Developing a dataset to assess ecosystem services in the Midwest United States

The Midwest United States produces around one-quarter of the world's grain supply. The demand for corn ethanol is likely to cause a shift toward greater corn planting. To be prepared for the potential impacts of increased corn production, we need a better understanding of the current state of ecosystem services in this region. In this article, we describe a unique procedure for developing a dataset containing multiple variables useful in modeling ecological responses and tradeoffs. We demonstrate how to construct a detailed land cover classification and link it to yield and agricultural practices. We used the 2001 National Land Cover Database (NLCD) to spatially constrain the datasets during overlay analysis. With this method, we found that the percent agreement between classifications was frequently greater than 80%, indicating little change to the original base layer accuracies. Using three different land cover datasets, we were able to add 18 classes for agriculture and 155 classes for natural cover. We then linked variables for yield, fertilizer, and pesticide application rates, field residue, irrigation percentages, and tillage practices to the land cover data. The final Midwest dataset contained 15.5 million grid values and 15 variables. Capturing the land cover and land management information at the 30-m grid scale allows for aggregation and modeling of the ecosystem services at a variety of spatial scales. As a final step, we demonstrate a tradeoff evaluation between corn yield and nitrogen loadings using our dataset. The effort required to develop the Midwest dataset was greater than initially anticipated; however, the benefit of being able to calculate derivative variables and add new variables justifies the time expenditure needed to create such a detailed database.