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Spatiotemporal risk forecasting to improve locust management.
Piou, Cyril; Marescot, Lucile.
Afiliación
  • Piou C; CIRAD, UMR CBGP, Montpellier, France; CBGP, INRAE, IRD, CIRAD, Institut Agro Montpellier, Montpellier University, Montpellier, France. Electronic address: Cyril.piou@cirad.fr.
  • Marescot L; CIRAD, UMR CBGP, Montpellier, France; CBGP, INRAE, IRD, CIRAD, Institut Agro Montpellier, Montpellier University, Montpellier, France.
Curr Opin Insect Sci ; 56: 101024, 2023 04.
Article en En | MEDLINE | ID: mdl-36958588
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.
Asunto(s)

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Saltamontes Tipo de estudio: Etiology_studies / Prognostic_studies / Risk_factors_studies Límite: Animals Idioma: En Revista: Curr Opin Insect Sci Año: 2023 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Saltamontes Tipo de estudio: Etiology_studies / Prognostic_studies / Risk_factors_studies Límite: Animals Idioma: En Revista: Curr Opin Insect Sci Año: 2023 Tipo del documento: Article