Fertilizer Use and Water Quality in the United States

Jayash Paudel* and Christine L. Crago**

* PhD Candidate (corresponding author), Department of Resource Economics, University of Massachusetts Amherst, email: [email protected]

** Assistant Professor, Department of Resource Economics, University of Massachusetts Amherst, email: [email protected]

Selected Paper prepared for presentation at the 2018 Agricultural & Applied Economics Association Annual Meeting, Washington, D.C., August 5 – August 7

Copyright 2018 by Jayash Paudel and Christine L. Crago. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies. Fertilizer Use and Water Quality in the United States

Jayash Paudel† Christine L. Crago‡

May 21, 2018

Abstract

Fertilizer use from agriculture is widely acknowledged to be a leading cause of water pollution, yet no national estimates exist on the effect of fertilizer application on concentrations of agricultural pollutants in US watersheds. This paper examines the impact of fertilizer use on water quality using the most comprehensive data available on agricultural pollutants, including over 2.9 million pollution readings on nitrogen and phosphorus concentrations in water sites across the country. Findings show that a 10% increase in the use of nitrogen and phosphorus fertilizers (kg) leads to a 1.47% increase in the concentration of nitrogen (mg/l) and a 1.68% increase in the concentration of phosphorus (mg/l), respectively. Results also indicate that there exists heterogeneity in these elasticity measures across 18 water resource regions, ranging from 0.3% to 4.75% in the case of phosphorus pollution. More broadly, empirical estimates suggest that a 100% increase in the use of nitrogen fertilizers expands the size of the hypoxic zone in the Gulf of Mexico by roughly 1,598 square miles on average, equivalent to about one-ﬁfth of the estimated size of the “dead zone” in the Gulf of Mexico.

Keywords: Fertilizer use, water quality, environmental externalities, agriculture JEL Codes: Q12, Q15, Q51 and Q53

†Corresponding Author: Department of Resource Economics at University of Massachusetts Amherst, Stockbridge Hall, 80 Campus Center Way, Amherst, MA 01003. Email: [email protected] ‡Department of Resource Economics at University of Massachusetts Amherst, Stockbridge Hall, 80 Campus Center Way, Amherst, MA 01003. Email: [email protected] 1 Introduction

The US agricultural sector has seen a huge increase in demand for chemical fertilizers.1 Nitrogen fertilizer application, which rose from 2.7 to 12.3 million tons between 1960 and 2005, incurs almost a quarter of total production cost among corn farmers in the US (Beckman et al., 2013; Bejan and Pozo, 2016). Although fertilizers are essential for agricultural productivity, they lead to “groundwater contamination, eutrophication of freshwater and estuarine ecosystems, tropospheric pollution related to emissions of nitrogen oxides and ammonia gas, and accumulation of nitrous oxide” (Zhang et al., 2015). Previous reports on toxic algal bloom in Lake Erie in 2014 and federal lawsuit ﬁled by a water utility company against three counties in Iowa in 2015 highlight the recurring problem of fertilizer overuse, agricultural runoff and subsequent deterioration in water quality.2 Nitrogen pollution, which has exceeded “planetary boundaries” (Steffen et al., 2015), originates from fertilizer run-off that “causes $200bn-800bn worth of damage” to the ocean every year (Economist, 2018). Although scientists agree that fertilizer use is responsible for water quality problems such as the hypoxic growth in the Northern Gulf of Mexico (Whittaker et al., 2015), there remains a need to precisely estimate the percentage change in water quality that can be attributed to changes in on-farm fertilizer use. This paper seeks to shed light on the linkage between agricultural fertilizer use and nutrient pollution in the entire US. We construct an empirical model of the determinants of water quality, and study the impact of fertilizer use on the concentration of agricultural pollutants such as nitrogen and phosphorus. We employ a rich panel of watersheds constructed from over 35 years of data and condition our estimates on watershed and water resource region-by-year ﬁxed effects to identify the impact of fertilizer use on water quality. To obtain fertilizer application at the watershed level, we overlay the 8-digit hydrologic unit (HU) watershed polygons with a raster of county boundaries and a raster of the cropland cover to derive a weighting factor for fertilizer application in a county within a watershed. We sum the weighted county-level fertilizer application data to derive the total fertilizer applied within each 8-digit HU watershed. We use temperature, precipitation, urban runoff, prior levels of agricultural pollution and economic variables such as population and Gross Domestic Product (GDP) as the main explanatory variables in our econometric model. We also include upstream rainfall shock as a determinant of downstream watershed pollution and account

1According to Lu and Tian(2017), global nitrogen and phosphorus fertilizer use has increased by approximately 8 times and 3 times, respectively, over the last ﬁfty ﬁve years. 2Farmers apply nitrogen and phosphorus fertilizers that contain major sources of nourishment for crops. Nutrient runoff from agriculture causes several water quality problems, such as eutrophication of the “Dead Zone” in the Gulf of Mexico and nitrate contamination in drinking water. Presence of nitrates in water can lead to several health problems such as fatal blood disorder among infants commonly known as blue-baby. Excess phosphorous levels in portions of Lake Erie that provided drinking water for residents led to poisonous algal blooms, making tap water unsafe to drink for several days (Cohen and Keiser, 2017).

1 for spatial spillovers when pollutants move downstream and traverse multiple locations. We find that a 10% increase in the use of nitrogen fertilizer (kg) leads to a 1.47% increase in the amount of nitrogen (mg/l) in water. Similarly, a 10% increase in the use of phosphorus fertilizer (kg) is associated with a 1.68% increase in the concentration of phosphorus (mg/l) in water. We find large effects of fertilizer use on water quality among (i) non-metropolitan areas and (ii) areas with low GDP. Our results are also robust to the use of fertilizer application intensity as measured by total fertilizer use per unit area of cropland. Furthermore, we observe heterogeneity in nutrient pollution elasticity estimates across eighteen water resource regions in the US. Among these regions, the marginal effect estimates on nitrogen pollution range from 2.2% to 7.3%, and those on phosphorus pollution range from 1.7% to 4.7%. We also combine our estimates with statistics reported in prior hydrology-based studies and find that a 100% increase in agricultural fertilizer use leads to an increase of 1,598 square miles in the size of the hypoxic zone, which is an area about the size of Rhode Island and almost 20% the estimated size of the “dead zone” in the Gulf of Mexico. In the context of the Hypoxia Task Force 2008 Action Plan, our estimates suggest that reducing the concentration of nitrogen in water by 9-15% between 5 and 20 years requires a significant decrease of 41-67% in the use of nitrogen fertilizers. This is the first study to apply the largest standardized water quality data access tool available in assessing the impact of fertilizer use on agricultural pollutants across the US. Previous studies using similar data have focused mostly on non-agricultural pollutants and investigated the role of the Clean Water Act in improving alternative measures of water quality such as dissolved oxygen deficit, biochemical oxygen demand and total suspended solids. For example, Keiser and Shapiro (2017) evaluate the impact of the 1972 Clean Water Act on water pollution concentrations and highlight that grants issued to municipal wastewater treatment plants led to a downward trend in US water pollution in the last forty years.3 Smith and Wolloh(2012) assess dissolved oxygen in freshwater lakes over a 36-year period from 1975 to 2011 and conclude that there has been no significant change in water quality since the Clean Water Act. Our work differs from literature exploring the Clean Water Act and examines the overall impact of fertilizer application on nitrogen and phosphorus concentration in water. To the best of our knowledge, this is the first study to employ over 2.9 million nitrogen and phosphorus readings collected at 149, 887 water sites from the Water Quality Portal (WQP) to assess the linkage between fertilizer use and nutrient pollution across the US. This study provides the first national estimates of how fertilizer application affects concentration of agricultural pollutants in water. Majority of studies in water pollution research have employed ex ante engineering simulations to explore nutrient pollution in agriculture (Thorp

3Keiser and Shapiro(2017) report that each grant decreases the probability that downriver areas violate standards for being ﬁshable by about half a percentage point.

2 et al., 2007; Gowda et al., 2008; Nangia et al., 2008; Burkart and Jha, 2012; Iho and Laukkanen, 2012; Hendricks et al., 2014; Bostian et al., 2015) These studies focus on a specific watershed or a water resource region and cannot readily provide generalizable estimates for the entire United States. This research takes an empirical approach that deviates from simulation-based studies to assess the impact of landcover and management practices on water and nutrients at the field and watershed scale.4 Our study makes use of directly measured water quality readings across the United States to evaluate the environmental effect of agricultural fertilizer use. Although quasi-experimental research design has received noticeably more attention in water pollution research (Brainerd and Menon, 2014; Cohen and Keiser, 2017), this is the first study to deviate from simulation-based approach and directly estimate the relationship between fertilizer application and agricultural pollutants using large-scale, nationwide water pollution readings. In addition to providing plausible estimates of the effects of agriculture fertilizer use, we construct a new watershed-level panel data set that will enrich future research in agriculture and environmental policy. This further allows us to illustrate the economic significance of our agricultural pollutant elasticities, which can be combined with existing hydrology-based studies to make policy recommendations in the context of the Hypoxia Task Force 2008 Action Plan. The paper proceeds as follows. Section2 presents a conceptual framework on determinants of ambient water pollution. Section3 describes data sources and presents summary statistics. Section4 describes econometric models and Section5 presents the empirical findings of the study. Section6 discusses implications of the main results and concludes.

2 Conceptual Framework

This section presents a conceptual framework on the relationship between agricultural fertilizer use and water quality.

2.1 Fertilizer Use and Water Quality

Consider a watershed w, an area of land that collects and delivers water to common points. Within watershed w, point sources and nonpoint sources result in emissions and runoff, respectively.5 Following the theoretical framework presented in Shortle and Horan(2013), we

4For example, other studies employ the Soil and Water Assessment Tool (SWAT), a continuous-time model in which calculations are performed on a day-by-day basis, to model non-point source contributions to nutrient and sediment loads within a watershed. More information can be found here: https://swat.tamu.edu/software/ 5Shortle and Horan(2013) consider point sources to be industrial ﬁrms and nonpoint sources to be farms. Emissions are the quantity of pollutants directly discharged from a discrete point into the aquatic environment. Runoff is the quantity of pollutants such as nutrients that move from a well-deﬁned parcel.

3 model the production of polluting runoff for the ith nonpoint source in watershed w by the following relation:

riw = riw(fiw, ciw, αiw, νiw) (2.1.1) where f represents agricultural production inputs such as fertilizer, c represents pollution control choice inputs, α represents site characteristics such as soil type and topography and ν represents stochastic environmental variables affecting runoff, respectively. Next, Shortle and Horan(2013) deﬁne the ambient pollution load aw from watershed w as a function of point emissions (eiw), nonpoint emissions (riw), stochastic natural background levels of the pollutant (ζw), watershed characteristics (ψw), characteristics of the receiving water body (φw) and stochastic environmental variables (δw), as shown below:

aw = aw(r1w, ..., rnw, e1w, ..., eKw, aw−1, ζw, ψw, φw, δw) (2.1.2) where ζw accounts for stochastic events such as volcano eruptions, forest ﬁres and dust storms, ψw incorporates characteristics inﬂuenced by topography and geography such as fate and transport parameters, φw represents physical, chemical and biological attributes of the receiving water body and δw includes variables such as temperature, wind speed, solar radiation and pH. The model above assumes that ∂aw/∂riw ≥ 0 ∀i, w; ∂aw/∂ekw ≥ 0 ∀k, w; ∂riw/∂fiw ≥ 0 ∀i, w; and ∂aw/∂aw−1 ≥ 0. Relation aw(.) describes the transport of pollutants from individual sources through the watershed. In our empirical model, we expect to see a positive relationship between fertilizer application and nutrient pollution in water. The partial derivative of pollution with respect to fertilizer use is given by:

ξaw ∂aw ∂r = iw ≥ 0 ∀i, w (2.1.3) ξfiw ∂riw ∂fiw

According to Shortle and Horan(2013), the water quality impacts of pollutants from different sources may be nonuniform, and will generally depend on the location of those sources relative to that of the receiving water body. It is also likely that there will be interactions between pollution from different sources.

3 Data Sources and Summary Statistics

To implement the analysis, we employ the most detailed and comprehensive data available on agricultural pollutants in water and fertilizer application. This section describes the data and reports some summary statistics.

4 3.1 Hydrologic Data

We make use of a detailed US hydrologic map to aggregate fertilizer application and measures of water quality at the watershed level (see Figure1). The United States is divided and sub-divided into successively smaller hydrologic units (HU), from the smallest (cataloging units) to the largest (regions). Each hydrologic unit is identified by a unique numeric hydrologic unit code consisting of two to eight digits based on the four levels of classification in the hydrologic unit system (Seaber et al., 1994). The first level of classification (2-digit HU) divides the entire United States into 21 water resources regions, which are hydrologic areas based on surface topography containing either the drainage area of a major river, such as the Missouri region, or the combined drainage areas of a series of rivers, such as the Texas-Gulf region that includes a number of rivers draining into the Gulf of Mexico.6 The second level of classification (4-digit HU) divides the 21 regions into 222 subregions, which include the area drained by a river system, a reach of a river and its tributaries in that reach, a closed basin(s), or a group of streams forming a coastal drainage area (see Figure1). The third level of classification (6-digit HU) subdivides many of the subregions into accounting units that are used by USGS for designing and managing the National Water Data Network (Seaber et al., 1994). For our primary empirical analysis, we use the fourth level of classification (8-digit HU), known as the cataloging unit, the fourth smallest element in the hierarchy of hydrologic units. A cataloging unit is a geographic area representing part or all of a surface drainage basin, a combination of drainage basins, or a distinct hydrologic feature. These units subdivide the subregions and accounting units into smaller areas (approximately 2,150 in the nation) that are used by USGS for cataloging and indexing water-data acquisition activities in the “Catalog of Information on Water Data” (Seaber et al., 1994). We also take advantage of information on upstream and downstream watersheds available from National Watershed Boundary Dataset (WBD). Within each 6-digit HU, the watersheds are numbered sequentially with the uppermost outlet at the beginning.7

3.2 Water Quality Data

We obtain water quality data developed by the United States Geological Survey (USGS), Environmental Protection Agency (EPA), and the National Water Quality Monitoring Council. To explore agricultural pollutants, we employ data on nitrogen and phosphorus from lakes, estuaries, rivers and impoundments in the contiguous 48 states between 1967 and 2015. Streams, rivers, and

6Since our focus is on the conterminous U.S., our sample doesn’t include Alaska (region 19), the Hawaiian Islands (region 20), and Puerto Rico and other outlying Caribbean areas (region 21). 7Downstream watersheds are in ascending order within each 6-digit HU level (lower numbers always ﬂow into higher numbers).

5 lakes comprise about 56.76%, 23.24% and 5.74% of the data, respectively. Table1 provides annual summary of nitrogen concentration in US water sites from 1987 to 2006. The entire sample consists of 1,729,307 nitrogen pollution readings from 87,883 water sites located in 2,963 counties. The average amount of nitrogen in water in the entire United States is 3.27 mg/l with a standard deviation of 5.57.8 Similarly, Table2 provides annual summary of phosphorus concentration in US water sites from 1987 to 2006. The entire sample consists of 1,246,363 phosphorus pollution readings from 77,742 water sites located in 2,477 counties. The average amount of phosphorus in water in the entire United States is 0.20 mg/l with a standard deviation of 0.44.9 Figure2 demonstrates the density plots of nitrogen and phosphorus in US water sites. The overall mean and variance of nitrogen and phosphorus pollution appear fairly uniform across streams, rivers, lakes and estuaries. Figure3 shows the state-level heterogeneity in amount of nitrogen and phosphorus across water sites. The map shows that states such as Idaho, Montana, Kansas, South Dakota, Minnesota, Illinois, Louisiana, Mississippi and Kentucky consist of large concentration of nitrogen and phosphorus in water sites. At the watershed-level, the average amount of nitrogen and phosphorus in water sites across the entire United States is 3.06 mg/l and 0.47 mg/l, respectively (see Table A.1) Table A.2 and Table A.3 provide summary of nitrogen and phosphorus pollution readings across a panel of water sites that are present in all of the twenty years. Figure A.1 compares the yearly trend of average nitrogen and phosphorus pollution readings between sites in the entire sample and those that appear across all the years in the sample. Figure A.2 shows density plots of nitrogen and phosphorus pollution readings across 18 water-resource regions (2 digit HUs) in the US. Figure A.3 shows that the concentration of US agricultural pollutants has decreased between 1967 and 2015. We use amount of total solids generated from construction sites, nonvegetated lands, eroding banks and road sanding in water (mg/l) as a proxy for urban runoff. The entire sample consists of 264,185 readings from 22,468 water sites located in 1,597 counties. Table A.1 shows that the average amount of log urban runoff at the watershed-level is 6.30 mg/l with a standard deviation of 1.62 in the entire United States. Table A.4 provides annual summary of urban runoff in water sites from 1987 to 2006.

8Estimates from the United States Environmental Protection Agency (US EPA) suggest that appropriate reference levels for total nitrogen range from 0.12 to 2.2 mg/l. 9Appropriate reference levels for total phosphorus range from 0.01 to 0.075 mg/l, depending on the ecoregion. However, nuisance algal growths are not uncommon in rivers and streams below 0.1 mg/l.

6 3.3 Measure of Fertilizer Application

We employ county-level data on (a) nitrogen fertilizer use, and (b) phosphorus fertilizer use for the conterminous United States from 1945 to 2012.10 Figure4 shows the geographic distribution of U.S. farm fertilizer use. Table A.1 reports that the average watershed-level use of nitrogen and phosphorus fertilizers in the entire sample is approximately 3.6 million kilograms (kg) and 0.61 million kg, respectively. Figure A.4 shows a steep rise in the use of nitrogen and phosphorus fertilizers applied in a county between 1987 and 1996. Regressions with linear trend speciﬁcations show that annual farm nitrogen and phosphorus applied in a county increased on average by about 73.383 metric ton and 10.185 metric ton per year, respectively (see Table A.7).

3.4 Additional Control Variables

We draw weather data from Schlenker and Roberts(2009), population data from the U.S. Census Bureau, and gross domestic product (GDP) data from Deschenesˆ and Greenstone(2011). Table A.1 provides a summary of these control variables in the study. Table A.1 reports that the average annual precipitation and temperature is roughly 48 centimeters (cm) and 20◦C, respectively. The average upstream precipitation in the sample is 37.20 cm. The empirical model employs quadratic terms of precipitation, temperature, upstream precipitation, log GDP and log population.

4 Econometric Strategy

This section describes the econometric mode used to estimate impact of fertilizer application on water quality.

4.1 Fertilizer Use and Water Quality

We estimate the following equation to evaluate the causal impact of fertilizer application on water quality: 0 log Nwt = θ log Ywt + Xwt.β + γw + δrt + wt (4.1.1)

10Annual sales data compiled by Association of American Plant Food Control Ofﬁcials (AAPFCO) include information on state, county, quantity (in tons) sold, fertilizer code, an optional code distinguishing the intended use as farm or non-farm, and the individual percentage content of nitrogen-phosphate-potash (N-P-K) for the fertilizer. Nitrogen and Phosphorus fertilizer for farm and non-farm use were estimated by the summation of tonnage sold times the percentage of N and P in each product and times the proportion sold for farm use. State-level N and P commercial fertilizer farm use data were disaggregated to the county-level based on proportion of fertilizer expenditure of a county in the state.

7 where Nwt is the concentration of agricultural pollutants such as nitrogen or phosphorus in 0 watershed w and year t. Ywt is the amount of fertilizer used in watershed w in year t. Xwt includes a vector of urban runoff, prior concentration of agricultural pollutant, quadratic terms of annual temperature, precipitation, upstream rainfall shock, total population and GDP. The fixed effects γw control for all time-invariant determinants of water quality specific to watershed w, such as size, main channel gradient and land slope. We also include water resource region-by-year fixed effects δrt that control for time-varying differences in water quality that are common across watersheds in a water resource region (2-digit HU), such as pollution cleanup intervention implemented in the Missouri water resource region in response to rising agricultural pollutants. Note that the estimation sample consists of 18 water resource regions in the conterminous United States. Our weather variables proxy for key components of the hydrologic cycle and collectively provide a foundation for how landscapess and water interact (Edwards et al., 2015). To account for environmental preferences and the scope of economic activities, we use quadratic terms of GDP and population (Aklin et al., 2013). The stochastic error term, wt, includes other factors that determine water quality. θ represents the average percentage change in water quality for every percentage change in fertilizer use and is the main coefficient of interest. Two methodological issues are worth highlighting. First, we take advantage of the Geographical Information Systems (GIS) to ensure that agricultural runoff originating from fertilizer application in county i flows to watershed w. Specifically, we overlay the 8-digit HU watershed polygons with a raster of county boundaries and a raster of the cropland cover to derive a county/cropland cover area weighting factor. We apply the weighting factor to the county-level fertilizer data for counties within each 8-digit HU and sum to yield the total fertilizer applied within each 8-digit HU watershed. This helps us aggregate primary variables of our interest at the watershed level. Second, we seek to incorporate differences in water quality for both upstream and downstream watersheds.11 We include upstream rainfall shock within each 6-digit HU as an explanatory variable for downstream watershed pollution and account for spatial spillovers when pollutants move downstream and traverse multiple locations. Previous literature has explored local spillovers in panel models by including shocks to one’s neighbors, trade partners, or aid providers as explanatory variables for local outcomes (Munshi, 2003; Dell et al., 2014). We follow this standard approach in trade literature involving cross-border effects that extend beyond isolated spatial responses, and apply in the context of our study to account for potential bias from omission of substantive spillovers.

11Note that hydrologic units (HUs) are numbered sequentially, beginning upstream at the hydrologic unit with the uppermost outlet. Downstream codes are in ascending order within each 6-digit HU level (lower numbers always ﬂow into higher numbers). For example, one can start at the upstream end of the drainage and code the ﬁrst 8-digit HU unit as 09080201, code the next 8-digit HU unit downstream as 09080202, and so forth.

8 5 Results

5.1 Fertilizer Use and Water Quality

Table3 evaluates the impact of agricultural fertilizer use on the concentration of agricultural pollutants in water sources. Moving from left to right in the table, we estimate the same empirical model with watershed fixed effects and hydrologic region-by-year fixed effects while adding a progressively richer set of control variables. In column (1), we include watershed fixed effects and hydrologic region-by-year fixed effects. In column (2), (3) and (4), we add quadratic terms of weather controls, population and GDP, respectively. Our weather controls include annual precipitation, annual temperature and their quadratic terms. Column (5) addresses potential concerns regarding spatial spillovers, which arise when stochastic runoff and pollution in an upstream watershed propagates to downstream watershed (e.g., through transport or rainfall). Omission of substantive spillovers in the OLS estimation could create bias.12 Previous literature has examined local spillovers in panel models by including shocks to one’s neighbors, trade partners, or aid providers as explanatory variables for local outcomes (Munshi, 2003; Dell et al., 2014). Following this approach, we include upstream rainfall shock as an explanatory variable for downstream watershed pollution and account for spatial spillovers when pollutants move downstream and traverse multiple locations.13 Specifically, we incorporate 8-digit HU watershed polygons in our empirical analysis, allowing us to differentiate both upstream and downstream watersheds.14. In addition, column (6) employs total amount of solids from construction sites as a proxy for urban runoff, which is linked with agricultural pollution. Finally, column (7) includes lagged concentration of agricultural pollutant to reflect the fact that historic levels of pollution in a given watershed may determine concentration of current agricultural pollutants.15 We find strong and positive impact of both nitrogen and phosphorus fertilizer use (kg) on the concentration of nitrogen (mg/l) and phosphorus (mg/l) in water sites across the United States (see Table3). Our preferred specification in column (7) of Panel A shows that a 10% increase in the use

12Assuming positive correlation between upstream and downstream pollution, this bias could be either positive or negative conditional on relationship between upstream pollution and downstream fertilizer use. 13An alternative approach to examine spatial spillovers involves using number of upstream watersheds for each downstream watershed within a 6-digit HU as an explanatory variable for downstream pollution. While our main empirical speciﬁcation doesn’t follow this approach, our results don’t change much when this approach is employed. Results are available upon request. 148-digit HUs are numbered sequentially, beginning upstream at the hydrologic unit with the uppermost outlet. Downstream watersheds are in ascending order within each 6-digit HU level. 15Although inclusion of lags of the dependent variable biases coefﬁcient estimates in short panel models, this bias declines at the rate 1/T , where T is the number of observations within a group (Nickell, 1981). The panel employed in this study is long enough that such biases can probably be safely considered “second-order” (Dell et al., 2014). It is also worth noting that exclusion of the lagged dependent variable may bias the estimates if it is an important part of the data-generating process.

9 of nitrogen fertilizer (kg) leads to a 1.47% increase in the concentration of nitrogen (mg/l) across water sites. Similarly, our preferred specification in column (7) of Panel B shows that a 10% increase in the use of phosphorus fertilizers (kg) leads to a 1.68% increase in the concentration of phosphorus (mg/l) across water sites. These estimates are robust across multiple specifications from column (1) to column (6), with values ranging from 1.36% to 1.47% in the case of nitrogen pollution and from 1.28% to 1.68% in the case of phosphorus pollution. We further break down our estimates across watersheds belonging to four different categories, namely (a) metropolitan areas, (b) non-metropolitan areas, (c) areas with high GDP, and (d) areas with low GDP.16 This is important because the extent to which urban and suburban ecosystems contribute to water use and nutrient pollution across the US is of significant policy concern (Groffman et al., 2016). Table4 runs our primary empirical specification across four specific sub-groups. Although this exercise results in a decrease of the sample size, we find that our parameter estimates are quite similar to those shown in the comparable columns of Table3. Across all four categories, Panel A shows that the effect of nitrogen fertilizer use on nutrient pollution ranges from 1.03% in metro areas to 1.53% in areas with low GDP. These estimates suggest that fertilizer use leads to poorer water quality in non-metro areas (1.47%) compared to metro areas (1.03%), and in areas with low GDP (1.53%) compared to areas with high GDP (1.37%). Panel B reports a similar finding in the case of phosphorus pollution, with large effects in non-metro areas (1.98%) and areas with low GDP (2.37%). Figure5 explores the state-level heterogeneity in impact of fertilizer use on both nitrogen and phosphorus pollution. Given that different environmental regulations such as phosphorus fertilizer bans have been implemented across states in recent years, environmental externalities from agricultural fertilizer use at the state-level have significant policy implications. The marginal effect of fertilizer on nutrient pollution is heterogenous across different states. In the case of nitrogen fertilizer, the elasticity estimates range from 0.2% to 4.4%. Missouri, Wisconsin, Maryland, Illinois, Kansas, Rhode Island and Delaware report to have significant increases in nutrient pollution from the use of nitrogen fertilizers with estimated values of 2.7%, 2.8%, 3.1%, 3.8%, 4.1%, 4.3% and 4.4%, respectively. The marginal effect of phosphorus fertilizer on nutrient pollution is even more heterogenous, with estimates ranging from 0.1% to 6.6%. For example, Pennsylvania, Mississippi, Tennessee, Florida and Wisconsin report estimates of 3.2%, 3.8%, 4.6%, 4.8% and 5.1%, respectively. Finally, figure6 explores the heterogeneity in nutrient pollution elasticity estimates across eighteen water resource regions. Figure A.5 shows the same figure with 95% confidence intervals.

16Our deﬁnition of metropolitan and non-metropolitan areas is based on the 2013 Rural-Urban Continuum Codes, which distinguish metropolitan areas by the population size of their metro area, and non-metropolitan areas by degree of urbanization and adjacency to a metro area. Similarly, we use the annual median GDP at the state-level as a cutoff to construct areas of high and low GDP.

10 Texas-Gulf, Ohio, Tennessee, Upper Mississippi, Lower Mississippi, Great Basin and Paciﬁc Northwest report high values of marginal effect estimates on nitrogen pollution, ranging from 2.2% to 7.3%. In the case of phosphorus pollution, estimates are between 1.7% to 4.7% across Ohio, Tennessee, Texas-Gulf, Arkansas-White-Red and Great Basin.

5.2 Robustness Checks

We perform four additional tests to strengthen the validity of our prior estimates on the effect of fertilizer use on nutrient pollution. First, we explore the impact of agricultural fertilizer use intensity (kg/acre) on concentration of agricultural pollutants in water sites. Table5 shows that a 10% increase in nitrogen fertilizer use intensity (kg/acre) leads to a 1.17% increase in nitrogen pollution. Moreover, a 10% increase in phosphorus fertilizer use intensity (kg/acre) is associated with a 1.37% increase in phosphorus pollution. These estimates are robust across multiple specifications from column (1) to column (6) across both panels in Table5. Second, we test the impact of phosphorus fertilizer use on amount of nitrogen in water, and expect the coefficient estimate to be negligible. Column (1) in Table6 shows that farm phosphorus fertilizer application doesn’t directly predict nitrogen concentration in water. Similarly, column (4) in Table6 shows that farm nitrogen fertilizer application doesn’t directly influence phosphorus concentration in water. This placebo test provides additional confidence in our primary finding. Third, we assess the linkage between non-farm fertilizer use and nutrient pollution, and anticipate the effect to be zero. As expected, columns (2) and (5) show that non-farm nitrogen fertilizer doesn’t have a significant economic impact on the concentration of nitrogen and phosphorus in water. Similarly, columns (3) and (6) show that non-farm phosphorus fertilizer doesn’t have any considerable impact on nitrogen and phosphorus concentration in water. Finally, we conduct a placebo test on the impact of agricultural fertilizer use on concentration of non-agricultural pollutant, an alternate measure of water quality. Specifically, we use Secchi depth as a measure of water quality, and hypothesize that fertilizer use has a statistically insignificant effect on Secchi depth.17 Table A.8 shows that farm fertilizer application has no economic impact on Secchi depth, and the estimates are statistically insignificant across multiple specifications.

17Sechhi depth is a measure of light penetration into water body and is a function of absorption or scattering of light in the water. Scientists measure water transparency with a Secchi disk. A Secchi disk is a metal disk, 8 inches in diameter that is lowered into the water on a cord. Sechhi depth is deﬁned as depth at which a white disc is indistinguishable from surrounding water. Three factors that affect the depth to which light will penetrate include (a) amount of color (b) presence of algae, and (c) inorganic or erosional materials. When the water transparency is high, the Secchi depth is high. When the water transparency is low and cloudy, the Secchi depth is low.

11 5.3 Discussion

We have shown that an increase in agricultural fertilizer use leads to substantial deterioration in water quality. To illustrate the economic significance of our agricultural pollutant elasticities, we use results from prior studies and estimate the effect of an increase in fertilizer use on the size of the hypoxic zone in the Gulf of Mexico. Specifically, Hendricks et al.(2014) use regression estimates from Obenour et al.(2012) and report that a 0.23% increase in nitrogen concentration translates to an increase of 25 square miles in the size of the hypoxic zone with a standard error of 5.45 square miles. Our estimates suggest that a 100% increase in the use of nitrogen fertilizers leads to a 14.7% increase in the concentration of nitrogen pollutant. Applying the statistics reported in Hendricks et al.(2014) and Obenour et al.(2012), a 14.7% increase in nitrogen concentration results in an increase of 1,598 square miles in the size of the hypoxic zone, which is an area about the size of Rhode Island and almost 20% the measured size of the “dead zone” in the Gulf of Mexico. From a policy perspective, it is useful to determine the magnitude of change in fertilizer use that is required to minimize nutrient pollution and the size of hypoxic area in the Gulf of Mexico. Given prior estimates on the relationship between the size of the hypoxic zone and nutrient concentrations (Forrest et al., 2011), we are able to provide more information on the percentage decrease in use of fertilizers required to achieve a statistically significant reduction in the size of hypoxic area. For example, table7 shows that achieving a 11.5% reduction in nitrogen pollution over a ten year time horizon requires a 51.2% decrease in the use of nitrogen fertilizers. Table7 suggests that reducing the concentration of nitrogen by 9-15% between 5 and 20 years requires a 41-67% decrease in the use of nitrogen fertilizers. In the context of the Hypoxia Task Force 2008 Action Plan, our estimates highlight the need to design policies aimed at addressing excess nitrogen and phosphorus loads across water sites and reducing the size of the Gulf hypoxic zone. Our primary findings can also be combined with other studies that delve into evaluating the impact of agricultural policies designed to reduce farm income variability. For example, Weber et al.(2016) find that a 10% increase in insurance coverage (measured by premiums per acre) is associated with a 0.44% increase in fertilizer expenses per acre. A back-of-the-envelope calculation, using our data, suggests that a 10% increase in insurance coverage translates to a 0.05% increase in the concentration of nitrogen pollution.18 In addition to providing plausible estimates of the effects of agricultural fertilizer use on environment, our paper complements prior literature focused on evaluating policies aimed at improving agricultural outcomes. For example, other studies can benefit from our pollution elasticity measures in quantifying environmental externalities from agriculture.

18Our estimates show that a 10% increase in fertilizer expenses information collected from the Census of Agriculture results in a 1.2% increase in the concentration of nitrogen in water. This allows us to generate a value of 0.05% change in agricultural pollution.

12 We compare our methods and results to four previous studies that link fertilizer use and nitrogen losses. For example, Gowda et al.(2008) apply the Agricultural Drainage and Pesticide Transport (ADAPT) field scale water table management model to show that a 50% reduction in applied fertilizer leads to an 11% reduction in nitrate-N losses from a 365 ha agricultural watershed in central Iowa. Our nitrogen loss elasticity with respect to the use of nitrogen fertilizers in the state of Iowa is approximately 0.145, implying that a 50% reduction in fertilizer use results in a 7.25% increase in the concentration of nitrogen pollution. Our approach differs from Gowda et al.(2008), who perform a calibration exercise to predict nitrogen losses and use water quality simulation model to quantify the effect of agricultural practices on nutrient pollution. Given that we employ an econometric model to estimate nutrient pollution using observed nitrogen readings over a wider time horizon, it appears that different methodological approaches used in these two studies lead a slight difference in estimates. In a different study, Thorp et al.(2007) find that a 27% reduction in nitrogen fertilizer application rate corresponds to an 18% simulated reduction in nitrogen loss to water resources between 1996 and 2005 in Iowa. Our model, when restricted to the same ten-year period, gives a comparable elasticity estimate of 6.2%. Nangia et al.(2008) find that a 31.6% decrease in the fertilizer application rate leads to a 13% reduction in nitrate losses in south-central Minnesota. This results in a nitrogen loss elasticity of 0.41, almost equivalent to our estimate of 0.39. In a more recent study, Burkart and Jha(2012) run a 10-year SWAT simulation runs on the Raccoon watershed and find that a 10% reduction in nitrogen fertilizer leads to a 5.9% change in nitrate at the watershed outlet between 1993 and 2004, which is almost identical to our estimate of 6.15% during the time period. This shows that pollution elasticity estimates generated from an econometric model in this study are robust across different methodological approaches employed in prior literature.

6 Conclusion

This study employs the most comprehensive data available on agricultural pollution readings across the United States to estimate the percentage change in water quality that can be attributed to changes in on-farm fertilizer use. Using over 2.9 million nitrogen and phosphorus readings and controlling for watershed and water resource region-by-year ﬁxed effects, the study shows that a 10% increase in the use of nitrogen fertilizer (kg) leads to a 1.47% increase in the amount of nitrogen (mg/l) in water. The study also ﬁnds that a 10% increase in the use of phosphorus fertilizer (kg) leads to a 1.68% increase in the concentration of phosphorus (mg/l) in water. This study also highlights the extent to which urban and suburban ecosystems contribute to water use and nutrient pollution across the US. While we observe stark heterogeneity in marginal effect estimates on nitrogen and phosphorus pollution across different states and water resource

13 regions, our pollution elasticity estimates are robust across different simulation-based approaches employed in prior literature. Our estimates further suggest that a 100% increase in nitrogen fertilizer use leads to an increase of 1,598 square miles in the size of the hypoxic zone, which is approximately 20% the estimated size of the “dead zone” in the Gulf of Mexico. A back-of-the-envelope calculation shows that farmers need to decrease fertilizer application by 41-47% to improve water quality by 9-15% between 5 and 20 years. Future work will therefore beneﬁt from evaluating policies such as phosphorus fertilizer bans aimed at minimizing the use of fertilizers and improving water quality.

14 Figure 1: Water-resources regions and hierarchy of hydrologic units in the U.S.

(a) Water-resources regions of the United States

(b) Hierarchy of hydrologic units shown on Hydrologic unit maps

15 Figure 2: Density plot of agricultural pollutants in US water sources

15 .6

10 Fraction .4 Fraction

Overall Overall 5 .2 Stream Stream River River Lake Lake Estuary Estuary 0 0 0 5 10 15 0 1 2 3 4 5 Nitrogen (mg/l) Phosphorus (mg/l)

(a) Nitrogen concentration (b) Phosphorus concentration

Figure 3: US map of nitrogen and phosphorus concentration in water sources

(a) Nitrogen concentration (b) Phosphorus concentration

16 Figure 4: Farm Fertilizer Use in the United States, 1945-2012

(a) Nitrogen (b) Phosphorus

Figure 5: State-level heterogeneity in impact of fertilizer use on agricultural pollutants

(a) Nitrogen (b) Phosphorus

17 Figure 6: Water resource region-level heterogeneity in impact of fertilizer use on agricultural pollutants

Heterogeneity across Water-Resource Regions .8

Marginal effect estimate .2

Phosphorus Nitrogen 0

Ohio

Missouri California Texas-Gulf Mid-Atlantic Great Lakes Tennessee Rio Grande Great Basin New England Upper ColoradoLower Colorado Pacific Northwest South Atlantic Gulf Upper MississippiLower MississippiSouris-Red-Rainy Arkansas-White-Red

(a) Heterogeneity in pollution elasticity estimates

18 Table 1: Data summary of nitrogen readings from the Water Quality Portal (WQP), 1987-2006

Year Number of Observations Nitrogen (mg/l)

Readings Sites States Counties Mean Median Std. Dev. Min. Max. (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) 1987 25,558 3,472 50 905 5.71 2.70 7.65 0.05 48 1988 25,602 3,331 50 976 5.23 2.40 7.29 0.06 48 1989 28,857 3,660 50 1,010 5.06 2.40 6.96 0.05 48 1990 28,851 3,473 50 1,040 4.99 2.40 6.86 0.05 48 1991 32,867 3,963 50 1,050 4.74 2.30 6.71 0.05 48 1992 31,150 3,208 50 956 4.58 2.20 6.46 0.05 48 1993 29,734 3,607 49 1,073 3.24 1.60 4.78 0.05 48 1994 31,190 4,018 51 1,048 3.31 1.60 4.90 0.05 48 1995 28,664 3,430 50 917 3.29 1.50 5.03 0.05 48 1996 27,560 3,282 49 868 2.91 1.30 4.79 0.05 48 1997 26,486 3,193 48 875 2.73 1.30 4.27 0.05 48 1998 27,886 3,586 49 900 2.55 1.20 3.79 0.05 48 1999 33,730 4,253 51 929 2.39 1.10 3.63 0.05 48 2000 35,344 5,132 50 1,048 2.50 1.10 4.01 0.05 47 2001 40,852 6,010 51 1,176 2.15 0.90 3.70 0.05 48 2002 40,332 6,651 50 1,216 1.91 0.83 3.38 0.05 48 2003 44,099 7,656 51 1,327 1.78 0.82 3.44 0.05 48 2004 56,166 9,458 51 1,619 2.07 0.85 3.90 0.05 48 2005 59,507 9,382 51 1,386 1.80 0.82 3.41 0.05 48 2006 58,207 9,026 51 1,295 1.95 0.80 3.80 0.05 48 Overall 1,729,307 87, 883 51 2,963 3.27 1.24 5.57 0.05 48

Source: Data developed by the U.S. Geological Survey (USGS), the U.S. Environmental Protection Agency (EPA), and the National Water Quality Monitoring Council, 1987- 2006. Note that overall values apply for water readings from 1967 to 2015.

19 Table 2: Data summary of phosphorus readings from the Water Quality Portal (WQP), 1987-2006

Year Number of Observations Phosphorus (mg/l)

Readings Sites States Counties Mean Median Std. Dev. Min. Max. (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) 1987 7,477 1,494 33 292 0.32 0.09 0.68 0.001 4.60 1988 9,269 1,704 35 361 0.27 0.08 0.57 0.003 4.60 1989 12,495 2,147 35 378 0.23 0.08 0.46 0.003 4.60 1990 13,767 2,806 49 757 0.25 0.07 0.53 0.001 4.60 1991 23,624 4,336 50 943 0.27 0.09 0.54 0.000 4.60 1992 24,261 3,902 49 920 0.26 0.09 0.54 0.001 4.60 1993 20,315 2,792 34 452 0.25 0.08 0.54 0.001 4.60 1994 20,503 3,004 35 456 0.20 0.06 0.43 0.001 4.60 1995 18,887 2,617 29 427 0.21 0.07 0.43 0.001 4.60 1996 17,413 2,360 28 392 0.20 0.06 0.43 0.002 4.60 1997 14,563 2,496 26 381 0.20 0.08 0.40 0.001 4.60 1998 17,084 2,698 32 421 0.23 0.08 0.47 0.000 4.60 1999 35,460 4,353 36 580 0.18 0.06 0.41 0.000 4.60 2000 37,693 4,896 40 622 0.19 0.05 0.43 0.000 4.58 2001 39,342 5,492 36 635 0.19 0.07 0.37 0.000 4.58 2002 39,266 6,039 39 740 0.17 0.07 0.34 0.000 4.53 2003 40,809 6,935 35 749 0.18 0.07 0.37 0.000 4.60 2004 47,024 7,448 39 745 0.19 0.07 0.39 0.000 4.60 2005 47,681 7,843 40 693 0.19 0.07 0.39 0.000 4.60 2006 46,985 7,986 40 701 0.17 0.06 0.36 0.000 4.59 Overall 1,246,363 77,742 51 2,477 0.20 0.06 0.44 0.00 4.60

20 Table 3: Impact of fertilizer use on agricultural pollutants in water

Panel A Dependent Variable: Log Nitrogen Pollutant (mg/l) (1) (2) (3) (4) (5) (6) (7) Log Nitrogen Fertilizer (kg) 0.136*** 0.145*** 0.144*** 0.144*** 0.144*** 0.145*** 0.147*** (0.010) (0.011) (0.011) (0.011) (0.011) (0.028) (0.029) N 57,546 47,973 47,973 47,973 47,973 11,392 10,943 R-Squared 0.317 0.392 0.392 0.392 0.392 0.465 0.469 Watershed fixed effects Yes Yes Yes Yes Yes Yes Yes Region-by-year fixed effects Yes Yes Yes Yes Yes Yes Yes Weather controls No Yes Yes Yes Yes Yes Yes Gross Domestic Product (GDP) No No Yes Yes Yes Yes Yes Population No No No Yes Yes Yes Yes Upstream precipitation No No No No Yes Yes Yes Urban runoff No No No No No Yes Yes Lagged nitrogen (mg/l) No No No No No No Yes Panel B Dependent Variable: Log Phosphorus Pollutant (mg/l) (1) (2) (3) (4) (5) (6) (7) Log Phosphorus Fertilizer (kg) 0.151*** 0.138*** 0.135*** 0.128*** 0.128*** 0.166*** 0.168*** (0.012) (0.015) (0.014) (0.014) (0.014) (0.036) (0.037) N 40,239 31,687 31,687 31,687 31,687 6,037 5,598 R-Squared 0.350 0.470 0.471 0.472 0.472 0.637 0.638 Watershed fixed effects Yes Yes Yes Yes Yes Yes Yes Region-by-year fixed effects Yes Yes Yes Yes Yes Yes Yes Weather controls No Yes Yes Yes Yes Yes Yes Gross Domestic Product (GDP) No No Yes Yes Yes Yes Yes Population No No No Yes Yes Yes Yes Upstream precipitation No No No No Yes Yes Yes Urban runoff No No No No No Yes Yes Lagged phosphorus (mg/l) No No No No No No Yes

Note: Standard errors are clustered by watershed. *** indicates significance at the 1% level, ** indicates significance at the 5% level and * indicates significance at the 10% level.

21 Table 4: Heterogenous impact of fertilizer use on agricultural pollutants in water

Panel A Dependent Variable: Nitrogen Pollutant (mg/l) Metropolitan Non-metropolitan High GDP Low GDP (1) (2) (3) (4) Nitrogen Fertilizer (kg) 0.103*** 0.147*** 0.137*** 0.153* (0.039) (0.052) (0.031) (0.088) N 5,511 5,447 8,319 2,639 R-Squared 0.559 0.557 0.535 0.517 Watershed fixed effects Yes Yes Yes Yes Region-by-year fixed effects Yes Yes Yes Yes Weather controls Yes Yes Yes Yes Gross Domestic Product (GDP) Yes Yes Yes Yes Population Yes Yes Yes Yes Upstream precipitation Yes Yes Yes Yes Urban runoff Yes Yes Yes Yes Lagged nitrogen (mgll) Yes Yes Yes Yes Panel B Dependent Variable: Phosphorus Pollutant (mg/l) Metropolitan Non-metropolitan High GDP Low GDP (1) (2) (3) (4) Phosphorus Fertilizer (kg) 0.029 0.198*** 0.150*** 0.237** (0.070) (0.055) (0.049) (0.102) N 2,333 3,289 4,053 1,569 R-Squared 0.696 0.705 0.654 0.686 Watershed fixed effects Yes Yes Yes Yes Region-by-year fixed effects Yes Yes Yes Yes Weather controls Yes Yes Yes Yes Gross Domestic Product (GDP) Yes Yes Yes Yes Population Yes Yes Yes Yes Upstream precipitation Yes Yes Yes Yes Urban runoff Yes Yes Yes Yes Lagged phosphorus (mgll) Yes Yes Yes Yes

Note: Our definition of metropolitan and non-metropolitan areas is based on the 2013 Rural- Urban Continuum Codes, which distinguish metropolitan areas by the population size of their metro area, and non-metropolitan areas by degree of urbanization and adjacency to a metro area. We use annual median GDP at the state-level as a cutoff to construct areas of high and low GDP. Standard errors are clustered by watershed. *** indicates significance at the 1% level, ** indicates significance at the 5% level and * indicates significance at the 10% level.

22 Table 5: Impact of fertilizer use per area on agricultural pollutants in water

Panel A Dependent Variable: Log Nitrogen Pollutant (mg/l) (1) (2) (3) (4) (5) (6) (7) Log Nitrogen Fertilizer (kg/acre) 0.114*** 0.114*** 0.113*** 0.114*** 0.114*** 0.120*** 0.117*** (0.008) (0.009) (0.009) (0.009) (0.009) (0.022) (0.023) (0.025) (0.024) (0.024) (0.025) (0.028) (0.051) (0.052) N 57,425 47,909 47,909 47,909 47,909 11,386 10,960 R-Squared 0.374 0.392 0.392 0.392 0.392 0.466 0.473 Watershed fixed effects Yes Yes Yes Yes Yes Yes Yes Region-by-year fixed effects Yes Yes Yes Yes Yes Yes Yes Weather controls No Yes Yes Yes Yes Yes Yes Gross Domestic Product (GDP) No No Yes Yes Yes Yes Yes Population No No No Yes Yes Yes Yes Upstream precipitation No No No No Yes Yes Yes Urban runoff No No No No No Yes Yes Lagged nitrogen (mg/l) No No No No No No Yes Panel B Dependent Variable: Phosphorus Pollutant (mg/l) (1) (2) (3) (4) (5) (6) (7) Phosphorus Fertilizer (kg/acre) 0.108*** 0.097*** 0.095*** 0.091*** 0.091*** 0.130*** 0.137*** (0.010) (0.011) (0.011) (0.011) (0.011) (0.031) (0.034) N 40,091 31,571 31,571 31,571 31,571 5,990 5,577 R-Squared 0.346 0.466 0.467 0.468 0.468 0.629 0.633 Watershed fixed effects Yes Yes Yes Yes Yes Yes Yes Region-by-year fixed effects Yes Yes Yes Yes Yes Yes Yes Weather controls No Yes Yes Yes Yes Yes Yes Gross Domestic Product (GDP) No No Yes Yes Yes Yes Yes Population No No No Yes Yes Yes Yes Upstream precipitation No No No No Yes Yes Yes Urban runoff No No No No No Yes Yes Lagged phosphorus (mg/l) No No No No No No Yes

Note: Standard errors are clustered by watershed. *** indicates significance at the 1% level, ** indicates significance at the 5% level and * indicates significance at the 10% level.

23 Table 6: Impact of non-farm fertilizer application on nutrient pollution

Dependent Variable: Nitrogen (mg/l) Phosphorus (mg/l)

(1) (2) (3) (4) (5) (6) Farm P Fertilizer (kg) 0.001 (0.001) Farm N Fertilizer (kg) 0.000* (0.000) Non Farm N Fertilizer (kg) -0.001 0.000* (0.001) (0.000) Non Farm P Fertilizer (kg) -0.012 0.000*** (0.008) (0.000) N 34647 34696 34696 19414 19424 19424 R-Squared 0.101 0.101 0.101 0.199 0.199 0.200

Watershed fixed effects Yes Yes Yes Yes Yes Yes Month fixed effects No Yes Yes No Yes Yes Year fixed effects No No Yes No No Yes

Note: Standard errors are clustered by watershed. *** indicates significance at the 1% level, ** indicates significance at the 5% level and * indicates significance at the 10% level.

Table 7: Estimates on required nitrogen fertilizer reduction to minimize the size of hypoxic area in the Texas-Gulf region

Time horizon (years) Nitrogen concentration reduction (%) Nitrogen fertilizer reduction(%)

(1) (2) (3) 1 31.6 140.9 2 22.7 101.2 5 15.1 67.3 10 11.5 51.2 20 9.2 41.0

Note: This table is based on work by Forrest et al.(2011), who compute required nutrient concentration reductions for a statistically resolvable reduction in hypoxic area. Calculations on nitrogen concentration reduction are based on the UEDC (U wind, post-1993 epoch, river discharge, and nitrogen concentration) model. The elasticity of nitrogen concentration with respect to nitrogen fertilizer use in the Texas-Gulf region (0.22) determined in this study is used to estimate the percentage reduction in nitrogen fertilizer. The time horizon represents the years of observation after the stated nutrient reduction has occurred.

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27 A Water Quality Trends

This section describes the (i) econometric models for investigating trends in nitrogen and phosphorus concentration in water sites, and (ii) results outlining change in water quality over time

A.1 Econometric Model

We use the following equation to evaluate year-by-year changes in water quality:

τ=2015 X 0 Nict = ατ 1[tt = τ] + Xictβ + γi + ict (A.1.1) τ=1967 where each observation in this analysis is an individual water quality reading at monitoring site i, calendar day-of-year c, and year t. The Nict represents the amount of nitrogen or phosphorus (mg / l) present in monitoring site i. Consistent with Keiser and Shapiro(2017), Xict includes a day-of-year and a month-of-year cubic polynomial. We include weather and county controls in Xict in sensitivity analyses. The fixed effects γi control for all time-invariant determinants of water quality specific to monitoring site i. The stochastic error term, ict, includes other factors that determine water quality. By plotting the year-by-year coefficients and the constant term, we are able to demonstrate national-level mean patterns of water quality, controlling for time and monitoring site characteristics. We also estimate linear water quality trends using the following equation: 0 Nict = αtt + Xictβ + γi + ict (A.1.2) where α represents the mean annual change in water quality and is the main coefficient of interest.

28 A.2 Trends in Water Quality

We begin with an investigation on change in water quality over time. To evaluate how US water quality has changed over time, we focus on concentration of nitrogen and phosphorus in water sites between 1967 and 2015. We find substantial improvement in nutrient pollution across the whole country.19 Figure A.3(a) shows that although we observe an increase in nitrogen concentration between 1967 and 1983, we find a drastic decrease in amount of nitrogen every year from 1983 onward Figure A.3(b) shows that phosphorus concentration in water sites has decreased significantly from less than 1 mg / l to 0.25 mg / l. Figure A.3(a) also suggests that agricultural pollution trends measured by amount of nitrogen in water sites have gradually slowed in recent years. This might be attributable to potential sources of nitrogen pollution in water: unregulated agricultural sources and nonpoint sources. It is also worth pointing out that our data set offers relatively small number of observations for water quality readings in the first four years, from 1967 to 1970. However, from 1974 onward, our sample includes at least 20,000 water quality readings. Year-by-year trends for nitrogen and phosphorus follow a similar pattern, suggesting that water quality has improved over time. Tables A.5 and A.6 show that concentration of nitrogen and phosphorus in water sites has fallen by 2.4 percent per year and 1.5 percent per year, respectively (Panel A, Column 1). Our finding of substantial improvement in water quality is consistent with Keiser and Shapiro(2017), who find positive trends in a wide range of pollutants since the Clean Water Act. We estimate a large number of sensitivity analyses, including restricting the sample to specific water sites (such as estuary, lakes, rivers and streams), census divisions (West, Midwest, South and Northeast), number of readings (at least 25) and time period (July and August only). All of these alternative approaches exhibit similar signs, magnitude and precision. This gives us additional confidence that trends in water quality have improved over the last fifty years.

19Here and throughout, we use a locally estimated scatterplot smoothing procedure to present time-series results.

29 Figure A.1: Yearly trend in pollution readings from the Water Quality Portal (WQP), 1987-2006

4.5

3.5

3 Nitrogen (mg/l)

2.5

All sites 2 Panel sites only

1987 1990 1993 1996 1999 2002 2005 Year

(a) Nitrogen readings

.35

.25 Phosphorus (mg/l) .2

All sites Panel sites only .15 1987 1990 1993 1996 1999 2002 2005 Year

(b) Phosphorus readings

30 Figure A.2: Concentration of agricultural pollutants in US water-resource regions

New England .5 Mid-Atlantic South Atlantic-Gulf Great Lakes .4 Ohio Tennessee Upper Mississippi .3 Lower Mississippi Souris-Red-Rainy Missouri

Fraction Arkansas-White-Red .2 Texas-Gulf Rio Grande Upper Colorado .1 Lower Colorado Great Basin Pacific Northwest California 0 0 5 10 15 Nitrogen (mg/l)

(a) Nitrogen readings

8 New England Mid-Atlantic South Atlantic-Gulf Great Lakes 6 Ohio Tennessee Upper Mississippi Lower Mississippi Souris-Red-Rainy 4 Missouri

Fraction Arkansas-White-Red Texas-Gulf Rio Grande 2 Upper Colorado Lower Colorado Great Basin Pacific Northwest California 0 0 1 2 3 4 5 Phosphorus mg/l)

(b) Phosphorus readings

31 Figure A.3: Trends in concentration of nitrogen and phosphorus in US water sites, 1967-2015

5 1.2

4 1

.8 3

.6 Nitrogen (mg/l)

2 Phosphorus (mg/l)

.4 Average Average 1 Upper Bound Upper Bound Lower Bound .2 Lower Bound

1967 1971 1975 1979 1983 1987 1991 1995 1999 2003 2007 2011 2015 1967 1971 1975 1979 1983 1987 1991 1995 1999 2003 2007 2011 2015 Year Year

(a) Nitrogen readings (b) Phosphorus readings

Note: Coefﬁcients are from regressions described in (A.1.1). Dashed lines show 95% conﬁdence interval.

Figure A.4: Trends in farm fertilizer application, 1987-2006

4.2 25

24 4

23 3.8

3.6 Farm Nitrogen (kg / acre)

21 Farm Phosphorus (kg / acre) Average Average Upper Bound Upper Bound Lower Bound Lower Bound 20 3.4 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 Year Year

(a) Nitrogen (b) Phosphorus

Note: Coefﬁcients are from regressions similar to (A.1.1). Dashed lines show 95% conﬁdence interval.

32 Figure A.5: Water Resource Region-level heterogeneity in impact of fertilizer use on agricultural pollutants 2 1

95% CI 0 Average -1 Marginal effect estimate -2 Ohio Missouri California Texas-Gulf Tennessee Rio Grande Mid-Atlantic Great Basin Great Lakes New England Upper Colorado Lower Colorado Pacific Northwest Upper Mississippi Lower Mississippi Souris-Red-Rainy South Atlantic Gulf Arkansas-White-Red

(a) Nitrogen 1 .5

95% CI Average 0 Marginal effect estimate -.5 Ohio Missouri California Texas-Gulf Tennessee Rio Grande Mid-Atlantic Great Basin Great Lakes New England Upper Colorado Lower Colorado Pacific Northwest Upper Mississippi Lower Mississippi Souris-Red-Rainy South Atlantic Gulf Arkansas-White-Red

(b) Phosphorus

33 Table A.1: Summary statistics of variables in the study

Variables Observations Mean Std. Dev. Minimum Maximum Nitrogen pollution (mg/l) 57,546 3.06 5.17 0.00 47.00 Phosphorus pollution (mg/l) 41,742 0.47 1.74 0.00 48.30 Farm nitrogen fertilizer (kg) 57,546 3,603,238 4,569,531 0 93,300,000 Farm phosphorus fertilizer (kg) 57,646 611,310 707,949 0 11,100,000 Lagged nitrogen pollution (mg/l) 55,013 3.11 5.21 0.00 47.00 Lagged phosphorus pollution (mg/l) 39,645 0.47 1.75 0.00 48.30 Precipitation (cm) 47,521 48.40 23.88 0.09 148.51 Temperature (◦C) 47,521 20.08 3.76 8.37 31.24 Log urban runoff (mg/l) 11,856 6.30 1.62 0.00 10.48 Upstream precipitation (cm) 48,858 37.20 28.84 0.00 142.54 Log Gross Domestic Product (GDP) 57,022 11.20 1.35 7.42 14.11 Log Population 57,022 15.25 1.10 12.69 17.37

34 Table A.2: Data summary of nitrogen readings across a panel of water sites from the Water Quality Portal (WQP), 1987-2006

Year Number of Observations Nitrogen (mg/l)

Readings Sites States Counties Mean Median Std. Dev. Min. Max. (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) 1987 3343 173 29 116 4.637 2.300 5.749 0.100 46 1988 3838 173 29 116 4.639 2.100 6.198 0.0700 48 1989 4105 173 29 116 4.181 2 5.050 0.0500 46 1990 3902 173 29 116 4.222 2.200 5.010 0.0600 46 1991 3548 173 29 116 4.186 2.100 5.305 0.0600 48 1992 3776 173 29 116 4.175 2.200 4.919 0.0500 46 1993 3140 173 29 116 3.099 1.700 3.675 0.0700 39 1994 3220 173 29 116 2.874 1.500 3.310 0.0500 31 1995 3026 173 29 116 2.972 1.487 3.480 0.0500 32 1996 3098 173 29 116 2.274 1.400 2.783 0.0500 33 1997 2642 173 29 116 2.281 1.388 2.492 0.0500 29 1998 2870 173 29 116 1.877 1.100 2.255 0.0500 20 1999 2846 173 29 116 1.886 1.100 2.073 0.0500 12 2000 2810 173 29 116 1.882 1.200 2.285 0.0500 17 2001 3073 173 29 116 1.794 1 2.186 0.0500 14 2002 2956 173 29 116 1.851 1 2.308 0.0500 21 2003 2727 173 29 116 1.524 0.950 1.941 0.0500 18 2004 2526 173 29 116 1.398 0.890 1.763 0.0500 20 2005 2355 173 29 116 1.422 0.880 1.874 0.0500 23 2006 2433 173 29 116 1.396 0.790 1.876 0.0500 22

Source: Data developed by the U.S. Geological Survey (USGS), the U.S. Environmental Protection Agency (EPA), and the National Water Quality Monitoring Council, 1987-2006.

35 Table A.3: Data summary of phosphorus readings across a panel of water sites from the Water Quality Portal (WQP), 1987-2006

Year Number of Observations Nitrogen (mg/l)

Readings Sites States Counties Mean Median Std. Dev. Min. Max. (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) 1987 977 110 5 49 0.274 0.0850 0.542 0.00100 4.460 1988 858 110 5 49 0.283 0.0810 0.551 0.00500 4 1989 918 110 5 49 0.306 0.0750 0.579 0.00500 4.450 1990 1069 110 5 49 0.325 0.0850 0.632 0.00500 4.480 1991 1198 110 5 49 0.280 0.0640 0.541 0.00100 4.530 1992 1337 110 5 49 0.217 0.0600 0.450 0.0100 4.150 1993 1267 110 5 49 0.218 0.0670 0.419 0.0100 4.250 1994 1328 110 5 49 0.184 0.0590 0.348 0.00700 4.280 1995 1167 110 5 49 0.195 0.0600 0.390 0.0100 3.970 1996 1081 110 5 49 0.146 0.0500 0.322 0.0100 4.300 1997 604 110 5 49 0.195 0.0809 0.354 0.0100 3.440 1998 888 110 5 49 0.217 0.0630 0.441 0.0100 4.470 1999 1611 110 5 49 0.176 0.0680 0.287 0.00300 3.780 2000 1441 110 5 49 0.246 0.0680 0.424 0.00200 3.830 2001 1479 110 5 49 0.267 0.0900 0.474 0.00200 4.420 2002 1415 110 5 49 0.220 0.0730 0.390 0.00100 4.380 2003 1353 110 5 49 0.359 0.100 0.631 0.00200 4.450 2004 1515 110 5 49 0.274 0.0920 0.467 0.00300 4.270 2005 1117 110 5 49 0.234 0.0710 0.374 0.00200 2.870 2006 1233 110 5 49 0.235 0.0710 0.442 0.00200 4.270

Source: Data developed by the U.S. Geological Survey (USGS), the U.S. Environmental Protection Agency (EPA), and the National Water Quality Monitoring Council, 1987-2006.

36 Table A.4: Data summary of urban runoff from the Water Quality Portal (WQP), 1987-2006

Year Number of Observations Log urban runoff (mg/l)

Readings Sites States Counties Mean Median Std. Dev. Min. Max. (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) 1987 9230 1311 28 361 4.622 4.787 1.720 0.693 9.770 1988 10010 1326 27 334 4.819 5.075 1.730 0.693 9.764 1989 11275 1716 26 417 4.940 5.100 1.699 0.693 9.687 1990 10966 1654 29 420 4.780 4.868 1.721 0.693 9.741 1991 12533 1861 28 388 5.122 5.193 1.717 0.693 9.749 1992 13550 1662 30 383 4.832 5.075 1.804 0.693 9.741 1993 12743 1754 33 411 4.861 5.136 1.807 0.693 9.766 1994 12089 1857 30 414 4.867 5.193 1.910 0.693 9.771 1995 12163 1576 35 406 4.799 5.124 1.959 0.693 9.758 1996 13196 1803 33 402 4.886 5.247 1.859 0.693 9.760 1997 10597 1891 35 393 5.944 5.799 1.308 0.693 9.769 1998 10626 2021 32 395 5.950 5.852 1.296 0.693 9.770 1999 11542 2117 35 458 6.075 6.004 1.310 0.833 9.770 2000 13728 2598 37 498 5.926 5.930 1.311 0.952 9.770 2001 18254 3771 39 607 5.601 5.513 1.423 0.693 9.772 2002 18852 3609 38 583 5.611 5.438 1.330 0.693 9.770 2003 16856 3536 37 604 5.644 5.425 1.401 0.693 9.772 2004 16173 3941 39 641 6.050 5.717 1.600 0.693 9.770 2005 14452 3651 38 638 5.820 5.529 1.494 0.693 9.763 2006 15350 3411 38 648 5.728 5.476 1.403 0.693 9.765 Overall 264,185 22,468 49 1,597 5.38 1.66 5.39 0.69 9.77

Source: Data developed by the U.S. Geological Survey (USGS), the U.S. Environmental Protection Agency (EPA), and the National Water Quality Monitoring Council, 1987-2006.

37 Table A.5: Trends in nitrogen concentration across US water sites, 1967-2015

Panel A, Dependent Variable: Log Nitrogen (mg/l) All All Estuary Lake River Stream (1) (2) (3) (4) (5) (6) Year -0.024*** -0.024*** -0.026*** -0.022*** -0.009*** -0.029*** (0.001) (0.001) (0.005) (0.002) (0.001) (0.001) Constant 1.450*** 1.508*** 1.030*** 1.071*** 0.277*** 1.907*** (0.031) (0.033) (0.276) (0.108) (0.054) (0.035) N 1,729,307 1,729,307 52,231 124,387 453,612 914,937 R-Squared 0.687 0.689 0.654 0.626 0.740 0.603 Water-Site FE Yes Yes Yes Yes Yes Yes Time Polynomial No Yes Yes Yes Yes Yes

Panel B, Dependent Variable: Log Nitrogen (mg/l) ≥ 25 readings July-August West Midwest South Northeast Year -0.025*** -0.025*** -0.031*** -0.021*** -0.019*** -0.032*** (0.001) (0.001) (0.001) (0.001) (0.001) (0.002) Constant 1.552*** 1.865*** 1.753*** 1.665*** 1.087*** 2.132*** (0.033) (0.053) (0.058) (0.072) (0.055) (0.079) N 1,388,772 362,601 351,975 315,089 760,389 301,854 R-Squared 0.653 0.731 0.748 0.642 0.634 0.695 Water-Site FE Yes Yes Yes Yes Yes Yes Time Polynomial No Yes Yes Yes Yes Yes

Note: Standard errors are clustered by county. *** indicates significance at the 1% level, ** indicates significance at the 5% level and * indicates significance at the 10% level. Each empirical specification includes time cubic polynomial and water-site fixed effects, except for the first column.

38 Table A.6: Trends in phosphorus concentration across US water sites, 1967-2015

Panel A, Dependent Variable: Log Phosphorus (mg/l) All All Estuary Lake River Stream (1) (2) (3) (4) (5) (6) Year -0.015*** -0.015*** -0.010*** -0.009*** -0.018*** -0.009*** (0.001) (0.001) (0.002) (0.001) (0.001) (0.002) Constant -1.984*** -2.139*** -2.571*** -2.642*** -1.940*** -1.577*** (0.028) (0.030) (0.097) (0.053) (0.040) (0.062) N 1,246,363 1,246,363 67,084 183,643 602,961 140,188 R-Squared 0.668 0.670 0.447 0.697 0.603 0.704 Water-Site FE Yes Yes Yes Yes Yes Yes Time Polynomial No Yes Yes Yes Yes Yes

Panel B, Dependent Variable: Log Phosphorus (mg/l) ≥ 25 readings July-August West Midwest South Northeast Year -0.015*** -0.014*** -0.018*** -0.014*** -0.010*** -0.016*** (0.001) (0.001) (0.001) (0.001) (0.001) (0.002) Constant -2.145*** -2.069*** -1.987*** -1.591*** -2.645*** -2.240*** (0.031) (0.049) (0.047) (0.056) (0.061) (0.096) N 952,382 290,012 244,840 351,607 551,300 98,616 R-Squared 0.638 0.712 0.733 0.695 0.609 0.798 Water-Site FE Yes Yes Yes Yes Yes Yes Time Polynomial No Yes Yes Yes Yes Yes

39 Table A.7: Trends in Fertilizer Application, 1987-2006

Dependent Variable: Nitrogen Fertilizer Phosphorus Fertilizer Farm Farm Intensity Non-Farm Farm Farm Intensity Non-Farm 1000 kg kg / acre 1000 kg 1000 kg kg / acre 1000 kg (1) (2) (3) (4) (5) (6) Year 73.383*** 0.223*** 3.496*** 10.185*** 0.026*** 0.496*** (8.073) (0.013) (0.308) (1.579) (0.003) (0.051) Constant 6107.079*** 21.483*** 34.265*** 958.130*** 3.589*** 10.428*** (84.349) (0.131) (3.222) (16.501) (0.028) (0.538) N 59735 59735 59735 59735 59735 59735 County Fixed Effects Yes Yes Yes Yes Yes Yes

Note: Standard errors are clustered by county. *** indicates significance at the 1% level, ** indicates significance at the 5% level and * indicates significance at the 10% level.

Table A.8: Impact of agricultural fertilizer use on a non-agricultural measure of water pollution

Dependent Variable: Secchi Depth (m) (1) (2) (3) (4) (5) (6) Log Farm N Fertilizer (kg) 0.100 0.097 0.096 (0.152) (0.152) (0.152) Log Farm P Fertilizer (kg) 0.152 0.149 0.164 (0.132) (0.132) (0.135) N 1,019,449 1,019,449 1,019,449 1,019,449 1,019,449 1,019,449 R-Squared 0.081 0.081 0.081 0.081 0.081 0.081

Watershed fixed effects Yes Yes Yes Yes Yes Yes Month fixed effects No Yes Yes No Yes Yes Year fixed effects No No Yes No No Yes

Note: Sechhi depth is a measure of light penetration into water body and is a function of absorption or scattering of light in the water. Scientists measure water transparency with a Secchi disk. A Secchi disk is a metal disk, 8 inches in diameter that is lowered into the water on a cord. Sechhi depth is defined as depth at which a white disc is indistinguishable from surrounding water. When the water transparency is high, the Secchi depth is high. When the water transparency is low and cloudy, the Secchi depth is low. Standard errors are clustered by watershed. *** indicates significance at the 1% level, ** indicates significance at the 5% level and * indicates significance at the 10% level.