Can we create a `race to the top' for weather forecasts to inform smallholder farmer decisions?
How to stop cheap, bad weather forecasts from pushing out good ones for farmers
AI has made it cheap to produce customized weather forecasts that could help hundreds of millions of small farmers in poor countries. But without clear standards to measure forecast quality, low-cost but unreliable predictions could dominate the market, crowding out genuinely useful ones. The authors propose evaluation principles and protocols that would let farmers and others judge which forecasts are actually worth using.
Farmers make critical decisions about planting, irrigation, and harvest timing based on weather forecasts. Bad predictions lead to crop failures and financial ruin. Without standards, farmers in low-income countries will have no way to distinguish between a forecast that can genuinely help them and one that's just cheap noise—forcing them to either ignore forecasts entirely or gamble on unreliable ones.