Consequences of climate change on food-energy-water systems in arid regions without agricultural adaptation, analyzed using FEWCalc and DSSAT

Effects of a changing climate on agricultural system productivity are poorly understood, and likely to be met with as yet undefined agricultural adaptations by farmers and associated business and governmental entities. The continued vitality of agricultural systems depends on economic conditions that support farmers’ livelihoods. Exploring the long-term effects of adaptations requires modeling agricultural and economic conditions to engage stakeholders upon whom the burden of any adaptation will rest. Here, we use a new freeware model FEWCalc (Food-Energy-Water Calculator) to project farm incomes based on climate, crop selection, irrigation practices, water availability, and economic adaptation of adding renewable energy production. Thus, FEWCalc addresses United Nations Global Sustainability Goals No Hunger and Affordable and Clean Energy. Here, future climate scenario impacts on crop production and farm incomes are simulated when current agricultural practices continue so that no agricultural adaptations are enabled. The model Decision Support System for Agrotechnology Transfer (DSSAT) with added arid-region dynamics is used to simulate agricultural dynamics. Demonstrations at a site in the midwest USA with 2008–2017 historical data and two 2018–2098 RCP climate scenarios provide an initial quantification of increased agricultural challenges under climate change, such as reduced crop yields and increased financial losses. Results show how this finding is largely driven by increasing temperatures and changed distribution of precipitation throughout the year. Without effective technological advances and operational and policy changes, the simulations show how rural areas could increasingly depend economically on local renewable energy, while agricultural production from arid regions declines by 50% or more

Relating agriculture, energy, and water decisions to farm incomes and climate projections using two freeware programs, FEWCalc and DSSAT

Context: The larger scale perspective of Integrated Assessment (IA) and smaller scale perspective of Impacts, Adaptation, and Vulnerability (IAV) need to be bridged to design long-term solutions to agricultural problems that threaten agricultural production, rural economic viability, and global food supplies. FEWCalc (Food-Energy-Water Calculator) is a new freeware, agent-based model with the novel ability to project farm incomes based on crop selection, irrigation practices, groundwater availability, renewable energy investment, and historical and projected environmental conditions. FEWCalc is used to analyze the interrelated food, energy, water, and climate systems of Finney County, Kansas to evaluate consequences of choices currently available to farmers and resource managers. Objective: This article aims to evaluate local farmer choices of crops and renewable energy investment in the face of water resource limitations and global climate change. Metrics of the analysis include agricultural and renewable-energy production, farm income, and water availability and quality. The intended audience includes farmers, resource managers, and scientists focusing on food, energy, and water systems. Methods: Data derived from publicly available sources are used to support user-specified FEWCalc input values. DSSAT (Decision Support System for Agrotechnology Transfer) with added arid-region dynamics is used to obtain simulated crop production and irrigation water demand for FEWCalc. Here, FEWCalc is used to simulate agricultural and energy production and farm income based on continuation of recent ranges of crop prices, farm expenses, and crop insurance; continuation of recent renewable-energy economics and government incentives; one of four climate scenarios, including General Circulation Model projections for Representative Concentration Pathway 8.5; and groundwater-supported irrigation and its limitations. Results and Conclusions: A 50-year (2018-2067) climate and groundwater availability projection process indicates possible trends of future crop yield, water utility, and farm income. The simulation during more wet years produces high crop production and slower depletion of groundwater, as expected. However, surprisingly, the simulations suggest that only the Drier Future scenario is commercially profitable, and this is because of reduced expenses for dryland farming. Although simulated income losses due to low crop production are ameliorated by the energy sector income and crop insurance, the simulation under climate change still produces the worst annual total income. Significance: FEWCalc addresses scientific, communication, and educational gaps between global- and local-scale FEW research communities and local stakeholders, affected by food, energy, water systems and their interactions by relating near-term choices to near- and long-term consequences. This analysis is needed to craft a more advantageous future

Simulating and Analyzing Use of Water and Renewable Energy in Agricultural Areas Using FEWCalc and DSSAT

As much of farmland in the middle USA is located in arid and semi-arid regions, agricultural practices depend primarily on irrigation. Widely prevalent large-scale groundwater extraction for agriculture began in the 1950s with the introduction of center pivot irrigation, each of which requires about 800 gallons/min, or 4,360 m3/day. Groundwater supported irrigation was dependable for decades, but now many areas of the High Plains aquifer is at risk for over-extraction, and farming is facing difficult circumstances. On the positive side, Kansas recently became the fifth leading producer of wind energy, with solar power production growing in recent years. However, opportunities to use this locally produced energy to improve prospects for the farming community face scientific and engineering challenges and, communities are not aware of many potentially promising alternatives. Food-Energy-Water Calculator (FEWCalc) is a freeware interactive computer program designed to inform farmers about economic and water resource consequences of land-use alternatives in the face of climate variability and long-term change. FEWCalc integrates the agricultural model, Decision Support System for Agrotechnology Transfer (DSSAT), and Agent-Based Modeling (ABM), and is novel in its attention to farm economy, groundwater quantity and surface water quality, and its ability to realistically account for arid climates. FEWCalc is demonstrated and tested using data from Garden City, Kansas, USA. It allows users to define model parameters such as the acreage planted in four crops (corn, wheat, soybeans, and grain sorghum); the number of solar panels and wind turbines, and their financial variabilities; and one of four 50-year projected scenarios. FEWCalc results show high variability of net farm income due to price uncertainty and weather conditions. FEWCalc outputs also present how water supplies threaten farm incomes and indicate that renewable energy development has the potential to support farm systems and provides economic opportunities to balance farming difficulties. Results from Scenario 1 (Repeat Historical) are repeated based on conditions from a 10-year base period (2008-2017) in sequence. For Scenario 2 (Wetter Future), FEWCalc can maintain irrigation operations for the entire 60-year simulation. Scenario 3 (Drier Future) resorts to dryland farming more quickly than other scenarios, but it produces the highest average annual net income. Scenario 4 (Climate Change) indicates increased challenges such as reduced crop yields and increased financial losses under climate change. This finding addresses the challenges of the future and provides a tool for research and education. The existing human interaction capabilities of FEWCalc would be improved by adding human decision-making characteristics such as avoidance of risk, maximizing profit, and evolution of policies and governmental institutions

Harvesting Sunshine: A Modular Integrated Framework for Modeling the Agrivoltaic System

Understanding the interplay between energy and water is central to effective natural resource management. With enough energy it is possible to secure sufficient water, with enough water it is possible to produce nearly boundless energy. The sun is the engine which drives the availability of both these resources; nearly every source of energy on earth can ultimately be tracked back to solar energy and solar radiation dictates climatic cycles, distributing water around the globe. Agriculture is the endeavor which most embodies these interactions. Effective natural resource decision making requires multi-scale models which consider the synergies and trade-offs within this Food – Energy – Water (FEW) nexus. These multi-scale models are dependent on research which explores these interactions at scales from the finely detailed field level up to the global level. This dissertation will focus on one technological approach, agrivoltaics, which sits firmly at the nexus of various natural resources, investigating it at multiple scales. First, context is provided about how the agrivoltaic approach fits into the broader FEW nexus, including a systematic review of the FEW nexus, and various perspectives that researchers use to characterize the nexus (Chapter II). This review finds that the volume of research on the FEW nexus has grown exponentially since 2010, that the research is coming from a wide geographic range, and that the majority of the research is related to the Ecosystems and Institutional Change perspectives. Next, an analysis of agrivoltaics at the national level is conducted, producing estimates of the various impacts of wide-scale agrivoltaic adoption in the United States (Chapter III). This analysis estimates that 20% of the US’ total electricity generation can be met with agrivoltaics if less than 1% of the annual US budget is invested into rural infrastructure and if less than 1% of the nation’s farmlands are converted to agrivoltaics. The analysis also finds that widescale installation of agrivoltaic systems can lead to a carbon dioxide (CO2) emissions reduction equivalent to removing 71 million cars from the road annually and the creation of over 100,000 jobs in rural communities. The dissertation concludes with the introduction of an integrated modular framework (AV-EAS) for field level analysis (Chapter IV). The AV-EAS framework incorporates a solar radiation model, a process-based crop growth simulation model, and an economic model in order to simulate an agrivoltaic field and identify optimal panel configurations for a particular location and particular crop based on multiple objectives. An example use of AV-EAS, modeling dry bean growth in western Oregon, finds that increasing the panel row spacing has a greater impact on crop yield than increasing the clearance height of the panels, that the relationship between panel configurations and yield is dependent upon whether the cropping system is energy-limited or water-limited, and that, with current steel-based racking methods, racking costs must be kept below $0.5/Watt for agrivoltaics to remain profitable. The process of characterizing the FEW nexus and developing multi-scale models is an iterative one. This work explores the agrivoltaic approach at multiple scales, introduces an integrated field-scale framework for simulating these systems, and provides a clear pathway for the NewAg lab at Oregon State to deepen the understanding of the potential role of agrivoltaics within the broader FEW nexus

An Approach for Building Efficient Composable Simulation Models

Models are becoming invaluable instruments for comprehending and resolving the problems originating from the interactions between humans, mainly their social and economic systems, and the environment. These interactions between the three systems, i.e. the socio-economic-natural systems, lead to evolving systems that are infamous for being extremely complex, having potentially conflicting goals, and including a considerable amount of uncertainties over how to characterize and manage them. Because models are inextricably linked to the system they attempt to represent, models geared towards addressing complex systems not only need to be functional in terms of their use and expected result but rather, the modeling process in its entirety needs to be credible, practically feasible, and transparent. In order to realize the full potential of models, the modeling workflow needs to be seen as an integral part of the model itself. Poor modeling practices at any stage of the model-building process, from conceptualization to implementation, can lead to adverse consequences when the model is in operation. This can undermine the role of models as enablers for tackling complex problems and lead to skepticism about their effectiveness. Models need to possess a number of qualities in order to be effective enablers for dealing with complex systems and addressing the issues that are associated with them. These qualities include being constructed in a way that supports model reuse and interoperability, having the ability to integrate data, scales, and algorithms across multiple disciplines, and having the ability to handle high degrees of uncertainty. Building models that fulfill these requirements is not an easy endeavor, as it usually entails performing problem description and requirement analysis tasks, assimilating knowledge from different domains, and choosing and integrating appropriate technique(s), among other tasks that require the utilization of a significant amount of time and resources. This study aims to improve the efficiency and rigor of the model-building process by presenting an artifact that facilitates the development of probabilistic models targeting complex socioeconomic-environmental systems. This goal is accomplished in three stages. The first stage deconstructs models that attempt to address complex systems. We use the Sustainable Development Goals (SDG) as a model problem that includes economic, social, and environmental systems. The SDG models are classified and mapped against the desirable characteristics that need to be present in models addressing such a complex issue. The results of stage one are utilized in the second stage to create an Object-Oriented Bayesian Networks (OOBN) model that attempts to represent the complexity of the relationships between the SDGs, long-term sustainability, and the resilience of nations. The OOBN model development process is guided by existing modeling best practices, and the model utility is demonstrated by applying it to three case studies, each relevant to a different policy analysis context. The final section of this study proposes a Pattern Language (PL) for developing OOBN models. The proposed PL consolidates cross-domain knowledge into a set of patterns with a hierarchical structure, allowing its prospective user to develop complex models. Stage three, in addition to the OOBN PL, presents a comprehensive PL validation framework that is used to validate the proposed PL. Finally, the OOBN PL is used to rebuild and address the limitations of the OOBN model presented in stage two. The proposed OOBN PL resulted in a more fit-for-purpose OOBN model, indicating the adequacy and usefulness of such an artifact for enabling modelers to build more effective models