# Ozone NAAQS Benefits Analysis: California Crops Table ofContents

loO 

### INTRODUCTION o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o 1

2o0 

### BACKGROUND: STUDIES OF OZONEIMPACTS ON CALIFORNIA AGRICULTURE

....................................................3 2.1 Introduction
..................................................... 3 2.2 Howitt et al., 1984.
. ..............................................6 2.3 Olszyk et al., 1988(a)
.............................................. 8 2.4 Olszyk, et al., 1988(b).
. .......................................... 11 2.5 Howitt and Goodman, 1989
....................................... 13 2.6 Winer et al., 1990
................................................ 14 2.7 Air Resources Board. 1992.
. ...................................... 18

3o0 

### YIELD CHANGE ESTIMATES
......................................21 3.1 Introduction
....................................................21 3.2 Exposure-Response Functions
.....................................21 3.3 Calculation ofOzone Indices
......................................21 3.4 Calculation ofPercent Change in Yield
.............................. 27

4o0 

### ECONOMIC MODELING o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o 30 

### 5.0 RESULTS

..........................................................32 REFERENCES

.....................................................- .........35 APPENDIX 1: BENEFITSESTIMATESBY CROP
. o. o. o o o. o o o o o o o o o o. o o ••• o o o 

### A-1

AbtAssociatesInc. July 5, 1995 Page i · California Crops Benefits MethodologyOZONENAAQS BENEFITANALYSIS: CALIFORNIA CROPS

1.0 INTRODUCTION To date, estimation of the agricultural benefits of ozone NAAQS has been limited to commodity crops, such as com, soybeans, wheat, and cotton. National agricultural sectormodels do not currently supportthe analysis oftheimpacts onchanges in supply orcostofnoncommodity crops, such as fruits orvegetables. These crops are economically important intwo ways, however. On anational scale, the value offruit and vegetableproductionwas nearly $20.8 billion in 1990 (the base year for this ozone NAAQS benefit analysis) (USDA 1994), compared to $62.4 billion for commoditycrops inthat year (USDA 1994). Fruits and vegetablesare also animportantcomponent ofseveral regional economies. As an indication ofthepotential importance offruits and vegetables to regional economies, Californiaalone accounted for 52 percentofthevalue ofU.S. vegetablecrop production in 1990 (USDA 1995), as well as 46.4percentofthe 1990 value ofcitrusproductionand 55.6percent ofthe 1990 noncitrus value offruitproduction (USDA 1993). · Because ofthe significance ofthe fruit and vegetable sector ofthe agricultural market, and because ofCalifornia's dominance ofthis sector, this analysis was conducted to estimate the potential changes in the California agricultural economy due to proposed Ozone NAAQS. The objective ofthisanalysis is to evaluate changes in the production offruit, vegetable, and commodity crops in California due to ozoneNAAQS, and to estimate the economic value ofthese changes in production
.. The results ofthis analysis will notprovide a national estimate ofthe impacts ofozone NAAQS onthe fruit and vegetable sector, but will provide an indicationofthe potential magnitude ofthesebenefits. This analysis evaluates several differentlevelsofstandards: five primary standards as well as three secondary standards. The secondary standards used in this analysis are defined in terms of levels ofprotectionofagricultural crops. The W126 index ofozone was selectedby OAQPS as the best indicator ofexposure for agricultural crops, primarilybecause itis cumulative andputs more emphasis on peaks, but does not completely ignore the effect oflow levels ofozone. Using all NCLAN

**exposure~response** functions for which adequate data are available, a composite W126 exposure-response function canbedefined. Thisfunction canbeused to calculatethe level ofozone associated with different levels ofcrop loss. The levels ofmaximumpermissible crop loss selected to bethebasis for secondary standards are 30percent, 20percent, and 10 percent loss of50 percent or more ofthe crops included in the composite function. The specific standards evaluated are reported below: Primary Standards:

• 0.12 ppm second highestdaily mean (2hdm) 1-hr (currentprimary standard)

• 0.10 ppm second highest daily mean (2hdm) 1-hr

• 0.09 ppm 8-hrdaily maximum average
AhtAssociatesInc. July 5, 1995 Page 1 California Crops BenefitsMethodology• 0.08 ppm 8-hr daily maximum average

• 0.07 ppm 8-hr daily maximum average Secondary Standards:

• 47.9 ppm-hrW126 index (maximum of30percent

**yi~ld** loss)

• 34.9ppm-hr W126 index (maximum of20percent yield loss)

• 21.0 ppm-hr W126 index (maximum of 10 percentyield loss) Thesecondary standards wereevaluatedinrelationto assumingcompliancewithaprimarystandard, resulting in five sets of secondary standard analyses (e.g., secondary standards compared to attainment ofa 0.12 ppm 1-hrprimary standard; secondary standards comparedto attainmentofa 0.10 ppm 1-hrprimary standard; and so on). One feature ofthis analysis that is important to note is that the form ofthe ozone index used isrelativelynewto benefits analysis. As a result, the indices evaluated inthis study are not directly comparable to those evaluated in previous ozone benefits analyses, such as those described in Section2, below. In addition, the selectionofa W126 standarddeterminedhowavailableexposureresponse studies couldbeused. These issues are discussed in moredetail in Section 3, aspartofthe analysisrequiredto estimateyield changes. Section 2 summarizes the results ofstudies onthe impactofozone on Californiaagriculture thathavebeenconductedto date. Sections 3through 4 describe the steps involved inthis analysis, and Section

### 5reports its results. Exhibit 1 graphically presentsthe steps andrequirements for this analysis, including the general areas ofestimating ozone concentrations, predictingyield changes, andestimating economic impacts.


### 2.0 BACKGROUND: STUDIES OF OZONE IMPACTS ON CALIFORNIA AGRICULTURE 

### 2.1 Introduction 

### A number ofstudies have been conducted in California evaluating the impact ofozone on agricultural crops and onthe agricultural economy. This review summarizes those studies, with particular emphasis on (1) the form ofozone standard(s) evaluated; (2) the sources and forms of exposure-response models used; (3) the yield change results obtained; and (4) the economic impact results obtained, ifany. The studies are presented in chronological order. Two studies were not included inthis review because they relied on regression analyses, rather than exposure-response analyses, and were limited in their geographic scope (Leung et al., 1982; Rowe and Chestnut, 1985).

AbtAssociatesInc. July 5, 1995 Page2 California Crops BenefitsMethodology u'

### One study (ARB, 1992) provides results for the same base year as current NAAQS agricultural benefit analyses, 1990. While most published analyses ofthe impacts ofozone on California agriculture report yield effects relative to a defined level of "clean air", this study evaluated the economic impact ofvarious standards relative to an ambient baseline. The ARB report presents the results ofan analysis using 1990 ozone data and a 1990 economic baseline to evaluate the impact of two ozone standards relative to the baseline. The analysis evaluated standards that are based on a different ozone index than is being used to define the secondary NAAQS under consideration by OAQPS, however, and so its results are not representative ofthe benefits that would be associated with potential secondary NAAQS. This analysis does provide a basis for comparing the magnitude ofthe results obtained for the ozone NAAQS evaluated in this study. The results oftwo additional studies (Howitt et al., 1984; Howitt and Goodman, 1989) are also potentially pertinent for evaluating the results ofthis analysis ofprimary and secondary ozone NAAQS. These studies also analyzed a variety ofstandards relative to an ambient baseline. In addition, the levels ofstandards evaluated are analogous to the primary and secondary NAAQS under consideration, because they reflect protection from 10 percent, 20 percent, and 30 percent yield loss for 50 percent ofcrops. Although the levels ofprotection are the same as those used to defme the secondary standards being evaluated in this study, the benefits associated with 7-hr seasonal mean standards will differ from those associated with W126 standards because the pattern of ozone concentrations, and associated plant responses, will differ. There are two additional limitations to the usefulness ofthese results for analysis ofthe benefits associated with primary or secondary NAAQS, however. The baselines used in these studies are 1978 (Howitt et al., 1984) and 1984 (Howitt and Goodman, 1989), making direct comparison with other NAAQSrelated agricultural benefits analyses impossible. An 

### additional limitation ofthe Howitt et al. (1984) analysis is that it is limited to NCLAN crops, and so does not incorporate exposureresponse functions available for a variety of important California crops. Finally, although it includes a large number ofadditional crops important to California, Howitt and Goodman's 1989 analysis does not appear to use exposure-response functions most suitable for analysis of 1990 conditions (see Winer et al., 1990, for comparison), and does not include a recently published exposure-response function representative ofstone fruits, a large component ofCalifornia fruit production (Retzlaffet al., 1992). In summary, the results ofHowitt et al. (1984) are too dated and limited to be. of use in secondary NAAQS analysis. The results obtained by Howitt and Goodman (1989) could be used as an indication ofthe magnitude ofimpacts ofpotential ozone NAAQS in California, but would not be expected to provide the same estimate as an evaluation ofthe proposed NAAQS using the 1990 California Agricultural Resources Model (CARM), with all available exposure-response functions.

AbtAssociatesInc. July 5, /995
. Page 3 California Crops BenefitsMethodology

## Exhibit 1: Steps in California Crops OzoneBenefits Analysis
SpecifyRegulatory Options '·
• Form ofozone index

(12-h Wl26)
• Levels ofprimaryand

secondary standards IdentifyExposure-ResponseFunctions
• Wl26NCLAN functions ifavailable
• Identifyaprioriminimwnand maximwn functions
• Review alternativefunctions forcrops with no Wl26 functions:

all indices 

## -~

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**J~·** ill

• Identityexpostminimumandmaximwnfunctions 

# I

**~~~·=·~.,=~,~-~,~==~~-~·-~-~~-~·~-.~~··· ~~~_.** 

## ...................

**f:~** • Selectthose thatgive rational response (inverse

relationship between ozoneconcentration andyield)



## I 

# I

• Calculate median duration ifseveral experiments existfor one crop and index

**~~** I FonnOzoneIndices
• Identifyexperimental duration foreach function, ifavailable

• Identify indices needed for selected functions
• Identify state orcounty-level growing season foreachcrop • Usehourlyozoneconcentrations undereach regulatory scenarioto calculate indexforcropspecific duration and growing season (anchorduration periodonharvest)

EstimateYield Changes
• Usecalculated ozone indexfor each scenario in appropriateexposure-response function to

estimate percentrelative yield loss (PRYL) foreach crop in each scenario, relativeto "clean air"
• Calculate overall PRYLforeach crop between scenario and associatedbaseline
• ApplyPRYLforeachcropto estimate change in per-acreyields for each scenario

EstimateChangein Economic Surplus
• Enternew per-acreyields into CARM inputtable
• Run baseline CARM
• Run CARM foreach scenario

- CARM allocates acreageto each crop based on newyield expectations

-- CARM calculates producerand consumersurplus basedon new acreageallocations andcrop production levels 

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AbtAssociatesInc. July 5, 1995 Page4 California Crops BenefitsMethodology

### 2.2 Howitt et al., 1984. 

### Effects ofalternative ozone concentrations and response data on 

### economic assessments: the case ofCalifornia crops. Base year: Comparison to: Exposure-response models used: Form ofevaluative standard: Exposure-response model functional form: Levels ofevaluative standards:

Air quality data: 1978; regional ozone concentrationbaseline reported. baseline from evaluative standards. NCLAN 1; 

### Oshima et al. (1976) (alfalfa). Specific models not reported. 7-hr seasonal mean (ppm), 0900 to 1600 hours. various: linear, quadratic, Weibull 0.04 ppm (improved air quality) 0.05 ppm (slight degradation ofair quality) 0.08 ppm (significant degradation ofair quality) EPA Storage and Retrieval ofAerometric Data system, and California Air Resources Board data. NCLAN studies were used to identify exposure-response functions for crops included in the CARM model. NCLAN functions for field corn (Heck et al., 1982; Heck et al., 1983(a); Heck et al., 1984(b)), cotton (Heck et al., 1982; Heck et al., 1983(a); Heck et al., 1984(b)), grain sorghum (Heck et al., 1984(b)), irrigated wheat (Heck et al., 1982; Heck et al., 1983(a); Heck et al., 1984(b)), dry beans (Heck et al., 1.982; Heck et al., 1983(a); Heck et al., 1984(b)), lettuce (Heck et al., 1982), and processing tomatoes (Heck et al., 1984(b)) were used. A response function for alfalfa was adapted from Oshima et al. (1976). When there were multiple functions available for a given crop, individual functions were selected based on significance and goodness offit. Specific functions used were not reported. Several crop response functions were used as surrogates for crops that had not been evaluated for ozone response:

• Irrigated wheat:

• Lettuce:

• Proc. tomatoes:

• Grain sorghum: • Hay alfalfa: Dryland wheat Barley Celery Onions Fresh tomatoes Potatoes Rice Seed alfalfa The CARM model includes 25 additional crops for which yield losses were not estimated, comprising eight annual crops and 17 perennial crops.

1

### Heck et al., 1982; Heck et al., 1983(a); Heck et al., 1983(b); Heck et al., 1984(b) AbtAssociatesInc. July 5, 1995 Page5 California Crops Benefits Methodology

### Results Howitt et al. do not report the percentage change in yield ofindividual crops for any ofthe evaluative scenarios, but do report changes (from the 1978 baseline) in 

### production and changes in acreage for the 0.04 and the 0.08 scenarios. Since change in

### production reflects the change in

### yields times the change in 

### acreage, we derived the estimated change in 

### yield for the 0.04 ppm scenario and for the 0.08 ppm scenario, presented in 

### Exhibit 2.

r········--.-...- ...-.-.........-.-.-.-.-.-.-..- ..-.-.........................·.·····-··········-·····----.-.-.........-.-.-.-. ----·-···············································-······················· -.-.----·-····························--.-.-.-.-.-.-.-...- .........-.-...- ...-

.........-

...- ...- ........................- ...- .......-.-...-.-.....................-.-.-...................._.. ................................... 

### j Exhibit 2 

# I ;·:·,'· Change in CropYields from 1978 Baseline to Two Alternative Concentrations



## ~~;::·:· (Percent) Crop Changeto 0.04 ppm Changeto 0.08 ppm



# ~~~



# ~:~~**:d,~d (Impro;~r•nt)**  **(Signm~~ljfrndation)** 

# I

Barley, irrigated 4.5 -13.25 

# II

0.90 Lettuce 5.88 -24.92 Onions 5.37 -14.79 Potatoes 2.02 -6.14 Rice 0.1 -3.99 Tomatoes, fresh 4.75 -8.59 Tomatoes, processing 1.91 -10.82 Wheat, dry land 1.11 -9.29 Wheat, irrigated 4.55 -13.3



## ~ 

# I tt!

, Source: Derived from Howitt et al., 1984, Exhibit4 ("Production" and "Acreage"



### ~~! j 

### data)

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AbtAssociatesInc. July 5, 1995 Page6 California Crops BenefitsMethodologyExhibit 3

**P.~e$~nts** the

**change~** in

**economi~ surpl~ses**  **esti~at~d** with the LARM model based 

### onestunated y1e10 cnanges. These cnanges m surpmses renect not oruy cnanges m the 13 

### crops for which yield changes could be estimated, but changes 

### in the acreage that are predicted to occur as high value crops are allocated more acreage iftheir yields decrease. 

### Exhibit 3 Changes in Economic SurplusCompared to 1978 Baseline UnderAlternative California Ozone Concentrations($Millions) Changein Changein ChangeinTotal Scenario (Relative to 1978 Baseline)
.

## ~o;1ri;r~r Su!!ltls



## r~i~l~er**~tlo~~1ls**..

## S

**(Mi~irplut/o** 0.04 ppm (Improvement) 0.05 ppm (Slight Degradation) 0.08 ppm (Significant Degradation) 13.7 0.8 -53.3 0.52 22.0 0.03 -3.7 -2.03 -104.0 1.28 35.7 0.82 -0.21 -2.9 -0.07 -6.04 -157.3 -3.62



### 2.3 Olszyk et al., 198S(a). Crop loss assessment for California: Modeling losses with 

### different ozone standard scenarios. Base year: Comparison to: Exposure-response models used:

Form ofevaluative standard: Exposure-response model functional form: Levels ofevaluative standards:

Air quality data:

AbtAssociatesInc. July 5, 1995 

### 1984; baseline ozone concentrations not reported. 0.025 "clean air" (for 12-hr exposure; 0.027 for 7-hr), from baseline and evaluative standards; 0.04 ppm was used as background for the 

## >0.10 ppm scenarios. NCLAN (Heck et al., 1984(a)), Heagle et al., (1979) (spinach), Thompson and Olszyk, (1986) (alfalfa, grapes, lemons, oranges, sweet corn, sugarbeets, rice), and McCool et al., (1986) (onions, strawberries, fresh tomatoes). Specific models used not reported. 7-hr and/or 12-hr (0800 to 2000 hours) seasonal mean (ppm), and maximum hourly concentration. not specified. 12-hr stnd: 0.04 ppm 0.045 ppm 0.05 ppm 0.06 ppm 1hr stnd: 

### > 0.10ppm 7-hr stnd: 0.044 ppm 0.049 ppm 0.054 ppm 0.065 ppm California Air Resources Board, Aerometric Data Division.

Page 7 California Crops BenefitsMethodology

### NCLAN studies were used when possible. NCLAN research in California was used for barley, cotton, lettuce, and processing tomatoes; research conducted in other regions was used for dry beans, field com, sorghum, and wheat (Hecket al., 1984(a)); all these are 7-hr seasonal mean functions. A study using protocols similar to 

### NCLAN was used for spinach (Heagle et al., 1979), for which a 12-hr seasonal mean function was derived. More limited ozone concentrations for 12- hr

### exposures were used in ARB-sponsored research (filtered, halfflltered, and ambient) on alfalfa, grapes, lemons, oranges, sweet com, and sugar beets (Thompson and Olszyk, 1986). In

### addition, ARB-sponsored research provided data to generate a 7-hr equation for rice (Thompson and Olszyk, 1986). A 12-hr average exposure was used to generate an exposure-response function for green onions, which was used for all onions in this analysis (McCool et al., 1986). A cumulative function ofhourly exposures over 0.10 ppm was used to generate exposure-response functions for strawberries and fresh market tomatoes (McCool et al., 1986). Thirty crops in the agricultural production database were assumed to have yield changes ofzero because no exposure-response information was available. To model how meeting current standards would be achieved, three different approaches were used. All represented compliance with the 1984 California 1-hr oxidant standard of0.10ppm. Specific scenarios were developed as follows:

• >0.10 = 

### 0.10: peak shaving all hours over 0.10; leaving all other hours unchanged.

• Modified rollback: proportional rollback, reducing peak at the site to 0.10ppm and with background of0.04 ppm: 0.04+{[(hourly value-0.04)*(0.10-0.04)]/(peak-0.04)}. No change in site data ifno values exceeded 0.10 ppm.
• Simple rollback: all hourly values reduced proportionately by the change from the statewide peak ozone to 0.10 ppm. No change in site data ifno values exceeded 0.10 ppm. Alternative 12-hr standards of0.04, 0.045, 0.05, and 0.06ppm were modeled for crops and counties with conditions exceeding these averages. (Corresponding 7-hr standards of 0.044, 0.049, 0.054, and 0.065 ppm were also modeled.)


### Results Exhibit 4 presents the results of all the scenarios evaluated, in terms ofpercentage yield losses compared to 0.025 ppm. Olszyk et al. (1988(a)) report that using abackground level of0.04 would reduce all losses for the simple rollback scenario to zero and would reduce the losses for the 0.045 and 0.05 standards by approximately 50 percent.

AbtAssociatesInc. July 5, 1995 Page8 California Crops BenefitsMethodology

# .• :...............................................................................................................................................................................................................................................**~·** 

### Exhibit4 Crop Losses Associated with 1984 Ambient Ozone and ThreeStandard Scenarios Compared to "Clean" Air, 0.025 ppm (Percent)
Crop 

## !L e at

### ~ Losses UnderStandard Scenarios(%)

### ·"'\~ : oss s**:·····················:··················:;·················:······················:,············••·•oO"•··········::"······························---~···--····---················**... 

### ! Ambient 

### i !

### -~**~** ! Peak !Modified! Simple

### ~ :Ozone(%)! 0.04 i 

### 0.045: 0.05 i 

### 0.06 !

## t~hOJO !Rollback!Rollback '''**··Aii~1i~.h~y··············!···········s·.·9··········I······s:·4······r·····6:·s·····'=······7:'8'·····**

## ······s·:s······1··········s·:6

.........

# l'

........6:·i··

.......·........2:·8

.......... Barley 0

## ~ 0 0 0 0 

## I 0 0 0 Beans, dry 

### Com, field Com, silage Com, sweet Cotton Grapes, all Lemons Lettuce Onions, all Oranges Rice Sorghum, grain Spinach Strawberries Sugarbeets Tomatoes, fresh 23.5 1.5 2.9 6.1 18.8 22.4 20.4

0 

### 24.4 19.8 2.2 0.7 3.5



### 0 0 2.9 11.3 0.5 0.6 3.8 7.3 9.8 12.2

0 

### 14.3 8.9 1.3 0.2 3.5



### 0 0 14.8 0.8 1.1 4.8 9.7 12.9 15.8

0 

### 18.6 11.8 1.7 0.3 3.5

0 0 

### 2.9 17.5 1.1 1.5 5.4 12.2 15.9 17.7

0 

### 21.4 14.8 2.0 0.4 3.5

0 

### 0 2.9 20.9 1.4 2.2 6.1 16.2 20.8 19.8

0 

### 24.0 18.7 2.1 0.5 3.5



### 0 0 22.7 1.5 2.6 5.9 18.3 21.9 19.1

0 23.4 18.6 2.2 0.6 3.5

0 0 0 17.2 0.9 1.5 3.6 12.8 15.6 14.0

0 15.6 13.0 1.9 0.4 2.9

0 0 0 11.7 0.4 0.6 0.7 8.4 8.6 5.2

0 

### 5.6 6.7 1.6 0.3 0.8



### 0 0 

# ! 'Tomatoes, proc 

### j Wheat, all 4.2 0.8 2.9 0.9 0.7 1.4 0.8 1.9 0.8 2.9 2.8 0.8 3.9 0.8 1.8 0.5 0 0.8 0.1
"l

# ISource: Olszyk et al. 1988(a), Tables 1and 2.

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AbtAssociatesinc. July 5, 1995 Page9 California Crops BenefitsMethodology

### 2.4 Olszyk et al., 1988(b). California statewide assessment ofthe effects ofozone on crop productivity. Base year: Comparison to: Exposure-response models used: Form ofevaluative standard: Exposure-response model functional form: Levels ofevaluative standards: Air quality data: 1984; baseline ozone concentrations not reported. 0.025 "clean air" (for 12-hr exposure; 0.027 for 7- hr), from baseline. see Exhibit 4, above. Specific models used reported. 7-hr and/or 12-hr (0800 to 2000 hours) seasonal mean (ppm), and maximum hourly concentration. various: linear, Weibull. None. California Air Resources Board, Aerometric Data Division. The same studies as used for the Olszyket al., 1988(a) analysis were apparently used for this paper, but are described in more detail. Exhibit 5 provides the citations for the individual exposure response functions used. The text notes that equations based onCalifornia research were used wherever possible, but the specific location ofthe experiments that generated the equations that were used is not reported. In one case (wheat), the cited source is described as an Illinois experiment, but an available and similar California result was mentioned in the text, but was not used. The reported estimated crop losses, comparing ambient conditions with "clean" air (0.025 ppm), are the same as reported in Exhibit 4, above. The authors list 15 crops that they categorize as potentially at risk from ozone because more than 50 percent ofthe state tonnage was exposed to relatively high levels ofozone during their growing season (i.e., 12-hr averages at or over 0.05 ppm): alfalfa seed, asparagus, avocados, cantaloupe melons, figs, grapefruit, honeydew melons, lima beans, nectarines, olives, pistachios, plums, potatoes, walnuts, and watermelons. No exposure response functions were available toestimate the yield losses associated with these crops. An 

### additional 15 crops were not exposed to ozone levels exceeding 0.05 ppm. Ofthese, three had growing season averages near 0.05 ppm, and so "should not be considered as totally without risk at this time": almonds, apricots, and peaches. If

### a year were to experience elevated ozone levels, any ofthe remaining 12 crops that were not generally exposed to high concentrations ofozone during their growing season would fall into the "potentially at risk" category as well.

AbtAssociatesInc. July 5, 1995 Page 10 California Crops BenefitsMethodology 

### ·.

### Exhibit 5 Sources for Exposure-Response Functions Used by Olszyket al., 1988(b) Crop NCLAN Studies Alfalfa Barley Beans, dry Com, field Cotton Lettuce Sorghum, grain Tomatoes, proc. 

### Citation Notes Temple et al., 1987 Normal irrigation, 1984 Temple et al., 1985(a) Heck et al., 1984(b); Kohut and Laurence, 1983 Kress and Miller, 1985(b) Used 

### = 11618.5 as meanof 10725 for cv "Pioneer 3780" and 12512 for cv "PAG 397" Temple et al., 1985(b) Temple et al., 1986 Kress and Miller, 1985(a) Heck etal., 1984(b); Temple et. al., 1985(c) Irrigated cotton, 1981

Wheat Kress et al., 1985 Illinois experiment. 

### m



# ~ NCLAN-Related Studies

·: 

### Spinach

Rice



### CARB Studies Grapes, all

Oranges Sugarbeets


### CDFA Studies Onions 

# l Strawberries 

# !Tomatoes, fresh 

### ! OtherStudies 

# !Lemons j 

### Com, sweet Heagle et al., 1979 Kats et al., 1985(a)

Brewer, 1983

Kats et al., 1985(b) Brewer, 1978

McCool et al., 1986
McCool et al., 1986 McCool et al., 1986 Thompson and Taylor, 1969 Thompson et al., 1976 **t.~** 

## w CARB study analyzed by NCLAN researchers.



# ~~ Ozone added in 5-hr Monday

- Friday period; ,

**.~** 

### equation assumed 0.025 ppm ozone ppm for all

**~l**

**~"'** 

### otherhours. 

### M

**·~** 

### Ozone concentrations calculated from Fresno County monitor sites, over 16 

### km from experimental plot; unpublished 1986 data. Thompson seedless used for all grapes. Unpublished 1986 data. Ozone concentrations calculated from Fresno County monitor sites, over 16 

### km from experimental plot. Recalculated by McCool on a 12-hrbasis. Original results for green onions, used for all omons. Cumulative dose> 0.10 ppm. Cumulative dose > 0.10 ppm. Based on oxidant, notozone, datafrom San Bernardino, CA. Based on 1974 oxidantdata from Riverside, CA. it



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**mrsUJ.~~** AbtAssociatesInc. July 5, 1995 Page 11 California Crops BenefitsMethodology

### 2.5 Howitt and Goodman, 

### 1989. The economic assessment of California field crop losses due to air pollution. Report to the California Air Resources Board, ContractNo. AS

### 105-32. June. (See also Howitt and Goodman, 1988.) Base year: Comparison to: Exposure-response models used: Form ofevaluative standard: Exposure-response model functional form: Levels ofevaluative standards: Air quality data: 1984; baseline ozone concentrations not reported. baseline from evaluative standards. Olszyk 1988(a). Specific models used notreported. 12-hr seasonal mean (ppm). various (Olszyket al., 1988(a)). 0.025 0.045 0.06 0.04 0.05 CaliforniaAirResources Board, Aerometric Data Division. This analysis estimates economic impact ofalternative ozone standards comparedto the 1984 baseline (ozone conditions and yield) using CARM model, based onyield response functions from Olszyk et al., 1988(a), withtwo variations:

• A 0.025 ppm standard was evaluated.

• A regional standard was evaluated: setting a 0.045 ppm standard for the San Joaquin Valley and a 0.05 ppm standard for the rest ofthe state. The 0.045 ppm 12 hr seasonal mean standard is equivalentto a 0.049ppm7hr seasonal mean standard, the level at whichTingey et al. (1990) indicate a 1 0 percent yield loss for 50 percent of crops would be experienced. The yield estimates associated with the comparison between baseline and each regulatory option were not presented, and cannot be discerned from the results presented in Olszyk et al., 1988(a). Results Exhibit 6presentsthe statewide benefits associated with seven alternative standards scenarios. The authors concludethatbecausethe regional standard (0.045 ppminthe SanJoaquin Valleywith 0.05 ppm in the rest ofthe state) captures 81 percentofthe benefits ofthe stricter standard (0.045 ppm statewide), with the expectation oflower implementation costs, this approach may be more efficient and equitable than adopting the stricter standard on a statewidebasis.

AbtAssociatesInc. July 5, 1995 Page 12 California Crops BenefitsMethodology 

### ,,........................................................................................................................................................................................... ':- 

### Exhibit6 Economic Benefits Associated with Alternative Ozone Standards Compared to 1984 Baseline Conditions
Alternative Standards (12-hrseasonal, statewide Consumer Producer unless noted) Surplus Surplus Total 0.025 ppm 160.0 172.9 332.9 0.04 ppm 98.1 107.4 205.5 0.045 ppm 88.2 70.4 158.6 0.05 ppm State/0.045 ppm SJV 84.0 67.1 151.1 0.05 ppm 66.2 52.7 118.9 **~:**

**:s!~**

**~~**

.,

§ 0.06 

### ppm 26.0 23.9 49.9

**·~** 

### Hourly0.10ppm 53.2 48.2 101.3

**.~~**



### ~1 Source: Howitt and Goodman, 1989, Tables 3 and 8.

. · 

## [!i

**··s:::~:::::~:::::::::::::::::::::::::::::::::;:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::':::::::::::::::::::::::::::::::::::::::::::::::::::':::::::::::'::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::** 2.6 Wineretal., 1990. Air Quality Impacts on CaliforniaAgriculture, 1990-2010. Base year: Comparison to: Exposure-response models used: Form ofevaluative standard: Exposure-response model functional form: Levels ofevaluative standards: Air quality data: 1986; forecasts to 1995 and 2010. Ozone concentrations not reported, butprecursoremissions reported. 0.025 "clean air" from actual and forecasted baselines, and from attainment ofstandard. not specified. maximum hourly average (ppm)
notreported. 0.09ppm. CaliforniaAir Resources Board. Using forecasted emissions inventories, this study estimates the impact ofozone oncrops in the Central Valley ofCaliforniainthe base yearof1986, andfuture years 1995 and 2010. Emissions of"reactiveorganic gases" (ROG) (similar in compositionto VOCs) and NOxwere predicted based onpredicted growth in both mobile and stationary sources. Ozone concentrations are assumedto increase in direct proportion to increases in NOx emissions (virtually no change for 1995, and an increase of 18 percent for 20I0). This increase is applied only to those hours over an assumed background level of0.04ppm. This "worst case" scenario is compared to a "best case" scenario of attainmentofCalifornia's 0.09 ppm highest hourly average ozone standard. Ozone conditionsunder attainment were estimated by a proportional rollback of all hours over 0.04 ppm (assumed AbtAssociatesInc. July 5, 1995 Page 13 California Crops BenefitsMethodology

### background). Yield losses for both cases estimated by comparison against a "clean air" ozone concentration of0.025 ppm (12 hr 

### seasonal average). The results reported below indicate that exposure-response functions for five crops were used that were not available for use by Olszyk et al., 1988(a) or 1988(b): alfalfa seed, cantaloupe, honeydew melons, potatoes, and watermelons. Crop losses associated with these functions are relativelyhigh, thus economicresultswouldbe expectedto differfrom HowittandGoodman (1989) ifthis standard had been included intheir analysis. (Howitt and Goodman, 1989, evaluated a 0.10 ppm maximum hourly standard, as opposed to the current 0.09 ppm maximum hourly standard evaluated inthis analysis). In addition, a comparison ofthe estimated crop losses associatedwith ambient 1984 and 1986 ozone conditions (relative to "clean

### air") indicate that it is likely thatWiner et al. (1990) used different functions for several crops than those used by Olszyk et al. (1988(a), 1988(b))andby Howittand Goodman (1989). These cropsare: lemons, dehydratedonions, oranges, spinach, and processing tomatoes. 

### Results Exhibit 7presentsthe estimated crop losses (comparedto "clean air") associated withthe 1986 baseline, the forecasted 2010 baseline, and attainment ofa 1-hourmaximumhourly average of0.09 ppm. The forecasted changes in ozone concentrations differ between long term average concentrations and peak concentrations, because the long-term averages includeunchanged hourly values less than or equal to 0.04 ppm. The cumulative index ofexposures exceeding 0.10 ppm is predictedto more than double for many counties. Ifa cumulative index were used to estimate yield losses, in general crop losseswould be predicted to be much higher. Yield changes for one crop, fresh tomatoes, are predicted from an exposure-response function using a cumulative index, which shows more than twice the yield losses between the 1986 and 2010 baselines.

AbtAssociatesInc. July 5, 1995 Page 14 California Crops BenefitsMethodology............................................................................................................................................................................................................................................... :

·= 

### Exhibit7 Estimated Crop Losses Relative to "Clean Air" (0.025 ppm)for Three Ozone Condition Scenarios in California'sCentralValley (PercentLoss)
Attainment 2010Forecast Crop Ambient, of0.09 ppm Ambient 1986 (no additional standard controls) Alfalfa, hay 10.6 6.7 11.5 Alfalfa, seed* 8.5 4.6 9.2 Barley 0 0 0 Beans, dry 20.9 13.4 22.9 Broccoli 0 0 0 Cantaloupe* 33.5 19.2 36.2 Com, field 1.7 0.8 2.1 Silage, com 2.1 0.9 2.5 Com, sweet 2.0 1.7 2.1 Cotton 15.7 8.6 17.5 Grapes, raisin 26.5 15.6 28.7 Grapes, table 25.3 15.9 27.6 Grapes, wine 22.5 14.1 24.4 Honeydew melon* 21.3 16.0 23.3 Lemons** 9.4 7.4 10.4 Lettuce 0 0 0 Onions, dehydrated** 1.0 0.5 1.1 Onions, fresh 0.9 0.5 1.0 Oranges** 9.3 4.8 11.9 Potatoes* 10.6 7.1 11.4 Rice 2.5 1.9 2.8 Sorghum, grain 1.0 0.5 1.3 Spinach** 0.4 0 0.5 Strawberries 0 0 0 Sugarbeets 0 0 0 Tomatoes, processed 2.4 0 5.3 Tomatoes, 1.6 0.7 2.0 Watermelon* 35.6 22.1 37.6 Wheat 0.7 0.4 0.8 Wheat, dryland 0.3 0.2 0.4 Wheat, irrigated 0.8 0.5 0.9 * Exposure-response function not available from Exhibit4. ** Potentially different exposure-response function from that used in Olszyk 1988(a) or 1988(b). Source: Winer, Olszyk, and Howitt, 1990, Table 2.

AbtAssociatesInc. July 5, 1995 Page 15 California Crops BenefitsMethodology

### Economic impacts ofattainment ofregulatory standards and ofexperiencing predicted ozone concentrations in 20I0 are reported only in terms ofproducerbenefits. Consumerbenefits were not calculated because the crop production changes were evaluated on a limited regional scope, while consumer impacts would need to reflect changes in the overall market. The economic model was calibratedto 1987, ratherthan the 1986 ozone baseline, butthe authors note that emissions did not change significantly between 1986 and 1987.2 

### Exhibit 8 reports the changes in producer surplus (reported as "producers' return to land and management") associated with the difference inyields from each ofthe baseline ozone levels to the attainment ofthe0.09 ppm standard. Changes in crop mix are predicted to occur regardless ofchanges in ozone levels due to (unspecified) predicted changes 

### in population and income: this explains the increasing returns associated withthe0.09ppm scenario, eventhough ozone and yields remain constant.

2 

### Alone, unchanged emissions would not necessarily result in stable ozoneconditionsbetween two years. The authors do not address the variability in meteorologic conditionsbetween 1986 and 1987, ortheirpotential impact on ambient ozone levels. AbtAssociatesInc. July 5, 1995 Page 16 California Crops BenefitsMethodology

# .. ........................................................................................................**~~~·~·~~~·~·············"'''''''''""'** ............................................................................. 

# _ CentralValley Producers' Surplus Comparison ofBaseline Ozone,**~·1** Forecasted Ozone, and Attainment of0.09 ppm Standard Scenario 1995 2010 No Further Action on Ozone 

### 1987 5,240.3 5,345.8 5,416.4 Attain 0.09 ppm Standard 5,445.4 5,558.6 5,693.4 Net Change 205.1 212.8 277.0 

## t.) 

## i**~** : Source: Winer, Olszyk, and Howitt, 1990, Table 5.

### ~**L~·~::::::::::;:~~~==~~=~=:::::::::::::~=::::::::::::::::::::~:::::~:::::::~~==:::::::~:;::::::::::::~:::::::;:::;:;:::::::::::::~;:;:;:::::::::::;:;:;:;:;:;:;:;:;:;:::::::::::::::~:~::::::::::~~=~:?..J::.~:::t~~~=;::::::..~====-~-=.:::::~==~==:::.~::::~:**



### Another interpretation ofthese results is that the returns to producers would be expected to increase 4.6 percentbetween 1987 and 2010 in the absence ofany changes in yields due to ozone, based on changes inpopulationand income alone. Dueto the production impactspredictedto occur due to ozone, however, this rate ofgrowth is predicted to decrease to 3.4 percent.


### 2.7 Air Resources Board. 1992. The Atmospheric Acidity Protection Program: annual reportto the Governorand theLegislature, 1991 and Effect ofairpollution on crops in the San Joaquin Valley: special report. Base year: Comparison to: Exposure-response models used: Form ofevaluative standard: Exposure-response model functional form: Levels ofevaluative standards: Air quality data: 1990. Ozone concentrations not reported. baseline from evaluative standards. not specified. 12-hr seasonal mean (ppm).
not reported. 0.04 ppm, 0.025 ppm. CaliforniaAirResources Board. Thisreport presents an analysis ofthe impacts ofozone onagricultural production in the San Joaquin Valley and statewide. Itprovides no details on the crops included, or on the method for yield change estimates. Economic impacts were estimated with the CARM for 1990. 

### Results Exhibit 9 presents the percent yield increases relative to 1990 for both standards for each of nine regions in the state. Exhibit 1 0 presents estimated benefits to producers and consumers associated with both standards.
AbtAssociatesInc. July 5, 1995 Page 17 California Crops BenefitsMethodologyCrop **Exh·i~i~··~··························································································································································b**

Estimated Yield Changes from 1990 Baseline due to 0.04 and 0.025 ppm Standards (percent).

Sac. l N. SJV C. SJV S. S.JV Imp. S. Coast N. Coast N.E. S.E

.04/.025 1 .041.025 .04/.025 .041.025 .04/.025 .04/.025 .04/.025 .04/.025 .04/.025 @l

**Alfalf~,hay** 4.6/5.6 j 5/5.8. 5.1/10.7 5.1/14.8 4.1/4.1 4.1/5.5 1.9/1.9 4.7/8.3 5.1/9.6 @

Beans,dry 11.2!15.7jll.9!17.8 11.9133.3 11.9148.8 010 11.3139.1 010 010 010 **~jjjj**

Cantaloupe 0/0 I 14.6126.6 14.6147.6 14.6170 12.6112.6 14.5127.7 010 010 14.7146.9 lill

Citrus 8.618.6 ! 010 10.4118 10.4124.2 9.419.4 9.2113.4 010 010 10.4137.2 **~\\l**

Cotton 010 ! 6.7127.9 6.7138.1 6.7150 5.8120.1 5.8120.1 010 010 010 **~][\j**

Grapes,dry 010 I 10119.9 10131.2 10143.6 010 010 010 010 12120 II!J

Grapes,table 010 j10112.3 10131.8 10143.6 010 10/45.2 010 010 10120.2 Ill

Grapes,wine 10/31.2 ! 10116.1 10130.2 10/43.6 010 7.6110.3 313 9.9127.9 10120.3 B

Lettuce 010 l 010.3 011 011.1 010 010.2 010 010 011.7 I

; @

Onions,dry 4.314.3 ! 3.713.7 8111.5 8124 5.315.3 5110.8 010 6.2/6.2 2.7/2.7 f.\

Rice 2.6 I 3.1 l 2.6 I 4 2.618 2.6110 0 I 0 0 I 0 0 I 0 2.6 I 6.6 0 I 0 **~**

l I.

Sugarbeets 1.2/3.9 ! 1.818.1 1.8113.6 1.819.9 1.313.8 1.113.4 010 1.8110.5 010 J

Tomato,proc. 318.7 ; 3.613.7 4.216.6 4.2110.1 2.712.7 2.212.2 010 010 010 ;

. Wheat 1.311.3 I 1.611.6 8.819.7 9.4115 4.914.9 6.116.3 o I o 7.218.8 o I o **~**

1 Source: ARB, 1992, Table 1 I

**L.~=~~~~~:~-,_t@Z:-** . **..-..:-~~-&:$.:r:~=~=~:w.~~~-=~~r:~*r:=~~=~~~:;:r:~~~~=~~=~=~~=~=::;:~===~~==~=~~~~~=~:;:::::r::::::~:~:;:i::::::~=======~====~=~*~~=r:r:~~:;:r:~~~=~===~=~==*~:~$j*f:~r:~:;§::*::::::=~=~=~~=~~=~"i====~~===~===~=::::;:~;:r:;?~:ffu~&W::~~~~*-==~=~~~~~~-~-R~**

Abt Associates Inc. July 5, 1995 Page 18 California Crops Benefits Methodology**················~~**.....................................................................................................................................................**,~** 

### Exhibit 10 California Statewide Economic ImpactofAlternative Ozone Standards Relative to 1990 Baseline ($millions)
Standard ConsumerSurplus 0.04 275 0.025 713 ProducerSurplus 215 779 

### TotalSurplus 490 1,492

. Source: ARB, 1992, Table 3 

## &

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### 3.0 YIELD CHANGEESTIMATES 3.1 Introduction There are several steps involved in generating yield change estimates. One step is that ozone exposure-response functions for eachcropneed to be identified. Another step is thatozone indices need to be calculated for both the baseline (ambient) ozone conditions, and for each regulatory option, to be used in the identified exposure-response functions. Finally, percent relative yield changebetweenaregulatory option and its associatedbaselineneeds tobecalculated. Eachofthese steps is detailedbelow. 

### 3.2 Exposure-Response Functions To estimate yield impacts from ozone, exposure-response functions are requiredfor eachcrop to be analyzed. EPA's NCLAN program concentrated primarily on commodity crops, but also includedresearchonkidneybeans, lettuce, potatoes, andtomatoes. Useofcumulativeozoneindices such as W126 is a relatively recent development in the study ofozone impacts oncrops, and few experimentshavebeen designed orreported in terms ofthis index. BecausetheNCLANprogram used a consistentprotocol and developed a database ofexperimental conditions and results for all its studies, U.S. EPAIERL was able to use the original datafrom NCLAN studiesto developW126 exposure-response functions for most NCLAN crops.3 (Adequate data do not exist to develop a Wl26form ofthe tomato exposure-response function.) To the extentpossible, exposure-response functions expressed interms ofa W126 ozone index were used for this analysis. NCLANwasnot ableto include awide variety offruits and vegetables, however; thus other sources ofinformation on crop response to ozone exposure were also used in this analysis. The level ofdatarequired to estimate W126 functions is not generally available from non-NCLAN exposure-response studies, and as aresult, derivation ofW126 exposure-response functions was notpossiblefor alargenumber ofcropsgrowninCalifornia. In orderto provide an indicationofthepotentialmagnitudeofbenefits associated with ozoneNAAQS, available exposure-response functions were used intheiroriginal form. For this analysis, the functions included 7-hour seasonal mean indices, 12-hour seasonal

3This analysis was conducted by U.S. EPA, Environmental Research Lab (ERL). Corvallis.
AbtAssociatesInc. July 5, 1995 Page 19 California Crops Benefits Methodologymeanindices, and AOT10 indices, and reflectbothlinearand Weibull functional forms. TheAOT10 indexwasused in someexperimentsfor severalreasons: 0.10ppmwasthe 1-hourCaliforniastandard at the time, and so made a reasonable threshold level; and use ofa threshold eliminated equal mathematical weighting ofdose from many low concentrationexposures and a few high ones.4 The exposure-response functions used and their sources are reported in Exhibit 11. Crop-specific .discussionsofthe source, use, and limitationsofthe exposure-responsefunctions followa discussion ofhowwehandled individual crops for which multiple exposure-response functions are available. 

### Minimum/Maximum Exposure-Response Functions Seven crops included in this analysis have several experimentally-derived exposure-response functions available. Two different approaches were used for modeling the benefits associated with these crops. In both cases, the functions demonstrating minimum and maximum responses were selected and used to represent the entire crop to present a range ofbenefitestimates. In actuality, a numberofdifferent cultivars are planted by producers, and so ozoneresponse will be a weighted average ofthe responsiveness ofeach cultivar to its ozone condition and its proportion oftotal acreage. It is important to note that these values do not necessarily bound the analysis, since the number ofcultivars evaluated is small relative to the number grown for many crops. In both cases, minimum and maximum functions were identified priorto theiruse inthe analysis, but ondifferent bases. For NCLAN crops (field corn, cotton, potatoes, and wheat), U.S. EPAIERL conducted an analysis to identify the ozone concentration required to result in a 10 percent yield loss for each experimentally-derived exposure-response function. The function with the lowest concentration demonstrates the maximum response, and the function with the highest concentration at 1 0 percent yield loss demonstrates the minimum response. In Exhibit 11, only the functions identifiedby US EPAIERL as minimum and maximum are reported for these crops. Thistype ofdirect comparison ofozone concentrations associated with a given level ofcrop protection is only possible if all the equations available use the same index. For some ofthe remaining crops with multiple functions, several different indices were used to characterize the responSe to ozone. As aresult, an alternative approach was used to selectminimum and maximum functions forallnon-NCLANcropswithmultipleexposure-responsefunctions. Using 1990baseline ozone data, each function was analyzed overarange ofozone values, the relative yield losses were arrayed from minimum to maximum, and the minimum and maximum functions were identified. There was no case where functions "crossed over", i.e., where under different ozone conditions, differentfunctions resulted inminimum or maximum estimates. The identified functions were then usedto estimate economic impacts. All the functions available for these crops are listed inExhibit 11, withthe maximum and minimum functions identified.

4McCool et al., (1986) note that use ofthis index eliminates low ozone concentrations, and so assumesthatthey are not important to plant growth and yields in California, butthatthis assumption requires further investigation. AbtAssociatesInc. July 5, 1995 Page20 California Crops BenefitsMethodology

### Alfalfa Hay Alfalfa hay is harvested several times in the season. Exposure-response studies generally measure the total yield in the season over several cuttings, compared to a seasonal ozone index. NCLAN experimentswere conducted on alfalfa, but a W126 function forthis crop wasnotincluded inthe national exposure-response analysis due to a number ofquestions about howto calculatethe correct cumulative ozone exposure statistic that reflects the number ofdays ofexposure between harvests, and at what point in the regrowth cycle ozone has an effect. Alfalfawas included inthe California analysis, however, because the issues associated with exposure duration do not affect seasonal mean indices in the same way as they do cumulative indices. Sincethe experiments used for the basis ofthe Californiaanalysis were conductedusing seasonal meanindices overan entire cutting season,these functions should give areasonable indication ofthe magnitudeofyield changes associated with different ozone levels. Because ofthe uncertainty associated with the exposureeffect dynamic for alfalfa, however, the benefits estimated for alfalfa should 

### not be considered accurate estimates. Rather, the value ofbenefits associated with alfalfa should be considered as indicators ofthe potential magnitude ofthe effect ofozone reduction on alfalfa, with a higher (but unquantifiable) level ofuncertainty than for most othercrops. The functions used for alfalfa come from a number of studies conducted in California. NCLAN studies (Temple et al., 1988) were conducted over two years, and so two functions are reported in Exhibit 11. One study (McCool et al., 1986) used the AOT10 ozone index but did not reportthe duration ofthe experiment. In estimating crop losses, we used a defaultduration ofMaySeptember for alfalfa.5 

### Yield losses for alfalfa seed were not estimated because the exposureresponse functions only evaluated the yield ofcut alfalfa. 

### Dry Beans The W126 function from NCLAN studies, derived by U.S. EPAIERL, was used. Cantaloupe Thompson et al. (1991) report adose-response function derived from datapresentedin Snyder et al. (1988) for muskmelon in Indiana. The SAPRC has recommended its use for all melons in California5
, 

### butbecause rimskmelon and cantaloupe are notthe same species, thebenefits estimated for cantaloupe should llOt

### be considered accurate estimates. Rather, the value ofbenefits associated with cantaloupe should be considered as indicators ofthe potential magnitude ofthe effect ofozone reduction, with a higher (but unquantifiable) level ofuncertainty than for mostothercrops. Field Corn The W126 functions from NCLAN studies, derived by U.S. EPAIERL, were used. U.S. EPAIERL identified both a minimum and a maximum exposure-response function.

5

### Personal communication, Randall Mutters, California StatewideAir Pollution Research Center. AbtAssociates Inc. July 5, 1995 Page21 California CropsBenefits Methodology

### SweetCorn Although experimental dataexist for sweet corn'sresponse to ozone (Thompson et al., 1976), this crop is not modeled in the economic model ofCaliforniaagriculture. This is a specialty crop with very small acreage. 

### Cotton The W126 functions from NCLAN studies, derived by U.S. EPAIERL, were used. U.S. EP AIERL identified a minimum and a maximum exposure-response function. ERL excludedtwo NCLAN studies because the experimental duration was very short compared to other NCLAN studies for cotton, and the exposure duration was also shorterthanthe growthperiodreported 

### inthe experiments. 

### Grain Sorghum The W126 function from NCLAN studies, derived by U.S. EPA!ERL, was used. 

### Grapes Two studies on grapes found yield responses to ozone for different varieties: Brewer(1983) studied Thompson seedless (used for both table grapes and wine), and Thompson and Kats (1970) studied Zinfandel (used for wine). (Musselman et al., 1985 studied concord grapes and found no effect ofozone on yields). Data from both studies were used to derive the exposure-response functions reported in Thompson et al., 1991, with an exposure duration of 138 days for 1969 data from Thompson and Kats (1970) and 152 days for April-August exposure for data reported in Brewer (1983). Lemons Thompsonet al. (1991)report an exposure-responsefunction based on datain Thompson and Taylor, 1969. The experimentwas amulti-yearexperimentwith exposure occurring 365 days per year. Citrusyield impacts, however, appearto bethe resultofexposure duringthe one ortwo years priorto harvest. Olszyket al. (1990) hypothesize that orangesrespondto ozone concentrations two years priorto harvest, due to the ability oftrees to support fruit set, as affectedby ozone'simpact on starch reserves in the previous year. Citrus flower buds usually form from about April to October; fruit is harvested inthe following year. The SAPRC indicates that flower budformation may be the most affected by ozone, in the year prior to harvest. 6 

### As a result, the ozone data that shouldbe used to estimateyield responses in one year shouldbe from either one ortwo yearsprior. Inthis analysis, it was not possibleto create a 1988 or 1989 ozone data setto be used 

### in conjunction with 1990 cropping data, and the literature is not clear on which would be most appropriate for

6

### Personal communication, Randall Mutters, California Statewide AirPollution Research Center. AbtAssociatesInc. July 5, 1995 Page22 California CropsBenefits Methodologyanalysis. As aresult, the yield change and benefitestimates from this study are based onusing 1990 ozone data as a proxy for prior years' exposure data. Thefunctionusedinthis analysis is amodificationofthe exposure-responsefunctionpresented in Thompson_et al., 1991 for lemons. Thompson et al. (1991) adjusted the lemon yield results to betterestimate changes in overall production due to ozone: lemons and oranges tend tobearevery otheryear, sothe percentrelative yield loss was reducedby an arbitrary 50 percentto reflectthe fact that some orchards in a region will be "on" in a given year, while others will be "off'. The CARM model, however, uses 

### per acre yields to estimate the resources that will be allocated to the production ofa given crop: the "on/off' adjustment for the nwnber ofacres bearing fruit is not appropriate for estimating the yield changesthat an individual producerwouldexpectto see dueto ozone impacts. Therefore, we modified Thompsonetal.'s (1990) function toremove the 50percent adjustment factor. Inthe CARM, all citrus (lemons, oranges, and grapefruit) constitute one crop. Therefore, to estimate the yield changes for "citrus" as an input to the CARM, we weighted the county 

### PRYL estimates for lemons and oranges, and a zero effect for grapefruit, by the proportionofeachto total citrus production, to derive a single yield change estimate. To the extent that ozone affects grapefruit in a manner similar to the effects on lemons and oranges, the citrus estimate will underestimate benefits. Because ofthe uncertainty associated with the mechanism ofozone effect on citrus fruit formation, the use ofproxy ozone concentrationdata, andthe uncertaineffectofozoneongrapefruit, the benefits estimated for changes in the yields oflemons and oranges should 

### not be considered accurate estimates. Rather, the value ofbenefits associated with citrus crops shouldbe considered indicators ofthe potential magnitude ofthe effect ofozone reduction onlemons and oranges, with a higher (butunquantifiable) level ofuncertainty than for most othercrops. 

### Lettuce The W126 function from NCLAN studies, derived by U.S. EPAIERL, was used. Onions Onions are a very early crop, planted inJanuary in California, when ozone concentrationsare not expected to be high. Temple et al. (1990) studied four cultivars ofonions (all bulb crops, as opposedto green onions). Only one variety showed significantyield losses atambient ozone levels: Rio Bravo. This analysis uses the yield estimates for Rio Bravo to representpotential yieldchanges for all types of onions, thus possibly overestimating the benefits associated with ozone improvements for this crop.

AbtAssociatesInc. July 5, 1995 Page 23 California Crops BenefitsMethodology

### Oranges Olszyk et al. (1990) studied continuous exposure ofValenciaoranges over a four-year period, withexposure from April to Octoberused to estimate yield impacts. Like lemons, orangeyields are affectedby ozone exposure one to two years prior to harvest, but in this analysis, 1990 ozone data are used as a proxy for data from prior years. An 

### orange function developed by Thompsonet al., (1990) to reflect a 50 percentreduction (see the discussion underLemons, above) was·notused for this analysis. Inthe CARM, all citrus (lemons, oranges, and grapefruit) constitute one crop. Therefore, to estimate the yield changes for "citrus" as an input to the CARM, we weighted the county PRYL estimates for lemons and oranges, and a zero effect for grapefruit, by the proportion ofeach to total citrusproductionto derive asingleyield change estimate. To the extentthatozone affectsgrapefruit in a mannersimilar to the effects on lemons and oranges, the citrus estimate will underestimate benefits. Because ofthe uncertainty associated with the mechanism ofozone effect on citrus fruit formation, or the use ofproxy ozone concentration data, and the uncertain affect ofozone on grapefruit, the benefits estimated for changes in the yields oflemons and oranges should 

### notbe considered accurate estimates. Rather, the value ofbenefits associated with citrus crops should be considered indicators ofthe potential magnitude ofthe effect ofozone reduction on lemons and oranges, with a higher (butunquantifiable) level ofuncertainty than for most other crops. 

### Plums Only one study has examined the effect ofozone on the yield ofstone fruits such as plums, peaches, nectarines, and apricots. Retzlaffet al. (1992) studied 1-yearold plumtrees for two years. Basedonthese data, Randall Mutters ofthe SAPRC providedtwo exposure-response functions, one for 1989, and one for 1990.7 

### The 1990 function was used in this analysis to be consistent withthe 1990 CARM baseline. These results may not fully represent the effect ofozone onthe yields of established orchards. 

### Potatoes The W126 functions from NCLAN studies, derived by U.S. EPAIERL, were used. U.S. EPAIERL identified atolerant and a sensitive exposure-response function. Rice Thompson etal. (1991) report three different functional forms based on datareported in Kats et al. (1985(a)). We used the Weibull function for this

**analysis~** 

### The Kats et al. (1985(a)) experimentexposed the crop to ozone for 5 days a week for atotal of346hours (we estimated an
'Personal communication, Randall Mutters, California StatewideAirPollution Research Center. AbtAssociatesInc. July 5, /995 Page 24 California Crops BenefitsMethodologyexposure duration of69 days from these data). The SAPRC used a linear extrapolation on the original Katset al. (1985(a)) data to obtainestimatesofyieldimpactsbasedon7-hour seasonal mean ozone indices (Thompson et al., 1986). The experimental design for Kats et al., 1985(a) included both charcoal filtered treatments and ozone addition treatments. Therefore, 

### it is notpossible to say ifthe effect ofno treatmenttwo days a week wouldtendto biasthe overallyieldresults up ordown. Rice does not appear to be .very sensitive to ozone; the effect ofuncertainties in yield change estimates is not likely to have a significant effect on overall benefit estimates. Nonetheless, the benefits estimated for changes in the yields ofrice should 

### not be considered accurate estimates. Rather, the value ofbenefits associated with rice should be considered indicators ofthe potential magnitude ofthe effect ofozone reduction on rice, with a higher (but unquantifiable) level of uncertainty than for most other crops. 

### Soybeans Soybeans are notmodeled inthe CARM because Californiadoesnotproduce a sufficientlevel ofthe crop to affectthe agricultural economy ofthe state. 

### Tomatoes (Processing) The CARM does not model fresh tomatoes, due to their specialty crop nature, and so the tomato analysis is restricted to processing tomatoes. Processing tomato data are available from NCLAN, but adequate data were not available to derive a W126 function. Therefore, this analysis is based on three types ofozone indices: 7-hour seasonal mean data, 12-hour seasonal mean data, andAOT10 data. Heck et al. (1984)reporttwo exposure-response functions, but only onewas used 

### inthis analysisbasedonthe recommendationofthe SAPRC (Thompsonet al., 1986). Theexcluded function predicted very large yield reductions under California ambient conditions, and did not appearreasonable. 

### 3.3 Calculation ofOzoneIndices The pattern ofozone concentrations associated with achieving standards set according to different ozone indices will vary, even ifthe target level ofprotection achieved is the same. For example, the pattern ofhourly ozone concentrations associated with meeting a 7-hr seasonal mean standardthat achieves 1 0percentmaximum crop loss will not necessarilyprovide the same level of protection 

### interms ofaW126 index. This analysis required thateitherexposure-response functions be converted to W126 terms to analyze the impact ofthe secondary standards, orthat the hourly ozone concentrations associated with meetingthese standardsbeusedto calculatethe indexused in theresponse study. Mostoftheexperimentaldataoncropresponsetoozonehasfollowed aprotocol similartothatused intheNCLAN analyses, andozoneexposurehasbeendesignedtoreflectvarious levels of7-hour or 12-hour seasonal mean concentrations. As long as hourly concentration data associatedwiththe experimentare available, otherindices canbecalculated andusedtocharacterize crop response; this was done for NCLAN crops other than tomatoes. In those cases where experimental data do not permit conversion ofthe exposure-response function to W126 terms, the hourly ozoneconcentrationspredictedto be associated witheach standardwereusedtocalculatethe index needed for use in available exposure-response functions.

AbtAssociatesInc. July 5, 1995 Page 25 California Crops BenefitsMethodologyr····················-·············--············································-··················· **················E~~-i~~~--~-~ ~--**  **-E~~~-~~-r~~~~~~~~~··;·~~-~~~~~--·······················** ······································································ .. **····················-~-**

: Crop ! Cultivar ! Ozone !Equation! Yield Function (PRYL, ppm) l Duration l Source

1 Index i Type 1

, l · [hom n ct al. 1991 · 01 k ct

Alfalfa Hay 1 NK 512 12 hr 1 Max p2.67-(139.02*12hr))/(32.67-(139.02*bascl2))(2-yr study) 1 114/40 al. (l::a,

**1986 ~)**  ). szy .,

.............................................. ..... Mao ... ii.69 ...... ................. : .................. ·r······ .................................................................................. ··· · ................................. : ............................. r............................................................ f

AlfalfaHay . . **W~**

**512** 12hr j . fll8.96-(408.8*12hr))/(118.96-(408.8*basel2)) ... ·. . 210 ['"ompsonetal(l991,1986) ili[i, **~A;::~~~:~:-~H~::·:::.·:·.·::::·:·:::·.:::~·:::r:·:·:·ww:·::·:Lf.~s~:l(4~::·:·::.:r::·:l(2f.:h~~:::·:r:.·::·:::·** .. **:.:::.:.:li~~-~~~!f.~~::!.[ii1J)~\~~12~~Nf2:~~~1tl~~ff.~:~t/~~e·l·~~i~-~-.:.:** ... ·:::·:::·:.· .... **·:::·:r·::::·:.:·::2~-o~l:::·::·:::::·::r~~PI!~.~~:TI~::·:((I(9?.8~-~8-f>::::** .... : .. ::::::·:::::·:·:: lfl

,a ,a ay r i {612S•base 12)) (Irrigated) : 1' emp e eta · ::{

Grain Sorghum DeKalb A28+ Wl26 ( . **~-exp(-(WI26/205.3)** 1 ... . . .. ..... ·.· ............ ····· f' ........................................ . **g~;t.~;.::·::·::::·:::·:·:·:·:·::·:·::::::·:::·:·:·:·:·:r·:·J~~;.t.~k:.:J·:·:·:·Jr~F-:J:·:·:·:·:·:·:~~-:·:·:·:·:·Jr~Mi:r& ::~~-a~ft?Xrw;~~~~:~t~~-;;~:~;)~lrv~~~~~;-;lt~~**  **:·:·:·:·:·:···:** .. :···:·:·:·:·:·:···:·:·:·:·:·:·J

Lemons . ! NA ! 12 hr j **l0.5004-(12hr•62.24))/(0.5004-(ba~el2•62.24))** i

: ,, : : use
**~---------J- -~~~-------** 

Plum Casselman '12 hr ) [377.9-(1373.1•12hr))/(377.9-(1373.1*basel2))(2-year study) 191/214 J'ers. Com. Randall Mutters I :::::: **~~)~)\**

**~r~** :::::: **~j~;;) f~~**  **fj~**

I -=·=·= **w~~~**

i:! **~-====** ijjjj I **~->** 1'.1 **~::::**

**~=~=**
**··miw.mm:~~H%t~M=~=~=mm.w.n~w~=~.,,&** ... **&,.-::,{m~=~~~W:w.:~mn:%~mt:i~=~=:t:=:::m:~:':;=:tr::::m:m~-w:==rwn:;1:1:1:mmmwm·:mm~tf'::::::=:::::=w=;~mmffif:fmmr:1:mf:=ft:=:;:~=;nw.m;~r:rm::mwm~.oo;** . **:w%mm:&Mi.J!~1~mw;ll**

Abt Associates Inc. July 5, 1995, Page 26 California Crops Benefits MethodologyOzone data were provided by U.S. EPAIERL (Corvallis). The regulatory options analyzed consist offive alternative primary standards, and three levels ofstringency for secondary standards associated with each ofthe primary standards. All secondary standards are defined in terms ofa W126ozone index, and reflect levels ofcropprotectionto be attained: no morethan 10percentyield loss, 20percent, and 30 percent, in order ofdecreasing stringency. Yield loss for the purposes of establishing secondary standards is derived from a composite exposure- response function that reflects most ofthe NCLAN ozone exposure-response experiments oncommodity crops. Eachozone exposure-response experiment exposedthe cropbeing studied overa season. The actual duration ofeach experiment varied, butto enable comparison ofresults, the medianduration wasusedforcrops with multipleexperiments.8 U.S. EPA/ERLprovidedall dataonamonthlybasis. ForWl26 functions, the duration ofNCLAN experiments was provided by ERL, and werounded to the nearest month. Based on planting and harvesting data developed by USDA (USDA, 1984) andemergenceperiodsused in agricultural pesticide fate andtransport models (U.S. EPA 1993), we developed state-specific growing seasons for all field crops. The standard growing seasonfor other crops in California was obtained from the California Statewide Air Pollution Research Center (Randall Mutters, personal communication). Since the W126 and AOTIO indices are cumulative, monthly data for these indices from ERL were summed for the experimental duration, with the exposureperiod anchored on the harvesting date. Forseasonal meanindices, the monthly datafor the sameperiod were averaged to obtain the statistic used in estimating yield losses. 

### 3.4 Calculation ofPercentChangein Yield To develop estimated changes in per-acre yields as input to the CARM, several steps were required. First, the exposure-response functions were used to calculate yield losses relativetothe "cleanair"treatment in exposure experiments, then the change in yield wasestimated betweeneach scenario and its relevant baseline. Finally, this change was applied to the per-acre yields for the relevant baseline. Ozone exposure-response functions are expressed as a percent relative yield loss (PRYL) function, where the ozone level being analyzed is implicitly being comparedto the level ofozone associated with the charcoal filtered air treatment, which represents "clean air". For cumulative ozoneindices, this level is essentially zero ozone, while for 7-hr seasonal mean indicesthis level is 0.027 ppm and for the 12-hr seasonal mean it is 0.025 ppm (Olszyk et al, 1988(a)). Tocalculate a percentyield changeto apply to anonzero baseline, we calculated a PRYLusingthebaseline ozone le'vel, and a PR YL using the regulatory ozone level. The change inyield, measured as a percentof thehypothetical zero-ozone crop production, is then: (PRYL(base)-PRYL(rollback)) To obtainthe change in terms ofour (non-zero) baseline yield, we divideby thatyield, and get:

8This approach is predicated on the assumption thatthe duration ofanexperimentdoes not affectthe coefficient for yield in any way other than through the value ofthe ozone concentration variable. AbtAssociatesInc. July 5, 1995 Page 27 California Crops BenefitsMethodology(PRYL(base)-PRYL(rollback))/(1-PRYL(base)). These percent yield losses were then used to modify input data on regional yield peracre in the economic model used to estimate benefits from ozoneregulation (seebelow). The per-acre yieldsunder 1990 baseline conditions were the basis for estimatingyields for eachprimary standard. The estimated per-acre yields for eachprimary standard then became the basis for estimating yields for its associated secondary standards. To modify the yield peracre input files to reflectchanges in yield due to ozone, it was necessary to constructweighted average percent yield change estimates for each region from county-level yield change estimates. We used a county'sproportion oftotal regional productionto weightthat county's predictedpercentyield change. Some counties are included in two CARM regions, so it was also necessary to specify theproportionofthe productioninthese counties that is allocatedto eachregion.


### 4.0 ECONOMICMODELING The California Agricultural Resources Model (CARM), developed by Dr. Richard Howitt at the University ofCalifornia, Davis (Howitt, 1995), was used to estimate the economic benefits associated with ozone NAAQS. The CARM is a nonlinear optimization (mathematical programming) model ofCalifornia agriculture. It assumes that producers maximize farm profit subject to land, water, and other agronomic constraints, and the model maximizes total economic surplus in its objective function. The CARM predicts producers' shifts in acreage planted to differentcrops due to changing market conditions orresource constraints. The CARMuses constant elasticity ofsubstitution (CES) production functions, whichpermits the substitution ofvariable inputs inresponse to a changein conditions, such asdue to changes in yields associated with ozoneexposure. To reach its optimum solution,the CARMusesa three stage approach to calculate equilibrium production function coefficients for variable inputs. In the first stage, a constrained profit maximization linearprogrc:un is specified. In the second stage,regional production and cost parameters that calibrate the nonlinear CES model to the base year data are derived from the results ofthe linear program. The third stage model is then specified with a nonlinearobjectivefunctionthatincorporatesthenonlinearCESproductionfunctions andlandcosts. Calibrated Production Equilibrium (CPE) models such as the CARM have a number of advantagesfrom theperspectiveofmicroeconomictheory. Linearprogrammingmodels oftenresult 

### ina step-wiseresponsetopolicy changes, wheretherangebetweenstepsmaybelargerthanthelevel of policy change, making the models inflexible over some range of data. CPE models are characterizedby nonlinear and continuous functions, allowing for analysis ofsmall policy changes, such as the small changes in yields that maybe associated with incremental regulationofozone. Becausetheprofit function is nonlinear, the slopes ofsupply and demand functions derived from CPEmodels are respectively positive and negative, as required for microeconomic analysis. TheCARMisdisaggregatedintonineproductionregions, anditincludes 38differentcropping activities. The CARM does not have a livestock sector, although the demand function for some

AbtAssociatesInc. July 5, 1995 Page 28 California CropsBenefitsMethodology

### crops reflects livestock feed demand overtime. The version ofthe CARM used inthis analysis was calibratedto 1990 production and price data. The CARM regions and their associated counties are reported in Exhibit 12, with the proportion ofsplit counties' production allocated to a given region reported in parentheses.

CARM Region Sacramento Northern San Joaquin Valley Central San Joaquin Valley Southern San Joaquin Valley Imperial

South Coast

North Coast

Northeast
Southeast Exhibit 12 CARM Regions and Associated Counties
Counties (Proportion ofProduction,%) Sacramento, Tehama, Butte, Glenn, Colusa, Yolo, Sutter, Solano (100), Placer (50), Yuba Merced, Stanislaus, SanJoaquin, Alameda (25), Contra Costs (25) Madera, Fresno, Tulare, Kings
Kern (100) Imperial Contra Costa (75), Alameda (75), San Francisco, SanMateo, Santa Clara, San Benito, Santa Cruz, Monterey, San Luis Obispo, Santa Barbara, Ventura, Los Angeles (75), Orange, Riverside (33), San Diego Del Norte, Humboldt, Mendocino, Lake, Sonoma,Napa, Solano (0) 

### Siskiyou, Trinity, Shasta, Modoc, Lassen, Plumas, Sierra,Nevada, Placer (50), Amador, ElDorado, Alpine, Calaveras, Tuolumne, Mono, Mariposa lnyo, San Bernardino, Kern (0), Los Angeles (25), Riverside (67)

AbtAssociatesInc. July 5, 1995 Page 29 California Crops BenefitsMethodology

### 5.0 RESULTS Because many crops have multiple exposure-response functions available, two different levels ofresponse were evaluated for each standard to reflect the minimum response and the maximum response. This approach treats all production ofa given crop as being typifiedby a single cultivar, which is not an accurate portrayal ofcropproduction. Itdoes, however, provide anindication oftherange ofbenefitsthat can be estimatedbased onthe available data. Exhibit 13 presents the total surplus associated with each standard evaluated. Estimated benefits associated with primary standards, including bothminimum and maximum estimates, range from $82.4 millionto $484.3 million. The largestbenefits are associated with the 0.07 ppm 8-hourprimary standard, followed by the 0.08 ppm 8-hour standard and the 0.10 ppm 1-hour standard. Under the 0.07 ppm 8-hour standard, grapes (takentogether) account for $162.4 million (minimum) to $212.6 million (maximum) ofthe estimated benefits. Raisins account for between $70 million and $91.5 million in estimated benefits, followed by table grapes ($56.8 million to $74.3 million), and wine grapes ($35.4 millionto 46.6 million). Citrus is responsible for approximately $97.9 million in benefits (both minimum and maximum). (Underthe minimum and maximum scenarios, there are differences in the benefits estimates for cropswith only a single exposure response function, due to changes in the yield peracre for othercrops.) As discussed in Section 3.2, this may be an underestimate ifgrapefruit have a response similarto that oforanges and lemons. Depending on whetherthe response ofthe predominant alfalfa cultivars grown in California is similarto the maximum or the minimum response functions, the benefits associated with alfalfa for the 0.07 ppm 8-hour standard could be as high as $88.4 million or as low as $11.9 million. Benefits associated with cantaloupe are approximately $38 million, dry beans $15 million, and plums $14 million. Processing tomato benefits could be as high as $28.3 million, based on the maximum exposure-response function available for this crop; the minimum estimate, however is only $2.4 million. Several crops actually show negative benefits underthe 0.07 ppm 8-hour standard (as well as under other standards). It

### is still profitable to growthese crops under the new standard, but their overall benefits have declined relativeto those associated with higher ozone levels due to shifts to other crops. Appendix.1 contains crop-by-crop results for each scenario. Inmost cases, secondary standards do not add significant incremental benefits. Benefits associated with a secondary standard exceed those ofthe primary standard in only one case: the most stringent secondary standard (associated with a maximum of 10percentyield loss) for the 0.12 ppm 1-hour standard. For both the 0.12 and the 0.10 ppm 1-hour standards however, benefits associated with one or more secondary standard are relatively large. Inthese cases, the benefits ofsecondary standards are primarily associated withcitrus, grapes, cantaloupe, dry beans, and alfalfa hay.

AbtAssociatesInc. July 5, 1995 Page 30 California Crops BenefitsMethodology

### Standard 

### Primary: 0.12 ppm 1-hr Secondary: 10 percent 20percent 30percent 

### Primary: 0.10 ppm 1-hr Secondary: 10 percent 20 percent 30 percent 

### Primary: 0.07 ppm 8-hr Secondary: 10percent 20percent 30 percent 

### Primary: 0.08 ppm 8-hr Secondary: 10 percent 20 percent 30 percent 

### Primary: 0.09 ppm 8-hr Secondary: 10percent 20percent 30percent 

### Exhibit 13: Changes in Economic Surpluses for Alternative Primary and Secondary Standards ($1,000) Producer Surplus Consumer Surplus TotalSurplUs 

### Minimum 69,554

113,907 42,460 28,521 

### 159,891 44,917

-313 1,086 

### 285,448 7,064 7,064 7,064 

### 203,713 19,031 -9,057 -9,057 

### 125,842 57,749 22,464 12,603 3 

### Maximum 87,236

153,949 63,236 37,813 

### 213,455 65,019 19,353 2,560 

### 387,076 15,104 15,104 15,104 

### 276,692 29,789 252 252 

### 171,590 86,123 29,577 7,097' 

### Minimum 12,854

25,291

1,497 6,545 

### 31,667 14,167 -7'147 3,386 

### 65,006

-1,807 -1,807 -1,807 

### 41,478 3,841 -8,785 -8,785 

### 15,143

15,762 16,1191 16,311· 

### Maximum 25,530

45,460

3,951 6,838 

### 54,433

20,138

6,139 5,304 

### 97,255 11,794 11,794 11,794 

### 75,795 8,762

293 293 

### 43,962 18,539 11,429" 3,9961 

### Minimum 82,408

139,198 43,957 35,066 

### 191,558 59,084 -7,460 4,472 

### 350,454 5,257 5,257 5,257 

### 245,191 22,872 -17,842 -17,842 

### 140,985 73,511 38,583 28,9141 

### Maximum 112,766

199,409 67,187 44,651 

### 267,888 85,157 25,492

7,864 

### 484,331 26,898 26,898 26,898 

### 352,487 38,551 545 545 

### 215,552 104,662 41;006 11,093" "Minimum estimates exceed maximum estimates because no change in the ozone index was estimated for the maximum function, but the index for the minimum function did vary between scenarios. 

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AbtAssociatesInc. July 5, 1995 Page 31 California Crops BenefitsMethodology- I 

### In some cases, not only do individual crops demonstrate negative benefits under more stringent ozone standards, but total benefits are estimatedto be negative as well. There are nine cases when minimum estimated benefits are, in fact, negative relative to the relevant baseline. In these cases, the CARM model is shifting resources between crops (such as land and water) very slightly in response to minor increases in yields, resulting in changes inthe slope ofthe supply curve. This type ofa shift can result in not only a newprice, but changedrelationships between the price, the quantity produced, total revenue, and economic surpluses. In some cases, these changesresult in decreases in consumer and producer surplus in response to increased yields. Finally,there are four cases where the minimum estimated benefits exceedthe maximum estimated benefits. This anomalous result is a function oftheestimated ozone concentrations for different indices in these scenarios. Specifically, the 12-hour seasonal mean indices, used for the maximum analyses, were not predicted by U.S. EPAIERL to changebetween these scenarios, but the AOTl0 (minimum) indices did

### change. Therefore, yields were estimated to increase for the minimum analysis but not for the maximum analysis, resulting inhigherbenefits for the former.

AbtAssociatesInc. July 5, /995 Page 32 California CropsBenefitsMethodology

## REFERENCES

Air Resources Board. (ARB) 1992. The Atmospheric Acidity Protection Program: annual report to the Governor and the Legislature, 1991 and Effect of air pollution on crops in the San Joaquin Valley: special report. Sacramento: California Environmental Protection Agency. September. Brewer, R.F. 1978. The effects ofpresent and potential air pollution on important San Joaquin Valley crops: sugarbeets. Final report. CaliforniaAirResources Board ContractNo. A6-161- 30. Brewer, R.F. 1982. The effects ofozone and S02

### on alfalfayields and hay quality. Final report to the CaliforniaAir Resources Board, ContractNo. A1-038-33. Brewer,R.F. 1983. The effects ofambient airpollution onThompson seedless grapes. Finalreport, CaliforniaAir Resources Board, ContractNo. A1-132-33. Heagle, A.S., R.B. Philbeck, M.B. Letchworth. 1979. Injury and yield responses of spinach cultivars to chronic doses ofozone in open-top field chambers. J. Env. Qual. 8:368-73. Heck, W.W., O.C. Taylor, R. Adams, G. Bingham, J. Miller, E. Preston, L. Weinstein. 1982. · Assessment ofcrop loss from ozone. JAPCA 32:353. Heck, W.W., R.M. Adams, W.W. Cure, A.S. Heagle, H.E. Heggestad, R.J. Kohut, L.W. Kress, J.O. Rawlings, O.C. Taylor. 1983(a). A reassessment ofcrop loss from ozone. Env. Sci. Technol. 17:572A. Heck, W.W., O.C. Taylor, R.M. Adams, G.E. Bingham, J.E. Miller, E.M. Preston, L.H. Weinstein. 1983(b). National crop loss assessment network (NCLAN) 1982 annual report. Corvallis Environmental Research Laboratory. Heck, W.W., W.W. Cure, J.O Rawlings, L.J. Zaragoza, A.S. Heagle, H.E. Heggestad, R.J. Kohut, L.W. Kress, and P.J. Temple. 1984(a). Assessing impacts ofozone on agricultural crops: I. Overview. JAPCA 34:729-35. Heck, W.W., W.W. Cure, J.O. Rawlings, L.J. Zaragoza, A.S. Heagle, H.E. Heggestad, R.J. Kohut, L.W. Kress, R.J. Temple. 1984(b). Assessingimpactsofozoneonagricultural crops: II. Crop yield functions and alternative exposure statistics. JAPCA 34:810-817. Howitt, R.E., T.W. Gossard, R.M. Adams. 1984. Effects ofalternative ozone concentrations and response data on economic assessments: the case of California crops. JAPCA 34:1122-1127.

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### Howitt, R.E. and C. Goodman. 1988. Economic impacts of regional ozone standards on agricultural crops. Env. Pollut. 53:387-395. Howitt, R.E. and C. Goodman. 1989. The economic assessment ofCalifornia field crop losses due to air pollution. Report to the California Air Resources Board, Contract No. AS-105- 32. June. Howitt, R.E. 1995. CalibrationModelling Methods for Measuring Agricultural and Environmental Interaction. J. Ag. Econ. (Forthcoming). Kats, G., P.J. Dawson, A. 

### Bytnerowicz, J.W. Wolf, C.R. Thompson,D.M. Olszyk. 1985(a). Effects ofozone or sulfurdioxide on growth and yield ofrice. Agric. Ecosys. and Env. 14:I03-117. Kats, G., D.M. Olszyk, C.R. Thompson. 1985(b). Open-top experimental chambers for trees. JAPCA 35:1298-1301. Kohut, R. and J.A. 

### Laurence. 1983. Yield response ofred kidney bean Phaseolus vulgaris 

### to incremental ozone concentrations in the field. Env. Pollut. 32:233. Kress, L.W., J.E. Miller, H.J. Smith. 1985. Impact ofozone on winterwheat yield. Environ. Exp. Bot. 25:211. Kress, L.W. and J.E. Miller. 1985(a). Impact ofozone on grain sorghum yield. Water, Air, and Soil Pollut. 25:377. Kress, L.W. and J.E. Miller. 1985(b). Impact ofozone on field-com yield. Can. J. Bot. 63:2408. Leung, S.K., W. Reed, S. Geng. 1982. Estimations ofozone damage to selected crops grown in southern California. JAPCA 32:160. McCool, P.M., R.C. Musselman, R.R. Teso, R.J. Oshima. 1986. Determiningyield losses from air pollutants for California agriculture. Calif. Ag. 40(7-8):9-10. Olszyk, D.M. A. Bytnerowicz, G. Kats, P.J. Dawson, J. Wolf, and C.R. Thompson. 1986. Crop effects from air pollutants in air exclusion systems vs. field chambers. J. 

### Environ. Qual. 15:417-422. Olszyk,D.M., C.R. Thompson, M.P. Poe. 1988(a). Crop loss assessmentfor California: Modeling losses with different ozone standard scenarios. Env. Poll. 53:303-311. Olszyk, D.M., H. Cabrera, C.R. Thompson. 1988(b). California statewide assessment ofthe effects ofozone on crop productivity. JAPCA 38(7):928-931. Olszyk, D.M. 1989. Mechanistic basis for the growth and yield effects of ozone on Valenciaoranges. Final report to the California AirResources Board, ContractNo. A733-087.
AbtAssociatesInc. July 5, 1995 Page 34 California Crops Benefits MethodologyOlszyk,D.M., G. Kats, C.L. Morrison, P.J. Dawson, 

### I. Gocka, J. Wolf, and C.R. Thompson. 1990. Valencia orange fruit yield with ambient oxidant on sulfurdioxide exposures. J. Am. Soc. Hortic. Science 115:878-883. Oshima, R.J., M.P. Pie, P.K.Braegelmann, D.W. Baldwin, V. van Way. 1976. Ozone dosage-crop loss function for alfalfa: a standardized method for assessing crop losses from airpollutants. JAPCA 26:861. Retzlaff, W.A., L.E. Williams, T.M. DeJong. 1992. Photosynthesis, growth, and yield response of 'Casselman' plum to various ozone partial pressures during orchard establishment. J. Am Soc. Hort. Sci. 117(5):703-710. Rowe, R.D and L.G. Chestnut. 1985. Economic assessment ofthe effects ofair pollution on agricultural crops inthe SanJoaquin Valley. JAPCA 35:728. Snyder, R.G., J.E. Simon, R.A. Reinert, M. Simini, and W.W. Heck. 1988. Yield responses of field-grown crops to acidic fog and ambient ozone. J. Environ. Qual. 17:19Vf97. Temple, P.J. 1990. Growth and yield responses ofprocessing tomato 

### (Lycopersicon escu/entum Mill.) cultivars to ozone. Environ. Exp. Bot. 30:283-291. Temple, P.J., O.C. Taylor, L.F. Benoit. 1985(a). Effects ofozone on yield oftwo field-grown barley cultivars. Env. Pollut. 39:217. Temple,P.J., O.C. Taylor, L.F. Benoit. 1985(b). Cottonyieldresponsesto ozone as mediatedby soil moisture and evapotranspiration. J.Env. Qual. 14:55. Temple, P.J., K.A. Surano; R.G. Mutters, G.G. Bingham, J.H Shinn. 1985(c). Airpollution causes moderate damage to tomatoes. Calif. Ag. 39:20. Temple, P.J., O.C. Taylor, L.F. Benoit. 1986. Yield response ofhead lettuce 

### (Lactuca sativa L.) to ozone. Environ. Exp. Bot. 26:53. Temple, P.J., L.F. Benoit, R.W. Lennox, C.R. Reagan, O.C. Taylor. 1987. Combined effects of ozone and water stress on alfalfa growth and yield. J. Env. Qual. 17:108. Temple, P.J., L.F. Benoit, R.W. Lennox, C.A. Reagan, and O.C. Taylor. 1988. Combined effects ofozone and water stress on alfalfa growth and yield. J. Environ. Qual. 17:108-113. Temple, P.J., T.E. Jones, and R.W. Lennox. 1990. Yield loss assessments for cultivars ofbroccoli, lettuce, and onion exposed to ozone. Environ. Pollut. 66:289-300. Thompson, C.R. and O:C. Taylor. 1969. Effects ofairpollutants on growth, leafdrop, fruit drop, and yield ofcitrus trees. Env. Sci. Technol. 3:934-940.

AbtAssociatesInc. July 5, 1995 Page 35 California Crops BenefitsMethodology

### Thompson, C.R., and G. Kats. 1970. Antioxidants reduce grape yield reductions from photochemical smog. Calif. Ag. 24: 12-13. Thompson, C.R., G. Kats, 

### J.W. Cameron. 1976. Effects ofambientphotochemical airpollutants on growth, yield, and ear characteristics oftwo sweetcomhybrids. J. Env. Qual. 5:410-412. Thompson, C.R. and D.M. Olszyk. 1986. Californiacrop loss assessmentproject. Interim reports to the CaliforniaAir Resources Board for contractNo. AS-031-33. Tingey, D.T., W.E. Hogsett, E.H. Lee, A.A. 

### Herstrom, S.H. Azevedo. 1990. An 

### Evaluation of variousalternativeambientozone standardsbased oncropyieldlossdata. In: R.L. Berglund, D.R. Lawson, andD.J. McKee, eds. Transactions: TroposphericOzoneandtheEnvironment. Pittsburgh, PA: Air and Waste Management Association. USDA 1984. Usual Planting and Harvesting Dates for U.S. Field Crops. Statistical Reporting Service Agricultural HandbookNo. 628. USDA 1993. Fruit and Tree Nuts: Situation and OutlookYearbook. ERS TFS-266. July. USDA 1994. National Financial Summary 1993. December. USDA 1995. Final Estimates for Vegetables 1987-1992. ERS Statistical Bulletin 902. January. US EPA 1993. PRZM-2, A Model for Predicting Pesticide Fate 

### in the CropRoot and Unsaturated Soil Zones. Users's Manual for Release 2.0. EPA/600/R-93/046. US EPA Unpublished. Air Quality Criteria for Ozone and Related Photochemical Oxidants. Winer, A.M, D.M. Olszyk, and R.E. Howitt. 1990. Air Quality Impacts onCaliforniaAgriculture, 1990-2010. Ch. 5 in Agriculture in California: Onthe Brink ofa New Millennium 1990- 2010,H.O. Carterand C.F. Nuckton, eds. Davis,CA: U.C. DavisAgricultural Issues Center, Issue Papers.

AbtAssociatesInc. July 5, 1995 Page 36 California Crops BenefitsMethodology

### APPENDIX 1 Benefits Estimates By CropCROP P.12 ppm 1hr 0.10 ppm 1hr

Min Max Min Max

ALFA-HAY 7,615 11,483 8,643 ALFA-SEED -654 70 -2
ALMONDS -403 -71 26 APPLE+PEAR -31 -10 -8
APRICOTS -25 -4 -7 ASPARAGUS -9 -8 -8 AVOCADOS 0 2 -8

BARLEY -199 -23 -50 BEANS-DRY 3,378 3,414 7,631 BROC+CAULI -95 -31 -60 CANTALOUPE 7,709 7,739 16,311
CARROTS -19 -19 -26

CELERY -4 -6 -4

CITRUS 16,790 16,861 39,055

CORN 52 5 2

COTTON 246 -59 -112
GRAINHAY -35 9 5 GRAINSORGH -28 -17 -4 GRAPE-DRY 12,236 18,939 35,309

GRAPE-TABL 9,789 13,285 25,140 GRAPE-WINE 4,740 6,702 15,493

LETTUCE -132 -32 -66 ONIONS-DRY 676 767 1,585
PASTURE 3,772 -326 637 PEACH+NECT -162 3 -42

PLUMS 1,948 2,195 6,215
POTATOES 342 806 664

PRUNES -24 -6 -2

RICE 580 657 717

SAFFLOWER 182 181 160

SILAGE 169 -51 -34 SUGARBEETS -84 -32 20

TOMATO-PRO 1,364 4,734 2,992
**~ALNUTS** -175 -24 -8

**~EAT** 235 13 -53

XFIELDCROP 175 183 28 XFRUITVEG -298 -96 -179 XTREENUTS -68 3 -67

Total PI'Qducer 69,554 87,236 159,891

Surplus FRUIT AND VEGETABLE CROPS: PRODUCER SURPLUS BY CROP ($1,000) PRIMARY STANDARDS RELATIVE TO BASELINE

0.07 ppm 8hr 0.08 ppm 8hr

Min Max Min

27,201 6,240 49,198 8,213

-40 -640 -656 -648

-399 -··- -182 ·-------=464 - -348 -29 -15 -32 -17

-21 -6 -25 -14

-

-8 4 -10 -6

-1 -13 1 -3

-133 44 -276 -124

7,643 10,991 10,979 9,487

28 8 -42 -39

16,332 29,647 29,640 20,734

-16 -16 -20 -15

-3 -3 -4 -3

38,927 72,720 72,713 51,117

52 48 56 63

-56 -185 -1,456 -387

-29 -28 -44 -5

-14 -9 -28 6

46,173 66,948 87,272 45,452

32,642 46,840 61,289 33,295

20,473 31,154 40,937 20,859

45 -112 -115 -38

1,486 2,832 2,826 2,018

2,571 2,407 -4,463 1,639

-444 -444 -447 -430

5,911 12,332 12,330 8,402

1,708 936 2,761 755

-23 -23 -24 -19

736 1,460 1,418 741

159 133 184 143

314 144 -5 124

-103 -102 -109 -34

12,956 2,237 25,031 2,829

-174 10 -175 -51

-323 -332 -344 419

373 326 -490 51

-163 --------

-391 -320 -385

-67 -67 -68 -67

213,455 285,448 387,076 203,713 0.09ppm 8hr

Max Min Max 32,702 8,762 20,601

-68 -681 -254

-213 -380 -172

-13 -35 -35

-11 -25 -8

-7 -15 -3

3 -2 6

-123 -278 -212

9,515 6,550 6,578

-29 -87 -60
20,768 13,613 13,667

-19 -24 -14

-2 -4 -3
51,314 32,986 33;191

-9 55 12

-102 -1,455 -580

2 -52 -20

-19 -44 -5
61,755 27,721 38,380

43,781 21,267 28,017
26,823 10,798 14,242

-49 -135 -57

2,010 1,205 1,314

-280 -345 253

-29 -448 -356

8,773 5,168 5,449

2,117 564 1,478

-2 -24 -23

812 672 682

176 184 113

49 -190 -210

-38 -108 -17
16,665 1,945 9,570

32 -175 -11

118 -369 -284

256 -344 359

40 -361 0

-6 -68 5 276,692 125,842 171,590CROP ALFA-HAY ALFA-SEED ALMONDS APPLE+PEAR APRICOTS ASPARAGUS AVOCADOS BARLEY BEANS-DRY BROC+CAULI CANTALOUPE CARROTS CELERY CITRUS CORN COTTON GRAINHAY GRAINSORGH GRAPE-DRY GRAPE-TABL GRAPE-WINE LETTUCE ONIONS-DRY PASTURE PEACH+NECT PLUMS POTATOES PRUNES RICE SAFFLOWER SILAGE SUGARBEETS TOMATO-PRO WALNUTS WHEAT XFIELDCROP XFRUITVEG XTREENUTS Total Producer Surplus FRUIT ANDVEGETABLE CROPS: PRODUCER SURPLUS BY CROP ($1,000) SECONDARY STANDARDS RELAnVETO 0.10 1-HR PRIMARY STANDARD 10% Loss 20°.4 Loss 30% Loss Min Max Min Max Min Max -2,198 6,108 -204 1,802 -450 -96 90 -655 -608 32 -210 300 -435 527 -20

-11 20 -28 42 -6

-1 14 -18 20 -13 1 8 ·2 3 1 -18 8 9 0 6 -156 -22 -228 22 160 992 979 -32 -40 27

5 -59 53 -17 64 3,790 3,765 1,352 1,338 193 7 -2 5 1 11 0 0 0 0 1 13,115 13,436 3,862 3,997 572 -20 -6 63 -3 45 174 -154 -1,344 -124 40 -51 26 -54 0 -39

-2 17 -24 7 -37 12,815 15,891 1,070 4,070 622 9,132 12,115 2,323 3,700 360 3,755 5,064 778 836 900 -5 -112 -48 -153 -26 400 476 -24 77 -55

19 -1,301 -4,923 1,911 371 41 344 -405 22 -401 2,485 3,052 698 1,025 84

116 434 3 78 6 -18 17 -21 18 -1 -35 13 -91 -26 -10

-8 -40 26 13 -66 350 172 48 76 219 -53 63 -142 53 -35 -59 3,565 -792 1,487 -757 -140 161 -170 4 -26 207 299 -295 54 40 180 -193 -465 -908 -533 314 394 -201 50 -191

96 80 -1 1 1 44,917 65,019 -313 19,353 1,086 -920 262 239

12 13 7 5 143

6 -60 121

-3 2 918 -36 -29 -30

-2 1,283

705 591 -58 147 -3,407

436 651

-5 21

9 53 -472

92 1,177

178 372 -247 329

57 2,560CROP ALFA-HAY ALFA-SEED ALMONDS APPLE+PEAR APRICOTS ASPARAGUS AVOCADOS BARLEY BEANS-DRY BROC+CAULI CANTALOUPE CARROTS CELERY CITRUS CORN COTTON GRAINHAY GRAINSORGH GRAPE-DRY GRAPE-TABL GRAPE-WINE LETTUCE ONIONS-DRY PASTURE PEACH+NECT PLUMS POTATOES PRUNES RICE SAFFLOWER SILAGE SUGARBEETS TOMATO-PRO WALNUTS WHEAT XFIELDCROP XFRUITVEG XTREENUTS Total Producer Surplus FRUIT ANDVEGETABLE CROPS: PRODUCER SURPLUS BYCROP ($1,000) SECONDARY STANDARDS RELATIVE TO 0.09 8-HR PRIMARY STANDARD 10% Loss 20% Loss 30% Loss Min Max Min Max Min Max -462 9,832 -1,639 3,077 -2,200

27 -395 576 334 881 -24 -228 60 159 331

3 5 11 21 30 0 -17 21 15 17 7 -4 7 21 -1 3 -4 -4 9 -8 78 385 324 189 67 1,830 1,803 50 30 34

100 -18 20 51 33 3,965 3,917 1,343 1,333 37 5 -2 5 8 -2 0 0 0 2 1 16,882 16,684 5,833 5,812 1,610 -2 35 -4 2 -85 -1 -809 1,080 642 1,345 13 -1 -5 -17 0 16 23 34 -26 19 14,867 17,140 7,261 6,826 2,523 11,617 15,138 4,015 5,196 992 4,811 6,343 1,878 2,436 1,543 3 -72 33 0 72 463 358 137 147 128

40 10,093 -876 370 934

1 -92 393 214 604 3,186 2,906 1,114 794 632 67 290 2 0 -4

0 -1 20 13 8 6 -5 -34 -18 34 -2 41 -35 60 -99 221 1,582 202 244 43

1 -84 56 -8 -40 141 2,945 95 1,462 775

0 -161 46 10 252 21 -38 71 532 643 -120 -1,008 294 -296 787

-13 -393 64 -68 580

0 -67 15 -2 85 57,749 86,123 22,464 29,577 12,603 1,176 320 -184

25 -5 -5 -18 211

22 50 13 -3 0 1,233

-33 328 -24 -15 959 918 1,263

-18 57 -734 258 304

-3 14 9 30 227

-9 895 -22 249 -329

-57

-5 7,097CROP [Q.12 ppm 1hr 0.10 ppm 1hr

Min Max Min Max

ALFA-HAY -1 10,358 558
ALFA-SEED -6 1 0
ALMONDS -518 -80 34 APPLE+PEAR -15 -10 -11
APRICOTS -2 0 -1 ASPARAGUS -3 -1 -2

AVOCADOS -34 -4 -24

BARLEY -259 -477 -547 BEANS-DRY 1,224 1,353 3,033

BROC+CAULI -54 -18 -37 CANTALOUPE 2,117 2,290 4,896
CARROTS -7 -8 -11

CELERY **~3** -5 -3

CITRUS 5,574 5,759 13,618

CORN -223 42 -42

COTTON 951 -391 -701
GRAINHAY -14 3 0

GRAINSORGH -6 -18 -1 GRAPE-DRY 398 945 1,771 GRAPE-TABL 1,927 2,819 5,384 GRAPE-WINE 355 976 2,258

LETTUCE -83 -37 -92

ONION8-DRY 49 94 210

PASTURE 1,860 318 . 1,185

PEACH+NECT -22 6 -30

PLUMS 187 295 915
POTATOES 49 170 137

PRUNES -49 -18 -8

RICE 138 130 268

SAFFLOWER -531 -234 -227

SILAGE 367 -618 -487 SUGARBEETS -159 -32 38

TOMATO-PRO -3 560 439
!WALNUTS -210 -21 -17

WHEAT 25 -421 -567 XFIELDCROP 126 -98 -24 XFRUITVEG -183 -105 -139 XTREENUTS -109 6 -107

Total Consumer 12,854 23,530 31,667

Surplus

- '--------- ·-- ----- FRUIT AND VEGETABLE CROPS: CONSUMER SURPLUS BY CROP ($1,000) PRIMARY STANDARDS RELATIVE TO BASELINE

0.07 ppm Shr 0.08 ppm Shr

Min Max Min

18,907 5,701 39,209 1,492

-

0 -6 -6 -6

-513 -232 -522 -448

-13 -5 -16 -11

- ·-

-2 -1 -2 -2

-3 2 -3 -2

-30 -9 -35 -17

-326 666 -395 -510

3,069 4,271 4,241 3,664

21 12 -20 -21

4,902 9,214 9,206 6,398

-5 -5 -7 -5

-3 -3 -3 -3

13,293 25,181 25,172 17,560

-221 -214 -231 -249

-326 -870 -6,098 -1,718

-13 -11 -18 -2

-11 0 -6 14

2,314 3,241 4,264 2,108

6,856 9,937 13,033 7,096

3,187 4,273 5,679 2,936

12 -69 -82 -33

159 339 338 263

1,738 1,674 -127 1,247

-154 -153 -156 -149

781 1,741 1,740 1,155

362 205 584 158

-48 -45 -53 -41

434 809 596 211

-428 -98 -536 -259

336 326 218 313

-189 -186 -198 -62

1,621 122 3,309 379

-211 12 -215 -64

---- -1,027 -1,043 -1,092 237

259 325 -203 139

-185 15 -197 -183

-108 -108 -109 1-----·--·- -107

54,433 65,006 97,255 41,478

-- L__ --- 0.09 ppm Shr

Max Min MaX 28,271 280 18,371

-1 -6 -2

13 -492 -226

-9 -22 -21

-1 -2 -1

-1 -3 0

-3 -41 -8

-768 -399 -878

3,786 2,476 2,563

-19 -51 -35

6,596 4,012 4,210

-10 -9 -6

-2 -3 -3
18,076 11,220 11,716

32 -231 -50

-526 -6,110 -2,564

2 -20 7

-15 -24 10

3,112 1,181 1,902

9,367 4,391 5,898

3,991 1,352 2,098

-51 -99 -48

260 121 171

535 18 271

-3 -150 -124

1,325 668 792

445 122 301

0 -54 -221

515 242 119

-238 -539 -2951

-1,080 -480 -2551

-58 -196 -28

2,297 75 1,275'

31 -215 291

-125 -1,131 -967

51 -400 -142

-52 -230 -99

54 -109 5
75,795 15,143 43,962CROP ALFA-HAY ALFA-SEED ALMONDS APPLE+PEAR APRICOTS ASPARAGUS AVOCADOS BARLEY BEANS-DRY BROC+CAULI CANTALOUPE CARROTS CELERY CITRUS CORN COTTON GRAINHAY GRAINSORGH GRAPE-DRY GRAPE-TABL GRAPE-WINE LETTUCE ONIONS-DRY PASTURE PEACH+NECT PLUMS POTATOES PRUNES RICE SAFFLOWER SILAGE SUGARBEETS TOMATO-PRO WALNUTS WHEAT XFIELDCROP XFRUITVEG XTREENUTS NetTotal Surplu1 FRUIT AND VEGETABLE CROPS: TOTAL SURPLUS BYCROP ($1,000) SECONDARY STANDARDS RELATIVE TO 0.07 8-HR PRIMARY STANDARD 10% Loss 20% Loss 30% Loss Min Max Min Max Min Max -397 494 -397 494 -397 705 551 705 551 705 137 14 137 14 137

-1 42 -1 42 -1 -2 4 -2 4 -2 -24 3 -24 3 -24 30 3 30 3 30 -1,436 1,064 -1,436 1,064 -1,436

156 242 156 242 156 -57 37 -57 37 -57 1,198 942 1,198 942 1,198 1 5 1 5 1 2 0 2 0 2 580 11 580 11 580 165 7 165 7 165 242 6,899 242 6,899 242

30 17 30 17 30 -2 9 -2 9 -2 2,967 2,419 2,967 2,419 2,967

624 805 624 805 624 2,182 1,122 2,182 1,122 2,182 68 146 68 146 68 158 9 158 9 158 -3,603 8,282 -3,603 8,282 -3,603

643 685 643 685 643 523 38 523 38 523 -25 4 -25 4 -25 66 11 66 11 66 -266 29 -266 29 -266 -115 368 -115 368 -115 -1,011 325 -1,011 325 -1,011

205 10 205 10 205 1,034 206 1,034 206 1,034 -138 15 -138 15 -138 1,091 1,222 1,091 1,222 1,091 -528 905 -528 905 -528 -202 -53 -202 -53 -202 259 3 259 3 259 5,257 26,898 5,257 26,898 5,257 494 551

14 42

4 3 3 1,064

242

37 942

5 0 11 7 6,899 17 9 2,419

805 1,122

146

9 8,282

685

38 4 11 29 368 325

10 206

15. 1,222

905 -53

3 26,898CROP ALFA-HAY ALFA-SEED ALMONDS APPLE+PEAR APRICOTS ASPARAGUS AVOCADOS BARLEY BEANS-DRY BROC+CAULI CANTALOUPE CARROTS CELERY CITRUS CORN COTION GRAINHAY GRAINSORGH GRAPE-DRY GRAPE-TABL GRAPE-WINE LETIUCE ONIONS-DRY PASTURE PEACH+NECT PLUMS POTATOES PRUNES RICE SAFFLOWER SILAGE SUGARBEETS TOMATO-PRO !WALNUTS WHEAT XFIELDCROP XFRUITVEG XTREENUTS NetTotal Surplue FRUIT ANDVEGETABLE CROPS: TOTAL SURPLUS BYCROP ($1,000)

**~ECONDARY** STANDARDS RELATIVE TO 0.08 8-HR PRIMARY STANDARD 10% Loss 20% Loss 30% Loss Min Max Min Max Min Max 1,916 5,290 -137 -626 -137

62 63 -8 -589 -8 661 -25 -129 -710 -129

-4 2 -19 -22 -19 11 15 -11 -13 -11 6 6 -5 -2 -5 -1 32 -14 -32 -14 -41 803 17 1,183 17 294 227 -30 83 -30 -70 31 83 31 83 2,689 2,472 108 -113 108 -4 18 -7 7 -7 0 5 0 -3 0 8,420 8,440 739 38 739

260 15 11
. -188 11 -3,363 -607 -5,450 2,468 -5,450 -58 -37 -58 -47 -58 -75 17 -54 0 -54 8,277 8,227 2 -2,388 2 4,517 6,143 -259 -320 -259 2,028 2,668 -345 -262 -345

-51 5 -125 -83 -125 262 207 -129 -112 -129 -2,664 891 -7,190 2,629 -7,190

380 -32 -25 -568 -25 1,964 1,446 -13 -553 -13 5 68 8 5 8 80 -11 -16 -64 -16 37 17 55 -28 55 -153 23 -233 -280 -233 -834 676 -127 1,576 -127 129 -137 -210 -193 -210 420 2,082 -910 -603 -910 -125 -81 -280 -438 -280 .-2,011 -229 -2,135 570 -2,135

-697 -232 -922 250 -922 547 31 -17 168 -17

57 19 -3 -224 -3 22,872 38,551 -17,842 545 -17,842 -626 -589 -710

-22 -13

-2 -32 1,183 83 31 -113 7 -3 38 -188 2,468

-47

0 -2,388

-320 -262

-83 -112 2,629 -568 -553 5 -64 -28 -280 1,576 -193 -603 -438 570 250 168 -224 545

### APPENDIX 1 

### Benefits Estimates By Crop



### • 'CROP **~.12** ppm 1hr 0.10 ppm 1hr

Min Max Min Max
ALFA-HAY 7,615 11,483 8,643 ALFA-SEED -654 70 -2
ALMONDS -403 -71 26 APPLE+PEAR -31 -10 -8
APRICOTS -25 -4 -7 ASPARAGUS -9 -8 -8 AVOCADOS 0 2 -8

BARLEY -199 -23 -50 BEANS-DRY 3,378 3,414 7,631

BROC+CAULI -95 -31 -60 CANTALOUPE 7,709 7,739 16,311
CARROTS -19 -19 -26

CELERY -4 -6 -4

CITRUS 16,790 16,861 39,055

CORN 52 5 2

COTION 246 -59 -112
GRAINHAY -35 9 5

GRAINSORGH -28 -17 -4 GRAPE-DRY 12,236 18,939 35,309

GRAPE-TABL 9,789 13,285 25,140 GRAPE-WINE 4,740 6,702 15,493

LETIUCE -132 -32 -66 ONIONS-DRY 676 767 1,585
PASTURE 3,772 -326 637 PEACH+NECT .. -162 3 -42

PLUMS 1,948 2,195 6,215
P<»TATOES 342 806 664

PRUNES -24 -6 -2

RICE 580 657 717

SAFFLOWER 182 181 160

SILAGE 169 -51 -34

SUGAR BEETS -84 -32 20

TOMATO-PRO 1,364 4,734 2,992
!WALNUTS -175 -24 -8

[WHEAT 235 13 -53 XFIELDCROP 175 183 28 XFRUITVEG -298 -96 -179 XTREENUTS -68 3 -67

,,

Total Producer 69,554 87,236 159,891

Surplus

-· - FRUIT AND VEGETABLE CROPS: PRODUCER SURPLUS BY CROP ($1,000) PRIMARY STANDARDS RELATIVE TO BASELINE

0.07 ppm 8hr 0.08 ppm 8hr

Min Max Min

27,201 6,240 49,198 8,213

-40 -640 -656 -648

-399 .. -182 --·-------=464 -348

-29 -15 -32 -17

-21 -6 -25 -14

-

-8 4 -10 -6

-1 -13 1 -3

-133 44 -276 -124

7,643 10,991 10,979 9,487

28 8 -42 -39

16,332 29,647 29,640 20,734

-16 -16 -20 -15

-3 -3 -4 -3

38,927 72,720 72,713 51,117

52 48 56 63

-56 -185 -1,456 -387

-29 -28 -44 -5

-14 -9 -28 6

46,173 66,948 87,272 45,452

32,642 46,840 61,289 33,295

20,473 31,154 40,937 20,859

45 -112 -115 -38

1,486 2,832 2,826 2,018

2,571 2,407 -4,463 1,639

-444 -444 -447 -430

5,911 12,332 12,330 8,402

1,708 936 2,761 755

-23 -23 -24 -19

736 1,460 1,418 741

159 133 184 143

314 144 -5 124

- -103 -102 -109 -34

12,956 2,237 25,031 2,829

-174 10 -175 -51 -323 -332 -344 419 373 326 -490 51

-391 163 -320 -385

-67 -67 -68 -67

213,455 285,448 387,076 203,713 0.09ppm8hr

Max Min Max ·. 32,702 8,762 20,601

-68 -681 -254

-213 -380 -172

-13 -35 -35

-11 -25 -a

-7 -15 -3

3 -2 6

-123 -278 -212

9,515 6,550 6,5781

-29 -87 -60'
20,768 13,613 13,667

-19 -24 -14

-2 -4 -3
51,314 32,986 33,191

-9 55 12

-102 -1,455 -580

2 -52 -20

-19 -44 -5
61,755 27,721 38,380

43,781 21,267 28,017
26,823 10,798 14,242

-49 -135 -57

2,010 1,205 1,314

-280 ,345 253

-29 -448 -356

8,773 5,168 5,449

2,117 564 1,478

-2 -24 -23

812 672 682

176 184 113

49 -190 -210

-38 -108 -17

16,665 1,945 9,570

32 -175 -11

118 -369 -284

256 -344 359

40 -361 0

-6 -68 5 276,692 125,842 171,590CROP ALFA·HAY ALFA-SEED ALMONDS APPLE+PEAR APRICOTS

.. ASPARAGUS AVOCADOS BARLEY BEANS-DRY BROC+CAULI CANTALOUPE CARROTS CELERY CITRUS CORN COTTON GRAINHAY GRAINSORGH GRAPE-DRY GRAPE-TABL GRAPE-WINE LETTUCE ONIONS-DRY PASTURE IPEACH+NECT PLUMS POTATOES PRUNES RICE SAFFLOWER SILAGE SUGARBEETS TOMATO-PRO WALNUTS WHEAT XFIELDCROP XFRUITVEG XTREENUTS Total Producer Surplus FRUIT ANDVEGETABLE CROPS: PRODUCER SURPLUS BYCROP ($1,000) SECONDARY STANDARDS RELATIVE TO 0.10 1-HR PRIMARY STANDARD 10% Loss 20% Loss 30% Loss Min Max Min Max Min Max -2,198 6,108 -204 1,802 -450 -96 90 -655 -608 32 -210 300 -435 527 -20

-11 20 -28 42 -6

-1 14 -18 20 -13 1 8 -2 3 1 -18 8 9 0 6 -156 -22 -228 22 160 992 979 -32 -40 27

5 -59 53 -17 64 3.790 3,765 1,352 1,338 193 7 -2 5 1 11 0 0 0 0 1 13,115 13,436 3,862 3,997 572 -20 -6 63 -3 45 174 -154 -1,344 -124 40

. -51 26 -54 0 -39

-2 17 -24 7 -37 12,815 15,891 1,070 4,070 622 9,132 12,115 2,323 3,700 360 3,755 5,064 778 836 900 -5 -112 -48 -153 -26 400 476 -24 77 -55

19 -1,301 -4,923 1,911 371 41 344 -405 22 -401 2,485 3,052 698 1,025 84

116 434 3 78 6 -18 17 -21 18 -1 -35 13 -91 -26 -10

-8 -40 26 13 -66 350 172 48 76 219 -53 63 -142 53 -35 -59 3,565 -792 1,487 -757 -140 161 -170 4 -26 207 299 -295 54 40 180 -193 -465 -908 -533 314 394 -201 50 -191

96 80 -1 1 1 44,917 65,019 -313 19,353 1,086 -920 262 239

12 13 7 5 143

6 -60 121

-3 2 918 -36 -29 -30

-2 1,283

705 591 -58 147 -3,407

436 651

-5 21

9 53 -472

92 1,177

178 372 -247 329

57 2,560CROP ALFA-HAY ALFA-SEED ALMONDS APPLE+PEAR APRICOTS ASPARAGU'S AVOCADOS BARLEY BEANS-DRY BROC+CAULI CANTALOUPE CARROTS CELERY CITRUS CORN COTION GRAINHAY GRAINSORGH GRAPE-DRY GRAPE-TABL GRAPE-WINE LETIUCE ONIONS-DRY PASTURE PEACH+NECT PLUMS POTATOES PRUNES RICE SAFFLOWER SILAGE SUGARBEETS TOMATO-PRO WALNUTS WHEAT XFIELDCROP XFRUITVEG XTREENUTS Total Producer Surplus FRUIT ANDVEGETABLE CROPS: PRODUCER SURPLUS BY CROP ($1,000) !SECONDARY STANDARDS RELATIVE TO 0.09 8-HR PRIMARY STANDARD 10% Loss 20% Loss 30% Loss Min Max Min Max Min Max -462 9,832 -1,639 3,077 -2,200

27 -395 576 334 881 -24 -228 60 159 331

3 5 11 21 30 0 -17 21 15 17 7 -4 7 21 -1 3 -4 -4 9 -8 78 385 324 189 67 1,830 1,803 50 30 34

100 -18 20 51 33 3,965 3,917 1,343 1,333 37 5 -2 5 8 -2 0 0 0 2 1 16,882 16,684 5,833 5,812 1,610 -2 35 -4 2 -85 -1 -809 1,080 642 1,345 13 -1 -5 -17 0 16 23 34 -26 19 14,867 17,140 7,261 6,826 2,523 11,617 15,138 4,015 5,196 992 4,811 6,343 1,878 2,436 1,543 3 -72 33 0 72 463 358 137 147 128

40 10,093 -876 370 934

1 -92 393 214 604 3,186 2,906 1,114 794 632 67 290 2 0 -4

0 -1 20 13 .8 6 -5 -34 -18 34 -2 41 -35 60 -99 221 1,582 202 244 43

1 -84 56 -8 -40 141 2,945 95 1,462 775

0 -161 46 10 252 21 -38 71 532 643 -120 -1,008 294 -296 787

-13 -393 64 -68 580

0 -67 15 -2 85 57,749 86,123 22,464 29,577 12,603 1,176 320 -184

25 -5 -5 -18 211

22 50 13 -3 0 1,233

-33 328 -24 -15 959 918 1,263

-18 57 -734 258 304

-3 14 9 30 227

-9 895 -22 249 -329

-57

-5 7,097CROP Q.12 ppm 1hr 0.10 ppm 1hr

Min Max Min Max

ALFA-HAY -1 10,358 558
ALFA-SEED -6 1 0
ALMONDS -518 -80 34 APPLE+PEAR -15 -10 -11

APRICOTS -2 0 -1 ASPARAGUS -3 -1 -2 AVOCADOS -34 -4 -24

BARLEY -259 -477 -547 BEANS-DRY 1,224 1,353 3,033 BROC+CAULI -54 -18 -37 CANTALOUPE 2,117 2,290 4,896
CARROTS -7 -8 -11

CELERY **~3** -5 -3

CITRUS 5,574 5,759 13,618

CORN -223 42 -42

COTTON 951 -391 -701
GRAINHAY -14 3 0 GRAINSORGH -6 -18 -1 GRAPE-DRY 398 945 1,771 GRAPE-TABL 1,927 2,819 5,384 GRAPE-WINE 355 976 2,258

LETTUCE -83 -37 -92

ONION$-DRY 49 94 210

PASTURE 1,860 318 1,185

PEACH+NECT .t" -22 6 -30

PLUMS 187 295 915
POTATOES 49 170 137

PRUNES -49 -18 -8

RICE 138 130 268

SAFFLOWER -531 -234 -227

SILAGE 367 -618 -487 SUGARBEETS -159 -32 38 TOMATO-PRO -3 560 439
WALNUTS -210 -21 -17

[WHEAT 25 -421 -567 XFIELDCROP 126 -98 -24 XFRUITVEG -183 -105 -139 XTREENUTS -109 6 -107

,.._

Total Consumer 12,854 23,530 31,667

Surplus

-- FRUIT AND VEGETABLE CROPS: CONSUMER SURPLUS BY t:ROP ($1,000) PRIMARY STANDARDS RELATIVE TO BASELINE

0.07 ppm 8hr 0.08ppm8hr

Min Max Min

18,907 5,701 39,209 1,492

0 -6 -6 -6

-513 -232 -522 -448

-13 -5 -16 -11

·-

-2 -1 -2 -2

-3 2 -3 -2

-30 -9 -35 -17

-326 666 -395 -510

3,069 4,271 4,241 3,664

21 12 -20 -21

4,902 9,214 9,206 6,398

-5 -5 -7 -5

-3 -3 -3 -3

13,293 25,181 25,172 17,560

-221 -214 -231 -249

-326 -870 -6,098 -1,718

-13 -11 -18 -2

-11 0 -6 14

2,314 3,241 4,264 2,108

6,856 9,937 13,033 7,096

3,187 4,273 5,679 2,936

12 -69 -82 -33

159 339 338 263

1,738 1,674 -127 1,247

-154 -153 -156 -149

781 1,741 1,740 1,155

362 205 584 158

-48 -45 -53 -41

434 809 596 211

-428 -98 -536 -259

336 326 218 313

-189 -186 -198 -62

1,621 122 3,309 379

-211 12 -215 -64

-· -1,027 -1,043 -1,092 237

259 325 -203 139

-185 15 -197 -183

-108 -108 -109 -107

-r----···
54,433 65,006 97,255 41,478

- 0.09 ppm 8hr

Max Min MaX 28,271 280 18,371

-1 -6 -2

13 -492 -226

-9 -22 -21

-1 -2 -1

-1 -3 0

-3 -41 -8

-768 -399 -878

3,786 2,476 2,563

-19 -51 -35

6,596 4,012 4,210

-10 -9 -6

-2 -3 -3
18,076 11,220 11,716

32 -231 -50

-526 -6,110 -2,564

2 -20 7

-15 -24 10

3,112 1,181 1,902

9,367 4,391 5,898

3,991 1,352 2,098

-51 -99 -48

260 121 171

535 18 271

-3 -150 -124

1,325 668 792

445 122 301

0 -54 -22

515 242 119

-238 -539 -295

-1,080 -480 -255

-58 -196 -28

2,297 75 1,275

31 -215 29

-125 -1,131 -967

51 -400 -142

-52 -230 -99

54 -109 5
75,795 15,143 43,962CROP ALFA-HAY ALFA-SEED ALMONDS APPLE+PEAR APRICOTS ASPARAGUS AVOCADOS BARLEY BEANS-DRY BROC+CAULI CANTALOUPE CARROTS CELERY CITRUS CORN COTTON GRAINHAY GRAINSORGH GRAPE-DRY GRAPE-TABL GRAPE-WINE LETTUCE ONIONS-DRY PASTURE PEACH+NECT PLUMS POTATOES PRUNES RICE SAFFLOWER SILAGE SUGARBEETS TOMATO-PRO WALNUTS WHEAT XFIELDCROP XFRUITVEG XTREENUTS NetTotal Surplus FRUIT ANDVEGETABLE CROPS: TOTAL SURPLUS BYCROP ($1,000) SECONDARY STANDARDS RELAnVETO 0.07 8-HR PRIMARY STANDARD 10% Loss 20% Loss 30% Loss Min Max Min Max Min Max -397 494 -397 494 -397 705 551 705 551 705 137 14 137 14 137

-1 42 -1 42 -1 -2 4 -2 4 -2 -24 3 -24 3 -24 30 3 30 3 30 -1,436 1,064 -1,436 1,064 -1,436

156 242 156 242 156 -57 37 -57 37 -57 1,198 942 1,198 942 1,198 1 5 1 5 1 2 0 2 0 2 580 11 580 11 580 165 7 165 7 165 242 6,899 242 6,899 242

30 17 30 17 30 -2 9 -2 9 -2 2,967 2,419 2,967 2,419 2,967

624 805 624 805 624 2,182 1,122 2,182 1,122 2,182 68 146 68 146 68 158 9 158 9 158 -3,603 8,282 -3,603 8,282 -3,603

643 685 643 685 643 523 38 ' 523 38 523 -25 4 -25 4 -25 66 11 66 11 66 -266 29 -266 29 -266 -115 368 -115 368 -115 -1,011 325 -1,011 325 -1,011

205 10 205 10 205 1,034 206 1,034 206 1,034 -138 15 -138 15 -138 1,091 1,222 1,091 1,222 1,091 -528 905 -528 905 -528 -202 -53 -202 -53 -202 259 3 259 3 259 5,257 26,898 5,257 26,898 5,257 494 551

14 42

4 3 3 1,064

242

37 942

5 0 11 7 6,899 17 9 2,419

805 1,122

146

9 8,282

£85

38

4 11 29 368 325

10 206

15 1,222

905 -53

3 26,898CROP ALFA-HAY ALFA-SEED ALMONDS APPLE+PEAR APRICOTs·· ASPARAGUS AVOCADOS BARLEY BEANS-DRY BROC+CAULI CANTALOUPE CARROTS CELERY CITRUS CORN COTTON GRAINHAY GRAINSORGH GRAPE-DRY GRAPE-TABL GRAPE-WINE LETTUCE ONIONS-DRY PASTURE PEACH+NECT PLUMS POTATOES PRUNES RICE SAFFLOWER SILAGE SUGARBEETS TOMATO-PRO WALNUTS WHEAT XFIELDCROP XFRUilVEG XTREENUTS NetTotal Surplus FRUIT ANDVEGETABLE CROPS: TOTAL SURPLUS BYCROP ($1,000)

**~ECONDARY** STANDARDS RELATIVE TO 0.08 8-HR PRIMARY STANDARD 10% Loss 20% Loss 30% Loss Min Max Min Max Min Max 1,916 5,290 -137 -626

**-1~7**

62 63 -8 -589 -8 661 -25 -129 -710 -129

-4 2 -19 -22 -19 11 15 -11 -13 -11 6 6 -5 -2 -5 -1 32 -14 -32 -14 -41 803 17 1,183 17 294 227 -30 83 -30 -70 31 83 31 83 2,689 2,472 108 -113 108 -4 18 -7 7 -7 0 5 0 -3 0 8,420 8,440 739 38 739

260 15 11 -188 11 -3,363 -607 -5,450 2,468 -5,450 -58 -37 -58 -47 -58 -75 17 -54 0 -54 8,277 8,227 2 -2,388 2 4,517 6,143 -259 -320 -259 2,028 2,668 -345 -262 -345

-51 5 -125 -83 -125 262 207 -129 -112 -129 -2,664 891 -7,190 2,629 -7,190

380 -32 -25 -568 -25 1,964 1,446 -13 -553 -13 5 68 8 5 8 80 -11 -16 -64 -16 37 17 55 -28 55 -153 23 -233 -280 -233 -834 676 -127 1,576 -127 129 -137 -210 -193 -210 420 2,082 -910 -603 -910 -125 -81 -280 -438 -280 -2,011 -229 -2,135 570 -2,135

-697 -232 -922 250 -922 547 31 -17 168 -17

57 19 -3 -224 -3 22,872 38,551 -17,842 545 -17,842



# .. -626 -589 -710

-22 -13

-2 -32 1,183

83 31 -113 7 -3 38 -188 2,468

-47

0 -2,388

-320 -262

-83 -112 2,629 -568 -553 5 -64 -28 -280 1,576 -193 -603 -438 570 250 168 -224 545

4 'CROP ALFA-HAY ALFA-SEED ALMONDS APPLE+PEAR APRICOTS

.. ASPARAGUS AVOCADOS BARLEY BEANS-DRY BROC+CAULI CANTALOUPE CARROTS CELERY CITRUS CORN COTTON GRAINHAY GRAINSORGH GRAPE-DRY GRAPE-TABL GRAPE-WINE LETTUCE ONIONS-DRY PASTURE PEACH+NECT PLUMS POTATOES PRUNES RICE SAFFLOWER SILAGE SUGARBEETS TOMATO-PRO WALNUTS WHEAT XFIELDCROP XFRUITVEG XTREENUTS Total Producer Surplus FRUIT ANDVEGETABLE CROPS: PRODUCER SURPLUS BYCROP ($1,000)

**~ECONDARY** STANDARDS RELATIVE TO 0.121-HR PRIMARY STANDARD 10% Loss 20% Loss 30% Loss Min Max Min Max Min :Max 538 15,425 745 7,017 -820

6 -28 -1 .fJ77 750 208 -2 -3

**-~89** 303

16 1 -4 -24 17 17 -2 0 -8 16 10 4 -1 5 4

1 -8 1 6 102 205 45 -83 -165 176 4,114 4,100 1,182 1,169 28 56 6 15 .fJ 57 8,967 8,964 4,124 4,097 2,496 5 14 -1 -1 5 0 3 0 3 2 30,880 31,029 14,714 14,692 7,614 19 13 7 18 -22 -883 -273 -1,702 -1,145 -742

-4 -36 -10 -47 22 .fJ 24 0 -16 9 30,698 37,935 12,720 16,388 9,352 22,645 29,160 9,910 12,489 5,805

9,760 11,926 3,637 4,546 2,940 67 -31 -1 .fJ7 68 917 945 357 323 251 -2,131 59 -4,764 -479 -3,457

-281 -36 -285 43 149 6,106 5,991 3,099 2,864 1,970

243 786 -9 115 -14

2 .fJ 0 -5 40 97 27 25 -10 81 -92 -120 21 30 36 367 178 -207 -17 -190 -19 -2 -26 -17 77 1,221 7,619 343 3,466 998 8 13 0 -139 164 139 38 -592 -308 -116 103 95 .fJ83 -318 59 -92 164 -83 -234 240

1 -72 0 .fJ1 51 113,907 153,949 42,460 63,236 28,521 5,190 -119

.fJ1

3 -1 1 -2 221

14 37 2,468 13 6 7,324 45 -19 -40 -10 6,765 7,021 3,088

-19 50 2,873 -439 1,744 5 8 .fJ -3 360 -71 2,499 -148 -352 -271 -293

-71 37,813CROP ALFA-HAY ALFA-SEED ALMONDS APPLE+PEAR APRICOTS ASPARAGl.JS AVOCADOS BARLEY BEANS-DRY BROC+CAULI CANTALOUPE CARROTS CELERY CITRUS CORN COTTON GRAINHAY GRAINSORGH GRAPE-DRY GRAPE-TABL GRAPE-WINE LETTUCE ONIONS-DRY PASTURE PEACH+NECT PLUMS POTATOES PRUNES RICE SAFFLOWER SILAGE SUGARBEETS TOMATO-PRO WALNUTS WHEAT XFIELDCROP XFRUITVEG XTREENUTS NetTotal Surplus FRUIT AND VEGETABLE CROPS: TOTAL SURPLUS BYCROP ($1,000) SECONDARY STANDARDS RELATIVETO 0.09 I·HR PRIMARY STANDARD 10% Loss 20% Loss 30% Loss Min Max Min Max Min Max 564 13,794 1,949 4,196 2,658

27 -398 581 338 889 -51 -515 141 370 756

9 12 20 30 38 0 -18 22 18 18 7 -6 8 28 -1 9 -29 11 38 16 212 1,666 899 1,117 -10 2,566 2,457 230 107 142

165 -25 33 81 46 5,348 5,109 1,843 1,777 228 7 0 7 11 -5 0 -1 0 4 1 22,593 21,908 7,848 7,835 2,605 3 -128 6 92 166 14 -4,097 5,509 3,439 6,706 17 -17 -7 -40 -8 34 21 52 -51 26 15,633 17,817 7,824 7,215 2,828 14,123 18,333 4,939 6,401 1,331 5,646 7,242 2,197 2,937 2,079 21 -94 54 ·2 102 532 378 165 181 184 993 13,756 -178 500 1,283

-6 -123 527 291 791 3,662 3,259 1,304 843 812 82 357 5 7 -15

3 -24 67 13 58 61 203 44 33 -128

8 -65 95 124 265 945 2,837 932 107 -358

3 -241 161 27 -84 163 3,246 114 1,716 1,081

4 -392 112 -16 549 50 -82 224 1,369 1,852 41 -844 698 -83 1,069 23 -457 108 -48 722

0 -173 41 0 220 73,510 104,662 38,583 41,007 28,914



### •• 903 323 -415

37 -5 -6 -32 1,183

140 79 61 -4 -1 1,347 5 1,686

-47 -43 989 1,171 1,536

-39 77 -85 349 343

-1 21 337 124 -209

-23 1,148

-96 643 -319

-76

-8 11,092

4 'CROP ALFA-HAY ALFA-SEED ALMONDS APPLE+PEAR APRICOTS.- ASPARAGUS AVOCADOS BARLEY BEANS-DRY BROC+CAULI CANTALOUPE CARROTS CELERY CITRUS CORN COTTON GRAINHAY GRAINSORGH GRAPE-DRY GRAPE-TABL GRAPE-WINE LETTUCE ONIONS-DRY PASTURE PEACH+NECT PLUMS POTATOES PRUNES RICE SAFFLOWER SILAGE SUGARBEETS TOMATO-PRO WALNUTS WHEAT XFIELDCROP XFRUITVEG XTREENUTS Total Producer Surplus FRUIT ANDVEGETABLE CROPS: PRODUCER SURPLUS BYCROP ($1,000) SECONDARY STANDARDS RELAnVETO 0.07 8-HR PRIMARY STANDARD 10% Loss 20% Loss 30% Loss Min Max Min Max Min Max 880 163 880 163 880 698 545 698 545 698

61 5 61 5 61

5 29 5 29 5 -2 4 -2 4 -2 -19 2 -19 2 -19 16 -1 16 -1 16 -157 383 -157 383 -157

34 53 34 53 34 -30 26 -30 26 -30 715 678 715 678 715

3 4 3 4 3 1 0 1 0 1 158 5 158 5 158 -17 -5 -17 -5 -17 39 1,348 39 1,348 39 21 12 21 12 21

3 15 3 15 3 2,629 2,150 2,629 2,150 2,629

391 552 391 552 391 1,688 756 1,688 756 1,688

64 90 64 90 64 106 6 106 6 106 -2,059 6,551 -2,059 6,551 -2,059

487 498 487 498 487 363 26 363 26 363

-8 2 -8 2 -8 24 2 24 2 24 -26 61 -26 61 -26 -51 -90 -51 -90 -51 30 178 30 178 30 77 4 77 4 77 727 179 727 179 727 -51 4 -51 4 -51 416 420 416 420 416 -155 515 -155 515. -155

-99 -67 -99 -67 -99 99 1 99 1 99 7,064 15,104 7,064 15,104 7,064



## ... 163 545

5 29

4 2 -1 383

53 26 678

4 0 5 -5 1,348 12 15 2,150

552 756

90

6 6,551

498

26

2 2 61 -90 178

4 179

4 420 515 -67

1 15,104CROP ALFA-HAY ALFA-SEED ALMONDS APPLE+PEAR APRICOTS ASPARAGUS AVOCADOS BARLEY BEANS-DRY BROC+CAULI CANTALOUPE CARROTS CELERY CITRUS CORN COTTON GRAINHAY GRAINSORGH GRAPE-DRY GRAPE-TABL GRAPE-WINE LETTUCE ONIONS-DRY PASTURE PEACH+NECT PLUMS POTATOES PRUNES RICE SAFFLOWER SILAGE SUGARBEETS TOMATO-PRO [YIIALNUTS rwtiEAT XFIELDCROP XFRUITVEG XTREENUTS Total Producer Surplus FRUIT AND VEGETABLE CROPS: PRODUCER SURPLUS BYCROP ($1,000)

**~ECONDARY** STANDARDS RELATIVE TO 0.08 8·HR PRIMARY STANDARD 10".b Loss 20% Loss 30% Loss Min Max Min Max Min ft'Aax -1,568 2,216 131 1,588 131 62 62 -8 -583 -8 287 114 -56 -186 -56

3 1 -14 ·17 -14 10 12 -10 -13 -10 4 6 -3 0 -3 -2 15 4 -3 4 -249 60 -131 191 -131 148 133 -9 17 -9 -41 17 49 15 49 2,008 1,985 88 59 88 -2 13 -5 2 -5 0 2 0 -1 0 6,248 6,263 531 340 531 9 38 -8 54 -8 -654 -120 -1,070 468 -1,070

-41 -25 -41 -33 -41 -42 11 -34 -9 -34 7,704 7,780 3 -2,092 3 3,693 5,022 -137 -164 -137 1,656 2,293 -139 -38 -139

-31 -5 -77 -63 -77 201 170 -86 -72 -86 -1,914 945 -5,872 1,711 -5,872

285 -18 -17 -418 -17 1,638 1,283 -9 -380 -9 -1 58 1 2 1 26 -4 -5 -21 -5 -23 10 -42 -68 -42 65 -35 39 3 39 -363 116 -71 126 -71

44 -43 -74 -65 -74 283 1,799 -641 -395 -641

-5 -46 -125 -205 -125 -761 -172 -780 273 -780 -180 -188 -443 204 -443 511 -2 5 86 5

23 23 -1 -62 -1 19,031 29,789 -9,057 252 -9,057



## ... 1,588 -583 -186

-17 -13

0 -3 191

17 15 59

2 -1 340

54 468 -33

-9 -2,092

-164

-38 -63 -72 1,711 -418 -380 2 -21 -68

3 126 -65 -395 -205 273 204

86 -62 252

.J 'CROP ALFA-HAY ALFA-SEED ALMONDS APPLE+PEAR APRICOTS

.. ASPARAGUS AVOCADOS BARLEY BEANS-DRY BROC+CAULI CANTALOUPE CARROTS CELERY CITRUS CORN COTION GRAINHAY GRAINSORGH GRAPE-DRY GRAPE-TABL GRAPE-WINE LETIUCE ONIONS-DRY PASTURE PEACH+NECT PLUMS POTATOES PRUNES RICE SAFFLOWER SILAGE SUGARBEETS TOMATO-PRO WALNUTS WHEAT XFIELDCROP XFRUITVEG XTREENUTS

Total Consumer Surplus FRUIT ANDVEGETABLE CROPS: CONSUMER SURPLUS BY CROP ($1,000) SECONDARY STANDARDS RELATIVE TO 0.121-HR PRIMARY STANDARD 10% Loss 20% Loss 30% Loss Min Max Min Max Min Max 1,619 15,370 1,189 1,663 3,311 0 0 0 -6 7 269 -15 -7 -414 385

10 -11 -5 -14 0

1 -1 0 -2 1 3 1 0 -2 3 28 -9 -1 -38 138 -188 669 -113 -455 -36 1,703 1,666 467 345 104 36 4 9 -36 32 2,930 3,004 1,371 1,228 967 2 5 .0 -6 0 0 3 0 2 2 10,566 10,897 5,077 5,004 3,179 190 -88 -14 -295 230 -2,389 1,297 -7,048 -4,822 -3,142 -1 8 -5 -28 22 -11 25 0 -1 -8 1,720 1,913 682 682 729 4,999 6,264 2,156 2,602 1,419 1,937 2,083 647 526 829 37 -22 -3 -79 24 134 146 53 17 76 -364 -334 -1,558 -93 -1,715 -131 -19 -138 -148 18

. 1,016 981 463 369 378 74 147 23 15 16

7 21 -7 -50 69 180 78 -60 -121 51 474 178 -38 -247 156 -284 -217 -166 -378 -888

-29 26 -41 -212 154 356 1,191 45 405 367

8 -8 -5 -166 177 199 49 -1,133 -583 -513 179 177 -318 -475 -151

6 95 -24 -141 74 2 -114 0 -99 82 25,291 45,460 1,497 3,951 6,545 -1,197 -1 -88

6 -1 -1 -28 933 63 24 754

6 5 2,400 -260

-32 -16 12 157 1,387

553

3 -23 1,421 -157 278

3 24 232 -277 1,063 -156 473 -186 -632 287 -79 -114 6,838CROP ALFA-HAY ALFA-SEED ALMONDS APPLE+PEAR APRICOTS ASPARAGUS AVOCADOS BARLEY BEANS-DRY BROC+CAULI CANTALOUPE CARROTS CELERY CITRUS CORN COTION GRAINHAY GRAINSORGH GRAPE-DRY GRAPE-TABL GRAPE-WINE LETIUCE ONIONS-DRY PASTURE PEACH+NECT PLUMS POTATOES PRUNES RICE SAFFLOWER SILAGE SUGARBEETS TOMATO-PRO WALNUTS WHEAT XFIELDCROP XFRUITVEG XTREENUTS

Total Consumer Surplus FRUIT ANDVEGETABLE CROPS: CONSUMER SURPLUS BYCROP ($1,000) SECONDARY STANDARDS RELAnVETO 0.10 1-HR PRIMARY STANDARD 10% Loss 20% Loss 30% Loss Min Max Min Max Min Max 4,043 7,916 462 1,869 1,000 -1 1 -6 -6 0 -272 383 -567 729 -22

-1 1 -8 24 5 0 1 -1 1 -1 -1 3 •1 1 0 -9 28 -12 1 -5 -367 -155 144 218 843 419 391 -148 -153 86

1 -40 38 -7 39 1,561 1,546 493 495 355 3 -3 4 1 6 0 0 0 0 0 4,193 5,003 1,068 1,401 -77 40 155 -209 5 -168 858 -281 -5,399 -442 305 -18 8 -22 1 -14

-4 15 -5 4 -14 698 825 -124 225 35 1,993 2,733 377 1,037 131

585 619 -143 -330 208

4 -75 10 -79 30 61 103 -40 12 -45 -784 -1,175 -1,248 530 140

31 119 -129 6 -123 285 535 15 159 -58

28 74 7 45 10 -39 30 -54 39 4 -93 -159 -352 1 210 95 257 -317 -13 150 559 -349 722 150 855 -83 132 -259 86 -63 -25 672 -333 252 -319 -165 166 -206 10 -23 331 807 -547 105 68

13 -354 -279 -263 -131 74 76 -77 24 -33 153 129 -2 1 1 14,167 20,138 -7,147 6,139 3,386



### ... 3,602 4 306 -'

5 1 3 29 436 -48 -40 222

-4 2 831 207 -132

-12

6 77 284 -180

-41 58 -1,404

153 191

-9 38 -254

58 -737 168 354 264 1,001 -339

73 131 5,304CROP ALFA-HAY ALFA-SEED ALMONDS APPLE+PEAR APRICOTS ASPARAGUS AVOCADOS BARLEY BEANS-DRY BROC+CAULI CANTALOUPE CARROTS CELERY CITRUS CORN COTTON GRAINHAY GRAINSORGH

**GRAPE~DRY** GRAPE-TABL GRAPE-WINE LETTUCE ONIONS-DRY PASTURE PEACH+NECT PLUMS POTATOES PRUNES RICE SAFFLOWER SILAGE SUGARBEETS TOMATO-PRO WALNUTS WHEAT XFIELDCROP XFRUITVEG XTREENUTS Total Consumer Surplus FRUITAND VEGETABLE CROPS: CONSUMER SURPLUS BY CROP ($1,000) SECONDARY STANDARDS RELATIVE TO 0.07 B·HR PRIMARY STANDARD 10% Loss 20% Loss 30% Loss Min Max Min Max Min Max -1,277 331 -1,277 331 -1,277 6 5 6 5 6 76 9 76 9 76 -6 13 -6 13 -6 0 0 0 0 0 -5 0 -5 0 -5 13 5 13 5 13 -1,278 681 -1,278 681 -1,278

122 189 122 189 122 -27 12 -27 12 -27 483 264 483 264 483

-2 2 -2 2 -2 1 0 1 0 1 423 7 423 7 423 181 13 181 13 181 203 5,552 203 5,552 203

9 5 9 5 9 -5 -5 -5 -5 -5 337 269 337 269 337 233 253 233 253 233 494 366 494 366 494

4 56 4 56 4 52 2 52 2 52 -1,544 1,,31 -1,544 1,731 -1,544

156 187 156 187 156 160 12 160 12 160 -17 2 -17 2 -17 41 9 41 9 41 -240 -32 -240 -32 -240

-64 458 -64 458 -64 -1,042 147 -1,042 147 -1,042 128 7 128 7 128 306 28 306 28 306 -87 10 -87 10 -87 675 801 675 801 675 -373 390 -373 390 -373 -103 14 -103 14 -103 160 2 160 2 160 -1,807 11,794 -1,807 11;794 -1,807



### •• 331 5 9 13 0 0 5 681 189

12 264

2 0 7 13 5,552 5 -5 269 253 366

56

2 1,731

187

12 2 9 -32 458 147

7 28 10 801 390

14 2 11,794CROP ALFA-HAY ALFA-SEED ALMONDS APPLE+PEAR APRICOTS ASPARAGUS AVOCADOS BARLEY BEANS-DRY BROC+CAULI CANTALOUPE CARROTS CELERY CITRUS CORN COTTON GRAINHAY GRAINSORGH GRAPE-DRY GRAPE-TABL GRAPE-WINE LETTUCE ONIONS-DRY PASTURE PEACH+NECT PLUMS POTATOES PRUNES RICE SAFFLOWER SILAGE SUGARBEETS TOMATO-PRO WALNUTS WHEAT XFIELDCROP XFRUITVEG XTREENUTS Total Consumer Surplus FRUIT ANDVEGETABLE CROPS: CONSUMER SURPLUS BYCROP

.($1,000) !SECONDARY STANDARDS RELAnVETO 0.08 8-HR PRIMARY STANDARD 10% Loss 20% Loss 30% Loss Min Max Min Max Min ·Max 3,484 3,074 -268 -2,214 -268 1 1 0 -5 0 374 -138 -73 -524 -73

-7 1 -5 -5 -5 1 2 0 0 0 2 0 -1 -2 -1 1 17 -19 -29 -19 208 743 149 991 149 146 94 -22 67 -22 -29 13 34 16 34 681 487 20 -173 20

-1 6 -2 4 -2 0 2 0 -1 0 2,171 2,177 207 -302 207

251 -23 19 -242 19 -2,709 -487 -4,381 2,000 -4,381 -17 -11 -17 -14 -17 -33 6 -20 9 -20 573 447 -1 -296 -1 825 1,122 -122 -156 -122 372 376 -207 -224 -207 -19 10 -48 -20 -48 61 38 -44 -40 -44 -750 -54 -1,318 918

**-1,3~8**

95 -15 -8 -151 -8 326 163 -4 -174 -4

6 10 7 3 7 55 -7 -12 -43 -12 60 7 97 39 97 -219 59 -272 -283 -272 -471 560 -56 1,450 -56

85 -95 -136 -129 -136 137 283 -269 -207 -269 -120 -35 -155 -234 -155 -1,250 -57 -1,355 297 -1,355

-518 -44 -479 47 -479

35 33 -22 82 -22 34 -4 -2 -162 -2 3,841 8,762 -8,785 293 -8,785



### •• -2,214 -5 -524

-5 0 -2 -29 991

67 16 -173 4 -1 -302 -242 2,000

-14

9 -296 -156 -224

-20 -40 918 -151 -174 3 -43 39 -283 1,450 -129 -207 -234 297

47 82

. -162

293CROP ALFA-HAY ALFA-SEED ALMONDS APPLE+PEAR APRICOTS ASPARAGUS AVOCADOS BARLEY BEANS-DRY BROC+CAULI CANTALOUPE CARROTS CELERY CITRUS CORN COTION GRAINHAY GRAINSORGH GRAPE-DRY GRAPE-TABL GRAPE-WINE LETIUCE ONIONS-DRY PASTURE PEACH+NECT PLUMS POTATOES PRUNES RICE SAFFLOWER SILAGE SUGARBEETS TOMATO-PRO WALNUTS WHEAT. XFIELDCROP XFRUITVEG XTREENUTS

Total Consumer Surplus FRUIT ANDVEGETABLE CROPS: CONSUMER SURPLUS BYCROP ($1,000) SECONDARY STANDARDS RELATIVE TO 0.09 8-HR PRIMARY STANDARD 10% Loss 20% Loss 30% Loss Min Max Min Max Min Max 1,027 3,961 3,588 1'119 4,857 0 

### .... 5 3 8 -28 -287 81 211 425

6 7 8 10 8 0 -1 1 3 1 0 -2 1 7 0 6 -25 15 29 24 134 1,281 574 928 -77 735 654 180 78 108

65 -7 13 30 13 1,383 1,191 500 444 191 2 1 2 3 -3 0 -1 0 2 1 5,711 5,223 2,014 2,023 995 5 -163 11 90 251 15 -3,288 4,429 2,797 5,361 4 -16 -2 -22 -7 18 -2 18 -24 7 765 677 563 389 305 2,506 3,195 924 1,205 339

836 899 318 501 535

18 -22 21 -2 30 69 20 28 34 56 953 3,664 698 130 349

-6 -32 134 77 187 476 353 190 49 180

16 67 2 7 -11 3 -23 47 0 50 55 208 78 51 -162 10 -107 129 64 364 724 1,255 730 -136 -401

1 -157 105 35 -44 22 301 19 254 306

4 -231 66 -25 297 29 -44 153 836 1,208 161 164 405 213 282 36 -65 45 20 142

0 -106 26 1 135 15,762 18,539 16,119 11,429 16,311



## ... -274 3 -231

11 -1 0 -14 972 117 30 47 -1 0 113 38 1,357

-23 -28 31 254 273 -21 20 649

91 39

2 7 328 94 -435

-14 253 -74 394

10 -19



### .... 3,996CROP **~.12** ppm 1hr 0.10 ppm 1hr

**~in** Max Min ALFA-HAY 7,614 21,842 9,201
ALFA-SEED -660 71 -2
ALMONDS -920 -151 60 APPLE+PEAR -46 -19 -19
APRICOTS -27 -4 -8 ASPARAGUS

-12 -10 -10 AVOCADOS

-34 -1 -32

BARLEY

-459 -500 -597 BEANS-DRY 4,602 4,767 10,664 BROC+CAUU

-149 -49 -97 CANTALOUPE

9,826 10,029 21,207
CARROTS -26 -27 -37

CELERY -7 -11 -7

CITRUS 22,364 22,620 52,672

- CORN

-171 47 -39

COTTON

1,197 -451 -812
GRAINHAY

-50 12 5 GRAINSORGH

-34 -35 -6 GRAPE-DRY 12,634 19,885 37,080 GRAPE-TABL

11,716 16,104 30,524 GRAPE-WINE 5,095 7,678 17,751

LETTUCE

-214 -69 -157 ONIONS-DRY

725 861 1,794
PASTURE

5,633 -8 1,821 PEACH+NECT

-185 9 -72

PLUMS 2,135 2,490 7,130
P<}TATOES 391 976 801

PRUNES -73 -23 -10

RICE 718 787 986 SAFFLOWER

-349 -53 -67

SILAGE

536 -669 -522 SUGARBEETS -243 -63 58 TOMATO-PRO

1,361 5,294 3,431
rwALNUTS -385 -45 -25

**~EAT** 259 -408 -620 XFIELDCROP 301 85 4 XFRUITVEG -481 -201 -319 XTREENUTS

-177 10 -174 --

Net Total Surplus 82;4o7 110,766 191,558 FRUIT AND VEGETABLE CROPS: TOTAL SURPLUS BY CROP ($1,000) PRIMARY STANDARDS RELATIVE TO BASELINE 0.07ppm8hr 0.08ppm 8hr

Max

Min Max Min
46,107 11,941 88,407 9,706

---

-40 1-·

-645 -662 -654

. -912 -414 -926 -796

- -43 -21 -48 -29

-22 -7 -27 -16

-10 5 -13 -8

-31

-22 -35 -20

-459

709 -671 -634

.. 10,712 15,262 15,220 13,151

49

21 -62 -60

21,233 38,861 38,846 27,132

-21 -21 -27 -20

-6 -7 -7 -6

52,220 97,901 97,885 68,677

-169

-166 -175 -186

-382 -1,055 -7,554 -2,104

-42

-39 -63 -6

-25

-8 -34 19

48,488 70,189 91,535 47,560

39,498

56,776 74,321 40,392

23,660 35,427 46,616 23,795

57

-181 -197 -72

1,644

3,171 3,164 2,281

4,309

4,081 -4,590 2,885

-599

-598 -604 -579

6,692 14,073 14,070 9,557

2,070 1,141 3,345 913

-71 -68 -77 -60

1,170 2,270 2,013 951

--269 35 -353 -116

650 470 213 437

-292 -288 -307 -96

14,577

2,359 28,340 3,207

-385 22 -390 -115

-1,350 -1,374 -1,436 656

631 651 -692 190

-· -575 178 -517 -568

-175 -175 -177 -174

267,888 350,454 484,331 245,192 0.09ppm8hr

Max Min Max 60,973 9,042 38,971

-68 -687 -256

-200 -872 -398

-22 -57 -56

-12 -27 -9

-8 -18 -4,

0 -43 -21

-892

-676 -1,089
13,301 9,026 9,140

-48

-138 -95
27,364 17,626 17,877

-29 -33 -20

-4 -7 -6
69,390 44,206 44,906

23

-176 -38

-629

-7,564 -3,144

4

-72 -13

-33 -68 6
64,867 28,903 40,282
53,148

25,658 33,915
30,814 12,149 16,340

-101 -234 -105

2,270

1,326 1,485

255 -328 524

-32

-597 -480
10,098 5,837 6,240

2,562 686 1,779

-2 -77

... 5

1,328 915 800

-62 -355 -182

-1,031 -670 -466

-96 -304 -45
18,962

2,019 10,844

63 -390 18

-8 -1,500 -1,251

307 -745 216

-12 -591 -99

48

-177 10 352,487 140,985 215,552CROP ALFA-HAY ALFA-SEED ALMONDS APPLE+PEARAPRICOTSASPARAGUS AVOCADOS BARLEY BEANS-DRY BROC+CAULI CANTALOUPE CARROTS CELERY CITRUS CORN COTTON GRAINHAY GRAINSORGH GRAPE-DRY GRAPE-TABL GRAPE-WINE LETTUCE ONIONS-DRY PASTURE PEACH+NECT PLUMS POTATOES PRUNES RICE SAFFLOWER SILAGE SUGARBEETS TOMATO-PRO WALNUTS WHEAT XFIELDCROP XFRUITVEG XTREENUTS NetTotal Surplus FRUIT AND VEGETABLE CROPS: TOTAL SURPLUS BY CROP ($1,000) SECONDARY STANDARDS RELATIVE TO 0.121-HR PRIMARY STANDARD 10% Loss 20% Loss 30% Loss Min Max Min Max Min Max 2,157 30,795 1,934 8,680 2,491 6 -28 -1 -683 757 477 -17 -11 -704 688

26 -10 -8 -37 17 18 -3 0 -10 18 13 6 -1 3 7 29 -17 -1 -31 240 17 713 -176 -620 140 5,817 5,766 1,648 1.514 132 92 10 24 -43 89 11,896 11,968 5,495 5,325 3,463 6 20 -2 -7 5 1 6 0 5 5 41,446 41,926 19,791 19,696 10,792 208 -75 -7 -277 208 -3,272 1,024 -8,750 -5,967 -3,884 -4 -27 -15 -75 44 -16 49 -1 -17 0 32,419 39,848 13,402 17,070 10,082 27,644 35,424 12,066 15,091 7,225 11,697 14,009 4,284 5,072 3,769 104 -53 -4 -146 92 1,051 1,091 410 340 328 -2,495 -275 -6,322 -572 -5,172

-411 -55 -423 -105 166 7,121 6,972 3,562 3,233 2,348

316 933 14 130 2

9 15 -7 -54 108 277 105 -.35 -131 132 382 57 -18 -217 192

82 -39 -373 -396 -1,078 -47 24 -67 -229 231 1,577 8,810 388 3,871 1,365 16 5 -5 -305 341 338 87 -1,725 -891 -630 283 272 -1,001 -793 -93 -86 259 -107 -374 314

3 -185 0 -160 133 139,198 199,410 43,958 67,187 35,066



# .. 3,993 -120 -149 9 -2 -1 -30 1,154

77 61 3,222 19 11 9,723 -214

-51 -56

2 6,922 8,409 3,641

-16 27 4,294

-595 2,023 8 32 225 -280 1,424 -227 2,972 -334 -985

16 -371 -184 44,652FRUITANDVEGETABLE CROPS: TOTAL SURPLUS BYCROP ($1,000) !SECONDARY STANDARDS RELAnVETO 0.10 1-HR PRIMARY STANDARD CROP 10% Loss 20% Loss 30% Loss Min Max Min Max Min Max ALFA-HAY 1,845 14,024 258 3,671 551 ALFA-SEED -97 90 -661 -614 32 ALMONDS -481 683 -1,002 1,255 -42 APPLE+PEAR -12 21 -36 66 -1 APRICOTS.· -1 15 -19 21 -14 ASPARAGUS 0 11 -3 4 0 AVOCADOS -26 36 -3 1 2 BARLEY -524 -178 -84 239 1,003 BEANS-DRY 1,411 1,370 -180 -193 113 BROC+CAULI 7 -99 92 -24 103 CANTALOUPE 5,351 5,311 1,845 1,834 548 CARROTS 10 -5 9 2 17 CELERY 0 1 1 0 1 CITRUS 17,308 18,439 4,929 5,398 495 CORN 20 149 -146 2 -123 COTTON 1,033 -435 -6,743 -566 345 GRAINHAY -69 33 -76 0 -52 GRAINSORGH -6 32 -29 12 -52 GRAPE-DRY 13,513 16,71'5 946 4,295 656 GRAPE-TABL 11,124 14,848 2,700 4,737 491 GRAPE-WINE 4,340 5,682 635 506 1,107 LETTUCE -1 -186 -38 -233 4 ONIONS-DRY 461 579 -64 89 -100 PASTURE -765 -2,476 -6,171 2,441 511 PEACH+NECT 72 464 -534 27 -524 PLUMS 2,770 3,586 713 1,183 26 POTATOES 145 508 10 123 16 PRUNES -57 46 -76 56 3 RICE -128 -147 -442 -25 201 SAFFLOWER 88 217 -291 1 84 SILAGE 909 -176 770 226 1,073 SUGARBEETS -136 195 -401 139 -98 TOMATO-PRO -84 4,236 -1,125 1,739 -1,076 WALNUTS -304 327 -375 15 -50 WHEAT 538 1,106 -842 159 108 XFIELDCROP 193 -547 -744 -1 '171 -664 XFRUITVEG 388 470 -278 74 -224 XTREENUTS 249 210 -2 2 2
NetTotal Surplus 59,084 85,157 -7,460 25,491 4,472



# .. 2,682 266 545

17 14 10 34 579 -42 -100 343

-7 4 1,749

171 -161

-42

5 1,360 .989 411 -98 205 -4,811

590 842 -14 60 -245 111 -1,209

260 1,532

442 1,373 -586 402 188 7,865

J '