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

Year: 2026
Type: preprint
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.
DOI: 10.48550/arxiv.2610.00782
Authors: Aitken, Colin, Tippett, Michael K., Hassanzadeh, Pedram, Kowal, Katherine, Mbuvha, Rendani, Marsham, John H., Nath, Shruti, Ndiaye, Ousmane, Parker, Douglas J., Wainwright, Caroline M, Kremer, Michael, Boos, William R.
Institutions: University of Chicago, Columbia University, University of the Witwatersrand, University of Leeds, University of Oxford, African Center of Meteorological Application for Development, NORCE Research AS, Lawrence Berkeley National Laboratory, University of California, Berkeley
Language: English
Open Access status: green
OpenAlex ID: W7215774796
Cites: 2
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Related to: 10