Exploring land quality effects on world food supply1

Geoderma 86 Ž1998. 43–59

Exploring land quality effects on world food supply 1 J. Bouma

a,)

, N.H. Batjes b, J.J.R. Groot

c

a

Laboratory of Soil Science and Geology, Agricultural UniÕersity, Wageningen, Netherlands b ISRIC (International Soil Reference and Information Center), Wageningen, Netherlands c AB-DLO (Institute for Agrobiological and Soil Fertility Research), Wageningen, Netherlands Received 7 November 1997; accepted 18 March 1998

Abstract In a previous study, simulations of agricultural production potentials were made for different exploratory scenarios considering population growth, type of diet and low and high input agriculture. Results indicated that future world populations can be fed, but problems are likely in South Asia. The simulations involved gross generalizations for soil conditions. For example, possible effects of soil degradation were not expressed. The current study was made to explore the effects of the different types of soil degradation on agricultural production, using major soil groupings of the Humid Tropics and Seasonally Dry ŽSub.Tropics as examples. Degradation Žcompaction, erosion and acidification. is expressed in terms of soil quality indicators relating potential to actual production. Results are characteristically different for different soil units Žgenoforms., and the suggestion is made to present such differences in future soil databases for phenoforms that express effects of different forms of degradation or improvement, allowing better assessments for exploratory land use scenarios. Field studies should be initiated to describe realistic phenoforms for any given genoform. q 1998 Elsevier Science B.V. All rights reserved. Keywords: simulation modelling; soil degradation; population growth

)

Corresponding author. Invited paper for the Symposium: Global Carrying Capacity: Feeding the People of the 21st Century, held at the 89th Annual Meetings of the American Society of Agronomy in Anaheim, CA, USA. The symposium was sponsored by the Divisions: S-5, A-3, A-5, A-6, C-3, S-4, S-6 and S-11. 1

0016-7061r98r$ - see front matter q 1998 Elsevier Science B.V. All rights reserved. PII: S 0 0 1 6 - 7 0 6 1 Ž 9 8 . 0 0 0 3 4 - 2

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1. Introduction Concerns are being expressed about the possibilities of producing enough quality food for a doubling world population by 2040 Ž United Nations, 1992. . Production will have to occur in a manner that is socially acceptable and economically feasible, while environmental quality is not degraded. A major challenge indeed! Early exploratory studies ŽBuringh et al., 1975. estimated maximum global food production at 50 Mt of dry matter grain, enough to feed at least 30 billion people. A better knowledge about soils, freshwater resources and cropping makes it possible to update this estimate. Global changes in soil resources and climate Ž Oldeman et al., 1991; Parry et al., 1987. make a revision necessary. Penning de Vries et al. Ž1995a,b. examined whether natural resources of the world will allow food security by 2040 in 15 major regions in agro-ecologically sustainable ways. They considered 3 population growth scenarios, 3 diets and 2 management systems, comparing high and low external inputs, the latter corresponding with a global vs. a local agricultural system Ž WRR, 1995. . In their work, gross simplifications were needed to arrive at manageable input data for their crop production model, which operated on a 18 = 18 global grid Ž f 110 km = 110 km at the equator. . Also, input of soil data was strongly restricted. The first objective of our current study was to review these restrictions and suggest possible, more effective alternatives. The second objective originated from the impression that soil input is not presented very effectively in many interdisciplinary projects. Soil science lacks simple, accessible indicators for soil quality that can convey a message effectively to colleague scientists and laymen Že.g., Karlen et al., 1997; Pieri et al., 1995.. We therefore tried to define a land quality indicator related to food production that reflects different soil conditions in a given soil as a result of different management practices.

2. The world food study The total food requirement in 2040 to feed the population in each of 15 regions ŽUnited Nations, 1992. was computed by Penning de Vries et al. Ž1995a,b. and compared with the amount of food that could be produced from an agro-ecological point of view in a sustainable manner while using natural resources efficiently. Demand for food is the product of population size and per capita consumption. For both features, 3 levels were considered, corresponding to minimum, medium and maximum population estimates and to a vegetarian, moderate and affluent diet, the latter requiring more than 3 times as much plant biomass as the first. Maximum food production was approximated taking into account crops, land, water and climate. In all, 15,500 land units, 700 climatic zones and 100 large river basins were considered by Penning de Vries et al.

Region

South America Central America Northern America Western Africa Southern Africa Southeast Asia Southern Asia Europe World total

HEI system

LEI system

Vegetarian diet, low population

Moderate diet, medium population

Affluent diet, high population

Vegetarian diet, low population

Moderate diet, medium population

Affluent diet, high population

89.2 15.6 49.3 16.0 31.0 11.8 3.7 13.5 19.7

41.7 7.2 22.3 6.4 14.8 5.1 1.6 6.4 8.8

20.0 3.5 10.5 2.9 6.9 2.4 0.8 6.4 4.2

30.1 6.8 25.0 6.8 14.6 3.8 2.0 6.5 8.4

14.1 3.1 11.3 2.7 7.0 1.7 0.9 3.1 3.7

6.8 1.5 5.3 1.2 3.3 0.8 0.4 1.6 1.8

For the demand, 2 extreme combinations of population and diet and a middle one are used. The potential food supply is shown for the alternative high external input ŽHEI. and low external input ŽLEI. farming systems ŽPenning de Vries et al., 1995b..

J. Bouma et al.r Geoderma 86 (1998) 43–59

Table 1 Ratio of potential food supply and demand, for some selected regions

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Ž1995a,b. . Crop production was simulated for high external input Ž HEI. and low external input Ž LEI. systems, with a simulation interval of 1 day. The first system uses chemical fertilizers and biocides, as it is based on a global economy, while the latter does not, as it focuses on regionally closed nutrient cycles ŽVereijken, 1989, 1992. . Expected food production and consumption were expressed in terms of ‘grain equivalents’, a hypothetical weight unit ŽGoudriaan et al., 1998. . Results of the study are reported by Penning de Vries et al. Ž 1995a,b. , while more detailed data are provided by Luyten Ž1995. . Three groups of regions can be distinguished when considering the ratio between potential supply and demand in 2040 Žsee Table 1.. Regions in the danger zone have ratios of 2 or less. Regions with ratios higher than 5 are considered to have sufficient food security, while conclusions vary in the intermediate group. For much of Asia, the ratio between supply and demand is in the danger zone. The Americas, Central Africa and Oceania are clearly in the second group. Other areas vary considerably and do present some problems with certain combinations of population growth and management scenarios. This holds for Central America, Northern, Western and Eastern Africa and Europe with an LEI, high population growth scenario. The approach followed here implicitly assumes a no-trade situation which is more or less realistic for LEI but certainly not for HEI conditions. Still, the data provide a basis for considering trade in the latter case.

3. How to generalize disciplinary data 3.1. Climate, crop growth and management The reported study presents interesting case studies for different disciplines in terms of their approach towards generalization of procedures and data gathering for the model. Climatic data were obtained for 700 climatic zones using existing databases which have been built up effectively in the context of global change studies. Population growth scenarios were coupled with 3 types of diets, using the population projections of the United Nations Ž1992. . Crop growth and management was generalized in an intriguing manner. Obviously, considering a large series of crops and management options at world level would be unmanageable. First, food production and demand were expressed in grain equivalents ŽGE.. For production, GE refers to the quantity of dry grain that would be produced if crops of only 1 type were grown Žwheat in moderate climates, rice in the tropics. plus the grain equivalent of grass grown on land considered unsuitable for arable farming. The simulation model used ŽGroot et al., 1997. calculates production considering radiation, temperature and the availability of water and nutrients. Also, multiple cropping harvest indexes Ž economic yield as a fraction of total above-ground biomass. and post-harvest losses are considered

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ŽLuyten, 1995.. The model also used simple functions relating to N-supply to contrast LEI and HEI management. 3.2. Soils When Penning de Vries et al. Ž1995a,b. did their study, the only available worldwide digital soil data set was that of Zobler Ž 1986. . This data set has been aggregated from the printed Soil Map of the World ŽFAO-UNESCO, 1971–1981. at NASA for global change studies at 18 = 18 resolution. In the data set, each grid cell is characterized in terms of its dominant FAO-UNESCO soil unit— based on centre-point sampling, slope, soil phase and topsoil textural class. Water holding capacity was derived from the texture of the dominant soil type and was applied to the entire grid cell, leading to gross simplifications Ž Batjes, 1996.. The simple water-balance model used by Penning de Vries et al. Ž 1995a,b. assumes soil to be: Ž 1. homogeneous without cracks, Ž 2. well-drained, and Ž 3. 60 cm deep. There was no run-off nor consideration of watershed hydrology in the model. Since completion of the above-mentioned study, a corrected digital version of the Soil Map of the World has been published by FAO Ž 1995. . Also, median values for selected soil chemical and physical attributes by FAO-UNESCO soil unit have been presented for use at a 5X = 5X resolution ŽFAO, 1995. , resp. 0.5 = 0.5 degree resolution with the WISE database Ž Batjes, 1997. . A cooperative effort is underway to arrive at a mutually agreed-upon database of derived soil properties for global change research, based on the best available soil profile data Ž Batjes et al., 1997. . 2 Such a set should represent variability observed within major land units: distinction of ‘representative’ profiles implies a risk that this variability is ignored. 3.3. Cropping Arable cropping is much more demanding than grass production in terms of soil requirements. A general soil suitability classification was therefore designed to define soils suitable for arable cropping. Potential production of arable crops was calculated next, while grass production was calculated for the remaining cells considered suitable for pasture. Clearly, the broad selections made can easily be criticised. In that case, alternatives should be presented to allow this important type of study to be made. We prefer to take this positive approach.

2

E-mail: http:rrwww.iiasa.ac.atrcgi-binrpubsrch?IR97025.

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3.4. Land use A major criticism of the study by Penning de Vries et al. Ž 1995a,b. related to the manner in which the land and its use were considered in calculating production levels, which assumed a high level of expertise by farmers Ž application of ‘best technical means’ both for HEI and LEI systems. . Calculations were made for grid cells covering tropical forests and large nature reserves. However, in the current political climate, such an approach has little appeal. Of course, grid cells covering such areas can easily be excluded and this will be done in future runs. Real land use has not been considered, but only theoretical production levels for 2 crops and 2 types of management. Clearly, potential productions are often much higher than productions achieved by farmers. A convincing case can be made that production is determined by economic policy, not by agro-ecology! This, however, makes it still relevant to explore the natural production limits of the systems which is done here. Even economists and politicians may have to bow to the laws of nature. Even though Penning de Vries et al. Ž1995a,b. cite good correspondence between calculated and measured yield levels in some areas and lack of data to test LEI systems, they still need to pay more attention to actual yield levels. But the aim of their study was to explore future developments. In this light, the results are alarming. If the scenarios for Asia are ominous here, taking into account the assumptions made, they may be much worse in reality! In this way, the study can serve a very useful purpose. Finally, soils are crudely represented and they are assumed to be virgin. Currently, at least 20% of the soils of the world are degraded Ž Oldeman et al., 1991. and this aspect should be expressed in simulation studies. In the remainder of this paper, we will address this issue and an alternative procedure for considering soils in these kinds of studies. 4. Soil classes as carriers of data The authors of the study under review, are not soil scientists. When looking for soil data, they chose the Zobler Ž1986. dataset, partly because they considered data derived from the Soil Map of the World ŽFAO-UNESCO, 1971–1981. to be descriptive only, and thus of limited value. This illustrates a lack of effective communication of the soil science discipline to outsiders, professionals, as well as laymen. We do feel that soil classes can be excellent ‘carriers’ of soil information, but we need to provide it ourselves, avoiding diverse explorations with varying degrees of success by non-soil scientists. This case has been made elsewhere Že.g., Bouma, 1994, 1997; Bouma and Hoosbeek, 1996; FAO, 1995; Batjes et al., 1997.. We will therefore focus here on the feasibility of using information from small-scale soil maps, such as the 1:1 million scale Soil Map of the World, to transmit relevant information for world-scale exploratory studies. The central concept here is the assumption that each major soil group,

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as distinguished in the FAO-UNESCO Ž 1974. Legend, has a characteristic range of behaviour within a given agro-ecological zone. This range originates from different forms of management which may or may not have led to erosion or other forms of soil degradation, but which may also—on the bright side—have resulted in soil improvements. Simulation modelling, as used by Penning de Vries et al. Ž1995a,b., can help to quantify effects of such ranges in characteristics on food production. In turn, when part of a database, such data can be used to interpret the occurrence of certain major soil groupings, even when several occur within a given grid cell. The remainder of this paper will be used to explore this approach for 5 major soil groupings that occur in different agro-ecological zones of the world. The use of major agro-ecological zones for the correlation of agricultural research and planning is being promoted by agencies such as the Food and Agriculture Organization ŽFAO, 1978–1981. , the Consultative Group of International Agricultural Research Ž CGIAR, 1992. and IBSRAM, the International Board for Soil Research and Management Ž Greenland et al., 1994. . 5. Agro-ecological zones and soils being studied Within the scope of the current exploratory study, the focus is on the soils of the ‘Humid Tropics’ and ‘Seasonally Dry Tropics and Subtropics’ Ž Table 2.. The overall agricultural potential of these agro-ecological zones, which account for about 39% of the earth’s land surface, is described by FAO Ž1991. . They are important globally, in that tropical agriculture alone is now charged with the challenge of feeding 70% of the world’s population Ž Lal and Sanchez, 1992. . The dominant major soil groupings ŽFAO-UNESCO, 1974. encountered in these agro-ecological zones are shown in Table 3. Descriptions for soil profiles, considered representative for the major agro-ecological zones and soil groupings listed in Table 3 were taken from the ISRIC Soil Information System Ž Van de Ven et al., 1995.. 6. Data requirements for virgin and degraded phenoforms Morphological descriptions and soil analytical data were analyzed by profile to arrive at characteristic values for each soil; these values were considered to be Table 2 Characterisation of agro-ecological zones Agro-ecological zone

Area Ž=10 6 ha.

LGP

Koppen climate ¨

Humid Tropics Seasonally dry Žsub.tropics

1925 2475

270–365 90–275

Af, Am Ac, Cw

LGP is length-of-growing period ŽFAO, 1991., in days.

50

Agro-ecological zone

Major soils a

Extent Ž10 6 ha.

ISIS profiles

Koppen climate ¨

Reference no.c

Humid tropics

Acrisols Ferralsols Gleysols Arenosols

589 507 168 127

Ferric Acrisol ŽP.R. of China, CN019. Orthic Ferralsol ŽIndonesia, ID022. yb Cambic Arenosol ŽColombia, CO0107.

Am Af y Af

1 2 y 3

Seasonally dry sub-tropics and tropics

Luvisols Arenosols Acrisols Ferralsols

429 320 239 231

Ferric Luvisol ŽNigeria, NG018. Ferralic Arenosol ŽNigeria, NG017. Orthic Acrisols ŽP.R. of China, CN021. Orthic Ferralsol ŽZambia, ZM004.

Aw Aw Cwa Aw

4 5 6 7

a

After FAO Ž1991.. Not included. c Reference number for profile, as used in text and this table. b

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Table 3 Main soil units considered by agro-ecological zone

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representative for a genetic soil type Ž genoform. and were estimated for different phenoforms, each of which formed by a particular type of management Žas defined by Droogers and Bouma, 1997. . Basic input parameters necessary for the food production model are: infiltration rate, depth of rooting and available water capacity in the rooting zone. Infiltration rates were estimated from textural and structural information, using tabular data of Landon Ž1991. Ž p. 71.. Available water capacity Ž AWC., for the rooting zone, was calculated by profile from the available moisture retention data for the y10 to y1500 kPa range Že.g. Groenendijk, 1989. . Information on rooting depth, was derived from soil structure, texture and root distribution with depth, as well as from analytical data on the depth and intensity of occurrence of possible chemical limitations Že.g., exchangeable aluminium levels toxic to crops. . Based on the genoform, new parameter values were created by profile for three phenoforms ŽDroogers and Bouma, 1997. as shown in Table 4. Phenoform A resulted from subsoil compaction, as expressed by an assumed 50% decrease of the infiltration rate and a reduced depth of rooting of 40 cm associated with a Table 4 Soil parameter values for the genoform and 3 phenoforms Profile no. 1

2

3

4

5

6

7

a

Variables

Genoform y1 .

Infiltration rate Žcm h Rootable depth Žcm. AWC a Žmm. Infiltration rate Žcm hy1 . Rootable depth Žcm. AWC Žmm. Infiltration rate Žcm hy1 . Rootable depth Žcm. AWC Žmm. Infiltration rate Žcm hy1 . Rootable depth Žcm. AWC Žmm. Infiltration rate Žcm hy1 . Rootable depth Žcm. AWC Žmm. Infiltration rate Žcm hy1 . Rootable depth Žcm. AWC Žmm. Infiltration rate Žcm hy1 . Rootable depth Žcm. AWC Žmm.

0.8 80 125 1.5 75 80 5.0 75 50 0.8 100 80 5.0 100 60 0.8 50 65 1.5 100 160

Phenoformsb A

B

C

0.4 40 60 0.75 40 45 2.5 40 25 0.4 40 30 2.5 40 25 0.4 40 50 0.75 40 65

0.6 50 80 1.25 45 50 5.0 45 30 0.6 70 55 3.5 70 40 0.6 20 25 1.3 70 110

0.8 120 185 1.5 115 125 5.0 100 65 0.8 100 80 5.0 100 60 0.8 100 130 1.5 100 160

AWC is computed for rootable depth Žand y10 to y150 kPa range.. Phenoform A is compaction, B is water erosion and C is liming Žsee text..

b

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plough-layer. Form B resulted from water erosion, as expressed by an assumed 30 cm loss of the solum, which in turn, resulted in reduced infiltration rates and rooting depths. Form C resulted from liming as an expression of nutrient management. Here, the natural exchangeable-Al saturation of the subsoil, and subsoil pH, was considered to express possible effects of liming in terms of depth of rooting and AWC. Surface crusting is an important form of soil degradation with a potentially strong impact on land quality if it inhibits germination of seeds and infiltration of water. Local observations of representative phenoforms are needed to express such effects realistically.

7. Results 7.1. Production Õalues For each of the major soil units in Table 3, crop production expressed in grain equivalents has been calculated using climatic inputs derived from the CLI-

Fig. 1. Climate diagrams showing the monthly distribution of air temperature Ž8C., radiation and precipitation Žmm..

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Fig. 2. Potential and water-limited yield for the genoform and attainable yields for 3 phenoforms Žcompaction, erosion and liming., by soil type ŽC4 crop..

MATE database Ž Leemans and Cramer, 1991. . Monthly climate data, considered representative for the different agro-ecological zones are presented in Fig. 1. Crop productions were calculated for multiple cropping, the number of growing seasons being determined by climatic conditions. Results are only presented for a multiple cropping sequences of C4 crops. The calculated production values are presented in Fig. 2. 7.2. Land quality indicators We propose the ratio between estimated production and potential production Ž=100 to obtain whole numbers. to be a land quality indicator for agricultural

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production. Potential production is a characteristic value for any site as it is a defined function of radiation levels and air temperatures, assuming ample supply of water and nutrients for the crop. Estimated productions, as derived in this study, reflect the range of conditions that may occur as a result of soil degradation, such as compaction, erosion and acidification. Also, water-limited yields are indicated, which, when compared with potential yields, provide a measure for irrigation need. As with the production values discussed before, characteristic ranges are found for each genoform Ž Fig. 3. . For each of the different soils in Table 4, the difference between potential and water-limited productions can directly be related to the amount and distribution of rainfall, as illustrated in Fig. 1. The large difference between potential and water-limited

Fig. 3. Relative soil quality indicators by soil type ŽC4 crop; expressed as 100=yield i rpotential yield, where i is water-limited, compaction, erosion and liming, respectively..

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production for soil 7 is due to the seasonal rainfall pattern with an annual precipitation of only 1140 mm, while crop production calculations for soil 3 under an evenly distributed rainfall of 3000 mm yry1 show little differences between potential and water-limited yields. More interesting are the differences in water-limited productivity between the genoform and different phenoforms, as illustrated in Figs. 2 and 3. Most striking is the difference in range in relative phenoform productivity for each of the soils. Soil 6, an ‘Orthic Acrisol’ in China shows a wide range in phenoform productivity, while soil 1 Ža ‘Ferric Acrisol’ in China, with a comparable precipitation. exhibits a rather small range. The strong effects of water erosion on rootable depth and available water content ŽTable 4. for soil 6 Ž Orthic Acrisol., together with the different rainfall distribution between the soils, explain this difference in range. The effects of liming were expressed in terms of increased depth of rooting, increasing the amount of water available to the crop.

Fig. 4. Absolute soil quality indicator by soil type Žexpressed as 100=water-limited yieldrMPY, where MPY is Maximum Potential Yield of C4 crop in the world: 41.4 ty1 dry mattery1 hay1 yry1 s100%..

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This explains why liming may result in productions exceeding the water-limited ones. As compaction reduces both infiltration rate and available water content, it generally has a severe effect on productivity. As well as this type of yield indicator which is focused on potential yield of a given genoform, we also may consider an absolute value which is related to maximum potential yield in the world. This value is relatively simple to estimate: a C4 crop with a closed canopy Žfull radiation interception. produces under nonlimiting growing conditions approximately 300 kg hay1 dayy1 dry matter. A crop like sugarcane, with a growing cycle of 14–16 months, has full soil cover for 12 months throughout its cycle. When grown under Sahelian conditions Ž90% of the year, no limitation due to radiation and temperature. , the theoretical potential above ground production will be slightly over 80 ty1 hay1. Applying the conversion to grain equivalents according to Goudriaan et al. Ž1998., it yields approximately 40 ty1 hay1 grain equivalents. Water-limited yields of each genoform can now also be expressed in relation to this absolute standard ŽFig. 4.. We thus obtain 2 land quality indicators, an absolute one based on water-limited yield in relation to the highest potential production, and a relative one, relating production levels to the potential production of a given genoform in a given agro-ecological zone.

8. Discussion and conclusions Major soil types, as defined by FAO-UNESCO Ž 1974. , contain much relevant data to estimate parameters for simulation models as used in exploratory world food studies. Two approaches can be taken. The one used by Penning de Vries et al. Ž 1995a,b. estimated soil texture from the soil type represented on the FAO-UNESCO Soil Map of the World within a given 18 = 18 grid. Other soil characteristics, such as rooting depth or drainage status, were not varied. The second approach is illustrated in the second part of this paper. The major soil type is defined and typical parameters needed by the model Ž here: infiltration rate, rooting depth and available water content. are derived from the profile description using measured data and expert knowledge. This is preferred over procedures where single soil characteristics Ž texture, organic matter content, etc.. are estimated from the map and used in pedotransfer functions. Model calculations of productions should be part of the data belonging to the particular major soil types in meta databases. Data on effects of compaction, erosion and liming, as used in this study, are examples only. Calculated production figures should therefore not be considered as absolute values. However, they clearly illustrate that the effects of the different forms of degradation cannot simply be extrapolated, but that they need to be related to soil type and geographical location Že.g., Mantel and van Ebgelen, 1997. . The real challenge is to go out into the field and find out how major processes of soil degradation manifest

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themselves in different major land units Ž for a recent example: Tengberg et al., 1997.. Again, effects have to be expressed in terms of parameters of the model. In this exploratory study, liming was expressed in terms of rooting depth which was sometimes shallow in genoforms with acid subsoils, while liming conceivably led to much deeper rooting. New field work is needed in addition to gathering much already available data on phenoform conditions. This activity implies new use of existing soil maps because observations are only made in areas where a given genotype is supposed to occur. Similar developments are found at field and farm scale where also a case has been made to document phenoforms of given genoforms by simulation modelling to broaden the potential usefulness of soil survey data ŽDroogers and Bouma, 1997. .

Acknowledgements The work reported here is a part of the research program of the C.T. de Wit Graduate School of Production Ecology of the Agricultural University, Wageningen, the Netherlands and reflects contributions by the Laboratory of Soil Science and Geology, ISRIC and AB-DLO. W. Stol Ž AB-DLO. made the production calculations. A.J.M. van Oostrum Ž ISRIC. assisted in preparing the figures.

References Batjes, N.H., 1996. Development of a world data set of soil water retention properties using pedotransfer rules. Geoderma 71, 31–52. Batjes, N.H., 1997. A world data set of derived soil properties by FAO-UNESCO soil unit for global modelling. Soil Use Manage. 13, 9–16. Batjes, N.H., Fischer, G., Nachtergaele, F.O., Stolbovoy, V.S., van Velthuizen, H.T., 1997. Soil data derived from WISE for use in global and regional AEZ studies, ISRICrFAOrIIASA, IIASA Report IR-97-025. Luxembourg, 27 pp. Bouma, J., 1994. Sustainable land use as a future focus of pedology. Soil Sci. Soc. Am. J. 58, 645–646. Bouma, J., 1997. Role of quantitative approaches in soil science when interacting with stakeholders. Geoderma 78, 1–12. Bouma, J., Hoosbeek, M.R., 1996. The contribution and importance of soil scientists in interdisciplinary studies dealing with land. In: Wagenet, R.J., Bouma, J. ŽEds.., The Role of Soil Science in Interdisciplinary Research. Soil Sci. Soc. Am. Special Publ. 45, pp. 1–15. Buringh, P., van Heemst, H.D.J., Staring, G., 1975. Computation of the absolute maximum food production of the world. Report Dept. of Tropical Soil Science, Wageningen Agricultural University, Wageningen, the Netherlands. CGIAR, 1992. Review of CGIAR priorities and strategies, Report No. AGRrTAC:IARr92r18, Part 1. TAC Secretariat. FAO, Rome, Italy. Droogers, P., Bouma, J., 1997. Soil survey input in exploratory modelling of sustainable land management practices. Soil Sci. Soc. Am. J. 61, 1704–1710.

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