Predictive model of the severity of leaf blight by Cercospora kikuchii using meteorological variables
DOI:
https://doi.org/10.15517/am.2023.54430Keywords:
Glycine max, fungus, pathology, plagueAbstract
Introduction. In integrated disease management, it is important to incorporate elements such as economic
damage thresholds, monitoring, and risk forecasting systems, which constitute tools to define disease control strategies. Objective. Develop a predictive model of the severity of Cercospora leaf blight (TFC) using meteorological variables for the north of the province of Buenos Aires, Argentina. Materials and methods. There were data on the incidence
and severity of TFC corresponding to five relevant Pergamino, Buenos Aires, soybean production cycles (2013-2017) in different reproductive stages R1 to R7. The dependent variable was the probability of occurrences of categorized levels of the rate of increase (TI) of the severity of TFC caused by C. kikuchii. The elements and meteorological variables used were daily records of maximum and minimum temperature, precipitation, and relative humidity. The
nonparametric Kendall Tau-b coefficient TI of connection between the TI binary categorized levels of TFC severity and the weather variables was calculated. Results. The meteorological variables with the greatest consequences in relation to the TI of the TFC were those related to relative humidity (DHR, MOJRO, DHRT). The inclusion of a thermal variable (GDTmax) was important for the adjustment of the predictive model. Conclusion. It was possible to develop a TFC severity prediction model that included two meteorological variables, one related to relative humidity
days and another thermal related to a maximum temperature limit for the development of the disease. To validate and strengthen the proposed model, it is necessary to have more severity data over the years.
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Copyright (c) 2023 M. Lavilla, M. Martínez, A. Ivancovich, A. Díaz-Paleo
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