North and South Dakota have experienced rapid land-use changes in the past decade. Recent studies have shown that these land-use changes are mainly characterized by conversions of grasslands to crop production, especially corn and soybeans. Approximately 271,000 hectares of grasslands were lost to corn and soy production in 2006-2011 period, almost seven times the losses in 1989-2003. The implications of these changing land-uses range from reduced biodiversity and loss of habitat for waterfowl species to low agricultural productivity on drought-sensitive marginal lands. While progress has been made in characterizing regional land-use changes, formal analyses establishing causal relationships at the local level are lacking. We construct a spatially delineated dataset for the Dakotas and utilize a Difference-in-Difference (DID) model in conjugation with Propensity Score Matching to estimate the impact of an ethanol plant on nearby corn-acres. We hold the advent of an ethanol plant to be a treatment that influences land-use on surrounding agricultural plots. In our preliminary work, based on the Parallel Paths assumption of the DID, we find that the effect of ethanol plants on corn production varies by plants and a single point estimate for all ethanol plants in a region, as usually provided in the literature, can be highly misleading. Surprisingly, we find both positive as well as negative effects of ethanol plants on corn-acres that may be statistically insignificant. Negative estimates are irreconcilable to the economic incentives due to these corn-based ethanol plants. We find intensified corn production and reduced soybeans due to the ethanol plants. Our analysis also reflects a difference in opportunity of converting from wheat to corn and from grass to corn. We use placebo tests and pre-treatment trends in corn acres to examine the Parallel Paths assumption that identifies the DID estimates. We find that this assumption fails and propose to carry out this analysis by incorporating differentiated trends into the DID framework through more flexible assumptions in future. An important contribution of this paper is that it presents a unique research design that uses quasi-experimental techniques to evaluate the impact of a change/policy upon availability of spatially delineated datasets. To this extent, our results are to be viewed as preliminary.