Can we create a `race to the top' for weather forecasts to inform smallholder farmer decisions?

Colin Aitken, Michael K. Tippett, Pedram Hassanzadeh, Katherine Kowal, Rendani Mbuvha, John H. Marsham, Shruti Nath, Ousmane Ndiaye, Douglas J. Parker, Caroline M Wainwright, Michael Kremer and William R. Boos

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Abstract: Artificial-intelligence weather prediction (AIWP) models have made it possible to produce high-quality tailored forecasts with limited computational resources. This advance has the potential to benefit hundreds of millions of farmers in low- and middle-income countries who lack access to forecasts of critical weather phenomena. However, it can be difficult for key stakeholders to evaluate forecast quality, risking a "race to the bottom" as cheap but low-quality forecasts crowd out forecasts that would benefit farmers. We propose a set of principles and protocols for evaluating agriculturally-relevant forecasts as a starting point for standards that would let forecasters credibly convey their forecasts' quality.

Date: 2026-09, Revised 2026-10
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