Innovation
Methods, ideas, or system contributions that make the work stand out.
Artificial-intelligence weather prediction
Agricultural forecast evaluation
Smallholder farmers
Forecast quality standards
Tailored forecasts
Can we create a `race to the top' for weather forecasts to inform smallholder farmer decisions?
🤖 AI Summary
This study addresses the lack of high-quality weather forecasts for smallholder farmers in low-income regions, where the absence of evaluation criteria drives a race to the bottom characterized by Gresham’s Law. To overcome this challenge, this work leverages AI weather prediction (AIWP) models alongside cost-effective computational optimization techniques to propose the first evaluation principles and protocol framework tailored for agricultural forecasting. By establishing a standardized mechanism for communicating forecast quality, the proposed approach aims to catalyze a race to the top. The primary contribution lies in constructing a trustworthy evaluation standard system for forecast quality, which supports hundreds of millions of smallholder farmers in accessing high-quality, customized weather services, thereby effectively empowering agricultural decision-making.
📝 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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