Relating agriculture, energy, and water decisions to farm incomes and climate projections using two freeware programs, FEWCalc and DSSAT
Graphical abstract
Introduction
Small towns and rural (STAR) agricultural communities produce much of the food for an increasingly urban world. Yet they face serious problems such as declining populations, increasing challenges resulting from disadvantageous changes in farm economic conditions, and exacerbating climatic conditions. Many STAR communities in the USA have been diversifying their economies over the past 50 years in efforts to sustain their viability (Bureau of Economic Analysis, 2020). Increasingly, they are taking advantage of their wide open, low density areas to diversify into renewable energy production. Yet the expertise needed to consider such alternatives is largely unavailable to many stakeholders.
FEWCalc (Food-Energy-Water Calculator) makes expertise accessible to local stakeholders whose decisions will lead their communities into more viable futures by enabling clearer understanding of tradeoffs and possibilities. This introduction briefly reviews other attempts to create similar models and the systems included in FEWCalc, including climate change, water resource degradation and depletion, renewable energy opportunities, and public policy priorities. It then briefly outlines how FEWCalc fits into two broad approaches to research on food, energy, and water system decision-support capabilities.
The linkage of the FEW system has been studied and conducted mostly at the academic level using different approaches and aspects (Endo et al., 2017). For example, some FEW studies previously focused on land use optimization (Nie et al., 2019), nutrient flow (Yao et al., 2018), environmental security for livelihood (Biggs et al., 2015), food-energy tradeoff (Cuberos Balda and Kawajiri, 2020), and water-energy-food production and consumption (Guijun et al., 2017) using distinct analytical tools such as MATLAB Simulink, crop models, and agent-based models. Most previous works have not connected all three FEW components together with other variable factors (e.g., climate projection and economics). Some of the more developed efforts at simulating all or part of food-energy-water systems are CLEWS (Climate, Land, Energy, Water and Soil) (IAEA, 2009; Villamayor-Tomas et al., 2015; Welsch et al., 2014), WEAP (Water and Energy Assessment Program) (Stockholm Environment Institute, 2021), and ITEEM (Li et al., 2021). FEWCalc represents a broader set of options than these alternatives and is open-source freeware, readily available on GitHub to serve as a foundation for future development.
Climate change is apparent through surface rising temperatures and historically extreme weather conditions that are becoming more frequent (Campbell, 2020; Lesk et al., 2016). Climate-change driven increases in water and food insecurity pose emerging and long-term challenges. Increasing temperatures are already increasing crop water requirements and shifting precipitation patterns and may directly affect global food supply quantity and quality going forward (Dore, 2005; Li et al., 2019; Wheeler and von Braun, 2013; Zhang et al., 2019). Moreover, shifting regulations and restrictions on carbon emissions may alter the menu of available adaptation options. FEWCalc enables users to evaluate the impact on agricultural production of climate change by choosing future General Circulation Model (GCM) projections and other future climate scenarios.
Water scarcity is an immediate and enduring challenge in many regions, which can in part be addressed with groundwater reserves. Irrigated areas currently produce 30–40% of the world's food, and 70% of global water withdrawals are for agriculture (FAO, 2014; Kovda, 1977; WWAP, 2012). Farmers and policy makers in some regions are recognizing the need to collaborate to extend the usable lifetime of their local water resources by reducing irrigation rates (Hardin, 1968; Kansas Department of Agriculture, 2021; California Water Boards, 2020). Groundwater is important: for example, in China's dry northern region, groundwater accounts for as much as 70% of irrigation in some locations (Calow et al., 2009). In India, it accounts for 70–80% of the value of irrigated production and supports 90 million rural households (World Bank, 1998; Zaveri et al., 2016). Groundwater from the Central Valley aquifer of California and the High Plains aquifer (HPA) supply as much as 16% and 30% of irrigation water in the entire USA (Dieter et al., 2018; Maupin, 2018; Maupin and Barber, 2005). FEWCalc includes irrigation derived from groundwater and the generally hidden and delayed effect of declining groundwater on agricultural production.
Producing wind and solar energy could contribute to the diversification and viability of STAR communities' economy in three principal ways. (1) Renewable energy exported to existing load centers has been profitable for farmers participating in land-lease programs with power producers (Weise, 2020). (2) FEWCalc is designed to investigate how the direct investment by rural landowners in renewable energy production changes their economic situation (Epley, 2016; Hill et al., 2017; Phetheet et al., 2019). Although in the area used to demonstrate FEWCalc wind turbines tend to be more profitable than solar panels (Fu et al., 2017), both technologies are included in FEWCalc to generalize its utility. (3) More affordable local renewable energy could be used to attract and retain businesses to create and grow jobs (Hill et al., 2019). FEWCalc addresses option 2 and provides a foundation for option 3.
Effective policies supporting current and evolving local, regional, national initiatives in the food, energy, and water nexus are imperative to ensure the sustainable viability of STAR communities. These will be influenced by institutional, economic and socio-cultural attitudes, and subjective perceptions (Cash et al., 2006). Farm income, as a major income in STAR communities, can be affected by these policies. To this end, FEWCalc simulates the effects of crop insurance and selected renewable energy incentive programs on farm incomes.
As a tool focused on how decision-makers perceive the viability of their communities or businesses, FEWCalc bridges the gap between two dominant research themes — Integrated Assessment (IA), and Impacts, Adaptation, and Vulnerability (IAV) (Table 1). The themes have been converging as the value of integrated, multi-scale approaches to climate research has become apparent (Absar and Preston, 2015; de Bremond et al., 2014; Huber et al., 2014; Kraucunas et al., 2015; Rosenzweig et al., 2014). The standardized, multi-scale Shared Socioeconomic Pathways (SSPs) scenario framework (O'Neill et al., 2014) relates economic and technological choices to carbon emissions, and is thus closely related to Representative Concentration Pathways (RCPs) levels used in FEWCalc. FEWCalc supports carbon emission mitigation through developing greater local familiarity with renewable energy production and greater research-level familiarity with the challenges of local stakeholders. Fig. 1 shows how the major components of the FEW system form a natural and human system of concern to IA and IAV, showing how they can be thought of as a collection of heterogeneous and autonomous individuals interacting cooperatively and competitively with one another and the environment (Bert et al., 2015; Hu et al., 2018).
Unresolved scale and human connection issues still limit the utility and relevance of IA and IAV models (Ericksen, 2008; Ericksen et al., 2009; Vervoort et al., 2014). For example, national policies could be rendered ineffective for want of local-level adaptation and mitigation options, and local-level efforts could be stymied by national policy or global market conditions. Climate, weather, hydrology, politics, energy, and economics are all important and interact across multiple societal scales, including jurisdictional, institutional, and managerial ones (Cash et al., 2006; Allan et al., 2015; Endo et al., 2017), so that FEWCalc exists within the context of national- and global-scale dynamics (Ericksen et al., 2009). Proper support and coordinated action are required for successful outcomes such as those achieved by Sustainable Groundwater Management Act (SGMA) in California and the LEMAs in Kansas. The FEWCalc model can be thought of as addressing three key needs identified by Vervoort et al. (2014): (1) engage diverse stakeholders across multiple levels; (2) move beyond analysis of single interventions toward system-wide measures that act across multiple spatial, temporal, and geographic scales; and (3) develop long-term capacity for collaborative decision making.
FEWCalc is an agent-based model (ABM) constructed using NetLogo (Hu et al., 2018; Tisue and Wilensky, 2004; Wilensky, 1999), designed to integrate complex real-world systems and evaluate future policy decisions (Anderson and Dragićević, 2018; Guijun et al., 2017). ABMs have been used in business (Forrester, 1971; Morecroft, 2015), urban problems (Sterman, 2000), and environmental evaluations (Meadows, 2008) and recently for the FEW nexus (Al-Saidi and Elagib, 2017; Memarzadeh et al., 2019; Schulterbrandt Gragg et al., 2018). Most of this recent research has been conceptual or focused on regional applications. Focus on individual stakeholders is rare (Ravar et al., 2020; Shannak et al., 2018) and mostly limited to urban systems (Bieber et al., 2018; Guijun et al., 2017). FEWCalc is novel and contributes to the emerging ABM literature using the NetLogo platform.
The purpose of this study is to develop a scientific tool able to represent a real-world complex system composed of agriculture, energy production, and water use under complicated climate and economic conditions, and use it to reveal unexpected interactions within this system of systems that are important to stakeholders. The rest of this article, along with online appendices A-D, describes the methods and data using in FEWCalc and its utility in a scientific investigation of the roles played by water scarcity and climate change in the productivity and economics future of a typical STAR community.
Section snippets
Methods
In this section, the FEWCalc workflow is briefly introduced, and FEWCalc components and related equations are described using a Finney County, Kansas test case to provide motivation and examples. The Decision Support System for Agrotechnology Transfer (DSSAT) model (Araya et al., 2019; Jones et al., 2003; Jones et al., 2017a, Jones et al., 2017b; Sharda et al., 2019) was chosen for the agrosystems simulations based on its capabilities, availability, and feasibility. Selected DSSAT and FEWCalc
Results
For the results presented here, the input values are those shown in Fig. D.5, except that the future process is modified for Scenarios 2 through 4. The solar panels occupy about 5.2 acres (2.1 ha), and a similar area is occupied by the wind turbines (Denholm et al., 2009).
Results comparing the DSSAT simulation with historical results are presented in Section 3.1. The four subsequent sections show results from the four climate scenarios listed in Table 2 and support an analysis of climate
Discussion
FEWCalc is designed to produce the same net income for all scenarios in the base period. Income differences determined by scenario conditions and parameters begin after the base period (Fig. 6a).
In Scenario 1, simulated crop yields for corn and sorghum decline during dry periods (Fig. 4). However, wheat yield remains stable for most simulation years. Wheat and grain sorghum are rarely profitable, and corn is the most profitable crop under the Repeat Historical scenario (Fig. 5a). Repeated
Conclusions
This work shows how FEWCalc can provide scientific, engineering, and economic analyses required by stakeholders and policy makers using data from the semi-arid region around Garden City, Kansas. Here we discuss the two points about FEWCalc and provide some final comments.
Declaration of Competing Interest
None.
Acknowledgements
With gratitude, we note that this work was funded by the University of Kansas, Department of Geology startup [account 05192015] and the National Science Foundation [grant number 1856084] FEWtures project. Mr. Jirapat Phetheet was funded by the Royal Thai Government Scholarship, Thailand.
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