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.

Physics Atmospheric and Oceanic Physics Submitted on 29 Oct 2026 Title:From extreme weather to agricultural consequences with agentic AI View PDF http://arxiv.org/pdf/2610.40641v1 HTML experimental https://arxiv.org/html/2610.40641v1 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. Current browse context: physics.ao-ph References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs https://info.arxiv.org/labs/index.html .