this. However, after heavy rains or spring thawing at the ploughed site, especially at the layer of 0-10 cm, the composition of soil solution in respective plots was sometimes hardly distinguishable (see Table 58). Table 59 illustrates the change of the liquid phase in the profile, and it is obvious that neutralisation takes place after lime application in the ploughed horizon. The minimum of redox potential is observed in the deeper, since this is less aerated although biologically quite active. Table 59 Selected characteristics of heavy loamy soddy podzolic soil at different depths Parameter
Depth (cm) 7
15
Eh (m)V
610
533
686
pH
6.74
6.59
4.90
Ca^^ (meq/L)
24.5
22.4
11.5
N03^ (meq/L)
2.9
1.1
5.1
35
Note: in situ measurements data of August, 1983
According to our experience, NOs-ions are among the most mobile components of the soil liquid phase and evidence is given by the high variance in in situ measurement (Tables 60, 61).
116 Such scattering of data is a cause for technical complications. Such was the relative measurement error of 10%, which is usual practice for ionometric monovalent ions analysis, and at a probability level of 90%, from 10 to 150 electrodes were needed to obtain a more or less reliable result (see Section 5.2).
Table 60 NO3" ions activity in the liquid phase of sod-podzolic soil at different periods under extensive croprotation (meq/L) Treatment*
Winter wheat
Fallow plot 29.IV-4.V.1984
29.VI.1984
13.IX.1984
18.V.1985
20.V1.1985
Comtrol
11.1±4.2**
8.7±1.7
4.2±1.9
1.710.6
1.1+0.5
NPK
15.7±3.1
9.2±1.7
66±10
12.9±2.2
9.2±9.1
Lime + NI^K
6.5±5.3
10.3±2.6
65.1+30.5
8.7±2.2
7.2±7.0
Lime
-
11.3±6.1
17.4±6.7
1.7±1.1
1.310.3
Vi manure + '/2NPK
10.0±6.7
13.6±4.5
14.9±8.7
8.0±5.0
1.611.0
Manure
19±12
9.4±3.8
7.8±5.7
4.0+1.3
2.312.1
Soil moisture (%)
19.8
19.6
21.8
18.4
16.2
Soil temperature (*^C)
10
19
13
14
13
* Base dose - see Table 56. ** X ± a
Table 61 NO3" ions activity in the liquid phase of sod-podzolic soil at different periods under intensive croprotation (meq/L) Treatment
Barley
Potatoes
1983
1985
1986
1984
27 .IV
4-8 VIII
24.x
6.V
3.VIII
17. IX
14.V
7.V
2-5. VII
Control
0.37
2.07
1.63
19.0
0.86+0.26**
1.1
1.6
6.92
0.85
1.37
Manure
0.57
5.67
3.87
-
0.62+0.28
4.3
5.7
13.9
1.80
3.59
NPK
2.40
6.89
12.9
1.32+0.42
3.5
8.5
13.1
4.37
0.39
1,5 NPK
-
-
10.5
1.44+0.13
-
-
11.2
4.27
-
4.10
1.87
5.22
11.4
1.48+0.77
4.1
7.1
16.8
8.39
3.05
-
-
-
2.96+0.88
12.2
-
-
2.81
4.89
7.24
33.5
5.1+1.1
-
-
3 NPK
7.3
17.4
15.5
8.24
3 NPK ^ manure
3.14
7.09
3.91
18.4
10.6+2.1
7.2
13.1
15.6
5.65
2.35
Soil moisture (°o)
21.4*
16.8
18.0
18.8
21.3
26.6
19.4
17.6
20.1
26.7
Soil temperature ('^C)
17**
17
3
14
16
14
18
13
16
12
2 NPK 2.5 NPK
* Base dose - see Table 54. ** X ± a
20.IX
117 NOs" ions activity in the liquid phase varied according to the amount of mineral fertilisers, both when nitrate fertilisers were in mixed form, barley fertilised by ammonium nitrate, as well as in ammonium form, potatoes being fertilised by ammonium sulphate. Differences in NO3" activity between treatments took place during the whole observation periods, but they were most drastically after fertiliser application. For example, at extensive croprotation fertihser was applied on 14* August and measurements were carried out on 13^ September (see Table 60). Significant changes in NO3' activity were observed in both control and fertilised plots, which is explained by fertilisers, their absorption by plants, denitrification and nitrification processes especially in fallow plots, as well as leaching of NO3'. Leaching resulted in higher NO3' activity in the subsurface horizon than in ploughed horizon (see Table 59). Active absorption of NO3" by plants takes place in the lower part of ploughed horizon, by which the activity of NO3" at a depth of 7 cm is higher than at 15 cm depth. Similar results were obtained for arable brown forest soil under winter wheat. Usually, at a depth of 12.5 cm the concentration of NO3" was similar or lower than at 5 cm as a result of plant uptake. At the same time, the concentration of NO3' at 5 cm depth was lower than at 20 cm depth due to NO3" leaching (Nair & Talibudeen, 1973). The above results show the usefulness of soil liquid phase analysis. Thus, when using agrochemical analysis techniques we were unable to obtain a reliable variation between fertilised plots (see Table 54).But the use of ion-selective electrodes for in situ measurements enabled us to observe differences in NO3' activity in the liquid phase of sod-podzolic soil at both types of crop rotation. The in situ measurements provide information on the 'momentary' supply of dissolved substances in soil. To estimate the nutrient supply of plants, it is necessary to take into account the soil buffer capacity with regard to these elements and the possible input from other sources (precipitation, nitrogen fixation). Studies on the processes of soil solutions formation allow us to influence their composition. It is sometimes more efficient to switch to other field management techniques or regimes to promote the transition of soil nutrient reserves into liquid phase available to plants, rather than to increase the amount of fertilisers. Is there really need for more K fertilisers when most soils contain high amounts of K and the additional fertiliser is fixed by the soil? It may be more efficient to apply Ca fertiliser in form of soluble salts, which is useful for plant nutrition and the improvement of soil physical properties, but also promotes the release of K from the soil
118 adsorbing complex. Further research in this field allows different views on the problems and methods of mineral fertiliser application.
4 7 2 SOIL RESISTANCE TO ACID RAIN
Researchers have been concerned about the systematic acidification of agricultural lands, leading to the substitution of cations (Ca^^, Mg^^, K^, NH^^) by H^, and at low pH values - by Al^"^. There are several reasons for soil acidification, but the main cause is the use of acid fertilisers and acid rain. Rapid acidification of agricultural lands in the USA over the last 35 years (Mahler et al., 1985) and in AustraHa (Porter, 1984) has arisen from long-term use of ammonium fertihsers. The raise in acidity of atmospheric precipitation (Bulatkin, 1980; Tabatabai, 1985, etc.) has also had negative impact on soil. The buffer capacity of soils maintains soil pH in a certain range diminishing the influence of atmospheric acids (Tabatabai, 1985; Grishina & Kondratieva, 1987). Even in chernozems after rains an acidification of the soil liquid phase could be detected (see Fig. 16), altough in theory an alkalinization of the soil solutions with dilution should take place (see Section 4.3). In the absence of buffer effect of natural steppe vegetation, the pH value of the chernozem decreased by almost one unit in 35 years (see Table 42). Studing the impact of acid rains on soil two processes may be identified. The first one is a rapid and significant change in pH of the soil liquid phase, which quickly returns to initial value due to the soil buffer capacity. The pH of ordinary chernozem decreased by 1-2 units after acid rain and then reached the original value for the given ecosystem in 5-6 hours under steppe vegetation and in 10-12 hours in calvitia (i.e. gaps among grass). In grey forest soil, the pH value recovered within 1-2 days at undisturbed stand and in 3-4 days in a fallow plot (Zykina et al, 1987). The second process is far more slower, leading to changes in SAC and global soil acidification. It is hard to give a quantitative estimation of this process. We suggest that in agroecosystems the role of fertilizers in acidification process is far greater than that of acid rains.
4 7 3 SOIL LIQUID PHASE UNDER RECULTIVATION
The influence of recultivation on the soil liquid phase was investigated in May-June 1981 (Ukraine). Measurements were carried out on:
119 •
2-3 days, 1 year and 8 years old loess loam spoil banks of the Verkhnednepr Metallurgical Combine;
•
the recultivation plot of the Dnepropetrovsk Institute for Agricultural Research with a stretched chernozem layer of 50 cm depth (no fertilisers added and with fertihsation by NgoPsoKgo);
•
an adjacent arable field under corn (ordinary chernozem), without fertilisation for four years;
•
ordinary chernozem under natural steppe vegetation (creeping-grass community, in the Khomutovskaya Steppe Reserve) (Snakin et al, 1984). The resuhs reveal a sophisticated pattern of the processes under the influence of natural
vegetation and agricultural activities (Table 62). The redox potential of loess spoil banks tends to increase with time, which is probably due to oxidation processes during lifl;ing of parent material to the surface. Similar increase in Eh takes place in chernozem under agricultural use which is illustrated by the values in natural and old field chernozem. This may cause problems since at high Eh the availability of Fe and Mn to plants decreases. Table 62 Physico-chemical properties of cultivated plots Investigated site
t(°C)
W (%)
Eh
pH
(mV)
Ca^^
r
NO,-
meq/L
"C" in soil •
( % )
CO2 (%) soil air
carbonates
Loess loam spoil bank 2-3 days
18
17.8
512±6*
7.6±0.3
10.0±5.2
0.42
1.2
0.26
0.13
5.35
0.26
0.04
5.49
1 year
21
17.6
528±19
7.7±0.2
10.3±5.1
0.11
3.3
8 years
23
9.8
536±13
7.2±0.2
30.2±8.4
3.40
3.1
0.32
0.07
5.38
22
21.9
561±8
7.3±0.1
37.0±11.6
0.24
6.3
1.84
0.08
0.86
24
21.1
563±7
7.3±0.1
33.714.2
0.41
5.5
1.79
0.06
0.65
677±9
5.5±0.2
31.0±9.3
0.17
2.3
2.45
0.04
0.15
25.6±0.4
3.10
1.7
4.81
0.07
0.29
Recultivation plot (chernozem bank, unfertilised) Recultivation plot (chernozem bank, fertiliser applied) Old arable ordinary chernozem
22
21.0
Virgin ordinary chernozem
15
22.3
607±25
5.7±0.3
* X ±a
The fertile soil layer consists of mixed upper horizons of ordinary chernozem and the loess loam spoil banks have a different pH value compared to zonal soils of the region. While the former have a low alkaline reaction and a significant amount of carbonate, the upper layer of the chernozem (0-10 cm) is acid. Such notable difference which levelled out with depth will have impact on plant growth and should be taken into account in the selection of crops. With time (fresh and 8-years old banks) the soil reaction becomes more acid. As shown by the carbonate content,
120 acidification is not determined by leaching of carbonates by precipitation, it results from the activities of micro-organisms and vegetation of the banks due to their acid root exudates. For example, the first year spoil bank vegetation is represented by isolated individuals of saltwort, horseweed, horse sorrel and other species, while in the 8-years plots a closed grass cover has been formed, which includes bromegrass, creeping-grass, Arctic clover, prickly lettuce, Agropyron, and coltsfoot. The upper layer (0.5 cm) of the 8-years old bank is covered by moss and is humified. With time the acidification of bank soil solution is accompanied by a significant increase in Ca^* activity in the liquid phase. This is explained by Ca transition into the soil solution from low soluble carbonate into the form of soluble bi-carbonate. Carbonate equilibrium analysis (see Section 6 1.1) has shown that in all investigated sites soil solution is enriched by CaCOs, except in the upper layer of the original zonal soil (virgin steppe and arable land). In the virgin chernozem, only the 0-10 cm layer was undersaturated and did not react with 10% HCl solution. At a depth of 15 cm, effervescence and saturation of soil solution by CaCOs took place (see Section 6.1). It is hard to make judgements on the content of K^ and NO3" in the liquid phase of the studied soils, for the activity of these ions is highly variable in time even within a day (see Section 5 3) A greater activity of K^ ions in SLP has been shown in virgin chernozem solution and in an 8year old loess banks compared to other sites. This is determined by a far greater root mass of wild plants which seem to mobilise K primary silicates and clay minerals and preserve it in soluble form involving it in an intensive biological cycle. Usually, NO3" unlike K^ is present in soluble form and its amount in the soil solution is determined to a great extent by microbiological activity and plant absorption. This is the why NO3' activity in fallow plots was somewhat higher than in the others. At the same time, relatively high NO3 activity is observed in the liquid phase of loess loams, which is possibly a cause for their high fertility (Bekarevich et al., 1975). However, it is worth remembering that only the composition of soil liquid phase was studied and not the total nitrogen amount in the soil. Total nitrogen showed considerable differences and serves as major source of nitrogen in soil solution. The cultivated plots showed low content of organic matter in comparison with zonal soils. This may be explained by mixing of top soil horizons with lower horizons at selective mining and cultivation. During the process of cultivation considerable changes took place in the soil liquid phase. Firstly, they were determined by mixing of various horizons of zonal soil during sampling and preparation of cultivated layers. Secondly, this was due to subsequent processes, caused by agricultural use of recultivated land.
121 In the process of natural vegetation of loess loam spoil banks the increase in their redox potential, acidification of soil liquid phase, the increase in Ca^"^, K^, NO3" and organic carbon was observed which is due to biological factors.
4.8. CONCLUSIONS
Summing up this chapter, there are a number of important in the process of soil liquid phase formation, but the identification of the main factors of soil liquid phase formation is very difficuh. The processes leading to dramatic changes in the composition of soil solution are as follows. The composition of soil solid phase, particularly, SAC, soil air, soil moisture regime, the downward transport of substances with precipitation and by capillary rise, temperature regime, and various field management techniques. In addition to this, the living matter of ecosystems is a multifactor component interacting with the soil liquid phase. For instance, plants: • release and absorb various chemical substances by roots immediately into and from the soil Uquid phase; • enrich soil liquid phase with various components leached by precipitation from aboveground plants parts; • enrich soil Hquid phase by products of decomposition of aboveground litter and standing dead biomass and roots; • activate the development of micro-organisms, which in turn, have a significant impact on the soil liquid phase composition. The problems of the interaction between vegetation and soil liquid phase the main subject of the next chapters. It is worth to note that ecosystem type has a much greater impact on soil liquid phase composition than soil type. So from an environmental point of view, the soil liquid phase should be considered not as a part of soil, but as a special structural element of ecosystem itself This is a pre-condition to a better understanding of the role of liquid phase in the functioning of ecosystems.
122 CHAPTER 5. SPATIAL AND TEMPORAL PROPERTIES OF SOIL LIQUID PHASE
There are many factors which influence the soil liquid phase and determine its high temporal and spatial variability. It reflect the diversity of soils and soil processes. The living matter of soil creates a significant heterogeneity within a given soil horizon and changes with time. Such heterogeneity of properties, combined with temporal changes is an important feature of soils as components of an ecosystem. The estimation of heterogeneity and variability of soil properties is important from a practical point of view in order to give an accurate quantitative description of soil processes. Heterogeneity and variability are deeply interconnected and interdependent phenomena. The temporal variability of processes is often the cause for spatial heterogeneity of soil properties and vice versa, and the spatial dynamics of soil liquid phase may be determined by temporal changes.
5.L THE COMPOSITION OF SOIL LIQUID PHASE
Here we present an analysis of soil liquid phase composition in various natural and agricultural ecosystems, carried out using the data base (see Section 2.7) of the sites of investigations mentioned in Chapter 3.
5 11 SOIL REDOX POTENTIAL (Eh)
Eh values in all ecosystems studied varied from 257 to 884 mV, whereas the most frequent values lay in the range from 500 to 700 mV (Tables 63 and 64). In the forest ecosystems, mean Eh values are much higher then in the hebaceous ones. Fluctuation between minimal and maximal values as well as the frequency of fluctuations in the forest ecosystems is much wider.In soils of coniferous ecosystems. Eh values are higher than in soils of broad-leaved forests. The range of minimum and maximum values in coniferous forests is wider than in broad-leaved forests. The most frequent Eh values vary from 600 to 800 mV, whereas in broad-leaved forests the variation is lower by 100 mV.
123 Table 63 Composition of the soil liquid phase in various ecosystems Ecosystem
*
Eh (mV)
c^
PH
r
NO3"
0.5
(meq/L) Forest ecosystems:
coniferous
broad-leaved
Grassland ecosystems:
meadows
meadow-steppes
steppes
Agricultural ecosystems
For all ecosystems
X
650
5.4
7.3
1.1
min-max
257-884
3.7-6.8
0.03-41.7
0.005-10.3
0.008-3.3
X
671
5.0
1.8
1.0
0.5
min-max
257-884
3.7-6.1
0.03-10.0
0.005-10.3
0.01-1.5
X
631
5.7
9.7
1.1
0.45
min-max
328-732
4.4-6.8
0.29-30.6
0.01-4.42
0.01-3.3
X
589
6.1
14.6
2.3
2.2
min-max
426-743
4.5-7.9
0.12-64.0
0.1-11.9
0.02-23.1
X
577
5.8
12.7
2.8
2.6
min-max
426-686
4.5-7.7
0.15-64.0
0.01-11.9
0.02-23.1
6.3
5.4
1.2
0.9 0.19-1.86
596
X min-max
574-620
5.7-7.1
1.9-10.8
0.06-3.3
X
606
6.6
19.0
1.8
1.7
0.12-54
0.02-7.9
0.13-10.0
min-max
478-743
5.4-7.9
X
565
6.4
14.2
1.5
6.9
min-max
360-740
5.0-8.2
0.38-74.0
0.008-25.1
0.21-66.3
X
581
6.1
12.2
1.7
4.6
min-max
257-884
3.7-8.2
0.03-74
0.005-25.1
0.008-66.3
* X - average value, min-max -range of minimum and ' maximum values
Table 64 Range of the most typical (80% frequency) values of soil liquid phase in various ecosystems Ecosystem
Eh (mV)
pH
Ca^^
Forest ecosystems:
500-800
4.5-6.5
0.03-10
0.03-1.1
0.01-0.8
coniferous
600-800
4.0-5.5
0.03-2
0.005-1.1
0.01-0.6
broad-leaved
500-700
5.0-6.2
0.3-15
0.01-1.2
0.01-0.9
Grassland ecosystems:
K^
NO3"
(meq/L)
510-620
5.2-7.2
0.1-25
0.1-4
0.02-3
meadows
540-640
5.0-7.0
0.2-25
0.01-4
0.02-4
meadow-steppes
575-625
5.8-7.0
1.9-10
0.06-1.8
0.2-1
steppes
510-700
5.7-7.2
0.1-38
0.02-3
0.2-3
Agricultural ecosystems
440-640
5.6-7.6
0.4-20
0.01-2
0.2-10
Throughout all ecosystems
500-700
5.0-7.0
0.03-20
0.03-3
O.Ol-lO
In grassland ecosystems the differences in soil redox potentials are not so distinct.
124 In soils of natural forest and grasslands, Eh values are higher than in the soils of agricultural ecosystems. The range of minimum and maximum and most frequent values in the soils of agricultural ecosystems is more narrow than in forest ecosystems, and wider compared to natural grasslands (see also Tables 63 and 64). We may establish the following sequence according to soil redox potential: coniferous forests > broad-leaved forests > steppes > meadow steppes > meadows > agricultural ecosystems. When considering redox potential in various soil type of natural communities, podzolic soils are characterised by the highest Eh values and the range of values is much wider than in other soil types (Table 65).
Table 65 Composition of the liquid phase of various soil types within the natural communities
*
Soil type
Eh (mV)
pH
c?^
r
NO3"
(meq/L) Podzolic
Grey forest
Chernozem
Chestnut
Brown forest
Alpine meadow
Alluvial soils
X
659
4^9
18
095
045
min-max
257-884
3.7-6.4
0.03-27.6
0.005-10.3
0.02-1.5
X
594
6.1
17.6
1.3
2.1
min-max
537-732
5.2-7.1
0.29-64.0
0.01-4.6
0.2-23.0
X
598
6.7
20.6
1.6
l.I
min-max
500-661
5.7-7.9
1.2-54.0
0.02-7.9
1.3-3.3 2.4
X
647
6.1
10.6
2.0
min-max
545-743
5.4-7.7
0.12-44.0
0.02-5.6
0.13-10.1
X
609
5.3
6.1
4.5
4.1
min-max
536-682
4.4-6.4
0.14-30.6
0.052-22.7
0.01-26.7
X
584
5.5
4.7
3.3
1.5
min-max
426-774
4.5-6.5
0.8-12.9
1.0-9.0
0.02-6.3
X
562
6.1
4.2
1.0
2.8
min-max
438-608
4.7-7.7
0.18-13.6
0.01-3.5
0.07-11.5
* X - average, value, mm-max - )^ange of minimum and maximum values
The lowest Eh values have been observed in the alpine meadow and alluvial soils (Table 65). The average values of redox potential in grey forest soils and chernozems differ only slightly but the range is wider in grey forest soils possibly due to a greater diversity in redox processes in forest ecosystems. One may order the soils according to the redox potential values in a following sequence: podzolic > chestnut > brown forest > grey forest > chernozems > alpine meadow > alluvial.
125 Virgin podzolic soils are very different from arable podzolic soils in Eh (see Tables 65 and 66). Generally, ploughed podzolic soils are more homogenous than virgin soils and the range is narrower. This demonstrates that the heterogeneity of soil properties is to a great extent determined by biological factors, which is largest in virgin soils. In our comparison of agricultural and virgin chestnut soils, similar results were obtained. Ploughed chestnut soils are more homogeneous and the range of most frequent values is narrower in arable soils. The redox potential of arable chestnut soils is lower than that of virgin soils (see Table 65, 66 and 67).
Table 66 Composition of the Uquid phase in various soil types of agricultural lands Soil type
*
Eh (mV)
pH
Podzolic
X
549
6.3
min-max
391-715
5.0-7.4
K^
N03-
9.1
1.9
8.1
0.38-28.8
0.008-25.1
0.37-66.3
Ca^^ (meq/L)
Grey forest
Chernozem
Chestnut
X
607
6.3
13.7
0.22
13.2
min-max
542-740
5.4-7.1
1.1-57.4
0.02-0.84
0.32-59
X
569
6.6
21.6
1.7
2.6
min-max
360-722
5.3-7.9
1.8-74.0
0.02-19.4
0.21-26.4
X
608
6.9
5.8
0.31
11.6
min-max
529-686
6.0-7.7
2.63-8.47
0.1-0.5
0.24-32.5
* X - average value, min-max -range of minimum and maximum values
Table 67 Range of most typical values (80% frequency) of physico-chemical parameters of the soil liquid phase in various soil types Soil type
Eh (mV)
pH
Ca^^
K^
NO3"
(meq/L) Virgm podzolic
600-800
4.3-6.2
0.03-4
0.005-1
0.02-0.6
Arable podzolic
470-670
5.3-6.8
0.4-16
0.01-2
0.4-15
Virgm grey forest
540-650
5.7-6.8
0.3-35
0.01-1.4
0.2-2.2
1.2-30
0.02-0.25
0.3-25
Arable grey forest
550-620
5.7-6.9
Virgin chernozem
520-620
6-7.2
1.2-35
0.02-3
1.3-2
Arable chernozem
500-640
5.7-7.4
1.8-35
0.02-2
0.2-3
Virgin chestnut
560-740
5.8-6.7
0.2-11
0.02-3
0.2-3
Arable chestnut
540-680
6.2-7.7
3-8
0.1-0.5
0.3-25
126 Virgin chernozems are more heterogeneous than arable chernozems. But redox potential of arable chernozems is lower than that of virgin chernozems. Grey forest soils of the natural and agricultural are similar. The arable grey forest soils are more homogeneous than virgin soils and the range of most frequent values is narrower. We have to emphasise the considerable variations in the redox potential of various ecosystems. In forest communities, the Eh is higher than in herbaceous communities. In soils of the natural ecosystems the redox processes are more diverse than in soils of agricultural lands. Due to cultivation, the redox regime of podzolic soils shows the most considerable changes. An attempt has been made to identify the determining factors of the composition of soil liquid phase, and to estimate their influence on various types of ecosystems. An analysis of all available data was carried out, where the influence of the factors on particular ecosystems and soils was considered. Table 68 shows the pair correlation coefficients between the physico-chemical parameters of the soil liquid phase. The correlation is herewith considered not directly with ion activity value, but with the value of pX = -Ig ax. This approach is justified by comparability of results in the pXpH-Eh range, and by the logarithmic distribution of ion activity in physical chemistry. As shown in Table 68, Eh and pH has a dependence of medium reliability. Eh and pCa has a dependency of low reliability, and no correlation was found between Eh - pK, Eh - pNOs. Let us consider the interrelationship of the values studied.
Table 68 Coefficients of correlation between the physico-chemical parameters of the soil liquid phase for the ecosystems studied Parameter
Eh
pH
pCa
pK
PNO3
^
-0.36
0.22
0.07*
0.01*
pH
-0.35
0.05*
-0.27
pCa
_
0.31
0.17
_
0.26
_
_
-
^h
pK pNO?
-
.
~~~
* Coefficients are not significant at P <0.05
Table 69 gives determination coefficients, and shows the share (percentage) of the changes, which in the given case depend on the factor studied.
127 Table 69 Impact of parameters of the soil liquid phase on the soil redox potential in various ecosystems Investigated sites
Multiple
Determination coefficient (%'I ^H
pCa
pK ^
pNOs
correlation coefficient
Ecosystems Agricultural ecosystems
33
2*
1*
1*
0.61
Natural communities:
9
1*
1*
3
0.37
21
4*
2*
1*
0.50
forests at whole coniferous forest
47
8*
4*
0*
0.77
broad-leaved forest
4*
29
8*
9*
0.71
1*
0*
13
0*
0.37*
meadows
4*
11*
10*
0*
0.50*
steppes
14
25
10*
0*
0.70
12
0.8*
0.8*
3.4*
0.40
grasslands at whole
All ecosystems together
Soils Virgin lands: podzolic
25*
1*
n*
7*
0.64*
grey forest
16*
44
4*
6*
0.83
chernozems
0*
0*
14*
11*
0.50
chestnut
16*
38*
11*
3*
0.83*
Arable lands: podzolic
43
0*
2*
3*
0.69
grey forest
32
40
14
1*
0.93
chernozems
42
0*
3*
1*
0.68
chestnut
13*
4*
4*
69*
0.95*
* Coefficients are not significant at P<0.05
In all ecosystems soil pH has a significant impact on redox potential, whereas this impact is much greater in agricultural lands than in natural communities. Among the natural communities, the major influence of pH on Eh was found in coniferous forests and steppes. The redox variability is not depending on pK and pNOs values. The significant influence of pCa value on Eh could be observed in grey forest soils in virgin and arable state and in the soils of broad-leaved forests and steppe communities.
5 1 2 pH PROPERTIES
The pH in various types of ecosystems varies from 3.7 to 8.2 (Table 63). The most frequently observed pH values is in the range of 5.0 to 7.0 (Table 64).
128 Soils of forest ecosystems are more acid and more heterogeneous than grassland ecosystems. In coniferous forest ecosystems, pH is lower than in broad-leaved forests. The range of pH values in coniferous forests is similar to broad-leaved forests but the range of most frequently observed pH values in broad-leaved forests is narrower than in coniferous forests. In grassland ecosystems, the lowest pH was observed in meadow communities and the highest pH in steppe communities. The variance of pH data is largest in meadow communities and the lowest is in the meadow steppe communities (see Table 63, 64). In agricultural lands the pH was higher than in natural grassland communities. The range of pH values in agricultural lands is wider than in forest, and is narrower than in grassland communities. The following order was found: steppes > agricultural lands > meadow steppes > meadows> broad-leaved forests > coniferous forests. The lowest pH values have been observed in podzolic soils, the highest pH in chernozems under natural vegetation (see Tables 65, 67). The pH variance is highest in alluvial soils and lowest in grey forest soils. The following order of soil types under natural vegetation according to their pH value was found: chernozems > grey forest ~ chestnut « alluvial > alpine meadow > brown forest > podzolic. In the comparison of arable and virgin soil types, it was noticed that the pH of podzolic soils have changed resulting from cultivation. The podzolic soils of agricultural lands are more homogeneous in their pH properties: the variance and the range of most frequent values in arable podzolic soils is lower than in virgin soils (Tables 65, 66, 67). Resulting from cultivation, pH values of chestnut soils also increased by 0.8 pH units. Little changes occurred in the grey forest soils (0.2 pH units) and chernozems, which were acidified by only 0.1 pH unit. The soil pH in various ecosystems differed largely. Soils under forest show lower pH, as compared to soils under herbaceous vegetation. The greatest change was observed in podzolic soils resulting from land cultivation. A influence of the studied parameters on the pH variability was found for all ecosystems, but the nature of this influence is different (Table 70). In coniferous forests, a significant influence of Eh, pCa and pNOs was found, whereas the influence of Eh was large. In meadow communities the significance of pCa, pK, pNOs impact reduced in the following order: pCa > pK > pNOs. In steppe communities, the Eh and pK dependence of pH is reliable, however, the influence of pK is greater as compared to that of Eh. In agricultural lands, the influence of Eh on pH variability is
129 predominating in all soil types, except in chestnut soils. Grey forest and arable chestnut soils show a relatively high influence of pCa which may be explained by Ca deficit.
Table 70 The effect of the soil Hquid phase on the variability of soil pH values in various ecosystems Investigated sites
Determination coefficient (%) Eh
pCa
33
0*
pK
Multiple
pN03
correlation coefficient
1*
0.61
Ecosystems Agricultural ecosystems
3
Natural communities:
9
8
3*
13
0.57
forests at whole
21
2*
0*
2*
0.50
coniferous forest
47
11
4*
13
0.87
broad-leaved forest
4*
6*
1*
7*
0.42*
1*
9*
30
0*
0.68
meadows
3*
19
15
10
0.68
steppes
14
2*
36
1*
0.73
12
7.5
3.5
13
0.60
1*
25
0.73*
grasslands at whole
All ecosystems together
Soils Virgin lands: podzolic
25
2*
grey forest
16*
16*
5*
0*
0.61*
chernozems
-
14*
36
1*
0.71*
chestnut
18*
6*
12*
32*
0.82*
podzolic
43
0*
10
7
0.77
grey forest
32
36
7*
0*
0.87
chernozems
42
0*
5*
5*
0.72
chestnut
13*
42
21*
21*
0.98*
Arable lands:
* Coefficients are not significant at P<0.05
The relation between pH of the liquid phase and the redox potential is one of the most important relations. When combined, the two factors present a more or less integral characteristic of soil chemical status. As in the work by Baas-Becking et al. (1960), we have tried to ordinate the soils of various ecosystems according to the Eh-pH co-ordinates. When analysing the pH and redox properties of soils of various ecosystems there is a large difference between forest ecosystems and agroecosystems (Fig. 23). Soils of forest ecosystems have the highest Eh and lowest pH values. Herbaceous ecosystems take a transitional position. The
130 variance of pH and Eh data is considerable for all ecosystems, except for Eh value in grassland ecosystems. Eh (mV) 800-
^
1 - forest 2 - grassland 3 - agricultural lands »- centroids
\ \
700-
\ I , . • • • 1
••.
600H A '
)
••
500-
5.0
-n— 6.0
— I —
7.0
pH
Fig. 23. Ordination of ecosystems by the most typical soil pH and Eh values (80% frequency^
1 - coniferous 2 - broad-leaved — • 3 - meadow • - - 4 - meadow-steppe • 5 - steppe * - centroids
600 H
7.0
pH
Fig. 24. Position of natural communities by their most typical soil pH and Eh values (80% freqi4ency)
Detailed analysis of the soil properties in natural communities, shows the segregation of the coniferous, broad-leaved and steppe communities according to their distinct acidic-alkaline and redox properties. Meadow communities take a transitional position between broad-leaved forests
131 and steppe communities and meadow-steppe communities overlap between meadow and steppe communities (Fig. 24). From the analysis of pH and redox properties it can be seen that soils of virgin lands (podzolic, chestnut and chernozems) are different. Grey forest soils may be placed between chestnut soil and chernozem (Fig. 25). In arable soils these differences are levelled out by a significant change in acidic-alkaline and redox properties of podzolic soils, acidic-alkaline properties of chernozems, chestnut and grey forest soils and redox properties of chernozems (Fig. 26). Eh (mV) 800-
700
/ I
1 - podzolic — 2 - grey forest • 3 - chernozem 4 - chestnut • • « - centroids
/ \
600
500 5.0
6.0
7.0
pH
Fig. 25. Position of various soil types of the natural communities in the Eh -pH co-ordinates Eh (mV)
700' \
1 - podzolic 2 - grey forest 3 - chernozenn 4 - chestnut . . - - . . >» - centroids
600
500 —r~ 5,0
6.0
7.0
pH
Fig. 26. Position of various soil types of agricultural lands in the Eh -pH co-ordinates
132 Oxygen content of soil solution and products of soil microflora are main agents determining redox systems in soils (Zyrin & Orlov, 1980; Vozbutskaya, 1968). According to some authors (Bohn, 1968; Ponnamperuma et al, 1966; Ponnamperuma, 1972; Gorshkova & Orlov, 1981), in automorphic soils it is the O2 - H2O system which determines redox. At normal conditions (P=l atm., t=20C^ pH=7) the Eh of water ranges from 810 to -410 mV. The pH dependence of Eh water is expressed by a linear equation with a 59 mV slope per pH unit (Garrels & Christ, 1965). The change in partial pressure of O2 and H2 causes some alteration of the straight line, but the slope remains unchanged. In the work by Gorshkova and Orlov (1981), the linear Eh - pH dependence was observed in all samples at pH range from 1 to 12, corresponding to the equation Eh = A - B • pH with A values from 720 mV to 890 mV and the angle coefficient in the range of 40-60 mV Similar results were obtained from an experiment on arenaceous quartz. This enables us to conclude that in low buffer capacity substrates the determining factor is the H2O - O2 - H^ system. Table 71 The analysis of the Eh - pH correlation in the soil liquid phase in various ecosystems types bivestigated sites
Linear regression equation
Correlation coefficient
Agricultural ecosystems
Ecosystems Eh= 849.9-43.8pH
-0.37
Natural communities:.
Eh=836-38.8pH
-0.39
forests at whole
Eh=926-57.3pH
-0.38
coniferous forest
Eh=1039-73.{)pH
-0.32
broad-leaved forest
Eh=779-32.9*pH
-0.39
Eh=658-11.2*pH
-0.18*
meadows
Eh=660-14.2*pH
-0.16*
steppes
Eh=848-36.6pH
-0.22*
Eh=821-38.6pH
-0.40
grasslands at whole
All ecosystems together
Soils Virgin lands: podzolic
Eh=l 160-102.3pH
-0.44
gre\' forest
Eh=935-54pH
-0.48
chernozems
Eh=523+11.3*pH
0.14*
chestnut
Eh=957-48.6*pH
-0.41*
podzolic
Eh=843-46.2pH
-0.38
grey forest
Eh=l 032-67.9pH
-0.59
chernozems
Eh=917-52.5pH
-0.50
chestnut
Eh=ll04-71.5pH
-0.68
Arable lands:
* Coefficients are not significant at P<0.05
133 The regression analysis of the Eh - pH correlation shows a negative correlation between Eh and pH in most soils (Table 71). Coefficient A varied from 660 mV in meadow communities to 1039 mV in coniferous forests. Coefficient B varied from 14.2 in meadow communities to 73 in coniferous forests. In arable soils, the correlation between Eh and pH values is more reliable and the variability of coefficient B is small. In soils of natural communities, coefficient B changes significantly which indicates the diversity of active biochemical processes.
5 13 CALCIUM ACTIVITY
The activity of Ca^^ ions in the soil liquid phase of different ecosystems varies between 0.03 to 74 meq/L and the most frequent Ca^^ activity is in the range from 0.03 to 20 meq/L (Table 63 and 64). The highest values of Ca^"^ activity in liquid phase were observed in soils of the steppe and the lowest in soils under coniferous forests. In soils of forest ecosystems, Ca^^ ions activity in the liquid phase is lower than that in soils of grassland communities. On average, Ca^^ ions activity in the soils Hquid phase of agricultural lands is about the same to soils of grassland communities. Calcium ions activity in the soil liquid phase of coniferous forests is lower than that in broad-leaved forests (Tables 64 and 65). The lowest values of Ca^^ activity were observed in the liquid phase of podzolic soils of natural communities and the variance of data is high in those soils (Tables 66 and 68). The highest Ca ^ activity of the soil liquid phase was observed in chernozems and the following sequence can be proposed: chernozems > grey forest > chestnut « brown forest > alpine meadow « alluvial > podzolic. Arable podzolic soils differ substantially from virgin soils as with cultivation Ca^^ activity has increased, which is demonstrated by the mean values and by the ranges of values and the most frequently occurring values (Tables 65-67). Chernozems have changed the least, and there is little difference between arable and virgin chernozems. The mean values of Ca^^ ions activity in arable grey forest and chestnut soils is lower than in analogous virgin soils. Arable grey forest and chestnut soils are more homogeneous compared to virgin soils and the variance of data is significantly lower.
134 There are notable differences in Ca^^ ions activity in soil liquid phase of various ecosystems. The lowest Ca ions activity was observed in coniferous forest soils. Resulting from soil cultivation, changes in Ca ions content in the soil liquid phase were the highest in podzolic soils. A marked influence of pH, pK and Eh values on Ca^^ ions activity has been found in all ecosystems but this influence differs depending on ecosystem type: in coniferous forests and meadow communities the influence of pH and pK values is reliable, whereas in broad-leaved forest and steppe communities the influence of redox potential prevails (Table 72).
Table 72 The influence of parameters of the soil liquid phase on Ca^^ ions activity (pCa) in soils of different ecosystems Investigated sites
Determination coefficient (%)
_
pH
pK
pN03
Multiple correlation coefficient
Ecosystems Agricultural ecosystems Natural coininunities.
2*
0*
17
0*
0.44
6
9
9
8
0.57
10
2*
4*
0
0.60
coniferous forest
0*
21
26
0*
0.69
broad-leaved forest
26
4*
7*
5*
0.65
0*
9
25
2*
0.61
6*
18
21
5*
0.71
27
2*
10*
0*
0.62
4
8
10
1*
0.48
forests at whole
grasslands meadows steppes All ecosystems together
Soils Virgin lands: podzolic
0*
0*
48
2*
0.71*
grey forest
60
8*
9*
3*
0.89
chernozems
0*
14*
0*
2*
0.40*
chestnut
53*
0*
12*
15*
0.89*
podzolic
0*
1*
19
18
0.61
grey forest
20*
44
0*
0*
0.80
chernozems
0*
0*
26
0*
0.51
chestnut
1*
50*
20*
15*
0.93*
Arable lands:
* Coefficients are not significant at P<0.05
It is hard to separate factors which influence calcium ions to a maximum extent. In podzolic soils, a significant influence of pK on pCa value was found and in natural communities this
135 influence is larger. The interrelation between pCa-pK-pNOs parameters determined by the use of fertilisers, is manifest mainly in arable podzolic soils showing to the important part of anthropogenic factors in those soils. A significant influence of Eh value on the Ca ^ ions activity in the liquid phase of virgin grey forest soils was found; while in their arable variants the influence of pH, and for arable chernozems the influence of pK is significant.
5.14 POTASSIUM ACTIVITY
The activity of K^ ions in the soil liquid phase in different ecosystems varies between 0.005 to 25 meq/L and the mostfi-equentK^ activity is in the range of 0.03 to 3 meq/L (Tables 63 and 64). In soils of grassland ecosystems, K^ ion activity was higher than in soils of forest ecosystems. The following K^ ion activity-based sequence can be proposed: meadows > steppes > agricultural lands > meadow steppes > broad-leaved forests > coniferous forests. The highest K^ ion activity values were observed in the soil liquid phase of brown forest under fir-trees and also in alpine meadow soils, while the lowest values were observed in podzolic and alluvial soils (Table 65). In arable podzoHc soils, the K^ ion activity is higher than in the virgin soils - both by average values and by the range of values. Potassium activity values in arable chernozems showed the least similarity to virgin soils of this type (see Tables 65 - 67). In arable grey forest, and arable chestnut soils, K^ ions activity is lower than in their virgin state (see Table 65 and 66). It is noteworthy that the variance of data is significantly lower, i. e. the soils of agricultural lands are more homogeneous (see Table 67). A reliable influence of pCa and pNOs on K"^ ions activity has been found in all ecosystems, whereas in the natural communities the influence of pNOs was higher than in agricultural lands (Table 73). There is a considerable influence of redox potential in the soils of coniferous forests systems. In meadow communities, the influence of pCa and pNOs is dominating and the influence of pCa is exceeding that of pNOs, while in steppe communities the influence of pH and Eh is large. When comparing arable and virgin soils, in arable podzoUc soils the influence of pH, pCa, pNOs is significant and they can be ordered as follows: pNOs > pCa > pH. In virgin podzolic soils the influence of pCa is significant. In arable and virgin grey forest soils a strong influence of Eh was found and the influence of pCa is also notable in arable grey forest soils. In arable chernozems, a strong influence of pCa and pNOs values was found (pCa > pNOs). Virgin chernozems soils are
136 under a considerable influence of pH, while chestnut soils in the natural communities have showed a significant impact of Eh. See also Section 6.3 for additional information on specificity of K^ ions activity in the "soil-soil solution-planf system.
Table 73 Estimation of the influence of the parameters of the soil liquid phase on K^ ions activity (pK) in soils of different ecosystems Investigated sites
Determination coefficient (%)
_
pCa
pH
pN03
Multiple correlation coefficient
Ecosystems Agricultural ecosystems
1*
5
16
9
0.56
Natural communities,
1*
0*
11
30
0.65
forests at whole
0*
2*
5
30
0.61
coniferous forest
32
0*
22*
5*
0.77
broad-leaved forest
4*
0*
10*
8*
0.47*
8
16
21
9
0.73
meadows
1*
4*
27
9
0.64
steppes
10
38
7*
9
0.80
1*
1*
11
13
0.51
grasslands
All ecosystems together
Soils Virgin land: podzolic
5*
2*
45
1*
0.73*
grey forest
53
0*
14*
0*
0.81
chernozems
7*
44
0*
4*
0.75
chestnut
47
9*
11*
25*
0.96*
Arable lands: podzolic
1*
13
17
24
0.73
grey forest
54
21
4*
1*
0.81
chernozems
1*
6*
24
10
0.64
chestnut
8*
13*
33*
19*
0.81*
* Coefficients are not significant at P<0.05
5 15 NITRATE ACTIVITY
The activity of NO3' ions in the soil liquid phase of different ecosystems varies from 0.008 to 66.3 meq/L and and the most frequent values of NO3' ion activity range between 0.008 and 10 meq/L (Tables 63 and 64).
137 The highest NO3" ions activity values were observed in agricultural lands by average values, as well as the range of values and the most frequent values. Nitrate ion activity in the soil liquid phase of grassland communities is higher than in forest communities. The highest values of NO3" activity in the soils of natural communities were observed in brown forest soils, and the lowest in podzolic soils (see Table 65). When comparing virgin and arable soils, one has to make special mention of the fact that the significant increase in nitrates ions activity in all soil types of agricultural lands for the average values, and especially the maximum of range and the most frequent values (see Tables 65-67). The least difference was found between arable and virgin chernozems.
Table 74 The impact of the parameters of the soil liquid phase on NO3" activity (pNOs) in soils of various ecosystems Investigated sites
Determination coetTicient (%)
___
pCa
PH
Multiple
pK
correlation coefficient
Ecosystems Agricultural ecosystems
I*
3*
2*
10
0.41
Natural communities.
0*
8
1*
31
0.63
forests at whole
5*
0*
10
29
0.66 0.80
coniferous forest
34
21
5*
4*
broad-leaved forest
32
8*
1*
7*
0.69
2*
2*
0*
16
0.46
2*
6*
0*
13
0.46*
9*
6*
2*
16
0.57*
0*
12
1*
13
0.51
grasslands meadows steppes All ecosystems together
Soils Virgin lands: podzolic
1*
33*
1*
2*
0.60
grey forest
0*
1*
24*
1*
0.51*
chernozems
8*
3*
0*
8*
0.44*
chestnut
52
I*
0*
36*
0.94*
Arable lands: podzolic
1*
17
30
18
0.81
grey forest
6*
18*
2*
11*
0.61*
14
0.48
5*
0,95*
chernozems
2*
3*
4*
chestnut
24*
54*
6*
* Coefficients are not significant at P<0.05
138 The influence of pH and pK on pNOa was found to be reliable and in the natural communities the influence of pK was higher (Table 74). In coniferous and broad-leaved forests a significant impact of Eh was found, which is possibly the results of micro-organism activity. Serdobolsky (1940) observed that at high soil moisture contents the increase in Eh values leads to reduction of NO3 into NO2 and NO2 into N2O. In coniferous forests, the influence of pCa value was also found. The impact of pK value was high in grassland communities. When comparing arable and virgin soils, special mention should be made of the influence of pH in the virgin podzols, but in arable podzolic soils pCa is predominant and pH and pK also have a significant impact. In virgin chestnut soils, the influence of Eh was significant. In arable chernozems only pK showed a considerable impact. It can be concluded that each parameter is closely related to other parameters of the soil liquid phase, which is shown by the high multiple correlation coefficients (Tables 68, 70-74). In arable soils, the correlation coefficient is usually higher.
5.2. SPATIAL HETEROGENEITY
The analysis of soil heterogeneity has been of interest to many researchers, starting from the works by Kirsanov et al. (1935) and Serdobolsky (1937). Most publications in this field, based on the analysis soil samples in the laboratory, consider mostly the conservative properties of soils such as the mechanical composition, humus content, etc. The investigation of the soil liquid phase composition using displaced soil solutions may be referred as averaged soil samples analysis. Analysis of gley brown soil solutions, taken in an oak grove, shows that in an area of 0.1 ha the spatial variation of concentration at different times of the year has the following variation coefficients (Cv): K" - 30-45%, Mg^^ - 60-98%, CI" - 20-55%. Seasonal variability amounted at 24-34% for K\ Mg^"^ - 35-45%, CI" - 32-53%, which allowed statistical evaluation during the year only for CI' ions (Grieve et al., 1984). According to Manderscheid & Matzner (1995), spatial heterogeneity of the soil solution in a Norway spruce was 36-298 % (Cv%), and maximum value are for NH4-N. In soil solutions displaced by ethanol from arable grey forest soil, the variation coefficients ranged from 11 to 68% for NO3', HCO?', CI", Ca^", Mg^\ K", Na"^ ions. It has been shown that for regular observations it is adequate to analyse 2 or 3 samples for each variant in order to reach 50% difference between average concentrations for the basic components of soil solutions (Ponizovsky &Polubesova, 1986).
139 To estimate the real heterogeneity within an ecosystem, it seems more appropriate to use of in situ measurements of the soil Hquid phase by ion-selective electrodes, which gives the pattern of ion activity for several points in of soil profile or a plot in an ecosystem. In our work particular attention has been given to the averaging technique of Eh, pH, pNOs, pK, and pCa. When working with ion-selective electrodes, the pX value (the negative logarithm of ion activity or concentration) is often being averaged, which is wrong. It is more correct to average the ion activity or concentration values themselves and to convert the obtained average value into pX. Table 75 Average ion activity in the liquid phase of soil in the Southern part of the Moscow region, expressed by ion activity (I) and pX values (II) Site
aK^ (meq/L)
aN03 (meq/L)
_
_
n
_
pH
1
_
ac/"( 'meq/L)
_
_
Agricultural grey forest soil
639
670
016
007
29.1
575
3.49
L78
Agricultural grey forest soil
6.14
6.50
0.07
0.05
8.9
2.7
2.89
2.29
Agricultural grey forest soil
5.58
5.87
0.53
0.32
21.9
16.2
57.4
43.6
Agricultural grey forest soil
5.90
6.07
0.15
0.14
18.0
13.5
30.4
24.0
Agricultural grey forest soil
6.32
6.48
0.47
0.21
0,4
0.31
20.2
17.4
Agricultural grey forest soil
6.64
6.73
0.18
0.11
16.4
9.3
21.5
17.0
Cultivated meadow
6.82
7.01
2.65
1.82
12.0
2.1
36.7
29.5
Cultivated meadow
6.24
6.55
1.25
0.59
1.2
0.59
26.5
19.0
Cultivated meadow
6.29
6.65
0.54
0.24
0.53
0.43
14.0
7.8
Natural meadow
5.42
5.59
3.79
0.98
0.6
0.47
9.81
2.0 10.5
Natural meadow
6.27
6.42
0.26
0.24
0.7
0.57
27.6
Natural meadow
5.77
5.98
1.46
1.26
1.2
0.98
9.2
7.4
Coniferous forest
4.32
4.5
5.13
0.89
39.4
1.32
7.0
2.7
Broad-leaved forest
5.65
5.81
15.7
4.5
41.7
26.9
31.4
18.6
Broad-leaved forest
5.75
5.8
0.66
0.46
0.7
0.56
1.3
1.0
Table 75 demonstrates that the negligence of this aspect may lead to wrong results, and the pH value may differ 0.1 - 0.4 units. For other ions this may be even higher and ion activity values may be underestimated. Large differences can occur with parameters of large spatial heterogeneity and for pH value the mean-square deviation is more than 0.2. Neglecting this factor leads to uncertainties in conclusions on averaging of pH values at mixing soil samples (Kim, 1989), as well as to discrepancies in the interpretation of data on soil cover spatial-temporal properties.
140 Table 76 The physical and chemical characteristics of soils in various ecosystems, measurements in June, 1981 (sites 9-11) and May, 1982 (sites 1-8) (average value of 3 readings of 30 electrodes) Number
Eh (mV)
pH
Cv,%
7
^
6.73
6L3
L98
12.8
5.56
43.4
5.0
18.1
9.78
54.2
0.39
5.3
25.8
19.8
76.8
0.34
4.6
3.29
2.37
72.2
6.8
0.44
6.5
2.29
1.30
56.8
0.80
5.2
6.9
0.42
6.2
5.80
3.61
62.3
0.15
38
5.3
6.0
0.55
9.2
4.68
3.45
73.8
679
33
4.9
5.6
0.37
6.6
6.1
3.1
10
697
22
3.2
5.6
0.27
4.8
13.4
11
755
23
3.1
3.5
0.21
6.1
7.3
of object* ~
a
Cv,%
J
1
660
34
5.2
2
644
29
3
638
4 5
G
Cv.%
J
6.8
0.39
5.7
10.9
4.5
6.8
0.36
5.2
18
2.8
7.1
0.37
586
24
4.1
7.4
625
40
6.2
7.4
6
669
46
6.8
7
680
35
8
710
9
NO3" (meq/L)
K^ (meq/L)
Ca^^ (meq/L)
Moisture (%)
Cv,%
~
a
Cv,%
~
a
1.64
84.1
1.38
1.02
74.1
23.2
1.85
8.0
1.90
1.51
79.5
1.50
0.62
41.3
22.7
2.14
9.5
0.53
0.47
88.8
1.41
0.57
40.6
16.9
1.11
6.6
1.13
1.21
107
2.58
1.12
44.3
30.0
3.19
13.3
0.33
0.38
117
1.28
0.61
47.6
27.3 2.79
10.2
0.98
122
0.82
0.57
69.9
36.4 3.92
10.8
1.75
1.29
73.9
19.5 2.47
12.7
2.14
2.38
110
22.9 2.15
9.4
50.7
32.3 4.35
13.5
3.7
27.8
11.7 0.99
8.5
1.4
18.5
4.1
21.2
<^
0.87
Cv,%
* 7 - virgin land steppe reserve (creeping-grass community) in thePriazov region (ordinary chernozem); 2 - virgin land steppe reserve (mixed fescue-stipa community) in the Priazov region (ordinary chernozem); 3 - agricultural land (2ndyear lupine) in the Priazov region {ordinary chernozem): 4 - agricultural land (sunflower crop) in the Priazov region (ordinary chernozem): 5 - virgin land steppe reserve (mixedgrass community) in the Kursk region (typical chernozem); 6 - broad-leaved forest reserve (oak grove) in the Kursk region (typical chernozem); ~ - agricultural land (winter wheat) in the Kursk region (typical chernozem): S ~ a plot of agricultural land in the Kursk region, left fallow from 1947 (typical chernozem); 9 - broad-leaved forest (lime. oak. aspen, birch) m the South of the Moscow region (grey forest soil); JO- agricultural land (barley) in the South of the Moscow region (grey forest soil); J J - pme forest in the South of the Moscow region (sandy low-podzolic soil).
Analysis of the physical and chemical properties of the top soil in various ecosystems reveals some variability, which is primarily due to soil heterogeneity (Table 76). Sometimes it is natural that methodical error that contributes to this variability, but this contribution is relatively small, which was shown by soil experiments without plants, mixed and homogenised by a 2 mm sieve. In these experiments the variation of redox value was 0.5%, which is significantly lower compared to the variability in natural soils (2.8±10.6%). Taking into account the instrument error
141 of ion activity measurements, the data shows that the variability due to instrumental error is about 20-15%. These experiments were carried out ceteris paribus with similar electrodes, which created a reUable basis to evaluate the effect of the soil heterogeneity. Our analysis of soil heterogeneity from a geographical point of view (Fig. 27) shows a decreasing trend in the heterogeneity in the following sequence: oak forest - broad-leaved mixed forest - pine forest without herbaceous cover. This may be explained by the decreasing role of herbaceous plant cover in that sequence, aboveground phytomass of herbaceous plant cover was from 130 to 3 g/m^ respectively. Since plant cover has a great influence on soil processes in the rooting horizon, therefore acting as one of the main sources of soil heterogeneity.
Cv(%)
Eh
8j 6J 4J 2J
n n
fin nnr
n ' ' '' ''
6 9 11
o^YYi Q(%)| 5] PI 4]
1 n
3 4 7 8 10
PH
I 1
•—iF
r
2J 0 M II M
1 2 5 6 9 11
Cv(%)|
3 4 7 8 10
1 ^^' 1n
[
60 ] n In 50] n 40 j n II II In 30] 20] 10] 1 2 5 6 9 Virgin lands
11
l"~i
n
1 II 1 n 3 4 7 8
10
Agricultural lands
Fig. 27. Variation coefficients of the soil physico-chemical properties in natural ecosystems and agroecosystems (see Table 76 for details)
142 The influence of plants cannot be given an uniform interpretation. Plant species diversity and abundance in ecosystems and the related use of soil resources is possibly a factor lowering heterogeneity. Except for moisture, the variation coefficient of the parameters studied in a monodominant creeping-grass community is higher than in a mixed fescue-stipa community. Human activity, such as various soil management techniques, the use of organic and mineral fertilisers and other measures are an important factor affecting the soil heterogeneity. Ploughing which seems to reduce heterogeneity, is the mixing of heterogeneous soil layers with the residues of cultivated plants, which creates greater heterogeneity. That is why the fallow plot (see object 8 in Table 76), ploughed the day before the measurements were carried out, is characterised by larger differences. Considerable heterogeneity is also observed in arable chernozems within one week sowing of sunflower (object 4), where a considerable amount of wheat plant residues was found in the topsoil. With time soil processes level out heterogeneity and on the adjacent 2nd year lupine plot (object 3) the heterogeneity was notably lower. Each of the investigated parameters can show heterogeneity, which is natural, since they reflect different soil processes. Therefore, comparative analysis by their degrees of heterogeneity is problematic, but let us try to compare the heterogeneity of different ions activity in the soil liquid phase. Variation coefficients of activity for the ions studied ( H \ Ca'", K' and NO3") are of the same order''^. The decrease in spatial variability has the following sequence: K^> H^> NO3' > Ca^^. Under natural conditions the larger heterogeneity in ion activity should be viewed as a resuH of higher ion mobility, but in this case the larger intensity biological turnover of K"^ compared to N and Ca is important. The closeness in the Eh and pH variation coefficients and that of H and Ca ions may be explained by the interdependence of these parameters. For example, correlation of horizontal and profile characteristics of arable and virgin grey forest soils (Tables 77, 78) have shown an average negative correlation between soil solution redox potential and pH (r=-0.27—0.68), on the other between hydrogen ions activity (pH) and calcium (r=-0.33~0.59). A closer correlation has been observed between the studied parameters in grey forest and low-podzolic soils, which is possibly due to the low buffer capacity of podzolic soils. With depth the correlation increases for both
'^ Table 76 shows pH variability coefficient values. Cv of hydrogen ion activity is very large. For object 5 pH variabihty coefficient value is 4.6%, while Cv of H^ ions equals 65.5%, for object 8 this is 9.2 and 84.9% respectively.
143 cultivated and virgin grey forest soils. Almost no correlation was observed between soil moisture and the physico-chemical parameters.
Table 77 Coefficients of correlation between pH and Eh, soil moisture (W) and Ca ion activity values in soil solutions, based on simultaneous measurements (average for a vegetation period). Number of object -see table 76 Parameter
Eh
Ca^^
pH
9
10
11
pH
-0.51
-0.43
-0.38
W
-0.14
-0.13
-0.06
9
10
11
0.08
0.13
0.21
9
10
11
-0.52
-0.52
-0.41
-0.35
-0.18
-0.12
Table 78 Coefficients of correlation between pH and Eh, soil moisture (W) and Ca ion activity values in soil solution of various horizons (A, B, BC) of grey forest soil, based on simultaneous measurements Parameter
pH
W
Soil
Eh
pH
Ca
BC
A
B
BC
A
B
BC
-0.59
-0.58 -0.62
-
-0.59
-0.68
-
-0.60
-0.37
-
-0.33 -0.42
-0.56
-0.61
-0.18
-0.14
-0.17
0.15
0.11
0.04
-0.02
-0.01
-0.02
-0.21
-0.17
0.24
0.05
0.16
0.12
-0.04
-0.04
-0.10
A
B
virgin
-0.27*
arable virgin arable
Statistical analysis of pH and Ca ion activity for objects 9-11 at different seasons showed that the distribution of these parameters may be characterised by the law of normal distribution (Kesovetal., 1983). The change in heterogeneity of soil physico-chemical properties with depth has been studied in broad-leaved forest on grey forest soil (object 9) and an arable field on grey forest soil (object 10). In the arable field, the heterogeneity of all parameters tended to decrease with depth, while in the adjacent forest stand this trend could not be detected (Table 79). Variation coefficients in the A horizon of the arable field was somewhat higher as compared to the A horizon under the forest. In horizons B and C, a reverse pattern was observed. Obviously, the regularities are the results of the interactions of different environmental factors. Grass roots in the arable field (oat) in the ploughed horizon are the cause for a significant heterogeneity. In lower horizons, the role of plants diminishes which decreases the variability of Eh and ion activity. In the forest, horizon A is exposed to the effect of plants in a lesser extent. At the
144 same time, tree roots are a source of heterogeneity in the lower horizons, as they have high penetrating ability.
Table 79 Physical and chemical properties of grey forest soil by horizons Object
Horizon, depth (cm)
Eh(mV) JC
Cv (%)
CT
X
CvC%)
X
a
Cv(%)
X
Moisture (°/
NO3" (meq/L)
K^ (meq/L)
Ca^^ (meq/L)
pH
a
cr
Cv(%)
X
a
Cv(%)
CT
X
») Cv(%)
9
A (7)
636
40
6.3
6.1
0.4
6.1
2.0
1.2
63.4
0.21
0.14
68.3
0.10
701
41
5.8
5.1
0.3
4.9
12.3
6.6
53.3
0.10
-
11.3
B(65)
-
33.3 3.4
forest
18.2 0.7
4.0
17.8 0.7
4.0
C(116)
717
44
6.1
5.2
0.3
5.5
11.6
6.6
57.2
0.10
-
-
-
10
A (7)
678
46
6.7
5.4
0.4
7.1
10.0
7.5
75.0
0.58
0.69
119
5.4
4.2
77.7
13.1
1.2
9.2
arable
B(56)
675
23
3.4
5.6
0.3
6.0
19.0
4.7
36.6
0.18
0.09
52.8
0.60
0.30
50.0
15.2 0.3
2.2
field
C(105)
685
21
3.0
5.5
0.2
3.8
16.9
5.7
33.8
0.14
0.05
36.0
0.52
0.20
38.9
15.3 0.5
3.2
It is worth to note the significantly lower differentiation in the cultivated grey forest soil as compared to virgin grey forest soil. While in the arable soils there is no significant difference in redox potential and pH, but under the forest the A horizon differs from lower horizons by both Eh (- 75 mV) and pH (+ 1.0 unit). Human activities, on one hand, create additional heterogeneity in soil properties at particular layers, but on the other hand, they may decrease heterogeneity.
Table 80 Comparison of heterogeneity of the characteristics in grey forest soil, obtained by conventional agrochemical and in situ measurements Parameter
Grey forest soil (under forest)
Grey forest soil (arable) X
8
Cv
JC
5
Cv
6.8
Agrochemical analyses pHsalt
4.8
0.3
6.3
5.7
0.4
pHwater
5.6
0.3
4.8
6.3
0.2
3.6
K2O by Maslova (mg/100 g)
15.2
4.5
29.3
20.8
4.9
23.6
Exchange Ca^^ (meq/100 g)
12.6
3.2
25.0
10.5
4.1
39.1
pH
5.4
0.4
7.1
6.1
0.4
6.2
K^ (meq/L)
0.58
0.69
119
0.21
0.14
68.3
Ca^" (meq/L)
10.0
7.5
75
2.0
1.3
63.4
hi situ measurements
145 Using grey forest soil as an example, an analysis of the heterogeneity of soil properties was carried out, based on results of conventional agrochemical techniques and the in situ measurements by ion-selective electrodes (ISE). For Ca, K and H (pH) ions activity when measured by ISE in undisturbed soil, the degree of heterogeneity was higher than for the similar parameters measured by conventional techniques in the same extracts of dry samples (Table 80). This is reasonable since ISE measurements are carried out in "living" soil almost at a fixed point, while conventional techniques measure in a relatively large average sample (300-400 g). The heterogeneity of soil physical and chemical properties at conventional analysis depends substantially on the sample size, which Hmits significantly the use of this technique in the analysis of soil heterogeneity.
5.3. TEMPORAL VARIABILITY
The variability of physico-chemical properties with time should indicates the intensity of soil processes, and in situ measurements by ISE give insight into these processes. It is probably the only method allowing to estimate changes in physico-chemical properties within a short period of time (hours, days). Our investigations in various ecosystems (Snakin et al., 1977; Bystritskaya et al., 1981; Snakin & Kesov, 1984; Kovacs-Lang et al, 1986; Snakin 1989) revealed the cyclic nature of the composition of the soil hquid phase in the course of a day. Such change in soil properties may substantially superimpose the observed spatial heterogeneity properties and creates uncertainties in the interpretation of results. Since it is almost impossible to perform simultaneously sampling and analysis for the studied parameter with the required regularity, the identification of the time scales of changes in soil physico-chemical properties is our special interest. Daily and seasonal changes of Eh, pH and Ca ion activity were measured in broad-leaved forest, agroecosystem and pine forest (objects 9-11, see Table 76) in the soil Hquid phase. The results in solutions of grey forest and low podzohc soils indicate a daily cycle in these properties (Fig. 28). As in the case of virgin ordinary chernozem (Snakin, 1983), maximum values for Eh and Ca ion activity in the soil solution at depth of investigation (7 cm) is registered at about 3.00 pm, and the minimum values at night and in the morning. For the pH a reverse trend has been observed. Usually, changes of ion activity and Eh take place from 6:00 to 11:00 am. Comparison of variation coefficients of the values, obtained at different times of the day at fixed points (n=4 for three days) and measured conditionally simultaneously within an hour in 30 points shows that the daily variability of physical and chemical properties is much lower than their spatial heterogeneity (Table 81). However, in the cultivated grey forest soil (object 10), daily
146
variability is significant, comparable to the spatial variability. Consequently, it is necessary to take into account the time of sampling or analysis, especially in the case of field measurements. It is advisable to take into account the time when measurements v^ere made and to compare the properties of various soils measured at the same time of the day . May 5-7 Spring
June 30-July 2 Summer
September 29 October 1 Autumn 9.11 10 11 10 9
9 10 11
6 12 18 24
6
12 18 24
6 12 18 24 Time (h)
Fig. 28. Diurnal dynamics of redox potential, pH and Ca ions activity in the soil liquid phase (n^3) in various ecosystems and in different seasons (9-11 - number of objects, see Table 76)
A low variability was observed in litter covered pine forest, which again proves the biologically determined nature of the observed phenomena due to the absence of herbaceous plant cover.
Probably, this conclusion cannot be extended to laboratory analysis of dry soil samples due to their insulation from "living" soil.
147 Table 81 Comparative characteristics of temporal variability and spatial heterogeneity (by variability coefficient, %) for Eh, pH and Ca ions in the liquid phase of grey forest and low podzolic soils at 7 cm depth Soil
Parameter
Spatial heterogeneity
Temporal variability daily spring
Grey forest soil
Eh
under the cover of pH broad-leaved
Ca
seasonal summer
autumn
spring
summer
autumn
1.6(15*)
0.6(12)
1.3(28)
5.3(110)
10.6
4.9
4.6
26 (43)
2.0 (30)
1.4(18)
0.6 (8.9)
6.1
6.6
7.6
20.3 (68)
14.2 (30)
24.6 (35)
10(20)
29.7
48
70
forest Arable grey forest
Eh
5.8 (89)
5.3 (166)
2.2 (82)
7.1(160)
6.5
3.1
3.2
soil
pH
7.3 (120)
8.0(167)
2.6(71.2)
6.1(130)
6.1
4.8
3.2
Ca
45.6(165)
10.7(47)
16.4 (25.9)
70(18)
27.7
23
63.1
Low podzolic soil
Eh
0.5(10.9)
0.6(19.4)
1.2(36.4)
2.2 (69)
4.6
1.8
3.3
in litter covered
pH
4.0 (47.6)
2.2(36.1)
2.7(30.3)
0.5 (6.4)
8.4
6.1
8.9
pine forest
Ca
10.5(37.1)
2.2 (22.7)
3.1(9.3)
88 (37)
28.3
9.7
334
* Share of variability (%) of respective spatial heterogeneity; in case of seasonal variability were taken average spring, summer• and autumn Cv values (of spatial heterogeneity))
5.4. THE ESTIMATION OF THE NECESSARY NUMBER OF COLLECTED DATA FOR THE RELIABLE DETERMINATION OF SOIL CHARACTERISTICS In planning experiments aiming at obtaining reliable results at a minimal cost, the necessary number of measurements is one of the key issues. Based on the investigation on the spatial heterogeneity of physical and chemical properties of soils in various ecosystems (see Table 76), sample size was estimated with an error of 10 and 30% at the following confidence levels: Pi=0.80; P2=0.90; P3=0.95, see the work by Dmitriev (1972) for the equations used. The results obtained were averaged for forest, steppe and agricultural soils. Estimations have shown that for a reliable measurement of Eh and pH almost in all soils five electrodes are sufficient (Table 82). A relatively precise measurement of ion activity including hydrogen requires a larger number of electrodes. Since ionselective electrodes produce 10-20% instrumental error depending on the ion valency and in situ field analyses bring in additional source of errors, it is justified to consider sample size with measurement error of 30%. For the considered
148 ecosystems, to obtain reliable data at such level of measurement error it is necessary to use at least 17 electrodes for Ca^\ for NO3' - 19; for K^ - 37 at the confidence levels 90%. Whereas measurements in agricultural lands require a slightly higher number of electrodes due to possible hot spots of heterogeneity, resulting from human activities. Table 82 Minimum number of electrodes for analysing the physico-chemical properties of soils at different confidence levels (P) Ecosystems
Parameter
__ 0.80
Forest
Steppe
0.95
0.90
error oflO%
error of 30%
error o n O %
error of 30%
error of 10%
error of 30%
Eh
3
2
3
2
4
3
pH
3
2
4
2
5
3
Ca^^
35
6
60
7
82
7
K"
109
20
270
30
385
46
NO3"
85
10
145
17
200
24
Eh
3
2
3
2
4
3
pH
3
2
3
2
4
3
Ca'^
62
8
106
13
146
18
K:
156
17
267
28
369
40
NOj-
54
8
92
12
127
16
Agricultural
Eh
2
2
3
2
3
2
land
pH
3
2
4
2
5
3
Ca^^
63
9
107
12
148
19
K"
190
21
333
37
459
50
NO3"
92
11
156
19
216
26
It is important to note that large number of electrodes are necessary due to soil spatial heterogeneity, and not because of the defects in the measurements technique. These assessment of heterogeneity of the soil physical and chemical characteristics exceeds those produced by conventional analysis of soils, what was proven by our own (Table 80) and literature data (Vazhenin, 1963; Karpachevsky, 1977; Vakulova et al., 1979). However, one should take into account that we have investigated dynamic ion activity in the liquid phase of a "living" soil, and not
149 in dried soil samples. Secondly, measurements were carried out at individual points and not in averaged samples.
5.5. DYNAMICS OF THE SOIL LIQUID PHASE
The soil liquid phase is probably the most dynamic component of a ecosystem, which reflects many environmental factors and has almost no buffer capacity due to low amount of dissolved components as compared to those participating in the biological cycle (Table 37). The composition and dynamics of the soil liquid phase is an indicator of environmental status of the soil and coincides with the concept of recent soil processes i.e. "soil-momenf, unlike the term of "soilmemory" in the interpretation of V. O. Targuilian and I. A. Sokolov (1978)^\ Chapter 4 is focused on factors determining the soil liquid phase composition. Considering its dynamics, we have aimed at quantifying the influence of a given environmental factor in various ecosystems. We carried out our investigations in the grassland ecosystems of Central and Eastern Europe (The Bugac and the Csaszartoltes sites in Hungary, The Khomutovskaya Steppe Reserve in Ukraine) and in the Colchid forests in the North Caucasus. Since ion-selective electrodes can only be used for a limited number of ions, we also performed measurements in soil solutions obtained by displacement technique.
5 5 1 SANDY SEMI-DESERT STEPPE
The investigations in the Bugac site were carried out in 1984-1986 using the technique, described in sections 2.3.2. and 2.4.2. The various ion-selective electrodes were installed at a depth of 5-10 cm into the rhizosphere of separate plants and in bare soil unoccupied by higher plants {calvitium and nudum). In calvitium the surface of soil was free of plants, but at the depth of investigation plant roots were found. Bare areas of sandy semi-desert steppe without roots were designated as nudum.
That included the concepts of "soil-moment" and "soil-memory". The concept of "soil-memory" implies a complex of sustainable and conservative properties of soil profile, an integral result of factors and processes of soil formation during the period of observation. "Soil-moment" is a range of dynamic soil properties, a sum of factors and processes at time of observation (years). This includes the properties of short formation periods.
150 From 9 to 16 ion-selective electrodes of each type were installed in the communities for 3-4 days, and readings were performed from 6 a.m. to 9 p.m. with an interval of 3 hours. Therefore, the quoted results are an average of a large data set. Table 83 Physical and chemical properties of a sandy low humus calcareous soil according to in situ measurements at June 5, 1985, 9 am {Festucetum vaginatae community, Bugac, Hungary) Plant
W
t
Eh (mV)
species
C'o)
fC)
~
a
Cv
n
JH J
^
Cv
n
pNO, J
cr
Cv
n
pCa J
Festuca
1.36
20
547
24
4.4
3
T98
0.14
1.8
4
2.42
0.60
25
14
1.63
2.37
21
-
.
7.67
0.24
3.1
4
2.24
0.41
18
14
1.13
21
548
24
4.2
3
7.42
0.21
2.8
4
2.28
0.26
12
Calvitmm
3.73
21
546
16
2.8
3
8.34
0.02
0.2
4
2.56
0.33
Mean
2.2
21
547
21
3.9
9
7.85
0.38
4.9
16
2.37
0.42
pK o
Cv
n
J
Cv
n
0.37
22
4
3.04 0.09
<7
3
4
1.38
0.37
27
4
3.58
0.38
U
4
14
1.59
0.30
19
4
3.14
0.36
11
4
13
14
2.54
0.22
90
4
4.22
0.16
4
4
18
56
1.76
0.51
29
16
3.50
0.54
15
16
vaginata Carex
.
.
lipancarpos Koelena glauca
Xoie: X - average value: a - mean-square deviation: Cv - variation coefficient: n - number of electrodes for simultaneous measurements
The properties of liquid phase of under the sparse steppe vegetation are characterised by a high variability coefficients (Table 83). This is to a large extent explained by the mosaic character of the plant cover. For the comparison of various indicators Table 83 shows pX values. The influence of plants on the heterogeneity of soil physico-chemical properties is proven by the lower Cv% in calvitium soils, where the role of plants is diminished. The lowest spatial heterogeneity is characteristic for the redox and pH values (Cv up to 5%). The variability of K^, NOs^ and especially Ca^" ions are much higher, and up to 30% for pX values as compared to the data in Table 76. The spatial heterogeneity of the physical and chemical parameters of the soil liquid phase, and also their absolute value are highly related to the activity of plants. Analysis of variance shows a high reliability of the differences between all parameters studied under the various plant species (Table 84). In the plant rhizosphere the ion concentration is higher than in the calvitium, and especially K^ and Ca^"^ ions are affected. A significant role belongs perhaps to water absorption by plants roots, which is shown by soil moisture and ion concentration data, expressed as soil weight (Table 85). Through transpiration and related water absorption by plants, the concentration of CI", Na\ Mg ^ ions increases in the rhizosphere. In the rhizosphere, an increased concentration of biogenous K"^ and NO^' ions can be observed as compared to calvitium. despite their active
151 absorption by plants. Obviously, micro-organisms play an important role in their mobilisation in the rhizosphere from SAC and dead organic matter.
Table 84 Analysis of variance on the influence of time, plant species and measurement date on soil liquid phase composition. July 4-6, 1985, Bugac, Hungary Parameter
Factors time of the day
plant species
other factors
date
R*
P*
R
P
R
P
R
P
pNOs
0.100
0.99
0.038
0.99
0.026
0.99
0.789
0.99
pK
0.009
**
0.752
0.99
0.007
0.200
0.99
0.450
0.99
pCa
0.034
0.90
0.464
0.99
0.002
** **
Eh
0.257
0.99
0.063
0.95
0.044
0.95
0.540
0.99
pH
0.027
0.95
0.570
0.99
0.055
0.99
0.305
0.99
* R - determination coefficients, P - confidence levels ** unreliability of factor impact
Table 85 Composition of ethanol-displaced soil solutions and the ground water of sandy desert steppe (average data from 20 May to 4 June, 1984) Bugac, Hungary Soil .solution
W
pH
(%)
HCOj-
NOj'
Cf
K^
Na^
Ca^^
Mg^^ HCOj'
(meq/L)
NOj'
CF
K^
Na*
Ca^*
Mg^^
(mg /100 g of dry soil)
Under plants
2.7
8.0
4.4
2.8
4.3
0.22
0.37
8.5
1.6
0.67
0.43
0.37
0.021 0.021 0.42
0.048
Under calvitium
4.6
8.0
3.2
1.2
3.6
0.08
0.23
5.3
0.8
0.85
0.32
0.55
0.014 0.023 0.47
0.042
Ground
-
7.6
4.0
0.05
1.0
0.01
0.1
3.5
water (7 m)
The seasonal dynamics of the composition of soil liquid phase was studied using nitrateions (Table 86). Minimum NO3" activity was observed in June when there is active plant growth and optimal weather conditions. In the following seasons the amount of NO3' in the soil liquid phase is substantially higher. In all periods of investigation, lower NO3" activity was observed in calvitium compared to other ecosystems. The sandy semi-desert steppe community is characterised by periods of drought. Such periods are marked by a sharp increase in concentration of the soil liquid phase - see data of August-September, 1984, July, 1986. However, no strong correlation was found between NO3' activity in the soil Hquid phase and soil moisture: 0.62 for data of 1984 and 0.56 for data of 1986,
152 for the amount of NO3 in the soil the respective values equalled 0.15 and 0.66. During the period of observation the amount of NO3 in the soil of the sandy semi-desert steppe varied from 0.15 to 1.9 mg/100 g. NOs" activity in the liquid phase varied from 2 to 50 meq/1.
Table 86 Dynamics of the soil liquid phase composition of a sandy low humus calcareous soil in 1984-1986 (5-7 cm depth) Bugac, Hungary Parame-
Plant
ter
.species
Date of mea.surements 1984
1986
1985
20-22.V
2-4. VI
1-3. VIII
17-18. IX
3-5X1
4-6. VII
4-5. VII
25-26.IV
16-17.IV
6-7. VI
9-lO.VII
Festuca
3.72
1.91
13.5
29.5
18.6
4.67
8.51
12.6
17.0
5.25
17.8
Koelena
6.31
-
29.5
46.8
10.5
5.62
10.7
7.41
17.8
4.79
20.0
Car ex
2.45
4.47
19.5
31.6
1.32
7.08
-
-
-
-
('alvitia
2.19
2.69
21.4
22.4
8.13
3.98
8.51
6.46
6.92
3.63
3.89
J*
3.67
3.19
21.0
32.6
9.63
5.34
9.25
8.82
13.9
4.56
13.9
.7**
0.61
0.55
1.83
1.43
1.85
0.68
1.61
1.18
1.05
0.87
0.93
-
-
-
;
-
aNoj (meci/L)
aca'* (meq L)
Festuca
-
Koelena
"
-
61.8
'
X
9.36
-
-
-
-
69.2
**
Festuca
(meq L)
Koelena
-
-
-
-
1.07
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
0.87
-
(. 'arex
0.26
Calvitia
0.05
-
7* 7**
-
-
-
0.56 0.044
Festuca
7.79
Koelena
7.83
Carex
7.40
7.71
8.03
7.72
7.59
-
7.75
-
-
7.90
452
457
499
510 529
-
7.87
7.82
7.72
7.75
Festuca
459
533
Koelena
429
466
409
531
(^arex
493
533
475
469
505
Calvitia
7* Eh (mV)
-
5.56
aK^
PH
"
110.0
Calvitia
.7*
"
95.8
-
Carex
Calvitia
-
8.24
-
-
-
-
-
-
-
-
-
-
529
513
480
472
499
510
7*
478
526
468
452
509
516
W.oi, (%)
2.9
3.2
1.1
0.9
3.3
2.2
3.0
2.3
1.4
3.2
1.3
tscl ("C)
21
24
28
20
6
25
15
20
25
17
24
* X - average wn activity values in soiI liquid phiise ( meq/L):
** 7 -
amounts of those ions, converted to 100 g, of dry sOiil(mg/100g).
153 For soil redox potential June is also marked by a maximum (see Table 86). But during the period of observation, Eh values changed insignificantly (492±27 mV, Cv=5.5%). This is comparable to the spatial heterogeneity values (see Table 83). We measured the daily dynamics of the composition of the soil liquid phase only by ionselective electrodes. Daily variability was found for all parameters studied (Fig. 29). Analysis of variance indicated high daily variability for separate ions in different seasons (see Table 84).
Eh(mV) pX
800 700 600 500
Eh
400 '9
•
300
•
pK
200 -• pCa 100 +
12
15
18 21 Time (h)
Fig. 29. The diurnal pattern of the composition of the soil liquid phase of a sandy lowhumus calcareous soil (July 4-6, 1985)
Daily dynamics of NO3 is the extensively studied subject in our research (see Table 86). Daily variability of NO3 activity in the soil liquid phase of the sandy semi-desert steppe is significant in the periods of active vegetation and is not significant at the beginning (April 4-5, 1986) and end (November 3-5, 1984) of the vegetation period. Nitrate is at its maximum in the second part of the day (3 p.m. - 6 p.m.). In the morning and evening hours, NO3 activity is considerably lower (Fig. 30). The period of September 1984 with its anomalously low soil moisture content (0.9%) differs considerably from this pattern.
154 pKO,
254
I -May, 20-22, 1984, 2 - J u n e , 2-4, 1984; 3 - J u l y 31-August 2, 1984; 4-September, 17-18, 1984; 5 - November, 3-5, 1984; 6-July, 4-6, 1985, 7-April, 4-5, 1986, 8 - A p n l , 25-26, 1986; 9 - M a y , 16-17, 1986; 10-June, 6-7, 1986; II -July, 9-10, 1986
2.OH
1.5
10
0.5 6
9
—I—
—I—
12
15
—r— 18
1 21 Time (h)
Fig. 30. Diurnal dynamics of nitrate-ion activity in sandy low humus calcareous soil
The daily amplitude of variability reached 0.4 pNO? units which is comparable to the spatial heterogeneity in the sandy desert steppe (see Table 83). The widest daily pNOs variability was observed for calvitium and average amplitude during 1984-1986 was 0.48 pNOs, while for the whole ecosystem it was 0.30 pN03. Since NO?" in the soil liquid phase of the calvitium was lower than in the rhizosphere (6.2 and 8.1 meq/L respectively), the amplitude of the variability in the calvitium was also lower. The dynamics of NO3" in the soil o^ calvitium suggest that it is caused by micro-organisms activities, compensated by the absorption activities of plants. The high biological activity of the rhizosphere gives a higher level of NO3 than in calvitium. Correlation of the fluctuation of NO3' activity with the soil temperature is weak (r = 0.2 to 0.4). There is also no evidence of a relation of the pNO? amplitude with the temperature (r= 0,06 to 0.30 in different years). This serves indirect evidence for a non-physical background of the phenomena The analysis of soil temperature dependence (see Fig. 20) of pNOs amplitude favours the biological cause for the diurnal dynamics of NO3 activity in the soil liquid phase, which maximum values coincide with the optimal temperature range for plant and micro-organism activities (see Section 4.4).
155 The daily variation of redox potential was reliable over the whole observation period. Maximum Eh values were registered during morning hours, and these values reached their minimum at 3-6 pm, where they increased again (Fig. 31). The daily range of Eh variation is relatively narrow (20-50 mV). Eh (mV) 600 2 ' 5
500
-- 2
. ^^"•^5
3 1 4 ,
- 1 ^ 3
400 1 '
^
4
1 -May, 20-22,1984; 2 - June, 2-41984; 3-July, 31-August 2 1984: 4-September, 17-18 1984; 5-November, 3-51 1984
I 300 6
9
12
15
18
21 Time(h)
Fig. 51. Diurnal dynamics of Eh of a sandy low humus calcareous soil under Festucetum vaginatae community
The daily dynamics of pH in the soil liquid phase was reliable during all the three periods (see Table 86). The variation range was about 0.2 pH, whereas no clear pattern was found. The daily dynamics of pK and pCa was indistinct (0.1-0.2 pX) and lower than the spatial heterogeneity (see Table 83). Only a sHght increasing trend in the activity of K and Ca from the morning hours until to 3-6 p.m., was found (see Fig. 29).
5 5 2 THE MIDDLE DANUBE STEPPE
We carried out our research in the liquid phase of a southern chernozem under Pannonic mixed fescue-feather grass steppe in May, June and July, 1985 at Csaszartoltes. The spatial heterogeneity of this grassland was less distinct than in the sandy semi-desert steppe (Table 87). Eh and pH values (0.7-4.4%) have a low variability and the variation coefficients of NO3", Ca^^, K^ ions activity are higher (4-24%). It is possible that the more developed denser grass cover as compared to the preceding community, levels out spatial differences. The vegetation determines the current soil formation processes and each plant species
156 has its own effects, resulting in significant differences in the soil liquid phase composition (Table 88).
Table 87 Physical and chemical properties of the soil of the mixed fescue-feather grass steppe (Csaszartoltes), in situ measurements data of July 11, 1985, 9 a.m. Plant
W
t
species
(%)
CC) ~^
Eh (mV) '^
Q
n
^H ^
'^
C^
n
pNOj ~
'^
Q^
ii
pCa ~
'^
Cy
n
pK ~
'^
C^, ii
13.9
-
630
5
0.5
4
7.95
.21
2.7
5
2.91
0.69
24
13
2.51
0.55
22
3
3.37
0.18
5.3
4
13.5
-
622
9
1.5
4
7.98
0.35
4.4
5
2.75
0.29
10
13
2.71
0.69
25
3
3.54
0.31
8.7
4
onohrychis
16.2
-
626
8
1.2
4
7.78
0.26
3.4
5
2.76
0.38
14
13
2.24
0.18
7
3
3.39
0.14
4.1
4
Mean
14.5
17.3
626
8
1.3
12
7.90
0.29
3.7
15
2.81
0.49
17
39
2.55
0.53
21
9
3.43
0.23
6.8
12
Festiwa rupwola Sup a capillata Astragalus
Note: X - average value; o ~ mean-square deviation: Cv - variation coefficient; n - number of simultaneous measurements
Table 88 Analysis of variance on the impact of time, plant species and measurement date on the composition of the soil liquid phase. July 10-12, 1985, Csaszartoltes, Hungary Parameter
Factors time of the day ~R*
P*
plant species R
date P
R
other factors P
R
P
pNOs
0.019
0.95
0.039
0.99
0.233
0.99
0.668
0.99
pK
0.061
0.95
0.112
0.99
0.078
0.99
0.686
0.99
pCa
0.096
0.95
0.116
0.99
0.014
**
0.700
0.99
Eh
0.015
0.041
0.90
0.055
0.95
0.703
0.99
pH
0.015
** **
0.222
0.99
0.008
**
0.675
0.99
* R- determination coefficients. P - confidence levels * * unreliability' of factor impact
It is hard to make conclusions on the seasonal dynamics of the soil liquid phase in SalvioFestucetum nipicolae pannonicum community due to the non-continuous character of our measurements (Table 89). According to the data, NO3' ion activity and concentration decreased from May to July.
157 Table 89 Composition of the liquid phase of a southern chernozem (Csaszartoltes, Hungary) Parameter
Plant species
aN03
Festuca rupicola
(meq/L)
Date of measurements. 1985 May 9-10
June 25-27
July 9-13
2.19
1.70
0.83
Stipa capillata
2.14
2.45
1.29
Astragalus onobrychis
-
-
1.32
2.16
2.08
1.15
X *
~ **
2.08 Festuca rupicola
ac.'-^ (meq/L)
Stipa capillata Astragalus onobrychis
-
-
X *
1.33
0.92
-
8.14
-
5.26 14.82 9.42 4.86
X -
aK"
Festuca rupicola
-
0.50
(meq/L)
Stipa capillata
-
0.30
Astragalus onobrychis X
*
X
**
-
-
0.49 0.43 0.34
-
-
W soil (%)
17.2
10.3
14.5
t soil (°C)
17
18
19
Festuca rupicola
pH
Stipa capillata Astragalus onobrychis
-
-
X *
Eh (mV)
Festuca rupicola Stipa capillata Astragalus onobrychis
X
*
-
8.01 7.96 7.75 7.91 614 603 601 606
* X - average ion activity values in soil liquid phase (meq/L); ** X ~ approximate amounts of those ions, converted to 100 g of dry soil (mg/100 g).
The diurnal dynamics of the soil liquid phase in this community was less distinct as compared to the sandy desert steppe (Fig. 32). This was possibly due to a lower amplitude of the daily soil temperature variability and the buffering effect of the soil adsorbing complex.
158 Eh (mV)
)X
8 - Ron
pH
700 6 ' 600
Eh
5 ' 500 4 - 400 ^
•
pK
~ PNO3
^00
pCa
2 - 200 1 1 100
t( "C)
^
^^
20 15
Q
6
9
12
15
18
21
Time(h)
Fig. 32 Diurnal dynamics of the soil liquid phase composition of the Pannonic steppe (July 1012, 1985)
Daily dynamics of nitrate-ions activity was poorly expressed during all three periods of measurements (see Table 88) and was reliable at the confidence level 0.95. Determination coefficients were 0.066-0.019, and the daily amplitude varied from 0.1 to 0.2 pNOs. The minimum NO^'-ion activity values were found during the evening hours of June-July, 1985. A similar shift in the maximum was recorded for photo synthetic activities of the dominant species of the Pannonic steppe (Snakin at al, 1991). Chapter 7 gives a detailed description of a possible correlation of those processes. Daily dynamics of K* ion activity were reliable at the confidence level 0.95. The maximum values were detected in the morning (6 a.m.), the minimum at 3 p.m., and fluctuation was at 0.2 pK. For Ca, a reverse pattern was observed whereby the maximum values occurred in the evening (9 pm) and the minimum from 6 to 9 a. m. with an amplitude up to 0.5 pCa. Daily dynamics of pH and Eh were not significant. Somewhat higher values were observed from 6 to 9 a m and the daily amplitude was 0.1 pH and 10 mV.
5 5 3 THE PRIAZOV STEPPE
We carried out studies of the liquid phase in virgin ordinary chernozem in 1977 and 1985 similar to studies at Bugac and Csaszartoltes sites.
159 The spatial heterogeneity of all parameters studied in the pontic steppe was almost equal and variation coefficients were 4-10% (Table 90). At whole, the properties of soil were more homogeneous, but in June 1985 differences in soil liquid phase composition in the rhizosphere were significant, except for the activity of Ca (Table 91). Emphasis should be given to the coefficient value, which determines the influence of plant species on K ions activity in the soil solution (0.65). Soluble K content in the soil under various plants differed substantially and was highest under Stipa and lowest under Vicia (Table 92).
Table 90 Physical and chemical properties of an ordinary chernozem in the Khomutovskaya steppe {in situ measurements, June 6, 1985, 9 a.m.) Plant
W
t
Eh (mV)
species
(%)
(^C)
J
25.0
16
666
69
10
4
6.64
0.40
6.0
8
4.05
0.39
9.7
11
2.05
0.21
22.0
16
610
41
6.8
4
6.53
0.33
5.0
8
3.98
0.28
6.9
11
2.07
0.04
cracca
25.1
16
650
21
3.3
4
6.59
0.33
5.0
8
3.77
0.36
9.7
11
2.18
0.13
6.2
3
Mean
24.0
16
642
53
8.3
12
6.58
0.36
5.5
24
3.93
0.37
9.4
33
2.10
0.16
7.5
9
a
^H
C
v
i
i
J
pNOj a
C
v
n
"
a
pC^^ C
v
i
i
"
pK a
C
y
n
J
a
10
3
3.07
2.0
3
2.45
C
v
i
i
0.29
9.3
3
0.09
3.7
3
3.20
0.11
3.5
3
2.91
0.37
13
9
Festuca nipicola Stipa capillata Vicia
Note: X - average value; a - mean-square deviation; Cv - vanationy coefficient; n - number of electrodes for simultaneous
measurements
Table 91 Analysis of variance on the influence of time, plant species and measurement date on the composition of the soil Hquid phase. July 4-7, 1985, Khomutovskaya Steppe Reserve, Ukraine Parameter
Factors time of the day
plant species
R*
P*
R
P
R
P
R
other factors P
** ** ** ** **
0.890
0.99
date
pN(:)3
0.009
«*
0.078
0.99
0.002
pK
0.101
0.99
0.650
0.99
0.002
pCa
0.315
0.99
0.006
**
0.015
Eh
0.020
0.033
0.95
0.005
pH
0.016
** **
0.045
0.99
0.009
0.219
0.99
0.451
0.99
0.872
0.99
0.863
0.99
* R - determination coefficients. P - confidence levels ** unreliability of factor impact
Seasonal dynamics of the composition of soil liquid phase was distinct for all parameters studied and somewhat exceeded spatieal heterogeneity except for potassium (Table 93). Maximum
160 NO? activity was observed in the period of early spring. In the following months, activity and the amount of NO3 decreased steadily. Potassium dynamics in the liquid phase was similar, but in late autumn a sharp increase in K activity took place, conceivably due to K leaching from dead plant matter by continuous rain. Table 92 Composition of the soil liquid phase in a virgin ordinary chernozem under different plant species (Khomutovskaya Steppe Reserve, June 4-7, 1985) Eh, mV species
W soil (%)
t soil ("C)
24
19
meq/L(mg/100g)
Festuca mpicola
0.09
24.0
0.71
6.50
629
Stipa capillata
0.12
23.0
3.02
6.32
601
Vicia cracca
0.17
22.0
0.50
6.35
624
Mean
0.13(0.17)
23.0(18.8)
1.41 (1.13) 6.39
618
Table 93 Seasonal dynamic of the liquid phase composition of virgin ordinary chernozem (Khomutovskaya Steppe Reserve, daily mean values of 1977-1978) Parameter
1978
Date of measurement, 1977 2-8. IV
9-15. V
23-29. VI
1-3. vm
18-22. XI
18-25. IV
aN03
9.3(17.4)*
1.55(1.41)
1.40(2.53)
0.61(1.08)
0.20(0.41)
0,%(1,60)
a,.^^
28.0(33.8)
39.0(23.0)
35.0(40.8)
54.0(61.8)
28.0(37.6)
12,0(12.8)
aK^
0.62(0.73)
0.39(0.22)
0.49(0.56)
0.34(0.38)
3.0(3.9)
0,12(0,13)
PH
6,8
6.8
6.7
6.5
6.3
6.8
Eh (mV)
611
600
590
586
566
609
-
**"C'^ (mg/L)
18,3
18.3
24.3
27.4
31.4
**Na" (meq/L)
0,19
0.23
0.20
0.20
0.21
0.20
**Cr (meq/L)
0,65
0.91
0.50
0.39
0.50
0.31
**Mg^^ (meq/L)
2.1
2.4
1.9
2.3
2.8
1.8
** HCO3' (meq/L)
3,1
4.2
3.7
3.8
4.8
4.1
W soil (%)
33,7
18.2
32.6
32.1
37.0
30.3
tsoil('^C)
10
16
18
21
6
9
* meq/L (mg/100 g) ** ethanol-displaced soil solutions analyses.
161 As soil temperature increased and the phytomass grew, Ca^^ ion activity increased and dropped toward the end of autumn. The pH of the soil liquid phase varied substantially within the vegetation period. A sharp increase in pH was measured due to acid rains in November 18-22, 1977. The Khomutovskaya steppe reserve is situated close to the Mariupol metallurgical plant and the Donetsk region. No precipitation took place in other periods of observation. From spring to autumn 1977, an increase in organic matter content of the soil solution was observed. But for soil humus this trend was not distinct (Table 94). Coefficient of correlation between organic matter content in soil solution and total soil organic matter (humus) was 0.51 Such correlation (r=-0.22 to 0.53) was also observed in the steppe community of the Kamennie mogyli (Stone graves) reserve (Snakin et al, 1991). The level of correlation was low, since high organic matter content in soil solution proves the accumulation of organic matter in soil and reflects the dehumufication processes. Seasonal changes in CI", Mg^^, HCO3' and particularly Na^ in the ordinary chernozem solution were indistinct (see Table 93).
Table 94 Dynamics of chemical composition of virgin land ordinary chernozem in the Priazov region (0-10 cm) Parameters
1978
Date of sampling (1977) 2-8. IV
9-15. V
23-29. VI
26. VII
26. VIII
18-22. XI
19. IV
PHwater
7.0+0.1*
7.1+0.2
7.1+0.2
7.2 +0.2
7.0+0.1
7.1+0.1
7.4+0.4
PHKCI
6.72+0.2
6.6+0.2
6.6+0.1
6.6 +0.2
6.5 ±0.1
6.6+0.2
6.9 +0.2
Humus (%)
6.0+0.3
5.6+0.4
5.9+0.3
5.6+0.3
5.8 +0.3
6.3 +0.5
6.2 +0.6
Total N (%)
0.42+0.04
0.28+0.06
0.37 +0.04
0.33 +0.06
0.36 +0.02
0.36 +0.05
0.33 +0.02
Hydrolyzable N (mg/100 g)
21.9+2.4
21.5+0.5
19.6+1.3
20.2+1.7
21.9+0.8
19.4+1.3
16.2+0.5
K by Maslova (mg/100 g)
47+3
42+7
40+2
45+3
44+2
48+6
49+3
P by Kirsanov (mg/100 g)
9.1+1.1
7.5+1.7
8.8+1.3
7.3+0.8
8.6+1.2
8.3 +0.3
9.1+1.6
Ca(meq/100g)
44+5
43+6
48+3
44+5
44+3
42+2
45+6
Mg(meq/100g)
5.6+2.3
6.9+3.9
5.7+3.6
6.8+2.6
6.7+2.6
7.0 +2.3
5.3 +2.7
K(meq/100g)
0.70+0.03
1.15+0.30
0.77+0.15
0.96+0.43
0.92+0.43
1.11+0.53
1.23+0.32
Na(meq/100g)
0.42+0.13
0.74+0.22
0.30+0.05
0.68+0.22
0.64+0.29
0.45 +0.22
0.45 +0.22
ZKt(meq/100g)
51
52
55
52
52
51
52
C:N
8.3
11.6
9.3
9.9
9.4
10.2
10.9
Exchangeable bases by Hedroitz:
* Mean-square deviation (c)
Analysis of seasonal measurements of the redox potential in a virgin ordinary chernozem showed that the highest Eh were observed in April and the lowest in November. From April to November, a systematic decrease in Eh occured. Comparison of soil seasonal dynamics to
162 temperature and moisture in the same periods for the upper 10 cm layer, allows to conclude that Eh changes were not caused by moisture or temperature changes. We have tried (see Section 6.2.4) to explain the seasonal dynamics of Eh value with the activity of the living components of ecosystem in a thermodynamic context (Snakin & Dubinin, 1980). The diurnal dynamics of the composition of liquid phase in virgin ordinary chernozem is distinct for almost all ions (Fig. 33, Table 95). pX
Eh (iTiV)
18 21 Time(h)
Fig. 33. Diurnal dynamics of the liquid phase composition in an ordinary chernozem in the Priazov region, June 4-7, 1985 (Khomutovskaya Steppe Reserve)
Table 95 Diurnal dynamics of the Hquid phase in an ordinary chernozem (Khomutovskaya Steppe Reserve) Periods of
Parameter
measurement
Time (h) 6
9
12
15
18
3
4
5
6
7
8
K"
0.65
0,54
0.49
0.59
0.59
0.60
(meq/L)
0.49
0.34
0.30
0.32
0.34
0.44
June 25-27, 1977
0.43
0.43
0.42
0.47
0.58
0.55
July 30-August 3, 1977
0.29
0.28
0.30
0.33
0.40
0.46
November 19-21, 1977
3.3
3.5
4.8
2.6
2.3
1.0
April 20-24, 1978
0.07
0.07
0.09
0.13
0.13
0.09
June 4-6, 1985
1.66
1.26
0.83
0.66
0.85
162
1
2
April 3-4, 1977 May 10-13, 1977
21
May 10-13, 1977
N03-
5.0
0.92
0.17
0.16
0.12
0.36
June 25-27, 1977
(meq/L)
2.7
0.37
0.66
0.43
0.59
2.7
0.86
0.50
0.55
0.64
0.53
0.48
July 30-August 3, 1977
163 Table 95 (continued)
1
2
_
4
5
6
_
8
November 19-21, 1977
022
009
O04
O04
O05
O07
April 20-24, 1978
1.1
0.74
0.39
0.33
0.31
0.29
June 4-6, 1985
0.11
0.13
0.14
0.14
0.12
0.11
51.8
55.2
45.2 73.6
April 3-4, 1977
Ca^^
39.4
47.0
57.0
May 10-13, 1977
(meq/L)
70.4
73.4
68.4
77.0
64.8
June 25-27, 1977
65.2
70.0
76.8
80.6
124.0
124.6
July 30-August 3, 1977
99.6
69.2
64.0
102.0
88.0
85.0
November 19-21, 1977
31.0
43.2
85.0
86.6
50.8
31.0
April 20-24, 1978
8.88
12.1
17.6
18.8
15.2
12.3
June 4-6, 1985
20.1
17.8
16.6
18.6
29.0
40.8
6.99
6.81
6.60
6.63
6.71
6.97
May 10-13, 1977
6.85
6.82
6.83
6.79
6.79
7.04
June 25-27, 1977
6.84
6.64
6.65
6.0
5.55
6.65
July 30-August 3, 1977
6.52
6.54
6.48
6.58
6.42
6.64
November 19-21, 1977
5.88
6.41
6.46
6.62
5.88
6.15
April 20-24, 1978
7.46
6.89
6.78
6.74
6.79
7.36
June 4-6, 1985
6.40
6.48
6.33
6.35
6.37
6.42
April 3-4, 1977
April 3-4, 1977
PH
595
612
622
620
615
602
May 10-13, 1977
587
584
563
561
563
575
June 25-27, 1977
582
591
590
588
591
592
July 30-August 3, 1977
589
590
584
578
593
592
November 19-21, 1977
551
548
564
582
584
568
April 20-24, 1978
601
614
609
604
607
613
June 4-6, 1985
618
638
615
615
614
608
April 3-4, 1977
Eh (mV)
t soil CC)
8.0
8.5
9.5
11.5
11.5
10.5
May 10-13, 1977
12.0
13.0
16.5
18.0
18.0
16.0
June 25-27, 1977
15.5
16.5
19.5
19.5
20.0
18.5
July 30-August 3, 1977
19.0
19.0
20.0
20.0
20.5
20.0
November 19-21, 1977
5.5
5.5
5.5
6.0
5.5
5.5
April 20-24, 1978
7.0
7.5
9.5
11.0
11.0
9.0
June 4-6, 1985
15.0
16.0
19.0
21.0
21.0
20.0
As a rule, NO3' showed maximum activity during the morning hours and in the evening. Perhaps, this is explained by NO3" absorption by plants which increases in the daytime, and by its supply to the liquid phase due to continuous activity of bacteria under stable temperature conditions. Sharp soil temperature changes are not a characteristic for the soil of the steppe ecosystem, such as in the other sites of investigation (Bugac, Csaszartoltes) due to the thick litter layer.
164 Potassium content in the soil liquid phase was at its minimum at daytime except for the extremely low level in April 20-24 and high in November 19-21. Maximum values were observed in the morning and evening hours and this has to deal with the K absorption and release by plants (Luttkus & Bottlicher, 1939). The observed decreasing in K ion activity may not be explained by the influence of soil temperature, for with increasing in temperature, K ions activity increased by 0.1 pK every 7-8 degrees (see Section 4.4). Calcium had its maximum in the second half of the day, and one of the possible reasons may be the changes in CO2 content in the soil air which is related to Ca through the carbonate equilibrium. Our data showed that CO2 content in soil air is at its maximum in the second half of the day in an ordinary chernozem (Snakin & Zavizion, 1979). The pH value in the liquid phase of a virgin ordinary chernozem is at its minimum from 12 p.m. to 6 p.m., which is harmonised with the CO2 dynamics in the soil air. An additional acidification of the soil solution takes place as a result of acidic substances exudation by plant roots (Snakin, 1980). In the period November 19-21, 1977 this pattern was disturbed by continuous rains. The amplitude of daily Eh variability of was 10-40 mV, and the fluctuations differs depending on the vegetation period. Maximum Eh values were observed in the period of active vegetation during morning hours but in the second part of the vegetation period during evening.
5 5 4 THE COLCHID FOREST
In 1987 and 1988 investigations were carried out in the Colchid forests which is preserved in the Caucasus State Reserve (Krasnodar region); see the works by Andreeva et al. (1990) and Andeeva (1990) for detailed description of the experimental sites. Tables 96 to 98 are on the seasonal dynamics of the soil liquid phase in the Colchid forest. The high variability coefficients deserve particular attention. And relatively stable properties as Eh and pH had variability coefficients from 4 to 22% and 7.0 to 9.3% respectively. The variability coefficients in steppe communities (see Tables 83, 87, 90) and various forests of the South of the Moscow region (see Table 76) were half or less. Such regularity is also observed for spatial heterogeneity. In the Colchid forests, soil temperature and moisture had low variability. It might be that the pattern was determined by the specificity of local soil formation processes and the intensive biological cycle in the humid Colchid type subtropical forests.
165 Table 96 Physical and chemical properties of soil under mixed broad-leaved boxwood forest (horizon Ai, depth 5 cm, n= 10), Colchid forest, Caucasus State Reserve Parameters
Statistical
Period of measurements
characteristics
1987
Temporal
17.V
8. VII
8.x
404
509
674
663
33.2
34.4
8.0
7.5
6.1
6.0
6.9
6.4
6.3
11.5
6.7
65.8
6.3
1.5
2.3
2.3
1.6
2.0
24. Ill
12.V
12.VII
4.x
24. Ill
X
572
628
620
581
Cv
20.0
25.6
8.4
15.4
X
6.1
6.0
5.9
4.9
Cv
9.8
15.0
16.9
X
2.3
2.3
1.6
Eh (mV)
pH
pCa
Cv pK
X
variability
1988
21.7
8.7
12.5
33.3
21.7
22.7
18.8
15.0
4.3
3.8
3.5
4.0
3.8
3.9
3.0
3.7
Cv
7.0
7.9
8.6
7.5
5.3
7.7
16.7
5.4
A-
4.5
5.0
4.0
4.0
5.1
4.5
4.4
3.5
Cv
6.7
10.0
-
-
X
49.5
X
8.5
PNO3
PNH4
X
Cv W (%) t('C)
X
Cv
581
15.3
6.0
9.3
2.0
18.3
3.8
10.2
4.4
12.2
4.3
3.5
15.0
3.9
13.3
13.6
14.3
4.5
4.5
4.2
4.3
4.2
-
8.9
6.2
5.4
7.2
7.1
54.9
53.9
36.5
76.9
68.8
61.9
39.6
55.3
25
10.5
18.2
12.3
7.5
13.0
17.2
15.0
13.4
26
10.0
Table 97 Physical and chemical properties of the soil under the hornbeam-oak forest (horizon Ai, depth 7 cm, n = 8), Colchid forest, Caucasus State Reserve Parameters
Eh (mV)
statistical
Period of measurements
characteristics
1987 24.III
12.V
12.VII
4.x
24.III
17.V
8.VII
8.x
630
597
662
617
608
596
604
631
19.1
20.9
11.3
15.1
7.9
13.6
11.9
10.1
.x:
5.1
5.5
5.3
5.3
6.1
5.6
6.2
5.8
Cv
23.5
18.2
20.7
11.3
9.8
14.3
12.9
12.1
X
Cv pH
pCa
pK
PNO3
pNH4
2.6
2.9
1.6
1.7
2.7
2.7
2.0
2.1
Cv
30.8
17.2
18.8
29.4
14.8
18.5
25.0
23.8
J
4.3
4.0
3.4
3.6
4.0
4.3
2.7
3.3
Cv
16.3
10.0
11.8
11.1
5.0
9.3
29.6
15.2
X
4.6
4.1
4.3
4.2
4.0
4.7
4.4
3.7
Cv
8.7
14.6
11.6
9.5
4.1
8.5
11.4
10.8
-
-
4.5
4.2
4.7
4.1
3.7
6.7
12.0
7.3
12.2
5.4
X
7 Cv
W (%)
iCo
Temporal variability 1988 X
Cv
618
3.6
5.6
7.0
2.3
21.9
3.7
15.0
4.4
8.7
4.2
9.1
X
32.4
74.3
32.4
34.1
55.9
46.1
36.8
25.5
42.2
38.0
X
10.0
12.0
19.0
15.5
8.5
12.0
20.0
16.5
14.7
26.2
166 Table 98 Physical and chemical properties of the soil in laurel-cherry yew woodland (horizon Ai, depth 5 cm, n = 8), Colchid forest, Caucasus State Reserve Parameters
Statistical
Period of measurements
characteristics
1987 24.III
Eh (mV)
PH
pCa
pNO.,
pNH4
W (%) I ("O
UN
12. VII
variability
143
Jx
24.III
ITV
8?vn
83<
473
434
328
625
"T
502
Cv
22.9
19.9
36.7
21.1
16.3
21.9
47.3
16.6
~
6.4
6.1
5.7
5.4
6.3
6.1
6.1
6.9
Cv
7.8
6.6
21.1
14.8
4.8
8.2
8.4
4.3
v
1.9
2.5
1.7
1.9
2.5
2.0
1.7
2.4
572
558
23.3
8.0
17.6
21.1
16.0
25.0
23.5
16.7
X
4.6
4.5
4.4
4.8
4.5
4.5
3.7
4.2
Cv
8.7
13.3
6.8
6.3
8.9
6.6
16.2
11.9
~
4.5
4.6
5.1
4.8
4.9
5.1
5.0
3.8
Cv
8.9
6.5
3.9
10.4
12.2
9.8
8.0
13.2
5.4
4.8
4.4
4.6
5.1
-
7.4
6.3
4.5
13.0
7.8
Cv pK
Temporal 1988
-
x
X
Cv
479
22.3
6.1
7.3
2.1
16.4
4.4
7.5
4.7
9.2
4.9
8.2
Cv
-
7 ~
50.1
64.6
79.4
77.0
105.2
98.6
80.9
86.1
80.2
21.9
7.5
12.0
19.3
13.0
6.5
12.0
19.5
15.5
13.9
30.7
The communities studied differed substantially in their soil liquid phase composition and dynamics (Table 99). The spatial heterogeneity of Eh decreased in the following order: yew (24%), box (19%), oak (14%)). The pattern of temporal variability is similar: yew (22.3%o), box (15.3%)), oak (3.6%o). The oak grove has the most developed herbaceous ground vegetation, while it is almost absent in the box-tree forest, that is why one may scarcely consider it a major cause in this sequence. Perhaps, the age of dominant trees (hornbeam-oak - up to 100 - 200 years, mixed broad-leaved boxwood - up to 300-400 years, laurel-cherry yew - 700-800 years) is affecting the heterogeneity of Eh in the soils. It is conceivable that the moist soils (yew forest) are characterised by the lowest Eh, which supports the common observation of moisture influence on Eh. However, seasonal moisture changes and Eh had no a reliable correlation (Table 100), except for the box-tree forest. Correlation between seasonal decrease in Eh and the period of maximum organic matter supply to soil through leaf and dead plant parts litter harmonise with our data (Ivakhnenko & Romanova, 1979). In the yew forest, a decrease in Eh was observed in the summer post-leaf fall period and in the box-tree forest low Eh values was observed in the leaf fall period. A pore correlation between Eh and pH values was found (see Table 100).
167 Table 99 Physical and chemical properties of soils within the ecosystems studied (depth 0-10 cm), MarchOctober, 1987 and 1988, Colchid forest, Caucasus State Reserve Parameters
Ecosystems mixed broad-leaved boxwood
hornbeam-oak
laurel-cherry yew
pH
6.0±0.8
5.6±0.8
6.1±0.7
Eh (mV)
581±120
618±80
479±140
Ca^^ (meq/L)
10.0±4.6
5.1±3.4
8.5±3.5
K" (meq/L)
0.16±0.07
0.19±1.3
0.04±0.02
N03' (meq/L)
0.08±0.08
0.06±0.05
0.03±0.04
N H / (meq/L)
0.05±0.02
0.08±0.06
O.OliO.Ol
Soil liquid phase (in situ)
Soil solutions Mg^^ (meq/L) *
0.7±0.2
0.6±0.4
0.5±0.3
Na^ (meq/L) *
0.12±0.04
0.12±0.06
O.lliO.Ol
HCCK (meq/L)*
0.34±0.06
0.17±0.12
0.16±0.05
Cr (meq/L) *
0.92±0.62
0.61±0.31
0.41±0.08
S04^-(meq/L)*
0.43±0.13
0.62±0.37
0.37±0.09
Si02(mg/L*^
4.6±2.2
24.4±11.7
13.2±9.8
X"(mg/L*^
350±202
42±23
58±70
Ca^^(meq/100g)
42.06±4.83
20.18±7.12
45.42±3.23
Mg^^ (meq/100 g)
2.53±0.79
3.27±0.89
2.27±0.86
K^(meq/100g)
L19±0.32
0.62±0.21
1.09±0.25
Na^ (meq/100 g)
0.24±0.07
0.16±0.05
0.27±0.06
7.21±2.89
12.82±3.80
6.53±2.16
Soil adsorbing complex
Hydrolytic acidity (by Kappen) (meq/100 g) Exchangeable acidity (by Sokolov) (meq/100 g)
0.12±0.04
0.16±0.04
0.11±0.05
Field moisture (%)
57±14
40±17
81±23
4.3±0.9
5.9±1.4
Hygroscopic moisture (%) 5.8±0.2 * Data of ethanol-displaced solutions
Soils of the oak grove are among the most acidic (average pH = 5.6). We carried out an investigation of water extracts of litter (1 : 10) to analyse its causes. Oak grove litter extract was the most acidic (pH = 5.9), while on average the pH of yew- and box-tree litter extracts were 6.3. Differences in K^ activity in the soil liquid phase may be most related to the biological cycle in these ecosystems. The lowest K"^ activity was observed in spring (March-May) and coincided with the period of intensive growth of summer plants and ephemeroids, and growth of assimilation
168 surface of the evergreen plants. Maximum activity was observed in summer for there was no growth and last year leaves of the spring evergreen plants died. Analogue regularities were observed for exchangeable K in forest soils (Kovrigin, 1952). Table 100 Coefficients of correlation between physical and chemical properties of soils Parameters
Ecosystems
Eh
pH
W (%)
box
5^64
038
oak
-0.16
0.15
yew
-0.19
0.02
box
0.75
oak
t(°C)
Eh
4176
+0.45
0.71
-0.31
0.07
0.02
0.39
-0.61
-0.70
-0.60
0.51
0.01
-0.81
-0.57
-0.76
yew
0.45
-0.15
-0.56
-0.70
0.04
box
-
0.30
-0.42
-0.52
-0.57
-0.43
-0.25
-0.65
-0.32
0.37
0.51
0.66
-0.78
-
-0.62
0.18
0.15
-0.51
0.09
0.12
-0.34
0.29
-0.72
-
0.49
0.11
0.70
0.37
0.19
-0.01
-
0.63
box oak yew box oak yew
pCa
pN03
0.30
yew
pK
pCa 044
oak
pH
~^K
box oak yew
^026
0.42 -0.47
Comparing data on seasonal activity changes of K in the soil liquid phase with the dynamics of green phytomass we found that the maximum activity period of K^ ions in the liquid phase coincides with the maximum content in the herbaceous layer in the oak forest, in Colchid ivy (yew) and in the green phytomass of box (Andreeva, 1990). The influence of precipitation on K content in the soil liquid phase was also measured. In summer 1987, the K content was lower compared to the same period in 1988, which was characterised by high precipitation. The increase in K content in the soil solution was due to its wash off by atmospheric precipitation, which was mentioned in the work of Volkova (1978 b). Correlation between pK and hydrothermal conditions is ambiguous. No correlation between K content in the soil liquid phase and soil moisture was observed for the ecosystems. The correlation is, however, always reliable in terms of temperature (see Table 100). Such correlation
169 is not explained only by changes of temperature, but the agreement of these changes with the rhythm of seasonal development of living components, as it was in the case of nitrates (see Sections 4.4 and 5.4.1). Comparison of pK and pH values shows that the higher the pH value, the more K^ is be found in the soil Hquid phase (correlation coefficient -0.34 to -0.62). Similar correlation with pH was observed for exchangeable K content in soil (Table 97). The activity of Ca^"^ ions in the liquid phase was quite high and comparable to that of chernozem. It decreased in the following order: box, yew, oak. Seasonal variability of pCa was analogue to pK. For box and oak groves, a correlation between Ca content in green phytomass and its content in the soil liquid phase was found (correlation coefficients 0.70 and 0.59 respectively). This corresponds with Karpachevsky (1981), who considered the consumption of Ca by grass and tree layer vegetation the main reason for its dynamics in the soil. A pCa - soil temperature correlation was also observed as in the case of pK - temperature (see Table 100). Results of the three factor analysis of variance showed that the dynamics is determined by ecosystem, season and year (Table 101). The soil redox potential is to a significant extent determined by ecosystem type. Some 30% of K, Ca and NO3 content are determined by season, whereas ecosystem type is also important for K.
Table 101 Analysis of variance on the influence of time (year, month) and ecosystem type on soil physicochemical properties Factors
Determination coefficient
_
_
Month
0.05
-
Ecosystem type
0.22
0.09
Year
_*
0.12
Month and ecosystem
0.16
Ecosystem and year
Month, ecosystem and year
-
-
Other factors (including errors)
0.47
Month and year
__
_
___
__
__
__
0.23
0.06
0.08
0.10 0.55
- ' means that determination coefficient is less than 0.05
0.49
0.49
0.49
170 The soil and environmental conditions of the Colchid type forests of the subtropical climate is distinctly different from the broad-leaved and coniferous forests of the forest steppe and taiga zones. Significant ecological differences in ecosystems of Colchid forest were discovered. For example, it has been demonstrated that in the relic tree vegetation (laurel-cherry yew, Colchid box) special environmental situations can develop which are determined by microclimatic features and by characteristics of geochemical cycle.
5.6. CONCLUSIONS
The results of our investigation with assistance of the Demetra database allowed to establish the most reliable range of parameters in the liquid phase of different soil types under various ecosystems. Analysis of the heterogeneity of soil physico-chemical properties (redox potential, H^, K^, Ca \ NO3' activity in soil liquid phase) by in situ measurements has shown that the heterogeneity and variability of soil properties are substantially determined by biological factors. The influence of vegetation on the heterogeneity of soil properties is expressed: on one hand, plant roots present the source of heterogeneity, on the other hand, plant species diversity decreases heterogeneity. Using the Colchid type ecosystems, it was found higher heterogeneity of soil redox potential occurred under older dominant trees. According to the literature, the spatial variability of pH increases with ecosystem age (Riha et al., 1986). Human activities are a significant factor in the creation of heterogeneity in soil properties, often leading to a temporary increase. At the same time, human activities can decrease heterogeneity of soil properties along the soil profile in vertical dimension. With depth there is a decreasing trend in spatial heterogeneity of soil physico-chemical properties under agricultural lands. In the forest, this trend is not existing, possibly due to a different distribution of roots in forest ecosystems. The study of the variability of soil properties has shown that diurnal changes may vastly contribute. Seasonal variability of Eh and Ca^^ activity values in the soil liquid phase is larger than their spatial heterogeneity. The dynamics of the soil liquid phase harmonises well with changes in the vegetation, especially in natural communities. For example, in all ecosystems of the Colchid forest the period of minimum K^ and Ca^^ activity in coincides with the period of active growth (spring), while in summer K^ and Ca^^ ions activity increases.
171 Soil formation in the ecosystems proceed under the influence of several factors: parent material, climate, relief, and living organisms. Let us try to elucidate a pattern in the spatiotemporal structure of the soils of the natural grassland communities. It is worth to emphasise similarities in soil formation regimes in the Bugac, the Csaszartoltes site and the Khomutovskaya Steppe Reserve. There are similarities in water regimes as they are situated on watershed territories, precipitation by exceeding evaporation, and the absence of shallow to ground water (non-ablutive type). There are also similarities in the redox regime: the predominance of oxidation processes along the profile, similar pH from low alkaline in the parent rock to neutral and low acidic in the humus horizon under the influence of vegetation. We shall focus on the peculiarities of soil formation processes as this array enables us to trace the influence of biological factors. Summarised results are presented in Table 102. We should emphasise the following soil formation processes under steppe vegetation: • Variable conditions of water supply to plants. Moisture content in the soil of the sandy desert steppe (Bugac) was significantly lower compared to other territories, water retention range was narrower due to low moisture holding capacity of the sandy soils and capillary water capacity close to zero. Dew has a role compared to other territories, supporting plants with moisture during morning hours in the dry summer period. • Significantly lower redox potential in the sandy semi-desert steppe (Bugac) compared to Csaszartoltes and the Khomutovskaya steppe. A possible reason is the low water content and a lower degree of organic matter decomposition due to less favourable climatic conditions. The level of soil redox potential may be characterised as the relation between bioproductivity (reduction) and decomposition (oxidation) processes (see also Section 6.2.3). For Eh we have tried to use the proportion of maximum values of living and dead aboveground phytomass (L:D value in Table 104). In fact, the value of Eh is at its maximum where this ratio is close to 1, plant matter decomposition takes on average place in one year. The redox potential is also low in Kamenicky , where the ratio L:D is almost the same as in Bugac, probably because of low temperatures limiting organic matter decomposition. • Significantly lower pH in the liquid phase of an ordinary chernozem in the Khomutovskaya steppe as compared to the soils of Bugac and Csaszartoltes, despite the presence of carbonates in soil and parent materials of all the three sites. It is probable that vegetation has less impact on soil properties in Bugac and Csaszartoltes. Even in Bugac under the developed
172 synusia of lichens at a 2-5 cm depth the pH value was 0.5-1.0 units lower than in the carbonate sand with no vegetation (see Table 41).
Table 102 Soil physico-chemical properties in the 0-10 cm layer of various grassland ecosystems Parameter
Bugac average
Eh, mV
^92
D:L*
Csaszartoltes range
average
range
410-530
606
600-615
0.20
Khomutovskaya steppe Kamenicky average
range
597
565-630
0.53
0.95
average range ""559
474-642**
0.26
Soil liquid phase: 7.8
pH
7.4-8.0
1.8
7.7-8.0
6.6
6.6-6.8
5.4
4.9-5.9
NO?' (meq/L)
11
1.3-47
1.8
0.8-2.5
2.0
0.2-9.3
0.04
0.02-0.06
K* (meq/L)
0.56
0.05-1.1
0.34
0.3-0.5
0.9
0.1-3.0
0.02
0.005-0.04
Ca^^ (meq/L)
70
9.4-96
9.4
5.4-14.
32
12.0-54
1.0
0.8-1.6
N()3" (meq/L)
3.2
2.5-44
8.0
5.8-9.0
4.0
3.1-4.8
1.2
0.8-1.5***
Cr (meq/L)
2.4
0.6-4.3
0.6
0.5-1.1
0.5
0.3-0.9
0.2
0.09-0.7***
Cox (%)
0.31
0.2-0.5
2.6
3.4
3.1-3.7
7.5
6.4-8.8
N (%)
0.016
0.012-0.023
0.30
0.23-0.33
0.35
0.28-0.42
0.45
0.34-0.87
K(mg/10()g)
1.7
0.5-2.4
14
45
40-49
5.8
P(mg/100g)
0.23
0.2-0.3
0.79
8.4
7.4-9.2
0.096
0.09-0.13
Ca (%)
62
61-64
83
84
81-87
69
56-73
Mg (%)
34
32-36
15
12
10-14
24
19-36
K (%)
2
1.8
2.2
1.4-4.2
3.7
2.8-5.3
Na (%)
1
0.2
0.9
0.5-1.4
3.8
2.5-5.9
lKt(meq/100g)
1.6
2.9
52
51-55
Soil solution:
Soil:
Exchangeable bases:
2-3
W hygroscopic (%)
0.2
0.13-0.22
1.6
1.48-1.78
3.5
3.04-4.14
W field (%)
2.4
0.9-3.3
14
10-17
30
18-37
11.2 4.7 42
34-50
* The ratio of maximum supplies of living and dead ahoveground phytomass; ** results of one day measurements of July 2, 1990; *** results oflysimetric water (15 cm) analysis
• Very high NO3' activity and very high content of other ions in the liquid phase of sandy soils (Bugac) at very low humus, total N and P content compared to the soils of the Csaszartoltes site and the Khomutovskaya Steppe Reserve. • High degree of heterogeneity of the soil physico-chemical properties under grassland vegetation. The degree of heterogeneity for as PNO3, pK and pCa decreases with the structural
173 development of vegetation and diversification of species (see Table 28) in the Bugac Csaszartoltes - Khomutovskaya steppe direction (see Tables 83, 87 and 90). In the Khomutovskaya steppe in the rhizosphere of different plant species both the composition of the soil liquid phase (see Table 91) and conservative properties as humus content (Bystritskaya et al., 1978) differ significantly. • High compositional stability of the liquid phase of an ordinary chernozem in the Khomutovskaya steppe (see Table 84, 88 and 91 for comparison to 'date' factor). It is possible that the more developed soil adsorbing complex of the chernozem provides a permanent equilibrium composition of soil liquid phase, despite considerable change in soil moisture. • Correlation between seasonal changes in soil chemical composition with plant growth and dying off has been observed in all ecosystems. Humus and available N, K and P decrease at the beginning of active vegetation and increases by autumn (see also Table 94). Similar seasonal changes in soil chemical composition were observed for humus in a virgin chernozem (Gertsyg, 1959) and mobile N in arable chernozem (Godunov, 1973), for exchangeable and soluble K in podzols (Peterburgsky, 1973, Kulikova, 1978), and for total salt concentration in typical chernozem solutions (Churilina et al., 1979). • Sharp concentration increases of the soil liquid phase in periods of drought, is a special feature of the sandy semi-desert steppe (Bugac) (see also Table 86). • Daily dynamics of the soil liquid phase, manifested differently for the various ions and at different vegetation period, determined primarily by biological factors, i. e. secretion-absorption activities of plants and micro-organisms. For sandy poorly developed soils (Bugac) the range of daily dynamics is smaller than the spatial heterogeneity of pK, pCa, pH. In the Khomutovskaya steppe, daily dynamics often exceeds the spatial heterogeneity (Cv 0.8-7.1 and 5-6% respectively) even for a stable parameter as pH. • Discrepancies between daily courses of NOB" activity in the sandy low humic soil (Bugac) and ordinary chernozem in the Khomutovskaya steppe: in the first soil it reaches a maximum from 3 to 6 p.m., in the second soil, during early morning and late evening hours. In both soils the existence of daily pNOs dynamics is rehable, whereas there are facts which prove its biologically determined nature (see Tables 84, 95, Fig. 19 and 30). Chapter 7 gives due consideration to this problem.
174 • A clear pattern in the daily dynamics of calcium ion activity in the soil liquid phase of all ecosystems studied: an increase in Ca activity from morning hours towards the second half of the day, and a decrease in the late evening. • Relatively low daily variability of Eh in grassland ecosystems (10-50 mV) compared to its spatial heterogeneity and variability during the vegetation period. The considered patterns of current soil formation processes allow us to conclude on the similarity of soil the "chernozem type" processes under the steppe vegetation of Salvio-Festucetum rupicolae stipetosum pannonicum (Csaszartoltes) and Salvio-Festucetum stipetosum ponticum (Khomutovskaya steppe) communities.
175
CHAPTER 6. MATERIAL AND ENERGY EXCHANGE IN ECOSYSTEMS
6. 1. SOIL CARBONATE EQUILIBRIUM
Carbonate equilibrium reflects a summing-up of several environmental processes in the biosphere. They regulate the composition of the atmosphere, natural waters and deposits. Soil carbonate equilibrium deals with many processes at the ecosystem level: soil air formation and emissions of CO2 into the atmosphere, dissolving of salts or crystalUsation; migration of different substances along the soil profile. The effect of soil alkalinization during irrigation is also explained by carbonate equilibrium. Carbonate equilibrium research in soils is most difficult in a methodical sense since it may be easily changed as it involves the components of all soil phases. Secondly, it is necessary to know the activity values in order to describe it thermodynamically, which involves the long and approximate estimation of activity coefficients. Resulting from these difficulties, an opinion among researches appeared that in natural waters and soil solutions in the presence of solid phase the Ca concentration may be higher by a ratio from 5 to 10 than in a saturated solution (Alekhin & Lyakhin, 1968; Minkin, Endovitsky, 1978). We consider this point of view doubtful, since even in pure solutions in the absence of solid phase crystaUisation centres the metastable saturation border of the CaC03-H20-C02 system does not exceed a threefold solubility rate for calcium bicarbonate; and at solid phase addition spontaneous crystallisation takes place (Tovbin, Kononenko, 1954).
6 1 1 ASSESSMENT OF CARBON EQUILIBRIUM STATUS
To elucidate the status of soil carbonate equilibrium, we have attempted to elaborate an in situ measurement-based technique (Snakin & Zavizion, 1979). Analysis of the formation processes leads to the following equation, which describes the equilibrium status between solid CaCOs, and soil solution and gas phase CO2 (Garrels & Christ, 1965):
2pH - pCa - PCO2 = p KH2C03 + pKco2 + pKncos - pKcacos,
(22)
176 the negative logarithms of H" and Ca^^ ions activity and CO2 partial pressure are in the left side of the equation, while negative logarithms of equilibrium constants are in the right-hand side. When using pure solutions (soil solutions, water extracts) it is more appropriate to use a different equation:
pH - pCa - pHCOs = PKHCO3 - pKcaco3
(23)
Let us designate the right-hand side of equations 22 and 23 comprised of standard values as AT and Bj. Substituting experimental data into equation (22) or (23) gives A or B values which are lower than AT and BT respectively, the solution is not saturated in relation to calcium carbonate. If A = AT or B = BT, solution is saturated. At A > AT or B > BT the solution is oversaturated. For greater accuracy, it is necessary to take into account the temperature of the soil solution or water. Table 103 shows AT and BT values estimated by equilibrium constants at different temperatures based on the equations of Zavodnov (1965).
Table 103 AT and BT values at different temperatures Value
t(«C)
10
12
15
17
18
20
22
25
30
Ax
9.92
9.90
9.84
9.83
9.81
9.78
9.77
9.72
9.69
Br
2.18
2.14
2.08
2.05
2.03
2.00
1.97
1.91
1.84
For the analysis of carbonate-calcium equilibrium, it is necessary to know three parameters (pH, pCa, PCO2 or pH, pCa, pHCOs) and the temperature. For carbonate equilibrium estimations in undisturbed soils, pH, pCa and pCOi values were used. pH and pCa obtained from in situ measurements, CO2 content were measured in 200 cm soil air samples collected in brass tubes at the respective depth (Matskevich, 1950). Estimation of CO2 was by titration of a 0.05 N barium hydroxide aliquote by 0.05 N sulphuric acid after it absorbed a sample of soil air in the presence of phenolphtalein and a reference sample, which had the same aliquote of Ba(0H)2 solution without soil air absorption. For the analysis of water extracts and ethanol-replaced soil solutions pH, pCa and pHCOs were used. HCO3' was measured as total alkalinity of the soil solutions and by titration with sulphuric acid in the presence of methyl orange.
177 According to Minkin and Andreev (1985) carbonate alkalinity is 85-95% of the total alkalinity of soil solutions. Therefore, total alkalinity may characterise carbonate alkalinity. There is a difference between carbonate equilibrium status in ordinary chernozem based on data of analyses of water extracts, ethanol-replaced soil solutions and in situ measurements (Table 104). The system is not saturated in the water extracts. The replaced soil solution is oversaturated, which is probably due to losses in CO2 during preparation of soil solution and errors in measurements of concentration and in estimations of the activity coefficient by Debye-Huckel equation of carbonate-ions. According to the in situ measurement in undisturbed soil, the liquid phase is close to saturation by CaCOs. Therefore, neither the extracted soil solutions nor water extracts from fresh samples can give the real status of soil carbonate equilibrium under natural conditions.
Table 104 Estimation of carbonate equilibrium status in an ordinary chernozem by water extracts, replaced soil solutions and in situ measurements Analytical
Sampling
technique
depth (cm)
Water extract
10-20
Soil solution
10-20
In situ
15
pH
pCa
pHCOj
7.6±0.1*
3.810.1
8.210.1
2.710.1
6.910.1
1.810.2
-
pCOi
BT
B
3.2410.03
1.91
0.610.2
2.4410.03
1.91
3.110.2
2.310.2 -
-
AT
1.81
A
9.710.5
measurements * Confidence borders at P <0.05.
Table 105 shows the carbonate-calcium status in various soils, based on in situ measurement. High degree of non-saturation of the carbonate-calcium system for non-carbonate soils, e. g. forest and sod-podzoUc was observed. The liquid phase of carbonate soils is always undersaturated by CaCOs in the upper layer, which is imposed to a maximum impact of precipitation, and biologically active components of through acidic excretions of plants and microorganisms, and CO2 diffusion into the atmosphere. The degree of saturation increases downwards in these soils with a parallel growth of carbonate content. In horizons where 10% HCl causes effervescence there is no difference between AT and A. In none of the soil A exceeded AT significantly, which proves the absence of oversaturation by CaCOs.
178 Table 105 Carbonate system in various soil types Soil
Depth (cm)
t("C)
W (%) CO2 carb. (%)
PH
pCa
PCO2
AT
A
Southern chernozem virgin land (mixed fescue steppe)
arable irrigated
arable non-irrigated
7
15
26
1.14
6.7
2.3
3.0
9.84
7.5±0.3
20
11
24
1.23
6.6
2.9
2.9
9.90
7.4±0.4
7
13
23
1.41
6.9
2.4
3.0
9.88
8.4 ±0.3
20
11
23
1.54
6.7
2.7
2.9
9.90
7.8 ±0.3
7
13
24
1.36
6.0
2.4
3.0
9.88
6.6 ±0.3
20
11
27
1.37
6.2
2.6
2.9
9.90
6.9 ±0.4
50
10
25
3.61
7.7
2.9
2.9
9.92
9.6 ±0.5
Ordinary chernozem virgin land (creeping grass
7
12
24
2.11
6.9
2.2
3.1
9.90
8.5 ±0.4
community)
50
9
24
4.84
7.5
2.9
2.8
9.94
9.3 ±0.5
virgin land (mixed fescue stipa
7
12
26
2.38
6.9
2.0
3.0
9.90
8.8 ±0.8
commimity)
35
11
28
2.68
7.6
2.3
2.8
9.90
10.1 ±0.5
arable
7
14
22
1.67
7.3
2.0
2.9
9.86
9.7 ±0.4
virgin land (steppe)
7
14
27
1.85
7.1
2.6
3.4
9.86
8.2 ±0.3
virgin land (oak grove)
7
14
37
1.50
6.3
2.8
3.1
9.86
6.7 ±0.5
arable
7
15
21
1.50
6.7
2.6
3.4
9.84
7.4 ±0.5
7
9
40
3.8
3.0
9.94
5.4 ±0.3
9
24
5.8
3.6
3.0
9.94
5.0 ±0.4
50
8
22
5.8
3.8
2.9
9.96
4.7 ±0.3
7
16
18
-
6.1
20
5.4
2.6
3.0
9.83
5.2 ±0.4
7
12
34
3.40
6.6
2.2
2.5
9.90
8.5 ±0.3
50
12
29
3.40
7.2
2.3
1.8
9.90
10.3 ±0.5
7
10
20
5.5
2.9
3.2
2.92
4.9 ±0.6
Typical chernozem
Grey forest forest area
arable area .Alluvial sod-meadow calcareous
Sod-podzolic arable
The liquid phase of non-irrigated southern chernozem is less saturated by CaCOs compared to the irrigated soils. This has been proven by laboratory experiments carried out on irrigated and non-irrigated typical Caucasus chernozem, in which the A values comprised 9.3 and 8.6 at AT = 9.78. Therefore, the method obtains true data on carbonate equilibrium with maximum accuracy as compared to those obtained from water extracts and replaced soil solutions. During application of this method a significant oversaturation of soil liquid phase by calcium carbonate was never found.
6 1 2 ATMOSPHERIC CO2 AND SOIL LIQUID PHASE
lonometry is a convenient method for clarification of the influence of various factors on the soil liquid phase composition. Two series of model experiments were carried out (Snakin, et al,
179 1987a) in order to determine the influence of the partial pressure of CO2 in soil air on the soil liquid phase composition (H^, Ca^^, Na"^ activity and Eh).
Fig. 34. Cross-section of experimental vessel: 1 - outflow of gravitational water; 2 - grid; 3 soil; 4 - lid with electrodes input holes; 5 -inflow of gas mixture
In the first series of experiments a vessel of 1 1 was used (Fig. 34) filled with the following soils: 1) virgin ordinary chernozem of the Priazov region, 0-20 cm depth; 2) alluvial sod-meadow calcareous soil, 0-20 cm depth; 3) grey forest soil, 0-20 cm depth. In the second series of experiments 1 litre vessels of similar design were placed in a climate chamber andfilledby: 1) subcaucasus ordinary deep chernozem with mycelium carbonates from a non-irrigated area, 050 cm depth; 2) subcaucasus ordinary deep chernozem solonetzic with mycelium carbonates from an irrigated area, 0-50 cm depth; 3) heavy loamy sod-podzolic soil, 0-10 cm depth. To minimise changes in moisture status, soil air was enriched by water vapour and mixed with CO2 in the necessary proportion by respective indications of rotameters. Carbon dioxide content was controlled by periodical 100 cm^ gas mixture samples, treated by 0.05 N Ba(0H)2 absorption and titration by 0.05 sulphuric acid in presence of phenolephtalein. Measurements of ion activities in soils were carried out by the following electrodes: EM-Ca-01, ESL-41G-05 and ESL-43-07 (pH), ESL-51-11 (Na), ETP-02 (Eh), EVL-1M3 (silver/silver chloride saturated
180 reference electrode - see Table 15). For comparison soil solution was replaced by ethanol from soil samples. Table 106 gives some properties of the studied soils.
Table 106. Properties of the experimentally studied soils Humus (C)
Water
CO2 from
Water extract (meq/100 g)
(%)
extract pH
carbonates
Ca^^
Na^
Non-imgated Subcaucasus chernozem
2.18
7.4
89
0.67
0.15
0.73
Irrigated Subcaucasus chernozem
1.98
7.6
39
0.44
1.62
0.86
Ordinary chernozem of the Priazov region
3.9
7.7
44
1.09
0.05
0.73
Alluvial sod-meadow calcareous soil
1.39
7.8
78
1.64
0.09
0.41
Grey forest soil
1.21
6.0
16
0.14
0.06
0.09
Sod-podzolic soil
1.15
5.4
12
0.20
0.05
0.08
Soil type
HCO3'
(meq/lOOg)
Particular attention should be paid to data reliability, since soil as well as soil samples tend to be under the influence of complicated processes, which have different directions. This is explained by microbiological activities, the change in soil moisture during the experiment, and possibly mixing with KCl from the reference electrode especially in experiments of longer duration, which occurred in the given experiment, and the subsequent replacement of cations from soil adsorbing complex by K. Significant changes in the soil liquid phase composition may be possible. In the second series of experiments, where the pH value of soil liquid phase was continuously recorded, ion-selective electrodes (ISE) showed a dramatic increase in H\ Ca^^ and Na^ ions. This increase was proven by analysis of ethanol replaced soil solutions^^ (Table 107). Therefore, we have failed to verify the data when repeating the treatment with carbon dioxide in the second series of experiments, that is why in the ftirther discussion of results primary attention will be paid to the trends in the processes, not to their quantitative side. Stable values of soil liquid phase parameters at changing CO2 pressure under the experimental conditions was rapid (Table 108). After CO2 concentration increased within 3 hours reliable indications were obtained. A more detailed study of the process showed that pH equilibrium was recorded in 30-40 min as CO2 partial pressure increased (Fig. 35). When decreasing CO2 pressure the period of obtaining a stable pH value took 7-15 h. Readings of the
"^ The fact that an increase in concentration of Ca^^, Mg^^ and H^ took place through KCl input was proven by a significant increase in CI" concentration, not equivalent to that of K^ ions concentration.
181 ion-selective electrodes were also obtained after a permanent pH value of the liquid phase had been achieved.
Table 107 The composition of soil solutions, replaced before and after the experiment (meq/L) Soil type
Ca^^
pH
Na^
K^
Mg^^
cr
before
after
before
after
before
after
before
after
before
after
before
after
SA3
T52
2l6
168
25l
90
058
23^7
Til
87
13
277
Non-imgated 7.88
7.68
19.3
180
10.3
25
0.55
6.0
0.75
10.6
3.8
175
4.55
2.53
59.2
1.07
10.4
0.56
5.0
1.09
4.5
2.0
70
Irrigated chernozem
chernozem Sod-podzolic
5.94
soil
Table 108 Changes in pH and pCa in the soil Hquid phase at PCO2 changes in the gas phase Soil type
Parameter Time from the begiiming of the experinent 0
3h
1 day
2 days
3 days
1.13
1.3
1.13
4 days
Ordinary chernozem
PCO2
3.8
1.13
of the Priazov region
pH
7.010.1
6.5±0.1 6.5±0.2 6.6±0.1 6.610.1
6.710.1
pCa
2.010.1
1.910.2 1.910.1 1.910.1 2.110.2
2.210.2
Alluvial sod-meadow PCO2
3.8
1.07
calcareous soil
pH
7.610.1 6.710.1 6.710.1 6.710.1 6.710.1 -
pCa
1.910.1
1.810.2 1.910.1 1.810.1
1.810.1 -
PCO2
3.8
1.22
1.22
pH
4.810.2 4.710.1 4.710.1 4.710.2 4.810.1 -
pCa
2.410.1 2.510.2 2.610.2 2.410.3 2.510.2 -
Grey forest soil
1.07
1.22
1.07
1.22
1.13
1.07
Correlation between the soil Uquid phase composition and soil air composition is also illustrated in Table 108. The pH is most sensible to CO2 content change in the gas phase. Correlation coefficients of pC02-pH for an ordinary chernozem of the Priazov region and for sodmeadow calcareous soil were almost the same (r=0.71). Correlation was not estabhshed for the grey forest soil which has an insignificant amount of CaCOs. For all soils studied correlation was absent between Ca activity in the Uquid phase and CO2 content.
182
4h
-I—I r I 6.6 6.1 6.8 pH
I L 6.6 6.7 6.8
pH
Fig. 35. The pH change of the liquid phase in a chernozem at increasing (a) and decreasing (b) of CO 2 concentration from 5 to 0.04% in the incoming gas mixture
pCO.
pH
4 h
O—-O
0—0
O
>—O—O
0
0-0—O
36
40
3
pCa
4
8 12
Time (h)
Fig. 36. The influence of CO2 concentration in the input gas mixture on Eh, pH, pCa andpNa in the liquid phase of irrigated (1) and non-irrigated (2) chernozems and sod-podzolic soil (3)
Similar results were obtained in the second series of experiments (Fig. 36). Ordinary Subcaucasus chernozem had close correlation between pH and pC02 values. Correlation
183 coefficient equals 0.95 for irrigated and 0.84 for non-irrigated chernozem at P=0.99. A ten-fold increase in CO2 concentration in these soils leads to a pH decrease by 0.4-0.5 units. It is worth to note the insignificant error in the pH electrodes. For sod-podzolic soil, correlation between pH and PCO2 values is weak (r=0.3). No PCO2 dependence of the change in Cd?^ activity was found. No reliable correlation between pCOi in the gas phase and Na^ ion activity in the liquid phase of the soils studied was established. The pC02 and Eh for irrigated and non-irrigated chernozems were characterised by a close reverse correlation (r = -0.80). Absence of such correlation for sod-podzolic soiP proves that changes in hydrogen ions activity is the most probable reason for the changes in the Eh values, according to the following equation: Eh = E^^A-ln}^-[H^Y.
(24)
[Red] ^ J
where y is stoechiometric coefficient in the total oxidation-reduction reaction; [Ox] and [Red] are respective activities of oxidized and reduced forms of substances, which take part in the process. This is proven by the fact that CO2 concentration increase (consequently, oxygen partial pressure decrease) should decrease Eh value, and not increase, as noted in Fig. 36. Our results for the Subcaucasus ordinary chernozem showed that with a pH unit decrease due to increasing CO2 concentration. Eh increases by 32-44 mV, whereas higher values are characteristic in irrigated soils. Similar values for the Eh-pH correlation curve (39 mV for chernozem and 44.4 mV for sodpodzolic soil) were obtained at artificial soil acidification (Gorshkova & Orlov, 1981). The absence of relevant changes in pH in the liquid phase of some soils and stability of Ca activity for all soil types have a simple explanation. In theory when CO2 increases in the soil air, a pH decrease should take place. However, this decrease has a certain limit. For the H20iiquid - C02gas system, CO2 concentration increase from 0.04 to 50% should be accompanied by water acidification^"^ at pH from 5.6 to 4.6 with a temperature t of 20 ^C. If the pH of the liquid phase is close to or below these values, then no significant acidification through CO2 is likely to take place, which was observed in sod-podzolic and grey forest soils. Estimations for the CaCOssoiid - H20iiquid - C02gas system showed that CO2 concentration increases by an order in the gas phase and is accompanied by acidification of 0.7 pH units. However, in the carbonated soils this change showed lower values (0.4-0.5) which can be explained by the buffer capacity of soils. ^^ Grechin and Kurlikova (1962) also found no correlation between CO2 concentration increase from 15 to 30% and the change in Eh in sod-podzolic soil, they therefore considered the O2 concentration as the decisive factor. In these and fiirther estimations use is made of the equations and constants based on the work by Drever (1982) at a temperature of 20 ^C.
184 Calcium ions activity in the soil liquid phase is an important indicator of carbonate equilibrium. Under natural conditions, changes in this parameter are often associated with changes in CO2 concentration according to the equation: CaCOssoHd + C02gas + H20Hquid<^ C a ( H C 0 3 ) 2 .
In theory, Ca activity should increase from 2.8 to 6.0 meq/L at a CO2 concentration increase from 0.5 to 5% in the presence of CaCOs in the solid phase. However, Ca activity is much higher (see Table 105, 108, Fig. 36), and we did not observe a significant aca value increase since activity growth was at the level of instrumental error (< 19%) for bivalent ions. The results showed that CO2 in the soil solution is not a decisive factor in the pH of the liquid phase of acidic soils and does not determine Ca activity to a significant extent. Data in Table 107 prove that the level of Ca activity influences the balance of monovalent ions in soils which replace Ca from the soil adsorption complex. These conclusions do not prove that higher CO2 content in the soil air is the reason for Ca losses of irrigated soils compared to non-irrigated soils (Buyanovsky, 1974; Zborishuk, 1979). Calcium replacement from the soil adsorbing complex by salts of irrigating water and subsequent leaching is a more probable reason for decalcification of irrigated soils.
6.2. SOIL LIQUID PHASE OXIDATION AND pH AS INDICES OF ECOSYSTEM FUNCTIONING
From a physico-chemical point of view, the redox reactions of photosynthesis, respiration, nutrition, decomposition are the basis for life processes. The redox potential may be a quantitative characteristic for such processes and can be used as an indicator of the state of natural objects. The redox potential is a ftinction of oxidised and reduced activity in solution in accordance with equation (24), which links Eh to the pH, which is another important property of soilecological processes. There are grounds to believe that Eh measured directly in soil by electrodes, reflects the proportion between oxidised and reduced substances in the soil liquid phase and characterise the oxidation rate of a system. That is why it is often called the oxidation potential. However, according to others, soil Eh is under the direct influence of gas and solid soil phases. Consequently, one should be careftil when interpreting Eh values. In one of our experiments, we observed the change in Eh depending on air composition changes, especially
185 hydrogen (Table 109). In most cases we observed significant differences between Eh, measured in soil, and Eh, measured in replaced soil solutions. For example, in an ordinary chernozem under laboratory conditions we obtained: 461±8 and 395+15 mV. This difference, as in the case of pH value (see Section 4.5) may be explained by changes in dissolved quantity of different gases and by the shift in the ratio of oxidized and reduced forms in the soil solutions. Hydrogen when penetrating into soil dissolves in the liquid phase, thus changing the oxidation rate of dissolved substances. Table 109 The influence of gas composition on soil Eh at short-term (10-15 min) expositions Input gas
Electrode types ETPL; ETP-02
EO-01
Platinum
Atmospheric air
461 ±8
329±3
400
Carbon dioxide
461 ±8
332±23
-
Oxygen
463±9
326±25
414
Hydrogen
-60±14
262±66
-227
The speed of change in the ratio of oxidized and reduced forms in the soil liquid phase is high. Experiments by Serdobolsky (1953) proved the significant change in Eh in soil samples within 10-30 min after extraction from the soil. The transformation of Fe^^ substances into soluble and exchangeable forms takes place within an hour, while complex forms appear within 4 hours (Xieming, 1985).
6 2 1 OXIDATION-REDUCTION IN SOILS Our first Eh in situ measurements carried out under virgin land steppe vegetation, revealed a diurnal cyclic character for its values (Snakin et al., 1977). The Eh reached its maximum value during daytime, and minimum at night (Fig. 37). Daily changes were observed in other soil types as well (see Fig. 28, 29, 31, 32, 33). The in situ pH value of the soil liquid phase is subject to daily variations. The cychc character of the pH has to do with the influence of CO2 in the soil air, which concentration tends to increase during daytime (Snakin & Zavizion, 1979), and is partially explained by plants root secretions which contains various organic acids. Daily changes of Eh may hardly be linked to changes in soil moisture, since no regular daily variations were observed (see Section 7.2).
186
' ' ' ' ' ' • ' « ' • ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' 12 18 0 6 12 12 18 0 12 18 0 Time(h)
Fig. 37. Daily dynamics of temperature, Eh andpH of an ordinary virgin chernozem under steppe vegetation.
Comparison of Eh and soil temperature did not reveal a mutual correlation, despite the same pattern of changes during the day. Investigation in the soil temperature dependence of Eh in the laboratory showed a reverse pattern with an increase of Eh at soil cooling (see Section 4.4). In our opinion, the daily Eh variability is determined by the activities of the living components. This is primarily due to the activities of higher plants and the closely related microorganisms^^ which was proven by laboratory experiments. During the experiments turning on the lights led to an increase in Eh and vice versa (Fig. 38), which is possibly determined by changes in the photosynthetic activity of plants. A laboratory container with a 6-months old grass mixture is not an adequate model of an ecosystem. Therefore, the quantitative dependence of soil Eh on changes of light intensity observed in laboratory (see Fig. 37 and 38 for comparison).
"'^ It is possible that the alteration of light and dark contributes to daily Eh change. See work by (Cheng & Pesant, (1984) for the influence on exchangeable and easily reducable Mn content in soil.
187 Eh (ImV)
On 650 h
Off
On
i i
I i i
irrigation
i i i i
W=50%
600 h
550
8
^^
21' 8
12
10
14
20
16
•y
/ :16 17 Time (h)
Fig. 38. Influence of light and irrigation on soil Eh. Laboratory experiment, ordinary chernozem, sowing of Vicia-Avena mixture; On - light on. Off- lights off.; W- soil moisture
Eh (mV) 640 1
^^^x—
'^'—X^
620 3
'^^Z^\^
600
j
.580
1 - March-April; 2 - May, 3 - June-July; 4 - July-August; 5 - November, 1977: 6 - April, 1978
^
" " ^ ^ ^
560 5/10
i
J
L ^
12
1
1
L
15
18
21 Time (h)
Fig. 39. Average daily dynamics of Eh registered by
ETP-02 electrodes in an ordinary
chernozem under virgin land vegetation at different time of the vegetation period
Biological determination of the phenomena is supported by the analysis of the diurnal variability at different times in the vegetation period (Fig. 39). Daily variability is distinct in April, May and June, i. e. the period of intensive growth of vegetation and less distinctly in August and November when the growth of vegetation stops. In November, the transfer of the minimal Eh was observed in the morning due to the shortening of the day.
Further investigations of Eh dynamics on young sandy soils (Bugac site) reveal a different pattern: a fall in Eh during daytime (see Fig. 31). Such Eh change was caused by the direct influence of temperature (see Fig. 18) while biological factors affected to a lesser extent.
7.0 h
65
g
20
^ 10 40 £ 30 20|620 1-
^600 E 580
560 III
IV V
VI VII VIII IX X 1977
XI
XII I
IV V 1978
Fig. 40. Seasonal changes in temperature, Eh, soil moisture and pH of liquid phase of ordinary chernozem under virgin steppe vegetation (7-10 cm depth)
Interrelation between Eh and the activities of living components is obviously through changes in Eh value (Fig. 40). From spring to autumn, a systematic decrease in Eh in an ordinary chernozem took place. Similar results were obtained for sod-podzolic soils under fir-tree forests: in spring a slight increase in Eh was observed, which was due to high content of dissolved O2 in melting water. The lowest Eh values was observed in autumn. In our opinion, this has to do with the maximum input of organic matter through litter and dead plant parts (Ivakhnenko & Romanova, 1979). However, in agricultural soils with the use of fertilisers, herbicides, drying out, irrigation, plowing, it may pose a significant impact on the Eh (Smith et al., 1978), and its dynamics may be different (Tikhonenko, 1977). The views expressed by many authors on the decisive influence of mineral composition of soil on its Eh value seems questionable. Living matter poses a significant impact on the mineral
189 components primarily due to absorptive-secretive activities of plants, and this has an effect on the redox potential. The relation between Eh and moisture is complicated and differs from the generally accepted view (Vozbudskaya, 1968) which is a reverse soil moisture dependence of Eh. Our observations showed that after rainfall with an increase in soil moisture from 21.2 to 34.5%, Eh decreased but recovered to the initial state after two sunny days, although moisture decreased only by 2.4% (see Fig. 16). In laboratory experiments, the fall in Eh after irrigation was rapidly compensated (see Fig. 38). However, the short-term nature of Eh decrease after the rain has been also found in sod-podzohc soil (Orlov & Jindil, 1974). In field moist soils no significant correlation between moisture and Eh was observed (Poddubny, 1959; Inisheva, 1977). Our data showed that continuous irrigation of ordinary and southern chernozems in the territory of the Stavropol region lead to a decrease in Eh by 40-100 mV compared to the neighbouring non-irrigated soils. Analysing the influence of moisture on Eh moisture regime should be taken into account. The other components of soil liquid phase, including pH (see Fig. 28, 30, 32, 33, 40) are also subject to diurnal and seasonal fluctuation. Equation (24) describes the relation between pH and Eh values. According to the equation, the correlation is either negative or absent if H^ is not involved in chemical reaction. The relation between Eh and pH of the soil solution is complex (Stepniewska, 1987). Formal estimation of correlation coefficient of conjugate changes in these values in a daily cycle, turns out that a close (r = -0.75) negative correlation exists for the first two periods (see Fig. 39) and an absence of correlation for the fifth period (r = 0.05). A detailed analysis on seasonal variability reveals a positive correlation (r = 0.92). The daily dynamics of Eh and pH values is probably determined by the same factors^^, while there are various factors affecting their changes in the seasonal cycle. A most possible reason for daily changes in Eh and pH is the activity of the ecosystem Hving components, particularly secretions of CO2, organic acids and other substances into the soil liquid phase. This assumption is proven by the pH results by means of antimony electrodes (Schaller & Fischer, 1981), which showed that close to the root surface the daily pH changes are larger (Fig. 41).
"'^ Where the biological factor is weak (see Fig. 29) the Eh-pH correlation is negative.
190
24
12
Hours
Fig. 41. Daily changes in soil pH values under peanut in direct contact (1) and at a distance of several mm from the roots (2) (according to Schaller and Fischer, 1981).
In the seasonal course the Eh may be determined by biological factors, while soil solutions acidification in autumn is caused by acid rain (Bystritskaya et al., 1981). In an experiment on grey forest soil of grass mixture (clover+timothy grass+fescue grass), we investigated the influence of herbicides on soil Eh. Herbicides applied in small amount did not affect the chemical composition of soil, and, the Eh value. Treatment of plants by symasine and region killed the plants and increased soil Eh value from 520 mV to 565 mV in two days. Such change in Eh is related to the death of plants, and to the suppression of micro-organism activities in soil which usually lowers the Eh. The above experiments show that the use of pesticides affects the value of Eh. Since there have been almost no simple criteria for pesticides norming with allowance made for their phytotoxicity and their influence on soil biota and biochemical processes (Sokolov 8c Strekozov, 1976), the value of soil Eh may proposed as a criteria. The efficiency of the Eh should be determined in comparison with a range of toxicological, hygienic and other methods of assessment, in particular with soil respiration, chemical and biological monitoring like analysis of pollutants residuum, estimations for suppression or death of organisms, weakening of reproductive function, etc. Due to the dynamic nature of the soil redox regime, a reliable comparison of different soils by their Eh value is a difficult task. Nevertheless, Table 110 presents mean daily values of Eh measured in a relatively short period. The results show a close negative correlation between Eh and pH values of soil liquid phase, and a dramatic increase in oxidation processes under forest
191 vegetation compared to steppe vegetation. High Eh values were registered in podzolic soils, and in cold mountainous podzols (Snakin, 1985).
Table 110 Eh and pH for virgin and arable lands (A horizon, depth 5-8 cm) Soil type
Date
Eh (mV)
(1982)
virgin land
arable*
virgin land
arable*
Southern chernozem
May, 5-9
677±40
667±23
6.36+14
6.91±0.07
(Askania-Nova Reserve)
Ordinary chernozem
May, 16-20
657+34
(Khomutovskaya Steppe Reserve)
Typical chernozem
May, 25-27
(Centralnochemozemny Reserve)
pH
609±26
6.74+0.15
672+33
6.03±0.29
572±29
6.86±0.28
637+15
7.35±0.31
684±23
6.67+0.38 6.30+0.42
697±11
under steppe
622±35
7.08±0.12
under forest
661±39
6.26±0.62
Grey forest soil
7.38±0.22
May, 11-14
700±0,22
724+36
6.02±0.22
5.38+0.59
June, 26-30
815±45
572+16
3.92+0.20
6.10+0.07
(Malino forest area)
Podzolic soil (Central-forest Reserve) * Under various crops
6 2 2 THE Eh-pH GRAPH
The Eh as an indicator of the ratio of oxidized and reduced forms substances in soils, and the pH may be used as indicators of the chemical status of soils. It is possible to develop a soil classification based on their reaction, the presence and the position of gravitational moisture horizon and the related redox properties (Glazovskaya, 1972). A concrete Eh-pH scale for determination of a definite soil formation type have not yet been developed. Due to failing to obtain reproducible data on Eh and pH, some authors come to the conclusion that the latter does not reflect the true situation and do not justify their research. Some authors still accept the use of rH concept, against the recommendations of the author of this concept (Clark, 1928). Some
192 scepticism remains in relation to in situ field analysis of soils by means of indifferent and ionselective electrodes, although it is known that separation of soil sample from ecosystem leads to a change in Eh (Serdobolsky, 1953) and soils reaction (Snakin & Zavizion, 1979). We agree with Baas-Becking et al., (1960) that from all the known methods only the direct insert of electrodes into soils is considered reliable. The goal of our research was to find the range of Eh and pH values for certain soil types on the basis of/>/ situ measurement in ploughed horizons. The work was carried out in April before braird and in August 1989 after cropping on the key plots of Kamensky district of the Voronezh region in quasi simultaneous mode with air photographic survey based on soil maps. We used only data of fields without vegetation, for two reasons: 1) the intention to obtain information, disregarding the influence of vegetation (consequently, with reduced variability); 2) the use of aerial photos for soil diagnostics. The results showed the following Eh and pH values for the investigated subtypes of chernozems: ordinary chernozem Eh - 480 to 560 mV; pH - 7.2 to 7.9; typical chernozem Eh 560 to 660 mV; pH - 5.3 to 6.4; leached chernozem Eh - 550 to 610 mV; pH - 5.6 to 6.6 (Table 111 and Fig. 42). Eh(mV) N.
650-
0
>.S. *s
y >K
0
/'
''^jg
y
600y <^
A
> s.
^H.
H^^^ ®
550y
N
y
0
- ordinary chernozem
A, - leached chernozem 500-
g] B 1
,
,
s.
^ '^
- typical chernozem - transitional between typical and ordinary chernozem , , ,
,
, 8
pH
Fig. 42. The ordination of various subtypes of arable chernozems according to Eh-pH coordinates
193 The Eh values of the soils can be arranged as follows: typical chernozem > leached chernozem > ordinary chernozem > solonetz chernozem, and for the pH a reverse array was found: soionetz chernozem > ordinary chernozem > leached chernozem > typical chernozem. The Eh and pH borders delimited in the maps of the Agrochimservice, do not always reveal the real pattern. Based on the position in the Eh-pH graph, we corrected the identifications of chernozem subtypes in six cases (see Table 111). This correction was confirmed by aerial photographs, reUef maps and thematic processing of scanned information. Sample No. 116 and 13 were the only exceptions, since correction brought ambiguous results. This may be explained by soil heterogeneity in these plots and by extreme conditions for the electrodes and ionometer during the investigation period (soil temperature 2-4 ^C, air temperature 0-5 ^C). Table 111 Values of Eh and pH for various arable soils of the Chernozem zone by in situ measurements Profile
Soil type according to Agrochimservice map (Snakin & Gurov,
Eh
number
1992)
498
High depth residuum solonetz chernozem
308 A
Ordinary low humic chernozem, medium-, highly eroded residuum
Corrected soil type
pH
7*
CT
Cv
507
10
2.0
478
17
3.6
a
Cv
8.17
0.06
0.7
7.80
0.09
2.2
-
X
salinized 30
Ordinary low humic imperfectly eroded carbonate chernozem
528
16
3.0
7.78
0.02
0.3
-
799
Typical low humic imperfectly eroded carbonate chernozem
553
14
2.5
7.88
0.02
0.3
Ominary chernozem
116
Typical medium humic glay medium depth chernozem
535
7
1.3
7.50
0.02
0.3
-„-
1
Ordinary low depth shortened eroded chernozem
546
8
1.5
7.20
0.02
0.3
-
377
(.Ordinary medium eroded residuum salinized chernozem
562
6
1.1
7.24
0.09
1.2
2B
Ordinary chernozem
552
6
1.1
7.31
0.04
0.6
2
Chernozem transitional between ordinary and typical
558
10
1.8
6.77
0.08
1.2 1.9
2A
Typical chernozem transitional with ordinary chernozem
572
15
2.6
6.73
0.13
104
Typical low humic medium depth loamy chernozem
614
15
2.4
5.74
0.08
1.4
-
13
Ordinary low humic high residual-salinized chernozem
620
11
1.8
6.39
0.06
0.9
Typical chernozem
110
Ordinary lowhumic residual-salinized chernozem
600
14
2.3
6.23
0.01
0.2
-»-
3
Typical low humic heavy loamy medium depth chernozem
617
20
3.2
5.80
0.17
2.9
320
Ordinary low humic imperfectly eroded carbonate chernozem
623
8
1.3
5.32
0.13
4.4
Typical chernozem
320 A
A Same, but medium eroded at the slopes
659
2
0.3
5.37
0.05
0.9
116 A
A Typical medium humic medium depth loamy chernozem
615
30
4.9
5.53
0.15
2.7
-»-
116 B
Same type
622
13
2.1
5.32
0.07
1.3
481
Leached low humic surface-low loamy medium eroded chernozem
609
20
3.3
6.62
0.14
2.1
114
Low humic leached loamy chernozem
578
46
8.0
6.11
0.14
2.3
411
Low humic low eroded heavy loamy leached chernozem
557
16
2.9
5.60
0.10
1.8
* X - mean value; a - mean-square deviation; Cv - variability coefficient, %
The above range of Eh and pH may be used to identify the various subtypes of arable chernozems and to determine their taxonomy. It is natural that there are no distinct borders
194 between various chernozems. The transition from one subtype to another takes place gradually. There are questionable cases (see profiles 2, 2 A), when it is hard to determine a concrete subtype. There are additional soil formation processes, which contribute to Eh and pH. For example, in the case of profile 13 and 110 an increased pH value (6.2-6.4) compared to the other profiles, is most likely due to residual salinization (see also profile 498). The dynamics of the indicators and under the influence of various factors is another reason, which determines the wide range of the above Eh and pH values for different types of chernozems. In our analysis, we used data measured at different times (April, August), which differed significantly in moisture and temperature conditions. However, the data on virgin sod-calcareous soil under mixed fescue stipa, adjacent to the investigated area, revealed stable Eh and pH values. The seasonal dynamics of physical and chemical properties of virgin ordinary chernozems in the Priazov region have shown that the variability for Eh comprised 30-40 mV, and for pH about 0.5 units (see Fig. 40). This is much less compared to the range in arable lands. To eliminate the daily dynamics of these values we carried out measurements from 10 a.m. to 4 p.m. at minimal changes in soil liquid phase composition (see Section 5.3). The influence of soil moisture was insignificant. After heavy rain Eh of ordinary virgin chernozems reached the initial value within a day (see Fig. 16). The pH value of ordinary virgin chernozem recovers in 5-6 h after precipitation. In grey forest soil under undisturbed grass this process required 1-2 days (Zykina et al., 1987). Therefore, to obtain data on physico-chemical parameters typical for the given type of soil formation, in situ measurements should be carried out 1 -2 days after precipitation. The representative Eh and pH values for soil formation processes of various chernozems subtypes are shown in their distribution graph (Fig. 43). The Eh reveals two peak values at about 550 mV, in ordinary chernozems, and close to 610 mV in typical chernozems. These peak values can also be traced for pH 5.5 in typical chernozems and 7.2 units in ordinary chernozems (Fig. 43 a). The graph does not include values for leached chermozems because of their poor representation (n=2). The graph from initial data of separate electrodes^^ (see Fig. 43b), shows maximum pH of 6.4, determined by residual salinization (profiles 13, 110, 481), and a peak in Eh of 490 mV related to salinized chernozems. Analysis of Eh-pH graph of the investigated soils (see Fig. 42) showed that typical and ordinary chernozem soils are positioned along the same line in accordance with the equation. Eh = 871 - 4 4 p H .
As a rule, reading of separate electrodes were repeated three times, an average of these was taken as result.
195
12
£2
i4
500
540
580
620
660 Eh(mV)
500
540
580
620
660 Eh (mV)
5.0
5.8
6.6
7.4
8.2
12 B s^ 8 0)
n
12
Z
14 5.0
5.8
7.4
8.2
pH
pH
Fig 43. Eh and pH distribution for various chernozem subtypes, based on mean values for investigated site (a) and on average reading of electrodes (b)
This suggests the existence of similar causes affecting the redox reactions which determine their formation. If we proceed from the conclusion that ordinary chernozems differ from typical chernozems only by reduced humus content (Egorov et al., 1977), then this equation may describe a summarised process of humus accumulation. However, the works by Gorshkova and Orlov (1981), obtained the data close to this regression equation at artificial acidification and alkalinization of various soils. They considered the predominant influence of water potential under redox conditions of normal aeration soils as a most capacitance redox system. The area of leached chernozems turned out to be skewed in different direction. Although their statistics is minimal, the likelihood exists, in which the change in pH value is not related to a change in Eh. The process of carbonate leaching from humic horizon is not a process of a redox nature. We may come to the conclusion that in situ measured redox and soil liquid phase pH values lie in a chacarteristic range for certain types of soil formation and may be used for identification of various chernozems subtypes.
196 6 2 3 SOIL REDOX REGIME AND GRASSLAND PRODUCTIVITY
Many attempts have been made to establish a correlation between Eh and the chemical properties of soil, based the equation: ,, 59 [Ox] Eh^E'+—lgf-f-59-pH. n {Rea\
m n
.^^. (25)
according to which in presence of equilibrium in the system ^ , ^ / , M . ; ^ ^ . ; ^ A ^ . ; ^ A ^
(26)
where Eh is in mV, [Ox] is the concentration of oxidized, and {Red\ of reduced components. Based on the equation (25), a correlation was established between soil Eh, pH and Fe ^ concentration (Ponnamperuma et al., 1967; Yu, 1985): Eh = 1058 - 59 Ig ( Fe^") - 177pH.
(27)
However, others, e.g. (Poddubny, 1959; Stashchuk, 1968 et al), did not observe a quantitative relationship (27) between Fe in soil and solid phase of bottom sediment and Eh. It proved to be unsuccessful. This may be explained by the use of inadequate data. For example. Eh values obtained by in situ measurements, and data obtained from chemical analysis of extracts belongs to soil samples of already altered properties. If the Eh is also determined in the sample then it depends heavily on the time passed since collection of the sample (Serdobolsky, 1953). Soil organic matter and the productivity process determine to a great extent the functioning of soil, at least in the upper horizons. Therefore, it is logical to suppose the presence of a close correlation between the soil redox potential and productivity. The process of production implies the reduction of carbon forms and other elements, while the process of decomposition and mineralization represent the oxidation of dead organic matter^^. Ecosystem productivity may be a measure of production process, which is often taken as living phytomass in the period of maximum standing crop. For the decomposition processes the maximum supplies of dead phytomass, subject to oxidation may be used. In accordance with (26), one may consider the correlation between Eh and the logarithm of live and dead aboveground plant matter supplies. It is natural that these values are at their maximum in different times of the vegetation period.
^^ The decisive role of soil organic matter in Eh changes at contrast changes in soil physical regimes especially at flooding and drying is well-known (Ponnamperuma et al., 1969; Yu, 1985; Kostenkov, 1987; Tararina, 1989 et al).
197 We have dedicated this section to the consideration of these two problems in grasslands. We used the following parameters for the analysis of Eh correlation: • correlation between living (L) and dead (D = standing dead + litter) aboveground plant matter. This value also characterises the intensity of turnover of plant organic matter; • daily net productivity (P) value of communities; • photo synthetic activity of the species dominating in the community. For the analysis we used the results of research groups of the former USSR, Hungary and Czech republic (Snakin et al., 1991) in the pontic Priazov steppe (Khomutovskaya Steppe Reserve), in the Pannon steppe (The Csaszartohes reserve), in the sandy semi-desert steppe (Bugac site) and on a secondary meadow (Kamenicky site). Productivity characteristics (net productivity, phytomass supplies) were estimated based on the dynamics of plant matter fractions in the vegetation period obtained by harvesting (Kovacs-Lang, 1991). Field measurements of photosynthetic activity were carried out on the level of predominant species by the C
method using
portable equipment with a glass exposure chamber (Kovacs-Lang & Meszaros-Draskovits, 1985).
Table 112 The range and mean value of soil physical-chemical indicators and production process in various grasslands Parameter
Sandy semi-desert
Pamion steppe
Pontic steppe
Secondary meadow
steppe (Bugac),
(Csaszartoltes),
(Khomutovskaya steppe),
(Kamenicky),
Hungary
Ukraine
Czech republic
Hungary
mean
mean
range
565-630
597
674-642*
559
6.6-6.8
6.6
3.9-5.9*
4.7
range
mean
range
mean
Eh (mV)
410-530
492
600-615
606
PH
7.4-8.0
7.8
7.7-8.0
7.9
Net productivity (P)
0.4-0.7
5-7
6-10
3-4
L:D
0.20
0.95
0.53
0.26
Eh (mV) (estimate)
491-518
591-620
603-581
569-535
range
(g/m^ per day)
* Based on one-day measurements of July 2, 1990
Analysis of the materials (Table 112) revealed a close correlation of Eh value with the L:D ratio and net productivity. Correlation coefficient (r) for the pair of Eh-L:D values was 0.80. The pair of Eh-lg (L:D), related to (26), has a higher correlation coefficient (0.89). The regression equation is as follows: Eh = 623 + 151 lg(L:D).
(28)
198 Coefficient of correlation for the Eh net productivity (P) pair equals 0.86. The correlation between Eh and Ig P (r=0.98) was close, and the regression equation is as follows: Eh = 516 + lgP.
(29)
The direct relationship between ecosystem productivity and Eh has been indicated by data from daily Eh measurements (4-6 times a day) and the photo synthetic intensity of predominant plant species (Table 113). All three investigated sites were characterised by a positive correlation, which decreased with the growing biodiversity.
Table 113 Correlation and determination coefficients of the influence of photosynthetic intensity of predominant plants species on Eh Sites
Species
Coefficients of
diversity*
correlation
detennination
32
0.64
0.51
44
0.69
0.31
80
0.15
0.03
Sandy semi-desert steppe (Bugac, Hungary), July 4-7, 1985 Pamion steppe (Csaszartoltes, Hungary), July 10-12, 1985 Khomutovskaya pontic steppe (Ukraine), July 4-7, 1985 * number of higher plant species
The values of Eh (Fig. 44), according to regression equations (28) and (29), was close to the experimental ones. In Table 112 the first estimated Eh value showed a relation to P, while the second one was related to L:D ratio.
Eh (mV)
_
Eh (mV)
600
600.
550H
550-
B
/
/ /
500-
5001
450
7. - T 0.2
^*^^-
1 0.4
1 0.6
1 0.8
r 1.0 L :D
450-
/ 1
1
1
3
1"
5
7
9 P, g/m" day
Fig. 44. Correlation between Eh maximum ahoveground living and dead plant (L:D) matter supplies (A) and net productivity in grassland ecosystems (B)
199 A close correlation between Eh with both L:D ratio and P suggest a close correlation between net productivity and L:D. There is such correlation, but it is much weaker (r=0.67). In accordance with data in Table 112, we carried out a analysis of correlation between ecosystems productive characteristics and the liquid phase pH. No correlation was discovered and correlation coefficient for the pH - L:D pair equalled 0.45, while for pH - P it was -0.06. Comparison of the theoretical (25) to the empirical equation (28) demonstrated their incompatibility. If to consider Red as a photosynthetically produced living phytomass, and Ox as dead plant matter, subject to oxidation, the ratios (28) are reversed compared (25). This incompatibility is easy to explain, if we suppose the two different, but closely related blocks; soil and plant. Then the processes of reduction in plant matter are accompanied by the occurrence of oxidized substances in soil. At the same time, with the change in L:D ratio, changes in Eh value become more distinct, which may be suggested from equation (25). The reflective pattern of redox processes in soil and plant matter was proven by the results in Table 113, and with an increase in reduction processes (photosynthesis) in plants there is an increase in the share of oxidized substances in soil, consequently in Eh growth. By regression equations (28) and (29) we have tried to estimate the production characteristics of the ecosystem by knowing the Eh value. Thus, our research showed that Eh on a non-fertihsed non-ploughed recreated meadow (South of the Moscow region, Experimental Field station of ISSP RAS, sowed in 1980 on grey forest soil) comprised on average 576 mV, varying from 537 to 622 mV. The net productivity of the given meadow was approximately 4 g/m^ per day; the live and dead overground plant matter supply ratio equals 0.47, varying from 0.3 to 0.8. Verification of L:D ratio (Ermolayev & Shirshova, 1988) has shown that this value is subject to variation and was 0.77 and 0.68. Both values fell within the estimated range based on the Eh values. One has to be careful trying to generalise relationships obtained in this work. Forest soils have higher Eh compared to the grassland. This harmonises well with equation (28) and is likely to be determined by the dominance of living matter above the dead matter. But equation (29) may hardly be used in these case. The situation becomes more complicated in the case of arable lands. The use of equation (28) is unacceptable since living matter decrease by yield removal. There are grounds to assume that equation (29) does not contradict to agricultural practices. Organic fertilisation i. e. the use of manure, led to an increase in reduction processes. Eh decrease, and consequently productivity decreased in accordance with the equation (29). But organic fertihsation increases Eh probably due
200
to an improvement in the soil physical regime and this was shown already by Remezov (1929). This illustrates once more the complexity of the Eh and the difficulties when trying to regulate redox regime in agricultural lands. The above illustrates the complexity and polyfunctionality, but there is a correlation between the value of soil Eh and production process in ecosystems. This correlation is revealed only when we use average results of many years observations in the natural ecosystems. The ratio of living and dead plant matter may be related to an average level of Eh in the ecosystem. There are many environmental factors which have direct or indirect influence on the production process and soil redox regime, and which may result in significant fluctuations. This investigation proves that in the natural grassland ecosystems the Eh has to do with the production process, and be closely connected with the net productivity of the system. This conclusion harmonises well with the one in the works by Reintam and Kylli (1989) on the role of soil as a product and a necessary pre-condition of production process.
6 2 4 THERMODYNAMIC INTERPRETATION OF Eh CHANGES Biological systems can be taken as non-stationary open systems, in which a range of irreversible processes take place. We used thermodynamic concepts to elucidate a number of natural regularities (Snakin & Dubinin, 1980) due to the fact that the thermodynamics of irreversible processes and its application to living systems has received much attention (Prigogine & Defay, 1962; Glansdorff & Prigogine, 1973). We quite agree with the opinion of Volobuev (1958) that any consideration in the field of energetic of soil formation is only approximate and schematic. The intensive studies on Eh of biosystems had begun before the theory of irreversible thermodynamic processes in open systems. Due to this fact some authors still recommend a very careful use of Eh values and consider a thermodynamic interpretation of the data impossible. Moreover, they often refer to the poor reproducibility of the readings of separate electrodes, while often forget the need to provide greater repeatability for their statistics due to great soil heterogeneity. In our experiments, the repeatability of the mean value was within the range of ±3-5 mV.
201 Based on the concept of «potential mediator)) (see Section 2.4), we have shown that the measured indifferent platinum electrode potential (cp msrd) complies with the potential of equilibrium status mediator (cpredox), which perceives Eh as a potential of a complex biosystem, i. e. CPmsrd= (predox = E h .
(30)
Using thermodynamic equations, we may re-write it as follows: E h ^ - ^ . nF
(31)
or ^^^_^r-T^^_m:_^T^^ nF nF nF
(32)
where zlG*, zl//* AS"^ are the respective changes in isobaric-isothermal potential, enthalpy and entropy of the system in the course of irreversible non-equilibrium processes; n is number of electrons which participate in the redox reaction. Let us differentiate equation (32): (DH^
(TBS'
dEh I \ n ) I \ n dt ~~ F dt '^ F dt
In case of normal development of a living organism, one may observe a range of biochemical redox reactions (photosynthesis, oxidative phosphorilation, etc.). Two-electron transitions are characteristic of many biochemical reactions (Leninger, 1976). That is why n should undergo sUght changes. It is also known that for the macrobodies of given composition and physical status thermodynamic characteristics like AH, will be equal independently of their structure (Kobozev, 1962). At small or insignificant temperature changes in warm-blooded animals or in soils, the equation may be written as follows: dr
nF
dr
or
dx
nF dr At a fixed initial point (Si*) in case if the system returns to the status, close to the initial
one (see Fig. 40) and S2* is the variable, we obtain the following equation:
202
(34) dx
YiF dx
If we consider a range of conditions and limitations (e. g. the presence of potential mediators in the biosystem, temporal permanence of composition, physical status, temperature), then the value of ^ i ^ is be directly related with the entropy factor. dv To prove that the use of the above approach is possible, we carried out an analysis of soil (n=16) and vegetation (n=4) samples. The concentration of the elements vary slightly, depending on the season (Table 114). This allows the use of equation (34) in the analysis of the observed dependence (see Fig. 40). The trend in Eh during the vegetation period complies with status, when ^3h. < 0. and is harmonized with ^
< 0.
Table 114 Composition of a virgin chernozem and its vegetation at different times Sample type
Parameter
1978
Sampling ttime (1977) April, 3-6
May, 8-12
June, 24-26
August, 1-3
November, 18-19
April, 19-23
C(%)
5.31
6.45
6.50
5.85
6.27
6.64
H(%)
2.00
1.93
1.97
1.77
1.92
2.04
N (%)
0.32
0.36
0.50
0.23
0.29
0.33
ash content (%)
81.67
81.37
81.84
81.27
83.31
80.14
C(%)
35.20
37.6
36.50
37.30
36.90
33.30
(mean value:
H (%)
4.94
5.36
4.88
5.26
5.17
3.75
green mass ^ standing dead
N (%)
1.49
1.66
1.56
1.80
1.76
1.69
^litter + roots in soil
ash content (%)
24.50
21.28
23.42
19.51
20.64
25.69
from 0 to 10 cm)
weight (t/ha)
21.7
21.7
25.7
26.4
21.5
21.6
Soil (0-10 cm)
Vegetation
It is known, that: 6/.V* = dS + deS.
where d,S is entropy change due to irreversible phenomena inside the system, and deS is entropy change explained by energy and material transfer to the environment and vice versa through the borders of any biological system (Prigogine & Defay, 1962). Consequently: (35) dr
nF
dr
nP
dr
203
At the same time, diS2/dT is always larger than zero, whereas deS2/dT may be larger or lesser than zero. The case of dS2*/dT<0 takes place under the condition deS2<0 and ldeS2/drl>diS2/dT. The latter goes well with the statement which says that the development of life leads to ordered structured and improvement of the biological substance due to the transformation of chaotic array of organic and mineral components into a well-established system of living matter. The material exchange between the biological entities and the environment, unlike the reactions inside the system, has a predominant influence on such a development. As shown (Fig. 40), Eh returns to the initial value and the system entropy increases (dS2/dT>0).
6 2 5 IN VIVO Eh MEASUREMENTS IN ANIMALS
We carried out experiments on warm-blooded animals in order to prove the important role of redox conditions, and to measure the Eh value in Hving systems^^. Warm-blooded animals are appropriate for the Eh measurement technique and the analysis of results obtained due to the permanent body temperature and the regulated way of biochemical reactions under normal ontogenesis. Various fluctuations (diseases, significant physical loads, etc.), cause of temperature changes influencing the intensity of biochemical redox processes, which increase by 5% at 1 C increase in body temperature (Villee & Dethier, 1971). There have been attempts of in vitro Eh measurements in living tissue (Mikhaelis, 1936; Clark, 1960), but the authors observed poor reproducibility. In our work, we used mature rabbits and the method of experiment was slightly different from the method described above (see section 2.4). As Eh electrode, a platinumized platinum wire 0.5 mm in diameter and as reference electrode EVL-IM type filled with a saturated KCl solution were used. The reference electrode was connected with a sah bridge. Blood was used as working medium because it is a substance, which contacts with almost all organs. Therefore, any change in the processes of organs should be reflected by the redox regime of blood, which would cause Eh change. Blood contains up to 0.9% of soluble salts which allows to use the electrochemical measurement technique for blood content analysis. Blood components also contain potential mediators such as f/Y (Snyder et al., 1975). Measurements were carried out in a flow of venous blood (in vivo), and the distance between the Eh electrode and reference electrode was 5 mm. ^ These experiments were carried out in collaboration with A. G. Dubinin and A. D. Shiryaev.
204
Eh stabilised within 15-20 min which is much less than in the experiments with liver cells or yeast (Mikhaelis, 1936). At normal conditions, the results could be reproduced with an accuracy of ±1 mV during the 20 days period of measurements. It is clear that Eh may referred to as a hard indicator of blood status (like pH), which simplifies the analysis of the data obtained. Preliminary experiments were carried out in order to elucidate the influence of medicine on Eh value in blood. By giving nembulate to a rabbit (30 mg by 1 kg of living weight) of soporific action, a parallel change in blood Eh from 414 to 392 mV during 25 min took place with the change in the intensity of respiration. Our experiments on warm-blooded animals proved correlation between the value of Eh and the functioning of living systems. By analysis of Eh changes, it is possible to determine whether life is stimulated or inhibited, moreover inhibition and stimulation are likely to be caused by a respective potential (or the density of current) in a given system. It was discovered that an increase in the content of Asotobacter agile microflora, lead to a change in species content under the impact of direct current on soil (Kravtsov & Kravtsova, 1971). At certain Eh this cultivate tuberculosis bacteria stable to streptomycine and isoniaside (lonue, 1959).
6. 3. POTASSIUM DYNAMICS IN THE SOIL LIQUID PHASE In various ecosystems, K ion activity in the soil liquid phase varies from 0.005 to 25 meq/L (Table 115). The most frequent K ion activity is in the range of 0.03 to 3 meq/L. The liquid phase of grey forest and alpine meadow virgin soils, had the highest values of K ion activity Lowest values were observed in podzolic soils. Potassium ion activity in the liquid phase of arable podzols was higher than in the virgin soils. Potassium ion activity in arable chernozems differ to a little from virgin chernozems. In grassland ecosystems, K ion activity is usually higher, as compared to forest ecosystems (Table 116). According to K activity, the order is as follows: meadows > steppes > meadow steppes > broad-leaved forests > coniferous forests. At the same time, there are no distinct differences between forest and grassland communities in concrete sites (Table 117).
205 Table 115 Potassium ion activity in the liquid phase of various soil types (meq/L) Soil type
n
X ±a
min - max*
min -max**
All soils
234
1.7 ±3.6
0.005-25.1
0.03 - 3
Brown forest
19
4.5+6.8
0.05-22.7
0.1-17.2
Alpine meadow
11
3.3+2.5
1.0-9.1
1.0-4.6
Chestnut
9
2.0±2.1
0.02 - 5.6
0.08-4.8
Chernozem
19
1.5 ±2.0
0.02 - 7.9
0.02 - 4.2
Grey forest
18
1.3 + 1.5
0.01-4.6
0.06-4.3
Natural communities
Alluvial
11
1.0+1.2
0.01-3.5
0.06-2.8
Podzolic
19
0.9 + 2.3
0.005 - 10.3
0.005-1.9
Cimiamonic
18
0.2 ± 0.4
0.02-1.8
0.04-0.37
130
2.0 + 3.4
0.005 - 22.7
0.03-4.0
Podzolic
41
1.9±4.6
0.008-24.1
0.01 -7.2
Chernozem
39
1.7 ±3.6
0.02-19.4
0.02 - 7.8
Chestnut
7
0.33 ±0.16
0.1 -0.5
0.2-0.5
Grey forest
15
0.22 ±0.26
0.02 - 0.84
0.03 - 0.82
104
1.5 ±3.7
0.008-25.1
0.01-2
Soils of the natural communities Agricultural lands
Soils of agricultural lands
Note: X - mean value; a- mean -square deviation; min-max - the range of minimum and maximum values; min - max** - the range of most frequent (80%) values
Table 116 Potassium ion activity in the soil liquid phase of various ecosystems (meq/L) Ecosystems
X
min - max*
Forest ecosystems at whole
1,1
0.005 - 10.3
0.03-1.1
1,0
0.005 - 10.3
0.005-1.1 0.01-1.2
coniferous broad-leaved
min-max**
1,1
0.01 -4.42
2,3
0.1-11.9
0.1 - 4
meadow
2,8
0.01-11.9
0.01-4
meadow-steppe
1,2
0.06-3.3
0.06-1.8
steppe
1,8
0.02-7.9
0.02 - 3
Grassland ecosystems at whole
Note: X - mean value; min - max* - the range of minimum and maximum values; min max** - the range of most frequent (80%) values
206 Table 117 Potassium ion activity in the soil liquid phase of various protected territories (meq/L) Site
r ± CT
min - max
The Central-forest State Reserve
0.20 ± 0.26
0.005 - 0.78
The Prioksko-terrace State Reserve:
1.8±3.0
0.06-10.3
forest communities
1.0 ±0.85
0.2-1.9
herbaceous communities
2.1 ±3.4
0.06- 10.3
The Mahno forest area, Tula region
0.04 ± 0.02
0.01 -0.06
The Zaokskoye forestry, Moscow region
0.54 ±0.32
0.09- 1.0
The Centralnochemozemny Reserve:
2.0 ±1.8
0.06-4.2
2.3 ± 1.9
0.48-4.2
grassland communities
1.7±1.6
0.06-3.3
fallow land since 1947
0.15±0.10
0.02 - 0.27
1.05 ±1.04
0.02-3.1
The Askania-Nova Reserve
1.8±2.0
0.02 - 5.6
The Caucasus State Reserve, Yew-box
0.38 ±0.86
0.016-4.42
3.9 ±5.0
0.3-22.7
forest conununities
5.3 ±8.7
0.3-22.7
grassland communities
3.3 ±2.1
1.0-9.7
forest communities
The Kliomutovskaya Steppe Reserve
grove The Caucasus State Reserve, the Juga station:
Note: X - mean value; a- mean-square deviation; min-max - the range of minimum and maximum values
Comparing different soils is a difficult task due to great spatial heterogeneity and temporal variability. We carried out analysis on spatial heterogeneity and temporal variability of K^ ions activity in the soil of 15 various natural and agricultural ecosystems in the Moscow region, based on variation coefficients. The investigation demonstrated high variability of K^ ions activity in the soil liquid phase. Average spatial variation coefficient was 77±26 while average temporal variation coefficient equalled 87±40 (see also Chapter 5). Variation coefficients showed that there are narrow limits in time and space during the active period of vegetation from May to August, although seasonal changes are greater. No reliable difference in spatial heterogeneity and temporal variability were discovered in: agricultural lands, reconstructed meadow, natural grassland communities and forest communities. Soddy low podzohc sandy soils were marked by the highest spatial heterogeneity. A correlation between pCa and pNOs and K^ ion activity values was found in for all sites (Table 118). The impact of Eh in coniferous forest ecosystems was significant. All parameters
207
studied have a great impact on K^ activity in grassland communities. The influence of pH and Eh is predominant in steppe communities and that of pCa and pNOs in meadow communities. Correlation between pK, pCa and pNOs values was found in the agricultural soils.
Table 118 The influence of some parameters of the soil liquid phase on K ion activity. Resuhs by multiple regression analysis. Ecosystems
Determination coetTicients (%) Eh
pH
pCa
All ecosystems
1*
1*
11
13
Natural coimnunities:
I*
0*
11
30
0*
2*
5
30
forest ecosystems at whole
pN03
coniferous
32
0*
22*
5*
broad-leaved
4*
0*
10*
8*
grassland at whole
8
16
21
9
meadow
1*
4*
27
9
10
38
7*
9
1*
5
16
9
steppe Agricultural lands * Coefficients are not significant at P <; 0.05
With an increase in moisture content in some ecosystems an increase in K ion activity was observed (Andreeva, 1990). Observations on the Hquid phase of ordinary chernozem under virgin vegetation before and after heavy rain, indicated a sHght decrease in Eh, whereas pH tended to decrease, Ca^^ and NO3' activity decreased and K^ activity increased (see Fig. 17). In modeling experiments was shown that K^ ions activity in the soil liquid phase increased with temperature increases. The extent of this increase was about the same for the chernozem, the grey forest soil, and for sod-podzolic soils showing some 0.1 pK for each 7-8^ C. Such increase was possibly caused by K^ release from SAC (see Section 4.4). For all data array a positive correlation between K ion activity in the soil liquid phase, field moisture and humus content was established by pair correlation coefficients. We carried out multiple regression analysis to determine the extent of influence of other soil properties on K^ ion activity in the Hquid phase of various soil types (Table 119). The influence of temperature, moisture and humus on potassium activity is not large for all data. Virgin grey forest and chestnut soils were marked by influence of soil temperature. The influence of humus was found in natural communities chernozems and cultivated grey forest soils. The influence of
208 moisture and temperature on pK variability in the soils of natural communities is small. It is not significant in agricultural soils.
Table 119 Assessment of the influence of different soil properties on pK value in various types of soils Investigated objects
Multiple correlation
Determination coetTicients, %
Tsoil(°C) W soil (%.) Humus (%) • coefficient Virgin land soils:
6
8
I*
0.39
podzolic
18*
1*
17*
0.6*
grey forest
47
0*
11*
0.76
chernozem
13*
4*
20
0.61*
chestnut
54
23*
2*
0.88
Arable soils:
1*
2*
0*
0.18*
podzolic
4*
0*
-
0.20*
grey forest
3*
1*
59
0.79*
chernozem
5*
0*
25*
0.55
chestnut
18*
33*
14*
0.82*
* Coefficients are not significant at P
0.05
Table 120 The influence of various factors on K^ ions activity in the soil liquid phase Factor
Index
Natural communities
Agricultural lands
r
n
r
n
Vegetation type
A
11.1
60
Ecosystem type
B
13.8
60
Vegetation period
C
25.4
60
3.9*
94
Soil type
D
37.4
60
2.8*
101
Fertiliser
E
1.8*
101
Note: r - determination coefficient; n - number of investigated objects * Coefficients are not significant at P < 0.05
We have carried out an analysis of variance on the effect of various factors on K ion activity in the soil liquid phase (Table 120). The factors considered are as follows: factor A vegetation type (woody and grassland vegetation); factor B - community or ecosystem type (coniferous, broad-leaved forests, meadow, meadow steppe and steppe communities); factor C vegetation period (for agricultural soils - before; beginning; peak; end; after; and for natural communities - beginning; peak; end of the vegetation period) factor D - soil type (podzolic, grey
209 forest, chernozem and chestnut); factor E - type of agricultural technique (the use of fertilisers). We estimated the extent of the influence of each factor on K^ ion activity in the soil liquid phase by a respective determination coefficient (see Section 4.6). In soils with natural communities, the dynamics of K^ ions activity in the soil liquid phase depends on the vegetation type, ecosystem and soil type. Seasonal changes in K^ ion activity in the liquid phase of virgin soils are significant (see Table 120). The factor analysis for the whole data set by K^ ion activity revealed a significant influence of soil type, natural community types and the period of vegetation on the variability. The respective determination coefficients are 16.7; 12.3; 6.6. According to the calculations based on all data for agricultural soils, the influence on K^ ion activity in the soil Uquid phase of the vegetation period and use of fertilisers is not significant. At the same time the investigation on concrete site show influence of these factors, in particular in podzolic soil (Table 121).
Table 121 The influence of fertiUsers on K^ ion activity in soil liquid phase (meq/L) Investigated site
Soil type
Moscow region
podzolic
Treatments
X ±CT
control
0.06 ±0.03
Dolgoprudninskaya
+ manure
0.13 ±0.08
agrochemical
N30P20 K30
1.8 ±2.4
experimental field station
N60P40 Keo
0.20 ±0.12
N90P60 K90
6.4 ±10.8
Stavropol sky region.
chernozem
Vodorazdelny sovkhoz
N90P60 K90 + manure
1.7±2.6
control
0.25 ±0.12
+ phosphogypsum
1.7±0.8
+ lime
1.3 ±1.0
+CaC03
9.9 ±9.5
CaC03 + HNO3 + H3PO4
1.8
Note: X - mean value; a-- mean-square deviation
Potassium ion activity in the liquid phase of natural communities is largest in spring and lowest in autumn. According to many researches, K^ ion dynamics have to do with the intensity of biological activity (Kovrygin, 1952; Volkova & Bystritskaya, 1977; Andreeva, 1990 etc.). Kovrygin (1952) pointed to the fact that lowest content of K in forest soils coincides with the period of leaf surface growth.
210 Similar results on K dynamics have been obtained (Donskich et al., 1975; Grieve et al, 1984; Edmeades et al., 1985; Andreeva, 1990). In the study of seasonal dynamics of replaced soil solutions, highest K concentration was observed in spring (Fernandes & Macias, 1987).
6. 4. POTASSIUM AND NITRATE IN THE LIQUID PHASE OF ARGICULTURAL SOILS
The behaviour of potassium and nitrate ions has much in common but reveals some differences. Both of them are biophylic, and have a role to play in growth of plants and in soil processes and their maximum activity is in the upper soil horizon (Table 122).
Table 122 The change in K^ and N0"3 activity in the soil liquid phase with depth, meq/L Soil
Horizon (depth, cm)
_
NO3"
Southern chernozem
A,(7)
OJT
099
AB(44)
0.08
0.23
Ai(7)
1.52
2.00
A,(22)
0.02
0.54
Ai(7)
0.05
0.45
A1A2 (20)
0.01
-
B(40)
0.01
0.29
Aper(4)
1.64
0.14
A2(10)
0.05
0.39
B,(20)
0.04
0.03
Ordinary chernozem
Grey forest soil
Podzolic soil
The maximum concentration of potassium in chernozem soil solutions (Volkova & Bystritskaya; 1977) and lowland peat soil (Donskikh et al., 1975) was observed in the upper horizon At the same time, potassium content in a non-fertilized agricultural field in the upper horizon was much lower than in the virgin soil, where the phytomass was higher both in aboveground and underground parts (Volkova & Bystritskaya, 1977). The similarity is the share of dissolved ions is small compared to total K and NO3 in soil (see Table 37). While for K the transition into dissolved state is not connected with the change in valency, the transformation of N compounds in soil is a sum of many redox processes, which are driven by micro-organisms and plants. However, in those soil horizons the activities of living organisms supports K^ and NO3" concentration at a relatively high level, and they are absorbed actively by plant roots.
211 Section 5.3 and Chapter 7 give a detailed analysis of dynamics of K^ and NO3" ions in the daily cycle, where they showed close correlation with the biological factor. The existence of the daily cycle of nutrients absorption and secretion by plants has been established long ago. The daily rhythm of sap flow from roots to aboveground plants parts is so stable that it continues after continuous dark-exposure of plants or stem ring-barking and is preserved even on the 23rd day after the cut off of aboveground parts (Trubetskova & Danilova, 1963). It has been shown that when illuminated consumption of K by cabbage roots increases by a factor of 1.5, NO3 - 16, and water - 2.4 (Maksimov et al, 1979). When placed in the dark, a corn plant may secrete from 15 to 30% of its K (Luttkus & Bottischer, 1939). Hydroponically grown oat roots consumed K to the greatest extent in the first part of the day, whereas at night time K and P absorption was insignificant (Cachioni-Walter, 1935). Intensity of amino acids flow with sap in sunflower plants and their root content are at a maximum in the day time, and are minimal at night. The synthesis of amino acids in roots weakens at night, although at night roots accumulate non-protein NO3 (Gunar etal., 1960). Based on the above facts, we can say that in the daytime of maximum absorption of K by plants, K^ activity in soil Hquid phase is lower than at night in almost all soils and during all periods of observation. A reverse dependence was observed for NO3 (see Chapter 7), which may be caused by intensive nitrification processes in the daytime. Seasonal changes in K^ and NO3' concentration in the soil liquid phase are also significant (see Tables 61, 93, 95; Fig. 30). In virgin steppe ecosystem a decrease in NO3" activity was observed from spring to autumn, which was especially strong in the rain season. Rain may cause liquid phase dilution and drainage of NO3, but also a shift in nitrate-ammonium equilibrium in favour of reduced form due to the increase of activity of denitrifying bacteria. This explains the decrease in redox potential (see Fig. 40). It is also known that when the number of irrigation events increase, the share of nitrogen in form of NO3" grows and is maximal in autumn (Boyarovich, 1967; Kudeyarov& Strekozova, 1985). On the contrary, K^ activity in the rainy period grew and reached its maximum in spring and autumn, and was lowest in the period of intensive growth of vegetation. Similar trends were observed by Donskikh et al. (1975) for peat soils. Soils are difficult to compare by K^ and NO3" activity in the liquid phase due to the dynamism in their composition. Table 123 contains daily mean values of K^ and NO3' activity, obtained in comparable periods of observation. Data analysis shows that maximum K^ and NO3" content for the virgin land series of the soils studied was observed in ordinary chernozem of the
212 Priazov region, marked by high biological productivity (Snakin & Bystriskaya, 1984). NO3' and K^ content in the soil liquid phase decreases south- and northward from Priazov region. The Hquid phase of sandy low humic soil is also marked by high NO3" activity, although the dissolved NO3 content is insignificant due to low moisture. This may be a consequence of sufficiently high activity of lower vegetation and nitrifying micro-organisms'^^ at low soil moisture, and the low adsorbing capacity of sand. As a result, the main part of nitrates remains in dissolved form. There are not such distinct features for agricultural soils, since K and NO3 content is to a great extent determined by fertilisation and output by yield.
Table 123 K" and NO3' ions activity in the liquid phase of various soils* Soil
Period of
Site (horizon)
measurements
NO3
Temperature
Soil moisture
(meq/L)
(%)
(V)
__
0.99±0.24
15A
0.42±0.44
11.08±10.4
23.7
12
1.52±0.62
2.2±0.9
23.1
12
agrocenoses (Aarabie)
0.44±0.29
1.75; 3.3
20.8
15
steppe (Ai)
0.33±0.26
1.3±0.6
27.3
14
forest (A,)
0.48±0.38
0.97±0.64
37.0
14
agrocenoses (Aarabie)
0.07±0.09
5.5±7.4
20.3
15
forest (A,)
0.06±-0.02
0.45±0.22
38.9
9
agrocenoses (Aan*ie)
0.02±0.02
2.4±2.6
17.4
15
forest (Ao)
1.64
0.14
22.5
13
forest (A:)
0.05
0.39
22.7
10
agrocenoses (A^bie)
0.03
0.49
22.8
20
May 20 - June 4,
steppe
0.18
1.85
3.4
24
1984
forest
0.40
3.45
3.8
no vegetation
0.05
0.62
3.4
-
Southern chernozem
May 5-9, 1982
Ordinary' chernozem
May 16-20, 1982
Typical chernozem
May 25-27, 1982
Gre\ forest
June 11-14. 1982
Podzolic
June 26-30, 1982
Sandy low humic**
-j7+
steppe (Ai)
0.37±0.02
agrocenoses (Arable) steppe (Ai)
* Mean values oftriph? measurements results during the day (about J0:00,
12:00pm, 2:00 pm) by 10- 30 electrodes, inserted to a depth of 5-7 cm
** By data ofethanol-,replaced solutions
6. 5. SILICON IN SOIL SOLUTIONS
Silicon is an active agent of the biological cycle and soil formation. Dissolution and redeposition of silicon oxide in its various forms is a universal and an important phenomenon in the process of soil formation. The development of soil horizons, their compaction and cementation. ^" High rate of nitrification here is also related to more alkaline reaction (see Tables 20, 112 for pH data on these soils). There is a close correlation between the pH of a soil solution and nitrification (Faller, 1982; Hem et al., 1985). Therefore, carbonate chernozem soil solutions are not only richer in Ca by a ratio of 2, but their NO3 content IS 3 times higher then that of ordinary chernozem (Krupennikov & Sinkevich, 1970).
213 solodization and podzolization of soils all are connected with silicates. One of the main indicators of these processes is the quantitative distribution in soil profile. Weathering crust types are distinguished in relation to Si02:R203 (or Si02:Al203). Under joint migration with sesquioxides the silicate compounds take part in the formation of hygrogenous allophanes and clay minerals; the composition and content of the latter determine coherence, stickiness, swelling and cation exchange capacity of soils. The process of transformation and synthesis of minerals, including feldspar weathering, depends on the concentration of silicon acid in soil solution. Silicon is worth our attention due to its ability to raise yield under certain conditions by increasing phosphates availability, stimulating plant resistance to fungal diseases (Askinazi, 1949; Russel, 1973; Her, 1979). The fact that Si is a potential reagent to other elements, silicates is responsible to some extent for nutrients immobilisation (Elgawhary & Lindsay, 1972). Under agricultural use of land silicon has a role to play in the development of such negative consequences as compaction, deterioration of water and air regimes. It is possible to judge on the role of silicon in soil formation process fi"om its concentration in soil solution. It varies in a wide range from 0.2 to 50 mg/L Si02, reaching 100-200 mg/L Si02 (Volkova, 1980; Kovda, 1985; Bystritskaya, 1987 et al.) under certain conditions (pH 10-11 in presence of sodium carbonate). It is in the in the range of respective values of amorphous silicon earth solubility rate (51 mg/kg Si), and quartz solubility rate (2.8 mg/kg). However, in heavily weathered soils, free silicon acid supplies may be depleted (Elgawhary & Lindsay, 1972; Lindsay, 1979). The following forms of silicon earth may exist in a solution: only orthosilicon acid H4Si04 at pH values below 7. From pH 8-9 the formation of mono- and dimerions can be observed: H3Si04", H2Si04^", HSi04^', Si03^ HSisOs", Si205^", HSi206^'. The amount of complex polymer ions may reach significant values only in solutions of pH 11-12 (Krauskopf, 1963; Kopeikin & Mikhailov, 1970; Her, 1979). In soil solutions, silicon earth is represented by monomer, dimer, polymer and silicon-organic forms; at pH below 8, siHcon is present in soil solution only in monosilicon acid form. There are some data on the predominance of silicon-humic complexes in water extracts from solonetz illuvial horizons (Her, 1979; Panov et al., 1987) and silicon acid colloid forms in solonetz, solod soil and chernozem meadow low saline soil solutions (Samoilova etal., 1972). The main sources of silicon acids in the soil solution are siHcon dioxide in various forms, the silicate minerals and plant residuum. The differences in their ability to provide the solution with
214 Si02, determined by the physico-chemical and energy parameters, are one of the factors decisive for H4Si04 concentration. In dispersed state, nepheline, diopside, augite may give 15-20 mg/L Si02 to the solution and bioptide, microcline, labradoride up to 5-7.5 mg/L Si02. At high extent of disintegration and downward flows, quartz dissolves in the amount of 3.5-4.0 mg/L Si02 (Keller, 1955). Feldspar and a number of complex silicates, such as turmaline, circone and garnet minerals of extremely low solubility are resistant to weathering but they also release an small amount of silicon into the soil solution (Kovda, 1985). The highest H4Si04 concentration was observed, when the soil solution was in balance with amorphous silicon acid, which covered the surface of soil particles. The lowest concentration were typical for soils, which did not contain redeposited Si02 or secondary aluminosilicates (Lindsay, 1979; Brown & Mahler, 1988). The process of Si02 transition from silicate material to soluble state takes place at conditions of predominant oxidation (Nazarov, 1976). In the soils of temporarily excessive moistening in the periods of contrast redox regime an intensive disintegration of aluminosilicates takes place with the transition of decomposition products, including silicon acid, into soil solution (Kaurichev, 1974). Solubility and mobility of silicon earth are increased under high acidity and alkalinity. The adsorption-desorption processes are predominant in determining H4Si04 concentration in soil solution (McKeaque & Cline, 1963), and are very sensitive to pH changes. Numerous investigations have shown that maximum adsorption proceeds at pH of 9-10, at higher or lower pH values it is reduced. The "salt effect", which stimulated adsorption, polymerization and coagulation of silicon acid, is no extraordinary phenomenon (Brown & Mahler, 1988). Calcium and magnesium sulphates, carbonates and bicarbonates decrease sharply Si02 solubility and cause sedimentation of alkali metal silicates or Si02 residuum formation (Krauskopf,
1963). The freshly formed
hydroxides of multivalent metals are most active in the adsorption of silicon acid, while allophanes adsorb less soil Si02 and iron-enriched crystal minerals. Carbonates and humic substances are not active. Evaporation, transpiration or freezing of solutions stimulates Si02 deposition in form of crusts, which are later included into dissolved silicon acids - solid phase silicon acids equilibrium (McKeaque & Cline, 1963). The concentration of H4Si04 in soil solution and its mobility are sensitive to anthropogenic load on soil. The use of phosphorous and ammonium fertilisers, which acidify soil, is a a cause for Si02 concentration increase in the soil solution. It may be alkalinised and redeposited in the lower horizons of the profile, acting as a cementing and blocking agent. Liming decreases silicon acid concentration and mobility (Bystritskaya, 1987; Panov et al., 1987). Organic matter acts in a
215 similar way (Bystritskaya, 1987; Allmarras et al, 1991). In the experiments of Allmarras et al. (1991) the addition of lime and organic material decreased the amount of H4Si04 in solution by 32 and 24% respectively, compared to control samples. Irrigation without application of chemical melioration resulted in dehumification accompanied by accumulation of soluble silicates in the ploughed horizon of solonetz soils (Her, 1979). Continuous a follow also leads to an increase in mobile sihcon acid content (Pereverzev, 1989), which is proved by the data in Table 124.
Table 124 Si02 and organic matter content in soil solutions of various soil types Investigated site
— The .Ajskania-Nova
Vegetation
_
Soil
__
sheep's fescue stipa
chestnut
Reserve
The Khotnutovskaya
creeping-grass
ordinary chernozem
Steppe Reserve
The Centralnochemozemny Reserve
The Mahno forest area.
Reserve
Tungiro-Neriungri area
(mg/L)
_
PH
"C" (mg/L)
__
__
26^9
Asoddy
0-10
21.03
7.25
Al
20-30
12.92
7.47
14.8
B,
55-65
18.13
7.40
26.0
Asoddy
0-10
16.3
7.58
52.0
Al
35-45
8.8
7.74
26.2
B,
55-65
5.13
7.58
18.0
Asoddy
0-10
13.6
8.35
29.7
A,
35-45
9.07
8.08
15.4
smooth brome-mixed-stipa
typical chernozem
Asoddy
0-10
19.4
7.82
42.0 32.0
oak
typical chernozem
A,
0-10
14.3
7.22
fallow since 1947
typical chernozem
Al
0-10
38.5
6.86
15.8
oak
grey forest
A,
0-10
15.8
7.06
32.8
A1A2
20-25
16.6
6.2
25.3
Bi
35-60
18.9
6.69
11.6
A,
0-20
12.4±1.4
6.4+0.1
Bi
35-45
20.7
6.3
B2
45-55
18.0
6.3
-
Ao
0-6
14.6±1.4
3.7±0.2
240+35
A2
8-10
14.1±1.3
4.3±0.6
48±16
B,
15-20
9.75
5.9
29.6 36.4
lime and oak grove
grey forest
spruce forest
podzolic
Reserve
The Caucasus Biosphere
Si02
(cm)
ordinary chernozem
Moscow region
The Central-forest State
1
Depth
mixed fescue-stipa
Tula region.
The Zaokskoye forestry,
Horizon
broad-leaved boxwood
B2
30-40
16.5
5.6
cinnamonic
A,
3-7
4.6±0.6
7.4±0.1
laurel-cherry yew
cinnamonic
A,
3-7
13.4±5.5
7.0±0.1
hornbeam-oak
brown forest
Al
3-7
24.4±4.2
6.2±0.1
mixed grass
alpine meadow
A,
3-7
8.3±0.8
5.6±0.3
broad-leaved forest
Peaty soil with permafrost
AT
7
6.8
5.7
72.7
Al
18
10.8
5.7
70.4
Ao
7
7.8
5.8
40.8
sedge, bluejoint grass
meadow-boggy with permafrost
216 Table 124 (continued) The Prioksko-terrace
green moss Pine forest
Ai
0-10
34.4±8.3
3.6±0.1
B
20-30
43.7
4.5
A,
0-10
13.5
6.6
B
10-20
15.3
5.8
Asoddy
0-10
23.0
6.9
Asoddy
0-15
5.9±06
6.610.4
Asoddy
0-20
11.1+0.3
7.0±0.3
Apiowing
0-10
34±2.0
6.2±0.1
41±15
Allowing
0-10
15.3+3.5
7.7±0.1
20+12
chernozem
Apiowing
0-10
30+9
7.2±0.4
podzolic
Apiowing
0-10
54
57
grey forest
Apiowing
0-10
29.0±2.5
5.1±0.5
podzolic
Reserve oak grove
mixed grass mixed grass mixed grass stipa
alluvial sod-acidic
Agricultural lands* The .Askania-Nova Reserve The Khomutovskaya Steppe Reserve The Centralno-
21±6
chemozemny Reserve The Central-forest State
328
Reserve MaHno forest area
64.9±39
* The case m point are the agroecosystems, adjacent to virgin reserve territories
Silicon earth concentration as a biophyle in soil solution is affected by seasonal dynamics. In grassland ecosystems, maximum concentration coincides with the period of increased biological activity - in the spring (Femandes et al., 1987) or in the summer (Volkova, 1980; Bystritskaya, 1987). Silicon acid concentration in soil solution under forest increases during leaf fall in autumn (Volkova, 1980; Pervova & Evdokimova, 1984). Silicon acid is found in large amounts in needles and in a more mobile form as that in the litter of broad-leaved species, which allows its quick transition into soil solution. According to our data (Table 124), the average concentration of silicon acid in ethanolreplaced soil solution in natural communities varies in the range from 4 mg/L (box tree, cinnamonic soil) to 46 mg/L (green moss pine forest, podzolic soil). The latter was marked by coloured soil solutions due to the large amounts of organic matter. The data indicate the presence of a reliable weak correlation (r=0.32) between silicon acid dissolved forms and organic matter. The correlation between SiOi and pH turned out to be absent. As a rule, silicon acid content decreases along the soil profile. No significant influence of mineral fertilisers on silicon acid content of soil liquid phase was found (Table 125). The grey forest arable soil has maximum Si02 concentration in soil solutions in early spring (the beginning of the vegetation of agricultural plants) and its distinct minimum in summer (the peak of vegetation) (Table 126).
217 Table 125 Changes of Si02 content in grey forest soil solution with different dosage of fertiliser Crop
Horizon
Treatments* Ci
Barley
C4
C3
C2 c
n
X
a
n
X
a
n
8.3
3.2
15
9.5
0.6
4
8.5
2.7
10
10
6.2
2.8
15
6.5
2.6
5
5.1
0.9
10
7**
a
n
•'^ploughed
9.3
2.1
18
B,
5.4
1.1
X
^ploughed
7.2
4.6
25
7.0
3.6
14
7.4
2.7
4
6.8
3.2
9
wheat
B,
4.8
2.0
10
4.8
3.2
15
4.1
1.2
5
3.9
1.3
10
Corn
^ploughed
7.4
3.8
67
1.1
3.5
29
8.4
2.2
8
7.7
3.0
19
2.4
10
4.5
1.3
20
Winter
5.1
Bi
1.6
20
5.5
3.1
30
5.3
* Ci - control;C2 - NsoPecKeoiC^ -- NgoPeoK 9o; C4 -- N90P60K90+ manure
** 7 -mean value,
a - root mean square deviation., n - sample size
Table 126 Seasonal changes in Si02 concentration, organic matter (C) and pH of grey forest soil solutions Horizon
Depth
Vegetation period 4th period (after)
1 St period (beginning) 2nd period (peak)
3rd period (ending)
7*
X
a
n
X
a
n
a
n
X
a
n
Si02 (mg/L) ^ploughed
0-20
11.13 3.5
23
5.4
2.9
34
7.54
3.34
40
7.15
2.07
29
Bi
30-50
7.94
2.5
16
3.6
1.1
21
4.81
2.11
24
4.81
1.45
19
^plouglied
0-20
18.2
6.3
7
0.6
1.6
23
0.04
0.21
24
12.3
10.3
29
Bi
30-50
13.8
6.3
8
0.29
1.1
15
-
-
"•
16.6
10.3
19
34
7.3
0.42
40
7.37
0.48
29
24
6.97
0.38
19
"C" (mg/L)
pH •'^plouglied
0-20
B,
30 -50
7.51 6.83
0.36 0.36
23 16
7.5 6.9
0.4 0.6
21
6.84
0.43
* x- - mean value. a - root mean square deviation; n - sample size
It is Ukely that the lack of expression in the data on silicon in soil solution is determined by the technique used to obtain soil solutions from soil samples. These differ from natural soil by both hydrothermic, air regime and their pH value (see Section 2.5).
218 6. 6. ORGANIC MATTER IN SOIL SOLUTIONS
Organic matter of the soil solution is the most mobile soil organic fraction and of interest for several reasons: •
It may be a substrate not proximal to a food source, such as those in aggregates, deeper soil horizons and aquifers. As a result, its consumption can strongly control the soil redox conditions;
•
Through physical and chemical binding it is involved in the co-transport of metals and some of the xenobiotica (see Section 6.7);
•
It stabilises soil colloids and aggregates;
•
It can be important in weathering and in the podzolization process;
•
It is a component of the organic carbon cycles. It can also contribute to some of the dissolved organic matter in surface waters;
•
It is involved in the nitrogen cycle in the same way as in the organic carbon cycle;
•
It may have value as an indicator of the "general condition" of soil (Zsolnay, 1966). The organic matter of soil solutions is heterogenous by its origin: soluble humic acids
(mainly, fiilvicacids), soluble organic residuum compounds and their decomposition products (sugars, organic acids, alcohols, aminoacids, ferments, etc.), metabolytes of soil microorganisms (ferments,
vitamines, toxines, etc.), various roots exudates,
substances
of animal and
anthropogenic origin. Therefore, there are certain difficulties in the interpretation of results of the analyses of total organic matter in soil solutions. Tables 124 and 127 show that the mean values of organic carbon content in soil solutions of various soils varies from 12 (grey forest soil under agricultural use) to 275 mg/L (peat horizon). We have not studied solonetz soils, for which high (> 1000 mg/L) soluble humus content have been observed (Vozbutskaya, 1968; Kovda & Rozanov, 1988). A sharp decrease of the organic carbon content in the soil liquid phase takes place with depth (see Table 124) and its maximum content is found in the surface horizon (Bystritskaya at al, 1981). In summer, at the peak of vegetation growth, we observed the minimum of organic carbon, while its maximum was observed at the beginning of the vegetation period, as in the case of silicon acid concentration for grey forest soil under agricultural use (see Table 126). The same was observed by other researchers (Dergachova, 1984; Shirshova, 1991).
219 Table 127 Average concentration of organic matter ("C") in soil solutions and other soil parameters for some ecosystems Ecosystem
Steppe
Soil
chestnut
Steppe
chernozem
Forest
chernozem
Horizon
Humus (%) Eh (mV)
"C" of soil solution
Amount
in situ
(mg/L)
of objects
pH
__
3.03±1.92
645147
6.910.7
22.616.7
_
Al
6.27±1.99
616126
6.910.4
45.6149.5
11
Al
8.90
661
6.3
32
1 4
Forest
grey forest
Al
2.9311.86
708121
5.710.4
24.619.2
Forest
podzolic
AO
51.0
751
4.1
205
1
Forest
podzolic
A2
1.4011.25
754184
4.310.7
40.4115.7
5
Forest
swampy-
AT
56.0
884
3.9
275
1
Al
2.15
678
5.8
71.6
2
AO
9.60
650
4.7
40.8
1
10
podzolic Forest
peaty with permafrost
Meadow
meadow-boggy with permafrost
Agricultural
chestnut
•^ploughed
1.3811.91
586166
7.110.6
37.7117.1
Agricultural
chernozem
"^ploughed
4.7010.90
5821117
7.110.6
30.2118.5
11
Agricultural
grey forest
•'^ploughed
1.7810.50
732111
5.610.2
12.5113.1
4
Agricultural
podzolic
^ploughed
2.90
580147
6.810.3
81.7135.5
4
Agricultural
meadow-boggy
Apioughed
5.50
612
5.4
74.2
1
with permafrost
Analysis of the relationships of "C" in soil solution with humus in soil, Eh and pH showed that the correlation was closest between organic carbon content in soil solution and soil organic matter (determination coefficient - 0.44) and Eh value (determination coefficient - 0.43). There was no influence of pH value. No reliable influence of these parameters on "C" in soil solution was established and the multiple regression coefficient comprised 0.89. If no attention is paid to the extremely high values of organic matter content in soil solutions from peat horizons then the correlation between the values considered may be different. In this case pH value is reliable (determination coefficient - 0.30), while the correlation with humus is absent and the influence of Eh value is small (0.18). At low soil humus content in mineral horizons the number of factors, which determine organic matter content in soil solutions increases and that of the hardly accountable ones, such as the activities of plants and micro-organisms. Their daily and seasonal dynamics seems to diminish the influence of the mentioned parameters.
220
6. 7. HEAVY METALS Heavy metals (HM) behaviour in the system of soil-soil solution-plant has become particularly acute due to their increased technogenous production. There are differences between forms of metal substances in pollutions and of those in the natural systems. Under edaphic conditions, the exogenous substances brought into soil by pollution, may be subject to ftirther transformation and change in their mobility, changing the availability to plants. The behaviour of technogenous TM is same as of HM in the natural conditions. HM are present in two phases of the soil: solid and liquid (soil solution). In the solid phase, they are present in exchangeable and fixed forms as components of highly dispersed particles and humus matter, they are absorbed by sesqui-oxides gels, and are the basic parts of insoluble salts. In soil solutions, HM are present in forms of soluble mineral and organic-mineral salts. Extraction of soil samples with 2 mol/L HNO3 at 100 ^C as proposed by Andersson (1976) was shown to be a suitable method for determining the total potentially available fraction of the metals in the soil. One of the first investigations of soil solution, carried out by Hodgson (Hodgson et al., 1966), showed that they contain significant amounts of HM in forms of complex substances, mainly in association with organic ligands. The methods used to obtain solutions from soil, differ immensely, but it is possible to get a notion of the average concentration of HM in soil liquid phase by data of Table 128.
Table 128 Heavy metal content in natural soil solutions of various soils (|ig/L) Method of soil solution replacement
Element Cd
Co
Cu
Fe
Hg
Mn
Mo
Ni
Pb
Zn
-
60
40
50
2.4
170
730
20
50
70
-
0.4-14
3-18
-
-
0.3-5
28-135
150-549
32-270
2-8
-
(Yamasaki et al.. 1975)
6
3
37
16
-
-
21-180
Centrifugation (Kabata-Pendia.s, 1972)
243
2
150
8
351
(Zmijewska, Minchewsky, 1969)
-
-
78
-
55
30
-
-
22
(Guliakin et al., 1976)
0.2
0.3-1.0
0.5-3.0
30-40
25-50
0.6-2.0
4-25
5-300
(Heinrichs, Mayer, 1980)
3-5
12-87
18-27
36
0.01-0.2
-
1-3
50-1000
-
3-8
(Itoh.Yuniara.1979)
Soil paste suction (Bradford et al., 1971) Replacement by 0.01 N CaCh solution (Hodgson et al., 1966)
73-270
Suction tlirough ceramic plate
Method not indicated* (Tiller, 1981)
14-44
* Data for rice fields soils after their flooding during 14 weeks
-
1000-2200 2000-8000
40-17000
20-25 3-15
5-63
190-570
-
•2
1-15
221 The state of metals primarily depends on the chemical composition (mainly, its anion part and soluble organic matter), Eh and pH. In liquid phase of acidic soils, there is only an insignificant amount of anions of mineral acids and there is ample fulvate type organic matter (Tiurin, 1944). In relation to this it is possible to suggest that HM in the soil solution of acidic soils form primarily the soluble organo-mineral complexes. In soils of neutral pH, such as chernozems, calcium bicarbonate and sulphate predominate (Afanasieva, 1966). The presence of significant amount of calcium in soil solution with humate type of organic matter, leads to a dramatic decrease in the share of soluble humus fraction. Therefore, Pb, Zn, Co in the solution of such soils react primarily with the mineral part, forming carbonates and sulphates of medium and low solubility. Calculations made by Kabata-Pendias (1972) showed that total heavy metal cation content varies in the range from 10 to 100 |ag/L in normal soil solutions, whereas in polluted soils it is much higher. When soluble HM substances are added to the soil, their concentration in equilibrium solutions grows proportionally. In the experiments of Cottenie et al. (1979) the relative amount of metal adsorbed by light sandy soil comprised: for Zn - 39% of the 1000 mg/kg dose, for Cu - 50% of the 5000 mg/kg dose; for Cd - 30% of the 5120 mg/kg and for Pb - 26% of the 2695 mg/kg added. HM substance in soil solution (soluble forms) are the most mobile, available for plants. Therefore, it is important to know the factors, which influence the quantitative distribution of HM in the system of solid soil phase - soil solution - plant. The mechanic composition, soil reaction (pH), organic matter content and cation exchange capacity (Black et al., 1973) are amongst those factors, which have notable influence upon heavy metals availability. Soil particle size distribution has a direct impact on heavy metal fixation and release, which results in lesser danger of plant absorption of excessive (toxic) heavy metals in heavy clay soils. Results of our investigations showed that in the soils of different texture in horizon Ai in the Caucasus State Biosphere Reserve, the content of heavy metals was different. Table 129 shows how the various texture determines the values of CEC and Zn content in acid-soluble and mobile forms. Usually, the share of mobile HM forms is maximal at low pH and Eh values. With the increase in soil pH, the mobility of HM decreases (Lindsay, 1979; Nemeth at al., 1994). HM content in soil solutions of alkaline and neutral soils is lower than in acid soils (Table 130). Hydroxides and carbonates of heavy metals are of low solubility, with the increase in soil solution pH the probability of the formation of insoluble hydroxides and carbonates increases.
222
Table 129 Soluble (1) and mobile* (2) Zn forms in cinnamonic soils (Caucasus State Biosphere Reserve) Horizon
Texture
A,
sundy loam
CEC,
pH of soil liquid
Zn, mg/kg
meq/100 g
1
2
phase
30.00
24.00
14.50
5.6 6.3 6.8
Ai
loam
39.00
28.50
15.00
Ai
clay loam
55.00
32.00
19.00
* Heavy metal forms were determined in soil extracts by spectrophotometric technique: acid soluble forms - in extract 1 MHNO3, mobik ^ forms - in extract of ammonium-acetate buffer solution with apH of 4.8 (Zyrin & Orlov, 1980)
Table 130 Heavy metal content* (^ig/L) in natural soil solutions, obtained by centrifugation from soils of different pH values (Kabata-Pendias & Pendias, 1992) Element
Soil acidic sandy
sandy
aleuritic
loamy
lime
(2.5-4)**
(4-4.5)
(5.5-6.5)
(7-7.5)
(7.5-7.8)
Cd
107
-
-
-
-
-
Co
0.5
5
Cu
783
76
20
50
50
Fe
2223
1000
500
200
100
Mn
5965
8000
5000
100
700
Mo
-
-
3
5999
-
5
Pb
-
-
Zn
7137
1000
5000
100
300
* Quoted are the average values of 4-5 samples * * Interval ofpH values
Metals are able to form complex compounds with soil organic matter, that is why in soils with high humus content HM are less available for plant uptake. Soil organic matter influences the content of HM in soil solution. Micro-organisms may exudate organic substances, which have ligands of high aflfmity to metals (Wildung et al., 1980). Resulting from the investigation of metal oxides interaction with aerobic decomposition of plant material, an assumption has been made that organic matter dissolves metal oxides due to peptization of hydroxides by colloid organic matter (Bloomfield et al., 1981). Another form for linking of metal oxides with organic matter is the soluble complexes of anion nature, which are most stable at pH 6 to 9 and less at pH < 6. Cadmium may be fixed by fulvic acids through formation of stable metalorganic substances up to
223
59% of its total content in solution (Truitt & Weber, 1979). Fraction of humic acids, extracted from humic-gley soils, peat and lignine, fixed cadmium by means of their carboxyl and phenolhydroxyl groups. Complexes formed by cadmium and humic acids are less stable than those of by copper and lead. Cadmium adsorption by acidic suspensions of humic acids changes, depending on the pH of solution, since acidity determines the properties of functional groups involved (Beveridge & Pickering, 1980). In addition to ionic links, the participation of low acid nondissociating functional groups through co-ordination Hnks is also supposed in complex formation. Fractional analysis of soil organic acids, carried out by Stepanova (1976), illustrates the great affinity of fulvic acids to heavy metals (Table 131). It is also known that Cu and Pb concentrations in fulvic acids are much greater as compared to humic acid (Mitzkevich et al., 1977).
Table 131 Heavy metal content in organic matter of soil clay fraction (mg/kg of dry mass) (Stepanova, 1976) Soil
Element
Clay fraction (< 0.001mm) at whole
Chernozem
Podzolic
organic matter
humic acid
fulvic acid 29.4
Cu
90
33.0
3.6
Zn
116
41.5
3.4
38.1
Mn
1110
262
trace
254
Mo
5
1.7
0.8
0.9
Cu
44
17.9
1.2
16.7
Zn
80
44.7
15.6
29.1
Mn
1830
307
44
267
Mo
3
0.7
0.2
0.5
Metals, entering the soil in form of organic complexes with the sludge of sewage water, in the initial period may be highly soluble (Cataldo & Wilding, 1978) and the degree of solubility depends on the ability of complexes for replacement by major concurrent ions and on hgand resistance to decomposition by micro-organisms. Disintegration of complexes may lead to a notable decrease in metal solubility, due to hydrolysis, adsorption and exchange reactions with components of soil adsorbing complex. Part of the ions, released during the disintegration of the initial complex, may form other complexes of various resistance with organic ligands. Some have noted the "induced toxicity" phenomena, which occurs after sewage input to soil as a result of free ion formation of more toxic metals than their compounds in the sewage. In
224
the course of time metals, contained in soil, transform into insoluble non-organic and organic substances resistant to microbiological decomposition (Tills & Alloway, 1983). Cation exchange capacity primarily depends on mineralogical composition of clay fraction and organic matter content. The higher the CEC the more the soil retention capacity for HM which rules out their penetration in toxic amounts into plants, and also in animals and man. Table 132 represents data on HM content in grey forest medium loamy soil, which illustrate the dependence of various HM forms, and particularly, CEC dependence of Zn.
Table 132 Acid-soluble (1) and mobile (2) Zn forms in grey forest medium loamy soils (Tula region, Malino forest farm) Horizon
Al
Humus (%) CEC,
5.20
pH of soil liquid
Zn (mg/kg)
meq/100 g
1
2
phase
24.30
23.00
11.00
6.1
A1A2
1.40
14.60
14.50
8.00
5.8
Bl
1.40
16.00
9.50
9.00
5.8
In HM distribution in the system of soil - solution an important part belongs to sorption and desorption phenomena. Experimental data on Cd sorption from its diluted solutions correspond to the Langmuir and Freundlich equation. The Freundlich equation coefficient is applied in order to characterise soil sorption capacity (Miragaya, 1980). HM sorption values depend on the chemical and structural properties of the different soil components. Sorption may take place depending on the soil organic matter, humic fractions, clay minerals, sesquioxides, organo-mineral compounds of the soil. The share of a particular component in HM sorption is different in different soils. Cadmium sorption in humic soils is primarily determined by humic organic compounds, while in low humic soils and in mineral horizons it is by clay minerals and sesquioxides (Gorbatov et al, 1988). In soils of various properties HM content and distribution with depth depends on the direction and intensity of soil formation processes (Beus et al., 1976). For example, in highly humified chernozems, where organic matter mobility is low and non flushing water regime is predominant, biogenous HM accumulation is observed. In sod-podzolic soils, which are being formed under the influence of sodization and podzolization, two peaks in HM content are observed: in the humic and in the illuvial horizons.
225 In meadow-boggy with permafrost soils of the Tungiro-Neniuginsk area, significant predominance of acid-soluble Cd has been observed in soils under grassland communities compared to agricultural soils, while mobile Cd was practically the same. As for alpine cold podzolic soils under forest, we could distinguish an accumulative type of cadmium distribution along the profile with a maximum in the upper horizon (Table 133). All the forms of metal compounds behave similar. Table 133 Acid-soluble (1) and mobile (2) Cd forms in soils of the Tungiro-Neniuginsk area (Chita region) Ecosystem
Soil
pH of soil
Humus
Cd(mg/kg)
Agricultural
meadow-boggy
liquid phase
(%)
1
2
Aploughed
5.4
5.50
0.07
0.07
with permafrost
AO
4.7
9.60
0.20
0.10
alpine cold
Al
5.1
9.80
0.13
0.01
podzolic
A2
4.6
4.10
0.04
0.02
B
5.5
4.10
0.04
0.02
Horizon
with permafrost Grassland
Forest
meadow-boggy
In podzoHc soils of the Central-forest Reserve under forest cover, an eluvial-illuvial type of distribution of soluble Cd form may be traced, with peaks in the humic and illuvial horizon (Table 134). This type of distribution is characteristic for podzohc soils. Metal distribution is determined by biogenous accumulation and leaching (eluviation), accompanied by accumulation in the illuvial horizon. Table 134 Acid-soluble (1) and mobile (2) Cd forms in soils of the Central-forest Reserve Ecosystem
Soil
Horizon
pH of soil
Humus
CEC
liquid phase
(%)
(meq/lOOg)
1
2
Forest
podzolic
AO
4.8
20.00
32.60
0.50
0.50
A2
5.0
1.40
21.30
0.14
0.12
Bl
5.2
0.40
15.00
0.06
0.03
Al
4.5
51.00
24.70
1.30
0.77 0.19
Cd (mg/kg)
swampypodzolic
Agricultural
podzolic
Asod
4.7
10.90
19.90
0.20
podzolic
Aploughed
6.4
2.90
6.30
0.10
0.05
podzolic
Aploughed
6.3
1.80
6.80
0.10
0.06
226 Tables 135 and 136 present our research data on HM content in soils of various types. The maximum Pb content was observed in Chech republic in the Beskidy Mountains and at the Kamenicky site for both acid-soluble and mobile forms. Increased acid-soluble Pb was also found in brown forest and cinnamonic soils of the Caucasus Biosphere Reserve (Sochi region), which may be due to anthropogenic factors (closeness to a motorway, airport) on heavily textured cinnamonic soils. At the same time, mobile Pb in this region did not exceed the background level of other territories studied, since the bedrock of these soils is lime which promotes the formation of low soluble Pb compounds. The lowest content of soluble and mobile Pb forms was observed in soddy carbonate soils of the Bugac site (Hungary).
Table 135 Acid-soluble heavy metal forms in soils of Central and Eastern Europe Investigated site
Soil
Ecosystem
Element (mg/kg) Pb
Cd
Zn
Cu
Co
Ni
1
2
3
4
5
6
7
8
9
T\ er region. Central-forest Reserve
podzolic
10.7*
0.24
27.0
3.5
1.4
3.8
6.2**
0.17
9.0
1.4
0.6
3.3
4***
4
3
4
4
4
3.5
1.2
4.0
Tver region. Central-forest Reserve
acid meadow alluvial
grassland
9.5
0.2
10.0
Moscow region. Prioksko-terrace
podzolic
forest
7.0
0.02
2.0
0.1
0.55
1.5
Reserve
podzolic
grassland
10.5
0.6
19.0
4.5
1.4
7.5
(protected territory)
sod-acid alluvial
grassland
9.0
0.12
12.0
2.5
1.8
6.0
Moscow region, Zaokskoe forestry
grey forest
forest
11.0
0.3
16.5
7.0
2.4
9.0
Kursk region, Centralnochemozemny
chernozem
forest
16.3
0.11
16.6
4.8
3.5
9.1
6.3
0.0
0.4
1.8
0.3
3.9
2
2
2
2
2
2
11.7
0.11
14.9
3.4
3.4
9.0
1.7
0.02
2.1
0.9
0.4
3.5
2
2
2
2
2
2
fallow land
10.3
0.09
30.3
4.9
3.1
11.0
since 1947
2.3
0.01
10.1
1.9
0.4
4.2
3
3
3
3
3
3
10.2
0.09
19.2
7.0
4.7
17.2
1.1
0.01
3.5
0.6
0.3
0.2
3
3
3
3
3
3
7.0
13.5
Reserve
Kursk region. Centralnochemozemny
chernozem
grassland
Resei-ve
Kursk region, Centralnochemozemny
chernozem
Reserve
Donetsk region, grassland
Kiiomutovskaya Steppe Reserve
Klierson region, grassland
Askania Nova Reserve
12.4
0.13
13.4
9.4
4.9
0.08
4.0
2.4
5.9
4.6
6
6
6
7
6
6
Tula region, Malino forest area
grey forest
forest
9.25
0.37
19.0
4.5
2.0
9.25
Krasnodarsky region, Caucasus Reserve
cinnamonic
forest
25.0
2.0
28.5
7.5
32.0
15.0
Krasnodarsky region. Caucasus Reserve
brown forest
forest
30.0
1.8
24.0
3.5
52.0
6.5
227
Table 135 (continued) 1
2
3
_
^
8
9
Krasnodarsky region.
alpine meadow
grassland
191
0.1
206
19!8
2J0
6^4
3.5
0.03
5.7
18.3
0.7
2.3
4
4
4
4
4
4 8.0
Caucasus Reserve, Juga station
_
~~6
Caucasus Reserve, Juga station
brown forest
forest
20.0
0.13
36.0
4.25
3.6
Hungaiy, Bugac site
sod-calcareous
grassland
2.8
0.14
17.5
1.25
1.2
2.30
0.8
0.05
0.5
0.25
0.3
0.75
2
2
2
2
2
2
Hungary'. Csaszaitoltes
chernozem
grassland
7.5
0.2
64.0
5.0
2.5
7.5
Czech Republic, Kamenicky site
brown forest
grassland
64.3
0.2
6.6
3.6
1.0
1.35
1.3
0.03
0.6
0.49
0.0
0.05 2
2
2
2
2
2
Czech Repubhc, Beskidy site
brown forest
forest
83.13
0.12
9.75
3.0
0.58
1.55
Tungiro-Neniuginsk area
meadow-boggy with
grassland
8.0
0.2
23.0
16.0
1.4
8.0
forest
7.0
0.09
9.5
9.7
1.4
3.2
2.0
0.04
9.5
0.7
0.4
1.7
2
2
2
2
2
2
10.0
0.07
permafrost Tungiro-Neniuginsk area
alpine cold podzolic
Agricultural lands Tungiro-Neniuginsk area
meadow-boggy with
agricultural
permalro.st Tver region, near Central-forest Reserve
Moscow region, Agrochemical Field
podzolic
podzolic
agricultural
agricultural
Station named Prynishnikov
Moscow region. Experimental Field
grey forest
agricultural
Station of ISSP RAS
Moscow region. Experimental Field
grey forest
Station of ISSP RAS
3.2
1.4
1.6
1.0
2.3
0.15
0.37 2
2
2
2
2
2
9.5
0.07
25.9
10.25
4.8
12.2
6.5
0.03
9.3
3.8
1.6
10.6
12
12
12
12
12
12
9.9
0.24
14.1
5.6
2.5
5.45
0.04
1.2
0.7
0.13
3.0
4
4
4
4
4
grassland
7.7
0.2
13.5
6.0
2.5
7.2
****
0.2
0.05
1.1
2.2
0.12
0.6
3
3
3
3
3
3
7.5
0.21
12.5
5.5
2.3
9.0
agricultural
Donetsk region, near Khomutovskaya
chernozem
agricultural
Steppe Reserve
agricultural
Reserve
chernozem
4.0
5.0
grey forest
Stavropol region, Vodorazdelny farm
0.1 0.0
4
Tula region, Ordzhonikidze farm
Kherson region, near Askania Nova
8.5 0.5
agricultural
14.5
0.1
22.0
8.0
3.9
17.5
6.5
0.01
2.0
1.9
0.1
0.0
2
2
2
2
2
2
13.6
0.5
15.6
6.2
4.3
17.8
1.0
00.6
3.8
2.7
0.3
1.9
5
5
5
5
5
5
7.0
0.16
34.0
10.3
2.7
8.5
0.35
0.02
15.6
1.8
0.5
3.2
4
4
4
4
4
4
Stavropol region, Vodorazdelny farm
chernozem
grassland
4.5
0.06
4.0
10.0
5.2
27.0
Stavropol region, Moskovsky farm
chernozem
agricultural
10.0
0.1
56.3
6.6
4.2
15.1
0.8
0.02
26.3
0.4
0.2
0.4
4
4
4
4
4
4
228 Table 135 (continued) Stavropol region, Russkoye farm
Stavropol region, Seraphimovsky farm
agricultural
chestnut
agricultural
9.4
0.1
39.5
12.4
3.4
12.0
0.74
0.01
3.6
8.0
0.04
0.0
4
4
4
4
4
4
9.3
0.1
11.0
5.6
3.7
13.8
2.7
0.01
1.5
0.1
0.1
1.3
2
2
2
2
2
2
* - average: ** - mean root square deviation; *** - number of objects; **** - recovered meadow
Table 136 Mobile heavy metal forms in soils of Central and Eastern Europe ln\'e.stigated site
Soil
Ecosystem
Element (mg/kg) Pb
Cd
Zn
Cu
Co
Ni
1
2
3
4
5
6
7
8
9
Tver region. Central-forest Reserve
podzolic
forest
6.0*
0.2
6.7
2.0
0.4
0.2
3.9**
0.19
6.9
2.0
0.4
0.1
5***
5
5
5
5
5
3.5
0.19
19.0
5.5
0.6
0.1
Tver region. Central-forest Reserve (protected
acid meadow alluvial
territorv) Moscow region, Prioksko-terrace Reserve
podzolic
forest
1.9
0.05
6.0
2.9
0.1
0.7
Moscow region. Prioksko-terrace Reserve
podzolic
grassland
0.7
0.2
19.0
3.7
0.3
0.8
Moscow region, Prioksko-terrace Reserve
sod-acid alluvial
grassland
0.9
0.08
6.5
0.2
0.1
0.1
Moscow region. Zaokskoe forestry
grey forest
forest
1.8
0.2
11.0
3.0
1.0
2.6
Kursk region, Centralnochemozemny Reserve
chernozem
forest
2.4
0.07
22.6
3.6
0.3
0.1
Kursk region. Centralnochemozemny Reserve
chernozem
Kursk region, Centralnochemozemny Reserve
Donetsk region,
3.4
0.0
0.05
0.0
2
2
2
2
2.0
0.05
12.6
5.7
0.2
0.05
0.01
0.6
5.0
0.07
0.04
2
2
2
2
2
2
fallow land
1.2
0.03
12.3
3.0
0.2
0.1
since 1947
0.5
0.01
2.3
0.8
0.04
0.0
4
4
4
4
4
4
4.3
0.04
19.2
3.6
0.3
0.10
2.5
0.01
2.8
0.4
0.1
0.0
3
3
3
3
3
3
1.8
0.1
9.8
6.1
0.6
0.86
0.35
0.02
2.9
3.5
0.5
0.66
9
9
9
9
9
9 0.8
grassland
Klierson region, grassland
.Askania Nova Reserve
0.02 2
0.65
grassland
chemozem
Klioniutovskaya Steppe Reserve
1.2 2
Tula region. Malino forest area
grey forest
forest
1.5
0.1
1.0
3.2
0.2
Kra.snodarsky region, CaucasusReserve
brown
forest
2.6
1.3
17.2
4.6
2.2
1.2
Kia-^nodarsky region, CaucasusReserve
brown forest
forest
5.1
0.7
14.8
6.9
1.9
0.7
Krasnodarsky region,
alpine meadow
grassland
3.6
0.1
13.5
5.8
0.8
1.0
1.25
0.02
2.7
2.8
0.3
0.6
3
3
3
3
3
3
2.0 0.05 2
0.05 0.01 2
21.5 0.5 2
3.8 3.2 2
0.1 0.02 2
0.1 0.0 2
Caucasus Reserve. Juga station
Hungary, Bugac site
sod-calcareous
grassland
229 Table 136 (continued) 1
2
3
4
5
6
7
8
9
Hungary, Csaszartoltes
chernozem
grassland
2.9
0.1
36.5
9.0
0.2
0.1
Czech Republic, Kamenicky site
brown forest
grassland
10.8
0.2
9.6
9.5
0.2
0.1
0.75
0.02
3.4
0.0
0.0
0.0
2
2
2
2
2
2
Czech Republic, Beskidy site
brown forest
forest
11.1
0.16
11.5
2.5
0.2
O.I
Tungiro-Neniuginsk area
meadow-boggy with
grassland
2.3
0.1
17.5
4.5
0.7
1.2
permafrost Tungiro-Neniuginsk area
alpine cold podzolic
forest
1.45
0.06
6.4
9.0
0.2
0.7
0.15
0.04
2.1
1.0
0.05
0.5
2
2
2
2
2
2
Agricultural lands Tungiro-Neniuginsk area
meadow-boggy with
agricultural
permafrost Tver region, near Central-forest Reserve
Moscow region, Agrochemical Field Station
podzolic
podzolic
agricultural
agricultural
named Pi"ynishnikov
Moscow region. Experimental Field Station of
grey forest
agricultural
ISSP RAS
Moscow region. Experimental Field Station of
grey forest
grassland ****
ISSP RAS
Tula region, Ordzhonikidze farm
grey forest
agricultural
Donetsk region, near Khomutovskaya Steppe
chernozem
agricultural
Resei-ve
Kherson region, near Askania-Nova Reserve
chestnut
agricultural
1.3
0.05
3.0
1.1
0.2
0.1
0.1
0.01
2.0
0.9
0.05
0.0
2
2
2
2
2
2
2.2
0.08
10.1
0.9
1.3
1.3
0.9
0.06
4.9
0.0
0.4
1.1
6
6
6
6
6
6
0.7
0.2
7.4
2.9
0.09
0.6
0.1
0.02
0.8
1.6
0.01
0.5
4
4
4
4
4
4
1.1
0.2
6.7
1.8
0.3
0.9
0.5
0.01
0.6
l.I
0.02
0.2
3
3
3
3
2
3
1.4
0.15
15.2
0.2
0.3
0.5
4.3
0.04
8.0
2.8
0.2
0.2
0.45
0.01
0.0
0.25
0.03
0.03
2
2
2
2
2
2
2.1
0.1
4.0
1.4
1.1
I.I
0.5
0.01
0.9
1.0
0.1
0.09 5
5
5
5
5
5
Stavropol region, Vodorazdelny farm
chernozem
agricultural
4.5
0.06
4.0
10.0
5.2
27.0
Stavropol region, Vodorazdelny farm
chernozem
grassland
1.3
0.1
11.0
0.9
0.13
2.7
0.5
0.03
3.0
0.0
0.13
1.7
5
5
5
5
5
5
2.9
0.09
4.7
2.6
I.I
0.7
0.6
0.01
0.6
1.2
0.1
0.04
4
4
3
4
4
4
3.1
0.1
30.9
1.5
1.1
0.35
0.4
0.01
8.6
0.6
0.1
0.08
4
4
4
4
4
4
2.3
0.1
10.0
0.9
0.5
0.8
0.4
0.0
2.0
0.7
0.3
0.0
2
2
2
2
2
2
Stavropol region, Moskovsky farm
Stavropol region, Russkoye farm
Stavropol region, Seraphimovsky farm
chernozem
chestnut
chestnut
agricultural
agricultural
agricultural
* - average: ** - mean root square deviation; *** - number of objects; **** - recovered meadow
230
As long as there is a close link between HM and soil components, the negative influence of heavy metals on the environment is not significant. If soil conditions allow HM to enter into soil solution, it creates the danger of soil contamination and the probability of their penetration into plants, man and animals increases (so-called "The bomb of delayed action"). The danger of contamination of soil and plants depends on plant species, forms of chemical compound in soil, the presence of elements resistant to the influence of heavy metals and related complex substances, processes of sorption and desorption; the amount of available forms of these metals in soil and on general climatic conditions. Consequently, the negative influence of HM depends essentially on their mobility. The ever-increasing extent of environmental pollution by industrial emissions containing heavy metals, leads to an increase in their levels in agricultural lands and, consequently, to a decrease in the amount and quality of crop. Heavy metals may penetrate the soil-plant system with atmospheric precipitation, dust from the industrial areas, motorways, municipal enterprises, and also with fertilisers, in form of sewage water of industrial and municipal origin, and of that used for irrigation and with phosphorous fertilisers (Snakin, 1998). Being accumulated in soils, metals are slowly removed by leaching, while taken up by plants, by water erosion and deflation. As estimated by limura et al. (1977), the first period of heavy metals half-removal (i.e. removal the half of initial concentration) for soils under lysimetric conditions varies, for Zn - from 70 to 510 years, for Cd - from 310 to 1500 years and for Pb from 740 to 5900 years. There are few reports on HM uptake into plants and its determining factors. The composition and other properties of soil solution have influence on metal penetration through root system. As a result an increased HM content can be detected in plant tissues, the concentration exceeds that of the plants on unpolluted soils by a ratio of tens and hundreds. That is why they require special processing before being used for food for man and animals. Table 137 shows the data of different authors on HM concentration, which are considered the maximum permitted concentrations in relation to phytotoxicity. Detailed environmental regulation on polluting substances must be determined with regard a distribution characteristics of HM in various ecosystems. In order to investigate HM distribution in soil-plant systems we have carried out work in various regions with a difference in the extent of anthropogenic load; the Moscow region (Prioksko-terrace Reserve, Zaokskoe forestry, Experimental Field Station of ISSP RAS); Czech Republic (Kamenicky meadow site and patches
231 of degraded pine woods in the Beskhidy Mountains); the Caucasus Reserve (subalpine and alpine meadows and European fir forests of the Juga Biospheric Station). Pb and Cd measurements were carried out by direct atomic-absorption technique with electrothermal powder atomization (limit of measurement - 0 . 1 - 1 mg/kg for Pb and 0.005-0.01 mg/kg for Cd).
Table 137 Total heavy metal concentration in surface horizon of soils, considered as maximum permitted in relation to phytotoxicity (mg/kg of dry mass) Element
HM concentration (Kovalskiy, (El-Bassam,
(Linzon,
(Lindsay,
(Kloke,
(Kitagishi,
1974)
Tietjen,1977)
1978)
1972)
1979)
Yamane, 1981)
Cd
-
5
8
5
3
-
Co
30
50
25
50
50
50
Cu
60
100
100
100
100
125
Hg
-
5
0,3
5
2
Mo
4
10
2
10
5
-
Ni
100
100
100
100
100
Pb
-
100
200
100
100
400
Zn
70
300
400
300
300
250
Pb and Cd content in soils of the Moscow region is low and comprises 16-18 mg Pb /kg of dry soil and 0.03-0.7 mg Cd /kg of dry soil. Heavy metal content in soils of the mountainous sites of the Caucasus Reserve was: 13-36 mg Pb /kg; 0.07-0.53 mg Cd /kg; in the parent rock: 4-35 mg Pb /kg, 0.01-0.8 mg Cd/kg (with the exception made for one sample, where Pb content in soil was 375 mg/kg). Pb and Cd concentration in the soils of mountainous sites of the Czech Republic are higher: 95-550 mg Pb /kg and 0.5-3 mg Cd/kg, the concentration decreases sharply down the soil profile, by a ratio from 10 to 40, and is comparable to the content in the parent rock. So the main part of these metals in the upper horizon is accumulated by anthropogenic input. Pb and Cd content in plant biomass (mainly, grass biomass) varied in the following range: 0.5-17.5 mg Pb /kg and 0.01-0.87 mg Cd /kg of dry grass mass. Heavy metals concentration in the direction of living biomass-standing dead-litter varies as follows: 2.1-2.6-4.0 for Pb and 0.14-0.200.26 for Cd by mean values. The dependence of the accumulation of the heavy metals by different plant species studied on the metal concentration in soils was not found.
232
Estimations have shown that accumulation coefficients (K) of heavy metals (HM concentration ratio in plants and in soil) do not differ at equal HM concentrations in the soil. The use of fertilisers in agricultural soils increases the values of Pb accumulation coefficients, but for Cd this effect is less distinct. The concentration dependence of HM accumulation in soil may be empirically expressed by the following equations: In Kpb = 0.02 - 0.84 In (Cpb); r = -0.83; n = 160; In Kcd = - 1.66 - 0.78 In (Cca); r = -0.72; n = 160, where Cpb and Ccd - total Pb and Cd concentrations in soil (mg/kg). Analysis of these equations and empiric data proves that at HM concentration in the soil, equal to maximum permitted concentration (MPC), adopted in Russia (20-33 mg Pb /kg and 5 mg Cd /kg), accumulation coefficients make about 0.6 for Pb and 0.5 for Cd (Table 138). This corresponds to HM content in plants at a minimal level, proposed as Pb and Cd maximum permitted concentrations in plants (2-50 mg Pb/kg and 0.2-3 mg Cd/kg according to data of various authors).
Table 138 MPC values of some pollution's in soils, plants and food products, mg/kg Pollution
MPC in soil
Lead
20-32
1.0-2.6
2-50
Cadmium
5
0.25
0.2-3
0.03-1.0
Nitrate
50
220
-
45-2000
Estimate MPC in plants MPC in plants
MPC in foods 0.3-10
6. 8. CORRELATION BETWEEN SOIL SOLID AND LIQUID PHASES COMPOSITION
As a rule, the study of ion exchange properties are based on modelling experiments in the soil-solution system with the use of physico-chemical equations of adsorption and cation exchange. We have carried out the analysis of Freundlich, Langmuir, Gapon, Nikol'skii and Gaines-Thomas equations using the results of in situ potentiometric measurements under field conditions by ionselective electrodes, and the Demetra computer database (see Section 2.7). The applicability of adsoption and ion exchange equations in the description of experimental dependence, i.e. models validity, was checked through dispersion analysis by Fcriterion. A respective determination coefficient was used as a criterion for models comparison (Pollard, 1977). Since there are only a limited number of ISE which meets the requirements oi in
233
situ measurements, we have limited ourselves to the consideration of the behaviour of Ca ^ and K^ ions. Substances adsorption on solid phase surface is a common phenomenon and has to do with almost all the components of soil liquid phase. The quantitative side of the process may be expressed from Freundlich equation: Q-^mc\
(36)
And Langmuir equation: e = ^ ^ •(?„„.
(37)
where Q - specific amount of adsorbed substance (per unit of soil mass); c - equilibrium concentration (activity) of adsorbed substance in solution; m and n ~ empirical coefficients, reflecting specific properties of soils; K - process equilibrium constant; Qmax - maximum amount of substance, adsorbed by a unit of soil mass, i. e. soil sorption capacity (according to Orlov, 1985). Cation exchange reaction, including K^ and Ca^^ cations, may be expressed from empirical equations, proposed by
Gapon(1937):
^
= KG ^""^^-^
(38)
Nikol'skii (1934):
^a ^l^
P^)
Gaines and Thomas (1953):
,JNZ-^CEC V^c-^LAC
V ^ = ,.K -^^^^ ^^,
V"c»» .
(40)
Where Nca and NK - the amounts of respective cations involved into exchange process in SAC (mg-eqv/100 g), acJ'^ and UK^ - cation activity in equilibrium solution; CEC - cation exchange capacity; KQ, KM and KG-T - exchange constants, which reflect soil selectivity in relation to cation adsorption in the respective equations. Table 139 shows K^ and Ca^"^ ions content in soil liquid and sohd phases of the natural and agricultural ecosystems, which prove the great variability of values within a given soil type. The variability of Ca and K ion activity significantly exceeds the variability of concentration of these ions under exchange condition. This is probably due to the analysis of sohd soil phase from mixed samples. In natural communities, there are distinct differences between various soil types by exchangeable Ca content; the highest values were observed in cinnamonic soil and in chernozems, and the lowest in podzolic soils. Soils of natural communities can be arranged in accordance with
234
their liquid phase Ca^^ ions activity (see Table 139 and Fig. 45) as follows: chernozems > cinnamonic > chestnut > brown forest > grey forest > podzolic. However, they are quite similar in relation to their exchangeable K content. It may be noted that the highest values were observed in brown forest, and the lowest in podzolic soils. By K^ ion activity in the liquid phase the natural communities can be arranged as follows, brown forest > chestnut > chernozems > grey forest > podzolic > cinnamonic (see also Section 6.3).
Table 139 K and Ca content in the liquid and solid parts of various soil types Soil type
Liquid phase (meq/L)
Soil adsorbing complex (meq/100 g) Caexch
K^xch
CEC
3.5 ±4.9
0.6 ±1.0
16.7 ±8.7
~C?^
K^
Natural ecosystems Podzolic
Grey forest
Chernozem
Chestnut
Cumamomc
Brown forest
X
±CI
1.4 ±1.9
1.0 ±3.0 0.005 -10.3
min - max
0.4 -6.0
0.04 -3.6
7.4 -32.6
0.03 -8.8
X ±a
12.2 ±4.0
1.2 ±1.2
20.8 ±3.4
2.5 ±2.6
1.3 ±1.2
min - max
7.2 -6.0
0.26 -3.7
16.2 -27.5
0.3 -7.2
0.01 -4.6
33.8 ±10.0
0.9 ±0.4
41.3 ±10.2
20.6 ±17.7
1.6 ±2.0
min - max
13.7-47.9
0.2-1.4
22.8 -54.7
1.2-54.0
0.02-7.9
X ±a
15.8 ±3.0
0.8 ±0.2
25.0 ±2.0
10.6 ±14.5
2.0 ±1.7
min - max
13.6-17.9
0.7-1.0
22.2-27.1
0.1 -44.0
0.02 -5.6 0.2 ±0.4
X
±CT
7 ±a
44.1 ±2.8
1.1 ±0.2
54.5 ±2.2
14.7±11.7
min - max
39.3 -48.4
0.8-1.5
49.0 -57.0
3.8-41.7
0.04-1.8
X ±a
15.4 ±8.4
1.7±2.1
42.2 ±15.5
6.1 ±9.4
4.5 ±7.4
min - max
4.2-28.1
0.3 -6.9
15.9-76.1
0.1-30.6
0.05 -22.7
X ±a
9.0 ±5.0
0.2 ±0.1
13.1 ±4.1
9.1 ±7.5
1.9 ±5.0
min - max
0.4-12.7
0.12-0.42
6.3-17.0
0.38 -28
0.008-25.1
Agroeco systems Podzolic
Grey forest
Chernozem
Chestnut
13.5 ± 1.7
0.4 ±0.1
18.0±2.1
13.7±17.1
0.2 ±0.2
min - max
7.1 -18.2
0.12-0.63
13.9 -20.0
1.1-57.4
0.02 -0.84
X ±(7
22.7 ± 8.0
1.1±1.7
35.0 ±4.3
21.6 ±16.5
1.7 ±3.7
min - max
17.7-37.0
0.3-1.3
28.1-39.8
1.8-74.0
0.02-19.4
X ±a
20.0 ±5.9
0.9 ±0.3
26.4 ±5.4
5.8 ±1.9
0.3+0.2
min - max
7.4 -27.4
0.3-1.3
11.1 -29.8
2.6-8.5
0.1-0.5
X
±CT
Note: X - mean value; a- mean-.square deviation; min-max - the range of minimum and maximum values
235 60
50i
h' />x'
40 H
30i LL
20i
10
J-
Fig. 45. Activity distribution curves for Ca (a, b) andfor K (c, d) in natural ecosystems (a, c) and agroecosystems (b, d): 1 ~ podzolic; 2 - grey forest; 3 - chernozems; 4 - chestnut soils (curves are based on different activity ranges for various soils)
Differences in soils of agroecosystems for Ca content are not very distinct. This is explained by a significant increase in Ca in both soHd and liquid parts of acid soils in the agricultural process. The differences in K content are also not significant, but in arable podzolic and grey forest soils, average exchangeable K content decreased and the range of the observed values narrowed. Potassium ion activity in soil liquid phase varies significantly, especially in spring after fertiliser appHcation. lion concentration in soil solution is buffered through surface adsorption of soil particles. The overall ion mobility depends on their amount and mobility in the solid phase. Analysis of correlation among all parameters of the Hquid and solid phase, carried out for the data of Demetra database, shows a low level of correlation (Table 140). That is why we have attempted to analyse
236 such correlation, using particular soil types as examples, and made use of adsorption and cation exchange equations.
Table 140 Coefficients of correlations between liquid and solid soil part parameters (for the data base - DDB) Solid part
Liquid phase
pH
Eh
Ca^^
K"
NO3'
Exchangeable cations: Ca^-
-0.18
0.38
0.37
-0.05*
-0.08*
Mg^^
-o.n*
-0.02*
0.01*
0.09*
-0.05*
K^
-0.05*
-0.18
-0.11*
0.14*
-0.05*
Na"
-0.13*
-0.10*
-0.05*
0.13*
-0.08*
HydroKlic acidity
-0.07*
-0.45
-0.22
0.48
0.06*
CEC
-0.12*
0.03*
0.17*
0.26
-0.05*
* Coefficients are not significant at P- 0.05
Table 141 Analysis of Freundlich equation for various soil types Soil type
Ca
K
m
n
R'
m
n
R'
6.9
0.43
0.31
-0.22*
0.1*
0.04*
podzolic
1.1*
0.18*
0.06*
-1.1*
0.07*
0.01*
grey forest
9.0
-0.25*
0.72*
1.1*
0.67*
0.76*
chernozem
20.0
0.2
0.55
-0.33*
0.005*
0*
cimiamonic
44.7
-0.02*
0.06*
0.2
0.06*
0.11*
brown tbrest
10.0
0.3
0.34
-0.26*
0.14*
0.14*
5.2
0.49
0.39
-0.12
0.34
0.43
grey tbrest
6.7
0.2
0.62
-0.83
0.10
0.64
chernozem
13.0
0.27*
0.81*
-0.18*
0.25*
0.81*
chestnut
18.2
0.03*
0.03*
0.33*
0.12*
0.76*
6.7
0.43
0.32
-0.25
0.15
0.08
Natural communities:
Agricultural lands:
All soils
Note: ni and n ~ equation coefficients; R^ -- determination coefficients. * Coefficients are not significant at P 0.05
Based on the data presented in Tables 141 and 142, one may conclude that Freundlich and Langmuir equations describe satisfactorily the experimental dependence between exchangeable Ca and Ca in the liquid phase. In agreement with determination coefficients the Langmuir equation provides better approximation to the values observed. Linear regression of the Langmuir equation.
237
leads to a high correlation coefficient value (0.88) even for all data collection (Fig. 46). Correlation between exchangeable and soluble calcium is stronger for agricultural soils.
Table 142 Analysis of Langmuir adsorption equation for various soil types Soil type
Ca Kca
K R'
model
KK
R'
validity*
Model validity *
0.33
0.11
+
0.02
0
podzolic
0.06
0.95
+
0.008
0
-
grey forest
0.37
0
-
0.23
0.82
+
chernozem
0.77
0.71
+
0.008
0
ciimamonic
0.60
0
0
0.18
0
-
0.02
brown forest
0.002
0
Agricultural lands:
0.47
0.85
+
0.07
0.04
0.28
0.78
+
0.05
0
-
Natural communities:
grey forest chernozem
0.38
0.81
+
0.05
0
chestnut
0.33
0
-
0.09
0
0.34
0.78
+
0.003
0
All soils
Note: Kca and KK -- equilibrium constants, R'- - determination coefficient * By F-ctiterion at P 0.05
Ca
exch(iTieq/100g) 60
Fig. 46. Experimental data approximation for all the soils studied by Langmuir equation (Kca = 0.34, see Table 142)
238 A reliable dependence between exchangeable and soluble potassium has been discovered in accordance with Freundlich equation only for agricultural soils (see Table 142). The applicability of adsorption equations for the description of experimental data on exchangeable Ca content and Ca^^ activity in the liquid phase may be indicative of adsorption mechanisms. In natural ecosystems, the behaviour of such biophylic element as potassium may be affected by other processes among them prevailing the biological ones. This was proved in the work by Volkova (1978) containing an analysis of various potassium forms in steppe ecosystem. Analysis of the possibilities to apply cation exchange equations, based on data of soil liquid phase composition, has demonstrated an appropriate model description for the agricultural soils (Table 143). The functional link between the ratio of exchangeable Ca and K and the respective correlation of their activity in the soil liquid phase of natural ecosystems is less distinctly expressed. In the evaluation of equations being analysed by the respective determination coefficients it is hard to distinguish a particular equation as the best.
Table 143 Analysis of cation exchange equations for various soil types of agricultural and natural ecosystems (uncorrected dispersion analysis*) Soil t\pe
Natural communities:
Gaines and Thomas equation
Gapon equation
Nikol'skii equation
R^
model validity**
R'
model validity **
R'
model validity **
0.24
+
0.17
+
0.23
+
podzolic
0.06
-
0.08
-
0.07
-
grey forest
0.78
+
0.84
+
0.75
+
chernozem
0.15
-
0.13
-
0.15
-
cimiamonic
0.49
+
0.49
+
0.50
+
brown forest
0.73
+
0.67
+
0.66
+
Agricultural lands:
0.47
+
0.54
+
0.49
+
grey forest
0.67
+
0.76
+
0.74
+
chernozem
0.51
-
0.57
-
0.54
-
chestnut
0.98
+
0.98
+
0.98
+
0.29
-f
0.25
+
0.28
+
For the whole database
* Having expressed equation left-hand parts through y, and right-hand parts x, we obtain that y=Kx for all exchange equations being analysed, since is it an axiom that linear regression curve should cross zero point of co-ordinates. In this case the dispersion analysis is non-corrected, and the determination coefficient will not be a square of ordinary correlation coefficient (Pollard, 1977). * * By F-criterion at P< 0.05.
239 In accordance with selectivity coefficients (Table 144), soils selectivity to Ca increases as follows: podzolic soils - grey forest - chernozems according to the increase in humus content. It is known that among soil soHd phase components, organic matter is the most Ca-selective. The lowest Ca selectivity and the highest K selectivity was observed for cinnamonic soils. Brown forest soils are the least K-selective. Based on selectivity coefficients, these values are comparable with data by Bolt (1982), except for the brown forest soils.
Table 144 Ca and K exchange selectivity coefficients for various soil types Soil types
Podzolic
X
min - max Grey forest
X
min - max Chernozem
X
min - max Chestnut
X
min - max Cinnamonic
X
min - max Brown forest
Agroecosystems
Natural ecosystems
X
min - max
KG
KN
KG-T
2.0
0.7
0.08-4.8
0.06-2.1
2.6
0.8
KG-T
KG
KN
4.4
1.3
0.5
1.9
0.36-11.4
0.06-4.1
0.1-1.1
0.3-6.8
4.5
2.8
0.7
3.3
0.1-3.2
0.5-14
0.6-5.6
0.24-2.0
1.2-9.3
0.4-12
6.5
1.1
7.7
4.1
0.8
4.2
1.1-21
0.16-3.2
1.2-23
0.4-10
0.1-20
0.5-11.3
1.5
0.4
1.9
2.5
0.5
2.8
1.3-1.7
0.3-0.5
1.6-2.2
0.8-3.1
0.2-0.7
1.0-3.7
1.5
0.3
1.7
-
-
-
0.5-3.3
0.07-0.5
0.5-3.6
-
-
"
20
10.5
52
2.4-77
0.4-42
2.4-182
Note: X - mean value; min-max - the range of minimum and maximum values; «-» - no data available
The pattern of change of selectivity coefficients at K - Ca exchange in the genetic order of agricultural soils corresponds to the changes, observed in the soils of the natural ecosystems. The selectivity of the soils studied tends to increase to K in the agricultural process, exluding chestnut soil, represented by only two natural ecosystems. A reliable SAC content dependence of K and Ca selectivity coefficients was not discovered for different types of soils. Analysis of data shows an increase in soil selectivity to Ca and a decrease in K at K content in SAC lower than 2% (Fig. 47). Since decreased K content in SAC leads to an increase in soil selectivity to K, the pattern obtained shows that there are other factors of significant influence upon the process considered.
240
80
60
40
20
I
1
I
>
I
0
0.05
0.1
0.15
0.2
1^
0.25
0.3
Fig. 47. The dependence of selectivity coefficient on the share of K in SAC for all soils studied (Nikol 'ska equation).
The analysis of experimental data, based on in situ measurements in undisturbed soils, allows the following conclusions: • Gapon, Nikol'skii and Gaines-Thomas equations describe satisfactorily the K - Ca exchange between soil soHd and Hquid phases; • adsorption of calcium by the soil in ecosystems is satisfactorily described by Langmuir and Freundlich equations, but the Langmure equation is more precise; • description of potassium adsorption in the ecosystems follows satisfactorily the Freundlich equation only for agroecosystems; in the natural ecosystems none of the quoted equations may be used to describe potassium adsorption; • in all cases the processes in the agricultural soils are described by adsorption and ion exchange equations more precisely as compared to those in the natural communities.
6. 9. CONCLUSIONS In situ ionometry has allowed to develop a method, which gives real assessment of carbonate equilibrium status in soils. By the application of the method it has been shown that none of the cases registered a reliable CaCOs oversaturation of the soil liquid phase. For carbonate soils, there is a close correlation between pH value and CO2 content in soil air. For acid soils containing
241 no CaCOs in the solid phase, such correlation is insignificant. As CO2 partial pressure increased no reliable change in Ca and Na ion activity in the liquid phase was found. For carbonate soils an increase in Eh was observed with CO2 concentration increase, which is determined by pH change in the liquid phase. For sod-podzolic soil no reliable correlation was observed within the limits of CO2 concentration in gas phase (up to 5%) and time of experiment. The above results of soil Eh study, based on in situ measurements, show that the Eh reflect the redox conditions in the soil liquid phase and may gain application, including thermodynamic analysis. The study of Eh dynamics in the daily and seasonal cycles has shown that in virgin soils under developed ecosystems it is the biological factor that directs many soil processes, whereas for the communities at the initial stage of succession physical factors are often predominant. Analysis of correlation between Eh and pH changes in soil liquid phase shows that this correlation may be either negative or may not exist at all. However, in practice cases of both negative and positive correlation's are frequent, which proves the complex nature of soilecosystem processes and the possibility of spontaneous changes of these parameters. As integral characteristics of soil-ecosystem processes, soil Eh and pH may become important soil diagnostical indicators. Using arable chernozems as an example, the possibility for the use of Eh and pH diagrams for identification of various chernozem subtypes was demonstrated. The study of the correlation between redox regime and production processes proves the interdendence of these characteristics. There may be a close correlation between Eh value and the ratio of maximum living and dead phytomass supply in the natural grassland communities. The same close relationship is true for the Eh value and netto productivity of ecosystem. Analysis of the regression equation shows the reflective pattern of soil redox processes and vegetation, the increase in the intensity of reduction processes (photosynthesis) in plants during the day is accompanied by an increase in oxidised substances in soil and, consequently. Eh increase. The value of Eh, thus, is a feature characterising ecological processes. Eh may also contain different information on the processes in ecosystems. In this sense it seems prospective to expand the works for the characterisation of soils with the use of the given value (Kovda, 1973). Eh in soils containing easily decomposable organic substances should differ significantly from the soils with
greater content of stable mineral and organic substances.
Significant Eh change may be also observed after pesticide application and under other anthropogenic loads on agricultural lands. Finally, soil erosion may be accompanied by the changes in redox conditions due to the destruction of soil aggregates, inside of which a more reduced
242
regime by 100-200 mV was observed than that at their surface (Serdobolsky & Sinniagina, 1953; Kaurichev & Tararina, 1972). The mentioned facts have to deal with the purely practical questions of agriculture, and in situ measurement of Eh may be one of the agrochemical express-methods deserving special attention. The dynamics of the composition of soil liquid phase in agrocenoses is significantly different from those in natural ecosystems. While for the natural communities, soil liquid phase composition is an indicator of the intensity of biogeocenotic processes. In agroecosystems both the concentration of components and their dynamics are most often determined by the degree of anthropogenic impact: the quantity and type of fertilizer applied, agronomical techniques and cropping. Under such conditions the soil liquid phase concentration may be high (see Table 123). Na , Cr, S04^' ions are often found in soil solutions in high concentrations. This may take place after fertiliser had been applied, resulting in adverse impact on crop and change in soil formation conditions (see Table 57, 58). Simultaneously, Ca is replaced in large amounts from the soil adsorbing complex, its concentration grows in the liquid phase, leading to ftarther migration down the soil profile and, as a consequence, to loss of Ca. Analysis of factors determining soil liquid phase composition, allows to develop a approach to its formation, aimed at optimising the productive process in the case of agriculture. Therefore, it is advisable to introduce the method of/>? situ ion activity measurements into practice of a network of agrochemical research stations. This could allow to create a scale of optimal values of ions activity in the liquid phase of various soils that ensure higher yield. Such an approach could solve many environmental problems, related to the excessive use of fertilisers, which application is often unjustified and leads not only to environmental pollution, but also to the deterioration of yield. To a great extent material and energy exchange in ecosystems takes place with active participation of soil liquid phase Therefore, it is important to compare the amount of substances contained from both the points of view of plant nutrition (potassium, nitrates, etc,) and the extent of soil contamination (nitrates, heavy metals, etc). The range of concentration of the components in soil liquid phase, due to their seasonal and diurnal dynamics, is wide (see Chapter 5). Thus, potassium activity in soil liquid phase of various ecosystems varies by a ratio of 5000. The differences in the cases of such complex indicators as silicon and organic matter content are smaller (up to 5-20 times). This makes the various types of soil difficult to compare. However, these differences are evident and significant.
243
A number of difficulties may occur when using existing physico-chemical equations for the description of the real material exchange in natural ecosystems. In agroecosystems, where the manifestation of biological component is sharply reduced and regulated, the applicability of ion exchange and adsorption equations seems more justified than in natural ecosystems.
244
CHAPTER 7. ENVIRONMENTAL PROCESSES AND SOIL LIQUID PHASE The process of photosynthesis is "a trigger mechanism of biosphere", i.e. the processes of phytomass increase, transpiration, biological turnover of chemical elements caused thereby and relate to the number of basic ecologo-functional characteristics of ecosystem. We have considered (see Section 4.5; 5; 6.2.3) the problems of interrelation between these processes and the properties of the soil liquid phase. It makes sense to consider this interrelation in a separate section, as bioproductivity is the main function of ecosystem. Natural changes in the composition of the soil liquid phase during the vegetation period set an inevitable question on causes for that changes. Preliminary analysis of data given above shows that apart from the effect of purely physical factors (temperature and soil moisture), the composition of soil liquid phase in seasonal and diurnal cycles is a biologically determined process. We try to consider, at least at a phenomenological level, this interrelation with functioning of the vegetation. First of all, we consider photosynthesis, transpiration and phytomass increase as well as hydrothermic regime of soil. 7.1. PHOTOSYNTHETIC INTENSITY In June-July 1985 we carried out combined studies of daily changes of photo synthetic intensity in the dominant plant species and ion composition of the soil liquid phase in different (field stations "Bugac", "Csaszartohes" and "Khomutovskaya steppe" reserve) ecosystems (Snakin et al., 1991). The correlation between these two processes (Table 145) was calculated on the basis of analysis of variance. Daily dynamics of NO3" ions in soil liquid phase closely correlated with temperature and photosynthetic intensity. The increase of photosynthesis during a day in all three communities was followed by the rise of activity of nitrate-ions^^^: subsequent correlation coefficients range from -0.29 to 0.86. The greater effect of biological factor in the sequence Bugac - CsaszartoltesKhomutovskaya steppe led to greater influence of photosynthesis on PNO3 (determination In tlie section correlation is considered not directly with the value of ion activity but with the value pX = -Ig ax (i.e.. the liigher pX, the less ion activity). Such an approach derives, first, from the comparative results in the succession pX-pH-Eh, and second, from tlie fact that in equations of physico chemistry ion concentration (activity) is often presented in logaritlunic form.
245 coefficient varies accordingly: 0.38 - 0.60 - 0.56) at simultaneous decrease of the effect in soil temperature. 0.96 - 0.66 - 0.15. The mechanism is effected by both the substitution of temperature factor by the factor of photosynthesis and in the thermo-stabilizing effect of vegetation (the more developed litter and the plants have a role to play in thermoinsulation, as a result daily fluctuations of soil temperature considerably decrease). Table 145 Resuhs of two factor variance analysis on the influence of photo synthetic intensity of dominant plant species (P) and soil temperature (t) on the SLP composition (pX) Objects, date
Parame-
Partial coefficients of
Regression coefficients in
Determination
ter
correlation
equation pX = Ao+ Ait + A2P
coefficients
~t
P
IJP
"~^%
A,
A2
t
P
__
Festucetum Vaginatae
PNO3
-0.97
-0.31
0.98
3.32
-0.041
-0.0087
0.96
0.38
(Bugac),
pK
-0.09
0.63
0.76
3.47
-0.0012
0.030
0.30
0.57
0.58
July 4-7, 1985
pCa
-0.99
0.09
1.00
2.38
-0.031
0.0009
0.99
0.45
0.99
pH
-0.01
-0.25
0.32
7.92
-0.0002
-0.013
0.04
0.10
0.10
Eh
-0.99
0.64
1.00
31.0
-4.65
1.20
0.99
0.51
0.99
Salvio-Festucetum
PNO3
0.46
-0.29
0.83
2.55
0.022
-0.010
0.66
0.60
0.69
mpicolae ponnonicum
pK
1.00
0.99
1.00
1.69
0.082
0.050
0.44
0.03
0.99
(Csaszartoltes),
pCa
-0.66
0.30
0.91
3.64
-0.068
0.020
0.81
0.69
0.83
M y 10-12, 1985
pH
0.46
0.80
0.87
7.72
0.0081
0.016
0.31
0.68
0.75
Eh
0.53
0.69
0.71
68.6
1.72
2.08
0.05
0.31
0.50
PNO3
-0.70
-0.86
0.88
4.09
-0.0074
-0.0074
0.15
0.56
0.78
1.92
0.050
0.017
0.69
0.17
0.94
Salvio-Festiicetum nipicolae ponticum
pK
0.96
(Kliomutovskaya
pCa
steppe),
pH
June 4-7, 1985
Eh
0.97
0.90
0.97
-0.58
0.93
0.94
1.99
-0.014
0.029
0.12
0.82
0.88
-0.76
-0.77
0.85
6.70
-0.013
-0.0078
0.32
0.33
0.72
-0.62
0.15
0.64
61.9
-2.74
0.28
0.40
0.03
0.41
Potassium activity in the soil liquid phase correlates with photo synthetic intensity to a greater extent (r: from 0.63 to 0.99) than nitrate. The growth of photo synthetic activity is accompanied by a decrease of the quantity of K in the soil liquid phase, most likely due to its uptake by plants during the production process (Liittkus & Botticher, 1939). The higher the soil temperature, the less K in soil solution (r: 0.96 and 1.00); this correlation is absent for high levels of K in Bugac. Potassium ion activity dependence on the temperature in case of soil without plants (laboratory experiments) present another regularity: K^ activity increases approximately by 0.1 pK for every 7-8"^ temperature rise (see Section 4.4). Discrepancies between these dependencies, which are observed also for nitrates, show that the influence of temperature in
246 natural communities is of indirect character, most likely through biological processes. The rise of soil temperature stimulates nutrient uptake including K uptake from the soil liquid phase. In Bugac, where the role of biological component is less, and the soil temperature inhibit biological processes (see Fig. 20), the influence of temperature on K content in the soil liquid phase SLP in daily cycle, as well as in the seasonal course is of opposite character. The activity of Ca in the soil liquid phase correlates with photosynthesis to a lesser degree, (see Table 145). The contribution of photosynthesis into its daily dynamics is significant: determination coefficient increases in the succession Bugac - Csaszartoltes - Khomutovskaya steppe: 0.45 - 0.69 - 0.82. An increase of photosynthetic activity was accompanied by a decrease of Ca * activity, whereas with the rise of temperature the quantity of Ca in the soil liquid phase increased. This is in agreement with temperature dependence, which was observed for carbonate soils (see Section 4.4). That is why we could often observe an increase in Ca^^ activity by noon (see Table 95) which was possibly derived from raised activity of microorganism respiration, but not from the respiration of plant roots. Such regularity makes it possible to explain the pattern of daily changes of Ca^* activity in soil in the different objects (compare Fig. 29 and Fig. 33). As compared to the other parameters, the value of pH and the ratio of oxidized and reduced forms of compounds (Eh) in the soil liquid phase change less with the increase of photosynthetic intensity and soil temperature. The only exception is temperature effect on Eh in Bugac, for which high daily fluctuations of soil temperature (lO'^C and more) are typical. The rise of temperature leads to a decrease of Eh (with Csaszartoltes). This corresponds to results obtained earlier studying Eh-temperature dependence under laboratory conditions with soil without plants (see Section 4.4.) The very high values of multiple determination coefficients of ion activity in the soil liquid phase of natural grassland ecosystems with photosynthetic intensity and soil temperature, allow to use the daily dynamics of the soil liquid phase composition the equation of linear regression as follows (see Table 145): pX = Ao+Alt + A2P .
(41)
The pH value of the sandy low humic calcareous soil in Bugac, that is at the initial stage of soil formation, was an exception to this rule. During the vegetation period of 1986 in the field station Bugac, interrelated changes of nitrate-ion activity in liquid phase of sandy low humic calcareous soil, photosynthetic intensity and soil temperature at a depth of 5-10 cm were studied (Table 146). The results demonstrate that
247
at the beginning of the vegetation period (4-5 April) the influence of photosynthetic intensity on the NO3 content of soil is absent (determination coefficient = 0). With the development of grass cover this value increases, but as in 1985 it is not dominant. The influence of temperature in the sandy semi-desert steppe is always significant. However, if at the beginning of the vegetation period the rise of temperature was accompanied by a decrease of NO3' ion activity (r=0.91) and this corresponded to laboratory experiments with soil without vegetation, then later this influence was of opposite character. Hence, the supposition that indirect temperature influence on SLP via the biological component of ecosystem (living matter) in the periods of active vegetation can be much superior to its direct effect has found support again.
Table 146 Results of the two factor correlation analysis on the effect of soil temperature (t), photosynthetic intensity (P) on NO3' activity in liquid phase of the sandy low humic soil (field station Bugac, Hungary). Tenns of
Partial coefficients of
Regression coefficients
Detennination
measurements
correlation
in equation
coefficients
Range of variations
PNO3 = Ao+A,t + A2P t
P
t,P
Ao
A,
A2
t
P
t,P
t
PNO3
4-5 April, 1986
0.91
-0.69
0.91
1.93
0.0075
-0.0013
0.67
0.00
0.83
10-20
1.99-2.07
25-26 April, 1986
-0.95
-0.55
0.97
2.62
-0.026
-0.0034
0.90
0.30
0.93
13-14
1.95-2.31
16-17 May, 1986
-0.84
0.46
0.88
2.33
-0.018
0.0026
0.72
0.24
0.78
18-28
1.76-2.06
6-7 June, 1986
-0.64
0.28
0.87
2.44
-0.0060
0.0024
0.74
0.60
0.76
12-20
2.30-2.37
9-10 July, 1986
-0.94
0.55
0.94
3.07
-0.050
0.015
0.84
0.00
0.89
18-28
1.74-2.17
-0.77
-0.86
0.87
2.85
-0.025
-0.027
0.10
0.42
0.76
14-25
1.89-2.35
Following daily average data of 1985 and 1986
In all measurements in 1986 in the sandy semi-desert steppe, highly reliable values of multiple determination coefficients were found specifying the effect of soil temperature and photosynthetic intensity on the content of NO3" ions in the soil liquid phase. The analysis of interdependence between the changes of average values of nitrate-ion activity in the soil liquid phase and photosynthetic intensity within the vegetation periods 1985 and 1986 (see Table 146), gives a negative correlation coefficients (-0.86). Consequently, the higher photosynthetic intensity, the more NO3" are found in the soil liquid phase. Such a regularity gives evidence on that the periods with high photosynthetic intensity are, at the same time, the periods of high intensity of nitrification processes.
248 The influence of photosynthetic intensity on the NO3' content of soil within a vegetation period was much higher than in the daily cycle. Inverse dependence has been found for temperature: its influence was higher in daily cycle relative to the effect of daily averages within the whole vegetation period in the sandy semi-desert steppe. For description
of dynamics of NO3" ions in the soil, of the Festucetum vaginatae
community (Bugac), within a vegetation period we found the following equation: PNO3 = 2.85 - 0.025 t - 0.027 P. 7.2. TRANSPIRATION AND EVAPORATION Transpiration and evaporation from soil surface are two factors leading to changes both in soil moisture content and the soil liquid phase composition. w (%)
4J
2-1
1 - Carex\ 2 - Koeleria; 3 - Festuca; A - CaMtium (plant-free space); 5 average on the site
1 0
12
15
18
21
Hours
Figure 48. Daily moisture dynamics of sandy soil under various plants. The dynamics was determined by gravimetry (Bugac, 20 May 1984). As was shown in Section 5.3, the daily course of transpiration has a sinusoid character with a maximum at 11-13 hours. Transpiration and evaporation could explain the daily rhythm of the composition of soil liquid phase. However, our attempts to study the rhythmic diurnal changes of soil moisture have failed both when determining moisture by gravimetry (Fig. 48) and electrometrically with the help of a sensor set at the depth of 7 cm in the soil for the whole period of measurements (Fig. 49). It is possible that the absence of plausible rhythmic diurnal variations of soil moisture is due to the low sensitivity of measurement technique used by us, but it is also obvious that in the soil of this grassland communities at a depth of 5-10 cm the moisture is kept
249 approximately at the same level for short periods of time (day) due to the continuous upward movement of moisture from the lower soil horizons. W(%)
16-^
15
14
15 ' 21 • 3 " 9 ' 15 ' 2 1 ' 3 ' 9 ' 15" 21 Hours 5 June 6 June 7 June
Figure 49. Daily moisture dynamics of ordinary chernozem under steppe vegetation. The dynamics was determined electrometrically (Khomutovskaya steppe, 1985) Table 147 Transpiration, soil moisture and nitrate content and activity in liquid phase of sandy soil (Bugac, 1986). Parameter
Correlation coefficient
Date of measurements 4-5
25-26
16-17
6-7
9-10
April
April
May
June
July
PNO3
2.04
2.07
1.89
2.35
1.95
aN03 (meq/L)
9.12
8.51
12.9
4.47
CNO3 (mg/100 g soil)
1.57
1.09
0.92
Transpiration* (g H20/dm ^ per day)
43
30.5
Evaporation* (g H20/dm^ per day)
62
Soil moisture (%)
2.98
PNO3
^NOS
11.2
-
-
0.85
0.78
-
-
28.5
15
0.19
-0.38
-0.74
64
-
48
41
-0.28
0.16
0.80
2.26
1.35
3.25
1.32
0.83
-0.88
0.48
CNO3
* the average for Koeleria and Festuca.
Analysis of interrelation between transpiration, the moisture content of sandy low humic soil (Bugac) and NO3" ion activity in the soil liquid phase at different stages of vegetation period (Table 147) points out to a different character of the impact of these factors. If changes in soil moisture are closely and negatively related to nitrate activity in the soil liquid phase (r = -0.88), then with quantity of NOs' in soil (mg/lOOg) this correlation is weaker and is of opposite direction (r = 0.48). Transpiration correlates to a lesser degree, and as a physiological process, is linked to ions' uptake as a result of which the nitrate activity in the soil liquid phase could decrease. The higher the transpiration is, the lower the NO3" activity in the soil liquid phase (r = -0.38). Close
250
correlation (-0.74 and 0.80 respectively) has been obtained for NO3" available in soil (mg/lOOg) under transpiration and evaporation. A comparative analysis of interrelations studied makes it possible to conclude that nitrate uptake by plant roots is a selective process opposing the gradient of concentration. 7.3. PLANT MATTER DYNAMIC The soil liquid phase is an intermediate link in the system: living matter - soil liquid phase - solid part of soil and hence it is interesting to consider the dynamics of its composition with regard to changes in plant and soil components. The soil solution contains a very small part of the total amount of chemical elements contained in other components of the ecosystem (see Table 37). Table 148 Seasonal dynamics of the amount of chemical elements (g/m^) in different components of steppe ecosystem (Khomutovskaya Steppe) Chemical
Ecosystem
Terms of measurements
elements
component
1977
Correlation coefficient 1978
3-6
8-12
24-26
1-3
18-19
19-23
with supplies
with supplies
April
May
June
August
November
April
in SLP
in vegetation
K
Vegetation
12.9
14.2
23.0
19.9
10.9
12.3
-0.43
K exchangeable
Soil (0-10 cm)
43.9
38.9
36.9
40.7
44.3
44.8
0.39
Soil liquid phase
0.66
0.24
0.50
0.31
3.75
-0.82
according to Maslova
N
Vegetation
32.3
36.0
40.1
47.4
37.8
36.4
-0.59
N common
Soil (0-10 cm)
370
320
435
280
325
330
0.32
-0.35
N easily hydrolyzable
Soil (0-10 cm)
21.9
21.5
19.6
20.2
19.4
16.2
0.50
-0.12
N-NO3
Soil liquid phase
3.56
0.34
0.51
0.20
0.09
0.32
Ca
Vegetation
24.5
26.9
32.8
32.6
23.9
24.6
0.68
Ca exchangeable
Soil (0-10 cm)
890
864
958
878
830
902
-0.01
Ca^^
Soil liquid phase
15.3
12.3
18.3
25.2
17.9
11.3
Moisture (%)
Soil
33.7
18.2
32.6
32.1
37.0
30.3
according to Hedroits
The analysis of seasonal dynamics of supplies of chemical elements in soil, vegetation and liquid phase of ordinary chernozem in the Priazov Region was made on the basis of data given in Table 148. First of all, the low correlation between ion content in the soil liquid phase and the
251 content of their exchangeable forms in the solid part of soil was marked: in case of K and Ca and the total amount or easily hydrolyzable part of N in the soil (r range from -0.01 to 0.50). A medium negative correlation was found between supplies of K and nitrates in vegetation and the contents of K^ and NO3" ions in the soil liquid phase (r = -0.43 and -0.59, respectively). For Ca the correlation was insignificant, and this means that the dynamics of the activity of Ca ions in liquid phase of chernozem within the vegetation period was not linked to the supply of Ca in vegetation. Analogous conclusion is drawn from the analysis of interrelation between the supplies of this element in the phytomass (living + dead aboveground + roots in a 0-10 cm layer) and the soil solid phase. For K there is a close correlation between the quantity in the phytomass and the amount of its exchangeable form in the soil (r = -0.82). For the various forms of nitrogen the corresponding correlation is also negative though much weaker (r = -0.12 + -0.35). Thus, dynamics of K and NO3 available in the liquid phase of ordinary chernozem proved to be determined to only a slight degree by supplies of K and nitrates in the steppe vegetation within the vegetation period. Dynamics of Ca^^ ion activity is caused by other reasons, including photo synthetic activity and temperature (see Section 7.1). The dynamics of SLP is result of a combined action of a number of factors among which cyclic changes of temperature of soil, and photosynthetic intensity, as well as transpiration and accumulated plant material have large effect. 7.4. ECOLOGICAL ESSESSMENT OF THE DEGREE OF ANTHROPOGENIC CHANGES IN SOIL Quantitative assessment of the status of the ecosystem and its components is one of the most important theoretical and practical problems. Determination of ecologically tolerable levels of anthropogenic loads for natural terrestrial ecosystems (ecological rationing) and assessment of damage caused by different impacts on ecosystems depend mostly on solution of this problem. The task is to estimate the degree of the deviation of the ecosystem or its components (e.g., soil) from their natural state, conditionally accepted as the norm, under the influence of external (anthropogenic) factors. Ecological estimate of state corresponds to the assessment of the degree of anthropogenic degradation of ecosystem or the soil. However, the common approach to assess soil degradation is anthropocentric, and often determines loss of valuable from economic point of view, agricultural qualities (e.g., reduction in soil fertility). The two approaches differ from each other by what is accepted as the norm: either natural state of the initial ecosystem (undisturbed pristine analogue) or some conditional state, considered to be the optimum one, or, sometimes, results of a previous investigation that establishes a control or bench-mark reference).
252 Complex systems such as ecosystems are difficult to describe, and the quantitative process of the final assessment of the degree of the ecosystem deviation from the norm has yet to be resolved (Bezel et all, 1992; Snakin et al., 1992). In our opinion, the approach of describing the state of an ecosystem or its components using a set of measurable attributes that characterize various aspects of the object under study, accessible for measurement, without overlapping seems to be needed. Ideally, the number of attributes to be analyzed should be low (5-10) yet sufficient to provide data for interpretation of major trends in ecosystem processes. We have suggested some sets of attributes to describe the state of soil and landscape (Snakin et al., 1992; Bashkin et al., 1993), and also a system for assessing the degree of soil degradation (Snakin et al., 1996). The next step is to estimate in relative units the value of the sets of attributes (qualimetry). The difference between the values for the considered ecosystem and its non-disturbed analogue will help to draw some conclusions for assessing the state and the degree of degradation of the ecosystem or one of the components. In our study, we used a collection of soil ecological attributes to estimate the deviation of agricultural from virgin soils, and to trace the direction of recent processes of soil formation. We estimate the value and the direction of the vector, describing soil changes from its initial (virgin) to the present (agricultural) state (Snakin & Prisyaznaya, 1997). For these purposes we used the following complex of soil ecological attributes: •
soil redox potential (Eh, mV), reflecting the degree of the state of oxidation of soil components that can be interpreted thermodynamically (Baas-Becking et al, 1960; Dubinin & Snakin, 1984) and has an intimate correlation with net productivity and the ratio between living and dead organic substances in the soil (see Section 6.2.3);
•
pH value of SLP, which determines the conditions under which physical, chemical and biological processes take place, and the chemical condition for soil nutrients; pH value in combination with Eh value can be used to diagnose the type and subtype of the soil also (see Section 6.2.2);
•
Ca^^ ions activity (meq/L) in SLP "the guard of fertility" according to A.N.Sokolovsky (1932), which plays an important part in forming soil physical properties and physiological processes;
•
K^ ions activity (meq/L) in SLP, showing the degree of the amount of this element available for plants; the degree of anthropogenic influence on soil (fertilizing); the degree of soil biogenity: absence or presence of vegetation, biomass, etc. (Volkova, 1978);
•
NO3' ions activity (meq/L) in SLP, reflecting the amount of nitrogen mineral forms available for plants, the degree of soil cultivation, soil productivity.
253 Thus, these soil attributes characterize recent processes of soil formation and can serve as indicator of the impacts of external factors on the soil ecosystem. The changes in these parameters may reflect changes in the soil state resulting from agriculture. Quantitative assessment of the degree of soil deviation from its initial state was carried out by calculating the Euclidean distance between two sets of data corresponding to the recent soil state and the control one. Fig. 50 shows the direction of recent process of soil formation and the degree of deviation of cultivated soils from their virgin analogues in two dimensional factor space of redox potential and pH properties. Euclidean distance can be estimated, in principle, for any ndimensional space, including the above-mentioned set of 5 attributes.
650 H
600
7.0
pH
Figure 50. Degree and direction of anthropogenic changes in different soils by Eh and pH: 1,2,3..., 8 - centroids of the groups corresponding to the numbers of the groups (Table 149)
To assess the degree of the similarity and differences between the SLP content of the different types of soil, discriminant analysis was carried out (Klecka, 1986). Discriminant analysis is used to classify data and is based on finding a hyperplane that divides the preliminarily chosen groups of data in the best possible way. The next stage is to determine the index of the classification effectiveness which makes it possible to see how efficiently the data are grouped, what number of the objects (%) is in this or that primary group and, consequently, which groups are similar. After discriminant fimctions have been calculated, we estimated the co-ordinates of the groups' centroids by counting Euclidean distance (E) between them using the following formula:
E^^{x,^-x^^f
,
254 where x are co-ordinates of centroids in indicative space of the studied type of soil in natural (Xy) and cultivated (x^) state;7 refers to the appropriate discriminant function. We analyzed the changes in the state of soils caused by long-term agricultural use for sodpodzolic, gray forest soils, chernozems and chestnut soil. Discriminant analysis was used to determine how the eight groups of four types of soils differ in their sets of physico-chemical attributes (Table 149).
Table 149 Classification of different type of soils in terms of their physico-chemical properties (5 attributes) according to the results of discriminant analysis Actual groups
Predicted groups (%) N of the group Type of soil
1
2
3
4
5
6
7
1
Chestnut
56
0
33
0
0
0
0
11
2
Chernozems
11
44
17
0
0
28
0
0
A' of the group
8
Virgin soils
3
Gray forest
28
0
36
7
0
29
0
0
4
Podzolic
6
0
0
88
0
6
0
0
5
Chestnut
14
28
0
0
0
0
29
0
5
Chernozems
13
29
21
2
3
21
3
8
Agricultural soils
7
Gray forest
36
9
0
0
0
9
46
0
8
Podzolic
7
14
11
0
3
3
24
38
The data of virgin soils form more distinct groups when compared to their cultivated analogues. 36 to 88% of individual virgin soils correspond to their specific groups while for the agricultural soils this index is 21 to 46%. 71 - 94% of pristine soils possess the properties of their specific groups. By contrast, only 35 to 68% of individual agricultural soils correspond to their specific group, the remaining 32 to 65% are similar to virgin soils of different types (28 to 29% of cultivated chestnut soils and chernozems proved to be similar to non cultivated chernozems, and 36% of gray forest soils fell into the group of non cultivated chestnut soil). Based on the measurements we selected, the podzolic soils show the strongest clustering among the non-cultivated soils considered (88%> enter the group of podzolic soils). The gray forest soils show the least clustering among the virgin soils (28% are similar to virgin chestnut soils, 29%) to cultivated chernozems, which possibly results from degradation of the latter or from the fact that many of the investigated gray forest soils are clearings with grassland cover).
255 During cultivation changes occurred in gray forest and podzolic soils, therefore none of the agricultural soils were grouped with their virgin analogues. Changes in chernozems and chestnut soils were not so large, however, only 29% and 14% (respectively) of the agricultural soils are similar to their virgin analogues. We may conclude that virgin soils make up more delimited groups in terms of their physico-chemical properties. Agricultural soils form rather vague groups, of which many do not correspond to their virgin analogues, and do not form specific groups. Table 150 shows quantitative data of the analysis of degree of ecological degradation of soils resuhing from their agricultural use. If we take Euclidean distance for chernozem as 1 the relative degree of anthropogenic changes in terms of the given set of physico-chemical attributes of soil (Eh, pH, and K^, Ca^^, NO3' ions activity) follows the sequence: chernozem, 1; chestnut soil, 7; gray forest soil, 8; podzolic, 12. That is, agricultural chernozems differ slightly from natural chernozems, while podzolic soils have been changed dramatically as a result of human activity. This suggests that chernozems are more resilient to anthropogenic impacts, while podzolic soils are very sensitive to agricultural disturbances. However, one should also take into consideration the direction and the degree of anthropogenic influence, which differs among soils.
Table 150 Degree of ecological degradation of the properties of different types of soils (according the results of discriminant analysis) Type of the soil
Euclidean distance
Number of ecosystems Natural ecosystems
Agroecosystems
Chestnut
9
7
1.8
Chernozems
18
38
0.25
Gray forest
14
11
2.1
Podzohc
16
29
2.9
Fig. 51 depicts the direction of changes during the period of soil cultivation (5 attributes), represented in two-dimensional form using discriminance analysis. It shows that changes in the properties of chestnut, gray forest and podzolic soils are directed towards the formation of specific types of agricultural soils possessing similar physico-chemical properties. Slight changes in chernozems also tend to the same direction. The latter depends on people's efforts to create some "ideal" type of soil similar to natural chernozems while forming man-made ecosystems. Some changes in chernozems under cultivation are related to degradation (e.g. acidification - see Fig. 50) rather than with "amelioration".
256 1.8 CM
7
c .o "o c
5
c -0.2 05
c
E Q -2.2 -2.1
-0.1
1.9
Discriminant function 1
Figure 51. Changes in a set of properties (5 attributes) of soils during cultivation: 1,2,3..., 8 centroids of the groups corresponding to the numbers of the groups in Table 149 The results are preliminary and need further consideration. More extensive sets of attributes, including stable indices (e.g., humus content, mineralogical composition) should be also used. For more precise assessment it is advisable to use a larger number of ecosystems, and to correlate parameters, while calculating the distance between the group centroids. The latter is considered when using the Mahalanobis distance (Webster, 1977), but it is a complicated mathematical problem. Nevertheless, the results show that cultivation crucially affects the state of soils, which changes the role of these soils in the functioning of ecosystems. •
The degree of anthropogenic changes in soils in terms of the studied set of physico-chemical properties during the cultivation is estimated as: chernozems, 1; chestnut soils, 7; gray forest soils, 8; podzolic soils, 12 (soils resistance to anthropogenic impact decreases in this order).
•
Virgin soils form more distinct groups when compared to their cultivated analogues, especially podzolic soils, i.e. virgin soils are more specific in their properties.
•
Analysis of a set of dynamic attributes (Eh, pH and K^, Ca^^, NO3' ions activity in SLP) shows that under cultivation the processes of soil formation are directed to forming a special type of agricultural soils, whose properties and ecological role do not correspond to those of their virgin analogues.
7.5. SOIL LIQUID AND ECOSYSTEM CONTAMINATION At present, ecosystems, and especially agroecosystems, endure pressure of different anthropogenic pollutants: sulfur and nitrogen oxides, pesticides, radionuclides, heavy metals.
257 mineral fertilizers. The problem of ecological norming to determine the tolerable levels of these pollutants is open to discussion. The norming of pollutants in soil as a component of an ecosystem has received the least study. The reason, among the above mentioned, is the complexity of the object, its heterogeneity and non-equilibrium character. The content of pollutants in living matter is weakly correlated with their total quantity in soil, and different extracts from soil do not provide information on their mobility and availability for plants. The wide spectrum of soil properties, which determine biogeochemistry of pollutants (i.e., ion exchange characteristics, reaction, redox potential, penetrability etc.) makes the creation of general standards for different soils a complicated problem. In our opinion, a perspective is given by studying of properties of SLP. From the 4 indices unveiling hazard of chemical pollutants, i.e. translocational, water migratory, air migratory, general sanitary (Guidelines for ..., 1982), SLP composition determines immediately two, regulating the two remaining. The concentration of pollutant in SLP is the basic factor of immediate effect on living organisms in ecosystem (plants, microorganisms, soil animals). The concentration of pollutant in SLP determines its translocation index of hazard: the ability of chemical substance to pass from soil via the root system into agricultural plants and accumulate in their biomass. Concentration of pollutant in SLP (in lyzimetric waters) also defines the water migratory hazard index: the ability of chemical substance to pass from soil into underground subsoil waters and surface sources. Air migratory index of hazard, the ability of chemical substance to pass from soil into the atmosphere, besides the physico-chemical peculiarities of the pollutant itself, is also related to the composition of soil solution since the transition into gaseous phase, excluding dust formation, occurs from liquid phase. General sanitary index of hazard, the influence of the chemical substance on self-purifying capacity of soil and its biological activity, has also been determined by the concentration of pollutant in SLP as directly acting on microorganisms. Zakharov (1931) considered soil solution to be one of the leading factors in formation of soil biological activity and soil biotic regime inseparably entwined with dynamics of the composition of soil solution. SLP provides environmental conditions for numerous soil microorganisms including soil protozoa and algae, many of them live direcfly in the soil solution and develop as typical hydrobionts. Estimates of the degree of heavy metal pollution by biotesting the soil solutions (Yakovlev & Reshetnikov, 1989) and by chemical extraction from contaminated soils (Bujtas at al., 1998) has been suggested.
258
For characterization of the translocational index of pollutants can serve the value of the coefficient of biological accumulation, which equals to the ratio between the pollutant's concentration in the living organisms and its concentration in the substrate. It is appropriate to use the accumulation coefficient relative to the fresh weight of living matter (not in a dried or ash state) and the SLP (Snakin, 1980). Specified accumulation coefficient (K) would give information on real distribution of the chemical element under study between soil and environment. But it would depend to a lesser degree on soil properties. Only few works have been performed
in biogeochemistry
on determining the
accumulation coefficient in such a way. Consequently we have a false impression of how the chemical elements are distributed between the living and nonliving material. We often speak about concentrating chemical elements by the living matter, while the concentration of those in the living matter is less than in the soil solution. Such distortion is caused by drying and ashing of the living matter, and finally by the calculation of the accumulation coefficient in relation to the total supplies of the elements in soil. Our investigations on determining the NO3' accumulation coefficient of different wild and cultivated herbaceous plants on gray forest (virgin and cultivated), sod-calcareous and low podzolic soils, showed that the coefficient depends on the concentration of NO3' in SLP. In general, accumulation coefficient varies from 7.8 to 6.9 (n=25). In fertilized plots it equals 1 (from 0.7 to 3). At pNOs = 3.2 (CNO3 = 40 mg/1), KNO3 = 11 ± 4, and at PNO3 = 2.2 (CNO3 =
400mg/l) KNO3 = 1,5 ± 0,8. Changes in the coefficient of accumulation with the concentration is one of the regulating mechanisms of the living matter composition. But its decrease is behind the concentration increase of the ion to be absorbed. In case with NO3", this is approximately four times. Hence, with increasing concentrations of the pollutant in SLP, its amount is increasing in plants. In order to prevent high level of NO3' accumulation of plants, it is necessary to monitor the SLP composition (soil solution). When solving the norming of pollutant concentration in soil it is appropriate to extend the maximal permissible concentration of pollutants from natural water to SLP. In case of food products the standards used for drinking water should be applied. As for NO3", it is reasonable^^ to establish for agricultural soils maximum NO3" concentration in soil solution at 50 mg/L. This allows to grow high quality agricultural goods and to prevent NO3 from active migration into subsoil waters. At the same time, such concentration is
^' Taking into account the translocation factors, in fertilized soils this concentration of SLP provides the NO3 concentration in plants approximately 50 mg/kg.
259 sufficient for normal plant nutrition, for in accordance with the studies of Kochergin (1965) the plants are well provided with nitrigen at concentrations of NO3 exceeding 20 mg/kg. Some cases, however, exist in practice (state farm "Sergievsky", Moscow region) when at concentrations of 1500 and 4600 mg/L in soil solution, in the crop 2400 mg/kg NO3' (vegetables, green mass) and 3400 mg/kg nitrate (beet, vegetable root crops) was found. From a biogeochemical point of view two attributes should be determined in soil: total concentration of pollutant and its concentration in SLP. If the first attribute is the factor of extensiveness, history and properties of the soil, then the second one is a factor of intensive attack of pollutant on the living matter, and it is the factor which is to be normed.
7.6. CONCLUSIONS
Production process and soil formation represent major creative mechanisms in the functioning of natural ecosystems. Studying the processes individually gives us valuable information about the potential of a ecosystem, the state of environment, and the direction of change occurring under the impact of natural or anthropogenic factors. Coupled investigation of these intimately interrelated and interconnected processes gives new information. The innovation of these investigations is that new field methods for measurements in situ are applied, and this is an assessment of real parameters of ecosystems processes. The results presented in the chapter on dynamics of plant substances, photo synthetic intensity and transpiration of dominant types, dynamic chemical composition of the solid part of soils and the composition of their liquid phase, allowed quantitative estimation of the interrelations among the processes. From the factors considered (accumulation of plant material, transpiration, photo synthetic intensity, soil temperature) soil temperature (t) and photo synthetic intensity (P) determine the dynamics of the SLP composition to the greatest extent. To describe the dynamics of parameters studied (Eh, pH, pK, pCa, pNOs in SLP) one can use the following equation: PX = Ao + Alt + A2P, where Ao, Ai, A2 are empirical coefficients, different for different types of ecosystem. Functioning of natural ecosystems makes up a complicated suite of interrelated and interdependent processes. Simultaneous analysis of dynamics of phytomass increase, its chemical composition, photosynthetic intensity and transpiration with recent processes of soil formation enables to discover some general regularities. Only the first steps have been taken in this direction. However, we believe that such an approach is quite promising, especially in terms of the development of/>? situ measurement technique.
260 The results of in situ measurements makes it possible to ecologically estimate the state of soils and to compare the development of soils in different ecosystems by a number of attributes (Eh, pH, pCa, pK, pNOs) characterizing recent soil formation processes. Analysis of the effect of cultivation (ecological degradation) of the main soil types on the European territory of Russia under agriculture shows that sod-podzolic and gray forest soils were more altered than chestnut soils and chernozems. Under cultivation a specific type of agricultural soils is formed, and the soils bear greater similarity to each other than when compared to corresponding virgin soil analogues. Monitoring of the soil state based on a set of attributes is an important part of ecological monitoring. Monitoring of SLP will help in solving the problem of norming the anthropogenic effect on soil, which has not yet found practical application. The concentration of pollutant in the SLP determines its quantity in plants, the degree of its migration among the landscape and is ought to be normed.
305 CORRELATION BETWEEN SOIL NAMES*
Soil name**
Synonym in FAQ UNESCO system
Meadow-boggy with permafrost
Gelic Gleysol
Peaty soil with permafrost
Gelic Histosol
Tundra soil
Leptosol
Typic podzolic
Dystric Podzoluvisol
Sod-podzolic
Albic Luvisol
Sod weak podzolic
Albic Luvisol
Grey forest
Luvic Phaezem
Brown forest
Eutric Cambisol
Gleysolic acid brown
Distric Cambisol
Typical chernozem
Haplic Chernozem
Ordinary chernozem
Calcic Chernozem
Southern chernozem
Calcic Chernozem
Leached chernozem
Luvic Chernozem
Shallow low humus calcareous southern chernozem
Calcic Chernozem
Deep chernozem with mycelium carbonates
Deep Calcic Chernozem
Solonetzic compact chernozem
Luvic Chernozem
Dark chestnut
Haplic Kastanozem
Chestnut
Haplic Kastanozem
Cinnamonic
Cambisol
Sierozem (grey dezert)
Calcic Xerosol
Grey-brown
Luvic Yermosol
Solonetze
Solonetze
Meadow solonchakous
Umbric Gleysol solonchakous
Meadow-steppe solonetze
Gleyic Solonetze
Sod-calcareous
Rendzina
Weakly developed sandy sod-calcareous
Rendzina
Alluvial sod-meadow calcareous
Calcaric Fluvisol
Alluvial soils
Fluvisols
Alpine meadow
Umbric Leptosol
* hy Glazovskaya (1990) ** according to Russian classification by Egorov et al, 1977
263 GLOSSARY The main method used for studing of soil Hquid phase is ionometry, which is a relatively young and special field of research. That is why some of the terms are not widely known and accepted or are subject to discussion. Here we include a brief glossary in order to clarify the techniques and to express the authors' point of view on some of the conceptual and practical problems of application of ionometry in soil researches. We used the guidelines (Handbook of Electrode Technology, 1982) and other publications (Camman, 1973; Morf, 1981; Rabinovich, 1985 et al)ACTIVITY - effective concentration of free ions in solution, a thermodynamic characteristic of ion ability to participate in chemical reactions. The correlation between activity and concentration is as follows: a = y • c, where a - activity, c - concentration, y - activity coefficient, close to 1 for very diluted solutions. A serious problem is the thermodynamic indefiniteness of the concept of individual ion activity, since the existing experimental methods for thermodynamic determination of activity may be applied to electroneutral components only. One may obtain an average ion coefficient only. A practical way out may be based on suggestion that in water solutions of potassium chloride of any concentration cathion and anion activity coefficients are equal. In a physical sense, activity and concentration are measured in the same way. We shall emphasize that the methods to express concentration are correspondent to the values of chemical potential in standard hypothetical solutions of zero concentration, and, consequently, to the values of activity in the same solution (Chemical..., 1983). ACTIVITY COEFFICIENT - a multiplier for concentration of electrolyte, proposed by G. Lewis, what makes possible the application of ideal systems laws for the description of many processes (see Activity). It reflects all the phenomena in the system, which cause a deflexion in ion behaviour in a real system in relation to an ideal one (electrostatic interaction, appearance of associates, etc.). Although it has no particular unit of measurement, it is qualitatively dependent on the method of concentration representation. For diluted solutions (ion strength below 0.1) activity coefficient (/) may be expressed from Debye-Huckel equation: Az'-yfl
\±Bb41 ^ where z - the ion's charge, b - the ion size parameter; A and B - constants, which depend on temperature and dielectric permeability of dissolving agent; / - ion strength. BUFFER SOLUTION - a solution, in which the activity of definite (buffered) particles remains constant at dilution or concentration of solution, or at addition or
264 subtraction of a limited amount of buffered particles (e.g. the permanence of H^ ion activity or ion capacity). CALIBRATION CURVE - a graphical representation of the measured ion activity or concentration dependence on potential of electrode pair applied. As a rule, it is built in semilogarithmic co-ordinates: E (mV) - Igax or E - IgCx. CALIBRATION OF ELECTRODES - a consequental determination of ionselective electrodes potential in standard solutions with known values of ion activity. It is recommended that calibration is performed before and after the measurements. COMBINED ELECTRODE - a sensor with two electrodes {electrode pair) built-in, such as indicator and as reference electrodes. CONCENTRATION - the amount of component per mass (or volume) unit of dissolving agent. It is often measured in moles, i.e. the number of gramm-moles of substance in one litre of solution - g-mole/1 (for ions - g-ion/1 or mg-ion/1). In precise thermodynamic calculations concentration is measured in shares of a mole (the number of g-moles of substance per 1 kg of dissolving agent). The latter are close to molar values for diluted solutions. DIFFUSION POTENTIAL (JUNCTION POTENTIAL) - potential in the place of liquid contact between the solution studied and the salt bridge electrolyte of reference electrode, created by the difference in their concentration. It is advisable to take the following measures in order to diminish the influence of diffusion potential on measurement results: • careful selection of salt bridge composition with the cathions and anions closest mobility so that difftision potential may be diminished; • utilization of concentrated solution in reference electrode and salt bridge to preserve of diffusion potential value more constant. ELECTRODE LIFETIME - time, during which the electrode functions. The lifetime of homogeneous, gas-sensitive and glass electrodes may be reduced by mechanical damage or chemical impact (membrane poisoning) and under normal conditions may last several years. Electrodes with liquid and plastified membranes may be broken, the electrode-active substance flow from the membrane in the process of utilisation. They usually have a lifetime from three months to one year. ELECTRODE PAIR - electrochemical element, including an indicator and a reference electrodes. ELECTRODE POTENTIAL DRIFT - the property of an electrode to change its potential in the course of time, irrespective of the change in the activity of ion measured. To prevent this mistake it is advisable to conduct repeated calibration as more as possible. ELECTRODE POTENTIAL SET-UP TIME (RESPONSE TIME) - a period of time, during which electrode pair potential stabilises after having been submersed into the measurement substrate. As a rule, it increases with the decrease in ion concentration measured.
265 FLOW POTENTIAL - a potential, which occurs at the reference electrodemeasurement substrate border due to leakage of reference electrode solution into analysed substrate. It is minimal at good sealing (but not hermetic, which disables all measurements) of reference electrode electrolytic key. It is of a constant and an insignificant value, and cause only slight error to the measurement. GAS-SENSITIVE ELECTRODE - an electrode, which contains a sensor (being often represented by a combined pH-electrode) with a solution of electrolyte, separated from the analysed substrate by a gas-permeable membrane. Penetrating through the membrane, the gases change the composition of the given solution, which results in electrode potential change. It may be utilized for in situ measurements of carbon dioxide content in soil air (Komisarova & Razumova, 1987). INDIFFERENT ELECTRODE - electrode, produced of a neutral metal (platinum, gold, graphite, specially processed glass), used as an indicator in the determination of redox potential. INDICATOR ELECTRODE - general name for ion-selective electrode and indifferent electrode. IONIC STRENGTH - concentration parameter of solution (averaged concentration of ions in solution), the measure of electric interaction between all ions in solution. It is calculated as a product half-sum of molar concentration of each ion (G) and charge square of respective ion (Z/):
At whole, ion strength determines ion activity coefficient in solution. ION-SELECTIVE ELECTRODE - electrochemical half-element, which potential changes in proportion to the logarithm of activity of ion measured in solution. Depending on the membrane material (sensor), the electrodes are classified as follows: • electrodes with a homogeneous membrane, made of powder (e.g. from Ag2S for Ag^ and S^" measurements) and monocrystallic (e. g. from LaFs for F' measurements) material; • electrodes with a heterogeneous membrane or plastified electrodes, in which electrode-active substance is distributed in an inert matrix (silicon latex or polyvinylchloride film), such EM-NO3-OI, EM-K-01 and others; • electrodes with a liquid membrane, represented by a solution of ion or neutral substances in organic dissolving agent; • glass electrodes, which membrane is made of special glass, selective in relation to particular ions (mostly to one-charge cathions). As a rule, electrodes are filled with a special solution for the functioning of a built-in reference electrode. However, there is a group of the so-called solid state electrodes, which contain no built-in reference electrode and have a solid output.
266 lONOMETRY - a potentiometric technique used to determine ion activity or concentration by means of ion-selective electrodes. The method is based on the Nernst equation, which at 25^ C takes the following form: 59
E^E'±—\ga^ Z " where a^ - activity of ion measured of charge Z, E^^ - constant, mV. ISOPOTENTIAL POINT - point on measured ion activity dependence of electrode pair potential, in which ion-selective pair potential is independent of temperature. For some ion-selective pairs the isopotential point lies either outside the working range or within it. The higher the ion concentration the more is the influence of temperature on the electrode potential. LIFETIME OF ELECTRODE - see Electrode lifetime. LIQUID JUNCTION POTENTIAL - see Diffusion potential. POTENTIOMETRY - an electrochemical analytical technique, based on the determination of the dependence between equilibrium electrode potential and thermodynamic activity of components, involved in a electrochemical reaction. Potentiometry is general term for ionometry and redoximetry. REDOX POTENTIAL - see Soil redox potential. REDOXIMETRY - a potentiometric method for the determination of redox potential through various indifferent electrodes, which potential at 25 C corresponds to the equation: ^ ^0 59, [Ox] 59/w ,, E^E + — Ig -— pH n [Re d] n where [Ox] and [Red] are the respective activity values of oxidised and reduced substances forms; n - the number of electrons involved in a overall redox reaction; m - stechiometric coefficient before hydrogen ions activity in this reaction. REFERENCE ELECTRODE - half-element, which comprises an electrode pair with an indicator {ion-selective or indifferent) electrode, which potential is independent of the composition of solution studied. The most widespread are chloride-silver and calomel reference electrodes, filled by KCl solution of various concentration (saturated; 3.5 m; 1 m; 0.1 m, etc.). RESPONSE TIME - see Electrode potential set-up time. SALT BRIDGE - a device, used to prevent direct contact of analysed solution with a half-element Dereference electrode. Comprises of an indifferent electrode with a maximum mobility of cathions and anions (see Diffusion potential), being a U-tube with a agar-stabilised solution, or is realized through special design of reference electrode with a cover, where salt bridge functions are given to external electrolyte. SELECTIVITY COEFFICIENT (SELECTIVITY CONSTANT) - a qualitative parameter, which represents the correlation between ion-selective electrode
267 respond to mixing and measured ions. It is predominantly used to assess the applicability of ion-selective electrode for a given measurement: CO
where K - selectivity coefficient of an y4-selective electrode to ions A with a charge of Z} in relation to ions B with a charge of z^. SOIL LIQUID PHASE - the sum of soil components in liquid form. Since water is the predominant component of this phase, it is often called soil water phase (Snakin, 1989). Since soil liquid phase is non-uniform, the right to use the notion of "phase" is still put into question (Orlov, 1985). SOIL REDOX POTENTIAL (Eh) - a function of ratio between oxidised and reduced forms of chemical elements in soil, which characterises the extent of system oxidation:
^, = / ( ^ . M ^ A
etc.).
It is determined through indifferent electrode potential, which occures during its submersion into measurement substrate. SOIL SOLUTION - a part oi soil liquidphase^ replaced (removed) from it by a particular technique (centrifugation, pressing, replacement by ethanol, etc.). When using replacement, one risks to change the composition of extracted solution. SUSPENSION EFFECT - the difference in the parameters, measured in suspence and from replaced centrifugated solution (supernatant). It was first described by G. Wiegner and H. Pallmann (1930). The origin of suspension effect remains a matter of discussion. ^tQ Diffusion potential, Flow potential etc.
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INTRODUCTION The liquid phase of soil (soil solution) is a very thin, penetrating and all-embracing water layer. It has the most extensive surface among the biosphere components and interacts with all these components, hivestigation of the soil liquid phase can of great significance in environmental research. Soil water is one of the most important natural water category in the biosphere (Vemadsky, 1960). V.I.Vemadsky considered it "the basic element of the biospheric mechanism" and "the basic life substratum". According to K.K.Hedroitz (1975a), "to move on in solving some theoretical as well as practical issues of agronomy we have to find another approach to solve the problem of soil solution; we should study the composition of the solution and its temporal changeability as depending on external conditions. It will not be an exaggeration to say that further achievements of agronomy depend on the solving of this problem". Soil liquid phase investigations have not become an efficient instrument in ecology or applied soil science, despite extensive soil solution data. This is due to the difficulties in studying soil solutions in unchanged state, spatial heterogeneity of soil properties (including soil liquid phase) and dynamic composition of soil solutions responding to environmental changes. The more difficult the problem, the more interesting it is to fathom its depths. Soil liquid phase investigation dates back to the start of experimental environmental research. Two trends have emerged from the very beginning: (i) attempts to separate soil solution from soil in order to analyze its composition (Schloesing, 1866; Ishcherekov, 1910), and (ii) experiments on soil liquid phase carrying out immediate investigation in soil, without preliminary extraction, through electrometric methods (Whitney &, Means, 1897; Briggs, 1899). The first trend was used for a long time, though it was noted that "all the attempts at extracting soil solution from soil at a low moisture content are bound to fail" (Hedroits 1975a). Development of the second trend was drawn back by the imperfection of electrometric techniques. It was not until the ion-selective electrodes technology (ISE) was introduced that progress was made and the first ISE (glass H^-electrode) was used in soil investigations (Nikol'skii, 1930). Development of different ISE technology and field ionometers allowed to expand the circle of determinable ions in water (liquid) phase of different soils, and to investigate natural soil liquid phase
under field conditions without breaking their internal physico-chemical balances (the so-called in situ measurements). A brand-new class of data is the case, which enables us to assess parameters of physico-chemical and biological processes in soil under natural conditions. It is often that analysis of soil samples resuhs in unreliable data, especially at the preliminary stage of investigations. Soil sample properties reflect the stages of selection and preservation, and its redox, gas-exchange and microbiological processes are different from soils in the field. Livestigation of soil as a component of natural and cultivated ecosystems should be dynamic and should reveal its nature and the links within the solid, liquid and gas phases. We agree with Ruellan (1983), that to study recent soil processes the newest technical means should be used in order tofindout the structure and composition of soil components in situ. This study is devoted to search and back-up of new approaches to soil liquid phase analysis and aims to fmd out, the role of soil liquid phase in thefimctioningof natural and agricultural ecosystems in recent soil-formation, formation of primary biological production, and in bio-geochemical turnover of elements. Direct investigation of soil liquid phase is the determination of the concentration (activity) of ions or redox potential in situ; while the analysis of soil solution implies that the soil solution is extractedfromsoil. The authors have aspired to give insight into the development of ideas and theories as well as certain results of Russian schools of soil science and ecology on problem of studying of soil liquid phase. The references therefore contain mainly articles in Russian. As compared with earlier publications on soil liquid phase investigation (Bystritskaya, Volkova, Snakin, 1981; Snakin, 1989; Snakin, Kovacs-Lang, Bystritskaya et al., 1991; Snakin, Prisyazhnaya, Rukhovich, 1997) this work is substantially expanded. It includes new field investigation data as well as all data generalization carried out by the means of a special complex database «Demetra» (developed by the authors of this work) on soil liquid phase composition and other soil-ecological properties in various ecosystems in Central and Eastem Europe.
306
SUBJECT INDEX
accumulation coefficient - 232, 258 acid r a i n - 118 acidification (soil) - 6 1 , 9 1 , 95-96, 105,109,112,118,120-121,164, 183,190,195,256 activity (ionic activity) - 25-27, 263 activity coefficient - 27-28, 263, 265 Agrochemical Field Station named Pryanishnikov - 70, 227, 229 alkalinisation of the soil solution - 96, 105,110,118,175,195 "Analitpribor" (NPO) - 47-48 Askania-Nova Reserve - 70, 191, 206, 215-216
B Bugac site - 70, 71, 73, 74, 77, 78, 95, 97,151-155,170-174,197,198, 226-229, 244-250 buffer solution - 38, 51, 52, 232, 263264
calculation of the results - 25, 26, 54, 55, 63, 85, 209, 258 calibration curves - 34, 41, 45, 52, 264 calibration of electrodes - 50-52, 264 calomel electrodes - 42, 46-47, 266 capillary water (moisture) - 10, 14-16, 171 carbonate equilibrium analysis - 175184 cation exchange capacity (CEC) - 65, 221,224,233,234,236 Caucasus State Reserve - 70, 71, 149, 164-174, 206, 215-216, 222, 226229,231 Central-forest State Reserve - 70, 71, 191, 206, 215-216, 225, 226-229 Centralnochemozemny Reserve - 70, 71,97,140, 191,206,215-216, 226-229
centrifugation of soil solution - 21, 23 choosing the pH/mV meter - 50 coefficient of selectivity of ISE - 28, 47-48 Colchid forests - 149, 164-174 combine electrode - 264 combined methods for soil solution extraction - 24 compensation of temperature dependence - 40-45, 54 concentration of ions - 25-26, 54, 264 contamination (soil) - 53, 230, 242, 256 cryptogamic lower plants - 97 crystallized water - 9, 10 "CRYTUR" - 47-48 Csaszartoltes site - 70, 71, 73, 74-75, 77,78,79-80,149,155-158,170174,227-229,244-250
D Danube-Tisza Interfluve - 70, 71, 72, 73 Debye-Huckel equation - 27, 28, 51, 54,177,263 degree and direction of changes in soils - 253-256 DEMETRA data base (DDB) - 7, 6667,170,232,235,236 determination of NH4^ ion activity 49 determination of CI" ion activity - 49 determination of Na^ ion activity - 49 determination of Ca^"^ ion activity - 44, 49 determination of pH - 43, 49 determination of K^ ion activity - 43, 49 determination of NO3' ion activity 44,49 diffusion potential (junction potential) -29,33-35,37,50,264,266 dissolving capacity - 13 displacement of soil solution - 21-24
307
diversity of water forms in soil - 9-10 Donnan's equilibrium - 29 dynamic of Ca'^^ ion activity - 91, 105, 146, 152, 155, 160, 162, 169-170, 173-174,246,251,256,259 dynamic o f E h - 9 1 , 105, 146, 152, 155, 158, 160-162, 169, 185, 187188,190,194,240,256,259 dynamic of K^ ion activity - 91, 105, 152, 155, 158, 160, 162, 168-170, 173, 204, 209-211, 242, 251, 256, 259 dynamic of NO3" ion activity - 91, 94, 151-154, 156, 158, 160, 162, 169, 173, 211, 244, 248,251, 256, 259 dynamic o f p H - 9 1 , 105, 146, 152, 155, 158, 160-162, 164, 169, 173, 185,194,256,259
E ecosystem t y p e - 103-106, 121, 123, 127, 129-130, 132,134, 136-137, 169,204-205,208-209,259 electrode lifetime - 264, 266 electrode pair - 35, 264, 266 electrode potential drift- 35, 49, 51, 264 electrode potential set-up time - 30, 264, 266 Eh-pH co-ordinates - 129-131, 192, 253, 262 entropy-201-203 environmental norming- 17, 190, 257258,260 equations: - Debye-Huckel - 27, 28, 51, 54, 177,263 - Freundlich - 85, 86, 224,232, 233, 236, 240, 262 - Gaines-Thomas - 85, 232,233, 238,240 - Gapon - 85, 90, 232, 233, 238, 240 - Langmuir - 85, 86, 224, 232, 233, 236, 237, 240, 262
- N e m s t - 2 5 , 41-43, 266 -Nikol'skii - 85, 90, 232, 233,238, 240 error of in situ measurement - 30, 36, 37, 38, 68, 261 Euclidean distance - 253, 255 evaporation- 24, 100, 171, 214, 248250 excretion of plants - 95, 96, 103,177 Experimental Field Station of ISSP R A S - 7 0 , 199,227,230 extraction of soil solution - 21-24
fertilisers (mineral) - 86, 108-113, 115,117-119,135,142,188,208, 209,214, 216, 217, 230, 232, 235, 242 flow potential - 37, 265, 267 Freundlich equation- 85, 86,224, 232, 233, 236, 240, 262
Gaines-Thomas equations - 85, 232, 233,238, 240 gas-sensitive electrode - 265 Gapon equations - 85, 90, 232, 233, 238, 240 "Gomel M E F " - 4 8 , 50 gravitational water - 10, 16-17
H heavy metals (HM) - 49, 66, 67, 220223, 226, 228, 230-232, 242, 256, 257 heterogeneity of soil - 6,14, 39, 52, 53,65,67,97,98,111,122,125, 138-145, 147,148, 150, 153-155, 159,164,166, 170, 172-174, 193, 200, 206, 261 heterogeneity of soil solution- 6,14, 15 herbicides-188, 190 hydrophilic substance - 15
308
impact of CO2 on SLP - 38, 58, 59, 61, 62,68,88,96,164,175-185,240, 241,261 impact of O2 on SLP - 68, 88, 132, 183,185 indifferent electrode - 54-57, 67, 265 indicator electrode - 265 in situ measurement in soil - 7, 14, 21, 22,33,36,39-68, 109, 115, 117, 139,144,145,150,156,159,167, 170, 175, 176, 177, 185, 192, 193196, 219, 232, 240-242, 259-262, 265 inhomogeneity of capillary water - 15 interception - 98-101 ion exchange - 13, 85, 88, 232-233, 236, 238, 243, 257 ion interaction (as factor) - 19 ion uptake intensity (as factor) - 17 ionic strength of solution - 26-28, 110, 263, 265 ion-selective electrodes (ISE) - 6, 14, 22, 24, 34, 37, 39-41, 43, 46, 49, 50, 56,57,64,65,67,68,86,92,109, 117,139,145,149,150,153,180, 181,192,232,261,264,265-267 ionometry - 21, 24-54, 58-65 Ishcherikov-Komarova method - 23 isopotential point - 38, 41-45, 266
junction potential (diffusion potential) -29,33-35,37,50,264-266
Langmuir equations - 85, 86, 224, 232, 233, 236, 237, 240, 262 lime application (liming) - 64, 110, 112,114-116,209,215,262 Uquid junction potential, 5^^ junction potential - 29, 33-35, 37, 264 lysimetric water- 10, 16-17, 21, 172, 257
M Mahalanobis distance - 256 Malinino forest area - 70, 191, 206, 215,226 maximal permitted concentration (MFC)-232, 258-259 mediator of redox system - 55, 56, 201-202 Michaelis constants - 20
N negative adsorption- 132 14, 34, 86 Nemst's equation - 25, 41-43, 266 net productivity (?) value - 197-200, 252 Nikol'skii equations - 85, 90, 232, 233, 238, 240 noncontact method - 30 non-solvent volume (NV) - 10-16, 21, 32, 34, 86, 90, 93
o organic matter in SLP - 161, 215-219 "ORION" R.I. - 35, 47, 48,
K Kamerdcky site - 70, 71, 73, 74, 76, 77, 81-83, 171, 172, 197, 226-230 Khomutovskaya Steppe Reserve - 69, 72,73,75,76,77-83,91,119,140, 149,158-164,170-174,191,206, 215,216,226-229,244-250 Kiskunsag National Park - 70-72
pellicular water (moisture) - 10-14, 17 pesticides-190, 241, 256 phase of soil - 9 photosynthesis - 184, 199, 201, 241, 244-246, 262 photosynthetic intensity - 244-248, 251,259
309 phytomass - 59-61, 64, 85, 141, 161, 168, 169, 171, 172, 191, 197, 199, 210, 241, 244, 251, 259, 261, 262 plant nutrition - 17-20, 262 polarization of redox electrode - 55-56 pollution- 101, 102, 109, 220, 230, 232, 242, 257 "potential mediators" - 55, 201, 203 ^-potential-33, 34 potentiometry - 266 precipitation (atmospheric) - 39, 73, 90,91,98-103,117-121,168,171, 177,194,230,261 press out of soil solution - 23 Prioksko-terrace State Reserve - 70, 206,216,226,228,230 production process-61, 196, 197, 200,241,245,259 productivity - 67, 113, 171, 196-200, 212,262
Q Quinhydrone electrode - 37, 38
R "Radelkis"-48 recultivation - 109, 118, 119 redox potential - 54-57, 122-127, 203204, 266 - 267 redoximetry - 54-58, 266 reference electrode - 25, 29, 33, 34, 37,39,41,43,44,49,50,53,54, 57, 264-266 replenishment factor - 18, 19 response time - 264, 266 rhizosphere - 78, 79, 95, 96, 110, 149, 150, 154, 159, 173,262 Richards' press - 24
saltbridge-37, 264, 266 saturation of soil solution - 63, 85, 120,175,177,178,240,261 sedimentation - 85, 87, 214
selection of sensing electrodes - 46-49, 56 selection the reference electrodes - 49 selectivity coefficients of ISE - 28, 47, 48, 266-267 selectivity in adsorption of ions - 86 silicon in SLP-212-217, 242 soil adsorbing complex (SAC) - 12, 13,81,86,88,93,109,110,113, 117,118,121,151,157,167,173, 180, 184, 207, 223, 234, 239, 242 soil liquid phase - 267 soil phase - 9 soil solution - 267 soil solution replacement: - by ethanol displaced - 15, 22, 23 - by pressure - 15, 23 soil redox potential - 22, 54-58, 67, 68, 94, 122-127, 153, 169-171, 196, 252,261,262,264-266 soil type - 13, 32, 35, 60, 62, 63, 77, 103-109,124,125,129,131,178, 180,181,191,193,205,208,209, 215,216,234,236-239,309 soil water forms - 10 soil-water ratio - 31, 32, 34, 90 solubility of soil solid phase - 85 spatial heterogeneity - 6, 53, 97, 138145, 147, 148, 150, 153-155, 159, 164,166,170,173,174,206,261 standardization of the activity scale 26 storage and transportation of ISE - 53, 54 suction of soil solution - 23 suspension effect (SE) - 25, 29-36, 267
temperature dependence of ISE - 3943 temporal variability - 122, 145-147, 149_174, 186-188,261 thermodynamic interpretation of redox potential - 55, 200-203
310 time of equilibrium establishment 86-87 transpiration- 150, 214, 244, 248-250, 259, 262 Tungiro-Neniuginsk area - 71, 225, 227, 229
u uptake of nutrient by plants - 18
variability (daily) of SLP - 145-147, 153, 154, 157, 164, 170, 174, 186, 187,261 variability (seasonal) of SLP - 104, 105, 138, 145-147, 169, 170, 189, 261 vegetation period - 12, 73, 74, 75, 96, 99, 103-105, 108, 143, 153, 161,
164, 173, 174, 187, 196, 197, 202, 208, 209, 217, 218, 244, 246-249, 261 Verkhnednepr Metallurgical Combine -70,119
w water extract from soil - 9, 22, 60, 63, 64,68,82,103,176-178,180,213 water potential - 10
yield-111, 113, 212, 213, 242
Zaokskoe foresty - 70, 226, 228, 230
261 SUMMARY
Analysis of the soil liquid phase's role in the functioning of ecosystems allows to consider it as part of soil, and a separate structural part of the ecosystem. Soil liquid phase is a link between ecosystem components and an indicator of the processes. Soil liquid phase composition depends on the quantity and composition of the atmospheric precipitation, the temperature, soil chemical composition and humidity, vegetation cover, carbonic acid content in the soil air and the input of mineral and organic fertilizers. Has been established the method for analysis of soil liquid phase in situ on the ionometric basis, which gives an essentially new information on the real functioning of ecosystems. The method is none disturbing the balance within the soil - air - living matter system, which is inevitable while analyzing extracted samples, as well as obtain otherwise inaccessible data on heterogeneity of soil properties under natural conditions within short time-cycles (hours, diurnal periods). The use of this method together with other techniques of ecosystem functioning analysis (investigation of the phytomass and the intensity of various parameters) allows to broaden the scope of environmental investigations. A number of methodological problems of/>/ situ measurements in soils have been solved, such as: ranges of errors (2-4%) and the uncertainty (up to 0.1-0.2 pX) have been assessed and the nature of the suspension effect causing the uncertainty has been examined. A way to compensate the temperature dependence of ion-selective electrode pairs while investigating soils in situ has been offered. The work also addresses the thermodynamic uncertainty of the soil redox potential value and shows the possibility to overcome it as well as to thermodynamically interpret the potential obtained by means of fme-plated platinumized electrodes. The spatial heterogeneity of the values of ionic activity (Ca^^, K^, NO3") in soil liquid phase usually makes up 20-120%, while that of the Eh and pH values is 3-10%o. As the ecosystem structure's complexity increases (the growth in species diversity), heterogeneity tends to decrease. The temporal variability of soil liquid phase within a day is much lower than the spatial heterogeneity, while the variability within the vegetation period is higher. The causes of the above phenomena are of biological nature. A method has been developed for assessment of the carbonate balance in soils on the basis of the data of the in situ measurements of pH, pCa in soil liquid phase and pC02 of the soil air. It has been shown that in none of the observed cases a veritable saturation of soil liquid phase with CaCOs was found.
262 The value of soil redox potential (Eh), one of the main parameters of ecosystem functioning, is, on the one hand, closely connected with the net-productivity value (P) of the grass phytocenosis and, on the other hand, with the value of the ratio between the maximum living and dead (L:D) aboveground phytomass. Here redox processes in soil and vegetation are of a 'mirror' character (see Section 6.2.3). The use of a diagram within the Eh-pH co-ordinates measured in situ was shown on the example of various chernozem subtypes. For example, various subtypes of arable chernozems match the following Eh and pH ranges: ordinary chernozem: Eh - 480 to 560 mV; pH - 7.2 to 7.9; typical chernozem: Eh - 570 to 660 mV; pH - 5.3 to 6.4; leached chernozem: Eh - 550 to 610mV;pH-5.6to6.6. It was suggested that the data of the in situ measurements of the agricultural soil liquid phase (pH, pNOs, pK, pCa) be used to characterize the conditions of plant nutrition and further improvement of agricultural measures in fields (quantity, type and time of fertilization, liming). It is very important to pay attention to the possible excess of nitrate (over 50 mg/1) in soil liquid phase, which can lead to accumulation of their toxic quantities in agricultural products. Analysis of the applicability of the equations of ionic exchange and adsorption for description of the processes occurring in real communities showed their limited character as to their use for ecosystems. Both Langmuir and Freundlich's equations turned out to be unsuitable to describe adsorption of K by soil in natural ecosystems. A close correlation between soil liquid phase composition and the processes of photosynthesis, transpiration, phytomass growth, the temperature and water regime of soil has been found. Soil liquid phase composition's dynamics in rhizosphere can be very accurately described by the following equation: pX = Ao + Ait + A2P, where Ao, Ai and A2 are empirical coefficients, P - photosynthesis intensity, t - soil temperature, ""C. For some ions (K^, Ca^^) photosynthesis intensity growth is accompanied by a decline in their activity in soil liquid phase, while for others (NO3') it is accompanied by an increase of their activity. A method of assessment of soil state on the basis of a complex of parameters to characterize the recent soil formation processes has been suggested. It has been shown that in agricultural soils, podzolic and gray forest soils have been changed to a greater degree as compared to chestnut soils and especially chernozems. Agricultural soils represent a separate group of soils more identical within the group than as compared to their natural analogues.
