A Bayesian method to support global out-scaling of water-efficient rice technologies from pilot project areas

Abstract

This article present a Bayesian probabilistic method to support out-scaling of technologies from pilot projects. The method is applied to aerobic rice, a water-saving technology with probable global potential. The method assumes that areas similar to pilot sites are more likely to adopt than those that are different or unfavourable. Similarity is defined from climate, landscape and socio-economic attributes. Favourability is further evaluated by project specialists. Scaling out is not a simple linear process, so the method is proposed as a complement to learning processes. Results can support prioritization and strategic planning over specific geographic areas.