PARTNERSHIP: AN OPTIMIZATION-BASED DECISION SUPPORT SYSTEM USING ARTIFICIAL INTELLIGENCE TECHNIQUES FOR COST-EFFECTIVELY IMPLEMENTING AGRICULTURAL BEST MANAGEMENT PRACTICES TO REDUCE NUTRIENT LOADINGS

• Sponsoring Institution: National Institute of Food and Agriculture
• Project Status: ACTIVE
• Funding Source: AFRI COMPETITIVE GRANT
• Reporting Frequency: Annual
• Accession No.: 1030982
• Grant No.: 2023-67021-40619
• Cumulative Award Amt.: $760,000.00
• Proposal No.: 2022-11134
• Project Start Date: Jul 1, 2023
• Project End Date: Jun 30, 2027
• Grant Year: 2023
• Program Code: [A1521]- Agricultural Engineering

Recipient Organization
RESEARCH FOUNDATION FOR THE STATE UNIVERSITY OF NEW YORK, THE
1400 WASHINGTON AVE
ALBANY,NY 12222

Performing Department
(N/A)

Non Technical Summary
Hypoxia and harmful algal blooms (HABs), primarily caused by excess nonpoint source (NPS) nitrogen and phosphorus loadings in U.S. waters, such as Lake Erie, the Chesapeake Bay, and the Gulf of Mexico, have caused significant economic losses (just one major HAB event can cost local coastal economies tens of millions of dollars) and health issues. Reducing NPS nutrient losses from agricultural areas is the key to solving HAB and hypoxia issues in the U.S.Agricultural best management practices (BMPs) are popular approaches to reduce NPS nutrient loadings.Given varied functionalities of different types of BMPs, the impacts of BMPs on hydrology and water quality vary. In addition, BMP performance is also significantly affected by local conditions, such as weather conditions (precipitation, temperature, relative humidity, solar radiation, and wind speed) and drainage area features (land use/land cover, in-situ soil, elevation, groundwater, and drainage area size). Therefore, the performance of BMPs with typical BMP designs according to BMP design standards would be determined by their types, quantities, and spatial locations. Computer models that accurately quantify the life cycle effectiveness and costs of commonly used BMPs are vital for developing optimization-based decision support systems to create optimal BMP implementation strategies (optimal types, quantities, and spatial locations of BMPs) that minimize nutrient loadings at minimum cost. Moreover, computationally efficient optimization methods are needed for applications in large watersheds. However, current tools lack these capabilities.Therefore, this project will address the challenges of creating cost-effective and sustainable agricultural BMPimplementation strategies by developing and applying an optimization-based decision support system (SWAT-BMP-OPT) incorporating improved AMALGAM (A Multi-ALgorithm Genetically Adaptive Multi-objective method, which is a popular optimization method using artificial intelligence techniques), enhanced SWAT (Soil and Water Assessment Tool), and modified BMP-COST (BMP Cost Evaluation Tool). The SWAT-BMP-OPT will be able to comprehensively evaluate the effectiveness, cost, and cost-effectiveness of commonly used agricultural BMPs in reducing NPSnutrient loadings; and reliably and efficiently develop optimal BMP implementation strategies (optimal types, quantities, and spatial locations of BMPs) to minimize nutrient loadings at minimum cost.The SWAT-BMP-OPT can be appliedinfuture agricultural BMP planning and implementation projects, resulting in increased cost-effectiveness and sustainability in implementing agricultural BMPs.

Animal Health Component

80%

Research Effort Categories

Basic

20%

Applied

80%

Developmental

(N/A)

Classification

Knowledge Area (KA)	Subject of Investigation (SOI)	Field of Science (FOS)	Percent
112	0399	2050	20%
112	0399	2020	80%

Knowledge Area
112 - Watershed Protection and Management;

Subject Of Investigation
0399 - Watersheds and river basins, general;

Field Of Science
2020 - Engineering; 2050 - Hydrology;

Keywords

agricultural conservation practice

cost-effective

decision support system

nutrients

optimization

Goals / Objectives
The long-term goal of the project is to address the challenges of creating cost-effective and sustainable agricultural best management practice (BMP) implementation strategies by developing an optimization-based decision support system incorporating improved AMALGAM (a popular optimization method using artificial intelligence techniques), enhanced SWAT (Soil and Water Assessment Tool), and modified BMP-COST (BMP Cost Evaluation Tool). The optimization-based decision support system SWAT-BMP-OPT will be able to comprehensively evaluate the effectiveness, cost, and cost-effectiveness of commonly used agricultural BMPs in reducing nonpoint source nutrient loadings, as well as reliably and efficiently develop optimal BMP implementation strategies (optimal types, quantities, and spatial locations of BMPs) to minimize nutrient loadings at minimum cost.To achieve the long-term goal, the following objectives and activities will be pursued: (1) Improve SWAT in simulating the effectiveness of commonly used agricultural BMPs in reducing nutrient loadings. (2) Improve BMP-COST for evaluating the life cycle costs of BMPs. (3) Improve SWAT-BMP-OPT to optimize agricultural BMP implementation strategies that minimize nutrient loadings at minimum costs. (4) Use the improved SWAT and BMP-COST to evaluate the performance (effectiveness, cost, and cost-effectiveness) of agricultural BMPs starting from BMPs already implemented in the case study area with various scenarios. (5) Demonstrate the improved SWAT-BMP-OPT, starting with agricultural BMPs already implemented in the case study area, to answer various questions.

Project Methods
Methods.This project will use innovative and feasible approaches to improve the SWAT, BMP-COST, and optimization-based decision support system SWAT-BMP-OPT in simulating BMPs; and demonstrate the improved decision support tools in the CCW to assist stakeholders in creating cost-effective BMP implementation strategies. (1) The type, quantity, spatial location, and efficiency representations of commonly used BMPs in current computer models are limited. The improved SWAT will comprehensively simulate the types, quantities, spatial locations, and efficiencies of commonly used BMPs, which are vital for accurately simulating existing and future BMPs. (2) Current computer models assume BMP performances are constant over time; but in reality, BMP efficiencies vary over time. The improved SWAT will simulate the temporal long-term life cycle BMP efficiencies. (3) Current BMP cost evaluation tools do not include all important items for BMP life cycle costs. An innovative BMP cost evaluation method will be created for BMP-COST 2.0 that can comprehensively evaluate BMP life cycle costs. (4) Current optimization-based decision support systems have limitations, such as insufficient types of BMPs simulated, inefficient optimization algorithms, as well as inaccurate representations of BMP life cycle effectiveness and costs. A computationally efficient SWAT-BMP-OPT 2.0 will be developed by linking the improved SWAT and BMP-COST 2.0 with improved AMALGAM and MLSOPT framework, which will assist future optimal BMP implementations (obtain most cost-effective types, quantities, and spatial locations of BMPs starting from existing BMPs), and provide insights for existing BMPs and current watershed-level nutrient reduction plans.How data will be analyzed or interpreted.(1) For Objective 1, the improved BMP simulation methods will be developed from scientific literature, public datasets, and/or expert opinions; and the improved simulations of BMPs in SWAT will be systematically tested at field scales and validated using data from databases and literature. R2, NSE, and PBIAS will be used to evaluate field scale results to further improve BMP representations.The data for long-term life cycle features of BMPs will be collected from available scientific literature and public datasets. These temporal variations in parameters will be added to SWAT, allowing users to choose whether to consider temporal variations of the parameters.(2) For Objective 2, the new method of calculating BMP costs will be based on the reality of BMP life cycle costs; and BMP cost data will be from literature and databases. (3) For Objective 3, SWAT-BMP-OPT will be improved by increasing the optimization efficiency and accuracy; and connect the improved SWAT and BMP-COST 2.0 with improved AMALGAM and MLSOPT framework. (4) For Objective 4, the improved SWAT and BMP-COST 2.0 will be applied in the CCW. Sensitive parameters will be obtained using Sobol′'s global sensitivity analysis method. SWAT simulated annual crop yields, as well as monthly streamflow rates and nutrient loads (TP, DRP, TKN, NH4-N, and NO2+NO3-N) at the watershed outlet will be calibrated and validated using data from public datasets or collaborators. The modeling results of BMP (individual BMPs and all BMPs implemented in series starting from existing BMPs) effectiveness, costs, and cost-effectiveness for various scenarios will be compared to provide insights for BMP implementations. (5) For Objective 5, the calibrated and validated SWAT setups in the CCW with no BMPs implemented and with existing BMPs will be used to set up SWAT-BMP-OPT 2.0. The results of optimal BMP implementation strategies (optimal types, quantities, and spatial locations of BMPs) for various scenarios (yearly and March-July) will be analyzed or interpreted.Efforts.(1) Besides personal communications, special sessions will be organized in international meetings (such as American Society of Agricultural and Biological Engineers--ASABE Annual International Meeting, and American Geophysical Union--AGU Fall Meeting) to focus on the topics of this project. Four papers will be published in distinguished peer-reviewed journals and the research results will also be presented at the above international meetings.(2) A project web portal will be created on the Scholars Archive repository of University at Albany's University Libraries (https://scholarsarchive.library.albany.edu/).(3) Annual non-technical reports (including general descriptions of methods used in the project; and the insights obtained from the modeling results) will be created and made available to the public on the project web portal.(4) The improved tools with their codes, user-friendly graphical interface, learning materials (tutorials and instructional videos), and other materials/data will be posted on the project web portal and the official SWAT model website (also will be incorporated in SWAT model official versions by working with SWAT developers).(5) By working with Purdue University Extension, Indiana Corn Marketing Council-Indiana Soybean Alliance, Indiana State Department of Agriculture, and USDA research engineers, we will connect with additional local stakeholders.(6) The simulation results of this project (results of simulating BMPs at the field scale and watershed scale in Objectives 4 and 5) will be posted on the project web portal.(7) Regular virtual seminars will be held every six months (more frequent if necessary) to disseminate the project's results and obtain feedback on different topics aiming to engage different groups of stakeholders with varied interests.Evaluation.Through the above activities: (1) local stakeholders, such as outreach/extension educators, will be engaged to help identify specific problems to be solved in the CCW; (2) NRCS engineers and land improvement contractors will be engaged to verify the BMP effectiveness and cost information collected and implemented in the model to ensure the values are reasonable; (3) all stakeholders will be engaged to provide evaluations, suggestions, and feedback on model improvement, such as model assumptions, and approaches to make the tool more useful and user-friendly (simple and easy to use for stakeholders through user-friendly graphical interface and guided tool application steps); (4) model users, SWAT model developers, and other researchers will further improve and/or apply the tools in specific areas to solve local problems; (5) all stakeholders will evaluate the simulation results of this project, and provide feedback on scientific questions answered and to be answered that can help maximize the support of practical decision making in planning and implementation projects; and (6) researchers will conduct further scientific research and practical application studies based on the simulation results of this project.

Progress 07/01/24 to 06/30/25

Outputs
Target Audience:(1) Model users, SWAT model developers, and other researchers, who can help further improve and/or apply the improved tools in specific areas to solve local problems, were reached through (a) an oral presentation in 2024 American Society of Agricultural and Biological Engineers (ASABE) Annual International Meeting on the topic of evaluating the impacts of nutrient management on agroecosystem sustainability, (b) a poster presentation in 2024 American Geophysical Union (AGU) Fall Meeting on the topic of an integrated tool for cost-effectively applying nutrient management practices for corn, soybeans, and wheat considering the 4Rs, (c) a poster presentation in USDA Agricultural and Food Research Initiative 2024 Project Director's Meeting on the topic of an integrated tool for cost-effectively applying nutrient management practices for corn, soybeans, and wheat, (d) a poster presentation in 2025 University at Albany's Showcase Day on the topic of a comparative analysis of wetland modeling approaches, (e) a poster presentation in 2024 Florida A&M University Student Research Forum on the topic of GIS and SWAT integration on watershed management, and (f) a poster presentation in 2025 Florida A &M University Graduate Poster Competition on the topic of enhancing residue management in SWAT watershed modeling. (2) NRCS engineers (Dr. Dennis Flanagan and Dr. Haw Yen), who can help verify the BMP effectiveness and cost information collected and implemented in the model, were reached by obtaining monitored data and feedback on the work "An integrated tool for cost-effectively applying nutrient management practices for corn, soybean, and wheat considering the 4Rs" and "A comparative analysis of wetland modeling approaches". (3) Stakeholders within Indiana Farm Bureau, Indiana Soybean Alliance, and Indiana Corn Marketing Council were engaged regarding modeling of effectiveness and costs of BMPs. These organizations represent producers who are directly impacted by and interested in conservation strategies, so demonstrating the return on investment of BMPs through clear, localized modeling supported by data is key. Modeling tools--such as watershed-scale simulations and cost-benefit analyses--can be powerful in illustrating both the environmental outcomes and economic trade-offs of various BMP scenarios. Ensuring current and evolving stakeholder interests, including agricultural sustainability, economic viability, and environmental stewardship, are incorporated in modeling scenarios to meet key agricultural producer led goals are critical. Changes/Problems:We were not able to find suitable graduate students immediately after the project started given the common student recruiting periods and project starting time. The graduate student at University at Albany started in August 2024. The graduate student at Purdue University started in January 2024. And the graduate student at Florida A&M University started in August 2024.Given student recruitment issues, some of the stated goals were not met. What opportunities for training and professional development has the project provided?Training and professional development were provided to a graduate student at University at Albany, a graduate student at Purdue University, and a graduate student at Florida A&M University. The students were trained with extensive water resources modeling skills through training activitiesand professional developmentactivities, such as study groups, workshops, seminars, individual study, courses, and one-on-one work with the mentor. How have the results been disseminated to communities of interest?(1) Model users, SWAT model developers, and other researchers, who can help further improve and/or apply the improved tools in specific areas to solve local problems, were reached through (a) an oral presentation in 2024 American Society of Agricultural and Biological Engineers (ASABE) Annual International Meeting on the topic of evaluating the impacts of nutrient management on agroecosystem sustainability, (b) a poster presentation in 2024 American Geophysical Union (AGU) Fall Meeting on the topic of an integrated tool for cost-effectively applying nutrient management practices for corn, soybeans, and wheat considering the 4Rs, (c) a poster presentation in USDA Agricultural and Food Research Initiative 2024 Project Director's Meeting on the topic of an integrated tool for cost-effectively applying nutrient management practices for corn, soybeans, and wheat, (d) a poster presentation in 2025 University at Albany's Showcase Day on the topic of a comparative analysis of wetland modeling approaches, (e) a poster presentation in 2024 Florida A&M University Student Research Forum on the topic of GIS and SWAT integration on watershed management, and (f) a poster presentation in 2025 Florida A &M University Graduate Poster Competition on the topic of enhancing residue management in SWAT watershed modeling. (2) NRCS engineers (Dr. Dennis Flanagan and Dr. Haw Yen), who can help verify the BMP effectiveness and cost information collected and implemented in the model, were reached by obtaining monitored data and feedback on the work "An integrated tool for cost-effectively applying nutrient management practices for corn, soybean, and wheat considering the 4Rs" and "A comparative analysis of wetland modeling approaches". (3) Stakeholders within Indiana Farm Bureau, Indiana Soybean Alliance, and Indiana Corn Marketing Council were engaged regarding modeling of effectiveness and costs of BMPs. These organizations represent producers who are directly impacted by and interested in conservation strategies, so demonstrating the return on investment of BMPs through clear, localized modeling supported by data is key. Modeling tools--such as watershed-scale simulations and cost-benefit analyses--can be powerful in illustrating both the environmental outcomes and economic trade-offs of various BMP scenarios. Ensuring current and evolving stakeholder interests, including agricultural sustainability, economic viability, and environmental stewardship, are incorporated in modeling scenarios to meet key agricultural producer led goals are critical. What do you plan to do during the next reporting period to accomplish the goals?Additional research activities will be conducted for the following objectives: (1) Improve SWAT in simulating the effectiveness of commonly used agricultural BMPs in reducing nutrient loadings. (2) Improve BMP-COST for evaluating the life cycle costs of BMPs. (3) Improve SWAT-BMP-OPT to optimize agricultural BMP implementation strategies that minimize nutrient loadings at minimum costs. Additional stakeholders will be engaged as planned. Some of the stated goals were not met because we were not able to find suitable graduate students immediately after the project started given the common student recruiting periods and project starting time. The graduate student at University at Albany started in August 2024. The graduate student at Purdue University started in January 2024. And the graduate student at Florida A&M University started in August 2024.

Impacts
What was accomplished under these goals? For objective 1, a comparative analysis of wetland modeling approaches was conducted and demonstrated in the AXL watershed, which is a typical agricultural area in the Maumee River watershed. The improvements of simulating different types of BMPs, including cover crops, blind inlets, wetlands, filter strips, grassed waterways, grade stabilization structures, two-stage ditches, conservation tillage, and residue management, are ongoing. For objective 2, the improvement of BMP-COST in evaluating the life cycle costs of above BMPs is ongoing. A comparative analysis of wetland modeling approaches was conducted and demonstrated in the AXL watershed, which provides insights of the best way to improve the simulation of wetlands in the SWAT model, can assist decision-makers in cost-effectively applying wetlands.

Publications

• Type: Conference Papers and Presentations Status: Published Year Published: 2024 Citation: Yaoze Liu, Siyu Li, Anh H. Nguyen, Bernard A. Engel, Jingqiu Chen, Dennis C. Flanagan, Tian Guo, Fawen Li, Dongyang Ren, Chengxu Liu. 2024. An integrated tool for cost-effectively applying nutrient management practices for corn, soybeans, and wheat. Poster presentation. USDA Agricultural and Food Research Initiative 2024 Project Directors Meeting. July 25-26, 2024, Manhattan, Kansas.
• Type: Conference Papers and Presentations Status: Published Year Published: 2024 Citation: Yaoze Liu, Siyu Li, Anh H. Nguyen, Bernard A. Engel, Jingqiu Chen, Dennis C. Flanagan, Tian Guo, Fawen Li, Dongyang Ren, Chengxu Liu. 2024. An integrated tool for cost-effectively applying nutrient management practices for corn, soybeans, and wheat considering the 4Rs. Poster presentation. 2024 American Geophysical Union Fall Meeting. December 9-13, 2024, Washington DC.
• Type: Conference Papers and Presentations Status: Published Year Published: 2025 Citation: Zhuohang Wu, Yaoze Liu. A comparative analysis of wetland modeling approaches. Poster presentation. 2025 University at Albanys Showcase Day. April 30, 2025, Albany, New York.
• Type: Peer Reviewed Journal Articles Status: Published Year Published: 2024 Citation: Liu, Y., Li, S., Nguyen, A.H., Engel, B.A., Chen, J., Flanagan, D.C., Guo, T., Li, F., Ren, D. and Liu, C., 2024. An integrated tool for cost-effectively applying nutrient management practices for corn, soybeans, and wheat. Science of The Total Environment, 955, p.177110.
• Type: Conference Papers and Presentations Status: Published Year Published: 2024 Citation: Yaoze Liu, Siyu Li, Anh H. Nguyen, Bernard A. Engel, Jingqiu Chen, Dennis C. Flanagan, Tian Guo, Fawen Li, Dongyang Ren, Chengxu Liu. Evaluating the impacts of nutrient management on agroecosystem sustainability. Oral presentation. 2024 American Society of Agricultural and Biological Engineers Annual International Meeting, July 28-31, 2024, Anaheim, California.
• Type: Conference Papers and Presentations Status: Published Year Published: 2025 Citation: Olaoluwa E. Anigboro-Napoleon, Yaoze Liu, Jingqiu Chen. Enhancing Watershed Modeling with ArcGIS and SWAT integration. The 2025 FAMU Graduate Poster Competition. March 27, 2025. Tallahassee, FL.
• Type: Conference Papers and Presentations Status: Published Year Published: 2024 Citation: Olaoluwa E. Anigboro-Napoleon, Yaoze Liu, Jingqiu Chen. Integrating SWAT and ArcGIS to Improve Watershed Management. The 2024 FAMU Student Research Forum. October 30, 2024, Tallahassee, FL.

Progress 07/01/23 to 06/30/24

Outputs
Target Audience:(1) Model users and other researchers, who can help further improve and/or apply the improved tools in specific areas to solve local problems, were reached through an oral presentation in 2023 AGU Fall meeting on the topic of improving SWAT model in simulating nutrient management. (2) A NRCS engineer (Dr. Dennis Flanagan), who can help verify the BMP effectiveness and cost information collected and implemented in the model, was reached by obtaining feedback on the work "An integrated tool for cost-effectively applying nutrient management practices for corn, soybean, and wheat considering the 4Rs". (3) Board members (primarily agricultural producers) of Indiana Soybean Alliance and Indiana Corn Marketing Council who are potential beneficiaries of the model were reached through discussion of needs the model can provide at their board meetings. Changes/Problems: major problems or delays that may have a significant impact on the rate of expenditure; The budget for Purdue University, which was originally for a postdoctoral researcher, was changed to support a graduate student.We were not able to find suitable graduate students immediately after the project started given the common student recruiting periods and project starting time. The graduate student at Purdue University started in January 2024. And a new graduate student at University at Albany will start in August 2024. In addition, Florida A&M University's Year 1 funds (Dr. Jingqiu Chen's summer salary and undergraduate students) have not been spent yet. Dr. Chen intends to strategically leverage the unspent funds from Year 1 and combine them with the budget for Year 2 to facilitate the recruitment of a postdoctoral researcher. significant deviations from research schedule or goals; Given student recruitment issues, some of the stated goals were not met. unexpected outcomes; None. or changes in approved protocols for the use or care of animals, human subjects, and/or biohazards encountered during the reporting period. None. What opportunities for training and professional development has the project provided?Training and professional development were provided to a graduate student at Purdue University. The student was trained with extensive water resources modeling skills through training activitiesand professional developmentactivities, such as study groups, workshops, seminars, individual study, courses, and one-on-one work with the mentor. How have the results been disseminated to communities of interest?(1) Model users and other researchers, who can help further improve and/or apply the improved tools in specific areas to solve local problems, were reached through an oral presentation in 2023 AGU Fall meeting on the topic of improving SWAT model in simulating nutrient management. (2) A NRCS engineer (Dr. Dennis Flanagan), who can help verify the BMP effectiveness and cost information collected and implemented in the model, was reached by obtaining feedback on the work "An integrated tool for cost-effectively applying nutrient management practices for corn, soybean, and wheat considering the 4Rs". (3) Board members (primarily agricultural producers) of Indiana Soybean Alliance and Indiana Corn Marketing Council who are potential beneficiaries of the model were reached through discussion of needs the model can provide at their board meetings. What do you plan to do during the next reporting period to accomplish the goals?Additional research activities will be conducted for the following objectives: (1) Improve SWAT in simulating the effectiveness of commonly used agricultural BMPs in reducing nutrient loadings. (2) Improve BMP-COST for evaluating the life cycle costs of BMPs. (3) Improve SWAT-BMP-OPT to optimize agricultural BMP implementation strategies that minimize nutrient loadings at minimum costs. In addition to the current graduate student at Purdue University, a new graduate student at University at Albany will start in August 2024 to work on this project. Additional stakeholders will be engaged as planned. Some of the stated goals were not met because we were not able to find suitable graduate students immediately after the project started given the common student recruiting periods and project starting time. The graduate student at Purdue University started in January 2024. And a new graduate student at University at Albany will start in August 2024.

Impacts
What was accomplished under these goals? The long-term goal of the project is to address the challenges of creating cost-effective and sustainable agricultural best management practice (BMP) implementation strategies by developing an optimization-based decision support system. The optimization-based decision support system SWAT-BMP-OPT will be able to comprehensively evaluate the effectiveness, cost, and cost-effectiveness of commonly used agricultural BMPs in reducing nonpoint source nutrient loadings, as well as reliably and efficiently develop optimal BMP implementation strategies (optimal types, quantities, and spatial locations of BMPs) to minimize nutrient loadings at minimum cost. Stakeholders: (1) model users, SWAT model developers, and other researchers (who would be interested in the tools based on their BMP related work) who can help further improve and/or apply the SWAT, BMP-COST, and SWAT-BMP-OPT in specific areas to solve local problems; (2) outreach/extension educators who can use research outcomes as educational materials for farmers and other local stakeholders who benefit from the modeling results in the specific areas; and (3) NRCS engineers and land improvement contractors (experienced engineers and construction teams) who can help verify the BMP effectiveness and cost information collected and implemented in the model. Stakeholders will be engaged through personal communications, a project web portal, conferences, journal publications, improved tools, seminars, and non-technical reports. For objective 1, the improvement of SWAT in simulating the effectiveness of nutrient management in reducing nutrient loadings is completed. For objective 2, the improvement of BMP-COST in evaluating the life cycle costs of nutrient management is completed. By combining an improved SWAT for nutrient management (SWAT-NM) and an improved BMP-COST for nutrient management (BMP-COST-NM) considering the 4Rs for corn, soybean, and wheat, an integrated tool SWAT-COST-NM was created. The SWAT-COST-NM was demonstrated in the AXL watershed, which is a typical agricultural area in the Maumee River watershed. The impacts of single nutrient management practices (single-NM, which separately changed the rate, place, time, or nutrient source of fertilizer applications) and combined-NM practices (a number of single-NM practices combined as one nutrient management practice) for corn, soybean, and wheat on crop yields, March-July/yearly nutrient losses (Total Phosphorus--TP, Dissolved Reactive Phosphorus--DRP, and Total Nitrogen--TN), yearly gross costs, yearly crop revenues, yearly net costs, and cost-effectiveness in reducing March-July/yearly nutrient loadings were evaluated. Tradeoffs in yearly net costs, crop yields, and March-July/yearly nutrient losses existed when determining the impacts of nutrient management practices. A combined-NM practice (Scenario 7d) could simultaneously reduce March-July TP, DRP, and TN losses by 5.89%, 8.19%, and 8.23%, respectively, while increasing crop yields with additional income (0.50 $/ha/yr of cropped area). The project developed SWAT-COST-NM, which can quantify various factors and tradeoffs when evaluating the impacts of nutrient management practices for corn, soybean, and wheat, can assist decision-makers in cost-effectively applying nutrient management practices considering the 4Rs.

Publications

• Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Yaoze Liu, Siyu Li, Anh Nguyen. Evaluating the impacts of nutrient management on agroecosystem sustainability. Oral Presentation. 2023 American Geophysical Union Fall Meeting, December 11-15, 2023, San Francisco, California.
• Type: Conference Papers and Presentations Status: Published Year Published: 2024 Citation: Bernard Engel. Addressing Runoff and Nonpoint Source Pollution Issues: Past, Present and Future. Oral Presentation. International Conference on Future of Water Resources at IIT Roorkee from January 18-20, 2024, Roorkee, India.