[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124797-en":3,"doc-seo-124797-105":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},124797,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Development of a Machine Learning System for Irrigation Decision Support with Disparate Data Streams - Thesis","Advances in irrigation technology enable data-driven scheduling and variable rate irrigation (VRI) to improve application efficiency and potentially reduce withdrawals from groundwater and surface water. However, higher efficiency does not always translate into greater future or downstream water availability, especially under consumptive use, where water is not returned to the local watershed. This thesis evaluates VRI impacts using consumptive use ratio and a newly defined marginal consumptive use ratio across multiple Nebraska site-years, and develops machine learning models using two years of weather and agronomic data to recommend irrigation timing with a “Latest Date” output. In-season validation achieves an RMSE of 2.23 and future work will expand training data and tune models for practical adoption.","University of Nebraska-Lincoln  \nDigitalCommons@University of Nebraska-Lincoln  \nDepartment of Biological Systems Engineering: Dissertations, Theses, and Student Research  \nBiological Systems Engineering  \n12-2023  \nDevelopment of a Machine Learning System for Irrigation Decision Support with Disparate Data Streams  \nEric Wilkening  \nUniversity of Nebraska-Lincoln, [ewilkening@huskers.unl.edu](ewilkening@huskers.unl.edu)  \nFollow this and additional works at: [https://digitalcommons.unl.edu/biosysengdiss](https://digitalcommons.unl.edu/biosysengdiss)  \n Part of the Bioresource and Agricultural Engineering Commons, Other Computer Engineering Commons, and the Systems Science Commons  \nWilkening, Eric, \"Development of a Machine Learning System for Irrigation Decision Support with Disparate Data Streams\" (2023) . Department of Biological Systems Engineering: Dissertations, Theses, and Student Research. 148.  \n[https://digitalcommons.unl.edu/biosysengdiss/148](https://digitalcommons.unl.edu/biosysengdiss/148)  \nThis Article is brought to you for free and open access by the Biological Systems Engineering at DigitalCommons@University of Nebraska-Lincoln. It has been accepted for inclusion in Department of Biological Systems Engineering: Dissertations, Theses, and Student Research by an authorized administrator of DigitalCommons@University of Nebraska-Lincoln.  \nDEVELOPMENT OF A MACHINE LEARNING SYSTEM FOR IRRIGATION  \nDECISION SUPPORT WITH DISPARATE DATA STREAMS  \nby  \nEric Wilkening  \nA THESIS  \nPresented to the Faculty of  \nThe Graduate College at the University of Nebraska  \nIn Partial Fulfillment of Requirements  \nFor the Degree of Master of Science  \nMajor: Agricultural and Biological Systems Engineering  \nUnder the Supervision of Professor Derek M. Heeren  \nLincoln, Nebraska  \nDecember 2023  \nDEVELOPMENT OF A MACHINE LEARNING SYSTEM FOR IRRIGATION  \nDECISION SUPPORT WITH DISPARATE DATA STREAMS  \nEric Wilkening, M.S.  \nUniversity of Nebraska, 2023  \nAdvisor: Derek M. Heeren  \nIn recent years, advancements in irrigation technologies have led to increased efficiency in irrigation applications, encompassing the adoption of practices that utilize data-driven irrigation scheduling and leveraging variable rate irrigation (VRI) . These technological improvements have the potential to reduce water withdrawals and diversions from both groundwater and surface water sources. However, it is vital to recognize that improved application efficiency does not necessarily equate to increased water availability for future or downstream use. This is particularly crucial in the context of consumptive water use, which refers to water consumed and not returned to the local or sub-regional watershed, representing a critical consideration in water conservation.  \nVariable Rate Irrigation (VRI) allows for management of in-field spatial variability of water requirements. To comprehensively assess the impact of VRI on consumptive water use and pumping, this study evaluated multiple site-years of field research data. The research employed a previously developed metric known as the\"consumptive use ratio\" which quantifies the change in consumptive use relative to the change in irrigation water applied. The study further developed a “marginal consumptive use ratio”, which was utilized to analyze different irrigation management scenarios,  \nincluding VRI with zone control prescriptions, across multiple years and field sites in Eastern and Western Nebraska.  \nBeyond the realm of current irrigation practices, this research explored the integration of machine learning to expedite irrigation recommendations while enhancing water usage efficiency. Leveraging two years of weather and agronomic data, machine learning algorithms were trained and tested. A novel method of this model is its recommendation of a \"Latest Date\" to indicate when to irrigate at a fixed application depth, allowing for flexibility across different irrigation systems.  \nThis approach, when applie","cbCaibVhiekFdVOk","https://ap.wps.com/l/cbCaibVhiekFdVOk","pdf",4877073,1,122,"English","en",105,"# Introduction\n## Irrigation efficiency and consumptive water use\n## Variable rate irrigation (VRI) and evaluation metrics\n# Machine Learning for Irrigation Recommendations\n## Weather and agronomic data\n## Model design and “Latest Date” recommendation\n# Validation and Results\n## In-season performance (RMSE)\n# Future Work and Applications\n## Data expansion and model tuning","[{\"question\":\"What problem does the study address in irrigation decision support?\",\"answer\":\"It addresses how to improve irrigation scheduling using data-driven methods while accounting for consumptive water use, where efficiency gains may not automatically increase downstream or future water availability.\"},{\"question\":\"How does the thesis quantify the impact of variable rate irrigation (VRI)?\",\"answer\":\"It evaluates multiple site-years using the consumptive use ratio and a newly developed marginal consumptive use ratio to analyze different irrigation management scenarios, including VRI with zone control prescriptions.\"},{\"question\":\"What does the machine learning model recommend and how is it validated?\",\"answer\":\"The model recommends a “Latest Date” indicating when to irrigate at a fixed application depth. In-season validation achieves a root-mean-square-error (RMSE) of 2.23.\"}]","Development of a Machine Learning System for Irrigation Decision Support with Disparate Data Streams - Thesis | PDF",1785894717,307,{"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},"development-of-a-machine-learning-system-for-irrigation-decision-support-with-disparate-data-streams-thesis","",{"@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/development-of-a-machine-learning-system-for-irrigation-decision-support-with-disparate-data-streams-thesis/124797/",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","2026-08-05",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},"What problem does the study address in irrigation decision support?","Question",{"text":75,"@type":76},"It addresses how to improve irrigation scheduling using data-driven methods while accounting for consumptive water use, where efficiency gains may not automatically increase downstream or future water availability.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the thesis quantify the impact of variable rate irrigation (VRI)?",{"text":80,"@type":76},"It evaluates multiple site-years using the consumptive use ratio and a newly developed marginal consumptive use ratio to analyze different irrigation management scenarios, including VRI with zone control prescriptions.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the machine learning model recommend and how is it validated?",{"text":84,"@type":76},"The model recommends a “Latest Date” indicating when to irrigate at a fixed application depth. 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