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A forecasting model for desert locust presence during recession period, using real-time satellite imagery

Marescot Lucile, Fernandez Élodie, Dridi Hichem, Benahi Ahmed Salem, Hamouny Mohamed Lemine, Maeno Koutaro Ould, Escorihuela Maria-José, Paolini Giovanni, Piou Cyril. 2025. A forecasting model for desert locust presence during recession period, using real-time satellite imagery. Remote Sensing Applications: Society and Environment, 37:101497, 17 p.

Article de revue ; Article de recherche ; Article de revue à facteur d'impact
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Url - jeu de données - Dataverse Cirad : https://doi.org/10.18167/DVN1/4IY2TG

Résumé : Desert locust (Schistocerca gregaria) is a major agricultural pest that poses significant socio-economic challenges to food security. This study aims to enhance preventive management of desert locusts in Western and Northern Africa by improving an operational model developed by Piou et al. (2019). The model employs satellite remote sensing data and machine learning to forecast locust occurrence at a 1 km2 resolution every ten days. Objectives include identifying environmental risk factors, training random forest models with high-predictive power and providing updated forecasts via a web interface. It is the first implementation of a statistical forecasting model for this species within an automated system, delivering updated locust presence probabilities every ten days. Validated through field surveys with a positive error rate of 23%, the forecasting tool shows a strong correlation between predicted probabilities and observed locust densities. This operational tool can guide survey teams, optimize resource allocation, and mitigate environmental impacts efficiently. We believe continuous evaluation and integration of the forecast system will enhance its effectiveness in preventing locust outbreaks, thereby safeguarding food security in the region.

Mots-clés Agrovoc : télédétection, Schistocerca gregaria, imagerie par satellite, modélisation environnementale

Mots-clés géographiques Agrovoc : Mauritanie, Sénégal

Mots-clés libres : Automatic forecast system, Locust outbreak, Machine Learning, Remote Sensing, Schistocerca gregaria

Agences de financement hors UE : Commission de lutte contre le criquet pèlerin dans la région occidentale, Food and Agriculture Organization, Agence Française de Développement

Projets sur financement : (ITA) Consolider les bases de la stratégie de lutte préventive et développer la recherche opérationnelle sur le Criquet pèlerin en région occidentale, (FRA) Accounting for Climate Change in Water and Agriculture management

Auteurs et affiliations

  • Marescot Lucile, CIRAD-BIOS-UMR CBGP (FRA) ORCID: 0000-0001-7625-5446 - auteur correspondant
  • Fernandez Élodie, CIRAD-BIOS-UMR CBGP (FRA)
  • Dridi Hichem, CLCPRO (DZA)
  • Benahi Ahmed Salem, CNLA (MRT)
  • Hamouny Mohamed Lemine, CLCPRO (DZA)
  • Maeno Koutaro Ould, JIRCAS (JPN)
  • Escorihuela Maria-José, isardSAT (ESP)
  • Paolini Giovanni, isardSAT (ESP)
  • Piou Cyril, CIRAD-BIOS-UMR CBGP (FRA) ORCID: 0000-0002-9378-9404

Source : Cirad-Agritrop (https://agritrop.cirad.fr/613047/)

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