Transcript David Mulla, University of Minnesota

Minnesota Nitrogen Science
Assessment and N
Reduction Planning Tool
D. J. Mulla,
Department of Soil, Water, and Climate
University of Minnesota,
W. F. Lazarus
Department of Applied Economics
University of Minnesota,
D. Wall
Minnesota 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
Reasons for Study
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
Minnesota technical assessments informing
nutrient reduction strategy
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
X
X
X
Nonpoint sources to waters
Nonpoint Pathways to Waters
BMPs effectiveness – watershed N reductions - cost/benefit
tool
BMP adoption constraints (social)
X
Past progress with existing programs – quantifying
reductions by sector
X
X
X
X
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
Tile Drainage
Leaching to GW
N losses
based on
extensive
literature
search
SWAT models, Water balance,
River discharge data, research data
Based on Precipitation and N rate
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
Milk, Eggs, Meat
MDA-USDA,NASS, 2010
Nass County data (weighted avg)
2005-2009, NASS, 2010
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 soybeans
 NO3-N losses for alfalfa
Tile Drainage-Nitrate Losses: Multivariate analysis
NO3-N Leaching
For 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
180
Dry years (431 mm)
100
y = 0.0602*Nrate + 22.245
R² = 0.0871
80
60
40
20
Cumulative NO3-N leaching (lbs ac-1)
Cumulative NO3-N leaching (lbs ac-1)
120
Wet years (687 mm)
160
140
120
100
80
60
40
y = 0.2945*Nrate + 37.6
R² = 0.459
20
0
0
0
100
200
N rate (lbs ac-1)
300
400
0
50
100
150
200
-1
N rate (lbs ac )
250
300
NO3-N Leaching Zones
For 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
Proportion of Wells Exceeding 3 mg/L Nitrate
Nitrogen per Agroecoregion
Percent
0 - 2.0
2.1 - 4.0
4.1 - 6.0
> 6.0
N
40
0
40
80 Kilometers
Groundwater NO3-N Denitrification Factors
(used to estimate groundwater nitrate discharge)
ndwater 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
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
N Losses = Discharge * Runoff (%) * N Concentration in Runoff
Nitrogen concentration in cropland runoff for each Agroecoregion.
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
Region
Forest N Export
•
2006 NLCD-Deciduous,
Evergreen, & Mixed Forest
•
~11 million acres statewide
• N export coefficients:
• 2 lbs ac-1 in average
year
Urban/Suburban Runoff
• 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
Septic Systems
•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)
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
25
50
Miles
100
Restorable Wetlands
Suitable Acres
0- 4,300
4,400 - 14,000
15,000 - 33,000
34,000 - 58,000
59,000 - 110,000
0
25
50
Miles
100
Perennial Cropland
Suitable Acres
0 - 5,200
5,300 - 14,000
15,000 - 33,000
34,000 - 92,000
93,000 - 230,000
0
25
50
Miles
100
Riparian Buffers
Suitable Acres
0- 11,000
12,000 - 28,000
29,000 - 49,000
50,000 - 83,000
84,000 - 220,000
0
25
50
Miles
100
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
Minnesota N Balance
(lb ac-1)
Manure and Fertilizer
Volatilization
14.1
Milk, Eggs
2.5
Senescence
37.3
Crop
Removal
111.6
Animal
Feed
38.6
Denitrification
26.8
Manure
20.0
Fertilizer
70.3
Runoff
0.8
Drainage
6.0
Leaching
8.6
Animals
Sold
5.7
Deposition
11.3
Net Mineralization
89.4
Fixation + Seeds
31.6
2.0
Purchased
Animals
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
Average cost/ac (see line)
Average cost/lb of N Reduced
N reduction from Current
Key
Reduce 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%
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 losses
 Agricultural 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