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Improving pest monitoring networks using a simulation-based approach to contribute to pesticide reduction

Author

Listed:
  • Cros, Marie-Josée
  • Aubertot, Jean-Noël
  • Gaba, Sabrina
  • Reboud, Xavier
  • Sabbadin, Régis
  • Peyrard, Nathalie

Abstract

Conventional pest management mainly relies on the use of pesticides. However, the negative externalities of pesticides are now well known. More sustainable practices, such as Integrated Pest Management, are necessary to limit crop damage from pathogens, pests and weeds in agroecosystems. Reducing pesticide use requires information to determine whether chemical treatments are really needed. Pest monitoring networks (PMNs) are key contributors to this information. However, the effectiveness of a PMN in delivering relevant information about pests depends on its spatial sampling resolution and its memory length. The trade-off between the monitoring efforts and the usefulness of the information provided is highly dependent on pest ecological traits, the damage they can cause (in terms of crop losses), and economic drivers (production costs, agriculture product prices and incentives). Due to the high complexity of optimising PMNs, we have developed a theoretical model that belongs to the family of Dynamic Bayesian Networks in order to compare several PMNs performances. This model links the characteristics of a PMN to treatment decisions and the resulting pest dynamics. Using simulation and inference tools for graphical models, we derived the proportion of impacted fields, the number of pesticide treatments and the overall gross margins for three types of pest with contrasting levels of endocyclism. The term “endocyclic†refers to an organism whose development is mostly restricted to a field and highly depends on the inoculum present in the considered field. The presence of purely endocyclic pests at a given time increases the probability of reoccurrence. Conversely, slightly endocyclic pests have a low persistence. The simulation analysis considered ten scenarios: an expected margin-based strategy with a spatial resolution of four PMNs and two memory lengths (one year or eight years), as well as two extreme crop protection strategies (systematic treatments on all fields and systematic no treatment). For purely and mainly endocyclic pests (e.g. soil-borne pathogens and most weeds, respectively), we found that increasing the spatial resolution of PMNs made it possible to significantly decrease the number of treatments required for pest control. Taking past observations into account was also effective, but to a lesser extent. PMN information had virtually no influence on the control of non-endocyclic pests (such as flying insects or airborne plant pathogens) which may be due to the spatial coverage addressed in our study. The next step is to extend the analysis of PMNs and to integrate the information generated by PMNs into sustainable pest management strategies, both at the field and the landscape level.

Suggested Citation

  • Cros, Marie-Josée & Aubertot, Jean-Noël & Gaba, Sabrina & Reboud, Xavier & Sabbadin, Régis & Peyrard, Nathalie, 2021. "Improving pest monitoring networks using a simulation-based approach to contribute to pesticide reduction," Theoretical Population Biology, Elsevier, vol. 141(C), pages 24-33.
    • Handle: RePEc:eee:thpobi:v:141:y:2021:i:c:p:24-33
    DOI: 10.1016/j.tpb.2021.06.002
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    References listed on IDEAS

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    1. David J. Pannell, 1991. "Pests and pesticides, risk and risk aversion," Agricultural Economics, International Association of Agricultural Economists, vol. 5(4), pages 361-383, August.
    2. Aka, Joël & Ugaglia, Adeline Alonso & Lescot, Jean-Marie, 2018. "Pesticide Use and Risk Aversion in the French Wine Sector," Journal of Wine Economics, Cambridge University Press, vol. 13(4), pages 451-460, November.
    3. Marie-Josée Cros & Jean-Noël Aubertot & Nathalie Peyrard & Régis Sabbadin, 2017. "GMDPtoolbox: A Matlab library for designing spatial management policies. Application to the long-term collective management of an airborne disease," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-18, October.
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    2. Rincon, Diego F. & Esch, Evan D. & Gutierrez-Illan, Javier & Tesche, Melissa & Crowder, David W., 2024. "Predicting insect population dynamics by linking phenology models and monitoring data," Ecological Modelling, Elsevier, vol. 493(C).

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