Crop variety management for climate adaptation supported by citizen science

Significance

Climate adaptation requires farmers to adjust their crop varieties over time and use the right varieties to minimize climate risk. Generating variety recommendations for farmers working in marginal, heterogeneous environments requires variety evaluation under farm conditions. On-farm evaluation is difficult to scale with conventional methods. We used a scalable approach to on-farm participatory variety evaluation using crowdsourced citizen science, assigning small experimental tasks to many volunteering farmers. We generated a unique dataset from 12,409 trial plots in Nicaragua, Ethiopia, and India, a participatory variety evaluation dataset of large size and scope. We show the potential of crowdsourced citizen science to generate insights into variety adaptation, recommend adapted varieties, and help smallholder farmers respond to climate change.

Abstract

Crop adaptation to climate change requires accelerated crop variety introduction accompanied by recommendations to help farmers match the best variety with their field contexts. Existing approaches to generate these recommendations lack scalability and predictivity in marginal production environments. We tested if crowdsourced citizen science can address this challenge, producing empirical data across geographic space that, in aggregate, can characterize varietal climatic responses. We present the results of 12,409 farmer-managed experimental plots of common bean ( Phaseolus vulgaris L.) in Nicaragua, durum wheat ( Triticum durum Desf.) in Ethiopia, and bread wheat ( Triticum aestivum L.) in India. Farmers collaborated as citizen scientists, each ranking the performance of three varieties randomly assigned from a larger set. We show that the approach can register known specific effects of climate variation on varietal performance. The prediction of variety performance from seasonal climatic variables was generalizable across growing seasons. We show that these analyses can improve variety recommendations in four aspects: reduction of climate bias, incorporation of seasonal climate forecasts, risk analysis, and geographic extrapolation. Variety recommendations derived from the citizen science trials led to important differences with previous recommendations.