Red leaf blotch (RLB) of almond, caused by the ascomycete Polystigma amygdalinum, is a severe foliar disease endemic in the Mediterranean Basin and Middle East. Airborne ascospores of P. amygdalinum were monitored from 2019 to 2021 in two almond orchards in Lleida, Spain, and a Bayesian beta regression was used to model its seasonal dynamics. The selected model incorporated accumulated degree-days (ADD), ADD considering both vapor pressure deficit and rainfall as fixed effects, and a random effect for the year and location. The performance of the model was evaluated in 2022 to optimize RLB fungicide programs by comparing the use of model predictions and action thresholds with the standard program. Two variants were additionally considered in each program to set the frequency between applications, based on (i) a fixed frequency of 21 days or (ii) specific meteorological criteria (spraying within 7 days after rainfalls greater than 10 mm, with daily mean temperatures between 10 and 20°C, and with a minimum frequency of 21 days between applications). Programs were evaluated in terms of RLB incidence and number of applications. The program based on the model with periodic fungicide applications was similarly effective as the standard program, resulting only in a 2.6% higher RLB incidence but with fewer applications (three to four, compared with seven in the standard program). When setting the frequency between applications by using the meteorological criteria, a higher reduction in the number of applications (two to three) was observed, while RLB incidence increased by roughly 16% in both programs. Therefore, the model developed in this study may represent a valuable tool toward a more sustainable fungicide schedule for the control of almond RLB in northeast Spain.
Keywords: Polystigma amygdalinum; Prunus dulcis; decision support system; disease control; epidemiology; modeling; red leaf blotch.