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Modeling Soil Carbon from Forest and Pasture Ecosystems of Amazon, Brazil
Author(s) -
Cerri C. E. P.,
Coleman K.,
Jenkinson D. S.,
Bernoux M.,
Victoria R.,
Cerri C. C.
Publication year - 2003
Publication title -
soil science society of america journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.836
H-Index - 168
eISSN - 1435-0661
pISSN - 0361-5995
DOI - 10.2136/sssaj2003.1879
Subject(s) - pasture , environmental science , soil water , soil carbon , amazon rainforest , biomass (ecology) , ecosystem , agronomy , soil organic matter , agroforestry , forestry , soil science , ecology , geography , biology
Conversion of tropical forest to agricultural management has important implications for C storage in soils and global climate change. The Nova Vida Ranch in the Western Brazilian Amazon basin provided a unique opportunity to study the conversion of tropical forests to pastures established in 1989, 1987, 1983, 1979, 1972, 1951, and 1911, in comparison with uncleared forest. Soils were analyzed for organic C, bulk density, total N, pH, clay content, and biomass C. The forest soil contained 34 Mg C ha −1 in the 0‐ to 30‐cm layer: modeling clearance and conversion to pasture caused an initial fall in the C stock, followed by a slow rise. After 88 yr, the pasture soil contained 53% more C than the forest soil. The increase in total N on conversion to pasture was less marked, which led to C/N ratios in the pasture soils being higher than in the forest soil. The Rothamsted C turnover model (RothC‐26.3) was used to simulate changes in the 0‐ to 10‐ and 0‐ to 30‐cm layer of soils when forest was converted to pasture. The model predicted that conversion to pasture would cause a 54% increase in the stock of organic C in the top 30 cm of soil in 100 yr. The modeled input of plant C to the 0‐ to 30‐cm layer of soil under pasture was assumed to be 8.28 Mg C ha −1 yr −1 The model provided a reasonable estimate of the microbial biomass (BIO) C in the 0‐ to 10‐cm soil layer. This was an independent test of model performance, because no adjustments were made to the model to generate output.