SG = Grain Sorghum

FEWture Farms

FEWture Farms is an interactive tool for farmers, students, and others. It is designed to provide insight into the productivity and economics of a farm with possible renewable energy generation capacity. FEWture Farms integrates agriculture, water, energy, and economic components. It calculates farm income, and provides a visualization of the system and graphs of results. Ammonia production locally on the farm is evaluated and relevant information is provided in a linked Excel file.

Hopefully, FEWtures Farms will help stakeholders in the agricultural, energy, and water communities identify economic opportunities for Small Town and Rural (STAR) agricultural communities through local renewable energy resources. This will hopefully encourage a resilient Food, Energy, and Water future. As an educational tool, FEWture Farms allows students and teachers to peek into the systems that put water in their glasses, food on their table, and power in their light bulbs.

Introduction

The conditions and practices simulated are for the period 2008 to as late as 2097. The model is developed using data from 2008 to 2017, which is referred to as the “historical” period of time. Results from 2018 to 2097 are estimated projections that are likely to correctly identify trends and unlikely to be precisely accurate. The projections can be thought of as accurately identifying future conditions that will need to be faced by farmers.

FEWture Farms can be used to address a question like

An alternatively, the question might be posed as follows.



The simulation does not account for inflation. Prices need to be considered in the same way we consider the prices in games such as Monopoly. Some of the proportions are likely to remain similar and the simulations can provide some good experiences that inform the user about system dynamics despite not being predictive in a specific sense.

These rest of these notes are organized with the following headings. Users can click the items in this list to go to the related section.

1. Notes for Users
2. More about the Test Case Simulated
3. FEWture Farms – Some Basics
4. Data
5. Model Function
6. Selected Topics for Running FEWture Farms
7. Model version
8. References

1. Notes for Users

This section briefly notes some ways FEWture Farms could be useful to different audiences. FEWture Farms was developed by a group of people with expertise in each of the component fields – food, energy, water, economics, and ammonia (a main component of the fertilizer mix) -- and can thus be used by people with expertise in a few or even none of these fields to learn something about these multicomponent systems.

FEWtures Farms is best thought of as a tool to introduce users to novel approaches. It is not a tool to plan a proposed endeavor in detail. Users are encouraged to seek advice from specialists before making major decisions about ways forward.

FEWture Farms is presented using a test case based on data from 2008 to 2017 from Garden City, Kansas, USA. Crop prices and expenses were such that in many cases farming was a difficult endeavor in which to make money. With FEWture Farms, users can run scenarios to identify circumstances in which farmers can earn money from agricultural and energy production in the face of projected (to as late as 2097) water resource limitations, anticipated (as of 2021) changes in climate, and world agro-economic conditions.

1a. Farmers

The representation of farming concerns in FEWture Farms will likely seem too simple for most farmers. Hopefully there is enough realism in the agricultural component that the interplay with the water, energy, and economic components provide some insight into how future water constraints and energy opportunities might play a role in the future of a given farm or cooperative farm venture.

1b. Teachers and Students

FEWture Farms can be the basis of class exercises to help students understand the dynamics behind food on the table, water emerging from a faucet into a sink or bathtub, and electricity that seems to magically appear at any time of the day or night when a light switch is turned on. It can provide logistical and economic understanding of the energy, water, and agricultural infrastructure on the landscape to open the world up to students and, sometimes, their teachers.

1c. Everyone

Food, Energy, water (FEW) systems are what we and our society depend on for our existence. Better understanding of these systems makes people better citizens and neighbors.

2. More About the Test Case Simulated

It would be wonderful someday to have a tool like FEWture Farms that could automatically collect data for a user-chosen location and provide site specific results. That is not available in this version of FEWture Farms. For the example provided, one location was chosen, relevant data collected, and a simulation was constructed to create an interactive platform. This example forms a foundation for a program applicable to user chosen locations in that it suggests what kinds of data would need to be readily available for such applications, and provides program structure and some programming specifics likely to be useful in such a development.

The test case chosen includes a number of circumstances of broad interest. These include diminishing water supply, local renewable energy opportunities, and an agricultural system that dominates world markets for some crops and does not for other crops. The test case includes one example of government support -- crop insurance that is active when crop productivity drops off due to loss of irrigation water supply.

The test case represents the area around Garden City in Finney County, Kansas, USA. It is in the southern High Plains aquifer (HPA), where groundwater levels decreased dramatically between the 1960s and the 2020s, due mostly to very large-scale water pumping to irrigate crops. The water level declines have meant that in many areas irrigation has become impractical and only dryland farming is now possible (see first figure below). As shown in the figure, more land is expected to convert to dryland farming going forward. Farmers are diversifying their income sources as groundwater supplies are depleted and agricultural production is negatively impacted.

Fortunately, Kansas is well positioned in the nation’s wind belt and local farmers have access to a robust renewable energy source (Anderson et al., 2012). Economically, Kansas is the second leading state in wind energy production, with almost 50% of the electricity sold in the state being met by wind in 2022 (https://windexchange.energy.gov/states/ks).

The data used for the text case is documented extensively in Phetheet (2021a, Appendices A, B, C, and D). The figures from this section and related updated figures are available from the Kansas Geological Survey (KGS) and National Renewable Energy Laboratory (NREL).

3. FEWture Farms – Some Basics

FEWture Farms is an extension of FEWCalc, the Food-Energy-Water Calculator assembled by Jirapat (Mos) Phetheet, a Master’s student who worked with Professor Mary C. Hill in the Department of Geology, University of Kansas. FEWCalc is documented and demonstrated in Phetheet et al. (2021a,b).

3a. TechnoEconomic Structure

The FEWture Farms calculations are divided into two parts.

• The first part consists of crop calculations using a crop model called Decision Support System for Agrotechnology Transfer (DSSAT) (Jones et al., 2003).
• The other is conducted using NetLogo agent-based modeling software by Uri Wilensky, 1999. This includes the energy, nitrate, groundwater level, and economic calculations, using the crop production and water and fertilizer use calculated using DSSAT.

3b. Repeat a Simulation

If the Export button is clicked, the data for the current simulation is saved in a file called “netlogo-world-file.csv”, called the World File in this document. This file can be used to repeat that simulation in either Netlogo web or the Netlogo desktop application by first loading the model file and then from the file dropdown menu selecting import > import world and selecting the downloaded World File.

The same filename is used for each save. To avoid overwriting files for each simulation, rename the files as you save them.

3c. Simulation of Changing Conditions Over Time

If things change going forward (as they surely will) FEWture Farms can be used to represent known or hypothesized changes as follows. To investigate effects of postulated changes, users can run one year at a time using the button Go Once at the top near the Go button. User-defined values can be changed after some number of years and the user can click the Go Once button to continue year by year. Alternatively, at any point the Go button can be clicked to proceed to the end of the defined time period. The export button preserves settings as they are at the end of a simulation. The World File will not include previous changes. Thus, FEWture Farms model runs with changing year-to-year conditions cannot be repeated using the method as defined here.

4. Data

The data used in this program is hard coded, user controlled, or used to convert units. These data types are described in the following sections. For additional information, see Phetheet (2021a,b)

4a. User Controlled Input Data

FEWture Farms allows users to specify parameters for their own simulation in the NetLogo interface. It is designed to let users control those numbers easily by using boxes, sliders, and dropdown menus. These are located on the left of the model page on the FEWture Farms web site. The parameter names are mostly self-explanatory. Additional information is described in the list below.

• Simulation_period: A period of simulation, in number of years. Up to 90 years is supported. In any simulation, the first 10 years use historical data (2008-2017), subsequent years apply Future Process (see below). In the graphs, the year on the horizontal axis represents a sequential year. Year 1 is 2008, year 60 is 2067 (the default simulation), and year 90 is 2097 (the latest possible end year). No adjustments for inflation are made over the simulation period.
• Agriculture
  • Corn-area, Wheat-area, Soybean-area and Milo-area: Total area of corn, wheat, soybeans and milo (grain sorghum), in acres.
• Energy
  • Energy_value: Energy buyback rate.
  • For solar energy
    • #Panel_sets: Number of solar panel set (one set is 1000 panels).
    • Capacity_S: Installed PV capacity, for each panel.
    • Nyear_S: Solar panel lifespan.
    • Degrade_S: Annual degradation rate.
    • Cost_S: Solar panel capital costs.
    • ITC_S: Investment Tax Credit for solar.
    • PTC_S: Production Tax Credit for solar.
  • For Wind energy
    • #Wind_turbines: Number of wind turbines.
    • Capacity_W: Installed wind capacity for each turbine.
    • Nyear_W: Wind turbine lifespan.
    • Degrade_W: Annual degradation rate.
    • Cost_W: Wind turbine capital costs.
    • ITC_W: Investment Tax Credit for wind.
    • PTC_W: Production Tax Credit for wind.
• Water
• Aquifer_thickness: Saturated thickness of the aquifer.
• Min_Aq_Thickness: Minimum available aquifer thickness.
• Future_Process: A drop-down menu of future process. Future process will be activated automatically after year 10 using historical data from 2008 to 2017 and GCM data (2018-2098).
  • Repeat Historical: Ten-year DSSAT results are repeated consecutively.
  • Wetter Years: A future that is wetter than historical period.
  • Dryer Years: A future that is drier than historical period.
  • Impose P, T, & S: A future involved climate change model (GCM) results.
    • Climate_Model: RCP4.5 or RCP8.5 (4.5 assumes a lower carbon, less hot future than does 8.5)

4b. “Hard Coded” Input data

Some input data in this model is “hard-coded,” meaning that the user cannot change these input values. These values correspond to eight input files in comma-separated values (.csv) format available for reference on the FEWture_Farms Github repository in the “netlogo” folder. The desktop app, also available on the FEWture_Farms Github repository, can run these and user-created custom input files. Details can be found in the model notes associated with that file. The adapted model file, which is compatible with Netlogo Web, is available in that repository within the “NetlogoWeb” folder.

Some hard-coded Input values (e.g., projected precipitation and crop prices for the Future_Process options Repeat Historical, Dryer and Wetter) were taken from historical data between 2008 and 2017. Others were calculated from DSSAT (e.g., yield and irrigation) using the same dataset (numbered 1-4 as shown below) and global climate models (GCMs) (5-8 below). The input files relate to the four major crop types in Kansas: corn, wheat, soybean, and milo (grain sorghum).

• 1_Corn_inputs.csv , 2_Wheat_inputs.csv , 3_Soybean_inputs.csv , 4_Milo_inputs.csv
• 5_Corn_GCMs.csv , 6_Wheat_GCMs.csv , 7_Soybean_GCMs.csv , 8_Milo_GCMs.csv

The file columns headers are not well-defined. Here is a detailed explanation of those values.

• Year: simulation year. (0, 1, 2, …)
• Precip (in): historical precipitation, in inches.
• Price ($/bu): historical crop price for 2008-2017, in US$/bushel (current dollars).
• Yield_1 (bu/ac): simulated yield from irrigated farming using historical data.
• Irrig_1 (in): simulated irrigation from irrigated farming using historical data.
• Yield_2 (bu/ac): simulated yield from dryland farming using historical data.
• Irrig_2 (in): simulated irrigation from dryland farming using historical data. Values = zero.
• N-app (kg/ha): Nitrogen fertilizer rate (kilograms per hectare; units from DSSAT).

The next columns list precipitation, crop yield and irrigation for RCP8.5 and 4.5.

• RCP8.5
  • Precip8.5 (in): Precipitation projection average for 20 GCM models
  • The following are averaged over DSSAT simulations of 20 GCM model precipitation, temperature and solar radiation projections.
    • Yield_3 (bu/ac): simulated yield from irrigated farming.
    • Irrig_3 (in): simulated irrigation from irrigated farming.
    • Yield_4 (bu/ac): simulated yield from dryland farming.
    • Irrig_4 (in): simulated irrigation from dryland farming.
• RCP4.5
  • Precip4.5 (in): Precipitation projection under PRPCP4.5
  • The following are averaged over DSSAT simulations of 20 GCM model precipitation, temperature and solar radiation projections.
    • Yield_5 (bu/ac): simulated yield from irrigated farming.
    • Irrig_5 (in): simulated irrigation from irrigated farming.
    • Yield_6 (bu/ac): simulated yield from dryland farming.
    • Irrig_6 (in): simulated irrigation from dryland farming.
  The units used are as follows.
  • Unit explanation: in is inch, $ is US dollar (Not adjusted for inflation), bu is bushel, and ac is acre.
  
  

  4c. Unit Conversions

  • 1 bushel corn or milo per acre = 62.77 kilograms per hectare
  • 1 bushel wheat or soybean per acre = 67.25 kilograms per hectare
  • 1 acre = 0.4047 hectares

  5. Model function

  FEWture Farms calculations are conducted for agriculture, energy, water, and economics. A note about ammonia, an important part of fertilizer and a significant expense to farmers, is also included.

  5a. Agriculture

  Crop production for four crops used in FEWture Farms are simulated by the DSSAT model. Crop production values from DSSAT are based on several types of data, including weather data. The weather data are temperature, precipitation, and solar irradiance.
  
  The historical period of the FEWture Farms simulation (from 2008 to 2017) uses measured weathered data from those 10 years. The simulated crop production for those years was compared to measured values as reported in Phetheet et al (2021a).
  
  The simulated projections start in 2018 and continue for up to 90 years, as defined by the user. For these years, the weather data needed by DSSAT is obtained from repeating the 10years of measured data results to create a future that is an exact repetition of 10 measured years, consists of more wet or dry years than the past 10 measured years, or uses statistically downscaled Global Climate Models (GCMs).
  
  GCMs can be run under different Representative Concentration Pathways (RCPs), which differ in assumptions about how society evolves going forward. The two options included in FEWture Farms are RCP4.5 and RCP8.5. RCP8.5 is consistent with societal evolution that continues to depend on fossil fuels as a dominant energy source. RCP4.5 is consistent with societal evolution that depends on fossil fuels less. As of this writing in 2025, global temperature increases have been more consistent with RCP8.5 that RCP 4.5.
  
  Users select one of the future processes under Climate Scenario section. There are 4 options: (1) Repeat Historical, (2) Wetter Years, (3) Dryer Years, and (4) Impose T, P, & S Changes. Choosing Climate Projection enables the user to used the GCM projections for RCP4.5 or RCP8.5. For the Repeat Historical option, the same 10-year sequence is repeated unless groundwater levels decline below the user-defined minimum. At that point, dryland farming is simulated. Dryland farming means that the crop yield will tent to be less, but costs of irrigation decline. Economic costs are discussed briefly below.
  
  

  Irrigated Farming

  FEWture Farms assumes that water for irrigation is all from groundwater. The model simulates irrigated farmland if the water is available and the aquifer thickness is not less than a minimum aquifer thickness defined by users.
  
  

  Dryland Farming

  During the simulation, groundwater is consumed to supply water through the system. When the aquifer thickness is below a minimum aquifer thickness, the model stops irrigating and then applies dryland farming in the system. During dryland farming, the groundwater level rises due to the recharge rate and irrigated farming may resume once the water level has recovered enough.
  
  

  5b. Energy

  The FEWture Farms interface allows users to define the length of time that installation cost is spread over. The default is 30 years. Users can also define the number of solar panels and wind turbines in the interface under the Energy section. The default wind turbine power is set at 2 megawatts and can be adjusted by the user.
  
  

  Equations:

  The variable names introduced in the section Input Data -- User Controlled are used where applicable. Otherwise, short phases are used to describe the quantity involved.
  • Solar production (MWh) = #solar panels * Capacity_S * average peak sun hours * Degrade_S * 365 days/yr
  • Wind production (MWh) = #wind turbine * Capacity_W * capacity factor * Degrade_W * 8,760 hrs/yr
  • Solar cost ($) = #solar panels * Capacity_S / 1000 * Cost_S / Nyear_S * (1 - ITC_S / 100)
  • Wind cost ($) = #wind turbines * [(Capacity_W * Cost_W / Nyear_W) + O&M costs] * (1 - ITC_W / 100)
  • Solar sell ($) = solar production * Energy_value
  • Wind sell ($) = wind production * Energy_value
  Default values are in Appendix C of Phetheet et al (2021a). For more information see Phetheet (2021a,b)
  
  

  5c. Water

  All irrigation water is derived from groundwater in the simulation. Fertilizer is applied to crops; part of the nitrate is used by plants and some tends to be retained in the soil and eventually washes away into streams during wet periods. Literature values are used estimated the resulting nitrate load to streams. Nitrate in the water system is important due to potential adverse health consequences.
  
  

  Surface Water

  Nitrogen Concentration contributions to Surface Water are estimated based on literature estimates. About 10% of applied nitrogen fertilizer remains in the soil during dry and moderate years until it is moved to surface-water bodies in wet years. See Phetheet et al. (2021a, App. C) for additional information.
  
  

  Equations:

  N_field = 10% × N_applied × N_acres / 1.12 -> Accumulated until moved N_stream = ∑time (N_field) -> Moved in wet or extremely wet years
  
  

  Groundwater

  Water-level changes annually have been found to be closely correlated to annual use. Whittemore et al (2016) computed linear regression equations for correlation of mean annual water-level changes with reported water use during 1996-2012. They also evaluated the predicted response of the HPA and concluded that (1) water pumped for irrigation is the major driver of water-level changes and (2) a pumping reduction of 22% would stabilize the water level, and this could help extend the usable lifetime of the aquifer.
  
  FEWture Farms employs a statistical method to calculate water-level change given values of water use for agriculture from DSSAT. A two-step process is used to calculate groundwater-level changes in this work (see section 2.5.2 in Phetheet et al. 2021a). One relates DSSAT calculated annual water use to regional reported annual water use. The second relates regional reported annual water use to annual water level changes. The linear regression equations below were calculated based on historical data from 2008 to 2017 in Finney County, Kansas and the regional area evaluated by Whittemore et al (2016).
  
  

  Equations:

  Step 1: Reported gw use (ft) = [0.114 * DSSAT water use (ft)] + 0.211
  Step 2: Average annual water-level change (ft) = [-32.386 * Reported gw use (ft)] + 8.001
  
  For more information see Phetheet et al (2021a,b)
  
  

  5d. Economics

  The crop prices and expenses simulated are described in Phetheet (2021a). FEWture Farms does not apply an inflation factor. For the net income trends, the underlying assumption is that the inflation rate for the income and expenses is the same. Clearly time will affect the income and expense numbers in ways not presently foreseen. The FEWture Farms program could provide a foundation for a program able to simulate theoretical scenarios of changing income and expense scenarios.
  
  Here, the methods used in FEWture Farms are discussed briefly related to two issues – how crop prices are affected by how dominant this geographic area is in the global pricing of the four crops, and the cost of ammonia used in FEWture Farms relative to historical pricing.
  
  

  Crop Prices and Crop Dominance Globally

  For some crops, like corn and grain sorghum, low crop productivity in the area considered means higher prices for those crops globally. That is, when production is down, price will tend to be high. In this situation, farmers can do quite well financially even in years with low productivity. This is reflected in some simulated results.
  
  For other crops, such as soybeans and wheat, no such dominance exists as of the timeframe of this model construction, so crop prices tend to vary independently of local production. This means that financially these crops can have some very bad years. Again, this is sometimes reflected in some simulated results.
  
  

  Net Present Value

  Net Present Value provides a way of measuring the value of a flow of money over time. Positive values likely suggest something worth doing. Larger positive values are even better.
  
  Net present value is calculated using the following formula:
  NPV = income / (1 + r)^t
  where t is the length of time, in years from present, and r is the discount rate applied. The discount rate is used to determine the present value of a dollar earned in a future year. Users may select higher or lower discount rates, which will decrease or increase the present value of future income.
  
  

  5e. Fertilizer -- Ammonia Costs and an Alternative

  Fertilizer provides nitrogen to plants and makes them grow larger faster. Fertilizer is responsible for about 40% of agricultural production worldwide. The presence of synthetic fertilizer in the food supply can be measured using the nitrogen isotopes in people. On average, about 40% of the nitrogen in people’s bodies are isotopically consistent with synthetic fertilizer. (Bird et al. 2021) Ammonia is a major component of fertilizer used in crop production. The remainder of this section will focus on ammonia because it is a large part of the mix of fertilizers that tend to be used in agriculture.
  
  The subsections below first present some brief historical information about ammonia prices. The price of ammonia in the FEWture Farms scenarios is static, and this section provides a background for how in error that may be. This is followed by a description of how the effect of an alternate fertilizer price on farm income might be estimated on FEWture Farms results. Finally, the finances of a new option of farms producing their own green ammonia locally using local renewable energy sources is presented. The analysis is provided in an Excel file. Use of results from the Excel file are discussed.
  
  

  A Brief History of Ammonia Prices

  The price of ammonia has varied substantially over time in recent years. It has historically nearly all been produced using fossil fuels as represented by natural gas. The price of ammonia historically varied pretty much in step with natural gas costs, but in the last decade or so has sometimes been priced above what might have been expected based on natural gas prices.
  
  For example, in 2022 the price of ammonia ranged from $200/tonne in January and was as high as $1600/tonne in August. Obviously, this was a difficult situation for farmers. As of this writing, in June 2025, the cost of ammonia is about $680/tonne.
  
  This version of FEWture Farms does not calculate the effects of a variable cost for ammonia. The ammonia costs used in FEWture Farms are not based on the fertilizer use calculated by DSSAT. Instead, the cost of fertilizer is set to a general estimate of quantity of fertilizer per acre times cost/tonne for each crop type as provided by Ibendahl et al. (2020) , as discussed by Phetheet et al. (2021a, table A.7). This is multiplied by the number of acres of that crop planted.
  
  

  Accounting for Alternative Costs of Ammonia

  The effect of a different ammonia price can be approximated by adjusted values produced by FEWture Farms, as follows.
  • Calculate New Cost per tonne minus Old Cost per tonne (from Ibendahl).
  • Multiply by the number of tonnes per acre for each crop to get one number for each crop.
  • Multiply each number by the number of acres for the associated crop.
  • If the number is positive, subtract it from the farm net income for agriculture, and the farm total net income.
  • If the number is negative, add it to the farm net income for agriculture and the farm total net income.
  In doing this calculation you will be able to see that the added cost of ammonia can be significant relative to the net income calculated by FEWture Farms. You can see that the high prices for ammonia discussed in the brief price history section can be devastating to farmers and motivate them to change farming practices. For example, they might leave more land unplanted in a year with very high ammonia prices.
  
  

  Considering Local Green Ammonia

  As an alternative to purchasing ammonia for fertilizer, the option now exists for farmers to produce their own ammonia locally. When this technology is powered by renewable energy such as wind or solar, the product is called green ammonia. Green ammonia is functionally the same product but instead of the needed hydrogen coming from natural gas, it comes from water. The amount of water needed is small – generally less than 0.1% of the water needed to irrigate the crop involved. (Pfromm 2025).
  
  An excel file that can be used to calculate green ammonia operating and capital costs, and cashflow over a defined time period was developed as part of the FEWtures project and is available here. Clicking this link allows users to download the Excel file. Users will need to open the file and proceed with the following instructions.
  
  First, you will need Excel on your computer.
  
  Save the downloaded file. Click (or double click depending on your computer settings) on it to start Excel. When the Excel file is opened, click the button to enable macros. As a macro-enabled Excel file, and it may be blocked by security settings on your system. Contact your system administrator if you experience any problems. Testing was conducted using a Windows OS.
  
  When the menu about “Enter Data” pops up, choose the right-hand button labeled “Exit Data Entry”. This will produce a starting point that provides a positive cash flow by year 20. It differs from the starting point produced when the “Enter Data” is chosen in that the discount rate is set to 0.05 instead of 0.15, and the price of ammonia is set to $1100/tonne instead of $1000.
  
  The sheet of the Excel file titled “Discounted cash flow” will be most useful for users. It provides the opportunity to define selected characteristics of a local green ammonia installation (like the assumed price of ammonia and discount rate). This sheet also includes a graph of cumulative discounted cash flow over a defined period of time. A larger version of the graph is provided in the preceding sheet, entitled “Output Graph”.
  
  Having the ability to produce ammonia means the farm no longer needs to pay for ammonia at market rates. However, it does need to pay for the facility. The discounted case flow exceeding zero means that the avoided payments for ammonia have paid off the equipment at that point in time. The farm will continue needing to pay for maintenance and, eventually, equipment replacement. Overall, the farm has gained ammonia price stability and might make money on the investment.

  6. Selected Topics for Running FEWture Farms

  Here, two issues are addressed: the steps for starting a simulation and how to export input and output from a model run.

  6a. Start the simulation

  1. Set model options (see “Initialize parameters”)
  2. Click Setup
  3. Click Go to run the entire simulation or click Go once to advance the simulation one time step.

  6b. Export Data

  The “Export data” button below the model illustration allows users to export both model inputs and result graphs.
  
  The model inputs are downloaded in a series of .csv files, which can be used to repeat the simulation in Netlogo web or on the desktop version.
  
  The results specific to each graph will be downloaded as separate csv files. The first 30 or so lines of these files can be ignored. The remaining lines contain x and y values corresponding to the graphical outputs in the web interface (x being years, starting at year 0 of the simulation so that the sequence goes to 59 for a 60 year simulation) along with color values from the graph and a “pen down” variable that denotes whether that point appears on the graph. The names of these files are described in the following section.
  
  

  6c. Filenames for Result Graphs

  Eight exported files correspond to the eight plots produced in the model interface. This allows results from each saved simulation to be easily identified. The filenames are as follows, in alphabetical order. To retain files from a given run, users are encouraged to copy files into a directory named for and dedicated to that model run.
  • “ag-net-income.csv”
  • “crop-production.csv"
  • "crop-groundwater-irrigation.csv"
  • "farm-energy-production.csv"
  • "total-net-income.csv"
  • "energy-net-income.csv"
  • "groundwater-level.csv"
  • "income-from-crop-insurance.csv"

  7. Model Version

  This version of the FEWture Farms model requires Netlogo version 6.1.0 or higher to run on desktop.

  8. References Cited

  References cited in this text are listed here. These and additional references related to the NSF FEWtures project can be found in a graphical display located on the Publications page.
  
  Anderson, A.C., Gibson, B., White, S.W., & Hagedorn, L. (2012). The Economic Benefits of Kansas Wind Energy. Retrieved from https://www.renewableenergylawinsider.com/wp-content/uploads/sites/165/2012/11/Kansas-Wind-Report.pdf
  
  Bird, M.I., Crabtree, S. A., Haig, J., Ulm, S., Wurster, C.M., 2021. A global carbon and nitrogen isotope perspective on modern and ancient human diet. Proc. Natl. Acad. Sci.118(19):e2024642118. DOI 10.1073/pnas.2024642118.
  
  Ibendahl, G., O’Brien, D., Haag, L., & Holman, J. (2020). Farm Management Guides. https://www.agmanager.info/farm-management-guides-0.
  
  Jones, J.W, Hoogenboom, G., Porter, C., Boote, K., Batchelor, W., Hunt, L., … Ritchie, J. (2003). The DSSAT cropping system model. European Journal of Agronomy, 18(3–4), 235–265. doi:10.1016/S1161-0301(02)00107-7.
  
  Kansas Geological Survey (KGS). (2007). Estimated Usable Lifetime for the High Plains Aquifer in Kansas, available at: http://www.kgs.ku.edu/HighPlains/maps/index.shtml.
  
  National Renewable Energy Laboratory (NREL). (2011). United States – Annual Average Wind Speed at 80 m., available at: https://www.nrel.gov/gis/wind.html.
  
  Phetheet, J., M. C. Hill, R. W. Barron, B. J. Gray, H. Wu, V. Amanor-Boadu, W. Heger, I. Kisekka, B. Golden and M. W. Rossi (2021a). Relating agriculture, energy, and water decisions to farm incomes and climate projections using two freeware programs, FEWCalc and DSSAT. Agricultural Systems 193. doi: 10.1016/j.agsy.2021.103222.
  
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