Predicting and mapping orthotospovirus risk in peanut crops using biometeorological data - Dottori, Carolina Andrea (2025)
Viral diseases significantly impact agricultural production and food security globally, with orthotospovirus species causing substantial damage to various crops. In peanut (Arachis hypogaea L.), outbreaks of Groundnut ringspot orthotospovirus (GRSV) are particularly detrimental, resulting in significant yield losses. This study introduces an innovative methodology that utilizes a logistic regression model to predict orthotospovirus occurrence based on publicly available monthly average biometeorological data. Data from 835 georeferenced peanut fields across 16 growing seasons in Argentina were analyzed. The results showed that wind speed, temperature, relative humidity, and precipitation from the winter months preceding peanut planting were key factors influencing GRSV presence. The model’s predictive capacity, validated with k-fold cross-validation (k = 10), demonstrated an accuracy of 79 %, a specificity of 87 %, and a sensitivity of 61 %. Moreover, this study underscores the importance of preseason climatic conditions in thrips population dynamics and highlights the need for further research on the role of wind in orthotospovirus-disease occurrence. Additionally, a phytopathological map was generated to delineate high and low-risk areas for GRSV within the main peanut-producing region of Argentina. This map categorizes regions based on the probability of GRSV occurrence and its variability across growing seasons, providing valuable insights for targeted disease management. Both tools, the predictive model and the phytopathological risk map of viral occurrence, constitute resources that could be easily adopted by stakeholders, facilitating the implementation of sustainable management practices for peanut crop protection.