Spatiotemporal risk forecasting to improve locust management

Piou Cyril, Marescot Lucile. 2023. Spatiotemporal risk forecasting to improve locust management. Current Opinion in Insect Science, 56:101024, 7 p.

Résumé : Locusts are among the most feared agricultural pests. Spatiotemporal forecasting is a key process in their management. The present review aims to 1) set a common language on the subject, 2) evaluate the current methodologies, and 3) identify opportunities to improve forecasting tools. Forecasts can be used to provide reliable predictions on locust presence, reproduction events, gregarization areas, population outbreaks, and potential impacts on agriculture. Statistical approaches are used for the first four objectives, whereas mechanistic approaches are used for the latter. We advocate 1) to build reliable and reproducible spatiotemporal forecasting systems for the impacts on agriculture, 2) to turn scientific studies into operational forecasting systems, and 3) to evaluate the performance of these systems.

Mots-clés Agrovoc : Schistocerca gregaria, gestion du risque, dynamique des populations, technique de prévision, distribution géographique, agroécologie

Mots-clés libres : Agricultural pest, Forecasting tool, Locust management, Spatio-temporal risk

Classification Agris : H10 - Ravageurs des plantes
U30 - Méthodes de recherche

Champ stratégique Cirad : CTS 4 (2019-) - Santé des plantes, des animaux et des écosystèmes

Agences de financement hors UE : Agence Nationale de la Recherche

Projets sur financement : (FRA) Etude de l'émergence du polyphénisme de phase et des risques associés

Auteurs et affiliations

• Piou Cyril, CIRAD-BIOS-UMR CBGP (FRA) ORCID: 0000-0002-9378-9404 - auteur correspondant
• Marescot Lucile, CIRAD-BIOS-UMR CBGP (FRA) ORCID: 0000-0001-7625-5446

Source : Cirad-Agritrop (https://agritrop.cirad.fr/604520/)

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