Question: How can we assist Iowa vegetable growers to expand their acreage and become more sustainable and profitable?

Answer: The best approach would be to engage growers and extension personnel through on-farm collaborative research and demonstration projects  for sustainable production systems with an emphasis on cropping systems, cover crops, nutrient management, season extension, vegetable transplant production and soil amendments.


Question: Why is it worthwhile to use models to examine food and agriculture systems?

Answer: Models are useful for making explicit our assumptions about the way the world is organized and how it works. After making our assumptions explicit, we can then evaluate the logical consequences of those assumptions and compare them to the real world. Finally, we can use models to examine the consequences of varying our initial assumptions.

Imagine that we built a model to assess the amount of nitrogen flowing into the water treatment plant for the city of Des Moines, given the current pattern of land use and farming practices upstream. Does the output of the model reflect the measurements of nitrogen concentrations made every day at the Des Moines Water Works? What does the model predict would happen to nitrogen concentrations in water if land use shifted toward more food and forage crops and fewer acres of commodity crops?


Question: What is a food system?

Answer: Food systems are made up of many parts. Most people think of planting, growing and harvesting fruits and vegetables, or raising livestock. In addition, food systems include processing, packaging, transporting, marketing, and consuming food, as well as disposing of food waste. A broad perspective is important because food systems are complex and important tradeoffs can be overlooked if the big picture is not taken into consideration.


Question: How can environmental impacts of food systems be measured?

Answer: Life Cycle Assessment (LCA) is a tool that can be used to understand the environmental impacts of complex systems. We are using LCA to understand environmental impacts of food systems from the point of production to waste disposal (carbon emissions, energy use, and water use).


Question: How are climate change and food systems connected?

Answer: Reducing carbon footprints at scale can help to preserve diverse plant and animal ecosystems. Many people do not realize the extent of environmental impacts associated with the food we eat. In fact, over 25% of global carbon emissions that accelerate climate change may be caused by our food systems. Significant levels of carbon emissions in food systems come from soil disturbance, fuel use, animal by-products and disposal of food waste.


Question: What is a SWAT model?

Answer: The Soil and Water Assessment Tool (SWAT) is a  physically-based eco-hydrological model developed to explore the effects of climate and land management practices on water, sediment, and pollution at scales that vary from a single watershed to an entire river basin. The model can also represent watershed processes such as hydrology, soil erosion, crop growth, and nutrient cycling on different time scales. SWAT is a well-documented model that has been applied in over 4000 peer-reviewed studies (https://www.card.iastate.edu/swat_articles/).


Question: What does SWAT provide as model outputs?

Answer: The SWAT model reports a large variety of outputswhich may include water balance values, streamflow rates, groundwater recharge, nutrient and sediment budgets, quanitities of other pollutants in water, as well as crop yield and biomass.


Question: What steps are involved in the SWAT modeling process?

Answer: The process includes three main steps:

  1. Model set-up: Spatial data input (soils, elevation, and land use), climate, and land management practices.
  2. Calibration/Parametrization: Model output is compared against observed data (e.g., streamflow, suspended sediments) and the parameters are adjusted until the model can replicate measured values based on a set of criteria.
  3. Validation:  Calibrated models are then run with data from other time periods and/or locations, and again tested for agreement between model output and observed data.


Question: What is a WRF model?

Answer: The Weather Research Forecasting (WRF) model is a mesoscale numerical weather prediction system that we are using to generate hourly air temperature data at a 1-km spatial resolution. The WRF model offers ways to represent land and atmosphere interactions over complex and variable land surfaces. More description of WRF model can be found at https://www.mmm.ucar.edu/weather-research-and-forecasting-model


Question: What does WRF provide as model outputs?

Answer: The WRF model can generate spatially gridded air temperature and land surface temperature at a 1-km spatial resolution and hourly scale. 


Question: What steps are involved in the WRF modeling process?

Answer: There are three main steps:

Data collection: We collected land surface data (e.g., albedo, leaf area index, green vegetation fraction, impervious surface area, land use and land cover) and weather data from Global Forecast System (https://www.ncei.noaa.gov/products/weather-climate-models/global-forecast).

Calibration/Parametrization: We calibrated the model by comparing model output against weather station observations.

Output datasets:  Data from calibrated models are converted to raster in GeoTIFF format.


Question: What is an Agent Based Model (ABM)? 

Answer: Agent-based modeling is a computational simulation modeling method that uses software agents to represent individual human beings.  Like their real-world counterparts, agents are capable of making decisions and taking action to achieve their goals.  They can also be programmed to interact with other agents – for example, they might share information (or their opinions) with their neighbors.  Over time, as agents acquire new knowledge through interactions and experiences, they may update their beliefs, which can lead them to make different decisions and to change their behaviors. 


Question: How can ABM help us understand food systems? 

Answer: With ABM, we can create agents that are simulated versions of Iowa producers.  These producer agents can be given a wide variety of characteristics, including business/personal goals, beliefs and abilities, production capacities, and openness to trying new things, just like real-life producers.  We can then use ABM to test the impact of different scenarios, such as increased consumer demand for local food, on the producer agents’ decisions about growing food for local markets.  The results of these simulation experiments can provide insights into the behavior of real-life food systems. 


Question: How do you create a producer agent? 

Answer: You need to create mathematical and/or logical statements to embed within each agent that give it a set of rules for decision making and evaluating the results of its decisions.  To make the agents behave realistically, we design their decision logic to mirror the logic of real-life producers. One way of doing this is to collect data from producers about their current production practices and their beliefs about producing food for local markets. We can then analyze and summarize the data to generate the logical statements that are needed to guide the producer agents’ decisions and behaviors.