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2020 National Emissions Inventory Technical
Support Document: Agriculture - Crops and
Livestock Dust
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EP A-454/R-23 -001 p
March 2023
2020 National Emissions Inventory Technical Support Document: Agriculture - Crops and
Livestock Dust
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Research Triangle Park, NC
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Contents
List of Tables i
16 Agriculture - Crops and Livestock Dust 16-1
16.1 Sector Descriptions and Overview 16-1
16.2 EPA-developed estimates 16-2
16.2.1 Activity Data 16-2
16.2.2 Allocation Procedures 16-4
16.2.3 Emissions Factors 16-4
16.2.4 Controls 16-6
16.2.5 Emissions 16-6
16.2.6 Point Source Subtraction 16-7
16.2.7 Sample Calculations 16-7
16.2.8 Improvements/Changes in the 2020 NEI 16-8
16.2.9 Puerto Rico and U.S. Virgin Islands Emissions Calculations 16-8
16.3 References 16-8
16.3.1 Agricultural Tilling 16-8
16.3.2 Dust kicked-up by animals Error! Bookmark not defined.
List of Tables
Table 16-16-1: SCCs for agricultural tilling and dust kicked up by animals 16-1
Table 16-16-2: Number of Passes or Tillings per Year 16-5
Table 16-16-3: Sample calculations for PM10-FIL emissions from conservation tilling from corn 16-7
Table 16-4: Sample calculations for PM10-PRI emissions from dust kicked up by animals 16-8
l
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16 Agriculture - Crops and Livestock Dust
16.1 Sector Descriptions and Overview
Fugitive dust emissions from agricultural tilling include the airborne soil particulate emissions produced
during the preparation of agricultural lands for planting. Dust kicked up by animals refers to the dust
emitted from different types of livestock feet. These emissions are primarily considered to be made by
cattle and swine, but poultry emissions of dust are also examined. Fugitive dust emissions from both
agricultural tilling and livestock feet are estimated for PM10-PRI, PM10-FIL, PM25-PRI, and PM25-FIL.
Since there are no PM-CON emissions for this category, PM10-PRI emissions are equal to PM10-FIL
emissions and PM25-PRI emissions are equal to PM25-FIL. Table 16-1 shows the SCCs assigned to the
various ag tilling and livestock feet; SCC level 1 are "Miscellaneous Area Sources" for all SCCs.
Table 16-1: SCCs for agricultural tilling and dust kic
ced up by animals
SCC
SCC Level 2
SCC Level 3
SCC Level 4
2801000003
Agriculture
Production - Crops
Agriculture -
Crops
Tilling
2805001000
Agriculture
Production -
Livestock
Beef cattle -
finishing
operations on
feed lots
(drylots)
Dust Kicked-up by
Hooves
2805001010
Agriculture
Production -
Livestock
Dairy Cattle
Dust Kicked-up by
Hooves
2805001020
Agriculture
Production -
Livestock
Broilers
Dust Kicked-up by
Feet
2805001030
Agriculture
Production -
Livestock
Layers
Dust Kicked-up by
Feet
2805001040
Agriculture
Production -
Livestock
Swine
Dust Kicked-up by
Hooves
2805001050
Agriculture
Production -
Livestock
Turkeys
Dust Kicked-up by
Feet
16-1
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16.2 EPA-developed estimates
The calculations for estimating emissions from agricultural tilling involves distributing state-level tilling
data by tilling type (conservation, no-till, and conventional) to the county level and calculating a ratio of
conservation, no-till, and conventional tilling for each county. That ratio is used to estimate the type of
tillage for each crop type for each tilling type in each county. The type of tillage is used to develop a
county-level emissions factor for each crop type and tilling type, which is used to calculate county-level
PM10-FIL, PM10-PRI, PM25-FIL, and PM25-PRI emissions.
The calculations for estimating the emissions from dust kicked up by animals involves multiplying the
livestock counts by an emission factor for PM10-PRI, PM10-FIL, PM25-PRI, and PM25-FIL.
16.2.1 Activity Data
16.2.1.1 Agricultural tilling
The basis of agricultural tilling emission estimates is the number of acres of crops tilled in each county
by crop type and tillage type. These data are obtained from the 2017 Census of Agriculture developed
by the United States Department of Agriculture [ref l].The USDA Census of Agriculture reports acres
harvested for a given crop at the county level but does not provide tilling data for each crop type at the
county level.
The USDA Census of Agriculture redacts some county level data to avoid disclosing data for individual
farms. Missing county-level data for acres harvested by crop type and tilling type are calculated using
the difference between the state and national level reported data and the sum of the county-level data
by state.
When county level tilling data are unavailable, the total state level tilling data by tilling type,
conservation, no-till, and conventional are distributed to the county level for each crop. The difference
between the county-level data for acres harvested by crop tilling type and the state-level data for acres
harvested by crop tilling are equally distributed to the counties without data.
as,t 2 Q-C,t (1)
am,t ~ T.
Lm,t
Where:
am,t = County-level land tilled by crop tilling type, t, for counties missing tilling data, m, in
acres
as,t = Land tilled by crop tilling type t in state s, in acres
oc,t = Sum of county-level land tilled by crop tilling type, t, in acres
Cm,t = Number of counties missing county-level land tilled data by crop tilling type, t
USDA provides data on the number of acres tilled by tillage type (conservation, no-till, and conventional)
in each county [ref 2], but not by tillage type and crop type in each county. To estimate tillage by crop
type in each county, a ratio is determined based on the number of acres in each county tilled by each
tillage type to the total acres tilled by all tillage types. This calculation uses either the data directly
reported by USDA or the data gap-filled by equation (1).
16-2
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y —
2 ®c,t (ftr
ac,t (or am,t)
(2)
Where:
Ratio of crop tilling type t to total all crop tilling types in county c
Land tilled by crop tilling type t in county c, in acres
om,t = Land tilled by crop tilling type t for counties missing data, m, in acres
The ratio is then used to estimate the county-level acres harvested by crop type from the 2017 Census of
Agriculture to the tilling type (conservation, no-till, and conventional) at the county-level.
ac,x = Acres harvested of crop type x in county c, in acres
Tilling data for permanent pasture followed a different methodology. Conventional tilling data are
available for the state of Utah [ref 3], For Utah, a ratio of the conventional tilling acres to the total acres
of permanent pasture is developed (0.0023) and applied to the total acreage data for permanent
pasture from the 2017 Census of Agriculture to determine the number of conventional tilled permanent
pasture acres by county in other states. It is assumed that the remainder of the permanent pasture
acres is not tilled, so the remaining distribution of permanent pasture acres is then distributed to no till
acres and conservation tilling acres are left as zero.
16.2.1.2 Dust kicked-up by animals
The activity data for this dust kicked up by animals is based on livestock counts (average annual number
of standing head) and population information by state and county used to develop U.S. EPA's
Greenhouse Gas Inventory [ref 8], This data set is derived from multiple data sets from the United States
Department of Agriculture (USDA), particularly the National Agricultural Statistics Service (NASS) survey
and census [ref 9], The USDA NASS survey dataset, which represents latest available, national livestock
data, is used to obtain the livestock counts for as many counties as possible across the United States.
For a full description of the GHG livestock population estimation methodology, refer to the above
referenced citation for the EPA's GHG inventory document.
Generally, counties not specifically included in the NASS survey data set (e.g., due to business
confidentially reasons) were gap-filled based on the difference in the reported state total animal counts
and the sum of all county-level reported animal counts. State-level data on animal counts from the GHG
inventory were distributed to counties based on the proportion of animal counts in those counties from
the NASS census.
®t,c,x ^c,£ ^ ^c,x
(3)
Where:
Ot,c,x
rc,t
Land tilled by crop tilling type t and crop type x in county c, in acres
Ratio of crop tilling type t to total all crop tilling types in county c
P
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Pa,c = Estimated population of animal type a in county c
Pa,s = NASS survey reported state-level population of animal type a in state s
ra,c,2oi2 = Ratio of animal county- to state-level animal counts from the 2012 NASS census for
animals type a in county c
16.2.2 Allocation Procedures
Activity data are reported at the county level for agricultural tilling, so allocation is not needed.
The USDA survey reports the livestock counts at the county level for many counties, so no allocation is
necessary for dust kicked-up by animals. The procedure for gap-filling missing county-level data using
state-level data is discussed in Section 16.2.1.2.
16.2.3 Emissions Factors
16.2.3.1 Agricultural tilling
The county-level emissions factors for agricultural tilling are specific to the crop and tilling type (e.g.
conventional tillage corn, no-till soybean, etc.) and are calculated using the following equation [ref 4, 5],
EFp,t,x,c c x k x sc x Pf (5)
Where:
EFp,t,x,c = Emissions factor for pollutant p, crop tilling type t, and crop type x in county c, in
Ibs./acre
c = Constant 4.8 Ibs./acre-pass
k = Dimensionless particle size multiplier (PM10-FIL and PM10-PRI = 0.21; PM25-FIL and
PM25-PRI = 0.042)
sc = Percent silt content of surface soil (%) in county c, defined as the mass fraction of
particles smaller than 50 pim diameter found in surface soil
pt = Number of passes or tilling in a year by crop tilling type, t
The U.S. Department of Agriculture and the National Cooperative Soil Survey define silt content of
surface soil as the percentage of particles (mass basis) of diameter smaller than 50 micrometers (nm)
found in the surface soil.1 The soil sample data used to estimate county-level, average silt content values
are from the National Cooperative Soil Survey Microsoft Access Soil Characterization Database [ref 8],
This database contains the most commonly requested data from the National Cooperative Soil Survey
Laboratories including data from the Kellogg Soil Survey Laboratory and cooperating universities.
EPA applied specific selection criteria to the database to ensure that all samples are comparable and
relevant to this analysis. The selection criteria included selecting only samples taken inside the United
States with a preparation code of S and a horizon top of zero centimeters or a master horizon of A or O.
A preparation code of S signifies that the sample is the air-dried whole soil passing through a 3 inch
sieve and a horizon top of zero or master horizon of A or O ensures that the sample is taken at the
surface.
1 Note that this definition is different than the U.S. Environmental Protection Agency's definition that includes all
particles (mass basis) of diameter smaller than 75 micrometers.
16-4
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In some cases, the sample metadata did not indicate a county, but included latitude and longitude
coordinates. In these cases, the state and county information are determined based on the latitude and
longitude coordinates and added to the sample entry in the database.
The average silt content for a county is calculated by summing the total silt content of all the samples in
the county and dividing by the number of samples in the county. For counties without samples, the
average silt content is calculated by summing the total silt content of soil samples in neighboring
counties and dividing by the number of samples in the neighboring counties. If neighboring counties also
lacked sample data, then the county is assigned the average silt value of soil samples within the state.
Table 16-2 shows the number of passes or tillings in a year for each crop for conservation use, no-till and
conventional use [ref 7], These values are used as pt in equation 1 to estimate the county-level
emissions factors. Mulch till and ridge till tillage systems are classified as conservation use, while 0 to 15
percent residue and 15 to 30 percent residue tillage systems are classified as conventional use.
Table 16-2: Number of Passes or Tillings per Year
Crop
Conservation Use
No-Till
Conventional
Use
Barley
3
3
5
Beans
3
3
3
Canola
3
3
3
Corn
1
0
2
Cotton
5
5
8
Cover
1
1
1
Fallow
1
1
1
Fall-seeded/Winter Wheat
3
3
5
Forage
3
3
3
Hay
3
3
3
Oats
3
3
5
Peanuts
3
3
3
Peas
3
3
3
Permanent Pasture
0
0
1
Potatoes
3
3
3
Rice
5
5
5
Rye
3
3
5
Sorghum
1
1
6
Soybeans
1
0
2
Spring Wheat
1
1
4
Sugarbeets
3
3
3
Sugarcane
3
3
3
Sunflowers
3
3
3
Tobacco
3
3
3
Source: Woodard 1996 [ref 7]
16-5
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16.2.3.2 Dust kicked-up by animals
Emission factors for dust from animals are provided in the "Wagon Wheel Emission Factor
Compendium" on the 2020 NEl Supporting Data and Summaries site. Dust emission factors are from
different literature articles for each livestock type. No references for PM25 emission factors were found
in the literature for Beef Cattle. To calculate PM25 for Beef Cattle, the Dairy Cattle PM10 to PM25 ratio
of 4.81 from this tool was used and is based on ratios in the PM Augmentation tool [ref 10]. In general, if
the study calculated an emission factor, it was converted to units of ton/year/head and is used in this
tool. If the study did not calculate an emission factor, then it was calculated by dividing the emission
rate in tons per year by animal units according to an equation used by the NRC's Scientific Basis for
Estimating Air Emissions from Animal Feeding Operations: Interim Report [ref 11], Animal units are
calculated by multiplying an equivalent factor by the livestock population according to an equation from
the Wisconsin Department of Natural Resources [ref 12]. After converting the AU to number of animals,
assuming that 1 AU is equivalent to 500 kilograms, the emission factor is calculated in units of tons per
year per head.
16.2.4 Controls
There are no controls assumed for either category.
16.2.5 Emissions
16.2.5.1 Agricultural tilling
Particulate matter emissions from agricultural tilling are computed by multiplying crop- and county-
specific emissions factors by crop- and county-specific data on tilling activity. The emissions are then
summed across all tilling types and crop types.
Ep,c = Annual total agricultural tilling county level emissions of pollutant p in county c from
all crop tilling types, in tons
EFP/t/x,c = Emissions factor for pollutant p, crop tilling type t, and crop type x in county c, in
Ibs./acre
at,x,c = Land tilled by crop tilling type t, and crop type x in county c, in acres
16.2.5.2 Dust kicked-up by animals
For dust from animal, each animal type and pollutant the livestock count is multiplied by the emissions
factors to estimate emissions.
T X
Ep,cEFptxc
t=1X=1
(6)
Where:
(7)
16-6
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Where:
Ep,c,a = Annual emissions of pollutant p in county c for animal type a, in tons per year
Pa,c = Population of livestock for animal type a in county c
EFp,a = Emissions factor for pollutant p and animal type a, in tons per year per head
16.2.6 Point Source Subtraction
There are no point source-specific SCCs for agricultural tilling or dust from hooves; therefore point
source subtraction is not performed for either category.
16.2.7 Sample Calculations
16.2.7.1 Agricultural Tilling
Table 16-3 provides a sample calculation for PM10-FIL emissions for conservation tilling from corn. For
total PM10-FIL emissions, the calculations below would need to be repeated for all crop types for all
three tilling types, and then summed in equation 5 for total emissions. The values in these equations are
demonstrating program logic and are not representative of any specific NEI year or county.
Table 16-3: Sample calculations for PM10-FIL emissions from conservation tilling from corn
Eq.
#
Equation
Values for Clay County, AL
Result
1
2 &c,t
311,942 acres — 298,042 acres
1,069.23 acres for
conservation tilling
am,t ~ p
Lm,t
13 missing counties
2
acX {or amX)
2 Q-c,t (PT
1,069.23 acres
1,489.23 acres
0.718 ratio of
conservation tilling
to all tilling
3
®-t,c,x l~c,t ^ &c,x
0.718 x 89 acres
63.9 acres corn
harvested using
conservation tilling
4
EFp,t,x,c = c x. k x. sc x pt
4.8 pounds x 0.21 x 28.930,6 x
acre-pass
1 pass
7.59 pounds per
acre for
conservation tilling
from corn
5
T X
Ep,c EFPitiXic ^ Q-t,c
t = 1 X=1
1 ton
x 2000 lb
pounds
7.59 x 63.9 acres
acre
1 ton
X 2000 lb
0.24 tons PM10-FIL
emissions from
conservation tilling
for corn*
* Note that this calculation must be completed for all crop types and tilling types in the county to determine the
total emissions for that county.
16.2.7.2 Dust kicked-up by animals
Table 16-4 lists sample calculations to determine PM10-PRI emissions from dust kicked up by animals.
The sample calculations use swine as an example, but the calculations would need to be repeated to
16-7
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calculate values for all livestock types. Again, the values in these equations are demonstrating program
logic and are not representative of any specific NEI year or county.
Table 16-4: Sample calculations for PM10-PRI emissions from dust kicked up by animals
Eq.
#
Equation
Values
Result
1
Pa,c ~ Pa,s * T~a,c.,20 \ 2
N/A
swine population
is available by
county and does
not need to be
calculated using
2012 NASS
Census ratios.
2
Ep,c,a ~ Pa,c * EFp a
5,813 swine in the county
x 0.000803607 tons PM10 per head of swine
4.67 tons PM10-
PRI emissions
from swine
16.2.8 Improvements/Changes in the 2020 NEI
There were no significant changes to the agricultural tilling or dust from hooves methodology.
16.2.9 Puerto Rico and U.S. Virgin Islands Emissions Calculations
Since insufficient data exists to calculate emissions for ag tilling for the counties in Puerto Rico and the
US Virgin Islands, emissions are based on two proxy counties in Florida: 12011, Broward County for
Puerto Rico and 12087, Monroe County for the US Virgin Islands. The total emissions in tons for these
two Florida counties are divided by their respective populations creating a tons per capita emissions
factor. For each Puerto Rico and US Virgin Island county, the tons per capita emissions factor is
multiplied by the county population (from the same year as the inventory's activity data) which served
as the activity data. In these cases, the throughput (activity data) unit and the emissions denominator
unit are "EACH".
16.3 References
16.3.1 Agricultural Tilling
1. U.S. Department of Agriculture. 2017 Census of Agriculture.
https://www.nass.usda.gov/Publications/AgCensus/2017/index.php
2. Personal communication from Christy Meyer, U.S. Department of Agriculture, National
Agricultural Statistics Service to Marissa Hoer, Abt Associates, September 2015.
3. Personal communication from Greg Mortensen, Utah Department of Environmental Quality to
Jonathan Dorn, Abt Associates, 2014_UtahDeptAg_DNR_Tilling_Stats.xlsx, February 2016.
4. U.S. Environmental Protection Agency. 1985. Compilation of Air Pollutant Emission Factors, 4th
Edition, AP-42, Volume I: Stationary Point and Area Sources, page 11.2.2-1. Research Triangle
16-8
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Park, North Carolina.
5. Midwest Research Institute. 1981. The Role of Agricultural Practices in Fugitive Dust Emissions,
page 117. Prepared for California Air Resources Board.
6. U.S. Department of Agriculture, National Cooperative Soil Survey, NCSS Microsoft Access Soil
Characterization Database, http://ncsslabdatamart.sc.egov.usda.gov/
7. Woodard, Kenneth R. 1996. Agricultural Activities Influencing Fine Particulate Matter Emissions,
Midwest Research Institute; corn and soybean tilling passes updated based on data from Kansas
and Iowa, https://www3.epa.gov/ttn/chief/old/ap42/ch09/s01/related/rel03 c09s01.pdf
.3.2 Dust kicked-up by animals
8. U.S. EPA. 2018. Inventory of Greenhouse Gas Emissions and Sinks, 1990-2016. Chapter 5.2,
Manure Management. EPA 430-R-18-003.
9. United States Department of Agriculture. 2020. National Agricultural Statistics Service Quick
Stats, https://quickstats.nass.usda.gov/
10. U.S. EPA. 2017. Air Emissions Inventories, PM Augmentation.
https://19ianuarv2017snapshot.epa.gov/air-emissions-inventories/pm-augmentation .html
11. National Research Council. 2002. The Scientific Basis for Estimating Air Emissions from Animal
Feeding Operations: Interim Report. Washington, DC: The National Academies Press.
https://doi.org/10.17226/10391.
12. State of Wisconsin Department of Natural Resources. 2012. Animal Unit Calculation Worksheet
Form 3400-025A. https://dnr.wi.gov/files/PDF/forms/3400/3400-Q25A.pdf
13. Joo, H.S., Ndegwa, P.M., Heber, A.J., Ni, J.Q., Bogan, B.W., Ramirez-Dorronsoro, J.C. and Cortus,
E.L., 2013. Particulate matter dynamics in naturally ventilated freestall dairy barns. Atmospheric
Environment, 69, pp.182-190.
16-9
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United States Office of Air Quality Planning and Standards Publication No. EPA-454/R-23-001p
Environmental Protection Air Quality Assessment Division March 2023
Agency Research Triangle Park, NC
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