Modeling of Soybean under Present and Future Climates in Mozambique

Authors

Manuel António Dina Talacuece, Federal University of ViçosaFollow
Flávio Barbosa Justino, Federal University of ViçosaFollow
Rafael de Ávila Rodrigues, Goias Federal UniversityFollow
Milton Edgar Pereira Flores, Federal University of ViçosaFollow
Jéssica Garcia Nascimento, Federal University of ViçosaFollow
Eduardo Eduardo Santos, Kansas State UniversityFollow

Date of this Version

6-17-2016

Document Type

Article

Citation

2016 by the authors

Comments

Climate 2016, 4, 31; doi:10.3390/cli4020031 www.mdpi.com/journal/climate

Abstract

This study aims to calibrate and validate the generic crop model (CROPGRO-Soybean) and estimate the soybean yield, considering simulations with different sowing times for the current period (1990–2013) and future climate scenario (2014–2030). The database used came from observed data, nine climate models of CORDEX (Coordinated Regional climate Downscaling Experiment)-Africa framework and MERRA (Modern Era Retrospective-Analysis for Research and Applications) reanalysis. The calibration and validation data for the model were acquired in field experiments, carried out in the 2009/2010 and 2010/2011 growing seasons in the experimental area of the International Institute of Tropical Agriculture (IITA) in Angónia, Mozambique. The yield of two soybean cultivars: Tgx 1740-2F and Tgx 1908-8F was evaluated in the experiments and modeled for two distinct CO2 concentrations. Our model simulation results indicate that the fertilization effect leads to yield gains for both cultivars, ranging from 11.4% (Tgx 1908-8F) to 15% (Tgx 1740-2Fm) when compared to the performance of those cultivars under current CO2 atmospheric concentration. Moreover, our results show that MERRA, the RegCM4 (Regional Climatic Model version 4) and CNRM-CM5 (Centre National de Recherches Météorologiques – Climatic Model version 5) models provided more accurate estimates of yield, while others models underestimate yield as compared to observations, a fact that was demonstrated to be related to the model’s capability of reproducing the precipitation and the surface radiation amount.