Regional Effects of Agricultural Conservation Practices on Nutrient

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Regional effects of agricultural conservation practices on nutrient transport in the Upper Mississippi River Basin Ana Maria Garcia, Richard Brown Alexander, Jeff Arnold, Lee Norfleet, Michael J. White, Dale Robertson, and Gregory E. Schwarz Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.5b03543 • Publication Date (Web): 31 May 2016 Downloaded from http://pubs.acs.org on May 31, 2016

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Regional effects of agricultural conservation

2

practices on nutrient transport in the Upper

3

Mississippi River Basin

4

Ana María García*†, Richard B. Alexander‡, Jeffrey G. Arnold§, Lee Norfleet¦, Michael J.

5

White§, Dale M. Robertson±, Gregory Schwarz‡.

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AUTHOR ADDRESS

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† U.S. Geological Survey, 3916 Sunset Ridge Rd., Raleigh, North Carolina 02906, United States

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‡ U.S. Geological Survey, 432 National Center, Reston, Virginia 20192, United States

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§ U. S. Department of Agriculture, Agricultural Research Service, Grassland Soil and Water

10

Research Laboratory, 808 East Blackland Rd. Temple, Texas 76502, United States

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¦ U. S. Department of Agriculture, Natural Resources and Conservation Service, 101 East

12

Blackland Rd. Temple, Texas 76502, United States

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± U.S. Geological Survey, Wisconsin Water Science Center, 8505 Research Way, Middleton,

14

Wisconsin 53562, United States

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ABSTRACT

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Despite progress in the implementation of conservation practices, related improvements in water

18

quality have been challenging to measure in larger river systems. In this paper we quantify these

19

downstream effects by applying the empirical USGS water-quality model SPARROW to

20

investigate whether spatial differences in conservation intensity were statistically correlated with

21

variations in nutrient loads. In contrast to other forms of water quality data analysis, the

22

application of SPARROW controls for confounding factors such as hydrologic variability,

23

multiple sources and environmental processes. A measure of conservation intensity was derived

24

from the USDA-CEAP regional assessment of the Upper Mississippi River and used as an

25

explanatory variable in a model of the Upper Midwest. The spatial pattern of conservation

26

intensity was negatively correlated (p = 0.003) with the total nitrogen loads in streams in the

27

basin. Total phosphorus loads were weakly negatively correlated with conservation (p = 0.25).

28

Regional nitrogen reductions were estimated to range from 5 to 34 percent and phosphorus

29

reductions from 1 to 10 percent in major river basins of the Upper Mississippi region. The

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statistical associations between conservation and nutrient loads are consistent with hydrological

31

and biogeochemical processes such as denitrification. The results provide empirical evidence at

32

the regional scale that conservation practices have had a larger statistically detectable effect on

33

nitrogen than on phosphorus loadings in streams and rivers of the Upper Mississippi Basin.

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INTRODUCTION

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Quantifying the environmental benefit of agricultural conservation practices has been a priority

36

to stakeholders and stewards of rivers, lakes and estuaries that have been deteriorated by the

37

intensification of agricultural production in the United States. Agricultural conservation


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programs, which range from voluntary technical assistance only to payment-based voluntary and

39

cross-compliance programs, have been implemented since the Food Security Act of 19851 with

40

an early focus on the viability of agricultural production through soil conservation. The Farm

41

Security and Rural Investment Act of 20022 substantially increased the level of public funding

42

for conservation and initiated the goal of maximizing environmental benefit 3,4. Subsequently,

43

the Conservation Effects Assessment Project (CEAP) was established to provide science-based

44

guidance on the best use of funding for conservation and to facilitate the alignment of

45

conservation programs with national environmental protection priorities such as the restoration

46

of the Gulf of Mexico.

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Much of the experimental research in conservation documents local (field and farm scale)

48

benefits of conservation practices5,6 but broader off-farm effects have been more difficult to

49

observe7. Reviews of 14 Agricultural Research Service (ARS) benchmark studies8 and the 13

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National Institute of Food and Agriculture watershed studies9 document the findings of a few

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watershed (1 – 2500 km2) experimental studies that demonstrate improvements in stream water

52

quality attributable to conservation practices. Examples include a paired watershed experimental

53

approach that found that nutrient management reduced nitrate concentrations in tile drained

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watersheds by 30% in small watersheds (~4-8 km2) in Iowa10. Kuhnle et al.11 documented a 60%

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reduction in sediment loads over a 17-yr time period in an experimental catchment in northern

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Mississippi (~21 km2) where 20% of cropland had been converted to permanent cover by

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enrollment in the Conservation Reserve Program. Literature reviews have documented decreases

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in particulate phosphorus losses with erosion control and increases in soluble phosphorus

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availability with some structural practices, such as conservation tillage6,12. Overall, few

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watershed experimental or monitoring studies have demonstrated improvements in water quality


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that could be attributed directly to the implementation of conservation practices9. At regional

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scales (> 10,000 km2) trend analyses of water quality records at monitored outlets of large,

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predominantly agricultural watersheds have shown both increases and decreases in nutrients

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during time periods when conservation practices were adopted. For example, Sprague et al13

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found that flow-normalized stream nitrate concentrations had increased at most sites in the Upper

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Mississippi during a time span that included some of the largest increases in conservation

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funding (1980 – 2008); however, this time period also included increased fertilizer

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consumption14,15 and significant hydrologic modification by artificial drainage16. By contrast,

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Murphy et al.17 found recent (2000-2010) decreases in in flow-normalized stream nitrate

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concentrations for the Iowa and Illinois Rivers. A key limiting factor of most of these

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environmental and observational studies is the inability to measure and control for simultaneous

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processes such as multiple nutrient sources, transport processes and hydrological variability8.

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To address limitations of observational studies and trend analysis we present an alternative

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empirical methodology to infer whether conservation practices have had a realized and

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statistically detectable effect on nutrient loads in large, predominantly agricultural watersheds of

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the Upper Mississippi River Basin. We apply the USGS SPARROW (SPAtially Referenced

77

Regression On Watershed attributes) model18,19 to investigate the correlations between water

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quality changes across multiple (~700 for the Upper Midwest) monitoring stations and adopted

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conservation practices. The SPARROW model was developed to extend existing regression-

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based data analysis by introducing a spatial-referencing methodology that allowed a space for

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time substitution, and therefore contrast trend studies which rely on inferences regarding changes

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over time. The methodology improves the correlation of explanatory variables with water quality


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measurements over non-spatially referenced data analysis, thereby improving the ability to

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discern causal influences.

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We employ data and farm-scale process modeling that was previously developed for the

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regional Upper Mississippi River Basin CEAP assessment20. This includes the NRI-CEAP

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Cropland Survey21, an unprecedented documentation of the location, extent, and types of

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conservation practices that have been implemented. As part of the regional CEAP assessment,

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the survey data were represented in field-scale simulation models using the Agricultural

90

Productivity and Extension (APEX) model22 incorporating the best-available process knowledge

91

of the expected effects on nutrient edge-of-field delivery. This approach allowed for multiple

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conservation practices to be summarized and aggregated into nutrient-specific, integrated indices

93

of conservation activities on farms while ensuring that specific farm-level practices were not

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disclosed, in compliance with disclosure clauses, specifically Section 1619 of the Food,

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Conservation, and Energy Act, 2008. The indices of conservation intensity derived from CEAP

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conservation modeling were incorporated into a SPARROW model of the Upper Mississippi

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River Basin to statistically test for the downstream effects of established practices on nitrogen

98

and phosphorus transport in streams.

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The Agricultural Research Service (ARS) benchmark studies8 and the National Institute

100

of Food and Agriculture watershed studies9 also documented the findings of watershed modeling

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that forecasts improvements in stream water quality. Most of these studies applied a priori

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scientific understanding of the physical system to project response and provide technologically

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feasible conservation effects. Yet, the level of complexity afforded by process models can lead to

104

uncertainties in the linkages established between conservation practices and in-stream water

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quality response given 8,23. The CEAP assessment19 for the Upper Mississippi River Basin


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coupled the APEX-predicted nutrient loads from cultivated cropland with the Soil Water

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Assessment Tool (SWAT) to physically represent how practices function in agronomic systems

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and would impact downstream water quality in large river basins. However, it is not currently

109

known whether the downstream benefits forecasted by process models have in fact been realized

110

in the watersheds of the Upper Mississippi River Basin. Studies that further integrate

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observational data and modeling have been advocated to advance conservation science and

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policy8,24,25.

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With the complimentary application of process understanding in the APEX modeling,

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conservation data derived from the CEAP cropland survey and the empirical assessment

115

framework in SPARROW we investigate the measured effect – if any – of agricultural

116

conservation practices, including those were not strategically designed for environmental benefit.

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With this, we intend to fill an important gap in conservation research and provide a regional-

118

scale empirical assessment of conservation effects and an estimate of the magnitude of

119

downstream impacts.

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METHODS

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We perform an inference analysis by evaluating the empirical associations that result from the

122

inclusion of conservation information into regression-based SPARROW analyses of nutrient

123

transport in the Upper Mississippi River Basin. The SPARROW model is a nonlinear least-

124

squares multiple regression on catchments of a hydrologic framework to solve a mathematical

125

expression of constituent mass. Mean annual in-stream constituent load at the outlet of

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catchment, i, is expressed as a function of landscape and instream characteristics such that,

127

= ∑ ∑∈ + ′∑ ∑ exp∑


(1)

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where is an explanatory variable representative of direct nutrient source, indexed by n,

129

having source coefficient , is a source-specific upland or land-to-water delivery

130

explanatory variable, indexed by m, mediated by source/delivery-variable-specific coefficient

131

, and the functional term, A(⋅), accounts for in-channel and reservoir processing, depending

132

on a set of k-indexed attenuation variables, , and associated coefficients, . The functional

133

term, A’, represents attenuation applied to load entering the reach network at catchment i, and is

134

evaluated as √ if reach i is a stream, and equal to if reach i is a reservoir. The term ∑∈

135

corresponds to constituent load leaving the set of reaches , directly upstream of reach i. In

136

model estimation, if any of the contributing upstream reaches are monitored, those reaches use

137

the monitored value of load to represent the load from that reach; otherwise, for predictions or

138

for unmonitored upstream reaches, the contributing load is estimated using the modeled load

139

given by equation (1). If reach i is monitored, a residual in logarithm space, , may be

140

determined from monitored load, , and predicted load, , given by equation (1) such that

141

= ln − ln . The model is estimated using nonlinear optimization methods to determine the

142

values of the coefficients , and that minimize the sum of squared residuals across all

143

monitored sites. The residuals are assumed to be independent, identically distributed, and have

144

zero mean. Further details on the theoretical development of the SPARROW model are provided

145

by Smith et al.18 and Schwarz et al.19

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The starting framework for this study was based on previously published SPARROW models24

147

developed for the U.S. portion of the Great Lakes, Upper Mississippi, Ohio, and Red River

148

Basins, a 1,379,100 km2 area in the Upper Midwest, represented by approximately 12,000

149

catchments of a 1 to 500,000 scale hydrologic framework. The models used nonlinear least-

150

squares regression methods to obtain coefficient estimates that minimize the squared residuals


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implied by the model in equation 1. The response variables in the regressions were mean annual

152

loads at 708 water quality stations for nitrogen and 810 for phosphorus. Robertson and Saad26

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compiled data collected from 1970-2006 (with most water-quality records spanning the period

154

1980 to 2004) at a spatial density of approximately 18 water quality monitoring sites per 4-digit

155

HUC (figure 1). Nutrient loads were estimated using rating curve estimation methods and de-

156

trended to represent long term mean annual conditions centered at 2002, removing the potentially

157

confounding effects of intra- and inter- annual variations in climate and hydrology25. Further

158

information on the previously published Upper Midwest models including the spatial data

159

sources used as explanatory variables in the models, model robustness discussion and comments

160

on the use of rating curve estimation methods are provided in the Supporting Information (SI).

161

In order to move the descriptive exercise represented by the published models into the

162

experimental framework needed for this study, modifications were made to explanatory variables

163

to accommodate information on agricultural conservation practices. The STATSGO erodibility

164

factor (K-factor) was added as an explicit representation of erosion, an important feature given

165

that many conservation practices have been developed to support erosion control. The

166

explanatory variable for tile drainage flow was removed from the published Upper Midwest; the

167

estimated coefficient associated lower phosphorus delivery with higher tile-drainage intensity as

168

limited surrogate for erosion processes. This change did not necessarily remove the

169

representation of tile-flow from the models. In contrast to mechanistic models, where explicit

170

representation is needed to fully ‘account’ for a particular transport mechanism, the empirical

171

nature of SPARROW implies that estimated coefficients implicitly aggregate the effect of

172

multiple transport processes that are spatially correlated with stream nutrient loads. The effects

173

of processes that are not explicitly described by the explanatory variables of the model and are


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uncorrelated with nutrient loads are relegated to the spatially explicit model error term and

175

quantified as part of the prediction uncertainties.

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As detailed later in this section, information about conservation practices were obtained for

177

crop agriculture in the Upper Mississippi River Basin, which is 35% of the Upper Midwest study

178

area (figure 1). Conservation practices are incorporated into the model as a land-to-water

179

delivery variable. To accommodate this it is necessary to develop a regionally stratified

180

SPARROW model specification. This allows for different agricultural source coefficients

181

between the UMRB and remaining portions of the UM basin. The estimates of the coefficients is

182

statistically informed by a subset of the observed loads from monitoring stations located in the

183

UMRB (252 for total nitrogen and 324 for total phosphorus). The remaining model coefficients

184

are constrained to have the same values in both the UMRB and remaining areas of the UM basin.

185

The modified nitrogen model contains four crop agriculture-related variables: cropland (UM),

186

cropland (UMRB), manure from confined sources (UM) and manure from confined sources

187

(UMRB). To limit correlated terms we retained cropland as an explanatory variable for fertilizer

188

loss and nitrogen fixation by legumes in crop rotations.

189


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Upper Mississippi R. Study Area Upper Midwest Study Area

UPPER MISSSIPPI MAJOR (HUC4) RIVER BASINS

Mississippi Headwaters (0701) Minnesota River Basin (0702) St. Croix River Basin (0703) Upper Mississippi-Black-Root Rivers (0704) Chippewa River Basin (0705) Upper Mississippi-Maquoketa-Plum Rivers (0706) Wisconsin River Basin (0707) Upper Mississippi-Iowa-Skunk-Wapsipinicon Rivers (0708) Rock River Basin (0709) Des Moines River Basin (0710) Upper Mississippi-Salt Rivers (0711) Upper Illinois River Basin only (0712) Lower Illinois River Basin (0713) Upper Mississippi exclusive of Missouri (0714)

Base from USDA-NRCS, USGS and EPA. Watershed Boundary Dataset 1:24,000, Accessed 05/26/2016.

190 191

Figure 1. Map of major river basins (4-digit HUCs) in the Upper Mississippi River Basin

192

(UMRB) and distribution of water quality monitoring sites used for SPARROW. Inset: Spatial

193

extent of the Upper Midwest SPARROW models.


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194

We term the modified Upper Midwest models, ‘SPARROW models without conservation

195

variable’ (columns 1-3, table 1 and table 2) although we hypothesized that conservation effects –

196

if any, were contained in the aggregate of estimated coefficients for cropland agriculture. The

197

models are the comparison basis for the empirical test of correlation of conservation practices

198

with stream nutrient loads. To perform this test, conservation intensity variable was added as a

199

land-to-water delivery term in the SPARROW model to be treated as intensive transport

200

properties in SPARROW, similar to a soil characteristic. Land-to-water delivery explanatory

201

variables and estimated coefficients (Znmi and in equation 1) establish associations to

202

climatic, natural or anthropogenic landscape processes that affect contaminant transport to

203

streams in the reach network. A positive coefficient indicates that catchments with large values

204

of the associated land-to-water variable have an increased rate of delivery of a nutrient source

205

( ) to the stream, as expressed by the exp∑ term in equation 1. Therefore, the

206

magnitude of any effect of a conservation variable on nutrient load was not presumed but was

207

instead inferred from the estimation process.

208

We used a conservation variable that was a spatially explicit representation of information

209

obtained through the NRCS-CEAP regional assessment strategy20,27,28. The NRCS-CEAP

210

regional assessment strategy involved developing predictive scenarios that represented the

211

farmer practice information obtained through the NRI-CEAP Cropland Survey21. The survey

212

results were modeled with the Agricultural Productivity and Extension (APEX) model22 which

213

simulated a ‘baseline’ of edge-of-field loss of phosphorus, nitrogen, pesticides and sediment that

214

from the documented farming activities. A second APEX modeling scenario (i.e., no practice)

215

simulated farming activities without the voluntary conservation practices and incentives that are

216

included in the baseline scenario. For example, for the no-practice scenario (figures S1-b and S1-


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e in SI), structural best management practices such as reduced tillage, grass terraces, grass

218

waterways, riparian buffers were removed and formerly cultivated land that is currently retired

219

was brought back into agricultural production. The results were aggregated to the HUC-8 level

220

and shared with USGS for this analysis. Further details on the CEAP modeling framework and

221

the HUC-8 APEX model results are provided in the SI.

222

The conservation variable was computed as the relative difference between the APEX loads for

223

the baseline and the no-practice scenario (figures S1-c and S1-f) such that,

224

∆

=

∗

−

/

(2)

225

where Pk* is the nutrient load for the kth HUC-8 for the no practice scenario in mass units (figures

226

S2-b and S2-e, Table S7, columns 5 and 6); Pk is the load for the baseline scenario, also in mass

227

units (figure S2-a and S2-d; Table S7, columns 2 and 3). To allocate the data to the Upper

228

Midwest SPARROW hydrologic framework, we calculated a nutrient-specific conservation

229

intensity variable, #$, defined as &crop

#$, = &

230

crop+

∆

(3)

231

where crop ⁄crop+ is the ratio of cropland the ith SPARROW catchment to area in cropland

232

within the kth HUC-8.

233

The conservation intensity variable, as defined by equation 3 (figures 2b and 2c) is dependent

234

on both process-model predictions of conservation effectiveness and the synthesis of

235

conservation implementation from gathered survey data. Therefore higher values are associated

236

with both higher expected effectiveness and higher levels of overall implementation. Using the

237

relative difference between modeled scenarios minimizes the dependence on assumptions and


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238

uncertainties related to the APEX model development and calibration. To illustrate this, we can

239

conceptualize the APEX scenarios as simple loading models where load P = SLC, with S are

240

nutrient inputs, L represent transport processes that embed process understanding, such as

241

algorithms that account for tile-drainage, and C an impact factor related to conservation-specific

242

activities. The relative difference between APEX edge-of-field loadings for the baseline

243

scenario, Po and the no-practice scenario, P* (figures S1-c and S1-f) becomes

244

∆ = ∗ - ∗ − - / - = ∗ - ∗ − - / -

245

The actual specification of APEX loading is more complicated than this simple description yet

246

we can expect that some multiplicative effect of process understanding, assuming they are equal

247

in both scenarios, ‘drop out’ (L drops out in equation 2) and the quantity depends primarily on

248

relative source inputs and conservation practice.

(4)

249

Because of the empirical nature of SPARROW, correlations between the conservation variable

250

and other explanatory variables with similar spatial geography could limit the interpretability of

251

the empirical experiment. The correlation to cropland agriculture is small (Pearson correlation =

252

0.1 p < 0.0001 for TN and 0.2, p < 0.0001 for TP). We further assessed these issues by

253

performing a multivariate standard regression on all transport variables present in SPARROW

254

models for the central USA: (1) the published Upper Midwest Model, (2) the Mississippi and

255

Atchafalaya River Basin model29. The residuals of the multivariate regression, which we here

256

term ‘conservation variable residuals’ were used as explanatory variables in SPARROW models

257

(tables S.5 and S.6 in the supporting information). The statistical significance of the correlation

258

between the conservation variable residuals and in-stream loads was essentially the same as the

259

results in table 1: p = 0.003 for both nitrogen SPARROW models and residual of the phosphorus

260

conservation intensity variable was found to remained statistically insignificant (p = 0.25 to p =


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261

0.51). We conclude that multi-collinearity does not impede process interpretations of results

262

obtained in the estimation: model statistics and estimated coefficients are essentially the same for

263

nitrogen and phosphorus models when correlations were removed.

264


14


(a) Extent of cropland

(b) Conservation variable, TN

Percent area

kg/kg

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(c) Conservation variable, TP

kg/kg

0% - 10.9%

0 - 0.0008

11% - 32.9%

0.00081 - 0.00419

33% - 52%

0.0042 - 0.0101

52.1% - 74.4%

0.0102 - 0.0232

74.5% - 100%

0.0233 - 0.381

0 - 0.0015 0.00151 - 0.0072 0.00721 - 0.0179 0.018 - 0.0402 0.0403 - 0.931

Base from USDA-NRCS, USGS and EPA. Watershed Boundary Dataset 1:24,000, Accessed 05/26/2016.

265 266 267

Figure 2. Spatial distribution of (A) cropland, the explanatory conservation intensity variable for (B) total nitrogen (TN) and (C) total phosphorus (TP) for the Upper Mississippi River basin. Interval bounds are quantiles of the distribution.

268


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RESULTS AND DISCUSSION

270

The conservation intensity explanatory variable was found to be inversely correlated with total

271

nitrogen delivery to streams as indicated by a statistically significant (p = 0.003) and negative

272

land-to-water delivery coefficient, indicating that higher levels of conservation intensity are

273

associated with lower levels of nitrogen delivery to nearby streams (columns 4-6, table 1). For

274

total phosphorus the inclusion of the conservation variable also resulted in a negative land-to-

275

water coefficient, but the statistical significance is weak by conventional statistical measures (p=

276

0.25) (columns 4-6, table 2). In fact, the confidence interval includes positive numbers (-4.09 –

277

3.55) implying that the mean coefficient is statistically indistinguishable from zero. Explanatory

278

variables in tables 1 and 2 differ in units and the estimated coefficients are not standardized and

279

not directly comparable. The magnitude of the conservation effects associated with the estimated

280

coefficients were evaluated at a regional-scale and are presented in the next section.

281

The SPARROW with conservation variable re-interprets the observed data set with an explicit

282

representation of conservation effects. Adding the conservation intensity variables to

283

SPARROW did not fundamentally change model interpretation: explanatory variables account

284

for 86% of the variance in the logarithm of observed nitrogen yield and 74% of the phosphorus

285

yield variability. Explanatory variables representing non-agricultural sources and transport

286

properties, unrelated to conservation practices remained unchanged. The eigenvalue spread of

287

each model increased slightly but remained well under 100 indicating that multi-colinearity

288

would not limit interpretation.


16


289 290

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Table 1. Estimated source (αn) land-to-water (./0 ) and in-channel (12 .) coefficients for SPARROW nitrogen models with and without conservation intensity variable*. SPARROW without conservation variable †

Explanatory variable

Coefficient‡

Standard Error

p§

0.112

*

SPARROW with conservation variable Coefficient

‡

[95% CI]

Standard Error

p

Source Point source (kg/yr)

0.801

0.799

0.111

*

[0.6 – 1.02] Atmospheric deposition (kg/yr)

0.545

0.0391

*

0.552

0.0391

*

1.530

*

2.784

*

0.0580

* *

[0.5 – 0.65] 2

Cropland, UM (km )

12.10

1.540

*

12.1 [8.78 – 15.3]

Cropland, UMRB (km2)

**

2.630

19.30

20.1 [13.5 – 27.1]

Manure from confined sources, UM 0.251 (kg/yr) Manure from confined source, UMRB ** (kg/yr)

*

0.0583

*

0.251 [0.173 – 0.35]

0.340

0.108

*

0.417 [0.07 – 0.75]

0.119

--

--

-6.4

2.134

Land to water delivery Conservation intensity (kg/km2)

--

0.003

[-9.67 – -0.0619] Fraction of catchments with tiles

1.250

0.132

*

1.22

0.131

.

[0.977 – 1.49] Drainage density (km/km2)

0.107

0.0561

0.0567

0.104

0.0557

0.0632

0.0186

0.0743

[-0.00092 – 0.216] Temperature (deg. C)

-0.033

0.0187

0.0801

-0.033 [-0.0745 – 0.00942]


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SPARROW without conservation variable †


Clay content (%)

Coefficient‡ 0.019

Standard Error

p§

0.00387

*

SPARROW with conservation variable Coefficient

‡

[95% CI] 0.02

Standard Error

p

0.00383

*

0.00026

*

0.0989

*

[0.01 – 0.03] Precipitation (mm)

0.002

0.00027

*

0.00173 [0.00126 – 0.00219]

In-channel processing Loss in small streams (m3/s)

0.432

0.0988

*

0.445 [0.315 – 0.675]

Loss in medium streams (m3/s)

0.212

0.0910

0.0203

0.221

0.0915

0.0161

[0.047 – 0.57] Loss in reservoirs (yr./m)

5.320

1.260

*

5.52

1.280

*

[0.0333 – 10.9] Model diagnostics Number of sites, UM

708

708

Number of sites, UMRB

252

252

0.400

0.400

R of nutrient yield

0.850

0.860

Eigenvalue spread

42.64

43.66

RMSE of residuals 2

*

Variables significant at the 1 percent significance level; UMRB, Upper Mississippi River Basin; UM, Upper Midwest drainage area exclusive of the Upper Mississippi River; CI, confidence interval. † Descriptions of the datasets used to derive the explanatory variables are provided as supporting information (table S.1.) ‡ Coefficient units are inverse of explanatory variable units. § Reported p-values are for a single-tailed t-test for source, channel transport, and reservoir-loss coefficients and a two-tailed test for land-to-water coefficients. ** Source terms that were associated with conservation


18


291 292

Page 20 of 34

Table 2. Estimated source (αn) land-to-water (./0 ) and in-channel (12 .) coefficients for SPARROW phosphorus models with and without conservation intensity variable*. SPARROW without conservation variable

SPARROW with conservation variable

†


‡

Coefficient

Standard Error

Coefficientǂ §

p

[95% CI]

Standard Error

p

Source Point source (kg/yr)

0.998

0.137

*

1.00

0.137

*

14.5

*

0.00313

*

0.00305

*

0.00795

0.0501

0.0114

*

0.0107

*

2.24

*

1.306

0.2

0.0966

*

[0.54 – 1.49] 2

Urban non-point (km )

63.19

14.47

*

64.2 [-1.83 – 128]

Fertilizer,UM (kg/yr)

0.019

0.00309

*

0.019 [0.01 – 0.03]

Fertilizer,UMRB (kg/yr)

**

0.013

0.00286

*

0.0133 [0.0053 – 0.02]

Manure from confined sources, UM (kg/yr)

0.015

0.00787

0.0524

0.0156 [0.00 – 0.03]

Manure from confined sources, UMRB (kg/yr)**

0.065

Manure from unconfined sources (kg/yr)

0.062

0.0103

*

0.0693 [0.0414 – 0.0924]

0.0107

*

0.061 [0.035 – 0.08]

2

Forest and wetlands (km )

20.06

2.251

*

19.8 [13.9 – 25.5]

Land to water delivery Conservation intensity (kg/km2)

-1.49 [-4.09 – 3.55]

Soil permeability

-0.306

0.0956

*

-0.323 [-0.508 – -0.0885]


19

Page 21 of 34


SPARROW without conservation variable

SPARROW with conservation variable

†


Coefficient‡ K factor

Standard Error

6.550

1.160

Coefficientǂ p§ *

[95% CI]

Standard Error

6.26

p

1.178

*

0.0684

0.0316

0.0967

.

0.948

.

[3.33 – 9.04] In-channel processing Loss in small streams (m3/s)

0.149

0.0686

0.0296

0.147 [0.0381 – 0.262]

Loss in medium streams (m3/s)

0.254

0.0959

*

0.262 [0.126 – 0.387]

Loss in reservoirs (m/yr)

3.860

0.944

*

3.89 [-1.29 – 6.37]

Model diagnostics Number of sites, UM

810

810

Number of sites, UMRB

324

324

RMSE of log-transformed residual

0.49

0.49

R2 of nutrient yield, UM Model

0.73

0.74

Eigenvalue spread

40.13

43.05

*

Variables significant at the 1 percent significance level; UMRB, Upper Mississippi River Basin; UM, Upper Midwest drainage area exclusive of the Upper Mississippi River; CI, confidence interval. † Descriptions of the datasets used to derive the explanatory variables are provided as supporting information (table S.1.). ‡ Coefficient units are inverse of explanatory variable units. §§ Reported p-values are for a single-tailed t-test for source, channel transport, and reservoir-loss coefficients and a two-tailed test for land-to-water coefficients. ** Source terms that were associated with conservation.


20


293

Page 22 of 34

Our finding of a statistically significant negative correlation between stream nitrogen loads and

294

conservation intensity is consistent with the hydrological and biogeochemical processes on land

295

surfaces and in the subsurface that can potentially interact with conservation practices to yield

296

nitrogen reductions in streams. The structural and erosion control practices that have been

297

implemented in the Upper Mississippi River Basin have been shown to reduce runoff and peak

298

flows, increasing water infiltration and soil water holding capacity6. Reactive forms of nitrogen

299

(ammonia, nitrate) are readily available and highly mobile in nitrogen-enriched agricultural soils

300

where high rates of nitrogen mineralization and nitrification favor dissolved inorganic over

301

organic, sediment-bound nitrogen30. The routing of larger quantities of water to the subsurface

302

by conservation practices contributes to increased hydraulic storage that can lead to higher

303

denitrification rates31,32 and reductions in nitrogen delivery to streams when compared to

304

equivalent areas without conservation practices. In areas where extensive tile drainage short

305

circuits natural flow paths and contributes to larger nitrate loads in streams33,34, structural

306

conservation practices, such as stream riparian buffers, can also increase hydraulic storage and

307

reduce nitrogen delivery to streams35–37. Soil conditions conducive to denitrification have been

308

observed in the shallow sub-surface throughout the Upper Mississippi region38,39 but substantial

309

leaching to ground water has also been noted, which can delay the delivery of nitrogen to

310

streams by several decades (10 – 40 years)40,41. Therefore, the effectiveness of conservation

311

practices in reducing nitrogen delivery to streams is highly dependent on subsurface hydrological

312

and biogeochemical conditions that favor the permanent removal of nitrogen via denitrification

313

and/or appreciably delay the transport and delivery of nitrogen to streams by leaching to deep

314

ground waters.


21

Page 23 of 34

315


The weak negative correlation between conservation intensity and stream phosphorus loads

316

may be explained by the effects of several key factors. First, the downstream response to

317

conservation-related reductions in particulate phosphorus6 is subject to long time lags: sediment-

318

bound phosphorus can be stored on farm fields and can take decades (~25 years) to move

319

downstream as particles are repeatedly deposited and re-suspended, which can delay measurable

320

in-stream responses to changes in upland particulate phosphorus delivery42. In our analysis,

321

observing the effects of conservation practices on particulate phosphorus may be limited by the

322

use of stream water quality data detrended to a baseyear (2002) that is approximately coincident

323

with the time period represented by the conservation intensity measure. Additional years of

324

observations that can support the use of a later base year for detrending (post 2005) in

325

SPARROW could provide a more complete evaluation of a lagged conservation response in

326

stream particulate phosphorus loads.

327

Second, some erosion control practices, specifically, no-till and reduced tillage have been

328

shown to increase soluble phosphorus levels in farm runoff12,43, which can potentially offset

329

expected downstream benefits derived from conservation-related reductions in particulate

330

phosphorus. In soils with no-till and other forms of reduced tillage where there is less vertical

331

mixing of the soil and integration of phosphorus fertilizers, phosphorus can accumulate near the

332

surface, where desorption processes can lead to elevated levels of soluble phosphorus in overland

333

runoff12. Reduced tillage can also facilitate the development of soil macropores with connections

334

to tile drainage systems that speed the delivery of soluble phosphorus to streams43. The

335

availability of soluble phosphorus is also enhanced in farm soils where the phosphorus sorption

336

capacity has been saturated by legacy applications of phosphorus-enriched manure and

337

fertilizers44. With a substantial fraction of total phosphorus delivered in soluble form, and


22


338

increasingly via sub-surface pathways, many conservation practices designed to remediate

339

erosion loss have limited effectiveness at controlling dissolved phosphorus losses. This

340

collection of confounding processes may explain the lack of statistical evidence in the

341

SPARROW analysis of the effects of conservation practices on total phosphorus in streams of

342

the Upper Mississippi.

343

Page 24 of 34

Predictions of regional-scale conservation effects.

344

To quantify the magnitude of possibly realized conservation effects in major rivers in the

345

UMRB, nutrient loads were predicted by using the estimated coefficients from the ‘SPARROW

346

with conservation’ columns (tables 1 and 2) and equation S1. Conservation effects were

347

subtracted from the SPARROW stream loads based on an assumed “no practice” condition

348

where the conservation intensity variable was set to reflect the absence of conservation practices

349

(Zcp, i = 0). A parametric bootstrap that presumes normality for all model coefficients was

350

performed to provide 90 percent confidence intervals for the SPARROW predictions. The results

351

were then summarized to the 4-digit HUC level by including the effects of nutrient processing

352

during instream transport and computing loads delivered to the outlet of the basin (figure 3). The

353

SPARROW predictions are presented alongside HUC-4 conservation effects predictions

354

documented in the Upper Mississippi CEAP20 assessment. The CEAP results were developed

355

using the Soil and Water Assessment Tool45 to route APEX-derived cropland loads downstream

356

and simulate nonpoint source loadings from land uses other than cropland.

357

While the comparison presented in figure 3 is of two differing assessment frameworks, it

358

allows us to make several conclusions. Our process inferences regarding the conservation impact

359

of nitrogen are explicitly represented in the mechanistic models. Most of the reduction presented


23

Page 25 of 34


360

by the CEAP assessment is the result of representing hydrologic modifications, such as changes

361

in soil moisture content and related effects on nitrogen cycling processes including enhanced

362

denitrification processes. The close agreement in predictions of nitrogen load reductions with the

363

two approaches is likely related to the high mobility of reactive nitrogen that is favored in

364

agricultural systems, especially those with tile drainage, which leads to a rapid downstream

365

water-quality response. Thus, the results can be considered to provide a limited validation of

366

existing process understanding and we isolate a possible benefit of conservation: an overall

367

increase in hydraulic storage, which when coupled with denitrification could lead to permanent

368

nitrogen losses that reduce nitrogen delivery to streams.

369

Although the predictions for total phosphorus reductions differed substantially (figure 3b) they

370

illustrate the difference in assessment approaches. The weak empirical association obtained with

371

SPARROW led to the minor reductions: on average 4 percent reduction at major river basin

372

outlets with some lower bounds for the 90% confidence interval below 0. The average reduction

373

for the CEAP approach was 30 percent, with a significant portion attributed to nutrient

374

management. The Upper Mississippi cropland CEAP survey documented substantially more

375

phosphorus management than for nitrogen20. The process models simulate nutrient reductions as

376

immediate and sustained which leads a technologically-feasible forecast of water-quality

377

improvements. With the SPARROW analysis -- an assessment of already realized benefits

378

during the 90s and early 2000s, it is not possible to forecast a response that is not represented in

379

the observational dataset. Nutrient management was fully incentivized in Farm Security and

380

Rural Investment Act of 2002 through the Environmental Quality Incentives Program. In

381

comparison to well established soil management practices that have been landscape features for

382

years, if not decades, nutrient management have dynamic implementation pattern that would lead


24


Page 26 of 34

383

to lags in measured effects. Therefore it is possible that additional water quality records, beyond

384

the early 2000s, may be needed to actually observe the downstream benefits of the managed

385

phosphorus reductions.

386

Our combined process-based and empirical assessments compliment temporal analyses of

387

aggregated water-quality monitoring records by providing an across the landscape exercise

388

assessment that leverages space over time and provides some degree of experimental control.

389

The process knowledge contained in the field-scale APEX loads facilitated the development of

390

nutrient-specific, integrated measures of multiple conservation activities on farms that allowed

391

an empirical regional-scale assessment of both our process-based understanding conservation

392

effects and documented levels of adoption and implementation. Applications of this

393

methodology in other environmental settings are needed to further evaluate the impacts of

394

conservation practices. With this study we identify a mitigating effect of conservation practices

395

for the delivery of total nitrogen streams. However, these relatively short-term benefits are

396

potentially offset in future years by the influence of deep leaching that introduces delays in the

397

delivery of nitrogen to streams and postpones the impacts of agriculture on water quality. For

398

phosphorus, the chemistry of agricultural soils and more recent research on soluble phosphorus

399

mobility and transport are leading to shifts in understanding that are either not fully incorporated

400

in process modeling or modeled with uncertainties. Obtaining an improved understanding of the

401

relative importance of these different processes in US watersheds is needed to optimize the

402

management of nutrients for environmental benefits.


25

Page 27 of 34


(a)

403

(b)

404


26


Page 28 of 34

405 4-Digit HUC

HUC Name

0701

Mississippi Headwaters

0702

Minnesota River Basin

0703

St. Croix River Basin

0704

Upper Mississippi-Black-Root Rivers

0705

Chippewa River Basin

0706

Upper Mississippi-Maquoketa-Plum Rivers

0707

Wisconsin River Basin

0708

Upper Mississippi-Iowa-Skunk-Wapsipinicon Rivers

0709

Rock River Basin

0710

Des Moines River Basin

0711

Upper Mississippi-Salt Rivers

0712

Upper Illinois River Basin only

0713

Lower Illinois River Basin

0714

Upper Mississippi, Kaskaskia and Meramec Rivers

406 407

Figure 3. Comparison for 4-digit HUC of nutrient reductions attributed to conservation.

408

ASSOCIATED CONTENT

409

Supporting Information. Additional details on modifications to previously published

410

SPARROW models, comments on the use of mean annual nutrient loads, description of CEAP

411

conservation effects datasets and nutrient loads from APEX models for scenarios. This material

412

is available free of charge via the Internet at http://pubs.acs.org.

413

AUTHOR INFORMATION

414

Corresponding Author

415

*U.S. Geological Survey, National Water Quality Program, 3916 Sunset Ridge Rd., Raleigh,

416

North Carolina 02906, United States. Phone: 919-571-4058 Fax: 919-571-4041Email:

417

[email protected]


27

Page 29 of 34


418

Author Contributions

419

The manuscript was written through contributions of all authors. All authors have given approval

420

to the final version of the manuscript.

421

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