(100a) Computing at the Nexus of Food, Energy, and Water

In coming decades, the world population is projected to grow significantly increasing the demand for food, water, energy, and other resources. Furthermore, these resource challenges may be amplified due to climate change and urbanization. In addition, Food, energy and water (FEW) systems were traditionally analyzed and planned independently to address the challenges of population growth, climate change and urbanization. However, such piece-meal approaches (e.g., bio-fuel subsidy, fertilizers in agriculture) to solving problems in one system (e.g., energy, food) led to unanticipated harms to other systems (e.g., food price increase, water resource depletion and degradation). Thus, understanding the interdependent and interconnected nature of food, energy, and water systems (FEW nexus) is a societal priority.

Computing is crucial for understanding the problem, the interconnections, and the impacts withing FEW nexus. It is also needed for monitoring a variety of Earth resources (e.g., agriculture fields, fresh water lakes, energy needs for cooling or heating, etc.), and trends (e.g., deforestation, pollution, etc.) for timely detection and management of risks, such as impending crop failures and crop-stress anywhere in the world. It is also needed to reduce waste and to improve efficiency, e.g., amount of water and energy needed to produce food.

Computing success stories go beyond the cyber-infrastructure for simulations (e.g., GCMs, AgMIP ) to include precision agriculture and GEOGLAM. Precision agriculture uses cyber-physical systems and data science to increase yield, and reduce fertilizer and pesticide runoffs. The Global Agricultural Monitoring (GEOGLAM) , an international system, uses remotely sensed satellite imagery to monitor major crops for yield forecasts to enable timely interventions to reduce disruptions in global food supply.

However, the FEW nexus presents new challenges and opportunities for computing. For example, data science methods need to not only re-examine assumptions such as non-stationarity (e.g., climate change) but also address nexus challenges such as high cost of false positives, (social) feedback loops, and multiple spatio-temporal scale.