[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122737-en":3,"doc-seo-122737-105":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":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},122737,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Predicting site-specific economic optimal nitrogen rate using machine learning methods and on-farm precision experimentation","Applying at the economic optimal nitrogen rate (EONR) can improve nitrogen (N) fertilization efficiency and profitability while lowering environmental risks. On-farm precision experimentation (OFPE) enables collecting extensive field data to estimate EONR. The study evaluates machine learning approaches, including generalized additive models (GAM) and random forest (RF), for yield and N response estimation. Results from 20 OFPE N trials show reliable yield modeling, but different predicted EONR across sites.","Agronomy & Horticulture--Faculty Publications Agronomy and Horticulture Department  \n3-29-2023  \nPredicting site‑specific economic optimal nitrogen rate using machine learning methods and on‑farm precision experimentation  \nAlfonso de Lara  \nTaro Mieno  \nJoe D. Luck  \nLaila A. Puntel  \nFollow this and additional works at: [https://digitalcommons.unl.edu/agronomyfacpub](https://digitalcommons.unl.edu/agronomyfacpub)  \n Part of the Agricultural Science Commons, Agriculture Commons, Agronomy and Crop Sciences Commons, Botany Commons, Horticulture Commons, Other Plant Sciences Commons, and the Plant Biology Commons  \nThis Article is brought to you for free and open access by the Agronomy and Horticulture Department at DigitalCommons@University of Nebraska-Lincoln. It has been accepted for inclusion in Agronomy & Horticulture--Faculty Publications by an authorized administrator of DigitalCommons@University of Nebraska-Lincoln.  \nPredicting site‑specific economic optimal nitrogen rate using machine learning methods and on‑farm precision experimentation  \nAlfonso de Lara1 · Taro Mieno2 · Joe D. Luck3 · Laila A. Puntel1  \nAccepted: 29 March 2023  \nThis is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023  \nAbstract  \nApplying at the economic optimal nitrogen rate (EONR) has the potential to increase nitrogen (N) fertilization efficiency and profits while reducing negative environmental impacts. On-farm precision experimentation (OFPE) provides the opportunity to collect large amounts of data to estimate the EONR. Machine learning (ML) methods such as generalized additive models (GAM) and random forest (RF) are promising methods for estimating yields and EONR. Twenty OFPE N trials in wheat and barley were conducted and analyzed with soil, terrain and remote-sensed variables to address the following objectives: (1) to quantify the spatial variability of winter crops yield and the yield response to N using OFPE,(2) to evaluate and compare the performance of GAM and RF models to predict yield and yield response to N and,(3) to quantify the impact of soil, crop and field characteristics on the EONR estimation. Machine learning techniques were able to model wheat and barley yield with an average error of 13 .7%(624 kg ha−1) . However, similar yield prediction accuracy from RF and GAM resulted in widely different economic optimal nitrogen rates. Across sites, soil available phosphorus and soil organic matter were the most influential variables; however, the magnitude and direction of the effect varied between fields. These indicate that training a model using data coming from different fields may lead to unreliable site-specific EONR when it is applied to another field. Further evaluation of ML methods is needed to ensure a robust automation of N recommendation while producers transition into the digital ag era.  \nKeywords Yield · Economic optimal nitrogen rate · On-farm precision experimentation (OFPE) · Variable rate · Machine learning · Checkerboard design · Wheat  \n* Laila A. Puntel[lpuntel2@unl.edu](lpuntel2@unl.edu)  \n1 Department of Agronomy, University of Nebraska, Lincoln, NE, USA  \n2 Department of Agriculture Economics, University of Nebraska, Lincoln, NE, USA  \n3 Department of Biosystems Engineering, University of Nebraska, Lincoln, NE, USA  \n1 3  \nIntroduction  \nNitrogen (N) management is one of the most critical management decisions to maximize yield and profit in cereal grain production. However, applying sufficient N to meet the crop demand while minimizing environmental N losses is still a challenge (Rockström et al., 2009 ; Zhang et al., 2015) . This challenge remains due to the high variability of the economic optimal N rate (EONR) associated with the spatial and temporal variability in the yield response to N (Kahabka et al., 2004 ; Mamo et al., 2003 ; Pierce & Nowak, 1999) and to the uncertainty in modeling the relationship between N rates and yields (Kyveryga","cbCaia4F1kdi8e7N","https://ap.wps.com/l/cbCaia4F1kdi8e7N","pdf",2757897,1,22,"English","en",105,"# Abstract\n## Objectives and methods\n## Key findings\n## Implications for site-specific nitrogen recommendation","[{\"question\":\"What problem does the study address in nitrogen management?\",\"answer\":\"It targets how to identify the economic optimal nitrogen rate (EONR) despite spatial and temporal variability in crop yield responses and uncertainty in modeling N-rate effects.\"},{\"question\":\"How do OFPE trials support the machine learning approach?\",\"answer\":\"On-farm precision experimentation generates large datasets from wheat and barley trials, including soil, terrain, and remote-sensed variables, to train models for yields and yield response to nitrogen.\"},{\"question\":\"Why can yield prediction accuracy still lead to different EONR estimates?\",\"answer\":\"The study finds that RF and GAM can produce similar yield-prediction accuracy, yet generate widely different economic optimal nitrogen rates, indicating sensitivity of EONR estimation to model behavior beyond yield accuracy.\"}]","Predicting site-specific economic optimal nitrogen rate using machine learning methods and on-farm precision experimentation | 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problem does the study address in nitrogen management?","Question",{"text":75,"@type":76},"It targets how to identify the economic optimal nitrogen rate (EONR) despite spatial and temporal variability in crop yield responses and uncertainty in modeling N-rate effects.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do OFPE trials support the machine learning approach?",{"text":80,"@type":76},"On-farm precision experimentation generates large datasets from wheat and barley trials, including soil, terrain, and remote-sensed variables, to train models for yields and yield response to nitrogen.",{"name":82,"@type":73,"acceptedAnswer":83},"Why can yield prediction accuracy still lead to different EONR estimates?",{"text":84,"@type":76},"The study finds that RF and GAM can produce similar yield-prediction accuracy, yet generate widely different economic optimal nitrogen rates, indicating sensitivity of EONR estimation to model behavior beyond yield 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