Are we using the right tools to understand land use change, particularly at the local level, and how can understanding of land use change be improved?

The Renewable Fuel Standard (RFS) has been around for 10 years, yet the policy continues to be dogged by concerns over its impact to U.S. and foreign land use.  In the United States, the USDA tracks aggregate land use conversion.  However, the impact of growing biofuels feedstocks (primarily corn and soy) on land use change at a local level has been difficult to ascertain.  Understanding of the issue is primarily hampered by the use of databases and tools that were not developed specifically for this task. The result is policy makers and stakeholders have insufficient and perhaps inaccurate information regarding the impacts of the statute on land conversions.

The RFS requires that no new land be utilized for renewable fuel production, in order to preserve carbon stores and ecological habitat, and ensure that biofuels have a net positive effect on greenhouse gas (GHG) emissions.  Scientists have used a variety of datasets to attempt to understand land use change (LUC), but these tools were not specifically designed for understanding land use change over time, particularly at a local level.

What’s at stake? There has been great concern by some that the RFS has driven the conversion of ecologically sensitive lands (wetlands, forest, and grasslands) that are important for wildlife and carbon storage to cropland, particularly in the Prairie Pothole Region, but also in regions of the Southern Great Plains. Unfortunately, it has been difficult to assess the veracity of these claims, not only due to other policy and economic drivers of LUC, but also because of land use datasets that simply aren’t designed to analyze these types of local conversions.  

Currently, several databases are primarily used to try to understand LUC. They include the USDA’s Cropland Data Layer (CDL), U.S. Geological Survey’s National Land Cover Database (NLCD), and the National Agricultural Imagery Program (NAIP).  None were designed to specifically understand land use change in the context of the RFS, especially at a local level.  There are shortcomings in applying these datasets to understanding the impacts of the RFS, according to a recent paper in the journal Biofuels, Bioproducts & Biorefining.  For example, CDL is not designed to assess land use over a long period of time, rather, it provides a snapshot of land use.  Meanwhile, NAIP provides a resolution of a few meters, and can be used to visually inspect land, but is not assessed for accuracy.  

Most recently, a study of land-use conversions in the journal Environmental Research Letters found a higher concentration of grassland conversion within a 100-mile radius of ethanol facilities, by utilizing CDL data.  The study finds that regions of the Dakotas and western Kansas have seen high grassland conversions to crop use, as opposed to top corn-producing states Iowa, Illinois, and Indiana.  The idea seems plausible since there are more grasslands in these regions, to begin with.  However, to what degree are conversions happening, what’s driving conversions in addition to ethanol (such as available conservation acres), and how accurate are these measurements remains an open debate.   

Of course, the ethanol industry shot back with its own arguments, including the fact that total cropland in counties with ethanol plants was lower in 2012 than previous years. Geoff Cooper, of the Renewable Fuels Association, contends that ethanol facilities aren’t driving grassland conversions, but farmers are instead converting fallow cropland to crop production.  

However, without a broadly accepted way to measure LUC, the debate will continue to be ruled by conflicting information.  What both sides can perhaps agree on is that quality monitoring and data is necessary to understand the environmental impacts of the RFS, and to ensure that the issue is looked at carefully and holistically.


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