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Lands unsuitable for growing food could be used to grow perennial grasses for biofuel. Photo by Randy Jackson

In their quest to make cellulosic biofuel a viable energy option, many researchers are looking to marginal lands—those unsuitable for growing food—as potential real estate for bioenergy crops.

But what do farmers think of that? Brad Barham, a CALS/UW-Extension professor of agricultural and applied economics and a researcher with the Great Lakes Bioenergy Research Center (GLBRC), took the logical next step and asked them.

Fewer than 30 percent were willing to grow nonedible cellulosic biofuel feedstocks—such as perennial grasses and short-rotation trees—on their marginal lands for a range of prices, Barham and his team found after analyzing responses from 300 farmers in southwestern Wisconsin.

“Previous work in the area of marginal lands for bioenergy has been based primarily on the landscape’s suitability, without much research on its economic viability,” says Barham, who sent out the survey in 2011. “What’s in play is how much farmers are willing to change their land-use behavior.”

Barham’s results are a testament to the complex reality of implementing commercial cellulosic biofuel systems. Despite the minority of positive responses, researchers found that there were some clusters—or “hotspots”—of farmers who showed favorable attitudes toward use of marginal land for bioenergy.

These hotspots could be a window of opportunity for bioenergy researchers since they indicate areas where feedstocks could be grown more continuously.

“People envision bioenergy crops being blanketed across the landscape,” says Barham, “but if it’s five percent of the crops being harvested from this farm here, and 10 percent from that farm there, it’s going to be too costly to collect and aggregate the biomass relative to the value of the energy you get from it.

“If we want concentrated bioenergy production, that means looking for hotspots where people have favorable attitudes toward crops that can improve the environmental effects associated with energy decisions,” Barham notes.

CALS agronomy professor Randy Jackson is also interested in the idea of bioenergy hotspots. Jackson, who co-leads the GLBRC’s area of research focusing on sustainability, says that just because lands are too wet, too rocky or too eroded to farm traditionally doesn’t mean they aren’t valuable.

“The first thing we can say about marginal lands is that ‘marginal’ is a relative term,” says Jackson. Such lands have a social as well as a biophysical definition. “This land is where the owners like to hunt, for example.”

The goal of GLBRC researchers like Barham and Jackson is to integrate the environmental impacts of different cropping systems with economic forces and social drivers.

The environmental benefits of cellulosic biofuel feedstocks such as perennial grasses are significant. In addition to providing a versatile starting material for ethanol and other advanced biofuels, grasses do not compete with food crops and require little or no fertilizer or pesticides. Unlike annual crops like corn, which must be replanted each year, perennials can remain in the soil for more than a decade, conferring important ecosystem services like erosion protection and wildlife habitat.

The ecosystem services, bioenergy potential and social values that influence how we utilize and define marginal land make it difficult to predict the outcomes of planting one type of crop versus another. To tackle that problem, Jackson is working with other UW–Madison experts who are developing computer-based simulation tools in projects funded by the GLBRC and a Sun Grant from the U.S. Department of Energy.

Jackson hopes that these modeling tools will help researchers pinpoint where farmer willingness hotspots overlap with regions that could benefit disproportionately from the ecosystem services that perennial bioenergy feedstocks have to offer.

“These models will include data layers for geography, crop yield, land use, carbon sequestration and farmer willingness to participate,” says Jackson. “There could be as many as 40 data layers feeding into these models so that you can see what would happen to each variable if, say, you were to plant the entire landscape with switchgrass.”