ACKNOWLEDGMENTS We should like to convey our thanks to B.P. Nikol'skii, E.A. Materova, P.A. Kriukov, V.A. Kovda, A.N. Ty...
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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^^+^^/^' 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
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
aK
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 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 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
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.
