Board 390: Student-Led Collaboration for Data-Driven Decisions in Food, Energy, and Water Systems

Sarah M. Ryan; Robert Brown; Amy Kaleita; Sergio Horacio Lence; Cynthia Lidtke; Cameron Alexander MacKenzie; Michelle Lynn Soupir

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Conference: 2024 ASEE Annual Conference & Exposition
Location: Portland, Oregon
Publication Date: June 23, 2024
Start Date: June 23, 2024
End Date: June 26, 2024
Conference Session: NSF Grantees Poster Session
Tagged Topic: NSF Grantees Poster Session
Page Count: 12
DOI: 10.18260/1-2--46976
Permanent URL: https://peer.asee.org/46976
Download Count: 122

Paper Authors

biography

Sarah M. Ryan Iowa State University orcid.org/0000-0001-5903-1432

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Sarah M. Ryan is the C.G. “Turk” and Joyce A. Therkildsen Department Chair and Professor of Industrial and Manufacturing Systems at Iowa State University. She directs the DataFEWSion National Research Traineeship.

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biography

Robert Brown

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Dr. Brown is Anson Marston Distinguished Professor in Engineering and Gary and Donna Hoover Chair in Mechanical Engineering at Iowa State University (ISU). Dr. Brown is the founding director of the Bioeconomy Institute (BEI), which coordinates ISUâ€™s re

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biography

Amy Kaleita Iowa State University orcid.org/0000-0002-8906-3408

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Amy L. Kaleita is Professor and Chair of Agricultural and Biosystems Engineering at Iowa State University, and a licensed professional engineer. She has a B.S. in Agricultural Engineering from Penn State University, an M.S. in Civil and Environmental Engineering and Ph.D. in Agricultural Engineering from the University of Illinois.

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Sergio Horacio Lence

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Professor of Economics

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Dr. Soupir's research focuses on soil and water quality, nonpoint source pollution control, watershed management, and water quality monitoring. She uses both lab and field scale studies to examine the occurrence, fate and transport of pathogens, pathogen

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Abstract

An INFEWS-themed National Research Traineeship (NRT) program aimed to build a community of researchers who explore, develop and implement effective data-driven decision-making to efficiently produce food, transform primary energy sources into energy carriers, and enhance water quality. Over five years, four cohorts of trainees, totaling 31 MS and PhD students from 16 graduate programs at a Midwestern land-grant university (approximately half drawn from five different engineering disciplines) have completed the major components of the two-year program. These include thesis or dissertation research on a food, energy, and water systems (FEWS) issue; a graduate coursework certificate in Data-Driven Food, Energy and Water Decision Making; and participation in a Graduate Learning Community that includes monthly workshops and weekly small-group activities designed to enhance the trainees’ interdisciplinary communication and collaboration skills, while preparing them for careers in diverse organizations.

As the program evolved, the students increasingly took on leadership of program elements. Student members of the leadership team collaborated with the faculty on programmatic decisions. Student-faculty working groups planned the learning community activities and annual research symposia. Most notably, small groups of trainees proposed, planned, and conducted multidisciplinary research projects. A trio of trainees from civil engineering, sustainable agriculture, and environmental science completed a systematic literature review on equity in FEWS that was published in a leading interdisciplinary journal. The final cohort of trainees divided into teams to collaborate on projects concerned with each of the three FEWS areas. The food team is studying food systems in Iowa to assess inequality in access to nutrition. They combine production, distribution, consumption, and nutritional data at the county level to assess causes of poor health outcomes of Iowans, while incorporating climate change components in their analysis to assess the sustainability of current food systems. The energy team is analyzing data collected from a microgrid that combines solar photovoltaic generation and storage to power a livestock feeding facility. Their goal is to assess the cost effectiveness of installed capacity relative to power purchased from the grid. The water team is studying how climate change affects regional drought and flood conditions, with the objective to produce an online, interactive map linked to low-income communities. Meteorological, climate, and demographic data are combined to identify hot spots of vulnerability to water excess or deficit. Feedback obtained at the annual symposium from the program’s external advisors will enhance the applicability of each project.

Citation
Format

Ryan, S. M., & Brown, R., & Kaleita, A., & Lence, S. H., & Lidtke, C., & MacKenzie, C. A., & Soupir, M. L. (2024, June), Board 390: Student-Led Collaboration for Data-Driven Decisions in Food, Energy, and Water Systems Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. 10.18260/1-2--46976

TY - CPAPER
AB - An INFEWS-themed National Research Traineeship (NRT) program aimed to build a community of researchers who explore, develop and implement effective data-driven decision-making to efficiently produce food, transform primary energy sources into energy carriers, and enhance water quality. Over five years, four cohorts of trainees, totaling 31 MS and PhD students from 16 graduate programs at a Midwestern land-grant university (approximately half drawn from five different engineering disciplines) have completed the major components of the two-year program. These include thesis or dissertation research on a food, energy, and water systems (FEWS) issue; a graduate coursework certificate in Data-Driven Food, Energy and Water Decision Making; and participation in a Graduate Learning Community that includes monthly workshops and weekly small-group activities designed to enhance the trainees’ interdisciplinary communication and collaboration skills, while preparing them for careers in diverse organizations.

As the program evolved, the students increasingly took on leadership of program elements. Student members of the leadership team collaborated with the faculty on programmatic decisions. Student-faculty working groups planned the learning community activities and annual research symposia. Most notably, small groups of trainees proposed, planned, and conducted multidisciplinary research projects. A trio of trainees from civil engineering, sustainable agriculture, and environmental science completed a systematic literature review on equity in FEWS that was published in a leading interdisciplinary journal. The final cohort of trainees divided into teams to collaborate on projects concerned with each of the three FEWS areas. The food team is studying food systems in Iowa to assess inequality in access to nutrition. They combine production, distribution, consumption, and nutritional data at the county level to assess causes of poor health outcomes of Iowans, while incorporating climate change components in their analysis to assess the sustainability of current food systems. The energy team is analyzing data collected from a microgrid that combines solar photovoltaic generation and storage to power a livestock feeding facility. Their goal is to assess the cost effectiveness of installed capacity relative to power purchased from the grid. The water team is studying how climate change affects regional drought and flood conditions, with the objective to produce an online, interactive map linked to low-income communities. Meteorological, climate, and demographic data are combined to identify hot spots of vulnerability to water excess or deficit. Feedback obtained at the annual symposium from the program’s external advisors will enhance the applicability of each project.

AU - Sarah M. Ryan
AU - Robert Brown
AU - Amy Kaleita
AU - Sergio Horacio Lence
AU - Cynthia Lidtke
AU - Cameron Alexander MacKenzie
AU - Michelle Lynn Soupir
CY - Portland, Oregon
DA - 2024/06/23
PB - ASEE Conferences
TI - Board 390: Student-Led Collaboration for Data-Driven Decisions in Food, Energy, and Water Systems
UR - https://peer.asee.org/46976
DO - 10.18260/1-2--46976
ER -