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The project targets field-scale high-yielding cultivars and links management choices to sustainability and bioeconomy outcomes, including ecosystem services such as greenhouse-gas emissions, nutrient leaching, evapotranspiration and biodiversity, supported by technoeconomic and machine-learning yield prediction.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/doe-bioenergy-technologies-office-beto-2023-project-peer-review-next-generation-feedstocks-for-the-emerging-bioeconomy-april-4-2023/271757/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/doe-bioenergy-technologies-office-beto-2023-project-peer-review-next-generation-feedstocks-for-the-emerging-bioeconomy-april-4-2023/271757.png","ImageObject",300,407,{"name":92,"@type":93},"Felix Montgomery","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-26","2026-09-15",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is the main goal of this BETO project?","Question",{"text":113,"@type":114},"Develop productive, cost-effective, sustainable warm-season perennial bioenergy feedstock production systems on marginal croplands across Midwest geographic locations.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which ecosystem services and sustainability indicators are evaluated?",{"text":118,"@type":114},"Assessments include CO2, N2O, CH4 emissions, nutrient leaching (N&P), water quantity via evapotranspiration (ET), biodiversity, and soil quality/soil carbon-related metrics.",{"name":120,"@type":111,"acceptedAnswer":121},"How does the project use machine learning in FY 21-22?",{"text":122,"@type":114},"It develops, validates, and publishes remote-sensing and machine-learning models to estimate biomass yield at harvest, plus biomass moisture and crude protein using a multi-year dataset.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},271757,1789461459,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},549768064778,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","DOE Bioenergy Technologies Office (BETO) 2023 Project Peer Review  \nNext-Generation Feedstocks for the  \nEmerging Bioeconomy 60% of Completion (2019-2024)  \nDate: April 4, 2023  \nFeedstock Technologies Program  \nD.K. Lee  \nUniversity of Illinois at Urbana-Champaign  \nHigh-level Project Goal  \nDevelop productive, cost-effective, and sustainable warm-season perennial bioenergy feedstock production systems on marginal croplands across geographic locations in the Midwest  \nHigh-yielding bioenergy switchgrass On-farm field scale production on marginal Land  \nSustainable feedstock Production across geographic  \nBiomass (dry ton ac-1)  \nNew cultivar  \nIndependence  \nLiberty  \nWS-10L  \nPredecessor cultivar  \n'Independence' Kanlow  \nShawnee  \nSunburst  \nregions in Midwest  \n21-acre field  \n* 2-acre CRP  \nWatertown, SD  \nMead, NE   \nAmes, IA  *  \n****  Urbana, IL  \nEdwardsville, IL  \n0.0 2.0  \n 1st Growing season  \n4.0 6.0 8.0  \n 2nd Growing season  \nContext and project history:  \n• Limited data availability on field-scale research with high-yielding switchgrass cultivars  \n• Need to demonstrate ecosystem service benefits of energy-type switchgrass on marginal lands  \nProject Framework  \nFeedstock  \nSustainability  \nBioeconomy  \nObjective 1&2  \n▪ Potentials of high-yielding switchgrass at field scale  \n▪  Best Management Practices  \n▪ Harvest logistics  \n▪ Feedstock quality  \nObjective 4  \nEcosystem services (ES)  \n▪ CO2 , N2 O, CH4 emissions  \n▪ Nutrient leaching (N&P)  \n▪ Water quantity (ET)  \n▪ Biodiversity  \n▪ Soil quality (SOC, WAS, etc. )  \nObjective 5  \n▪ Develop a regional feedstock cost-rate model for delivering switchgrass to biorefinery locations  \nPlot-scale (100 ft2) to field-scale (1 acre)  \nObjective 3  \nHarvest logistics  \n Machine learning (ML)  \nInput  \n• Soil data  \n• Weather data  \n• Field management  \n• Growth and development  \n• Phenotypical data  \n• ES measurements  \n• GPS coordinates +  \nTechnoeconomic analysis  \nPrediction Yield&ES  \n2 – Approach (Technical Metrics) M✓etr6icston:/ac biomass yield  \n2 – Approach (Technical metrics)  \nEstablishment  \n▪ Seed bed preparation/Planting  \n▪ Weed control  \nMaintenance and harvest  \n▪ Fertilization/Weed control  \n▪ Sustainable biomass harvest  \nEcosystem service  \n▪ Water quality/quantity  \n▪ Soil health/C sequestration/GHG  \nLiberty Switchgrass Iowa, Sept 2022  \n▪ Biodiversity Field preparation Fertilization  \nVegetative growth  \nCarthage switchgrass SD, Sept 2022  \nLow diversity mixture NE, Aug 2022  \nIndependence Switchgrass IL, Urbana Sept 2022  \nHarvest  \nJan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec  \nSoil sensors with 3 depths for evapotranspiration (ET)  \nBiodiversity measurements  \n▪ Avian acoustic monitoring (Left)  \n▪ Insects & pollinators (Middle, Right)  \n  Ecosystem service measurements    \n2 – Approach (potential challenges)  \nChallenge 1:  \n▪ Establishment challenges on marginal lands (spring flooding) during the first year, delay weed control and fertilization  \nChallenge 2:  \n▪ Cold winter in South Dakota during the third growing year causing stand damage, winter kill  \nChallenge 3:  \n▪ Drought challenge in the third growing year in ILand NE impacted on productivity  \nChallenge 3:  \n▪ Machine breakdown limits timely operations such as seeding, spraying, harvesting, and ultimately limits data collection points  \nSpring flooding  \nWinter damage  \nMachine breakdown  \n4 – Progress and Outcomes (Field-scale, FY 21-22)  \nTask 1: Management of field-scale switchgrass production system  \n▪ Comparing switchgrass and corn production system: Productivity, ecosystem services and biodiversity benefits  \n▪ Annual nitrogen application rates  \n▪ Switchgrass: 25 and 50 lbs N /acre  \n▪ Corn: 180 lbs N /acre  \nGrain and Biomass yield of corn in 2022  \n\n| Site | Grain yield (ton/acre) | Total Biomass\u003Cbr>(ton/acre) |\n| --- | --- | --- |\n| IL Urbana | 2.5 | 5.0 |\n| Nebraska | 2.3 | 4.6 |\n| South Dakota | 2.8 | 5.5 |\n\nCorn Switchgrass  \nJune 2022  \nHarvesting in Fall 2022 ","cbCaiatLoWnnVVoQ","https://ap.wps.com/l/cbCaiatLoWnnVVoQ","pdf",2980972,31,"English","# High-level Project Goal\n## Project Framework and Objectives\n## Technical Approach and Metrics\n## Potential Challenges\n## Progress and Outcomes (Field-scale, FY 21-22)\n## Progress and Outcomes (Small-scale, FY 21-22)\n## Progress and Outcomes (Machine Learning, FY 21-22)","[{\"question\":\"What is the main goal of this BETO project?\",\"answer\":\"Develop productive, cost-effective, sustainable warm-season perennial bioenergy feedstock production systems on marginal croplands across Midwest geographic locations.\"},{\"question\":\"Which ecosystem services and sustainability indicators are evaluated?\",\"answer\":\"Assessments include CO2, N2O, CH4 emissions, nutrient leaching (N\\u0026P), water quantity via evapotranspiration (ET), biodiversity, and soil quality/soil carbon-related metrics.\"},{\"question\":\"How does the project use machine learning in FY 21-22?\",\"answer\":\"It develops, validates, and publishes remote-sensing and machine-learning models to estimate biomass yield at harvest, plus biomass moisture and crude protein using a multi-year dataset.\"}]","DOE Bioenergy Technologies Office (BETO) 2023 Project Peer Review - Next-Generation Feedstocks for the Emerging Bioeconomy - April 4, 2023 | PDF",78]