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A modelling assessment of the maize crop growth, yield and soil water dynamics in the Northeast of Brazil

Authors :
Marshall Victor Chagas Santos
André Luiz de Carvalho
José Leonaldo de Souza
Mauricio Bruno Prado da Silva
Rui Palmeira Medeiros
Ricardo Araújo Ferreira Junior
Gustavo Bastos Lyra
Iêdo Teodoro
Guilherme Bastos Lyra
Marco Antonio Maringolo Lemes
Federal University of Viçosa
Federal University of Alagoas
Universidade Estadual Paulista (UNESP)
Federal Rural University of Rio de Janeiro
Source :
Scopus, Repositório Institucional da UNESP, Universidade Estadual Paulista (UNESP), instacron:UNESP
Publication Year :
2020
Publisher :
Southern Cross Publishing, 2020.

Abstract

Made available in DSpace on 2022-04-28T19:29:12Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-06-01 The present study aims to evaluate the APSIM-Maize model performance to use it as a decision-making tool to help improve production rates, reduce production costs and assess the potential impacts of climate change on crop yields in the Northeast of Brazil. The crop, soil and weather data used in the simulations were obtained from field experiments carried out in maize crops in 2008 and 2011 in two different edaphoclimatic regions in Alagoas State, Northeast Brazil. The approach we used explored the ability of APSIM to simulate growth variables and soil water dynamics of a maize variety (AL Bandeirante). During parametrization, we made some adjustments regarding the variety and soil organic matter to attain a better representation of the growth and soil water dynamics, respectively. The APSIM-Maize model predicted the leaf area index with a RMSE (Root Mean Square Error) ranging between 0.14 and 1.06 cm2 cm-2 and the biomass production with an RMSE between 2.30 and 3.34 Mg ha-1. The volumetric soil water content was satisfactorily predicted with RMSE ranging between 0.02 and 0.08 mm mm-1. Results showed that this model is a useful tool for decision-making, which can be potentially used as a support in climate risk management and policies, aiming to improve regional production, provided it has been previously validated with independent datasets. Department of Agricultural Engineering Federal University of Viçosa Department of Agrometeorology Federal University of Alagoas Department of Rural Engineering São Paulo State University Department of Meteorology and Climatology Federal Rural University of Rio de Janeiro Department of Rural Engineering São Paulo State University

Details

ISSN :
18352707 and 18352693
Database :
OpenAIRE
Journal :
June 2020
Accession number :
edsair.doi.dedup.....ddaa620d0d1f499b377a00465d906f03