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Advances in crop insect modelling methods—Towards a whole system approach

Author

Listed:
  • Tonnang, Henri E.Z.
  • Hervé, Bisseleua D.B.
  • Biber-Freudenberger, Lisa
  • Salifu, Daisy
  • Subramanian, Sevgan
  • Ngowi, Valentine B.
  • Guimapi, Ritter Y.A.
  • Anani, Bruce
  • Kakmeni, Francois M.M.
  • Affognon, Hippolyte
  • Niassy, Saliou
  • Landmann, Tobias
  • Ndjomatchoua, Frank T.
  • Pedro, Sansao A.
  • Johansson, Tino
  • Tanga, Chrysantus M.
  • Nana, Paulin
  • Fiaboe, Komi M.
  • Mohamed, Samira F.
  • Maniania, Nguya K.
  • Nedorezov, Lev V.
  • Ekesi, Sunday
  • Borgemeister, Christian

Abstract

A wide range of insects affect crop production and cause considerable yield losses. Difficulties reside on the development and adaptation of adequate strategies to predict insect pests for their timely management to ensure enhanced agricultural production. Several conceptual modelling frameworks have been proposed, and the choice of an approach depends largely on the objective of the model and the availability of data. This paper presents a summary of decades of advances in insect population dynamics, phenology models, distribution and risk mapping. Existing challenges on the modelling of insects are listed; followed by innovations in the field. New approaches include artificial neural networks, cellular automata (CA) coupled with fuzzy logic (FL), fractal, multi-fractal, percolation, synchronization and individual/agent-based approaches. A concept for assessing climate change impacts and providing adaptation options for agricultural pest management independently of the United Nations Intergovernmental Panel on Climate Change (IPCC) emission scenarios is suggested. A framework for estimating losses and optimizing yields within crop production system is proposed and a summary on modelling the economic impact of pests control is presented. The assessment shows that the majority of known insect modelling approaches are not holistic; they only concentrate on a single component of the system, i.e. the pest, rather than the whole crop production system. We suggest system thinking as a possible approach for linking crop, pest, and environmental conditions to provide a more comprehensive assessment of agricultural crop production.

Suggested Citation

  • Tonnang, Henri E.Z. & Hervé, Bisseleua D.B. & Biber-Freudenberger, Lisa & Salifu, Daisy & Subramanian, Sevgan & Ngowi, Valentine B. & Guimapi, Ritter Y.A. & Anani, Bruce & Kakmeni, Francois M.M. & Aff, 2017. "Advances in crop insect modelling methods—Towards a whole system approach," Ecological Modelling, Elsevier, vol. 354(C), pages 88-103.
    • Handle: RePEc:eee:ecomod:v:354:y:2017:i:c:p:88-103
    DOI: 10.1016/j.ecolmodel.2017.03.015
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    4. Johnson, Bethany J. & Gomez, Marcella M. & Munch, Stephan B., 2025. "Empirical dynamic modeling for prediction and control of pest populations," Ecological Modelling, Elsevier, vol. 504(C).
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    6. 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).

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