### 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 i s not possib le 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, distr i bution data consists

of state and cou nty

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

habitat designation geospatial fi les are needed to properly generate a co

occurrence ana l ysis.

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/critical

h abitat.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, w e 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 i tems, and domains from th e 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 popul ate the table found i n 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 agr i cu l tural classes were further refined by

comparing county level NASS CoA acreage reports to count y 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

a pplication 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 (CD L) 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 tota l 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 metho dology 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 i n 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 la yer 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 Attach ment 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 u niq ue species (t he README file i nd icates 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

spec ies 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 d ifferent 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 Bui l 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

" wi ll 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 Apri l 11

, 2016). To date

, the location o f 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

- pesticide

assessments. 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 i ntroduction to Attachment 4

- 1 EPA

states tha t " each individual species spreadsheet was populated by l i nking root

### 5 spreadsheet files to the i nd ivi d ual species files. A database was constructed in the root

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

species in formation 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" i n their WoE assessment. Nowhere,

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

EPA cont i nues to state that the aquatic and terrestrial WoE and "species template"

files re q uire four root files to populate, make comparisons of relevant estimated

expos u re concentrations (EECs) to t hresholds, and make corresponding weighted risk

and confidence calls." It would appear that these fi l es are extremely pertinent to the

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

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

f i les" 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://ww w.epa.gov/endangered

- species/provisional

- models

- endangered

- species

- pesticide

assessments#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 provi ded 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 i n the form

of a GIS spatial file to ru n 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 Apri l 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 t he co

- occurrence analysis are presented i n

Attachment 1

- 6 and Chapter 4, both of which were only made available i n Apri l 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 designate d 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

, inc l uding the addition of new species profile informati on

, have been

### 6 made to Attachments 1

- 11, 1

- 12, 1

- 20, and 1

- 21 since the draft BEs were originally

released in December 20 1 5.

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

assessment endpoints us ed 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 prov ided in the

taxon

- specific attachments (Attachments 1

- 11 through 1

- 21).

4. Chapter 1

- Section 1 .4.2.2.e. A qualitative evaluation of m i xtures based on

env i ronmental monitoring data, formulated products and mixes was conducted assum i ng

additive toxicity, unl ess ava i lable data indicate that antagonism or synergism is likely.

EPA states that the risks of environmental mixtures conta i n i 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 Chap ter 1 and Attachment 1

- 7 of

al l three biological evaluation documents, the Agency states that dermal, inhalation

and dri nking water exposure routes will be considered.

a. In Attachment 1

- 7 of all three biological evaluations the EPA presents meth ods

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

Subsection 3 in all three documents is missing d iscussions of these routes of

exposure, as well as dose

- based exposure estimates. W i thout these exposure

estimates, the Agency's r esults ( 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 fo r

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/endangered

species/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 estima ted 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 exposu re assessment (Subsection 3) are the concentrations in feed

items. Dose

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

the document. W i thout these exposure estimates, the Agency's results (i.e., risk

characterizations/effects determinatio ns) 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

- s i te exposure. In Attachment 1

- 7 of biological eva l uations,

the Agency presents a method for determining the d istance 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 i ntent to do so, it appears as though the Agency has

neglected to include off

- field exposure est i mates 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 diazi non are currently

### 8

registered. The following values were considered when defining the foliar dissipation half

lives 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 chlorpy rifos, 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 resi dues 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, sec tion 2.5.1 refers to a 'BEAD memo' for additional information on common

crop rotation scenarios for vegetable crops i n four region s

, however there is no prior

reference to this memo i n the document and apparently no reference information.

EPA Response : T he 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 scen ario titled "CALett uce w/irrigation" is assigned to HUC 15 and 1 6

, Vegetables/Ground Frui t crop group (Attachment 3

- 1; Table A 3

- 1.4). This scenario is not

avai l able i n the PRZM standard scenario download.

EPA Response : The PRZM scenario listed is a typ o. 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. Howev er, 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 chl orpyrifos, 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 l b a.i./A is not presented in the

table. Due to this omission the dietary exposure estimates for this exposure scenar io 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 r ate 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 e stimates:

- Download the chemical

- specific ZIP file under WoE tool description on the

provisional models web page ( https://www.epa.gov/en dangered

species/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

maxi mum 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 wr i tten 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 pre sent 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 1 999 (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 spec i es 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 general ly 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 re

reviewing the species informatio n, 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

a lready 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 inc l ude 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, i ncluding references, will be provided in this section.

4. Chapter 2 (All BEs): There are sever a l instances where EPA descr i bes a number of stu d ies

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

i ncluded in the ECOTOX referenc e files (Appendix 2

- 2) or the registrant submitted

reference fi les (Appendix 2

- 4). With the i nformation provided (generally only author

names and year of p u bl ication )

, it is not possible to identify and access these studies

. For

exam p l e

, from the mala thion BE

, Sections 5 and 6 the follow i ng studies are mentioned with

no accompan ying reference details:

a. Conte and Parker (Texas A&M University

, 1975)

b. Procto r

, Corliss

, and Lightner (NMFS, 1966)

c. Tagatz (1974)

d. Kennedy and Walsh

, USFWS (1970)

e. Fi n layson, Faggella, Jong, Littrell

, Lew (1981 )

### 11 f. Kuhajda (1996)



### g. Kucera, 1987



### h. Engbrin g

, 1989

L Parsons and Davis, 1971 J

.

Giles and Rober t

, 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 A ppendix 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. s etiferus

. 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 Serv ice, Galveston Laboratory.

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

Environments in Northwestern Florid a. 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, (1 981). 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.

- Kuc era, 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 Experim ental 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. Sin ha. (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. It is 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

- 1 76, the paragraph beneath the summary of Hoda et al., 19 93

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

between both stu d ies). However, no reference is given for this study.

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

E162358). [Hac kenberger 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 th e 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 i ncorrect

, and when no other study

reference details are listed it is d ifficult for the reader to determ i ne and confirm which study

i s being referred to. For example

, i n the diazinon BE:

a. E64966 is reported in error in Section 3.4.2.5 as an amphipod st udy. 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 s tudy. The correct

ECOTOX ID appears to be 50 3 86.

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 A l

- 2 2) : In the dis cussion of the Aq uaWoE animal matrices

EPA ind i cates that the EECs used i n the assessment are based on output from ana l yses

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

to generate "Species Summar y " reports whic h are located i n the PWC postprocessor (See

Provisional Models Website). EPA d id not provide the l i nk to the Provisional Models

website here.

EPA Response : The provisional models can be found at: https://www.epa.gov/endangered

species/provisional

- models

- endangered

- species

- pesticide

- assessments . This link will be

provided in Attachment 4

- 1 in the final BE.

8. Appendix 2 (Diaz i non 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 Appendi ces 2

- 6 to 2

- 9 are included in A ppendices 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 e ffects 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 sou rces are missing for the effects metrics entered for malathion. These

omissions make cross

- referencing of i nputs difficult particularly if the input units do not match

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

EPA Response : E FED 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 t he 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 pr ovided for any of

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

appendix (Majewski and Cape l

, 1995) was cited and referenced. With the exception of this

### 14

study

, none of the references included are cited with in 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 c itations 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 fin al 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

c hlorine 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 wit h an ECOTOX number or MRID number), the full citations are available

in either the Excel spreadsheet found in Appendix 2

- 2 or, for registrant

- subm itted 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

Pesticide s 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 )

. Pestici des 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 )

. Moni tored Aquatic Incidents

during Broad S cale 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 Chapte r 3 for specific references will be provided in the final BEs.

11

. Appendix 1

- 9 (Degradate Li ne of Evidence) (Diazinon only): EPA states on page 7 that

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

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

were obta i ned 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

La ird

, 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. Availabl e 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

appendice s

, 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., 19 98; 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., O kamoto, 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.

Env ironmental 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 Ra nge, 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 Toxic ology and C hemistry

, 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. Pesticid es 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

- R esources

Investigations Report. 2002

- 4100

.

b. Chlorpyrifos citations: PANNA Drift Catcher report

, 2005 ; PANNA Drift

### 17 Catcher repor t

, 2006 ; CARB repor t

, 1998; CARB report , 2003

EPA Response : The information for these references is provided below. This i nformation 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/Lindsay

chlorpyrifos7_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/Drift Catcher/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 20 03, EH03

- 02.

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

13

. Appendix 4

- 7, Section 3.2 (Malathion and Chlorpyrifos on l y): There is no explanation f or

how fecu nd ity declines are estimated w i th MCnest, and no reference to the actual model

or a document descr i bing the details of the model. Without these details it is difficult to

piece together how the analysis was con d ucted 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

- pesticide

asse ssments). 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 Biolog ical Evaluation s

, EPA

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

### 18

biological evaluations. However, there are problems w i th more than 60 of the hyperlinks

presented. For example

, a significant n um ber of links are broken, leading to " page not found"

or " inval id locatio n " errors. In addition, many hyperlinks redirect the reader to the EPA

Pesticides Homepage instead of to specific areas of i nterest. Finally

, several l i nks point to

proprietary information not directl y access i ble to the public. Appendix 1 provides a l ist of

hyperlinks that are either broken or lead to incorrect or u navailable 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 vali date 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 o f Agriculture (CoA) acreage reports 2 

### for the c rops 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 (h tt p:/ /"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 re ference should have been for

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

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

used i n many sim u lations. The PRZM5 user manual (Young and Fry, 2014) ind icates that

CAM 3 has been removed i n PRZM5

, stating on p. 1 5 that "Several u nused and/or

unrealistic CAM schemes from PRZM3 were el i m in ated in PRZM5 (CAM= 3, 5, 6, 9,

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

ne eded t o evaluate the extent of the error and potential impac t s 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 t he 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 er rors were identified in the ErrorSummary.txt PWC results files

provided i n Appendix 3

- 4f (PWC Postprocessor Output). These files repo 1 i potentially

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

Notes' worksheet in Appe ndix 3

- 4 Aquatic EECs contains the statement "Separate post

processing simulations were completed for urban uses and combi ned i n one output

summary file. Only a few errors were noted i n batch run error summary file

- these files

were not rerun because the y didn't i mpact the overall assessment." These and related

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

assessment was not im pacted.

EPA Response : Although t he ErrorSummary.txt file s for the chlorpyrifos PWC ru ns indicate s

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 f ile “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 mo deled using PWC may not be consistent with the Master Use

Summary Table (Appendix 1

- 3). For example, for chlorpyrifos foliar uses on corn

, the

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

applications at 1.5 l b/A 10 days apart), and yet the PWC batch i nput file shows 5 foliar

applications were modeled (2 apps with a non

- approved method (CAM 3) and 3 apps at

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

batch inputs against the Mast er 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 pattern s. Based on Appendix 1

- 3, the maximum annual applicatio n 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 a ppendices have been made available to the public since the December 2015

posting

5. Malathion spray drift b uffer 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 groun d 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 chlorpyrif os and the uncertainty with where the adulticide, wide

area, and non

- agricultural uses could occur, the entire Uni ted States is considered the

### 20

action area for chlorpyrifos for Step I

. " (Appendix 3

- 5) The same rationale was applied

for Step 2. However, th e 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 grass l and).

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 use s, 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

- agric ultural 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 i nto account when dete rmining 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

sc heduled 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 i n the terrestrial exposure

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

clear to which append i 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 biologic al 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 estimat es 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,

i ncl uding 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 d ifficult to discern how these

files are to be interpreted in the context of the biological eva l uations and their revi ew.

EPA Response : As discussed in Section 9 of C hapter 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 informati on 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 supp l emental files associated with Attachment 4

- 3,

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

in Attachment 4

- 3 or Chapter 4. As such, i t is difficult to d iscern how these files are to be

interpreted in the context of the biological eva l u ations and their review. Supplemental

files 2

, 3 and 4 present species sens itivity d istribution (SSDs) estimates for the three pilot

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

SSD s

, nor t h e 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 descri bed 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

- s pecies/provisional

- models

- endangered

- species

- pesticide

assessments#Effects) using the same approach described in Attachment 1

- 5 (Method for

Deriving Species Sensitivity Distributions for Use in P esticide Effects Determinations for Listed

Species).

1 1

. Ma lathion, Chapter 1

- Problem Formulation, Section 1.4.1.1.c.1

, EPA's a q uatic

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

Variable Vo l ume Water Model (VVWM ) , us i 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 m ust be verified by reproducing the

reported results. Prelim i nary 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 reproducin g EPA's results are li sted 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 ava i lable online (v

1.5 Dec 8 2015) or an earlier version was used in these re ports 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 specifi c model i nputs are not descr i bed i n 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 speci fies 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 conta i ning a subset of the model i nputs 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 e ach 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 sc enario assignment by crop and

HUC2

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

,

so the actual input parameters used cannot be conf i rmed. The renamed scenario fi les were

not provided.

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

Models link (https://www.epa.gov/endangered

- species/provisional

- models

- endangered

species

- pesticide

- assessments#Aquatic), under the discussion of PWC ESA Scenarios.

5. An attempt to reproduce HUC2 05 corn bi n 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, t he 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 ‘ApplicationMe thod’ 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 s preadsheet tools that require a review of

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

against the orig i nal model output files.

EPA Response : A preliminary evaluation of the PWC postprocessor tool indicates the to ol 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 ex posure 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 P F AM simulations used to assess rice and watercress uses cannot be reproduced

because the scenario files w ere not included with the reports and are not publically

available otherwise. W i thout complete input parameter i nformation 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

presente d 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 t he BEs , EPA is responsible for addressi ng 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

dev eloped 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 Re sponse : Exposure estimates for plants are provided in the TED tools that are

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

- D ownload the chemical

- specific ZIP file under WoE tool description on the

provisional models web page ( https://www.epa.gov/endangered

species/provisional

- models

- endangered

- species

- pesticide

- assessments ).

- Open the file titled “TEDtool_v1.0.xl sx” 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 TerrPl ant User’s guide, which is available online at:

https://www.epa.gov/pesticide

- science

- and

- assessing

- pesticide

- risks/terr plant

- version

122

- 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 esti mating

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

invertebrate species. EECs for listed terrestrial invertebrate specie s 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 Attachme nt 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) i n the

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

14. Appendi x 3

- 6, TEDtool worksheets. These MS Excel worksheets conta i n the inputs for

the TEDtool

, inc l uding exposure assumptions and effects metrics. For every numerical

I n put there is a ce l l allocated for the source of the value. For all thr ee pi lot OPs there are

severa l sources missing for physical

, chemical and fate inputs. These omissions make

cross

- referencing of inp u ts difficult particularly if the i nput 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 i n the text of Chapter 3 or its other associated appendices (e.g., daily fraction

retained in mammals and daily fraction retai ned 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 par ameters 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 acco unt 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 d ata processing for formatting the Cen sus 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

G enClass11 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 Relat ionship 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

CA BBAGE 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 V egetables/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 Cr ops

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/Grou nd 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

S WEET 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. Sugges ted 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 Len nartz 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_10 15.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 Ec ological 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 rem ap 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 man ual 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.Check OutExtension( "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 in

22. ## workspace.

23. inws

= r "../CDL_Recodes/CDL_Recla ss_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

= dat etime.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_manage ment( "{}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 t o 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" .form at(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.BuildPy ramids_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(cd l))

== 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 & Vineyard s 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" .forma t(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 1 1 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__

= 'Ste ven 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

- w orkspace 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. ## E nvironmental 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.e xtent

= "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.Exis ts(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_f ile

= "{}{}" .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_f ile, 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 "C ompleted 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 da tetime

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 f rom 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 fr om 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 correspond ing 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 Sit e 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 cultivat ed 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.parallelProcessingFa ctor

= "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( "{}2 015_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 variab le 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(out dir)

### 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

= datetim e.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 t he 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/NA SS2012_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 directo ry

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. prin t "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.MakeQueryTab le_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 counti es 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. cr optime1

= 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, "Ov erwrite" )

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 a rea"

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 "../A nalysis/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, classfi eld)}

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 Catal og

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_ma nagement(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 "Loa ding 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(c at, 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

tet ime.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,

cr op))

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),

"N ONE" )

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 co des 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 "Be gun 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

= d atetime.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 MAPPIN G

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 Value ToValue

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 ValueToValu e

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 70 7 5 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 ValueToValu e

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 93 9 3 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

104 104 1 04 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 Val ueToValue

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 ValueToVa lue

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

13 5 135 135 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 Va lueToValue

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 ValueToV alue

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

1 76 176 110 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

196 196 196 V alueToValue

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 ValueToVal ue

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 V alueToValue

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 ValueTo Value

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/soybe ans

### 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 Veg etables/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 Document 1 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 u nder 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/text

idx?SID=26b254e3ec275241162f b666aa219c7b&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?ZyActi onD=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%5C0 0000020%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&ZyEn try=1&SeekPage=x&ZyPURL

Malathion (5) Chapter 1 Page 1

– 40 and

page 58 Environmental risk assessment models

https://www.epa.gov/pesticid e

- science

- and

- assessing

- pesticide

- risks/models

- pesticide

- risk

assessment

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

- guidelines

ecological

- toxicity

- data

- open

Malathion (7) Chapter 1 Page 63 Guidance for using incident da ta in evaluating listed and non

- listed species

https://www.epa.gov/pesticide

- science

- and

- assessing

- pesticide

- risks/guidan ce

- using

- incident

data

- evaluating

- listed

- and

Malathion (8) Chapter 1 Page 65 McNest details

https://www.epa.gov/pesticide

- scie nce

- and

- assessing

- pesticide

- risks/models

- pesticide

- risk

assessment#mcnest

Malathion (9) Appendix 1

- 1 Page 3 Organophosphate cumulative risk assessment

https://arch ive.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 Document 1 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.ep a.gov/pesticide

- science

- and

- assessing

- pesticide

- risks/guidance

- selecting

- input

parameters

- 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

Nor th 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/text

idx?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/x150y 150g16/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

- risk

assessment

Chlorpyrifos (7) Chapter 1 1

- 49 Guidance for evaluating open literatu re studies

https://www.epa.gov/pesticide

- science

- and

- assessing

- pesticide

- risks/evaluation

- guidelines

ecological

- 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

- incident

data

- evaluating

- listed

- and

Chlorpyrifos (9) Chapter 1 1

- 55 McNest

### 49

Chemical Document 1 Section (paragraph

or page #) 1 Corrected Link/Comment

https://www.epa.gov/pesticide

- science

- and

- assessing

- pesticide

- risks/models

- pesticide

- risk

assessment#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/text

idx?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=&TocE ntry=&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&ImageQ uality=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 an d non

- listed species

https://www.epa.gov/pesticide

- science

- and

- assessing

- pesticide

- risks/guidance

- using

- incident

data

- eval uating

- listed

- and

Diazinon (7) Chapter 1 Page 58 McNest

https://www.epa.gov/pesticide

- science

- and

- assessing

- pesticide

- risks/mo dels

- pesticide

- risk

assessment#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

- cholinesterase

inhibition

- 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

- and

calculating

- degradation

### 50

Chemical Document 1 Section (paragraph

or page #) 1 Corrected Link/Comment

Diazinon (11) Appendix 3

- 1 B1

- 23 N AFTA guidance for degradation kinetics

https://www.epa.gov/pesticide

- science

- and

- assessing

- pesticide

- risks/nafta

- guidance

- evaluatin g

and

- calculating

Diazinon (12) Appendix 3

- 1 B1

- 23 SAP meeting

– 2008



### https://www.regulations.gov/#!docketBrowser;rpp=50;po=0;D=EPA

- HQ

- OPP

2008

- 0550

Di azinon (13) Appendix 3

- 1 B1

- 24 KABAM

https://www.epa.gov/pesticide

- science

- and

- assessing

- pesticide

- risks/models

- pesticide

- risk

assessment#kabam

Diazinon (14) Appendix 3

- 1 B1

- 25 Reporting fate data under ESA

overview document?

https://www.epa.gov/sites/production/files/2014

- 11/doc uments/ecorisk

- overview.pdf

Diazinon (15) Appendix 3

- 1 B1

- 25 Diazinon and red

- legged frog litigation

https://iaspub.epa.gov/apex/pestici des/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

h ttps://www.epa.gov/pesticide

- science

- and

- assessing

- pesticide

- risks/nafta

- guidance

- evaluating

and

- 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

- risk

assessment#terrestrial

Malathion/

Chlorpyrifos/

Diazinon (3) Attachment 1

-

13 A13 (PF)

- 13 US NMFS Species Profile for Guadalupe F ur Seal

http://www.fisheries.noaa.gov/pr/species/mammals/seals/guadalupe

- fur

- seal.html

Malathion/

Chlorpyrifos/

Diazinon (4) Attachment 1

-

16 Page 156 USFWS crit ical 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 Document 1 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.mamma lsociety.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/articl es/2007/06/19/07

- 2823/endangered

- and

- threatened

- wildlife

and

- 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 f or Key Largo Woodrat

https://www.fws.gov/verobeach/MSRPPDFs/KeyLargoWoodrat.pdf

Malathion/

Chlorpyrifos/

Diazinon (12) 150, 160, 197 Recovery plan for Florida grasshopper sparro w

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

- threatened

wildlife

- and

- plants

- endangered

- species

- status

- for

- sierra

- nevada

Malathion/

Chlorpyrifos/

Diazinon (16) Attachment 3

-

1 Page 15 NAFTA degradation kine tics

https://www.epa.gov/pesticide

- science

- and

- assessing

- pesticide

- risks/nafta

- guidance

- evaluating

and

- calculating

1 As provided in Appendix 1 (in Attachment A) of the extension request.