Assessing coffee yield: predictive modeling based on phenological sensitivity to climate variability.
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This study aimed to validate the agro-meteorological model developed by Santos and Camargo (2006) for predicting Arabica coffee yield, using 14 years of data (2011–2024) from Agrotechnological District (DAT) Caconde and the Vulcanic Region of Poços de Caldas. The model, based on climatic variables and phenological sensitivity coefficients (Ky), was tested under 16 scenarios combining genotypes, productivity ranges, and two spatial levels: plot and regional. Results indicated better performance at the regional scale, with lower errors (MAE, RMSE) and high R² values (> 0.9), especially in areas with homogeneous productivity. In contrast, plot-level scenarios, such as those for the Bourbon cultivar, showed high variability and lower predictive accuracy. Water deficit was identified as the main factor associated with yield losses. The findings highlight the model’s potential for strategic planning at broader spatial scales and the need for local calibration to improve accuracy in heterogeneous environments.
Organização: Silvia Maria Fonseca Silveira Massruhá, Durval Dourado Neto, Luciana Alvim Santos Romani, Jayme Garcia Arnal Barbedo, Édson Luis Bolfe, Ivan Bergier, Maria Angelica de Andrade Leite, Vitor Del Alamo Guarda, Catarina Barbosa Careta.
Organização: Silvia Maria Fonseca Silveira Massruhá, Durval Dourado Neto, Luciana Alvim Santos Romani, Jayme Garcia Arnal Barbedo, Édson Luis Bolfe, Ivan Bergier, Maria Angelica de Andrade Leite, Vitor Del Alamo Guarda, Catarina Barbosa Careta.
Palabras clave
Produtividade agricola, Modelos agrometeorológicos, Variabilidade climática, Distrito agrotecnológico de Caconde, Projeto Semear Digital, Agricultural productivity, Agro-meteorological models, Climate variability, Produtividade, Models