PREVISÃO DA PRODUTIVIDADE DO CAFÉ COM BASE EM DADOS AGROCLIMÁTICOS E APRENDIZAGEM DE MÁQUINA / FORECASTING COFFEE YIELD BASED ON AGROCLIMATIC DATA AND MACHINE LEARNING

Autores

DOI:

https://doi.org/10.48075/ijerrs.v3i1.26255

Palavras-chave:

Coffea arábica, Temperatura do ar, Déficit hídrico.

Resumo


Objetivou-se prever da produtividade do café com modelos regressivos usando dados meteorológicos em diferentes tipos de solo. O trabalho foi realizado em 15 localidades produtoras de C.arabica do Paraná. Os dados climáticos foram coletados por meio da plataforma NASA/POWER de 1989 e 2020 e os dados de produtividade do Coffea Arabica (sacas/ha) foram obtidos pela CONAB de 2003 a 2018. Para o calcula da evapotranspiração de referência (ETo) foi utilizado o método de Penman e Monteith, e o balanço hídrico climatológico (BH) de Thornthwaite e Mather (1955). Na modelagem dos dados, foi utilizado a regressão linear múltipla, em que a produtividade do C.arabica foi a variável dependente e as variáveis independentes foram temperatura do ar, precipitação, radiação solar, déficit hídrico, excedente hídrico e armazenamento de água no solo. Modelos de regressão linear múltipla são capazes de prever a produtividade do cafeeiro arábica no estado do Paraná com dois a três meses de antecedência a colheita. O elemento meteorológico que mais influencia o cafeeiro é a temperatura máxima do ar, principalmente durante a formação do fruto (março). Temperaturas máximas do ar em março de 31.01°C reduzem a produção do cafeeiro. Os modelos podem ser usados para previsão da produtividade do cafeeiro arábica auxiliando no planejamento dos cafeicultores da região do norte do Paraná.

Biografia do Autor

João Antonio Lorençone, Instituto Federal de Mato Grosso do Sul

Técnico agrícola e graduando de agronomia no Instituto Federal de Mato Grosso do Sul. Participa do grupo de pesquisa Recursos naturais e tecnologias agropecuárias (RENTA) do IFMS, desenvolvendo pesquisas nas áreas de agrometeorologia e modelagem agrícola. Possui experiência em feiras científicas, congressos e elaboração de artigos científicos. Experiência na área de design gráfico e produções audiovisuais. Representante do Mato Grosso do Sul no Parlamento Juvenil do Mercosul, mandato 2018-2020.

Lucas Eduardo de Oliveira APARECIDO, Instituto Federal de Mato Grosso do Sul

Prof.  Dr. do Instituto Federal de Mato Grosso do Sul, departamento de agrometereologia, Naviraí, Mato Grosso do Sul, Brazil.

Pedro Antonio LORENÇONE, Instituto Federal de Mato Grosso do Sul

Graduando em Agronomia pelo Instituto Federal de Mato Grosso do Sul, Naviraí, Mato Grosso do Sul, Brasil.

José Reinaldo da Silva Cabral de MORAES, Universidade estadual de São Paulo – Unesp

Msc. Doutorando em Produção Vegetal pela Universidade estadual de São Paulo – Unesp, campus Jaboticabal, Jaboticabal, São Paulo, Brazil.

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05-05-2021

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LORENÇONE, J. A.; APARECIDO, L. E. de O.; LORENÇONE, P. A.; MORAES, J. R. da S. C. de. PREVISÃO DA PRODUTIVIDADE DO CAFÉ COM BASE EM DADOS AGROCLIMÁTICOS E APRENDIZAGEM DE MÁQUINA / FORECASTING COFFEE YIELD BASED ON AGROCLIMATIC DATA AND MACHINE LEARNING. International Journal of Environmental Resilience Research and Science, [S. l.], v. 3, n. 1, 2021. DOI: 10.48075/ijerrs.v3i1.26255. Disponível em: https://e-revista.unioeste.br/index.php/ijerrs/article/view/26255. Acesso em: 4 dez. 2021.

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