LOGISTIC MODELS FOR ESTIMATING EPIDEMIC INCREASE RATES OF BLUEBERRY FOLIAR DISEASES, BASED ON METEOROLOGICAL AND LEAF SENESCENCE VARIABLES

Abstract

Alternaria tenuissima is one of the most important pathogens on blueberry (Vaccinium corymbossum) in Argentina, causing leaf spots, twig blight and fruit rot. The severity of leaf spots caused by A. tenuissima was estimated in crops located in San Pedro, Gualeguaychu and Concordia (Argentina), during spring and summer of 2008 and 2009. Each linearised disease progress curve (3-4 observations per crop cycle) adjusted to Logistic and Gompertz epidemiological models (R2>0.77 to 0.98) for Concordia and San Pedro. Every 14 days, epidemic daily increase rate (DI) was estimated from disease epidemic curves. DI values were binary categorized according to median values (DI=0.04253) for the 36 severity percentages estimated every 14 days, into severe (DI≥0.04253), and slight to null (DI 0.55). The best logistic regression model of binary response, that integrated LS, RF and the interaction MMT×RF, showed a prediction accuracy of 97%. Among ordinal response models, the bivariate one that integrated LS factor and the interaction MMT×RF was chosen (92% prediction accuracy). These results add information for the development of a predictive system for blueberry leaf diseases, in a context of sustainable crop health management.