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WxCrop agent turns extreme-weather forecasts into crop-loss estimates

Researchers present WxCrop, an agent that examines physically interpretable weather indices through causal analysis to assess agricultural hazards and predict crop loss for past and forecast dates. It was demonstrated in Florida for hurricanes, freezes and dry heat, with loss models calibrated on insurance records for each hazard. Reported checks include a hurricane index AUC of 0.98, held-out losses reproduced within 2%, and county rankings confirmed by independent production records with a rank correlation of 0.63. Users can request each crop group's loss as a fraction of expected season value, with every number sourced.

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It demonstrates agentic AI converting weather forecasts into sourced, crop-specific loss estimates that could support faster agricultural disaster response and risk assessment.

arXiv:2610.40641v1 Announce Type: cross Abstract: Extreme weather is forecast days ahead, yet its consequences for agriculture remain hard to predict: a forecast says where a storm will strike, not which crops will suffer or how much is at risk, and the records that could tell span many sources. We present WxCrop, the first agent that judges agricultural hazards from physically interpretable weather indices examined by causal analysis, and the first that combines weather and agricultural information to predict crop loss reliably for past and forecast dates. We demonstrate it in Florida on hurricanes, freezes and dry heat, with a loss model calibrated on insurance records for each, and the same procedure extends to other hazards and areas. Three checks show reliability: the hurricane index detects recorded cyclone days with an AUC of 0.98, recorded losses in held-out years are reproduced to within 2%, and independent production records confirm the county rankings (rank correlation 0.63). Users ask the agent for each crop group's loss as a fraction of expected season value, every number sourced, so that governments, growers and scientists respond to agricultural disasters quickly.
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