Spring Nitrate Flux in the Mississippi River Basin: A Landscape Model

Jun 20, 2007 - Spring Nitrate Flux in the Mississippi River Basin: A Landscape Model with Conservation Applications. Mary S. Booth*. National Park Ser...
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Environ. Sci. Technol. 2007, 41, 5410-5418

Spring Nitrate Flux in the Mississippi River Basin: A Landscape Model with Conservation Applications MARY S. BOOTH* National Park Service, 4175 Geist Road, Fairbanks, Alaska 99709 CHRIS CAMPBELL Environmental Working Group, 1436 U Street, NW, Suite 100, Washington, DC 20009

Nitrogen derived from fertilizer runoff in the Mississippi River Basin (MRB) is acknowledged as a primary cause of hypoxia in the Gulf of Mexico. To identify the location and magnitude of nitrate runoff hotspots, and thus determine where increased conservation efforts may best improve water quality, we modeled the relationship between nitrogen inputs and spring nitrate loading in watersheds of the MRB. Fertilizer runoff was found to account for 59% of loading, atmospheric nitrate deposition for 17%, animal waste for 13%, and municipal waste for 11%. A nonlinear relationship between nitrate flux and fertilizer N inputs leads the model to identify a small but intensively cropped portion of the MRB as responsible for most agricultural nitrate runoff. Watersheds of the MRB with the highest rates of fertilizer runoff had the lowest amount of land enrolled in federal conservation programs. Our analysis suggests that scaling conservation effort in proportion to fertilizer use intensity could reduce agricultural nitrogen inputs to the Gulf of Mexico, and that the cost of doing so would be well within historic levels of federal funding for agriculture.

Introduction For at least the last 20 years, the summer occurrence of the “Dead Zone” has threatened marine life in the water column over thousands of kilometers in the Gulf of Mexico (1, 2). Nutrients conveyed to the Gulf by the Mississippi and Atchafalaya Rivers support the development of massive algal blooms, which eventually decompose, consuming oxygen in the water column. The natural stratification of the Gulf, whereby lighter river water overlies heavier salt water, impedes overturn and oxygen recharge at depth. Once oxygen concentrations fall to about 2 mg/L or below, a condition known as hypoxia occurs whereby any aerobic organism that cannot leave the area or reach the surface suffocates. The size of the Dead Zone varies from year to year, but the 5-year average for 2001-2005 puts it at about 15 000 km2 (N. N. Rabalais, personal communication) and in 2001 it was over 20 000 km2 (1). While the degree to which algal productivity is controlled by nitrogen or phosphorus may vary with time or location within the plume of freshwater entering the Gulf (3-6), * Corresponding author phone: (907) 455-0665; fax: (907) 4550601; e-mail: [email protected]. 5410

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freshwater nitrogen inputs appear to be the main driver for algal bloom development (7-9). Inputs from human and animal waste, atmospheric deposition, and natural ecosystems all contribute to total loads, but most studies have concluded that fertilizer runoff is the main source of nitrogen to rivers and streams in the Mississippi River Basin (MRB) (10-18). Nitrate constitutes about 62% of total nitrogen discharged to the Gulf (14) and is considered the most important driver of algal productivity (7, 15, 19). The importance of agricultural sources in the MRB is reflected by an increasing freshwater nitrate peak around May since the 1990s (18), and recent analysis suggests that May nitrate discharge to the Gulf is now the best nutrient-related predictor of hypoxic zone size in July (9). Federal farm policy, manifested as commodity payments that support fertilizer and chemical-intensive corn, soy, wheat, cotton, and rice cropping, is arguably the most important factor structuring the agricultural landscape of the Midwest and the biogeochemical consequences of land use. Farm programs provide a safety net for farmers, lessening the effects of price fluctuations, but they have also contributed to the homogenization of the agricultural landscape and expansion of fertilizer-intensive crops at the expense of diversified land use. One model has estimated that three primary “program” cropsscorn, soybean, and wheats accounted for 86% of agricultural nitrate losses in the MRB for the 1990-1994 period, despite occupying less than 20% of the land area (18). Corn production appears to be especially implicated in agricultural nutrient losses, with nitrate comprising a greater proportion of total nitrogen loading as corn acreages increase (16). In the MRB, approximately 45% of federal commodity payments go to corn (20), a trend that will likely intensify if more land is brought into production for corn-based ethanol. While commodity payment programs promote intensive agriculture, federally funded programs also exist to mitigate its effects, paying farmers to create streamside buffers and wetlands and carry out other conservation practices. Such practices effectively reduce nutrient loading (21-23), but the continued problem of aquatic nutrient loading indicates that conservation efforts are not yet adequately protective. Our work therefore had two objectives. First, we wished to create a model that would employ easily available data to identify those areas of the MRB where agricultural nitrate loading is highest. Other models have characterized nitrogen loading in the MRB, but calibration data are not always recent, computing and data requirements can be intensive, and some models characterize year-round loading of total nitrogen, rather than spring nitrate loading, which has been greatly implicated in development of hypoxia (7, 9, 18). Our second objective was to use data from federal commodity support and conservation programs to calculate the cost of increasing conservation effort in areas identified by the model as disproportionately responsible for agricultural nitrate runoff.

Methods We developed a regression model to describe the relationship between springtime nitrate loading to the Gulf of Mexico and runoff, fertilizer N inputs, population, animal waste inputs, and atmospheric nitrate deposition within site catchments for the years 1990-2002. We then used the same framework of analysis to examine how federal agricultural commodity support payments and conservation payments are distributed, and how distributions might be changed to direct more conservation effort and funding toward areas where agricultural nitrate loss is highest. The National 10.1021/es070179e CCC: $37.00

 2007 American Chemical Society Published on Web 06/20/2007

Hydrography Dataset (NHD) (24) served as the model’s spatial framework. We were not able to use the set of water quality monitoring sites that have been used to calibrate models such as the USGS SPARROW model (10, 12), because data collection has been discontinued at several of these sites. We therefore identified a new set of 27 USGS sites (25) and their catchments (which included some used in other models) for which we calibrated the model. All sites chosen had data collected for a minimum of 4 years in 1990-2002. The mean catchment area of calibration sites was 32 846 km2 and the median was 32 242 km2. Time- and flow-weighted nitrate flux (kg nitrate-N ha-1 d-1) for the spring period (March-June) was calculated for each water-quality monitoring site and year for which there were data using discharge and nitrate concentration data from the USGS National Water Information System (25). As an input to the model, monthly runoff data for each watershed in the MRB were obtained from USGS (Dave Wolock, personal communication) and averaged to produce a spatially weighted estimate for the March-June period that describes runoff (mm d-1) for each of the 27 calibration site catchments. County-level data on cropping acreages (26) were combined with fertilizer use data (27) to estimate yearly fertilizer use by crop, and were checked against a commercial database of county-level fertilizer sales (28). A rate for per-capita human waste nitrogen inputs to aquatic systems was calculated using an independent database of 32 000 records of municipal waste discharge data at the county level (29) which was regressed on population data obtained from the Census, with the assumption that 75% of waste nitrogen is nitrate (30, 31). Data on numbers of cows, sheep, hogs, and poultry were compiled at the county level from National Agricultural Statistics Service agricultural census data, using linear interpolation for years between the 5-year census, and were multiplied by waste output and nitrogen content of waste following the protocol used in the SPARROW model (32), checking for consistency with data from Smil (53). Atmospheric nitrate deposition data were obtained in raster form from the National Atmospheric Deposition Program (33) and re-expressed at the watershed level using ARC GIS Spatial Analyst (34). Federal commodity and conservation payment data used in the analysis were obtained from the Environmental Working Group (20). Data that were acquired on a county level (fertilizer inputs, human and animal populations, and federal commodity support and conservation payments) were re-expressed at the watershed level by using ARC GIS (34) to calculate the proportion of each county and its associated inputs that lay within each watershed. The model was configured so that yearly non-point inputs (fertilizer, animal waste, and atmospheric nitrate deposition) were multiplied by the area-weighted catchment runoff term. We pre-set a value for discharge of nitrate as municipal waste by multiplying our per-capita nitrate discharge rate by annual watershed population density. We allowed the model to determine the importance of the fertilizer, animal waste, and nitrate deposition inputs by using the solver function in Microsoft Excel to assign the coefficients and exponents to each term that would minimize the sum of squares in a linear regression of modeled flux on measured flux. Finally, we used the GLM procedure in SYSTAT to determine the significance level of terms in the model, regressing modeled nitrate flux against measured spring flux at the 27 calibration sites. To estimate spring nitrate loading from the MRB as a whole, we applied the coefficients and exponents determined by the model to nitrogen inputs data for every watershed in the MRB for each year in the 1990-2002 period, multiplying the resulting per-hectare nitrate flux by watershed size to estimate the watershed nitrate-N load to rivers and streams. Each watershed’s nitrate load estimate was then multiplied

FIGURE 1. Comparison of whole-MRB modeled nitrate-N flux (kg N d-1) for March-June with USGS flux measured April-July at the Melville (Atchafalaya) and St. Francisville (Mississippi) sites, 19902002. by a watershed-specific delivery efficiency value from the SPARROW model that takes into account stream size and time of travel to the Gulf to estimate the amount of nitrogen remaining after in-stream denitrification (12). Watershedlevel losses were summed to estimate the total daily nitrate load leaving the MRB and entering the Gulf. The contributions of nitrogen inputs as fertilizer, animal waste, atmospheric nitrate deposition, and human waste were each expressed as a proportion of the total modeled daily load for the MRB as a whole. Where possible, model inputs and estimates were compared with data from other sources. Estimated fertilizer use was compared with a commercial database of fertilizer sales by county (28), and population waste estimates were compared with estimates from EPA (35). An additional source of “population-related” nitrate inputs may be fertilizer runoff from gardens, lawns, and golf courses. We thus examined non-farm fertilizer sales for three states where such data were well-documented in the fertilizer sales database to estimate the potential magnitude of urban fertilizer runoff. To evaluate the model’s performance relative to actual water quality measurements at the individual watershed level, we selected an additional set of 47 USGS sites with singlewatershed catchments that did not overlap the catchments of the 27 calibration sites. Mean watershed area for these sites was 3750 km2 and median was 3223 km2. We compared modeled output (not including the denitrification efficiency parameter, which was only employed to calculate the summed nitrate load entering the Gulf) with actual nitrate loading data at these independent sites. Finally, we compared modeled nitrate load estimates for the MRB as a whole with USGS nitrate loading data from the St. Francisville (Mississippi River) and Melville (Atchafalaya) monitoring sites. Following development of the nitrate loading model, we wanted to estimate the increase in conservation acreage that would be required to make conservation effort commensurate with agricultural intensity in core agricultural areas, and to estimate what such an increase would cost. Again using data from the 27 catchments from 1990-2002, we used the solver function in Excel to describe the relationship between measured nitrate flux and the proportion of a catchment under fertilized agriculture. Using 2003 data on cropping and conservation acreages as a basis, we then calculated the amount of new conservation acreage that would be required if conservation effort were to be allocated commensurate with the increase in nitrate flux that occurs as the proportion of a watershed under fertilized agriculture increases. To estimate the cost of this scenario, we multiplied the area of hypothetical new conservation area by the per hectare conservation payment rate within each watershed, assuming that the area thus created would be at the expense of fertilized land and its associated commodity support payments. Where conservation acreage in a given watershed already exceeded VOL. 41, NO. 15, 2007 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 2. Catchments for the 27 sites used to calibrate the model and the 47 “single-watershed” sites (in black). the amount calculated by our model, we estimated no change in its area.

Results and Discussion Model Structure and the Proportional Contribution of Inputs. The best-fitting version of the model comparing estimated and observed nitrate-N flux (kg nitrate-N ha-1 d-1) for the 27 water quality monitoring stations in years from 1990 - 2002 had the following form:

modeled flux (kg nitrate-N ha-1 d-1) ) catchment runoff × [1.0376 × 10-5 × (kg fertilizer-N ha-1 yr-1)3.083 + 1.118 × 10-4 × (kg animal waste-N ha-1 yr-1)1.1486 + 9.497 × 10-4 × (kg atmospheric nitrate-N ha-1 yr-1)] + 6.53 × 10-3 kg nitrate-N d-1 × (population ha-1) with catchment runoff (mm d-1) serving as a dimensionless weighting factor that controls delivery of non-point N inputs to aquatic systems. As determined by GLM, the runoff × fertilizer (p < 0.0001), runoff × atmospheric nitrate deposition (p ) 0.025), and population terms (p ) 0.004) were all significant, and the runoff × animal waste term was marginally significant (p ) 0.09). The overall regression statement for modeled flux on measured flux at the 27 sites was modeled flux (kg nitrate-N d-1) ) 0.83 × measured flux + 0.0025 (R 2 ) 0.84, p < 0.00001, n ) 208). The total modeled nitrate-N load for 1990-2002 averaged 65% of the summed measured load at the St. Francisville (Mississippi) and Melville (Atchafalaya) monitoring stations for the months of March-June (R 2 ) 0.65) and 80% of the measured load for the April-July period (R 2 ) 0.86; Figure 1). The model estimates nitrate loading to rivers for the months of March-June, but that water actually arrives at the Gulf weeks later, which may explain the better relationship between our loading estimates and USGS-measured loading for the April-July period. As a proportion of the total modeled nitrate load for the years 1990-2002, fertilizer runoff accounts for 59%, municipal waste sources for 11%, atmospheric nitrate deposition for 17%, and animal waste for 13%. These results are reasonable in the context of other nitrogen loading models for the Gulf. For instance, the SPARROW model calculates total nitrogen inputs to the Gulf at around 6.1 million kg d-1 5412

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in the late 1980s (10, 12), with 49% coming from fertilizer runoff, 18% from atmospheric deposition, 14% from livestock wastes, 6% from point sources, and 13% from nonagricultural sources, such as forest soils. Influence of Site Location on Modeled Nitrate Flux. Sites used to calibrate the model were not located randomly by USGS and can represent accounting sites of interest for particular water quality issues, which may compromise how well they represent the MRB as a whole. We postulate that some calibration sites probably have a slight urban bias, which might occur if sites are located in poorly mixed regions downstream of urban areas, since allowing the model to find an optimal coefficient for the municipal loading term produces a value almost 50% higher than the regressionbased per-capita value that we used. Urban bias in the data, if it exists, could cause our model to underestimate the proportion of the load contributed by other sources such as fertilizer. Interestingly, however, the increase in municipal loading calculated when the human waste coefficient is determined by the model decreases the proportional contribution by fertilizer by just one percent, coming instead at the expense of loading calculated for atmospheric nitrate deposition, which co-varies spatially with municipal loading in this dataset. Despite the potential urban bias in the data, we consider that the 27 calibration sites should be broadly representative of the MRB, given that sites are widely distributed, mostly located on large rivers, and have a summed catchment area that constitutes 52% of total basin area (Figure 2). As an additional check on the validity of the model, however, we applied the model’s terms to data from 47 independent single-watershed USGS water-quality monitoring sites, producing a nitrate flux estimate that was 56% of measured flux (R 2 ) 0.60, p < 0.00001, n ) 202). Many of the “single-watershed” sites are located in high fertilizeruse areas of the MRB (Figure 2), suggesting that the model, calibrated as it is for sites across the MRB, probably underestimates fertilizer nitrate loading in intensively cropped areas. Underestimation of Measured Nitrate-N Loading by the Model. Several factors may account for the model’s underestimation of the total MRB nitrate load measured at the St. Francisville and Atchafalaya water quality monitoring stations. The SPARROW denitrification loss values attenuate the final modeled nitrate load considerably, thus if these

values overestimate rates of denitrification in the spring, the period for which our model is calibrated, the modeled load may be underestimated. Lending support to this idea, a study examining agricultural runoff in Illinois concluded that denitrification is an ineffectual means of nitrate loss when stream nitrate peaks in the spring, but operates more efficiently when concentrations diminish in summer (36), and another 2-year study in the MRB found that denitrification rates are much higher in August than in May (37). However, the model also may underestimate nitrate loading because it does not include some known inputs, such as industrial nitrogen inputs, soil nitrogen mineralized in natural or agricultural systems, and residual nitrogen left in soils after harvest of leguminous crops. Further, because the model only includes the current year’s nitrogen inputs, it is not parameterized to capture the response of nitrate flux to runoff events that mobilize nitrogen stored in soils and groundwater (14, 15, 38). Agricultural nitrogen inputs are the major source of groundwater nitrate, and patterns of groundwater contamination in the MRB show a pattern similar to that of surface water nutrient loading (39). River systems may derive from a few percent to almost half their flow from groundwater (40), thus the potential for nitrate loading from groundwater is high, and represents an additional, albeit residual, agricultural input to surface water. McIsaac et al. (15) found that surface water nitrate flux in the MRB was related both to current year nitrogen inputs and a fractional contribution of inputs from up to the previous 9 years, highlighting the importance of residual nitrogen. Nonlinearity of the Nitrate Flux-Fertilizer Relationship. Our model treats nitrate flux as an exponential function of fertilizer inputs, but the nonlinearity of this relationship may partially reflect collocation of high fertilizer use and enhanced field drainage. Much of the intensively fertilized land in the Midwest is normally too wet to farm without intervention, thus to accelerate drainage, farmers use perforated tile drains and contour their fields to move water to drainage ditches. About 35% of Illinois’ agricultural lands are tile-drained (41), and drainage can greatly increase fertilizer runoff from these areas (42). Despite the difficulty of distinguishing the influence of drainage and high fertilizer use rates, however, various models have treated fertilizer loss to aquatic systems as a nonlinear function of either nitrogen inputs (15, 17) or the proportion of the land area under fertilization (14). Nonlinear loss dynamics in response to inputs are indeed observed at a variety of scales, occurring in nitrogen addition experiments in nonagricultural systems once the retention capacity of plants and soils is exceeded (43, 44), or at the ecosystem level in response to atmospheric N deposition (45-47). Temporal data from Europe (48) show an abrupt nonlinear increase in the extent of the Black Sea hypoxic zone once fertilizer use in its basin increased beyond about 1.75 million metric tons a year, and for the major drainage basins of the United States (Figure 3), nitrate flux is highest where the sum of fertilizer, biologically fixed N, and atmospheric nitrate deposition exceeds about 20 kg N ha-1 (49). The exponential relationship between nitrate flux and fertilizer inputs in our model reflects a nonlinear increase in nitrate flux measured at the 27 calibration sites once watershed fertilizer inputs exceed about 30 kg ha-1 (Figure 4). A strong linear relationship between fertilizer inputs per unit area and the proportion of watershed area under fertilized agriculture in these catchments (proportion fertilized ) 0.0108 × kg fertilizer N ha-1 + 0.0099, R 2 ) 0.93, p < 0.00001, n ) 208) translates to a nonlinear increase in measured nitrate flux once 30-40% of watershed area is fertilized. Because modeled nitrate flux increases steeply once fertilizer inputs reach a certain threshold, the model identifies a relatively small number of intensively fertilized watersheds

FIGURE 3. Relationship between nitrate-N discharge and net anthropogenic input (fertilizer, biologically fixed N, and atmospheric nitrate deposition) for major watersheds of the United States (reproduced with permission from ref 49).

FIGURE 4. Relationship between nitrate discharge and fertilizer use for model calibration sites (solid line) and “single watershed” sites (dashed line). Curves were fitted using SYSTAT’s “locally weighted scatterplot smoothing” procedure. as contributing a large proportion of fertilizer inputs to the Gulf (Figure 5). A particular watershed’s contribution to modeled nitrate loading differs from year to year, probably due to temporal shifts in precipitation and runoff, since other model inputs tend to change smoothly over time. However, averaged modeled values for the years 1995-2002 suggest that fertilizer losses from about 5% of the MRB (44 watersheds) account for about 49% of aquatic nitrate loading from fertilizer, and about 20% of land area accounts for about 90% of spring nitrate-N loading. These results are similar to those of Donner et al. (18), who found that 20% of basin area was responsible for about 86% of year-round nitrate flux for the years 1990-1994. Overall, the model suggests that spring fertilizer losses in the MRB averaged over 3 million kg nitrate-N d-1 for the years 1995-2002, with total losses over the 120-day modeled period constituting 2.6-7.2% of yearly fertilizer-N inputs worth about $234 million at then-current fertilizer prices (28) or $334 million at more recent prices (50). Not surprisingly, intensively cropped areas that the model identifies as major sources of fertilizer runoff also tend to receive a large proportion of commodity support payments (Figure 6), and there is a strong linear relationship between average fertilizer use and average commodity payments at the watershed level for the 1995-2002 period (average kg fertilizer-N ha-1 ) 0.619 × commodity dollars + 2 217 540; n ) 848, R 2 ) 0.79, p < 0.00001). However, watersheds with the highest rates of modeled fertilizer runoff tend to have relatively less land enrolled in conservation programs than watersheds with lower agricultural losses. For instance, watersheds in the 5% of the MRB ranked as having the highest rates of agricultural nitrate loss had on average 0.028 hectares of land in conservation per fertilized hectare in 2003, significantly lower than the 0.056 and 0.063 hectares of conservation land per fertilized hectare in the next two highest ranked blocks of 5% area (F ) 9.24, p ) 0.0002). VOL. 41, NO. 15, 2007 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 5. Cumulative percent of MRB accounting for proportion of modeled nitrate-N flux from agricultural sources. Totals mapped are for nitrate-N flux prior to adjustment with SPARROW delivery efficiency coefficients.

FIGURE 6. Agricultural commodity payments (dollars ha-1 on a whole-watershed basis) for 2003. Evaluating Model Structure and Terms: Runoff. Because the model required multiple datasets and assumptions, it was important to evaluate the model’s inputs and structure 5414

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relative to external criteria. Runoff was an important driver in the model, improving the model’s regression R 2 from 0.66 to 0.84 with its inclusion. Runoff volume alone can exert

strong control over aquatic nitrogen flux in undisturbed systems, explaining about 66% of the variance in nitrate flux from the mostly forested watersheds of the Hydrologic Benchmark Network of the United States (51). However, runoff alone only explained about 11% of the variance in our dataset, reflecting the primary role of anthropogenic nitrogen inputs in aquatic nitrate loading. Work estimating the separate contributions of hydrology and nitrogen inputs in the MRB has concluded that precipitation-related increases in river discharge only account for about 25% of the increase in nitrate loading that has been observed for the years 19661994 (52). Fertilizer Inputs. Comparing our modeled fertilizer inputs with fertilizer sales data suggests that our estimates were close to or slightly below actual fertilizer use in the MRB. While sales data were not available for every state, countylevel fertilizer sales calculated from the fertilizer sales database (28) exceeded our estimates of fertilizer use by 8-35% in Arkansas, Illinois, Indiana, and Ohio, even after subtracting non-agricultural fertilizer sales. However, our estimates closely matched fertilizer sales data in Iowa, Kansas, and Missouri, and somewhat overestimated fertilizer sales in Minnesota. On average, our calculations translated to an estimate of 87.5 kg fertilizer-N yr-1 applied per person in the MRB for 1990-2002. Population-Related Nitrogen Inputs. Using a fixed estimate of per-capita municipal nitrate loading in the model was a necessary compromise, decreasing the number of parameters estimated and increasing the model’s stability. Nitrate inputs to aquatic systems as human waste vary based on the prevalence and efficacy of municipal waste treatment, but our regression-based estimate of about 2.38 kg nitrate-N person-1 yr-1 constitutes 89% of an EPA estimate for human waste inputs as total N in 1996 (35), and is reasonable in light of estimations of per capita waste production (53). We did not include industrial nitrogen inputs in the model, due to the difficulty of obtaining historical discharge data at the watershed level. However, an EPA study estimates total nitrogen inputs to the MRB from industrial sources at about 87 million kg in 1996 (35), which amounts to about 239 000 kg d-1, not all of which is nitrate. An additional source of “population-related” inputs that is not explicitly included in the model is fertilizer runoff from lawns and golf courses. Analyzing state-level totals from the fertilizer sales database (28), we found that non-farm fertilizer sales were 2.09% of farm sales in Indiana, 1.57% in Missouri, and 1.45% in Illinois, amounting to around 1.75 kg person-1 yr-1 for 1993-2000. There is no way to determine what percentage of non-farm fertilizer nitrogen is lost to aquatic systems, but where urban and suburban fertilizer runoff is channeled by impervious surfaces into storm sewers, the proportion may be relatively high, and could increase the importance of urban nitrogen in aquatic nitrate loading. Such additional population-related inputs may partly explain why the municipal waste coefficient calculated by the model was higher than the value we determined by regressing waste outputs on population. Animal Waste and Atmospheric Deposition. Our calculations using animal numbers from the Census of Agriculture and animal waste production estimates (32, 53) suggest that total production of nitrogen in animal waste is about 38% of nitrogen applied as fertilizer in the MRB. Nevertheless, animal waste inputs were only a marginally significant term in the model. Nitrogen content of animal waste can vary widely, and significant amounts can be volatilized (54), which may reduce the amount of nitrogen available to enter aquatic systems. Additionally, Smith et al. (10) found that the spatial referencing of animal waste inputs significantly improved the fit of the SPARROW model, since large animal operations are more likely to contribute runoff if they are located near rivers. Our model was only spatially

FIGURE 7. Relationship of modeled nitrate-N flux, and the proportion of the watershed enrolled in a federal conservation program, to the proportion of the watershed in fertilized agriculture (data from 2003). explicit to the catchment level, which may have reduced its ability to detect the influence of animal waste on nitrate flux. Interestingly, although rates of atmospheric nitrate deposition in the MRB are relatively low compared to those in the Northeast (mean 2.09, median 2.19 kg ha-1 yr-1), the model estimated nitrate deposition as the second largest source of aquatic nitrate in the MRB. Because we did not include ammonium deposition, which is high in agricultural regions, dry deposition of nitrate, or organic N deposition, the importance of atmospheric nitrogen inputs to the Gulf is probably more important than our model indicates. A Model-Based Approach for Increasing Conservation Enrollment. One reason for high rates of agricultural nitrate loss in intensively cropped regions may be that conservation acreage is presently inadequate to buffer the volume of fertilizer lost from fields. Supporting this idea, our analysis suggests that agricultural nitrate loss increases as a nonlinear function of the proportion of a watershed in fertilized agriculture, but the proportion of a watershed enrolled in a federal conservation program actually declines with cropping intensity (Figure 7). Because this analysis combines conservation acreages from all programs, the negative relationship between the proportion of a watershed fertilized and the proportion enrolled in a conservation program is largely driven by high Conservation Reserve Program enrollment in the western part of the MRB, where large tracts of land have been taken out of production and where low runoff reduces aquatic nitrogen loading. Nonetheless, conservation acreage is arguably low in an absolute sense in the 44 watersheds identified by the model as contributing nearly 50% of fertilizer loss in the MRB, constituting on average 2.1% of watershed area while fertilized agriculture constitutes 76%. Increasing the amount of land enrolled in conservation programs might reduce agricultural nitrate loss, but how much more acreage is needed? While there is probably no fixed relationship between cropping intensity and the conservation effort required, making conservation enrollment a nonlinear function of agricultural effort, so that it constitutes an increasing proportion of land area in watersheds where cropping intensity and other factors most favor nutrient loss, is a possible approach. As a simple initial scenario, we inverted the current negative relationship that exists between conservation and agricultural effort (Figure 7), instead making the proportion of a watershed in conservation mirror the nonlinear rise in nitrate flux that occurs as fertilization intensity increases. We first used the solver function in Excel to describe the relationship between measured nitrate flux and the proportion of a catchment fertilized at the 27 calibration sites:

modeled flux (kg nitrate-N ha-1 d-1) ) 0.217 × proportion fertilized2.93 VOL. 41, NO. 15, 2007 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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This relationship described about half the variability in measured flux:

modeled flux (kg nitrate-N ha-1 d-1) ) 0.56 × measured flux + 0.0029 (n ) 208, R 2 ) 0.52, p < 0.00001) We then rephrased the model to calculate a hypothetical increase in conservation enrollment. Using 2003 data on conservation and fertilized acreages as the basis for calculations, we replaced the flux term in the model with an estimate for the amount of conservation acreage required for each watershed in the MRB. This calculation is straightforward, because although the proportion of a watershed in conservation and its daily modeled nitrate flux have different units (Figure 7), they fall on a similar scale:

proportion in conservation required ) 0.217 × proportion fertilized2.93 Under this simple scenario, land enrolled in conservation programs would be increased by about 2.71 million hectares, a 29% increase over 2003 enrollments, while land taken out of traditional fertilized agriculture and enrolled in conservation programs would constitute about 3% of 2003 fertilized hectares. Would increasing conservation to this extent be sufficient to reduce nitrate loading and the size of the Gulf hypoxic zone? It has been estimated a 30% reduction in total nitrogen inputs would shrink the hypoxic zone by 20-60%, though still greater reductions may be required (8). Our model characterizes nitrate loading, not total nitrogen loading, but under our scenario, a 30% reduction in total nitrate inputs to the Gulf would require a 50% reduction in agricultural loading to aquatic systems, assuming that other sources were not also reduced. Many variables can affect nutrient loading to streams, thus it is difficult to determine whether a 30% increase in conservation effort would reduce agricultural nitrate loading by 50%. Grassy and wooded stream buffers can be highly effective in reducing nitrogen inputs in both overland flow and groundwater, with load reductions well above 50% (21, 23), and wetlands that are restored or created can be effective sinks for nitrogen, promoting plant and microbial nitrogen assimilation and denitrification (55). However, it seems likely that achieving a 50% overall reduction in agricultural nitrate loss will require not only increased conservation enrollment, but also reductions in overall fertilizer use. Increasing conservation acreage would itself reduce total watershed fertilizer inputs by taking land out of production, but changes in fertilization practices would render increased conservation acreage even more effective, especially in tile-drained areas with the highest rates of nitrate loss (56). Data from USDA indicate that about 17% of farmers in the Corn Belt test soils for nitrate prior to applying fertilizer, but that those who test reduce their applications by almost 7 kg ha-1 (57). Timing of fertilizer application can also influence runoff. A multi-year watershed-level USDA study found that spring fertilizer application decreased aquatic nitrate loads by 30% in contrast to fall application, and involved no reduction in corn production (58). Data from USDA’s Agricultural Resource Management Survey (ARMS) indicates that as of 2001, 48% of corn growers surveyed in Illinois, and 36% in Iowa, applied N fertilizers in the fall rather than in the spring (59), suggesting that significant reductions in nitrate loading remain to be realized with increasing adoption of spring application. Reducing the concentration of nitrogen in runoff with more efficient fertilizer use and spring application could also increase denitrification efficiency in streams and wetlands, which can be overwhelmed by high nitrogen concentrations (36). 5416

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Our analysis suggests that a federal conservation program initiative of this scale would not require a large increase in overall funding. New and existing conservation acreage funded at the average 2003 rate in each watershed would increase total conservation spending about 55% from the 2003 baseline, from $1.38 billion to $2.14 billion, with farmers increasingly benefiting from “green” federal payments at the expense of commodity support payments. However, the combined cost of commodity support and conservation programs under this scenario would be about $8.6 billion, 6.2% more than the combined cost of these programs in 2003. This is a relatively small increase in the context of historical funding levels, which fluctuate with market and weather conditions. For instance, commodity subsidies alone averaged $13.28 billion a year for 1999-2001 in the MRB, a 260% increase over the previous 3 years, while conservation subsidies averaged $1.21 billion a year, largely unchanged from the previous 3-year period (20). The reductions in fertilized acreage called for under a scenario of increased conservation effort may be difficult to reconcile with historic patterns of farming and an increasing federal commitment to corn-based ethanol production, which will likely bring more land into intensive corn production. In light of this trend, flexible solutions to reduce aquatic nitrate loading could also include structuring agricultural funding to support production of perennial crops, including cellulosic ethanol crops like switchgrass, that build soil organic matter stocks and retain nitrogen inputs more efficiently than corn and other annuals (60, 61). Whatever the long-term trajectory of agricultural production in the MRB, however, large-scale increases in conservation effort are needed to install and restore the “ecological infrastructure” that protects aquatic systems. Further, increased conservation acreage at such a level would be accompanied by a variety of additional benefits. Wetlands and riparian forests restored or created under conservation programs would provide significant flood retention and control (62). Creating and restoring wetlands and diversifying land use could also promote pesticide retention and degradation (6365), reduce farm field sediment loading in streams (21, 66), reduce municipal water treatment costs, improve fish and bird habitat, sequester carbon, and provide miles of connected habitat corridors for wildlife. The collective benefit of all these functions, and a greater hope for restoring the Gulf of Mexico, may be achieved with a relatively modest shift in federal agricultural spending priorities.

Acknowledgments Sara Duke, Nancy Rabalais, Dave Wolock, Bill Battaglin, Greg McIsaac, Thomas Jordan, Don Boesch, and especially Richard Alexander all provided valuable input to this project, for which we are very grateful. We also thank the excellent scientists of the USGS and NADP for providing help and guidance in utilizing their data.

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Received for review May 23, 2007. Revised manuscript received January 23, 2007. Accepted May 16, 2007. ES070179E