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The state of Iowa is known for its high-yield agriculture, supporting rising demands for food and fuel production. But this productivity is also a significant contributor of nitrogen loading to the Mississippi River basin causing the hypoxic zone in the Gulf of Mexico. The delivery of nutrients, especially nitrogen, from the upper Mississippi River basin, is a function, not only of agricultural activity, but also of hydrology. Thus, it is important to consider extreme weather conditions, such as drought and flooding, and understand the effects of weather variability on Iowa’s food-energy-water (IFEW) system and nitrogen loading to the Mississippi River from Iowa. In this work, the simulation decomposition approach is implemented using the extended IFEW model with a crop-weather model to better understand the cause-and-effect relationships of weather parameters on the nitrogen export from the state of Iowa. July temperature and precipitation are used as varying input weather parameters with normal and log normal distributions, respectively, and subdivided to generate regular and dry weather conditions. It is observed that most variation in the soil nitrogen surplus lies in the regular condition, while the dry condition produces the highest soil nitrogen surplus for the state of Iowa. 

Sustainable provision of food, energy and clean water requires understanding of the interdependencies among systems as well as the motivations and incentives of farmers and rural policy makers. Agriculture lies at the heart of interactions among food, energy and water systems. It is an increasingly energy intensive enterprise, but is also a growing source of energy. Agriculture places large demands on water supplies while poor practices can degrade water quality. Each of these interactions creates opportunities for modeling driven by sensor-based and qualitative data collection to improve the effectiveness of system operation and control in the short term as well as investments and planning for the long term. The large volume and complexity of the data collected creates challenges for decision support and stakeholder communication. The DataFEWSion National Research Traineeship program aims to build a community of researchers that explores, develops and implements effective data-driven decision-making to efficiently produce food, transform primary energy sources into energy carriers, and enhance water quality. The initial cohort includes PhD students in agricultural and biosystems, chemical, and industrial engineering as well as statistics and crop production and physiology. The project aims to prepare trainees for multiple career paths such as research scientist, bioeconomy entrepreneur, agribusiness leader, policy maker, agriculture analytics specialist, and professor. The traineeship has four key components. First, trainees will complete a new graduate certificate to build competencies in fundamental understanding of interactions among food production, water quality and bioenergy; data acquisition, visualization, and analytics; complex systems modeling for decision support; and the economics, policy and sociology of the FEW nexus. Second, they will conduct interdisciplinary research on (a) technologies and practices to increase agriculture’s contributions to energy supply while reducing its negative impacts on water quality and human health; (b) data science to increase crop productivity within the constraints of sustainable intensification; or (c) decision sciences to manage tradeoffs and promote best practices among diverse stakeholders. Third, they will participate in a new graduate learning community to consist of a two-year series of workshops that focus in alternate years on the context of the Midwest agricultural FEW nexus and professional development; and fourth, they will have small-group experiences to promote collaboration and peer review. Each trainee will create and curate a portfolio that combines artifacts from coursework and research with reflections on the broader impacts of their work. Trainee recruitment emphasizes women and underrepresented groups.