PreprintCC BY 4.0arXiv
Can we create a `race to the top' for weather forecasts to inform smallholder farmer decisions?
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.
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BibTeX
@misc{aitken2026create,
title = {{Can we create a `race to the top' for weather forecasts to inform smallholder farmer decisions?}},
author = {Colin Aitken and Michael K. Tippett and Pedram Hassanzadeh and Katherine Kowal and Rendani Mbuvha and John H. Marsham and Shruti Nath and Ousmane Ndiaye and Douglas J. Parker and Caroline M Wainwright and Michael Kremer and William R. Boos},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.00782},
eprint = {2610.00782},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.00782},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Can we create a `race to the top' for weather forecasts to inform smallholder farmer decisions?
AU - Colin Aitken
AU - Michael K. Tippett
AU - Pedram Hassanzadeh
AU - Katherine Kowal
AU - Rendani Mbuvha
AU - John H. Marsham
AU - Shruti Nath
AU - Ousmane Ndiaye
AU - Douglas J. Parker
AU - Caroline M Wainwright
AU - Michael Kremer
AU - William R. Boos
PY - 2026
DA - 2026/10/07
PB - arXiv
DO - 10.48550/arxiv.2610.00782
UR - https://arxiv.org/abs/2610.00782
AB - 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.
ER -