Computers have enabled people to create virtual realities, and many people interact with these worlds on many levels, from games they can play on their phones to elaborate worlds that can be explored and conquered on their gaming systems. These kinds of games aren’t usually associated with farming, but what if a farmer had a virtual reality that was a replicate of their farm? What if they could test their decisions with no risks and no consequences? What if they could look back and see what their yields would have looked like if they had planted later, or chosen a different maturity group, or gone with a higher seeding density? How much could they learn about their crops and soils and how they interact with the weather on their farm?

What is a cropping systems model?Cropping systems models are computer programs that create virtual farms (Fig. 1). This fall the Iowa Soybean Association has started to work with cropping systems modeling through funding a new project with Iowa State University to use a cropping systems model to identify site specific management practices that could increase soybean yields in Iowa.

Building a simulation of a cropping system could go a little like this: A farmer would input a variety of information for the programmers to start with. The first step would be inputting the soil while being as specific as possible. Including silt, sand and clay content so the model can understand water flow.

Organic matter levels should also be included so the model can analyze nutrient availability, and then the pH and micronutrients are added. A crop is then chosen for the soil and more information is inputted, such as maturity group, plant population, row spacing and planting time.

Now that the soil and the crop are in the model, something needs to drive the system – weather. Once the system is hit with sunlight and precipitation allowing the soil to warm up, plant growth begins. Plant growth changes the soil moisture, changes in soil moisture lead to changes in nutrient cycling and changes in nutrient availability affect plant growth. Things get complicated very quickly, but now that the system is running, it is ready to not only test the what-if questions, but may also answer questions that start with “Why?”

Of course, building a cropping systems model is more difficult than that, but there are decades of agronomic research to draw upon when attempting to simulate how the natural world works. For example, how much mass a plant gains over a day depends upon how much sunlight (radiation) is available in a given location, how much of this sunlight is intercepted by the leaves which depends on leaf area (LAI) and leaf angle (k) and how efficient the plant is at converting the sunlight to dry mass (RUE). We can express the relationship of these things in an equation:

Plant mass = radiation * (1 – e-k*LAI)*RUE.

When the daily values over the season are summed up, total plant mass can be estimated. The estimate from the above equation refers to potential plant mass with no water or nutrient stresses involved into the equation.

When water and nitrogen stressors are added the equation gets more complex and draws upon other components of the system, like soil. Hundreds of equations like this, and their interactions make up a cropping systems model. Cropping systems models are in turn tested against cropping systems data to determine how close the simulation comes to the real thing.