University of Maryland researchers will lead a five-year, $10 million project funded by the U.S. Department of Agriculture’s National Institute of Food and Agriculture to help farmers in the Corn Belt navigate efficient water and nutrient use in order to increase crop production.
The researchers plan to develop a Dashboard for Agricultural Water use and Nutrient management (DAWN) that will help maximize corn, soybean and bioenergy crop production in the Midwestern United States. They expect DAWN to identify innovative ways of increasing land- and water-use efficiency given competing resource demands and varying water availability and quality.
“Our goal is to create a predictive tool that translates complex system science into reliable, usable information for agricultural decision-makers so that they can optimize pre-season, in-season and longer-term practices,” said the project’s lead investigator Xin-Zhong Liang, a professor of atmospheric and oceanic science at UMD with a joint appointment in the Earth System Science Interdisciplinary Center (ESSIC). “To do this, we have to link local land-use and water-use practices to large-scale feedbacks and deliver that information effectively to stakeholders.”
Routine decisions such as crop choice, fertilizer use, irrigation scheduling and reservoir operations can have wide-ranging and long-term impacts on water availability, nutrient loss, agricultural production and sustainability. The changing climate and enhanced extremes also threaten production—rainfed crops are vulnerable to droughts, heat stress raises water demand, and floods threaten crop growth and water quality.
“We will build models and decision support tools that represent the complex interactions among agriculture, climate, land and water use, and economic and environmental impacts,” Liang said. “If we can find ways to increase agricultural productivity and reduce input costs and losses due to environmental and biological stresses, and thus increase profitability, this project will be a success.”
According to Liang, current decision support tools evaluate only conditions and tradeoffs at individual points and fail to capture larger system feedbacks. DAWN will include data from large-enough scales to capture feedbacks across different regions, times and sectors.
The project team includes researchers, extension specialists, educators and stakeholders. Partners in the project include researchers at Colorado State University, the University of Illinois, the University of Minnesota, the University of Nebraska and FamilyFarms Group.
DAWN will be designed collaboratively with end-users to provide short-term forecasts for real-time decision-making, seasonal outlooks for mid-range planning, and scenario projections for long-range planners and policymakers to address adaptation strategies for improving agricultural and food system sustainability.
“Ultimately, we hope DAWN will be a holistic framework of tools that will help bridge the gap between advanced modeling systems and the practical needs of crop producers, water managers and policymakers,” Liang added.
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In addition to Liang, investigators on the project from UMD include Applied Environmental Health Professor and Director of CONSERVE and UMD Global STEWARDS, Amy R. Sapkota; Atmospheric and Oceanic Science Professor and Chair and ESSIC Director Fernando Miralles-Wilhelm; ESSIC Assistant Research Professors Michael Gerst and Thomas Wild; ESSIC Visiting Research Scientist Xuesong Zhang; ESSIC Project Manager Michael Maddox; ESSIC Assistant Research Scientists Junyu Qi and Mitchell Schull; and ESSIC Postdoctoral Associates Yufeng He, Chao Sun and You Wu.
Media Relations Contact: Abby Robinson, 301-405-5845, abbyr@umd.edu
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The College of Computer, Mathematical, and Natural Sciences at the University of Maryland educates more than 9,000 future scientific leaders in its undergraduate and graduate programs each year. The college’s 10 departments and more than a dozen interdisciplinary research centers foster scientific discovery with annual sponsored research funding exceeding $200 million.
