3

A. EPA's spatial-product analysis of whether a species' range or critical habitat

overlaps with product use relies on nonpublic data.

1. It is not possible to recreate the co-occurrence analysis found in Attachment 1-6 because:

a. Distribution data are not provided for all listed species and designated critical

habitats. Where provided in species attribute tables, distribution data consists

of state and county-level data but it is inconsistent. Species range and critical

habitat designation geospatial files are needed to properly generate a cooccurrence analysis.

EPA Response: Publicly available critical habitat files can be downloaded from the U.S Fish

and Wildlife Service’s ECOS website or a description can be found in the federal register 

notice designating the critical habitat. (https://ecos.fws.gov/ecp/report/table/criticalhabitat.html). In collaboration with the Services, EPA is exploring options for making all of the 

species and critical habitat geospatial files available. We are committed to finding a solution 

that allows these data to be publically available; however, we do not currently have an 

estimated timeframe for their release.

b. Although methodology is provided on how the action area was derived,

duplication of the spatial footprint is difficult due to interpretation of

commodities, data items, and domains from the publically available

National Agricultural Statistics Census of Agriculture 2012 (NASS) data. 

To properly duplicate this process and identify the uses that need to be

modeled for each HUC 2 Region, the algorithm in Excel developed to

automatically populate the table found in Appendix 1-6, data provided by

Steve Lennartz as mentioned in footnote 3 in Appendix 1-6, as well as

further description of how the agri cultural classes were further refined by

comparing county level NASS CoA acreage reports to county level CDL

acreages (from Section 1.4.1.1.a and Attachment 1-3) are needed.

EPA Response: NASS Census of Agriculture commodities on acres harvested were obtained for 

the 2012 census period and consolidated into normalized tables. The online QuickStats 

application was used at the county level for acres harvested (https://quickstats.nass.usda.gov/). 

A cross-walk of the relationship between 73 different crops identified in the 2012 NASS Census 

of Agriculture with the 11 General Class Cropland Data Layer (CDL) categories is provided in 

Table 1 of Appendix A. A summary table at the county level was then made which totaled the 

acreage of each individual NASS record for the crop within the CDL general class. From this, a 

cross-tab table was made showing the total acreage value at the intersection of a given Federal 

Information Processing Standard (FIPS)/county with the General Class. Counties where the 

acreage total was withheld to protect farmer confidentiality were assigned the value of -1 in the 

compiled data and this was folded into the total sum. Most of the data processing was 

performed via a series of Structured Query Language (SQL) queries in MS Access 2013. For 

details on the specific queries run see Appendix A.

Processing CDL Rasters

The overall methodology for processing the Cropland Data Layer is thoroughly described in, 

“ATTACHMENT 1-3: Method for Establishing the Use Footprint (DOCX)”. EPA implemented this 

method using a series of python scripts (see APPENDIX B in this response).
4

c. Golf course turf is listed as a chlorpyrifos use in Appendix 1-3 and overlap data

are provided in the "Range Overlap" tab in Attachment 1-6 but the data source

used to spatially model this use are not provided in Attachment 1-3.

EPA Response: The Golf course use layer is a proprietary dataset provided by the FIFRA 

Endangered Species Task Force (FESTF). The results of the analysis were provided; however, the 

data and its sources are protected as Confidential Business Information (CBI). 

 

2. The u pdates made to Attachment 1-10 since the initial December, 2015 document

release are not clear; the HUC Assignments tab does not show HUC information and

does not list only species for which HUC info has changed and the Species Updates Bin

worksheet shows 104 unique species (the README file indicates bin assignments were

updated for 40 species) and does not contain bin information so it is not clear what this

worksheet represents.

EPA Response: The HUC Assignments tab identifies the spatial file that was used to place the 

species into the HUC. All HUC assignments were checked and updated between the December 

2015 and April releases of the draft BEs using the refined species ranges. The HUCs for each 

species can be viewed in the Bins_20160324 tab. Additional information on the different tabs 

can be found in the ReadMe tab. 

The lists were provided to identify the species where updates occurred since the release of the 

original table in December 2015. The bins used in the final analysis can be found in the 

Bins_20160324 tab. The numbers in the ReadMe tab were inadvertently not updated to 

represent the updated table in the April 2016 release (there were 93 updated HUCs, and 123 

updated species). This will be corrected in the final BEs.

B. EPA failed to disclose multiple data files used for its Effects Characterization,

providing an insufficient opportunity to comment on EPA's analysis.

1. Chapter 2, Appendix 2-6, Attachment 1-5, and Attachment 1-22 (All three BEs): EPA

does not provide reference to the location of the Data Array Buil der v.1.0 tool that was

used to generate data arrays. Similarly, EPA does not provide reference to the location

of the SSD tool used to generate SSDs. In Attachment 1-5, it is stated that the tool

"will eventually be made publicly available, along with a user's guide". The location of

these tools was made available after communicating with the EPA contact for

malathion to request the data for the terrestrial EECs (email from Steven Snyderman to

Sara Rodney dated April 11, 2016). To date, the location of this webpage has yet to be

referenced in the BEs or corresponding attachments.

EPA Response: Tools to create SSDs and Data Arrays are provided in the Provisional Models link 

at https://www.epa.gov/endangered-species/provisional-models-endangered-species-pesticideassessments. Specific references to the website will be provided in the final BE.

2. Attachment 4-1 (TerrWoE and AquaWoE Matrices -tool methodology and

description), page A 1-1 (All three BEs): In the introduction to Attachment 4-1 EPA

states that "each individual species spreadsheet was populated by li nking root
5

spreadsheet files to the individual species files. A database was constructed in the root

data files that can be updated and maintained on a regular basis, including altering

species information as well as changi ng the chemical of concern and its relevant

chemical and toxicity properties and geospatial use data." EPA goes on to describe the

file structure and purpose of each "root file" in their WoE assessment. Nowhere,

however does EPA provide the location where these files can be found.

EPA continues to state that the aquatic and terrestrial WoE and "species template"

files require four root files to populate, make comparisons of relevant estimated

exposure concentrations (EECs) to thresholds, and make corresponding weighted risk

and confidence calls." It would appear that these files are extremely pertinent to the

transparency of EPA's assessment in terms of understanding the exact comparisons of 

EEC values to effects threshold values. As such, EPA should provide these "root

files" for the purposes of review.

EPA Response: Fully parameterized individual root files for each chemical are provided in the 

Provisional Models page under the Weight of Evidence Tools section at 

https://www.epa.gov/endangered-species/provisional-models-endangered-species-pesticideassessments#woe. A link to a zip file is presented for each chemical. The zip file includes 

parameterized TEDtool files (base and alt, which provide both dietary and dose-based EECs for 

all terrestrial species), TerrWoE, AquaWoE, data input template and species template files which 

can be used to generate weight of evidence matrices for any applicable species. Specific 

references within Attachment 4-1 to the website will be provided in the final BE.

C. EPA's Problem Formulation relies on unverifiable data and incorrect or missing

cross-references.

1. Chapter 1 -Section 1.4.1.2. EPA states that "species ranges were provided in the form

of a GIS spatial file to run the co-occurrence analysis (see Attachment 1-6 for details)".

However, Attachment 1-6 was still under development when the Draft BE was released

in December 2015, and as such, EPA's co-occurrence analysis could not be verified

until the revised BE was made available in April 2016.

EPA Response: None needed.

2. Chapter 1 -Section 1.4.1.3. EPA states that the results of their co-occurrence analysis

are presented i n Section 1 .4.2.2.e. This reference is incorrect and refers to the

Mixtures Analysis section. The results of the co-occurrence analysis are presented in

Attachment 1-6 and Chapter 4, both of which were only made available in April 2016.

EPA Response: This reference will be corrected in the final BEs. It will be changed to, “See 

Attachment 1-6 and Chapter 4 for the results of the co-occurrence analysis”

3. Chapter 1 -Problem, Section 1.4.2.2.c. EPA gives vague descriptions of what

primary constituent elements (PCEs) and physical and biological features (PBFs) will

be considered when assessing effects to designated critical habitat. No references are 

provided for the PCEs/PBFs considered for individual species in this assessment. 

Some information on PCEs is available in Attachments 1-1 1 through 1-21, however

several updates, including the addition of new species profile information, have been
6

made to Attachments 1-11, 1-12, 1-20, and 1-21 since the draft BEs were originally

released in December 2015.

EPA Response: As stated in Section 1.4.2.2.c, “(i)n many cases, EPA anticipates that the 

assessment endpoints used to evaluate the potential for direct and indirect effects to listed 

species are equivalent to those used to evaluate potential effects to PCEs for a given designated 

critical habitat.” And as stated in the comment, information on specific PCEs are provided in the 

taxon-specific attachments (Attachments 1-11 through 1-21).

4. Chapter 1 -Section 1.4.2.2.e. A qualitative evaluation of mi xtures based on

environmental monitoring data, formulated products and mixes was conducted assuming

additive toxicity, unless available data indicate that antagonism or synergism is likely. 

EPA states that the risks of environmental mixtures containi ng malathion is discussed

further in Section 1.4.2.2.g, however this section does not exist in the Problem

Formulations for all three chemicals.

EPA Response: The Problem Formulations for all three chemicals refer to ‘Section 1.4.2.2.g”, 

however, none of the BEs contain a ‘Section 1.4.2.2.g”. This will be corrected in the final BEs. It 

will be changed to, “Further discussion of the risks to environmental mixtures containing 

[SPECIFIC CHEMICAL] is discussed in APPENDIX 4-2.”

5. Attachment 1-1: Ecological Incidents, Page 1. EPA collected incident data from the

Incident Data System (IDS), Ecological Incident Information System (EIIS) and

Avian Incident Monitoring System (AIMS). However, the AIMS database is

currently inaccessible through the American Bird Conservancy's website and has

been for some time.

EPA Response: EPA does have access to all of the incident data described in the BEs 

[including the data available in the Avian Incident Monitoring System (AIMS)]. These data 

were considered in the BEs. Since the EPA does not manage the AIMS, we cannot directly 

address its current availability through the American Bird Conservancy. These data can be 

obtained via a Freedom of Information Act (FOIA) request.

D. EPA's conclusions on Exposure Characterization and its methodologies for

characterizing such exposures are unverifiable.

1. Chapter 3, Subsection 3 (Al l three BEs): In both Chapter 1 and Attachment 1-7 of

all three biological evaluation documents, the Agency states that dermal, inhalation 

and drinking water exposure routes will be considered.

a. In Attachment 1-7 of all three biological evaluations the EPA presents methods

for estimating exposure doses for all three routes. However, Chapter 3,

Subsection 3 in all three documents is missing discussions of these routes of

exposure, as well as dose-based exposure estimates. Without these exposure

estimates, the Agency's results (i.e., risk characterizations/effects 

determinations) cannot be reproduced.
7

EPA Response: Species-specific dose based exposures for diet, dermal, inhalation and 

drinking water exposure routes are provided in the TED tools that are parameterized for 

each of the chemicals. To access these exposure estimates:

- Download the chemical-specific ZIP file under WoE tool description on the 

provisional models web page (https://www.epa.gov/endangeredspecies/provisional-models-endangered-species-pesticide-assessments). 

- Open the file titled “TEDtool_v1.0.xlsx” or “TEDtool_v1.0_alt.xlsx”. 

- Click on the tab titled “Min rate doses” to access the estimated doses based on 

the lower rate entered into the input page of the spreadsheet. 

- Click on the tab titled “Max rate doses” to access the estimated doses based

b. In all three biological evaluations the only concentrations presented in the

terrestrial exposure assessment (Subsection 3) are the concentrations in feed

items. Dose-based EECs are not provided. These are critical omissions from

the document. Without these exposure estimates, the Agency's results (i.e., risk

characterizations/effects determinations) cannot be reproduced.

EPA Response: See previous response for location of dose-based exposure estimates.

c. In the Analysis Plans (Section 1.4.1.1.c.2) the Agency states that it will use

AgDrift to assess off-site exposure. In Attachment 1-7 of biological evaluations,

the Agency presents a method for determining the distance off-field to effects

thresholds using drift curves based on AgDrift output. However, the terrestrial

exposure assessments (Chapter 3) do not contain off-field (or off-site) exposure

estimates. Given the stated intent to do so, it appears as though the Agency has

neglected to include off-field exposure estimates in the exposure assessment

chapters of all three biological evaluations. Without these estimates, and the

missing on-field dose-based EECs, it is impossible to reproduce the Agency's

results (i.e., risk characterization/effects determination).

EPA Response: The TED tool also generates estimated distances to which risks extend (based on 

AgDRIFT). Once the TED tool with parameter values for a specific chemical is accessed (see 

response above), these estimates of distances where risk extends from the edge of field are 

provided on the following worksheets of the tool: “Plants”, “Min rate – dietary conc results”, 

“Max rate – dietary conc results”, “Min rate doses”, and “Max rate doses”.

2. Chapter 3, Section 3.2 (page 3-53, second paragraph- CPY; Section 3.2 (page 3-39, third

paragraph) - DIA) (Chlorpyrifos and Diazinon). The Agency states that a foliar dissipation

half-life of 5.3 days based on data reported by Willis and McDowell (1987) was used.

However, EPA does not report (in Chapter 1, Chapter 3, nor Appendix 3-1) which data

were used from the reported source, nor how the single half-life estimate was calculated. 

This omission impedes the accurate reproduction of the Agency's foliar dissipation half-life

estimate.

EPA Response: For diazinon, foliar dissipation half-lives were obtained from Table III of 

Willis and McDowell (1987). Half-lives representing measures from total residues were 

included. Half-lives representing measures of dislodgeable residues were excluded, as they 

may under-represent the residues of diazinon in plants. The values from granular 

formulations were excluded, as no granular formulations of diazinon are currently 
8

registered. The following values were considered when defining the foliar dissipation halflives relevant to diazinon: 

Half-life (days) Crop Location

2.2 Endive Ontario

5.3 Kale DC

1.3 Corn IA

0.4 Corn IA

5.0 Wheat TX

For chlorpyrifos, as stated in Chapter 3, a foliar dissipation half-life of 4 days is used, based on 

data reported by Willis and McDowell (1987). Half-lives representing measures from total 

residues were included. Half-lives representing measures of dislodgeable residues were 

excluded, as they may under represent the residues of chlorpyrifos in plants. As only two total 

residue values were available, the 90th percentile could not be calculated and the highest of the 

two listed values was used (4 days).

3. Chapter 3, section 2.5.1 refers to a 'BEAD memo' for additional information on common 

crop rotation scenarios for vegetable crops in four regions, however there is no prior

reference to this memo in the document and apparently no reference information.

EPA Response: The reference to the BEAD memo in Chapter 3 of the chlorpyrifos BE is an error. 

Information from BEAD regarding crop rotations is provided in Chapter 3, Section 2.5.1 of the 

diazinon BE and will be incorporated in the final BE for chlorpyrifos.

4. A PRZM scenario titled "CALettuce w/irrigation" is assigned to HUC 15 and 16,

Vegetables/Ground Fruit crop group (Attachment 3-1; Table A 3-1.4). This scenario is not

available in the PRZM standard scenario download.

EPA Response: The PRZM scenario listed is a typo. The scenario for HUC 15 should be 

CALettuceSTD and for HUC 16 STXMelonNMC. The scenarios will be corrected in the final BE.

5. For malathion, Chapter 3 section 2.1 refers the reader to Appendix 3-1 for additional 

model input parameter information. However, Appendix 3-1 provides supplementary 

information on environmental transport and fate data analysis.

EPA Response: Appendix 3-1 contains fate and transport information used to develop inputs for 

the aquatic modeling. Additional information for aquatic modeling can be found in Chapter 3, 

Section 2.7, as well as Appendices 1-7 (Scenario Development) and 3-3 (Spray Drift). Section 2.1 

in the malathion exposure chapter will be revised to reflect this information with the release of 

the final BE.

6. For chlorpyrifos, Chapter 3 section 2.7 table 3-5 refers the reader to Table 1 for values of 

drift fractions. However, there is no Table 1 in the document.

EPA Response: The reference to Table 1 is an error. The information cited here can be found 

in Section 2.4.2 of Chapter 3. This will be corrected in the final BEs. It will be changed to, “See 

Section 2.4.2.”
9

7. Chlorpyrifos, Chapter 3, Section 3.2 (page 3-40, Table 3-17), the table provides "Mean and

upper bound EECs calculated for food items..." Only three application scenarios are

presented. However, in the Introduction on page 3-39 (third paragraph), the Agency states

that four different chlorpyrifos application scenarios were used to estimate terrestrial

exposure. The upper bound single application rate of 4.0 lb a.i./A is not presented in the

table. Due to this omission the dietary exposure estimates for this exposure scenario cannot

be verified.

EPA Response: EECs for all application rates modeled were not specifically listed in Chapter 3. 

Table 3-17 was intended to bracket the upper and lower application rates modeled. For all rates 

modeled, EECs are captured in the TED tools that are parameterized for each of the chemicals. 

Rates modeled in the TED tool include minimum, upper bound, maximum single rate and a 

multiple application rate. These rates were derived from an overall assessment of the range of 

rates available for all chemical uses. Specific rates associated with particular uses are located in 

the master use table. To access these exposure estimates:

- Download the chemical-specific ZIP file under WoE tool description on the 

provisional models web page (https://www.epa.gov/endangeredspecies/provisional-models-endangered-species-pesticide-assessments). 

- Open the file titled “TEDtool_v1.0.xlsx” for EECs associated with the minimum and 

upper bound rates modeled or “TEDtool_v1.0_alt.xlsx” for EECs associated with the 

maximum single and multiple application rates modeled.

Clarification on the location of all terrestrial EECs will be added to Chapter 3 for the final BE.

E. EPA entirely failed to provide proper citations to studies (or sections)

considered in the Biological Evaluations.

Study citations are critical for proper review of any written scientific document. Lack of 

bibliographic information decreases the confidence that a reviewer may have on the particular

document under scrutiny. In some cases EPA does not present full references for studies or

modeling tools described in a particular section, nor does it provide relevant MRID or only provide

ECOTOX codes.

1. Citations are not provided for the following studies mentioned in Chapter 4

(chlorpyrifos): Eidels, et al. 2007 (pg. 36); Land 2001 (pg. 36); Macfarland 1998 (pg. 

36); and Sandel 1999 (pg. 36).

EPA Response: The full citations for these references are provided below. These references will 

be included in the final BEs.

- Eidels, R.R., J.O. Whitaker Jr., and D.W. Sparks (2007). Insecticide residues in bats 

and guano from Indiana. Proceedings of the Indiana Academy of Science, 116 (1): 50 

– 57.

- Land, T.A. (2001). Population Size and Contaminant Exposure of Bats Using Caves on 

Fort Hood Military Base, Texas. M.S. thesis, Texas A&M University, College Station, 

Texas.
10

- McFarland, C.A. (1998). Potential Agricultural Insecticide Exposure of Indiana Bats 

(Myotis sodalis) in Missouri. M.S. thesis, University of Missouri, Columbia, Missouri.

- Sandel, J.K. (1999). Insecticides and Bridge-Roosting Colonies of Mexican Free-Tailed 

Bats (Tadarida brasiliensis) in Texas. M.S. thesis, Texas A&M University, College 

Station, Texas.

2. Citations are not provided for all species attributes in Attachments 1-1 1 through 1-21.

EPA Response: Attachments 1-11 through 1-21 provide biological information summary reports 

for all taxa considered in the BEs, including details on the species life history. For birds, 

mammals, reptiles, amphibians and coral, all references are provided within each species 

summary report. For fish, aquatic invertebrates, plants and terrestrial invertebrates, detailed 

information and appropriate references are provided in the supplemental information 

spreadsheets for each attachment. References are generally provided for every data element on 

every line of the spreadsheet. In some instances, a reference may not be provided for a specific 

element if the answer to the element question was “No” (no information available). In rereviewing the species information, we have identified a few other instances in which reference 

information has inadvertently not been provided for a specific element. In those cases, the 

information is from a reference already provided for that species (i.e., it would be from a source 

already cited in the species profile for other elements). The species information, however, will 

continue to be reviewed and updated for the final BE. 

3. Chapter 1 (Section 1.2.2) (Malathion and Chlorpyrifos only): EPA does not include any

references for the data summarized in their Fate Overview section.

EPA Response: The specific references for the fate overview information are provided in 

Chapter 3 and Appendix 3-1 for each chemical. In the final BEs, a statement to see Chapter 3 for 

specific details, including references, will be provided in this section.

4. Chapter 2 (All BEs): There are several instances where EPA describes a number of studies

for which no MRID or ECOTOX ID are provided. These studies do not appear to be

i ncluded in the ECOTOX reference files (Appendix 2-2) or the registrant submitted

reference files (Appendix 2-4). With the i nformation provided (generally only author

names and year of pu blication), it is not possible to identify and access these studies. For

example, from the malathion BE, Sections 5 and 6 the following studies are mentioned with

no accompanying reference details:

a. Conte and Parker (Texas A&M University , 1975)

b. Proctor, Corliss, and Lightner (NMFS, 1966)

c. Tagatz (1974)

d. Kennedy and Walsh , USFWS (1970)

e. Finlayson, Faggella, Jong, Littrell, Lew (1981)
11

f. Kuhajda (1996)

g. Kucera, 1987

h. Engbring, 1989

L Parsons and Davis, 1971 J.

Giles and Robert, 1970

k. Hoda et al., 1993

EPA Response: For studies obtained from the ECOTOX database (open literature studies), the 

full citation is available in the Excel spreadsheet found in Appendix 2-2 (Accepted ECOTOX 

Database for Malathion) or in Appendix 2-5 (Rejected ECOTOX Bibliography for Malathion). For 

registrant-submitted studies, the full citations are available in Appendix 2-4 (OPPIN Bibliography 

for Malathion). For the citations used as examples, full citations are provided below.

- Conte, F. S. and Parker, J. C. (1975). Effect of Aerially-Applied Malathion on Juvenile 

Brown and White Shrimp Penaeus aztecus and P. setiferus. Trans. Am. Fish. Soc. 

104: 793-799.

- Proctor, Raphael R. Jr., Jane P. Corliss, and Donald Lightner (1966). Mortality of Post 

larval and Juvenile Shrimp Caused by Exposure to Malathion -A Laboratory and Field 

Study. National Marine Fisheries Service, Galveston Laboratory.

- Tagatz, M.E., 1974. Effects of Ground Application of Malathion on Saltmarsh 

Environments in Northwestern Florida. USEPA Gulf Breeze, Environmental Research 

Laboratory, Gulf Breeze, Florida.

- Kennedy, H. D. and Walsh, D. F. (1970). Effects of Malathion on Two Warmwater 

Fishes and Aquatic Invertebrates in Ponds. Tech. Pap. No. 55, Bureau of Sport Fish. 

Wildl., Fish Wildl. Service, U.S.D.I., Washington, D.C., 13 p.

- Finlayson, B.J., G. Faggella, H. Jong, E. Littrell, and T. Lew, (1981). Impact on Fish and 

Wildlife from Broad Scale Aerial Malathion Applications in South San Francisco Bay 

Region, Pesticide Investigation Unit, Water Pollution Control Laboratory, California 

Fish and Game Department.

- Kuhajda, B.; Blanco, C.; and Green, M. (1996). Impact of Malathion on Fish and 

Aquatic Invertebrate Communities and On Acetylcholinesterase Activity in Fishes 

within Stewart Creek, Fayette County, Alabama: Final Report. Unpublished study 

prepared by University of Alabama. MRID 47587601.

- Kucera, E. (1987). Brain Cholinesterase Activity in Birds after a City-Wide Aerial 

Application of Malathion. Bull. Environ. Contam. Toxicol. 38: 456-460.

- Engbring, John (1989). Fluctuations in Bird Populations on the Island of Biota as 
12

Related to an Experimental Program to Control The Melon Fly. U.S. Fish and Wildlife 

Service, Honolulu, Hawaii. 

- Parsons, Jack K. and Billy Don Davis, (1971). The Effects on Quail, Migratory Birds 

and Non-Game Birds from Application of Malathion and Other Insecticides. Texas 

Parks and Wildlife Dept., Study conducted from 1964 to 1968. 

- J. Giles and Robert, 1970, Giles, R.H. Jr. (1970). The Ecology of a Small Forested 

Watershed Treated With the Insecticide Malathion-S35. Wildl. Monogr. 24:81.

- Hoda Q, M.A. Azfer, and S.P. Sinha. (1993). Modificatory Effect of Vitamin C and 

Vitamin B-Complex on Meiotic Inhibition Induced by Organophosphorus Pesticide in 

Mice Mus musculus. Int. J. Vitam. Nutr. Res. 63(1): 48-51. ECOTOX # 75127.

5. There are also cases where EPA appears to refer to a study without giving any details of

the study reference at all. Itis thus not possible to identify which studies are being

referred to. For example, in the malathion BE:

a. In Section 9.4.2.5, p 2-176, the paragraph beneath the summary of Hoda et al., 1993

appears to refer to a separate study (as the supplier of malathion was different

between both studies). However, no reference is given for this study.

EPA Response: The reference was provided in the paragraph (Hackenberger et al. 2010; 

E162358). [Hackenberger B.K, D. Jaric, D. Hackenberger and S. Stepic. 2010. Effects of Chronic 

Dietary Exposure to a Low-Dose of Malathion, Aroclor-1254 and 3-Methylcholanthrene on Three 

Biomarkers in Male Mice. Acta Biol. Hung. 61(4): 423-433.]

b. In Section 11.5, in the last paragraph of this section, reference is made to a study in

which effects on weight (biomass) were reported at 5040 ppm for yellow spruce. No

reference is given for this study.

EPA Response: The citation is provided below and will be included in the final BE.

- Straw N.A., N.J. Fielding, and A. Waters. (1996). Phytotoxicity of Insecticides Used to 

Control Aphids on Sitka Spruce, Picea sitchensis (Bong.) Carr. Crop Prot. 15(5): 451-

459. ECOTOX # 67965.

6. Some of the ECOTOX IDs provided by EPA appear to be incorrect, and when no other study

reference details are listed it is difficult for the reader to determi ne and confirm which study

is being referred to. For example, in the diazinon BE:

a. E64966 is reported in error in Section 3.4.2.5 as an amphipod study. The correct

ECOTOX ID appears to be 64955.

EPA Response: EPA agrees that the correct ECOTOX ID for Anderson and Lydy 2002 is E64955. 

This will be corrected in the final BE.

b. E070452 is reported in error in Table 10-1 as the bee contact study. The correct
13

ECOTOX ID appears to be 070542.

EPA Response: EPA agrees that the correct ECOTOX ID for the honey bee study cited in Table 

10-1 is E070542. This will be corrected in the final BE.

c. E50396 is reported in error in Section 8 as the acute bullfrog study. The correct

ECOTOX ID appears to be 50386.

EPA Response: EPA agrees that the correct ECOTOX ID for the bullfrog study referenced in 

Section 8 is E50386. This will be corrected in the final BE.

7. Attachment 4-1, Section 3 (page Al -22): In the discussion of the AquaWoE animal matrices

EPA indi cates that the EECs used in the assessment are based on output from analyses

completed with the Pesticide in Water Calculator (PWC). The output from the PWC was used

to generate "Species Summary" reports which are located in the PWC postprocessor (See

Provisional Models Website). EPA did not provide the link to the Provisional Models

website here.

EPA Response: The provisional models can be found at: https://www.epa.gov/endangeredspecies/provisional-models-endangered-species-pesticide-assessments. This link will be 

provided in Attachment 4-1 in the final BE.

8. Appendix 2 (Diazinon only): There is no reference section located in this appendix

(whereas the same append ix for malathion and chlorpyrifos references are provided).

EPA Response: It is not clear what appendix is specifically being referenced to in this 

comment. Appendices 2-1 through 2-5 include references. The references for the data 

included in Appendices 2-6 to 2-9 are included in Appendices 2-1 to 2-5.

9. Appendix 3-6 (Malathion only): These MS-Excel worksheets contain the inputs for the

TEDtool, including exposure assumptions and effects metrics. Appendix 3-6 associated with

the malathion biological evaluation is mislabeled "Chlorpyrifos Input Parameters for Weight of

Evidence Matrices. " For every numerical input there is a cell allocated for the source of the

value. Dozens of sources are missing for the effects metrics entered for malathion. These

omissions make cross-referencing of inputs difficult particularly if the input units do not match

the reported units in the text (e.g., conversions from diet to dose).

EPA Response: EFED acknowledges the typographical error in the title under the “read me” tab, 

and it should read “Malathion” instead of “Chlorpyrifos.” This will be corrected in the final BE. 

The values in the input tab in the workbook are for malathion. In regard to the missing sources, 

in many cases, values without accompanying sources were cited in previous cells (e.g., values for 

terrestrial-phase amphibians and reptiles are from bird values which are used as surrogate 

values, LOAEC values are from study cited above for NOAEC values). Additionally, threshold 

values (e.g., 1/million mortality, 10% mortality) are found in threshold tables for each taxon in 

Chapter 2 (Effects Characterization).

10. Appendix 1-9 (Malathion and Chlorpyrifos only): Few full references are provided for any of

the data cited in the malathion and chlorpyrifos append ices. Only one study in the malathion

appendix (Majewski and Capel, 1995) was cited and referenced. With the exception of this
14

study, none of the references included are cited within the malathion and chlorpyrifos

appendices and all appear to have originated from the diazinon appendix.

EPA Response: For chlorpyrifos, all of the ecotoxicity data provided in Tables B 1-9.1 and B 

1-9.2 are cited by MRID or ECOTOX numbers. The full citations for all of the MRID numbers 

are provided in Appendix 2-4 and the full citations for the ECOTOX numbers are provided in 

Appendix 2-2. The full citations for the remaining references are provided below. These 

references will be included in the final BEs.

- Magara, Y., T. Aizawa, N. Matumoto, and F. Souna (1994). Degradation of 

pesticides by chlorination during water purification. Water Sci. Technol., 30(7): 

119 – 128.

- Duirk, S.E., and T.W. Collette (2006). Degradation of chlorpyrifos in aqueous 

chlorine solutions: Pathways, kinetics, and modeling. Environ. Sci. Technol., 

40(2): 546 – 551. 

- Wu, J.G., and D.A. Laird (2003). Abiotic transformation of chlorpyrifos to 

chlorpyrifos oxon in chlorinated water. Environ. Toxicol. Chem., 22(2): 261 –

264.

For malathion, for references cited in Table 1-9.1, 1-9.2, and 1-9.3 (as well as other 

references cited with an ECOTOX number or MRID number), the full citations are available 

in either the Excel spreadsheet found in Appendix 2-2 or, for registrant-submitted studies, 

the full citations are available in Appendix 2-4. The full citations for the remaining 

references are provided below and will be included in the final BE:

- Magara, Y., Aizawa, T., Matumoto, N., and Souna, F. (1994). Degradation of 

Pesticides by Chlorination During Water Purification. Water science and 

technology 30: 119-128.

- Wu, J., and Laird, D.A. (2003) Abiotic transformation of chlorpyrifos to 

chlorpyrifos oxon in chlorinated water. Environmental Toxicology and Chemistry

22:2 261-264.

- Duirk, S.E., and Collette, T.W. (2006) Degradation of Chlorpyrifos in Aqueous 

Chlorine Solutions: Pathways, Kinetics, and Modeling. Environmental Science & 

Technology. 40:2 546-551.

- Fellers, G. M., McConnell, L. L., Pratt, D., & Datta, S. (2004). Pesticides in 

Mountain Yellow-Legged Frogs (Rana Mucosa) from the Sierra Nevada 

Mountains of California. Environmental Toxicology and Chemistry. 23(9), 2170-

2177.

- LeNoir, J. S., McConnell, L. L., Fellers, G. M., Cahill, T. M., & Seiber, J. N. (1999). 

Summertime Transport of Current-use pesticides from California’s Central 
15

Valley to the Sierra Nevada Mountain Range, USA. Environmental Toxicology 

and Chemistry, 18(12), 2715-2722.

- Mcconnell, L. L., Lenoir, J. S., Datta, S., and Seiber, J. N. (1998). Wet Deposition 

of Current-Use Pesticides in the Sierra Nevada Mountain Range, California, USA. 

Environmental Toxicology and Chemistry 17: 1908-1916.

- Sparling, D. W., Fellers, G. M., & McConnell, L. L. (2001). Pesticides and 

amphibian population declines in California, USA. Environ. Toxicol. and Chem., 

20(7), 1591-1595.

- Majewski , M.S. and P.D. Capel. (1995). Pesticides in the Atmosphere: 

Distribution, Trends, and Governing Factors. Ann Arbor Press, Inc. Chelsea, MI.

- California-Administrative Report 82-2, (1982). Monitored Aquatic Incidents 

during Broad Scale Aerial Application over San Francisco, Bay Area, 1981 , Dept. 

Of Fish and Game, Environmental Services Branch.

References for the monitoring data in Table B 1-9.3 are found in Chapter 3. A notation to 

see Chapter 3 for specific references will be provided in the final BEs.

11. Appendix 1-9 (Degradate Line of Evidence) (Diazinon only): EPA states on page 7 that

the "USGS analyzed a total of 1,499 samples across sites throughout the United States

for diazoxon between 2002 and 2014." However, the USGS source from which data

were obtained is not specified. Several documents are cited within the appendix, but full

references are not provided (e.g., Magara et al ., 1994; Duirk and Collette, 2006; Wu and

Laird , 2003; Fellers et al., 2004; Sparling et al., 2001; LeNoir et al., 1999; McConnell et

al., 1998).

EPA Response: The source of the USGS information is: USGS. 2015. National Water-Quality 

Assessment Program (NAWQA). U.S. Geological Survey. Available at 

http://water.usgs.gov/nawqa/ (Accessed January 15, 2015). The full citations for the other 

references are provided below.

12. Appendix 1-10: Summary of Malathion/Chlorpyrifos Monitoring Data (Malathion and

Chlorpyrifos only): Several documents are cited within the malathion and chlorpyrifos 

appendices, but full references are not provided.

a. Malathion citations: USEPA, 2006; CEPA, 1996; CDFG, 1982; Petreas et al.,

1992; Pederson et al., 2006; Fellers et al., 2004; LeNoir et al., 1999; McConnell

et al., 1998; Sparling et al., 2001 ; Majewski et al., 2005; Majewski and Baston,

2002.

EPA Response: For references cited with a MRID number, the full citations are available in 

Appendix 2-4 (OPPIN Bibliography for Malathion). Other citations in this appendix are provided 

below and will be included in the final BE:

- USEPA. (2006). Organophosphorus Cumulative Risk Assessment 2006 - Update. 
16

Prevention, Pesticides and Toxic Substances (EPA-HQ-OPP-2006-0618-

0002).http://www.epa.gov/pesticides/cumulative/2006-op/op_cra_main.pdf

- California Environmental Protection Agency (CaEPA). September (1996). 

Environmental Monitoring Results of the Mediterranean Fruitfly Eradication 

Program, Riverside County 1994. Department of Pesticide Regulation. 

Environmental Hazards Assessment Program.

- California-Administrative Report 82-2, (1982). Monitored Aquatic Incidents during 

Broad Scale Aerial Application over San Francisco, Bay Area, 1981, Dept. Of Fish and 

Garrle, Environmental Services Branch.

- Petreas, M., Brown, M., Mischke, T., Okamoto, H., and Stephens, R. (1992). 

Environmental Monitoring for Malathion and Co-Products Following Aerial 

Application in an Urban Area. 203rd ACS (American Chemical Society) national 

meeting, San Francisco, California, USA, April 5-10, 1992. Abstr Pap Am Chem Soc

203: Envr319.

- Pedersen, J. A., Yeager, M. A., and Suffet, I. H. (2006). Organophosphorus 

Insecticides in Agricultural and Residential Runoff: Field Observations and 

Implications for Total Maximum Daily Load Development. Environmental Science & 

Technology [Environ. Sci. Technol.]. Vol. 40, no. 7, pp. 2120-2127. 1 Apr 2006.

- Fellers, G. M., McConnell, L. L., Pratt, D., & Datta, S. (2004). Pesticides in Mountain 

Yellow-Legged Frogs (Rana Mucosa) from the Sierra Nevada Mountains of California. 

Environmental Toxicology and Chemistry, 23(9), 2170-2177.

- LeNoir, J. S., McConnell, L. L., Fellers, G. M., Cahill, T. M., & Seiber, J. N. (1999). 

Summertime Transport of Current-use pesticides from California’s Central Valley to 

the Sierra Nevada Mountain Range, USA. Environmental Toxicology and Chemistry, 

18(12), 2715-2722.

- Mcconnell, L. L., Lenoir, J. S., Datta, S., and Seiber, J. N. (1998). Wet Deposition of 

Current-Use Pesticides in the Sierra Nevada Mountain Range, California, USA. 

Environmental Toxicology and Chemistry, 17: 1908-1916.

- Sparling, D. W., Fellers, G. M., & McConnell, L. L. (2001). Pesticides and amphibian 

population declines in California, USA. Environ. Toxicol. and Chem., 20(7), 1591-

1595.

- Majewski , M.S. and P.D. Capel. 1995. Pesticides in the Atmosphere: Distribution, 

Trends, and Governing Factors. Ann Arbor Press, Inc. Chelsea, MI.

- Majewski, M. S., and Baston, D. S. (2002). Atmospheric Transport of Pesticides in the 

Sacramento, California, Metropolitan Area, 1996-1997. USGS Water-Resources 

Investigations Report. 2002-4100.

b. Chlorpyrifos citations: PANNA Drift Catcher report, 2005 ; PANNA Drift
17

Catcher report, 2006 ; CARB report, 1998; CARB report, 2003

EPA Response: The information for these references is provided below. This information will be 

included in the final BEs.

- PANNA Drift Catcher report, 2005: Air Monitoring for Chlorpyrifos in Lindsay, 

California Jun-July 2004 and July-August 2005: Technical Report. Pesticide Action 

Network North America. July 2006. 

(http://www.pesticideresearch.com/site/docs/DriftCatcher/Lindsaychlorpyrifos7_18_06wc.pdf)

- PANNA Drift Catcher report, 2006: Air monitoring for Chlorpyrifos in the Yakima 

Valley, Washington, April 2006: Technical Report. Pesticide Action Network North 

America. December 2006. 

(http://www.pesticideresearch.com/site/docs/DriftCatcher/FWPP-CP-12-14-

06wc.pdf)

- CARB report, 1998: Final Report for the 1996 Monitoring in Tulare Country, 

California Air Resources Board, California Environmental Protection Agency. Also 

known as “Report for the Application and Ambient Air Monitoring of Chlorpyrifos 

(and the Oxon Analogue) in Tulare County during Spring/Summer 1996. Project no. 

C96-041 (ambient), C96-040 (application). April 7, 1998. 

(http://www.cdpr.ca.gov/docs/emon/pubs/tac/tacpdfs/chlrpfs.pdf)

- CARB report, 2003: Ambient Air Monitoring for Pesticides in Lompoc, California, 

Volume 1: Executive Summary. State of California Environmental Protection Agency, 

California Department of Pesticide Regulation, March 2003, EH03-02. 

(http://www.cdpr.ca.gov/docs/specproj/lompoc/exec_sum_march2003.pdf)

13. Appendix 4-7, Section 3.2 (Malathion and Chlorpyrifos only): There is no explanation for

how fecundity declines are estimated with MCnest, and no reference to the actual model

or a document describing the details of the model. Without these details it is difficult to 

piece together how the analysis was conducted and to reproduce results.

EPA Response: Fecundity declines were estimated using the provisional model that integrates 

TIM and MCnest. This model is available on the provisional models page 

(https://www.epa.gov/endangered-species/provisional-models-endangered-species-pesticideassessments). In this analysis, fecundity was estimated for birds without exposure to 

chlorpyrifos or malathion (referred to as “control”). Then, fecundity was estimated in birds 

exposed to chlorpyrifos or malathion. The difference in fecundity was attributed to exposure 

to the simulated pesticide.

F. EPA repeatedly referenced incorrect hyperlinks.

All three Biological Evaluations have multiple hyperlinks that are either broken or direct the

reader to the incorrect location. In many places throughout the Biological Evaluations, EPA

uses hyperlinks to direct the reader to reference materials and websites considered in their
18

biological evaluations. However, there are problems with more than 60 of the hyperlinks

presented. For example, a significant number of links are broken, leading to "page not found"

or "invalid location" errors. In addition, many hyperlinks redirect the reader to the EPA

Pesticides Homepage instead of to specific areas of interest. Finally, several links point to

proprietary information not directly accessible to the public. Appendix 1 provides a list of

hyperlinks that are either broken or lead to incorrect or unavailable information.

EPA Response: The corrected links are provided in APPENDIX C of this response. The 

corrected links will be provided in the final BEs.

G. Based on preliminary identification of a large number of errors and discrepancies

in the data that was provided, it is especially critical that EPA makes publicly

available missing and omitted data to allow the public to validate EPA's analysis.

A. Spatial data

1. Appendix 1-6 (all 3 compounds) states that "To determine whether to model a use

pattern for each HUC2 region 2014 National Agricultural Statistics Service (NASS)

Census of Agriculture (CoA) acreage reports

2

for the crops specific to diazinon were

crossed with the HUC2 regions based using ArcGIS

3

·" It is assumed this should read

"2012 National Agricultural Statistics Service (NASS) Census of Agriculture (CoA)

acreage reports..." because the USDA CoA website (http://"vww.agccnsus. usda. gov/)

indicates that the most recent complete Census was completed in 2012.

EPA Response: The assumption that the sentence should read, “2012 National Agricultural 

Statistics Service (NASS) Census of Agriculture (CoA) acreage reports ..." is correct. This will be 

corrected in the final BE.

B. Exposure modeling

1. Appendix 3-3 refers to AgDRIFT version 2.2.1, while the EPA website provides

download for AgDRIFT 2.1.1 only.

EPA Response: The reference to AgDRIFT 2.2.1 is an error. The reference should have been for 

AgDRIFT version 2.1.1. This will be corrected in the final BEs.

2. Review of PWC batch run files indicates that an application method flag (CAM) of 3 was

used in many simulations. The PRZM5 user manual (Young and Fry, 2014) indicates that

CAM 3 has been removed in PRZM5 , stating on p. 15 that "Several unused and/or

unrealistic CAM schemes from PRZM3 were eliminated in PRZM5 (CAM= 3, 5, 6, 9,

1O)." Therefore, the simulations using CAM 3 may be in error and additional time is

needed to evaluate the extent of the error and potential impacts on exposure concentrations.

EPA Response: The inputs in the batch run file correspond directly to the application methods 

available in PWC, not the CAMs in PRZM. The batch input file uses the term ‘ApplicationMethod’ 

to differentiate that these values are not CAMs. An input of 3 corresponds to an application 

method of a Uniform Application Incorporated Below a specified depth, which corresponds to a 

PRZM CAM of 4. 
19

3. Apparent modeling errors were identified in the ErrorSummary.txt PWC results files

provided i n Appendix 3-4f (PWC Postprocessor Output). These files repo1i potentially

hundreds of errors related to PRZM and VVWM execution. For chlorpyrifos, the 'PWC 

Notes' worksheet in Appendix 3-4 Aquatic EECs contains the statement "Separate post

processing simulations were completed for urban uses and combined in one output

summary file. Only a few errors were noted in batch run error summary file - these files

were not rerun because they didn't impact the overall assessment." These and related

errors need to be identified and i nvestigated to confirm the conclusion that the overall

assessment was not impacted.

EPA Response: Although the ErrorSummary.txt files for the chlorpyrifos PWC runs indicates

errors, these errors are a result of the PWC program reading blank rows, not errors in the actual 

modeling. For instance, in the “Excludes Seeds\All HUCs but AK”, errors are listed for rows 5219 

to 7828. However, an inspection of the input file “1. Chlorpyrifos Aquatic Modeling Batch Input 

File (ES) 111615 FINAL.csv” shows that 5219 rows (5218 runs) were available. If the input file is 

opened in a text editor program, such as Notepad, one can see the empty rows filled with 

commas at the end of the file. The same errors occur for all of the chlorpyrifos runs for all HUCs 

but AK. Error files for the other chemicals did not contain these issues. The ErrorSummary file 

for chlorpyrifos will be corrected in the final BE.

4. Application patterns modeled using PWC may not be consistent with the Master Use

Summary Table (Appendix 1-3). For example, for chlorpyrifos foliar uses on corn, the

maximum application rate in the Master Use Summary Table is 3 lb/A annually (2

applications at 1.5 lb/A 10 days apart), and yet the PWC batch input file shows 5 foliar

applications were modeled (2 apps with a non-approved method (CAM 3) and 3 apps at

1.5 lb/A (cam 2)). Given the above, additional time is need to verify all of the PWC

batch inputs against the Master Use Summary Table and the Master Use Summary Table 

needs to be verified against product labels.

EPA Response: EPA used the Master Use Summary Table as the basis for its modeled 

application patterns. Based on Appendix 1-3, the maximum annual application rate for 

chlorpyrifos on corn is 8.1 lbs a.i./A/yr. In Appendix 1-7, this is the annual rate used for corn, 

split into 5 applications: 1 at preplant at 1.6 lbs a.i./A (application method 3, uniform below 

surface, CAM 4), 1 at plant at 2 lbs a.i./A (application method 3, uniform below surface, CAM 4) 

and three post-emergent foliar applications at 1.5 lbs a.i./A (application method 2, above crop, 

CAM 2). These appendices have been made available to the public since the December 2015

posting

5. Malathion spray drift buffer setbacks in Chapter 3 section 2.7 Table 3-5 are not correct

and are not consistent with Appendix 3-3 (Table B 3-3.1). Chapter 3 suggests a 25 ft

ground buffer, while Table B 3-3.I has no ground buffer.

EPA Response: The reference to a 25 ft ground buffer should have indicated the buffer was for 

aerial applications only. This error will be fixed in the final BE for malathion.

6. Downstream dilution was not conducted for chlorpyrifos (or malathion) because "Because

of the widespread use of chlorpyrifos and the uncertainty with where the adulticide, wide

area, and non-agricultural uses could occur, the entire United States is considered the
20

action area for chlorpyrifos for Step I." (Appendix 3-5) The same rationale was applied

for Step 2. However, the tables in Appendix 1.6 and I .7 indicate that developed uses

(which include mosquito control) are separate from agricultural uses and there are certain

agricultural use sites that are not applicable across all HUCs (e.g., rice and grassland).

Therefore, the use extent is defined inconsistently in different sections of the report.

EPA Response: Unlike agricultural uses, which can be mapped in GIS using crop data layers, 

usage information on mosquito adulticide uses, as well as other non-agricultural uses, is not 

available; therefore, adulticide applications are assumed to occur in every HUC and every 

watershed. EPA will review Appendices 1.6 and 1.7 to ensure they are consistent with other 

sections of the final BE with regards to the extent of non-agricultural uses.

7. Attachment 3-1 explicitly states that due to the use of conservative scale factors, there is

uncertainty around aquatic habitat 3 and 4 exposure predictions, however these uncertainties

do not appear to have been taken into account when determining confidence levels in the

weight of evidence matrices.

EPA Response: In the evaluation of species in Bins 3 and 4, there is uncertainty surrounding the 

exposure estimates. This will be a topic of discussion at the upcoming Stakeholder Workshop 

scheduled for June 2016.

8. Chapter 3, Section 3.1 (first paragraph), in the introduction to the Measures of Terrestrial 

Exposure Section (3), the Agency states, "As discussed in the terrestrial exposure

appendix, KABAM will not be run for chlorpyrifos, diazinon or malathion." First, it is not

clear to which appendi x EPA is referring. There are multiple appendices in the document

that are pertinent to the terrestrial exposure modeling. Second, the Agency does in fact use

KABAM estimates in all three biological evaluations when empirical data were

unavailable.

EPA Response: The terrestrial exposure appendix referred to by EPA is Attachment 1-7. EPA 

uses empirical BCF values to calculate pesticide concentrations in aquatic organisms (in place 

of KABAM estimates of concentrations in aquatic organisms). When empirical BCFs are not 

available (e.g., for aquatic plants exposed to plants), KABAM is used to estimate BCFs.

9. Attachment 4-2. There are three supplemental files associated with Attachment 4-2,

including two MS Excel workbooks. None of these supplemental files are referred to in

Attachment 4-2 or Chapter 4 of the document. As such, it is difficult to discern how these

files are to be interpreted in the context of the biological evaluations and their review.

EPA Response: As discussed in Section 9 of Chapter 4, the analyses described in Attachment 4-2 

did not factor into the weight of evidence approach employed for Step 2. Instead, these 

analyses were provided in the BE because they may contain information relevant for the Step 3 

process. With regards to the supplemental files:

Supplemental Information 1 is referred to as Appendix A in the attachment

Supplemental Information 2 is referred to as Appendix B in the attachment

Supplemental Information 3 is referred to as Appendix C in the attachment 

Supplemental Information 4 is referred to as Appendix D in the attachment
21

l0. Attachment 4-3. There are six supplemental files associated with Attachment 4-3,

including one MS Excel workbook. Five of these six supplemental files are not referred to

in Attachment 4-3 or Chapter 4. As such, it is difficult to discern how these files are to be

interpreted in the context of the biological evaluations and their review. Supplemental

files 2, 3 and 4 present species sensitivity distribution (SSDs) estimates for the three pilot

OPs. Notably, none of these files presents the software that was used to generate the

SSDs, nor the statistical approach used to generate the models. These omissions make it

impossible to critique or reproduce the analyses.

EPA Response: Similar to the response above, the analyses described in Attachment 4-3 did not 

factor into the weight of evidence approach employed for Step 2. Instead, these analyses were 

provided in the BE because they may contain information relevant for the Step 3 process. 

Supplemental Information 1, 2, 3 and 4 are referenced on page 8 of Attachment 4-3. 

Supplemental Information 1.1 and 1.2 are referenced as Supporting Information SI 001 and SI 

002 and are described on page 15 of Attachment 4-3 Supplemental Information 1. For 

Supplemental Information 2, 3, and 4, the species sensitivity distributions were created using 

the SSD Toolbox v. 1.0 provided on the provisional models page 

(https://www.epa.gov/endangered-species/provisional-models-endangered-species-pesticideassessments#Effects) using the same approach described in Attachment 1-5 (Method for 

Deriving Species Sensitivity Distributions for Use in Pesticide Effects Determinations for Listed 

Species).

11. Malathion, Chapter 1 -Problem Formulation, Section 1.4.1.1.c.1, EPA's aquatic

exposure modeling was conducted "using a Pesticide Root Zone Model (PRZM) and

Variable Volume Water Model (VVWM), usi ng the Pesticide Water Calculator (PWC)

shell". However, EPA refers to SWCC throughout the remainder of the Problem

Formulation. It is unclear what, if any, differences exist between PWC and SWCC.

EPA Response: Remaining references to SWCC were made in error. References to SWCC should 

instead be made to PWC. This will be corrected in the final BE. 

H. Preliminary attempts to reproduce EPA results show many discrepancies and

errors that will require extensive analysis and time to reconcile and correct.

The vast quantity of exposure data and modeling results must be verified by reproducing the

reported results. Preliminary attempts to reproduce reported results were not successful and so

additional time is needed to understand the reason for apparent differences. Several examples of 

specific challenges to reproducing EPA's results are listed below.

1. The exact version of the PWC user interface and corresponding version numbers of

PRZM and VVWM are not documented. Whether the latest version available online (v

1.5 Dec 8 2015) or an earlier version was used in these reports cannot be determined.

EPA Response: The currently available public version of PWC, version 1.5 published on 

December 8, 2015, was used in the development of the BE aquatic EECs. Model versions will be 

included in the final BE.

2. Many of the specific model inputs are not described in the text documents. Information

needs to be extracted from large unformatted batch files. For example, the specific date
22

for every application is needed to reproduce the results. However, the Chapter 3 text

only specifies that initial application dates were randomly selected from the wettest

month of the growing season or at planting.

EPA Response: Given the volume of runs required for the development of the nation-wide BEs

and to avoid redundancy throughout the BEs, the methods for selecting the specific inputs, such 

as the application dates, were generally discussed in Chapter 3. The specific Input parameters 

are provided in Section 2.7 of Chapter 3, Appendices 1-3 (Master Use Summary Table), 1-6 (Use 

site and HUC2 Matrix), and 1-7 (Scenario Development), as well as Appendix 3-3 (Spray Drift), 

and the input runs provided in Appendix 3-4f.

3. Neither the PWC scenario files nor the PRZM and VVWM input files were provided.

Only batch run files were provided containing a subset of the model inputs needed to

reproduce the simulations.

EPA Response: The PWC scenario files are available under the Provisional Models link (see 

response below). When running PWC in batch mode, the PRZM and VVWM input files are 

overwritten as each run is conducted, so individual PRZM/VVWM files for each model run are 

not available. The batch input file and the individual results files are sufficient to reproduce and 

evaluate each simulation.

4. Attachment 3-1 Table A 3-1.4 provides a surrogate scenario assignment by crop and

HUC2, however the PWC batch run file uses a different scenario file naming convention ,

so the actual input parameters used cannot be confirmed. The renamed scenario files were

not provided.

EPA Response: The renamed scenario files are available as a zip file under the Provisional 

Models link (https://www.epa.gov/endangered-species/provisional-models-endangeredspecies-pesticide-assessments#Aquatic), under the discussion of PWC ESA Scenarios.

5. An attempt to reproduce HUC2 05 corn bin 7 simulations based on information provided

on the model inputs in tables and in the batch run file was unsuccessful. The batch run

file shows use of CAMs 1, 2, 3 and 4. However, the PWC GUI does not permit the user

to choose a CAM of 3 so this application method cannot be reproduced.

EPA Response: As previously mentioned, the inputs in the batch run file correspond directly to 

the application methods available in PWC, not the CAMs in PRZM. The batch input file uses the 

term ‘ApplicationMethod’ to differentiate that these values are not CAMs. An input of 3 

corresponds to an application method of a Uniform Application Incorporated Below a specified 

depth, which corresponds to a PRZM CAM of 4. 

6. PWC results were post-processed with excel spreadsheet tools that require a review of 

excel macro code (over 700 lines of code) and verification of cell data and equations

against the original model output files.

EPA Response: A preliminary evaluation of the PWC postprocessor tool indicates the tool is 

operating properly. The use of Excel allows for verification of the tool’s results fairly rapidly and 

should not require significant time to review.
23

7. The publically available version of PWC reports 1 in 10 year EECs, not 1 in 15 year

EECs, so the exposure concentrations used for effects determinations are not readily

reproduced. The calculation of 1 in 15 year EECs needs to be verified.

EPA Response: The publicly available version of PWC does allow for reporting of the 1-in-15 

year EECs. The user needs to select the Additional Frequency of Return (years) option, under 

the More Options tab, and input 15 years in the available text box.

8. The PFAM simulations used to assess rice and watercress uses cannot be reproduced

because the scenario files were not included with the reports and are not publically

available otherwise. Without complete input parameter information on weather, location,

soil characteristics and other assumptions about the environment, results cannot be verified.

EPA Response: All of the information necessary to run PFAM is provided in Chapter 3, Sections 

2.5.2.1 and 2.7, Tables 3.5 and 3.6 for chlorpyrifos and Tables 3.7-3.9 for diazinon.

9. Downstream dilution methods were not developed for step 3. Methods were not

presented for steps 1 and 2, however Append ix 1.6 indicated that the use extent may be

less than the entire US, therefore downstream dilution should have been conducted and

the approach needs validation.

EPA Response: In the BEs, EPA is responsible for addressing the overlap of the action area

footprint and the species range and critical habitats (Step 1) and, for those species and critical 

habitats that have been categorized as a May Affect, whether an individual of a species is Likely 

to be Adversely Affected (Step 2). The downstream dilution methods were developed for Steps 

1 and 2 and address these questions as presented in Appendix 3-5. While information from the 

BEs will be used in Step 3, the downstream dilution methods for Step 3 have not been fully 

developed at this time. However, EPA and the Services are working collaboratively to 

accomplish this. These methods will be available for evaluation and comment when the draft 

Biological Opinions are made available. 

12.Chapter 3, Subsection 3, the exposure assessments do not present a method for

estimating EECs for listed terrestrial plant species, nor are EECs presented for listed

terrestrial plant species. EECs for listed terrestrial plant species cannot be confirmed

with the information provided.

EPA Response: Exposure estimates for plants are provided in the TED tools that are 

parameterized for each of the chemicals. To access these exposure estimates:

- Download the chemical-specific ZIP file under WoE tool description on the 

provisional models web page (https://www.epa.gov/endangeredspecies/provisional-models-endangered-species-pesticide-assessments). 

- Open the file titled “TEDtool_v1.0.xlsx” or “TEDtool_v1.0_alt.xlsx”. 

- Click on the tab titled “Plants” to access the estimated exposures for spray drift 

and runoff.

Runoff EECs are calculated according to the method incorporated into TerrPlant v. 1.2.2. 

That method is described in the TerrPlant User’s guide, which is available online at: 

https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/terrplant-version122-users-guide-pesticide-exposure.
24

Distances from the edge of the field to which spray drift transport represents a risk to 

plants are calculated using equations adapted to AgDRIFT outputs. These equations are 

described in Attachment 1-7.

13. Chapter 3, Subsection 3, The exposure assessments do not present a method for estimating

EECs for listed terrestrial invertebrate species, nor are EECs presented for listed terrestrial

invertebrate species. EECs for listed terrestrial invertebrate species cannot be confirmed

with the information provided.

EPA Response: Three types of EECs are calculated for terrestrial invertebrates: dietary 

based (mg a.i./kg-food), dose based (mg a.i./kg-bw) and in soil (mg a.i./kg-soil). EECs for 

different food items consumed by terrestrial invertebrates (e.g., broad leaves) are 

calculated in the same manner as described in Attachment 1-7 (see section 4). Dose-based 

EECs for terrestrial invertebrates that are above ground or soil-dwelling are also described 

in Attachment 1-7 (see section 4). The method for calculating exposures in soil is described 

in section 5 of Attachment 1-7. EECs are provided in the TED tools parameterized for each 

chemical (see response to previous comment for information on accessing this file) in the 

worksheets titled “Min rate concentrations” and “Max rate concentrations”.

14. Appendi x 3-6, TEDtool worksheets. These MS Excel worksheets contain the inputs for

the TEDtool , including exposure assumptions and effects metrics. For every numerical

Input there is a cell allocated for the source of the value. For all three pi lot OPs there are

several sources missing for physical , chemical and fate inputs. These omissions make

cross-referencing of inputs difficult particularly if the input units do not match the reported

units in the text. Further, more than one input is presented herein (Appendix 3-6) but is not

mentioned in the text of Chapter 3 or its other associated appendices (e.g., daily fraction

retained in mammals and daily fraction retained in birds).

EPA Response: On the Title line of the Physical, chemical and fate properties table it states 

“see Chapter 3 for more complete information on parameters in this section”, which is 

where more complete information and references for these parameters can be found. The 

daily fraction retained values for mammals and birds are from rat and chicken (respectively) 

metabolism studies submitted by registrants to fulfill FIFRA data requirements for specific 

pesticides. These parameters are used to account for elimination of the pesticide from one 

day to the next in mammal and bird prey of listed species. This approach is similar to the 

one used in TIM.
25

APPENDIX A: Formating Census of Agriculture Data

Most of the data processing for formatting the Census of Agriculture Data was performed via a 

series of Structured Query Language (SQL) queries in MS Access 2013. 

In MS Access 2013 perform the following queries:

This first query takes the compiled NASS table from QuickStats and aggregates it into the 

GenClass11 categories (Table 1). It filters on Acres >0. It is called 

qMkTbl_FIPS_GenClass_Acres_2012:

SELECT ztbl_NASS2012_compiled.FIPS, trel_NASS_GenClass11.GenClassID_fk, 

Sum(ztbl_NASS2012_compiled.Value) AS SumOfValue INTO tbl_FIPS_GenClass_Acres_2012

FROM trel_NASS_GenClass11 INNER JOIN ztbl_NASS2012_compiled ON 

trel_NASS_GenClass11.NASS2012_crop = ztbl_NASS2012_compiled.Commodity

GROUP BY ztbl_NASS2012_compiled.FIPS, trel_NASS_GenClass11.GenClassID_fk

HAVING (((Sum(ztbl_NASS2012_compiled.Value))>0));

The next series takes the above table and pivots it by the classes which become headers. It is 

called qxTab_FIPS_CDL_xTab:

TRANSFORM Sum(tbl_FIPS_GenClass_Acres_2012.SumOfValue) AS SumOfSumOfValue

SELECT tbl_FIPS_GenClass_Acres_2012.FIPS

FROM tbl_FIPS_GenClass_Acres_2012

GROUP BY tbl_FIPS_GenClass_Acres_2012.FIPS

PIVOT tbl_FIPS_GenClass_Acres_2012.GenClassID_fk;

Table 1 Relationship between NASS Census and CDL general class categories.

NASS Census of Ag 2012 CDL General Class

ARTICHOKES Vegetables/Ground Fruit

ASPARAGUS Vegetables/Ground Fruit

BARLEY Other Grains

BEANS Vegetables/Ground Fruit

BEETS Vegetables/Ground Fruit

BROCCOLI Vegetables/Ground Fruit

BRUSSELS SPROUTS Vegetables/Ground Fruit

BUCKWHEAT Other Grains

CABBAGE Vegetables/Ground Fruit

CANOLA Other Grains

CARROTS Vegetables/Ground Fruit

CAULIFLOWER Vegetables/Ground Fruit

CELERY Vegetables/Ground Fruit

CHICORY Vegetables/Ground Fruit

CORN Corn
26

NASS Census of Ag 2012 CDL General Class

COTTON Cotton

CRAMBE Vegetables/Ground Fruit

CUCUMBERS Vegetables/Ground Fruit

DAIKON Vegetables/Ground Fruit

DILL Vegetables/Ground Fruit

EGGPLANT Vegetables/Ground Fruit

EMMER & SPELT Other Grains

ESCAROLE & ENDIVE Vegetables/Ground Fruit

FIELD CROPS, OTHER Other Row Crops

FLAXSEED Other Grains

GARLIC Vegetables/Ground Fruit

GINGER ROOT Vegetables/Ground Fruit

GRASSES & LEGUMES TOTALS Other Crops

GUAR Other Crops

HAY & HAYLAGE Hay/Forage/Silage

HERBS Vegetables/Ground Fruit

HOPS Vegetables/Ground Fruit

JOJOBA Orchards/Vineyards

LEGUMES Other Crops

LENTILS Vegetables/Ground Fruit

MILLET Other Grains

MINT Vegetables/Ground Fruit

OATS Other Grains

ORCHARDS Orchards/Vineyards

PEANUTS Vegetables/Ground Fruit

PEAS Vegetables/Ground Fruit

PEPPERS Vegetables/Ground Fruit

POPCORN Vegetables/Ground Fruit

POTATOES Vegetables/Ground Fruit

PUMPKINS Vegetables/Ground Fruit

RADISHES Vegetables/Ground Fruit

RAPESEED Other Grains

RHUBARB Vegetables/Ground Fruit

RICE Rice

RYE Other Grains

SAFFLOWER Other Grains

SESAME Other Grains

SORGHUM Other Grains

SOYBEANS Soybeans

SPINACH Vegetables/Ground Fruit

SQUASH Vegetables/Ground Fruit
27

NASS Census of Ag 2012 CDL General Class

SUGARBEETS Vegetables/Ground Fruit

SUGARCANE Other Grains

SUNFLOWER Other Row Crops

SWEET CORN Vegetables/Ground Fruit

SWEET POTATOES Vegetables/Ground Fruit

SWEET RICE Rice

SWITCHGRASS Hay/Forage/Silage

TARO Vegetables/Ground Fruit

TOBACCO Vegetables/Ground Fruit

TOMATOES Vegetables/Ground Fruit

TRITICALE Other Grains

TURNIPS Vegetables/Ground Fruit

VEGETABLES, MIXED Vegetables/Ground Fruit

VEGETABLES, OTHER Vegetables/Ground Fruit

WATERCRESS Vegetables/Ground Fruit

WHEAT Wheat

WILD RICE Rice
28

APPENDIX B: CDL Processing Python Scripts

This appendix contains text copied from 8 sequential python scripts. All sections have been copy/pasted 

from Python Files into this word document, and as a result, the integrity of indentation, wrapping, and 

comment symbology (##) should be carefully reviewed when repasting into integrated development 

environments (IDE). All paths are relative and need updating. Suggested directory structure needs 

manual creation in most cases. These scripts demonstrate 6 years of CDL (2010-2015); the previously 

released draft was 5 years (2010-2014). The user should edit scripts as needed for timeframe desired. 

Please contact Steve Lennartz via email (Lennartz.Steven@epa.gov) to receive the *.py files.

Contents

Appendix B: CDL Processing Python Scripts................................................... Error! Bookmark not defined.

Script 1: “1_GenClass11_Recode_Script1_1015.py” ..............................................................................28

Script 2: “2_GenClass11_Recode_Script2_1015.py” ..............................................................................30

Script 3: “3_CDL_CountyClipping_1015.py” ...........................................................................................32

Script 4: “4_CDL_UpdateAreaValues_1015.py” .....................................................................................33

Script 5: “5_Cultivated_CountyClipping.py” ...........................................................................................34

Script 6: “6_CDL_Expand_1015.py”........................................................................................................36

Script 7: “7_CDL_MosaicExpanded_1015.py” ........................................................................................38

Script 8: “8_CDL_FixHoles_1015.py” ......................................................................................................40

Supplemental Table: “cdl_recode_new.txt”...........................................................................................41

Supplemental Table: “EPA_CDL_Recode_Key.txt” .................................................................................45

Script 1: “1_GenClass11_Recode_Script1_1015.py”

1. ## Script 1. First Script to aggregate CDL classes. Requires each year of CDL to be rec

oded as describe in, "ATTACHMENT 

2. ## 1-

2: CDL Crosswalk (DOCX)", of Implementing NAS Report Recommendations on Ecological Risk

Assessment for Endangered 

3. ## and Threatened Species, https://www3.epa.gov/pesticides/nas/attachment-1-

2.docx . This can be done using ArcToolbox 

4. ## Spatial Analyst Tools/Reclass/Reclass by Table, using "cdl_recode_new.txt" loaded as

a remap table. These scripts 

5. ## demonstrate 6 years of CDL (2010-

2015); the previously released draft was 5 years (2010-2014). Edit scripts as needed 

6. ## for timeframe desired. All paths are relative and need updating. Suggested directory

structure needs manual creation 

7. ## in most cases. 

8. 

9. __author__ = 'Steven Lennartz' 

10. 

11. ## Import Site Packages 

12. import arcpy 
29

13. from arcpy import env 

14. from arcpy.sa import * 

15. import datetime 

16. 

17. ## Check out the ArcGIS Spatial Analyst extension license 

18. arcpy.CheckOutExtension("Spatial") 

19. 

20. ## Set variables. All paths are relative and need updating. 

21. ## Create a File Geodatabase, populated with recoded CDL layers as described in the hea

der. This will be your in22. ## workspace. 

23. inws = r"../CDL_Recodes/CDL_Reclass_1015.gdb/" 

24. ## Create a File Geodatabase for an out-workspace 

25. outws = r"../CDL_Recodes/CDL_GenAggregates_1015.gdb/" 

26. ## The cultivated layer from the CDL website 

27. cult = Raster("../Cultivated_Layer/2015_Cultivated_Layer/2015_Cultivated_Layer.img")

28. 

29. ## Environmental settings 

30. arcpy.env.workspace = inws 

31. arcpy.env.overwriteOutput = 1 

32. arcpy.env.snapRaster = cult 

33. arcpy.env.extent = cult 

34. arcpy.env.scratchWorkspace = outws 

35. arcpy.env.parallelProcessingFactor = "100%" 

36. 

37. ## Create a list of the rasters in the in-workspace 

38. ras = arcpy.ListRasters() 

39. 

40. ## The Key is included as a txt (add file extension if using txt format) 

41. key = "EPA_CDL_Recode_Key" 

42. valueList = {row[0] for row in arcpy.da.SearchCursor(key, "Value")} 

43. 

44. start_t1 = datetime.datetime.now() 

45. 

46. ## Aggregate multiple years of CDL 

47. ## For loop 

48. for value in valueList: 

49. if arcpy.Exists("{}CDL_1015_{}".format(outws, value)): 

50. print "{} Exists".format("CDL_1015_{}".format(value)) 

51. else: 

52. start_t = datetime.datetime.now() 

53. print "Temporally Aggregating Class {} at {}".format(value, start_t) 

54. ## Conditional statement aggregating 6 years of CDL 

55. out = Con(((Raster(ras[0]) == value) | (Raster(ras[1]) == value) | (Raster(ras[

2]) == value) | (Raster(ras[ 

56. 3]) == value) | (Raster(ras[4]) == value) | (Raster(ras[5]) == value)), 1,

0) 

57. out.save("{}CDL_1015_{}".format(outws, value)) 

58. print "Building Pyramids....." 

59. arcpy.BuildPyramids_management("{}CDL_1015_{}".format(outws, value)) 

60. print "Completed Temporal Aggregation of Class {} in: {}".format(value, datetim

e.datetime.now() - start_t) 

61. 

62. print "Completed Temporal Aggregation of all Classes in: {}".format(datetime.datetime.n

ow() - start_t1) 
30

Script 2: “2_GenClass11_Recode_Script2_1015.py”

1. ## Script 2. Second script to aggregate CDL classes over multiple years. 

2. 

3. __author__ = 'Steven Lennartz' 

4. 

5. ## Import Site Packages 

6. import arcpy 

7. from arcpy import env 

8. from arcpy.sa import * 

9. import datetime 

10. 

11. ## Check out the ArcGIS Spatial Analyst extension license 

12. arcpy.CheckOutExtension("Spatial") 

13. 

14. ## Set variables 

15. ## In-workspace is the out-workspace from step 1 

16. inws = r"../CDL_Recodes/CDL_GenAggregates_1015.gdb/" 

17. ## Create a File Geodatabase for an out-workspace 

18. outws = r"../CDL_Recodes/CDL_GenClass11_1015.gdb/" 

19. ## The cultivated layer from the CDL website 

20. cult = Raster("../Cultivated_Layer/2015_Cultivated_Layer/2015_Cultivated_Layer.img") 

21. ## From and to years of CDL. This example is 6 years, 2010-

2015, "CDL_1015_". For use in formatting strings. 

22. cdl = "CDL_1015_" 

23. 

24. ## Environmental settings 

25. arcpy.env.workspace = inws 

26. arcpy.env.overwriteOutput = 1 

27. arcpy.env.snapRaster = cult 

28. arcpy.env.extent = cult 

29. arcpy.env.scratchWorkspace = outws 

30. arcpy.env.parallelProcessingFactor = "100%" 

31. 

32. start_t1 = datetime.datetime.now() 

33. 

34. ## Conditional statements producing 11 general CDL layers 

35. start_t = datetime.datetime.now() 

36. print "Recoding Corn at {}".format(start_t) 

37. out1 = Con(((Raster("{}10".format(cdl)) == 1) | (Raster("{}14".format(cdl)) == 1) | (Ra

ster("{}15".format(cdl)) == 1) 

38. | (Raster("{}18".format(cdl)) == 1)), 1, 0) 

39. out1.save("{}{}10".format(outws, cdl)) 

40. print "Building pyramids........" 

41. arcpy.BuildPyramids_management("{}{}10".format(outws, cdl)) 

42. print "Completed Corn recode in: {}".format(datetime.datetime.now() - start_t) 

43. 

44. start_t = datetime.datetime.now() 

45. print "Recoding Cotton at {}".format(start_t) 

46. out2 = Con(((Raster("{}20".format(cdl)) == 1) | (Raster("{}25".format(cdl)) == 1) | (Ra

ster("{}26".format(cdl)) == 1) 

47. | (Raster("{}42".format(cdl)) == 1)), 1, 0) 

48. out2.save("{}{}20".format(outws, cdl)) 

49. print "Building pyramids........" 

50. arcpy.BuildPyramids_management("{}{}20".format(outws, cdl)) 

51. print "Completed Cotton recode in: {}".format(datetime.datetime.now() - start_t) 

52. 

53. start_t = datetime.datetime.now() 

54. print "Copying Rice at {}".format(start_t) 

55. arcpy.Copy_management("{}30".format(cdl), "{}{}30".format(outws, cdl)) 
31

56. print "Completed Rice copy in: {}".format(datetime.datetime.now() - start_t) 

57. 

58. start_t = datetime.datetime.now() 

59. print "Recoding Soybeans at {}".format(start_t) 

60. out4 = Con(((Raster("{}40".format(cdl)) == 1) | (Raster("{}42".format(cdl)) == 1) | (Ra

ster("{}45".format(cdl)) == 1) 

61. | (Raster("{}48".format(cdl)) == 1) | (Raster("{}14".format(cdl)) == 1)), 1

, 0) 

62. out4.save("{}{}40".format(outws, cdl)) 

63. print "Building pyramids........" 

64. arcpy.BuildPyramids_management("{}{}40".format(outws, cdl)) 

65. print "Completed Soybeans recode in: {}".format(datetime.datetime.now() - start_t) 

66. 

67. start_t = datetime.datetime.now() 

68. print "Recoding Wheat at {}".format(start_t) 

69. out5 = Con(((Raster("{}50".format(cdl)) == 1) | (Raster("{}56".format(cdl)) == 1) | (Ra

ster("{}58".format(cdl)) == 1) 

70. | (Raster("{}15".format(cdl)) == 1) | (Raster("{}25".format(cdl)) == 1) | (

Raster("{}45".format(cdl)) == 

71. 

1)), 1, 0) 

72. out5.save("{}{}50".format(outws, cdl)) 

73. print "Building pyramids........" 

74. arcpy.BuildPyramids_management("{}{}50".format(outws, cdl)) 

75. print "Completed Wheat recode in: {}".format(datetime.datetime.now() - start_t) 

76. 

77. start_t = datetime.datetime.now() 

78. print "Recoding Veg & Ground Fruit at {}".format(start_t) 

79. out6 = Con(((Raster("{}60".format(cdl)) == 1) | (Raster("{}61".format(cdl)) == 1) | (Ra

ster("{}68".format(cdl)) == 1) 

80. | (Raster("{}26".format(cdl)) == 1) | (Raster("{}56".format(cdl)) == 1)), 1

, 0) 

81. out6.save("{}{}60".format(outws, cdl)) 

82. print "Building pyramids........" 

83. arcpy.BuildPyramids_management("{}{}60".format(outws, cdl)) 

84. print "Completed Veg & Ground Fruit recode in: {}".format(datetime.datetime.now() - sta

rt_t) 

85. 

86. start_t = datetime.datetime.now() 

87. print "Copying Orchards & Vineyards at {}".format(start_t) 

88. arcpy.Copy_management("{}70".format(cdl), "{}{}70".format(outws, cdl)) 

89. print "Completed Orchards & Vineyards copy in: {}".format(datetime.datetime.now() - sta

rt_t) 

90. 

91. start_t = datetime.datetime.now() 

92. print "Recoding Other Grains at {}".format(start_t) 

93. out8 = Con(((Raster("{}80".format(cdl)) == 1) | (Raster("{}18".format(cdl)) == 1) | (Ra

ster("{}48".format(cdl)) == 1) 

94. | (Raster("{}58".format(cdl)) == 1)), 1, 0) 

95. out8.save("{}{}80".format(outws, cdl)) 

96. print "Building pyramids........" 

97. arcpy.BuildPyramids_management("{}{}80".format(outws, cdl)) 

98. print "Completed Other Grains recode in: {}".format(datetime.datetime.now() - start_t)

99. 

100. start_t = datetime.datetime.now() 

101. print "Copying Other Row Crops at {}".format(start_t) 

102. arcpy.Copy_management("{}90".format(cdl), "{}{}90".format(outws, cdl)) 

103. print "Completed Other Row Crops copy in: {}".format(datetime.datetime.now() - s

tart_t) 

104. 
32

105. start_t = datetime.datetime.now() 

106. print "Copying Other Crops at {}".format(start_t) 

107. arcpy.Copy_management("{}100".format(cdl), "{}{}100".format(outws, cdl)) 

108. print "Completed Other Crops copy in: {}".format(datetime.datetime.now() - start

_t) 

109. 

110. start_t = datetime.datetime.now() 

111. print "Copying Pasture, Hay, Silage at {}".format(start_t) 

112. arcpy.Copy_management("{}110".format(cdl), "{}{}110".format(outws, cdl)) 

113. print "Completed Pasture, Hay, Silage copy in: {}".format(datetime.datetime.now(

) - start_t) 

114. 

115. print "Copying Key....." 

116. arcpy.Copy_management("../CDL_Recodes/CDL_GenClass11_1015.gdb/CDL_GenClass11_key

", 

117. "{}CDL_GenClass11_key".format(outws)) 

118. 

119. print "Completed 11 Class Recode in: {}".format(datetime.datetime.now() - start_

t1) 

Script 3: “3_CDL_CountyClipping_1015.py”

1. ## Script 3. This script clips each raster from script 2 by county. These will serve as

an input for the expansion script. 

2. 

3. __author__ = 'Steven Lennartz' 

4. 

5. ## Import Site Packages 

6. import arcpy 

7. import datetime 

8. from arcpy import env 

9. from arcpy.sa import * 

10. 

11. ## Check out the ArcGIS Spatial Analyst extension license 

12. arcpy.CheckOutExtension("Spatial") 

13. 

14. ## Set variables 

15. ## In-workspace is the out-workspace from step 2 

16. inws = r"../CDL_Recodes/CDL_GenClass11_1015.gdb/" 

17. ## Output directory for 

18. outdir = r"../Cnty_Clips/1015/" 

19. ## Tiger Line County boundaries, vector 

20. cnty = r"../Boundaries/St_Cnty_Boundaries.gdb/tl_2013_cnty_conus_alb" 

21. ## Unique ID for counties, "FIPS code" 

22. fipscode = "GEOID" 

23. ## The cultivated layer from the CDL website 

24. cult = Raster("../Cultivated_Layer/2015_Cultivated_Layer/2015_Cultivated_Layer.img") 

25. 

26. start_t = datetime.datetime.now() 

27. 

28. ## Environmental Settings 

29. arcpy.env.overwriteOutput = 1 

30. arcpy.env.workspace = inws 

31. arcpy.env.snapRaster = cult 

32. arcpy.env.parallelProcessingFactor = "100%" 

33. print "Starting process at: {}".format(start_t) 

34. 
33

35. ## List county FIPS codes 

36. uniqueFips = {(row[0], row[1]) for row in arcpy.da.SearchCursor(cnty, (fipscode, "SHAPE

@"))} 

37. 

38. ## Create list of rasters in current workspace 

39. rasList = arcpy.ListRasters() 

40. 

41. ## Clip each raster by county FIPS code 

42. ## Environmental setting, extent 

43. arcpy.env.extent = "MAXOF" 

44. clipfc = "clip_lyr" 

45. ## Make county feature layer 

46. arcpy.MakeFeatureLayer_management(cnty, clipfc) 

47. 

48. ## Loop through raster list 

49. for raster in rasList: 

50. start_t2 = datetime.datetime.now() 

51. ## return string crop code from raster name 

52. arcpy.env.workspace = inws 

53. desc = arcpy.Describe(raster) 

54. name = desc.name 

55. val = name.split('_')[2] 

56. 

57. ## check for existing geodatabase 

58. outws = r"{}CDL_{}_cnty.gdb".format(outdir, val) 

59. if not arcpy.Exists(outws): 

60. arcpy.CreateFileGDB_management(outdir, "CDL_{}_cnty.gdb".format(val)) 

61. arcpy.env.workspace = outws 

62. 

63. ## Loop through counties to clip raster layers 

64. for fips, shape in uniqueFips: 

65. start_t1 = datetime.datetime.now() 

66. extent = shape.extent 

67. extent = "{} {} {} {}".format(extent.XMin, extent.YMin, extent.XMax, extent.YMa

x) 

68. arcpy.SelectLayerByAttribute_management(clipfc, "NEW_SELECTION", "{} = '{}'".fo

rmat(fipscode, fips)) 

69. in_file = "{}{}".format(inws, raster) 

70. out_file = "{}/CDL_{}_{}".format(outws, val, fips) 

71. if not arcpy.Exists(out_file): 

72. print "Clipping CDL_{}_{}...".format(val, fips) 

73. arcpy.Clip_management(in_file, extent, out_file, clipfc, "2147483647", "Cli

ppingGeometry", "NO_MAINTAIN_EXTENT") 

74. print "Finished clip in: {}".format(datetime.datetime.now() - start_t1) 

75. print "Completed Clipping {} in: {}".format(val, datetime.datetime.now() - start_t2

) 

76. print "Completed All County Clipping in: {}".format(datetime.datetime.now() - start_t)

Script 4: “4_CDL_UpdateAreaValues_1015.py”

1. ## Script 4. This script updates CDL acreage values for the table that compares CDL and

Census county acreages. It requires 

2. ## a geodatabase populated with tables generated using 'Spatial Analyst/Zonal/Tabulate

Area' using each layer generated 

3. ## in script 2 and the Tiger county boundaries. 

4. 

5. __author__ = 'Steven Lennartz' 

6. 
34

7. ## Import Site Packages 

8. import arcpy 

9. import datetime 

10. from arcpy import env 

11. from arcpy.sa import * 

12. 

13. ## Set variables 

14. ## In-workspace 

15. inws = r"../Analysis/NASS2012_CDL1015.gdb/" 

16. ## Master table. Table header should have FIPS code, 11 CDL columns, and 11 Census colu

mns. 

17. intbl = "NASS2012_Genclass11" 

18. 

19. ## Environmental Settings 

20. arcpy.env.workspace = inws 

21. arcpy.CheckOutExtension("Spatial") 

22. arcpy.env.overwriteOutput = 1 

23. arcpy.env.parallelProcessingFactor = "100%" 

24. 

25. start_t1 = datetime.datetime.now() 

26. 

27. ## Create a table view from master table 

28. print "Converting {} to Table View at: {}".format(intbl, start_t1) 

29. tblView = "TableView" 

30. arcpy.MakeTableView_management(intbl, tblView) 

31. 

32. ## List tables generated from "tabulate area" command, described at the top of this scr

ipt.

33. tblList = arcpy.ListTables("*area") 

34. 

35. ## Loop individual area tables to update master table 

36. for tbl in tblList: 

37. start_t = datetime.datetime.now() 

38. print "Processing table {} at: {}".format(tbl, start_t) 

39. ## return string crop code from tbl name 

40. desc = arcpy.Describe(tbl) 

41. name = desc.name 

42. val = name.split('_')[2] 

43. 

44. ## addjoin intbl with tbl (FIPS, GEOID) 

45. arcpy.AddJoin_management(tblView, "FIPS", tbl, "GEOID", "KEEP_COMMON") 

46. 

47. ## calculate corresponding CDL field by converting VALUE_1 from square meters to acr

es (1 sq meter is 0.000247 acres) 

48. outval = "{}.CDL_{}".format(intbl, val) 

49. inval = "CDL_1015_{}_cnty_area.VALUE_1".format(val) 

50. arcpy.CalculateField_management(tblView, outval, "(!{}! * 0.000247)".format(inval),

"PYTHON") 

51. arcpy.RemoveJoin_management(tblView) 

52. print "Completed {} table update in: ".format(datetime.datetime.now() - start_t) 

53. 

54. print "Completed updating all tables in: {}".format(datetime.datetime.now() - start_t1)

 

Script 5: “5_Cultivated_CountyClipping.py”

1. ## Script 5. This script populates a geodatabase with CDL Cultivated Mask rasters by co

unty. These serve as a mask for 

2. ## the expansion script. 
35

3. 

4. __author__ = 'Steven Lennartz' 

5. 

6. ## Import Site Packages 

7. import arcpy 

8. import datetime 

9. from arcpy import env 

10. from arcpy.sa import * 

11. 

12. ## Check out the ArcGIS Spatial Analyst extension license 

13. arcpy.CheckOutExtension("Spatial") 

14. 

15. ## Set variables 

16. ## Output directory same as cultivated layer 

17. outdir = r"../Cultivated_Layer/2015_Cultivated_Layer/" 

18. ## Tiger Line county boundaries, vector 

19. cnty = r"../Boundaries/St_Cnty_Boundaries.gdb/tl_2013_cnty_conus_alb" 

20. ## Unique ID for counties, "FIPS code" 

21. fipscode = "GEOID" 

22. ## The cultivated layer from the CDL website 

23. cult = Raster("{}2015_Cultivated_Layer.img".format(outdir)) 

24. 

25. start_t = datetime.datetime.now() 

26. 

27. ## Environmental Settings 

28. arcpy.env.overwriteOutput = 1 

29. arcpy.env.snapRaster = cult 

30. arcpy.env.parallelProcessingFactor = "100%" 

31. 

32. print "Starting process at: {}".format(start_t) 

33. 

34. ## List county FIPS codes 

35. uniqueFips = {(row[0], row[1]) for row in arcpy.da.SearchCursor(cnty, (fipscode, "SHAPE

@"))} 

36. 

37. ## Check for output geodatabase 

38. if not arcpy.Exists("{}2015_Cultivated_Layer.gdb".format(outdir)): 

39. print "Creating 2015_Cultivated_Layer.gdb" 

40. arcpy.CreateFileGDB_management(outdir, "2015_Cultivated_Layer.gdb") 

41. if not arcpy.Exists("{}2015_Cultivated_Layer.gdb/cultmask_2015".format(outdir)): 

42. ## Recode CDL Cultivated Layer 

43. start_t2 = datetime.datetime.now() 

44. print "Recoding 2015_Cultivated_Layer.img" 

45. arcpy.env.workspace = "{}2015_Cultivated_Layer.gdb".format(outdir) 

46. arcpy.env.scratchWorkspace = "{}2015_Cultivated_Layer.gdb".format(outdir) 

47. arcpy.gp.Reclassify_sa(cult, "Value", "0 0;1 0;2 1;3 0", "{}2015_Cultivated_Layer.g

db/cultmask_2015".format( 

48. outdir), "DATA") 

49. print "Completed Recode in: {}".format(datetime.datetime.now() - start_t2) 

50. ## Cast variable for agricultural mask 

51. agmask = Raster("{}2015_Cultivated_Layer.gdb/cultmask_2015".format(outdir)) 

52. print "Building Pyramids....." 

53. arcpy.BuildPyramids_management(agmask) 

54. 

55. ## Clip CDL cultivated raster by county FIPS code 

56. if not arcpy.Exists("{}2015_Cult_Cnty.gdb".format(outdir)): 

57. arcpy.CreateFileGDB_management(outdir, "2015_Cult_Cnty.gdb") 

58. 

59. ## Set environments 

60. arcpy.env.workspace = "{}2015_Cult_Cnty.gdb".format(outdir) 

61. arcpy.env.scratchWorkspace = "{}2015_Cult_Cnty.gdb".format(outdir) 
36

62. arcpy.env.extent = "MAXOF" 

63. 

64. ## Make County Feature Layer 

65. clipfc = "clip_lyr" 

66. arcpy.MakeFeatureLayer_management(cnty, clipfc) 

67. 

68. ## Loop through counties to clip CDL cultivated layer 

69. for fips, shape in uniqueFips: 

70. start_t1 = datetime.datetime.now() 

71. extent = shape.extent 

72. extent = "{} {} {} {}".format(extent.XMin, extent.YMin, extent.XMax, extent.YMax) 

73. arcpy.SelectLayerByAttribute_management(clipfc, "NEW_SELECTION", "{} = '{}'".format

(fipscode, fips)) 

74. out_file = "{}2015_Cult_Cnty.gdb/Cult_2015_{}".format(outdir, fips) 

75. print "Clipping Cult_2015_{}....".format(fips) 

76. arcpy.Clip_management(agmask, extent, out_file, clipfc, "2147483647", "ClippingGeom

etry", "NO_MAINTAIN_EXTENT") 

77. print "Finished clip in: {}".format(datetime.datetime.now() - start_t1) 

78. print "Completed All County Clipping in: {}".format(datetime.datetime.now() - start_t)

Script 6: “6_CDL_Expand_1015.py”

1. ## Script 6. This script uses the master table (updated in script 4) to expand the use

footprint of counties where CDL 

2. ## acreage is lower than Census acreage. 

3. 

4. __author__ = 'Steven Lennartz' 

5. 

6. ## Import Site Packages 

7. import arcpy 

8. import datetime 

9. from arcpy import env 

10. from arcpy.sa import * 

11. 

12. ## Set variables 

13. ## Master table from script 4 

14. acres = r"../Analysis/NASS2012_CDL1015.gdb/NASS2012_Genclass11" 

15. ## Unique ID for counties, "FIPS code" 

16. geoid = "FIPS" 

17. ## Key table for general class crop names and codes 

18. classcode = r"../Analysis/NASS2012_CDL1015.gdb/NASS2012_Genclass11_codes" 

19. ## Crop code field name. These codes are arbitrary, increments of 10, from 10 through 1

10. 

20. classfield = "GenClassID" 

21. 

22. ## In-workspace is the output directory from script 3 

23. inws = r"../Cnty_Clips/1015/" 

24. ## Output directory 

25. outws = r"../Expand/1015/" 

26. ## The cultivated layer from the CDL website 

27. maskws = r"../Cultivated_Layer/2015_Cultivated_Layer/2015_Cult_Cnty.gdb/" 

28. 

29. start_t = datetime.datetime.now() 

30. 

31. ## Check out the ArcGIS Spatial Analyst extension license 

32. arcpy.CheckOutExtension("Spatial") 

33. ## Environmental settings 

34. arcpy.env.overwriteOutput = 1 
37

35. arcpy.env.snapRaster = Raster("../Cultivated_Layer/2015_Cultivated_Layer/2015_Cultivate

d_Layer.img") 

36. arcpy.env.parallelProcessingFactor = "100%" 

37. 

38. print "Begin processing at: {}".format(start_t) 

39. 

40. ## Get list of class codes 

41. uniqueCrops = {row[0] for row in arcpy.da.SearchCursor(classcode, classfield)} 

42. 

43. ## Define function to get a value's cell count 

44. def get_raster_count(raster_obj, value): 

45. 

46. counts = [row.COUNT for row in arcpy.SearchCursor(raster_obj) if row.VALUE == value

] 

47. if not counts: 

48. print "No matching value" 

49. elif len(counts) > 1: 

50. print "Too many counts" 

51. else: 

52. return counts[0] 

53. 

54. ## Expand looping 

55. for crop in uniqueCrops: 

56. crop = int(crop) 

57. crop = str(crop) 

58. 

59. ## check for existing geodatabase 

60. if not arcpy.Exists("{}CDL{}x_cnty.gdb".format(outws, crop)): 

61. print "Creating CDL{}x_cnty Geodatabase".format(crop) 

62. arcpy.CreateFileGDB_management(outws, "CDL{}x_cnty.gdb".format(crop)) 

63. 

64. ## Return a unique set of values in the FIPS field for counties with higher NASS ac

reage than the CDL 

65. gtacres = "gtlayer" 

66. arcpy.MakeQueryTable_management(acres, gtacres, "USE_KEY_FIELDS", "OBJECTID", "", "

NASS_{0} > CDL_{0}".format( 

67. crop)) 

68. uniqueValues = {(row[0], row[1]) for row in arcpy.da.SearchCursor(gtacres, (geoid,

"NASS_{}".format(crop)))} 

69. 

70. ## Loop through counties needing expansion 

71. for fips, nassacres in uniqueValues: 

72. arcpy.env.workspace = "{}CDL{}x_cnty.gdb/".format(outws, crop) 

73. arcpy.env.scratchWorkspace = "{}CDL{}x_cnty.gdb/".format(outws, crop) 

74. fips = str(fips) 

75. croptime1 = datetime.datetime.now() 

76. print "expanding CDL{}_{}".format(crop, fips) 

77. ## Set mask from script 5 

78. mask = Raster("{}Cult_2015_{}".format(maskws, fips)) 

79. infile = Raster("{0}CDL_{1}_cnty.gdb/CDL_{1}_{2}".format(inws, crop, fips)) 

80. ## Expand flagged county by 1 pixel 

81. outfile = Expand(infile, 1, 1) 

82. ## Using the environmental setting for mask does not work here. Use mask in map

algebra instead. 

83. outfile = outfile * mask 

84. arcpy.BuildRasterAttributeTable_management(outfile, "Overwrite") 

85. count = get_raster_count(outfile, 1) 

86. ## Iteratively compare and expand county use footprint until area >= Census, or

mask area buildout. 

87. if count > 0: 

88. cdlacres = float(count * (900 / 4046.86)) 
38

89. while cdlacres < nassacres: 

90. outfile = Expand(outfile, 1, 1) 

91. outfile = outfile * mask 

92. arcpy.BuildRasterAttributeTable_management(outfile, "Overwrite") 

93. count = get_raster_count(outfile, 1) 

94. lastacres = cdlacres 

95. cdlacres = float(count * (900 / 4046.86)) 

96. if not (lastacres - cdlacres): 

97. print "No change in area" 

98. break 

99. outfile.save("{0}CDL{1}x_cnty.gdb/CDL{1}x_{2}".format(outws, crop, fips)) 

100. print "Building pyramids...." 

101. arcpy.BuildPyramids_management("{0}CDL{1}x_cnty.gdb/CDL{1}x_{2}".format(

outws, crop, fips)) 

102. print "..{} expansion complete in {}".format(outfile, (datetime.datetime

.now()-croptime1)) 

103. 

104. print "completed expansion in: {}".format(datetime.datetime.now() - start_t) 

Script 7: “7_CDL_MosaicExpanded_1015.py”

1. ## Script 7. Mosaic the county based expansions from script 6. 

2. 

3. __author__ = 'Steven Lennartz' 

4. 

5. ## Import Site Packages 

6. import arcpy 

7. import datetime 

8. from arcpy import env 

9. from arcpy.sa import * 

10. 

11. ## Set Variables 

12. ## Key table for general class crop names and codes 

13. classcode = r"../Analysis/NASS2012_CDL1015.gdb/NASS2012_Genclass11_codes" 

14. ## Crop code field name. These codes are arbitrary, increments of 10, from 10 through 1

10. 

15. classfield = "GenClassID" 

16. ## Master table from script 4 

17. acres = r"../Analysis/NASS2012_CDL1015.gdb/NASS2012_Genclass11" 

18. ## Unique ID for counties, "FIPS code" 

19. geoid = "FIPS" 

20. 

21. ## In-workspace is the output directory from script 3 

22. inws = r"../Cnty_Clips/1015/" 

23. ## Output directory 

24. outws = r"../Expand/1015/Mosaic/" 

25. 

26. start_t = datetime.datetime.now() 

27. 

28. ## Environmental settings 

29. arcpy.env.overwriteOutput = 1 

30. arcpy.env.snapRaster = Raster("../Cultivated_Layer/2015_Cultivated_Layer/2015_Cultivate

d_Layer.img") 

31. arcpy.env.parallelProcessingFactor = "100%" 

32. 

33. print "Begun processing at: {}".format(start_t) 

34. 

35. ## Check out the ArcGIS Spatial Analyst extension license 

36. arcpy.CheckOutExtension("Spatial") 
39

37. 

38. ## Get list of class codes 

39. uniqueCrops = {row[0] for row in arcpy.da.SearchCursor(classcode, classfield)} 

40. 

41. ## Get FIPS codes 

42. uniqueFips = {row[0] for row in arcpy.da.SearchCursor(acres, geoid)} 

43. 

44. ## check for existing geodatabase 

45. if not arcpy.Exists("{}CDL1015x_mosaic.gdb".format(outws)): 

46. print "Creating CDL1015x_mosaic.gdb" 

47. arcpy.CreateFileGDB_management(outws, "CDL1015x_mosaic.gdb") 

48. 

49. ## Set output geodatabase 

50. outgdb = "{}CDL1015x_mosaic.gdb".format(outws) 

51. 

52. ## Mosaic loop by crop 

53. for crop in uniqueCrops: 

54. crop = int(crop) 

55. crop = str(crop) 

56. 

57. croptime1 = datetime.datetime.now() 

58. print "starting {} mosaic at: {}".format(crop, croptime1) 

59. 

60. ## Expanded geodatabase from script 6 

61. xgdb = "../Expand/1015/CDL{}x_cnty.gdb".format(crop) 

62. arcpy.env.workspace = xgdb 

63. ## Create Raster Catalog 

64. cat = "{}/CDL{}x_cat".format(outgdb, crop) 

65. if arcpy.Exists(cat): 

66. print "{} Exists".format(cat) 

67. if not arcpy.Exists(cat): 

68. print "Creating Raster Catalog CDL{}x_cat".format(crop) 

69. arcpy.CreateRasterCatalog_management(outgdb, "CDL{}x_cat".format(crop)) 

70. print "Copying Expanded Rasters to Catalog CDL{}x_cat".format(crop) 

71. arcpy.WorkspaceToRasterCatalog_management(xgdb, cat) 

72. 

73. ## Load non-expanded rasters into Raster Catalog 

74. print "Loading non-expanded rasters into CDL{}x_cat".format(crop) 

75. for fips in uniqueFips: 

76. timex2 = datetime.datetime.now() 

77. cdlx = "{}/CDL{}x_{}".format(xgdb, crop, fips) 

78. if arcpy.Exists(cdlx): 

79. print "CDL{}x_{} exists".format(crop, fips) 

80. if not arcpy.Exists(cdlx): 

81. if arcpy.Exists("{}/CDL_{}_{}".format(cat, crop, fips)): 

82. print "{}/CDL_{}_{} Exists".format(cat, crop, fips) 

83. if not arcpy.Exists("{}/CDL_{}_{}".format(cat, crop, fips)): 

84. arcpy.CopyRaster_management("{0}CDL_{1}_cnty.gdb/CDL_{1}_{2}".format(in

ws, crop, fips), "{}/CDL_{}_{}".format(cat, crop, fips)) 

85. print "Added CDL_{}_{} to Catalog in {}".format(crop, fips, datetime.da

tetime.now() - timex2) 

86. 

87. ## Mosaic Raster Catalog to new dataset 

88. print "Raster CDL{}x_cat to Raster Dataset at: {}".format(crop, datetime.datetime.n

ow()) 

89. arcpy.RasterCatalogToRasterDataset_management(cat, "{}/CDL_1015_{}x".format(outgdb,

crop)) 

90. print "......Building pyramids" 

91. arcpy.BuildPyramids_management("{}/CDL_1015_{}x".format(outgdb, crop)) 

92. print "......Building attribute table" 
40

93. arcpy.BuildRasterAttributeTable_management("{}/CDL_1015_{}x".format(outgdb, crop),

"NONE") 

94. print "..CDL_1015_{}x mosaic complete in {}".format(crop, (datetime.datetime.now()-

croptime1)) 

95. 

96. print "completed mosaic in: {}".format(datetime.datetime.now() - start_t) 

Script 8: “8_CDL_FixHoles_1015.py”

1. ## Script 8. Some county FIPS codes from Tiger County Boundaries are not reported in th

e Ag Census. Those counties do 

2. ## not join in the expansion script 6 and result in NoData values. This script corrects

for NoData. 

3. 

4. __author__ = ' Steven Lennartz' 

5. 

6. ## Import Site Packages 

7. import arcpy 

8. import datetime 

9. from arcpy import env 

10. from arcpy.sa import * 

11. 

12. ## Set Variables 

13. ## In-workspace is the mosaic geodatabase from script 7 

14. inws = r"../Expand/1015/Mosaic/CDL1015x_mosaic.gdb/" 

15. ## Out-workspace from script 2 supplies replacement values for NoData 

16. patch = r"../CDL_Recodes/CDL_GenClass11_1015.gdb/" 

17. 

18. ## Environmental settings 

19. arcpy.env.workspace = inws 

20. arcpy.env.overwriteOutput = 1 

21. arcpy.env.snapRaster = Raster("../Cultivated_Layer/2015_Cultivated_Layer/2015_Cultivate

d_Layer.img") 

22. arcpy.env.parallelProcessingFactor = "100%" 

23. arcpy.env.scratchWorkspace = patch 

24. 

25. ## Check out the ArcGIS Spatial Analyst extension license 

26. arcpy.CheckOutExtension("Spatial") 

27. 

28. start_t = datetime.datetime.now() 

29. print "Begun processing at: {}".format(start_t) 

30. 

31. ## check for existing geodatabase 

32. if not arcpy.Exists("../Expand/1015/Mosaic/Final/CDL1015x_mosaic2.gdb"): 

33. print "Creating CDL1015x_mosaic2.gdb" 

34. arcpy.CreateFileGDB_management("../Expand/1015/Mosaic/Final/", "CDL1015x_mosaic2.gd

b") 

35. outws = r"../Expand/1015/Mosaic/Final/CDL1015x_mosaic2.gdb/" 

36. 

37. ## List rasters in workspace 

38. rasterList = arcpy.ListRasters() 

39. ## Loop through rasters to correct NoData 

40. for raster in rasterList: 

41. start_t1 = datetime.datetime.now() 

42. print "Processing {} at {}".format(raster, datetime.datetime.now()) 

43. inras = Raster(raster) 

44. desc = arcpy.Describe(inras) 

45. name = desc.name 

46. val = name.split('_')[2] 
41

47. val = val[:-1] 

48. true = Raster("{}CDL_1015_{}".format(patch, val)) 

49. out = Con(IsNull(inras), true, inras) 

50. out.save("{}CDL_1015_{}x2".format(outws, val)) 

51. print "Building Pyramids....." 

52. arcpy.BuildPyramids_management("{}CDL_1015_{}x2".format(outws, val)) 

53. print "Completed NoData holes for Class {} in: {}".format(val, datetime.datetime.no

w() - start_t1) 

54. print "Completed NoData holes for all classes in: {}".format(datetime.datetime.now() -

start_t) 

Supplemental Table: “cdl_recode_new.txt”

FROM TO OUT MAPPING

0 0 0 ValueToValue

1 1 10 ValueToValue

2 2 20 ValueToValue

3 3 30 ValueToValue

4 4 80 ValueToValue

5 5 40 ValueToValue

6 6 90 ValueToValue

7 7 7 ValueToValue

8 8 8 ValueToValue

9 9 9 ValueToValue

10 10 90 ValueToValue

11 11 90 ValueToValue

12 12 60 ValueToValue

13 13 60 ValueToValue

14 14 60 ValueToValue

15 15 15 ValueToValue

16 16 16 ValueToValue

17 17 17 ValueToValue

18 18 18 ValueToValue

19 19 19 ValueToValue

20 20 20 ValueToValue

21 21 80 ValueToValue

22 22 50 ValueToValue

23 23 50 ValueToValue

24 24 50 ValueToValue

25 25 80 ValueToValue

26 26 45 ValueToValue

27 27 80 ValueToValue

28 28 80 ValueToValue

29 29 80 ValueToValue

30 30 80 ValueToValue

31 31 80 ValueToValue

32 32 80 ValueToValue

33 33 80 ValueToValue

34 34 80 ValueToValue

35 35 60 ValueToValue

36 36 110 ValueToValue

37 37 110 ValueToValue

38 38 80 ValueToValue

39 39 80 ValueToValue
42

40 40 40 ValueToValue

41 41 90 ValueToValue

42 42 60 ValueToValue

43 43 60 ValueToValue

44 44 100 ValueToValue

45 45 80 ValueToValue

46 46 60 ValueToValue

47 47 60 ValueToValue

48 48 60 ValueToValue

49 49 60 ValueToValue

50 50 60 ValueToValue

51 51 60 ValueToValue

52 52 60 ValueToValue

53 53 60 ValueToValue

54 54 60 ValueToValue

55 55 61 ValueToValue

56 56 90 ValueToValue

57 57 60 ValueToValue

58 58 110 ValueToValue

59 59 110 ValueToValue

60 60 110 ValueToValue

61 61 100 ValueToValue

62 62 110 ValueToValue

63 63 140 ValueToValue

64 64 160 ValueToValue

65 65 200 ValueToValue

66 66 70 ValueToValue

67 67 70 ValueToValue

68 68 70 ValueToValue

69 69 70 ValueToValue

70 7075 ValueToValue

71 71 70 ValueToValue

72 72 70 ValueToValue

73 73 73 ValueToValue

74 74 70 ValueToValue

75 75 70 ValueToValue

76 76 70 ValueToValue

77 77 70 ValueToValue

78 78 78 ValueToValue

79 79 79 ValueToValue

80 80 80 ValueToValue

81 81 200 ValueToValue

82 82 123 ValueToValue

83 83 180 ValueToValue

84 84 84 ValueToValue

85 85 85 ValueToValue

86 86 86 ValueToValue

87 87 195 ValueToValue

88 88 200 ValueToValue

89 89 89 ValueToValue

90 90 90 ValueToValue

91 91 91 ValueToValue

92 92 100 ValueToValue

93 9393 ValueToValue

94 94 94 ValueToValue

95 95 95 ValueToValue
43

96 96 96 ValueToValue

97 97 97 ValueToValue

98 98 98 ValueToValue

99 99 99 ValueToValue

100 100 100 ValueToValue

101 101 101 ValueToValue

102 102 102 ValueToValue

103 103 103 ValueToValue

104 104104 ValueToValue

105 105 105 ValueToValue

106 106 106 ValueToValue

107 107 107 ValueToValue

108 108 108 ValueToValue

109 109 109 ValueToValue

110 110 110 ValueToValue

111 111 180 ValueToValue

112 112 200 ValueToValue

113 113 113 ValueToValue

114 114 114 ValueToValue

115 115 115 ValueToValue

116 116 116 ValueToValue

117 117 117 ValueToValue

118 118 118 ValueToValue

119 119 119 ValueToValue

120 120 120 ValueToValue

121 121 121 ValueToValue

122 122 122 ValueToValue

123 123 123 ValueToValue

124 124 124 ValueToValue

125 125 125 ValueToValue

126 126 126 ValueToValue

127 127 127 ValueToValue

128 128 128 ValueToValue

129 129 129 ValueToValue

130 130 130 ValueToValue

131 131 200 ValueToValue

132 132 132 ValueToValue

133 133 133 ValueToValue

134 134 134 ValueToValue

135 135 135 ValueToValue

136 136 136 ValueToValue

137 137 137 ValueToValue

138 138 138 ValueToValue

139 139 139 ValueToValue

140 140 140 ValueToValue

141 141 140 ValueToValue

142 142 140 ValueToValue

143 143 140 ValueToValue

144 144 144 ValueToValue

145 145 145 ValueToValue

146 146 146 ValueToValue

147 147 147 ValueToValue

148 148 148 ValueToValue

149 149 149 ValueToValue

150 150 110 ValueToValue

151 151 151 ValueToValue
44

152 152 160 ValueToValue

153 153 153 ValueToValue

154 154 154 ValueToValue

155 155 155 ValueToValue

156 156 156 ValueToValue

157 157 157 ValueToValue

158 158 158 ValueToValue

159 159 159 ValueToValue

160 160 160 ValueToValue

161 161 161 ValueToValue

162 162 162 ValueToValue

163 163 163 ValueToValue

164 164 164 ValueToValue

165 165 165 ValueToValue

166 166 166 ValueToValue

167 167 167 ValueToValue

168 168 168 ValueToValue

169 169 169 ValueToValue

170 170 170 ValueToValue

171 171 110 ValueToValue

172 172 172 ValueToValue

173 173 173 ValueToValue

174 174 174 ValueToValue

175 175 175 ValueToValue 176 176 110 ValueToValue

177 177 177 ValueToValue

178 178 178 ValueToValue

179 179 179 ValueToValue

180 180 180 ValueToValue

181 181 110 ValueToValue

182 182 182 ValueToValue

183 183 183 ValueToValue

184 184 184 ValueToValue

185 185 185 ValueToValue

186 186 186 ValueToValue

187 187 187 ValueToValue

188 188 188 ValueToValue

189 189 189 ValueToValue

190 190 190 ValueToValue

191 191 191 ValueToValue

192 192 192 ValueToValue

193 193 193 ValueToValue

194 194 194 ValueToValue

195 195 195 ValueToValue

196 196 196ValueToValue

197 197 197 ValueToValue

198 198 198 ValueToValue

199 199 199 ValueToValue

200 200 200 ValueToValue

201 201 201 ValueToValue

202 202 202 ValueToValue

203 203 203 ValueToValue

204 204 70 ValueToValue

205 205 80 ValueToValue

206 206 60 ValueToValue

207 207 60 ValueToValue
45

208 208 60 ValueToValue

209 209 60 ValueToValue

210 210 70 ValueToValue

211 211 70 ValueToValue

212 212 70 ValueToValue

213 213 60 ValueToValue

214 214 60 ValueToValue

215 215 215 ValueToValue

216 216 60 ValueToValue

217 217 70 ValueToValue

218 218 70 ValueToValue

219 219 60 ValueToValue

220 220 70 ValueToValue

221 221 61 ValueToValue

222 222 60 ValueToValue

223 223 70 ValueToValue

224 224 110 ValueToValue

225 225 15 ValueToValue

226 226 18 ValueToValue

227 227 60 ValueToValue

228 228 228 ValueToValue

229 229 60 ValueToValue

230 230 56 ValueToValue

231 231 60 ValueToValue

232 232 26 ValueToValue

233 233 68 ValueToValue

234 234 58 ValueToValue

235 235 80 ValueToValue

236 236 58 ValueToValue

237 237 18 ValueToValue

238 238 25 ValueToValue

239 239 42 ValueToValue

240 240 48 ValueToValue

241 241 14 ValueToValue

242 242 61 ValueToValue

243 243 60 ValueToValue

244 244 60 ValueToValue

245 245 60 ValueToValue

246 246 60 ValueToValue

247 247 60 ValueToValue

248 248 60 ValueToValue

249 249 60 ValueToValue

250 250 61 ValueToValue

251 251 251 ValueToValue

252 252 252 ValueToValue

253 253 253 ValueToValue

254 254 48 ValueToValue

255 255 255 ValueToValue

Supplemental Table: “EPA_CDL_Recode_Key.txt”

Value CDL_General_Class

10 Corn

14 Corn/soybeans
46

15 Corn/wheat

18 Corn/grains

20 Cotton

25 Cotton/wheat

26 Cotton/vegetables

30 Rice

40 Soybeans

42 Soybeans/cotton

45 Soybeans/wheat

48 Soybeans/grains

50 Wheat

56 Wheat/vegetables

58 Wheat/grains

60 Vegetables and ground fruit

61 (ground fruit)

68 Vegetables/grains

70 Orchards and grapes

75 Other trees

80 Other grains

90 Other row crops

100 Other crops

110 Pasture/hay/forage

121 Developed - open

122 Developed - low

123 Developed - med

124 Developed - high

140 Forest

160 Shrubland

180 Water

190 Wetlands - woods

195 Wetlands - herbaceous

200 Miscellaneous land
47

APPENDIX C: Corrected Links

Chemical Document1 Section (paragraph 

or page #)1

Corrected Link/Comment

Malathion (1) Chapter 1 Page 1 - 6 Cancellation procedures under FIFRA 6(f)

https://www.epa.gov/pesticide-tolerances/pesticide-cancellation-under-epas-own-initiative

Malathion (2) Chapter 1 Page 1 - 7 Reporting adverse effects information under FIFRA 6(a)(2)

https://www.epa.gov/pesticide-incidents/incident-reporting-pesticide-manufacturers-registrants

Malathion (3) Chapter 1 Page 1 - 9 eCFR inert ingredient list (inert list starts at section 180.920)

http://www.ecfr.gov/cgi-bin/textidx?SID=26b254e3ec275241162fb666aa219c7b&mc=true&node=pt40.24.180&rgn=div5

Malathion (4) Chapter 1 Page 1 - 9 Fact sheet on tolerances

http://nepis.epa.gov/Exe/ZyNET.exe/9100CFQL.TXT?ZyActionD=ZyDocument&Client=EPA&I

ndex=1986+Thru+1990&Docs=&Query=&Time=&EndTime=&SearchMethod=1&TocRestrict=

n&Toc=&TocEntry=&QField=&QFieldYear=&QFieldMonth=&QFieldDay=&IntQFieldOp=0&

ExtQFieldOp=0&XmlQuery=&File=D%3A%5Czyfiles%5CIndex%20Data%5C86thru90%5CTx

t%5C00000020%5C9100CFQL.txt&User=ANONYMOUS&Password=anonymous&SortMethod

=h%7C-

&MaximumDocuments=1&FuzzyDegree=0&ImageQuality=r75g8/r75g8/x150y150g16/i425&Di

splay=p%7Cf&DefSeekPage=x&SearchBack=ZyActionL&Back=ZyActionS&BackDesc=Result

s%20page&MaximumPages=1&ZyEntry=1&SeekPage=x&ZyPURL

Malathion (5) Chapter 1 Page 1 – 40 and 

page 58

Environmental risk assessment models

https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/models-pesticide-riskassessment

Malathion and 

Chlorpyrifos (6)

Chapter 1 Page 60 Guidelines for review of ecological toxicity open literature studies

https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/evaluation-guidelinesecological-toxicity-data-open

Malathion (7) Chapter 1 Page 63 Guidance for using incident data in evaluating listed and non-listed species

https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/guidance-using-incidentdata-evaluating-listed-and

Malathion (8) Chapter 1 Page 65 McNest details

https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/models-pesticide-riskassessment#mcnest

Malathion (9) Appendix 1-1 Page 3 Organophosphate cumulative risk assessment

https://archive.epa.gov/pesticides/reregistration/web/pdf/phosalone_red.pdf

(includes more than risk assessment of phosalone)

Malathion (10) Appendix 1-10 8 Rio La Plata watershed TMDL

https://ofmpub.epa.gov/waters10/attains_impaired_waters.show_tmdl_document?p_tmdl_doc_blo

bs_id=75535
48

Chemical Document1 Section (paragraph 

or page #)1

Corrected Link/Comment

Malathion (11) Chapter 3 Page 3 - 26 PRZM

http://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P100K3R4.txt

Malathion (12) Chapter 3 Page 3 - 26 Input parameter guidance

https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/guidance-selecting-inputparameters-modeling

Malathion (13) Appendix 4-7-

sup 1

Tables 3, 6, 9, 12, 

13

The BNA links will be replaced with the following: “See specific species pages in The Birds of 

North America Online (accessed 2015). A. Poole, Editor. Ithaca: Cornell Lab of Ornithology.”

Chlorpyrifos (1) Chapter 1 1 - 6 Cancellation procedures under FIFRA 6(f)

https://www.epa.gov/pesticide-tolerances/pesticide-cancellation-under-epas-own-initiative

Chlorpyrifos (2) Chapter 1 1 - 7 Endangered species bulletins

https://www.epa.gov/endangered-species/endangered-species-protection-bulletins

Chlorpyrifos (3) Chapter 1 1 - 7 Reporting adverse effects data under FIFRA section 6(a)(2) 

https://www.epa.gov/pesticide-incidents/incident-reporting-pesticide-manufacturers-registrants

Chlorpyrifos (4) Chapter 1 Page 1 - 9 eCFR inerts list

http://www.ecfr.gov/cgi-bin/textidx?SID=26b254e3ec275241162fb666aa219c7b&mc=true&node=pt40.24.180&rgn=div5

Chlorpyrifos (5) Chapter 1 Page 1 - 9 Fact sheet on tolerances

http://nepis.epa.gov/Exe/ZyNET.exe/9100CFQL.TXT?ZyActionD=ZyDocument&Client=EPA&I

ndex=1986+Thru+1990&Docs=&Query=&Time=&EndTime=&SearchMethod=1&TocRestrict=

n&Toc=&TocEntry=&QField=&QFieldYear=&QFieldMonth=&QFieldDay=&IntQFieldOp=0&

ExtQFieldOp=0&XmlQuery=&File=D%3A%5Czyfiles%5CIndex%20Data%5C86thru90%5CTx

t%5C00000020%5C9100CFQL.txt&User=ANONYMOUS&Password=anonymous&SortMethod

=h%7C-

&MaximumDocuments=1&FuzzyDegree=0&ImageQuality=r75g8/r75g8/x150y150g16/i425&Di

splay=p%7Cf&DefSeekPage=x&SearchBack=ZyActionL&Back=ZyActionS&BackDesc=Result

s%20page&MaximumPages=1&ZyEntry=1&SeekPage=x&ZyPURL

Chlorpyrifos (6) Chapter 1 1 - 48 Pesticide risk assessment models web page

https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/models-pesticide-riskassessment

Chlorpyrifos (7) Chapter 1 1 - 49 Guidance for evaluating open literature studies

https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/evaluation-guidelinesecological-toxicity-data-open

Chlorpyrifos (8) Chapter 1 1 - 53 Guidance for using incident data in evaluating listed and non-listed species

https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/guidance-using-incidentdata-evaluating-listed-and

Chlorpyrifos (9) Chapter 1 1 - 55 McNest
49

Chemical Document1 Section (paragraph 

or page #)1

Corrected Link/Comment

https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/models-pesticide-riskassessment#mcnest

Diazinon (1) Chapter 1 Page 6 Cancellation procedures under FIFRA 6(f)

https://www.epa.gov/pesticide-tolerances/pesticide-cancellation-under-epas-own-initiative

Diazinon (2) Chapter 1 Page 7 Endangered Species Bulletins

https://www.epa.gov/endangered-species/endangered-species-protection-bulletins

Diazinon (3) Chapter 1 Page 7 Reporting adverse effects data under FIFRA 6(a)(2)

https://www.epa.gov/pesticide-incidents/incident-reporting-pesticide-manufacturers-registrants

Diazinon (4) Chapter 1 Page 9 eCFR inerts list

http://www.ecfr.gov/cgi-bin/textidx?SID=26b254e3ec275241162fb666aa219c7b&mc=true&node=pt40.24.180&rgn=div5

Diazinon (5) Chapter 1 Page 1 - 9 Fact sheet on tolerances

http://nepis.epa.gov/Exe/ZyNET.exe/9100CFQL.TXT?ZyActionD=ZyDocument&Client=EPA&I

ndex=1986+Thru+1990&Docs=&Query=&Time=&EndTime=&SearchMethod=1&TocRestrict=

n&Toc=&TocEntry=&QField=&QFieldYear=&QFieldMonth=&QFieldDay=&IntQFieldOp=0&

ExtQFieldOp=0&XmlQuery=&File=D%3A%5Czyfiles%5CIndex%20Data%5C86thru90%5CTx

t%5C00000020%5C9100CFQL.txt&User=ANONYMOUS&Password=anonymous&SortMethod

=h%7C-

&MaximumDocuments=1&FuzzyDegree=0&ImageQuality=r75g8/r75g8/x150y150g16/i425&Di

splay=p%7Cf&DefSeekPage=x&SearchBack=ZyActionL&Back=ZyActionS&BackDesc=Result

s%20page&MaximumPages=1&ZyEntry=1&SeekPage=x&ZyPURL

Diazinon (6) Chapter 1 Page 56 Guidance for using incident data in evaluating listed and non-listed species

https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/guidance-using-incidentdata-evaluating-listed-and

Diazinon (7) Chapter 1 Page 58 McNest

https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/models-pesticide-riskassessment#mcnest

Diazinon (8) Chapter 2 2 - 86 Use of data on cholinesterase inhibition for organophosphates

https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/use-data-cholinesteraseinhibition-risk-assessments

Diazinon (9) Chapter 2 2 - 86 Risk dose-response (benchmark dose guidance) 

https://www.epa.gov/sites/production/files/2015-

01/documents/benchmark_dose_guidance.pdf

Diazinon (10) Chapter 3 2 - 64 Guidance for calculating degradation kinetics

https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/guidance-evaluating-andcalculating-degradation
50

Chemical Document1 Section (paragraph 

or page #)1

Corrected Link/Comment

Diazinon (11) Appendix 3-1 B1 - 23 NAFTA guidance for degradation kinetics

https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/nafta-guidance-evaluatingand-calculating

Diazinon (12) Appendix 3-1 B1 - 23 SAP meeting – 2008

https://www.regulations.gov/#!docketBrowser;rpp=50;po=0;D=EPA-HQ-OPP2008-0550

Diazinon (13) Appendix 3-1 B1 - 24 KABAM

https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/models-pesticide-riskassessment#kabam

Diazinon (14) Appendix 3-1 B1 - 25 Reporting fate data under ESA 

overview document?

https://www.epa.gov/sites/production/files/2014-11/documents/ecorisk-overview.pdf

Diazinon (15) Appendix 3-1 B1 - 25 Diazinon and red-legged frog litigation

https://iaspub.epa.gov/apex/pesticides/f?p=CHEMICALSEARCH:7:::NO:1,3,31,7,12,25:P3_XC

HEMICAL_ID:2086

Diazinon (16) Appendix 3-1 B1 - 25 NAFTA degradation kinetics

https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/nafta-guidance-evaluatingand-calculating

Diazinon (17) Appendix 4-7-

suppl 1

Tables 3, 6, 9, 13 The BNA links will be replaced with the following: “See specific species pages in The Birds of 

North America Online (accessed 2015). A. Poole, Editor. Ithaca: Cornell Lab of Ornithology.”

Malathion/ 

Chlorpyrifos/ 

Diazinon(1)

Attachment 1-

3

Page 9 USDA Cropland Data Layer

https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php

Malathion/ 

Chlorpyrifos/ 

Diazinon (2)

Attachment 1-

7

Page 3 T-REX and T-HERPS

https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/models-pesticide-riskassessment#terrestrial

Malathion/ 

Chlorpyrifos/ 

Diazinon (3)

Attachment 1-

13

A13 (PF) - 13 US NMFS Species Profile for Guadalupe Fur Seal

http://www.fisheries.noaa.gov/pr/species/mammals/seals/guadalupe-fur-seal.html

Malathion/ 

Chlorpyrifos/ 

Diazinon (4)

Attachment 1-

16

Page 156 USFWS critical habitat list

https://ecos.fws.gov/ecp/report/table/critical-habitat.html

Malathion/ 

Chlorpyrifos/ 

Diazinon (5)

Page 246 FWS species recovery plan 990518

http://ecos.fws.gov/tess_public/pub/speciesRecovery.jsp?sort=2
51

Chemical Document1 Section (paragraph 

or page #)1

Corrected Link/Comment

Malathion/ 

Chlorpyrifos/ 

Diazinon (6)

Page 282, 284 and 

309

USFWS critical habitat table

https://ecos.fws.gov/ecp/report/table/critical-habitat.html

Malathion/ 

Chlorpyrifos/ 

Diazinon (7)

Attachment 1-

17

A17 (PF) - 25 American Society of Mammalogists

http://www.mammalsociety.org/

Malathion/ 

Chlorpyrifos/ 

Diazinon (8)

Attachment 1-

17

Page 47 USFWS pronghorn recovery plan

https://www.fws.gov/southwest/es/arizona/Sonoran_Pronghorn.htm or

https://www.fws.gov/southwest/es/arizona/Documents/RecoveryPlans/SonoranPronghorn.pdf

Malathion/ 

Chlorpyrifos/ 

Diazinon (9)

Attachment 1-

17

A17 (PF) - 25 Designation of critical habitat for Kangaroo Rat

https://www.federalregister.gov/articles/2007/06/19/07-2823/endangered-and-threatened-wildlifeand-plants-revised-critical-habitat-for-the-san-bernardino

Malathion/ 

Chlorpyrifos/ 

Diazinon (10)

Attachment 1-

17

Page 138 Ohio species guide for Little Brown Bat

http://wildlife.ohiodnr.gov/species-and-habitats/species-guide-index/mammals/little-brown-bat

Malathion/ 

Chlorpyrifos/ 

Diazinon (11)

Attachment 1-

17

Page 141 USFWS Recovery Plan for Key Largo Woodrat

https://www.fws.gov/verobeach/MSRPPDFs/KeyLargoWoodrat.pdf

Malathion/ 

Chlorpyrifos/ 

Diazinon (12)

150, 160, 197 Recovery plan for Florida grasshopper sparrow 

https://www.fws.gov/verobeach/MSRPPDFs/FloridaGrasshopperSparrow.pdf

Malathion/ 

Chlorpyrifos/ 

Diazinon (13)

Attachment 1-

18

Page 62 This link will be replaced with the following citation: Small, C.R., and L.A. MacDonald (2001). 

Reproduction and Growth in Relocated and Resident Gopher Tortoises (Gopherus polyphemus) 

on Reclaimed Phosphate-Mined Lands: Final Report. The Florida Institute of Phosphate 

Research. Barstow, FL.

Diazinon (14) Attachment 1-

18

This link will be replaced with the citation: Hines, J.G., L.M. Hardy, D.C. Rudolph, and S.J. 

Burgdorf (2002). Growth rates and mortality of the Louisiana pine snake (Pituophis ruthveni). 

Journal of Herpetology, 36(4): 686 – 687.

Malathion/ 

Chlorpyrifos/ 

Diazinon (15)

Attachment 1-

19

USFWS Rana mucosa critical habitat

https://www.federalregister.gov/articles/2014/04/29/2014-09488/endangered-and-threatenedwildlife-and-plants-endangered-species-status-for-sierra-nevada

Malathion/ 

Chlorpyrifos/ 

Diazinon (16)

Attachment 3-

1

Page 15 NAFTA degradation kinetics

https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/nafta-guidance-evaluatingand-calculating

1 As provided in Appendix 1 (in Attachment A) of the extension request.