Soil Organic Carbon and Land Use Change in the Brazilian Amazon: A Modeling Approach.
Carlos E.P. Cerri1, Mark Easter2, Keith Paustian3, Kendrick Killian2, Kevin Coleman4, Martial Bernoux5, Pete Falloon6, David Powlson7, Eleanor Milne8, and Carlos C. Cerri1. (1) Centro de Energia Nuclear na Agricultura, Avenida Centenario 303, Sao Dimas, Piracicaba, Brazil, (2) Natural Resource Ecology Laboratory, Colorado State Univ, Fort Collins, CO 80523-1499, (3) Natural Resource Ecology Laboratory and Dept of Soil and Crop Sciences, Colorado State Univ, Fort Collins, CO 80523, (4) Rothamsted Research, Agriculture and Environment Division, Harpenden, AL52JQ, United Kingdom, (5) IRD, BP 64501, Montpellier, France, (6) The Met Office, Hadley Centre for Climate Prediction and Research, Fitzroy Road, Exeter, EXi3PB, United Kingdom, (7) Rotahmsted Research, Agriculture and Environment Division, Harpenden, AL52JQ, United Kingdom, (8) The Univ of Reading, The Dept of Soil Science, PO Box 233, Reading, RG6 6DW, United Kingdom
Land use and land cover changes in the Brazilian Amazon have major implications for regional and even global carbon cycling. Cattle pasture represents the largest single use (about 70%) of this once-forested land in most of the region. The main objective of this study was to use a modeling approach to examine the dynamics of soil carbon when forest is converted to pasture in the Brazilian Amazon. We used data from eleven site-specific ‘forest to pasture' chronosequences with the Century Ecosystem Model (Century 4.0) and the Rothamsted Carbon Model (RothC 26.3). The Century and RothC models predicted that forest clearance and conversion to well managed pasture would cause an initial decline in soil C stocks (0-20 cm depth), followed by a slow rise to levels exceeding those under native forest. The only exception to this pattern was found for a chronosequence in Suia-Missu, which is under degraded pasture. Statistical tests were applied to determine levels of agreement between simulated soil organic carbon stocks and observed stocks for all the sites within the 11 chronosequences in the Brazilian Amazon. The models also provided reasonable estimates (coefficient of correlation = 0.8) of the microbial biomass C in the 0-10 cm soil layer for two chronosequences when compared with available measured data. The Century model adequately predicted the magnitude and the overall trend in delta13C for the six chronosequences where measured delta13C data were available. This study gave independent tests of model performance, as no adjustments were made to the models to generate outputs. Our results suggest that modeling techniques can be successfully used for monitoring soil C stocks and changes, allowing both the identification of current patterns in the soil and the prediction of future conditions. Results were used and discussed not only to evaluate soil carbon dynamics but also to indicate soil C sequestration opportunities for the Brazilian Amazon region. Moreover, modeling studies in these ‘forest to pasture' systems have important applications, for example, for calculating CO2 emissions from land-use change in national greenhouse gas inventories of countries such as Brazil.