{"slug": "from-extreme-weather-to-agricultural-consequences-with-agentic-ai", "title": "From extreme weather to agricultural consequences with agentic AI", "summary": "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.", "body_md": "# Physics > Atmospheric and Oceanic Physics\n\n  [Submitted on 29 Oct 2026]\n\n# Title:From extreme weather to agricultural consequences with agentic AI\n\n[View PDF](http://arxiv.org/pdf/2610.40641v1)\n\n[HTML (experimental)](https://arxiv.org/html/2610.40641v1)\n\nAbstract: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.\n    \n\n### Current browse context:\n\nphysics.ao-ph\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer \n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers \n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps \n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations \n\n*(*[What are Smart Citations?](https://www.scite.ai/))\n# Code, Data and Media Associated with this Article\n\nalphaXiv \n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers \n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub \n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub \n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face \n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast \n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))\n# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower \n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender \n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))\n# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth 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.\n\nHave an idea for a project that will add value for arXiv's community? [**Learn more about arXivLabs**](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/from-extreme-weather-to-agricultural-consequences-with-agentic-ai", "canonical_source": "http://arxiv.org/abs/2610.40641v1", "published_at": "2026-10-30 15:54:08+00:00", "updated_at": "2026-10-30 16:19:12.696303+00:00", "lang": "en", "topics": ["ai-agents", "artificial-intelligence", "ai-research"], "entities": ["WxCrop", "arXiv", "Florida"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/from-extreme-weather-to-agricultural-consequences-with-agentic-ai", "markdown": "https://wpnews.pro/news/from-extreme-weather-to-agricultural-consequences-with-agentic-ai.md", "text": "https://wpnews.pro/news/from-extreme-weather-to-agricultural-consequences-with-agentic-ai.txt", "jsonld": "https://wpnews.pro/news/from-extreme-weather-to-agricultural-consequences-with-agentic-ai.jsonld"}}