Abstract

LAVILLA, M.; MARTINEZ, M.; IVANCOVICH, A.  and  DIAZ-PALEO, A.. Predictive model of the severity of leaf blight by Cercospora kikuchii using meteorological variables. Agron. Mesoam [online]. 2023, vol.34, n.3, 54430. ISSN 2215-3608.  http://dx.doi.org/10.15517/am.2023.54430.

Introduction. In the integrated management of diseases, it is important to incorporate elements such as economic damage thresholds, monitoring, and risk forecasting systems, which constitute tools to define d control strategies. Objective. To develop a predictive model of the severity of Cercospora leaf blight (TFC) using meteorological variables in the northern region of Buenos Aires Province, Argentina. Materials and methods. Data on the incidence and severity of TFC corresponding to five soybean production cycles (2013-2017) in Pergamino, Buenos Aires, at different reproductive stages (R1 to R7) were available. The dependent variable was the probability of occurrences of categorized levels of the rate of increase (TI) in TFC severity caused by C. kikuchii. Daily maximum and minimum temperature, precipitation, and relative humidity were recorded. The non-parametric Kendall Tau-b correlation coefficient was calculated between the binary categorized levels of TI in TFC severity and meteorological variables. Results. Meteorological variables with the highest correlation in relation to TFC TI were those related to relative humidity (Days with relative humidity (HR) >76 % (DHR), days with maximum temperature (Tmax) <28 °C, minimum temperature (Tmin) >15 °C, and HR >76 % (DHRT), days with Tmax <28 °C, Tmin >15 °C, rainfall >0.5 mm, and HR >75 %(MOJro)). The inclusion of a thermal variable (on days where Tmax <28 °C, GDTmax: ∑maximum temperature-28 °C) was important for the adjustment of the predictive model. Conclusion. A predictive model for TFC severity was developed, which included two meteorological variables, one related to days of relative humidity and another thermal related to a maximum temperature threshold for disease development. To validate and strengthen the proposed model, it is necessary to have more severity data over the years.

Keywords : fungus; pathology; plague; Glycine max.

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