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Agriculture, Ecosystems and Environment 122 (2007) 58–72

Predicted soil organic carbon stocks and changes in the Brazilian Amazon between 2000 and 2030 C.E.P. Cerri a,*, M. Easter b, K. Paustian b, K. Killian b, K. Coleman c, M. Bernoux d, P. Falloon e, D.S. Powlson c, N.H. Batjes f, E. Milne g, C.C. Cerri a a Centro de Energia Nuclear na Agricultura, Av. Centenario 303, CEP 13400-970, Piracicaba, Brazil The Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523-1499, USA c The Agriculture and Environment Division, Rothamsted Research, Harpenden, Herts AL5 2JQ, UK d Institut de Recherche pour le De´veloppement, BP 64501, 34394 Montpellier, Cedex 5, France e The Met. Office, Hadley Centre for Climate Prediction and Research, Fitzroy Road, Exeter EX1 3PB, UK f ISRIC – World Soil Information, P.O. Box 353, 6700 AJ Wageningen, The Netherlands g The Department of Soil Science, The University of Reading, P.O. Box 233, Reading RG6 6DW, UK


Available online 9 February 2007

Abstract Currently we have little understanding of the impacts of land use change on soil C stocks in the Brazilian Amazon. Such information is needed to determine impacts on the global C cycle and the sustainability of agricultural systems that are replacing native forest. The aim of this study was to predict soil carbon stocks and changes in the Brazilian Amazon during the period between 2000 and 2030, using the GEFSOC soil carbon (C) modelling system. In order to do so, we devised current and future land use scenarios for the Brazilian Amazon, taking into account: (i) deforestation rates from the past three decades, (ii) census data on land use from 1940 to 2000, including the expansion and intensification of agriculture in the region, (iii) available information on management practices, primarily related to well managed pasture versus degraded pasture and conventional systems versus no-tillage systems for soybean (Glycine max) and (iv) FAO predictions on agricultural land use and land use changes for the years 2015 and 2030. The land use scenarios were integrated with spatially explicit soils data (SOTER database), climate, potential natural vegetation and land management units using the recently developed GEFSOC soil C modelling system. Results are presented in map, table and graph form for the entire Brazilian Amazon for the current situation (1990 and 2000) and the future (2015 and 2030). Results include soil organic C (SOC) stocks and SOC stock change rates estimated by three methods: (i) the Century ecosystem model, (ii) the Rothamsted C model and (iii) the intergovernmental panel on climate change (IPCC) method for assessing soil C at regional scale. In addition, we show estimated values of above and belowground biomass for native vegetation, pasture and soybean. The results on regional SOC stocks compare reasonably well with those based on mapping approaches. The GEFSOC system provided a means of efficiently handling complex interactions among biotic-edapho-climatic conditions (>363,000 combinations) in a very large area (500 Mha) such as the Brazilian Amazon. All of the methods used showed a decline in SOC stock for the period studied; Century and RothC simulated values for 2030 being about 7% lower than those in 1990. Values from Century and RothC (30,430 and 25,000 Tg for the 0–20 cm layer for the Brazilian Amazon region were higher than those obtained from the IPCC system (23,400 Tg in the 0–30 cm layer). Finally, our results can help understand the major biogeochemical cycles that influence soil fertility and help devise management strategies that enhance the sustainability of these areas and thus slow further deforestation. # 2007 Elsevier B.V. All rights reserved. Keywords: Soil organic carbon; Regional estimates; Land use change; Brazilian Amazon

1. Introduction * Corresponding author. Tel.: +55 19 34294724; fax: +55 19 34294610. E-mail address: [email protected] (C.E.P. Cerri). 0167-8809/$ – see front matter # 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.agee.2007.01.008

Land use change usually alters land cover and the terrestrial change in C stocks (Bolin and Sukumar, 2000).

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The change from one ecosystem to another can occur naturally or through human activity. Anthropogenic changes are prominent in tropical rain forest, which is a diverse and complex ecosystem occupying approximately 17% of the world’s land area and is a habitat for about 40–50% of the earth’s species (Meyers, 1981). Tropical rain forests represent significant sources/sinks of trace gases and the exchange of CO2 between forest and the atmosphere and are an important component of the global C cycle. The economic damage through global warming from tropical deforestation alone is estimated at US$ 1.4–10.3 billion per year (Pearce and Brown, 1994). Fearnside (1996) estimated the economic damage by global warming in the Amazon region at approximately US$ 1200–8600 ha 1. Despite the relatively small proportion (14%) of forest that has been cleared in the Amazon, the total deforested area is larger than France. In addition to its sheer expanse, this eco-region also presents ideal climatic conditions for plant growth, expressed by high temperatures throughout the year and well-distributed precipitation. Therefore, the Amazonian tropical rainforest has, in principle, great potential for soil C sequestration, despite poor to moderate soil fertility. Reliable tools to quantify change in C sequestration in soils from the Brazilian Amazon would therefore be a very valuable addition to existing methodologies for C offset negotiations. They would also help identify the most promising strategies for decreasing C losses (or increasing C stocks) and where their application would be most effective. There have been a number of previous approaches to estimating regional changes in C stocks or C sequestration potential. These include statistical approaches where relationships between land management and changes in C stocks are applied to regional C pools (Smith et al., 1997, 1998) and the application of dynamic ecosystem simulation models using spatially explicit data at a variety of spatial scales (Paustian et al., 1997; Falloon and Smith, 2002). The latter method was used in this study, through the application of the GEFSOC modelling system. This is the only approach that accounts for a changing environment, especially in the case of land use change. Therefore, the aim of this study was to predict soil C stocks and changes in the Brazilian Amazon between 2000 and 2030, using the GEFSOC soil C modelling system.

2. Materials and methods 2.1. The Brazilian Amazon The Amazon Basin covers an area of approximately 700 Mha (Pires and Prance, 1986) and occupies large portions of the national territories of Venezuela, Colombia, Peru, Guyanas, Bolivia, Ecuador and Brazil (Fig. 1). The basin is bounded in the west by the Andean Mountains, in the north by the crystalline mass of Guyana and the savannahs of


Fig. 1. The Amazonian tropical rain forest and the Brazilian Amazon.

Colombia and Venezuela, in the south by the plateau of Mato Grosso, and in the east by the Atlantic Ocean. The Basin is more than 3000 km from west to east, and its width ranges from 300 km in the west, to 800 km in the east. Its central part is almost entirely located within the Brazilian territory and forms the Brazilian Amazon area (Fig. 1). The latter was developed mainly on sediments from the Pleistocene (Prance and Lovejoy, 1984). On the whole, the Amazon has a hot and humid climate, characterized by small variations in the diurnal and monthly temperatures. However, because of its vast size and geomorphological heterogeneity, this region represents a wide range of local climates, with different annual rainfall distribution and temperature extremes (Marengo and Nobre, 2001; Sombroek, 2001). This region has the highest rate of deforestation in the world (Skole and Tucker, 1993). Deforestation has been higher in Latin America than in Asia or Africa not only in terms of area (4.3 Mha year 1) but also in percentage of forest cleared (0.64% year 1) (Anderson, 1990). Of the Latin American forests that remained in 1850, 370 Mha (28%) had been cleared by 1985. Of this cleared area, 44% was converted to pasture, 25% to cropland, 20% became degraded and 10% changed to shifting cultivation (Houghton, 1991). In the Brazilian Amazon, estimated deforestation rates range from 1.1 to 2.9 Mha year 1. The total area cleared has reached approximately 55 Mha, about 14% of the total area of the Brazilian Amazon (INPE, 2004). Laurance et al. (2001a,b) identified several factors that have led to this rapid rate of deforestation. First, non-indigenous populations in the Brazilian Amazon have increased 10-fold since the 1960s, from about 2 to 20 million people, as a result of


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immigration from other areas of Brazil and high rates of intrinsic growth. Second, industrial logging and mining are growing dramatically in importance and road networks are expanding, increasing access to forests for ranchers and colonists. Third, the spatial patterns of forest loss are changing; past deforestation has been concentrated along the densely populated eastern and southern margins of the basin, however new highways, roads, logging projects and colonisation are now penetrating deep into the heart of the area. Finally, human-ignited fires are becoming an increasingly important cause of forest loss, especially in logged or fragmented areas (Laurance et al., 2001a). Despite the expanse of the deforested area, the Brazilian Amazon still accounts for approximately 40% of the world’s remaining tropical rainforest and plays a vital role in maintaining biodiversity, regional hydrology and climate, and terrestrial C storage (Laurance et al., 2001a). The forest accounts for 10% of the world’s terrestrial primary productivity and for a similar percentage of the C stored in land ecosystems (Keller et al., 1997). Cattle pastures dominate this once-forested land in most of the basin (Pires

and Prance, 1986; Skole and Tucker, 1993; Fearnside and Barbosa, 1998; Dias-Filho et al., 2001). Knowledge of landforms and soil conditions of the Amazon region remained limited until the 1960s (FAOUnesco, 1971; Sombroek, 1966). Jacomine and Camargo (1996) reported that two main soil divisions of the Brazilian soil classification, Latossolos (Oxisols or Ferralsols) and Podzo´licos (mainly Ultisols or Alisols), cover nearly 75% of the Amazon Basin (Fig. 2a). The remaining area is comprised of 13 soil divisions, only two of which cover more than 5% of the Amazon: Plintossolos (Inceptisols, Oxisols and Alfisols) and Gleissolos (Entisols and Inceptisols) representing 7.4 and 5.3%, respectively (Fig. 2b). The Brazilian Latossolos (Oxisols in the US Soil Taxonomy) are old, deep, permeable and well-drained soils. The clay mineral component is predominantly kaolinite, a low-activity clay, with varying amounts of iron and aluminum oxides (Cerri et al., 2000). Generally, the cation exchange capacity is only partially saturated with bases and the exchangeable Al3+ is relatively high (Lal, 1987). The Podzo´licos are mainly Ultisols of the US Soil Taxonomy.

Fig. 2. Simplified soil map of the Brazilian Amazon (a) and relative distribution of main soil types in the Amazon (b). Adapted from Cerri et al. (2000).

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They also contain low-activity clays and unsaturated cation exchange capacity. Eutrophic soils (Alfisols) are rare. Ultisols usually occupy younger geomorphic surfaces than Oxisols, with which they are often associated in the landscape. These are deep mineral soils with profiles often deeper than 2 m (Moraes et al., 1996). Moraes et al. (1995) estimated that approximately 47 Pg C are contained in the soils of the Brazilian Amazon to 1 m depth. Of this total, 21 Pg is contained in the top 20 cm, the depth at which changes in soil C stocks following land use change are generally most rapid. Cerri et al. (2000) estimated C stocks of 41 Pg C in 0–100 cm depth and 23.4 Pg C (i.e., 57% of the total) in the 0–30 cm depth. Ecological characteristics of the Brazilian Amazon are described by many researchers. McClain et al. (2001) report high plant biomass containing high concentration of nutrients (Bernoux et al., 2001), rapid rates of nutrient recycling (Cuevas, 2001), high annual rainfall with little seasonal variation in temperature and humidity (Sombroek, 2001; Marengo and Nobre, 2001), and a relatively closed system for nutrient and water cycling (Melack and Forsberg, 2001). 2.2. The GEFSOC modelling system The GEFSOC modelling system, described in detail by Easter et al. (2007), was used to integrate the data presented below and conduct analysis with three well-recognized models and methods: (i) the Century# general ecosystem model, (ii) the RothC# soil C decomposition model and (iii) the Intergovernmental Panel on Climate Change (IPCC) method for assessing soil C at regional scales. The modelling system requires six basic data classes to build the datasets necessary for a regional simulation: native vegetation, historic and current land use management, climate, soils and latitude/longitude. 2.3. Geographically distributed data used in the regional simulations There are several sources of data from which the model inputs could have been compiled for the Brazilian Amazon. In-country data, were selected as these are likely to provide the most detailed and accurate sources of information. 2.3.1. Climate Data were collated on temperature and precipitation for some regions within the Brazilian Amazon from the national weather service records (CPTEC/INPE/MCT and INMET) and scientific projects such as ABRACOS and LBA ( Precipitation data were identified and collated from approximately 1085 stations within the Brazilian Amazon (Fig. 3). Records on monthly air temperature were obtained from the ‘Carbon in the Amazon River Experiment’ (CAMREX) project, the ABRACOS project and the National Institute of Meteorology (INMET).


Fig. 3. Location of the pluviometric stations and number of stations in each state of the Brazilian Amazon.

Over the last decade the objective of the CAMREX project has been to define by mass balances and direct measurements those processes that control the distribution of bioactive elements (C, N, P and O) in the mainstream of the Amazon River in Brazil. The period over which air temperature measurements were made varies, however the majority of the data are from 1958 to 1995. The data were organized by month, averaging information for the period 1960–1991. 2.3.2. Native vegetation A native vegetation map (Fig. 4) of the Brazilian Amazon at the scale 1:5,000,000 was also used as input for the regional simulations. The original data set consisted of a digital version of the vegetation map of Brazil (IBGE, 1988) and was constructed from 2020 map units, divided in 94 vegetation classes. It is important to note that this map represents the ‘‘potential natural vegetation’’, that is the native vegetation that would result in the absence of human disturbance at the time of the mapping regarding climatic and soil conditions. 2.3.3. Soil Both the soil map of the Brazilian Amazon and the dataset that we describe below were used. The soil map was digitized from EMBRAPA data (1981) at the scale 1:5,000,000 containing 2698 map units representing 69 soil types for the whole of Brazil. The soil and terrain (SOTER) dataset presents a harmonized set of soil parameter estimates for the Brazilian Amazon, developed to permit modelling of soil C stocks and changes (Batjes et al., 2007). The soil and terrain (SOTER) Database for Brazil (of which the Brazilian Amazon data is part), compiled in the framework of the 1:5 M SOTER work for Latin America and the Caribbean, provided the basis for the current study. Results are presented as summary files and can be linked to the 1:5 M scale SOTER map for Brazil and Brazilian Amazon in a GIS through the unique SOTER-unit code (Batjes et al., 2004). 2.3.4. Land cover The land cover map of the Brazilian Amazon for the year 2000 was generated using radar data from the Joint Research


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Fig. 4. Native vegetation types in the Brazilian Amazon. Adapted from Bernoux et al. (2002).

Center JRC-ISPRA (Eva et al., 2002). In addition to the land cover map, we also used statistical data from the agricultural census. Data from 170 agricultural census bulletins were transferred from hardcopy to digital format. Data relating to areas of pasture, native vegetation and cultivated (crop) land were organized in EXCEL spreadsheets, on a county scale basis for the Brazilian Amazon. The Institute of Applied Economy (IPEA, Rio de Janeiro, Brazil, kindly provided data for the years 1975, 1980 and 1985. Using these sources land use data for the years 1940, 1950, 1960, 1970, 1975, 1980, 1985 and 1995 were assembled for

the entire Brazilian Amazon. This information was used to elaborate current and future land use scenarios. 2.4. Land use scenarios current and future Land use scenarios for the Brazilian Amazon, current and future, were devised taking into account; deforestation rates during the past two decades, census data on land use from 1940 to 2000 including the expansion and intensification of agriculture in the region, available information on management practices (mainly related to well managed pasture

Fig. 5. Example of the census data for counties within the Brazilian Amazon.

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versus degraded pasture and conventional systems versus no-tillage systems for soybean) and FAO predictions on agricultural land use and land use changes for the years 2015 and 2030 (FAO, 2002). Our baseline land use scenario was obtained using information from census data for the entire Brazilian Amazon. Census data for the years 1940, 1950, 1960, 1970, 1975, 1980, 1985 and 1995 were organised for the entire Brazilian Amazon on a county (‘‘Municipio’’) basis. An example of the data is shown in Fig. 5. When working with time series data of land use, we had to address the issue of county dynamics (e.g., new counties being created over the years) for each state of the Brazilian Amazon, as illustrated for the state of Rondonia in Fig. 6. Therefore, an essential step was to make an association between the agricultural census data and the county maps. In order to do this, we prepared sets of maps showing county evolution throughout time for all the states of the Brazilian Amazon. The result is a series of maps of different land use/ land cover, by county for the entire Brazilian Amazon. According to the census data, the population in the Brazilian Amazon increased at an average annual rate of 4% during the period 1970–1990. The urban part of the population expanded much faster than the rural, leading to a dramatic change in the composition of the population. Both rural and urban inhabitants have an impact on deforestation rates. Urban inhabitants typically work in the service sector and are therefore assumed to have no direct impact on deforestation. There will, nevertheless, be an indirect effect through the demand for agricultural goods and timber. In recent years the Brazilian Amazon has been faced with high annual deforestation rates. In 2002 and 2003, the rate of


deforestation rose to nearly 2.4 Mha year 1 (Table 1). This increase was mostly the result of the rapid destruction of seasonal forests in the southern and eastern sections of the basin. Relative to preceding years (1990–2001), forest loss has increased by a massive 48% in the states of Para, Rondonia, Mato Grosso and Acre (Table 1). The net deforestation rate in these four states increased from 1.43 Mha year 1 from 1990 to 2001, to 2.12 Mha year 1 in 2002–2003 (based on data from the Brazilian National Space Agency (INPE, 2004)). This increase was evidently driven by rising deforestation and land speculation along new highways and planned highway routes and the dramatic growth of Amazonian cattle ranching and industrial soybean farming (Laurance et al., 2001a,b). For our scenarios of land use change, we have assumed a constant deforestation rate of 2.0 Mha year 1 for the next 30 years, considering ‘‘business as usual’’. This value was obtained by averaging the official annual deforestation rates for the last decade (INPE, 2004) (Table 1). Cattle ranching is the leading cause of deforestation in the Brazilian Amazon. This has been the case since at least the 1970s. Government figures attributed 38% of deforestation from 1966 to 1975 to large-scale cattle ranching. However, today the situation may be even worse. Several factors have spurred Brazil’s recent growth as a producer of beef: (i) currency devaluation: the devaluation of the Brazilian real against the US dollar effectively doubled the price of beef and created an incentive for ranchers to expand their pasture at the expense of the rainforest. The weakness of the Brazilian currency also made Brazilian beef more competitive on the world market; (ii) control over foot-and-mouth disease: the eradication of Foot-and-Mouth Disease in much of Brazil has

Fig. 6. Municipio evolution/dynamics through time for Rondonia State, Brazil.

1016 1330 7578 8697 3605 54 259 23266

549 4 797 766 10416 7293 3463 326 136 23750 727

720 1230 6963 5111 2358 220 216 17259

612 1065 6369 6671 2465 253 244 1652326

419 7 634 958 7703 5237 2673 345 189 18165 547 441

536 30 670 1012 69636 5829 2041 223 576 17383 1023 1061 6543 6135 23582 214 320 182261

358 18 589 409 5271 4139 1986 184 273 13227 433

370 372 6220 4284 2595 240 333 14896

1208 9 2114 1745 10391 7845 4730 220 797 29059 482

400 36 799 1135 4674 378037 2595 281 409 13786 380 410 980 670 2840 3780 1110 420 440 13730 550 250 720 1100 4020 4890 1670 150 580 13730 a


Decade mean. Biennium mean.

540 130 1180 1420 5960 5750 1430 630 730 17770 620 60 1510 2450 5140 6990 2340 290 1650 21050 Acre Amapa Amazonas Maranhao Mato G rosso Para Rondonia Roraima Tocantins Amazon

Amazon states 1977/1988a 1988/1989 1989/1990 1990/1991 1991/1992 1992/1994b 1994/1995 1995/1996 1996/1997 1997/1998 1998/1999 1999/2000 2000/2001 2001/2002 2002/2003

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Table 1 Mean rate of gross deforestation (100 ha year 1) by state for the Brazilian Amazon (adapted from INPE, 2004)


increased price and demand for Brazilian beef; (iii) infrastructure: road construction gives developers and ranchers access to previously inaccessible forest lands in the Amazon. Infrastructure improvements can reduce the costs of shipping and packing beef; (iv) interest rates: rainforest lands are often used for land speculation purposes. When real pastureland prices exceed real forestland prices, land-clearing is a good hedge against inflation. At times of high inflation, the appreciation of cattle prices and the stream of services (milk) they provide, may outpace the interest rates earned on money simply left in the bank and (v) land tenure laws: in Brazil, colonists and developers can gain ownership of Amazon lands by simply clearing forest and placing a few head of cattle on the land. As an additional benefit, cattle are a low risk investment relative to cash crops, which are subject to severe price swings and pest infestations. Essentially, cattle are vehicles for land ownership in the Amazon. A significant amount of deforestation is also caused by the subsistence activities of poor farmers who are encouraged to settle on forestlands by government land policies. In Brazil, each squatter acquires the right (known as an usufruct right) to continue using a piece of land after having lived on a plot of unclaimed public land (no matter how marginal the land) and ‘‘using’’ it for at least 1 year and a day. After 5 years the squatter acquires ownership and hence the right to sell the land. Up until at least the mid1990s this system was exacerbated by a government policy that allowed each claimant to gain title for an amount of land up to three times the amount of forest cleared. Poor farmers use fire to clear land and every year satellite images show tens of thousands of fires burning across the Amazon (INPE, 2004). Typically, under-story shrubbery is cleared and then forest trees are cut. The area is left to dry for a few months and then burned. The land is planted with crops such as bananas (Musa X paradisiaca L.), palms (Bactris gasipaes Kunth), manioc (Manihot esculenta), maize (Zea mays) or rice (Oryza sativa L.). After a year or two, the productivity of the soil declines and the transient farmers move deeper into the forest clearing new forest to use as short-term agricultural land. The old, now infertile fields are used for small-scale cattle grazing or left for waste. Recently soybean production has become one of the most important contributors to deforestation in the Brazilian Amazon. The soybean expansion became possible following the production of a new variety of soybean, developed by Brazilian scientists to flourish in the rainforest climate. Brazil is on the verge of supplanting the United States as the world’s leading exporter of soybeans. Soybean farms promote some forest clearing directly, but have a much greater impact on deforestation by consuming cleared land, savannah and ecotonal forests, thereby pushing ranchers and slash-and-burn farmers ever deeper into the forest. Equally important, soybean farming provides a key economic and political impetus for massive infrastructure projects, which accelerate deforestation by other groups and individuals. Therefore, soybean expansion was included in our current

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and future scenarios. For the year 2015, estimates indicate that approximately 60% of the new deforested area in the Brazilian Amazon will be used for soybean cultivation. Pasture will be established on the remaining 40% of the newly cleared area. According to our scenarios, in 2030, the proportion of soybean to pasture would be even greater with approximately 70% of the newly cleared areas used for soybean and only 30% for pasture. Moreover, the pasture areas would also be split according to the management system: 20% of the pasture areas would be under well managed systems and the remaining 80% under degrading systems. Degraded pasture can be further split into three main categories: (1) remain as degraded pasture, (2) become well managed pasture after a rehabilitation and/or (3) be converted to row crops (mainly soybean). The suggested scenarios do not necessarily need to occur as they are described here; they are projections. It is difficult to devise scenarios for 2015 and 2030 for such a large area that is currently under development. However, we consider that they are likely and some have previously been used in other publications (Castro et al., 2001; Godinho et al., 2003; FNP, 2003; Saraiva et al., 2004; EMBRAPA, 2005). Our estimates of soybean expansion are in agreement with FAO predictions on agricultural land use and land use changes for the years 2015 and 2030 (FAO, 2002). Nevertheless, the purpose of the present study is not to define the most probable future scenarios, but rather to estimate the changes in SOC were these scenarios to occur.

3. Results and discussion 3.1. Regional simulations The Brazilian Amazon plays an important role in the global C cycle due to its large extent, its relatively high C


Fig. 8. Modelled SOC stocks in the 0–20 cm layer for the Brazilian Amazon in 1990, 2000 and 2030 derived from Century.

density and high deforestation rates. As mentioned in the previous sections, there are multiple variables that determine SOC distribution in the Brazilian Amazon. The GEFSOC modelling system was used to provide estimates of current and future SOC for the entire Brazilian Amazon as shown in Figs. 7–10. The system can produce a variety of outputs (Easter et al., 2007), of which only a selection are presented here namely SOC stocks and stock change rates. These were estimated by three methods: (i) the Century ecosystem model, (ii) the Rothamsted C model and (iii) the intergovernmental panel on climate change (IPCC) method for assessing soil C at the regional scale. Fig. 7 shows the current SOC stock distribution in the Brazilian Amazon for the 0–20 cm layer, obtained by the Century model. SOC stocks vary from 20 to 150 Mg C ha 1 in the top 20 cm of soil. SOC in the majority of the area is in the 60–80 Mg C ha 1 range. Fig. 7 shows agreement with information from other maps. For instance, soils adjacent to the Amazon River have the highest SOC stocks

Fig. 7. Map of year 2000 SOC stocks from the Century model for the 0–20 cm layer in the Brazilian Amazon.


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Fig. 9. Modelled SOC stock change rate for the Brazilian Amazon (1990– 2030).

(100–150 Mg C ha 1) due to their hydric conditions. The most northern and southeastern parts of the map show the lowest SOC stocks, which is as expected as the native vegetation is savannah, rather than evergreen forest (Fig. 4). The GEFSOC modelling system allows estimating SOC stocks, not only for different land uses but also for different soil hydrological conditions. According to SOTER and other data sets for Amazonian soils, the majority of Amazonian soils are non-hydric or freely drained. Among the different land uses under hydric conditions, the largest contribution to SOC stocks comes from soils under native vegetation (mainly evergreen forests, classes coloured with black and dark grey in Fig. 7). Estimated SOC stocks (0–20 cm) for the years 1990, 2000 and 2030 from Century are presented in Fig. 8. Stocks of SOC under native vegetation are projected to decrease through time due to the area reduction according to the land use scenarios described earlier. If the projected deforestation

in 2030 is realised, approximately 4200 Tg C will be lost from the soil compared to the estimate for 1990 (Fig. 8). It should be stressed that following deforestation, large amounts of C are also lost from biomass, much of which is emitted to the atmosphere as CO2. According to DiasFilho et al. (2001), forest-to-pasture conversion releases 100–200 Mg C ha 1 from above-ground forest biomass to the atmosphere. Thus the loss of forest area in the scenario represents a total additional loss of C from above ground biomass of 8000 Tg between 1990 and 2030. In the Brazilian Amazon, areas that have been converted from pasture to agriculture are mainly cropped with soybean, associated with a cover crop (usually millet) and cultivated under conventional tillage during the first 1–2 years before moving to a no-tillage system. Conversion of native vegetation to cultivated cropland under a conventional tillage system has resulted in a significant decline in soil organic matter content (Paustian et al., 2000; Lal, 2002). Farming methods that use mechanical tillage, such as the mouldboard plough for seedbed preparation or disking for weed control, can promote soil C loss by several mechanisms: they disrupt soil aggregates, which protect soil organic matter from decomposition (Karlen and Cambardella, 1996; Six et al., 1999), they stimulate shortterm microbial activity through enhanced aeration, resulting in increased emissions of CO2 and other radiatively active gases to the atmosphere (Bayer et al., 2000a,b; Kladivko, 2001), and they mix fresh residues into the soil where conditions for decomposition are often more favourable than on the surface (Karlen and Cambardella, 1996). Furthermore, tillage can make soils more prone to erosion, resulting

Fig. 10. Estimated SOC stocks in the agricultural expansion area within the Brazilian Amazon (1990–2030).

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in further on-site loss of soil C (Lal, 2002). Conversely, notillage practices cause less soil disturbance, often resulting in significant accumulation of soil C (Sa et al., 2001; Schuman et al., 2002) and consequent reduction of gas emissions (especially CO2) to the atmosphere (Lal, 1998; Paustian et al., 2000). However, there is considerable evidence that the main impact occurs in the topsoil with little overall effect on C storage in deeper layers (Six et al., 2002; Machado et al., 2003). In the humid tropics, minimum tillage methods may need to be combined with herbicide use for weed control, possibly leading to environmental pollution and a reduction in biodiversity (Batjes and Sombroek, 1997). Due to the adoption of no-tillage systems, SOC stocks are projected to increase under soybean cultivation in the period to 2030 (Fig. 8). The effects of agricultural expansion are evident in the modelled SOC stock change rates (Fig. 9), where soybean shows the highest change rate among the different land uses in the Brazilian Amazon. There are no estimates for SOC stock change rate (Fig. 9) or SOC stocks (Fig. 8) for soybean in the year 1990, as soybean production was not widespread in the region at the time. The projected increase in areas cultivated with soybean that adopt notillage systems would lead to an increase in SOC stocks. This is illustrated Fig. 10 for the years 2000 and 2030. In 1990, the area of agricultural expansion was dominated by SOC stock class of 20–40 Mg C ha 1. In the year 2000, some areas began to be cultivated with soybean and finally in 2030, most of this region is projected to move to a higher class of SOC stock (40–60 Mg C ha 1; Fig. 10). However, if the main effect of no-tillage is to concentrate organic C near the soil surface (Machado et al., 2003), the accumulation of SOC will have been overestimated. The estimates presented here are comparable with those obtained from other studies considering no-till systems in the mid-western part of Brazil (Lima et al., 1994; Riezebos and Loerts, 1998; Vasconcellos et al., 1998; Peixoto et al., 1999; Resck et al., 2000). These studies reported SOC stock accumulation rates of 0–1.2 Mg C ha 1 year 1 for the surface soil layer. For the land use scenario used, SOC stocks increase in the abandoned areas, from 217 Tg in 1990 to 620 Tg in 2030 (Fig. 8). These areas follow a clear pattern of development. During pasture use, burning and weeding delay succession, however the forest begins to regenerate once the area is abandoned. Secondary vegetation establishes itself through four main processes: regeneration of remnant individuals, germination from the soil seed bank, sprouting from cut or crushed roots and stems, and dispersal and migration of seed from other areas (Tucker et al., 1998). Variation in the speed of forest re-growth is evident across regions and along a soil fertility gradient in the Brazilian Amazon. The rate of forest succession is determined by several factors. Original floristic composition, neighbouring vegetation and soil fertility and texture may affect re-growth. In addition, farmers’ land use decisions (such as clearing size, clearing procedures, frequency and duration of use and crops) influence tree


establishment and the pathway of secondary succession. At the regional scale, soil fertility and land use history are the critical factors influencing the rate of forest re-growth (Tucker et al., 1998). Secondary forests in the Amazon have high rates of regeneration, both following slash-and-burn agriculture and after abandonment of degraded pasture. Brown and Lugo (1990) reported that abandoned agricultural lands that reverted to forest accumulate C at rates proportional to the initial forest biomass. Rates range from about 1.5 Mg C ha 1 year 1 in forests with initial biomass of 190 Mg C ha 1. Woomer et al. (1999) observed a rate of 6.2  1.3 Mg C ha 1 year 1 of C sequestration in secondary forest re-growth in the Brazilian Amazon. Watson et al. (2000) suggested a range of C accumulation rates of 3.1–4.6 Mg C ha 1 year 1 for tropical regions over 40 years. Schroth et al. (2002) reported that secondary forest on an infertile upland soil in central Amazon accumulated C in above and belowground biomass and litter at a rate of about 4 Mg ha 1 year 1. The rate of accumulation in aboveground biomass reported by Nepstad et al. (2001) ranged from 2.5 to 5 Mg C ha 1 year 1 for 20year-old Amazon secondary forest. Feldpausch et al. (2004) reported C accumulation for the central Amazon of 128 Mg C ha 1 for a 12-year-old secondary forest dominated by Vismia ssp. The secondary vegetation was regenerated on an abandoned, severely degraded pasture near Manaus. Forests that develop on abandoned land also counteract many of the deleterious impacts of past forest conversion to agriculture and cattle pasture. They play an important role in the regional C budget, as they re-assimilate part of the C that was released upon cutting and burning of the original forest vegetation. They also help to restore hydrological functions performed by mature forests and reduce the flammability of agricultural landscapes. Secondary forests transfer nutrients from the soil to living biomass, thereby reducing potential losses of nutrients through leaching and erosion. They also facilitate the expansion of native plant and animal populations from mature forest remnants back into agricultural landscapes (Nepstad et al., 2001). Pastures have the potential to re-introduce large amounts of organic matter into the soil (Fisher et al., 1994; Boddey et al., 1996; Rezende et al., 1999; Guo and Gifford, 2002). Pasture is usually introduced after a slash-and-burn technique used to clear the forest, with only two or three trees per hectare being harvested before clearance. Pasture grasses, such as Brachiaria brizantha, one of the best adapted to the regional conditions, are sown by plane or even by hand. After 2–4 years of establishment, weed control becomes necessary, which can be done by manually or with herbicide. The pasture can feed a varying number of cattle, on average 1.5 animals per hectare. Numbers vary according to the season and the rancher’s economic situation. Usually no fertilizers are used due to the high costs of chemical


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products that have to be transported from the southern states of Brazil. If the pasture is well managed (depending on soil type, weed control, use of adapted grass species and ideal grazing pressure), the system can be productive for more than 35 years. Increased soil C concentrations in surface horizons are a common consequence of pasture formation after forest clearance in the Amazon basin (Bonde et al., 1992; Moraes et al., 1995,1996; Trumbore et al., 1995; Neill et al., 1997). Moraes et al. (1996) found that total soil C contents to 30 cm in 20-year-old pasture were 17–20% larger than in the original forest sites of the western part of the Amazon. Trumbore et al. (1995) compared C budgets for forest and pastures in the eastern Amazon. In a rehabilitated and fertilized pasture of Brachiaria brizantha, they estimated gains, relative to forest soil C stocks, of over 20 Mg soil C ha 1 in the top 1 m of soil and a loss of about 0.5 Mg C ha 1 in the 1–8 m soil depth interval during the first 5 years following pasture rehabilitation. More than 50% of the forest-derived C in surface soils of pastures established on converted Amazon forest, turns over in 10–30 years (Chone´ et al., 1991; Trumbore et al., 1995). The estimated SOC stocks derived from the GEFSOC modelling system show an increase for well-managed pasture from 1990 to 2030 (Fig. 8). The area under degraded pasture, i.e., areas dominated by herbaceous and woody invaders (Uhl et al., 1988; Serra˜o and Toledo, 1990) should diminish according to our scenarios (mainly due to land prices). This is reflected in the reductions in SOC stocks in the year 2030. Under the scenarios considered, soil physical, chemical and biological properties would probably change due to alterations in the quantity and quality of C inputs to the soil, nutrient changes and stimulation of decomposition. Some of the issues discussed here are in agreement with the general patterns reported in other point or site scale studies. Fearnside and Barbosa (1998), for example, showed that trends in soil C were strongly influenced by pasture management. Sites that were judged to have been under poor management generally lost soil C, whereas sites under ideal management gained C. Trumbore et al. (1995) reported soil C losses in overgrazed pasture, but soil C gains from fertilized pasture in the Amazon region. According to Neill et al. (1997), degraded pastures with little grass cover are less likely to accumulate soil C because inputs to SOC from pasture roots will be diminished. However, this may not be true under the more vigorous re-growth of secondary forest. Greater grazing intensity and soil damage from poor management would, in all likelihood, cause soil C losses. 3.2. Comparison of current stocks with outcome from existing methods There are relatively few estimates of SOC stocks for the Brazilian Amazon (Moraes et al., 1995; Batjes and Dijkshoorn, 1999; Bernoux et al., 2002; Batjes, 2005) and

these mainly focus on the situation under native vegetation. To our knowledge, no prior study has considered stocks of SOC under actual land use (e.g., including areas under pasture and agriculture). 3.2.1. Estimates by Moraes et al. (1995) Moraes et al. (1995) determined stocks of C and nitrogen (N) for soils under undisturbed vegetation across the Brazilian Amazon basin using 1162 soil profiles from the RadamBrasil survey and a digitized Brazilian soil survey map. Mean basin soil C density was 10.3 kg C m 2 to 1 m depth. About 47 Pg C and 4.4 Pg N were contained in the top 1 m of soil. According to the authors, 45% of the total basin soil C (21 Pg C) and 41% of total soil N (1.8 Pg N) were contained in the top 20 cm over a 500 Mha area of the Brazilian Amazon. As these data represent sites with forest vegetation in the absence of significant disturbances, they represent a valuable baseline for evaluating the effects of land-use changes on soil C stocks in the Amazon. 3.2.2. Estimates by Batjes and Dijkshoorn (1999) Soil organic N and C stocks, to a depth of 0.3 and 1 m, respectively, were determined for the entire Amazon Region (about 700 Mha) using the soil and terrain database for Latin America and the Caribbean (SOTERLAC, FAO et al., 1998). Mean C densities to a depth of 1 m were estimated to, range from 4.0 kg m 2 for coarse textured Arenosols to 72.4 kg m 2 for the poorly drained Histosols. Mean C density for the mineral soils, excluding Arenosols and Andosols (30.5 kg C m 2), was estimated as 9.8 kg m 2. In total, the surface 1 m of soil was estimated to hold 66.9 Pg C and 6.9 Pg N. Approximately 52% of this C pool (about 34.8 Pg) was in the top 0.3 m of the soil, the layer most prone to changes upon deforestation. For the Brazilian Amazon (about 500 Mha) the calculated organic C stock was 25 Pg C (0–30 cm) and 46.5 Pg C (0–100 cm), similar to the values reported by Moraes et al. (1995). 3.2.3. Estimates by Bernoux et al. (2002) The objective of the study by Bernoux et al. (2002) was to give reliable values and a distribution map, for the 0–30 cm reference layer used by IPCC/UNEP/OECD/IEA (1997), for the Brazilian Amazon (an area that was ‘clipped’ from the map of Brazil’s total SOC stock under undisturbed vegetation). The study comprised: (i) elaboration of a map of representative Soil-Vegetation Associations (SVA) for Brazil; (ii) organization of a soil profile database, gathering information such as C concentration, bulk density, soil type and native vegetation; (iii) calculation of representative C stock (RCS) values for each SVA category and subsequent production of a map of their distribution and (iv) extraction of part of the Brazilian Amazon from the stock produced for the whole of Brazil. The potential total soil C stock of Brazilian Amazon under native vegetation, estimated by Bernoux et al. (2003) was 22.7 Pg C in the 0–30 cm layer. Using the standard error

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(SE) calculated for each SVA category (using an arbitrary level of 25% of the representative value when it is derived from only one C stock value) as a mean to estimate the accuracy of the total C stock, the entire error would be 2.3 Pg C. 3.2.4. Estimates by Batjes (2005) SOC stocks were calculated using an updated 1:5,000,000 scale SOTER database for Brazil (Batjes et al., 2004). Each SOTER unit is composed of one to five soil components, depending on the complexity of the mapping unit. Individual soil components were characterized by a typical profile selected as being regionally representative. Use of a single representative profile, however, ignores the variability within a soil component. Therefore, the procedure included the simulation of phenoforms, using the typical profile as the genoform. The information resulting from the simulations was linked to the soil geographical information, through the unique profile identifier for each soil component, to arrive at n (300) realizations of the regional organic C stocks. Results are presented as 95% confidence limits for the population median. For the Brazilian Amazon, median SOC stocks to 1 m were estimated to be 42.3–43.8 Pg C of which 23.9– 24.2 Pg C occur in the top 30 cm (Batjes, 2005). Estimates of SOC stocks for the Brazilian Amazon obtained from the different mapping methods are presented in Table 2; they are all somewhat lower than those obtained using the GEFSOC modelling system. The discrepancies may be related to the fact that our modelling results consider possible changes in land use (pasture and agriculture) and management practices (well managed pasture versus degraded pasture and conventional soybean cultivation versus no-tillage soybean cultivation) as opposed to native vegetation only. Conversely, the mapping approaches implicitly account for possible effects of soil properties other than soil wetness and clay content on SOC stocks – for example activity of the clay minerals (Wattel-Koekkoek et al., 2001), aluminium toxicity and content of coarse fragments – which are not yet considered in the dynamic models. Table 2 Estimates of soil organic carbon (SOC) stocks for the Brazilian Amazon obtained from different studies Source

Soil layer (cm)

SOC stocks (Pg)

Moraes et al. (1995) Batjes and Dijkshoorn (1999) Bernoux et al. (2002) Batjes (2005)

0–20 0–30 0–30 0–30

21 25 22.7  2.3 23.9–24.2

GEFSOC projecta Century RothC IPCC

0–20 0–20 0–30

32.6 27.0 26.9


Estimates for the year 2000 and includes land use changes and management practices.

Fig. 11. SOC stocks for the Brazilian Amazon estimated from different methods of the GEFSOC modelling system.

A clear advantage of the GEFSOC modelling system is that it permits the estimation of SOC stocks not only for current conditions, but also for past (historic) conditions and future scenarios. The mapping methods discussed earlier, however, can only calculate SOC stocks for the period for which data is available, they are not dynamic and hence do not allow presentation of ‘windows-of-opportunity’ for future land use/management scenarios, as required for policy making and in the context of the international conventions. Estimates of mean SOC density in the top 20 cm layer from RothC and Century models using the GEFSOC modelling system (4 and 6 kg C m 2, respectively) are within the range of values presented by previous studies in the Brazilian Amazon. For instance, Bernoux et al. (2002) reported that three quarters of all areas in Brazil had C density varying between 3 and 6 kg C m 2 in the 0–30 cm layer. Batjes (2005) found mean C densities varying from 2.4 (Arenosols) to 9.3 kg C m 2 (Gleysols) for the 0–30 cm layer. 3.3. Comparison of different methods of the GEFSOC modelling system The GEFSOC modelling system includes three different methods for assessing changes in SOC stocks (Fig. 11): two process models (Century and RothC) and an empirical method (IPCC). The IPCC method considers a deeper soil layer (0–30 cm) than Century and RothC (0–20 cm). Nonetheless, all three methods showed a decrease in SOC over time for the given scenarios. This decrease is slightly more accentuated for the IPCC method, compared to the outputs from the two process models. SOC stocks modelled using Century are up to 18% larger than those using RothC, but they both show the same trend (Fig. 11).

4. Conclusions Pasture productivity and longevity, and agricultural expansion in the Amazon basin are closely related to soil organic matter dynamics. Thus, understanding the major


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biogeochemical cycles that influence soil organic matter under cleared lands is vital for predicting the consequences of continued conversion of tropical forest to cattle pastures and agriculture. This understanding is also important in order to devise management technologies that enhance the sustainability of these areas and thus slow further deforestation. Therefore, more field data is needed on the effect of human-induced land use changes on tropical SOC dynamics and their relationship to soil type and climate condition. Such data can be used, together with remotely obtained imagery and geographic information systems and base maps of land use conversions, to improve overall estimates of current and future stocks of SOC in the Brazilian Amazon. The GEFSOC system provided a means of efficiently handling complex interactions among biotic-edapho-climatic conditions (>363,000 combinations) in a very large area (500 Mha). Modelling studies, using for example the GEFSOC modelling system, in these pasture and agricultural systems can help us to evaluate the magnitude of these impacts and determine the effect of possible new management strategies on pasture and cropland sustainability. Our findings for land conversion from forest to pasture/agriculture have important implications, for example, for calculating CO2 emissions from land-use change in national greenhouse gas inventories.

Acknowledgements This work was supported by the Global Environment Facility (project number GFL/2740-02-4381), Fundac¸a˜o de Amparo a` Pesquisa do Estado de Sa˜o Paulo (FAPESP), Coordenac¸a˜o de Aperfeic¸oeamento de Pessoal de Nivel Superior (CAPES), Conselho Nacional de Desenvolvimento Cientı´fico e Tecnolo´gico (CNPq), National Science Foundation, Consortium for Agricultural Mitigation of Greenhouse Gases (CASMGS) and the Ecosystem Center (Woods Hole/ USA).

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