[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127854-en":3,"doc-seo-127854-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127854,2336474466712,"Maeve","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Predicting fumonisins in Iowa corn - Gradient boosting machine learning","Fumonisins (FUM), secondary metabolites produced by Fusarium species, threaten U.S. corn production and feed safety. This study used gradient boosting machine (GBM) learning to evaluate an Illinois-centric predictive model against historical FUM contamination data from Iowa, and compared it with an Iowa-centric model. Corn samples from 99 Iowa counties (2010, 2020, 2021; n=529) were tested, with 2011 data (n=89) for independent validation. Using a 2 ppm threshold, Illinois- and Iowa-centric overall accuracy reached 71.08% and 85.39% (2011), while influential predictors differed by region and month. Results suggest meteorological and agronomic conditions may drive the probability of high FUM levels.","Received: 5 December 2023  \nRevised: 6 June 2024  \nAccepted: 29 July 2024  \nDOI: 10.1002/cche.10824  \nRESEARCH ARTICLE  \nPredicting fumonisins in Iowa corn: Gradient boosting machine learning  \nEmily Branstad‐Spates1  | Lina Castano‐Duque2 | Gretchen Mosher1  | Charles Hurburgh Jr.1 | Kanniah Rajasekaran2 | Phillip Owens3 |  \nH. Edwin Winzeler3 | Erin Bowers1  \n1Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, Iowa, USA 2USDA, Agriculture Research Service, Southern Regional Research Center, New Orleans, Louisiana, USA 3USDA, Agriculture Research Service, Dale Bumpers Small Farms Research Center, Booneville, Arkansas, USA  \nCorrespondence  \nLina Castano‐Duque, USDA, Agriculture Research Service, Southern Regional Research Center, New Orleans, LA 70124, USA.  \nEmail: [lina.castano.duque@usda.gov](lina.castano.duque@usda.gov)  \nGretchen Mosher, Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, IA 50011, USA.  \nEmail: [gamosher@iastate.edu](gamosher@iastate.edu)  \nFunding information  \nNational Institute of Food and Agriculture, Grant/Award Number: 2022‐ 690008‐36645  \nAbstract  \nBackground and Objectives: Fumonisin (FUM), a secondary metabolite from Fusarium spp., poses major concerns for the United States corn industry. This study evaluated a prepublished Illinois‐centric predictive model with historical Iowa FUM contamination data using gradient boosting machine (GBM) learning and compared influential predictors with an Iowa‐centric model. Corn samples (n = 529) were collected from 2010, 2020, and 2021 in Iowa's 99 counties, and 2011 data were used for independent validation (n = 89). Findings: Applying a 2 ppm (mg/kg) threshold for FUM high and low contamination events, the overall accuracy was 71.08% and 85.39% for the Illinois‐ and Iowa‐centric models in 2011. Balanced accuracies were 60.23% and 50.00% for the Illinois‐ and Iowa‐centric models. For Iowa's remaining years (testing data), the overall accuracy was 98.10%, and balanced accuracy was 50.00%. Conclusions: FUM‐GBM analyses determined the top influential predictor for the Illinois‐centric model was satellite‐acquired normalized difference vegetation index (NDVI) (Veg_index) in March, whereas the top predictor for the Iowa‐centric model was precipitation (PRCP) in October.  \nSignificance and Novelty: Results indicate that meteorological andagronomic events, such as PRCP and Veg_index in early planting stages and during harvest, may influence the probability of high FUM levels in corn.  \nKEYW OR DS  \ncorn, fumonisin, gradient boosting, prediction modeling, validation  \n1 | INTRODUCTION  \nFumonisins (FUMs) are secondary metabolites produced from fungal agents, namely, Fusarium verticillioides and Fusarium proliferatum are predominant corn pathogens globally that cause Fusarium ear rot disease (Munkvold  \net al., 2019; Shephard et al., 1996) . FUM occurs mainly in corn and corn‐based products; however, there are occurrences in other feed products, including rye, oats, wheat, rice, and barley (Kamle et al., 2019) . Over 28 analogs of FUM have been discovered, but the most extensively researched metabolites in prevalence and  \nThis is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.  \n© 2024 The Author(s) . Cereal Chemistry published by Wiley Periodicals LLC on behalf of Cereals & Grains Association.  \n2  \n|  \nBRANSTAD‐SPATES  \nET AL.  \ntoxicity rate order (high to low) are FB1, FB2, FB3, and FB4 (Voss et al., 2007) . FUM is an essential mycotoxin for monitoring and managing commodity corn products due to crop yield loss, contamination of grain due to Fusarium ear rot, effects on animal productivity, and human health costs (Battilani et al., 2008; Charmleyet al., 1994; Haque et al., 2020; Rheeder et al., 2002)","cbCaiaxqefxP7Xb5","https://ap.wps.com/l/cbCaiaxqefxP7Xb5","pdf",2069443,1,12,"English","en",105,"# Abstract\n## Background and Objectives\n## Findings\n## Conclusions\n## Significance and Novelty\n# Introduction","[{\"question\":\"What is the main goal of the study on Iowa corn fumonisins?\",\"answer\":\"To predict fumonisin (FUM) contamination in Iowa corn using gradient boosting machine learning, and to compare an Illinois-centric model with an Iowa-centric model.\"},{\"question\":\"How were the predictive models evaluated, and what data were used?\",\"answer\":\"Corn samples from Iowa’s 99 counties were collected across 2010, 2020, and 2021 (n=529), and 2011 data (n=89) were used for independent validation.\"},{\"question\":\"Which predictors were most influential in the Illinois- vs Iowa-centric models?\",\"answer\":\"For the Illinois-centric model, the top predictor was satellite-acquired normalized difference vegetation index (NDVI, Veg_index) in March, while for the Iowa-centric model it was precipitation (PRCP) in October.\"}]","Predicting fumonisins in Iowa corn - Gradient boosting machine learning | PDF",1785942370,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"predicting-fumonisins-in-iowa-corn-gradient-boosting-machine-learning","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-fumonisins-in-iowa-corn-gradient-boosting-machine-learning/127854/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the main goal of the study on Iowa corn fumonisins?","Question",{"text":76,"@type":77},"To predict fumonisin (FUM) contamination in Iowa corn using gradient boosting machine learning, and to compare an Illinois-centric model with an Iowa-centric model.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were the predictive models evaluated, and what data were used?",{"text":81,"@type":77},"Corn samples from Iowa’s 99 counties were collected across 2010, 2020, and 2021 (n=529), and 2011 data (n=89) were used for independent validation.",{"name":83,"@type":74,"acceptedAnswer":84},"Which predictors were most influential in the Illinois- vs Iowa-centric models?",{"text":85,"@type":77},"For the Illinois-centric model, the top predictor was satellite-acquired normalized difference vegetation index (NDVI, Veg_index) in March, while for the Iowa-centric model it was precipitation (PRCP) in October.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":122},"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]