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Predicting Heliothis (Helicoverpa armigera) pest population dynamics with an age-structured insect population model driven by satellite data

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  • Blum, Moshe
  • Nestel, David
  • Cohen, Yafit
  • Goldshtein, Eitan
  • Helman, David
  • Lensky, Itamar M.

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.

Suggested Citation

  • Blum, Moshe & Nestel, David & Cohen, Yafit & Goldshtein, Eitan & Helman, David & Lensky, Itamar M., 2018. "Predicting Heliothis (Helicoverpa armigera) pest population dynamics with an age-structured insect population model driven by satellite data," Ecological Modelling, Elsevier, vol. 369(C), pages 1-12.
    • Handle: RePEc:eee:ecomod:v:369:y:2018:i:c:p:1-12
    DOI: 10.1016/j.ecolmodel.2017.12.019
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    References listed on IDEAS

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    1. Feng, Hongqiang & Gould, Fred & Huang, Yunxin & Jiang, Yuying & Wu, Kongming, 2010. "Modeling the population dynamics of cotton bollworm Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae) over a wide area in northern China," Ecological Modelling, Elsevier, vol. 221(15), pages 1819-1830.
    2. Yves Carrière & Peter B Goodell & Christa Ellers-Kirk & Guillaume Larocque & Pierre Dutilleul & Steven E Naranjo & Peter C Ellsworth, 2012. "Effects of Local and Landscape Factors on Population Dynamics of a Cotton Pest," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-8, June.
    3. Blum, Moshe & Lensky, Itamar M. & Rempoulakis, Polychronis & Nestel, David, 2015. "Modeling insect population fluctuations with satellite land surface temperature," Ecological Modelling, Elsevier, vol. 311(C), pages 39-47.
    4. Jörn P W Scharlemann & David Benz & Simon I Hay & Bethan V Purse & Andrew J Tatem & G R William Wint & David J Rogers, 2008. "Global Data for Ecology and Epidemiology: A Novel Algorithm for Temporal Fourier Processing MODIS Data," PLOS ONE, Public Library of Science, vol. 3(1), pages 1-13, January.
    5. Hoque, Ziaul & Farquharson, Robert J. & Dillon, Martin & Kauter, Greg, 2001. "An approach to modelling and evaluating alternative management strategies for insecticide resistance in the Australian cotton industry," 2001 Conference (45th), January 23-25, 2001, Adelaide, Australia 125664, Australian Agricultural and Resource Economics Society.
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    1. Pasquali, S. & Soresina, C. & Gilioli, G., 2019. "The effects of fecundity, mortality and distribution of the initial condition in phenological models," Ecological Modelling, Elsevier, vol. 402(C), pages 45-58.
    2. Siti Aisyah Ruslan & Farrah Melissa Muharam & Zed Zulkafli & Dzolkhifli Omar & Muhammad Pilus Zambri, 2019. "Using satellite-measured relative humidity for prediction of Metisa plana’s population in oil palm plantations: A comparative assessment of regression and artificial neural network models," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-15, October.
    3. Augustinus, Benno A. & Blum, Moshe & Citterio, Sandra & Gentili, Rodolfo & Helman, David & Nestel, David & Schaffner, Urs & Müller-Schärer, Heinz & Lensky, Itamar M., 2022. "Ground-truthing predictions of a demographic model driven by land surface temperatures with a weed biocontrol cage experiment," Ecological Modelling, Elsevier, vol. 466(C).
    4. Neta, Ayana & Levi, Yoav & Morin, Efrat & Morin, Shai, 2023. "Seasonal forecasting of pest population dynamics based on downscaled SEAS5 forecasts," Ecological Modelling, Elsevier, vol. 480(C).
    5. Pasquali, S. & Soresina, C. & Marchesini, E., 2022. "Mortality estimate driven by population abundance field data in a stage-structured demographic model. The case of Lobesia botrana," Ecological Modelling, Elsevier, vol. 464(C).

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