Agricultural Adaptation to Climate Change: Implications for Fertilizer Use and Water Quality in the United States

Transcription

1 Agricultural Adaptation to Climate Change: Implications for Fertilizer Use and Water Quality in the United States Jayash Paudel Christine L. Crago Job Market Paper This version: December 2, 2018 Please click here for the most updated version Abstract Knowledge of adaptive behavior in response to warming temperatures is fundamental to understanding agricultural damages induced by global warming, and designing efficient policies to address climate change. This paper presents empirical evidence that farmers adjust fertilizer application in response to variation in temperature and precipitation trends during the growing season in the corn belt of the United States. Estimates indicate that farmers increase nitrogen and phosphorus fertilizer use by 0.172% and 0.238% in response to moderate heat. However, farmers decrease nitrogen and phosphorus fertilizer application by 0.260% and 0.323% in response to temperature exceeding a threshold that leads to damaging effects on crop production. Results imply that potential adaptation through decreased fertilizer use in response to extreme temperatures can lower input expenditures, which can partly offset adverse effects of extreme heat on yields. We further find that farmers will apply 37.41% more nitrogen fertilizers by mid-century when compared to a world without climate change, leading to deterioration of water quality. We show that the resulting nutrient runoff will increase nitrogen and phosphorus pollution by 9.72% and 12.91% under a business-as-usual scenario. These results imply that policy makers need to consider the impact of changing patterns in temperature and precipitation on fertilizer use when designing policies aimed at improving water quality. Corresponding Author: Department of Resource Economics at University of Massachusetts Amherst, Stockbridge Hall, 80 Campus Center Way, Amherst, MA jpaudel@umass.edu Department of Resource Economics at University of Massachusetts Amherst, Stockbridge Hall, 80 Campus Center Way, Amherst, MA ccrago@resecon.umass.edu

2 1 Introduction There is a growing consensus that rapid changes in temperature, precipitation levels, and weather variability are likely to have a large effect in the agricultural sector worldwide. A number of studies have found a wide range of estimates of how climate change 1 influences the agricultural sector in the United States (Mendelsohn et al., 1994; Deschenes and Greenstone, 2007; Schlenker and Roberts, 2009; Burke and Emerick, 2016a). The majority of these studies employ either the production function or hedonic approach, and focus on agricultural outcomes such as annual yields, profits and land prices. Although there have been previous discussions of fertilizer application as one of the adaptation mechanisms in response to changing weather conditions (Rosenzweig and Parry, 1994; Mendelsohn et al., 1994), empirical evidence of the effect of climate change on the use of agricultural inputs such as fertilizers is limited. Empirical estimation of this adaptive behavior in response to warming temperatures among profit-maximizing farmers is fundamental to understanding agricultural damages induced by global warming, and designing efficient policies to address climate change. Changes in agricultural use of fertilizers also have implications for water quality. The resulting nutrient runoff is the leading cause of water quality problems such as eutrophication of the Dead Zone in the Gulf of Mexico and nitrate contamination in drinking water. This necessitates a comprehensive understanding of the impact of climate change on water quality through the agricultural channel of fertilizer use. This paper evaluates the linkage between climate change, agricultural fertilizer use and water quality using three methodological steps. The first step exploits plausibly exogenous year-to-year variation in temperature and precipitation at the county level to estimate the causal effect of weather on nitrogen and phosphorus fertilizer application. We quantify short-run and long-run adaptation to climate change through adjustments in fertilizer application in response to temperature and precipitation. The second step makes use of climate projections from 18 general circulation models (GCMs) and simulates how future climate change will affect both nitrogen and phosphorus fertilizer application. Finally, the third step evaluates the impact of future climate change on concentration of agricultural pollutants through the channel of projected fertilizer use. We generate estimates of change in nitrogen and phosphorus pollution led by climate change-induced change in fertilizer use under a business-as-usual scenario in which the future world responds to climate change similarly to how it has responded in the past. We find evidence that farmers adjust fertilizer application within the growing season in response to long-run trends in temperature and precipitation among counties that are east of the one hundredth meridian. Our long difference estimates indicate that farmers increase nitrogen and 1 Dell et al. (2014) and Hsiang (2016) provide a detailed literature review on how climate affects society and the economy. Weather describes the short-term variation of temperature or precipitation, while climate change describes the average level of changes of weather outcomes that cover a long period of time. 1

3 phosphorus fertilizer use by 0.172% and 0.238% with every additional degree-day of heat between 8 C and 29 C. Farmers, however, decrease nitrogen and phosphorus fertilizer application by 0.260% and 0.323% in response to each additional degree-day of heat above 29 C. This reduction in fertilizer application in response to extreme heat suggests that temperature exceeding a threshold that leads to damaging effects on crop production discourages farmers from applying more fertilizers. Our results imply that potential adaptation through decreased fertilizer use in response to extreme temperatures can lower input expenditures, which can partly offset adverse effects of extreme heat on yields. We also find that farmers apply 0.488% more nitrogen fertilizer and 0.313% more phosphorus fertilizer with an additional centimeter increase in precipitation. We show that our estimates of adaptation through changes in fertilizer adjustment are robust to a wide variety of specification checks, and remain largely unchanged when addressing changes in local land use and measurement error (more on Section 5.3 and Appendix A). We further combine long difference estimates with temperature and precipitation projections belonging to the A1 scenario family (business-as-usual) and show how farmers will alter their use of fertilizers in response to future climate change. Assuming a fixed growing season and no major change in crop acreage under a business-as-usual setting, our estimates indicate that farmers will apply 37% more nitrogen fertilizer by mid-century when compared to a world without climate change. Across the 18 climate models, nitrogen fertilizer increase ranges between 21.5% and 52.24% with a median of 37.42%. This finding is important because prior work suggests that future climate change will reduce corn productivity by roughly 15 percent (Burke and Emerick, 2016a). We find that exposure to moderate heat has a much larger effect on fertilizer application compared to annual yields, resulting in predictions of a large increase in nitrogen fertilizer use. 2 Our simulations indicate that an increase in future nitrogen fertilizer application induced by climate change comprises 10.6% of the overall growth in fertilizer use observed in the last fifty years. We also predict estimates of change in nitrogen and phosphorus pollution linked with climate change-induced change in fertilizer use. Under a business-as-usual scenario, we show that future changes in fertilizer use induced by temperature and precipitation alone will substantially increase concentrations of nitrogen and phosphorus in water sites by mid-century. We find that the resulting nutrient run-off from changes in fertilizer applications will further deteriorate water quality, mostly in the Upper Mississippi Atchafalaya River Basin, the Northeast, and the Great Lakes basin. We find that average nitrogen and phosphorus pollution will increase by 9.72% and 12.91%, respectively. Combining our estimates with statistics reported in Hendricks et al. (2014) 2 For example, Burke and Emerick (2016a) find that exposure to each additional degree-day of heat below 29 C results in a 0.02% increase in overall corn yield. We show that exposure to moderate heat has a larger effect on fertilizer application, causing nitrogen and phosphorus fertilizer to increase by 0.172% and 0.238%. 2

4 and Obenour et al. (2012), a 9% increase in nitrogen pollution results in an increase of 978 square miles in the size of the hypoxic zone, which is an area about half the size of Rhode Island and 11% of the estimated size of the Dead Zone in the Gulf of Mexico. Higher concentrations of nitrogen and phosphorus in water have important economic implications. Studies report substantial property value losses associated with harmful algal blooms and poor water quality (Poor et al., 2007; Wolf et al., 2017). For example, Wolf and Klaiber (2017) use housing market data across six counties in Ohio and report that poor water quality resulting from blue green algae costs homeowners $152 million in lost property values over six years. A back-of-the-envelope calculation suggests that water pollution resulting from climate change-induced fertilizer application results in an additional expense of $20,065 a day for a single water treatment facility to remove the harmful toxins (Arenschield, 2015). This is likely a lower bound estimate of algal damages given additional costs on health, recreation and property values. As climate change is expected to increase the frequency of algal blooms, our results imply that policy makers need to consider the impact of changing patterns in temperature and precipitation on fertilizer use when designing policies aimed at improving water quality. This paper contributes to the literature in two ways. First, this paper contributes to the growing environmental economics literature on quantifying the economic impact of climate change on agriculture. Specifically, we provide the first estimates of the effects of climate change on U.S. fertilizer application. Prior research focused on agricultural yields does not provide us with information on the marginal effect of climate on adaptive actions that profit-maximizing farmers take in response to exogenous shocks to production (Schlenker and Roberts, 2009; Burke and Emerick, 2016a; Zhang et al., 2017). This means that yield declines in response to extreme heat may potentially hide other adjustments in agricultural inputs that can lower economic losses. We are able to provide evidence of behavioral responses in terms of input usage both in the short run and the long run among farmers in the corn belt of the United States. Our focus on fertilizer application as a key adaptive technology is important because nitrogen fertilizer use accounts for almost a quarter of total production cost among U.S. corn farmers (Huang, 2007; Beckman et al., 2013). Our findings suggest that efforts to mitigate the adverse impact of climate change on agriculture should address not only production losses from crop yields, but also changes in the use of agricultural inputs. Second, this paper builds on the growing literature on how climate change exacerbates other existing externalities such as water pollution. This is the first study to combine climate change projections of fertilizer use with an empirical model of the determinants of water quality and generate estimates of change in nutrient pollution led by climate change-induced agricultural fertilizer application under a business-as-usual scenario. To examine the impact of changes in future temperature and precipitation patterns on nutrient pollution in water bodies, we fit our 3

5 climate change-induced fertilizer use in the empirical model developed by Paudel and Crago (2018). This allows us to evaluate the resulting changes in concentrations of agricultural pollutants such as nitrogen and phosphorus. While our study is related to Sinha et al. (2017), we employ fertilizer application as a primary channel through which climate change influences water pollution. This, to our knowledge, is the first empirical study to evaluate the linkage between climate change and nutrient pollution with agricultural fertilizer use as a primary mechanism. This study is relevant to the broader literature on how economic agents adjust in the long run to mitigate the impact of negative environmental shocks (Tack et al., 2017; Manning et al., 2017; Hendricks, 2018). For example, Burke and Emerick (2016a) find limited evidence of agricultural adaptation in response to climate change based on yield responses, suggesting that future impacts of climate change on corn and soybean production could be disastrous. By showing that farmers promptly adapt fertilizer applications within the season in response to temperature and precipitation, our study points out that previous yield-based analyses can overestimate the negative impact of climate change on agriculture. While Burke and Emerick (2016a) show that climate change leads to reduction in yields, our results extend the literature and highlight the possibility that yield-focused studies can potentially overlook adjustments in other agricultural inputs such as fertilizers in response to extreme heat. This study is also broadly related to a longstanding literature showing that farmers can adapt to new information as it emerges and adjust production decisions quickly (Antle, 1983; Fafchamps, 1993). Recent work, exploring the linkage between weather variability and application of productivity-enhancing inputs, is mostly focused in Africa (Asfaw et al., 2016; Sesmero et al., 2017; Jagnani et al., 2018). Although weather conditions affect crop nutrient uptake, there exists no prior empirical evidence on how changing weather patterns influence the use of agricultural inputs such as fertilizers in the U.S. agricultural setting. Our findings have implications for profitability of farmers under future climate change. Under an assumption that farmers do not make any adjustments on the use of agricultural inputs (other than fertilizers) in response to extreme heat, our results suggest that climate change will result in higher fertilizer expenditures and lower profits. However, to the extent that farmers adjust the use of pesticides (Diffenbaugh et al., 2008; Deutsch et al., 2018) and herbicides for weed control in response to temperature, climate change-induced economic losses could be offset by such adaptive mechanisms. For example, farmers are more likely to apply herbicides under warm temperatures to protect corn yields from weeds. 3 Our results indicate that farmers may adjust their use of other agricultural inputs such as pesticides and herbicides in response to changing temperatures. This needs to be taken into account to determine the overall economic impact of climate change on 3 Warm temperatures result in a soft, fluid consistency that allows herbicides to penetrate the leaf surface and enter plant cells. On the other hand, cool conditions harden these structures and make herbicide entry more difficult. 4

6 profitability of farmers. The paper proceeds as follows. Section 2 presents a conceptual framework on determinants of fertilizer application. Section 3 describes data sources and presents summary statistics. Section 4 describes econometric models and Section 5 presents the empirical findings of the study. Section 6 uses data from global climate models to build projections of future fertilizer use and resulting changes in water quality. Section 7 discusses implications of the main results and concludes. 2 Conceptual Framework 2.1 Weather and Fertilizer Application Fertilizer is any material of natural or synthetic origin applied to soils for supplying nutrients required for crop growth. Fertilizers are commonly used for growing crops, with site-specific application rates based on crop type, soil and weather conditions. 4 Given that inorganic fertilizers contain nutrients such as nitrogen and phosphorus that are lost from the soil quickly, fertilizer is usually split into two to three applications throughout the growing season. 5 Agronomists recommend looking at short-term and long-term weather forecasts when planning fertilizer applications. Specifically, timely and moderate rainfall can be beneficial to dissolve dry fertilizer and move nutrients into the soil rooting zone, but excessive rain can increase leaching potential of nutrients. Pang et al. (1977) point out that temperature is one of the environmental factors affecting the transformation of nitrogen in soils. Past literature shows that nitrogen fertilizer use varies from year to year depending on weather and soil spatial variability (Hollinger and Hoeft, 1986; Raun and Johnson, 1999; Tremblay et al., 2012). Weather affects root growth and distribution in soils as well as nutrient uptake. It is also well-established that interactions among soil properties, rainfall and temperature determine water and nutrient availability as well as mineralization of fertilizers during the growing season (Schröder et al., 2000; Kay et al., 2006). Agronomists report that effective fertilizer management involves consideration of soil texture as well as seasonal conditions of temperature and precipitation (Sogbedji et al., 2001; Derby et al., 2005; Shanahan et al., 2008). More recently, Tremblay et al. (2012) evaluate the role of temperature and precipitation on nitrogen use efficiency, concluding that weather has a significant influence on fertilizer use. Amstrong (1999) explore factors that determine the use of phosphorus fertilizer, and highlight the role of temperature and moisture. Specifically, low soil temperatures reduce root growth rates and the 4 Some crops generally don t require nitrogen fertilizer. For example, legumes fix nitrogen from the atmosphere. 5 Coarser textured soils, for example, don t have water or nutrient holding capacity of slim loam soils, and they tend to dry out faster and have a higher risk for leaching. This necessitates several applications of fertilizers throughout the growing season. 5

7 rate of diffusion of phosphorus, causing a decrease in the amount of phosphorus accessed by roots. According to Amstrong (1999), the amount of phosphorous in crops due to fertilizer is highest when moisture availability is lowest. These studies highlight that nitrogen and phosphorus fertilizer application tends to be higher under moderate heat conducive to the growth of the crops, and lower under extreme heat. Nitrogen and phosphorus fertilizers have complementary functions. Nitrogen fertilizer contributes to the growth of lush green leaves, and enables the plant to trap energy from sunlight. Phosphorus fertilizer facilitates the actual use of the energy, and enhances the growth of new shoots and the development of flowers. Nitrate, the primary form in which plants absorb nitrogen from the soil, is very mobile in soils, while movement of phosphorus in soils is very limited. 2.2 Optimal Fertilizer Use A representative farmer maximizes her total profit (π) by using two inputs: fertilizers (F ) and a combination of other products (O) such as pesticides, planting materials, animal feeds, farm machinery and agricultural tools. Yield (y) is a concave function of fertilizer use (F ), other inputs combined (O) and climate variables (C) such as temperature and precipitation. Consider the case where a farmer faces output price P, unit fertilizer price w and unit input price r for the use of O. Assuming the farmer is a price taker, the farmer s maximization problem can be expressed as: max F,O π = P y wf ro subject to y = f(f, O, C) (2.2.1) where P, w and r are treated as exogenous parameters. Taking the derivative of π with respect to F and O gives the following first-order conditions: which respectively define the optimal input-demand equations: π F = P f ( ) F, O, C w = 0 F (2.2.2) π O = P f ( ) F, O, C r = 0 O (2.2.3) F = F ( w, r, P, C ) (2.2.4) O = O ( w, r, P, C ) (2.2.5) Equation (2.2.4) gives the optimal fertilizer use which is a function of input prices, output price and climate variables. In Section 4.1, we discuss how we control for these determinants of fertilizer demand in our empirical model. 6

8 3 Data Sources and Summary Statistics To implement the analysis, we employ the most detailed and comprehensive data available on fertilizer application, temperature, precipitation and water quality. This section describes the data and reports some summary statistics. 3.1 Measure of Fertilizer Application We obtain annual county-level data on fertilizer application available from U.S. Geological Survey s National Water-Quality Assessment Program (NWQAP) (Gronberg and Spahr, 2012). We employ county-level data on nitrogen and phosphorus fertilizer use for the conterminous United States from 1945 to Figure 1 shows the geographic distribution of U.S. farm fertilizer use. Table A.1 reports that the average use of nitrogen and phosphorus fertilizers in the entire sample is approximately 2.45 million kilograms (kg) and 0.53 million kg, respectively. Figure A.2 shows a steep rise in the use of nitrogen and phosphorus fertilizers applied in a county between 1950 and Regressions with linear trend specifications show that annual farm nitrogen and phosphorus applied in a county increased on average by about metric ton and metric ton per year, respectively (see Table A.2). 3.2 Climate and Weather Data We draw weather data from Schlenker and Roberts (2009), which consist of daily interpolated values of precipitation totals and maximum and minimum temperatures for 4 kilometer (km) grid cells covering the entire United States over the period We use pixel-level data obtained from Schlenker and Roberts (2009) that are aggregated to the county level and weighted by farmland areas. The county-level daily average temperature and precipitation data are further aggregated to generate the growing-season variables. The growing season is defined as April 1st to September 30th. The concept of growing degree days (GDD) measures the amount of time a crop is exposed to temperatures between a given lower and upper bound. Moderate heat is defined in terms of temperature between 8 C and 29 C and extreme heat is defined in terms of temperature exceeding 29 C. GDD are calculated as follows: 6 Gronberg and Spahr (2012) report that annual sales data compiled by the 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-phosphatepotash (N-P-K) for the fertilizer. Nitrogen (N) and Phosphorus (P) 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

9 GDD ct,8 29 = ( Tcdt 8 ) X1 { T cdt ( 8, 29 )} d D GDD ct,>29 = max { 0, T cdt 29 } d D where T cdt is average temperature in each county c, day d, and year t. D represents the set of days in the growing season. Table A.1 reports that the average precipitation and temperature during the annual growing season is roughly 55 centimeters (cm) and 20 C, respectively. The average number of growing degree days between 8 C and 29 C is approximately 2,215. The average number of growing degree days exceeding 29 C is approximately Additional agricultural outcomes and controls We obtain county-level data on cropland and irrigation from the Census of Agriculture (NASS, 2007). Other agricultural data come from the United States Department of Agriculture s National Agricultural Statistics Service (USDA, 2018). This includes information on county-level agricultural inputs such as expenditures on seeds, chemicals, petrol, and hiring labor, and state-level prices on crops such as corn, soy and wheat. We obtain population data from the U.S. Census Bureau (Census, 2018). Table A.1 provides a summary of these agricultural outcomes and additional control variables in the study. 3.4 Climate Change Predictions We obtain climate projections from 18 general circulation models (GCMs) that belong to the World Climate Research Programs Coupled Model Intercomparison Project phase 3 (WCRP CMIP3) multimodel dataset (Meehl et al., 2007). Table A.3 reports a list of GCMs in the CMIP3 database. Section 6.1 provides details on our climate projections between and Water Quality Data We obtain water quality data from the Water Quality Portal (WQP), the largest standardized water quality data access tool available, developed by the United States Geological Survey (USGS), Environmental Protection Agency (EPA), and the National Water Quality Monitoring Council (Read et al., 2017). To identify agricultural pollutants, we employ data on dissolved nitrogen and phosphorus from lakes, estuaries, rivers and impoundments in the contiguous 48 states between 8

10 1967 and Streams, rivers, and lakes comprise about 56.76%, 23.24% and 5.74% of the data, respectively. The entire sample consists of 1,729,307 nitrogen pollution readings from 87,883 water sites located in 2,963 counties, and 1,246,363 phosphorus pollution readings from 77,742 water sites located in 2,477 counties. The average amount of nitrogen in water in the contiguous United States is 3.27 mg/l with a standard deviation of Similarly, the average amount of phosphorus in water in the contiguous United States is 0.20 mg/l with a standard deviation of We also make use of a detailed U.S. hydrologic map to aggregate fertilizer application and measures of water quality at the watershed level. Paudel and Crago (2018) provide detailed summary statistics on water quality data employed in this study. 4 Econometric Strategy This section describes the econometric models for evaluating (i) the impact of temperature and precipitation shocks on fertilizer use in the short run, and (ii) the impact of long-run trends in temperature and precipitation on fertilizer use. Our analysis is focused on counties that are east of the one hundredth meridian, which accounts for over 90 percent of corn and soy production in the United States. 4.1 Fertilizer Application in the Short Run We estimate the following equation to evaluate the causal impact of temperature and precipitation shocks on nitrogen and phosphorus fertilizer application in the short run: Y ct = θ 1 GDD ct, θ 2 GDD ct,>29 + θ 3 P ct + θ 4 P 2 ct + X ct β + α c + γ t + ɛ ct (4.1.1) where Y ct is the log amount of fertilizer used in county c in year t. We follow the standard agronomic approach and use growing season degree-days and precipitation to model the effect of weather on fertilizer use. GDD ct,8 29 denotes growing season degree-days between 8 C and 29 C, GDD ct,>29 denotes growing season degree-days greater than 29 C and P denotes the amount of precipitation. Growing degree days are effective in explaining the nonlinear response of U.S. corn and soybean yields to heat accumulation within the growing season (Schlenker et al., 2006; Schlenker and Roberts, 2009; Miao et al., 2015). The specification also includes total precipitation level and its squared term to characterize the nonlinear relationship of precipitation and fertilizer application. The last term above is the stochastic error term, ɛ ct. The equation includes a full set of county fixed effects, αc. The appeal of including county fixed effects is that 9

11 they absorb all unobserved county-specific time invariant determinants of fertilizer application. In addition, the equation includes year fixed effects, γ t, that control for time-varying differences in fertilizer use that are common across counties such as fertilizer price shocks. The highly integrated nature of the agricultural commodity market allows us to capture prices shocks (e.g. fertilizer prices) that transmit smoothly across regions with year fixed effects. To obtain reliable estimates of θ i, we control for a wide range of potential time-varying explanatory variables, X, based on the conceptual framework outlined in Section 2.2. These variables include corn and soybean prices to account for output price in the model. We use expenditure on agricultural inputs to proxy for prices of inputs other than fertilizers such as seeds, chemicals, petrol and hiring labor. We also include crop acreage and population to account for the scope of agricultural activities in a county. The validity of any estimate of the impact of weather hinges on the assumption that the estimation of equation (4.1.1) will produce unbiased estimates of the θ i vectors. Noting that variation in temperature and precipitation can be considered as random draws from the distribution in a given spatial area, the weather-shock approach offers strong identification properties (Dell et al., 2014). By conditioning on county and year fixed effects, these vectors are identified from county-specific deviations in weather about the county averages after controlling for time-varying shocks common to all the counties. Due to the unpredictability of weather fluctuations, it is reasonable to presume that variation in temperature and precipitation is orthogonal to unobserved determinants of fertilizer application. Improved technology is commonly considered as a primary factor driving the use of nitrogen and phosphorus fertilizers. Although we do not directly control for this factor, it does not confound the identification strategy. Consistent with Schlenker and Roberts (2009), it is reasonable to assume that advances in technology are relatively smooth and uniform within a state, suggesting that these effects can be captured by state-level trends. We also employ a more conservative specification with state-by-year fixed effects as an additional robustness check to effectively absorb any shock specific to a state in a given year. 4.2 Fertilizer Application in the Long Run The panel approach explained in Section 4.1 is appropriate for estimating responses to temperature and precipitation in the short run. However, a different empirical strategy is needed to estimate the effect of long-run trends in temperature and precipitation. This is necessary because it is difficult to determine the degree to which the short-run variation in temperature and precipitation shocks incorporates the full range of changes that may occur in the future (Dell et al., 2014). Moreover, farmers may adapt agricultural production processes over a long period in response to climate 10

12 change. We adopt the long differences approach to evaluate the degree of fertilizer adjustment in response to long-run trends in temperature and precipitation. We construct long-run averages of fertilizer application and weather variables at two different points in time for a given county, and calculate changes in average fertilizer application as a function of changes in average temperature and precipitation (Burke and Emerick, 2016a). We consider two multi-year periods, a ( ) and b ( ). We generate average log fertilizer application, temperature and precipitation in each period a and b for county c as shown below: Y ca = θ 1 GDD ca, θ 2 GDD ca,>29 + θ 3 P ca + θ 4 P 2 ca + X caβ + αc + ɛca (4.2.1) Y cb = θ 1 GDD cb, θ 2 GDD cb,>29 + θ 3 P cb + θ 4 P 2 cb + X cb β + α c + ɛ cb (4.2.2) Taking the long difference over the two periods a and b, the county-specific characteristics that are fixed drop out and we obtain the following: Yc = θ 1 GDD c, θ 2 GDD c,>29 + θ 3 P c + θ 4 P 2 c + X cβ + ɛc (4.2.3) To make sure that weather parameters θ i are unbiased, our analysis hinges on the assumption that changes in temperature and precipitation between the two periods are not correlated with timevarying unobservables that may affect fertilizer application outcomes. Consistent with Burke and Emerick (2016a), we include state fixed effects to control for any unobserved state-level trends. To test the robustness of our baseline long difference estimates outlined above (more on Section A.5), we construct a two period panel of long differences using equation (A.5.1) and show that our estimates from equation (4.2.3) are robust to potential bias linked with time-varying unobservables at the state level. 5 Results 5.1 Fertilizer Application in the Short Run We estimate equation (4.1.1) to evaluate the short-run impact of temperature and precipitation shocks on fertilizer application and present the results in Table 1. Table 1 shows that both nitrogen and phosphorus fertilizer use increase with moderate heat, and decrease with extreme heat. We find that one additional degree-day exceeding 29 C leads to a decrease in nitrogen and phosphorus fertilizer application of % and %, while accounting for county fixed effects and year fixed effects in columns (1) and (3). Our preferred specification, however, includes county 11

13 fixed effects, year fixed effects and baseline control variables. In the case of nitrogen fertilizer, although we lose statistical significance in relation to moderate heat in column (2), we find that one additional degree-day exceeding 29 C leads to a 0.017% decrease in the amount of fertilizer application. The effect of temperature is equally pronounced in the case of phosphorus fertilizer, which increases by % in response to moderate heat and decreases by % in response to extreme heat. These results show that our estimates on fertilizer application in response to extreme heat are robust across multiple specifications with and without baseline controls. Across all four specifications, the estimated coefficients on precipitation are positive, while the coefficients on their squared terms are negative. Our preferred specification in columns (2) and (4) with county and year fixed effects and baseline controls suggests that precipitation increases the use of nitrogen and phosphorus fertilizers in areas with low precipitation, and decreases fertilizer application in areas with high precipitation. In areas with low precipitation, one additional centimeter of precipitation, for example, increases nitrogen and phosphorus fertilizer application by 0.102% and %, respectively (see columns (2) and (4)). In areas with high precipitation, one additional centimeter of precipitation decreases nitrogen and phosphorus fertilizer application by % and %, respectively. 5.2 Fertilizer Application in the Long Run We estimate equation (4.2.3) to evaluate the impact of long-run trends in temperature and precipitation shocks on fertilizer application and present the results in Table 2. We find that both nitrogen and phosphorus fertilizer application increase with long-run averages of moderate heat but decrease with long-run averages of excessive heat. Columns (2) and (4) show that one additional degree-day between 8 C and 29 C in the long run is associated with a 0.172% and a 0.238% increase in nitrogen and phosphorus fertilizer application, respectively. On the other hand, one additional degree-day exceeding 29 C in the long run is associated with a significant decrease of 0.260% and 0.323% in nitrogen and phosphorus fertilizer application, respectively. Although we lose statistical significance for the coefficient of extreme heat in columns (1) and (3), we find that moderate heat between 8 C and 29 C in the long run increases nitrogen and phosphorus fertilizer application by 0.243% and 0.207%. Our estimates in columns (2) and (4) illustrate that long-run trends in extreme heat lead to a significant decrease in the amount of nitrogen and phosphorus fertilizer applied. Table 2 shows that the estimated coefficients on precipitation are positive and statistically significant in the case of nitrogen fertilizer application. In areas with low precipitation, column (2) shows that one additional centimeter of precipitation increases nitrogen fertilizer application by % in the long run. Contrary to findings in Table 1, we do not see any statistical 12

14 significance in the estimated coefficients of the squared term of precipitation for both nitrogen and phosphorus fertilizer application in the long run. We find our results to be intuitive, given that warming in cool areas is expected to increase the number of growing degree days. This creates an ideal environment for more corn and soybean production, resulting in an increase of agricultural fertilizer use. Similarly, warming in hot areas is expected to increase the number of degree days exceeding 29 C, which results in a decrease of agricultural production and subsequent reduction in the use of fertilizers. Our long differences estimates provide strong evidence that farmers adjust fertilizer application within the growing season in response to long-run averages in temperature and precipitation in the U.S. corn belt. 5.3 Robustness Checks We perform several robustness checks in Appendix A that confirm our findings on the long-term impact of temperature and precipitation shocks on agricultural fertilizer use. This section focuses on two primary robustness checks. First, we assess the sensitivity of our baseline long difference estimates to our choice of multi-year periods. Specifically, we estimate equation (4.2.3) varying the baseline year from 1955 to 1995 in five year increments, and for each value of baseline year, we estimate 5, 10, 15, 20, 25, and 30-year difference models. We display the difference between the baseline estimate of θ (extreme heat) during and the estimate of θ for the period determined by the starting year and length of differences. We find that only 7 out of 39 estimates of θ are statistically different from our main estimate and none of the estimates are statistically different in the positive direction, suggesting that our baseline point estimates in Table 2 are conservative. Figure 2 shows that our results on both nitrogen and phosphorus fertilizer application reported in Table 2 are consistently robust when we change the time period under study. This implies that the choice of specific multi-year periods is not driving our estimates on nitrogen and phosphorus fertilizer application. Second, we conduct an alternative approach to evaluate the impact of climate change on nitrogen and phosphorus fertilizer as an additional robustness check. Following Dell et al. (2014), we estimate the same basic panel specification in equation (4.1.1) but over a longer period of fifteen years. Specifically, we exploit medium-run variation averaging the weather variables across a 15-year period, with the assumption that these averages represent climate change. We find that the estimates derived from this approach in Table 3 are fairly similar in magnitude compared to our baseline estimates from the long differences approach. For example, Columns (2) and (4) show that one additional degree-day exceeding 29 C in the long run is associated with a 0.219% and a 0.433% increase in nitrogen and phosphorus fertilizer application, respectively. 13

15 Although we lose statistical significance for the slope coefficient of moderate heat in case of phosphorus fertilizer, Columns (1) and (2) show that moderate heat between 8 C and 29 C increases nitrogen fertilizer application by 0.076% and 0.088%. These results indicate that farmers adjust nitrogen fertilizer application in response to moderate and extreme heat. 6 Climate Change Projections Our final empirical exercise involves two primary steps. The first step builds projections of the impacts of future climate change on agricultural fertilizer use in the U.S. The second step combines projected fertilizer use in the U.S. agricultural sector with prior estimates on the impact of fertilizer application on concentration of agricultural pollutants across the U.S. water sites. 7 This final step allows us to evaluate the linkage between climate change and water quality through the channel of agricultural fertilizer use. 6.1 Impact on Fertilizer Use To evaluate the impact of future climate change on agricultural fertilizer use, we carry out two specific tasks. First, we obtain climate projections from 18 general circulation models (GCMs) that belong to the World Climate Research Programs Coupled Model Intercomparison Project phase 3 (WCRP CMIP3) multimodel dataset. Second, we combine these temperature and precipitation projections with our long difference estimates to derive projected changes in nitrogen and phosphorus fertilizer use due to climate change. Using the historical response of agricultural fertilizer use to climate, we are able to predict how future climate change will influence fertilizer use in the U.S. agricultural sector. Our climate projections belong to the A1 storyline and scenario family, which describes a future world of very rapid economic growth, global population that peaks in mid-century and declines thereafter, and the rapid introduction of new and more efficient technologies (Nakicenovic et al., 2000). These projections use the A1B emissions scenario, which assumes that the technological change in the energy system will occur in such a way that similar improvement rates apply to all energy supply and end-use technologies. 8 Table A.3 reports a list of 18 climate models in the CMIP3 database employed in the study. The A1B emissions scenario 7 These estimates are available from Paudel and Crago (2018), who construct an empirical model on determinants of ambient water quality using over 2.9 million pollution readings on nitrogen and phosphorus to 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) across the U.S. water sites, respectively. 8 According to the Special Report on Emissions Scenarios (SRES), the A1 family can be categorized into three groups in terms of technological emphasis: fossil intensive (A1F1), non-fossil energy sources (A1T) and a balance across all sources (A1B). 14

16 excludes the role of climate initiatives, while accounting for government policies that might affect greenhouse gas emissions. The resolution of our GCMs is approximately 2.8X2.8 degree, which is equivalent to a grid width of 300 kilometers (186.5 miles) at the equator. Under this scenario, we cannot use GCM projection at a grid cell as a direct, unbiased forecast of future climate at any location in that grid cell, as prior literature has documented the severity of temporal and spatial aggregation bias (Auffhammer et al., 2013). 9 Our estimates of climate change by mid-century account for aggregation bias, given that we follow the common practice outlined in Auffhammer et al. (2013) and use predicted changes in temperature added to the observed values of baseline temperature in a given county. Specifically, our estimates of future climate change come from Burke and Emerick (2016b), who calculate GCM-projected changes in temperature ( C) and precipitation (%) between and , and then add (for temperature) and multiply (for precipitation) these changes to observed historical values in a given county. Figure 3(a) presents projections of average changes in nitrogen fertilizer use by 2050 across the 18 climate models. We follow Auffhammer et al. (2013) and report the impact for a number of climate models separately, while showing the variability in impacts across different models. For example, under climate change projections from the Meteorological Research Institute Coupled Global Climate Model (MRI), Figure 3(a) shows that long difference and panel estimates predict an increase in nitrogen fertilizer use of 29.69% and 11.5%, respectively compared to a world that did not experience any climate change. We also note that long differences projections are somewhat larger in magnitude primarily because the coefficient on GDD below 29 C under long differences estimation (0.17%) is four times as large compared to the coefficient under panel estimation (0.04%). 10 This finding is important because prior work suggests that future climate change will reduce corn productivity by roughly 15 percent (Burke and Emerick, 2016a). We find that exposure to moderate heat has a much larger effect on fertilizer application compared to annual yields, resulting in predictions of a large increase in nitrogen fertilizer use. 11 Using long difference estimates, we find that nitrogen fertilizer increase ranges between 21.5% and 52.24% with a median growth rate of 37.42%. Across all these models, the long difference approach projects an average nitrogen fertilizer increase of 37.41% (almost identical to the median) with a standard deviation of 9.67%. Compared to projected values of nitrogen fertilizer under the long differences approach, those under the simple panel approach are relatively smaller in magnitude 9 This problem arises because GCMs divide the earth s surface into a discrete grid. Although there exists variation in climate across discrete grid cells, climate-related statistics are always homogenous within each cell. This means that we might end up with a constant value of temperature and precipitation among different locations for a specific period of time within a single grid cell, suggesting that temporal and spatial aggregation can be problematic. 10 This also leads to larger confidence intervals among estimates derived from long difference projections. 11 For example, Burke and Emerick (2016a) find that exposure to each additional degree-day of heat below 29 C results in a 0.02% increase in overall corn yield. We show that exposure to moderate heat has a larger and significant effect on fertilizer application, causing nitrogen and phosphorus fertilizer to increase by 0.172% and 0.238%. 15

17 and display narrower confidence intervals. The panel estimates, across all different models, predict an average nitrogen fertilizer increase of 15.98% with a standard deviation of 5.2%. Figure 3(b) presents similar projections using both long differences and panel estimates in the case of phosphorus fertilizer use across the same set of climate models. We observe a similar pattern here, with long difference approach-based projections much larger in magnitude compared to panel approach-based projections. We find that long difference estimates predict an average phosphorus fertilizer increase of 54.68% with a standard deviation of 14.34% by mid-century when compared to a world without climate change. Figure 3 shows that the median climate model relying on long differences estimation projects an average nitrogen fertilizer and phosphorus fertilizer increase of 37.42% and 54.37%, respectively. The caveat with these projections is that we assume a fixed growing season and no major change in crop acreage within the U.S. throughout this empirical exercise. In the case of nitrogen fertilizer, our data show that nitrogen fertilizer use has increased by approximately 353% between 1960 and We note that our climate projections showing an increase in the use of nitrogen fertilizers constitute approximately 10.6% of the overall increase in the last fifty years. In the case of phosphorus fertilizer, we find that our median climate projection is equivalent to 25% of the overall growth over the last mid-century. Our projections indicate that climate change-induced increase in future nitrogen and phosphorus fertilizer application comprises 10.6% to 25% of the overall growth in agricultural fertilizer use observed in the last fifty years. 6.2 Impact on Water Quality To examine the impact of changes in future temperature and precipitation patterns on nutrient pollution in water bodies, we fit our climate change-induced fertilizer use (estimated in Section 6.1) in the empirical model developed by Paudel and Crago (2018) and evaluate the resulting changes in concentrations of agricultural pollutants such as nitrogen and phosphorus. 13 Combining our climate change projections of fertilizer use with an empirical model of the determinants of water quality, we generate estimates of predicted change in nitrogen and phosphorus pollution coming from climate change-induced change in fertilizer use by mid-century under a business-as-usual scenario. There are two caveats regarding our projections of future changes in concentrations of agricultural pollutants. First, we rely on a watershed-level empirical model, which requires that we assign the correct weight for projected fertilizer use in a county within a watershed and 12 This is consistent with Huang (2007), who report that annual use of chemical nitrogen fertilizers in U.S. agriculture increased by 355% from 1960 to Given that nutrient enrichment of water bodies is a major environmental problem, we focus on both phosphorus (associated with enrichment in freshwater systems) and nitrogen (associated with enrichment in estuaries and coastal waters) for a more comprehensive analysis. 16

18 aggregate weighted fertilizer values to the watershed level. 14 Second, we use concentration of agricultural pollutants as a useful measure of water quality. This rests primarily on the basis that concentration of nitrogen and phosphorus has biological significance to organisms in water bodies. 15 Alternatively, pollutant loading, a simple function of concentration and flow, can be used as a proxy for water pollution. Our choice of water quality measure, concentration, is based on both data availability and prior economic literature investigating the scope of water pollution in the United States (Keiser and Shapiro, 2017). We find that climate change-induced fertilizer use will have a strong impact on water quality, mostly in the corn belt of the United States. Our results show that future changes in fertilizer use led by temperature and precipitation alone will substantially increase concentrations of nitrogen by mid-century under a business-as-usual scenario in which economic agents in the future world respond to climate change similarly to how they have responded in the past. We also find that the projected changes in nutrient pollution estimates linked with climate change-induced fertilizer use are larger in magnitude among states within the Lower Mississippi Atchafalaya River Basin, the Northeast, and the Great Lakes basin. Figure 4(a) shows that Minnesota, Ohio, Mississippi, Louisiana, Pennsylvania, Michigan, New York, Virginia, Georgia, West Virginia, New Jersey and Massachusetts will have a significant increase in concentrations of nitrogen pollution (mg/l) of 5.08%, 6.38%, 6.64%, 8.91%, 11.21%, 11.33%, 14.80%, 22.07%, 23.06%, 28.36%, 28.85% and 32.25%, respectively. We find that the spread between the first and third quartile of nitrogen pollution change ranges from 0.73% to 16.31% for the continental US under a business-as-usual scenario. These findings are noteworthy because watersheds belonging to these states have high historical nitrogen fluxes (Sinha et al., 2017), and discharge to coastal areas reported to have impaired water quality resulting from eutrophication (Bricker et al., 2008; Rabalais et al., 2007). We find that our results are consistent with Sinha et al. (2017), who use changes in precipitation as projected by 21 different Climate Model Intercomparison Project Phase 5 (CMIP5) models for three climate scenarios to show that total nitrogen loading will accumulate more in the Northwest and corn belt of the US. Our study differs from Sinha et al. (2017), as we choose medium emissions climate scenario and use concentrations (instead of loads) of both nitrogen and phosphorus as measures of water quality. Sinha et al. (2017) employ a statistical model for predicting total nitrogen flux, using a total of 56 variables that include characteristics related to 14 This process involves two steps. First, watershed polygons are overlaid with shapefiles for cropland cover and county boundaries. Second, weighted county-level fertilizer values are summed to generate the total fertilizer used within each watershed. 15 For example, high concentration of agricultural pollutants in surface water results in growth of phytoplankton, which leads to a decrease in levels of dissolved oxygen and cause harm to aquatic organisms relying on oxygen for their survival. 17

19 net anthropogenic nitrogen input, precipitation, land use, temperature, tile drainage and different combinations of land use types. Sinha et al. (2017) also assume that the relationship between nitrogen flux and different covariates in the statistical model remains constant over time. We also evaluate the impact of climate change-induced phosphorus fertilizer use on concentrations of phosphorus pollution across the continental US. Figure 4(b) shows that Michigan, Florida, Minnesota, Pennsylvania, Maryland, New York, Ohio and Illinois will have a significant increase in concentrations of phosphorus pollution (mg/l) of 11.43%, 11.90%, 14.18%, 14.93%, 16.04%, 21.03%, 21.99% and 22.69%, respectively. We find that the average change in phosphorus pollution is 12.91% with a standard deviation of 6.69%, and the spread between the first and third quartile of the predicted estimate ranges from 9.73% to 15.17%. Prior literature shows that current global climate models tend to underestimate internal climate variability (Laepple and Huybers, 2014). This implies that the actual increase in concentrations of agricultural pollutants in response to climate change may be even greater. To the extent that our predicted impacts on concentrations of agricultural pollutants are conservative in magnitude, our estimates suggest that policy makers need to account for the impact of changing patterns in temperature and precipitation when designing policies aimed at reducing nutrient pollution. 7 Conclusion This paper quantifies the impact of climate change on adjustment of inputs such as fertilizers in the U.S. agricultural sector. Our long differences estimates show that farmers increase nitrogen and phosphorus fertilizer application by 0.172% and 0.238% in response to each additional degree-day of heat between 8 C and 29 C, and decrease nitrogen and phosphorus fertilizer application by 0.260% and 0.323% in response to each additional degree-day of heat above 29 C. This suggests that moderate temperature conducive to crop production induces farmers to apply more fertilizers, whereas extreme temperature causing severe damage on crop growth discourages farmers from applying fertilizers. This is important because prior studies focused on the relationship between yield changes and temperature do not provide information on how farmers adjust fertilizer applications in response to temperature and precipitation shocks. Results imply that potential adjustment in the use of other agricultural inputs such as pesticides and herbicides in response to changing temperatures needs to be taken into account to evaluate the overall economic impact of climate change on profitability of farmers. The study further combines long differences estimates with temperature and precipitation projections from 18 general circulation models to show that farmers will alter their use of fertilizers in response to future climate change. Under a business-as-usual scenario, estimates indicate that farmers will apply 37.41% more nitrogen fertilizers by mid-century when compared 18

20 to a world without climate change. This increase in fertilizer use will lead to increased nitrogen and phosphorus concentrations, deteriorating water quality. Water quality impairment in turn leads to losses in property values, clean up costs, and revenue losses. Our estimates show that nitrogen and phosphorus pollution will increase by 9% and 12%, respectively. We report heterogeneity in predicted nutrient pollution across different states, which has significant policy implications for prioritizing state-level interventions. Our results highlight the need to consider water quality problems linked with agricultural input applications in response to climate change. 19

21 Figure 1: Agricultural fertilizer use in the United States, (a) Nitrogen (b) Phosphorus 20

22 Figure 2: Sensitivity of results to starting year and length of differencing period Difference with coefficient Year Differences Starting Year Difference with coefficient Year Differences Starting Year Difference with coefficient Year Differences Starting Year Difference with coefficient Year Differences Starting Year Difference with coefficient Year Differences Starting Year Difference with coefficient Year Differences Starting Year (a) Nitrogen Difference with coefficient Year Differences Starting Year Difference with coefficient Year Differences Starting Year Difference with coefficient Year Differences Starting Year Difference with coefficient Year Differences Starting Year Difference with coefficient Year Differences Starting Year Difference with coefficient Year Differences Starting Year (b) Phosphorus Note: This graph shows differences between main estimates from (specifications 2 and 4 in Table 2 and other estimates under various starting years and differencing lengths. Dots are differences in estimates and whiskers are 95 percent confidence intervals of the differences. 21

23 Figure 3: Projected impact of climate change on fertilizer application by Change in log nitrogen fertilizer LD Panel mirochires mirocmedres gfdl0 inmcm3 gfdl1 ccsm ipsl 0 hadcm3 cnrm cccmat63 echam iap (a) Nitrogen gisseh mri gissaom pcm gisser csiro 1.5 Change in log phosphorus fertilizer 1.5 LD Panel 0 mirochires ipsl gfdl0 mirocmedres inmcm3 gfdl1 ccsm hadcm3 cnrm cccmat63 echam iap gisseh mri gissaom pcm gisser csiro (b) Phosphorus Note: This shows the projected impact of combined temperature and precipitation changes across 18 climate models reporting the A1B ( business-as-usual ) climate scenarios based on long differences (LD) and panel estimates of historical sensitivities to climate. The median projection is shown as a dashed line. 22

24 Figure 4: Projected estimates of change in nutrient pollution linked with climate change-induced fertilizer applications (a) Nitrogen (b) Phosphorus Note: This gives us projected estimates of change in nutrient pollution among states to the east of the hundredth meridian. 23