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

Author

Listed:
  • 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
    College of Natural Sciences, Forestry and Agriculture, The University of Maine, Orono, ME 04469, USA)

  • 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
    Instituto de Ciências Agrárias e Ambientais (ICAA), Campus Universitário de Sinop, Universidade Federal do Mato Grosso, Avenida Alexandre Ferronato, 1200, Sinop 78550-728, 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)

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.

Suggested Citation

  • Jessica Lima Viana & Jorge Luiz Moretti de Souza & Aaron Kinyu Hoshide & Ricardo Augusto de Oliveira & Daniel Carneiro de Abreu & Wininton Mendes da Silva, 2023. "Estimating Sugarcane Yield in a Subtropical Climate Using Climatic Variables and Soil Water Storage," Sustainability, MDPI, vol. 15(5), pages 1-18, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:5:p:4360-:d:1084139
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    References listed on IDEAS

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    1. Fabio Marin & James Jones & Abraham Singels & Frederick Royce & Eduardo Assad & Giampaolo Pellegrino & Flávio Justino, 2013. "Climate change impacts on sugarcane attainable yield in southern Brazil," Climatic Change, Springer, vol. 117(1), pages 227-239, March.
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    Cited by:

    1. Aaron Kinyu Hoshide, 2023. "Sustainable Development Agricultural Economics and Policy: Intensification versus Diversification," Sustainability, MDPI, vol. 15(12), pages 1-4, June.
    2. Jessica Lima Viana & Jorge Luiz Moretti de Souza & André Carlos Auler & Ricardo Augusto de Oliveira & Renã Moreira Araújo & Aaron Kinyu Hoshide & Daniel Carneiro de Abreu & Wininton Mendes da Silva, 2023. "Water Dynamics and Hydraulic Functions in Sandy Soils: Limitations to Sugarcane Cultivation in Southern Brazil," Sustainability, MDPI, vol. 15(9), pages 1-22, May.
    3. Guojun Zheng & Shengfeng Long & Guanghu Zhu & Qinlong Wang & Ting Luo & Hairong Huang & Lu Liu & Hui Fang & Pengcheng Ma & Yaoyang Shen & Zeping Wang, 2024. "Spatiotemporal Dynamic Relationship of Meteorological Factors and Sugar Content of Sugarcane by Vector Autoregression Model," Agriculture, MDPI, vol. 14(11), pages 1-19, October.
    4. Yifang Zhou & Mingzhang Pan & Wei Guan & Changcheng Fu & Tiecheng Su, 2023. "Predicting Sugarcane Yield via the Use of an Improved Least Squares Support Vector Machine and Water Cycle Optimization Model," Agriculture, MDPI, vol. 13(11), pages 1-23, November.

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