{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/113103"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/113103","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Quantifying the agronomic and water quality tradeoffs of fertilizer management with multiobjective evolutionary algorithms","abstract":"When combined with agricultural systems models, multiobjective evolutionary algorithms (MOEAs) can efficiently quantify optimal tradeoffs (Pareto fronts) among conflicting economic and environmental objectives to help farmers identify compromise decision alternatives. Realizing the potential of MOEAs to agricultural decision support, however, will require corroborating that approximate solutions accurately represent system tradeoffs through extensive algorithm comparisons and integrating model uncertainty analysis into the optimization workflow. This thesis contributes to these broader goals by comparing the ability of five MOEAs (e-MOEA, e-NSGA-II, OMOPSO, GDE3, and MOEA/D) to calibrate the USDA's Root Zone Water Quality Model (RZWQM2) and identify fertilizer management decisions that maximize profits and corn yields while minimizing nitrate loads for two corn-soybean production sites in east-central Illinois. For both test problems, I evaluated solution accuracy by tracking three performance metrics for all five MOEAs along with their contributions to the best-known Pareto front throughout the optimization run. After pooling the calibration results to assess model error tradeoffs, I applied the top-performing MOEA with sets of parameter vectors from the calibration Pareto fronts to stochastically optimize fertilizer rate, method, and timing decisions. e-MOEA stood out as the most effective algorithm for model calibration by achieving the highest performance metric values within the fewest generations. The accuracy differences among MOEAs were much less apparent for the fertilizer management problem, implying that the algorithm selection should not influence the optimization results. Nonetheless, I applied OMOPSO for the stochastic optimization because it achieved the highest metric values by a small margin. Despite wide uncertainty bounds and high maximum pro fit nitrogen rates, the optimization results support that sidedress fertilizer applications from 20 to 50 days after planting not only maximize profit for injection and surface broadcast but also reduce outcome sensitivity to the placement method and offer the best compromise between pro fit and drainage nitrate loads. Although testing different calibration parameters could further support these results and re fine the uncertainty bounds, this analysis provides a flexible simulation-optimization framework for stochastically optimizing best management practice selection, design, and placement at field to watershed scales.","abstract_html":"When combined with agricultural systems models, multiobjective evolutionary algorithms (MOEAs) can efficiently quantify optimal tradeoffs (Pareto fronts) among conflicting economic and environmental objectives to help farmers identify compromise decision alternatives. Realizing the potential of MOEAs to agricultural decision support, however, will require corroborating that approximate solutions accurately represent system tradeoffs through extensive algorithm comparisons and integrating model uncertainty analysis into the optimization workflow. This thesis contributes to these broader goals by comparing the ability of five MOEAs (e-MOEA, e-NSGA-II, OMOPSO, GDE3, and MOEA/D) to calibrate the USDA's Root Zone Water Quality Model (RZWQM2) and identify fertilizer management decisions that maximize profits and corn yields while minimizing nitrate loads for two corn-soybean production sites in east-central Illinois. For both test problems, I evaluated solution accuracy by tracking three performance metrics for all five MOEAs along with their contributions to the best-known Pareto front throughout the optimization run. After pooling the calibration results to assess model error tradeoffs, I applied the top-performing MOEA with sets of parameter vectors from the calibration Pareto fronts to stochastically optimize fertilizer rate, method, and timing decisions. e-MOEA stood out as the most effective algorithm for model calibration by achieving the highest performance metric values within the fewest generations. The accuracy differences among MOEAs were much less apparent for the fertilizer management problem, implying that the algorithm selection should not influence the optimization results. Nonetheless, I applied OMOPSO for the stochastic optimization because it achieved the highest metric values by a small margin. Despite wide uncertainty bounds and high maximum pro fit nitrogen rates, the optimization results support that sidedress fertilizer applications from 20 to 50 days after planting not only maximize profit for injection and surface broadcast but also reduce outcome sensitivity to the placement method and offer the best compromise between pro fit and drainage nitrate loads. Although testing different calibration parameters could further support these results and re fine the uncertainty bounds, this analysis provides a flexible simulation-optimization framework for stochastically optimizing best management practice selection, design, and placement at field to watershed scales.","abstract_has_math":false,"creators":["Peterson, Chelsea Marie"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Agricultural & Biological Engr","degree_department":null,"school":null,"contributors":["Rodriguez, Luis F","Bhattarai, Rabin","Sowers, Richard"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-01-12T21:47:03Z","date_published":"2022-01-12T21:47:03Z","updated_at":"2026-07-22T22:24:53Z","subjects":["fertilizer, agriculture, multiobjective, evolutionary, algorithms, RZWQM2"],"languages":["en"],"rights":["Copyright 2021 Chelsea Peterson"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/113103","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Rodriguez, Luis F","Bhattarai, Rabin","Sowers, Richard"]},{"key":"dc:creator","label":"Author","values":["Peterson, Chelsea Marie"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-01-12T21:47:03Z","2021-07-23","2021-08"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Agricultural & Biological Engr"]},{"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":["fertilizer, agriculture, multiobjective, evolutionary, algorithms, RZWQM2"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Chelsea Peterson"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/113103"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["When combined with agricultural systems models, multiobjective evolutionary algorithms (MOEAs) can efficiently quantify optimal tradeoffs (Pareto fronts) among conflicting economic and environmental objectives to help farmers identify compromise decision alternatives. Realizing the potential of MOEAs to agricultural decision support, however, will require corroborating that approximate solutions accurately represent system tradeoffs through extensive algorithm comparisons and integrating model uncertainty analysis into the optimization workflow. This thesis contributes to these broader goals by comparing the ability of five MOEAs (e-MOEA, e-NSGA-II, OMOPSO, GDE3, and MOEA/D) to calibrate the USDA's Root Zone Water Quality Model (RZWQM2) and identify fertilizer management decisions that maximize profits and corn yields while minimizing nitrate loads for two corn-soybean production sites in east-central Illinois. For both test problems, I evaluated solution accuracy by tracking three performance metrics for all five MOEAs along with their contributions to the best-known Pareto front throughout the optimization run. After pooling the calibration results to assess model error tradeoffs, I applied the top-performing MOEA with sets of parameter vectors from the calibration Pareto fronts to stochastically optimize fertilizer rate, method, and timing decisions. e-MOEA stood out as the most effective algorithm for model calibration by achieving the highest performance metric values within the fewest generations. The accuracy differences among MOEAs were much less apparent for the fertilizer management problem, implying that the algorithm selection should not influence the optimization results. Nonetheless, I applied OMOPSO for the stochastic optimization because it achieved the highest metric values by a small margin. Despite wide uncertainty bounds and high maximum pro fit nitrogen rates, the optimization results support that sidedress fertilizer applications from 20 to 50 days after planting not only maximize profit for injection and surface broadcast but also reduce outcome sensitivity to the placement method and offer the best compromise between pro fit and drainage nitrate loads. Although testing different calibration parameters could further support these results and re fine the uncertainty bounds, this analysis provides a flexible simulation-optimization framework for stochastically optimizing best management practice selection, design, and placement at field to watershed scales.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-01-12 without embargo terms","The student, Chelsea Peterson, accepted the attached license on 2021-07-23 at 08:57.","The student, Chelsea Peterson, submitted this Thesis for approval on 2021-07-23 at 09:12.","This Thesis was approved for publication on 2021-07-23 at 10:26.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17078 on 2022-01-12 at 12:46:58","Made available in DSpace on 2022-01-12T21:47:03Z (GMT). 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Realizing the potential of MOEAs to agricultural decision support, however, will require corroborating that approximate solutions accurately represent system tradeoffs through extensive algorithm comparisons and integrating model uncertainty analysis into the optimization workflow. This thesis contributes to these broader goals by comparing the ability of five MOEAs (e-MOEA, e-NSGA-II, OMOPSO, GDE3, and MOEA/D) to calibrate the USDA's Root Zone Water Quality Model (RZWQM2) and identify fertilizer management decisions that maximize profits and corn yields while minimizing nitrate loads for two corn-soybean production sites in east-central Illinois. For both test problems, I evaluated solution accuracy by tracking three performance metrics for all five MOEAs along with their contributions to the best-known Pareto front throughout the optimization run. After pooling the calibration results to assess model error tradeoffs, I applied the top-performing MOEA with sets of parameter vectors from the calibration Pareto fronts to stochastically optimize fertilizer rate, method, and timing decisions. e-MOEA stood out as the most effective algorithm for model calibration by achieving the highest performance metric values within the fewest generations. The accuracy differences among MOEAs were much less apparent for the fertilizer management problem, implying that the algorithm selection should not influence the optimization results. Nonetheless, I applied OMOPSO for the stochastic optimization because it achieved the highest metric values by a small margin. Despite wide uncertainty bounds and high maximum pro fit nitrogen rates, the optimization results support that sidedress fertilizer applications from 20 to 50 days after planting not only maximize profit for injection and surface broadcast but also reduce outcome sensitivity to the placement method and offer the best compromise between pro fit and drainage nitrate loads. Although testing different calibration parameters could further support these results and re fine the uncertainty bounds, this analysis provides a flexible simulation-optimization framework for stochastically optimizing best management practice selection, design, and placement at field to watershed scales.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-01-12 without embargo terms","The student, Chelsea Peterson, accepted the attached license on 2021-07-23 at 08:57.","The student, Chelsea Peterson, submitted this Thesis for approval on 2021-07-23 at 09:12.","This Thesis was approved for publication on 2021-07-23 at 10:26.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17078 on 2022-01-12 at 12:46:58","Made available in DSpace on 2022-01-12T21:47:03Z (GMT). No. of bitstreams: 2 PETERSON-THESIS-2021.pdf: 1413584 bytes, checksum: ac3666575305de28389da9dc50f7bbcf (MD5) LICENSE.txt: 4213 bytes, checksum: f3be422013edcc3dbe4a41f8449a2a25 (MD5) Previous issue date: 2021-07-23"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/113103"],"dc:language":["en"],"dc:rights":["Copyright 2021 Chelsea Peterson"],"dc:subject":["fertilizer, agriculture, multiobjective, evolutionary, algorithms, RZWQM2"],"dc:title":["Quantifying the agronomic and water quality tradeoffs of fertilizer management with multiobjective evolutionary algorithms"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Agricultural & Biological Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:53Z"}