Other authors have explored whether ethanol refineries have an effect on land use. Fatal and Thurman use county-level data to estimate the corn acreage effect of ethanol re- fineries. They find that a typical ethanol refinery increases corn acreage in its home county by over 500 acres and has effects that can persist for up to 300 miles. Miao also uses county-level data and finds a significant effect of ethanol refineries on corn acreage, as well as a differential effect between locally-owned and non-locally-owned refineries. Turnquist et al. , in contrast to more recent studies, fail to find any significant agricultural land conversion in areas near Wisconsin ethanol refineries. Finally, Feng and Babcock explore the full general equilibrium effect of increased ethanol production and find an unambiguous increase in corn acreage. Several researchers have focused on how ethanol production affects water quality and nitrate runoff. Donner and Kucharik highlight how the aggregate impact of the EISA will likely make achieving nitrate level goals in the Mississippi impossible. Thomas et al. use hydrologic models to estimate the water quality impacts of corn production caused by increased demand due to bio-fuel mandates. They find significant negative results. While it is likely true that “refineries cause corn,” it is also likely true that “corn causes refineries.” Ethanol refineries are not located at random,blueberries in pots and several researchers have explored the topic of ethanol refinery placement.
A series of papers have shown, unsurprisingly, that ethanol refineries are more likely to locate near areas with large corn production, near transportation infrastructure, and not near existing ethanol refineries . This finding is important because it highlights that ethanol refinery placement cannot be treated as truly random in econometric analyses without accounting for the underlying drivers of this placement. In my analysis, I argue that field-level fixed effects appropriately account for the major determinants of refinery placement. In particular, I study how distance-to-nearest-refinery affects the probability of a field being planted to corn. Whenever a new refinery is built, its presence differentially affects fields close to it relative to fields slightly farther away. However, due to the spatial characteristics of soil quality and topology, “more-treated” and “less-treated” fields are qualitatively comparable. My project improves upon previous work by leveraging new sources of field-level land use data and exploiting a finer-scaled panel of observations than previous authors. I exploit both the Cropland Data Layer and Common Land Unit to create annual observations of field-level land use. These agricultural micro-data allow for much more nuanced econometric estimation than in previous studies. Other authors have exploited similar micro-data in agricultural research to great effect . I also highlight the locality effect of ethanol refineries rather than the general equilibrium effect, focusing on small-scale heterogeneous effects that have not been well identified in previous work. The remainder of this paper is divided into a theoretical framework , a summary of my data, an overview of my econometric methods, a discussion of my results, and a conclusion. According to the Farm Service Agency of the USDA, a Common Land Unit is “an individual contiguous farming parcel, which is the smallest unit of land that has a permanent, contiguous boundary, common land cover and land management, a common owner, and/or a common producer association” .
Practically, a CLU represents a single agricultural field. Polygon shapefiles of CLUs are maintained by the FSA, but are not currently publicly available. I obtain CLU data for Illinois, Indiana, Iowa, and Nebraska from the website Geo Community . These data contain shapefiles from the mid 2000s, before CLU data were removed from the public domain. In this research, I implicitly assume that individual CLUs do not change over time: a reasonable assumption given the FSA definition. In reality, the FSA does adjust individual CLU definitions on a case-by-case basis if necessary, but I assume these adjustments to be negligible as in previous similar studies . Using the geospatial software ArcGIS, I overlay the CDL raster data with CLU polygons as shown in Figure 3.4. Upon visual inspection, the fit is quite good: CLU boundaries line up with crop changes in the CDL, roads appear clearly in both datasets, and geographical features such as waterways and elevation changes are visible. One concern is that many CLUs are quite small and appear to outline geographical features such as gullies, rather than larger constituent fields. This is particularly pronounced in areas near urban sprawl. Therefore, to maintain confidence that the fields I study are actually “fields” in the way we think of them, I drop all CLUs from my dataset with areas of less than 10 acres. I also drop CLUs with areas of greater than 10,000 acres, based on an assumption that these CLUs are incorrect.4 To assign each CLU a single crop cover, I calculate the modal value of the raster pixels contained within each CLU polygon. I then assign that modal value to the entire CLU. This procedure enforces the assumption that each field is planted to a single crop – an assumption strongly supported by a visual examination of the data. To my knowledge, this is the first instance of using modal statistics to interact the CDL and CLU datasets.
Previous research has used an off-center centroid to sample a single point of the underlying raster data. My procedure is preferable in that it reduces the chance of idiosyncratic mis-measurement of the field’s true land cover.In this paper, I have demonstrated that ethanol refineries exert a statistically significant effect on the land use of surrounding fields. Increases in corn acreage and nitrogen application occur within 30 miles of ethanol refineries, suggesting a highly localized effect. These findings are consistent with a model of ethanol refineries lowering corn basis for nearby farmers. Within a sample of almost 114 million acres, I find nearly 300,000 acres of the corn grown in 2014 can be attributed to ethanol placement effects accumulated over the years between 2002 and 2014. This project makes several important contributions to the existing literature and improves upon previous research. Most importantly, I leverage field-level observations of land use to create a thirteen year panel of over two million observations. This allows me to estimate a highly nonlinear relationship between distance to a field’s nearest ethanol refinery and that field’s probability of growing corn. My panel also allows me to include field-level fixed effects that control for time-invariant characteristics of each field such as soil type. In three econometric specifications that condition on the previous year’s land use, I find interesting patterns. Ethanol refineries seem to strongly incentivize nearby fields to grow corn-after-corn, while the effect appears opposite for corn-after-soy. The result for cornafter-soy is puzzling and is not predicted by my model. Future work may attempt to better understand this result. Nonetheless, the net effect of these two individual effects is that farmers appear to be growing more corn near ethanol refineries in the way the most stresses crop rotations and most exacerbates the use of nitrate-producing fertilizer. There is considerable room for further work on these questions. First, this analysis treats all refineries as identical. In reality, different refineries have different production capacities and ownership structures, and may have heterogeneous effects on surrounding land. Second, there is room to explore a wider range of econometric specifications beyond the linear probability model estimated in this paper. Finally, future work should explore data from the US Geological Survey to test whether water nitrate levels directly reflect the effect derived in the current project. While the findings of this paper appear relatively small in the context of the entire US Corn Belt, they are strongly statistically significant and demonstrate a real and important localized effect of ethanol refinery placement. My results are useful for anyone interested in a fuller understanding of the spatial forces driving land use change and nitrogen application in agriculture. Managing urban runoff and its associated pollutants is one of the most challenging environmental issues facing urban landscape management. The conversion of naturally pervious land surfaces to buildings, roads, parking lots,growing berries in containers and other impervious surfaces results in a rapid surface runoff response for both time of concentration and peak flow. Impervious land surfaces adversely impact the quantity and quality of surface runoff because of their effects on surface water retention, infiltration, and contaminant fate and transport. Large volumes of storm runoff from urbanized areas cause flooding, sewer system overflows, water pollution, groundwater recharge deficits, habitat destruction, beach closures, toxicity to aquatic organisms, and groundwater contamination.
Traditional urban runoff management focuses on removing the surface runoff from urban areas as soon as possible to protect public safety. However, as excess surface water is quickly drained from urban areas, it is no longer available for recharging groundwater, irrigating urban landscapes, sustaining wildlife habitat and other uses. Green infrastructure uses natural or engineered systems that mimic natural processes to control storm water runoff . For example, traditional detention ponds have been widely used to treat storm runoff and permeable paving promotes infiltration of rain where it falls. Importantly, decentralized green infrastructure strategies control runoff and contaminants at their source . Vegetation is a green infrastructure strategy that can play an important role in surface runoff management. Large-scale tree planting programs have been established in many cities to mitigate the urban heatis land effect, improve urban air quality, and reduce and treat urban runoff . There are municipal storm water credit programs in a growing number of cities that promote retaining existing tree canopy, as well as planting new trees. Although these programs encourage planning and management of urban forests to reduce runoff impacts, fertilizer is required to promote plant growth, and these added nutrients may contribute to contamination of surface runoff. Thus, reducing nutrients in storm runoff is a challenging task for landscape and water managers. Bioswales are shallow drainage courses that are filled with vegetation, compost, and/or riprap. As a part of the surface runoff flow path, they are designed to maximize the time water spends in the swale, which aids in the trapping and breakdown of certain pollutants. Bioswales have been widely recognized as an effective decentralized storm water BMP to control urban runoff. Their effects are threefold; vegetation intercepts rainfall reducing net precipitation; plant uptake of water via transpiration reduces soil moisture, thereby increasing subsurface water storage capacity, and root channels improve infiltration. Traditional bioswales are designed to remove silt and other pollutants from surface runoff waters. New bioswales are being developed for harvesting surface runoff and supporting urban tree growth . Bioswales that integrate engineered soil mixes and vegetation are being used to enhance treatment and storage of surface runoff . The composition of ESMs varies widely, from simple mixtures of stones and native soil to patented commercial products. Highly porous ESM mixes provide ample infiltration and pore space for temporary storage of surface runoff. Also, they support tree growth by providing more water and aeration to roots than compacted native soil alone. ESMs can reduce conflicts between surface roots and sidewalks by promoting deeper rooting systems. In California alone, over $70 million is spent annually to remediate damage by shallow tree roots to sidewalks, curbs and gutters, and street pavement. In Davis, California, a bioswale installed next to a parking lot reduced runoff from the parking lot by 88.8% and the total pollutant loading by 95.4% during the nearly two year monitoring period. Furthermore, a bioswale installed next to a turf grass patch at the University of California-Davis campus eliminated dry weather runoff from an irrigated urban landscape The ESM used in these studies offered several advantages over other ESMs because the main structural element was locally quarried and relatively inexpensive lava rock . This ESM had a high porosity, high infiltration rate, and a high water storage capacity . It effectively fostered the growth of biofilms that retain nutrients and degrade organic pollutants. Because vegetated bioswale research is in its infancy, very few studies have monitored vegetation growth and its impacts on bioswale performance. Moreover, evaluation of system performance is generally conducted before vegetation is fully established. In contrast, this study evaluated the effectiveness of two bioswales on surface runoff reduction, pollutant reduction, and tree growth eight years after construction.