Where can switchgrass production be more profitable than corn and soybean? An integrated subfield assessment in Iowa, USA
Perennial bioenergy crops are considered an important feedstock for a growing bioeconomy. However, in the USA, production of biofuel from these dedicated, nonfood crops is lagging behind federal mandates and markets have yet to develop. Most studies on the economic potential of perennial biofuel crops have concluded that even high-yielding bioenergy grasses are unprofitable compared to corn/soybeans, the prevailing crops in the United States Corn Belt. However, they did not account for opportunities precision agriculture presents to integrate perennials into agronomically and economically underperforming parts of corn/soybean fields. Using publicly available subfield data and market projections, we identified an upper bound to the areas in Iowa, United States, where the conversion from corn/soybean cropland to an herbaceous bioenergy crop, switchgrass, could be economically viable under different price, land tenancy, and yield scenarios. Assuming owned land, medium crop prices, and a biomass price of US$ 55 Mg1, we showed that 4.3% of corn/soybean cropland could break even when converted to switchgrass yielding up to 10.08 Mg ha-1. The annualized change in net present value on each converted subfield patch ranged from just above US$ 0 ha-1 to 692 ha-1. In the three counties of highest economic opportunity, total annualized producer benefits from converting corn/soybean to switchgrass summed to US$ 2.6 million, 3.4 million, and 7.6 million, respectively. This is the first study to quantify an upper bound to the potential private economic benefits from targeted conversion of unfavorable corn/soybean cropland to switchgrass, leaving arable land already under perennial cover unchanged. Broadly, we conclude that areas with high within-field yield variation provide highest economic opportunities for switchgrass conversion. Our results are relevant for policy design intended to improve the sustainability of agricultural production. While focused on Iowa, this approach is applicable to other intensively farmed regions globally with similar data availability.