carbon and nitrogen stocks in the soils of the amazon region · 2008-03-18 · for the mineral...

Ž .Geoderma 89 1999 273–286

Carbon and nitrogen stocks in the soils of theAmazon Region

N.H. Batjes ), J.A. Dijkshoorn( )International Soil Reference and Information Centre ISRIC , Wageningen, Netherlands

Received 8 April 1998; accepted 24 August 1998

Abstract

Soil nitrogen and organic carbon stocks, to a depth of 0.3 m and 1 m respectively, wereŽ .determined for the Amazon Region using the soil and terrain SOTER-LAC database for Latin

America and the Caribbean. Mean carbon densities, to a depth of 1 m, range from 4.0 kg my2 forcoarse textured Arenosols to 72.4 kg my2 for the poorly drained Histosols. Mean carbon density

Ž y2 . y2for the mineral soils, excluding Arenosols and Andosols 30.5 kg C m , is 9.8 kg m . In total,the top 1 m holds 66.9 Pg C and 6.9 Pg N. About 52% of this carbon pool is held in the top 0.3 mof the soil, the layer which is most prone to changes upon land use conversion and deforestation.q 1999 Elsevier Science B.V. All rights reserved.

Keywords: soil organic carbon; soil nitrogen; Amazon Region; SOTER database; Latin America

1. Introduction

The favourable effects of organic matter on the physical and chemicalproperties of soils, on biological activity through cycling of C and N, and insustaining soil productivity are well documented. Important factors controllingorganic matter levels in soils include climate, hydrology, parent material, soil

Ž .fertility, biological activity, vegetation patterns and land use Jenny, 1941 . Inthe short term, the carbon balance of terrestrial ecosystems is particularlysensitive to impact of human activities, including deforestation, biomass burn-ing, land-use changes and environmental pollution. On the longer term, plantgrowth and soil carbon sequestration may increase due to the so-called physio-

) Corresponding author.

0016-7061r99r$ - see front matter q 1999 Elsevier Science B.V. All rights reserved.Ž .PII: S0016-7061 98 00086-X

( )N.H. Batjes, J.A. DijkshoornrGeoderma 89 1999 273–286274

logical ‘CO -fertilization’ effect, associated with increased atmospheric CO2 2Ž .levels Bazzaz et al., 1996 , plus the associated improved water-use efficiency

Ž .Van de Geijn and Goudriaan, 1996 and more favourable temperatures andŽ .increased anthropogenic nitrogen emissions Mellilo, 1996 .

To assess the possible impacts of land use and global climate change on soilorganic matter content and quality in relation to potential greenhouse gas

Ž .emissions, inventories of past Houghton, 1995 and contemporary soil carbonŽ .stocks Eswaran et al., 1993; Sombroek et al., 1993; Batjes, 1996 are needed.

This information can then be compiled and analyzed in spatially-explicitdatabases of environmental driving variables that may be used as input to drive

Ž .simulation models on a regional or global basis e.g., Paustian et al., 1997 .In this paper we present revised estimates of soil carbon and nitrogen

densities and stocks in terrestrial ecosystems of the Amazon Region, using soilŽ .geographic and attribute data held in the soil and terrain SOTER-LAC digital

Ž .database for Latin America and the Caribbean FAO–ISRIC–UNEP–CIP, 1998 .Ž .Our study area corresponds with the Amazon Region of Sombroek 1996 .

Geographically, this region is considered a significant, potential source of futureŽ .emissions of greenhouse gases Watson et al., 1996; Fearnside, 1997 , where

Žrapid degradation of soil quality is possible upon land use changes de Moraes et.al., 1996; Kauffman et al., 1998 .

2. Materials and methods

2.1. Soil geographic data

Until recently, the major source of soil information for Latin America wasŽ .held in the Soil Map of the World FAO–UNESCO, 1974 . This 1:5 million

scale map was compiled using survey data collected prior to 1970. Since thattime, many countries in the region have carried out new soil surveys. These newmaterials have been used in the compilation of a 1:5 million scale soil andterrain digital database for Latin America and the Caribbean, using the interna-

Žtionally endorsed SOTER methodology Oldeman and Van Engelen, 1993; Van.Engelen and Wen, 1995 .

The SOTER methodology uses physiography as the main entry for subdivid-ing terrain units, the basic map units, into terrain components and soil compo-

Ž .nents Van Engelen and Wen, 1995 . Land areas showing a distinctive, andoften repetitive, pattern of landform, parent material, surface form, slope andsoils are mapped as SOTER units. These are identified by unique labels on themap. In the database, each SOTER unit is characterized further by its geometric

Ž . Ž .data i.e., location and topology and attribute data e.g., soil characteristics . Ata scale of 1:5 million, each SOTER unit may consist of up to three terrain

( )N.H. Batjes, J.A. DijkshoornrGeoderma 89 1999 273–286 275

Ž . Ž .Fig. 1. SOTER units SU as shown on the geometric database map and characterized in theattribute database.

Ž .components, each with their own soil components i.e., major soils as shown inFig. 1. As such, spatially small but carbon-wise significant inclusions in amapping unit can be accounted for, contrary to what has been the case for earliertraditional soil maps.

Each polygon in the SOTER-LAC database has been described in terms of itsmain component soils, characterized at the soil unit level of the Revised Legend

Ž .of the FAO–UNESCO Soil Map of the World FAO–UNESCO, 1988 , andtheir relative extent. Thus the Revised Legend can be used to aggregate theavailable soil profile data and to link derived interpretations of soil propertieswith the polygons demarcated on the SOTER-LAC map. The usefulness of soil

Žclasses as carriers of soil information is well documented FAO, 1995; Batjes,.1997; Bouma et al., 1998 .

The SOTER-LAC database was compiled from soil monographs and mapsoriginating from the various countries of Latin America and the Caribbean. Asthese surveys were commissioned for different purposes, they are of varyingresolution and quality. Therefore, the source of the data has been stored inSOTER-LAC together with aggregated information on the inferred reliability ofthe available information.

2.2. Soil profile data

The most appropriate way to study the organic and nitrogen content of soil ison a unit area basis, for a specified depth interval. This requires information on


the spatial distribution of different types of soil and of their carbon and nitrogencontent, bulk density, and stoniness as a function of depth. Commonly, referencedepth intervals of 0–0.3 m and 0–1 m are used in studies of soil organic carbon

Ž .pools Eswaran et al., 1993; Sombroek et al., 1993; Batjes, 1996 . The first layerencompasses the depths that are most directly involved in interactions with theatmosphere, and that are most sensitive to land use and environmental changesŽ .see reviews by Bouwman, 1990; Batjes and Sombroek, 1997 .

Litter is not included in our calculation of soil organic carbon mass as thisŽ .superficial layer is seldom sampled see Buringh, 1984; Sombroek et al., 1993 .

The amounts of carbon stored in the litter layers of many virgin and forestedsoils, however, can be considerable.

Generally, fewer samples are taken from the deeper layers than from thesuperficial ones, implying that the results are less reliable for the deeper layers.No attempt was made in the current study to assess soil C stocks stored below adepth of 1 m.

Primary soil data were taken from the appropriate SOTER-LAC attributefiles. All profiles have been described and coded using the uniform SOTER

Ž .conventions for database compilation Van Engelen and Wen, 1995 . Analyticalprocedures, used by the various laboratories in the region, have been docu-mented in a separate data file. This provides a handle for screening profiles withrespect to the comparability of the analytical procedures used. Possible sourcesof uncertainty in soil carbon, nitrogen and bulk density data related to differ-ences in sampling methodology, season of sampling, land use history, and

Žlaboratory methods have been reviewed elsewhere Batjes, 1996; Sombroek et.al., 1997 . Local effects of differences in microclimate, parent material and land

use for soil carbon stocks of a particular FAO soil unit were not taken intoaccount explicitly in our study.

About two-thirds of the soil profile data held in SOTER-LAC were sampledand described in the period from 1965 to 1985. There are no data to indicate thatthe available profile descriptions are statistically representative of the regionaldistribution of the soil units in the region, nor is the land use history known formost profiles. These problems are commonly encountered and recognized in

Ž .small scale, soil databases Sombroek et al., 1993; Lal et al., 1995 .The attribute data component of the SOTER-LAC database currently holds

1828 profile descriptions, 618 of which occur in the study area proper. However,there are 1258 data sets for similar soils, in terms of Revised Legend name, inthe database. This larger subset has been used for our study, corresponding witha total of 4608 horizons to a depth of 1 m. Out of these, 93% have measureddata for organic carbon, 76% for total nitrogen, 35% for bulk density, and 90%

Ž .for the amount of fragments )2 mm expressed as classes .Most determinations of soil organic carbon content for the profiles under

consideration are according to Walkley–Black and Tiurim. The results of theseŽ .two methods are considered comparable Vogel, 1994 at the considered obser-


vational scale. Total nitrogen content in the soil was determined by the Kjeldahlmethod in all cases. Bulk density was measured according to either the core orparaffin method.

2.3. Calculation of soil C and N stocks

Ž .Weighted C and N densities by depth zone 0–0.3 m and 0–1 m respectivelywere calculated for each soil profile as a product of depth of horizon, concentra-tion of C or N, bulk density, and volume percentage of fragments coarser than 2

Ž .mm see Batjes, 1996 , prior to the actual statistical analyses. In case measureddata were lacking for some of these attributes, surrogate values were determined

Ž .using pedotransfer functions or rules Bouma and Van Lanen, 1987 .Firstly, the mean bulk density was computed by profile; this mean then was

used for all horizons with missing bulk density data within a given profile. Allsubstituted values were flagged in the ‘derived’ database to differentiate them

Ž .from the measured ones. Secondly, the mean measured bulk density wascalculated for each FAO soil unit represented in the database. Thirdly, if there

Ž .were no bulk density data for a particular soil profile, the mean measured bulkdensity for the corresponding FAO soil unit was used in the ‘derived’ database.Finally, if there were no measured data at all for a certain soil unit, mean values

wfor bulk density for the corresponding FAO major soil grouping as tabulated byŽ .xBatjes 1996 were used as best estimates. In developing these pedotransfer

rules, the full complement of compatible profiles from the SOTER-LAC database has been used.

The volume percentage of coarse fragments, by horizon, was approximated asthe midpoint value of the class intervals used in SOTER. If there was no fieldinformation on the abundance of coarse fragments in the database, the default

Ž . Ž .midclass value for ‘few’ 2–5% was applied, except for Histosols ‘none’, 0% ,Ž . Ž .Leptosols ‘many’, 15–40% and Regosols ‘abundant’, 40–80% .

After weighted, mean densities for carbon and nitrogen had been calculatedby FAO soil unit and depth zone, areal estimates of soil C and N stocks weremade. This was done by combining our mean data on C and N densities with theavailable information on the relative distribution of soil units within each mapunit and their total extent. Results were then mapped using GIS.

3. Results and discussion

Ž .The Revised Legend FAO–UNESCO, 1988 consists of 28 major soilgroupings. These are differentiated at the highest level on the basis of effects ofdifferent soil forming processes, in so far as these are reflected in observableand measurable properties.

Twenty of these major soil groups occur in the Amazon Region. AcrisolsŽ . Ž . Ž .24.8% and Ferralsols 24.6% are most common, followed by Gleysols 8.1% ,


Ž . Ž . Ž . Ž .Leptosols 8.0% , Cambisols 7.2% , Plinthosols 4.4% and Regosols 4.4% .ŽIn total, these major soil groups account for about 80% of the surveyed area see

.Table 1, column 4 . The Acrisols correspond mainly with low-activity clayŽ .members of Ultisols of U.S. Soil Taxonomy Soil Survey Staff, 1992 , while

Ferralsols correspond with the well drained members of Oxisols of the U.S. SoilŽ .Taxonomy for further details see Van Wambeke, 1992 .

Although all our analyses were carried out by FAO soil unit, the results arepresented by major soil grouping only. This has been done to enhance thelegibility of the tabular output.

Mean bulk densities for Arenosols of 1500 kg my3 are similar to they3 Ž .1500–1550 kg m reported by Sombroek et al. 1997 , while our values for

Plinthosols of 1320 kg my3 are lower than the 1550–1650 kg my3 of SombroekŽ . Ž .et al. 1997 Table 1 . Computed mean bulk density for Histosols, based on the

Table 1Mean bulk density by FAO major soil grouping, and their relative extent in the Amazon Region

Soil N Mean CV Areaa y3 bŽ . Ž . Ž .group kg m % %

AC 131 1390 12 24.8AL 26 1260 20 1.8AN 96 900 31 0.4AR 103 1500 17 4.1CM 368 1290 14 7.2FL 91 1270 15 3.3FR 127 1300 18 24.6GL 204 1210 14 8.1

cHS 11 1040 15 0.6LP 79 1230 21 8.0LV 139 1330 13 2.5LX 47 1390 7 2.8NT 26 1230 14 0.3PH 210 1260 14 0.4PL 32 1320 13 0.8PT 18 1260 16 4.4PZ 15 1460 8 1.3RG 106 1220 15 4.4SC 26 1130 6 0.2SN 145 1230 10 0.1

Ž .N is the number of observations; CV is coefficient of variation % .a Ž .Abbreviations for major groups FAO–UNESCO, 1988 : AC, Acrisols; AL, Alisols; AN,Andosols; AR, Arenosols; CM Cambisols; FL, Fluvisols; FR, Ferralsols; GL, Gleysols; HS,Histosols; LP, Leptosols; LV, Luvisols; LX, Lixisols; NT, Nitisols; PH, Phaeozems; PL, Planosols;PT, Plinthosols; PZ, Podzols; RG, Regosols; SC, Solonchaks; SN, Solonetz.b Ž . 3 2Area of Amazon Region, as defined by Sombroek 1996 is 7,618=10 km .c y3 Ž .In this study a mean bulk density of 310 kg m Batjes, 1996 has been used for Histosols.


SOTER-LAC data set, is 1040 kg my3. Peat soils of permanently submergedlowlands, however, typically have a bulk density of less than 500 kg my3

Ž . y3Sombroek et al., 1997 . Consequently, a default value of 310 kg m has beenŽ .used for Histosols see Batjes, 1996 when computing soil C and N stocks.

Andosols in the Amazon Region have a mean bulk density of 900 kg my3. Thisis somewhat higher than the global mean of 730 kg my3 derived from the WISE

Ž .database Batjes, 1996 , yet in agreement with the diagnostic criteria of FAO–Ž .UNESCO 1988 .

In preparing a map of the spatial distribution of organic carbon density in theŽ .top 1 m of the soils of the Amazon Region Fig. 2 , we only considered the

Ž .spatially dominant soil component of each SOTER unit see Fig. 1 . This hasbeen done to enhance the legibility of the map. The median, relative extent ofthe dominant soil component of a SOTER unit is 60% in our study area, withthe quartiles being 50% and 70% respectively.

Coarse textured Arenosols, Regosols and shallow Leptosols predominate inŽ y2 .class 1 i.e., SOC-5.5 kg m to a depth of 1 m . Fluvisols, Plinthosols and

Planosols are the main soils of class 2. The third class mainly comprisesAcrisols, Lixisols and some areas of deep Podzols. Cambisols are prevalent inclass 4, towards the watershed with the Andes, while Ferralsols predominate inthe central part of the Amazon Region with minor extents of Nitosols occurringalso. Gleysols are the dominant soils in class 5, occurring mainly in depressedbasins. Alisols are spatially dominant in class 6, which occurs mainly in AcreProvince, Brazil. Finally, class 7 occurs both towards watershed with the Andes

Ž . Žin the west mainly Andosols and towards the Guyana coast in the east mainly.Histosols .

Ž .Mean soil organic carbon content in the upper 1 m see Table 2 is highest forŽ y2 .the Histosols 72.4 kg C m , and is associated with the slow decomposition of

organic matter under water-saturated conditions. The high value for AndosolsŽ y2 .30.5 kg C m can be explained by the protection of organic carbon by

Ž .allophane Mizota and Van Reeuwijk, 1989 . Generally, the stabilizing effect ofclay particles on soil organic matter decreases in the sequence: allophane)

Žamorphous and poorly crystalline Al-silicates)smectite) illite)kaolinite Van.Breemen and Feijtel, 1990 . The mean carbon density for the mineral soils,

Ž y2 . Ž y2 .excluding Arenosols 4.0 kg C m and Andosols 30.5 kg C m , is 9.8 kgmy2. This value compares well with the mean value of 10.3 kg C my2 which de

Ž .Moraes et al. 1995 found for soils of the Brazilian Legal Amazon.Mean nitrogen densities to a depth of 1 m range from 0.46 kg my2 for

y2 Ž .Arenosols to 3.13 kg N m for Histosols Table 3 . Typically the CrN ratiosŽ .are from 9–12, except for acid Podzols CrNf14 where organic matter

breakdown proceeds more slowly. Similarly, the weighted CrN ratio of 21 forthe 0.3–1 m layer of Histosols reflects a lower degree of decomposition of theorganic materials present in poorly drained environments. A CrN ratio above12–14 often is considered indicative for a shortage of nitrogen in the soil.

()

N.H

.Batjes,J.A

.Dijkshoornr

Geoderm

a89

1999273

–286

280

Ž y2 .Fig. 2. Carbon density in soils of the Amazon Region kg C m in top 1 m .


Table 2Mean carbon and nitrogen density by major FAO grouping of the Amazon Region

Soil Depth Organic carbon NitrogenŽ .group cm N Mean CV N Mean CV

y2 y2Ž . Ž . Ž . Ž .kg m % kg m %

AC 0–30 162 4.40 50 159 0.42 430–100 110 8.47 42 108 0.94 39

AL 0–30 18 8.57 42 18 0.83 230–100 12 15.25 45 12 1.71 21

AN 0–30 35 12.39 46 35 0.86 290–100 21 30.50 46 21 2.01 26

AR 0–30 65 2.07 50 60 0.21 330–100 45 4.01 42 40 0.46 26

CM 0–30 201 5.59 61 200 0.55 600–100 116 9.53 46 115 1.11 39

FL 0–30 45 3.42 52 43 0.42 440–100 33 7.48 46 33 0.94 43

FR 0–30 155 5.05 48 154 0.44 420–100 95 10.16 40 96 0.96 36

GL 0–30 89 6.74 62 89 0.76 620–100 59 12.66 59 59 1.45 47

HS 0–30 10 26.30 47 10 1.37 550–100 7 72.38 35 7 3.13 38

aLP 0–30 38 5.15 63 36 0.54 40LV 0–30 79 4.67 51 75 0.48 46

0–100 45 8.86 34 43 1.05 35LX 0–30 37 3.85 45 37 0.37 41

0–100 26 8.02 26 26 0.89 32NT 0–30 18 5.65 34 17 0.58 17

0–100 14 11.13 34 14 1.23 20PH 0–30 88 7.32 52 88 0.69 39

0–100 58 13.32 40 58 1.54 27PL 0–30 39 3.40 48 35 0.38 36

0–100 19 6.60 31 18 0.87 18PT 0–30 21 3.97 59 21 0.34 45

0–100 17 6.13 45 17 0.68 27PZ 0–30 14 5.49 54 14 0.43 35

0–100 10 9.00 42 10 0.87 21RG 0–30 90 2.52 66 88 0.27 57

0–100 30 4.02 50 28 0.52 35SC 0–30 11 2.31 53 10 0.22 33

0–100 6 5.25 38 6 0.61 35SN 0–30 43 3.46 55 40 0.39 50

0–100 31 5.77 39 30 0.76 31

Ž .N is the number of observations. CV is coefficient of variation % .See footnote of Table 1 for definitions of major soil group codes.aMaximum depth for Leptosols has been set at 0.3 m.


Table 3Weighted CrN ratio by FAO major soil grouping and depth zone

Ž . Ž .Soil Topsoil 0–0.3 m Subsoil 0.3–1 mgroup N Mean CV N Mean CV

AC 134 11.1 22 130 9.5 29AL 13 11.5 20 11 9.3 27AN 21 11.6 8 18 12.3 8AR 49 11.1 30 41 9.9 39CM 165 10.6 25 136 9.2 35FL 38 10.2 17 35 9.4 33FR 131 12.0 25 131 10.5 35GL 69 10.9 22 65 9.7 31HS 5 12.4 8 7 21.0 64LP 68 11.1 21 – – –LV 71 10.0 20 53 10.1 27LX 35 10.2 18 34 8.8 34NT 18 9.8 19 17 8.4 18PH 77 10.8 16 68 9.3 23PL 35 9.7 20 34 7.4 29PT 19 10.8 21 17 7.9 26PZ 11 14.0 28 10 13.5 47RG 70 10.8 24 45 10.4 32SC 11 12.3 17 7 11.8 13SN 41 10.3 18 30 8.2 22

Ž .N is the number of observations; CV is coefficient of variation % .See footnote of Table 1 for definitions of major soil group codes.

The full composition of each SOTER unit, in terms of its component soilunits, has been considered when computing the stocks of soil carbon and totalnitrogen. The amount of organic carbon held in the first 0.3 m and 1 m of the

Ž 15 .soils of the Amazon Region is 36.1 and 66.9 Pg C 1 Pgs10 g respectively.Ž . Ž .For soil nitrogen, this is 3.4 Pg N 0–0.3 m and 6.9 Pg N 0–1 m . About 52%

of the carbon pool is held in the top 0.3 m, the layer which is most prone tochanges upon land use conversion and deforestation. Soil carbon reserves in thetop 1 m of the Amazon Region account for about 4% of global carbon stocksŽ .f1550 Pg C; see Eswaran et al., 1993; Batjes, 1996 . Most of the terrestrial

Žcarbon in the region, however, is stored in the standing forest biomass 384 to3 y1 .510=10 kg ha ; Fearnside, 1997 , forming a large potential source of

greenhouse gas emissions upon deforestation.Ž 3 2.The C stock calculated for the part i.e., 5,552=10 km of our study area

Ž .that coincides with the Brazilian Legal Amazon, is 25.0 Pg C 0–0.3 m andŽ . Ž .46.5 Pg C 0–1 m , respectively. For total nitrogen this is 2.3 Pg N 0–0.3 m

Ž .and 4.9 Pg N 0–1 m . The figures found for the top 1 m of soil are similar toŽ .the 47 Pg C and 4.4 Pg N reported earlier by de Moraes et al. 1995 .


4. Conclusions

The operational and scientific problems associated with the accuracy andregional representativeness of the various spatial and attribute data considered in

Žsmall scale soil databases are well known e.g., Lal et al., 1995; Cramer and.Fischer, 1997 , yet difficult to remedy in compilations based on available

historic data.Our study allows to put reasonable bounds on soil organic carbon and total

nitrogen stocks for the Amazon Region, providing useful baseline data forstudying effects of recent land use changes on soil organic matter dynamics atthe regional level. However, the spatial variability in the content of soil organiccarbon and total nitrogen that may occur within a given major soil group, and its

Ž .various subunits, remains high see Table 2 .The need remains for critical soil data collection in the field and for sustained

data harmonization and analysis efforts. In the case of the Amazon Region theseshould focus on the soil physical parameters, notably bulk density, moistureretention and hydraulic conductivity. Suited locations for these field observa-tions, in terms of regional representativeness of the soil and agro-ecologicalunits represented, can be derived from SOTER-LAC and auxiliary climatic andland cover databases. Remote sensing will be critical for mapping land covertypes and monitoring their changes. In a GIS framework, such spatially explicitdatabases of driving variables can provide the basis for scaling-up and integrat-

Žing site data and model output to a regional and global level e.g., Mantel and.Van Engelen, 1997; Paustian et al., 1997; Bouma et al., 1998 . For example,

geographically explicit information on soil carbon pools, as presented in thispaper, may be combined with simple scenarios of land cover change, inducedfor example by deforestation or human-induced land degradation, to makeestimates of possible releases of carbon dioxide and other trace gases from thesoils of the Amazon Region upon anthropogenic disturbance. In addition, theSOTER-LAC database can be used to provide an indication of possible amountsof carbon that could be sequestered in various soil types subsequent to the

Žintroduction of sound management or conservation practices see Cole, 1996;.Batjes and Sombroek, 1997 .

Acknowledgements

ŽThe soil and terrain database for Latin America and the Caribbean SOTER-.LAC has been compiled in a joint project of the Food and Agriculture

Ž .Organization FAO of the United Nations, the International Soil Reference andŽ . Ž .Information Centre ISRIC , the International Society of Soil Science ISSS ,

Ž .the United Nations Environment Programme UNEP and the InternationalŽ .Potato Centre CIP , through subcontracts with national soil institutions in the


respective countries concerned. The central SOTER-LAC database was com-piled at ISRIC from the various national contributions, under overall coordina-tion by V.W.P. van Engelen and J.A. Dijkshoorn. The authors particularly thankJ.W. Resink for preparing the GIS maps.

References

Batjes, N.H., 1996. Total carbon and nitrogen in the soils of the world. European Journal of SoilScience 47, 151–163.

Batjes, N.H., 1997. A world data set of derived soil properties by FAO-UNESCO soil unit forglobal modelling. Soil Use and Management 13, 9–16.

Batjes, N.H., Sombroek, W.G., 1997. Possibilities for carbon sequestration in tropical andsubtropical soils. Global Change Biology 3, 161–173.

Bazzaz, F.A., Bassow, S.L., Berntson, G.M., Thomas, S.C., 1996. Elevated CO and terrestrial2

vegetation: Implications for and beyond the global carbon budget. In: Walker, B., Steffen, W.Ž .Eds. , Global Change and Terrestrial Ecosystems. Cambridge Univ. Press, Cambridge, pp.42–76.

Bouma, J., Van Lanen, H.A.J., 1987. Transfer functions and threshold values: from soil character-Ž .istics to land qualities. In: Beek, K.J., Burrough, P.A., McCormack, D.E. Eds. , Quantified

Land Evaluation. ITC Publication 6, International Institute for Aerospace Survey and EarthSciences, Enschede, pp. 106–110.

Bouma, J., Batjes, N.H., Groot, J.J.R., 1998. Exploring soil quality effects on world food supply.Geoderma 86, 43–59.

Ž .Bouwman, A.F. Ed. , 1990. Soils and the Greenhouse Effect. Wiley, Chichester.Ž .Buringh, P., 1984. Organic carbon in soils of the world. In: Woodwell, G.M. Ed. , The Role of

Terrestrial Vegetation in the Global Carbon Cycle. Measurement by Remote Sensing, SCOPE23. Wiley, New York, pp. 91–109.

Ž .Cole, V. Ed. , 1996. Agricultural options for mitigation of greenhouse gas emissions. In: Watson,Ž .R.T., Zinyowerea, M.C., Moss, R.H. Eds. . Climate Change 1995. Impacts, Adaptations and

Mitigation of Climate Change: Scientific-Technical Analyses. Cambridge Univ. Press, Cam-bridge, pp. 747–771.

Cramer, W., Fischer, A., 1997. Data requirements for global terrestrial ecosystem modelling. In:Ž .Walker B., Steffen, W. Eds. , Global Change and Terrestrial Ecosystems. Cambridge Univ.

Press, Cambridge, pp. 529–565.de Moraes, J.L., Cerri, C.C., Mellilo, J.M., Kichlighter, D., Neill, C., Skole, D.L., Steudler, P.A.,

1995. Soil carbon stocks of the Brazilian Amazon Basin. Soil Science Society of AmericaJournal 59, 244–247.

de Moraes, J.L., Volkoff, B., Cerri, C.C., Bernoux, M., 1996. Soil properties under Amazon forestand changes due to pasture installation in Rondonia. Brazil. Geoderma 70, 63–81.ˆ

Eswaran, H., Van den Berg, E., Reich, P., 1993. Organic carbon in soils of the world. Soil ScienceSociety of America Journal 57, 192–194.

Ž .FAO, 1995. Digital Soil Map of the World and Derived Soil Properties version 3.5 . CD-ROM,Food and Agriculture Organization, Rome.

Ž .FAO–UNESCO, 1974. Soil Map of the World 1:5,000,000 , Vol. 4: South America. UnitedNations Educational, Scientific and Cultural Organization, Paris.

ŽFAO–UNESCO, 1988. FAO–UNESCO Soil Map of the World—Revised Legend Reprint of.FAO World Soil Resources Report 60 . Technical Paper 20, International Soil Reference and

Information Centre, Wageningen.


FAO-ISRIC-UNEP-CIP, 1998. Soil and Terrain Database for Latin America and the Caribbean—Ž . Ž .SOTERLAC version 1.0 . CD-ROM, Food and Agriculture Organization FAO of the United

Ž .Nations, the International Soil Reference and Information Centre ISRIC , the United NationsŽ . Ž .Environment Programme UNEP and the International Potato Centre CIP . Land and Water

Digital Series, 5. FAO, Rome.Fearnside, P.M., 1997. Greenhouse gases from deforestation in Brazilian Amazonia: net commit-

ted emissions. Climatic Change 35, 321–360.Houghton, R.A., 1995. Changes in the storage of terrestrial carbon since 1850. In: Lal, R.,

Ž .Kimble, J., Levine, E., Stewart, B.A. Eds. , Soils and Global Change. Lewis Publishers, BocaRaton, pp. 45–65.

Jenny, 1941. Factors of Soil Formation: A System of Quantitative Pedology. McGraw-Hill, Neww xYork Reprinted by Dover Publications, New York, 1994 .

Lal, R., Kimble, J., Levine, E., Whitman, C., 1995. Towards improving the global database on soilŽ .carbon. In: Lal, R., Kimble, J., Levine, E., Stewart, B.A. Eds. , Soils and Global Change.

Lewis Publishers, Boca Raton, pp. 343–436.Kauffman, S., Sombroek, W.G., Mantel, S., 1998. Soils of rainforests: characterization and major

Ž .constraints of dominant forest soils in the humid tropics. In: Schulte, A., Ruhiyat, D. Eds. ,Soils of Tropical Forest Ecosystems: Characteristics, Ecology and Management. Springer-Verlag, Berlin, pp. 9–20.

Mantel, S., Van Engelen, V.W.P., 1997. The impact of land degradation on food productivity:Ž .Case studies for Uruguay, Argentina and Kenya Vols. 1 and 2 . Report 97r01, International

Soil Reference and Information Centre, Wageningen.Mellilo, J.M., 1996. Carbon and nitrogen interactions in the terrestrial biosphere: Anthropogenic

Ž .effects. In: Walker, B., Steffen, W. Eds. , Global Change and Terrestrial Ecosystems.Cambridge Univ. Press, Cambridge, pp. 431–450.

Mizota, C., Van Reeuwijk, L.P., 1989. Clay mineralogy and chemistry of soils formed undervolcanic material in diverse climatic regions. Soil Monograph 2, International Soil Referenceand Information Centre, Wageningen.

Ž .Oldeman, L.R., Van Engelen, V.W.P., 1993. A world soils and terrain digital database SOTER—an improved assessment of land resources. Geoderma 60, 309–325.

Paustian, K., Levine, E., Post, W.M., Ryzhova, I.M., 1997. The use of models to integrateinformation and understanding of soil C at the regional scale. Geoderma 79, 227–260.

Soil Survey Staff, 1992. Keys to Soil Taxonomy, 5th edn., SMSS Tech. Monograph No. 19.Pocahontas Press, Blacksburg.

Sombroek, 1996. Amazon landforms and soils in relation to biological diversity. In: Prance, G.L.,Ž .Lovejoy, T.E., Rylands, B.E., Dos Santos, A.A., Miller, C. Eds. , Priority Areas for

Conservation in the Amazonian Rainforest. Smithonian Institute Press, WA.Sombroek, W.G., Fearnside, P.M., Cravo, M., 1997. Geographic assessment of carbon stored in

Amazonian terrestrial ecosystems and their soils in particular. International Workshop onŽ .Carbon Pools and Dynamics in Tropical Ecosystems 10–15 December 1997 , Belem, Brazil.`

Sombroek, W.G., Nachtergaele, F.O., Hebel, A., 1993. Amounts, dynamics and sequestrations ofcarbon in tropical and subtropical soils. Ambio 22, 417–426.

Van Breemen, N., Feijtel, T.C.J., 1990. Soil processes and properties involved in the productionof greenhouse gases, with special relevance to soil taxonomic systems. In: Bouwman, A.F.Ž .Ed. , Soils and the Greenhouse Effect. Wiley, Chichester, pp. 195–223.

Van de Geijn, S.C., Goudriaan, J., 1996. The effects of elevated CO and temperature change on2Ž .transpiration and crop water use. In: Bazzaz, F., Sombroek, W.G. Eds. , Global Climate

Change and Agricultural Production. Food and Agriculture Organization of the United NationsŽ .FAO and Wiley, Chichester, pp. 101–121.

Van Engelen, V.W.P., Wen, T.T., 1995. Global and national soils and terrain digital databasesŽ . Ž .SOTER : Procedures manual revised edition . FAO–ISRIC–ISSS–UNEP, International Soil


wReference and Information Centre, Wageningen also issued by FAO as World Soil ResourcesxReport 74 .

Van Wambeke, A., 1992. Soils of the Tropics: Properties and Appraisal. McGraw-Hill, NewYork.

Vogel, A.W., 1994. Comparibility of soil analytical data: determinations of cation exchangecapacity, organic carbon, soil reaction, bulk density, and volume percent of water at selectedpF values by different methods. Working Paper 94r07, ISRIC, Wageningen.

Ž .Watson, R.T., Zinyowerea, M.C., Moss, R.H. Eds. , 1996. Climate Change 1995. Impacts,Adaptations and Mitigation of Climate Change: Scientific-Technical Analyses. Contribution ofWorking Group II to the Second Assessment Report of the Intergovernmental Panel on ClimateChange. Cambridge Univ. Press, Cambridge.

carbon and nitrogen stocks in the soils of the amazon region · 2008-03-18 · for the mineral...

Documents