Pesquisa Agropecuária Brasileira
URI permanente para esta coleçãohttps://thoth.dti.ufv.br/handle/123456789/9886
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2 resultados
Resultados da Pesquisa
Item Forecasting of the annual yield of Arabic coffee using water deficiency(Empresa Brasileira de Pesquisa Agropecuária - Embrapa, 2018-12) Aparecido, Lucas Eduardo de Oliveira; Rolim, Glauco de SouzaThe objective of this work was to develop agrometeorological models for the forecasting of the annual yields of Arabic coffee (Coffea arabica), using monthly water deficits (DEFs) during the coffee cycle, in important locations in the state of Minas Gerais, Brazil. For the construction of the models, a meteorological data set spanning of 18 years and multiple linear regressions were used. The models were calibrated in high‐ and low-yield seasons due to the high-biennial yields in Brazil. All calibrated models for high- and low-yield seasons were accurate and significant at 5% probability, with mean absolute percentage errors ≤2.9%. The minimum forecasting period for yield is six months for southern Minas Gerais and Cerrado Mineiro. In high‐yield seasons, water deficits affect more the reproductive stage of coffee and, in low-yield seasons, they affect more the vegetative stage of the crop.Item Economic and climatic models for estimating coffee supply(Empresa Brasileira de Pesquisa Agropecuária - Embrapa, 2017-12) Moraes-Oliveira, Adriana Ferreira de; Aparecido, Lucas Eduardo de Oliveira; Figueira, Sérgio Rangel FernandesThe objective of this work was to estimate the coffee supply by calibrating statistical models with economic and climatic variables for the main producing regions of the state of São Paulo, Brazil. The regions were Batatais, Caconde, Cássia dos Coqueiros, Cristais Paulista, Espírito Santo do Pinhal, Marília, Mococa, and Osvaldo Cruz. Data on coffee supply, economic variables (rural credit, rural agricultural credit, and production value), and climatic variables (air temperature, rainfall, potential evapotranspiration, water deficit, and water surplus) for each region, during the period from 2000–2014, were used. The models were calibrated using multiple linear regression, and all possible combinations were tested for selecting the variables. Coffee supply was the dependent variable, and the other ones were considered independent. The accuracy and precision of the models were assessed by the mean absolute percentage error and the adjusted coefficient of determination, respectively. The variables that most affect coffee supply are production value and air temperature. Coffee supply can be estimated with multiple linear regressions using economic and climatic variables. The most accurate models are those calibrated to estimate coffee supply for the regions of Cássia dos Coqueiros and Osvaldo Cruz.