A framework for assessing the value of seasonal climate forecasting in key agricultural decisions

A framework for assessing the value of seasonal climate forecasting in key agricultural decisions

While climate information services are widely available, translating climate information into actionable solutions to reduce climate risk, which are readily taken up by producers, remains a critical challenge. Here, we apply a bio-economic approach to assess the potential economic value of seasonal climate forecasts (SCFs) as a basis for climate services for use in agricultural decision-making. We use a case study approach, quantifying the impacts of seasonal precipitation on rice cropping, a dominant farming system in the Greater Mekong Region (GMR) in Southeast Asia. We demonstrate values of seasonal precipitation forecasts for a range of forecast skill levels from low to perfect skill for three seasonal precipitation conditions (wet, normal and dry), as well as extreme conditions (extreme wet and extreme dry). Based on our integrated bio-economic assessment and seasonal variation in precipitation, we identify an optimal rice sowing window, which potentially results in improved yield and economic benefits compared with the currently applied sowing window. Applying this approach using common rice varieties grown by farmers – specifically, the medium growth duration Jasmine rice and the short duration Vietnamese long grain white rice variety OM 5451 – we find significant value in using seasonal precipitation forecasts to identify optimal sowing windows, ranging from an average of $135 ha−1 for precipitation forecasts at the current level (70% accuracy) of forecast skill to $220 ha−1 for perfect (100% accurate) precipitation forecasts.

We propose that such a framework can be used to examine the value of using seasonal climate forecasts, even at current skill levels, in farm adaptive operational decision-making. We envisage that demonstration of the value of using seasonal forecasts in crop production system decisions will build user confidence and help in upscaling the use of climate information in the region and more broadly.