Insight
Satellite Monitoring for Smallholder Farmers: A Cost-Effectiveness Analysis
D4Act's pilot deployment of satellite-based crop monitoring systems across three Sahelian countries demonstrated that machine learning models trained on Sentinel-2 imagery can predict smallholder crop yields with over 85% accuracy - at a fraction of the cost of traditional ground-truth surveys. The system processed over 50,000 farm plots across Burkina Faso, Niger, and northern Togo, providing near-real-time estimates of crop health, growth stage, and expected yield throughout the growing season.
Satellite-based monitoring reduces per-farm assessment costs by 70-80% compared to enumerator-based field visits, while enabling continuous monitoring rather than single-point-in-time snapshots.