Predictive model to forecast desert locust outbreaks in Kenya using maximum entropy

Abstract

Desert locusts (Schistocerca gregaria) are one of the most destructive transboundary pests, posing significant threats to food security, livelihoods, and vegetation. In Kenya, a severe outbreak of desert locust outbreak occurred between December 2019 and June 2021, causing extensive damage to crop and vegetation, specially in the eastern and northeastern parts of the country. Using forecasted climate and environmental data as well as historical occurrence data, it is possible to Predict possibility of an outbreak which can facilitate relevant stakeholders to put in place necessary measures to mitigate the effects. This prediction can help enhance early warning systems by facilitating timely intervention towards mitigating risks efforts. This research study aimed at coming up with a predictive model for desert locust outbreaks in Kenya using the MaxEnt algorithm and historical presence data together with environmental variables such as precipitation, soil moisture, temperature, and vegetation indices to identify areas susceptible to infestations. The research used used Maxent algorithm and latest technologies of GIS and machine learning techniques to generate maps that classify areas in terms of risks levels (low, medium, high) based on climate data and historical locust occurrence data. The output will help enhance locust monitoring and forecasting, providing critical insights for policymakers, stakeholders, and farmers. The output includes a validated prediction model, maps, and recommendations for locust control strategies. The findings revealed that precipitation and soil moisture were the strongest predictors of habitat suitability, followed by temperature and vegetation indices. The MaxEnt model produced validated habitat suitability maps, classifying areas into low, medium, and high-risk zones. High-risk areas were concentrated in northeastern and eastern Kenya, aligning with regions historically affected by locust invasions. These results demonstrate that combining presence-only data with climatic and environmental predictors provides reliable forecasts of potential outbreak zones. The study concludes that the predictive model and generated risk maps can strengthen early warning systems, guide surveillance and control operations, and support policymakers, stakeholders, and farmers in mitigating the impact of desert locust outbreaks.