Árvore de decisão aplicada à análise de risco da severidade da ferrugem do cafeeiro na Guatemala

Abstract

The rust, caused by the fungus Hemileia vastatrix Berk & Br., is the main disease of coffee (Coffea arabica L.) in Latin America. The principal damage caused is defoliation and death of lateral branches, which causes premature fruit losses. Guatemala produces coffee in 270,000 hectares, and near of the 82% is cultivated with susceptible varieties to coffee rust races. Coffee rust epidemic is a complex process based on the relationships between the environment, plant growth, and crop practices. The objective of this study was to develop models for risk analysis based on decision trees in order to understand how cropping patterns determine the progress of the disease in Guatemala to identify and prioritize the important factors. For this work were used 1215 observations, obtained in 35 coffee plots from April 2013 to December 2014. The modeled variable was the leaf severity. Using the CHAID (Chi-Square Automatic Interaction Detection) algorithm were developed two decision trees. The first predicts leaf severity in plots where the producer does not follow the disease, while the second requires rust monitoring 28 days before the date of the severity risk analysis. In the trees, the main predictor was the fungicide spraying per year. The following predictor variables on the tree were related with the tissue availability for new infections, which also stimulates microenvironments with high relative humidity, warm temperatures, and foliar wetness prevalence. Only for non-monitoring tree was included the average rainfall, which suggests that climate relationship with the epidemic, is at microclimate level. The tree for plots with disease monitoring includes in all levels the 28 before severity and replaced management or climate variables getting similar predicted values. The accuracy of the tree for monitored plots was 65.85% with an estimated accuracy by cross validation of 73.34%, and for the monitored plots, the accuracy was 62.53% and 68.54%, respectively. Risk analysis models prove to be tools of support in making management decisions to implement the control of coffee rust and allow list in order of importance, management practices, and climatic factors that influence disease severity in different crop patterns.