Modelagem agrometeorológica para a previsão de produtividade de cafeeiros na região sul do estado de Minas Gerais

• Autor(a) principal: Victorino, Euler Cipriani
• Data de Publicação: 2016
• Outros Autores: Carvalho, Luiz Gonsaga de, Ferreira, Daniel Furtado
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Repositório Institucional da UFLA
• Texto Completo: http://repositorio.ufla.br/jspui/handle/1/13902; http://www.coffeescience.ufla.br/index.php/Coffeescience/article/view/1049
• topic: Cafeicultura; Cafeeiro; Coffee; Déficit hídrico; Seleção backward; Water deficit; Backward selection

Knowledge of effective crop forecasting techniques is of great importance for the coffee market, enabling better planning and making more sustainable this activity. This study aimed to adapt a predictive model of coffee yield, based on water availability, to the cities of Lavras and Varginha, in southern Minas Gerais, Brazil. The models were generated from multiple linear regression of productivity loss (Ye/Yp) as a function of the previous year productivity (Ya/Yp) and water deficit in the different phenological phases, represented by relative evapotranspiration (ETR/ETP)i. During the parameterization, the water deficit response coefficients (Kyi) and the previous year production coefficient (Ky0) were obtained. By the backward selection methodology, were obtained models that presented only significant coefficients. In this process, in general, the models were highly sensitive to the rainy season (November to April), and variables related to important periods such as flowering were not significant. It was concluded that the models have good potential for coffee crop forecasting. In these, previous year’s yield should be considered and the phenological sequence with best performance was Sep./Oct, Nov./Dec., Jan./Feb., Sep. /Apr.