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From extreme weather to agricultural consequences with agentic AI

Researchers posted WxCrop to arXiv on 29 October 2026, describing it as the first agent that judges agricultural hazards from physically interpretable weather indices examined by causal analysis and the first to combine weather and agricultural data to predict crop loss for past and forecast dates. Demonstrated in Florida on hurricanes, freezes and dry heat with loss models calibrated on insurance records, WxCrop's hurricane index detected recorded cyclone days with an AUC of 0.98, reproduced held-out-year losses to within 2%, and matched independent production records at a rank correlation of 0.63. Users query the agent for each crop group's loss as a fraction of expected season value, with every number sourced, so governments, growers and scientists can respond to agricultural disasters quickly.

read2 min views2 publishedOct 30, 2026
From extreme weather to agricultural consequences with agentic AI
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  [Submitted on 29 Oct 2026]


[View PDF](http://arxiv.org/pdf/2610.40641v1)

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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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