Estimating Field and Watershed Parameters Used in
USEPA's Office of Pesticide Programs Aquatic Exposure Models -
The Pesticide Water Calculator (PWC)/Pesticide Root Zone Model (PRZM)
and Spatial Aquatic Model (SAM)
U.S. Environmental Protection Agency
Office of Pesticide Programs
12/31/2019
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Contents
1. Field Scenario Inputs for PWC and SAM 5
1.1 Background 5
1.2 Creating Soil-Land Cover-Weather Field Scenarios from Spatial Datasets 6
1.3 Document Organization 7
2. Scenario Input Parameters 7
2.1 Soil and Landscape Inputs Derived from the Soil Map Unit Data 7
2.1.1 Hydrologic Soil Group (hydro_group) 9
2.1.2 Runoff curve numbers (cn_cov, cn_fal) 9
2.1.3 Soil Erodibility (K) Factor (usle_k) 10
2.1.4 Slope, Slope length, and USLE Slope Length/Steepness (LS) Factor (usle_ls) 10
2.2 Soil Inputs Derived from Horizon Data 11
2.2.1 Number of horizons (n_horizon) 11
2.2.2 Horizon thickness (thickness_#) 11
2.2.3 Bulk density (bc/_#) 12
2.2.4 Soil organic carbon content (orgC_#) 12
2.2.5 Water capacity (water_max_#, water_min_#) 12
2.2.6 Sand (sand_#) and Clay (c/oy_#) content 12
2.3 Crop-Related Inputs 12
2.3.1 Crop Milestone Dates 13
2.3.1.1 Planting dates (plant_begin, plant_end, plant_date) 13
2.3.1.2 Harvest dates (harvest_begin, harvest_end, harvest_date) 14
2.3.1.3 Blooming dates (bloom_begin, bloom_end) 14
2.3.1.4 Emergence dates (emergence_begin, emergence_end, emergence_date) 14
2.3.1.5 Maximum canopy cover dates (maxcover_begin, maxcover_end,
maxcover_date) 15
2.3.1.6 Annual crops with missing milestone dates 15
2.3.1.7 Milestone dates for perennial crops 15
2.3.1.8 Crops with multiple growing seasons in a year 15
2.3.2 Other Crop-Specific Inputs 16
2.3.2.1 Crop intercept (crop_intercept) 16
2.3.2.2 Maximum canopy cover (max_cover) 16
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2.3.2.3 Maximum active rooting depth (root_depth) 16
2.3.2.4 USLE practice (P) factor (usle_p) 16
2.3.2.5 USLE cover management (C) factor (usle_c_coi/, usle_c_fal) 17
2.3.3 Irrigation Inputs 17
2.3.3.1 Irrigation type (irrigation_type) 17
2.3.3.2 Allowable moisture depletion (depletion_allowed) 18
2.3.3.3 Leaching fraction (leaching_frac) 18
2.3.3.4 Maximum irrigation rate (irrigation_rate) 18
2.4 Weather Inputs 18
2.4.1 Daily precipitation (precipitation) 19
2.4.2 Daily mean air temperature (temperature) 19
2.4.3 Daily evapotranspiration (et) 19
2.4.4 Daily wind speed (windspeed) 19
2.4.5 Daily solar radiation flux (solarradiation) 19
2.4.6 Rainfall distribution (ireg) 19
2.4.7 Soil evaporation available depth (anetd) 20
2.4.8 Snowmelt factor (sfac) 20
3. References 20
Appendix A: Primary Scenario Input Parameters 24
Appendix B: Soil Input Fields Extracted from SSURGO 33
Appendix C: Land Cover Classes and General Crop Groups from CDL 34
Appendix D: Determining the Runoff Curve Number 41
Appendix E: Estimating Crop Milestone Dates 43
Appendix F. Aggregating Soil Horizons and Map Units for SAM 46
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Supporting Spreadsheets and Data Tables
Spreadsheet Name
Content
Crop Dates.xlsx
Milestone cropping dates - planting, emergence, full canopy
cover, harvest/canopy removal - for major crops and crop
groups in support of Section 2.3.1 and Appendix E.
Crop Input Data.xlsx
Miscellaneous crop inputs - rooting depth, USLE C Factor - in
support of Section 2.3.2.
Irrigation Input Data by Crop
and State.xlsx
Irrigation inputs in support of Section 2.3.3.
Acronyms and Abbreviations
Abbreviation
Description
CDL
Cropland Data Layer, provided by USDA NASS
EFED
Environmental Fate and Effects Division, Office of Pesticide Programs
ESRL
NOAA Earth System Research Laboratory
FIFRA
Federal Insecticide, Fungicide, and Rodenticide Act
HUC/HUC2
Hydrologic Unit Code
MUSS/MUSLE
Modified Universal Soil Loss Equation
NASS
USDA National Agricultural Statistics Service
NCEP/NCAR
National Centers for Environmental Prediction and Atmospheric Research
NOAA
National Oceanic and Atmospheric Administration
NRCS
USDA Natural Resources Conservation Service
OPP
USEPA Office of Pesticide Programs
ORD
USEPA Office of Research and Development
PRZM / PRZM5
Pesticide Root Zone Model / version 5 (the current version)
PWC
Pesticide Water Calculator, the interface that combines PRZM5 and VVWM
SAM
Spatial Aquatic Model
SSURGO
Soil Survey Geographic database, the geospatial soil dataset developed by
the USDA Natural Resources Conservation Service
USDA
United States Department of Agriculture
USEPA
United States Environmental Protection Agency
USLE
Universal Soil Loss Equation
VVWM
Variable Volume Water Model
NOTE: Appendix A lists the individual field scenario input parameter acronyms and names,
along with definitions.
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1. Field Scenario Inputs for PWC and SAM
1.1 Background
As a part of the requirements for pesticide registration and periodic review of existing
registrations, the U.S. Environmental Protection Agency's Office of Pesticide Programs (OPP)
conducts aquatic exposure assessments to determine whether pesticides that are applied
according to label directions can result in concentrations in water that have the potential to
adversely impact human health or aquatic organisms. To do this for the hundreds of
registration and registration review actions each year, OPP estimates pesticide concentrations
in water using models that account for a combination of soil, weather, hydrology, and
management/use conditions that are expected to influence the potential for pesticides to move
into water.
These models include the Pesticide in Water Calculator (PWC) (Young, 2019) and the Spatial
Aquatic Model (SAM) (USEPA OPP, 2015). SAM is pending further development. PWC uses field,
watershed, and waterbody properties to simulate environmental conditions. Underlying the
PWC, the Pesticide Root Zone Model, PRZM5 (Young and Fry, 2016), simulates pesticide fate
and transport in the field for defined pesticide applications, estimating pesticide loads to both
surface water and ground water. Both field and waterbody parameters define the
environmental scenario simulated in PWC. The term field scenario refers to the set of
parameters that describe the field/environmental conditions used in PRZM5 (Figure 1).
Field Water Body
Runoff &
Erosion
Washout
Volatilization
Volatilization
Degradation
Leaching
Hydroiy^is,
Biodegradation, Photolysis.
Uptake to Sediment
Figure 1. Conceptualization of an environmental scenario for surface water assessments in the PWC.
The scenario includes both a field and a waterbody. The field scenario refers only to the field inputs.
PWC uses a single field scenario to represent an entire contributing area (watershed) for the
crop/use for which the pesticide active ingredient is registered. In PWC, PRZM5 simulates
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pesticide fate and transport in the field, delivering pesticide loads to both surface water bodies
and ground water aquifers. The model simulates the fate/transport of the mass of pesticide
from this single pesticide use area into a fixed water body. In contrast to PWC, SAM accounts
for the contributions of multiple soil-land cover-weather combinations as they occur together
in the watershed and can directly account for multiple pesticide uses in watersheds throughout
the use area. Thus, runoff, erosion, and pesticide transport loading reflect the aggregated
contributions of multiple fields in the watershed. SAM incorporates contributions of pesticide
loadings for a range of water bodies (rivers, streams, ponds, lakes, and reservoirs) and accounts
for the time of travel differences in larger watersheds.
1.2 Creating Soil-Land Cover-Weather Field Scenarios from Spatial Datasets
Field scenarios represent a combination of soil, land cover, and weather conditions. OPP
identified all possible soil-land cover-weather combinations for the conterminous 48 U.S. states
by overlaying spatial data layers for soils from USDA's Natural Resources Conservation Service
(NRCS) Soil Survey Geographic (SSURGO) database (USDA NRCS SSS, 2018), crops/land cover
from the latest five years of USDA National Agricultural Statistics Service's (NASS) Cropland
Data Layer (CDL) (USDA NASS, 2014-2018), and meteorological files/weather station grids
generated from NOAA data (Fry et al, 2016). Section 2 provides more detail on these datasets.
USEPA OPP (2019) describes the code used to create the overlays, extract field scenario inputs,
and create the input data matrix for each soil-land cover-weather grid combination.
The three spatial data sets (SSURGO, CDL, Weather Grids) are overlaid in GIS to generate a
spatial index. The input datasets are joined to the spatial index and subsequently collated into
field scenarios (Figure 2).
NOAA
Climate/
Rain Gauge
Analysis
USDA Soil
Survey
Geographic
Database
(SSURGO)
USDA
Cropland
Data Layer
(CDL)
Figure 2: Scenario combinations are built from the overlap of primary spatial data layers - weather
grids (NOAA), soils (SSURGO), and land cover (CDL).
Weather
grid
(Met)
_
Crop
Corn
1 Soy
Combinations
| Soil A, Met 1, Corn
| Soil A, Met 2, Corn
Soil A, Met 2, Soy
| Soil B, Met 1, Corn
| Soil B, Met 1, Soy
| Soil B, Met 2, Corn
Soil B, Met 2, Soy
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1.3 Document Organization
This guidance document updates the Parameter Estimation chapter from the PRZM5 manual
(Young and Fry, 2016) and the Data Inputs chapter and associated appendices from the
Scientific Advisory Panel (SAP) materials on SAM (USEPA OPP, 2015). Updates include
• Specifying up-to-date input data sources to be used in developing field scenarios
• Updating runoff curve number and soil loss cropping practice factors to reflect current
common agriculture practices
• Identifying primary data sources for crop-related inputs described in Section 2.3
• Identifying general crop groupings used for developing field scenarios (Appendix C)
Section 2 describes the field input parameters used in OPP's aquatic models, along with any
calculations used to derive the inputs. The inputs are organized by the primary data sources
used to generate the field scenario input parameters. Appendices provide additional
information on the input parameters and sources (A and B), land cover classes (C), and details
for deriving runoff curve numbers (D), crop milestone dates (E), and aggregating soil horizons
and map units for SAM scenarios (F).
2. Scenario Input Parameters
The field scenario input parameters used in PWC and in SAM are organized by primary data
source. The descriptions and parameter estimation guidance are adapted from documentation
for PRZM 5 (Young and Fry, 2016) and SAM (USEPA OPP, 2015). Appendix A summarizes the
parameter names, data sources, extraction and derivation methods, and relationships with
other input parameters.
Field scenarios are identified by a unique scenario identification (scenario_id), which is a
combination of the 2-letter state name, SSURGO soil map unit key (soil_id), the weather grid
designation (weather_grid), and the cropland data layer category (cdl) (USEPA OPP, 2019). For
instance, the field scenario identifier ILS208621W20916LC24 consists of the state IL, the
SSURGO map unit key 208621 (which can be used to search for the map unit in SSURGO), the
weather grid 20916, and the CDL value 24 (for winter wheat, see Table 8 in Appendix C).
The inputs are organized by primary data sources: soil and landscape inputs from SSURGO,
crop-related inputs from a variety of crop data sources, and weather inputs from NOAA.
2.1 Soil and Landscape Inputs Derived from the Soil Map Unit Data
USDA's Natural Resources Conservation Service (NRCS) Soil Survey Geographic (SSURGO)
database (USDA NRCS SSS, 2018) is the primary source for soil-related inputs. The SSURGO
database contains a wealth of information about soils on the landscape, stored and presented
in spatial and tabular formats. Soils are grouped into map units based on similarities in
properties (such as hydrologic soil group and slope class). Each map unit has unique properties
and interpretations for use. Map units may contain one or more components (individual soil
series or non-soil features such as rock outcrops) that are likely to occur together in a landscape
(Figure 3). Within each component, soil properties vary with depth. Soil horizons reflect
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differences in properties with depth (Figure 3). USDA NRCS's resources on soil surveys and
SSURGO (USDA NRCS SSS, 2018) has more information related to soil properties, data
organization, and map scale.
Figure 3: Illustration of a single soil map unit composed of 3 individual soil components. Each
component has multiple soil horizons. The USDA NRCS SSURGO dataset contains data for the soil map
unit, individual components within the map unit, and horizons for each component.
Soil data are stored in unique data tables in SSURGO. Pertinent to field scenario inputs, data
specific to the map unit are stored in the muaggatt table; data for individual components
within the map unit are stored in the component table; and data for individual horizons within
each component are stored in the chorizon table. Appendix B cross-references the soil inputs
used in PWC and SAM with the SSURGO table in which these data are stored.
Soil inputs for each map unit represent the major component comprising the greatest
percentage of the map unit. However, if that component is missing data required for model
inputs, then the highest-percentage component with soil data is selected. When one or more
major components make up an equal percentage of the map unit, the more runoff-prone
component is used, according to hydrologic soil group designation in order: D > C > B > A.
The SSURGO database includes non-soil map units, such as quarries, landfills, rock outcrops,
and unspecified developed land, that do not contain data. These non-soil map units are not
used in PWC field scenarios. For SAM, a default runoff curve number of 99 is used for these
map units to account for runoff contributions within the watershed. Neither model simulates
pesticide transport from non-soil map units.
SOIL MAP UNIT
Component #1
Component #2
Component #3
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Soil and landscape inputs derived from SSURGO use a representative value of the reported
range in soil properties. While a single representative number doesn't capture the range in
properties, it is expected to be value-neutral in terms of impact on concentration estimates.
The runoff curve number is the major soil/landscape input affecting runoff (Jones and Russell,
2001; Jones and Mangels, 2002). It is dependent upon the hydrologic soil group associated with
the major soil map unit, general land cover types, crop production system, predominant
agricultural practice, and hydrologic condition (Table 1).
Soil erodibility (usle_k) and slope steepness/length (usel_ls) describe landscape factors that
affect the amount or erosion/sediment that can occur. These factors may be important for
chemicals that have a high affinity to sorb to soil.
2.1.1 Hydrologic Soil Group (hydro_group)
Although not a direct input into PWC or SAM, the hydrologic soil group and land cover are used
to determine the runoff curve number (Table 1). The hydrological soil group reflects the general
runoff characteristics of the soil based on its hydraulic conductivity and depth to drainage-
restrictive layers. Designations range from A (lowest runoff potential) through D (highest runoff
potential). The runoff curve number is based on the dominant hydrologic soil group
(HYDGRPDCD in the SSURGO muaggatt table) for the map unit. If that designation is not
available, the hydrologic soil group of the major map unit component with data is used.
Some wet soils have dual hydrologic groups (e.g., A/D, B/D, C/D). In their natural (undrained)
state, these soils behave as hydrologic group D because they are saturated at or near the
surface and have little capacity to accommodate additional water from precipitation. If drained,
the soil's capacity to hold rainfall increases and the less runoff-prone hydrologic group
designation is used (USDA NRCS, 2007). For dual hydrologic groups associated with cultivated
cropland, the better-drained soil group is used while the D designation is used for non-
agricultural land classes.
2.1.2 Runoff curve numbers [cn_cov, cn_fal)
Runoff curve numbers represent runoff conditions under full crop canopy (cn_cov) and post-
harvest/canopy removal (cn_fal) conditions. The default condition for cn_fal assumes fair to
good hydrologic condition and contour cropping with crop residue left on the surface after
harvest. The treatment/practice and cover condition can be updated to reflect regional
differences as such information becomes available. The cover types used for curve number
determination (USDA NRCS 2004a, 2008) are more general than either the individual CDL cover
classes or the general cover classes (Appendix C). Table 1 shows the curve numbers used for
scenario development. Appendix D describes how curve numbers are determined from the
original USDA NRCS (2004a, 2008) tables.
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Table 1 - Curve Numbers for Cover Type-Hydrologic Soil Group (HSG) Combinations from USDA NRCS
(2004a, 2008).
Cover Type
Treatment
or Practice1
Hydrologic
Condition
Cover
HSG A
HSG B
HSG C
HSG D
Fallow
Bare Soil
...
77
86
91
94
Residue left
Good
74
83
88
90
Row crops
C + CR
Good
Crop
64
74
81
85
Residue
74
83
88
90
Small grain
C + CR
Good
Crop
60
72
80
83
Residue
74
83
88
90
Close-seeded
legumes/rotation
meadow
C
Good
Crop
55
69
78
83
Residue
74
83
88
90
Pasture, grassland,
range
Fair
49
69
79
84
Meadow - continuous grass,
protected from grazing
—
30
58
71
78
Brush - brush-weed-
grass
Fair
35
56
70
77
Woods - grass combination
(orchards)
Fair
43
65
76
82
Woods
Fair
36
60
73
79
Developed Open
Space
>75% grass
cover
Good
39
61
74
80
Residential
(developed-low)
1/3 ac lots
30%
impervious
57
72
81
86
Residential
(developed-
medium)
1/8 ac lots
or less
65%
impervious
77
85
90
92
Urban (developed
high)
Commercial,
business
85%
impervious
89
92
94
95
1 - Cropping practice: C = contour cultivation; CR = crop residue left on the surface.
2.1.3 Soil Erodibility (K) Factor (usle_k)
The K factor (usle_k), an indicator of the inherent susceptibility of the soil to water erosion, is
used in the universal soil loss equation (USLE) to estimate erosion loss. Each soil horizon in
SSURGO has a K factor (KWFACT in the chorizon table in SSURGO). Because the topmost layer is
most exposed to runoff, the K factor for the surface layer is used. In cases where SSURGO
doesn't include a K factor for the surface layer, the K factor for the next layer is used.
2.1.4 Slope, Slope length, and USLE Slope Length/Steepness (LS) Factor (usle_ls)
Slope represents the area-weighted average gradient for the soil map unit in SSURGO
(SLOPEGRADWTA in the muaggatt table). Slope length is the average slope length for the major
component in the map unit (SLOPLENUSLE_R in the component table). If no value is available
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for the slope length, a default length of 300 feet, the maximum representative slope length
value in SSURGO, is used.
Slope and slope length determine the USLE Slope Length/Steepness (LS) Factor (usle_ls), using
equation [1], from Wischmeier and Smith (1978):
[1] LS = (X/72.6)M * {65.41 sin20 + 4.56 sin 0 + 0.065)
where
X = slope length, feet
0 = angle of slope (converted from % slope)
M = adjustment factor (0.5 if slope is >5%; 0.4 for slopes of 3.5-4.5%; 0.3 on slopes of 1-
3%, and 0.2 for slopes <1%)
2.2 Soil Inputs Derived from Horizon Data
SSURGO reports organic matter content, bulk density, field capacity, wilting point, sand and
clay contents for each soil horizon. The impact of these soil properties on pesticide fate and
transport vary with pesticide properties but are less than the impact of rainfall and curve
number (Sinnathamby et al, 2019; D'Andrea et al, 2020). Properties of the surface soil layer,
which includes the zone from which runoff extracts chemicals, have the largest impact. Organic
carbon content impacts sorption for high Koc pesticides; field capacity, wilting point, and bulk
density impact water storage capacity.
Soil components in SSURGO may have data for up to 14 different horizons. The current version
of PRZM in PWC can accommodate up to 8 horizons. Because SAM is focused on surface water
runoff rather than leaching to groundwater, properties for the surface horizon are the most
critical for estimating pesticide transport and properties for the subsurface horizons have been
aggregated in depth-weighted layers (Appendix F). The # symbol in the input parameter names
below refer to the horizon, numbered consecutively from surface to subsurface.
SSURGO reports a high, low, and representative value for each input. Soil inputs in the field
scenario are based on the representative value.
2.2.1 Number of horizons (n_horizon)
PWC/PRZM uses the number of SSURGO horizons to set the number of soil layers with inputs in
the model. This parameter refers to the number of horizons with complete inputs for bulk
density, soil organic content, and water capacity (minimum and maximum). Those horizons
with missing data, usually lower-most horizons that describe weathered rock or thin leaf-litter
surface horizons, are not included in the model routines.
2.2.2 Horizon thickness (thickness_#)
Horizon thickness (thickness_#) is the representative value for the total thickness of the
individual horizon (HZTHK_R in the SSURGO chorizon table) in centimeters (cm). Thickness
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defines the extent through which water and any associated chemical moves downward through
the soil by leaching.
2.2.3 Bulk density (bd_tf)
Soil bulk density (mass per unit volume of soil) is used in chemical transport equations to
estimate total soil porosity and soil moisture content. The bulk density for each layer (bd_#) is
the representative value for soil bulk density at one-third bar moisture content (DBTHIRDBAR_R
in the chorizon table).
2.2.4 Soil organic carbon content (orgC_#)
SSURGO reports soil organic matter (OM_R in the chorizon table) as a percent for each layer.
This conversion from organic matter to soil organic carbon (orgC_#) assumes soil organic
matter contains approximately 58% carbon (USDA NRCS, 2009):
[2] Soil orgC = OM / 1.724
2.2.5 Water capacity (water_max_#, water_min_#)
PRZM uses a tipping bucket concept for vertical water movement and this requires a maximum
and minimum level for the "bucket." Because such values are not directly available from
SSURGO, OPP uses the water content of the soil at 1/3-bar pressure (WTHIRDBAR_R in the
chorizon table) to represent water_max_# and the water content of the soil at 15 bars pressure
(WFIFTEENBAR_R in the chorizon table) to represent water_min_#. The 1/3-bar value is often
used to approximate the amount of water remaining after free drainage (i.e., field capacity) and
is a first approximation for the maximum value of the bucket. The 15-bar value is frequently
used as the wilting point or the least amount of water accessible to transpiration.
2.2.6 Sand (sand_#) and Clay [clay_#) content
The percent of sand (SANDTOTAL_R in the chorizon table) and clay (CLAYTOTAL_R) determine
the texture class of the soil. Sand is used as an input for the soil temperature routine in PWC.
Sand and clay are included as criteria in the soil grouping classes for SAM (Appendix F).
2.3 Crop-Related Inputs
USDA National Agricultural Statistics Service's (NASS) Cropland Data Layer (CDL) provides spatial
distributions of numerous crops using satellite imagery, remote sensing, and data training with
independent crop data (USDA NASS, 2014-2018). The CDL provides the land cover footprint for
collecting crop-related data and determining the general crop cover class (Appendix C) used for
field scenarios. OPP uses the most recent five years of CDL to capture representative cropping
patterns and to take advantage of improvements in land cover estimation methods.
The CDL provides the most detailed spatial resolution for various crops and crop groups at the
national level. Key areas of uncertainty include (a) the relative accuracy of the CDL in identifying
actual crops, which is covered in detail in the CDL accuracy assessments, (b) generic land cover
classes used for crops that, individually, have poor accuracy in CDL, and (c) year-to-year
variation in crop cover. Appendix C lists the current generic agricultural cover classes OPP will
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use to develop field scenarios - corn, cotton, soybeans, wheat, vegetables and ground fruit,
grapes, citrus, other orchards, other grains, other row crops, other cultivated crops, and
pasture/hay/forage. These general crop groups provide viable distinctions among inputs that
impact the use of pesticides and subsequent fate/transport from the field, such as land cover
class as it impacts runoff (curve number) or erosion (crop practice factor) and time of plant or
harvest as it relates to potential timing of pesticide application.
No single national dataset exists to supply all the crop/management-related inputs. Sections
2.3.1 and 2.3.2 identify available defaults for initial development. These will be updated as state
and/or regional crop data are developed.
Crop planting and harvesting dates are used to estimate crop emergence and the timing and
duration of canopy growth and to provide a framework that can be used to estimate the timing
and duration of pesticide applications. The crop stages have a greater impact on model outputs
when used for estimating timing of pesticide applications than for estimating canopy growth
and resulting crop intercept of rainfall and pesticide applications.
OPP used USDA reports on usual planting and harvest dates (USDA NASS, 2006, 2007, and
2010) to estimate crop growth dates by state. These reports provide the usual range in planting
and harvest dates by state for many field, vegetable, and fruit/nut tree crops. These dates are
used to estimate the key plant growth stages needed for modeling.
OPP compiled crop-related inputs for the current generic agricultural cover classes it plans to
use to develop field scenarios - corn, cotton, soybeans, wheat, vegetables and ground fruit,
grapes, citrus, other orchards, other grains, other row crops, other cultivated crops, and
pasture/hay/forage (Appendix C). Additional crops or crop groups may be added if differences
in specific management practices or environmental factors would impact the management and
transport of pesticides.
2.3.1 Crop Milestone Dates
Appendix E provides more detail on the methods used to derive the crop milestone dates,
including steps for estimating missing dates or for estimating dates for crops not included in the
USDA publications. The file Crop Dates.xIsx contains the detailed methods, crop dates, and
documentation for the dates.
2.3.1.1 Planting dates (plant_begin, plant_end, plant_date)
The beginning plant date (plant_begin) is either the first date in the "Most active" planting
dates in USDA NASS (2006, 2007, and 2010) or, if the most active range is not provided, the
"Begin" date for planting. The ending plant date (plant_end) is either the last date in the "Most
active" planting dates in USDA NASS (2006, 2007, and 2010) or, if the most active range is not
provided, the "End" date for planting. These dates, reported as month-day in the USDA
publications, are converted to Julian days (1 to 365). For PWC, a single plant date (plant_date) is
derived from the midpoint of plant_begin and plant_end. In SAM, the full range is used. Plant
date is not a direct input in PWC but is used to estimate the emergence date for modeling. In
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SAM, the plant date range provides a framework for linking pesticide applications to crop
milestones.
2.3.1.2 Harvest dates (harvest_begin, harvest_end, harvest_date)
"Harvest" for modeling purposes refers to the time when the crop canopy is removed, and root
depth returns to zero (effectively zeroing transpiration). For many annual crops, this coincides
with the agricultural harvest of the crop when the crop and canopy are actually removed. For
these crops, the beginning harvest date (harvest_begin) is either the first date in the "Most
active" harvesting dates in USDA NASS (2006, 2007, and 2010) or, if the most active range is not
provided, the "Begin" date for harvesting. The ending harvesting date (harvest_enc/) is either
the last date in the "Most active" harvesting dates in USDA NASS (2006, 2007, and 2010) or, if
the most active range is not provided, the "End" date for harvesting. These dates, reported as
month-day in the USDA publications, are converted to Julian days (1 to 365). For PWC, a single
harvest date (harvest_c/ote) is derived from the midpoint of harvest_begin and harvest_end. In
SAM, the full range is used.
For perennial field crops, such as alfalfa, the cutting dates best approximate canopy removal.
For perennial deciduous tree crops, leaf drop, which may occur well after the fruit/nut is
harvested, best approximates canopy removal. For these orchard crops, leaf drop dates will be
approximated from the first frost/freeze date in the fall.
2.3.1.3 Blooming dates (bloom_begin, bloom_end)
USDA includes the usual blooming dates (the period of time in which most orchards come into
full bloom) for fruit and nut trees (USDA NASS, 2006). For pesticides that may be applied during
bloom, the reported beginning date for bloom sets the start of the pesticide application
window in SAM and the range in blooming dates defines the length of the pesticide application
window in SAM. In some instances, bloom will be used to estimate other milestone dates, such
as beginning of leafing (for emergence) or full canopy cover, for fruit and nut crops.
2.3.1.4 Emergence dates (emergence_begin, emergence_end, emergence_date)
The emergence date for modeling purposes refers to the beginning of canopy cover for a crop.
Beginning and ending emergence dates are estimated by adding 7 days to the corresponding
planting dates for the crop in the state. For many annual crops, planting dates are available in
USDA NASS (2006, 2007, and 2010). In PWC, a single emergence date (emergence_date) is
derived from the midpoint of emergence_begin and emergence_end. In SAM, the full range is
used.
For perennial field crops, such as alfalfa, the beginning of growth either in the spring or
immediately after a cutting approximates emergence dates. For perennial deciduous tree crops,
the beginning of canopy development or leaf bud approximates emergence. For these orchard
crops, the midpoint between beginning and ending bloom dates approximates the beginning of
canopy development.
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2.3.1.5 Maximum canopy cover dates (maxcover_begin, maxcover_end, maxcover_date)
In the absence of data, the timing to maximum canopy cover is estimated as the midpoint
between emergence and harvest. This represents the maximum capacity of the crop to
intercept rainfall and pesticide applied above canopy. Crop growth in PWC and SAM assume the
canopy coverage and root depth increase proportionally with time from emergence date to full
canopy coverage. In PWC, a single date (maxcover_date) is derived from the midpoint of
maxcover_begin and maxcover_end. In SAM, the full range is used.
For perennial field crops, such as alfalfa, the maximum canopy is estimated as the midpoint
between cuttings/harvest dates. For perennial deciduous tree crops, maximum canopy cover is
estimated as the midpoint between canopy development and canopy removal/leaf drop.
2.3.1.6 Annual crops with missing milestone dates
In some instances, a crop occurs in a state, but not in sufficient acreage for inclusion in the
usual planting/harvest date publications (USDA NASS, 2006, 2007, and 2010). In these
instances, OPP used surrogates to fill-in data gaps. Surrogate dates are assigned in order of:
1. Same crop with dates from an adjacent state, with preference to states with similar
latitude
2. Another crop in the same state that is in the same General CDL Class (Appendix C)
Appendix E and the CropDates.xIsx spreadsheet describe the methods used to estimate missing
dates.
2.3.1.7 Milestone dates for perennial crops
Non-annual/perennial crops do not have reported planting dates in USDA NASS (2006, 2007,
2010), such as alfalfa and grass hay, pasture, orchards, and some berries/fruits. Emergence
(beginning of active growth), maximum canopy cover, and harvest (loss of foliage cover) are still
needed to set canopy and root growth model routines (for rain/pesticide interception and
evapotranspiration) and to define the times of crop cover and crop removal for runoff curve
numbers.
One approach for perennial crops is to tie canopy and root growth to the beginning and end of
the growing season based on the timing of the last and first killing frost, respectively. Canopy
cover differences can be linked to reported harvest dates - in the case of hay crops, multiple
harvests may occur - or, for deciduous orchards, the timing of leaf bud to leaf fall. Proposed
approaches to define significant crop milestone dates for use in modeling are described in this
section.
2.3.1.8 Crops with multiple growing seasons in a year
Some crops, such as vegetables, may have multiple growing seasons in a year in some states.
These show up in the USDA planting/harvesting reports with multiple planting and harvest
dates (USDA NASS, 2007). Although rare, some vegetable crops have up to four seasons in a
year. The current approach, outlined in Appendix E, is to develop a generic vegetable scenario
15
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with options for up to four growing seasons in a year (spring, summer, fall, winter). In states in
which only a single growing season occurs, only the first growing season will be active. In states
with more than one growing season in a year, the additional seasons would be activated.
2.3.2 Other Crop-Specific Inputs
Many of the crop-related factors described below are based on guidance in PRZM 3 (Carousel et
al, 2005) or PRZM 5 (Young and Fry, 2016) manuals. These crop inputs, along with the
supporting data used to derive the inputs, can be found in the file Crop Input Data.xlsx.
2.3.2.1 Crop intercept (crop_intercept)
Crop intercept is the maximum rainfall interception storage of the crop (cm). This parameter
estimates the amount of rainfall that is intercepted by a fully developed plant canopy and
retained on the plant surface. The PRZM3 manual (Carousel et al, 2005) provided a range of 0.1
to 0.3 cm for a dense crop canopy. USDA NRCS (2016) noted up to 0.1 inch (0.25 cm) can be
temporarily intercepted and stored on plant foliage.
2.3.2.2 Maximum canopy cover (max_cover)
The maximum areal crop coverage (or ground cover), as a percentage of the surface, sets the
maximum cover value. As a crop grows, its ground cover increases and captures proportionally
more pesticide from above canopy applications. Similarly, rainfall intercept and storage
capacity increases. For most crops, the maximum coverage is on the order of 80% to 100%
(Carousel et al, 2005; Young and Fry, 2016).
2.3.2.3 Maximum active rooting depth (root_depth)
The maximum active rooting depth is the depth to which plant roots draw water from the soil.
This is used in estimating irrigation needs and soil moisture content over time. The USDA
National Engineering Handbook (NEH) provides ranges in depth to which roots of mature crops
will extract water from deep, well-drained soils (USDA NRCS, 1997 and 2016). The root_depth
input for the crop is the average of the depths reported in Table 3-4 of NEH 652 Irrigation Guide
(USDA NRCS, 1997) and Table 11-3 of NEH 632 Sprinkler Irrigation Guide (USDA NRCS, 2016).
The SSURGO database reports maximum rooting depths (ROOTZNEMC in the valu_fy2018.gdb
in SSURGO) for soils that have a root- or drainage-restrictive layer in the soil. If the maximum
active rooting depth for the crop is greater than the depth of the maximum soil root zone
depth, the rooting depth for the crop is truncated at the soil depth for that crop-soil map unit.
In PWC/PRZM, the maximum active rooting depth must be less than the total soil depth. If the
maximum rooting depth for the crop equals the total soil depth, subtract 0.5 cm from the plant
root depth.
2.3.2.4 USLE practice (P) factor (usle_p)
The P factor in the Universal Soil Loss Equation estimates the impact of agricultural practices on
erosion. Values range from 0.10 (extensive practices) to 1.0 (no supporting practices). Specific
16
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values in Table 2 are based on Table 5.6 from Carousel et a I, 2005, which are based on values in
Wischmeier and Smith (1978). The default practice assumes cultivation along contour (C).
Table 2: USLE P Values for Selected Agricultural Practices and Slope Ranges (from Table 5.6, Carousel
et al, 2005, adapted from Wischmeier and Smith, 1978)
Practice
Slope, percent
1-2
2-7
7-12
12-18
18-24
Contouring (C), Default for field scenarios
0.60
0.50
0.60
0.80
0.90
No support practice
1.0
1.0
1.0
1.0
1.0
Contour Strip Cropping (CP)
0.30-0.60
0.25-0.50
0.30-0.60
0.40-0.80
0.40-0.90
Contour Listing or Ridge (CL)
0.30
0.25
0.30
0.40
0.45
2.3.2.5 USLE cover management (C) factor (usle_c_cov, usle_c_fal)
The C factor in the Universal Soil Loss Equation estimates the impact of crop cover management
practices on erosion. Values for USLEC range from 0.001 (well managed) to 1.0 (fallow or tilled
condition). Table 5.7 in the PRZM3 manual (Carousel et al, 2005) is based on crop,
management, and rotation practices, which can vary greatly with crops. The USLEC factors in
the file Crop Input Data.xlsx represent the high and low range for the crop, based on data
provided to OPP by USDA in 2000.
2.3.3 Irrigation Inputs
The need for including irrigation in model simulations depends on a combination of crop water
needs, rainfall amounts and timing, and the capability of the soil to retain water and supply the
crop. When the irrigation routine is triggered (based on the irrigation type parameter), the
allowable moisture depletion parameter indicates when irrigation occurs based on soil
moisture content.
The file Irrigation Input Data by Crop and State.xlsx contains irrigation inputs by crop and state,
along with the supporting data used to derive the inputs.
2.3.3.1 Irrigation type (irrigation_type)
Irrigation type triggers the irrigation routine in PWC/PRZM and SAM. The dominant type of
irrigation can be simplified based on how water is applied:
0 = no irrigation
1 = Over canopy is applied above the crop canopy, such as with pivot or spray booms. Over-
canopy irrigation triggers crop intercept and hold-up of applied water.
2 = Below canopy is applied below the crop canopy, such as with furrow or flood irrigation.
No crop intercept occurs.
OPP used the 2013 Farm and Ranch Irrigation Survey (USDA NASS, 2014) to determine the
appropriate irrigation type. Irrigation is triggered in the scenario when more than 40% of the
crop acreage in the state is irrigated. Over-canopy irrigation is used when the majority of
irrigated crop acreage in the state is irrigated using pressure or sprinkler systems. Below-
canopy irrigation is used when the majority of irrigated crop acreage in the state is irrigated
17
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with gravity or low-flow irrigation systems. An update to the irrigation and water management
survey, based on the 2017 Census of Agriculture, is expected to be released late in 2019. OPP
will update the irrigation triggers based on that report.
2.3.3.2 Allowable moisture depletion (depletion_allowed)
Allowable moisture depletion, the fraction of the available water capacity that triggers
irrigation for the crop, depends upon the soil moisture-holding characteristics, the type of crop
planted, and agricultural practices. The fraction generally ranges between 0.0 (irrigation begins
when soil moisture is depleted to the wilting point) and 0.6 (irrigation is applied at 60 percent
of the available water capacity) (USDA NRCS, 1997, 2016). Table 3-3 in the National Engineering
Handbook (NED), Part 652-lrrigation Guide (USDA NRCS, 1997) provides management-allowable
depletion (as a percent of available water capacity) for select crops at different growth stages.
OPP used the minimum depletion fraction as an irrigation trigger, with a default depletion of
0.5 for crops not included in the irrigation guide.
2.3.3.3 Leaching fraction (leaching_frac)
This refers to the fraction of excess water added by irrigation that is allowed to leach below the
root zone, usually to reduce salt build-up from evaporation losses. This factor represents a
fraction of the amount of water required to meet the soil water deficit. A default value of 0.1 is
used, indicating that 10% more water is used than that required to meet the water deficit.
2.3.3.4 Maximum irrigation rate (irrigation_rate)
The irrigation rate specifies the maximum daily amount of irrigation water that is applied. This
amount, dictated by soil properties (i.e., curve number), is set to minimize losses of irrigation
water by runoff. Irrigation rates for crops will vary with the curve number, following the runoff
calculation in the USDA NRCS Curve Number (CN) method (USDA NRCS, 2003):
[3] S = (2540/CN)-25.4
where
S = potential maximum daily water retention in soil (cm), setting the maximum daily
irrigation rate
CN = Curve Number value, based on Table 1, using the value for crop cover
2.4 Weather Inputs
Weather data come from publicly-available, gridded meteorological datasets that provide daily
values for precipitation, temperature, wind speed, solar radiation, and potential
evapotranspiration at uniform spatial resolution across the country. OPP used the Unified
Gauge-Based Analysis of Daily Precipitation from the Climate Prediction Center (CPC) (NOAA
ESRL, 2014) and Reanalysis Data from the National Centers for Environmental Prediction
(NCEP). Fry et al (2016) describe the data processing and quality assurance steps taken to
derive the weather data.
18
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The NCEP Reanalysis data are available as points at 2.5° x 2.5° (latitude/longitude) grid
resolution; the CPC precipitation data are at 0.25° x 0.25° US grid resolution. OPP combined the
data using grids (Theissen polygons) drawn around each datapoint in GIS and resampled at the
finer grid resolution of the CPC data (0.25° x 0.25°). Each weather station contains historical
daily weather data - precipitation, temperature, wind speed, solar radiation, and potential
evapotranspiration - collected from NOAA.
Additional weather-related parameters - rainfall distribution, depth of evapotranspiration, and
snowmelt factor - are based on descriptions and maps provided in the PRZM3 (Carousel et a I,
2005) and PRZM5 (Young and Fry, 2016) manuals. The maps for these data sources were
digitized into GIS and combined with the 0.25° x 0.25° grids using a spatial overlay.
2.4.1 Daily precipitation (precipitation)
Daily precipitation, in cm, comes from the NOAA Climate Prediction Center (CPC) Unified Rain
Gauge Analysis. Precipitation in the CPC dataset is converted from mm/day to cm/day.
2.4.2 Daily mean air temperature (temperature)
The daily mean air temperature at 2 meters above the surface is converted to degrees Celsius
by subtracting 273.15 from the original Kelvin values reported in the NOAA NCEP/NCAR
Reanalysis dataset.
2.4.3 Daily evapotranspiration [et)
Daily evapotranspiration, in cm, is not directly available in the NOAA data. The calculation,
described in Fry et al (2016), uses the Hargreaves-Samani method (FAO, 1998; Hargreaves and
Samani, 1985; Lu et al., 2005).
2.4.4 Daily wind speed (windspeed)
The wind speed as calculated by Fry et al. (2016) represents the speed at 10 m above the
surface, in cm/s.
2.4.5 Daily solar radiation flux (solarradiation)
The daily downward solar radiation flux, MJ nr2 day1, is used to calculate daily
evapotranspiration (Fry et a I, 2016).
2.4.6 Rainfall distribution (ireg)
The time of concentration calculation of peak flow is based on the USDA NRCS rainfall
distribution region (Young and Fry, 2016, adapted from USDA NRCS, 1986) for the time period
from May 1 to September 15. Figure 4 shows the approximate geographic range for the four
distribution regions. OPP overlaid the weather station grid to a digital version of Figure 4 to
derive the rainfall region by weather station grid.
19
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Figure 4: Approximate geographic boundaries for NRCS rainfall distributions, adapted from USDA
NRCS (1986) and linked to weather station grids.
2.4.7 Soil evaporation available depth (anetd)
This value establishes a minimum depth for which soil water is available for evaporation
Thomson and Troeh (1978) reported usual ranges of 5 to 8 cm. The current default is 8 cm.
2.4.8 Snowmelt factor (sfac)
The snowmelt Factor (cm/°C/day) is the amount of accumulated snow that melts per °C above
0°C. USDA NRCS (2004b) recommended a default value of 0.274 when no other information is
available.
3. References
Amos, J J., CM, Holmes, C.G. Hoogeweg, and S.A. Kay. 2010. Development of Datasets to Meet
USEPA Threatened and Endangered Species Proximity to Potential Use Sites Data
Requirements. Report Number: 437.01-Overview. Prepared by Waterborne Environmental, Inc.
for the Generic Endangered Species Task Force.
Baker, N.T., and Capel, P.D., 2011, Environmental factors that influence the location of crop
agriculture in the conterminous United States: U.S. Geological Survey Scientific Investigations
Report 2011-5108, 72 p.
Carousel, R.F., J.C. Imhoff, P.R. Hummel, J.M. Cheplick, A.S. Donigian, Jr., and L.A, Suarez. 2005.
PRZM-3, A Model for Predicting Pesticide and Nitrogen Fate in the Crop Root and Unsaturated
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Soil Zones: Users Manual for Release 3.12.2. Center for Exposure Assessment Modeling (CEAM),
National Exposure Research Laboratory - Ecosystems Research Division, Office of Research and
Development (ORD), U.S. Environmental Protection Agency (U.S. EPA).
D'Andrea, M.F., Letourneau, G., Rousseauc, A.N., Brodeur, J.C. 2020. Sensitivity analysis of the
Pesticide in Water Calculator model for applications in the Pampa region of Argentina. Science
of The Total Environment, 698,134232. Doi: https://doi.Org/10.1016/i.scitotenv.2019.134232
FIFRA SAP. 2015. A Set of Scientific Issues Being Considered by the Environmental Protection
Agency Regarding Development of a Spatial Aquatic Model (SAM) for Pesticide Risk
Assessments. FIFRA Scientific Advisory Panel Minutes No. 2015-03. Available in the public e-
docket, Docket No. EPA-HQ-OPP-2015-0424, accessible through the docket portal:
https://www. regulations.gov/docket?D=EPA~HQ~QPP~2015~04 24
FAO, 1998. Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements.
Irrigation and Drainage Paper 56. FAO, Rome, Italy.
Fry, M.M., G. Rothman, D.F. Young, and N. Thurman. 2016. Daily gridded weather for exposure
modeling. Environmental Modelling & Software, 82, 167-173,
https://doi.Org/10.1016/i.envsoft.2016.04.008
Hargreaves, G.H., and Z.Q. Samani. 1985. Reference crop evapotranspiration from temperature.
Appl. Engrg. Agric. 1 (2), 96e99.
Jones, R.L., and M.H. Russell (ed.). 2001. FIFRA Model Validation Task Force Final Report. The
FIFRA Environmental Model Validation Task Force.
Jones, R.L., and G. Mangels. 2002. Review of the validation of models used in federal
insecticide, fungicide, and rodenticide act environmental exposure assessments. Environ. Tox.
Chem. 21:1535-1544.
Lu, J., G. Sun, S. McNulty, and D.M. Amatya. 2005. A comparison of six potential
evapotranspiration methods for regional use in the southeastern United States. J. Am. Water
Resour. Assoc. 41 (3), 621e633.
Sinnathamby, S., Minucci, J., Denton, D., Raimondo, S., Oliver, L., Yongping, Y., Young, D., Hook,
J., Pitchford, A., Eric, W., Purucker, S.T. 2019. A Sensitivity Analysis of Pesticide Concentrations
in California Central Valley Vernal Pools. Journal of Environmental Pollution (In Press). Doi:
https://doi.Org/10.1016/i.envpol.2019.113486
Thomson, L.M., and Troeh, 1978. F.R. Soils and Fertility, 4th edition. Mcgraw-Hill Book Co. New
York.
USDA Agricultural Research Service (ARS). 2016. Revised Universal Soil Loss Equation 2 - How
RUSLE2 Computes Rill and Interrill Erosion. Available at https://www.ars.usda.gov/southeast~
area/oxford-ms/national-sedimentation-laboratory/watershed-physical-processes-
research/research/rusle2/revised~universal~soil~loss-eauation~2~how~rusle2~computes-rill~and~
interrill-erosion/ . Last modified 10/7/2016.
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USDA National Agricultural Statistics Service (USDA NASS). 2006. Fruit and Tree Nuts: Blooming,
Harvesting, and Marketing Dates. Agricultural Handbook Number 729. December 2006.
USDA National Agricultural Statistics Service (USDA NASS). 2007. Vegetables: Usual Planting and
Harvesting Dates. Agricultural Handbook Number 507. May 2007.
USDA National Agricultural Statistics Service (USDA NASS). 2010. Field Crops: Usual Planting and
Harvesting Dates. Agricultural Handbook Number 628. October 2010.
USDA National Agricultural Statistics Service (USDA NASS). 2012. Census of Agriculture. National
Agricultural Statistics Service, U.S. Department of Agriculture, Washington, D.C. Available at
https://auickstats.nass.usda.eov/ (accessed July 2018).
USDA National Agricultural Statistics Service (USDA NASS). 2014. Farm and Ranch Irrigation
Survey (2013). Volume 3, Special Studies, Part 1, AC-12-SS-1. 2012 Census of Agriculture.
National Agricultural Statistics Service, U.S. Department of Agriculture, Washington, D.C.
Available at
https://www.nass.usda.gov/Publications/AeCensus/2012/Qnline Resources/Farm and Ranch
Irrigation Survey/
USDA National Agricultural Statistics Service (USDA NASS). 2014-2018. Cropland Data Layer.
Published crop-specific data layer [Online]. Available at
https://nasseeodata.emu.edu/CropScape/ (accessed Feb 2019). USDA-NASS, Washington, DC.
USDA Natural Resources Conservation Service (USDA NRCS). 1986. Urban Hydrology for Small
Watersheds, TR-55, Natural Resources Conservation Service, United States Department of
Agriculture, Washington DC.
USDA Natural Resources Conservation Service (USDA NRCS). 1997. Irrigation Guide, Part 652,
National Engineering Handbook. Available:
https://www.nrcs.usda.eov/lnternet/FSE DQCUMENTS/nrcsl44p2 033068.pdf
USDA Natural Resources Conservation Service (USDA NRCS). 2003. National Engineering
Handbook Section 4: Hydrology. Natural Resources Conservation Service, United States
Department of Agriculture, Washington DC.
USDA Natural Resources Conservation Service (USDA NRCS). 2004a. Chapter 9 - Hydrologic Soil-
Cover Complexes, Part 630 Hydrology, National Engineering Handbook. Available:
https://www.nrcs.usda.eov/wps/portal/nrcs/detailfull/national/water/manaee/hvd roloev/?cid
=stelprdb!043063
USDA Natural Resources Conservation Service (USDA NRCS). 2004b. National Engineering
Handbook, NEH -4 Part 630, Chapter 11 Snowmelt. Natural Resources Conservation Service,
United States Dept. of Agriculture.
USDA Natural Resources Conservation Service (USDA NRCS). 2007. Chapter 7 - Hydrologic Soil
Groups, Part 630 Hydrology, National Engineering Handbook. Available:
https://directives.sc.eeov.usda.eov/OpenNonWebContent.aspx?content=17757.wba
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USDA Natural Resources Conservation Service (USDA NRCS). 2008. 2C-5 NRCS TR-55
Methodology. Cited in Iowa Storm Water Management Manual, Chapter 3 - Storm Water
Hydrology, Section 5 - NRCS TR-55 Methodology. Available:
https://www.iowadnr.gov/Environmental~Protection/W3ter~Quality/NPDES~Storm~
Water/Storm-Water-Manual
USDA Natural Resources Conservation Service (USDA NRCS). 2016. Chapter 11 - Sprinkler
Irrigation, Part 623, National Engineering Handbook. Available:
https://www.wcc.nrcs.usda.gov/ftpref/wntsc/waterMgt/irrigation/Nb H tiy\;hll,pdf
USDA Natural Resources Conservation Service Soil Survey Staff (USDA NRCS SSS). 2018. Gridded
Soil Survey Geographic (gSSURGO) Database. United States Department of Agriculture, Natural
Resources Conservation Service. Available online at http://datagateway.nrcs.usda.gov/ .
October 11-12, 2018 (FY2018 official release).
USDA Soil Survey Division Staff (USDA SSDS). 1993. Soil survey manual. Soil Conservation
Service. U.S. Department of Agriculture Handbook 18. Chapter 4. Soil Mapping Concepts.
Available: https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/ref/?cid=nrcsl42p2 054254
U.S. Environmental Protection Agency (USEPA). 2018. Section A§ 180.41 - Crop group tables.
Title 40 - Protection of Environment. CHAPTER I - ENVIRONMENTAL PROTECTION AGENCY
(CONTINUED). SUBCHAPTER E - PESTICIDE PROGRAMS. PART 180 - TOLERANCES AND
EXEMPTIONS FOR PESTICIDE CHEMICAL RESIDUES IN FOOD. Subpart B - Procedural Regulations.
Available: https://www.govinfo.gov/content/pkg/CFR~20iy~title40~vol26/xml/CFR~2Q17~title40~
vol26~secl80~41.xml
U.S. Environmental Protection Agency Office of Pesticide Programs (USEPA OPP). 2015.
Development of a Spatial Aquatic Model (SAM) for Pesticide Risk Assessments. Presented to the
FIFRA Scientific Advisory Panel, September 15-17, 2015. Available in the public e-docket, Docket
No. EPA-HQ-OPP-2015-0424, accessible through the docket portal:
https://www. regulations.gov/docket?D=EPA~HQ~QPP~2015~04 24
U.S. Environmental Protection Agency Office of Pesticide Programs (USEPA OPP). 2019.
Automated Methods for Field Scenario Generation. Draft.
Wischmeier, W.H., and D.D. Smith. 1978. Predicting rainfall erosion losses - a guide to
conservation planning. U.S. Department of Agriculture, Agriculture Handbook No. 537.
Young, D.F. 2019. The USEPA Model for Estimating Pesticides in Surface Water, in Pesticides in
Surface Water: Monitoring, Modeling, Risk Assessment, and Management. American Chemical
Society, editors Goh, Kean, and Young, American Chemical Society, Washington DC
Young, D.F. and Fry, M.M. 2016. PRZM5: A Model for Predicting Pesticide in Runoff, Erosion,
and Leachate, Revision A. USEPA/OPP 734S16001, U.S. Environmental Protection Agency,
Washington, DC. Available for download with the Pesticide in Water Calculator from the USEPA
OPP Water Models web site at https://www.epa.gov/pesticide~science~and~assessing~pesticide~
risks/models~pesticide~risk~assessment#aauatic
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Appendix A: Primary Scenario Input Parameters
Tables 3 to 5 summarize the scenario input parameters that are derived for each soil /land cover/weather station combination.
Parameters in bold are inputs for SAM and PWC; other parameters are used in deriving inputs or providing quality assurance. These
tables do not include the scenario identification fields described in Section 2.
Table 3 - Soil and Landscape Inputs used in PWC and SAM. Parameters in bold are direct inputs on both models.
Parameter
Name
Parameter
Description
Units / Range
Source
Extraction/ Derivation
Notes
Relationships/
correlation
A. Soil and landscape inputs derived from the soil map unit
hydro_group
Hydrologic soil
group, used to
determine curve
number
Numeric,
based on
alpha
designation:
1 (A), 2 (A/D),
3 (B), 4 (B/D),
5 (C), 6 (C/D),
7(D)
SSURGO for Soil Map
Unit: HYDGRPDCD
(dominant HSG) from
muaggatt table; if
missing, use HYDGRP
from the component
table for the major
component.
Converted to numeric
values for processing.
Slash groups (A/D,
B/D, C/D) provide
clues to tile drainage:
if cultivated land, use
left side; if not
cultivated, use D.
Used in combination
with land cover to
determine runoff curve
number
Based on hydraulic
conductivity, depth
to restrictive layers,
which are influenced
by soil texture,
structure, organic
matter content, bulk
density, mineralogy
cn_cov
cn_fal
Runoff curve
numbers of
antecedent
moisture condition
for crop cover and
fallow/residue.
dimensionless
whole
number, less
than 100
Hydrologic soil group
from SSURGO, land use
from CDL Curve number
guidance from USDA
NRCS (2004, 2008).
Follow curve number
guidance (Appendix
D).
Determines the portion
of rainfall that runs off
the land, potentially
carrying pesticides with
it.
Based on hydrologic
soil group, land cover
type, and
management
practices
usle_k
Universal soil loss
equation K factor
for soil erodibility
factor, whole-soil
dimensionless
fraction,
range from 0
to 0.64
SSURGO Soil Map Unit,
joined to chorizon table
through muaggatt,
component tables:
KWFACT for topmost
horizon (hzdept_r = 0)
Extract kwfact (K
factor, whole soil) for
surface horizon of
major component in
chorizon table
Sensitivity analysis on
soil erosion (RUSLE)
model inputs is
needed. Expected to
have greater impact on
high-Koc chemicals, but
the extent has not been
tested.
Erodibility depends
on soil texture,
organic matter
content, structure
(how well soil
particles aggregate),
permeability
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Parameter
Name
Parameter
Description
Units / Range
Source
Extraction/ Derivation
Notes
Relationships/
correlation
slope
Land slope, the
average slope
gradient for the
soil mapping unit.
%
Range from 0
to 100
SSURGO for Soil Map
Unit: SLOPEGRADD (wtd.
avg. slope) from
muaggatt table
Weighted average %
slope for the soil map
unit (SLOPEGRADD)
from SSURGO
muaggatt table
Used with slope length
to estimate USLE LS
(slope/length) factor.
Impacts soil erosion
potential in
combination with the
slope length. The
combined impact is
characterized in the
USLE LS factor below.
slopejength
Slope length used
in USLE LS factor.
Distance from
point of origin of
overland flow to
the point where
gradient decreases
and deposition
begins
feet
SSURGO component
table: SLOPELENUS_R
(linked to mapunit)
SLOPELENUS_R in
component table; if
no value is available
for SLOPELENUS_R,
use a default slope
length of 300 feet
(max. SLOPELENUS_R
value in SSURGO)
Used with slope to
estimate USLE LS
(slope/length) factor.
Correlation with
slope gradient is
weak. Impacts soil
erosion potential in
combination with the
slope length. The
combined impact is
characterized in the
USLE LS factor below.
uslejs
Universal soil loss
equation (LS)
length-slope
topographic factor
dimensionless
fraction
SSURGO for slope,
slopejength (above).
Equation from
Wischmeier and Smith
(1978).
Derive using equation
from Wischmeier and
Smith (1978):
LS = (A/72.6)M *
{65.41 sin20 +4.56
sin 0 + 0.065)
where
A = slope length, feet
0 = angle of slope
M = adjustment factor
Sensitivity analysis on
soil erosion (RUSLE)
model inputs is
needed. Expected to
have greater impact on
high-Koc chemicals, but
the extent has not been
tested.
Derived from slope
gradient, slope
length parameters
above.
root_zone_
max
Maximum depth of
the root zone (cm)
based on soil
properties
cm
SSURGO value added
data table (valu):
rootznemc, available for
the major component in
each map unit
Serves as maximum
depth of active root
zone. Adjust
maxrootdepth, anetd.
Used to define depth
of depletion for
irrigation.
Potentially serves as
cut-off depth for
active root zone
depth.
25
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Parameter
Name
Parameter
Description
Units / Range
Source
Extraction/ Derivation
Notes
Relationships/
correlation
B. Soil inputs derived from horizon data
n_horizons
Number of soil
horizons with
input data
Count
SSURGO chorizon table
Count the number of
horizons within each
component that have
values for bd, orgC, fc,
wp
Tracks number of
horizon inputs
thickness_#
Thickness of#
horizon
cm
SSURGO chorizon table
(linked to major
component, map unit):
HZTHK_R for each
horizon
No further
calculations
Used for accounting for
thickness
org C_#
Percent soil
organic carbon
(orgC) for #
horizon
%
Organic soils
>35% org. C;
mineral soils
less
SSURGO chorizon table
(linked to major
component, map unit):
OM_R for each layer
Convert from percent
organic matter to
organic carbon using
the equation:
orgC=(OM_R)/1.724
With the pesticide
sorption coefficient,
determines how much
pesticide is held
(sorbed) to soil and
how much is dissolved
in water (available for
runoff)
Impacts (directly or
indirectly) density,
water holding
capacity, erodibility;
with chemical Koc,
impacts the amount
of pesticide held on
soil
bd_#
Bulk density for#
horizon
g/cm3
generally
>1.00 (except
for organic or
volcanic soils),
<2.00
SSURGO chorizon table
(linked to major
component, map unit):
DBTHIRDBAR_R for each
horizon
No further
calculations
Reflects relative
porosity, water
capacity. Because of
narrow range in bulk
density values, the
impact of its
uncertainty is lessened.
Related to soil
texture (sand, silt,
clay), structure,
organic content.
Influences soil
permeability/
drainage, which
influences hydrologic
soil groups
26
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Parameter
Name
Parameter
Description
Units / Range
Source
Extraction/ Derivation
Notes
Relationships/
correlation
water_max_#
Maximum water
capacity for #
horizon; the
amount of water
retained after large
pores have drained
(1/3 bar water
content)
cm3/cm3
fraction <1.0
SSURGO chorizon table
(linked to major
component, map unit):
WTHIRDBAR_R for each
horizon
Divide by 100 to
convert units from
percent to cm3/cm3
Combined with
water_min, defines
water holding capacity
of soil, which influences
curve number, and the
irrigation trigger.
Related to soil
texture (sand, silt,
clay), structure, bulk
density, organic
content.
water_min_#
Minimum water
capacity for #
horizon; minimum
water content at
which plants
cannot draw water
from the soil (15
bar water content)
cm3/cm3
fraction <1.0
SSURGO chorizon table
(linked to major
component, map unit):
WFIFTEENBAR_R for
each horizon
Divide by 100 to
convert units from
percent to cm3/cm3
Affects irrigation trigger
and curve number, but
generally less influence
than water_max
Similar to field
capacity
sand_#
Percent sand
(total) for #
horizon
%
Range from >0
to <100
SSURGO chorizon table
(linked to major
component, map unit):
SANDTOTAL_R for each
horizon
No further
calculations
Affects temperature
routine for volatility, if
used. Indirect impact
on water holding
capacity of soil. Affects
bulk density, total
porosity, and
movement of water in
soil, K factor.
Varies with clay
content; influences
bulk density, field
capacity, wilting
point, total porosity,
K factor values.
clay_#
Percent clay (total)
for # horizon
%
Range from >0
to <100
SSURGO chorizon table
(linked to major
component, map unit):
CLAYTOTAL_R for each
horizon
No further
calculations
Affects temperature
routine volatility, if
used. Indirect impact
on water holding
capacity of soil. Affects
bulk density, total
porosity, and
movement of water in
soil, K factor.
Varies with sand
content; influences
bulk density, field
capacity, wilting
point, total porosity,
K factor values.
27
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Table 4 - Crop-related inputs used in PWC and SAM. Parameters in bold are direct inputs in PWC.
Parameter
name
Parameter
Description
Units
Source
Extraction/ Derivation
Notes
Relationships/
correlation
Crop/plant factors that can be used to guide pesticide application window
plant_begin
(SAM)
Julian day for
beginning of most
active crop
planting window
day number
(julian)
USDA Usual Planting
and Harvesting Dates
(2006, 2007, 2010)
Beginning of most
active planting date.
Convert day/ month
to Julian day.
Impact depends on
timing of pesticide
application; model
outputs less sensitive
to an application
window than a single
date.
Vary from year to year
based on weather (rain,
temperature/ growing
degree days), soil
moisture content.
plant_end
(SAM)
Julian day for last
date of most active
crop planting
window
day number
(julian)
USDA Usual Planting
and Harvesting Dates
(2006, 2007, 2010)
End of most active
planting date. Convert
day/ month to Julian
day.
Impact depends on
timing of pesticide
application; model
outputs less sensitive
to an application
window than a single
date.
Vary from year to year
based on weather (rain,
temperature/ growing
degree days), soil
moisture content.
plant_date
(SAM)
Julian day for
midpoint date of
most active crop
planting window
day number
(julian)
USDA Usual Planting
and Harvesting Dates
(2006, 2007, 2010)
Midpoint between
beginning and end of
most active planting
date. Convert day/
month to Julian day.
Impact depends on
timing of pesticide
application; model
outputs less sensitive
to an application
window than a single
date.
Vary from year to year
based on weather (rain,
temperature/ growing
degree days), soil
moisture content.
harvest_begin
(SAM)
Julian day for
beginning of most
active crop harvest
window
day number
(julian)
USDA Usual Planting and
Harvesting Dates (2006,
2007, 2010)
Beginning of most
active harvest date.
Convert day/ month
to Julian day.
Impact depends on
timing of pesticide
application.
Dictated by planting
date, time to maturity
for plant, weather
conditions.
harvest_end
(SAM)
Julian day for last
date of most active
crop harvest
window
day number
(julian)
USDA Usual Planting and
Harvesting Dates (2006,
2007, 2010)
End of most active
harvest date. Convert
day/ month to Julian
day.
Impact depends on
timing of pesticide
application.
Dictated by planting
date, time to maturity
for plant, weather
conditions.
28
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Parameter
Parameter
Units
Source
Extraction/ Derivation
Notes
Relationships/
name
Description
correlation
harvest_date
Julian day for
day number
USDA Usual Planting and
Midpoint between
Impact depends on
Dictated by planting
(PWC)
midpoint date of
(julian)
Harvesting Dates (2006,
beginning and end of
timing of pesticide
date, time to maturity
most active crop
2007, 2010)
most active harvest
application.
for plant, weather
harvest window
date. Convert day/
month to Julian day.
conditions.
emergence_be
Julian day for
day number
Estimated from USDA
Estimated from
Impact depends on
gin (SAM)
beginning of crop
(julian)
Usual Planting and
beginning planting
timing of pesticide
emergence
Harvesting Dates (2006,
date, in Julian days.
application.
window
2007, 2010)
emergence_en
Julian day for end
day number
Estimated from USDA
Estimated from
Impact depends on
d (SAM)
of crop emergence
(julian)
Usual Planting and
ending planting date,
timing of pesticide
window
Harvesting Dates (2006,
2007, 2010)
in Julian days.
application.
emergence_da
Julian day for
day number
Estimated from USDA
Estimated from
Impact depends on
te (PWC)
midpoint of crop
(julian)
Usual Planting and
midpoint planting
timing of pesticide
emergence
Harvesting Dates (2006,
date, in Julian days.
application.
window
2007, 2010)
bloom_begin
Julian day for
day number
USDA Usual Planting and
Where available
Impact depends on
(SAM)
beginning of
(julian)
Harvesting Dates (2006,
(primarily orchards,
timing of pesticide
bloom window, if
2007, 2010) or Crop
vegetables)
application.
relevant
Profiles
bloom_end
Julian day for end
day number
USDA Usual Planting and
Where available
Impact depends on
(SAM)
of bloom window,
(julian)
Harvesting Dates (2006,
(primarily orchards,
timing of pesticide
if relevant
2007, 2010) or Crop
Profiles
vegetables)
application.
maxcover_begi
Julian day for
day number
Estimated from USDA
Estimated from
Impact depends on
n (SAM)
beginning of
(julian)
Usual Planting and
beginning planting
timing of pesticide
maximum canopy
Harvesting Dates (2006,
date, in Julian days.
application.
cover window
2007, 2010)
maxcover end
Julian day for end
day number
Estimated from USDA
Estimated from
Impact depends on
(SAM)
of maximum
(julian)
Usual Planting and
ending planting date,
timing of pesticide
canopy cover
Harvesting Dates (2006,
in Julian days.
application.
window
2007, 2010)
29
-------
Parameter
Parameter
Units
Source
Extraction/ Derivation
Notes
Relationships/
name
Description
correlation
maxcover dat
Julian day for
day number
Estimated from USDA
Estimated from
Impact depends on
e (PWC)
midpoint of
(julian)
Usual Planting and
midpiont planting
timing of pesticide
maximum canopy
Harvesting Dates (2006,
date, in Julian days.
application.
cover window
2007, 2010)
Factors Used to Estimate Amount of Rainfall Reaching Ground from Canopy
crop_intercept
Maximum rainfall
cm
Table 5.4 in PRZM3
Link crop-related data
Used to define amount
Related to crop type.
interception
manual (Carousel et al,
to major crops in CDL
of rainfall intercepted
storage of crop
2005) for major crops.
general land cover
before reaching the
(cm)
classes
soil.
max cover
Maximum areal
%
Carousel et al (2005);
Link crop-related data
Used to define amount
Related to crop type.
coverage of
Young and Fry (2016)
to major crops in CDL
of rainfall intercepted
For most crops, 80-
canopy (%)
general land cover
classes
before reaching the
soil.
100% maximum.
Crop-specific Inputs for Irrigation
root_depth
Maximum active
cm
Table 3-4 of NEH 652
Average of depths
Defines depth for
Related to crop, soil
rooting depth of
Irrigation Guide (USDA
reported in tables;
activating irrigation.
depth.
crop (cm)
NRCS, 1997) and Table
11-3 of NEH 632
Sprinkler Irrigation
Guide (USDA NRCS,
2016)
adjust by root_zone_
max (lesser depth);
must be at least 0.5
cm less than soil
depth
irrigation_fract
Percent of crop
%
2013 Farm and Ranch
Table 35, Irrigation
Determines whether
Irrigation is triggered
ion
area irrigated (area
Irrigation Survey (USDA
Survey (USDA NASS,
irrigation will be
when >40% of the crop
to be designated)
NASS, 2014)
2014) - divide
irrigated acres by total
acres for crop
simulated.
acres in a state are
irrigated.
irrigation_type
Dominant
blank=none;
2013 Farm and Ranch
Table 36, Irrigation
irrigation type
over=over
canopy (e.g.,
spray, pivot);
under=below
canopy (e.g.,
furrow, flood)
Irrigation Survey (USDA
NASS, 2014)
Survey (USDA NASS,
2014) - compare over
canopy acres
(pressure, sprinkler) to
under canopy (gravity,
low flow)
30
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Parameter
Parameter
Units
Source
Extraction/ Derivation
Notes
Relationships/
name
Description
correlation
depletion_allo
Fraction of water
fraction
US DA NRCS National
Minimum depletion
wed
capacity depletion
allowed by crop
Engineering Handbook
(USDA NRCS, 1997,
2016).
fraction from NEH 652
Table 3-3;
default of 0.5.
leaching_frac
Extra fraction of
water added by
irrigation for
leaching
fraction
USDA NRCS National
Engineering Handbook
(USDA NRCS, 1997,
2016).
Default of 0.1.
lrrigation_rate
Maximum daily
irrigation rate
Cm/da
USDA NRCS National
Engineering Handbook
USDA NRCS, 2003),
Chapter 4: Hydrology.
Calculate based on
NRCS Curve Number
method
Crop-specific factors affecting erosion
usle_p
Universal soil loss
dimensionless
Ag Handbook 703;
Derive from slope,
Sensitivity analysis on
Derived from slope
agricultural
fraction
RUSLE data tables; Table
practice, using Table
soil erosion (RUSLE)
gradient, agricultural
practices factor (P
2, Section 2.3.2.4
2.
model inputs is
practice.
value)
needed. Expected to
have greater impact on
high-Koc chemicals, but
the extent has not been
tested.
usle_c_cov,
Universal soil loss
dimensionless
Ag Handbook 703;
Link crop-related data
Sensitivity analysis on
Unknown; related to
usle_c_fal
cover
fraction
RUSLE data tables; data
to major crops in CDL
soil erosion (RUSLE)
crop type, cultivation
management
provided by USDA in the
general land cover
model inputs is
practice.
factors for fallow
file Crop Input Data.xlsx
classes.
needed. Expected to
and crop cover (C
have greater impact on
value)
high-Koc chemicals, but
the extent has not been
tested.
31
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Table 5 - Weather inputs used in PWC and SAM.
Parameter in SAM
code
Parameter Name/
Description
Units
Source
Extraction/
Derivation
Notes
Relationships/
correlation
Each set of weather data are stored as a separate file identified by the WeatherlD number
month
Calendar month
mm
NOAACPC Unified
Rain Gauge Analysis
(US) and NOAA
NCEP/NCAR
Reanalysis (Global)
See Fry et al, 2016
day
Calendar day
dd
year
Calendar year
yyyy
precipitation
Precipitation, daily
total
cm/day
Precipitation in
relation to timing of
pesticide
application is an
important driver.
ET
Evapotranspiration
cm/da
Temperature
Air temperature at
2m
degrees (°) Celsius
WindSpeed
wind speed at 10 m
cm/s
SolarRadiation
Solar radiation flux
at surface
La/day (Sl)
Climate Factors Linked to Weather Station Grids
sfac
Snowmelt factor
(cm/C), used to
calculate snowmelt
rates in relation to
temperature.
cm/C
Table 3.6 in PRZM5
manual (Young and
Fry, 2014) gives
range in SFAC
related to forest
covers.
Use 0.36 for crops,
non-forested land
covers; 0.16 for
forest covers
Model sensitivity
has not been
evaluated.
rainfall
Rainfall distribution
region used to
calculation time of
concentration of
peak flow
Value
1 to 4
Figure 3.3 in PRZM5
manual (Young and
Fry, 2014), based on
US DA TR-55.
IREG assigned to
weather grids based
on rainfall
distribution map
(Figure 5)
Model sensitivity
has not been
evaluated. Broad
regions across
country.
anted
Min. depth from
which evaporation
is extracted during
fallow period (cm)
cm
10 cm for soil w/
limited drainage.
Figure 3.1 in PRZM5
manual (Young and
Fry, 2014) for free-
drainage soils.
Assigned midpoint
value from range in
Figure 6 for each
associated weather
grid
Model sensitivity
has not been
evaluated.
32
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Appendix B: Soil Input Fields Extracted from SSURGO
Soil data in the gridded version of SSURGO (gSSURGO) are in geodatabases that must be joined
to the spatial grids by keys (USDA NRCS SSS, 2018). Table 6 lists the fields and tables for the
input parameters extracted from SSURGO.
Table 6 - Soil Input Fields Extracted from SSURGO
SSURGO
Field
SSURGO
Table
SSURGO Field Name
PWC/SAM Input Name
Application
mukey
muaggatt,
component
Mapunit Key
soiljd
Link muaggatt, component
tables in SSURGO
muname
muaggatt
Mapunit Name
Used to identify soil for
PWC field scenarios
QA to identify type of missing
map units, PWC scenario name
slopegradwta
muaggatt
Slope gradient, weighted
average for map unit (%)
slope
Slope input
hydgrpdcd
muaggatt
Hydrologic Soil Group,
dominant for mukey
hydro_group
Used to derive runoff curve
number
cokey
component,
chorizon
Component Key
cokey
Link component, chorizon
tables; used in QA
comppct_r
component
Component % -
Representative Value
not in final scenario
inputs
% of component in map unit:
sort major components
majcompflag
component
Major Component flag
not in final scenario
inputs
Identify major components in
the map unit (flag = Yes)
hydgrp
component
Hydrologic Soil Group for
the component
hydro_group
(alternate)
Alternate hydrologic soil group
input if hydgrpdcd is missing
sloplenusle_r
component
Slope Length USLE -
Representative Value
slopejength
Soil input to determine USLE LS
value (along with slope)
hzdept_r
chorizon
Top Depth -
Representative Value
not in final scenario
inputs
Sort horizon inputs by depth
hzthk_r
chorizon
Thickness of horizon -
Representative Value
thickness_#
Horizon input; depth-weighted
calculations for SAM
sandtotal_r
chorizon
Total Sand -
Representative Value
sand_#
Horizon input (for volatility),
soil grouping parameter
claytotal_r
chorizon
Total Clay-
Representative Value
clay_#
Horizon input (for volatility),
soil grouping parameter
om_r
chorizon
OM - Representative
Value
orgC = om_r/1.724
Derive horizon input for
organic C
dbthirdbar_r
chorizon
Db 0.33 bar H20 [bulk
density] - Representative
Value
bd_#
Horizon input for bulk density
(BD)
wthirdbar_r
chorizon
0.33 bar H20 [field
capacity] -
Representative Value
water_max_#
Horizon input for maximum
water capacity
wfifteenbar_r
chorizon
15 bar H20 [wilting point]
- Representative Value
water_min_#
Horizon input for minimum
water capacity
kwfact
chorizon
K-Factor Whole Soil
usle_k
Soil input (uppermost layer) for
soil erodibility
rootznemc
valu_fy2018
¦gdb
Maximum depth of the
root zone (cm), based on
soil properties
not in final scenario
inputs
Used, in conjunction with crop
rooting depth to define
root_depth
33
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Appendix C: Land Cover Classes and General Crop Groups from CDL
USDA NASS (2014-2018) accuracy assessments show that, on a state-by-state basis, the
Cropland Data layer (CDL) is relatively accurate (90% or greater) for states that are major
producers of major commodity crops, such as corn, soybeans, wheat, and cotton, which are
grown over extensive contiguous areas, and for which the USDA has independent data for
training and quality assurance analysis1. However, a high frequency of error for other crops
suggests that CDL may not be suitable for representing non-commodity minor crops. To address
this, OPP aggregated minor crops into broader crop groupings to reduce the level of uncertainty
in spatial footprints in individual crops.
These general crop groups should provide viable distinctions factors that impact the use of
pesticides and subsequent fate/transport from the field, such as land cover class as it impacts
runoff (curve number) or erosion (crop practice factor) and time of plant or harvest as it relates
to potential timing of pesticide application. Thus, it is more critical to distinguish between
vegetable crops and orchards than between apple and peach orchards or between tomatoes
and peppers.
OPP evaluated aggregating CDL categories into more general crop groupings similar to those
used by the U.S. Geological Survey (Baker and Capel, 2011) and the Generic Endangered Species
Task Force (Amos et al, 2010) to improve the accuracy and year-to-year matches.
The full error matrices are available by year on the NAS CDL website2. The accuracy assessment
looks at two types of accuracy (described in the NASS documentation for Accuracy
Assessment): how well the ground truth crop pixels are correctly identified by the CDL (called
"Producer's Accuracy") and how well the CDL pixels correctly match the underlying ground
truth (called "User's Accuracy"). "Omission error," associated with Producer's Accuracy, refers
to the frequency in which the ground truth pixels are missed in the validation data.
"Commission error," associated with User's Accuracy, refers to the frequency in which CDL
pixels misclassify the underlying ground truth pixels in the validation data.
Commodity crops, such as corn, cotton, and wheat, generally have a relatively high accuracy
because of the wealth of training data available, while small/minor crops often have a relatively
low accuracy because of insufficient training data. Field size can also have an impact on
accuracy, as more identification errors are likely to occur along field boundaries than in the
middle of a field with uniform crop coverage.
The CDL error matrices spreadsheets are used to determine whether the accuracy can be
improved when the individual crops are aggregated into general class groups. To determine
whether the accuracy for the overall general groupings are sufficiently improved to be viable as
1 Metadata that include error analysis are available for download at
httpsi//www,nass,usda.gov/Research and Science/Cropland/metadata/meta.php.
2 Available on the USDA NASS CDL site in the FAQ section at
https://www.nass.usda,gov/Research and Science/Cropland/sarsfaqs2,php#Sectionl 11,0
34
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a surrogate for the individual crops in that group, OPP aggregated both the CDL categories and
the underlying actual land covers into the respective groups.
OPP then evaluated whether some of the broader general crop groups (vegetables, orchards,
grains, ground fruit) could be divided into smaller crop groupings based on the CFR label crop
groups (Table 7). In most cases, the smaller label crop groupings are less accurate than the
broader general land cover classes. The key here is whether the change in accuracy is
sufficiently small to have a small impact on the overall accuracy.
In that analysis, only the orchard and vineyard group was further divided. The accuracies of
individual orchard and vineyard crops in CA (2016 CDL) varies from 2 to 90% (producer's
accuracy) while the aggregate orchard/vineyard group has a producer's accuracy of 92%.
Further analysis indicates that, at least in CA, grapes can be separated with minimal loss in
producer's accuracy (89%). Among the CDL Orchard classes, the resulting producer's accuracy
was decent for citrus (88%), pome fruit (83%), and tree nuts (90%), but poor for stone fruit
(34%). Because the stone fruit categories in CA tended to be mis-identified as tree nuts, a
lumped stone fruit/tree nut subgroup might be supported by the accuracy assessment.
Further refinements in the accuracy assessments could be made using NASS CDL confidence
layers3, which provide a confidence value for each pixel based on how well it fit into the
decision tree used to classify it, and/or the national cultivated layer, which is based on the most
recent five years of data.
The resulting general land cover class groupings (with numeric designation in parentheses) used
in scenario development are:
Corn (10): Corn and double-cropped classes with corn in the rotation.
Cotton (20): Cotton and double-cropped classes with cotton in the rotation.
Rice (30): Cultivated rice.
Soybeans (40): Soybeans and double-cropped classes with soybeans in the rotation.
Durum (22), Spring (23), and Winter (24) Wheat: Durum, spring, and winter wheat classes,
along with those double-cropped classes with wheat in the rotation.
Vegetables (60) and ground fruit (61): This pulls together the individual vegetable crops, which
have low accuracy rates in CDL. It also includes ground fruit which, while using different
cultivation patterns, aren't well distinguished from surrounding vegetable (or other) classes.
Grapes (71): Grapes/vineyards, originally grouped as "Orchards and Vineyards", but separated
after evaluating the most recent accuracy assessments for grapes and orchards.
Citrus (72): Oranges and other citrus, originally grouped as "Orchards and Vineyards", but
separated after evaluating the most recent accuracy assessments for grapes and orchards.
3 National confidence layers and national cultivated layers are available for download at
https://www.nass.usda,gov/Research and Science/Cropland/Release/index.php
35
-------
Other orchards (70): This includes all nut and fruit trees and other tree orchards that could not
be reasonably distinguished due to a relatively high error rate. A number of orchard areas are
often misidentified as pasture, grassland or pasture (presumably the grass lanes between tree
rows contribute more to the optical signal than do the trees), shrubland, or forest.
Other grains (80): This includes all small grain crops other than wheat.
Other row crops (90): Sunflower, peanuts, tobacco, sugar beets, and hops that could not be
reasonably distinguished due to a relatively high error rate.
Other crops (100): CDL classes - other crops, aquaculture, idle cropland - that don't fit in the
above groups. These are of minor extent in CDL.
Pasture/hay/forage (110): This includes specific hay crops, such as alfalfa, clover, and vetch,
and general pasture, hay, and forage classes. It also includes grassland, which may include
pasture land in some parts of the country.
Open (121), Low (122), Medium (123), and High (124) Intensity Developed categories were
kept separate because the intensity differences can be used to estimate turf area and relative
impervious surface area and curve number determination also depends on intensity of
development.
Forest (140): This merges deciduous, evergreen, and mixed forest classes, along with more
generic forest classes.
Shrubland (160).
Water (180) includes all water body types identified in CDL.
Woody (190) and herbaceous (195) wetlands were kept as separate groups because of
differences in curve number determinations.
Miscellaneous lands (200) include other land classes that don't fit in any of the above
groupings. These are of minor extent in CDL.
Table 7 lists the individual CDL class values and names, as reported in the yearly cropland data
layer spatial data. The general crop groups are described above. The curve number cover class
is used in combination with hydrologic soil groups to determine curve number (Appendix D).
The FIFRA label crop group will be used to link pesticide label specifications to the scenarios.
Table 7 - Crosswalk between CDL Classes and General Land Cover Classes.
CDL
Value
CDL Category
(name)
General CDL Class
(number)
Curve Number
Cover Class
CFR Label Crop Group
(USEPA,2018)
1
Corn
Corn (10)
Row Crop
Cereal Grains (15B)
2
Cotton
Cotton (20)
Row Crop
Oilseed (20C)
3
Rice
Rice (30)
Small Grain
Cereal Grains (15C)
4
Sorghum
Other grains (80)
Small Grain
Cereal Grains (15B)
5
Soybeans
Soybeans (40)
Row Crop
Legumes (6)
6
Sunflower
Other row crops (90)
Row Crop
Oilseed (20B)
10
Peanuts
Other row crops (90)
Row Crop
not listed
36
-------
c
Va
11
~12
"13
14
~21
22
~23^
24
~25
26
~27
28
~29
30
~31
32
~33^
34
^35
36
37
^38
39
~41
42
~43
44
~45
46
~47
^48
~49
~50
51
~52
53
~54
55
~56
CDL Category
(name)
General CDL Class
(number)
Curve Number
Cover Class
CFR Label Crop Group
(USEPA,2018)
Tobacco
Other row crops (90)
Row Crop
not listed
Sweet Corn
Vegetables (60)
Row Crop
Cereal Grains (15B)
Pop or Orn Corn
Vegetables (60)
Row Crop
Cereal Grains (15B)
Mint
Vegetables (60)
Row Crop
Herbs and Spices (25)
Barley
Other grains (80)
Small Grain
Cereal Grains (15A)
Durum Wheat
Wheat, Durum (22)
Small Grain
Cereal Grains (15A)
Spring Wheat
Wheat, Spring (23)
Small Grain
Cereal Grains (15A)
Winter Wheat
Wheat, Winter (24)
Small Grain
Cereal Grains (15A)
Other Small Grains
Other grains (80)
Small Grain
Cereal Grains (15A)
Dbl Crop WinWht/
Soybeans
Wheat (24) / Soybeans
(40)
Small Grain /
Row Crop
Cereal Grains (15A) / Legumes (6)
Rye
Other grains (80)
Small Grain
Cereal Grains (15A)
Oats
Other grains (80)
Small Grain
Cereal Grains (15A)
Millet
Other grains (80)
Small Grain
Cereal Grains (15B)
Speltz
Other grains (80)
Small Grain
Cereal Grains (15A)
Canola
Other grains (80)
Small Grain
Oilseed (20A)
Flaxseed
Other grains (80)
Small Grain
Oilseed (20A)
Safflower
Other grains (80)
Small Grain
Oilseed (20B)
Rape Seed
Other grains (80)
Small Grain
Oilseed (20A)
Mustard
Vegetables (60)
Row Crop
Vegetables, Brassica Leafy (4-
16 B)
Alfalfa
Pasture/hay/forage (110)
Close-seeded
legumes
Nongrass Animal Feeds (18)
Other Hay/Non-
Alfalfa
Pasture/hay/forage (110)
Pasture, grass,
range
Nongrass Animal Feeds (17)
Camelina
Buckwheat
Other grains (80)
Other grains (80)
Small Grain
Small Grain
Oilseed (20A)
Cereal Grains (15A)
Sugarbeets
Other row crops
Row Crop
Vegetables, Root and Tuber (1A)
Dry Beans
Vegetables (60)
Row Crop
Vegetables, Legume (6C)
Potatoes
Other Crops
Vegetables (60)
Other crops (100)
Row Crop
Row Crop
Vegetables, Root and Tuber (1C)
Mixed
Sugarcane
Other grains (80)
Small Grain
not listed
Sweet Potatoes
Vegetables (60)
Row Crop
Vegetables, Root and Tuber (ID)
Misc Vegs & Fruits
Watermelons
Vegetables (60)
Vegetables (60)
Row Crop
Row Crop
Mixed
Vegetables, Cucurbit (9A)
Onions
Vegetables (60)
Row Crop
Vegetables, Bulb (3-07A)
Cucumbers
Vegetables (60)
Row Crop
Vegetables, Cucurbit (9B)
Chick Peas
Vegetables (60)
Row Crop
Vegetables, Legume (6C)
Lentils
Vegetables (60)
Row Crop
Vegetables, Legume (6C)
Peas
Vegetables (60)
Row Crop
Vegetables, Legume (6A, 6B, 6C)
Tomatoes
Vegetables (60)
Row Crop
Vegetables, Fruiting (8-10A)
Caneberries
Ground fruit (61)
Row Crop
Berries and Small Fruit (13-07A)
Hops
Other row crops (90)
Row Crop
not listed
37
-------
CDL
CDL Category
General CDL Class
Curve Number
CFR Label Crop Group
Value
(name)
(number)
Cover Class
(USEPA,2018)
57
Herbs
Vegetables (60)
Row Crop
Herbs and Spices (19)
58
Clover/Wildflowers
Other crops (100)
Close-seeded
legumes
Nongrass Animal Feeds (18)
59
Sod/Grass Seed
Other crops (100)
Pasture, grass,
range
not listed
60
Switchgrass
Pasture/hay/forage (110)
Pasture, grass,
range
Nongrass Animal Feeds (17)
61
Fallow/Idle Cropland
Other crops (100)
Fallow
not listed
62
Pasture/Grass
Pasture/hay/forage (110)
Pasture, grass,
range
Nongrass Animal Feeds (17)
63
Forest
Forest (140)
Woods
64
Shrubland
Shrubland (160)
Brush-weed-
grass
65
Barren
Miscellaneous land (200)
Fallow
66
Cherries
Other Orchards (70)
Woods-grass
Stone Fruit (12-12A)
67
Peaches
Other Orchards (70)
Woods-grass
Stone Fruit (12-12B)
68
Apples
Other Orchards (70)
Woods-grass
Pome Fruit (11-10')
69
Grapes
Grapes (71)
Woods-grass
Berries and Small Fruit (13-07F)
70
Christmas Trees
Other trees (75)
Woods-grass
71
Other Tree Crops
Other Orchards (70)
Woods-grass
Mixed
72
Citrus
Citrus (72)
Woods-grass
Citrus Fruits (10-10A)
73
unidentified
Miscellaneous land (200)
Fallow
74
Pecans
Other Orchards (70)
Woods-grass
Tree Nuts (14-12)
75
Almonds
Other Orchards (70)
Woods-grass
Tree Nuts (14-12)
76
Walnuts
Other Orchards (70)
Woods-grass
Tree Nuts (14-12)
77
Pears
Other Orchards (70)
Woods-grass
Pome Fruit (11-10')
81
Clouds/No Data
Miscellaneous land (200)
Fallow
82
Developed
Developed-med (123)
Residential-
1/8 ac
83
Water
Water (180)
na
87
Wetlands
Wetlands -herbaceous
(195)
Meadow
88
Nonag/Undefined
Miscellaneous land (200)
Fallow
92
Aquaculture
Other crops (100)
na
111
Open Water
Water (180)
na
112
Perennial Ice/Snow
Miscellaneous land (200)
Fallow
121
Developed/Open
Space
Developed-open (121)
Developed
open space
122
Developed/Low
Intensity
Developed-low (122)
Residential-
1/3 ac
123
Developed/Med
Intensity
Developed-med (123)
Residential-
1/8 ac
124
Developed/High
Intensity
Developed-high (124)
Urban
131
Barren
Miscellaneous land (200)
Fallow
38
-------
CDL
CDL Category
General CDL Class
Curve Number
CFR Label Crop Group
Value
(name)
(number)
Cover Class
(USEPA,2018)
141
Deciduous Forest
Forest (140)
Woods
142
Evergreen Forest
Forest (140)
Woods
143
Mixed Forest
Forest (140)
Woods
152
Shrubland
Shrubland (160)
Brush-weed-
grass
176
Grassland/Pasture
Pasture/hay/forage (110)
Pasture, grass,
range
Nongrass Animal Feeds (17)
190
Woody Wetlands
Wetlands-woods (190)
Woods
195
Herbaceous
Wetlands
Wetlands -herbaceous
(195)
Meadow
204
Pistachios
Other Orchards (70)
Woods-grass
Tree Nuts (14-12)
205
Triticale
Other grains (80)
Small Grain
Cereal Grains (15A)
206
Carrots
Vegetables (60)
Row Crop
Vegetables, Root and Tuber (1A)
207
Asparagus
Vegetables (60)
Row Crop
Vegetables, Stem and Stalk (22A)
208
Garlic
Vegetables (60)
Row Crop
Vegetables, Bulb (3-07A)
209
Cantaloupes
Vegetables (60)
Row Crop
Vegetables, Cucurbit (9A)
210
Prunes
Other Orchards (70)
Woods-grass
Stone Fruit (12-12C)
211
Olives
Other Orchards (70)
Woods-grass
Tropical fruit, edible peel (23A)
212
Oranges
Citrus (72)
Woods-grass
Citrus Fruits (10-10A)
213
Honeydew Melons
Vegetables (60)
Row Crop
Vegetables, Cucurbit (9A)
214
Broccoli
Vegetables (60)
Row Crop
Vegetables, Brassica (5-161)
216
Peppers
Vegetables (60)
Row Crop
Vegetables, Fruiting (8-10B, 8-
10C)
217
Pomegranates
Other Orchards (70)
Woods-grass
Fruit (24B)
218
Nectarines
Other Orchards (70)
Woods-grass
Stone Fruit (12-12B)
219
Greens
Vegetables (60)
Row Crop
Vegetables, Leafy (4-16A)
220
Plums
Other Orchards (70)
Woods-grass
Stone Fruit (12-12C)
221
Strawberries
Ground fruit (61)
Row Crop
Berries and Small Fruit (13-07G)
222
Squash
Vegetables (60)
Row Crop
Vegetables, Cucurbit (9B)
223
Apricots
Other Orchards (70)
Woods-grass
Stone Fruit (12-12C)
224
Vetch
Pasture/hay/forage (110)
Close-seeded
legumes
Nongrass Animal Feeds (18)
225
Dbl Crop WinWht/
Corn
Wheat (24)/Corn (10)
Small Grain/
Row Crop
Cereal Grains (15B / 15A)
226
Dbl Crop Oats/Corn
Other Grains (80) / Corn
(10)
Small Grain/
Row Crop
Cereal Grains (15B / 15A)
227
Lettuce
Vegetables (60)
Row Crop
Vegetables, Leafy (4-16A)
229
Pumpkins
Vegetables (60)
Row Crop
Vegetables, Cucurbit (9B)
230
Dbl Crop Lettuce/
Wheat/Vegetables
Small Grain/
Vegetables, Leafy (4-16A) /
Durum Wht
Row Crop
Cereal Grains (15A)
231
Dbl Crop Lettuce/
Cantaloupe
Vegetables (60)
Row Crop
Vegetables, Leafy (4-16A) /
Vegetables, Cucurbit (9A)
232
Dbl Crop Lettuce/
Vegetables (60) / Cotton
Row Crop /
Vegetables, Leafy (4-16A) /
Cotton
(20)
Row Crop
Oilseed (20C)
39
-------
CDL
Value
CDL Category
(name)
General CDL Class
(number)
Curve Number
Cover Class
CFR Label Crop Group
(USEPA,2018)
233
Dbl Crop Lettuce/
Barley
Vegetables (60)
Row Crop
Vegetables, Leafy (4-16A) /
Cereal Grains (15A)
234
Dbl Crop Durum
Wht/Sorghum
Wheat (22) / Other Grains
(80)
Small Grain /
Small Grain
Cereal Grains (15A/15B)
235
Dbl Crop Barley/
Sorghum
Other grains (80)/Other
grains (80)
Small Grain /
Small Grain
Cereal Grains (15A/15B)
236
Dbl Crop WinWht/
Sorghum
Wheat (24) / Other Grains
(80)
Small Grain /
Small Grain
Cereal Grains (15A/15B)
237
Dbl Crop Barley/Corn
Other Grains (80) / Corn
(10)
Small Grain/
Row Crop
Cereal Grains (15A/ 15B)
238
Dbl Crop WinWht/
Cotton
Wheat (24) / Cotton (20)
Small Grain/
Row Crop
Cereal Grains (15A) / Oilseed
(20C)
239
Dbl Crop Soybeans/
Cotton
Soybeans (40) / Cotton
(20)
Row Crop /
Row Crop
Legumes (6) / Oilseed (20C)
240
Dbl Crop
Soybeans/Oats
Soybeans (40) / Other
Grains (80)
Row Crop /
Small Grain
Legumes (6) / Cereal Grains (15A)
241
Dbl Crop Corn/
Soybeans
Corn (10) / Soybeans (20)
Row Crop /
Row Crop
Cereal Grains (15B) / Legumes (6)
242
Blueberries
Ground fruit (61)
Row Crop
Berries and Small Fruit (13-07B,
13-07G)
243
Cabbage
Vegetables (60)
Row Crop
Vegetables, Brassica (5-16')
244
Cauliflower
Vegetables (60)
Row Crop
Vegetables, Brassica (5-16')
245
Celery
Vegetables (60)
Row Crop
Vegetables, Stem and Stalk (22B)
246
Radishes
Vegetables (60)
Row Crop
Vegetables, Root and Tuber (1A)
247
Turnips
Vegetables (60)
Row Crop
Vegetables, Root and Tuber (1A)
248
Eggplants
Vegetables (60)
Row Crop
Vegetables, Fruiting (8-10B, 8-
10C)
249
Gourds
Vegetables (60)
Row Crop
Vegetables, Cucurbit (9B)
250
Cranberries
Ground fruit (61)
Row Crop
Berries and Small Fruit (13-07H)
254
Dbl Crop
Barley/Soybeans
Other Grains (80) /
Soybeans (40)
Small Grain/
Row Crop
Cereal Grains (15A) / Legumes (6)
40
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Appendix D: Determining the Runoff Curve Number
Runoff curve numbers are based on hydrologic soil group, general land cover class,
agricultural/land use practices, and general hydrologic condition (USDA NRCS 2004a, 2008). Use
the following steps to assign a curve number for both full canopy cover (cn_cov) and post-
harvest (cn_fal) conditions:
1. Identify the hydrologic soil group (HSG) for the dominant component in the soil mapping
unit. If the HSG is a combination (i.e., C/D, B/C), select the most runoff-prone hydrologic
value (runoff vulnerability follows the order D > C > B > A).
2. Determine Curve Number Land Cover class from the crosswalk with USDA Cropland Data
Layer (CDL) categories (Table 7 in Appendix C).
3. Determine the Curve Number under full canopy cover (cn_cov) using Table 9 below.
a. Unless otherwise specified, assume crops are planted on contour with crop
residue left after harvest (C+CR) for treatment practice in Table 9.
b. Unless otherwise specified, assume good or fair hydrologic conditions.
c. The curve number cover classes for the developed classes are based on the
percent of impervious surface (50-75% grass cover for open; 30% impervious for
low; 65% impervious for medium; 85% impervious for high).
4. Determine the curve number for fallow conditions (cn_fal)
a. For the crop/agricultural land cover classes, use the curve numbers for the crop
residue cover under fallow in Table 8.
b. For the woods, meadow, brush, and developed/residential classes, use the same
curve number as for the cn_ag
Table 8 - Curve Number Guidance based on NRCS TR-55 Methodology (USDA NRCS, 2008).
Cover Type
Treatment or Practice
Hydrol. Cond.
HSG A
HSG B
HSG C
HSG D
Fallow
Bare Soil
—
77
86
91
94
Crop residue cover (CR)
Poor
76
85
90
93
Good
74
83
88
90
Row crops
Straight Row (SR)
Poor
72
81
88
91
Good
67
78
85
89
SR + CR
Poor
71
80
87
90
Good
64
75
82
85
Contoured (C)
Poor
70
79
84
88
Good
65
75
82
86
C+CR
Poor
69
78
83
87
Good
64
74
81
85
Contoured and terraced (C&T)
Poor
66
74
80
82
Good
62
71
78
81
C&T + CR
Poor
65
73
79
81
Good
61
70
77
80
Small
grain
SR
Poor
65
76
84
88
Good
63
75
83
87
SR + CR
Poor
64
75
83
86
Good
60
72
80
84
C
Poor
63
74
82
85
41
-------
Cover Type
Treatment or Practice
Hydrol. Cond.
HSG A
HSG B
HSG C
HSG D
Good
61
73
81
84
C+ CR
Poor
62
73
81
84
Good
60
72
80
83
C&T
Poor
61
72
79
82
Good
59
70
78
81
C&T + CR
Poor
60
71
78
81
Good
58
69
77
80
Close-seeded or
broadcast legumes
or rotation meadow
SR
Poor
66
77
85
89
Good
58
72
81
85
C
Poor
64
75
83
85
Good
55
69
78
83
C&T
Poor
63
73
80
83
Good
51
67
76
80
Pasture, grassland, or range;
continuous forage for grazing
Poor
68
79
86
89
Fair
49
69
79
84
Good
39
61
74
80
Meadow - continuous grass, protected from grazing;
generally mowed for hay
—
30
58
71
78
Brush - brush-weed-grass mixture w/ brush as the major
element
Poor
48
67
77
83
Fair
35
56
70
77
Good
30
48
65
73
Woods - grass combination (orchard or tree farm)
(based on 50% woods, 50% grass)
Poor
57
73
82
86
Fair
43
65
76
82
Good
32
58
72
79
Woods
Poor
45
66
77
83
Fair
36
60
73
79
Good
30
55
70
77
Farmsteads - buildings, lanes, driveways, surrounding lots
—
59
74
82
86
Developed Open Space
<50% grass cover
Poor
68
79
86
89
50 - 75% grass cover
Fair
49
69
79
84
>75% grass cover
Good
39
61
74
80
Impervious: paved lots, roofs, driveways, etc
98
98
98
98
Streets & Roads
Paved curbs, storm sewers
98
98
98
98
Paved, open ditches, incl ROW
83
89
92
93
Gravel, incl ROW
76
85
89
91
Dirt, incl ROW
72
82
87
89
Urban
Commercial, business
85''-.. imperv
89
92
94
95
Industrial
72'.'.'. imperv
81
88
91
93
Residential
1/8 ac lots or less
(townhouse)
65% imperv
77
85
90
92
1/4 ac lots
38"'., imperv
61
75
83
87
1/3 ac lots
30''-.. imperv
57
72
81
86
1/2 ac lots
25% imperv
54
70
80
85
1 ac lots
20''-.. imperv
51
68
79
84
2 ac lots
12'.''" imperv
46
65
77
82
42
-------
Appendix E: Estimating Crop Milestone Dates
Both PWC (PRZM module) and SAM require crop emergence, maturity and harvest dates to
characterize crop canopy and root growth. For annual field crops, OPP selected state-level
"most active" planting and harvesting dates to parameterize the PRZM crop growth module
(USDA NASS, 2010).
In some instances, the crop will occur in a state, but not in sufficient acreage to have a reported
date range (USDA NASS, 2006, 2007, and 2010). To address these data gaps, OPP combined the
USDA Cropland Data Layer, Census of Agriculture, and Usual Plant and Harvest Dates for Field
Crops to estimate missing crop growth dates.
Data Sources
The USDA, NASS Cropland Data Layer (CDL) provides planted acreage estimates for major
commodities and digital, crop-specific, 30-meter geo-referenced output products. Data were
accessed through the USDA NASS Cropland internet portal (USDA NASS, 2014-2018).
The Census of Agriculture (CoA) is a complete count of U.S. farms and ranches and provides the
only source of uniform, comprehensive and impartial agricultural data for every county in the
nation. The CoA provides more crop-specific classifications and acreage resolution than CDL.
Data were accessed through the USDA NASS Quick Stats internet portal (USDA NASS, 2012).
The USDA Field Crops: Usual Plant and Harvest Dates (UPHD) identifies state-level periods when
annual crops are planted and harvested based on 20 years of crop progress data. Beginning
dates indicate when planting or harvesting is about 5 percent complete and ending dates when
operations are about 95 percent complete. The "most active" range indicates when between 15
and 85 percent of the crop is planted or harvested (USDA NASS, 2010).
For pesticide registration purposes and establishing residue tolerances, USEPA organizes
agricultural commodities into crop groups, that are botanically and agronomically related. OPP
accounted for the on-going multi-year joint project with NAFTA partners in Canada and Mexico
to revise the existing crop groups in 40 CFR 180.41 (USEPA, 2018).
Estimating Surrogate Dates for Annual Field Crops
The CDL provides the spatial footprint for the field scenarios. The area of individual CDL
categories and associated general land cover groups and CFR label groups (see Appendix C) are
tallied by state to identify the occurrence of the crop/group in the state. This is combined with
state-level acreage for each crop from the CoA and state-wide plant and harvest dates from the
UPHD.
OPP used both the CDL and the CoA to confirm that the crop was present in the state. If the
UPHD listed plant and harvest dates are used for that crop in that state, these dates were used
to estimate crop milestone dates. If the UPHD listed no dates for the crop in a state where CDL
and CoA confirm that it occurs, OPP estimated surrogate plant and harvest dates.
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Where the UPHD were missing for some crops in some states, OPP identified surrogate dates
for those states in which the crop occurs but has no reported dates, in the following order of
availability:
1. Same crop in an adjacent state
2. Another crop in the same general CDL class in the same state. If more than 1 crop
within the general CDL class has dates, select the one that is most similar in terms of
agronomic practices
The goal is to ensure that crop milestone dates are available for each of the general scenarios
being developed. The dates and estimation methods are documented in the accompanying
Crop Dates.xlsx file.
Estimating Dates for Double Cropping Categories
The CDL data includes double cropping categories. While USDA NASS focuses largely on
summer crops, ground truthing identifies whether a single or double crop was planted in a
given year. CDL captures the major crop rotations/patterns, but not winter fruits and
vegetables (USDA NASS, 2010-2017). Since published UPHD for double crops were not
reported. EFED retained the harvest dates of the second crop but used the harvest dates of the
initial crop as the surrogate planting date for the unpublished second crop. The dates and
estimation methods are documented in the accompanying Crop Dates.xlsx file.
Deriving Emergence, Maturity and Harvest Dates for Annual Field crops
The plant and harvest dates were used to derive the emergence, maturity and harvest dates.
The average planting and harvest dates were calculated for each state. The date of emergence
was estimated as 7 days after the average planting date for each state. The maturity was
calculated as the midpoint between the average planting and harvest dates for each state.
Estimating Dates for Perennial Cropping Categories
Cropping milestone dates for perennial crops - primarily pasture/hay/forage crops, tree
orchards, and vineyards - need to be defined in terms of equivalent stages. Emergence for
perennials reflects the beginning of active growth, such as new growth in hay/forage crops or
the onset of leaf bud in trees. This can be defined based on the last frost/freeze day in the
spring, or to the beginning of bloom in orchard trees. Full, or maximum, canopy cover may be
tied to timing of growth between cuttings for hay/forage or to full leaf-out in trees. Harvest,
which represents the time of foliage/canopy removal, would be tied to times of actual harvest
for hay/forage crops but would be better represented by leaf drop, rather than nut/fruit
harvest, for orchards. Methods for estimating these equivalent dates for both
pasture/hay/forage crops and for fruit/nut orchards and vineyards are documented in the
accompanying Crop Dates.xlsx file.
Selecting Milestone Dates for General Scenarios
While crop dates are initially developed for individual CDL crops (where available), scenarios
will be developed for major crops or general crop classes described in Appendix C. The crop
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milestone dates will be aggregated by general crop class/group. If more than one crop within
the general scenario has dates, OPP evaluated the range in active planting and harvest dates to
determine whether distinct differences in timing are evident (i.e., midpoints fall in different
seasons).
1. If the active planting and/or harvest date ranges largely overlap, select the crop with
the greatest acreage in the state to represent the general group.
2. If distinct differences occur in planting and harvest dates, determine whether a
separate scenario should be developed.
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Appendix F. Aggregating Soil Horizons and Map Units for SAM
For the Spatial Aquatic Model (SAM), OPP made two modifications in processing the soil inputs
in order to reduce computer processing and storage demands needed to run national-scale risk
assessments on a routine basis. The modifications resulted in minimal impact on estimated
pesticide concentrations in water (USEPA OPP, 2015). The SAP panel noted that, while the
modifications are defensible and do not impact model outputs, OPP should remain open to
technological advances that may negate the need for condensing data in the future (FIFRA SAP,
2015).
Depth Weighting
PWC scenarios use data for individual horizons, leaving a variable number of columns for each
soil map unit, based on the number of horizons present. For SAM, four standardized layers of
fixed depth intervals - 0-5, 5-20, 20-50, and 50-100 cm - are used for processing millions of soil
map unit inputs. Properties for the 0-5 cm layer are based on the surface horizon data in
SSURGO. Properties for the remaining three layers are depth-weighted averages of the soil
properties by horizon (see Figure 6 for illustration):
SJayer = sum(S hor x thickness of horizon)
total thickness of layer
Where
SJayer = soil property (orgC, bd, fc, wp, s, c, ph) value calculated for the layers used for
SAM inputs (0-5, 5-20, 20-50, 50-100 cm)
S_hor= soil property (orgC, bd, fc, wp, s, c, ph) value for the soil horizon identified by
SSURGO
Horizon
Depth/
Layer
n
A1 8
5
o
A2
20
25
Calculating depth-weighted values
for soil property S:
B
50
S_5 = S_A1
75
C
S 20 = (S ai x 3cm) + (S_A2X 12cm)
15 cm
S 50 = (S A2 x 5cm) + (S bx 25cm)
30 cm
c
100
115
L_^
S loo = (S b x 25cm) + (S c x 25cm)
50 cm
Figure 5: Illustration for calculating depth-weighted values for soil horizon data, standardized to four
layers (0-5, 5-20, 20-50, 50-100 cm).
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Aggregating Soil Map Units
For the 2015 SAP, USEPA OPP (2015) explored grouping individual soil map units into classes
based on USDA's soil water quality index (WQI) values for hydrologic soil group, slope, soil
erodibility and surface organic matter content (Lai and McKinney, 2012). Initial analyses found
no impacts on estimated pesticide concentrations for hydrologic soil groups and slope classes;
no appreciable impacts for organic matter classes >2%, and limited impacts on high-sorbing
pesticides for soil erodibility classes. The SAP Panel noted that the aggregated soil classes were
a viable option for reducing the number of scenarios in a national model, but suggested that
OPP reconsider those simplifying assumptions if the driving rationale is current
storage/computational limitations (FIFRA SAP, 2015).
OPP revisited the soil grouping classes/criteria, exploring the relationships between
independent soil variables (e.g., organic carbon content, sand, clay) in the 0-5 cm and 5-20 cm
layers. Properties for the surface (0-5 cm) layer had the greatest impacts on runoff estimates.
Correlations were evident between organic C content and bulk density and between clay and
sand content and minimum/maximum water capacity. The revised soil groupings are based on
hydrologic soil group, slope, surface organic C content, sand, and clay content for the surface
(0-5 cm) layer (Table 9).
Table 9 - Soil Parameter Classes Used to Derive Aggregated Soil Groups.
Soil Parameter
No. Classes
Class Breaks (Aggregated Soil ID)
Hydrologic soil
group
7
A, AD, B, BD, C, CD, D
Slope (%)
6
0-2 (sll), 2-5 (sl2), 5-10 (sl3), 10-15 (sl4), 15-25 (sl5), >25 (sl6)
Organic C (%)
11
0-0.5 (ol), 0.5-1 (o2), 1-1.5 (o3), 1.5-2 (o4), 2-3 (o5), 3-4 (06), 4-5 (o7),
5-6 (08), 6-12 (o9), 12-20 (olO), >20 (oil)
Sand content (%)
10
0-10 (si), 10-20 (s2), 20-30 (s3), 30-40 (s4), 40-50 (s5), 50-60 (s6),
60-70 (s7), 70-80 (s8), 80-90 (s9), 90-100 (slO)
Clay content (%)
10
0-5 (cl), 5-10 (c2), 10-15 (c3), 15-20 (c4), 20-25 (c5), 25-30 (c6),
30-40 (c7), 40-60 (c8), 60-80 (c9), 80-100 (clO)
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