Rainfall forecasts, learning subsidies and conservation agriculture adoption: Experimental evidence from Zambia - Ngoma, Hambulo; Simutowe, Esau; Silva, João Vasco; Nyagumbo, Isaiah; Kalala, Kelvin; Habeenzu, Mukwemba; Thierfelder, Christian (Elsevier, 2025)
Adapting smallholder rainfed farming systems to climate change requires adoption of technologies that build resilience to climate shocks. One such technology is conservation agriculture, yet its adoption by smallholders in Southern Africa is not widespread. We use incentivized economic field experiments in Zambia to test, ex-ante, whether providing rainfall forecasts and a time-bound learning subsidy can help increase the adoption of conservation agriculture. We found that providing rainfall forecasts predicting low rainfall significantly increased the probability of adopting conservation agriculture by 8 percentage points, while offering a subsidy increased the chances of adoption by 11 percentage points. Bundling rainfall forecasts and subsidies did not significantly influence adoption, perhaps because these were not complementary. Having experienced normal rainfall in the previous experiment round (cropping season) was associated with 6 percentage points higher odds of adopting conservation agriculture, while past exposure to low rainfall significantly reduced the probability of adoption by 6 percentage points. These results suggest that farmers do not expect two subsequent seasons to be the same given the increase in rainfall variability in the region. Other important drivers of adoption are hosting demonstration plots and education level of the participant. These findings provide evidence that providing rainfall forecasts and time-bound learning subsidies may be effective ways to enhance the adoption of conservation agriculture in Zambia and imply a need to reframe conservation agriculture as means to address low and erratic rainfall. Future research can evaluate the persistence of such effects using randomized controlled trials.