Spatiotemporal Analyses of Tomato Brown Rugose Fruit Virus in Commercial Tomato Greenhouses

Tomato brown rugose fruit virus (ToBRFV) is an emerging pathogen affecting tomato-production systems in several countries, including Mexico. This situation involves challenges due to the negative impact on yield and the lack of disease-management measures. This work analyzes the spatiotemporal distribution of ToBRFV in commercial tomato greenhouses. The presence or absence of diseased plants was evaluated weekly, assigning a location in space (x, y). Temporal analysis consisted of fitting the incidence to the monomolecular, logistic, log-logistic, Gompertz, exponential, Weibull, and Richard models, evaluated using the Akaike information criterion, significance, correlation, coefficient of determination, and root mean square error. Spatial analysis consisted of determining spatial aggregation using the Moran, Fisher, and Lloyd indices. In addition, spatial distribution was assessed by sequence observations, point patterns using the inverse distance index, and analysis by SADIE distance indicators. Results indicated that the logistic models (log-logistic and logistic) best described the temporal progress of ToBRFV. This disease also had slightly aggregated patterns in the initial phase, highly aggregated in the exponential phase, and uniform in the deceleration and stationary phases. This study demonstrates that the spatial and temporal dynamics of ToBRFV have important implications for the monitoring, diagnosis, management, and risk prediction of this disease.

Automated summary

This study analyzed the spatiotemporal distribution of Tomato brown rugose fruit virus (ToBRFV) in commercial tomato greenhouses and found that logistic models best described the temporal progress of the virus. The disease exhibited slightly aggregated patterns in the initial phase, highly aggregated in the exponential phase, and uniform in the deceleration and stationary phases, with important implications for disease monitoring, diagnosis, management, and risk prediction.