Estimating Sugarcane Yield in a Subtropical Climate Using Climatic Variables and Soil Water Storage

Jessica Lima Viana (), Jorge Luiz Moretti de Souza, Aaron Kinyu Hoshide, Ricardo Augusto de Oliveira, Daniel Carneiro de Abreu and Wininton Mendes da Silva
Additional contact information
Jessica Lima Viana: AgriSciences, Universidade Federal de Mato Grosso, Caixa Postal 729, Sinop 78550-970, MT, Brazil
Jorge Luiz Moretti de Souza: Departamento de Solos e Engenharia Agrícola (DSEA), Campus Universitário de Curitiba, Universidade Federal do Paraná, Rua dos Funcionários, 1540, Curitiba 80035-050, PR, Brazil
Aaron Kinyu Hoshide: AgriSciences, Universidade Federal de Mato Grosso, Caixa Postal 729, Sinop 78550-970, MT, Brazil
Ricardo Augusto de Oliveira: Departamento de Fitotecnia e Fitossanidade (DFF), Campus Universitário de Curitiba, Universidade Federal do Paraná, Rua dos Funcionários, 1540, Curitiba 80035-050, PR, Brazil
Daniel Carneiro de Abreu: AgriSciences, Universidade Federal de Mato Grosso, Caixa Postal 729, Sinop 78550-970, MT, Brazil
Wininton Mendes da Silva: Empresa Mato-Grossense de Pesquisa, Assistência e Extensão Rural (EMPAER-MT), Centro Político Administrativo, Cuiabá 78049-903, MT, Brazil

Sustainability, 2023, vol. 15, issue 5, 1-18

Abstract: Brazil is the largest producer of sugarcane ( Saccharum spp.) in the world, and this crop’s response to climate and soil water storage is essential for optimal management and genetic/yield improvements. The objective of our study was to build a multivariate model to estimate sugarcane yield in the subtropical conditions of the northwestern Paraná region using climatic and soil water storage variables. Observed yield data was used from experiments conducted at the Experimental Station of the Sugarcane Genetic Improvement Program of the Universidade Federal do Paraná. The sugarcane varieties RB72454, RB867515, RB966928, and RB036066 were analyzed in the 1998–2006, 2008, 2018 and 2019 harvest years. Stepwise multiple linear regression analysis with repeated cross-validation was developed to estimate sugarcane yield given climate and soil water storage variables for crop growth phases. The accumulated degree days in Phases I and II and soil water storage in Phase II of development significantly impacted sugarcane yield. The multiple linear regression model, with accumulated degree days and soil water storage in Phases I and II of development, successfully predicted sugarcane yield for analyzed varieties. Sugarcane production models like the one we developed can improve crop management for greater sustainability and climate change adaption in Brazil and other areas.

Keywords: agrometeorological modeling; multiple linear regression; statistical model; sugarcane; yield prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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