Co-Authors:
Blum, M., Department of Geography and Environment, Bar-Ilan University, Ramat Gan, Israel, Department of Entomology, Agricultural Research Organization, The Volcani Center, Rishon LeZion, Israel
Nestel, D., Department of Entomology, Agricultural Research Organization, The Volcani Center, Rishon LeZion, Israel
Cohen, Y., Department of Agricultural Engineering, Agricultural Research Organization, The Volcani Center, Rishon LeZion, Israel
Goldshtein, E., Department of Agricultural Engineering, Agricultural Research Organization, The Volcani Center, Rishon LeZion, Israel
Helman, D., Department of Geography and Environment, Bar-Ilan University, Ramat Gan, Israel, Department of Agricultural Engineering, Agricultural Research Organization, The Volcani Center, Rishon LeZion, Israel
Lensky, I.M., Department of Geography and Environment, Bar-Ilan University, Ramat Gan, Israel
Abstract:
The cotton bollworm (Helicoverpa armigera) is among the most damaging agricultural insect pests in the world. The life cycle of H. armigera is temperature dependent and as such modeling its population dynamics for integrated pest management (IPM) purposes requires accurate temperature information throughout the area of interest, which is not always available. We present, for the first time, a continuous age-structured insect population model driven by satellite-derived land surface temperature (LST) to derive population dynamics of H. armigera. We use LST data from the Moderate resolution imaging spectroradiometer (MODIS) conducting model simulations and validating the model with H. armigera larvae counts from in-field scout reports in nine sweet corn (Zea mays convar) and four tomato (Solanum lycopersicum) crop fields in Northern Israel. We compared our results with a similar model that uses air temperature derived from the nearest weather station as an input. To accurately predict population dynamics, we used different model initiation scenarios considering pesticide application and migration patterns between neighboring corn and tomato fields, which were identified as sink and source of the adult population. Results show that our LST-driven model outperformed the model driven by ambient air temperature. Model simulations generally followed the larval population development observed in the field when the model was initiated the day before the first larvae were detected, providing realistic population dynamics. Simulations with different adult population migration rates showed the importance of including between-field migration in the LST-driven model. In conclusion, this study provides a basis for future development of real-time IPM support systems, particularly when combining a temperature-driven age-structured insect population model with real-time satellite-derived information. © 2017 Elsevier B.V.
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