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Increasing Local Production of Table Food in Iowa to Improve Agricultural Sustainability: A Food-Energy-Water Systems (FEWS) Project Case Study

Tássia M. Brighenti, Tiffanie F. Stone, Phillip W. Gassman, and Janette R. Thompson

In 2021, harvested crop areas accounted for over 24 million acres of land in Iowa. More than 94% of this area was dedicated to production of just two commodity crops: corn and soybeans (USDA-NASS 2022). In spite of Iowa’s productive landscape, the state is a net table food (for direct human consumption) importer, with approximately 95% of table food grown elsewhere (Stone, Thompson, and Rosentrater 2021). As the human population of Iowa becomes more concentrated in urban areas, the opportunities to expand local table food production to meet nutritional, environmental, and social sustainability targets in these places increase. The ongoing Iowa UrbanFEWS project (Thompson et al. 2021) focuses on identifying opportunities to increase table food production in the six-county Des Moines Metropolitan Statistical Area (DMMSA) in Iowa.

US agricultural systems are efficient for crop productivity (Hunt et al. 2021) but also generate close to 25% of total US greenhouse gas emissions (GHGE). The Des Moines River Basin (DMRB), which leads to and surrounds much of the DMMSA, has a high proportion of agricultural land use and contributes to a number of negative environmental impacts in addition to GHGE. For example, excessive exports of nitrogen and phosphorus to the DMRB stream system contribute to both local water quality degradation and exacerbate the seasonal oxygen-depleted hypoxic zone in the northern Gulf of Mexico (Brighenti et al. 2022; Schilling et al. 2019; Gassman et al. 2017). In 2018, corn and soybeans were grown on approximately 70% of DMRB land area (Brighenti et al. 2022).

The USDA Agricultural Census provides important information about land area and crop yields for different crops. However, yield data has not historically been collated by the agency for fruits and vegetables in Iowa due to generally low production levels. The limited yield information collected for the region makes it difficult to develop assumptions for current average yields using publicly-available datasets. Estimating average fruit and vegetable yields at the DMMSA scale for different potential future climatic conditions is even more challenging. One way to generate this data over time and for various crop types is by using simulation models. For example, the spatially oriented Soil and Water Assessment Tool (SWAT) ecohydrological model can incorporate estimates for climate parameters to generate yield and water balance indicators for the future. Water quality, GHGE, and economic viability must all be considered to provide stakeholders and policy makers with information useful to design more sustainable food systems (Raschke et al. 2021). In this context, life cycle analysis (LCA) modeling techniques can be integrated with SWAT (figure 1a) to evaluate the impacts of increasing table food production in urban and peri-urban landscapes.

Figure 1
Figure 1. (a) Methodological flowchart. (b) Study area location, Des Moines River Basin outlet (DMRB outlet), Des Moines Metropolitan Statistical Area (DMMSA), and subbasins where potential future land use changes (LUC) were modeled.
Note: The highlighted (red) subbasins in (b) are the same areas included in subsequent figures.

This study focuses primarily on the DMMSA, the largest urban center in Iowa (~700,000 inhabitants including satellite communities). The total area of the DMMSA is roughly 9,356 km2 (US Census Bureau 2021), in contrast to the considerably larger 31,893 km2 upper portion of the DMRB, which surrounds the primary DMMSA and is required to accurately capture the natural watershed drainage necessary for SWAT simulations (figure 1b). We use a scaled-down input-output (USE-EIO) LCA model (Yang et al. 2017) to interface with the SWAT model to comprehensively measure and evaluate the environmental and economic impacts of increasing table food production in this landscape, combining global warming potential and water quality impacts with the economic impacts of these changes in the metropolitan area. We run the combined LCA-SWAT models for two DSMMSA scenarios: (a) a (current) baseline in which 5% of table food is grown locally (an approximation of contemporary conditions); and, (b) a future condition in which 50% of dietary requirements for the population is grown locally (based on current food consumption patterns and foods that can be grown in Iowa) (see table 1). The table foods in both models are apples, blueberries, broccoli, cabbages, carrots, cherries, collard greens, corn, cucumbers, dry beans, grapes, green peas, iceberg lettuce, kale, melons, onions, pears, potatoes, pumpkins, raspberries, snap beans, soybeans, spinach, squash, strawberries, sweet corn, sweet potatoes, tomatoes, and winter wheat.

Table 1. Description of Current Condition and Future Scenario
DMMSA Food System Current amount of local food production Increased local food production within the DMMSA
LCACurrent conditions: Models 50% of dietary requirements in 2020 with current production (about 5% local and 45% distant) based on consumption patternsFuture scenario: Models 50% of dietary requirements in 2040 with all local production based on current consumption patterns
SWATCurrent conditions: Models land use and yield conditions for 2020Future scenario: Models future land use with 50% local production and future yield conditions for 2040

Study design

We use LCA and SWAT models to generate environmental and economic impacts associated with changes in land use and climate (figure 1a). We estimate current average yields (kg/ha) for the 30 food types for our study area using input from fruit and vegetable extension specialists at Iowa State University. We incorporate land areas associated with production of each fruit and vegetable into the SWAT model for each scenario. We perform the SWAT simulation for current and future climates—the same set of climate models generated both sets of climate data. We obtain future climate projections and historical (current) data through the CORDEX initiative and use a combination of one RCM and two GCMs (RegCM4-MPI-ESM-MR and RegCM4-GFDL-ESM2M). SWAT model simulation outputs include values for streamflow, nutrients, sediment, and crop yields. We then executed the LCA model using the SWAT crop yield outputs for current and potential future conditions to estimate environmental and economic impacts for each scenario. Land use for production of table food increases by about 370 km2, or approximately 4% of the total DMMSA area. Our configuration for land use change includes distribution of table food production areas among several subbasins (outlined in figure 1b).


We present our SWAT simulations at two spatial scales: one for the whole DMRB as measured at the outlet (figure 1b) and one for a set of subbasins for which we project land use change (figure 1b). We also include five output variables: streamflow, sediments, nitrogen (in three forms, NO3, NO2, NH4), phosphorus (mineral), and crop yield. We use the LCA model to simulate the current condition and future scenarios. Both models use the two climate models for simulation. Environmental impacts assessed in the LCA simulation include global warming potential (GWP), energy use, and agricultural land use area. To enable comparisons between the two scenarios, environmental and land use outputs include impacts associated with growing table food to meet a total of 50% of dietary needs.

For the SWAT model at the DMRB outlet scale, outputs indicate no significant change in the water quantity or quality for the two scenarios, which is what we expected since the proportion of land use change for the entire DMRB area is minimal (~1%). Climate projections indicate potential reductions in streamflow, sediments, nitrogen, and phosphorus for the medium (50% of the cumulative distribution function; CDF) and lower (95% of the CDF) segments (figure 2). Trends for the peaks (10% of CDF) are mixed and there is no clear pattern of increase or reduction in the values (figure 2).

Figure 2
Figure 2. Cumulative distribution function for future projections of water quality and quantity variables.
Note: The nitrogen behavior is represented by NH4 (NO2 and NO3 were similar).

At the subbasin scale, changes in model outputs are more noticeable. Here, changes in land use appear to lead to reductions for both total nitrogen and phosphorus. The three subbasins (highlighted in figure 1b) had different proportions of land use change, which their respective water quality outputs reflect (figure 3). Analyses of all 13 headwater subbasins indicates that a minimum of 5.6% land use change is required to result in model output changes.

Figure 3
Figure 3. Three-basin representation for total nitrogen and phosphorus loads that corresponded to different proportions of land use change.
Note: The same three subbasins are shown spatially in figure 1b.

We use two climate models, RegCM4-GFDL and RegCM4-MPI, to simulate current and future yields. We find that RegCM4-GFDL results in higher average yield estimates compared to RegCM4-MPI in our study area. We notice that changes in future precipitation patterns result in increases or decreases in yields for fruits and vegetables based on our SWAT outputs (kg/ha). Among the crops we simulate, 18 show increased yields and three show decreased yields (the other nine crops vary for the two climate models). For example, future simulations show yield reductions for apples and tomatoes, while cucumbers show a significant potential yield increase (figure 4). Variations in crop yields may be a result of the combination of climate models.

Figure 4
Figure 4. Crop yield (kg/ha) trends (increasing or decreasing) when comparing current and future climate projections.

According to LCA model projections, energy use and GWP are reduced on a per person basis for the future scenario (with more local food production) compared to current conditions (figure 5). Energy use reduces by 16% for simulations using the climate projections in RegCM4-GFDL, and 10% using RegCM4-MPI. Similarly, GWP reduces by 16% using RegCM4-GFDL and 9% using RegCM4-MPI. At the scale of the DMMSA, this leads to an average energy reduction of 298 million megajoules and a GWP reduction of 89 million kg CO2 equivalent annually. The area of agricultural land required to grow 50% of dietary requirements for the MSA population increases by about 3% for the future scenario using projections in RegCM4-GFDL and 5% using RegCM4-MPI. Interestingly, agricultural land use decreases on a per-person basis and only land area used for forage and grazing associated with livestock production increases. Assuming production occurs across the United States (current condition), the total area of cropland needed to meet all nutritional needs is 16% greater using climate projections in RegCM4-GFDL and 17% greater using RegCM4-MPI.

Figure 5
Figure 5. Per person energy and global warming potential to meet 50% of dietary requirements given GFDL and MPI climate projections.
Note: The salmon color represents the current conditions and the blue represents the future scenario.

From an economic perspective, we are interested in knowing how the current and future scenarios compare economically for the DMMSA using current producer prices. We assume prices for individual food types do not change between the current condition and future scenario. We compare the two scenarios by isolating the increase in table food production for current and potential future food systems. We find that table food production at a level that could meet 50% of DMMSA nutritional requirements would have a gross value of approximately $157 million, or about 13% of the estimated $1 billion dollars currently generated by production of commodity crops (e.g., corn and soybean) in the DMMSA based on consistent producer prices in 2012 dollars. In the future scenario, the DMMSA can capture an additional $97 million dollars by growing more (up to 50%) of its own table food, increasing the value of DMMSA agricultural production by 15%. However, it is likely that the many necessary infrastructure and policy factors would need to change to support this additional table food production within the DMMSA.

Conclusion and future considerations

Several factors, including science, markets, and policies influence decisions regarding agricultural systems (e.g., crop type and farming practices). This study indicates potential strengths and weaknesses of different crop production scenarios and how they affect agricultural sustainability. A shift in land use to increase table food production in the DMMSA landscape indicates the potential for improvement in water quality and reductions in energy use and GWP. For future analyses, we plan to include a larger ensemble of climate models and in-depth uncertainty analyses to increase our confidence in the results.


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Suggested citation:

Brighenti, T., T. Stone, P. Gassman, and J. Thompson. 2022. "Increasing Local Production of Table Food in Iowa to Improve Agricultural Sustainability: A Food-Energy-Water Systems (FEWS) Project Case Study." Agricultural Policy Review, Fall 2022. Center for Agricultural and Rural Development, Iowa State University. Available at