Minnesota Nitrogen Science Assessment and N

Reduction Planning Tool

D. J. Mulla, Department of Soil, Water, and Climate

University of Minnesota,

W. F. LazarusDepartment of Applied Economics

University of Minnesota,

D. WallMinnesota Pollution Control Agency

GOALS• Assess nonpoint source nitrogen contributions to

Minnesota rivers from a) the primary land use sources, and b) the primary hydrologic pathways under dry, average and wet climatic conditions

• Determine the watersheds which contribute the most nitrogen to the Mississippi River, and combination of land uses and hydrologic factors having the greatest influences on the elevated nitrogen

• Develop a nitrogen decision tool to estimate reductions in N loadings to surface waters at the watershed scale with various BMPs

Provide technical information to help establish Minnesota goals and strategies to address its contribution to:

Nitrogen export to Gulf of Mexico

Nitrate concentration impairments in surface waters which may arise due to new numerical nutrient criteria

Reasons for Study

Technical Assessment Nitrogen Phosphorus

Watershed outlet load monitoring (70+ watersheds)

X X

Major River Monitoring X X

SPARROW Modeling of all HUC8 watersheds X X

Stream Concentration monitoring 700 sites X X

Water Quality Standards effects on loads X X

Twin Cities effects on loads X X

Temporal trends (50+ sites 1976-2010) X X

Seasonal variability in loads and concentrations X  

In-stream losses X X

Point source contributions X X

Nutrient budgets to cropland X  

Nonpoint sources to waters X X

Nonpoint Pathways to Waters X X

BMPs effectiveness – watershed N reductions - cost/benefit tool

X  

BMP adoption constraints (social) X  

Past progress with existing programs – quantifying reductions by sector

X X

Minnesota technical assessments informing nutrient reduction strategy

Agroecoregions

• Minnesota has 39 agroecoregions which represent broad regional variations in soil, landscape, climate, and crop or animal management systems

• Agroecoregions are finer scale geographic units than aquatic ecoregions or Major Land Resource Areas

• Each agroecoregion has unique limitations to production, for example drainage, irrigation, erosion, precipitation, growing degree days

• Each agroecoregion also has unique features that influence non-point source pollution, for example drainage, erosion, leaching, karst, sandy soils, etc

Agroecoregion Based N Inputs• Point data are available for crop

acreage and livestock numbers

• County statistics are available for crop harvest and fertilizer sales

• N transformations in soil (mineralization, denitrification) and N losses (volatilization, leaching, drainage, etc) are based on soil and landscape factors (represented by agroecoregions)

• Our approach is to estimate N inputs and outputs for agroecoregion units and then transform results back to watershed units

Methods - N Sources & Pathways

INPUTS METHODS /SOURCE

Net Mineralization  Burkart and James (1999

Inorganic Fertilizer MDA, NASS, Bierman (2011)

Atmospheric Deposition EPA

Legume Fixation Russelle and Birr, 2004; Meisinger and Randall, 1991

Planted Seeds Meisinger and Randall (1991)

Purchased Animals MDA-USDA,NASS 2010

Animal Feed MDA, Stuewe (2006)

OUTPUTS METHODS/SOURCE

Crop Removal NASS

Senescence Burkart and James (1999)

Denitrification Burkart and James, 1999; Meisinger and Randall, 1991

Runoff SWAT models, Water balance, River discharge data, research data

Tile Drainage Based on Precipitation and N rate

Leaching to GW Based on N rate for four groundwater pollution zones differing in risk for leaching

Fertilizer Volatilization Meisinger and Randall, 1991

Manure Storage Losses Midwest Plan Service MWPS-18 2004, Univ. of MN Extension Service 2001

Animals Sold MDA-USDA,NASS, 2010

Milk, Eggs, Meat Nass County data (weighted avg) 2005-2009, NASS, 2010

N losses based on extensive literature search

Tile Drainage

For each agroecoregion we used an extensive research database to estimate drainage losses based on:

Precipitation during the growing season for dry, average, and wet years

N rate (sum of fertilizer + manure application)

Tile drainage loss categories

NO3-N losses for corn, corn silage, wheat, barley, oats, sugarbeets, potatoes

NO3-N losses for soybeansNO3-N losses for alfalfa

Tile Drainage-Nitrate Losses: Multivariate analysis

NO3-N LeachingFor selected agroecoregions with high leaching loss:

Estimated the rate of NO3-N leaching for dry, and wet years, using research data based on N rate (sum of fertilizer + manure application)

“Average year data” is mean value of dry and wet years

y = 0.0602*Nrate + 22.245R² = 0.0871

0

20

40

60

80

100

120

0 100 200 300 400

Cu

mu

lati

ve N

O3-

N le

ach

ing

(lb

s ac

-1)

N rate (lbs ac-1)

Dry years (431 mm)

y = 0.2945*Nrate + 37.6R² = 0.459

0

20

40

60

80

100

120

140

160

180

0 50 100 150 200 250 300

Cu

mu

lati

ve N

O3-N

leac

hin

g (

lbs

ac-1

)

N rate (lbs ac-1)

Wet years (687 mm)

NO3-N Leaching ZonesFor other agroecoregions:

We scaled the rate of NO3-N leaching according to the potential risk of NO3-N contamination of groundwater in each agroecoregion based on a water quality monitoring database of 40,000 drinking water wells

N

40 0 40 80 Kilometers

Proportion of Wells Exceeding 3 mg/L Nitrate Nitrogen per Agroecoregion

Percent0 - 2.02.1 - 4.04.1 - 6.0> 6.0

Groundwater denitrification factor assigned to different agroecoregions. 

Agroecoregion   Denitrification factor 

Blufflands, Rochester Plateau  0.25 

Anoka Sand Plains, Alluvium and Outwash, Inter-Beach Sand Bars, Steep Valley Walls, Steeper Alluvium.  0.40 

Forested Lake Sediments, Mahnomen Lake Sediments, Poorly Drained BE Till, Poorly Drained Lake Sediments, Red Lake Loams, Somewhat Poorly Drained Lake, Swelling Clay Lake Sediments, Very Poorly Drained Lake Sediments 

0.60 

Other agroecoregions  0.50 

Drained soils  0.60 

 

Groundwater NO3-N Denitrification Factors(used to estimate groundwater nitrate discharge)

Surface runoff

• An extensive database of river monitoring was used to provide river discharges in dry, avg and wet years

• SWAT modeling for the following areas was available to estimate the percent of discharge attributable to runoff

7 Mile Creek (Wetter Clays and Silts) Root River (Undulating Plains) Karst (Blufflands, Rochester Plateau) Red River (Swelling Clay lake sediments, Very poorly drained lake

sediments) Sunrise Creek (Central till, Anoka Sand Plains, Alluvium and Outwash)

• For the remaining agroecoregions, runoff percentages were estimated from the closest SWAT results based on a runoff classification of agroecoregions

Nitrogen concentration in cropland runoff for each Agroecoregion.

Region  Agroecoregion  N 

concentration (mg L-1) 

1 

Drift & Bedrock Complex, Forested Lake Sediments, Mahnomen Lake Sediments, Northern Till, Northshore 

Moraine, Peatlands, Poorly Drained Lake Sediments, Red Lake Loams, Somewhat Poorly Drained Lake, Steep Poorly 

Drained Moraine, Swelling Clay Lake Sediments, Very Poorly Drained Lake Sediments,Wetter BE Till, Wetter 

Clays & Silts 

3.51 

     

2 

Central Till, Coteau, Drumlins, Dryer BE Till, Dryer Clays &Silts, Dryer Till, Forested Moraine, Inner Coteau, Mesabi Range, Poorly Drained BE =Till, Rolling Moraine, Steep 

Dryer Moraine, Steep Stream Banks, Steeper Till, Stream Banks 

1.82 

     

3 Bufflands, Inter-Beach Sand Bars, Level Plains, Steep 

Valley Walls, Steep Wetter Moraine, Steeper Alluvium, Undulating Plains 

0.73 

     

4  Alluvium & Outwash, Anoka Sand Plains, Rochester Plateau  0.244 

 

N Losses = Discharge * Runoff (%) * N Concentration in Runoff

Forest N Export

• 2006 NLCD-Deciduous, Evergreen, & Mixed Forest

• ~11 million acres statewide

• N export coefficients:

• 2 lbs ac-1 in average year

• 2006 NLCD- Developed >20% impervious

• ~1 million acres statewide

• Avg N export coefficients:

2.9 lbs ac-1 for surface runoff

1.1 lbs ac-1 for movement to GW

Urban/Suburban Runoff

•Septic N based on county data from MPCA

Septic N to Groundwater  = [(# Septics per county) *(Persons per household by county)*({9.1 lbs N per person}*{85% for denitrification losses})] *(% NOT IPHT)

Septic N to Surface Water = [(# Septics per county)*(Persons per household by county)*(9.1 lbs N per person)] * (% IPHT)

•Weighted to 2008 ZIP code populations to improve spatial accuracy of county data (MSP excluded from analysis)

Septic Systems

Methods – Watershed N Reduction Decision Tool• The Decision Tool is an Excel spreadsheet linked to a database of Minnesota soils, landscapes, cropping systems, management practices and crop enterprise budgets

• Estimates of N reductions are based on research meta-data and BMP specific reduction coefficients 

• Estimates are tied to site specific characteristics such as soil, slope, climate, and baseline farm management practices and cropping systems

N Reduction Decision Tool BMPs

• Rate and timing of N fertilizer• Controlled drainage• Bioreactors• Planting cover crops• Planting perennial grass• Installing riparian buffer strips• Installing wetlands

• Effects of individual BMPs as well as combinations of BMPs can be evaluated

N Fertilizer BMPs• Existing N rates can be reduced to target rates which average 117 lb/ac for fall application in a corn-soy rotation

• Reductions in N loading are estimated based on empirical relationships derived from extensive research databases for tile drainage, leaching and runoff

• Spring or sidedress N rates are 30 lb/ac lower than fall applications and reduce N losses by 8% compared to fall applications

• Spring application costs an extra $7/ac, while sidedress costs an extra $50/ac

• Costs of N fertilizer average $0.55/lb• Price of corn is assumed $6.00/bu

Controlled Drainage BMP

•Controlled drainage reduces N losses from treated area in tile drainage by 40%

• Installation costs are estimated at $162/ac on 1% slopes

•Annual repair and maintenance costs are $2.82/ac

Bioreactor BMP

• Each bioreactor treats 40 ac, and has an area of 471 ft2

•N reductions are 13% based on the assumption that each bioreactor treats 30% of the drainage system

• Total annualized net present value to install, maintain and replace bioreactors is $440

Cover Crop BMP

•Cover crops can be successfully grown one in five years

•Rye seed costs $0.22/lb, aerial seeding costs $25/ac, killing cover crop costs $22/ac

•Overall reduction in N loadings in drainage and leaching average 10% over a five year period

Perennial Grass and Riparian Buffer BMPs• Rye seed costs $11/lb or $8/ac• Other costs are $36/ac, including $10/ac for fertilizer (e.g. 60 lb N/ac)

• Reduction in N loadings arise partially from replacing annual crops that require higher rates of N fertilizer

• N loadings from perennial grass plantings and riparian buffers are assumed to be negligible

Wetland BMP

•Wetlands are assumed to cover 2% of the upland contributing area treated

•Costs to install wetland are $1,565/ac•Annual capital and maintenance costs are $103/ac

•Reductions in N loadings from wetlands are assumed to be 50%

Suitable acres for BMPs• Fertilizer rate reductions are only possible in areas where existing application rates exceed University recommendations

• Controlled drainage and bioreactors can be installed on tile drained land with slopes of 0.5%, 1% or 2%

• Perennial grass can be planted on ag land with crop productivity ratings of 60% or less (marginal land)

• Riparian buffers can be installed on ag land within 30 m of waterways

• Wetlands can be restored on tile drained land with hydric soils and high Compound Topographic Index values

Controlled Drainage

Suitable Acres

0- 5,700

5,800 - 18,000

19,000 - 33,000

34,000 - 62,000

63,000 - 100,000

0 50 10025Miles

Restorable Wetlands

Suitable Acres

0- 4,300

4,400 - 14,000

15,000 - 33,000

34,000 - 58,000

59,000 - 110,000

0 50 10025Miles

Perennial Cropland

Suitable Acres

0 - 5,200

5,300 - 14,000

15,000 - 33,000

34,000 - 92,000

93,000 - 230,000

0 50 10025Miles

Riparian Buffers

Suitable Acres

0- 11,000

12,000 - 28,000

29,000 - 49,000

50,000 - 83,000

84,000 - 220,000

0 50 10025Miles

User Inputs and Model Outputs

• Select watershed and type of climate of interest• Select types of BMPs to install• Select percent of suitable acres in watershed for installation of BMPs

• Model estimates effectiveness of each BMP at reducing N loadings

• Model estimates cost (per lb of N removed or per ac) of installing each BMP

• Model estimates overall watershed scale effectiveness and cost of installing multiple BMPs

Results

•Nonpoint Source N Loadings to Surface Waters

•Watershed N Reduction Decision Tool

Agricultural N Inputs

Agricultural N Outputs

Leaching8.6

Manure20.0

Fertilizer70.3

Deposition11.3

Runoff0.8

Drainage6.0

CropRemoval

111.6 AnimalFeed38.6

Milk, Eggs2.5

AnimalsSold5.7

Net Mineralization89.4

Fixation + Seeds31.6 2.0

Denitri-fication

26.8

Senescence37.3

Manure and Fertilizer Volatilization

14.1

Minnesota N Balance(lb ac-1)

PurchasedAnimals

1.6

N Loadings to Surface Water by Source

Comparison between Predicted and Measured Average N Loads

Nonpoint Source N Loadings by Source

Effect of Climate on N Loadings

N Reduction Decision Tool

KeyReduce N rate 20%Reduce N rate 20%, spring preplant N 5.2%Reduce N rate 20%, spring preplant N 5.2%, restore wetlands 2.7%Reduce N rate 20%, spring preplant N 5.2%, restore wetlands 2.7%, cover crops 15%Reduce N rate 20%, spring N 5.2%, buffers 2.9%, wetlands 2.7%, cont. drain. 2.3%N rate 20%, spring N 5.2%, buffers 2.9%, wetlands 2.7%, cont. drain. 2.3%, cover crops 15%

N reduction from Current

Averag

e co

st/a

c (see

 line

)

Averag

e co

st/lb of N

 Red

uced

Conclusions

• Total nonpoint source N loadings to Minnesota surface waters were estimated at 254 million lb during an average climatic year. This is about 6% of the total inputs of N on all Minnesota cropland

• Statewide, losses of N to surface water from agricultural sources represent 88% of total nonpoint source lossesAgricultural N loadings to surface waters from groundwater and

drainage are about equal and each far exceed runoff losses

• Statewide loadings of N to surface waters from forest, urban and septics represent 12% of total nonpoint source losses

• The Minnesota River Basin accounts for 34% of N loadings from nonpoint sources, the Lower Mississippi accounts for 21%, the Upper Mississippi accounts for 18%, and the Red River of the North accounts for 9%

Conclusions – Nonpoint Source N Loadings to Surface Waters• A comparison between the modeled nonpoint source N

loadings to Minnesota surface waters (in an average climatic year) and monitored N loadings (average of two typical years) was conducted for 33 MPCA monitored major watersheds across Minnesota

• Monitored N loadings were not used to calibrate the modeled nonpoint source N loadings, as the modeled N loadings were estimated independently, without calibration

• Linear regression between modeled and MPCA monitored N loads was very good, with an R² value of 0.69

• Modeled N loadings across all monitored watersheds were 10% higher than monitored N loads, which is not surprising given that additional losses in predicted N loadings may occur as nitrate travels downstream to the mouth of the watershed

Conclusions – Nonpoint Source N Loadings to Surface Waters• Climate has a significant effect on nonpoint source N loadings

to Minnesota surface waters

• Total statewide nonpoint source N loadings to surface waters for dry, average and wet years were predicted to be 106, 254 and 409 million lb, respectively

• During a dry year, the majority (46%) of nonpoint source N losses to surface waters arises from groundwater discharge

• During an average year, the nonpoint source losses from agricultural drainage (45%) increase relative to the losses from agricultural groundwater discharge (37%) in comparison with the losses during a dry year

• During a wet year, the majority of nonpoint source N losses statewide arise from agricultural drainage (49%)

• Discharge of groundwater from agricultural regions contributes another 34%

Conclusions – N BMP Decision Tool

• A watershed based N BMP Decision Tool was developed to assist planners evaluate strategies for reducing N loadings to Minnesota surface waters

• The Tool allows users to select a target watershed, climate, and extent of adoption of various N reduction BMPs

• BMPs are limited by an analysis of acres suitable for implementation

Conclusions – N BMP Decision Tool

• The Tool estimates N loading reductions for individual practices

• The Tool estimates cumulative N loading reductions for combinations of BMPs at the watershed scale

• The Tool estimates costs associated with implementing BMPs• Cost/lb of individual practices• Cost/ac of individual practices• Net annual costs for implementing all BMPs in a selected watershed

Conclusions – N BMP Decision Tool

• BMPs that are suitable for implementation over larger areas generally give larger watershed scale N loading reductions than BMPs that are limited to implementation in smaller areas, even though the latter may have high N reduction efficiencies per acre

• Approaches to achieving N load reductions greater than 25% are challenging

Thank you

• Support for this research was provided by MPCA