Development and Evaluation of a Disease Predictive Model for Bemisia tabaci Population Management in Tomato Crops
Development and Evaluation of a Disease Predictive Model for Bemisia tabaci Population Management in Tomato Crops
Nawaz Haider Bashir1, Salman Ahmad2*, Yasir Ali3, Saqi Kosar Abbas4, Umbreen Shahzad5, Azhar Abbas Khan6, Muhammad Zeshan Majeed7, Muhammad Atiq8, Muhammad Ghayoor Husnain2
ABSTRACT
Abstract | This study investigated environmental variables for developing an anticipated model for giving prediction of tomato leaf curl virus vector Bemisia tabaci. A stepwise regression model was designed with maximum and minimum temperatures, relative humidity, rainfall, and wind speed. The model is strongly associated with B. tabaci population and environmental variables during two growing seasons 2021-2022. Maximum and minimum temperatures and relative humidity significantly contributed to disease development. Bemisia tabaci populations increased with maximum (34.56-40.45°C) and minimum (20.53-26.78°C) temperatures. All tomato genotypes showed a significant population reduction when relative humidity increased from 30-57%. The model explained 38-65% of B. tabaci variability during both rating seasons. The RMSE and error (%) were below 20, showing that the model effectively predicted Bemisia tabaci population development. This innovative study showed how a predictive climate model may guide strategic pest control treatments, enhancing agricultural systems’ climate change resilience.
Novelty Statement | The present study revealed that temperature and humidity are the most significant factors, with a 1.07-unit increase in temperature resulting in a corresponding increase in whitefly populations, accounting for 3865% of the variability in the whitefly population. By utilizing this approach, farmers can substantially enhance crop protection measures under changing climate conditions by predicting and reducing the spread of the tomato leaf curl virus and its vector, Bemisia tabaci, thereby addressing a critical gap in sustainable pest management.
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