Checking the daily weather forecast is a matter of habit for most of us—it’s a handy tool that helps us decide which jacket to wear or whether to grab an umbrella on the way out. But for farmers, forecasting is more than a convenience: It can be an integral tool that allows for economically and environmentally sound pest management. And while forecasting the arrival of pests has always played an important role in agriculture, today’s growers can get more specific, up-to-date information than ever before through computer-based, pestprediction models.

Biological engineer Jeff Catchmark is studying the construction of plant cell walls in an effort to unlock the biofuels potential they protect.

In one ongoing, cooperative project, entomologist Dennis Calvin and his research team look at how climate and weather influence the timing of insect emergence in field crops. Focusing on European corn borer, corn rootworm, common stalk borer, and alfalfa weevil, the researchers integrate data on temperature, climate, insect development, and plant development to create computer models that predict insect phenology: the timing of biological events. Scientists have long known that insect development is related to temperature. During their life cycle, insects move through instars, the discrete stages of larval development. Each one of these stages is temperature dependent: The cooler it is, the longer insects stay in a particular stage; the warmer it is, the faster they move through that stage.

“To create models of insect development, we express this concept mathematically,” Calvin explains. “If you know an insect completes a tenth of its development every day at a certain temperature, you can say it takes ten days to go through that stage. To develop models, you have to take, say, a hundred insects and rear them in a laboratory growth chamber at a constant temperature, keeping track of how many hatch or transition to the next stage. From that information you get an average time they spend in each stage. Then you take another hundred insects and keep them at a different temperature, and so on. Gradually you build a range of points from which to build equations, and from those equations you build models.”

The next piece of the puzzle involves getting the best possible sitespecific temperature measures. In the past, temperature records from National Weather Service stations provided the best estimates for weather predictions. But because stations could be as many as 50 miles apart, and local weather varies greatly between stations, predictions often were not accurate. In the mid-1980s, former assistant professor Joe Russo developed techniques to acquire sitespecific temperature measurements between weather stations, taking into account factors such as topography. (For example, weather stations are usually near airports, and temperatures there would differ from those in farm fields.)