A Machine Learning Approach to Predicting and Mitigating Climate-Induced Agricultural Risks in Nigeria

Agricultural productivity in Nigeria is significantly challenged by climate change given that the nation is heavily reliant on rain-fed farming systems. This study explores the role of machine learning in agroclimatic risk modelling, discussing its capability to predict and mitigate climate-induced risks such as droughts, pest outbreaks and floods. The study further investigates the integration of climatic and non-climatic factors in risk evaluation and the application of machine learning algorithms for predictive purposes. Furthermore, it discusses the practical implications for relevant stakeholders including farmer, extension workers, and policymakers, focusing on strategies to enhance resilience and sustainability. The findings illustrate the transformative potential of machine learning in mitigating agro-climatic risks in a changing climatic condition.