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APSIM model parameterization to estimate tree-pasture interactions and Piatã palisadegrass growth in a silvopastoral system

Grant number: 16/01532-4
Support type:Scholarships abroad - Research Internship - Doctorate
Effective date (Start): November 01, 2016
Effective date (End): February 28, 2017
Field of knowledge:Agronomical Sciences - Agronomy - Agricultural Meteorology
Principal Investigator:Paulo Cesar Sentelhas
Grantee:Cristiam Bosi
Supervisor abroad: Neil Ian Huth
Home Institution: Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil
Local de pesquisa : CSIRO Agriculture, Australia  
Associated to the scholarship:14/11931-8 - Parameterization and evaluation of mechanistic crop simulation models to estimate Urochloa brizantha cv. Piatã productivity in a silvopastoral system, BP.DR

Abstract

Agroforestry systems (AFS) are forms of land use, where timber trees or fruit trees are combined with crops and/or livestock, simultaneously or in a sequence of time, aiming to improve ecological interactions and economic returns. Currently, these systems are of great importance, since they promote degraded pasture recovery, emit less carbon than conventional systems and can be considered as an adaptation measure to climate changes. In this context, this project aim to parameterize and test the Agricultural Production Systems Simulator (APSIM) to estimate the competition between trees and pasture for solar radiation, water, and to estimate the pasture growth in a silvopastoral system integrating eucalyptus and Piatã palisadegrass. This project will use data of microclimate, soil moisture, pasture growth and tree growth from the silvopastoral system above mentioned, collected from December 2014 to January 2016, in São Carlos, SP, Brazil. The simulations will be done using the multi-point simulation approach, combined with the APSIM-Growth module. The model calibration will be done by changing model´s parameters in order to reduce the difference between observed and estimated values, which will be defined by statistical indices of precision and accuracy and by mean and mean absolute errors. (AU)