[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117185-en":3,"doc-seo-117185-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},117185,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Can machine learning models provide accurate fertilizer recommendations?","Accurate modeling of site-specific crop yield response is essential for delivering economically optimal input rates (EOIRs) recommendations. While machine learning can predict yield effectively and has been used to estimate EOIRs from fertilizer application effects, accurate yield models may still produce misleading fertilizer rate recommendations. This study quantifies the uncertainty introduced by machine learning choices and covariate selection using real on-farm precision experimental data for winter wheat.","Department of Agricultural Economics: Faculty Publications  \nAgricultural Economics Department  \n3-25-2024  \nCan machine learning models provide accurate fertilizer recommendations?  \nTakashi S. T. Tanaka Gerard B. M. Heuvelink  \nTaro Mieno  \nDavid S. Bullock  \nFollow this and additional works at: [https://digitalcommons.unl.edu/ageconfacpub](https://digitalcommons.unl.edu/ageconfacpub)  \n Part of the Agribusiness Commons, Agricultural and Resource Economics Commons, and the Food Studies Commons  \nThis Article is brought to you for free and open access by the Agricultural Economics Department at DigitalCommons@University of Nebraska-Lincoln. It has been accepted for inclusion in Department of Agricultural Economics: Faculty Publications by an authorized administrator of DigitalCommons@University of NebraskaLincoln.  \nCan machine learning models provide accurate fertilizer recommendations?  \nTakashi S. T. Tanaka1,2,7 · Gerard B. M. Heuvelink3,4 · Taro Mieno5 · David S. Bullock6  \nAccepted: 8 March 2024 / Published online: 25 March 2024 © The Author(s) 2024  \nAbstract  \nAccurate modeling of site-specific crop yield response is key to providing farmers with accurate site-specific economically optimal input rates (EOIRs) recommendations. Many studies have demonstrated that machine learning models can accurately predict yield. These models have also been used to analyze the effect of fertilizer application rateson yield and derive EOIRs. But models with accurate yield prediction can still provide highly inaccurate input application recommendations. This study quantified the uncertainty generated when using machine learning methods to model the effect of fertilizer application on site-specific crop yield response. The study uses real on-farm precision experimental data to evaluate the influence of the choice of machine learning algorithmsand covariate selection on yield and EOIR prediction. The crop is winter wheat, and the inputs considered are a slow-release basal fertilizer NPK 25–6–4 and a top-dressed fertilizer NPK 17–0–17. Random forest, XGBoost, support vector regression, and artificial neural network algorithms were trained with 255 sets of covariates derived from combining eight different soil properties. Results indicate that both the predicted EOIRs and associated gained profits are highly sensitive to the choice of machine learning algorithm and covariate selection. The coefficients of variation of EOIRs derived from all possible combinations of covariate selection ranged from 13.3 to 31.5% for basal fertilization and from 14.2 to 30.5% for top-dressing. These findings indicate that while machine learning can be useful for predicting site-specific crop yield levels, it must be used with caution in making fertilizer application rate recommendations.  \nKeywords Economically optimal input rate · On-farm experimentation · Site-specific management · Variable-rate application · Winter wheat  \nIntroduction  \nSite-specific crop management aims to use information about within-field variability of soil and topographic properties to increase farming profitability and sustainability. Understanding of site-specific crop yield response facilitates effective site-specific crop management  \nExtended author information available on the last page of the article  \n(Bullock et al., 2019) . Until recently, site-specific crop management was principally based on farmers’ and agronomists’ experiences and expectations about crop responses to agronomic inputs. The expectations are based largely on inferences obtained from conventional small-plot trials that are presumed to represent what is occurring elsewhere. But these trials are expensive and labor-intensive, and it may be inappropriate to draw inferences from small-plot trials to improve management across many farms (Bullock et al., 2019; Lacosteet al., 2022). In contrast, on-farm experimentations have a potential to provide more actionable and practical insights to farmers as an alternative ","cbCaigPgC6tq7QrK","https://ap.wps.com/l/cbCaigPgC6tq7QrK","pdf",4214036,1,19,"English","en",105,"# Abstract\n# Introduction\n## Limitations of conventional and simulation-based approaches\n## On-farm precision experimentation and machine learning","[{\"question\":\"Why can accurate machine learning yield predictions still lead to inaccurate fertilizer recommendations?\",\"answer\":\"Because EOIR recommendations depend not only on yield prediction accuracy but also on how model uncertainty propagates from inputs to yield response and optimal rates. The study shows that recommended EOIRs vary substantially with algorithm and covariate choices.\"},{\"question\":\"What data and crop were used to evaluate fertilizer recommendation uncertainty?\",\"answer\":\"The study uses real on-farm precision experimental data for winter wheat, considering slow-release basal fertilizer NPK 25–6–4 and top-dressed fertilizer NPK 17–0–17.\"},{\"question\":\"Which machine learning methods were compared in this analysis?\",\"answer\":\"Random forest, XGBoost, support vector regression, and artificial neural networks were trained using covariates derived from combining eight soil properties to model yield response and EOIRs.\"}]","Can machine learning models provide accurate fertilizer recommendations? | PDF",1785674269,48,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"can-machine-learning-models-provide-accurate-fertilizer-recommendations","",{"@graph":36,"@context":85},[37,54,68],{"@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/can-machine-learning-models-provide-accurate-fertilizer-recommendations/117185/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why can accurate machine learning yield predictions still lead to inaccurate fertilizer recommendations?","Question",{"text":75,"@type":76},"Because EOIR recommendations depend not only on yield prediction accuracy but also on how model uncertainty propagates from inputs to yield response and optimal rates. The study shows that recommended EOIRs vary substantially with algorithm and covariate choices.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and crop were used to evaluate fertilizer recommendation uncertainty?",{"text":80,"@type":76},"The study uses real on-farm precision experimental data for winter wheat, considering slow-release basal fertilizer NPK 25–6–4 and top-dressed fertilizer NPK 17–0–17.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning methods were compared in this analysis?",{"text":84,"@type":76},"Random forest, XGBoost, support vector regression, and artificial neural networks were trained using covariates derived from combining eight soil properties to model yield response and EOIRs.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"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":21,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},"General","general"]