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

Lucile Marescot (), Elodie Fernandez (), Hichem Dridi, Ahmed Benahi, Mohamed Hamouny, Koutaro Maeno, Maria-José Escorihuela (), Giovanni Paolini and Cyril Piou
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Lucile Marescot: UMR CBGP - Centre de Biologie pour la Gestion des Populations - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement - IRD [Occitanie] - Institut de Recherche pour le Développement - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Montpellier - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement - UM - Université de Montpellier, Cirad-BIOS - Département Systèmes Biologiques - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement
Elodie Fernandez: UMR CBGP - Centre de Biologie pour la Gestion des Populations - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement - IRD [Occitanie] - Institut de Recherche pour le Développement - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Montpellier - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement - UM - Université de Montpellier
Hichem Dridi: FAO - Food and Agriculture Organization of the United Nations, Regional Office for the Near East and North Africa - FAO - Food and Agriculture Organization of the United Nations [Rome, Italie], CLCPRO - Commission de Lutte Contre le Criquet Pèlerin en Région Occidentale
Ahmed Benahi: CNLA - Centre National de Lutte Antiacridienne
Mohamed Hamouny: FAO - Food and Agriculture Organization of the United Nations, Regional Office for the Near East and North Africa - FAO - Food and Agriculture Organization of the United Nations [Rome, Italie], CLCPRO - Commission de Lutte Contre le Criquet Pèlerin en Région Occidentale
Koutaro Maeno: JIRCAS - Japan International Research Center for Agricultural Sciences
Maria-José Escorihuela: isardSAT
Giovanni Paolini: isardSAT
Cyril Piou: UMR CBGP - Centre de Biologie pour la Gestion des Populations - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement - IRD [Occitanie] - Institut de Recherche pour le Développement - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Montpellier - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement - UM - Université de Montpellier, Cirad-BIOS - Département Systèmes Biologiques - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement

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Abstract: Highlights: • We built an operational forecasting system for Desert locust preventive management. • We used random forest model for real-time forecasting of locust presence and update every decade. • Pest distribution was explained by sand cover, ecoregions, temperature, precipitations and vegetation cover. • Field evaluation revealed a strong correlation between predicted probabilities and observed locust densities. Abstract: Desert locust (Schistocerca gregaria) is a major agricultural pest that poses significant socioeconomic 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 km 2 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.

Keywords: Automatic forecast system; Locust outbreak; Machine learning; Remote sensing; Schistocerca gregaria (search for similar items in EconPapers)
Date: 2025-01
New Economics Papers: this item is included in nep-agr and nep-for
Note: View the original document on HAL open archive server: https://hal.inrae.fr/hal-04995261v1
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Published in Remote Sensing Applications: Society and Environment, 2025, 37, pp.101497. ⟨10.1016/j.rsase.2025.101497⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04995261

DOI: 10.1016/j.rsase.2025.101497

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