Agricultural productivity patterns are affected by growing season weather risks such as rainfall deficit (meteorological drought), which can develop into persistent soil moisture deficit (agricultural drought), leading to crop yield shortfall or wholesale losses. Weather index-based insurance (WII) is a financial instrument designed to assist smallholder farmers in coping with the impacts of drought through payouts when an index threshold is breached.
Critical for operational WII schemes is the skill of capturing the progression of meterological drought (rainfall deficit) to agricultural drought (crop failure), which is not fully represented in statistical correlations of historical data on rainfall deficit and crop yield. This is a non-trivial task. At the core of designing WII products is the requirement for high-quality data over the long-term and in near-real time to provide spatially explicit and internally consistent information on rainfall. Crop yield data with matching space-time characteristics are virtually non-existent and are prone to errors. Moreover, crops yield shortfalls or losses can result from factors other than rainfall such as soil properties and temperature, as well as evaporation and soil moisture dynamics, as crops differ in their sensitivities to the dynamics of water and energy fluxes during the different phonological stages within the growing season. As these factors are key in determining crop production outcomes, we evaluate new space-time volatility indicators based on land surface and crop process modelling using cotton grown in Zambia as a case study.
The results from our analysis of cotton production in Zambia suggest that combining rainfall and soil moisture information can inform WII applications at both the design and implementation stages. This is achieved through relating area-specific probabilities of rainfall deficit occurrence and severity to crop-specific water requirements and their sensitivities to agricultural drought. We discuss how this approach can be used to inform operational WII applications by capturing the physical dynamics of agro-meteorological risk along the trajectory of meteorological to agricultural drought.