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Estimating net anthropogenic nitrogen inputs (NANI) to US watersheds: comparison of methodologies Bongghi Hong, Dennis P. Swaney, and Robert W. Howarth Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/es303437c • Publication Date (Web): 15 Apr 2013 Downloaded from http://pubs.acs.org on April 26, 2013

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Environmental Science & Technology

Estimating net anthropogenic nitrogen inputs (NANI) to US watersheds: comparison of methodologies Bongghi Hong*, Dennis P. Swaney, Robert W. Howarth Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY 14853, USA *

Corresponding Author, 103 Little Rice Hall, Department of Ecology and Evolutionary Biology,

Cornell University, Ithaca, NY 14853, USA; email: [email protected]; phone: 1-607-255-1502; fax: 1-607-255-8088

Keywords NANI, nitrogen, riverine N flux, methodology, sensitivity, tile drainage.

Abstract The Net Anthropogenic Nitrogen Input (NANI) approach is a simple quasi-mass-balance that estimates the human-induced nitrogen inputs to a watershed. Across a wide range of watersheds, NANI has been shown to be a good predictor of riverine nitrogen export. In this paper, we

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review various methodologies proposed for NANI estimation since its first introduction, and evaluate alternative calculations suggested by previous literature. Our work is the first study in which a consistent NANI calculation method is applied across the US watersheds and tested against available riverine N flux estimates. Among the tested methodologies, yield-based estimation of agricultural N fixation (instead of crop area-based) made the largest difference, especially in some Mississippi watersheds where the tile drainage was a significant factor reducing watershed N retention. Across the US watersheds, NANI was particularly sensitive to farm N fertilizer application, cattle N consumption, N fixation by soybeans and alfalfa, and N yield by corn, soybeans, and pasture, although their relative importance varied among different regions.

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1. Introduction Over the past several decades, many regions of the world have experienced large increases in riverine nitrogen (N) fluxes to coastal waters, leading to eutrophication and other ecological damage.1,2 In the United States, a majority of coastal marine waters are moderately to severely degraded from this nitrogen pollution.3 Thus, it is important to understand and characterize the sources of human-induced N inputs to the landscape and their impacts on the riverine export. NANI (Net Anthropogenic Nitrogen Input), first introduced by Howarth et al.4 and since applied in modified forms in several subsequent studies,5−7 estimates the human-controlled nitrogen inputs to a watershed and has been shown to be a good predictor of riverine nitrogen export across watersheds of the US, Europe and Asia on a large scale, multi-year average basis.810

It is typically calculated as the sum of four major components: atmospheric N deposition,

fertilizer N application, agricultural N fixation, and N in net food and feed imports. The net food and feed imports in turn are composed of crop and livestock N production, representing negative fluxes removing N from watersheds, and livestock and human N consumption, positive fluxes adding N to watersheds. Among the spectrum of models for riverine nutrient export estimation,11,12 NANI is detailed enough to incorporate available data on individual crops, livestock, and people into a mass balance approach (Figure 1), but simple enough to be calculated with a relatively small set of parameters that are estimated from existing literature. Since its first introduction, alternative methods for calculating NANI have been proposed by numerous researchers. For example, Schaefer and Alber13 proposed that N in non-food crops be included in NANI in southeastern US watersheds to account for the nitrogen associated with


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the production of crops that are not consumed as food (e.g., cotton and tobacco; Figure 1). Livestock N production has been estimated as the difference between livestock N intake and N excretion6 or from slaughtered livestock sales data and the nitrogen content of their edible portions.14 Other examples include area-based versus yield-based estimation of agricultural N fixation (e.g., Han and Allan7 for the Lake Michigan watersheds), and weighting by county areas versus by land use areas for distribution of data to watersheds (e.g., Schaefer et al.15 for the western US watersheds). Most of these alternative approaches have been developed and tested regionally (Figure 2), rather than nationally across the US watersheds. A recently developed set of tools, referred to as the NANI calculator toolbox,16 attempts to incorporate these variants of the NANI calculation proposed in the earlier literature, so that any national-scale assessment across the US watersheds may be evaluated without regional discrepancies in methods, assumptions, or data sources. Alternative NANI calculations may be selected by the user and tested through a sensitivity analysis. Here we review modifications to the NANI calculation since its first introduction, evaluate the incorporation of alternative methodologies suggested by the previous literature, and identify key components controlling the variability of NANI and riverine N export in different regions of the US.

2. Material and methods 2.1. Datasets used A total of 106 US watersheds were used in this study (Figure 2), including 16 northeastern US watersheds (NE),5 12 southeastern US watersheds (SE),13 18 Lake Michigan


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watersheds (LM),7 18 western US watersheds (W),15 and 42 Mississippi watersheds (MS).17 Previous studies reported NANI and its components for these watersheds, or closely related variables that may be used to estimate NANI. Also reported in these studies are riverine N flux estimates and stream discharge, along with climatic variables such as temperature and precipitation available for most watersheds. Here, we use watershed boundaries delineated in the studies to determine consistent estimates of NANI using the toolbox, and compare these to the corresponding reported riverine N flux estimates. The publications with riverine N fluxes used in this study (Figure 2) all included the N flux estimates in the year 1992. Since these fluxes often represent multi-year averages centered on 1992, or estimates were made based on N concentration data collected over multiple years, we estimated NANI values averaged over three Agricultural Census years (1987, 1992, and 1997) to compare to the reported riverine N fluxes (Section 2.2). Sensitivity analysis was also performed using the NANI values averaged for the same periods (Section 2.3). Watersheds that did not have riverine N flux estimates are not included in this study.

2.2. NANI calculation Howarth et al.5 describe calculations of NANI and its components for the 16 northeastern US watersheds and discuss their underlying assumptions. Briefly, atmospheric N deposition includes only the oxidized form in NANI calculation, assuming that most of the ammonia/ammonium emission from a watershed is redeposited on the same watershed.4 Agricultural N fixation was estimated from the acreages of N fixing crops, reported in the Agricultural Census, multiplied by corresponding area-based fixation rates. Human N


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consumption was calculated by multiplying the watershed population, obtained from the Census, by a per-capita annual rate of 5 kg-N/person/yr.6 Similarly, livestock N consumption was calculated as the product of the inventory number of each class of livestock (obtained from Agricultural Census) and the corresponding annual N intake parameters, and summing over all livestock groups. Livestock N production was estimated as the difference between the livestock N consumption and livestock N excretion (estimated by applying the annual N excretion parameters), with a 10% loss assumed during the processing of animal products.6 Crop N production was calculated by converting the harvested quantities of major crops, reported in the Agricultural Census, into N in harvested crops, which are then distributed between humans and livestock, each of which can exhibit losses during the processing for the food and feed production, respectively. Based on the methodology described in Howarth et al.,5 NANI has been calculated for all the counties of the contiguous US using the NANI calculator toolbox.16 Although the toolbox provides the user different options for calculating NANI, the base calculation (hereafter referred to as version 1 or “v1”) reported in Hong et al.16 was different from that in Howarth et al.5 in the following ways: (1) oxidized N deposition was estimated from nationwide CMAQ (Community Multiscale Air Quality) model simulation output,18 instead of from Ollinger et al.19 and Lovett and Lindberg,20 (2) fertilizer N application, made available by the USGS (Ruddy et al.21 and J. Brakebill, personal communication) for all US counties, was used instead of from Battaglin and Goolsby,22 (3) data for missing or withheld items in the county-level Agricultural Census were estimated from state totals; state-level missing or withheld data from the US totals,


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(4) following Schaefer and Alber,13 calculation of non-food crop exports was included (Figure 1), and (5) crops of regional importance were added to the crop list (rice for crop N production and peanut for agricultural N fixation). These changes made little difference in the NANI calculated for the 16 northeastern US watersheds.16 A second version of the toolbox (referred to as “v2”) was developed for application in catchments of the transnational Baltic Sea drainage basin,10 where substantial differences in agricultural practices and dietary preferences are exhibited among the European countries whose watersheds comprise the basin, as well as variations and gaps in the data collected by different countries. The changes made in the toolbox (allowing spatial variation of NANI parameters, distributing regional data into smaller spatial units, and accepting auxiliary datasets) did not affect the NANI calculation for the US, and so are not discussed here. (Interested readers may find more detail on “v2” in Hong et al.10,23). In the following sections, we describe additional changes in the NANI calculation for the US (referred to as “v3”), incorporating most of the earlier considerations and suggestions from the previous studies. The toolbox also incorporates a new tool for repeated calculation of NANI with varying parameters, useful for a sensitivity or Monte Carlo analysis. The sensitivity analysis application is described in Section 2.3 and in Supporting Information.

2.2.1. Data allocation to watersheds


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Relatively high variability in the NANI-riverine N flux relationship in small watersheds has been previously attributed in part to errors associated with misattribution of characteristics near the level of spatial resolution of data (e.g., county-based population distributed to small urban watersheds, underestimating watershed human N consumption).16 Following the procedure described in Schaefer et al.15 and Han and Allan,7 components of NANI can be estimated based on the spatial distribution of land use areas within a watershed, or simply based on the proportions of the county areas. In this study, we tested the land-use-distribution option by distributing agricultural data (e.g., livestock numbers) to agricultural lands, and humanrelated data (e.g., population) to urban lands, as determined from 1992 National Land Cover Dataset (NLCD). Also, the estimates of farm and non-farm fertilizer applications (Ruddy et al.21 and J. Brakebill, personal communication) were distributed to agricultural and urban lands, respectively. CMAQ oxidized N deposition estimates were distributed based on simple deposition grid-cell area-weighted proportions as done earlier.16

2.2.2. Agricultural N fixation Agricultural N fixation has often been estimated from the areas of N fixing crops multiplied by the area-based N fixation coefficients.5,13,17 Estimates of the N fixation rate show some variation.17,24-26 For example, the reported estimates of N fixation by soybeans ranged from 1500 to 31,000 kg/km2/yr,27 possibly due to variation in soil nitrogen availability, crop variety, and previous cropping history.26,28,29 Alternatively, Han and Allan7 evaluated their calculation of agricultural N fixation based on the yield of soybeans and alfalfa for the 18 Lake Michigan watersheds and reported moderate improvements in the NANI-riverine N flux


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relationship compared to the area-based approach. The yield-based approach, as described and discussed in detail in Meisinger and Randall,30 estimates crop N fixation as the product of yield (estimated from harvested quantities as reported in Agricultural Census) and the percentage of this N that can be attributed to fixation:

F = H ×c×

D N p × × × (1 + b ) 100 100 100

(1)

where: F = crop N fixation (kg-N) H = harvested quantities as reported in Agricultural Census c = conversion factor to kg D = percent dry matter N = percent N in dry matter p = percent of harvested N that can be attributed to fixation b = nonharvested N as the fraction of harvested N Several values have been suggested for the percent of harvested N that can be attributed to fixation, again affected by various factors such as soil nutrient availability; for soybeans, values ranging from 0.53-0.82 have been estimated by Han and Allan.7 Experimental approaches yielded variable estimates, with an overall average of about 50-67%.29,31 McIsaac and Hu32 assumed 1.55 kg-N/bushel for N in harvested soybeans and 0.9 kg-N/bushel of N


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fixation, implying that about 58% of harvested N is from N fixation. In Midwestern areas, about 50% of harvested N in soybeans has been attributed to N fixation28,33,34 but recent studies indicate higher proportions (60-77%).35 In this study, we used an area-weighted average of values reported in Han and Allan7 (74% for soybeans and peanuts and 82% for alfalfa and clover). Sensitivity to this estimate was assessed in Sections 3.1 and 3.3, and also in Supporting Information. The nonharvested portion of N fixing crops may consist of belowground biomass and, in part, the nongrain aboveground portion of the plant that may be estimated from the N harvest index,36 representing an available source of N for mineralization and transport for streams.37 In some studies, not all these portions are explicitly considered as a part of “net” N inputs, assuming that they are mineralized each year.34 Since the NANI approach does not include N mineralization from soil organic matter, part of which is from the N sources that are already accounted for (e.g., fertilizer, N deposition, etc.), we include the nonharvested portion of the N fixing crops (50% of harvested N)30 as “new” net N inputs. The non-alfalfa hay areas were assumed to have 25% leguminous plants such as clovers.38,39 Because the Agricultural Census does not report harvested quantities of snap beans, the original area-based approach was applied to snap bean crops, as well as to pasture, as in Howarth et al.5

2.2.3. Human N consumption Human N intake rates, previously estimated at 5 kg-N/person/yr in Boyer et al.6 for the northeastern US, were estimated at the following values using US statistics on daily protein consumption: 103 g/day (1987), 108 g/day (1992), 108 g/day (1997), 110 g/day (2002), 111


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g/day (2007).40-42 Taking the 1987-1997 average value, 6.21 kg-N/person/yr of human N intake was used in this study. No attempt was made at assessing regional differences in human N consumption associated with regional dietary variation.

2.2.4. Livestock calculations In Boyer et al.,6 the “young cattle” category (sometimes referred to as “other cattle”) was estimated as the difference between all cattle numbers and the numbers of adult beef and milk cows, and their N intake and N excretion rates were assumed to be equal to their adult counterparts, possibly overestimating N inputs. In reality, this category represents several groups of livestock such as beef and dairy calves, heifers and stockers that are not reported as disaggregated categories in the Agricultural Census. Russell et al.43 assumed relatively low P intake rates for this “other cattle” category when calculating net anthropogenic phosphorus input (NAPI) for the Chesapeake Bay region. Alternatively, Han and Allan7 divided the livestock in the Agricultural Census into 18 more detailed groups (including 9 cattle groups) based on Kellogg et al.44 and estimated their N intake and N excretion rates, as well as their life cycle information (number of days on the farm). Such adjustments resulted in improvements in the NANI-riverine N flux relationship in some Lake Michigan watersheds. In this study, we adopted the list of livestock from Han and Allan7 and applied it to the NANI calculation for all watersheds. Additional assumptions are required to estimate livestock categories that are not specifically reported in the Agricultural Census, adding some uncertainties; sensitivity to adopting different livestock groups was assessed in Sections 3.1 and 3.3. The “goat” category


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was missing from Han and Allan.7 We added it to the list of livestock and assigned its parameters from Boyer et al.6 The toolbox permits the calculation of livestock N production either from the difference between N intake and N excretion6 or from slaughtered livestock sales data as the product of live weights, edible portions, and the nitrogen content in the edible portion.14 Although both approaches yield generally comparable results,16 here we applied the livestock product-based approach, considering its potential utility in future scenario analyses (e.g., dietary scenarios related to egg and milk consumption). N in the edible portion of pigs and chickens was estimated from Heinz and Hautzinger,45 and all other products from Han et al.14

2.2.5. Crop calculations In Hong et al.,16 peanut was included in the calculation of agricultural N fixation as a crop of regional importance, but not in the calculation of crop N production. In this analysis, it was included in both calculations, so that both its yield and fixation of N would be considered (Figure 1). David et al.46 noted that corn protein content had been decreasing from 10%47 in 1985 to approximately 8.5% in 2006. Following David et al.,46 we linearly interpolated corn protein content between these years and estimated a 1987-1997 mean value (9.5% protein = 1.5% N). The “loss” terms in the crop production calculation, and also in livestock, represent the loss of food and feed during storage and processing due to spoilage, consumption by insects and vermin, and other processes turning them into inedible components.48, 49 While the actual values for these terms are highly uncertain, higher values than were originally used in Boyer et al.6 (10%) might be possible, and other sources of loss such as the consumer-side loss might also


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need to be explicitly considered.50,51 In a recent study of NANI in the drainage basin of the Baltic Sea, the loss terms were estimated using the Food Balance Sheets calculations from FAO, yielding about 17% for corn, 30% for barley and wheat, and 34% for potatoes.10 Given the lack of information to determine the exact amount and location of these losses, we used our previous estimates from Boyer et al.,6 except for pasture where we adopted the “take half-leave half rule,” i.e., 50% of total annual pasture production may be grazed, and assumed a 50% loss of pasture production.52

2.3. Sensitivity analysis Relative sensitivity of a variable y to a parameter x is evaluated by examining the effect of a change of x on the response of y relative to the baseline value. In this study, we applied a ±10% change for each of the NANI components and estimated its sensitivity from the resulting proportional change in NANI. Detailed description of the sensitivity analysis applied in this study, as well as its results and discussion, can be found in Supporting Information (Figure S1).

3. Results and discussion 3.1. NANI in US watersheds The modifications of the NANI calculations described in Section 2.2 resulted in a variable degree of change in NANI and its components across US watersheds (Figure 3). NANI estimates greatly increased in some Mississippi and Lake Michigan watersheds, moderately


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decreased in many other watersheds (including some Mississippi and Lake Michigan watersheds), or showed no difference in others (Figure 3A). The area-weighted average of NANI for all the 106 US watersheds changed relatively little overall between “v1” and “v3” (a 9.8% increase, from 1935 kg-N/km2/yr to 2125 kg-N/km2/yr), with increases in Mississippi and southeastern US watersheds, reductions in northeastern and Lake Michigan watersheds, and little change in western US watersheds (Table 1). The most noticeable changes occurred in the agricultural N fixation component of NANI (Figure 3E). The yield-based approach (Section 2.2.2) increased N fixation estimation where it had been already high, and reduced it in the low N fixing areas. The overall change was again small (from 970 kg-N/km2/yr to 1046 kgN/km2/yr), but with a relatively large increase in Mississippi watersheds (from 1151 kg-N/km2/yr to 1311 kg-N/km2/yr; the largest estimated change was from 3813 kg-N/km2/yr to 7115 kgN/km2/yr). It should be noted that division of 106 US watersheds into 5 groups is not based on similarity of watershed characteristics, and examining regional average values may mask significant variability within each group of watersheds (e.g., Mississippi River Basin, a portion of which has intensive soybean production and tile drainage). Inclusion of the nonharvested portion of the N fixing crops (excluded in some studies or considered as part of soil N mineralization34) may partly explain the high N fixation estimated for some Mississippi watersheds, as well as the values used for the percentage of harvested N attributed to fixation (74% for soybeans and peanuts and 82% for alfalfa and clovers). Based on estimations by Gentry et al.35 (77% and 60% in 2001 and 2002, respectively; the 77% in 2001 was observed in dry conditions, limiting soil N availability from mineralization of soil organic matter), David et al.46 assumed a linear increase of this estimate for soybeans from 50% in 1985 to 60% by 2006 in a central Illinois watershed. Applying 50% attributed to fixation for all N


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fixing crops (except for snap beans and pasture, to which area-based approach was applied) for all years (1987, 1992, and 1997) would result in about 900 kg-N/km2/yr of N fixation for the Mississippi watersheds used in this study (700 kg-N/km2/yr for all US watersheds). Different species would respond differently to changes in calculation; for example, Han and Allan7 indicated that the yield-based approach often increased N fixation estimates for soybeans but decreased them for alfalfa compared to the area-based approach. Their sensitivities to NANI calculation were further investigated in Supporting Information. A similar pattern of change was observed for the net food and feed imports (Figure 3G), partly in response to the change in corn N content, shifting them higher (i.e., less negative) where they were highly negative, resulting in slight overall changes (from -1028 kg-N/km2/yr to -914 kg-N/km2/yr). The lower net food and feed imports in some Lake Michigan watersheds (Figure 3G) were mainly caused by replacing the livestock list from Boyer et al.6 with that from Han and Allan,7 reducing N consumption estimated for the “young cattle” or “other cattle,” as previously reported.7 Relatively large uncertainties exist in this category of livestock in terms of overall N budget; assigning adult N intake parameters to “other cattle” results in 9.9×109 kg-N of livestock N consumption summed over all US counties,16 whereas assigning the parameters for calves from Han and Allan,7 representing the other end of the spectrum of possibilities, yields 5.4×109 kg-N. Applying the livestock list from Han and Allan7 that divides “other cattle” into calves, heifers, stockers, etc. gives a total US livestock N consumption of 9.0×109 kg-N. At the scale of this study, land use-based allocation of data had little impact on NANI estimation except for a few small western US and Lake Michigan watersheds (Figures 3D and 3H), as reported from previous studies.7,15 The small changes in fertilizer N application (from Figure 3C to Figure 3D) were caused by distributing farm fertilizer to the agricultural lands and


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non-farm fertilizer to urban lands, instead of allocating the total county fertilizer application based on watershed areas. There was little overall impact on NANI or its components in any region (Table 1). The revised method (v3) showed no dramatic changes in the relative balance of NANI components, expressed as a percentage of NANI, for the nation as a whole (i.e., averaged over all watersheds) or within any region, compared to v1 (Figure 4). Midwestern watersheds are dominated by fertilizer inputs, and have large net exports of food/feed, though v3 indicates that the percentages of both of these terms are slightly smaller than for v1. This is due in part to the increase in magnitude of NANI in absolute terms associated with higher N fixation estimates in this region (Figures 3E and 3F), and in part to absolute decreases in estimates of net food/feed exports (Figures 3G and 3H). Components of NANI in northeastern US watersheds are more evenly balanced. Western US and Lake Michigan watersheds show contributions similar to those of the Mississippi Basin, and are net exporters of N in food/feed. Southeastern US watersheds are intermediate; they are net importers of N in food/feed like those in the northeastern US, but exhibit more dominance by fertilizer inputs, as do all other regions except the northeast, as well as the nation overall.

3.2. Riverine N flux comparison Plotting riverine N flux against NANI over all watersheds yielded linear regression lines with slopes between 0.25-0.27 in v3 (Figures 5B and 5C), an increase from earlier calculations (0.21; Figure 5A), suggesting that on average approximately 25-27% of NANI is exported from the landscape. This result is consistent with recent studies performed with larger datasets that


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included watersheds of the US, Europe, and Asia (0.24),8,9 as well as an earlier study applied at a larger and coarser scale.4 The NANI-N flux relationship tightened after incorporating changes in the NANI calculation, as indicated by the increase of R2 from 0.24 (v1) to 0.62 (v3). Relatively large changes were noted in some Mississippi and Lake Michigan watersheds, as discussed in Section 3.1, whereas there was relatively small difference in northeastern US watersheds. Land use-based allocation of data made little difference in the overall NANI-N flux relationship at the scale of this study (Figure 5C). Regional variation in the proportion of NANI exported by rivers has been shown to be largely explainable by regional variation in climate and discharge.8,9 Mississippi watersheds exhibit relatively higher slope in the NANI-N flux relationship (0.35; Figure S2A, Supporting Information) than other regions do. Attributing 50% of nitrogen in soybeans and alfalfa production to N fixation would increase this slope to 0.48 (Figure S2B). A higher fraction of riverine N export in the Mississippi River Basin, especially in some Corn Belt areas of the Midwest, has been attributed to the presence of extensive tile drainage systems.32,46 To investigate possible contributions from tile drainage system, tile drainage data obtained from World Resources Institute53 (Table S1) were used in multiple linear regression models. Both NANI and tile drainage were significant factors explaining the observed variation of riverine N fluxes in the Mississippi watersheds (Table S2). The regression analyses indicate that after considering tile drainage, which reduces watershed N retention, riverine N export is expected to increase by 0.19-0.24 kg-N/km2/yr for each unit increase of NANI, a result similar to that obtained in earlier studies.4,5,8 The interactive effect of tile drainage was interpreted as affecting retention of NANI in the watershed, rather than as a separate source term (Table S3). Details of these analyses are given in Supporting Information.


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3.3. Sensitivity analysis Sensitivities of NANI components to NANI calculation were generally consistent among different versions of NANI calculation (Supporting Information; Figure S1), except for some components to which specific changes were made (e.g., young cattle; Section 3.1). Overall, farm fertilizer N application, cattle N consumption, N fixation by soybeans and alfalfa, and N yield by corn, soybeans, and pasture were sensitive to NANI, indicating that the parameters used for the calculation of these variables would be important for accurate estimation of NANI. Relative importance of different NANI components varies among different regions of the US (Figure 4). The northeastern US was the only region where oxidized N deposition was more sensitive than fertilizer N application (Figure S1). Human and livestock N consumption was also important in the northeastern US watersheds relative to fertilizer N application. In contrast, fertilizer application and crop production were the most important NANI components in heavily cultivated regions like the Mississippi watersheds. In the context of regional management, it is worth knowing whether a significant source of nutrient loading is related to atmospheric deposition, fertilizer, or human or livestock waste. Determination of the sensitivity of humaninduced N input and the regional importance of its components should help target N management appropriately (e.g., waste treatment versus fertilizer management) by providing first-order estimates of the relative importance of different sources of N loading to a watershed.

3.4. Discussion


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Some recent refinements in data and parameters describing net anthropogenic nitrogen inputs in the US have not generally led to major changes in our understanding of the patterns of nitrogen loading to the US as a whole. However, the changes in methodology do seem to have improved the explanatory power of NANI and its linear relationship with riverine N export, as measured by the coefficient of determination of the linear regression. While riverine export from watersheds in various regions are not all equally well predicted, the refinements in NANI calculations have improved the relationship in the Mississippi, in part due to changes in estimates of crop N fixation, and also to changes in food/feed export. The NANI toolbox permits assessment of the regional variation in the proportions of major N inputs to watersheds, and thus should be a useful tool for regional nutrient management planning. The regional assessment of human-induced N inputs and sensitivities of their components to the NANI calculation presented in this study may in part depend on the areas considered for the analysis. For example, human N consumption is an important term for the net food and feed calculation. The net food/feed term may be positive or negative, depending upon whether regions are net importers (as in the east) or exporters of food (Midwest and west). However, its importance for the NANI calculation may only be appropriately addressed when all the urban areas are included, a large portion of which may have been excluded in this analysis (Figure 2). For example, the importance of human N consumption is likely to be greater in all areas of the northeastern US, where many large coastal metropolitan areas such as the New York City are located; NANI for the New York City and the surrounding NY-NJ-PA MSA (Metropolitan Statistical Area) was estimated to be ~60,000 kg-N/km2/yr and ~8000 kg-N/km2/yr, respectively, major portion of which is food import.54 Many metropolitan areas like New York City are typically located near the coast beyond the boundaries of the areas studied here, as the watershed


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boundaries are often defined based on the location of monitoring stations and availability of stream nutrient measurements.54 One way to complement our study would be to perform a similar sensitivity analysis on county-level data divided into major regions. The assessment presented in this study is based on NANI calculated using spatially uniform parameters (i.e., single NANI parameter set applied to all US watersheds). For example, agricultural N fixation was calculated in this study applying a single percentage value of N from fixation, a simplification of the original procedure.7,30 Assuming a fixed value for this parameter can be problematic when applied to large regions where soil N availability varies spatially, as the proportion of crop N derived from fixation has been shown to be affected by soil N mineralization.46 Developing spatially varying parameter sets for NANI calculation for the entire US, that may also vary temporally, is an important research topic beyond the scope of this paper. Our approach in this study was to assess sensitivities of key NANI parameters to NANI calculation by applying a range of possible values used in earlier studies. Previous application of the NANI toolbox (v2) to the transnational Baltic Sea drainage basin10 used spatially varying NANI parameters (agricultural N fixation, human N intake, and livestock N intake and N excretion) estimated for the European countries whose watersheds comprise the basin, and their impacts were assessed by comparing with the “baseline” calculation obtained using spatially uniform parameters. Future efforts may be directed toward developing regional NANI parameter sets for the US and assessing their importance in the estimation of human-induced N inputs, both regionally and nationally.


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Figures

Figure 1. Calculation of NANI and its components.


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Figure 2. Map of 106 US watersheds used in this study.


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Figure 3. Comparison of NANI and its components calculated using method v1 and v3. Oxidized N deposition, which is identical between v1 and v3, is not shown.


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Figure 4. Regional variation of average percent contribution of NANI to the total NANI value for watersheds across the US. Percentages, including negative values, are calculated relative to the total NANI value; values exceeding 100% are possible because NANI includes negative values in its sum. Here, v3 is based on county-areas weights, not land use areas; land use area weighting produces similar results.


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Figure 5. NANI vs riverine N fluxes in 106 US watersheds calculated using method v1 and v3.


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Tables Table 1. Area-weighted means of NANI and its components (kg-N/km2/yr; values for 1987, 1992, and 1997 were averaged) for 106 US watersheds calculated using method v1 and v3. Name

Calculation

NANI

v1 v3 (area weighted) v3 (land use weighted)

2624 2030 1985


699 699 699

689 689 689

603 603 603


482 482 472

1096 1096 1089


741 425 424

v1 v3 (area weighted) v3 (land use weighted) v1 v3 (area weighted) v3 (land use weighted)

oxidized N deposition

fertilizer N application

agricultural N fixation

net food and feed imports

non-food N exports

NE SE US US WaterWater- sheds sheds

Lake W US Michi- Watergan sheds Watersheds 2764 3558 1035 3046 2912 1018 3029 2876 1049

Mississippi Watersheds

All Watersheds

1933 2272 2263

1935 2125 2119

174 174 174

402 402 402

410 410 410

1839 1839 1810

835 835 827

1901 1901 1899

1593 1593 1589

556 358 354

1336 1355 1328

326 314 316

1151 1311 1314

970 1046 1048

703 425 391

470 950 943

-219 -884 -865

-299 -304 -268

-1511 -1332 -1342

-1028 -914 -918

0.81 0.81 0.79

47 47 46

0.01 0.01 0.01

0.44 0.44 0.28

11 11 11

10 10 10


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Corresponding Author Bongghi Hong, 103 Little Rice Hall, Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY 14853, USA; email: [email protected]; phone: 1-607-255-1502; fax: 1607-255-8088

Author Contributions The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.

Acknowledgment This research was supported by NOAA award NA05NOS478120, “Watershed-EstuarySpecies Nutrient Susceptibility,” NOAA Center for Sponsored Coastal Ocean Research, Coastal Hypoxia Research Program (http://sitemaker.umich.edu/chrpwe/home). This paper is Contribution #177 in the NOAA Coastal Hypoxia Research Program series. We would like to acknowledge Beth Boyer, Sylvia Schaefer, Haejin Han, and William Battaglin for sharing supporting data from their published papers. We also thank Christine Costello for discussion and help on tile drainage data and analysis.

Supporting Information Available Details of sensitivity analysis and assessment of tile drainage effects on N retention and delivery for the Mississippi drainage basin. This information is available free of charge via the Internet at http://pubs.acs.org.


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(52) Natural Resources Conservation Service (NRCS) guideline for pasture management; http://www.nrcs.usda.gov/. (53) Sugg, Z. Assessing U. S. farm drainage: Can GIS lead to better estimates of subsurface drainage

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Calculation of NANI and its components. 82x121mm (300 x 300 DPI)


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Map of 106 US watersheds used in this study. 82x76mm (300 x 300 DPI)



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Comparison of NANI and its components calculated using method v1 and v3. Oxidized N deposition, which is identical between v1 and v3, is not shown. 177x186mm (300 x 300 DPI)


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Regional variation of average percent contribution of NANI to the total NANI value for watersheds across the US. Percentages, including negative values, are calculated relative to the total NANI value; values exceeding 100% are possible because NANI includes negative values in its sum. Here, v3 is based on county-areas weights, not land use areas; land use area weighting produces similar results. 82x46mm (300 x 300 DPI)



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NANI vs riverine N fluxes in 106 US watersheds calculated using method v1 and v3. 82x129mm (300 x 300 DPI)


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150x183mm (96 x 96 DPI)


Estimating Net Anthropogenic Nitrogen Inputs to ... - ACS Publications

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