While biofuels may yield renewable fuel benefits, there could be downsides in terms of water quality and other environmental stressors, particularly if corn is relied upon exclusively as the feedstock. The consequences of increased corn production will depend importantly on where (and how) the additional corn is grown, which, in turn, depends on the characteristics of land and its associated profitability. Previous work has relied on rules of thumb for allocating land to increased acreage based on historical land use or other heuristics. Here, we advance our understanding of these phenomena by describing a modeling system that links an economics‐driven land use model with a watershed‐based water quality model for the Upper Mississippi River Basin (UMRB). This modeling system is used to assess the water quality changes due to increased corn acreage, which is associated with higher relative corn prices. We focus on six scenarios based on six realistic pairs of corn and soybean prices which correspond to a scale of decreasing soybean to corn price ratio. These price‐driven land use changes provide estimates of the water quality effects that current biofuel policies may have in the UMRB. Our analysis can help evaluate the costs and environmental consequences associated with implementation strategies for the biofuel mandates of the new energy bill. The amounts of total N and P delivered to the outlet of the UMRB (located at Grafton, Illinois, USA) rise as corn production becomes more intensive in the region. Our results indicate that a 14.4% in corn acreage in the watershed due to corn intensification in the most economically profitable locations would result in a 5.4% increase in total nitrogen loads and in a 4.1% increase in total phosphorus loads at Grafton. Our most aggressive scenario, driven by high but not out of reach crop prices, results in about a 57% increase in corn acreage with a corresponding 18.5% increase in N and 12% increase in P. These are somewhat conservative increases in nutrients, compared to those of previous studies, likely due to our focus on cultivated cropland which is already heavily fertilized.

Understanding the best way to allocate limited resources is a constant challenge for water quality improvement efforts. The synoptic approach is a tool for geographic prioritization of these efforts. It uses a benefit-cost framework to calculate indices for functional criteria in subunits (watersheds, counties) of a region and then rank the subunits. The synoptic approach was specifically designed to incorporate best professional judgment in cases where information and resources are limited. To date, the synoptic approach has been applied primarily to local or regional wetland restoration prioritization projects. The goal of this work was to develop a synoptic model for prioritizing watersheds within which suites of agricultural best management practices (BMPs) can be implemented to reduce sediment load at the watershed outlets. The model ranks candidate watersheds within an ecoregion or river basin so that BMP implementation within the highest ranked watersheds will result in the most sediment load reduction per conservation dollar invested. The model can be applied anywhere and at many scales provided that the selected suite of BMPs is appropriate for the evaluation area’s biophysical and climatic conditions. The model was specifically developed as a tool for prioritizing BMP implementation efforts in ecoregions containing watersheds associated with the USDA-NRCS conservation effects assessment project (CEAP). This paper presents the testing of the model in the little river experimental watershed (LREW) which is located near Tifton, Georgia, USA and is the CEAP watershed representing the southeastern coastal plain. The application of the model to the LREW demonstrated that the model represents the physical drivers of erosion and sediment loading well. The application also showed that the model is quite responsive to social and economic drivers and is, therefore, best applied at a scale large enough to ensure differences in social and economic drivers across the candidate watersheds. The prioritization model will be used for planning purposes. Its results are visualized as maps which enable resource managers to identify watersheds within which BMP implementation would result in the most water quality improvement per conservation dollar invested.