Modeling the impact of agrometeorological variables on soybean yield in the Mato Grosso Do Sul: 2000–2019

Lucas Eduardo Oliveira Aparecido (), Guilherme Botega Torsoni (), José Reinaldo Silva Cabral de Moraes (), Kamila Cunha Meneses (), João Antonio Lorençone () and Pedro Antonio Lorençone ()
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Lucas Eduardo Oliveira Aparecido: Federal Institute of Mato Grosso do Sul (IFMS) - Navirai
Guilherme Botega Torsoni: Federal Institute of Mato Grosso do Sul (IFMS) - Navirai
José Reinaldo Silva Cabral de Moraes: Federal Institute of Mato Grosso do Sul (IFMS) - Navirai
Kamila Cunha Meneses: State University of Sao Paulo (FCAV/UNESP) - Jaboticabal
João Antonio Lorençone: Federal Institute of Mato Grosso do Sul (IFMS) - Navirai
Pedro Antonio Lorençone: Federal Institute of Mato Grosso do Sul (IFMS) - Navirai

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2021, vol. 23, issue 4, No 19, 5164 pages

Abstract: Abstract The study of the soybean yield variability influenced by the climate contributes to the planning of strategies to mitigate its negative effects. Thus, our aim was to calibrate agrometeorological models for soybean yield forecast and identify the weather variables that most influence soybean yield. This study used historical series of climate and soybean yield data from soybean-producing locations in the Mato Grosso do Sul state, Brazil. The historical climate series was 20 years (2000–2019). The soybean production, yield, and planted area data of the localities were in the period from 2009–2018. Multiple linear regression analysis was the statistical tool used for data modeling. The models from the north and central regions forecast of anticipation of 2 months since the final data necessary to apply the model were EXCJANc and PJANc, respectively. The models calibrated for the southern region reported anticipation of one month since the final data necessary to apply the model was EXCFEVc. The calibrated models used to forecast soybean yield as a function of climatic conditions have a high degree of significance (p

Keywords: Crop modeling; Climate; Yield zoning; Spatial error model; Glycine max L. (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10668-020-00807-w

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