{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/122150"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/122150","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Harvesting data: A methodology for analyzing innovations in agricultural policy","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-12-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-12-01","abstract_has_math":false,"creators":["Knepp, Aleksi"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Agricultural & Applied Econ","degree_department":null,"school":null,"contributors":["Coppess, Jonathan W"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-12","date_published":"2023-12","updated_at":"2026-07-22T22:25:00Z","subjects":["Agricultural Policy","Data Analysis","Data Visualization","Commodity Policy","Conservation Policy"],"languages":["en","eng"],"rights":["Copyright 2023 Aleksi Knepp"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/122150","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Coppess, Jonathan W"]},{"key":"dc:creator","label":"Author","values":["Knepp, Aleksi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-12","2023-12-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Agricultural & Applied Econ"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Agricultural Policy","Data Analysis","Data Visualization","Commodity Policy","Conservation Policy"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2023 Aleksi Knepp"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/122150"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-12-01","The student, Aleksi Knepp, accepted the attached license on 2023-11-30 at 18:57.","The student, Aleksi Knepp, submitted this Thesis for approval on 2023-11-30 at 18:58.","This Thesis was approved for publication on 2023-12-05 at 15:52.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20047 on 2024-03-01 at 13:31:19","The use of economic modeling, data analysis, and spatial visualization has great potential application in agricultural policymaking. Agricultural production is deeply connected to geographic factors, such as climate, soil composition, and water availability. Policymakers would greatly benefit from the availability of data-driven models that allow visualization of policy outcomes to make more informed decisions. Spatial models and regional specific data offer unique insights into how different policies might impact payment outcomes for agricultural subsidy programs as well as the economic viability of these programs across regions. By leveraging these tools, policymakers can adapt their political strategies and opinions to the outcomes of these policies in the context of the agricultural landscapes they represent. This method of policymaking will ensure that their decisions are grounded in comprehensive data but also are linked to the specific needs of their region’s agricultural sector. This approach has the potential to pave the way for developing a variety of policies that effectively address the challenges facing agriculture, and this paper will provide a case study using two fictional but realistic policy changes to existing farm programs. Congress reauthorizes federal agricultural policy approximately every five years in legislation commonly referred to as the Farm Bill. This paper delves into the specifics of one income support program for farmers, the Agriculture Risk Coverage (ARC) program, and presents a methodology for using data to analyze two enhancements specifically tailored to the Midwest's agricultural landscape as specific case studies. By examining the program's provisions, this thesis demonstrates a method for analyzing concrete programmatic changes aimed at improving payout structures and risk coverage. Furthermore, I explore the potential of integrating conservation incentives, specifically cover crops, into ARC, thereby not only elevating the program's economic outcomes but also fostering environmental stewardship of agricultural land. This approach seeks to explore policy that combines economic and natural resource outcomes that could align the ARC program with broader environmental objectives. Each of the case studies is found to increase payments as compared to the current ARC-County (ARC-CO) program. Under a few assumptions, this increase in payments is hypothesized to drive an increase in enrollment in the altered ARC-CO programs, creating a better suite of options for the midwestern grower as they engage in risk management for their farms."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Harvesting data: A methodology for analyzing innovations in agricultural policy"]}]}],"canonical_facts":{"dc:contributor":["Coppess, Jonathan W"],"dc:creator":["Knepp, Aleksi"],"dc:date":["2023-12","2023-12-05"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-12-01","The student, Aleksi Knepp, accepted the attached license on 2023-11-30 at 18:57.","The student, Aleksi Knepp, submitted this Thesis for approval on 2023-11-30 at 18:58.","This Thesis was approved for publication on 2023-12-05 at 15:52.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20047 on 2024-03-01 at 13:31:19","The use of economic modeling, data analysis, and spatial visualization has great potential application in agricultural policymaking. Agricultural production is deeply connected to geographic factors, such as climate, soil composition, and water availability. Policymakers would greatly benefit from the availability of data-driven models that allow visualization of policy outcomes to make more informed decisions. Spatial models and regional specific data offer unique insights into how different policies might impact payment outcomes for agricultural subsidy programs as well as the economic viability of these programs across regions. By leveraging these tools, policymakers can adapt their political strategies and opinions to the outcomes of these policies in the context of the agricultural landscapes they represent. This method of policymaking will ensure that their decisions are grounded in comprehensive data but also are linked to the specific needs of their region’s agricultural sector. This approach has the potential to pave the way for developing a variety of policies that effectively address the challenges facing agriculture, and this paper will provide a case study using two fictional but realistic policy changes to existing farm programs. Congress reauthorizes federal agricultural policy approximately every five years in legislation commonly referred to as the Farm Bill. This paper delves into the specifics of one income support program for farmers, the Agriculture Risk Coverage (ARC) program, and presents a methodology for using data to analyze two enhancements specifically tailored to the Midwest's agricultural landscape as specific case studies. By examining the program's provisions, this thesis demonstrates a method for analyzing concrete programmatic changes aimed at improving payout structures and risk coverage. Furthermore, I explore the potential of integrating conservation incentives, specifically cover crops, into ARC, thereby not only elevating the program's economic outcomes but also fostering environmental stewardship of agricultural land. This approach seeks to explore policy that combines economic and natural resource outcomes that could align the ARC program with broader environmental objectives. Each of the case studies is found to increase payments as compared to the current ARC-County (ARC-CO) program. Under a few assumptions, this increase in payments is hypothesized to drive an increase in enrollment in the altered ARC-CO programs, creating a better suite of options for the midwestern grower as they engage in risk management for their farms."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/122150"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Aleksi Knepp"],"dc:subject":["Agricultural Policy","Data Analysis","Data Visualization","Commodity Policy","Conservation Policy"],"dc:title":["Harvesting data: A methodology for analyzing innovations in agricultural policy"],"dc:type":["text"],"thesis:degree_discipline":["Agricultural & Applied Econ"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:00Z"}