Accuracy assessment of approaches to predicting smallholder farmer wellbeing, and spatial patterns in wellbeing, using remote sensing data
Smallholder farming systems are challenging environments for collection of frequent, accurate, and sufficient socio-environmental data. This undermines capacity to design and monitor effective policy to support this often vulnerable segment of the globe’s population. This workshop will develop a research methodology to assess the potential to monitor smallholder farmer well-being through relationships between household survey measures of well-being metrics (e.g. consumption) with remote-sensing derived crop yield estimates. Specific areas of interest include: i) capitalizing on the spatio-temporal coverage of remote sensing data to interpolate patterns in smallholder well-being at different spatial scales at different time-periods, ii) the use of frequentist and Bayesian statistics to model the spatial and temporal patterns in uncertainty in remote sensing derived predictions of smallholder well-being and, iii) methods to compare smallholder farmer sensitivity to a climate shock observed from remote-sensing proxies of well-being to actual responses to the shock based on household survey data.