AI-Afforded Weather Forecasts in Agriculture: Adoption Dynamics Among Farmers in East African Nations and Benin
Domaine:
agricultureclimateType de record:
AI-powered agricultural weather forecasting systems are becoming increasingly prevalent in various regions to enhance crop yields and reduce risks associated with climate variability. A mixed-methods approach was employed, including surveys and interviews to gather data from a diverse sample of farmers across the targeted regions. The analysis revealed that approximately 45% of farmers in East African nations have adopted AI weather forecasting systems, with significant improvements in their crop yields reported by over 60% of participants who used these tools. Farmers perceive AI-driven forecasts as valuable for planning and decision-making processes, contributing to more sustainable agricultural practices. Policy makers are encouraged to support infrastructure development and training programmes to facilitate wider adoption of AI in agriculture among farmers in the region. Agriculture, AI, Weather Forecasting, Adoption Dynamics, East Africa, Benin Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.