Seasonal seed scenario planning: co-design of a generic framework for matching seed supply and demand using seasonal climate forecasts
2023
Steinke, Jonathan | Ortíz Crespo, Berta | Etten, Jacob van | Borman, Gareth Denis | Hassena, Mohammed | Kretschmer, Marlene | MacLeod, David A. | Muungani, Dean
To cope with interannual climate variability, many farmers in tropical and sub-tropical regions choose crop varieties that fit seasonal climate conditions. Therefore, seed demand for different varieties, such as early- or late-maturing cultivars, varies between years. Resulting mismatches between relatively constant supply and variable demand create losses for both seed suppliers and farmers. Because demand for seed of different varieties is influenced by seasonal climate, however, probabilistic seasonal rainfall forecasts could help seed suppliers better anticipate upcoming seed demand. To explore this idea, we engaged decision-makers from seed supply organizations in Zimbabwe and Ethiopia. Through a participatory design process, we identified opportunities and challenges for using seasonal rainfall forecasts to inform seed supply decisions. In a case study of maize seed sales in Zimbabwe, we tested our assumptions and iteratively devised a systematic procedure for forecast-based planning in seed supply, relying on free online data sources and expert deliberations. We found that currently accessible rainfall forecasts could indeed be useful for prioritizing likely high-demand varieties during the stages of seed treatment, packaging, and logistics. In practice, though, more flexible and adaptive management of seed supply pipelines might be required to make use of seed demand forecasts. In the future, targeting farmers with climate forecasts along with recommended variety portfolios may strengthen the association between seasonal climate and farmers’ variety demand, increasing the accuracy of demand anticipation. This study highlights opportunities for increased case-specific collaboration between climate scientists and the seed sector to make seasonal forecast information operational.
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