[{"_1":2,"_87":-5,"_88":-5},"loaderData",{"_3":4,"_26":27},"root",{"_5":6,"_17":10,"_18":19,"_20":21,"_22":10,"_23":-5,"_24":-5,"_25":10},"user",{"_7":8,"_9":10,"_11":10,"_12":-5,"_13":-5,"_14":15,"_16":-5},"roles",[],"isAuthenticated",false,"isPremium","planName","subscriptionStatus","featureFlags",{},"insightsBudget","isDevelopmentHost","userPreferences",{},"recaptchaSiteKey","6LcvOJcUAAAAAHsB-gPGfGktcRlKpH7OTIU_dMTD","isImpersonating","impersonation","affiliateStatus","isFreedomPortal","routes/v2/article/layout",{"_28":29,"_30":31},"notFound",true,"relatedArticles",[32,75],{"_33":34,"_35":36,"_37":38,"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56,"_57":58,"_59":60,"_61":62,"_63":64,"_65":66,"_67":68,"_69":70,"_71":72,"_73":72,"_74":72},"mediaId",6232458,"mediaKeyId","3426132-67eb2c3329be9fbf5bf4f3efd7f7c755","parentId",3426132,"title","Members Warn USDA, NOAA, EPA Cuts and Data Purges Threaten Agrochemical Research and AI Models","audioSeconds",1769,"startMs",734000,"endMs",2503000,"viewUrl","/Search/View?dp=1&key=3426132-67eb2c3329be9fbf5bf4f3efd7f7c755&start=734&end=2503","thumbnailUrl","https://assets.pipeline.soar.com/3426132-67eb2c3329be9fbf5bf4f3efd7f7c755/thumbnail_734000.jpg","articleUrl","/articles/6232458/Members-Warn-USDA-NOAA-EPA-Cuts-and-Data-Purges-Threaten-Agrochemical-Research-and-AI-Models","mediaUrl","https://secure.pipeline.soar.com/6232458-eb8794702b1b6c02799c48d152adcc7d/orig.mp4?Expires=1770421008&Signature=oRnixkWpw1IrKe6Vv8iWASsxdHXCcanGKOb0NwYIO2TVQcAfCHw-DL6VzYPLAibha768xYwrWvZKp9kPse6wsPFRhsuVX3noMvRx2jH~-QDKH9-NKXCfhNy~ijZ1kC336UuXSOn2DnyCbhlpaYBYbmP7OhXhmkVTh0tlnR8skKNjJVTuwNic4tOIgA2Ux6IUM2MkSUNEjwseKSgEwLDDLoI8o9CkuNIEgIxcxYRgjKwaJb7HHU2pqGc9~EPdeZ6WJKh2CO5K~7ehMzPIEyFDm9drwUG21LVmu52vPH1rEEzU8cSBMw0Shd4j8OpQpiqZvWabOq5Leh5mav6LNcyBWg__&Key-Pair-Id=KUT5TJCACFTXG","author","U.S. House Committee on Science, Space, and Technology","date","2025-05-21T16:47:56.243Z","article","Members of the House Science, Space, and Technology Subcommittee on the Environment used opening statements and questioning to flag recent federal data removals and proposed science funding cuts as direct threats to AI‑enabled agrochemical research.

Speakers described how AI models depend on long‑running, high‑quality public datasets and on agency science workers who collect, curate and publish those data. They said workforce reductions at NOAA, USDA and EPA and proposed budget cuts to NSF and EPA Office of Research and Development risk degrading the inputs that drive AI models and could slow or invalidate regulatory health assessments.

A member warned bluntly that \"No algorithm is better than the data that it runs on,\" arguing that dismantling data systems will leave farmers \"in the dark.\" Representative Lofgren, the committee ranking member, told the panel that the fiscal year 2026 proposed budget \"essentially eliminat[es]\" EPA's Office of Research and Development funding and noted the role of ORD's Integrated Risk Information System (IRIS) in producing toxicity and exposure data used in health risk assessments. \"Using AI to approve new chemicals without the critically necessary scientific health data to ensure they're safe is not innovation,\" Lofgren said. \"I think that's recklessness.\"

Witnesses described concrete ways the cuts and data gaps affect their research. Boris Cameletti said his team needs to sample many fields and relies on student and postdoctoral researchers supported by USDA and NSF grants; he said placing proposals on hold has hindered data collection and workforce support. Brian Lutz and other witnesses said their models depend on public climate and phenology data for fungicide‑timing tools and that academic advances build on decades of basic research funded by federal agencies.

Members also cited specific incidents. One representative described a late‑January order to remove climate‑related material from USDA websites; the transcript records that following a lawsuit the Department agreed to restore climate change related content to resource pages. The hearing record includes repeated citations to proposed cuts at NSF (a cited reduction of $4,900,000,000 or about 55% in the discussion) and to reported staffing and service disruptions at NOAA and USDA.

Witnesses and members urged priority for preserving public datasets, sustaining grant programs that support land‑grant extension and university research, and ensuring that AI applications are evaluated against robust, widely available toxicity and exposure data before regulatory decisions. Several members said they would use the open record and follow‑up submissions to press agencies on data restoration and funding decisions.

No formal committee action was taken at the hearing; members left the record open for 10 days for written questions and additional materials.","mediaType","Video","parentTitle","Environment Subcommittee - Innovations in Agrichemicals: AI’s Hidden Formula Driving Efficiency","oneSentenceSummary","Several members told the same House subcommittee that recent agency staffing cuts, proposed budget reductions and removal of climate data undermine the public datasets and scientific capacity that AI systems and university researchers need to develop safer agrochemicals.","taxTitle","Science, Space, and Technology: House Committee, Standing Committees - House & Senate, Congressional Hearings Compilation, Legislative, Federal","taxId",1823,"location","","headline","shortSummary",{"_33":76,"_35":36,"_37":38,"_39":77,"_41":78,"_43":79,"_45":80,"_47":81,"_49":82,"_51":83,"_53":84,"_55":56,"_57":58,"_59":85,"_61":62,"_63":64,"_65":86,"_67":68,"_69":70,"_71":72,"_73":72,"_74":72},6232462,"Experts Tell House Subcommittee AI Is Accelerating Agrochemical Discovery, But Data, Funding And Regulatory Bottlenecks Remain",1068,436000,1504000,"/Search/View?dp=1&key=3426132-67eb2c3329be9fbf5bf4f3efd7f7c755&start=436&end=1504","https://assets.pipeline.soar.com/3426132-67eb2c3329be9fbf5bf4f3efd7f7c755/thumbnail_436000.jpg","/articles/6232462/Experts-Tell-House-Subcommittee-AI-Is-Accelerating-Agrochemical-Discovery-But-Data-Funding-And-Regulatory-Bottlenecks-Remain","https://secure.pipeline.soar.com/6232462-e2d059bc1d281f5e8905498260ca4fc4/orig.mp4?Expires=1770419512&Signature=XHtYtJV4QFsk5Ucmrki-GKNSwn3sKBB6ITokUC6mjlGRZmMugM~aqh1yrJl3oQ~4xqlhFrrk~2DdxMtx8OQ46rcJYKO9gGwDMer1w8y7IbcYCoJ2thG5UtPO9OLfxcv4~DcDJxQQ6Rv6QW~qgKm0rxDv6JW8NbyiyBTe7SdAr2~piFqqzWqEF4gSosXRlQ75PnXb-zv5QUuYi76WFAf-oWX-wnfMZWqsyx6N99z8m2rDAC9r~i4NkW6-rcPajRYiwrWkwnrs07nhit3w5hplFcklRy47xfJF7QT0TYmBhzqg90TJTLy5kMgtQRzjdTy3Z1PXV2PEZHomVK0GZ-rYHw__&Key-Pair-Id=KUT5TJCACFTXG","At a hearing of the House Science, Space, and Technology Subcommittee on the Environment titled \"Innovations in Agrochemicals, AI's Hidden Formula for Driving Efficiency,\" witnesses from industry and land‑grant universities described concrete ways artificial intelligence is changing discovery, manufacturing and field application of pesticides and fungicides.

The panel heard that AI can compress discovery timelines, help manufacturers optimize production and generate site‑specific treatment maps that could sharply reduce chemical use. Committee members and witnesses urged continued federal support for public datasets and for research partnerships so AI outputs have reliable inputs and can be validated before field deployment.

Brian Lutz, vice president of agricultural solutions at Corteva Agriscience, told the subcommittee that AI is changing discovery by allowing researchers to move from randomness to prediction and design. \"AI has revolutionized discovery by allowing us to trade randomness and chance for prediction, specificity, and design,\" Lutz said, describing work in which models analyzed roughly 10,000 molecules in weeks and flagged dozens of candidates his team would not have found otherwise. He described three distinct AI roles: discovery, development and manufacturing, and field guidance. Lutz said Corteva invests \"nearly $4,000,000 every day\" in research and development and cited a manufacturer site in Midland, Michigan, as an example of using AI to optimize fermentation and reaction conditions.

Daniel Swale, associate professor at the University of Florida, testified that AI is opening access to natural‑product chemistry and can help universities re‑enter early‑stage discovery that historically has been dominated by private companies. \"The outputs of AI driven discovery are only as good as the inputs you provide the system,\" Swale said, urging public investment to create and share foundational datasets that currently are often locked inside private firms. He also described efforts to find alternatives for pests such as the Asian citrus psyllid and noted that synthetic compounds his lab has identified face a prolonged and costly regulatory pipeline.

Boris Cameletti, assistant professor at the University of Illinois Urbana‑Champaign, described using satellite and spectral remote sensing with machine learning to detect Red Crown Rot in soybeans, a soilborne disease that can cause \"yield losses of up to 50%.\" His team trains models with visible to near‑infrared spectral bands and ground truth observations to generate prescription maps that target only affected patches. \"If only 25% of a field is deceased, our approach could reduce fungicide yield by up to 75%,\" Cameletti said, adding that on‑farm trials are planned to compare traditional uniform applications with AI‑guided site‑specific treatments.

Members pressed witnesses on safety, validation and the pace of regulatory review. Several members reiterated a recurring theme in the hearing: AI models require robust public data. Chairman Franklin told the panel he had secured $4,500,000 in federal funding for an AI center at the University of Florida and noted EPA's reported backlog of \"over 504 new chemical reviews and 12,000 pesticide reviews\" under the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA) and review timelines under the Toxic Substances Control Act (TSCA). Members from both parties asked how to speed needed products to growers without sacrificing scientific review.

Witnesses described concrete benefits and limits. Lutz said a fungicide‑timing model combining field‑specific information with internal data has helped some growers increase yields by \"4 to 10 bushels per acre\" in pilot work. Swale argued that natural‑product pathways can shorten registration timelines for some compounds compared with synthetics and highlighted a Boulder, Colorado, company (Invata) that his team is working with to identify structures in complex natural mixtures. Cameletti emphasized that land‑grant research, checkoff funding and public computing infrastructure enabled the work at Illinois, and urged continued public support so tools serve the public good.

All three witnesses acknowledged technical and governance risks. Lutz and others urged model validation, human‑in‑the‑loop testing and regulatory clarity to prevent misuse or overreliance on unvalidated outputs. On risks and regulation, Lutz warned against a patchwork of state rules and called for federal clarity on oversight and testing frameworks.

The hearing record will remain open for written questions and comments; members may use those submissions to press agencies on data access, funding priorities and options to accelerate registration while preserving safety reviews.","Industry and academic witnesses told the House Science, Space, and Technology Subcommittee that artificial intelligence is speeding discovery, manufacture and field targeting of crop‑protection products, but they said public data, research funding and a clearer regulatory pathway are needed to translate models into usable tools for farmers.","actionData","errors"]

Article not found

Members Warn USDA, NOAA, EPA Cuts and Data Purges Threaten Agrochemical Research and AI Models

Experts Tell House Subcommittee AI Is Accelerating Agrochemical Discovery, But Data, Funding And Regulatory Bottlenecks Remain