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Contemporary food and agricultural systems degrade soils, pollute natural resources, and contribute to greenhouse gas emissions. The waste output from these systems, however, can be repurposed as an agricultural input, reducing emissions associated with organics disposal while actively sequestering atmospheric carbon in soils—thus transitioning the sector from a carbon source to a carbon sink. This research estimates the near-term technical and economic potential of utilizing composted organic feedstocks as a soil amendment to mitigate climate change and improve long-term soil quality, in line with California’s organics diversion policies, by connecting food scraps and organics residuals in California’s municipal solid waste to existing infrastructure and working lands in the state. The multi-objective spatial optimization results indicate considerable carbon sequestration benefits in the range of −1.9 ± 0.5 MMT CO₂eq annually, by applying compost to 6 million hectares of California rangelands at a price of approximately $200 per ton, presenting a cost-effective climate change mitigation strategy within proposed federal sequestration credits. Expanding composting capacity is predicted to increase the total amount of carbon sequestered while reducing the cost per ton and per hectare treated. This model aids decision makers in considering the technical, economic, and institutional potential of actively managing the State’s organic materials in municipal waste streams for climate change mitigation.

System-level integration and optimization of food-energy-water systems (FEWS) require coordination of multiple agencies and decision-makers and incorporating their interdependence. In general, such coordination might be hard to achieve. As a result, the literature on FEWS management either optimizes the operations for one sector (or one decision-maker), or models interdependence among the sectors without optimizing their operations. In this article, we develop a novel multi-agent management optimization approach that is able to incorporate stochasticity and uncertainty in the system’s dynamics and interdependence of the water and energy resources for food production. The proposed method is the first attempt to utilize fundamentals of decision and game theories to optimize operations of multi-agent FEWS. We specifically focus on differentiating between (1) cooperative decision optimization of the operations, where all decision-makers cooperate to achieve the best outcome for the whole system, the social optimum, and (2) non-cooperative decision-making of the agents, the Nash equilibrium. Illustrating with a real-world case study of FEWS in Ventura County, California, we show the difference between the cooperative and non-cooperative decision making in terms of long-term expected cost of managing the system. We further show how the extra costs associated with utilizing the renewable sources of water and energy could be incentivised, so that the non-cooperative solution (the Nash equilibrium) would naturally converge to the best outcome for the whole system (the social optimum).

Under the risk of drought, unreliable water supplies, and growing water demand, there is a growing need worldwide to explore alternative water sources to meet the demand for irrigation in agriculture and other outdoor activities. This paper estimates stocks, production capacities, economic costs, energy implications, and greenhouse gas (GHG) emissions associated with recycled water, desalinated brackish and seawater, and stormwater in California, the largest US state and the most significant fresh and processed food producer. The combined recycled water and stormwater supply could increase the share of alternative water use in urban land irrigation (parks and golf courses) from the current rate of 4.6% to 48% and in agriculture from 0.82% to 5.4% while increasing annual water costs by $900 million (1.8% of California’s annual agricultural revenue) and energy use by 710 GWh (0.28% of California’s annual electricity consumption). The annual supply of alternative water greatly exceeds the amount of water currently used in the food processing industry. In case studies of high-value agricultural produce, conventional water use was found to contribute approximately 17%, 12%, 4.1%, and 1.7% to the total GHG emissions of avocados, lemons, celery, and strawberries, respectively. However, materials (mostly packaging) contribute 46%, 26%, 47%, and 66%, and diesel use on farms 18%, 28%, and 14% for lemons, celery, and strawberries, respectively (data for avocados were not available). Switching to recycled water or stormwater would increase the total GHG emissions of one serving size of packaged strawberries, celery, lemons, and avocados by 3.0%, 7.8%, 11%, and 27%, respectively, desalinated brackish water by 23%, 58%, 150%, and 210%, and desalinated seawater by 35%, 88%, 230%, and 320%. Though switching to alternative water will increase costs, energy demand, and GHG emissions, they could be offset by turning to less environmentally damaging materials in agricultural production and sales (especially packaging).

Integrated management of food–energy–water systems (FEWS) requires a unified, flexible and reproducible approach to incorporate the interdependence between sectors, and include the risk of non-stationary environmental variations due to climate change. Most of the recently developed methods in the literature fall short of one or more aspects in such integration. In this article, we propose a novel approach based upon fundamentals of decision theory and reinforcement learning that (1) quantifies and propagates uncertainty, (2) incorporates resource interdependence, (3) includes the impact of uncontrolled variables such as climate variations, and (4) adaptively optimizes management decisions to minimize the costs and environmental impacts of crop production. Moreover, the proposed method is robust to problem-specific complexities and is easily reproducible. We illustrate the framework on a real-world case study in Ventura County, California.