TY - JOUR
T1 - Dashboard for Agricultural Water Use and Nutrient Management—A Predictive Decision Support System to Improve Crop Production in a Changing Climate
AU - Liang, Xin Zhong
AU - Gower, Drew
AU - Kennedy, Jennifer A.
AU - Kenney, Melissa
AU - Maddox, Michael C.
AU - Gerst, Michael
AU - Balboa, Guillermo
AU - Becker, Talon
AU - Cai, Ximing
AU - Elmore, Roger
AU - Gao, Wei
AU - He, Yufeng
AU - Liang, Kang
AU - Lotton, Shane
AU - Malayil, Leena
AU - Matthews, Megan L.
AU - Meadow, Alison M.
AU - Neale, Christopher M.U.
AU - Newman, Greg
AU - Sapkota, Amy Rebecca
AU - Shin, Sanghoon
AU - Straube, Jonathan
AU - Sun, Chao
AU - Wu, You
AU - Yang, Yun
AU - Zhang, Xuesong
N1 - Publisher Copyright:
© 2024 American Meteorological Society. All rights reserved.
PY - 2024/2/1
Y1 - 2024/2/1
N2 - Climate change presents huge challenges to the already-complex decisions faced by U.S. agricultural producers, as seasonal weather patterns increasingly deviate from historical tendencies. Under USDA funding, a transdisciplinary team of researchers, extension experts, educators, and stakeholders is developing a climate decision support Dashboard for Agricultural Water use and Nutrient management (DAWN) to provide Corn Belt farmers with better predictive information. DAWN’s goal is to provide credible, usable information to support decisions by creating infrastructure to make subseasonal-to-seasonal forecasts accessible. DAWN uses an integrated approach to 1) engage stakeholders to coproduce a decision support and information delivery system; 2) build a coupled modeling system to represent and transfer holistic systems knowledge into effective tools; 3) produce reliable forecasts to help stakeholders optimize crop productivity and environmental quality; and 4) integrate research and extension into experiential, transdisciplinary education. This article presents DAWN’s framework for integrating climate–agriculture research, extension, and education to bridge science and service. We also present key challenges to the creation and delivery of decision support, specifically in infrastructure development, coproduction and trust building with stakeholders, product design, effective communication, and moving tools toward use.
AB - Climate change presents huge challenges to the already-complex decisions faced by U.S. agricultural producers, as seasonal weather patterns increasingly deviate from historical tendencies. Under USDA funding, a transdisciplinary team of researchers, extension experts, educators, and stakeholders is developing a climate decision support Dashboard for Agricultural Water use and Nutrient management (DAWN) to provide Corn Belt farmers with better predictive information. DAWN’s goal is to provide credible, usable information to support decisions by creating infrastructure to make subseasonal-to-seasonal forecasts accessible. DAWN uses an integrated approach to 1) engage stakeholders to coproduce a decision support and information delivery system; 2) build a coupled modeling system to represent and transfer holistic systems knowledge into effective tools; 3) produce reliable forecasts to help stakeholders optimize crop productivity and environmental quality; and 4) integrate research and extension into experiential, transdisciplinary education. This article presents DAWN’s framework for integrating climate–agriculture research, extension, and education to bridge science and service. We also present key challenges to the creation and delivery of decision support, specifically in infrastructure development, coproduction and trust building with stakeholders, product design, effective communication, and moving tools toward use.
KW - Agriculture
KW - Climate prediction
KW - Climate services
KW - Communications/ decision making
KW - Decision support
KW - Water resources
UR - http://www.scopus.com/inward/record.url?scp=85191023899&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85191023899&partnerID=8YFLogxK
U2 - 10.1175/BAMS-D-22-0221.1
DO - 10.1175/BAMS-D-22-0221.1
M3 - Article
AN - SCOPUS:85191023899
SN - 0003-0007
VL - 105
SP - E432-E441
JO - Bulletin of the American Meteorological Society
JF - Bulletin of the American Meteorological Society
IS - 2
ER -