ISSN 0079-0419

PEDOLOGIE Edité avec l'aide financière de la Fondation Universitaire

et du Ministère de l'Education nationale et de la Culture française et du Ministère de l'Education nationale et de la Culture néerlandaise

Uitgegeven met de financiële steun van de Universitaire Stichting en van het Ministerie van Nationale Opvoeding en Nederlandse Cultuur

en van het Ministerie van Nationale Opvoeding en Franse Cultuur

Bulletin de la Société Belge de Pédologie

Bulletin van de Belgische Bodemkundige Vereniging

1983

XXXIII,2

Comité de rédaction Redactiecomité Editorial Board

P. Bullock (Rothamsted, V.K.), J. D'Hoore (Leuven, België), R. Dudal (FAO, Roma, Italy) , R. Frankart (Louvain-Ia-Neuve, Belgique), M. Girard (Grignon, France), G. Hanotiaux (Gembloux,

Belgique), M. H. B. Hayes (Birmingham, V.K.), A. Herbillon (Louvain-Ia-Neuve, Belgique), V. Schwertmann(Munchen, BRD),

C. Sys (Gent, België), M. Van Ruymbeke (Gent, België).

Chief Editor: W. Verheye (Gent, België).

D/1984/0346/1

SOCIÉTÉ BELGE DE PÉDOLOGIE

Secrétariat : Institut Géologique, Krijgslaan 281, B - 9000 Gand

Compte de chèques postaux : 000-0566241-52 : Société BeIge de Pédo­logie, Institut Géologique, Krijgslaan 281, B 9000 - Gand.

Prix d'abonnement par an pour la revue PEDOLOGIE: 600 FB

BELGISCHE BODEMKUNDIGE VERENIGING

Secretariaat: Geologisch Instituut, Krijgslaan 281, B - 9000 Gent.

Postrekening: 000-0566241-52 : Belgische Bodemkundige Vereniging, Geologisch Instituut, Krijgslaan 281, B - 9000 Gent.

Abonnementsprijs per jaar voor het tijdschrift PEDOLOGIE: 600 BF

PRESIDENT D'HONNEUR ERE-VOORZITTER

J. Baeyens

SECRETAIRES GENERAUX HONORAIRES ERE-SECRETARISSEN-GENERAAL

R. Tavemier J. Ameryckx c. Sys

ANClENS PRESIDENTS OUD-VOORZITTERS

V. Van Straelen t (1950-1953) G. Hanotiaux F. Jurion t (1954-1955) M. De Boodt L. De Leenheer ( 19 56-19 57) A. HerbUlon G. Manil t (1958-1959) P. Avril A. Van den Hende (1960-1961) J. D'Hoore G. Scheys (1962-1963) M. Van Ruymbeke L. Sine t (1964-1965) R. Frankart A. Cottenie (1966-1967)

( 1968-1969) ( 1 970-1 971) (1972-1973 ) (1974-1975) (1976-1977) ( 1978-1979) (1980-1981)

92

PEDOLOGIE, XXXIII~ p. 93-104, 5 tab., 5 fig., Ghent 1983

DEGREE OF SOIL WEATHERING AS DETERMINED BY ABRASION pH : APPLICATIONS IN SOIL STUDY ANDINPALEOPEDOLOGY

G. A. FERRARI D.MAGALDI

Abstract

The use of the abrasion pH, measured on the 125-250 J..Lsize fraction of the soil sample and, in particular, of a new index (DpH = pHab - pHsoiO derived from it,

is shown to be very useful for a rapid evaluation of the weathering degree of the soU horizons and of their mineral reserves.

The authors illustrate the results obtained on a sequence of soils developed on surfaces of different ages in the ancient lacustrine basin of Mugello (Tuscany, . Italy).

Recent and lowly weathered soils show lower DpH values than old ones. The DpH presents a clear correlation with : (1) the sum of cations contained in 125-250 J..L fraction of the soU horizon; (2) the sum of exchangeable bases; (3) the base saturation percentage.

In the conclusions, suggestions for using DpH in the evaluation of soil fertility .and in paleo-pedological research are presented.

Key-words

Abrasion-pH, weathering degree, mineral reserve.

1. INTRODUCTION

The abrasion pH is obtained by grinding the minerals into distilled water, followed by the measuring of the hydrogen ion concentration in the solution. The value of the abrasion pH (pHab) is affected by the re-

Research n° 76 del C.N.R. Centro di Studio per la Genesi, Classificazione e Carto­grafia del Suolo. c/o Istituto di Geopedologia e Geologia applicata, Università 'di Firenze - Italia.

G. A. Ferrari - Istituto di Geopedologia e Geologia Applicata, Piazzale delleCascine, 15 - Firenze. Italia. D. Magaldi - Istituto Sperimentale pedo Studio e la Difesa del Suolo , Piazza d' Azeglio, 30 - Firenze. ItaHa.

93

lease of ions from crystalline lat tic es af ter the increase in specific surface obtained by the grinding of the sample. In the hydratation process the hydrolysis and replacemen t of part of the cations by hydrogen continues to the point at which a state of equilibrium is reached :

M+ [mineral]- + H+ OH- ~ H+ [mineral]- + M+ OH- (Loughnan, 1969)

Relations. between the grinding intensity and the quantity of ions released have already been studied by Clarke (1900). A complete investi­gation of the abrasion pH of the most common minerals (Stevens & Carron, 1948) has been ~tilized as a field method for identification of mineral species. More recently, the abrasion pH was proposed as an index of chemical weathering by Grant (1969). In his work, Grant applies the measure of abrasion pH to rocks and to their weathering products, con­duding that the abrasion pH is controlled not only by the ions re1eased &om the primary minerals present in the sample, but also by the amount of day present:

abrasion pH = f [Ca + M~ + Na + K ] . day minerals

An examination ofsaprolites in various weathering stages is reported by Hendricks & Whittig (1968). These authors observe that the abrasion pH decreases with lower molar ratios of the oxides with respect to saprolite and to fresh parent material (andesite).

This study proposes a rapid method for the determination of the abrasion pH of a soil sample, avoiding the interference of day minerals and the possible presence of organic matter in the soil sample at various stages of weathering. The possibility of utilizing this procedure in the estim~tion of the degree of weathering and of the potential fertility as a reserve of nutrients in the primary minerals of the soils is considered.

As weathering progresses, there is a reduction in the quantity of resistant minerals in the soil.Moreover, during the weathering process, the quan-

tity of easily displaced ions is reduced (i.e. Ca2+, Mg2+, K+, Na +). In fact during the epimorphic process there is a movement of matter fr om the interior to t:he surface of the mineraIs. This movement increases in the zones of greater ionic density, like along deavages, edges and fractures (Ferrari & Magaldi, 1974; Stoops et al., 1979), thus contributing to an impoverishment of primary minerals in respect of the more mobile ca­tions.

2. MATERlALS AND METHODS

The soils examined in the present study come from surfaces of fluvial terraces forming a sequence within the old lacustrine basin of Mugello (Florence, Italy). This basin was formed in the Late pliocene as a con-

94

---------------- -- -----

sequence of collapses in the final uplifting phase of the Apennine moun­tain chain. Almost all pre-Iacustrine rocks in this area are composed by quartzose-micaceous-feldspathic sandstones with a day matrix, alternating

'with silty day layers (Marnoso-arenacea and "Macigno del Mugello" formations).

There are very limited outcrops of allochtoneous dayey forma­tions (Ligurides and Alherese units). The origin of the terraces dates back to the Glacial Periods, while the pedogenesis on these sedi­ments refers to the Interglacial stages (Sanesi, 1965).

Field research and sedimentological studies allow to state -that the rc~cks constituting the parent material of the soils are substantially homogene­ous. The pedogenetic trend also appears to be very similar in the differ­ent terraces. There is, in fact, a general evolution towards soils character­ized by a subsurface diagnostic horizon of the argillic type. This diagnos­tic horizon is barely recognizable in the Wurm terraces, quite well developed in the soils of the Riss terraces and becomes dearly expressed in the highest ones (Mindei); in the latter a certain desaturation may as weIl he observed. A synthetic description with a dassificàtion of the soils according to "Soil Taxonomy" (USDA, 1975) and the main analyt­ical data are given in table 1. This information comes from previous studies by Sanesi (1965), Rodolfi et al. (1978) and Dimase & Desideri (in press).

The field samples were thorougly mixed, air dried and sieved to ob­tain the 125-250 IJ. fraction. The aforementioned samples, treated with H202 to eliminate organic matter were boiled with oxálic acid in the presence of aluminium for a more thorough removal of skins of oxides and hydroxides. This preparation allows the elimination of the effects due to the organic matter and day fraction in the determination of the abrasion pH (Grant, 1969).

The sandy fraction thus obtained has the following average compo­sition : quartz = 66 %, feldspars = 22 %, phyllosilicates = 12 %, while that for the fresh rock gives, on average, the following : quartz = 20 %, feldspars = 40 %, calcite = 20 %, dolomite = 5 %, phyllosilicates = 15 %.

The (Ca + Mg + Na + K) content in the sandy fraction of the parent material was also calculated (meq/l00 g of sand) :

Ca2+ = 145, Mg2+ = 165, K+ = 74, Na+ = 109.

On the 125-250 IJ. fraction the sum of cations (Ca2+, Mg2+, K+, Na +), expressed in meq/l00 g of sand, was determined by atomic absorption, in a solution obtained with acid digestion (see tab Ie 2).

F or the determination of the abrasion pH, lOg of sand were ground for thirty minutes in a ball-grinder, obtaining a powder with about 65 % silt and 35 % day. Distilled water was added in a 1 :2.5 ratio to the

95

Table 1 Physical and chemical properties of the soils used in this study.

Horizon Color Structure Texture C.E.C. Base Sat. (meq/ ( %) 100 g. soil)

Prome : Rialto Ap 10 YR 4/3 cru mb SL 14.6 62 Surface : Wurm B1t 7.5 YR 5/7 fine prism. SCL 19.0 74 Classification : Typic Hapludalf 2.5 YR 6/3

B2t 7.5 YR 4/6 medium pris. CL 22.6 78 2.5 YR 6/3

Prome: Pian Vallico Ap 10 YR 6/3 ang. blocky SL 15.1 67 Surface : Wurm B2 10 YR 5/4 ang. blocky SL 12.2 78 Classification : Fluventic Eutro- 10 YR 6/8 chrept C 10 YR4/5 suban. blocky SCL 17.9 65

7.5 YR 4/4

Pro me : Pian dell'lmbuto Ap 10 YR 4/3 ang. blocky SL 15.8 60 Surface : Wurm (?) B2t 10 YR 6/6 medium pris. CL 23.7 68 Classmcation : Typic Hapludalf 2.5 YR 7/5

C as above ang. blocky SCL 26.1 64

Profile: Tabernacolo B21 10 YR 5/5 ang. blocky SCL 17.3 82 Surface : Wurm 10 YR 4/6 Classifi~ation : Fluventic Eutro- B22 10 YR 4/6 medium pris. SCL 18.0 83 chrept 2.5 YR 6/4

C 10 YR6/4 massive SCL 21.1 76

Profile : Uva secca Al 10 YR 7/2 fine crumb L 4.6 33 Surface : Riss B1 10 YR 7/5 suban. blocky L 9.3 26 Classification : Ultic Hapludalf B2t 10 YR 7/5 ang. blocky CL 11.0 43 on Thapto-Aeric Glossaqualf 10 YR 8/3

B2tg 10 YR 5/7 ang. blocky CL 10.2 45 10 YR 8/1

B2tgb 10 YR 6/8 coarse pris. CL 22.2 77 B3tgb 10 YR 6/8 coarse pris. CL 29.9 71

Profile : Casaldi B21t 7.5 YR 5/6 ang. blocky SCL 10.5 54 Surface : Riss B22t 7.5 YR 5/6 ang. blocky SCL . 11.4 33 Classification : Ultic Hapludalf B3t 7.5 YR4/4 ang. blocky L 11.2 36

2.5 YR 7/2

Profile: Gabbianello B21t 7.5 YR 5/6 ang. blocky SiC 27.6 52 Surface : Riss B22t 10 YR 5/6 ang. blocky SCL 16.7 40 Classification : Ultic Hapludalf IIB2t 10 YR 5/6 ang. blocky L 18.8 34

Profile : Lucigliano B21t 2.5 YR 3/6 ang. blocky CL 24.5 31 Surface : Mindel B22t 5 YR 4/8 ang. blocky CL 24.5 31 Classification : plinthudult B/C 5 YR 4/8 massive CL 30.5 35

Profile : Casaccia B1t 2.5 YR 4/6 ang. blocky C 14.9 22 Surface : Mindel 7.5 YR 5/4 Classification : Typic Paleudult B2t 2.5 YR 4/6 ang. blocky C 14.6 24

7.5 YR 5/4 IIC 5 YR 4/6 ang. blocky C 18.7 37

7.5 YR 7/2

96

Tabie 2 pH-water, abrasion-pH, DpH and sum of cations (meq./100 g.) contained in 125-25011 fraction of Mugello soils.

Profile : Rialto Horizon pH soil pH ab DpH Sum of cations Surface : Wurm (meq./l00 g. Classification : Typic Hapludalf sand)

Ap 5.9 6.56 0.7 126.4 'Blt 6.5 6.43 - 0.1 160.3 B2t 7.0 7.23 0.2 202.4

Profile: Pian Vallico Ap 6.6 7.19 0.6 187.3 Surface : Wurm B2 6.5 7.43 0.9 184.4 Classification : Fluventic Eutro- C 6.3 7.33 1.0 242.3 chrept

Profile: Pian dell'lmhuto Ap 6.2 6.61 0.4 n.d. Surface : Wurm (?) B2t 6.5 6.13 -0.4 n.d. Classificaiion : Typic Hapludalf C 6.2 6.26 0.1 n.d.

Profile: Tabernacolo B21 7.4 7.11 -0.3 n.d. Surface : Wurm B22 7.4 7.79 0.4 n.d. Classification : Fluventic Eutro- C 7.2 7.12 - 0.1 n.d. chrept

Pro me : Uva secca Al 4.8 6.93 2.1 98.3 Surface : Riss B1 5.1 7.73 2.6 75.1 CIassÏfication : Uitic Hapludalf B2t 5.6 7.24 1.6 77.0 on Thapto-Aeric Glossaqualf B2tg 5.7 6.79 1.1 99.5

B2tgb 5.9 7.05 1.1 128.1 B3tgb 5.8 6.48 0.7 106.5

Prome : Casaldi B21t 5.6 5.45 - 0.2 45.31 Surface : Riss B22t 5.5 5.54 0.0 64.37 Classification : Uitic Hapludalf B3t 5.6 5.23 -0.4 57.33

Prome : Gabbianello B21t 5.1 6.60 1.5 74.08 Surface : Riss B22t 5.3 6.15 0.8 61.55 Classification : uhic Hapludalf IIB2t 5.5 4.95 -0.6 77.85

Prome : Lucigliano B2lt 4.4 5.90 1.5 101.2 Surface : Mindel B22t 4.4 6.21 1.8 101.3 Classification : Plinthudult BIC 4.5 5.74 1.3 95.6

Prome : Casaccia Blt 5.1 7.18 2.1 47.97 Surface : Mindel B2t 5.3 6.00 0.7 43.08 Classification : Typic Paleudult IIC 5.2 6.60 1.4 60.80

97

Table 3 Relationship between DpH and some soil chemical data, using linear regression techniques (1 n = naturallogarithmic function).

1) Sum ofK+, Na+, Ca++, Mg++, in fresh minerals (Y, meq/100 g) and DpH (X): ln(Y) = 5.22 - 0.35 X

r = 0.70

2) Sum of extractable bases (Y, meq/100 g) and DpH (X) : ln(Y) = 2.79 - 0.66 X

r = 0.79

3) Base saturation (Y, %) and DpH (X) : ln(Y) = 4.48 - 0.48 X

r = 0.84

4) Sum ofK+, Na+, Ca++, Mg++in fresh minerals (Y, meq/100 g) and base satu­ration (X, %) : In(Y) = 2.07 + 0.70 In (X)

r = 0.79

Table 4 Relationship between DpH, soil fertility and weathering classes.

Base saturation Status of soil fertility Degree of DpH value mineral reserve weathering

< 35 % low = < 95 meq/100 g high > 1.9

35-80 % moderate = 95 - 170 meq/100 g moderate 1.9 - 0.2

> 80 % high = > 170 meq/100 g low < 0.2

Table 5 Mean DpH value in relation to age of the surface (from all samples).

DpH

Age of the surface x s

Wurm 0.3 0.45

Riss 0.9 1.01

Mindel 1.5 0.48

powder and af ter six minutes the pH of the suspension was read. The time of grinding ànd of contact with the distilied water was selected, so that the abrasion pH curve was not undergoing further variations with respect to these two parameters.

98

3. RESULTS AND DISCUSSION

The abrasion pH values referred to in table 3, were compared with the pH values of the solution of soil in figure 1 (pH measured in H20 in a 1: 10 ratio). The point distribution c1early demonstrates that the three groups of soils are fairly spread out. This resuIt leads to the assumption that a more precise estimate of the degree of weathering of the Iliinerals could be obtained from the difference between the abrasion pH and the solution pH. This new index is indicated as :

DpH (from pH difference) = - log Hab - (- log Hsol) = log Hsol - log Hab

= log Hsol. Hab

The DpH is therefore a function of the content of cations (Ca, Mg, K, Na) in the primary minerals of the soils and of the % saturation in the soil material, because the H+ concentration is roughly related to saturation percen tage.

From the above discussion the equation emerges :

DpH = f(S, m)

whereby S =saturation and m = sum of cations contained in· the primary minerals.

In order to recognize different groups within the total sample popula­tion the DpH values and the sum of cations in the minerals were elabo:­rated through Euc1idian distances with agglomerative processes known as the "average weighted pairgroup method" (AWPGM) (Sanesi & Wolf, 1972) af ter standardization of the values. :rhe re sult of this work is compiled in the dendrogram shown in figure 2.

• pH

soil

•• • ••

• • •

pH ab

• • ••

• • I • • • • • • •

•

•

Fig. 1. Relationship between abrasion pH and soil solution pH . (abrasion pH is measured in 125-250/l fraction of the soil horizon;soil solution pH is the pH value of whole horizon; squares = soils on Wurm surfaces; circles = soils on Riss surface ; triangles = soils on Mindel sur­face,. according to · the Classic Alpine subdivision) .

99

N (J)

M (J) .. (7)

at) (7)

fD (7)

... (7)

Cl) (J)

(J) (7)

o o ........................ ~

=

2,_ • 2,2 •

A

• A A

• • DpH 10 • • • • • 0,2 • o' •

~ o.e

50 .00 .50 200 250

~ ( Mg . C •• K.N.,m.q / 100 9

2,6 • 2,2 • ' ,8 ..

• • • ' ,4

DpH • ' ,0 • • • 0,6 •• •

• • 0 ,2 • •• - 0 ,2 • • • - 0,6 •

.0 • 5 20

~ exchangeabl. ba. e 5

Fig. 2. Dendrogram established by the method of average weighted pairs (A WPG M), resulting from the comparison between DpH and the sum of cations in 125-250 Jl minerals. (symbols as in figure 1).

Fig. 3 . Relationship between DpH and sum of ca-tions (meq/100 g) . (symbols as in figure 1).

Fig. 4 . Relationship between sum of exchangeable bases (meq/lOO g'of soil) and DpH . (symbols as in figure 1) .

100

2.6

2.2

1.8

1.4

DpH

1.0

0.6

0.2

- 0.2

- 0.4

• •

• • • • • •• • • •

• • • •

• • • • 20 30 40 50 ilO 70 80 90

SAT. %

Fig. 5 . Relationship between base saturation percentage of soils and DpH (symbols as in figure 1).

The diagram defines three distinct groups of samples. Groups land II include soils of different ages (mainly post-Wurmian in the fust and older ones in the second) all developed from the same parent material (Mamoso-arenacea and Macigno del Mugello formations), while Group III includes soils derived from different rocks (Ligurides and Alberese formations : shales, limestones, sandstones). Because of this, the soils in Group III have not been considered for the following determinations.

In Group land II the existing relationship between the DpH, the sum of the cations within the 125-250 J.l fraction, the % saturation of the soil and consequently the sum of ex changeable bases were analysed. The distribution of the representative points is shown in figures 3, 4 and 5, while the corresponding equations, calculated by expressing some ana­lytical data in logarithmic form, .are shown in table 3.

In spite of the different "sizes" of the samples examined, some corre­lations between DpH and the various chemical data are evident. In fact, the measurements of the abrasion pH and of the sum of the cations still contained in the minerals were made on the 125- 250 J.l fraction, while the other analyses were made on the less than 2 mm fraction.

4. CONCLUSIONS

The study confirms the importanee of the abrasion pH for a semi­quan titative evaluation of the degree of weathering undergone 'hy the rocks (regolith) and soil materials developed on them. A more precise evaluation can however he drawn from DpH.

The DpH, whose utilization is for the moment limited to particular types of pedogenetic substrata (non-carbonatie rocks) or to cDmpletely decarbonated soils, is a function of the potential mineral reserves in the soil. The DpH, easily and rapidly obtained, is useful not only for a semi­quantitative evaluation of the weathering degree of a horizon in paleo-

101

pedological and Quaternary studies, but also in order to obtain quickly, though roughly, a preliminary information on the mine ral reserves in the soil (tabie 3, eq uation nOl), on the saturation percentage (tabie 3, equation n° 2), and on the total content of the exchangeable bases (tabie 3, equation n° 3), obviously in conditions of similar parent materials to those studied in Mugello.

The re1ationship between the mine ral reserves and the saturation percen tage, as weU as the saturation classes, used in soil profile descrip­tions, is taken into consideration in order to define classes of mineral reserves. A preliminary sc ale of the potential mine ral reserves based on the DpH values is given in table 4.

Assessment of the degree of weathering of the whole soil is of great help in paleo-pedological research, whereby it may be useful to establish a direct relationship between soil evolution and age.

The DpH may furnish information enabling chronological separation of doubtfull soils or even of soil sediments. Por example the mean DpH values of all samples being examined, grouped according to the age of fluvial terraces, clearly separate recent soils from old ones (tabie 5).

ACKNOWLEDGEMENTS

We thank Prof Sergio Cecconi and Prof Fiorenzo Mancini of the University of Florence for critical reviews and fruitful discussions and we are grateful to Dr. Donatella Bidini and to Dr. Antonietta Raspi for their assistance with some of the laboratory work.

The critical reading and english translation of the manuscript by Dr. A. Piccolo and Dr. M. F. Purnell (FAO-AGLS, Rome) is gratefully acknowledged.

REFERENCES

Clarke F. W. (1900) Thc alcaline reaction of some natural silicates. u.s. Geol. Survey Bull., 167 : 156-158.

F.A.O.-Unesco (1974) Soil map of the world. Vol. 1. Legend. Unesco, Paris, 59 p.

Ferrari G. A. & Magaldi D. (1974) Micromorphological aspects of feldspars weatherlng in same paleosols of Tuscany (Italy). In G. K. Rutherford (Edit.) : Soil microscopy. Proceedings of the fourth international working meeting on soil micromorphology. Kingston, Ont., Canada: 383-393.

Grant W. M. (1969) Abrasion pH, an index of chemical weathering. Clay and clay minerals, 17 : 151-155.

102

Hendricks D. M. & Whittig L..D. (1968) Andesite weathering. 11. Geochemical changes from andesite to saprolite. J. Soil Sc., 19 : 147-153.

Loughnan F. C. (1969) Chemical weathering of the silicate minerals. Elsevier, New Vork, 154 p.

Magaldi D., Bazzoffi P., Bidini D., Frascati F., Gregori E., Lorenzoni P. & Mic1aus N. (1981) Studio interdisciplinare sulla c1assificazione e la valutazione del territorio : un esem­pio nel Comune di Pescia (PT). Cartografia di base. CNR Progetto Finallzzato "Conservazione del Suolo" e Istituto Sperimentale per 10 Studio e la Difesa del Suolo (Firenze).

Rodolfi G., Savio S., Martens P. (1978) Esperienze di cartografia tematica nel Mugello centrale (FI). Verifica di una metodo­logia di analisi delle risorse agricole del territorio. Annali Ist. Sper. Studio e Difesa Suolo, 9 : 67-138.

Sanesi G., (1965) Geologia e geomorfologia dell'antico bacino lacustre del Mugello, Firenze. Boll. Soc. Geol. It., 84, 3 : 169-252.

Sanesi G. & Wolf U. (1972) L'applicazione di alcuni metodi di analisi multivariata alla cartografia del suolo. Atti della Tavola Rotonda sul tema: La cartografia dei suoli : scopi, metodi, applica­zioni. S.I.S.S. - 5a Commissione.

Stevens R. E. & Carron.M. K. (1948) Simple field test for distinguishing minerals by abrasion pH. Am. Mineraiogist ,33: 31-49.

Stoops G., Altemüller H. J., Bisdom E. B. A., Delvigne J., Dobrovolsky V. V., Fitz­patrick E. A., Paneque G., Sleeman J. (1979) Guidelines for the description of alterations in soil micromorphology. Pedologie, 29, (1) : 121-135.

U.S.D.A. (1975) Soil Taxonomy. A basic system of soil classification for making and interpreting soil surveys. Agriculture Handbook n° 436,752 p.

Bepaling van de verweringsgraad door middel van de abrasie-pH: toepassingen in bodemonderzoek en paleopedologie

Samenvatting

Het gebruik yan de abrasie-pH, gemeten op de 125-250 micron bodemfraktie, en in het bijzonder van een nieuwe index (DpH = pHab - pHbodem) die er is van afge­leid, blijkt van groot nut te zijn voor een snelle beoordeling van de verweringsgraad en van de minerale reserve van bodemhorizon ten.

De auteurs tonen dit aan met resultaten die werden bereikt in een bodemsekwen­tie ontwikkeld op niveaus van verschillende ouderdom in het oude rivierbekken van Mugello in Toscanië, Italië.

103

Recente en weinig verweerde bodems vertonen hierbij lagere DpH waarden dan oudere profielen. De DpH-waarde vertoon t een duidelijke correlatie met (1) de som van de kationen uit de 125-250 micron bodemfraktie, (2) de som van de uitwissel­bare basen en (3) de basenverzadigingsgraad.

In de besluiten worden suggesties naar voren gebracht om de DpH waarde te ge­bruiken bij de beoordeling van de bodemvruchtbaarheid en bij het paleopedologisch onderzoek.

Détermination du degré d'altération par la valeur du pH d'abrasion : applications en pédologie et paléopédologie '

Résumé

L'usage du pH d'abrasion, mesuré sur la fraction de 125 à 250 microns, et plus particulièrement de l'index (DpH = pHab - pHsoO qui en est dérivé, paraît être d'une grande utilité pour une évaluation rapide du degré d'altération et de la réserve miné­rale des horizons du sol.

Les auteurs démontrent cette idée par des résultats obtenus dans une séquence de sols développés sur des niveaux d'age différent dans Pancien bassin lacustre de Mugello en Toscanie, !talie.

Les sols récents et peu développés ont des valeurs DpH inférieures à celles des sols plus agés. La valeur DpH est bien correlée (1) avec la somme des cations dans la fraction du sol entre 125 et 250 microns, (2) avec la somme des bases échangeables et (3) avec Ie degré de saturation en bases.

Les conclusions contiennent des suggestions quant à l'usage de la valeur DpH pour l'évaluation de la fertilité du sol et pour des recherches paléo-pédologiques.

Riassunto

L'uso del pH di abrasione, misurato sulla frazione granulometrica 125-250/1 degH orizzonti pedologici ed in particolare di un parametro da esso derivato (DpH = pHab - pHsoil), si rivelano assai utili per una stima rapida del grado di alte '­razione di un orizzonte e delle sue riserve minerali.

Gli Autori illustrano i risultati ottenuti da una sequenza di suoli svilupp;:ttisi su superfici di età diversa nell'antico bacino lacustre del Mugello (Toscana, ItaHa).

11 DpH tende ad essere piu elevato nei suoli piu alterati ed è correlabile con la somma dei cationi (Ca + Mg + K + Na) contenuti nei rninerali della frazio-ne 125-250/1 dei vari orizzonti pedologici, con la somma delle basi di scambio e con la percentuale di· saturazione in basi.

Nelle conclusioni si presentano proposte per l'utilizzazione del DpH nella stima della fertilità del suolo e nelle ricerche di paleopedologia.

104

PEDOLOGIE, XXXIII, 2, p. 105-115,3 tab., 5 fig., Ghent, 1983

SOIL AERATION DATA OF SANDY AND SANDY LOAM PROFILES IN BELGIUM

Abstract

O. VAN CLEEMPUT L. BAERT

Research project supported by I.W.O.N.L. (Institute for encouraging Scientific Re­search in Industry and Agriculture, Brussels)

The 02 and C02 contents at different depths in two sandy and one sandy loam proHle from Belgium have been followed during several years. Soil atmos­phere samples were taken by permanent and instant gas collection systems and analysed by gas chromatography. The sampling depth was 30, 60 and 90 cm respectively.

It was found that in spring and summer time the 02 content can decrease to values below 5 %. During the same period, the C02 content can increase up to 10 %. During the fall and winter time, the 02 and C02 contents of the soil atmos­phere are comparable with those of the aerial atmosphere. The type of soil, crop or sampling depth are of minor importance as compared to the time of the year the observation is made.

Key-words

Soil aeration, oxygen, aerobiosis, anaerobiosis.

1. INTRODUCTION

In a very general way, a soil consists of four major components : mineral materials, organic matter, water and air. Under natural condi­tions, the proportion of air and water is subject to great fluctuations, depending mostlyon climatological parameters and plant growth.

Soil air differs from that of the atmosphere in several respects. First, the soil air content is not uniform; air is moreover concentrated in the

O. Van Cleemput & L. Baert - Faculty of Agriculture, University of Ghent, Coupure, 653, 9000 Ghent, Belgium.

105

ma ze of soil pores, separated by soil solids, making that its composition can vary from place to place. Second, soil air has a moisture content and composition which is different from those of the atmosphere (Buckman & Brady, 1971).

Soil aeration is the process whereby gas of the soil pore space is ex­changed with gas from the atmosphere above the soil surface. According to Grable (1966), it is that part ofthe gaseous cycle involving the inter­change of C02 and 02 between living organisms, the soil, and the aerial atmosphere.

Optimum plan t growth migh t be hindered when the gas exchange be­tween the soil and the aerial atmosphere is limited, although the literature shows many anomalies on that point (Grable, 1966). On the other hand, out of the oxygen level in the soil, some information might be obtained on the possibility of having aerobic and (or) anaerobic conditions, which are of influence on the availability of other plant nutrients.

This paper describes the methodology to sample the soil atmosphere by permanent and by instant sample collectors. It presents data on the 02 and C02 content at different depths in sandy and sandy loam soils in Belgium covered by different crops.

2. MATERIALS AND METHODS

2.1. Gas sampling and analysis

Permanent gas sample collectors are instalied at different depths in the soil profile, according to the technique described by Dowdell et al. (1972). The gas collectors (fig. 1) are made of porous bronze material. A 1.5 mm LD. tube, protected by an outer tube, connects the collector with a 3-way valve above the soil surface. Gas samples are taken by means of a gastight syringe equiped with a 3-way valve, through which the connection is made with the other one. Before takmg the definite sample, and in order to clean the syringe and connecting tubes their total content is sucked out twice. The sample collectors are installed at 30, 60, and 90 cm depth and gas samples are taken on a monthly basis.

Next to the permanent gas sample collectors, also mobile gas sample collectors have been used. A detailed description of the gas sampling tube is given in figure 2. It consists of a stainless steel tube driven into the soil by hand or by an electrical motor. At the end of the stainless steel tube a ceramic filter is mounted, through which gas can enter. Before taking the samplè, the collector tube is sucked out twice by a hand pump. By proper use of the two 3-way valves between the collector tube and the syringe, a gas sample can be taken (fig. 3). In order to pre­ven t atmospheric gas to en ter the soil while driving in the collector tube, the soil surface around the collector is watersealed.

106

28mm

Fig. 1. Design of the permanent gas sampling system.

ounRTUBE 8/12

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Gas samples have subsequently been analysed by gas chromatography, according to the procedure described by Van Cleemput (1969).C02 is separated from the other air-components on Porapak Q and 02 is separated from N2 on Molecular Sieve 5 Ä. All components are identi­fied by a thermal conductivity detector.

107

Fig. 3. Schematic outline of the mobile gas sampling system.

4

CD gas sampling tube

Q) gastight syringe

Q) 'vacuum handpump

@ walerseal

2.2. Profile deseription and agronomie information

A brief textural deseription of the sandy loam and sandy soil profiles is given in table 1. In profile 1, gas samples have been eolleeted from 1977 to 1980; in profile 2 th is was done from 1978 to 1982 and in pro:.

Table 1 Textural profile of the studied soils.

ProfIle 1 Profile 2 Profile 3 Wortegem Sint-Laureins Evergem Soil type : Ldc (*) Soil type : (1) Zdh Soil type : Zchy

Dep th Tex tu re Dep th Texture Dep th Tex tu re cm cm cm

0-25 sandy loam 0-26 sand 0-26 loamy sand 25-38 sandy loam 26-34 sand 26-54 sand 38-62 (fine) sandy 34-56 sand 54-69 ( coarse) sandy

loam loam 62-85 (fine) sandy 56-80 (fine) sandy 69-86 (coarse) sandy

loam loam loam 85-120 sandy loam 80-120 loam to light 86-110 dayey sand

day 120-150 (fine) sandy + 100 sand

loam

(*) Soil types as iden tified in the Belgian soil dassification system.

108

Table 2 Survey of the different crops on the studied profiles.

1977 1978 1979

PROFILE 1 : WORTEGEM wheat sugarbeets wheat

PROFILE 2 : SINT-LAUREINS rye grass

PROFILE 3 : EVERGEM

maize

1980

barley

badey

1981 1982

maize maize

badey maize

file 3 from 1981 to 1982. A review of the different crops grown during these periods on the soils is given in table 2.

3. RESULTS

The evolution of the % 02 and C02 in the soil air at the three loca­tions is given in figures 4 and 5. In profile 1, the 02 content at 30 cm depth seldom drops be10w 15 %; it comes however to lower values at 60 and 90 cm depth, and especially in 1978, very low values have heen noted. The 02 content in profile 2 and 3 drops to lower values than in profile 1. During spring and early summer, it decreases to lessthan 10 % and in some cases to even less than 5 %. There is also less variation at the different depths. On all soils and during all years the 02 level in­creases again from August on, such as to reach almost .20 % during the winter months.

In all cases the C02 evolution shows the same trend: an important increase from March on to re ach a maximum in J une-J uly and a decrease to low values during the winter months. As for the 02 level, only in pro­file 1 there is a clear difference at the three depths. In the other profiles, the C02 content is not much different at 30, 60, or 90 cm depth. In general, the C02 con ten t in early summer reaches values hetween 5 and 10 %. Only in 1982, the C02 content never exceeded 4 %.

4. DISCUSSION

According to Greenwood (1968) a moist soil can he visualized as a unit consisting of fully water-saturated pockets surrounded hy gasfilled, unsaturated regions. Within this matrix micro-organisms and plant roots are continuously absorbing 02 and producing C02. Low 02 and high C02 values, as seen in figures 4 and 5 are common in conditions of high oxygen consumption through root and microbial activity and of restrict­ed aeration through excess of water. Only in the spring and summer months these conditions can be attained at the same time. The differ-

109

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1980

Evolution of % 02 in the soil air during several years at the 3 study sites.

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Table 3 Correlation coefficients between % 02 and % C02 for the different promes, years and depths (the figure between brackets refers to the number of samples considered).

PROFILE 1. WORTEGEM Depth (cm) 1977 30 - 0.63 (8) 60 - 0.75 (7) 90 - 0.57 (6) Tota! (depth) - 0.69 *** (21) Tota! (depth + year)

PROFILE 2. SINT-LAUREINS 30 60 90 Tota! (depth) Tota! (depth + year)

PROFILE 3. EVERGEM 30 60 90 Tota! (depth) Tota! (depth + year)

1978 1979 - 0.98*** (10) - 0.87*** (10) - 0.99 *** (7) - 0.74* (10) - 0.79 *** :(10) - 0.88*** (10) - 0.87*** (27) - 0.86 *** (30)

- 0.79*** (102 )

- 0.96 *** (7) - 0.87 ** (10) - 0.99*** (6 ) - 0.73* (9) - 0.98*** (6) - 0.70 (9) - 0.94*** (19) - 0.74*** (28)

- 0.77*** (93)

1980 1981 1982 - 0.96 *** (8) - 0.70 (89) - 0.79* (8) - 0.72*** (24)

- 0.67 (6 ) -0.91 ** (7) - 0.82* (6) - 0.78 (5) - 0.60 (5 ) -0.47 (6) - 0.87 (3) - 0.95 (3 ) -0.45 (5) -0.72** (14) - 0.88*** (15) -0.45 (17)

- 0.73 (7) - 0.84 * (7) - 0.84* (6 ) - 0.95 ** (6) - 0.36 . (6) . - 0.73*** (19)

- 0.94** (6) - 0.86 *** (19)

- 0.74 *** (38)

ences between the years, depths, and locations are the consequences of different moisture regimes, different microbial and plant activities and different energy supplies (organic matter distribution). Similar consider­ations were also made by Greenwood (1970), Enoch & Dasberg (1971), Elliott & Mc Calla (1972), Burford (1976) and Dowdell et al. (1979).

The inverse relation between 02-consumption and C02-production is demonstrated in table 3. In this tabie, the correlation coefficients are given for the different years and soil depths. All global data as well as some individual observations show that the consumed 02 is significantly negatively correlated with the produced C02.

Replacement of 02 and C02 in the soil atmosphere occurs mainly by mass flow and diffusion (Currie, 1970). Mass flow occurs only when th ere is a difference in total pressure between the soil air and the atmos­phere. This difference is induced by temperature changes, barometric pressure changes, wind or water movement, whereby the latter factor seems to have the greatest effect. Diffusion processes occur when there is a gradien t in partial pressure of the individual gases. I t is the most important mechanism of soil air renewal. Transfer of gas goes from the atmosphere to gas-filled soil pores and from these pores to the centers of biological or chemical activity. Since soil particles and living material are hydrated, the transfer from the gas-filled pores to the active sites must go through water films of varying thickness. Because of a 10,000 times slower diffusion of gas through water than through air (Grable, 1966), the waterfilm thickness is very important. Because of a higher solubility of C02 than 02 in water, its concentration gradient and rate of transfer may be greater than that of 02. It is generally recognized that C02 has a stimulating effect on plants rather than having a toxic effect. The level of 02 is more critical. To maintain the activity of aerobic organisms, a sufficient 02 diffusion rate should exist. This rate depends on the demand for 02 by the organisms. The demand itself depends on number and activity of the organisms and on the available energy source. When the 02 supply gets limited, aerobic activities are transfered into anaerobic ones, which can have important economie and environmental-hygienic consequences. Indeed, in an aerobic soil environ­ment, nitrate can be formed out of the organie material through ammo­nium; in anaerobic conditions, this nitrate will be transformed into gaseous nitrogen compounds, resuIting in nitrogen loss and eventual air contamination by gaseous nitrogen oxides.

The results also show that the type of soil profile, the type of crop and the sampling depth are of minor influence as compared to the sam­pling time. This is quite reasonable because the energy supply (organic matter) is not limited, so that the biological activity mainly depends on

113

the temperature evolution. It should be mentioned that out of a few tests, it was found that the 02-content as well as the C02-content in soil atmosphere samples taken close to each other, could differ importantly. It is known (Smith & Dowdell, 1974) that large differences between concentrations in individual sampling probes can occur. The observed trends with time, however, were more consistent.

It can be concluded that in spring- and summertime the 02 content in the soil profile can decrease to values below 5 %. At the same time, the C02 content can mount up to 10 %. Under these conditions, it would not be exceptional that anaerobic conditions develop within an overall aerobic soil profile.

REFERENCES

Buckman H. O. & N. C. Brady (1971) The nature and properties of soils. Macmillan Cy, New Vork, 7th edition.

Burford J. R. (1976) Effect of the application of cow slurry to grassland on the composition of the soil atmosphere. J. Sci. Fd. Agric., 27 : 115-126.

Currie J. A. (1970) Movement of gases in soil respiration. In (( Sorp tion and transport processes in soils ", Monograph No 37, Society of Chemical Industry, London, 152-169.

Dowdell R. J., Smith K. A., Crees R., & Restall S. W. F. (1972) Field studies of ethylene in the soH atmosphere-equipment and preliminary results. Soil Biol. Biochem., 4 : 325-31.

Dowdell R. J., Crees R., Burford J. R. & Cannell R. Q. (1979) Oxygen concentrations in a day soil af ter ploughing or direct drilling. J. 50i1 5ci., 30 : 239-245.

Elliott L. F. & T. M. Mc Calla (1972) The composition of the soil atrnosphere beneath a beef cattle feedlot and a cropped field. Soil Sci. Soc. Amer. Proc., 36 : 68-70.

Enoch H. & S. Dasberg (1971) The occurrence of high C02 concentrations in air. Geodetma, 6 : 17-21.

Grable A. R. (1966) Soil aeration and plant growth. Adv. Agron., 18 : 57-106.

Greenwood D. J. (1968) Root growth and oxygen distribution in soil. Trans. 9th Intern. Soil Sci.Congr., Australia : 823-832.

114

Greenwood D. J. (1970) Distribution of carbon dioxide in the aqueous phase of aerobic soils. J. Soil Sci., 21 : 314-329.

Smith K. A. & R. J. Dowdell (1974) Field studies of the soil atmosphere. 1. Relationships between ethylene, oxygen, soil moisture content, and temperature. J. Soil Sci., 25 : 217-230.

Van Cleemput O. (1969) Gas chromatography of gases emanating from the soil atmosphere. J. Chrom., 45 : 315-316.

Gegevens betreffende bodemaëratie van zand- en zandleemgronden in België

Samenvatting

De 02 en C02 gehalten op verschillende diepten in twee zand- en één zandleem­grond uit België werden gedurende verscheidene jaren gevolgd. De bodematmosfeer werd bemonsterd met permanente en instant gasbemonsteringssystemen en gaschro­matografisch geanalyseerd. De bemonsteringsdiepte was 30,60 en 90 cm.

Er werd vastgesteld dat in de lente en de zomer, het 02 gehalte kan dalen tot minder dan 5 %. Terzelfdertijd kan het C02 gehalte tot 10 % toenemen. Gedurende de herfst en winterperiode, zijn de 02 en C02 gehalten van de bodematmosfeer ver­gelijkbaar met die van de luch t. Bodemtype , gewas of bemonsteringsdiepte zijn van minder belang dan het tijdstip van het jaar waarop de meting wordt uitgevoerd.

Données concernant l'aération du sol dans des profils sableux et sablo-limoneux en Belgique

Résumé

La quantité d'02 et de C02 a été déterminée à différentes profondeurs dans deux sols sableux et un sol sablo-limoneux beige pendant différentes années. L'atmos­phère du sol a été échantillonnée au moyen d'un système de collecteurs de gaz per­manents et instantannés, et analisée par chromatographie gazeuse. La profondeur d'échantillonnage était 30, 60 et 90 cm.

On a trouvé que la teneur en 02 peut diminuer jusqu'à moins de 5 % au printemps et en été. En même temps, la teneur en C02 peut augmenter jusqu'à 10 %. Pendant l'automne et l'hiver les teneurs en 02 et en C02 dans l'atmosphère du sol sont com­parables aux valeurs enregistrées dans l'air. Le type du sol, la végétation et la pro­fondeur d'échantillonnage sont moins importants que la période de l'année à la­queUe I'observation est faite.

115

PEDOLOGIE, XXXIII, 2, p. 117-136,8 tab. Gand, 1983

LA PRÉVISION DE L'ÉROSION EN EUROPE ATLAN­TIQUE : LE CAS DE LA ZONE LIMONEUSE DE BELGIQUE

Résumé

A. BOLLINNE A. LAURANT

Recherches subventionnées par l'lnstitut pour l'Encouragernent de la Recherche Scientifique dans l'lndustrie et I' Agricul­ture (I.R.SJ.A.).

En vue de vérifier I'applicabilité de l'équation universelle de perte de sol (u.S. L.E.) à la zone à climat ternpéré océanique d'Europe Atlantique, des rnesures d'érosion ont été réalisées en parcelles expérirnentales standard (22,13 X 4 rn) de la région limoneus(' de Moyenne Belgique.

De l'analyse des résultats, il ressort que cette équation ne peut être appliquée telle quelle. Certains ajusternents s'irnposent : (1) Ie seuil de précipitations pris en considération pour Ie calcul de EI30 (12,7 rnrn) est trop élevé, il conduit à consi­dérer 33,5 % du ruissellernent cornrne non érosif; on propose de Ie rarnener à 1 rnrn; (2) les valeurs du facteur couvert végétal (C) utilisées aux Etats-Unis sont tout à fait inadéquates chez nous; d'autres valeurs calculées à partir de rnesures au charnp sont proposées.

Moyennant ces ajusternents, l'équation perrnet une estirn~tion satisfaisante d~ risque érosif. Par contre l'équation appliqüée telle quelle con duit à une sous-esti­rnation du risque érosif supérieure à 50 %. Une version rnodifiée de l'équation uni­verselle de perte de sol est proposée pour estimer Ie risque érosif dans la zone à clirnat ternpéré océanique d'Europe Atlantique.

Mots-clés

Erosivité, risque d'érosion.

A. Bollinne - Lab. Science du Sol, 27, Avenue Maréchal Juin - B 5800 Gernbloux -Belgique. A. Laurant - Lab. Géornorphologie et Géologie du Quaternaire, 7, Place du xx Août, B 4000 Liège - Belgique.

117

1. INTRODUCTION

Dans les limons de Moyenne Belgique l'érosion accélérée consécutive à la mise en culture a provoqué diverses modifications et dégradations ayant entr3Îné une diminution de productivité : troncature des sols (5 à 10 % du territoire), mise en place de sols colluviaux (1/3 du territoire), appauvrissement des sols érodés en éléments fins (0-10 pm) et en matière organique. De plus, chaque année, l'érosion provoque des dégats dans les cultures.

L'ensemble des pertes moyennes annuelles de rendement résultant des dégats aux sols et aux cultures a été estimé entre 3 et 5 % de la valeur de la récolte, soit une somme de 1.500 à 2.500 fr/ha/an * ou encore 18 à 30 % du bénéfice net moyen à l'hectare ou 10 à 17 % du revenu moyen du travail (revenu net + main-d'oeuvre imputée) *.

Des pertes de cette importance justifient que soient prises des mesures pour protéger Ie sol et les cultures et, par conséquent, que soient connus avec une précision satisfaisante Ie risque érosif et l'efficacité des diverses mesures anti-érosives envisageables.

2. LE MODELE AMERICAIN

Le modèle de prévision de l'érosion, appelé Universal Soil Loss Equa­tion (U.S.L.E.), mis au point aux Etats-Unis par Wischmeier & Smith (1965,1978) est à présent bien connu. C'est un modèle empirique établi à partir du traitement statistique des résultats de nombreuses mesures en parcelles expérimentales. 11 a pour expression : A = R.K.L.S.C.P. (eq. 1), A étantl'érosion en t/ha. Chacun des facteurs de l'équation prend en compte l'effet sur l'érosion, respectivement, de la pluie (R), du sol (K), de la longueur de la pente (L), de la pente (S), du couvert végétal et des pratiques culturales (C) et des pratiques anti-érosives (P).

Aux Etats-Unis des tables et nomogrammes permettent, pour la.plu­part des situations, de calculer la valeur des facteurs de l'équation et d'estimer Ie risque érosif. En vue de tester l'applicabilité des valeurs des paramètres du modèle américain à la zone à climat tempéré océanique d'Europe Atlantique nous avons entrepris une campagne de recherches.

La Moyenne Be1gique connaît des conditions très représentatives de la zone à climat tempéré océanique de la façade atlantique de l'Europe. Des mesures ont été réalisées pendant 6 ans (1974-1979) à Sauvenière (Gembloux , Moyenne Be1gique) sur 12 parcelles expérimentales de 22,13 m de long et 4 m de large installées sur un versant de 6,5 %. Ce

* Estimation basée sur les rendements moyens dans une rotation triennale en région limoneuse et sur les prix de la campagne 1979-1980. Valeurs que nous ont aima­blement communiquées J. F. Breuer & B. Lange, Chaire d'Economie Rurale (F.S.A.E. Gx), que nous remercions vivement.

118

versant est recouvert d'une épaisse couche de limon éolien dans lequel s' est développé un sol brun lessivé (Orthic Hapludalf) . Les résultats des mesures son t discutés ci-dessous et pour plus de clarté chacun des fac-teurs de l'équation (1) est envisagé séparément. .

2.1. LE FACTEUR PLUIE (R)

Afin de faciliter l'exposé, on discutera d'abord Ie problème du choix de la lame précitée à prendre comme seuil pour Ie calcul de R. Ensuite on examinera la corrélation entre EI30 et l'érosion.

2.1.1. Choix d'un seuil de lame précitée pour Ie calcul de R

Le facteur pluie ou indice d'érosivité (R) a été défini comme étant Ie produit de l'énergie de la pluie (E) par son intensité maximum en 30 mi­nutes (130)" (Wischmeier, 1959). Les valeurs de l'indice mensuel et de l'indice annuel sont égales à la somme des valeurs de EI30 des pluies in­dividuelles de la période correspondante. Aux Etats-Unis, seules les pluies ~ 1/2 pouce (12,7 mm) sont prises en considération pour calculer l'indice d'érosivité (R = EI30)' Deux raisons sont invoquées pour justi­fier cette façon de procéder : l'indice d'érosivité des pluies < 12,7 mm est habituellement faible et Ie fait d'éliminer les pluies < 12,7 mm a per­mis de réduire sensiblement Ie coût de la carte d'érosivité des pluies (Wischmeier & Smith, 1978).

A ce seuil, caractérisé par une hauteur de pluie, a été associé un seuil caractérisé par une in tensité : pour Ie calcul de EI30, en plus des pluies ~ 1/2 pouce (12,7 mm) on a retenu les pluies ~ 1/4 pouce (6,35 mm) en 15 mn, soit les pluies dont l'intensité est ~ 1 pouce/h (25,4 mm/h) pendant 15 minutes (Wischmeier & Smith, 1978). Ce critère d'intensité correspond au se uil proposé par Hudson (1976) en Rhodésie (Intensité ~ 1 pouce/h). Ces deux limites sont empiriques.

Le meilleur critère de sélection ne devrait retenir que les pluies qui provoquent ruissellement et érosion. C'est l'objectif habituellement pour­suivi par ceux qui se sont penchés sur Ie problème de l'érosivité des pluies (Wischmeier & Smith, 1965, 1978; Hudson, 1976). La difficulté majeure réside dans Ie fait que plusieurs facteurs inteiviennent dans Ie controle de l'érosion (intensité, durée et hauteur des pluies, humidité et structure du sol au momen t des pluies, ... ) et qu'il est difficile et souvent peu rentable de prendre en compte tous ces terrnes pour un indice d'éro­sivité empirique pratique (Wischmeier, 1959; Hudson, 1961).

L'étude de l'adaptation de EI30 à la Moyenne Belgique se base sur les résultats des mesures effectuées en jachère nue. Ceci perrnet d'éviter les in terférences provenan t des façons culturales ou du couvert végétal. L'année a été divisée en deux périodes sur base de l'état hydrique du

119

sol: une période "estivale", d'avril à septembre et une période "hiverna­Ie", d'octobre à mars. La période hivernale con traste nettement avec les conditions estivales : vu la température basse, l'évapotranspiration est faible et la viscosité de l'eau élevée. De ce fait, les sols sont souvent gor­gés d'eau et se ressuient lentement.

L'étude comparative des enregistrements pluviographiques et des en­registrements du ruissellement perrnet de déterminer aisément les pluies qui donnent du ruissellement et qui sont supposées provoquer de l'éro­SIon.

L'indice d'érosivité (EI30) a été cumulé par classe de pluie en dis tin­guant les pinies érosives et non érosives pour chaque classe de pluie indi­viduelle (tableau 1). Les précipitations < 1 mm n'ont pas été prises en considération et celles ~ 13 mm ont été regroupées en une seule classe.

L'analyse du tableau 1 montre qu'en été on enregistre des pluies non érosives de hauteur importante (> 13 mm). Par contre, en hiver, la hau­teur des pluies non érosives ne dépasse pas 6 mmo Cependant, la propor­tion de EI30 des pluies non érosives est du même ordre de grandeur au cours des périodes estivales (12 %) et des périodes hivernales (10 %). Mais à l'inverse, que ce soit en été ou en hiver, des pluies de faible hau-

Tableau 1 Indice d'érosivité (EI30) cumulé et groupé par c1asse de pluie, en distinguant les pluies érosives (E) et non érosives (N.E.) pour les périodes estivale et hivernale et pour l'ensemble de l'année.

Précipitations (P) Eté Hiver Année enmm E NE E NE E NE

1~P<2 0,62 2,99 0,21 0,40 0,83 3,39 2~P<3 1,40 1,17 0,38 0,24 1,78 1,41 3~P<4 2,59 2,27 0,31 0,05 2,90 2,32 4~P<5 3,61 3,54 0,69 0,46 4,30 4,00 5<P<6 5,19 2,85 0,35 0,55 5,54 3,40 6<P<7 5,73 1,03 3,73 - 9,46 1,03 7<P<8 1,57 3,49 0,50 - 2,07 3,49 8~P<9 6,54 - 0,22 - 6,76 -

9 <P< 10 17,60 2,07 0,65 - 18,25 2,07 10<P<1~ 6,60 - - - 6,60 -11~P<12 0,76 - 0,28 - 1,04 -12~P<13 11,98 1,38 - - 11,98 1,38 13<P 96,31 1,21 7,50 - 103,81 1,21

Totaux 160,50 22,00 14,82 1,70 175,32 23,70 I I I I I

Totaux par I I I

période 182,50 16,52 199,02

Totaux en valeur 88 % 12 % 90 % 10 % 88 % 12 % relative

I

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Tableau 2 Pourcentage du ruissellement provoqué par des pluies de hauteurs différentes.

Précipitations (mm) Ruissellement : pourcentages cumulés Eté Hiver Année

1~-l-<2 2,0 5,9 3,0 2~l<3 2,4 8,9 4,1 3~-l-<4 4,5 9,5 5,7 4~-l-<5 6,4 10,8 7,6 5~-l-<6 10,3 21,9 13,2 6~-l-<7 12,3 31,9 17,3 7~-l-<8 12,6 32,7 17,7 8~-l-<9 20,2 32,7 23,3 9 ~.J, < 10 24,4 37,2 27,6 10~-l-<11 31,4 37,2 32,8 11~-l-<12 32,2 37,2 33,5 12 ~ -l- < 13 32,3 37,2 33,5

-l-~ 13 100,0 100,0 100,0

teur peuvent provoquer ruissellement et érosion. L'importanee du ruissellement provoqué par les pluies de faible hauteur (quelques mm) n'est pas négligeable (tableau 2).

A la station de Sauvenière, les pluies < 2 mm sont à elles seules res­ponsables de 2 % du ruissellement estival et de 5,9 % du ruissellement hivernal tandis· que les pluies < 13 mm sont à'l'origine de 32,3 % du ruissellement estival, 37,2 % du ruissellement hivernal et 33,5 % du ruissellemen t annuel.

De telle·s pluies peuvent provoquer du ruissellement eorrespondant à à l'éeoulement d'une lame d'eau de 4 mm soit 40 m3/ha (Bollinne, 1982). Or l'expérienee a montré que l'entraînement de matières solides par des éeoulemen ts de eet ordre et même ne ttemen t inférieurs (1 mm par exemple) est loin d'être négligeable.

Par ailleurs, l'érosivité des précipitations ~ 1,27 mm (1/20 pouee) a été ealeulée pour une période de 40 années (1934-1973) à partir des en­registrements pluviographiques de la station d'Uecle (Bruxelles) (Lau­rant & Bollinne, 1976).

En moyenne, par rapport à l'ensemble des préeipitations ~ 1,27 mm (tableau 3), l'érosivité des préeipitations < 12,7 mm est de 39,0 % pour l'année, 34,8 % pour la période estivale (avril à septembre) et 52,4 % pour la période hivernale (oetobre à mars).

Des valeurs similaires ont été ob tenues pour 3 autres stations de Bel­gique méridionale (Florennes, Saint-Hubert et Spa) (Bollinne et al., 1979).

De eette analyse des résultats des mesures d'érosion à la station de

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Tableau 3 Valeurs de l'indice d'érosivité moyen exprimé en valeur relative pour différentes hau­teurs de pluies. A partir des valeurs de EI30 calculées pour Uccle (A. Laurant & A. Bollinne, 1976).

P(mm) EI30 (%) EI30 (% cumulé)

été hiver année été hiver année

1,27 - < 2 0,4 0,9 0,5 0,4 0,9 0,5 2-<3 1,1 2,5 1,5 1,5 3,4 2,0 3-<4 2,~ 4,1 2,8 3,8 7,5 4,8 4-<5 2,5 4,9 3,1 6,3 12,4 . 7,9 5 - <6 3,1 6,4 3,9 9,4 18,8 11,8 6 - <7 3,4 5,6 3,9 12,8 24,4 15,7 7-<8 4,2 5,3 4,5 17,0 29,7 20,1 8 - <9 3,8 5,9 4,3 20,8 35,6 24,3 9 - <]0 3,8 5,9 4,3 24,6 41,6 28,6

10 - < 11 4,2 4,6 4,3 28,8 46,2 32,9 11 - < 12 3,0 4,5 3,3 31,8 50,7 36,2 12 - < 12,7 3,0 1,7 2,7 34,8 52,4 39,0

~ 12,7 65,2 47,6 61,0 100,0 100,0 100,0

Sauvenière et des mesures d'érosivité à la station météorologique d'Uccle il ressort que : - l'érosivité des précipitations < 12,7 mm est importante (tableau 3); - ces mêmes précipitations < 12,7 mm sont aussi responsables d'une

part importante du ruissellement (tableau 2); - même les pluies les plus faibles peuvent provoquer ruissellement et

érosion (tableau 1). Par conséquent, pour un climat tempéré océanique tel que celui que

nous connaissons en Europe Occidentale, Ie seuil de pluie de 12,7 mm est certainement trop élevé. Provisoirement Ie se uil de 1 mm est pris en considération. Bien que des différences apparaissent entre les périodes estivale et hivernale (tableaux 1, 2 et 3), les résultats dont on dispose sont encore trop peu nombreux (Bollinne, 1982) pour déterminer avec précision Ie seuille mieux adapté à ces deux périodes.

2.1.2. La corrélation entre EI30 et l'érosion

Comparée aux résultats obtenus dans d'autres régions du monde (Wischmeier, 1959; Hudson, 1961; Lal, 1976), la relation entre la perte de sol et EI30 obtenu en Belgique est nettement plus faible (r ~ 0,6) (Bollinne, 1982) .. Aux Etats-Unis, rest systématiquement supérieur à 0,9, du moins lorsque la relation perte de sol - - EI30 est calculée sur une base saisonnière.

Vu la période de mesure assez courte - 6 ans - et la dispersion des

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résultats, l'étude de cette relation sur une base saisonnière a dû être reje­tée, n'ayant aucune valeur du point de vue statistique. 11 est donc normal que la relation perte de sol - EI30, étudiée sur base des données des re­levés individuels, soit plus faible que si elle avait été étudiée sur base des données cumulées par saison (Wischmeier, 1959, Wischmeier & Smith, 1965). Cependant au Nigéria, pour des pentes comprises entre 5 et 15 %, Lal (1976) a obtenu pour cette relation, calculée pluie par pluie, des coefficients de corrélation nettement supérieurs (r ~ 0,80). En Europe Atlantique cette relation est donc particulièrement faible.

Ceci résulte en partie des multiples interactions sol-précipitations qui revêtent sans aucun doute une grande importance en climat océanique (Laurant & Bollinne, 1976). Cependant, en vu d'une corrélation aussi faible, on se doit de s'interroger sur la validité de l'indice d'érosivité (EI30) dans une zone caractérisée par des pluies de faible intensité. Les deux facteurs de l'indice d'érosivité, l'énergie et l'intensité maximum en 30 minutes doivent donc être reconsidérés.

Des recherches sont en cours visant à-.vérifier la validité du mode d'es­timation de l'énergie des pluies (Wischmeier & Smith, 1965). Quant au second facteur (I30) il ne semble pas particulièrement bien adapté à la prévision de l'érosion en Europe Atlantique : en effet, les pluies y sont généralement de faible intensité et celles de forte intensité sont souvent de courte durée (~30 mn, Laurant & Bollinne, 1978). Cependant s'il s'avère nécessaire de procéder à la mise au point d'un nouvel indice d'éro­sivité mieux adapté aux conditions climatiques qui prévalerit sur la fa­çade atlantique de l'Europe, ce travail ne pourra être réalisé que lorsqu' on disposera d'un mode d'évaluation de l'énergie des pluies satisfaisant dans nos conditions climatiques et de suffisamment de données pour tester de nouveaux indices. Les mesures se poursuivent en vue de rassem­bIer suffisamment de données à cet effet.

Provisoirement nous proposons d'estimer Ie risque d'érosion d'une si­tuation donnée par référence aux parcelles en jachère nue (AJN) de la station de Sauvenière (L = 22,13; S = 6,5 %) en utilisant la relation éta­blie entre EI30 et la perte de sol au seuil de 1 mmo Cette relation est

AJN = 0,19 EI30 + 0,64 r = 0,58 (2)

La relation ayan t été calculée à partir de mesures réalisées approxima­tivement à une fréquence mensuelle, nous suggérons d'estimer Ie risque érosif sur une base mensuelle.

Les valeurs de EI30 peuven t être obtenues à partir de la carte d'érosi­vité des pluies de Belgique (Bollinne et al., 1979); de même les valeurs mensuelles de l'indice ont été caIculées pour les différentes régions du pays (Laurant & Bollinne, 1976, 1978; Bollinne et al., 1979).

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2.2. LE FACTEUR SOL (K)

Le facteur sol caracterise l'érodibilité du sol. Celle-ci peut être définie comme la "sensibilité" ou la "susceptibilité" du sol à l'érosion. Dans l'équation de perte de sol (USLE) elle est égale à la perte de sol par unité de l'indice d'érosivité des pluies ~ 12,7 mm pour des mesures effectuées dans une parcelle unitaire (longueur 22,13 m, pente 9 %) en permanence sous jachère nue et travaillée de haut en bas. Dans ces conditions

K = Et . Si la pente est différente, K = EIA S' 30 30'

La valeur du facteur K du sol de la station de Sauvenière a été estimée à l'aide du nomogramme utilisé aux Etats-Unis (Wischmeier et al., 1971); exprimée en unités métriques, elle est de 0,62. Cette même valeur calcu­lée sur base des résultats obtenus à la station de Sauvenière est de 0,53 (calculée conformément au modèle américain) en prenant en compte EI30 des seules pluies ~ 12,7 mmo

La valeur exprimée à l'aide du nomogramme est donc 17 % plus élevée que la valeur calculée à partir des mesures in situ. Ced peut être attribué au fait que la période de mesure (6 ans) est trop courte et que, contrai­rement aux parcelles de référence utilisées aux Etats-Unis, les parcelles de la station ne sont pas continuellement en jachère nue mais entrent dans une rotation triennale (betterave, froment, jachère nue). De cette façon, des résidus incorporés au sollors du labour peuven t modifier la valeur K. Par conséquent la valeur K,estimée à l'aide du nomogramme, peut être considérée comme une bonne approximation.

Des observations in situ semblent cependant indiquer que l'amplitude des variations de K soit nettement plus importante que l'amplitude cal­culée (Bollinne & Rosseau, 1978). Des mesures qui visent à vérifier ces observations son t en cours.

Etant donné que la perte de sol en jachère nue est estimée sur base d'une relation établie au seuil de pluie de 1 mm (éq. 2), il est nécessaire de procéder à un ajustemen t de la valeur K obtenue à partir du ~omo­gramme (Kw) avant d'estimer Ie risque d'érosion d'autres sols:

Km = (Kw - 0,48) 1,292 . ~':~ + 0,43 (3), ou après simplification , Km = (Kw ~ 0,48) 1,16 + 0,43 (eq 3').

- Km : facteur sol en unités métriques en tenant compte de l'érosivité

des précipitations au seuil de 1 mm;

- Kw : facteur sol en unités anglaises estimé à partir du nomogramme;

- 1,292 : facteur de conversion des unités anglaises en métriques pour K;

. - 0,48 :valeur K du soL de la station de Sauvenière, calculée à partir du

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nomogramme; - 0,43 : valeur K calculée à partir des mesures in situ à la station de

Sauvenière et utilisée comme valeur de référence.

La perte de sol d'un versant donné peut être estimée par la formule:

A = A JN . Km . ~'~36 . C . P (4) , - AJN : perte de sol de la jachère nue à la station de Sauvenière

(L = 22,13 m; S = 6,5 %) calculée à partir de l'équation 2;

- 0,636 : valeur des facteurs L. S. de la station de Sauvenière.

L'estimation de la perte de sol en jachère nue à la station de Sauve­nière par Ie modèle américain (USLE) est 21 % plus faible que par l'équa­tion 4 (tableau 4). Les différences principales entre les deux estimations apparaissent durant la période hivernale (tableau 5). L'estimation glo­bale de la perte de sol est nettemen t plus faible pour cette période (tableau 4) alors qu'elle n'est pas significativement différente pour la période estivale.

2.3. LE FACfEUR PENTE (L.S)

11 tient compte à la fois de la longueur (L) de la pente et de son incli­naison (S). L'érosion ,augmente avec la longueur et l'inclinaison des pentes. Dans la pratique les deux facteurs de pente, L et S, sont combi:.. nés en un seul facteur topographique qui permet d'évaluer globalement l'influence de la pente sur la vitesse de l'érosion. Des formules, tables et abaques permettent de quantifier aisément les valeurs du facteur topo­graphique .(Wischmeier & Smith, 1978; Foster & Wischmeier, 1974).

L'étude de l'influence de la pente sur la vitesse de l'érosion suppose que soient mis en oeuvre des moyens considérables; notamment que soit réalisée l'installation d'un nombre important de stations sur des pentes d'inclinaisons différentes et que chaque station soit équipée de parcelles de longueurs différentes en un nombre de répétitions suffisant pour que soit pondérée la variabilité entre les parcelles. De plus, les me­sures doivent être poursuivies pendant un nombre d'années suffisant, de Tableau 4 Erosion moyenne annuelIe estimée par la formule américaine (U.S.L.E.) et par la formule modifiée (U.S.L.E. modifiée) pour une parcelle nue à la station de Sauve­nière.

Période hivernale (0, N, D,], F, Mars)

Période estivale (A, M, J, J, A, Sept.)

Erosion moyenne annuelIe estimée (t/ha) U.S.L.E. U.S.L.E. modifiée

3,0 6,5

12,8 13,5

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façon à fournir des chiffres moyens tenant compte de la variabilité inter­annuelle due aux interactions entre les différents facteurs culturaux et climatiques. Faute de moyeIis nous n'avons pu entreprendre ces recher­ches.

Cependant nous disposons d'observations et de mesures (Bollinne, 1974, 1977) qui montrent que, conformément au facteur topographique (Wischmeier & Smith, 1978), l'érosion croît avec la longueur et l'inclinai­son des pentes. 11 a donc été décidé d'utiliser les valeurs du facteur' topo­graphique sans modification.

11 faut noter que Ie facteur topographique (L.S.) est considéré comme Ie plus "universellenient applicable" de tous les facteurs de l'équation américaine (USLE), du moins pour des pentes comprises entre 10 et 100 met entre 3 et 25 % (Moldenhauer & Foster, 1981), voire entre 1 et 3 % (Mutchler & Murphee, 1981). Son application peut donc théori­quement être étendue à toutes les pentes de la région limoneuse.

On verra au § 3 que les estimations de l'érosion à l 'aide des valeurs du facteur topographique sont satisfaisantes sauf cependant sur les pentes faibles « 4 %) ou elles conduisent à une sous-estimation importante.

2.4. LE COUVERT VEGETAL ET LES PRATIQUES CULTURALES (C)

Le facteur C est défini dans l'équation américaine (USLE) comme Ie rapport entre la perte de sol d'une parcelle cultivée dans des conditions définies et la perte de sol correspondante d'une parcelle identique en ja­chère nue continue.

La valeur du facteur C est conditionnée par plusieurs variables et leurs interactions. Les principales sont : Ie couvert végétal, Ie couvert de rési­dus (mulch), l'incorporation des résidus, les façons culturales et l'effet résiduel de celles-ci. Chaque variabie est traitée comme un sous-facteur et C est Ie produit de ceux-ci. Pour les deux premières variables on dis­pose de nomogrammes qui permettent de calculer aisément leurs valeurs et celTes de leurs interactions. De plus, des tables fournissent les valeurs des facteurs C pour les principales cultures et les rotations pratiquées aux Etats-Unis (Wischmeier & Smith, 1978).

Dans notre agriculture, les façons culturales étant pratiquement tou­jours les mêmes, du moins pour les principales cultures, l'estimation des valeurs du facteur C s'en trouve singulièrement simplifiée.

Dans une rotation triennale traitée classiquement, la valeur du fac­teur C est essentiellement controlée par les ameublissements, les traces de passages d'outils au cours du semis et des pulvérisations par l'évolu­tion du couvert au cours du cycle végétatif des cultures; après celle-ci, par la quantité de résidus laissés en surface et par la densité de la repousse après escourgeon.

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Tableau 5 Estimation de l'érosion mensuelle moyenne parla formule américaine pourune parcelle à lastation .de Sauvenière (pente de 6,5 %, longue de 22,13 m) et entre parenthèses les valeurs correspondantes obtenues par la formule modifiée. R = EI 30; K estimé par Ie nomogramme pour la valeur américaine et à partir des mesures en parcel1es; Ajn : érosion en jachère nue; A : érosion sous culture.

R K L.S. Ajn (t/ha) C P A (t/ha)

{.~ ({. ~ 12,7 mm 1,27 mm)

] 0,69 (1,78) 0,62 (0,43) 0,636 0,3 (1,0) 0,43 (0,10) 1,0 0,1 (0,1) F 0,71 (1,62) " " " 0,3 (0,8) 0,43 (0,30) " 0,1 (0,2) M B 0,71 (1,87) " " " 0,3 (0,8) 0,43 (0,50) " 0,1 (0,4) E A T 1,25 (2,74 ) " " " 0,5 (1,2) 0,64 (1,40) " 0,3 (1,7) M T 2,43 (4,68) " " " 1,0 (1,5) 0,64 (1,12) " 0,6 (1,7)

E ] R 4,41 (7,55 ) " " " 1,7 (2,1) 0,56 (0,87) " 1,0 (1,8) ] A 15,38 (18,74) " " " 6,1 (4,3) 0,36 (0,54) " 2,2 (2,3) A V 6,04 (9,93) " " " 2,4 (2 ,6) 0,36 (0,23) " 0,9 (0,6) E S 2,72 (5,75) " " " 1,1 (1,8) 0,26 (0,04) " 0,3 (0,1) 0 2,42 (4,41) " " " 1,0 (1,5) 0,26 (0,04) " 0,3 (0,1)

N 1,61 (3,26 ) " " " 0,6 (1,3) 0,79 (1,76) " 0,5 (2,3)

Ir 1,24 (2,57 ) " " " 0,5 (1,1 ) 0,62 (1,76) " 0,3 (1,9)

F 0,69 (1,78) " " " 0,3 (1 ,0) 0,62 (1,76) " 0,2 (1,8) R 0,71 (1,62) " " " 0,3 (0 ~ 8) 0,62 (1,76) " 0,2 (1,4 )

M 0 0,71 (1,87) " " " 0,3 (0,8) 0,62 (1,76) " 0,2 (1,4) M

A E 1,25 (2,74 ) " " " 0,5 (1,2) 0,62 (1,23) " 0,3 (1,5) M N 2,43 (4,68) " " " 1,0 (1,5) 0,17 (0,28) " 0,2 (0,4) ] T 4,41 (7,55 ) " " " 1,7 (2,1 ) 0,11 (0,20) " 0,2 (0,4) ] 15,38 (18,74 ) " " " 6,1 (4,3) 0,11 (0,00) " 0,7 (0,0) A 6,04 (9,93) " " " 2,4 (2,6) 0,11 (0,09) " 0,3 (0,2)

S 2,72 (5,75) " " " 1,1 (1,8) 0,79 (0,10) " 0,9 (0,2) I 0 2,42 (4,41) " " " 1,0 (1,5) 0,79 (0,85) " 0,8 (1,3) I

N E 1,61 (3,26 ) " " " 0,6 (1,3) 0,42 (0,85) " 0,3 (1,1) D S 1,24 (2,57) " " " 0,5 (1,1) 0,42 (0,85) . " 0,2 (0,9)

C ] 0 0,69 (1,78) " " " 0,3 (1,0) 0,42 (0,85) " 0,1 (0,9) F U 0,71 (1,62) " " " 0,3 (0,8) 0,42 (0,85) " 0,1 (0,7) M R 0,71 (1,87) " " " 0,3 (0,8) 0,42 (0,85) " 0,1 (0,7) G A E 1,25 (2,74 ) " " " 0,5 (1 ,2) 0,42 (0,50) " 0,2 (0,6) M 0 2,43 (4,68) " " " 1,0 (1,5) 0,17 (0,20) " 0,2 (0,3) ]

N 4,41 (7,55 ) " " " 1,7 (2,1) 0,06 (0,01) " 0,1 (0,0) ] 15,38 (18,74 ) " " " 6,1 (4,3) 0,11 (0,10) " 0,7 (0,4)

A 6,04 (9,93) " " " 2,4 (2,6) 0,29 (0,10) " 0,7 (0,3) S 2,72 (5,75) " " " 1,1 (1,8) 0,06 (0,05) " 0,1 (0,1) 0 2,42 (4,41) " " " 1,0 (1,5) 0,06 (0,05) " 0,1 (0,1) N 1,61 (3,26 ) " " " 0,6 (1,3) 0,06 (0,05) " 0,0 (Os!) D 1,24 (2,57) " " " 0;5 (1,1) 0,06 (0,05) " 0,0 (0,1)

--- ---47,4 (60,0) 13,6 (28,1)

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Les résidus étant enfouis à grande profondeur par les labours prófonds ils n'ont aucun effet mécanique favorable sauf, pendant une courte pé­riode, après déchaumage. L'effet résiduel à long terme se traduit par des changements, notamment dans la structure, la détachabilité, la densité, la teneur en matière organique, facteurs qui peuventmodifier l'érodibilité du sol.

Les valeurs du facteur C ont été calculées mois par mois pour les 3 cultures de la rotation triennale (betterave, froment, escourgeon) (tableau 5). Pour la betterave et Ie froment d'hiver les valeurs de C ont été calcu­lées à partir des résultats des mesures d'érosion dans les parcelles culti­vées et en jachère nue à la station de Sauvenière.

Pour l'escourgeon les valeurs de C ont été estimées à partir des valeurs de C en froment et des résultats des mesures de splash en froment et escourgeon. On a procédé de la manière suivante pour l'estimation du facteur C pour l'escourgeon :

C (escourgeon) = C (froment) X sPlast [fscourget~ sp as roment

Cette façon de procéder trouve sa justification dans l'existence d'une corrélation entre splash et érosion (r ~ 0,8) obtenue au cours des mesu­res en plein champ. De plus les équations de régression entre splash et érosion obtenues en parcelles nues et en parcelles sous différentes cul-

. tures ne sont pas significativement différentes (Bollinne, 1982). Les esti­mations du facteur C pour l'escourgeon ont été effectuées à partir des valeurs de C en froment, les deux céréales ayant une couverture et un développement similaires mais décalés dans Ie temps.

Les valeurs mensuelles moyennes de C ayant été établies sur base des résultats de mesures peu nombreuses (6 années de mesures), elles donnent seulement la ten dance de l'évolution moyenne de la valeur du facteur C en fonction de la croissance des cultures. Elle devront être con­firmées par des mesures ultérieures, d'autant plus que les différences entre les valeurs mensuelles moyennes ne sont pas significatives. ·Ceci résulte des fluctuations du facteur C en fonction des interactions sol­plante, des conditions météorologiques et de la variabilité interannuelIe de ce dernier facteur qui est extrêmement importante.

Les résultats de ces mesures et estimations du facteur C ont été repris au tableau 5. L'analyse de ce tableau perrnet de dégager deux faits essen tiels : 10 Dans un semis de froment ou de betterave effectué parallèlement à

la pen te, Ie risque érosif est, pendant plusieurs mois, supérieur à celui de lajachère nue (C = 100 %). Ceci est dû aux traces de passage d'ou­tils qui favorisent Ie ruissellement et l'érosion.

20 Un couvert efficace (C ~ 50 %) apparaît dès avril en escourgeon, en mai en froment et seulement en août en betterave. De ce fait, au cours

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de la période du maximum d'érosivité des pluies (juin, juillet et août, tableau 5) les sols emblavés en céréales sont bien protégés tandis qu'en betterave Ie risque d'érosion reste important en juin et juillet (C> 50 %).

Les valeurs du facteur C de novembre à mars en froment (176 %) peu-vent paraître aberrantes. En effet, Ie témoin en jachère nue, préparé de la même façon que Ie froment, reçoit conventionnelIement une valeur 100 % et il ne porte aucun couvert, tandis qu'en froment Ie couvert moyen pour l'hiver, estimé sur base des mesures de splash, est de 17,9 % (Bollinne, 1982). Cependant ces parcelles en froment sont marquées des traces du passage des roues laissées lors du semis. Ces traces de passage parallèles à la pen te, en légère dépression par rapport à la surface embla­vée, ont tendance à collecter l'eau des surfaces avoisinantes. Ceci favorise un ruisselIement concentré plus érosif que Ie ruisselIement diffus qui se produit dans les parcelIes témoins.

La période d'occupation du sol par une culture étant défmie comme l'espace de temps alIant de la récolte précédente à la récolte de la culture considérée, l'érosion moyenne de chacune des cultures a été calculée pour les parcelles de la station (tableau 6).

Dans la rotation triennale classique, c'est en froment qu'on enregistre l'érosion la plus forte (11,3 t) et en escourgeon l'érosion la plus faible (7,1 t). Par contre, si on considère l'érosion moyenne mensuelle au cours de l'occupation du sol par les cultures, celle-ci est égale en escour­geon et en betterave (0,6 t/ha/mois) mais nettement supérieure en fro-ment (1,1 t/ha/mois). .

Ce fait est assez inattendu. En effet, c'est en betterave qu'on pré-

Tableau 6 Erosion moyenne de chacune des cultures de la rotarion triennale à la station de Sauvenière (Km = 0,43; LS = 0,636) (x) érosion correspondante en jachère nue.

Betterave Froment Escourgeon Rotation

Erosion moyenne durant la période '),7 (25,9) 11,3 (16 ,7) 7,1 (17,4 ) 28,1 (60,0) culturale (t/ha)

Erosion moyenne durant la période culturale en % par 37,5 % 67,7 % 40,8 % 46,8 % rapport à la jachère nue

Durée de la période 15 10 11 36

culturale (mois)

Erosion moyenne 0,6 1,1 0,6 0,8

mensuelle (t/ha)

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voyait de constater l'érosion la plus importante, car, c'est dans cette cul­ture que l'érosion est toujours la plus spectaculaire (rigoles, dépots, dé­gats). Au printemps, une seule pluie peut y provoquer une érosion consi­dérable. Par contre, en hiver, quel que soit Ie couvert, Ie phénomène est mieux réparti dans Ie temps. De ce fait, il est plus progressif et moins spectaculaire. 11 n'est pas pour autant moins important.

Exprimée en valeur absolue l'érosion est la plus importante, non pas au cours des mois ou l'érosivité est la plus élevée (l'été) mais essentielle­ment après les semis, périodes durant lesquelles Ie couvert végétal protège moins bien les sols (tableau 5). A cela on notera deux exceptions : 10 Le labour avant betterave : Ie couvert végétal y est nul, mais vu la ru­

gosité et la perméabilité du sol engendrées par Ie labour, Ie ruisselle­ment et l'érosion y sont faibles, d'autant plus faibles que l'érosivité des pluies, elle aussi, est faible à cette époque Uanvier, février, mars).

20 Juillet en betterave : l'érosion moyenne mensuelle y est élevée alors que, comme Ie montrent les mesures de splash, Ie couvert végétal est déjà bien développé - il couvre près de 80 % de sol (Bollinne, 1982); cependant, vu l'existence de traces de passage d'outil parallèles à la pente, la valeur du facteur C est encore élevée au cours de ce mois (C = 53,6 %, tableau 5); d'autre part l'érosivité des pluies est la plus élevée au cours de ce mois (tableau 5); ces deux facteurs (R et C) per­mettent d'expliquer l'érosion moyenne mensuelle particulièrement élevée de juillet en betterave (2,3 t/ha/mois).

L'érosion moyenne mensuelle est supérieure à 0,8 t/ha/mois (moyenne mensuelle des 36 mois de la rotation, tableau 6) d'avril à juillet en bette­rave (4 mois), de novembre à avril en froment (6 mois) et d'octobre à janvier en escourgeon (4 mois), soit 14 mois sur les 36 mois de la rotation.

Exprimée en valeur relative par rapport à la jachère nue, l'érosion moyenne durant la période d'occupation du sol par les cultures (tableau 6) est respectivement de 37,5 % en betterave, de 67,7 % en froment et de 40,8 % en escourgeon. Elle est donc sensiblement plus élevée "en fro­ment que dans les deux autres cultures. Ceci résulte essentiellement du fait que pendan t 6 mois - de novembre à avril - la valeur du facteur C en froment es~ largement supérieure à 100 % (tableau 5) tandis qu'en escourgeon elle n'atteint jamais 100 % et qu'en betterave elle ne dépasse 100 % qu'en mars et avril. Etant donné Ie faible développement du cou­vert pendant la période hivernale et au début du printemps Ie froment est de loin la culture qui expose Ie plus les sols à l'érosion, bien que Ie phénomène n'y soit pas habituellement aussi spectaculaire qu'en bette­rave à l'occasion d'orages violents.

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2.5. LE FACTEUR PRATIQUES ANTI-EROSIVES (P)

Actuellement, aucune valeur du facteur P n'est disponible pour la Moyenne Belgique. Des mesures en cours visent à quantifier l'efficacité des semis perpendiculaires à la pente. Les résultats seront publiés dès qu'on disposera d'un nombre suffisant de données pour pondérer les in­teractions sol-climat-végétation sur ce facteur. Provisoirement nous suggé­rons d'utiliser les valeurs du facteur P proposées pour l'utilisation de l'équation aux Etats-Unis sans pour autant perdre de vue que leur validi­té n'est pas établie pour la Moyenne Belgique.

3. PRECISIONS DES ESTIMATIONS

L'équation est censée prévoir l'érosion moyenne annuelIe au cours d'une longue période et la précision des estimations ne peut être évaluée que par rapport à l'érosion mesurée au cours d'une longue période.

Pour les Etats-Unis, W. H. \Vischmeier & D. D. Smith signalent que les différences entre l'érosion mesurée en parcelles et l'érosion estimée sont d'autant plus importantes que la période de mesure est courte et sujette à des écarts qui résultent de variations cycliques et de fluctuations cli­matiques qui sont prises en compte dans l'équation mais qu'une trop courte période de mesure ne couvre pas.

Aux Etats-Unis, d'un point de vue statistique une période de 22 ans est considérée comme nécessaire pour donner une image correcte des précipitations. On ne s'étonnera donc pas de trouver chez nous des écarts considérables entre l'érosion mesurée en parcelles et l'érosion estimée. En effet, les mesures ne couvrent que 6 années et ont été interrompues pendant plusieurs mois pour permettre les façons culturales. Il serait donc illusoire de tester la valeur des estimations par rapport à une pé­riode de mesure aussi, courte.

Cependant, à coté des mesures en parcelles expérimentales, nous dis­posons de valeurs moyennes annuelles de l'érosion calculée pour une très longue période (± 150 ans) dans 3 cuvettes fermées.

L'érosion moyenne annuelIe de ces trois cuvettes a été estimée à l'aide de la formule (4) en vue de comparer les résultats de ces estima­tions à ceux obtenus par sondages (tableau 7).

L'analyse du tableau 7 montre que l'érosion moyenne annuelIe esti­mée par la formule est systématiquement inférieure à l'érosion moyenne annuelIe estimée à partir des sondages, de l'ordre de 20 % pour les 2 premières cuvettes, mais inférieure de plus de 50 % pour la troisième. Il est à noter que cette dernière est la plus petite et que ses pentes y sont plus courtes et plus faible.s que les pentes moyennes de la région (Bollin­ne, 1977).

L'utilisation de la formule conduit donc à une sous-estimation de

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Tableau 7 Comparaison entre l'érosion moyenne annuelle estimée (1) à partir de l'étude par sondage des colluvions accumulées depuis Ie dernier défrichement, (2) à partir de la formule américaine de perte de sol (USLE) légèrement modifiée.

Cuvettes Erosion moyenne annuelle (t/ha) :

(1) sondage (2) formule modifiée

1 15,3 11,8 2 15,6 12,3 3 12,8 5,0

l'érosion moyenne; il faudra en tenir compte par exemple dans Ie cas d'aménagements anti-érosifs. L'estimation réalisée par la formule modi­fiée devrait être considérée comme un risque minimum.

4. NECESSITE DES MESURES

La comparaison des résultats obtenus par la formule américaine (USLE) avec les résultats que donne la formule modifiée à laquelle nos travaux ont abouti démontre à suffisance la nécessité de telles mesures.

Le tableau 5 présente Ie détail du calcul deJ'érosion moyenne annuel­Ie effectué suivant les directives des auteurs américains pour la parcelle

. de la station de Sauvenière (pente de 6,5 %, longue de 22,13 m) et pour une rotation triennale. Pour faciliter la comparaison on a repris entre paranthèses les valeurs obtenues à partir de l'équation modifiée (éq. 4).

Epinglons les observations suivantes : 10 L'estimation de l'érosion en jachère nue (Ajn) par la formule améri­

caine est de 21 % inférieure à celle réalisée par la formule modifiée; la différence est peu importante durant la période estivale (- 5 %) mais très élevée durant la période hivernale (- 54 %). Les mesures hivernales étant peu nombreuses tant aux Etats-Unis (Wischmeier & Smith, 1978) que chez nous, il est hasardeux de tenter une explication de cette différence.

20 Les valeurs moyennes mensuelles du facteur C sélectionnées dans les tableaux (Wischmeier & Smith, 1978) sont dans l'ensemble nettement plus faibles que celles calculées in situ. Les différences les plus impor­tantes s'observent aux premiers stades des cultures. Aux derniers stades, les valeurs sont beaucoup plus proches. 11 semble que l'effet des traces de passage d'outil parallèles à la pente n'ait pas été pris en compte. Aux Etats-Unis, de nombreuses mesures ont été réalisées à partir de parcelles étroites (2 m) cultivées à la main (Moldenhauer & Foster, 1981) : dans ce cas, les traces de passage d'outil étaient inexis­tantes. Or, celles-ci favorisent Ie ruissellement et l'érosion. Les résul­tats des mesures (tableau 6) ne fon t que confirmer les observations (Bollinne, 1982).

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Tableau 8 Erosion moyenne durant la période culturale et pour la rotarion (A en t/ha) estimée par la formule américaine (U.S.L.E.) et par la formule modifiée.

A (t/ha) Betterave Froment Escourgeon Rotation

U.S.L.E. 6,8 3,1 3,7 13,6

U.S.L.E. 9,7 11,3 7,1 28,1 modifiée

30 L'érosion moyenne mensuelle (A) estimée par la formule américaine est presque toujours inférieure à celle estimée par la formule modifiée. Ceci est la conséquence de l'utûisation d'un facteur C généralement plus faible et, pour certains mois de l'année, d'une sous-estimation de l'érosion en jachère nue. L'érosion moyenne d'une culture ou de la ro­tation, estimée par la formule américaine, est toujours inférieure (ta­bleau 8) à celle estimée par la formule modifiée.

L'érosion estimée par la formule américaine est nettement inférieure à l'érosion estimée par la formule modifiée: < 50 % pour une rotarion triennale (tableau 5). Or, nous avons montré précédemment que l'utûi­sation de la formule modifiée conduit à une sous-estimation de l'érosion moyenne annuelle dans les conditions actuelles de culture. Ceci fait ressortir la nécessité de cultiver les parcelles de mesure en respectant Ie mieux possible les conditions dans lesquelles s'effectuent le,s cultures dont on veut estimer Ie risque érosif.

5. CONCLUSIONS

L'équation universelle de perte de sol (USLE) ne peut être appliquée telle queUe dans la zone à climat tempéré océanique de la façade atlanti­que d'Europe. Certains ajustements s'imposent : - Facteur pluie (R) : Le seuil de précipitation de 12,7 mm est beaucoup

trop élevé, un seuil nettement plus faible doit être déterminé. Provi­soirement l'érosivité des précipitations au seuil d,e 1 mm est prise en compte. - Le modèle américain semble mal adapté à l'estimation de l'énergie des pluies (§ 2.1. 2.). - Le choix de 130 (intensité maximum en 30 mn) doit être reconsi­déré, les pluies de fortes intensités étant Ie plus souvent de courte du­rée (~30 mn) (§ 2.1.2.).

- Facteur sol (K) : L'estimation de l'érodibûité du sol apparaît comme satisfaisante. Cependant un doute subsiste quant à la possibilité d'es­timer correctement l'amplitude des variations de ce facteur.

- Facteur topographique (L.S.) : Les valeurs de ce paramètre se mon-

133

trent généralement adéquates, sauf sur les pentes faibles « 4 %) ou elles semblent conduire à une sous-estimation du risque érosif.

- Facteur couvert végétal et pratiques culturales (C) : Les écarts entre les valeurs du facteur C ob tenues à partir des tables et nomogrammes et celles obtenues à partir des mesures au champ sont considérables. Les valeurs américaines ne reflètent pas la réalité du risque érosif pour l'Europe atlantique.

Cette brève revue des différents paramètres de l'équation (USLE) montre à quel point il est illusoire de prévoir avec une fiabilité accepta­bIe Ie risque érosif sans èn vérifier au préalable les valeurs par des mesu­res au champ. Sans cela, on s'expose à des erreurs considérables dans l'estimation du risque érosif. L'équation n'en reste pas moins un ou til remarquable.

Provisoirement il est proposé d'estimer Ie risque érosif par référence à la parcelle nue de Sauvenière à l'aide de l'équation 4, en tenant compte cependant, que celle-ei entraîne une sous-estimation, prineipalement sur les versants faibles.

La lutte anti-érosive suppose que soient connues avec une préeision suffisante l'importance de l'érosion et sa distribution au cours de l'année afin de procéder au choix des techniques les plus judicieuses.

Pour répondre à ce besoin les travaux en cours ont pour objectif: - la mise au point de l'indice d'érosivité Ie mieux adapté à la prévision

de l'érosion en Europe Atlantique; - la vérification de l'amplitude des varia ti ons du facteur sol; - la précision du risque érosif Hé aux cultures autres que celles de la ro-

ta tion triennale envisagée iei (maïs, colza, ... ); - la quantification de l'efficacité de techniques anti-érosives dans nos

conditions de culture.

BIBLIOGRAPHIE

Bollinne A. (1974) L'érosion des sols limoneux cultivés. Aperçu général. Première estimation. Bull. Reeh. Agron. Gembloux, 9 (3) : 353-369.

Bollinne A. (1977) La vitesse de l'érosion sous culture en région limoneuse. Pédologie, 27 (2) : 191-206.

Bollinne A. (1982) Etude et prévision de I'érosion des sols limoneux cultivés en Moyenne Belgique. Thèse, Uriiv. Liège, 356 p.

Bollinne A. & Rosseau P. (1978) L'érodibilité des sols de Moyenne et Haute Belgique. Utilisation d'une méthode de

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calcul du facteur K de l'Equation Universe1le de Perte de Sol. Bull. Soc. Géogr. Liège, 14 : 127-140.

Bollinne A., Laurant A. & Boon W. (1979) L'érosivité des précipitations à Florennes. Révision de la carte des isohyètes et de la carte d'érosivité de la Belgique. Bull. Soc. Géogr. Liège, 15 : 77-99.

Foster G. R. & Wischmeier W. H. (1974) Evaluating irregular slopes for soilloss prediction. Transactions Amer. Soc. Agric. Eng., 17 : 305-309.

Hudson N. W. (1961) An introduction to the mechanics of soil erosion under conditions of subtropical rainfall. Proeeedings and Transactions Rhodesia Sci. Ass., 49 (1) : 15-25.

Hudson N. W. (1976) Soil conservation. Batsford, London, 320 p.

Lal R. (1976) Soil erosion on alfisols in western Nigeria Geoderma, I-V: 363-431.

Laurant A. & Bollinne A. (1976) L'érosivité des pluies à Ucc1e (Belgique). Bull. Reeh. Agron. Gembloux, 11 (1-2) : 149-168.

Laurant A. & Bollinne A. (1978) Caractérisation des pluies en Belgique du point de vue de leur intensité et de leur érosivité. Pédologie, 28 (2) : 214-232.

Moldenhauer W. C. & Foster G. R. (1981) Empirical studies of soil conservation techniques and design procedures. In : R. P. C. Morgan (ed.), Soil Conservation, Problems and Prospects, pp. 13-29.

Mutchler C. K. & Murphee C. E. (1981) Prediction of erosion on flatlands. In: R. P. C. Morgan (ed.), Soil Conservation, Problems and Prospects, pp. 321-325.

Wischmeier W. H. (1959) A rainfall erosion index for a universal soil-Ioss equation. S.S.S. Am. Proc., 23 : 246-249.

Wischmeier W. H. & Smith D. D. (1965) Predicting rainfall-erosion losses from cropland. u.S. Dept. Agr. Handbook n. 282, 47 p.

Wischmeier W. H. & Smith D. D. (1978) Predicting rainfall erosion losses. A guide to conservation planning. u.S. Department of Agriculture, Agriculture Handbook, n. 537, 58 p.

Wischmeier W. H., Johnson C. B. & Cross B. V. (1971) A soil erodibility nomograph for farmland and construction sites. J. Soil and Water Conserv., 26 : 189-193.

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Soil-loss prediction in Atlantic Europe. The example of the loarny region in Central Belgiurn.

Summary

In order to vedfy the applicability of the universal soil-Ioss equation in the area dominated by the temperate oceanic climate of Atlantic Europe, erosion measure­ments we re carried out in standard experimental plots (22,13 X 4 m) in the loamy region of Central Belgium.

From the analYsis of the results it appears that this equation cannot be applied without certain modifications : (1) the threshold value of precipitation, considered for the calculation of EI30 (12,7 mm), is too high, and hence permits to conc1ude that 33,5 % of the runoff is non erosive; we propose to decrease this ft.gure to 1 mrn; (2) the values of the crop and management'factor (C) used in U.S.A., are quite inadequate in Atlantic Europe; other values calculated from measurements in the field are suggested.

Using these adjustments it is possible to estimate the erosion risk with a satis­factory precision. Otherwise, the equation .used as proposed for the U.S.A.leads to an underestimation of more than 50 % of the risk of erosion. A modified version of the universal soil-Ioss equation is proposed in order to estimate the erosion risk in the temperate oceanic climate of Atlantic Europe.

Voorspelling van de erosie in de Atlantische sector van Europa. Het voorbeeld van de Belgische Leemstreek.

Samenvatting

Teneinde de toepasselijkheid van de universele erosievergelijking te toetsen in het Atlantisch deel van Europa dat beïnvloed wordt door een gematigd oceanisch kli­maat, werden erosiemetingen uitgevoerd op een aantal standaard-proefpercelen (22,13 X 4 m) in de Leemstreek van Midden België.

Uit de analyse van de bekomen resultaten blijkt dat deze vergelijking als dusdanig niet kan toegepast worden en dat sommige wijzigingen zich opdringen : (1) de kriti­sche neerslag-grens die in overweging wordt genomen voor de berekening van EI30 (12,7 mm)ïs te hoog en brengt mee dat 33,5 % van de waterafvoer als niet-erosief moet beschouwd worden; er wordt daarom voorgesteld om deze grens tot 1 mm terug te brengen; (2) de waarden voor de faktor met betrekking tot het plantendek (C), die gebruikt worden in de Verenigde Staten, gaan in het geheel niet op voor on­ze streken; andere waarden berekend op basis van terreingegevens, worden daarom voorgesteld.

Mits deze aanpassingen laat de vergelijking toe een goede schatting te maken van het erosie-risico. 'De oorspronkelijke, niet-verbeterde vergelijking leidt tot een onder­schatting van dit risico met minstens 50 %. Een· gewijzigde versie van de universele vergelijking is daarom voorgesteld teneinde het erosierisico te schatten in de Atlan­tische zone van Europa, beïnvloed door een gematigd oceanisch klimaat.

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PEDOLOGIE, XXXIII, p. 137-145, 1 tab., 3 fig. Ghent 1983

CHARACTERIZATION OF METAL-HUMIC AND" -PUL VIC ACID COMPLEXES

NANDRAM K. V. RAMAN

Abstract

Complexes of humic and fulvic acids with divalent metal ions, viz. : Ca2+, Mn2+, Zn2+ and Cu2+, were characterized by potentiometric, conductometric and absorption spectroscopie methods. Potentiometric titrations of humic and fulvic acids in the presence of metal ions indicated the formation of metal­complexes. The magnitude of the pH drop on the addition of metal ions both in humic and fulvic acids followed the order: Cu > Zn > Mn > Ca. Conducto­metric titrations of humic and fulvic acids showed the complexation of Ca2+. J ob's plots suggested the formation of 1:1 metal-fulvic acid complexes.

Key-words

Metal-complexes, Potentiometric and conductometric titrations, J ob's plots.

1. INTRODUCTION

Complexes of humic (HA) and fulvic acids (FA) with metal ions are formed as a result of ion exchange, surface adsorption and chelation processes. Lewis & Broadbent (1961) as weIl as Schilitzer & Skinner (1965) have reported that carboxyl (-COOH) and phenolic hydroxyl (- OH) functional groups of HA and FA participate in the metal-com­plexing. F or the characterization of this process potentiometric titra­tions (Khanna & Stevenson, 1962~ Meelu & Randhawa, 1971; Banerjee & Mukherjee, 1972), conductometric titrations (Schnitzer & Skinner, 1963; Khanna & Bajwa, 1966) and spectroscopie methods (Schnitzer & Hansen, 1970; Adhikari et al., 1972) have been used. In the present study, those methods were employed to characterize metal-humic (Metal-HA) and -fulvic acid (Metal-FA) complexes.

Nand Ram & K. V. Raman - Department of Soil Science : G. B. Pant University of Agriculture & Technology Pantnagar, District Nainital, India. Present address K. V. Raman : A. P. Agricultural University, Hyderabad, India.

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2. MATERlAL AND METHODS

Three soils, namely Phoolbagh, Chamba and Nilgiri, representing respectively, forest, hill and peat soils of India, were used to extract humic substances. Their physico-chemical properties and general descrip­tions are given in tahle 1.

Table 1 Physico-chemical properties of the soils used.

Soil Location ph ysiograph y Soil order pH Organic Textural No. carbon (%) Class

1. Phoolbagh Forest Mollisol 7.6 2.20 Clay loam

2. Nilgiri Peat Histosol 5.8 23.20 Sandy

3. Chamba Hin Alfisol 6.7 1.97 Loam

Af ter in i ti al decalcification, humic suhstances from these soils were extracted hy repeated treatments with 0.1 NNaOH and fractionated in­to HA and FA hy acidifying the alkaline extract (Kononova, 1966). Humic acid was purified hy redissolving in 0.1 N NaOH, centrifuging, treating with HF + HCI in order to remove siliceous material, and finally dialysing against distilled water to make it salt free. Af ter the sample was dried. Fulvic acid was purified hy adsorption on activated charcoal; it was washed with 1 NH2S04 until free of Fe 2+ and eluted with 1 N NH40H hefore it was finally dried. The ash content of the purified humic and fulvic acids was less than 1 % and the molecular weights of HA and FA, as determined by the freezing point depression method (Schnitzer & Desjardins, 1962), were 1000 and 670 respectively.

2.1. Potentiometric titrations

Potentiometric titrations of the humic and fulvic acids were carried out according to the procedure descrihed hy Meelu & Randhawa (1971). Humic and fulvic acids were dissolved in 0.1 N NaOH to give a final concentration of 5 mg per mI of alkali. In 100 mI heakers, 10 mI of these solutions containing 50 mg of HA or FA were kept alongwith 25 mI of 0.1 N KCI; pH was adjusted to 3.0 and finally the volume was made up to 50 mI wuh O.lN KCI. Titrations of HA and FA samples were carried out by adding NaOH in increments of 0.02 mI; changes in the pH of the system were recorded hy a pH meter. Similarly, titrations of HA and FA in the presence of metal ions were also carried out under identical conditions, where 4 mI of 0.05 N of the appropriate metal ion (Ca2+, Mn2+, Zn2+ and Cu2+) were added to 10 mI solutions of HA or FA.

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2.2. Conductometric titrations

For conductometric titrations of HA and FA with 0.01 NNaOH and 0.01 N Ca (OH)2 the procedure of Schnitzer & Skinner (1963) was adopted. During the titration, changes in the conductance were recorded with the help of a digital conductivity meter and no increments of the base were added unless the conductance remained constant for at least 5 minutes.

2.3. Spectroscopie methods

To determine the molar composition of metal-fulvic acid complexes by Job's method, the procedure ofMac Carthy & Mark (1976) was utilized with some modifications. Fulvic acids extracted fr om Phoolbagh and Chamba soils were used for this purpose.

S ix aliq uots of 1.5 X 10-3 M fulvic acids varying from 0 to 20 mI in 4 mI increments were pipetted into a series of beakers to which 20 to o mI aliquots of l.S X 10-3 Mof the appropriate metal ions (Zn2+, Mn2+ and Ca2+) in 4 mI decrements were added and the pH was adjusted to 7.0. The solutions were transferred to 25 mI volumetric flasks and diluted up to the mark. The absorbance of the solutions was measured by a spectrophotometer at 410 nm for Zn- and Mn-F A complexes and at 490 nm for Ca-FA complexes. Earlier experiments indicated that these were the regions of maximum absorption.

3. RESUL TS AND DISCUSSION

3.1. Potentiometric titrations

The potentiometric titration curves of humic acids with metal-HA and fulvic acids with metal-F A are presented in figures la and b, respec­tively. Complex formation is evident from figure 1, as the addition of the metal ions to humic and fulvic acids led to a drop in pH. It could be explained that the formation of metal-complexes involved the displace­ment of protons (H+) from the complexing agents (HA or FA) accord­ing to the following phenomenon :

Mn+ + HA = MAn-1 + H+ (1)

where M is the metal ion and HA is the complexing agent, whieh con­tains large numbers of acidie groups; several H+ ions are moreover re­leased during the complexation process, usually involving a drop in the pH of the solution. The magnitude of the pH drop on the addition of metal ions both in humic and fulvic acids followed the order: Cu> Zn> Mn > Ca up to pH 7.5 and changed to the sequence Zn> Cu > Mn > Ca at higher pH. This is in agreement with the Irving-

139

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willium stability series. The order suggested that Cu2+ had a higher tend­ency for complexation than the other metal ions. Martell & Calvin (1952) had already noted earlier that the greater the tendency of metals to combine wi th a chelating agent the greater was the drop in pH. The displacemen t of curves in fulvic acids (figure 1 b) was higher than that of humic acids (figure la); th is may be attributed to the higher hydrophillic nature of fulvic acids (Banerjee & Mukherjee, 1972).

3.2. Conduetometric titrations

The conductometric titration curves of humic and fulvic acids depict­ed in figure 2 indicate that owing to the slow rise in conductance during titrations with N aOH they behaved as weak acids. The end points in Phoolbagh, Nilgiri and Chamba humic acids were obtained at 2.9, 2.0 and 2.0 mI of 10-2 N NaOH respectively, while they were obtained at 6.0,6.2 and 5.6 mI of 10-2 N NaOH in Phoolbagh, Nilgiri and Chamba fulvic acids, respectively. This suggested that these soil organic matter fractions were more acidic in Phoolbagh, as compared to other soils. Moreover, fulvic acids showed a higher acidity than humic acids obtained from the same soils; this could be attributed to a higher amount of oxygen containing functional groups in the fulvic acids (Nand Ram &

Raman, 1981). In titration curves with Ca(OH)2, the conductance generally remained

almost constant in humic acids initially and rose later on. In fulvic acids however, the conductance first declined to a minimum due to the neut­ralization of strong acidic groups, then remained almost constant and finally rose due to free Ca(OH)2. The relatively flat portion of the con­ductance curve in both humic and fulvic acids suggested the complexa­tion of Ca2+ due to the disappearance of Ca2+ and OH- ions. The Ca2+ was complexed with humic and fulvic acids, and OH- reacted with H to form H20. The conductometric titration curves of fulvic acids ~ere al­most similar to that of Schnitzer & Skinner (1963).

3.3. Spectroscopie methods

Absorption .spectra obtained from the difference in absorbance be­tween metal-fulvic acid complexes and fulvic acids in the range of 400-800 nm indicated that a maximum difference occurred at 410 nm for Zn2+ and Mn2+ and at 490 nm for Ca2+; hence these were selected as Àmax. Plotting of the absorbance versus the concentration of fulvic acid yielded a straight line at 410 nm, obeying Beer's law. A similar calibra­ti on curve was also obtained at 490 nm. These calibration curves were used to calculate Y whereby :

142

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JC..o- 1C _" Chamba Fig. 3. J ob's plots of metal-fulvic acid complexes.

Y = absorbance of metal-FA- absorbance of FA (2)

at each concentraion as suggested by Martell & Calvin (1952). Since the absorbance of all metal ions, even at the highest coneen tration used in this study, were less than 0.01, they were neglected in the calculation ofY values.

In order to obtain the J ob's graph, Y was plotted as a function of X : whereby:

X = mole fraction of one of the reactants. (3)

J ob's plot of metal-fulvic acid complexes, as illustrated in figure 3, re­veals that maxima occurred at 50 % concentration of the reactants (X = 0.5); th is suggested that one mole of metal reacted with one mole of fulvic acid, leading to the formation of 1: 1 complexes between metal ions (Zn 2+, Mn2+ and Ca2+) and fulvic acids extracted fr om Phoolbagh and Chamba soils.

4. CONCLUSIONS

Owing to the release of protons (H+) from the complexing agents (HA or FA) during the potentiometric titrations of humic and fulvic acids in the presence of metal ions, a drop in the pH of the system gave an index of complexation of metal ions (Ca2+, Mn2+, Zn2 + and Cu2+) with HA and FA. Among the metal ions, Cu2+ showed the highest degree of complexation. In conductometric titration curves of both humic and fulvic acids, a relatively flat portion of the curve indicated a complexation ofCa2+ due to the"disappearance ofboth Ca2+ and OH-

143

ions. Maxima of J ob's plots, which occurred at 50 per cent of the react­ants suggested the formation of 1: 1 complexes between metal ions and fulvic acids.

REFERENCES

Adhikari M., Chakravorty G. & Hazra G. C. (1972) Fulvic acid metal complexes. J. lndian Soc. Soil Sci., 20 : 311-321.

Banerjee S. K. & Mukherjee S. K. (1972) Physico-chemical studies of the eomplexes of divalent transitional metal ions with humic and fulvic acids of Assam soils. J. lndian Soc. Soil Sci., 20 : 13-18.

Khanna S. S. & Stevenson F. J. (1962) Metallo-organic complexes in soU. 1. Potentiometric titrations of some soU organie matter isolates in the presence of transition metals. Soil Sci., 93 : 298-305.

Khanna S. S. & BajwaJ. S. (1966) A study of the metallo-organic eomplexes in soU. 1. Potentiometrie and eondueto­metrie titrations of humic acid in the presence of metal ions. J. Res. Punjab Agric. Univ., 3 : 356-363.

Kononova M. M. (1966) Soil organic matter, its nature, its role in soil formation and in soil fertility (2nd ed.). Pergamon Press. Oxford, London.

Lewis T. E. & Broadbent F. E. (1961) Soil organic matter - metal complexes. 3. Exchange reactions of model compounds. soil Sci., 91 : 341-348.

Mae Carthy P. & Mark H. B. Jr. (1976) An evaluation of J ob's method of eontinuous variations as applied to soU organic matter - metal ion interaetions. Soil Sci. Soc. Am. J., 40 : 267-276.

Martell A. E. & Calvin M. (1952) Chemistry of me tal chelate compounds. Prentice Hall, Ine., New York.

Meelu O. P. & Randhawa N. S. (1971) Potentiometric titrations of zinc-humie acid complexes. lndian J. Agric. Sci., 41 : 848-851.

Nand Ram & Raman K. V. (1981) Characterization of humic and fulvie acids extracted from different Indian soUs. J. lndian Soc. Soil Sci., 29 : 179-183.

Schnitzer M. & Desjardins J. G. (1962) Molecular and equivalent weights of the organic matter of a podzol. Soil Sci. Soc. Am. Proc., 26 : 362-365.

144

Schnitzer M. & skinner S. I. M. (1963) Organo-metallic interactions in soils. 1. Reactions between a number of metal ions and the organic matter of a podzol Bh horizon. Soil Sci., 96 : 86-93.

Schnitzer M. & Skinner S. I. M. (1965) Organo-metallic interactions in soils. 4. Carboxyl and hydroxyl groups in soil organic matter and metal retention. Soil Sci., 99 : 278-284.

Schnitzer M. & Hansen E. H. (1970) Organo-metallic interactions in soils. 8. An evaluation of methods for the determina­tion of stability constants of metal-fulvic acid complexes. Soil Sci., 109 : 333-340.

Karakterisatie van metaal-complexen van humine en fulvinezuren

Samenvatting

Complexen van humine- en fulvinezuren met tweewaardige metaalionen zoals : Ca2+, Mn2+, Zn2+ en Cu2+ werden gekarakteriseerd aan de hand van potentiome­trische, conductometrische en spectrometrische methodes. Potentiometrische titratie van huminezuur en -'fulvinezuur in aanwezigheid van metaalionen duidt op de vor­ming van metaal-complexen. De grootte van de pH daling door toevoeging van me­taal-ionen aan humine en fulvinezuren verloopt volgens de volgende sekwentie : Cu > Zn > Mn > Ca. Conductometrische titratie van huminezuur en fulvinezuur toont complexatie aan van Ca2+. Job's grafieken suggereren de vorming van 1:1 metaal-fulvinezuurcomplexen.

Caractérisation de complexes métalliques des acides humiques et fulviques

Résumé

Les complexes d'acides hurnique et fulvique avec les ions metalliques Ca2+, Mn2+, Zn2+ et Cu2 + sont caractérisés au moyen de méthodes d'absorption potentiométri­que, conductométrique et spectrométrique. Les titrations potentiométriques d'acides humique et fulvique en présence d'ions métalliques indiquent la formation de com­plexes-métalliques. La diminution du pH par l'addition de métaux aux acides hurni­que et fulvique se présente dans l'ordre suivante : Cu> Zn > Mn> Ca. Les titra­tions conductométriques indiquent une complexation de Ca2+. Les graphiques de Job suggèrent la formation de complexes métalliques-acides fulviques 1: 1.

145

PEDOLOGIE, 1983, p. 147-170, 1 tabIe, 8 maps. Ghent, 1983.

AGRICULTURAL POTENTlAL AND CONSTRAlNTS IN RELATION TO GROWING PERIODS AND THEIR V ARIABILITY IN TANZANIA

E.DEPAUW

Abstract

In Tanzania the analysis of growing periods in terms of duration, onset dates and their variability allows a first approximation of agricultural potential and constraints. From this analysis, based on a waterbalance approach explained in an earlier paper, some conclusions are drawn with significance to rainfed agriculture. In about half of the country soil moisture storing properties appear to have little influence on the length of the growing period but in the remainder it may be possible to maximize the yield potential by growing longer-maturing and higher yielding varieties on soils with favourable moisture storing properties. Whereas the range of growing periods is very wide in Tanzania, it is the variability of growing period duration and onset date rather than insufficient length that constitutes the major limit at ion for rainfed crop production. The direct and indirect effe cts of growing period variability on agricultural production in Tanzania are discussed.

Keywords .

Agroclimatology - Land evaluation - Tanzania - Waterbalance.

1. INTRODUCTION

Tanzania is characterized by a wide variation of growing periods : according to the area, growing periods may occur once or twice a year, their duration may vary from less than 2 months to one year, and their onset date may vary over a range of 8 mon ths. In response to the severity of rainfall variability growing periods at one place may also show considerable variation in time: durations may vary by less than 10 % to more than 40 % from year to year.

This high degree of growing period variability, both on geographical as on temporal basis, makes Tanzania a representative case area to study

E. De Pauw - Soil surveyor, National Soil Service, P.O. Box 5088 Tanga, Tanzania. Present Address : Geologisch Instituut,.Rijksuniversiteit, Krijgslaan 281,9000 Ghent, Belgium.

147

general conditions of moisture availability and moisture-related limita­tions pertaining to the wet and dry tropics. At the same time, the con­siderable amount of rainfall data available in the country made it a logical choice to apply the concepts and methods described in a compan­ion paper (De Pauw, 1982). The main points of the latter paper are brief­ly ou tlined below.

The growing period is essentially the period of the year with sufficient moisture in the s6il for crop growth. In areas with pronounced rainfall variability this concept, however, is difficult to apply because growing periods may- vary considerably from year to year; this is a common situa­tion in the wet and dry topics. To correct this limitation an alternative concept, the dependable growing period, was defined. The dependable growing period is the duration of growing periods that can be expected to be equalied or exceeded in 4 years out of 5. The concept incorporates a probability level and is therefore able to account for the effects of rain­fall variability on long-term moisture availability. It is also responsive to variations of soil moisture storage capacity. The dependable growing period and likely onset date of the growing period are calculated by means of a simple waterbalance model that makes use of long-term monthly rainfall data .

. 2. STUDIES OF RAINFALL VARIABILITY IN TANZANIA

One of the major limitations for rainfed agriculture in Tanzania is the fact that most areas with seasonally concentrated rainfall are in a vary­ing degree affected by rainfall variability. This variability affects not only the amount of rain that may fall in a particular period of the rainy season, but also the start and the end of the. rainy season. The implica­tions of rainfall variability for rainfed agriculture are therefore twofold: first, the inherent drought hazard, especially in low-rainfall areas, and secondly, the problem of planning agricultural activities, especially planting.

The significance of rainfall variability for rainfed agriculture has long been recognized in East Africa and the first attempts to quantify this factor and rel':lte its severity to crop suitability date back to""the early fifties. These early approaches (ex. Glover et al., 1954; Manning, 1956; Kenworthy & Glover, 1958) were based on the concept of reliable or dependable rainfall, which is the rainfall amount that can be expected with a certain confidence over a given return period. The usu al proce­dure was either to assess the minimum rainfall amounts that could be expected in 4 years out of 5 or, alternatively, to assess the probability that a certain rainfall amount would be equalied or exceeded.

The main limitations of these approaches were that the probability

148

assessmen ts related to rather long periods (usually a year) and that the results could not be quan tified in respect of specific crops with their particular water needs and sensitivity or tolerance to moisture stress. Neither was the role of soils in storage of moisture considered.

More recent approaches based on shorter periods have been proposed to overcome some of the earlier shortcomings. Nieuwolt (1973) related 80 % probability rainfall with potential evaporation on a monthly basis for the whole of Tanzania. Similarly, fr om the probabilities that month­ly rainfall would equal or exceed certain percentages of the potential evapotranspiration, Gommes and Houssiau (1982) differentiated 7 agro­climatic types of growing seasons in Tanzania and estimated probabilities of crop failure for maize and sorghum. Similar studies based on shorter time units (pentades, weeks, decades) were carried out in limited parts of the country.

These approaches are more useful for agricultural planning purposes because moisture availability from rainfall is matched with general water requirements for crops. Yet, they do not sufficiently consider the role of stored soil moisture in compensating rainfall deficits. Also the rain­fall registered in individual time units is treated as an independent varia­bie and the possibilities of rainfall compensation within the growing season are thus ignored. This may lead to an underestimation of actual moisture availability.

A very recen t technique to assess the impact of rainfall variability on crops is the production of simulated rainfall data based on Markov-chain analysis of observed daily rainfall (ex. Bennett & Stern, 1979; Stern & Coe, 1982). The models are able to predict probabilities of dry spells within the growing season and, by inc1uding the simulated rainfall data into Crop-specific waterbalance modeis, they may also be applied to specific crops. The statistical techniques employed are very powerful and do not require long rainfall records, usually lacking in developing countries. However, they do require extensive computing facilities, special software packages and the data needed to fit the models are usually difficult to obtain (daily rainfall). F or these reasons the tech-

. nique may eventually fail to be applied extensively. A fairly simple and established tooI to assess moisture availability for

crop production is the waterbalance. Waterbalances allow comparison of available water from rainfall and soil moisture storage with water demand for evapotranspiration during specific time units. Waterbalances have been extensively used in East Africa by numerous workers for a variety of land evaluation and agricultural planning purposes at differ­ent levels of detail. Some of these applications are outlined by Jackson (1977). In comparison with other techniques, soil-water balances have

149

a particular appeal: they in tegrate all factors affecting soil-plant-water re1ationships and they recognize that crop yield reductions are more strongly corre1ated with soil moisture deficits than with rainfall deficits. They are easy to comprehend and apply, allow flexible data utilization and produce results that agree well with reality.

3. METHODS AND MATERlALS

The present study of growing periods in Tanzania is based on a water­balance approach, similar to the one used by F.A.O. in the Agro-Ecologic­al Zones Project (1978)~ The details of the method have been given by De Pauw (1982).

The data base used is essen tially agroclimatic : montly rainfall data for about 27 years per station are compared with potential evapotranspi­ration normals, calculated according to the Penman method. The soil variables affecting moisture availability (available waterholding capacity AWC and soi! depth) are incorporated in the model by means of general levels of moisture storage capacity.

On the basis of the waterbalance model the duration and onset dates of growing periods were calculated for 88 stations in Tanzania, consider­ing rainfall data for almost 30 years and three moisture storage capacity levels. This corresponds with about 2400 analyzed seasons and more than 7000 seasonal waterbalances. Sufficient climatic data on tempera­ture, relative humidity, sunshine or cloudiness and windspeed were available to calculate poten ti al evapotranspiration normals by Penman for 27 stations. For the remaining stations, Penman evapotranspiration normals were estimated from data for comparable reference stations, af ter corrections for different altitude, temperature and/or geographical latitude. For most stations very complete rainfall records for about 27 consecutive years within the 30-year period 1931-1960' were available : only two stations, Seronera and Usinge, had rainfall data for less than 20 yeaxs. .

For each station the average growing period duration (AGP), standard deviation, coefficient of variation (CV) and dependable growing period (DGP) were calculated and the onset dates were grouped into classes with two-week intervals. The waterbalance calculations and statistical analysis were carried out on a HP 9825 microcomputer with specially developed software written in HPL.

Although the present study állows fairly reliable predictions of key growing period characteristics, the data base made it an analysis of re­connaissance nature. It does not all ow in terpretation of, for instance, the impact of short-duration dry spells or of crop-specific rainfed yield potentials. Neither does it take the inf1uence of landforms, vegetation

150

cover, land use and land degradation on actual moisture availability into account. It was ·a1so difficult to use actual soil moisture data in view of catenary soil variability, unmappable at this small scale, and considerable knowledge gaps for large parts of the country. Nevertheless, at Icirger scales, for instance 1: 2,000,000to 1: 500,000 it should be possible, and even desirabie, to calculate growing period characteristics on the basis of actually observed or inferred soH moisture characteristics.

4. GROWING PERIODS AND THEIR V ARIABILITY IN TANZANIA

To visualize growing period characteristics in Tanzania, 8 maps have been prepared at scale 1 :4,000,000. They are reproduced in this paper at an approximate scale of 1: 10,000,000.

4.1. Spatial and temporal variations of the growing period

Map 1 depicts the length of the growing period that is likely to be equalied or exceeded in 4 years out of 5, if the moisture storage capacity within the rooting depth is arbitrarily taken at 50 mmo This situation is applicable to crops with shallow rooting habits on sandy or loamy soils.

Map 2 depicts the dependable growing period for a situation in which a maximum of 150 mm total available soil moisture can be utilized by crops in any mon th of the growing period. This situation would apply, for instance, to deeply rooting crops on sandy or loamy soils or to shallow rooting crops on heavy textured soils.

Map 3 depicts the dependable growing period for a situation where crops could utilize a maximum of 300 mm soil moisture in any month of the growing period. Such situation may apply to very deeply rooting crops on medium or heavy textured soils, tree crops or crops growing on soils with exceptionally high waterholding capacity (for instance volcanic ash soils).

These maps indicate that the range of growing periods in Tanzania is very wide. Durations of the dependable growing period may vary from less than 60 to 360 days. If 90 days were considered the minimum grow­ing period length required for profitable crop production, less than 15 % of Tanzania should be considered as unsuitable for crops. In more than 85 % of the country growing periods would be long enough for some form of crop production and crops can be grown with growth cycles that are adapted to the dependable growing period.

The variability that may affect growing periods is already built into the concept of dependable growing period. Another way of presenting the variability of growing periods is by mapping the coeffieient of varia­tion (C.V) (map 5). The latter has the advantage ofbeing independent of moisture storage capacity. In about 1/3 of the country, C.V-values

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of growing period duration are low (less than 20 %); in another third, C. V .-values exceed 30 %. This clearly indicates that variability of grow­ing periods is quite considerable in large parts of the country, and justifies the use in agricultural planning of PTobable growing period dura­tions rather than average ones.

4.2. Dependahle growing periods as a function of soil moisture storage capacity

Prom maps 1-3 and particularly map 4 it is clear that in some areas of the country, particularly in the west and southwest of the country, the dependable growing period is highly responsive to variations in moisture storage capacity of the soil. In some cases the dependable growing period ean be extended by more than 2 months on soil moisture only. Sueh differences in growing period duration in relation to soil depth, A WC and rooting depths may seem very high, but they are comparable to the results obtained by Virmani et al. (1978) in Hyderabad, India, and to the soil moisture data reported by Van de Weg and Mbuvi (1975) for Kindaruma, Kenya. The implieation is that in sueh areas, on deep soils with high AWC,crops with longer growth cycles ean be grown than on shallow soils with low AWC.

In other areas, partieularly in the northeastern and central parts of Tanzania, the dependable growing period length is little influeneed by variations in moisture storage eapaeity. The yield potential is more climatically determined and the climatic variables are such that a high transfer of soil moisture from surplus to deficit periods is not possible, whatever the storage capacity of the soil. This is not a suggestion that in these areas the soil type is less important for producing a good erop: yield reductions due to short-term moisture stress are, naturally, likely to he smaller on soils with high moisture retention. However, it would not be possible to grow on soils with high moisture storage eapacity crops with longer growth cycle and higher yield potential.

4.3. Most likely onset dates of growing periods

A wide range of onset dates exists in Tanzania. The earliest likely on­set dates oeeur in the area around Bukoba in August, the latest in April in the northeast of the country. The pattern of onset dates in Tanzania seems to move in a pendulum swing fr om the northwest to the north­east (map 6).

The likelihood of onset dates occurring in a partieular month and therefore the reliability of onset dates, varies eonsiderably. The areas with the highest probabilities of growing periods starting in a given month occur in the west and southwest of the country (map 6). These

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159

are as are more reliable in terms of onset dates than the northeast and east of the country, where very of ten Ie ss than one third of the growing periods are likely to start in the "most likely" onset month.

A similar pattern arises when the variability of the growing period onset dates is represented by the period required to group 80 % or more of all onset dates (map 7). In the west, centre and southwest of Tanzania 80 % or more of all analyzed onset dates are concentrated in a period of Ie ss than 1 1/2 month. For stations in the north, east and southeast 80 % or more of the analyzed onset dates occur in a period of at least 2 1/2 months, and may be spread in some areas over a period of more than 4 months. As will be discussed later, the insecurity created by such highly variabIe onset dates may considerably influence farmers in their choice of cropping patterns with concomitant fluctuations in agricultural pro­duction.

The results for the 88 analyzed stations indicated that, while soil moisture storage capacity was a factor of paramount importance for the duration of the growing period, it had little influence on the onset dates.

5. AGRICULTURAL POTENTlAL AND CONSTRAINTS IN RELA­TI ON TO GROWING PERIODS IN TANZANIA

By an analysis of the key elements of growing periods (duration, on­set dates, responsiveness to soil moisture storage capacity levels and their variability), a considerable number of zones with specific moisture characteristics can be distinguished, even at reconnaissance level. In Tanzania 30 moisture zones have been differentiated in this way (map 8).

The legend of this map as weIl as a summary of the agricultural potential and constraints of each moisture zone is given in table 1. It may be noted that the temperature factor has not been considered in this study and that the crops described in each zone are compatible with local temperature conditions. It was also felt that an evaluation of forestry capability based on the growing period concept would not provide more valuable information than given by the curren t vegetation map of Tanzania (Surveys and Mapping Division, 1976) (tabIe 1).

6. CONCLUSIONS

Growing periods in Tanzania show considerable variation both on a geographical and temporal basis in their key characteristics, duration and onset date.

The geographical variation of growing periods in Tanzania can be summarized in the following observations : - the duration of the dependable growing period can vary from less

160

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161

Table 1 Moisture zones of Tanzania.

Map Growing period eharaeteris- Agricultural potential and constraints symbol tics

AREAS WITH SINGLE GROWING PERIOD

1. Areas with single growing period, unresponsive to moisture storage capacity

SUl Duration DGP less than 60 DGP too short and onset dates too unreliable for erop pro-days. Onset date unreliable. duetion. Very low livestoek earrying eapaeity. High risk of

overgrazing and erosion.

SU2 Duration DGP 60-75 days. Very short DGP and unreliable onset dates result into low Onset date unreliable. rainfed erop yield potential and low livestoek earrying

eapacity. High risk of dry spells within the growing period, overgrazing and erosion.

SU3(r) Duration DGP 90-105 days. Short DGP unaltered by soil types. Moderately suitable for Onset date reliable. extensive grazing and drought resistant annuals (ex. sor-

ghum, bulrush millet, groundnuts). High risk of dry spells within the season, overgrazing and erosion.

SU3(u) Duration DPG 90-105 days. Growing periods, erop and range land yield potential eom-Onset date unreliable. parabie to previous unit. However, rainfed eropping is

more risky as unreliable onset dates may result into plant-ing losses, yield reduetions or erop failures.

SU4(r) Duration DGP 120-150 days. DGP of medium length, unaltered by soil types, suitable Onset date reliable. for extensive grazing, drought resisting and drought evadin~

annual erops (ex.g. sorghum, millet, groundnuts, sesame, eotton, sweet potatoes, quiek-maturing maize var.) and drought resistant perennials (sisal, eassava). Moderate risk of overgrazing and erosion. Some risk of mid-season dry spells.

SU(u) Duration DGP 120-150 days. Growing periods, erop and rangeland yield potential eom-Onset date unreliable. parabie to previous unit. Rainfed produetion of drought-

sensitive erops (ex. maize) more risky due to unreliable on-set dates and possibility of mid-season dry spells, ereating difficulties in matching erop water demand to aetual rain-fall pattern.

SUS DuratÎon DGP 150-180 days. DGP of medium length, unaltered by soil types. Suitable Onset date reliable. for extensive grazil1g and a wide range of annual erops (ex.

sorghum, millet, maize, sesame, eotton, groundnuts, sweet potatoes, tobaeeo, sunflower) and drought resistant peren-nials (sisal, eassava). Some risk of mid-season drought. Moderate risk of overgrazing and erosion.

162

Table 1 (Continued)

Map Growing period eharaeteris~ symbol tics

Agrieultural potential and constraints

2. Areas with single growing period, moderately responsive to moisture storage capacity

SMI(r) Duration DGP 90-135 days. DGP short to medium, with possible variation of 3-4 weeks Onset date reliable. aeeording to soil moisture storage capacity and erop root-

ing habits. Moderately suitable for extensive grazing and for drought-resistant annuals (ex. sorghum, sesame,. sun-flower) and perennials (ex. eashew, eassava). Moderately suitable for quick maturing, drought-sensitive erops (ex. maize) on soils with high moisture storage capacity. Mod .. erate risk of overgrazing and erosion.

SMI(u) Duration DGP 90-135 days. Growing periods, erop and rangeland yield potential eom-parabie to previous unit. Rainfed production of drought-sensitive erops more risky than in previous unit due to un-reliable onset dates.

SM2 Duration DGP 90-150 days. DGP short to medium, with possible variation of 3-4 weeks Onset date unreliable. aeeording to soil moisture storage capacity and erop root-

ing habits. Crop and rangeland yield potential eomparable to unit SMI(u) but suitability for drought-sensitive erops slightly better on soils with high moisture storage capacity. Unreliable onset dates entail some risk of planting losses, yield reductions or erop failures due to poor matching of erop water demand with aetual rainfall pattern.

SM3 Duration DGP 120-150 days. DGP of medium length with possible variation of 3-4 weeks Onset date unreliable. according to soil moisture storage capacity and erop root-

ing habits. Suitable for extensive grazing and rainfed pro-duetion of a wide range of annual and perennial erops with some degree of drought-resistanee, including eotton. Moderately suitable for drought-sensitive erops on better soils with good waterholding capacity. Some risk of plant-ing losses, yield reductions due to unreliable onset dates, but less risk of complete erop failures than in adjacent unit SU4(u).

SM4 Duration DGP 150-180 days. DGP of medium length with a possible variation of 3-4 Onset date reliable. weeks aeeording to soil moisture storage capacity and erop

rooting habits. Suitable for extensive grazing and rainfed production of a wide range of annual and perennial erops. Crop and rangeland yield potential and constraints eom-parabie to unit SUS, but more variation in function of soil types.

SM5 Duration DGP 180-225 days. Long DGP with possible variation of 3-4 weeks aeeording Onset date reliable. to moisture storage capacity and erop rooting habits. High-

ly suitable for extensive grazing and rainfed production of a wide range of annual and perennial erops, including maize, beans, potatoes, eoffee, eassava, bananas, partieular-lyon better soils with favourable moisture retention prop-erties.

163

Table 1 (eontinued)

Map Growing period eharaeteris-symbol ties

Agrieultural potential and eonstraints

3. Areas with single growing period, highly responsive to moisture storage capacity

SHI Duration DGP 90-150 days. DGP short to medium with possible variation of 5 weeks Onset date unreliable. aeeording to soil moisture storage eapacity and erop root-

ing habits. Suitability and eonstraints : erop and rangeland yield potential eomparable to unit SM2, but somewhat longer growing periods on soils with high moisture storage eapacity.

SH2 Duration DGP 120-195 days. DGP of medium length with possible variation of 6-10 Onset date unreliable. weeks aeeording to soil moisture storage eapacity and erop

rooting habits. Suitable for extensive grazing and a wide range of annuals and perennials with some degree of drought-resistanee. Moderately to highly suitable for drought-sensitive erops on better soils with good water-holding eapacity. Substantial risk of planting losses or yield reduetions due to unreliable onset dates.

SH3- Duration DGP 150-210 days. Medium to long DGP with possible variation of 1-2 months Onset date reliable. aeeording to soil moisture storage eapacity and erop root-

ing habits. Highly suitable for extensive grazing and a wide range of rainfed annuals and perennials. Very high yield potential for annual erops, partieularly on soils with high moisture storage eapacity, due to the reliability of the growing periods, both in duration and in onset dates.

SH4 Duration DGP 150-240 days. Medium to long DGP with a possible variation of more Onset date unreliable. than 2 months aeeording to soil moisture storage eapacity

and erop rooting habits. Suitable for intensive grazing and a wide range of rainfed annuals and perennials. Very high yield potential for annual erops on soils with high moisture storage eapacity, due to possibility of double eropping. Some risks of planting losses or yield red~etions due to un-reliable onset dates.

SH5 Duration DGP 180-270 days. Long DGP with possible variation of 2-3 months aeeording Onset date reliable. to soil moisture storage eapacity and erop rooting habits.

Highly suitable for intensive grazing and a wide range of annuals and perennials of medium and high altitude, in-cluding maize, beans, eoffee, tea, bananas, upland rice, potatoes, tree erops. Very high yield potential for annual erops on soils with high moisture storage eapaeity, in view of possibility of double eropping and reliability of growing periods, both in duration and onset date.

164

Table 1 (eontinued)

Map Growing period eharaeteris-symbol tics

Agrieultural potential and constraints

4. Areas with single, of ten continuous growing periods

Usually one growing period DGP usuaUy long but highly variabie according to soil per year, but of ten transition moisture storage capacity and erop rooting habits (differ-of one growing period into enees of several months possible due to bridging of rainy next without intervening dry periods by stored soil moisture. Highly suitable for inten-period. Growing periods sive grazing. High yield potential for a wide range of high usually long and highly alti tu de rainfed annual and perennial erops, including variabie over short distanees eoffee, tea, tree erops, in view of possibility of double due to orographic factors. erop ping, partieularly in situations of high moisture stor'-Growing periods highly re- age capacity. sponsive to moisture storage (SC1,SC2,SC4,SC5,SC6,SC7,SC8) capacity . Onset dates un-reliable due to overlapping of growing periods.

SCl Duration DGP 120-210 days

SC2 Duration DGP 150-240 days

SC3 Duration DGP 150-300 days DGP medium to very long, with possible variation of up to

SC4 Duration DGP 150-330 days 5 months aeeording to soil moisture storage capacity and

SC5 Duration DGP 150-360 days erop rooting habits. Highly suitable for intensive grazing and a wide range of low-altitude annual and perennial

SC6 Duration DGP 180-300 days erops, including rubber, oil palm, coconuts, citrus, bananas.

SC7 Duration DGP 210-360 days Double eropping possible in high moisture storage capacity situations.

SC8 Duration DGP 240-360 days (SC3)

AREAS WITH DOUBLE GROWING PERIOD

DU Two growing periods per Two very short DGP per year of approximately equal year, unresponsive to mois- length, unaltered by soil types, moderately suitable for ex-ture storage eapaeity. tensive grazing. Growing periods too short for high rainfed Duration DGP (main season) production of annuals. Marginally suitable for double 75 days; duration DGP (see- eropping of drought-resistant, quick maturing annuals. ondary season) 60 days. On- Moderately suitable for drought resistant perennials. set date main growing period unreliable.

DM1 Two growing periods per Two short DGP per year, with possible variation of 2-3 year, moderately responsive weeks aeeording to soil moisture storage capacity and erop to moisture storage capacity. rooting habits. Highly suitable for extensive grazing. Suit-Duration DGP (main season) able for drought resistant annuals and perennials of low 120-135 days; duration DGP altitude, including sorghum, eassava, sisal, eashew, eoeo-(seeondary season) 75-90 nuts, citrus and quick-maturin.g maize. Double eropping days. Onset date seeondary possible in many years with a quick-maturing drought-growing period unreliable resistan.t erop in the seeondary growing period (ex. sor-

ghum, grams, eowpeas, groundnuts). High risk of planting losses, yield reductions or erop fallures in seeondary grow-ing period due to unreliable onset dates.

165

Table 1. (continued)

Map Growing period characteris- Agricultural potential and constraints symbol tics

DM2 Two growing periods per Two DGP per year, main DGP of medium length, seeond-year, moderately responsive ary DGP very short. Variation of DGP length in function to moisture storage capacity . of soil moisture storage capacity and erop rooting habits is Duration DGP (main season) 3 weeks. Highly suitable for extensive grazing. Suitable for 120-150 days; duration DGP same range of annuals and perennials as in unit DMI. (seeondary season) 45-75 Double eropping not possible in most years due to very days. Onset date seeondary short duration and unreliable onset dates for the seeondary growing period unreliable. growing period.

DM3 Two growing periods per Two DGP per year, main DGP short, secondary DGP very year, moderately responsive short. Variation of DGP length in function of soil moisture to moisture storage capacity . storage capacity and erop rooting habits is 3-5 weeks. Duration DGP (main season) Moderately suitable for extensive grazing. Suitable for 90-120 days; duration DGP drought-resistant annuals and perennials and for quick-(secondary season) 30-60 maturing maize varieties only on soils with high moisture days). Onset date both grow- storage capacity. Yield potentiallower than in previous ing periods unreliable. unit due to shorter growing periods. Double eropping not

possible in most years due to very short durations and un-reliable onset dates for secondary growing periods.

No te DGP : dependable growing period.

than two months to one year; - according to soil type and erop rooting habits the dependable growing

period can be extended on soil moisture by Ie ss than 10 days to more than 2 months; .

- likely onset dates may be spread over a period of 8 months : the earliest onset dates occur in August, the latest in April.

The temporal variation of growing periods in Tanzania - their varia­bility sensu stricto - can be summarized as follows : - C. V -values of growing period durations may vary from less than 10 %

to more than 40 %; - the percentage of growing periods that is likely to start in the most

likely onset month may be as high as 75 % or as low as 20 %. Alter­natively, the period required to contain at least 80 % of all onset dates may vary from less than 6 weeks to more than 4 months.

For the country as a whole a fair amount of covariance suggests itself between the variability of growing period durations and onset dates. The west and southwest of Tanzania are characterized by a low variabil­ity of growing period duration and onset date; the north and the east,

166

by contrast, show considerable variability of both factors. However, there are many transitional cases: in some areas, such as Singida or Dodoma, the duration of growing periods is highly variabie but the onset dates are reliable. In other areas, for instance Arusha, dependable growing periods are long but the onset dates areunpredictable. In the double growing period areas of Tanga and Coastal Regions the main growing period shows considerable variability in duration, but the onset dates are fairly reliable. By con trast, the secondary growing periods in these areas show extreme variability, both in duration and onset date and are therefore very unreliable.

The present analysis based on the incorporation of rainfall variability in to a waterbalance approach allows a more accurate perception of moisture related potential and limitations for rainfed crop production than one based on average rainfall data. In view of the direct relevance of the analyzed factors to agriculture, a moisture zonification of the kind given in section 5 may be used in its own right for broad planning purposes, but would probably be even more effective as an input to a broader-based agro-ecological zonification at country level. Most relevant in this respect would be the estimation of dependable growing periods on different soil types.

Using the dependable growing period as a yardstick for seasonal moisture availability in most years, the present analysis indicates th at about 85 % of the country (not considering unsuitable areas such as swamps, rock outcrops etc.) is suitable in a varying degree to some form of crop production. It also appears that in about 55 % of the country the yield potential of some crops could be increased by growing longer­maturing, higher-yie1ding varieties on good soils with high moisture­storing properties. In only 15 % of the country the growing periods are too short in most years to allow economical rainfed crop production.

The overwhelming factor that restricts rainfed crop production in Tanzania is not the short duration of growing periods, but their variabil­ity. The impact of growing period variability is exacerbated because it is most prominent in the highly settled areas th at contribute most to the marketed crop production (Arusha, Moshi, Tanga, Mwanza). By contrast, the areas where growing periods are least variabie and where more constant yields and production levels can be expected (the Southern Highlands, Ruvuma Region, Rukwa Region and the Bukoba­Kibondo-Kasulu area) have a less developed agriculture owing to their excentric location and lack of adequate roads.

The impact of growing period variability on agricultural production in much of Tanzania can not be overemphasized. In the first place, the yie1d reductions in poor seasons re sult into a drop of marketed produc-

167

tion of food crops and may necessitate food imports and even famine relief for drought-stricken areas. However, the indirect effect of season­al variability on agricultural production may be even more important: delays in the onset date of the growing period may convince farmers that the season will be short and poor and encourage them 'to concentrate on survival crops with low market value rather than cash crops. Thus, whereas in most areas the growing period would not vary by more than 30-40 % from the average, actual crop production levels may show larger fluctuations. This is a fairly common situation for instance, in the Mwanza area where sharp fluctuations in cotton production have more to do with farmer's perception of the coming season than with the actual quality of that season.

Seasonal variability may also be a factor of considerable importance in the economics of fertilizer use. In Tanga Region (N.E. Tanzania) care­fully conducted fertilizer trials carried out over several years have not been able to prove significant yield responses to fertilizers and the con­clusion that fertilizer use may be uneconomical suggests itself for most of the Region. The natural fertility of the soils of Tanga Region is low to moderate and certainly not sufficient to explain this lack of response. It would now appear that the unreliability of growing seasons is the main reason for the poor response to fertilizer application. Most of Tanga Region is a double growing period area with'high variability affect­ing both seasons, but particularly the short one. In years with weIl distributed rainfall good responses occur. The unprofitability of fertilizer use in the Region is further suggested by the low fertilizer demand and use by smallholders, notwithstanding better access to fertilizer, than anywhere else in ' the country.

By contrast, in Ruvuma Region with an excentric and isolated loca­tion and poor road network, the demand for and use of fertilizer by smallholders is the second highest in the country (Harrop and Samki, 1980), although the soils are not particularly poor. It would appear that in this region the reliability of growing periods allows the consistent yield increases that are the prime requirement for profitable fertilizer use.

AGKNOWLEDGEMENTS

The study summarized in the present and an earlier companion paper was carried out in the framework of the FAO/UNDP project "National Soil Service", Mlingano, Tanzania. I should like to thank Mssrs. R. Gommes and M. Houssiau for the very substantial material and moral support received from the FAO Grop Monitoring and Early Warning Sys­tems Project, Dar es Salaam. I should also like to express my apprecia-

168

non to Mssrs. Dewan, Harrop and Samki of the National Soil Service and to Mssrs. Higgins and Purnell, FAO Headquarters, for their valuable criticism and comments.

REFERENCES

Bennett M. D. & Stem R. D. (1979) Incidence of dry spe lIs in the cropping season. Paper presented to Conference on "Soil and Climatic Resources and Constraints in relation to maize, cowpea, upland rice and cassava production" . Annual Research Conference, IITA, Ibadan, October 1979.

De Pauw E. (1982) The concept of dependable growing period and its modelling as a tooI for land eval­uation and agricultural planning in the wet and dry tropics. Pédologie, XXXII (3) : 329-348.

F.A.O. (1978) Report on the Agro-Ecological Zones Project. Vol I. Methodology and Results for Africa. World Soil Resources Report 48, FAO, Rome, 158 p.

Glover J., Robinson P. & HendersonJ. P. (1954) Provisional maps of the reliability of annual rainfall in East Africa. Q. Jl. Royal Met. Soc., 80 : 602-609.

Gommes R. A. & Houssiau M. (1982) Rainfall variability, types of growing seasons and cereal yields in Tanzania. Paper presented at Technical Conference on Climate for Africa, Arusha, Tanzania, 25-30 J anuary 1982, 28 p.

Harrop J. F. & SamkiJ. K. (1980) Review of soil fertility work in Tanzania related to fertilizer recommendations for annual crops. National Agricultural Conference, Uyole, Mbeya, November 1980, Mimeo, 13 p.

Jackson I. J. (1977) Climate, water and agriculture in the Tropics. Longman, London, 248 p.

Kenworthy J. M. & Glover J. (1958) The reliability of the main rains in Kenya. E. Afric. Agric. For. J., 23 : 267-272.

Manning H. L. (1956) The statistical assessment of rainfall probability and its application in Uganda agri­culture. Proc. Roy. Soc. B., 144 : 460-480.

Nieuwolt S. (1973) Rainfall and evaporation in Tanzania. BRALUP Research Paper 24, Univ. of Dar es Salaam, 46 p.

Stem R. D. & Coe R. (1982) The production of rainfall models and their use in agricultural planning.

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Draft Paper HOOS/SIMPAPER/B6, Dept. of Applied Statistics, University of Reading, Reading.

Surveys and Mapping Division (1976). Vegetation Map in "Atlas of Tanzania". Second Edition Ministry of Lands, Housing and Urban Development, Dar es Salaam, Tanzania.

Van de Weg R. F. & MbuviJ. P. (eds) (1975) Soils of the Kindaruma Area (Quarter Degree Sheet 136). Min of Agric., Nat. Agricult. Laboratories, Kenya Soil Survey, 138 p.

Virmani S. M., KampenJ. & Krantz B. A. (1978) Some aspects of the agricultural climate in relation to land evaluation for sorghum and millet in the semi-arid tropics. in "Land evaluation standards for rainfed agriculture" , World Soil Resources Report 49, FAO Rome: 45-57.

Landbouwmogelijkheden en beperkingen van groeiseizoenen in Tanzania

Samenvatting

Het is mogelijk een vrij goed beeld te bekomen van de landbouwmogelijkheden en beperkingen in Tanzania door een studie van de groeiperiodes, gebaseerd op de analyse der hoofdkarakteristieken, duur, begintijd en hun variabiliteit. Deze studie leidt tot enkele pertinente conclusies met betrekking tot de neerslag-gehonden landbouw. In ongeveer de helft van Tanzania zou de bodemvochtreserve weinig bij­dragen tot de lengte van de groeiperiode doch in de andere helft kan men aanzien­lijke verschillen verwachten in de duur van de groeiperiode in funktie van de vocht­bewarende eigenschappen van de bodem. De neerslag-gebonden landbouw wordt vooral geremd door de variabiliteit in de duur en het begin van de groeiperiode, en in veel mindere mate door een onvoldoende lengte. De direkte en indirekte gevolgen van de variabiliteit der groeiperioden op de landbouwproduktie in Tanzania worden besproken.

Potentialités et contraintes agricoles en fonction des périodes de eroissance et leur variabilité en Tanzanie

Résumé

En Tanzanie l'analyse des éléments principaux de la période de croissance, de sa durée, début et variation, permet de faire une première approximation des potentia­lités et des contraintes agricoles. De cette analyse, basée sur un modèle de bilan hydrique, il en résulte qu'il existe de grandes différences dans la mesure ou les capa­cités de rétention d'eau des sols influencent la durée des périodes de croissance. 11 paraît aussi que la variabilité des durées et des débuts des périodes de croissance est un facteur très important qui limite la production agricole pluviale. Les effets directs et indirects sur la production agricole en Tanzanie de la variabilité des périodes de croissance sont examinés.

170

PEDOLOGIE, XXXIII, 2, p. 171-197,2 tab., 1 fig., Ghent 1983.

A PEDOLOGICAL SOIL CLASSIFICATION SYSTEM FOR DANISH SOILS

H. B. MADSEN

Abstract

A Danish soil classification system has been developed for evaluating soils in the field. About 9000 soil promes have been classified in connection with the establishment of the main gas pipeline system in Denmark. The soil classification is a hierarchical system with four levels: order, group, series, and phase. The sys­tem is based on the defmition of diagnostic horizons and profile characteristics, indicating the horizon sequence, within the upper 120 cm of the prof tIe. All observations are stored in numerical form in computers with reference to the UTM-coordinate system. In this way it is possible to search for different charac­teristics as fragipans, peaty toplayers and degradated Bt-horizons and to relate these features to different landforms.

Key-words

Soil classification.

1. INTRODUCTION

In 1975 the Danish Ministry of Agriculture started a soil classification for the country based mainly on texture and the content of organic matter in the topsoil. 'Soil maps at scale 1: 50000 were constructed and stored in computers (Mathiesen, 1978). In 1981 the establishment of pipelines from the North Sea gasfields across Denmark represented a unique possibility to study soil profiles in different parent materials and to correlate the results with the former soil maps. Thus pedological in­vestigations were carried out by the Bureau of Land Data in co-operation with the Government Laboratory for Soil and Crop Research, the Geo­graphical Institute of the University of Copenhagen, and the Chemical Institute of the Royal Veterinary and Agricultural University, Copen­hagen.

H. B. Madsen - Geographical Institute, University of Copenhagen, Haraidsgade 68, DK-2100 Copenhagen ~; Ministry of Ag ricultu re , Bureau of Land Data (ADK), Enghavevej 2, DK-7100 Vejle, Denmark.

171

The investigations comprised a classification of the soi! profile at every 25 m in the two meter deep pipeline trench; 2-3 detailed profile studies, including sampling for analyses, we re carried out at every one km along the trench. The profile descriptions and analytical data were stored in èomputers at the Bureau of Land Data, and in the light of the results the profiles were classified according to FAO-Unesco (1974) and USDA-Soil Survey Staff (1975). As none of these systems are · optimal for classifying soils in the field and for computer handling, it was found necessary to build up a Danish soil classification system based on easily detectable features in the field. Through this system soils were classified at every 25 m along the greater part of the more than 500 km long main pipeline system in Denmark.

The soil classification system is based on former Danish profile inves­tigations (Fobian, 1966; Petersen, 1976; Madsen,1979 and 1983; Jacobsen, 1981; Dalsgaard et al., 1981) and classifies soils according to parent material, pedological development, and soil properties in regard to cultivation. The classification is a hierarchical system, which is easy to store in a numerical form in computers. As it is constructed for classifying soils without drawing boundaries, it is an open system with too many soil types for mapping in e.g. scale 1: 25000. For this purpose it will be necessary to group together some of the soil types with e.g. nearly the same horizon sequence and agricultural value. The system is constructed in such a way that it is possible to search for single charac­teristics in the soils, e.g. thin organic layers or hydromorphic features. Thus, a change from one order to another at the highest level in the sys­tem should not change much the classification at lower levels. It is in­deed found of importance to group together soils with almost the same content of organic matter and texture, soils with very similar horizon sequences, e.g. those having root- and water-impeding l~yers, and soils with the same natural drainage conditions.

It is essential that the used parameters in the classification system, e.g. mottles, texture, and horizon sequence, refer to stabie factors in order to make the classification valid for a long period. Therefore, base saturation and pH of the topsoil are not included in the classification, because fertilization and liming are widely used in Denmark. On the other hand pH at 1 m depth is included at the lowest level, as it has been shown that low pH-values at this depth can be fairly stabie in clayey soils in spite of liming. Low pH-values tend to impede root devel­opment (Foy et al. 1964,1967,1974; Armiger et al. 1968).

The proposed Danish soil classification is a hierarchical system with four levels: order, group, series and phase (table 1).

The soils are named partIy in accordance with other systems (FAO,

172

Table 1 The pedological soil classification system for Danish soils.

SERIES

Ol kolluvial Al: 40-80 cm thick

02 humusfattig Al < 1 % organic

matter

03 hUlTl0S Al: 7 -20 % organic

matter

04 histic peatlayer 10-40 cm thick

05 entic Al < 10 cm thick

06 mor morlayer > 10 cm thick

07 gleyey 08 stagnogleyey 09 pseudogleyey

10 bleget pale subsoil

11 degraderet degradated Bt or Bs

12 fragi fragipan

13 placic placic horizon

14 hardnet cemented layer

15 natric high content of sodium

16 kalkholdig weakly calcareous layers

17 rendzin 18 ranker ·

31-39 name of order+like

41-52 top+name of order

61-72 sub+name of order

Group level

3 GROUP LEVEL 2 GROUP LEVEL

11 Gley 12 Stagnogley 13 Pseudogley

11 Gley 12 Stagnogley 13 Pseudogley

11 Gley 12 Stagnogley 13 Pseudogley

11 Gley 12 Stagnogley 13 Pseudogley

23 Brunsol 11 Gley 24 Brunjord 12 Stagnogley 26 Podzol 13 Pseudogley

11 Gley 25 Lessive 12 Stagnogley

13 Pseudogley

11 Gley 12 Stagnogley 13 Pseudogley

6 Kalk

22 Bleg 6 Kalk 25 Lessive

26 Podzol

7 R~ndzin

6 Kalk 8 Ranker 25 Lessive 26 Podzol

23 Brun 22 Bleg 25 Lessive

11 Gley

26 Podzol

11 Gley

1 - 5: special horizon sequences for the orders.

1 GROUP LEVEL ORDER

1 Typi 22 Bleg

6 Kalk 1 Rendzin

Ol rajord "Regosol"

8 Ranker

1 Typi 2 Blandings 6 Kalk 7 Rendzin

02 blegsol "Arenosol"

8 Ranker

1 Typi 2 Struktur 3 Blandings 03 brunsol 6 Kalk "Arenosol" 1 Rendzin 8 Ranker

1 Ty"i 2 Struktur 3 Blandings 04 brunjord 6 Kalk "Cambisol" 1 Rendzin 8 Ranker

1 Typi 2 Blandings 3 Band 05 Lessive 4 Degra "Luvisol" 6 Kalk "Acrisol" 1 Rendzin 8 Ranker

1 Typi 2 Humus 3 Sesqui 4 Brun 06 podzol 5 Initial "Podzol" 7 Rendzin 8 Ranker

1 Typi 26 Podzol 25 Lessive 01 kolluvial-

6 Kalk jord 7 Rendzin 8 Ranker

1 Typi 26 Podzol 25 Lessive

7 Rendzin

08 stagnogley "Gleysol"

8 Ranker

1 Typi 2 Vad 3 Brun 09 gley 4 Kolluvial "Gleysol" 7 Rendzin 8 Ranker

1 F'ibri 2 Hemi 3 Sapri 4 Kopro

10 histosol "Histosol"

5 Blandings

1 Typi 2 Sedi 3 Blandings

11 rendzina "Re'ndzina"

1 Typi 2 Kolluvial 3 Litho

21 Rajords 22 Bleg 23 Brun

12 ranker "Lithosol" "Rankers"

26 Podzol 25 Lessive

Al< 2 cm no diagnostic B horizon exc. Bj

2 cm< Al < 80 cm below Al pale colours

2 cm< Al< 80 cm sandy brown soil without Bh or Bs horizon

2 cm< Al< 80 cm clayey brown seil without Bt horizon

Al < 80 cm soil having Bt horizon

Al < 80 cm seil having Bs and or Bh horizon

Al> 80 cm

stagnogley within the uppermost' 40 cm

ground water gley within the uppermost 40 cm

peatlayer ( > 20 % organic matter) more

than 40 cm thick

:o;!rl;a~èhin the uppermost 40 cm

lirne free rock normally within the uppermost 40 cm

6 - 8: ~~!\~~;:':s~ ~C~. o~e~~:i~r~;;'i~. % CaC03 • ranker = limefree rock. All three horizons begin within

11 - 13: Groundwater gley or stagnogley beginning between 40-80 cm depth or pseudogley beginning between 0-80 cm depth. 21 - 26: Presence of other diagnostic horizons which might have qualified the soil at order level.

Series level 7 - 9: Groundwater gley. stagnogley and pseudogley beginning between 80-120 cm depth.

10 - 16: Horizon beginning within the uppermost 120 cm of the soil. .

~i = ~~ ~ ~:~~;n e~;;~s~ Ca~~Jio~~~~~r d=v!î~~~~~Sr~~hO~e f;~~~~~z~~s d~*~S~~w~~~i:;;;:O cm depth. 41 - 72: Buried soils. The deepest soil profile beginning in the uppermost 60 cm qualifies the profile at order level.

The other profile is described at series level with top or sub in front of the name of order.

0 ::0 -< (fl 0 r lf)

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1974; Soi! Survey Staff, 1975; Mückenhausen, 1977; Duchaufour, 1977) and partly given new Danish designations. The system describes mainly the horizon sequence in the upper 120 cm of the soil and includes all diagnostic horizons (see 3.1.) within this depth unless specially mention­ed. Certain diagnostic horizons may be deeper, e.g. in soils with thick A1-horizons.

As the pedological and edaphological importance of certain soil cha­racteristics deperids on depth, the upper 120 cm of the soil are sub­divided in to 3 sections of 40 cm each. In this way it is possible to weigh the soi! characteristics, depending at what depth they occur. Thus the following combination of names may occur in podzolized soils with groundwater gley at different depths : groundwater gley beginning : between 0-40 cm depth = PodzoltypigIey; between 40-80 cm depth = Gleytypipodzol; between 80-120 cm depth = gleyey Typipodzol.

2. HORIZON SYMBOLS

In the definition of the soil types different horizon sequences are given. The following symbols are used for describing the horizons.

2.1. MASTER HORIZONS

Organic horizons are all horizons having more than 20 % organic mat­ter. All peat and mor layers are described by 0, limnic material by L.

Mineral horizons are all horizons having less than 20 % organic matter. The following symbols are used : Al, A2, B, C, R. These symbols are equal to the definition of A, E, B, C, R layers in the FAO-Unesco Scheme (1974), except that in the Danish system no mineral horizon has more than 20 % of organic matter.

If lithologic discontinuities are present in the profiles a Roman numeral is written to the left of the master horizon designation, e.g. A-B-IIC.

2.2. LETTER SUFFIX

Smaliletters may be added to the capitalietter to qualify the master horizon designation. The suffix letters do not always indicate th at the horizon is a diagnostic horizon. The suffix letters used to qualify the master horizons in this paper are as follows : a, e, i = sapric, hemic and fibric peat (Soil Survey Staff, 1975); t = illuvial accumulation of clay; s = illuvial accumulation of sesquioxides; v = alteration in situ as reflected by colour; j = alteration in situ as reflected only by structure;

174

c = calcium carbonate-con taining horizon; x = fragipan; b = buried horizon; h = A 1 horizon having 7 -20 % organic matter or dark B-horizon~ having

illuvial accumulation of humus; y = degradated B-horizon; z = polysequum; r = groundwater gley, reduced zone; o = groundwater gley, oxidized zone; s = surface water gley.

3. DEFINITION OF SOIL TYPES AT ORDER LEVEL

The profiles are classified into 12 orders; these can be regrouped into three categories :

a. Deep, non hydromorphic soils = soils without· mottles within the upper 40 cm and without high organic matter content due to imperfect drainage;

b. Deep hydromorphic soils = soils with mottles within the upper 40 cm and/or high organic matter content due to imperfect drainage;

c. Shallow soils = soils with rock or very calcareous material within the uppermost 40 cm.

The 12 orders, with their correlation according to FAO-Unesco (1974), are listed as follows :

a. Deep, non hydromorphic soils : Rajord (raw soil) Blegjord (pale soil) Brunsol (brown soil) Brunjord (brown earth) Lessivejord Podzol

FAO : Regosols FAO : Arenosols FAO: Arenosols FAO: Cambisols FAO : Luvisols, Acrisols FAO : Podzols

Kolluvialjord (soils with more than 80 cm Al horizon)

b. Deep hydromorphic soils : Stagnogleyjord (stagnogley soil) FAO: GIeysols GIeyjord (gley soil) FAO: GIeysols Histosol FAO : Histosols

c. Shaliow soils : Rendzina Ranker

FAO : Rendzinas FAO: Lithosols, Rankers

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3.1. DIAGNOSTIC HORIZONS

The profiles are grou ped: in the 12 orders on the basis of the presence or absence of diagnostic horizons. A total of 15 diagnostic horizons have been defined. These describe the thickness of the Al-horizon, the thickness of soillayers above a solid rock, the thickness of very calcare­ous material; they describe also certain B horizons, very bleached or sandy horizons, gleyed horizons or thick peat layers. The 15 diagnostic horizons are :

a. Diagnostic thin A 1 horizon. Al horizon less than 2 cm thick. Used to define Rajord.

b. Diagnostic thick A 1 horizon. A 1 horizon more than 80 cm thick. The A 1 horizon is defined as the A horizon in the FAO-Unesco System (1974). Used to define Kolluvialjord.

c. Diagnostic bleg (pale) horizon (en). More than 40 cm pale material within the uppermost 50 cm below the Al. The pale horizon must not be an A2. The pale material has one of the following chromas and values when moist : 6/1, 7/1, 7/2, 7/3, 8/1, 8/2, 8/3 or 8/4. Used to define Blegsols.

d. Diagnostic structural B horizon (Bj). A more than 10 cm thick B horizon just below the Al, where the original sediment structure is completely altered due to bioturbation or other pedological processes. The horizon must not be aBt, Bs, Bh or Bv horizon. Used to define Brunsols or Brun jorde.

e. Diagnostic coloured B horizon (Bv). A more than 10 cm thick B horizon just below the Al. The Bv horizon has a lower chroma or value than the C horizon due to the weathering of iron-rich silicate minerais. The horizon must not be aBt, Bs or Bh. Used to define Brunsols or Brunjorde.

f. Diagnostic sandy horizon. A more than 40 cm thick horizon just be­low the Al with Ie ss than 8 % day and less than 30 % silt. The horizon reaches 80 cm depth unless there is solid rock or loose material with more than 30 % calcium carbonate within the upper 80 cm of the profile. Used to define Brunsols.

g. Diagnostic humus-B horizon (Bh). A more than 1 cm thick horizon containing illuvial humus due to podzolization. The moist colour is black or very dark grey according to the Munsell Soil Color Charts. Used to define Podzols.

h. Diagnostic reddish sesquioxide-B horizon (Bs). A more than 10 cm thick podzol-B horizon which does not fulfill the colour requirements for a Bh horizon. The layer is cemented and/or has colours with

176

hues of 5 YR or redder. Used to define Podzols.

i. Diagnostic brownish sesquioxide-B horizon (Bsv, Bvs). A more than 10 cm thick podzol-B horizon. The layer is not cemented and. has colours with hues of 7.5 YR or yellower and the colours do not ful­fill the requiremen ts for a Bh:.horizon. Used to define Podzols.

]. Diagnostic lessivé horizon (Bt). A more than 10 cm thick B horizon containing illuvial day. The amount of illuvial day has to fulfill the demands as defined in the FAO-Unesco System (1974). Used to define Lessivejorde.

k. Diagnostic stagnogley horizon (Bg, Cg). A more than 10 cm thick, strongly reduced blue or grey surface water gley horizon beginning within the uppermost 40 cm of the profile. The horizon has to be more blue or grey (reduced) above the water-impeding layer than be­low. The water-impeding layer is e.g. a placic horizon or a slowly permeable Bt horizon. Used to define Stagnogleyjorde.

1. Diagnostic gley horizon (Bo, Br, Co, Cr). Groundwater gley beginning within the uppermost 40 cm of the profile. Groundwater gley soils are separated from surface water gleys as described by Mückenhausen (1977). Used to define Gleyjorde.

m.Diagnostic histic horizon (Oa, Oe, Oi)o More than half of the upper­most 80 cm of the horizon has more than 20 % organic matter. Used to define Histosols.

n. Diagnostic rendzin horizon. High-Iying limestone or calcareous mate rial. There is more than 30 % calcium carbonate in the upper 10 cm of the horizon, and in more than half of the uppermost 80 cm of the horizon. The maximum thickness of the soil above the diagnostic horizon is 80 cm. If the Al is less than 30 cm thick, then the diagnos­tic rendzin horizon must begin within the upper 40 cm of the soil; is the Al thicker than 30 cm, then the diagnostic rendzin horizon must begin at less than 10 cm below the Al. Used to define Rendzinas.

O. Diagnostic ranker horizon. High-Iying lime-free rock. The maximum thickness of the soil above the rock is 80 cm. rf the Al horizon is less than 30 cm thick, then the rock begins within the upper 40 cm of the soil; is the Al thicker than 30 cm, then the rock begins at Ie ss than 10 cm below the Al. Used to define Rankers.

3.2. DESCRIPTION OF THE DIFFERENT SOIL TYPES

3.2.1. Rajorde

Rajorde are weakly developed soils in loose sediments. These are of ten young soils due to recent sedimentation or erosion phenomena.

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Rajorde have an Al or Al + A2 horizon less than 2 cm thick; they have no diagnostic horizons except a bleg (pale),structural B, or sandy horizon.

3.2.2. Blegsols

Blegsols are characterized by pale colours below the Al. They have an Al horizon 2-80 cm thick and a diagnostic bleg (pale) horizon. Bleg­sols do not have a diagnostic histic, gley or stagnogley horizon or any diagnostic B horizon, except a structural B. Blegsols do not have a diag­nostic rendzin or ranker horizon. They are of ten young soils developed in dune sands near coastal plains.

3.2.3. Brunsols

Brunsols are sandy soils without podzolization features developed in parent material with Ie ss than 8 % day and less than 30 % silt. The soil is normally brownish, and there is no diagnostic bleg horizon. Brunsols must have a diagnostic sandy horizon, but there is no other diagnostic B horizon than a structural or coloured B horizon. The Al horizon must be 2-80 cm thick if a structural B horizon (Bj), or Chorizon begins immediately below the Al; the A11ayer is less than 80 cm thick if a coloured B horizon (Bv) begins immediately below the Al. Brunsols have no diagnostic histic, gley, stagnogley, rendzin or ranker horizon. They are of ten developed in glaciofluvial deposits or in sandy till.

3.2.4. Brunjorde

Brunjorde are like Brunsols, except that they have no diagnostic sandy horizon. Brunjorde are found in dayey tillor silty deposits but are rare in these parent materials, compared to Lessivejorde.

3.2.5. Lessivejorde

Lessivejorde have an Al horizon less than 80 cm thick and a diagnos­tic lessivé horizon (Bt). The Bt horizon must not fulfill the defmition of a podzol B horizon; however, a diagnostic podzol-B horizon may be developed in the lessivé A2 horizon. Lessivejorde do not have a diagnos­tic histic, gley, stagnogley, rendzin or ranker horizon. They are the most common soils in dayey till.

3.2.6. Podzols

Podzols have an Al horizon less than 80 cm thick and a diagnostic podzol-B horizon (Bh, Bs, Bsv or Bvs). The diagnostic podzol-B horizon is not developed in a lessivé A2 horizon. Podzols do not have a diagnos­tic histic, gley, stagnogley, rendzin or ranker horizon. They are the most

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common soil type in the outwash plains, in old aeolean deposits and in glaciofluvial deposits.

3.2.7. Kolluvialjorde

Kolluvialjorde have an A 1 horizon thicker than 80 cm. These soils do not have a diagnostic histic, gley or stagnogley horizon. They are of ten developed in depressions due to an accumulation of Al material as a result of erosion in higher parts of the landscape.

3.2.8. Stagnogleyjorde

Stagnogleyjorde have a diagnostic stagnogley horizon but no diagnos­tic histic, rendzin or ranker horizon. This very rare soil type is e.g. developed in materials with very impermeable Bt horizons or with placic horizons.

3.2.9. Gleyjorde

Gleyjorde have a diagnostic gley horizon or a histic horizon less than 40 cm thick or have a more than 10 cm thick Al horizon with more than 7 % organic matter due to imperfect drainage. Gleyjorde do not have a diagnostic histic horizon. They are of ten developed in depressions with groundwater near to the surface and in marine forelands.

3.2.10. Histosols

Histosols are soils having a diagnostic histic horizon due to an imper­fect drainage. The upper limit of the diagnostic histic horizon must be­gin within the upper 60 cm of the profile, except if it is Al material. In that case the diagnostic histic horizon must begin within the upper 80 cm of the profile. Histosols are of ten observed in depressions with groundwater near the surface.

3.2.11. Rendzinas

Rendzinas have developed over shallow limestone or calcareous material. Rendzinas must have a diagnostic rendzin horizon. They do not have a diagnostic histic horizon. These soils are only found in a few places, e.g. in North Jutland where cretaceous limestone reaches the surface.

3.2.12. Rankers

Rankers are soils having a diagnostic ranker horizon. They do not have a diagnostic histic horizon. In Denmark these soils are only found on the island Bornholm.

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4. DEFINITION OF THE SOILS AT GROUP LEVEL

The 12 orders are subdivided at group level on the basis of horizon sequence, thickness of the Al horizon, depth to the solid rock or calcar­eous material, soil colour, texture, decomposition status of the organic matter and depth to a groundwater gley, pseudogley or stagnogley horizon. Pseudogley refers to surface water gley without a strongly re­duced upper part. Stagnogley and groundwater gley are described as diagnostic horizons.

The naming is made by writing up to 3 prefixes to the left of the order name, starting at the right with the first group name. The names are written together and start always with a capitalietter, for example : Gleytypipodzol. Typi is thus the first group name and gley the second one.

Most deep, non-hydromorphic soils may be given up to three charac­teristics at group level, namely : a) horizon sequence (lst group designation); b) groundwater gley, stagnogley or pseudogley (2nd group designation); c) double profile development in the same soil (3rd group designation).

When characterizing the horizon sequence much care must be taken to ensure that it is typical for the order, that the profile is homogeneous in texture, and that the colour fulfills certain demands. In the case of groundwater gley, or stagnogley beginning between 40-80 cm depth, these characteristics are written to the left of the horizon sequence. In the case of an absence of the two first-named gley types, and if pseudo­gley is found within the upper 80 cm of the profile, this is added to the left of the name describing the horizon sequence. If the upper limit of a gley characteristic lies above or helow the just mentioned limits, this will characterize the soil at order and series level respectively, and no description of drainage conditions at group level will he made. The third characteristic at group level is used to describe two pedological processes with formation of diagnostic B horizons within the same profile, e.g. a podzol developed in the A2 horizon of a Lessivejord. As soils with double profile development are rare, three characteristics at group level are seldom. A Podzolpseudogleytypilessivé can he mentioned as an example of su eh a profile; it means a profile with day illuviation (lessivé) developed in a textura1ly homogeneous sediment (typi). The profile has pseudogley within 80 cm from the surface and a podzol has developed in the A2 horizon.

For deep hydroniorphic soils, up to three designations at group level are used, except for stagnogley soils. For gley soils the first group name describes the depth to a strongly reduced horizon, the thickness of the surface horizon, the organic matter content in the Al, and the presence

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of rock or very calcareous material within the upper 80 cm of the profile. The second group name is used for the description of other distinct pedological developments in the profile, e.g. podzolization and day migration. The third group name is used to separate calcareous gley soils. As to Histosols, the first group name is used to describe the decomposi­tion status of the organic matter, the second one to describe the pedological developmen t below the diagnostic histic horizon and the presence of shallow rock or very calcareous material, whereas the third group name is used to separate calcareous Histosols.

In shallow soils the Rendzinas have three group names, whereas Ran­kers have so far only two group names. F or Rendzinas the first group name designates the status of the calcareous layers, whereas the second group name describes gley characteristics within the upper 80 cm of the soil. The third group name describes the pedological development in the soillayers above the calcareous material. For Rankers the first group name describes the thickness of the sediment and the horizon sequence, whereas the second group name describes distinct hydromorphic charac­teristics in the profile.

Before listing the soil types at group level, the following defmitions and remarks should be made.

Calcareous horizon. More than 1/3 of the upper 120 cm of the soil has more than 5 % calcium carbonate.

Rendzina-like horizon. Horizon having more than 30 % calcium carbonate in the upper 10 cm; more than half of the uppermost 80 cm of the horizon has more than 30 % calcium carbonate. The horizon must begin within the upper 80 cm of the profile, but it must not qualify the soil as Rendzina.

Reduced groundwater gley horizon (Cr). Horizon dominated by blue or grey colours due to an imperfect drainage. Less than 10 % of the horizon is oxidized, e.g. as red spots around root channels.

Oxidized groundwater gley horizon (Co J. Horizon dominated by red, brown and light grey mottles due to imperfect drainage. More than 10 % of the horizon is oxidized.

At second group level the drainage conditions may be described. Except for the Rendzinas and Rankers the system distinguishes between gley, stagnogley and pseudogley. Gley refers to groundwater gley; stagno­gley and pseudogley are surface water gleys. Stagnogley is a strongly developed pseudogley with a reduced (blue or grey) upper part and a more aerated lower part (see definition of diagnostic stagnogley horizon). The normal surface water gley in Denmark is a pseudogley.

On the next pages the soils are defined at group levels. At the first

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group level the typic soil is defined by listing the specific points which must be fulfilled. The other soil types at the first group level are listed below and are defined in relation to the points listed for the typic soi!. If the points are fulfilled they are listed with a + in front; -means that the points are not fulfilled, while ! means that the points do not need to be fulfilled. The description + (a,b,c), - (e), ! (f) means that the points a, b, care fulfilled, point e is not fulfilled, and point f does not need to be fulfilled. Soils at the second and third group level are also defined in relation to the poin ts listed for the typic soi!.

4.1. RA]ORDE

First group level

lA) Typirajord : (Al) - C, where the following characteristics are fulfilled: a) The C horizon is not a diagnostic bleg (pale) horizon. b) There is no calcareous horizon. c) There is no rendzina-like horizon beginning within the upper 80 cm

of the soi!. d) There is no lime-free rock beginning within the upper 80 cm of the

soil. e) There are no gley features within the upper 80 cm of the soil.

1B) Blegrajord : (A)-Cn : + (b,c,d,e), - (a).

1C) Kalkrajord: (A)-Cc : + (c,d,e), - (b), ! (a).

1D) Rendzinrajord : (A)-(R) : + (d,e), - (c), ! (a,b).

1E) Rankerrajord : (A)-R : + (e), - (d), ! (a, b,c).

Second group level

If point e is not fulfilled, gley, stagnogley or pseudogley is written in front of the name at the first group level, e.g. :

1BA) Gleyblegrajord : (A)-Cn-Cno : + (b,c,d), - (a,e).

1AC) Pseudogleytypirajord: (A)-C-Cg: + (a,b,c,d), - (e).

Third group level

So far, no soi! type has been defined at this level.

4.2. BLEGSOLS

First group level

2A) Typiblegsol : A - (Bjn) - Cn where the following characteristics are fulfilled :

a) There is less than 20 cm not-pale material within the upper 120 cm

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of the profile. h) There is no calcareous horizon. c) There is no rendzina~like horizon heginning within the upper 80 cm

of the soil. . d) There is no lime-free rock within the upper 80 cm of the soil. e) There are no gley features within the upper 80 cm of the profile.

2B) Blandingshlegsol : A- (Bjn) - Cn - C : + (h,c,d,e), - (a).

2C) Kalkhlegsol: A-(Bjn)-Cn: + (c,d,e), _. (h),! (a).

2D) Rendzinhlegsol: A-Bjn-Cn-(R) : + (d,e), -(c),! (a,h).

2E) Rankerhlegsol : A-Bjn-Cn-R: + (e), - (d), ! (a,h,c).

Second group level

If point e is not fulfilled, gley, stagnogley or pseudogley is written in front of the name at the first group level e.g. :

2AA) Gleytypihlegsol : A-Bnj-Cn-Cno : + (a,h,c,d), - (e).

2CA) Gleykalkhlegsol: A-Bnj-Cn-Cno: + (c,d), - (h,e),! (a).

Third group level

So far, no soil type has heen defined at this level.

4.3. BRUNSOLS

First group level

3A) Typihrunsol : A - Bv - C where the following characteristics are ful­filled :

a) There is a diagnostic coloured B horizon. h) The lower limit of the diagnostic sandy horizon is helow 120 cm

depth. c) There is no calcareous horizon. d) There is no rendzina-like horizon. e) There is no lime-free rock heginning within the upper 80 cm of the

soil. f) There are no gley features within the upper 80 cm of the soil.

3B) Strukturhrunsol : Al- (Bj) -C : + (h,c,d,e,f), - (a).

3C) Blandingshrunsol : AI - (Bv,j) -C-IIc : + (c, d, e, f), - (h), ! (a).

3D) Kalkhrunsol : Al-(Bv,j)-C : + (d,e,f), - (c), ! (a, h).

3E) Rendzinhrunsol : Al-(Bv,j)-C-(R) : + (e,f), - (h,d), ! (a,c).

3F) Rankerbrunsol : Al-(Bv,j)-C-R : + (f), - (b,e), ! (a,c,d).

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Second group level

If point f is not fulfilled, gley, stagnogley or pseudogley is written in front of the name at the first group level e.g. :

3BA) Gleystrukturbrunsol : A-(Bj)-C-Co : + (b,c,d,e)" - (a, f).

3CC) Pseudogleyblandingsbrunsol: A-(Bj)-Cg-IICg : + (c,d,e), -(b,f), ! (a).

Third group level

So far, no soil type has been defined at this level.

4.4. BRUN]ORDE

First group level

4A) Typibrunjord : A-Bv- C where the following characteristics are ful­filled :

a) There is a diagnostic coloured B horizon. b) Layers with Ie ss than 8 % day and less than 30 % silt must not, in

total, exceed 10 cm within the upper 120 cm of the soil. c) There is no calcareous horizon. d) There is no rendzina-like horizon. e) There is no lime-free rock beginning within ' the upper 80 cm of the

soil. f) There are no gley features within the upper 80 cm of the soil.

4B) Strukturbrunjord: A-(Bj)-C: + (b,c,d,e,f), - (a).

4C) Blandingsbrunjord: A-(B)-C-IIC : + (c,d,e,f), - (b), ! (a).

4D) Kalkbrunjord : A-(B)-C : + (d,e,f), - (c), ! (a,b).

4E) Rendzinbrunjord: A-(B)-C-(R): + (e,f), - (d),! (a,b,c).

4F) Rankerbrunjord : A-(B)-C-R: + (f), - (e), ! (a,b,c,d).

Second group level

If poin t f is not fulfilled, gley, stagnogley or pseudogley is written in front of the name at the first group level e.g. :

3AC) Pseudogleytypibrunjord : A-Bv-Cg : + (a, b,c,d,e), - (f).

3DA) G1eykalkbrunjord : A-Bv-Co : + (d,e), - (c, f), ! (a, b).

Third group leve I

So far, no soil type has been defined at this level.

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4.5. LESSIVE]ORDE

First group level

SA) Typilessivé : AI-A2-Bt-C where the following characteristics are fulfilled:

a) There is no lithologie discontinuity in the parent material, e.g. dune sand superimposing clayey til!.

b) The Bt horizon is not dominated by Bt-bands. c) The uppermost 20 cm of the Bt-horizon are not degradated so that

more than 50 % of the horizon is A2 material. d) There is no calcareous horizon. e) There is no rendzin-like horizon. f) There is no lime-free rock beginning within the up per 80 cm of the

soi!. g) There are no gley features within the upper 80 cm of the profile. h) There is no other diagnostic B horizon in the soil than a Bt horizon

and a structural B horizon (Bj).

SB) Blandingslessivé : AI-A2-(II)Bt-IIC : + (b,c,d,e,f,g,h), - (a).

SC) Bandlessivé : AI-A2-Btd-C : + (c,d,e,f,g,h), - (b), ! (a).

SD) Degralessivé : AI-A2-Bty-C: + (d,e,f,g,h), - (c), ! (a,b).

SE) Kalklessivé : AI-A2-Bt-C : + (e,f,g,h), - (d), ! (a, b,c).

SF) Rendzinlessivé : AI-A2-Bt-C-(R) : + (f,g,h), - (e), ! (a, b,c,d).

SG) Rankerlessivé : AI-A2-Bt-C-R: + (g,h), - (f), ! (a,b,c,d,e).

Second group level

If point g is not fulfilled, gley, stagnogley or pseudogley is written in fr on t of the name at the first group level e.g. :

SAB) Stagnogleytypilessivé : AI-A2-Btg-C : + (a,b,c,d,e,f,h), - (g).

SDC) Pseudogleydegralessivé : AI-A2g-Btg-Cg : + (d,e,f,h), - (c,g), ! (a,b).

Third group level

If point h is not fulfilled due to the development of a Podzol, Brun­sol or Brunjord in the lessivé A2 horizon, this is described at the 3rd group level. The name Podzol, Brunsol or Brunjord is written in front of the name at second group level or first group level if no gley feature is present within the upper 80 cm of the soi!.

SC3) Brunsolbandlessivé : Alz-Bvz-A2-Btd-C : + (c,d,e,f,g), -(b,h), ! (a).

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SDC6) Podzolpseudogleydegralessivé : Alz-A2z-Bsz-A2-Bty-C : + (d,e,f), - (c,g,h), ! (a,b).

4.6. PODZOLS

First group level

6A) Typipodzol : Al - (A2)-Bh-Bs-C, where the following characteristics are fulfilled : .,

a) There is a diagnostic Bh horizon. b) There is a diagnostic Bs, Bsv or Bvs horizon. c) The diagnostic Bh horizon is thinner than the Bs, Bsv or Bvs horizon. d) The upper limit of Cis be10w 30 cm depth. e) There is no rendzin-like horizon. f) There is no lime-free rock beginning within the upper 80 cm of the

soi!. g) There are no gley features within the upper 80 cm of the soi!. h) There is no, diagnostic Bt horizon in the soi!.

6B) Humuspodzol : Al-(A2)-Bh-(Bs)-C : + (a,d,e,f,g,h), -(c), ! (b).

6C) Sesquipodzol : Al-(A2)-Bs-C : + (b,d,e,f,g,h), - (a). Note : Bs = diagnostic reddish sesquioxide B horizon.

6D) Brunpodzol: Al-(A2)-Bsv,vs-C: + (b,d,e,f,g,h), - (a). Note : Bsv, vs = diagnostic brownish sesquioxide-B horizon.

6E) Initialpodzol : Al-(A2)-Bs-C : + (e,f,g,h)+ (a and/or b), - (d), ! (a or b).

6F) Rendzinpodzol : Al-(A2)-(Bh)-Bs-C-(R) : + (f,g,h) + (a or b), - (e), ! (a, b,d).

6G) Rankerpodzol : Al (A2)-(Bh)-Bs-R : + (g,h) + (a or b), - (f), ! (a,b,d,c)

Second group level

If point g is not fulfilled, gley, stagnogley or pseudogley is written in front of the name at the first group level e.g. :

6AA) Gleytypipodzol: Al-(A2)-Bh-Bs-Co : + (a,b,c,d,e,f,h), - (g).

6DA) Gleybrunpodzol : Al-(A2)-Bsv-Co : + (b,c,d,e,f,h), - (a,g). N ote : Bsv = diagnostic brownish sesquioxide-B horizon.

Third group level

If point h is not fulfilled due to the deve10pment of a diagnostic lessivé B horizon at equal depth as the diagnostic podzol B horizon, this is described at the third group level e.g. :

186

6AS) Lessivétypipodzol : A1-(A2)-Bht-Bst-C : + (a,b,c,d,e,f,g), - (h).

6DS) Lessivébrunpodzol : A1-(A2)-Bsvt-C : + (b,c,d,e,f,g), - (a,h). N ote : Bsv = diagnostic brownish sesquioxide-B horizon.

4.7. KOLLUVIAL]ORDE

First group level

7 A) Typikolluvialjord : A1-(Bj, v) -C,where the following characteristics are fulfilled :

a) The Al is thicker than 120 cm. b) If the A 1 is between 80 and 120 cm thick, there is no diagnostic

Bt horizon below the Al. c) If the Al is between 80 and 120 cm thick there is no diagnostic

podzol B horizon below the Al. d) There is no calcareous horizon. e) If the Al is between 80 and 120 cm thick there is no rendzin-like

horizon beginning within 10 cm below the Al. f) If the Al is between 80 and 120 cm thick there is no lime-free rock

beginning within 10 cm below the Al. g) There are no gley features within the upper 80 cm of the profile.

7B) Lessivékolluvialjord: A1-(A2)-Bt-C : + (c,d,e,f,g), - (a,b).

7C) Podzolkolluvialjord : A1-(A2)-Bh-Bs-C :, + (d,e,f,g), - (a,c), ! (b).

7D) Kalkkolluvialjord : Al -C : + (e,f,g), - (d), ! (a, b,c).

7E) Rendzinkolluvialjord : A1-(B)-(R) : + (f,g), - (a,e), ! (b,c,d).

7F) Rankerkolluvialjord: A1-(B)-R : + (g), - (a,f), ! (b,c,d,e).

Second group level .

If point g is not fulfilled, gley, stagnogley or pseudogley is written in front of the name at the first group level e.g. :

7 AA) Gleytypikolluvialjord : A11-A12o-Co : + (a, d) or + (b,c,d,e, f), - (g) ..

Third group level

So far, no soil type has been defined at this level.

4.8. STAGNOGLEY]ORDE

First group level

8A) Typistagnogley : A1g-Cg-C where the following characteristics are fulfilled :

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a) There is no diagnostic podzol B horizon. b) There is no diagnostic lessivé B horizon. c) There is no calcareous horizon. d) There is no rendzin-like horizon. e) There is no lime-free rock within the upper 80 cm of the soil.

8B) Podzolstagnogley : A1g-A2g-Bsvg-Cg-C : + (b,c,d,e), - (a).

8C) Lessivestagnogley : A1g-A2g-Btg-Cg-C : + (c,d,e), - (b), ! (a).

8D) Rendzinstagnogley : A1g-Cg-C-(R) : + (e), - (d), ! (a,b,c).

8E) Rankerstagnogley : A1g-Cg-C-R : + (c), - (e), ! (a, b,d).

Second group level

So far, no soil type has been defined at this level.

Third group level

If point c is not fulfilled, except for 8D, kalk (= calcium carbonate, calcareous) is written in front of the name at first group level e.g. :

8CA) Kalklessivéstagnogley : A1g-A2g-Btg-C : + (d,e), - (b,c), ! (a).

4.9. GLEYJORDE

First group level

9A) Typigley : A1(h)-(Bo)-Co-Cr, where the following characteristics are fulfilled :

a) There is either a strongly reduced horizon (Cr) beginning between 80 and 120 cm depth and/or 7 to 20 % organic material in a 10 cm interval in Al due to imperfect drainage.

b) There is no Cr horizon beginning within the uppermost 80 cm of the soil and/ or a peat layer between 10 and 40 cm thick.

c) There is an Al horizon less than 80 cm thick. d) There is no calcareous horizon. e) There is no rendzin-like horizon. f) There is no lime-free rock beginning within the upper 80 cm of the

soil. q) There is no diagnostic podzol-B horizon, lessivé-B horizon or bleg

(pale) horizon.

9B) Vadgley (vad = wet) : (O)-Alo-(Bo)-Cr : + (c,d,e,f,g), - (b), ! (a). Vadgley has alO to 40 cm thick peat layer and/ or a strongly re­duced horizon beginning within the uppermost 80 cm of the profile.

9C) Brungley : A1o-(Bo)-Co: + (b,c,d,e,f,g), - (a). Brungley has no strongly reduced horizon beginning within the

188

upper 120 cm of the soil and has not more than 7 % organic matter within a 10 cm interval in the Al.

9D) Kolluvialgley : A1o-Co,r : + (d,f,g), - (c), ! (a,b,e).

9E) Rendzingley : A1o-Co,r-(.R) : + (c,f,g), - (e), ! (a,b,d).

9F) Rankergley : Alo-Co, r-(R) : + (c,d,g), - (f), ! (a,b,e).

Second group level

If point g is not fulfilled due to the presence of a diagnostic podzDI-B horizon, lessivé-B horizon or bleghorizon, the words podzol, lessivé or bleg are written in fr on t of the name at the first group level.

9A6) Podzoltypigley : A1h-A2o-Bso-Co-Cr : + (a,b,c,d,e,f), - (g).

9D2) Blegkolluvialgley : A1-Cno : + (d,f) , - (c,g), ! (a,b,e).

Third group level

If point d is not fulfilled, this is described, except for 9E, by the term kalk e.g. :

9AA) Kalktypigley : A1(h)-(Bo)-Co-Cr: + (a,b,c,e,f,g), - (d).

9ASA) Kalklessivetypigley : A1-A2o-B2o-Cr: + (a,b,c,e,f), - (d,g).

4.10. HISTOSOLS

First group level

10A) Fibrihistosol : Oi-Co, r where following characteristics are fulfilled: a) More than 75 % of the diagnostic histic horizon consists of weakly

decomposed peat (more than 66 % fibres). b) There is no calcareous horizon. c) There is no rendzin-like horizon. d) There is no lime-free rock within the upper 80 cm of the soil. e) There is no diagnostic podzol B horizon or lessivé-B horizon.

lOB) Hemihistosol : Oe-Co, r : + (b,c,d,e), - - (a) . . More than 75 % of the diagnostic histic horizon consists of some­what decomposed peat (between 33 % and 66 % fibres).

lOC) Saprihistosol : Oa-Co, r : + (b,c,d,e), - (a). More than 75 % of the diagnostic histic horizon consists of very decomposed peat (less than 33 % fibres).

10D) Koprohistosol : L-Co, r : + (b,c,d,e), - (a). More than 75 % of the diagnostic histic horizon consists of coprogenous deposits (gytje).

10E) Blandingshistosol : O-Co, r : + (b,c,d,e), - (a).

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N either the peat types nor the coprogenous deposits make up 75 % of the diagnostic histic horizon.

Second group level

Ifpoint c, d or e are not fulfilled, t~is is describe~ at second group level. As the classification at second group level is the same for 10A, lOB, IOC, 10D and 10E, only the classification for Fibrihistosols is described.

10AA) Rendzinfibfihistosol : Oi-Co-(R) : + (a,d), - (c), ! (b,e).

1 OAB) Rankerfibrihistosol : Oi -Co - R : + (a, b), - (d), ! (c, e).

10AC) Podzolfibrihistosol : Oi-A2o-Bso-Co : + (a, b,c,d), - (e).

10AD) Lessivefibrihistosol : Oi-A2o-Bto-Co : + (a, b,c,d), - (e).

Third group level

If poin t b is not fulfilled, th is is described, except for 10AA, by the term kalk e.g. :

10ADE) Kalklessivéfibrihistosol : Oi-A2o-Bto-Co : + (a,c,d), - (b,e).

10AE) Kalkfibrihistosol : Oi-Co + (a,c,d,e), - (b).

4.11. RENDZINAS

First group level

11A) Typirendzina : A-(R) where the following characteristics are ful­filled :

a) The horizon that qualifies the soit as rendzina is limestone. b) The horizon that qualifies the soit as rendzina has more than 30 %

calcium carbonate continuing for more than 40 cm. c) There is no mineral soi! thicker than 10 cm between the Al and

the upper limit of the horizon that qualifies the soit as rendzina. d) There are no gleyey layers more than 10 cm thick within the upper­

most 80 cm of the soil.

11B) Sedirendzina : A-C : + (b,c,d), - (a). The horizon that qualifies the soi! as rendzina is a loose sedimentary deposit.

11C) Blandingsrendzina : A-(C)-(R) : + (c,d), - (b), ! (a).

Second group level

If point d is not fulfilled, this is described at second group level e.g. :

10AE) Gleytypirendzina : A1o-(Bo)-(R) : + (a, b,c), - d.

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Third group level

If point c is not fulfilled, the pedological development in the mineral soil be10w the A 1 is described at third group level. As the same classifica­tion is used for 10A, lOB and 10e, the c1assification is only described for Typirendzinas.

11AA) Bruntypirendzina : A1-(Bj,v)-(R) : + (a,b,d), - (c). In the not pale mineral soil below the Al a structural or coloured B-horizon is present.

11AB) Blegtypirendzina : A1-Bn-(R) : + (a, b,d), - (c). The mineral soil below the Al has pale colours.

11AC) Podzoltypirendzina : A1-A2-Bs-(R) : + (a,b,d), - (c). The mineral soil below the Al has a diagnostic podzol-B horizon.

11AD) Lessivétypirendzina : A1-A2-Bt-(R) : + (a, b,d), - (c). The mine ral soil below the Al has a diagnostic lessivé-B horizon.

'4.12. RANKERS

First group level

12A) Typiranker : A1-R where the following characteristics are fulfilled: a) The mineral soil is more than 10 cm thick. b) The mineral soil is less than 40 cm thick. c) The soil between the Al and R is less than 10 cm thick. d) There is no gleyey layer more than 5 cm thick.

12B) Kolluvialranker : A1-(B)-R : + (a,c,d), - (b).

12C) Lithoranker : A1-R : + (b,c,d), - (a).

12D) Rajordsranker : (A1)-C-R : + (a, b,d), - (c). A Rajord is developed in the mineral soil.

12E) Blegranker : A1-Bn-R : + (a,b,d), - (c). The mineral soil has pale colours.

12F) Brunranker : A1-Bv, j-R : + (a, b,d), - (c). A diagnostic structural or coloured B-horizon is deveIoped in the mineral soil.

12G) Podzolranker : A1-(A2)-Bs-R: + (a,b,d), - (c). A diagnostic podzol-B horizon is develofed in the mineral soil.

12H) Lessivéranker : A1-(A2)-Bt-R : + (a, b,d), - (c). A diagnostic lessivé-B horizon is developed in the mineral soil.

191

Second group level

If point d is not fulfilled, gley is written in front of the name at first group level e.g. :

12HA) Gleylessivéranker : A1-A2o-B2to-R : + (a,b), _. (c,d).

Third group.level

So far, no soil type has been defined at this level.

5. DESCRIPTION OF SOILS AT SERIES LEVEL

When subdividing the soils at series level, profile characteristics are used. These describe either the presence of certain horizons within given depths, or the soil-chemical and -physical state of different horizons. If these characteristics are absent, the naming terminates at group level. In the case of several profile characteristics, these are consecutively added to the left part of the group name. All names at series level start with a smallietter. A soil classified at series level may for example be a placic gleyey Typipodzol, whereby placic and gleyey are the terms refer­ring to the series level. Theoretically, there is no limitation in the num­ber of terms to be used at series level.

Until now, 21 profile characteristics have been defined, but presum­ably more will follow. These profile characteristics are easily detectable in the field, and normally no laboratory analyses are needed to make a complete classification at series level. The defined profile characteristics fall naturally into four groups according to Al or 0 horizons drainage conditions, horizons below the Al and buried soils.

5.1. PROFILE CHARACTERISTICS DESCRIBING Al OR 0 HORIZONS

a: kolluvial : Al horizon, 40-80 cm thick;

b: humusfattig (fattig = poor) : a more than 10 cm thick Al horizon with less than 1 % organic matter in all 10 cm intervals within the Al layer;

c: humcps : a more than 10 cm thick Al horizon which has 7-20 % organic matter in at least one 10 cm interval within the Allayer;

d: histic : a 10 to 40 cm thick 0 horizon (more than 20 % organic matter);

e: mor: a more than 10 cm thick mor-Iayer, where the humus and fermen tation layers are more than 6 cm thick;

f: entic: Al horizon not thicker than 10 cm.

192

5.2. PROFILE CHARACTERISTICS DESCRIBING DRAINAGE CONDITIONS

g: gleyey : groundwater gley (mottles or "wet colours") beginning be­tween 80 and 120 cm depth;

h :stagnogleyey : stagnogley beginning between 80 and 120 cm depth;

i: pseudogleyey : pseudogley beginning between 80 and 120 cm depth.

5.3. PROFILE CHARACTERISTICS DESCRIBING HORIZONS BELOW THE Al

j: bleget (pale) : more thap 40 cm pale material within the upper 120 cm of the soil; the horizon is not a diagnostic pale horizon, but the pale material has the same colours;

k: degraderet (degradated) : Bt or Bs horizons with tongues or inter­fingering of A2 material, which do not qualify the soil at group level;

I : fragi : a more than 10 cm thick fragipan beginning within the upper 120 cm of the soil;

m:placic : placic horizon beginning within the upper 120 cm of the soil;

n:hardnet (cemented) : a continuous, more than .10 cm thick cemented layer beginning within the upper 120 cm of the. soil;

c: natric : a more than 10 cm thick horizon within the upper 120 cm of the profile, where the exchangeable sodium constitutes more than 15 % of the CEC-value;

p: kalkholdig (calcareous) : a more than 10 cm thick layer below the plowlayer, but within the upper 120 cm of the soil, containing more than 1 % calCium carbonate; the calcium carbonate content must not qualify the soil at order or group level;

q: rendzin : a more than 10 cm thick layer beginning between 80 and 120 cm depth containing more than 30 % calcium carbonate;

r: ranker: lime-free rock beginning between 80'and 120 cm depth;

s: order name + like, e.g. podzol-like : this profile characteristic is used when a pedological process forming a B horizon has not been strong enough to qualify the soil at order or group level; a lessivélike Brun­jord is a brown soil with traces of clay illuviation not strong enough to qualify the soil as Lessivéjord.

5.4. PROFILE CHARACTERISTICS DESCRIBING BURIED SOILS

t : top + name of order, e.g. Toppodzol : this profile characteristic describes the pedological development in a 10 to 60 cm thick younger parent material above an older soil; the older soil qualifies the profile at order and group level; in the younger parent materialless than 10

193

cm thick, no classification is made; if a podzol is developed in 50 cm thick blown sand, overlying clayey ti11 in which a Lessivéjord is developed, the soil is classified e.g. as Toppodzol Typilessivé;

u: sub + name of order, e.g. Sublessivé : this profile characteristic de­scribes an older soil buried by more than 60 cm but less than 120 cm younger parent material; the pedological development in the younger parent material qualifies the profile at order and group level; if a podzol is developed in 70 cm blown sand, overlying clayey till in which a Lessivéjord is developed, the soil is classified e.g. as Sublessivé Typipodzol.

6. DEFINITION OF SOILS AT PHASE LEVEL

When classifying the soils at phase level, the pH (CaC12) measured at about 1 m depth and the dominating texture at 0-40 and 80-120 cm depths are diagnostic terms. Unlike the three other levels, there is no proper naming of profiles at the phase level, texture and pH being stated just to the right of the profile name. The dominating texture at 0-40 and 80-120 cm depth is to be indicated af ter the division shown on figure 1.

90 80 70 60 50 40 30 20 10

SAND (63-2000,u)

L: clay Q : sil t S : sand

I : clayey q : si I ty s : sandy

k: we a k I Y c I a y e y R: heavy clay

T: very heavy clay

Fig. 1. Texture diagram used in the classification of Danish soils.

194

The texture at 0-40 cm is indicated first and if the texture is the same at 80-120 cm depth, the texture is only described with one name. For Histosols, the texture is described only at 80-120 cm depth, bec.ause the terming of Histosols at group level also designates the peat type in the upper layers. F or Rendzinas only the texture of the mine ral' soil within the upper 40 cm of the soil is indicated.

Af ter the description of texture, the pH (O.OlM CaCI2) at about 1 m depth is indicated as listed below: pH (CaCI2) value : 2-3 : veFY strongly acid,

3-4 : strongly acid, 4-5 : acid, 5-6 : weakly acid, 6-7 : neutral, 7-8 : basic, 8-9 : very basic.

A complete description of a soil profile can then be e.g. fragi Pseudo­gleytypilessivé, clayey silty sand, clay/acid. In this naming, the term lessivé is the order name, pseudogley and typi are group names, and fragi is a series name, while dayey silty sand, day / acid is the phase level naming.

7. THE STORAGE OF THE SOIL CLASSIFICATION IN COMPUTERS

Ultimo 1983 about 9000 classifications of soil profiles have been made along the main pipeline system in Denmark. Furthermore about 1000 detailed soil profile descriptions have been carried out. All these classifications up to series level are stored in computers at the Ministry of Agriculture, the Bureau of Land Data. The classifications are stored in numerical form and the location of the profiles is indicated by UTM­coordinates. In table 1 the figures describing the different characteristics are given; table 2 shows examples of soil types described in a numerical form.

The storage of the classified soils in the numerical form makes it easy to find soils with certain characteristics. Soils havirig fragipans are e.g.

Table 2 Different soil types described in numerical form according to the Danish soil classifi­cation system .

Soil type Series Group Order

histic Vadgley 4 0 0 2 9 fragi Pseudogleytypilessivé 12 0 13 1 5 Lessivébrunpodzol 0 25 0 4 6 humus Kalktypigley 3 6 0 1 9

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found hy searching for figure 12 at series level, while all soils having peat layers more than 10cm thick are found hy searching for figure 10 at order level, and figure 4 at series level. Soils with Bt-horizons are found hy searching for figure 5 at order level and figure 25 at group level.

REFERENCES

Armiger W. H., C. D. Foy, A. L. Fleming & B. E. Caldwell (1968) Differential tolerance of soybean varieties to an acid soil high in exchangeable alu­minum. Agron. J., 60 : 67-70.

Dalsgaard K., E. Baastrup & B. T. Bunting (1981) The influence of topography on the development of Alftsols on calcareous dayey till in Denmark. Catena, 8 : 111-136.

Duchaufour P. (1977) Pedologie, Masson, Par is.

FAO-Unesco (1974) Soil map of the world, 1 Legend. Unesco, Paris.

Fobian A. (1966) Studie über Parabraunerden in Dänemark. Pedologie, 16 : 183-198.

Foy C. D. & J. C. Brown (1964) Toxie factors in acid soils. 11. Differential aluminum tolerance of plant species. Soil Sci. Soc. Proc., 28 : 27-32.

Foy C. D., W. H. Armiger, A. L. Fleming & C. F. Lewis (1967) Differential tolerance of cotton varieties to an acid soil high in exchangeable alu-minum. Agron. J., 59 : 415-417.

Foy C. D., H. N. Lafever, J. W. Schwartz & A. L. Fleming (1974) Aluminium tolerance of wheat cultivars related to region of origin. Agron. J., 66 : 751-758.

Jacobsen, B. H. (1981) . Soil survey and land evaluation in an area near TiPnder. (In Danish with English summary). Geogr. Tidsskr., 81 : 17-32.

Madsen H. B. (1979) Soil survey and land evaluation. (In Danish with English summary). Fol. Geogr. Danica, X, 5. Reitzei, Copenhagen.

Madsen H. B. (1983) Soils of Himmerland. (In Danish with English summary). Thesis, Fol. Geogr. Danica, XVI. Reitzel, Copenhagen.

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Mathiesen, FI. Duus (1978) Soil Classification in Denmark. lts results and applicability. EEC seminar on land resource evaluation. Wexford, Ireland, 7th-9th November, 1979, 26 p.

Mückenhausen E. (1977) Entstehung, Eigenschaften und Systematik der Böden der Bundesrepublik Deutsch­land. DLG-Verlag, Frankfurt am Main.

Petersen L. (1976) Podzols and podzolization. Thesis, DSR, Copenhagen.

U.S.D.A.-Soil Survey Staff (1975) Soil Taxonomy. U.S. Dep. Agric., U.S. Govt. Print Office, Washington D.C., 752 p.

Un système de classification pédologique pour les sols au Danemark

Résumé

Un système de classification des sols situés en dehors de la zone urbaine, a été développé au Danemark. Environs 9000 observations de terrain, en relation avec la construction du système principal d'un gazoduc, ont été effectuées et classées. La classification est un système hiérarchique a quatre niveaux : ordre, groupe, série et phase. Le système est basé sur des horizons diagnostiques et les caractéristiques du profil indiquant la séquence d'horizons dans les premiers 120 cm. Toutes les obser­vations sont stockées sous forme numérique dans un ordinateur, et basé sur un sys­tème de coordonnées UTM. De cette manière il est possible de rechercher différentes caractéristiques, telles que les fragipans, la couche de tourbe et les horizons Bt dé­gradés, et d'établir les relations avec les différentes unités du relief.

Een bodemklassifikatiesysteem voor Deense gronden

Samenvatting

Een bodemklassifikatiesysteem werd ontwikkeld voor de gronden buiten de ste­delijke agglomeraties in Denemarken. Ongeveer 9000 terreinwaarnerningen werden langs een belangrijke gasleiding uitgevoerd, waarna de bestudeerde bodems werden geklasseerd. Het gevolgde systeem vormt een hiërarchische klassifikatie met 4 ni­veaus : orde, groep, serie en fase. Het systeem houdt rekening met diagnostische horizonten en met profielkarakteristieken die een aanduiding geven voor de hori­zontensekwentie in de bovenste 120 cm. Alle gegevens worden numerisch verwerkt in een computer, rekening houdend met een coördinatiesysteem VTM. Op die ma­nier is het mogelijk verschillende karakteristieken terug te vinden, zoals o.a. fragi­pans, veenlagen of gedegradeerde Bt-horizonten, en de relatie vast te stellen met de verschillende reliëfseenheden.

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PEDOLOGIE, XXXIII, 2, p. 199-218, 3 tab., 4 fig. Ghent 1983

A QUALITATIVE APPROACH OF THE TIME­DEPENDENCY OF THE'NUTRIENT-CONCENTRATION IN A SOIL PROFILE BY MEANS OF AUTO­CORRELATION AND V ARIOGRAM-ANAL YSIS

L. VERDEGEM

Summary

The application of the theory of the regionalised variabie on the analytical data, obtained in the period 1979-1983 on four experimental fields in Belgium, resulted in a better understanding of the temporal structure of the concentration­distributions of (N03N), Cl-, K+, Na+ and Ca++ at different depths.

By a consequent structural analysis, it was possible to get an idea about the time-limits, wherein the parameters of interest may be considered as temporally dependent or correlated.

The direct practical profit of this study is that the existing sampling- and analysis-program could be thoroughly simplified, by enlarging considerably the interval between twosamplings and analyses, without loosing reliability.

Key-words

Auto-correlation, regionalised variabie, structural analysis, variogram-analysis, time-series.

1. INTRODUCTION

Until recently the statistical interpretation of pedological data has al­most exclusively been carried out by an application of Fisher-statistics (Fisher, 1956), considering the data as spatial independent realisations of random variables. In the past years techniques have been developed (Matheron 1963, 1971;] ournel & Huijbregts, 1978), that treat the data as individual realisations of a 'regionalised' variable, i.e. realisations of a numerical one- or more-dimensional function, in order to express better the spatial variability. These techniques were introduced in mining geostatistics, where they we re applied to obtain more precise estimates

L. Verdegem, Faculty of Agricul tu re , University of Ghent, Coupure 653, 9000 Ghent, Belgium.

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of ore deposits in lenses or veins, mineral-concretions, etc ... In this paper a study is reported on the time-dependency, i.e. the

dependency in the one-dimensional time-space, of the concentration of the most important soluble (macro)-nutrients (N03N, Cl-, K+, Na+, Ca+t+) in soil-water and/or soil at different depths in the profile.

The calculations are based on the results obtained on 4 experimental fields of different texture, sampled at a two-week interval during the period 1979-1983. The aim was to elaborate roughly the time-limits wherein the parameters of interest may be considered as temporally de­pendent or correlated. Consequently the interval between two samplings and analyses could be enlarged to some extent, resulting in practice in an important saving of time and labour.

A detailed analysis, including precise curve-fitting and calculation of acceptable precision errors, did not seem necessary within the limits of the running research project (see section 3).

2. THEORETICAL

2.1. Properties of a regionalised variabie

When a variabie is characterised by a (one- or more-dimensional) spatial or a temporal distribution, it is said to be regionalised. The phenomenon, of which the variabie is the expression, is called the regionalisation. From the mathematical point of view, the variabie, written as ReV, is a function of x, x being a random point in the space: ReV = f(x). Usually those functions have very irregular shapes, although on average some structure seems to be demonstrated, referring to two paradoxal properties of the Re V : 1: locally, e.g. at a place Xl, it has got the properties of a classic al ran­

dom variabie (RV), whereby the measured value z(x1) is a realisation of the random variabie Z(x1); in other words z(x1) is one of the numerical values Z(x1) can take corresponding to some probability­distribution;

2: on the other hand the Re V demonstrates agiobal, ave rage or struc­tured aspect, in such a way that for each pair Xl and xl +h, h being a distance-vector in the space, the corresponding RV's Z(x1) and Z(x1 + h) are correlated.

This ambiguous aspect of randomness and structure is resumed by the definition of a random function, RF (Journel & Huijbregts, 1978), noted as Z(x). Consequently a single observation z(x) of the ReV can be considered as a specific realisation of Z(x).

In environmental studies, an expected spatial/temporal structure of a phenomenon can be found out by checking the hypothesis of station­arity, which includes in the minimal form (Journel & Huijbregts, 1978):

200

--------------------------------------~ ~-----

1: the expected value E {Z(x)} exists and is independent of x : E {Z(x)} = m, Vx, m = constant; .

2: the variogram 21'(h), by definition equal to : 21'(h) = 2(C(o)-C(h)) = E{[Z(x+ h) - Z(x)]2}, Vx, (Delhomme, 1978) exists and d"oes not depend on x, but only on h . . In the preceding mathematical expression C( 0) is the variance, C(o) = E{(Z(x)-m]2}, Vx, and C(h) is the covariance, C(h) = E {Z(x+ h) . Z(x)} - m 2, Vx.

When studying a regionalised variabie, the former requirement of the variogram can be modified to the requirement of the correlogram. This is the result of an autocorrelation calculation, being a process of self­comparison, reflecting the linear correlation between a spatialor time­series and an identical series translocated in space over a distance equal to a multiple of h, the distance-vector. By definition the correlogram is equal to

R(h) = Cth~ = 1 - Iihl (1) C 0 C(o)

2.2. Structural analysis : correlogram - and variogram - analysis of experimental data

Following the former section, the spatial variability, or dependency, of e.g. pedological variables can be studied either hy constructing a variogram, or a correlogram.

Within the limits of the hypothesis of stationarity, the formula of the

correlogram, R(h) = ~1' supposes that C(h), the covariance of 2RV's

Z(xi) and Z(xi + h), depends only on h, VXi, while C( 0) should be the variance for the RV's Z(xi) and Z(xi + h), VXi. In practice the latter seems to hold to some degree only, hence the denominator in the for­mul a of R(h) can better be denoted as the product of the square-roots of the variance of Z(xi) and Z(xi + h) giving (Nieisen et al., 1982) :

R(h) = (auto)covariance (x,x+h) " (2) y'variance(xr:- vvariance(x+ hf

or in a more explicit vers ion (Webster and Cuanalo, 1975)

1 n-h - h . L (Zi-Zi) (zi+h - zi+h)

R(h) = n- 1=1 (3)

[l ~ih (Zi-Zi)2. ~ ~ih (zi+h _ Zi+h)2]1!2 n-h 1= 1 n-h 1= 1

R(h) the autocorrelation-coefficient

201

Z· = - ~ Z· - 1 n-h l 1 n-h i= 1 1

Zi and Zi+h being individual realisations of the Re V _ 1 n-h z'+h = - . LZi+h

1 n-h 1=1

n = the total amount of realisations of the Re V used in the statistical analysis;

h = the distance-vector, absolute or relative parameter, with the restric­tion that h can take only integer values, e.g. h = 1,2,3,.. in cm, m, km (absolute parameter)

h = 1, 2, 3,.., the unity of h corresponding to a period of 14 days (relative parameter).

The autocorrelogram is a re1ative statistical tooI, with values between -1 and + 1.

A second and better tooI for the study of the spatial/temporal dependency between neighbouring observations is the (semi)variogram r(h) : firstly its calculation is simpier in comparison to the correlogram and secondly the variogram is absolute, in. such way th at its value depends directly on the value of the individual realisations of the ReV, as weIl as on the variability of these values. Moreover the variogram is essentiaIly positive, making the interpretation easier. The theoretical semi-variogram, r(h) = variance-covariance =-}E{Z(x+ h) -Z(x)]2} (see section 2.1), is estimated by (Journel and Huijbregts, 1978)

1 N(h) . 2 r *(h) = -- 1; [z'+h - z·]

2N(h) i= 1 1 1

with N(h) the amount of experimental pairs (zi, zi+h) of individual realisations of the RF Z(x), separated by the vector h.

A plot of r*(h) versus h is called the experimental variogram, starting at the origin and usually reaching a so-called 'sill'-value, equal to the a priori variance C( 0), for values of h ~ a, where a is called 'the range' and can be considered as the maximal interspace wherein two RV's Z(xï) and Z(xi +h) are spatially/temporally correlated. The dimension of r(h) is the square of the dimension of the single realisations of the RF.

Although by definition r(h) = 0 when h= 0, it is of ten found in practice that the variogram demonstrates a so-called nugget-effect, so that for 0 < h ~ € - € being a very small value in comparison with the smallest value of h considered - , r(h) = C (Journel and Huijbregts, 1978). This nugget effect is the characterisation of the impact of all variabilities with ranges much smaller than the distances of observation. It is shown in the variogram as an obvious discontinuity at the origin,

202

essentially meaning that part of the variogram remains unknown, due to an insufficiently detailed sampling.

of ten the value C of the nugget effect is much smaller than C( 0), the sill value, corresponding to the a priori variance of the Re V, but it is possible that C becomes equal to C( 0). This particular case is called a 'pure' nugget effect, as such corresponding to a total absence of spatial correlation, at least at the level of the chosen sampling distance.

Though, if a pure nugget effect is manifested in combination" with a valid hypothesis of stationarity, it is the expression of a very homogene­ous spatial distribution of the Re V. In fact, then it means that for every point of the regionalisation the best estimation of e.g. the nutrient­concentration, is the mean concentration of the regionalisation.

An experimental variogram should be considered as a basic tooI in the structural analysis. Once it has been calculated, one should come to the construction of an adequate model-variogram based on the experi­mental variogram and representing the most important characteristics of the regionalisation. That way the variogram is made operational for the quantitative analysis of the phenomenon.

An experimental variogram of ten doesn't correspond to a single vario­gram-model. Therefore it may be useful to mention the property of ad­ditivity of variograms, saying that any linear combination of variograrn with positive coefficients is a variogram again :

n 'Y(h) = i!l Ài 'Yi(h)

The most frequent occuring theoretical models in the study of experi­men tal variograms are given in table 1 and figure 1.

An important remark concerning the model-approximation is that practice has shown that experimental variograms calculated for neigh­bouring domains (e.g. the time-distribution of the concentration of a soilnutrient at several depths), as weIl as the consequent model-vario­grarns, of ten become more comparabie if each of them is divided by a function of the corresponding experimental mean. This is caIled a pro­portional effect, and the resulting transformed varlograms are called relative-grade variograms.

2.3. Some practical rules concerning a structural analysis

An experimental variogram should be interpreted for small distances h in comparison to the dimension L of the space related to the region­alised variabie, supposing that sufficient data-pairs, N, are available : maXimum h < L/2 N> 30 to 50 pairs. As mentioned before, the experimental variogram is calculated accord-

203

tv o ~

Table 1 Most frequently occuring theoretical var!ogram-mo~els (af ter Journel and Huijbregts, 1978).

Transition models = models with a sill-value* Models without sill-value Hole-effect models (no variance or covariance)

Linear behavióur at the Parabolic behavióur MODEL OF THE FORM hO origin at the origin mode Is with a cyclic form !

SPHERICAL MODEL GAUSSIAN MODEL -y(h)- hO ,OE ]0,2[ The formula for a one-dimensional, strongly

1 (h) =3h _ 1h3 1(h) = l-exp( -h2Ja2) LOGARITHMIC MODEL directional hole-effect is : 2a 2a3

1(h) "" log h 1(h)= 1-exp(-ph) cos h VhE [0, a[ the practical range = p = the dampening

V3.a the model-form is adapted the amplitude of this hole-1(h) = 1 vh ~ a in practice so that 1(0) effect is defmed as follows : a = the range becomes °

a = min C(h) EXPONENTlAL MODEL C(O) 1(h) = 1 -exp(-hJa) with 1 > a> 0.217 the practical range 7' 3a

(The theoretical sill-value

I is reached onlY'~t infinity),

- - - L-.--.-

* All the models of th is group are normalized to 1.

Fig. 1. Representation of the most y(h)

+==~~~=='5;;:::::-===::::;::::::;;=======1 common theoretica! vario­

y(h)

h/a

l=transition-models 2=models of the form

y(h)=h 6

3=hole-effect models

gram-models. The transition­models are normalised to 1. (af ter Journel and Huij­bregts,1978).

ing to formula (4). The following step is a model approximation, where­by the global shape of the experimental variogram may serve as an in­dication for the type of model: a transition-model or a model without sUI-value, keeping in mind that for high values of h the experimental variogram is subject to important fluctuations.

For detaUed estimation procedures the model-fitting can be executed using a non linear least squares solution. Yet for our purpose (see see­tion 1) it may be sufficient to use some practical rules proposed by Journel and Huijbregts (1978) : 1. The sill-value ean be found approximately by fitting a straight line

through the experimental variations of the flat part of the variogram.

Sometimes the experimental dispersion-varianee s*2 = 1 . ~ (zi-z)2, n 1=1

with n = the number of data, may be a useful tooI thereby, provided that it is in fact a good estimator of the sUl, i.e. of the a priori var i­anee. This supposes large values of Land more or less equally distrib­uted observations carried out on point-basis.

205

2. The behaviour at the origin and the eventual existence of a nugget effect can be evaluated by fitting a straight line through the first three points of the experimental variogram, extrapolating that line to the ordinate-axis.

3. EXPERIMENTAL

A research-project was started in 1979 to evaluate the loss of the most important soluble macro-nutrients (N03N, Cl-, K+, Ca ++, Na +) out of the soil-profile under normal agricultural conditions. Special attention was thereby given to the N-dynamics in the soil. For th at purpose four profiles of different texture were chosen, situated in the Flemish Valley. The most important characteristics of these fields are given in tab Ie 2.

Sampling of soil-water and soil"'cores was carried out every 2 weeks, at the following depths : 0.5, 1.0, 1.5, 2.0, 3.0 and 4.0 m for soil-water in St. Laureins and Evergem, and 0.5, 1.0, 1.5 and 2.0 for soil-water in Watervliet and for soil-material on all fields. A detailed description of the sampling techniques, was given by Verdegem et al. (1981).

In figure 2, the seasonal mean concentrations of the different nu trien ts in the soil-water of the four experimental fields, are depicted for the spring 1981 till spring 1983. It is shown that those concentra­tions highly fluctuate in the root-zone (on the average up to 1.0 m), whereas deeper in the profile the variations become smaller. Whether these fluctuations have a structured character (e.g. seasonal bounded), and thus have to be interpreted as such, or are due to the a priori vari­ability, caused by the medium itself, the sampling, the analysis, and th us essentially are unimportant, should be derived via a structural analysis. In fact, this is the only way to verify the temporal dependency of the concentrations of the nutrients in soil-water and soil.

Consequently a structural analysis was carried out on all available results of the four experimental fields, covering the period 6/7/79-19/4/83 for St.-Laureins, 5/5/81-19/4/83 for Evergem and 2/9/80-13/4/ 83 for Watervliet. This study was preceded by a preparatory study con­cerning the experimental data up till january 1983. Out of this prepar­atory work, it seemed preferabie to interprete every nutrient and every depth individually, in order to find out to what extent the theory of the regionalised variabie could be applied to the specific cases consider­ed.

In practical terms this means an establishment of rough time-limits wherein the parameters of interest may be considered as temporally dependen t or correlated.

A detailed structural analysis, covering precise model-fitting and cal­culation of acceptable precision errors was considered redundant for the field work purposes. In fact, for practical reasons it is necessary to

206

N o -.....J

Table 2 Some characteristics of the experimental fields.

Location Soil type according to the pHH20 Belgian Classification system

St-Laureins (1) zdh - Pleistocene sand on 0-20 cm : 5.9 (SL 12) aIluvial sandy loam at ± 75cm; 20-40 cm : 5.8

Postpodzol. 40-60 cm : 5.6

Evergem Zchy - Pleistocene sand above 0-25 cm : 6.6 (E 1) loamy sand at ± 75 cm; 25-50 cm : 6.5

Postpodzol 50-75 cm : 6.5

Watervliet sEdp : aIluvial (heavy) day 0-25 cm: 8.3 field 1 on a day-sand complex at 25-50 cm : 8.3 (WA 1) ± 50 cm depth; young soil 50-75 cm : 8.4

without proftle development.

Watervliet Udp - alluvial heavy day 0-25 cm : 8.3 field 2 (layer of 1.5 m) on a sand- 25-50 cm : 8.3 (WA 2) peat complex: young soil 50-75 cm : 8.5

without proftle development.

%C Drainage conditions Rooting Land use over the last depth five years

1.38 imperfect 50-60 cm 1979 : Italian ryegrass 1.16 + maize 0.64 1980 : Winter.:barley

+ Italian ryegrass 1981 : ltalian ryegrass + maize I

I

1982 : maize + Italian ryegrass 1983 : Italian ryegrass ,

0-25 cm : 1.29 moderately weil ± 70 cm 1981 : Winter-barley 25-50 cm : 0.46 1982 : maize 50-75 cm : 0.14 1983 : maize

0-40 cm : 1.0 moderately weil ± 85 cm 1979 : potatoes 40-60 cm : 0.95 drained - artificial 1980 : winter-wheat

drains at 60 cm with 1981 : sugarbeets an interspace of ± 20 1982 : winter-wheat m 1983 : potatoes

0-30 cm : 0.89 moderately weil 75-100 cm 1979 : sugarbeets 30-75 cm : 0.84 drained - artificial 1980 : spring-barley

drains at 90 à 100 cm 1981 : potatoes with an interspace of 1982 : winter-wheat 10 m 1983 : sugarbeets

mg/1

0.5

1.0 NOjN

1.5

2.0

3.0

4.0

Ê m /1

~ ..... 0.5 Cl. QJ

Cl

1.

1.

2.

3.

4. mg/1

0.5

1.0

1.5

2.0

3.0

4.0

Fig.2a.

50 50 mg/1

0.5

1.0

1.5

2.0

3.0

4.0

50

~ v Spring 1981 --_ Sunmer 1981

Autumn 1981 *---~ Winter 1981-1982 ~ ~ Spri ng 1982 ~ t Sunrner 1982 ~ ~ Autumn 1982 z z Wi nter 1982-1983

50

0.5

1.0

1.5

2.0

3.0

4.0

Spring 1983

50 100

\

1 0

150 mg/1

Seasonal evolution of the nutrient-concentration in the soil-water of St. Laureins soil.

208

0.5

1.0

1.5

2.0

3.0

4.0

Ê

m 1

(:9:9-----E!) Spri n9 410410----_1> Surrmer +------+1 Autumn »f-(---~.( Winter ~~----4>e Spri n9 + t SUllIIIer ~ S? Autumn z zWinter

Spring 'f

5 50 100 mg/1

3.

4. 1981 1981 /1 50 1981 ~--------=mQ~~---~~. 1981-1982 I 1982 0 5 1982 . 1982 1982-1983 1 0 1983 .

(9

6

1

)E

e

eJ Winter 1981-1982 6 Spring 1982 I SlJ1IIIer 1982 ~ Autumn 1982 6 Winter 1982-1983 • Spring 1983

50 m /1 100 mg/1

0.5

l.

1.

2.

3.

4.

Fig.2b.

150

50

Seasonal evolution of the nutrient-concentration in 'the soil-water of Evergem,soil.

209

0.5

1.0

1.5

2.0

0.5

1.0

1.5

2.0 "

O.

1.

1.

2.0

0.5

1.0

1.5

2.0

0.5

1.0

1.5

2.0i

Fig.2c.

/1 50

WAl - NO;N

/1 50

K+

e)Spring 1981 06Summer 1981 IAutumn 1981 KWinter 1981-1982 ~Spri ng 1982 .Summer 1982 ~Autumn 1982 zWinter 1982-1983

'~ v"Spri ng 1983

m /1 50

WAl - Na+

50 mg/l 100

50 100 150 200 250

mg/1

WAl - Ca++

mg/1 50

WA2 - NO;N

50 mg/l 100

0.5

1.0

1.5

2.0

mg/l 50

0.5

1.0

E'1.5 or:. 62.0 QJ Cl

75 150 225 300 375 450 mg/l

O. WA2 - Na+

1.

2.

50 100 150 mg/1

0.5

1.0 WA2 - Ca++

1.5

2.0

Seasonal evolution of the nutrient-concentration in the soil-water of Watervliet soil.

210

take a somewhat arbitrary mean value of the obtained time-intervals, , which can be used for all nutrients on sevenil depths and for all expei-Ï­men tal fields.

A second important remark concerning the preparatory work is that in this study only, the results of N03N-concentrations were included, and not in the detailed structural analysis. Indeed, one of the most important aims of th~ running project may be defined as : 'achieving a better understanding of the processes involved in soil-N-dynamics'. As a lot of these processes can take place very momentarily, it seems rat her unreasonable to extend the interval between two samplings, which is the main purpose of the study.

All calculations were executed on the Siemens computer of the 'Centraal Digitaal Rekencentrum' (CDR), of Ghent State University, Belgium.

4. RESULTS

To establish a thorough insight in the practice of a structural analysis, some examples of the construction of an experimental variogram are given in figure 3. These variograms are the re sult of the preparatory study men tioned above.

The examples for 0.5 m depth (SL12 N03N and Cl-) have definitely a cyclic form, correlated to the groundwaterlevel-fluctuations (see also section 5). For the cations the influence of the groundwaterlevel-fluctua­tions may be dampened or even waved away at 0.5 m depth (WA1-Na+, WA2-K+). This"is due to the regulating effect of the soil, explained by the interaction of the soil-matrix with the cations and by its buffering capacity in "the soil-moisture management.

Considering the variograms for 1.0 m depth, most of them correspond to a transition-model. Moreover their shape refers to a rather high temporal stability of the respective variabie. In this context the resem­blance between the variograms of N03N and Cl- - two equally mobile anions - for El at 1.0 m depth may be explained by the absence of roots at 1.0 m depth (no more N-uptake), and because denitrification is practically inexistent in this profile.

Out of preparatory work it also appeared that the values of r*(h) usually are strongly different per depth zone and per nutrient, in rela­tion to the highly variabie concentrations of the respective nutrients on the different depths. In fact, those concentrations have direct influence on the value of r*(h).

For that reason the structural analysis was carried out using a relative grade variogram (see section 2.2) obtained from the division of r *(h) by m*, the experimental mean. The dimension of these relative grade varia-

211

-------~

y(h)

300

y(h)

20

10

y(h)

50

y(h}

0.3

0.2

0.1

y(h)

• 300 • • • • • • • •• • • 200 •• •• • • •• • •••• • •• •

•• SL 12 - 0.5m • SL 12 - 0.5m ••• • 100 • • •• • . NO-N •• • Cl 3 • • • •

10 20 h h

• y(h)

• • • • • • • • • 2

• • • • • • • • • • • • • • •• • • • • • ••• • • • • • •

WA 1 - 0.5m • WA 2 - 0.5m Na+ •••• K+

•• •

10 20 h 10 20 h

y(h)

• •• •• • 1000

• ••• E 1 - l.Om • ••• • • • Cl •• •••• • • ••• • E 1 - l.Om •••• • • NOJN • •• • •• • ••• •• • 10 20 h 10 20 h

·y(h) •

• • • • •• •

•• • • • • -.- 50 ••••••• • • - • • ••••••• • - • • • • • WA 1 - l.Om 25 • WA 2 - l.Om

K+ • Na + • •

10 20 h 10 20 h

Fig. 3. Same examples of variogram-analysis of the nutrient-concentration in the soil-water, as ReV over a time-sequence. -y(h) in (mg/1)2, the unity ofh is 14 days.

212

.. 4> .., '" ~ 0 Vl

0 Vl

.. 4>

'" ~ 0 Vl

0 Vl

Ele-ment

-Cl

K+

CaH

Na +

K+

CaH

Na+

-Cl

K+

Ca H

Na +

K+

Ca H

~a +

-

St .-laurelns (SU2) . Watervliet (WAl)

O. Sm LOm 1.5m 2.0Ift 3.0tII 4.DIa O.Sm loOn! lo5m 2. Om Ho 1 e-effect mode 1 Exponentia 1 mode 1 Gaussian model Gaussian model Gaussian IIIOdel Pure nU!!!'t +Gausslan /ll)del with Exponenti a I /ll)de I Gaussian model Exponenti a I model s·2/rrf1t2 = 0.3 C-8.1 - a-20/3 :i2rm ~ 2 ~ 0 ~eks Range > 48 weeks Rlnge>4fweeks C-3.4 hl gh range (too 1i tt le C-20-a=10 C=13-a=12 •• C=2-a=4 m·=26 .. 5 Range = 40 weeks S.2/m·2 • 0.1 S·2/,,2 .0.1 S·2/m·2 • 0.04 data) Range>lyear . Range=40weeks Range=24weeks

S·2/m·2=0. 2-m"-36 . 2 ~=58.9 m·-60 ~=57. 7 m·.go.9 m· = 35 s~ /rrf1t2_O .3-rrf1t·46·4 s·2/rrf1t2=0.2-m·=51.5 S· 2/ m· 2=0 .03-m·= 73 .2

Hole-effect rOOde 1 Exponentl a I mode I Pure nugget Pure nugget Pure nugget Pure nugget Ho Ie-effect Exponentlal /ll)del Pure nugget Pure nugget s. ' /m . 2=0 . 4 C=0.24 - a=13/3 C=0.2 C-0.3 CaO .5 C=0 . 2 m· '3.3 C· 0.3 C·O .06-a= C=0 . 2 m·= 15.2 Range • 26 weeks m.= 0.3 m · ·0.3 m· = 0 . 5 m· =0 . 6 S·2/ m·2=0 . 6 Range-24weeks S·2/rrf1t2=0 .09 s·2/ me2=0.07

S·' /m"2=0. 4-m"=0 . 3 s·2/rrf1t2=0 . 3-m·=1 . 0 rrf1t=0.7 m-=3.0

Hole-effect model Exponenti al mode 1 Exponentia I mode I Exponenthl model Exponent i al /ll)de 1 Exponential /ll)del Pure nugget Exponentia 1 /ll)de I Exponential model Pure nugget s·? /m·~ =0.5 C=4. 5-Cl= 1. 5-a=6 C=3-Cl-6-a- 6 C.3.2- Cl-1-a-8 C- 2 . 2-Cl= 1 .8-a= 10 C-2-Cl=2-1-8 C· 14 C=10-a=4 C= 7-C 1=3-a=6 C=4.5 m· =64 Range=36 weeks Range=36 weeks Range-48 weeks Range>l year • Range.48 weeks ~=128 Range=24weeks Range = 36weeks S· 2/m· 2=0. 02

S·2/ m· 2=0 .07-m·= 128 S·2/ m· 2_0 .06-m· = 167 S·2/m·' -0. 03-m"= 1 12 s·2/m·2_0.02-m·_170 S.2/m"2 _0 .02-m·. 19 s·' /m·2=0 . 1 s·2/me2 z O.07-m·- 137 . 9 s·2/ m· 2=0 .06-m·=21 .9 m·=19 .9

Ho I e-effect mode I Exponenti al mode I Exponential /ll)del Pure nugget Pure nugget Pure nugget Exponenti al model Ho 1 e -e ffect /ll)de 1 Pure nugget Pure nugget s·2/rrf1t 2=0 . 3 C=2.5-a=8 C=O . 5-CI-0. 5-a=8 C·0.8 C· 0 . 7 C=0.6 C- O. 7-Cl=0 . 6-a=5 S·2/rrf1t2=0 .08 C·0.4 C=0.2 m·=18 . 9 Range=48 weeks Range=48 weeks S·2/m· 2 =0. 03 S·2/ m· 2= 0 .03 s·2/ m· '=0 .04 Range=30 weeks "'-"17 . 8 S·2/ m· 2=0 .02 s·2/m· 2=0 .01

S·2/ m· 2=0 .1-m·=28 . 6 S·2/ m· 2=0. 03-rrf1t=30.8 m·=28 .9 m·.25 . 6 m·= 15.5 S·2 /m.' =0 .08-m·= 16 .2 ntt=21.9 m·.1g . 9

Spheri ca 1 mode 1 Pure nugget Hole-effect model Exponential model Pure nugget Pure nugget C= l-C1=3-a= 11 C=0.8 m·=109.1 C= 1. 75-C1=0. 5-a =3 C=1.6 C=3.0 Range=22 weeks S· 2/ m· 2=0 .0 7-m·=?g m·= 29

S·2/ m· 2=0 .03 m· =22 . 4 m·=20 . 6

s· ' /rrf1t' =0.05 Range= 18weeks 5·' / m"2=0 .03 S. 2 /m. 2 =0.04 s· ' /m"2=0 .04-m"=48. 6 m· =48.8 m·=77 . 4

Pure nugget Pure nugget Pure nugget Expoaentia 1 mode 1 Pure nugget Pure nugget C=0 . 3 C=2 .9 C=0.7 C=O. 9-C1=0 . 5- a=4 C=0.3 C=O .5 S·2/m· 2=0.6 s.2/ m• 2=0 .09

m· =38 m· =36 Range=24weeks 5. ' /m· 2=0.01 5. 2 /rrf1t2=0 .01

m· = 0 .49 m·= 27 m·=34 s · ' /m · '=0.02 s·7/ m· 2=0.00-m·=43 . 1 m·=36 . 2 /fi· =37.4 Exponent i al model Spheri ca 1 mode 1 Exponential model Exponent i al modt'! Exponent 1 a 1 mode I C=1.9-C1 =0 . 6-a=7 C=3. 3-a= 10 C=3.1-a=5 C=3 . 5-a =5 C= 11-a=6 Range =42weeks Range=20weeks Range=3Oweeks Range=30weeks Range=36weeks 5.2/,",,2=0 .OS-m·-30. 7 s·· 2ïrrf1t2=0 .07-m.=47 . 1 s.2jm· 2=0 .06-m· =53 . 1 S·2/me2=0 .06-m·=56 . 2 m· =112 . 1 m·=168.6 se, / rrf1t2=0 .07-m-= 150] m· = 160.0

EverQem L E IJ Watervl iet (WA2)

Spheri ca I mode 1 Exponential model Gauss i an model Pure nugget e=15-a=12 ~ = 5-a=8 C=1-C1 =0 .4-a=1 C·0.8 Range=24weeks Range=48weeks Range > 1year s · 2/ mte=0.01 s.2/m. 2=0. 3-m-=35 . 4- s·'/m.2=0 .06-m· =50 5.' /m"2=0 .02-m-=68 . 1 m· = 89. 5

Hole-effect model Exponenti al model Gaus 5 i an mode 1 Pure nugget Exponential mode l Pure nugget Gaussian model Ho I e-effect model linear model Pure nugget S·2/m· 2=0.2 C=1. 6-a=7 C=2 . 9-a =13 CaO .4 C·O. 25-a= 7 C·0 . 3 C= 1-C1=0 . 3-a= 10 ' 5.2 /rrf1t2:O .09 Yh <a .. C=006-Yh>a-+C=06 C~0.3 m·=10.1 Range=42weeks Range=44weeks ~7/rn"2=0.6 Range=42weeks S·2/m" 2= 0.2 Range=36weeks m·=3.4 a= tl - Range= 22weeks S. 2 /m· Z=O .01

S·2/ m· 2=0. l-m·=6. 9 S·2/ me2 = 1. 1-m-= 1. 3 me,,0 . 6 S·2/ m· '=0 .03-m·=4 .8 m·=1.6 s· 2 /rn·2= 1.4-~=1.05 s·2/rrf1t2=0 .03-m-=V.3 m·=41.8

Ho 1 e-effect mode 1 li nea r mode 1 Exponenti al model Exponenti a I model ±Hole-effect model ±Hole-effect model Hole-effect model Exponentia I model Exponential model Spheri ca I mode 1 5.' /rrf1t2=0. 7 Yh <a"C=6/l1-Yh>a .. C= II C=3 . 3-a= 7 C=6-a= 7 s·2/ m.2·0 .03 s· '/~2=0 . 02 s.' /m·2~0 . 1 C=2. 7-C1=3. 5-a =4 . 7 C.3-C 1= 1. 5-a=5 . 3 C= 1. 25-C1=0. 5 m·=38.7 a=22-Range=44weeks Range=42weeks Range= 42weeks m·=51.4 m"= 115.5 m·. 116 . 4 Range=28weeks Range=32weeks a= 7-Range= 14weeks

s·2/ m· 2=0 .1-m"=44.4 5.2/111*2_0. 04-m·=4 7 S.2/1II*2=0 .05-rn"-63. 5 s.2/me2.o.05-.... 125 B 5.2 /m. 2=0 .03-111*= 140.!i s·2/me2=0 .04-m·=44 . 6

Hole-effect mode I Pure nugget Exponential model Gaussian model Exponential model Pure nugget Hole-effect model ~~~~~~~ ~i~~~~!--H~ Exponential /ll)del Pure nugget S·2/m· 2=0.7 C=0 . 6 C=0.4-a=6 Cl=0.2-C·1-a=1 CaO .5-a-6 C=0.2 S·2/me2.0 .07 C= 7-a= 7 C=3 m·=15 .9 S· 2 /rrf1t 2 ~0.04 Range=36weeks Range> lyear Range.36weeks s·2/m· 2,, 0.01 m·.18.0 a -8- Range. 16week s Range ,,42weeks s·2/m· 2=0.01

m"15.4 s·2/1II*2=0.02-me,,16 S·2/m·2_0 . 02-m·= 19 . 3 s·2/me2=0 .02- 111*.13.1 me=22 .6 s·2/me2=0 .05-m·"32.7 S·2/1II*2=0 .05-m.= 171 9 m·= 406 . 2

Exponenti a 1 model Pure nugget Pure nugget Pure nugget Expónential model Pure nugget Pure nugget C=9.3-a=2 C=1.4 C=3.1 C-0 .9 ",-2.l-a- 3 C=3.3 C=4 .7 Range= 12weeks S·2/ m· 2=0 .01 5.2/"2_0.04 s·2/rrf1t2_O.02 Range-18weeks

m·-169.9 s.2/me2=0 .01 5.2/11,. 2=0 .02

s·2/me2 =0. 2-m-= 55.3 m·=130 . 3 m"86.4 m·.47 . 2 ~.2/m«l.0 • 02-m"140 • 6 m"240 . 9 m"200.9

Pure nugget Pure nugget Pure nugget Pure nugget Exponentia 1 /ll)de 1 Exponentia 1 /ll)de 1 Pure nugget Pure nugget C=O .08 C=14 . 1 C=5.2 C-4.0 =0 .2-C1=0. 25-a,,4 C"O .22-a=4 C=0 .5 C· 1.6 S·2/ m· 2=0 .08 S·2/ m· 2=0.02 5.2/"2=0.01 S·2/me2,,0.01 ~ange"24weeks Range·24 .eks m·-26.7 m*z28.7 "=84.5 m·-832 . 3 me,,804 . 4 me,,5l7 . 1 ~.2 /m.2_0 .01-me,,42.4 S·2/,,-2.0.oo- III·-55.4 S·2/ m· 2:0.02 S·2/1ff*2=0.06

Ho 1 e-effect model Pure nugget Pure nugget Pure nugget xponenti al I'l1O de 1 Exponentl al model Exponent1al model Exponenti al model s·2/me2=0 .03 C=3.6 C=4.4 C-0.9 =3 .6-a,,6 C=9.3-a=7 C~18.9-a=6 C- 26.6-a=6 m·=30 .0 S.2/m· 2=0 .1 s·2/ m· 2=0 . 1 s.2/m· 2=0 .03 ~ange"36weeks Range=42 weeks Range=36 weeks Range=36 weeks

me=37.9 m-=40.5 m·"34 . 6 _ ~'-=O .05-m·=80 . 7 S.2/m*2zO. 06-m*= 155 S.2/ me2 ,,0 . 1-m·= 196.4 s·2/rr;e 2=0 . 1-m·,, 262. 2

Table 3 Structural analysis of the concentration~volution of the different nutrients in soil-water and soil, in the one-dimensional time-space. The analysis was executed for the four experimental fields and the different observation-depths. Cis the constant to transform the normalised

tv to the effective variogram.Cl is the nugget constant. The dimension of C and ~Cl is mg/1 for soil-water, mg/kg for soil and glkg for Ca++ c; in the soil of SL12. WAl and WA2.

I

grams is mg/ I or mg/kg for soil-water or soil analytical data respectively, except for Ca++ in the soil-samples of SLI2, WAl and W A2 where the dimension is g/kg.

In table 3 the results of the structural analysis of the concentrations ofCl-, K+, Ca++ and Na+ in the soil-water, as weIl as ofK+, Ca++ and Na+ in the soil are described. NO)N is not considered in this particular study for reasons explained above.

In table 3 every case-study encloses the derivation of an approximative formula for the model-variogram, and a calculation of the practical range-value (except for the cYclic type variograms for which further re­search and calculations are necessary - see section 5). Moreover , the experimental mean value m* is given - being an estimation of the abso­lute mean value m of the parameter considered -, as weIl as the relative grade dispersion-variance s*2/m*2. The latter is a dimensionless numher, and is in fact the representation of the relative difference between the data and their mean value. In other words that part of the variance which should be attributed to the relative amplitude of the concentra-" tion-fluctuations.

It is obvious that if a (more or less) stabie model-variogram is coupled to a (very) small value of s*2/m *2, one has to do with a (very) stable Re V in the time-space. F or the underlying study the practical meaning of this statement is that, if concentration-fluctuations are satisfying these conditions, the interval between two samplings can be extended, without loosing reliability.

5. DISCUSSION

Reviewing table 3 some important observations and statements may be made. Concerning the contents in the soil-water, it seems that from 1.0 m onwards the concentration-distributions in time may be consider­ed as (very) stabIe with (very) small s*2/m *2 values. In many cases even a pure nugget effect is established, meaning that the concentration­fluctuations are not time-dependent and yet not systematic or structured, but simply due to the a priori variability of the surrounding, the sam­pling and the analysis. As the pure nugget is coupled to a constant mean value, the concentration-distribution in time may be considered as extremely stabie for these cases.

For the concentrations at 0.5 m, of ten a cyclic model is observed, sometimes a spherical or exponential model, with smaller range and higher s*2/m*2 values. This is in close relation with the influence of the seasonal and the secondary groundwatertable (GWT)-fluctuations, causing the cYclic time-functions. Indeed, it is weIl-known that the GWT-depth varies seasonally, foIlowing a sine-function with a period

214

R(h) 1.0

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Fig. 4.

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2000

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.20 h

• 0.5 ••

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0.5.

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Variogram- and correlogram-analysis of the groundwaterlevel-fluctuations on the four experimental fields. 'Y(h) (circles) in cm2 j the unity ofh is 14 days.

of abou t one year. The variograms and correlograms of the Re V 'GWT' are obviously in proportion (fig. 4).

There is a striking similarity between the four variograms, which apparently depend only on the seasonal fluctuations and not in the secondary GWT-fluctuations. This view follows the theory of the ReV adapted to the RF 'GWT'. In fact, in section 2.1 it was stated that a RF is characterised by two paradoxal properties : locally, at a 'point' there

215

4000

2000

are the properties of the R V (secondary fluctuations ), whereas globally the RF is showing a structured aspect ' due to the seasonal run. The impact of the secondary fluctuations can however be deduced fr om the variogram, because the higher their amplitude, the higher the value of C(o), the a priori variance and hence the higher the value of ')'(h). The study of the GWT as Re V is of poor interest as, such, but no doubt it is important in relation to the nutrient-concentrations in the soil-water at 0.5 m depth. Therefore it seems opportune for further research to inter­prete the co-regionalisation of the GWT with the aforesaid concentrations.

when considering the contents of the cations in the soil-material, one may state that the re'spective time-distributions are very stabie, with high range values and small s*2/m*2 values. In fact, more than half of the cases are characterised by a pure nugget effect. A few exceptions may be mentioned for 0.5 m depth, e.g. Na+ in Evergem and K+ on WAl, where the variogram approximates the hole-effect type.

It should be admitted that the results presented in table 3 are rather rough approximates and that, from the statistical point of view, they constitute certainly not a fully achieved work. Nevertheless, they are very indicative and accomodate with the practical purposes of the stu.dy.

6. CONCLUSIONS

As a re sult of the former calculations and structural analyses, it can be stated that in the future the sampling- and analysis-program can be thoroughly simplified as follows : 1: sampling and analysis every 14 days : the concentration in the soil­

water of NO)N on all observation-depths:. followin~ the specific purpose of the running project, and of Cl ,K+, Ca + and Na+ at 0.5 m depth;

2: sampling and analysis every 3 months : the concentration in the soil­water of CI-, K+, Ca++ and Na+ from 1.0 m depth onwards;

3: sampling and analysis every 6 months : the concentration of K+, Ca++ and Na+ in the soil on all observation-depths. .

The interpretation of the 'GWT' as ReV has shown to be in many cases in close relation with the concentration-fluctuations in the soil­water at 0.5 m depth. Hence, a detailed study of the co-regionalisation of the GWT and the concentration in the soil-water of the respective nutrients at 0.5 m depth should be one of the first points on the agenda of further statistical research. Indeed, a thorough understanding of these co-regionalisations seems very interesting as the GWT can be easily measured in comparison to a nutrient-concentration.

Finally, a general condusion is that the theory of the Re V is able to explain fully the temporal/spatial structure of a variabie considered,

216

which is an important advantage over the theory of the RV. In soil science, and more specifically in applied soil science, it may

result in a more thematic and better defined interpretation of the avail­able data, taking into account the position of the sampling. points with­in the 'space' considered. As such, one may become aware of the individ­uality of singular measurements, instead of interpreting only mean values, a common practice up till now.

REFERENCES

Delhomme J. P. (1978) Application de la théorie des variables régionalisées dans les sciences de l'eau. Bulletin B.R.CM, sect. IIl, 4 : 341-375.

Fisher R. A. (1956) Statistical methods and scientific interference. Oliver and Boyd, Edinburgh.

Journel A. G. & Huijbregts eh. J. (1978) Mining geostatistics. Academie Press, London, 600 p.

Matheron G. (1963) Principles of geostatistics. Econ. Ceol., 58 : 1246-1266.

Matheron G. (1971) The theory of regionalized variables and its applications. Les Cahiers du Centre de Morphologie Mathématique, 5, C. G. Fontainebleau, France, 211 p.

Nielsen D .. R., Biggar J. W. & Wierenga P. J. (1982) Nitrogen transport processes in soil. In : Nitrogen in agricultural soils. Agronomy Monograph no. 22, ASA-CSSA-SSSA, Madison, USA, 940 p.

Verdegem L., Van Cleemput O. & Vanderdeelen J. (1981) Some factors inducing the loss of nutrients out of the soil proftie. Pedologie, XXXI, 3 : 309-327.

Webster R. & de la Cuanalo H. E. (1975) Soil transect correlograms of North Oxfordshire and their interpretaHon. Joumal of Soil Science, 26, 2 : 176-194.

Een kwalitatieve benadering van de tijdsafhankelijkheid van de concentratie aan voedingselementen in een bodemprofiel aan de hand van autocorrelatie en vario­gram.:malyse.

Samenvatting

Door toepassing van de theorie van de geregionaliseerde variabele op de analyse-

217

gegevens, bekomen in de periode 1979-1983 op vier proefvelden, werd een beter inzicht verkregen in de ruimtelijke structuur - in de tijd - van de concentratiever­delingen van (NO)N), Cl-, K+, Na+ en Ca++, op verschillende diepten.

Een daaropvolgende structurele analyse leidde tot het benaderend vastleggen van tijdsgrenzen, waarbinnen de onderzochte parameters aktijdsafhankelijk en gecorre­leerd te beschouwen zijn.

Een direct practisch nut van deze studie is dat het bestaaride monstername- en analyseprogramma grondig vereenvoudigd kon worden, in die zin dat de tussentijd tussen twee monsternamen en analyses in belangrijke mate kon worden opgedreven, zonder verlies van betrouwbaarheid.

Une approximation qualitative de la dépendance temporelle des teneurs en éléments nutritifs dans un prom de sol au moyen d'auto-corrélation et d'analyse structurelle.

Résumé

En appliquant la théorie de la variabie regionalisée sur les données d'analyse ob­tenues durant la période 1979-1983 sur quatre champs d'essais, une meilleure com­préhension fut rendue possible de la structure temporale, des gradients de concen­trations en (N03N), Cl-, K+, Nat et Ca++ à plusieurs profondeurs.

Une analyse structurelle subséquente a permis de défmir des limites dans Ie temps au seull duqueiies paramètres examinés sont à considérer comme temporale­ment dépendants et corrélés.

L'utilité pratique et directe de cette étude réside dans Ie fait que l'échantillon­nage appliqué et Ie programme d'analyse en sont profondément simplifiés , dans Ie sens que Ie temps d'intervalle entre deux prises d'échantillon et d'analyse peut être sensiblement augmentée, sans perte de fidelité.

218

INHOUD

G. A. Ferrari & D. Magaldi Degree of soU weathering as determined by abrasion pH : Applications in soU study and in paleopedology

O. Van Cleemput & L. Baert SoU aeration data of sandy and sandy loam profiles in Belgium

A. Bollinne & A. Laurant La prévision de l'érosion en Europe atlantique. Le cas de la zone limoneuse de Belgique

Nand Ram & K. V. Raman Characterization of metal-humic and -fulvic acid complexes

E. De Pauw Agricultural pot.ential and constraints in relation to growing periods and their variability in Tanzania

H. B. Madsen A pedological soU, classification system for Danish soils

L. Verdegem A qualitative approach of the time-dependency of the nutrient­c'oncentration in a soU profile by means of auto-correlation and variogram-analysis

SOMMAIRE

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