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

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.