Acta Scientiarum: Agronomy (Oct 2023)

Simulating soil carbon and nitrogen trends under an integrated system in the Brazilian Cerrado

  • Renato Falconeres Vogado,
  • Henrique Antunes de Souza,
  • Tiago Diniz Althoff,
  • Rômulo Simões Cezar Menezes,
  • Adriano Veniciús Santana Gualberto,
  • João Rodrigues da Cunha,
  • Luiz Fernando Carvalho Leite

DOI
https://doi.org/10.4025/actasciagron.v46i1.62574
Journal volume & issue
Vol. 46, no. 1

Abstract

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Management systems that include trees tend to provide higher amounts of plant biomass to the soil, contributing to the increase in carbon (C) and nitrogen (N) stocks. This study simulated C and N stocks and their compartments in a crop-livestock-forest integration system in the edafoclimatic conditions of the Maranhão Cerrado using the Century 4.5 model. The evaluated areas were native Cerrado vegetation (NV) and crop-livestock-forest integration (CLFI). The calibration process gradually modified the model parameters to better fit the simulated and observed soil C and N stocks. The best fit between the data was obtained after changes in the main parameters (DEC3(2), DEC4, and DEC5) that controlled the rate of decomposition of soil organic matter. C and N stocks increased by 14% and 15%, respectively, over 14 years after replacing NV with CLFI. The slow compartment of C presented greater sensitivity to changes in management, with an increase of 47% compared with that of NV. The active compartment increased by 31% and the passive compartment remained constant for over 14 years. Future scenarios, where pasture was maintained between the eucalyptus trees and the scenario that allowed the soybean, corn, and Brachiaria rotation between the trees, were more effective, accumulating approximately 37 Mg C ha-1. The continuous contribution of residues from the trees and pasture increased C and N stocks in the long-term in the slow fraction, where the total organic carbon increased from 32 to 36 Mg ha-1 when NV was replaced with CLFI. The model predicted the C and N stocks with accuracies ranging from 1 to 11% of the observed values

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