xvEPA
United States
Environmental Protection
Agency
Environmental Research
Laboratory
Corvalhs OR 97333
EPA-600/3-84-090
September 1984
Research and Development
The Economic
Effects of
Ozone on Agriculture
PROPERTY OF
D/V'tt/
OF
-------
EPA-600/3-84-090
September 1984
THE ECONOMIC EFFECTS OF OZONE ON AGRICULTURE
by
Richard M. Adams
Scott A. Hamilton
and
Bruce A. McCarl
Department of Agricultural
and Resource Economics
Oregon State University
Corvallis, Oregon 97331
CR810707-01-0
Project Officer
Eric M. Preston
Air Pollution Effects Branch
Environmental Research Laboratory
Corvallis, Oregon 97333
ENVIRONMENTAL RESEARCH LABORATORY
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
CORVALLIS, OREGON 97333
-------
NOTICE
The information in this document has been funded wholly or in part by the
United States Environmental Protection Agency under Cooperative Agreement
CR810707-01-0 to Oregon State University. It has been subjected to the
Agency's peer and administrative review and it has been approved for
publication as an EPA document. Mention of trade names or commercial products
does not constitute endorsement or recommendation for use.
n
-------
THE ECONOMIC EFFECTS OF
OZONE ON AGRICULTURE
Abstract
Past attempts to assess the monetary impacts of ozone or other air
pollutants on agriculture have had only sparse plant science information on
which to base an assessment. This paper reports on an economic assessment of
the effects of simulated changes in ambient ozone on U.S. agriculture using
recent crop response data from the National Crop Loss Assessment Network
(NCLAN). The economic analysis is limited to those ozone effects directly
associated with production and consumption of a set of agricultural
commodities. Effects on non-agricultural commodities and compliance costs of
achieving the simulated changes in ozone are not evaluated here and hence,
these economic estimates are not necessarily net economic effects. The
results are derived from an economic analysis based on a U.S. agricultural
sector model that includes major crop and livestock production as well as
processing and export uses. The economic effects of four hypothetical ambient
ozone levels are measured and compared with a 1980 base situation. The
analysis indicates that the economic effects as measured in income-equivalents
to producers and consumers of agricultural commodities of moderate (25
percent) ozone change below ambient levels are approximately $1.7 to $1.9
billion. A similar increase in ozone pollution results in costs (losses in
income) of $2.1 to $2.4 billion. These economic estimates display varying
sensitivity to the form of the response and meteorological data incorporated
in the assessment.
This report was submitted in fulfillment of cooperative agreement number
CR810707-01-0 by Oregon State University under the sponsorship of the U.S.
Environmental Protection Agency. This report covers a period from
October 1, 1983 to September 1, 1984 and work was completed as of
August 27, 1984. The research reported here is an extension of the analytical
framework developed in fulfillment of an earlier cooperative agreement
(CR 810296-01-1) between Oregon State University and the U.S. Environmental
Protection Agency.
iii
-------
TABLE OF CONTENTS
I. Introduction 1
II. A Review of Assessment Issues and Past Studies 3
Biological and Practical Issues 4
Economic Issues 5
Assessment Methodologies Applied to Agriculture 7
A Review of Regional Assessments 8
A Review of National Assessments 14
III. Methodol ogy 18
The Farm Model 20
Generation of Other State Activities and Crop Mixes 23
The Sector Model 23
IV. Procedure 28
V. The Data: Sources and Assumptions 32
Plant Response 32
Ozone Data 35
Moisture Stress - Ozone Interactions 42
VI. Results and Implications 47
Base Model Results 48
Analysis 1 52
Analysis II 61
Analysis III 63
-------
Page
Analysis IV 65
Analysis V 66
VII. Limitations of the Analysis 68
VIII. Sunmary 70
References 73
Appendix A: Analytics of the Assessment Model 81
Appendix B: Estimation of Acreage-Yield Response 98
Appendix C: General Crop Model and Moisture Stress 101
Appendix D: Yield Response and Ozone Data 103
LIST OF FIGURES
Figure
1 Structural Features of the Sector Model 26
2 Example Model Tableau 27
3 Regional Ozone Levels, by Year 41
4 The Effect of Moisture Stress 46
5 USDA Production Regions 53
LIST OF TABLES
Table
1 1981 Acreage and Value of Major U.S. Commodities 4
2 Sunmary of Regional Economic Assessments 9
3 Sunmary of National Economic Assessments 15
4 Primary Commodities in the Economic Model 22
vi
-------
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Secondary Commodities in the Economic Model
Summary of Alternatives Assessment Analyses
Wei bull Model Response Function Estimates
Seasonal Average Ozone Levels
July Drought, Yield Reduction and Moisture Stress Factors
Actual and Model 1980 Prices and Quantities for Crops
Actual and Model 1980 Livestock Product Prices and Quantities.
Model and Actual 1980 Regional Cropped Acreage
The Economic Effects of Ozone: Analysis I
Distribution of Consumer Surplus Between Domestic and Export
Markets
Prices of Selected Feed Grain, Oil Seed and Livestock
Commodities
Effect of Ozone on Regional Producer Surplus
The Economic Effects of Ozone: Analysis II
The Economic Effects of Ozone: Analysis III
The Economic Effects of Ozone: Analysis IV
The Economic Effects of Ozone: Analysis V
Page
28
34
36-37
39-40
44-45
49
51
54
56
59
60
62
64
65
67
68
Vll
-------
ACKNOWLEDGEMENTS
Many individuals contributed to the preparation of this assessment. Tom
Crocker, Bruce Dixon, Dick Howitt and Darrell Hueth provided valuable guidance
and encouragement at project initiation. Deb Brown and Jim Pheasant assisted
in the development of farm level economic data for the Corn Belt. Andy Lau
and Mike Hanrahan developed economic data and provided computational support.
The biological data that lie at the heart of this assessment were provided by
the numerous plant scientists working in the NCLAN program. Special
appreciation is also extended to the Research Management Committee of NCLAN
for overall support of this effort. Meteorological information and data on
rural ambient ozone levels were provided by Jim Reagan. Finally, Eric
Preston, EPA project officer, provided support and guidance through all phases
of this project and we are most grateful for his untiring efforts.
We thank many reviewers for constructive comments on an earlier version of
this report. Specifically, Don Davis, Don Holt, Dave King, Al Heagle and
Lance Kress offered valuable comments and insights on the biological data and
assumptions employed in the assessment. Dave King also prepared Appendix C
describing the crop model used in the moisture-stress analysis. Rick Freeman,
Bill Martin, Earl Swanson, Bob Taylor and Norm Whittlesey provided extensive
comments concerning the economic model, assumptions and assessment results.
The report was reviewed for policy content by USEPA's Office of Air Quality
Planning and Standards, Research Triangle Park and the Office of Environmental
Processes and Effects Research, Washington, D.C. While the collective reviews
improved the style and substance of the final report, any remaining errors are
solely the responsibility of the authors.
VI 1 1
-------
EXECUTIVE SUMMARY
THE ECONOMIC EFFECTS OF OZONE ON AGRICULTURE
The harmful effects of ozone and other air pollutants on vegetation have
been documented for at least 35 years (Middleton et al., 1950; Brisley and
Jones, 1950; Bleasedale, 1952). Attempts to assess the monetary impact on
agriculture soon followed the recognition of an ozone problem, motivated in
part by the need for such information in the formation of environmental
policy. Until recently however, attempts to measure the agricultural benefits
of reduced ozone have been hampered by incomplete or even contradictory
biological information. Where data did exist, the assessment procedures by
which the economic estimates were generated were not always consistent with
economic concepts.
The National Crop Loss Assessment Network (NCLAN) recently has attempted
to improve on the state of knowledge in the area of crop response to air
pollutants (Heck et al., 1982). The output from the NCLAN crop experiments
are intended ultimately to form the basis for regulatory policy making and
support economic assessments of the national consequences of ozone on
agriculture. Performance of a preliminary economic assessment using these
data is one objective of the NCLAN program. A final assessment using
additional data from further NCLAN experiments is scheduled for 1986.
The purpose of this manuscript is to report on the preliminary NCLAN
national economic assessment of ozone effects on agriculture. The economic
analysis is limited to those ozone effects directly associated with the
production and consumption of a set of agricultural commodities. Effects on
non-agricultural commodities as well as compliance costs of achieving any
changes in ambient ozone levels are not evaluated here, hence the estimates
are not net economic effects. The analyses, data and results underlying this
assessment represent the collective biological, meteorological and economic
knowledge gained from the NCLAN program through 1983. The results are derived
from an economic analysis based on a U.S. agricultural sector model (adapted
from Chattin et al., 1983). Empirical emphasis is on six major crops (corn,
soybeans, wheat, cotton, grain sorghum, and barley) which account for over 75
percent of U.S. cropped acreage (USDA, 1982). Potential ozone effects on hay
are also evaluated using the average yield response of other crops as a
surrogate. In addition, the assessment accounts for the derived-demand
relationship between feed grains, such as corn, and livestock, by including a
livestock production and feeding component.
-------
The specific objectives of the analysis are:
1. to provide assessments of the economic consequences of alternative
ambient ozone levels to agriculture, including those accruing to both
producers and consumers of agricultural commodities;
2. to test the sensitivity of these economic estimates to varying yield
forecasts reflecting sources of uncertainty within the NCLAN response
data; and
3. to use these results to provide insight on the importance of improved
information for possible inclusion in subsequent economic assessments
of ozone effects on agriculture.
The analysis and methodology are conceptually similar to the numerous
induced change analyses found in the agricultural economics literature. The
assessment methodology for the case of ozone was implemented using both
detailed farm-level models and a macro or sector model. The agricultural
sector model component is a price-endogenous mathematical programming model of
the agricultural sector, i.e., an activity analysis spatial equilibrium model
(Takayama and Judge, 1971). Such sector models have been used extensively by
agricultural economists to simulate the effect of alternative agricultural
policies or technological change (Heady and Srivistava, 1975; Duloy and
Norton, 1973). Among the various methodologies available to formulate policy
models, mathematical programming has proven to be a particularly useful tool
given its ability to predict potential consequences of as yet unrealized
policies. This general methodology has been applied to air pollution effects
in a number of recent regional studies; e.g., Adams et al., 1982; Adams and
McCarl, 1984; Howitt et al., 1984; and Rowe et al., 1984.
Data for the sector model were provided in part by a series of farm
models based on linear programming (LP) models and analyses of historical data
which were used to model producer behavior. The LP-based farm models
(REPFARM, McCarl, 1982b) were implemented for representative farms in the Corn.
Belt. For areas outside the Corn Belt, historical data were used to generate
whole farm plans. Crop records by state for 1970-1981 were used to develop
representative state level crop mixes and to derive econometrically estimates
of crop yield changes when crop mix changes. These whole farm plans from both
sources were then used to pregenerate a number of cropping activities for the
sector model (originally developed by Baumes, 1978 and documented in Chattin
et al., 1983) utilizing the USDA FEDS budgets. The economic model is thus a
sector model using data generated by the farm models or historical data
following the procedures in McCarl (1982).
The economic model uses alternative ozone-induced yield changes to
estimate the economic effects due to ozone changes. Each of a series of
hypothetical ozone scenarios is judged against a 1980 ambient ozone base
solution. The difference between the base solution reflecting 1980 parameters
and the hypothetical ozone analyses provides an estimate of the benefits or
costs of the ozone change.
-------
A plausible solution is established for a base case; then the model is
solved using alternative yield values (modified from actual 1980 yields) that
reflect hypothetical ozone levels in each production area (state or sub-state
levels). The yield adjustments are derived by use of a mix of NCLAN response
functions applied to the ozone levels. In this analysis four alternative
ozone levels are evaluated which represent: 10, 25, and 40 percent reductions
in ambient ozone concentrations and a 25 percent increase in ozone. The four
ozone adjustments are applied to both 1980 and 1978-82 mean levels.
For some crops within the NCLAN data there are several cultivars
(varieties) on which to base yield adjustments for use in the economic
analysis. Screening of the multiple cultivar data for those crops indicates
that a common or "pooled" response model estimated across cultivars is
appropriate (Heck et al., 1984b). For other crops, only one or two cultivars
are available in the NCLAN data. Specifically, pooled response models using
multiple cultivars are available for corn, soybeans and wheat, with two
cultivars of cotton (one irrigated or western cultivar and one nonirrigated or
southern cultivar) and individual cultivars for grain sorghum and barley.
Using different combinations of response functions drawn from this set, the
four ozone alternatives are then translated into corresponding yield changes.
The economic effects of the four yield change situations are evaluated by
solving the economic model under each yield change.
Five distinct analyses are performed, each with the four alternative
ozone levels. Analyses I and II use various combinations of pooled responses
for certain crops in addition to the single cultivar crops. Analysis I uses
pooled response functions estimated across data from statistically similar
cultivars of each crop, while Analysis II is based on a pooling of cultivars
grown in a given region. In the pooled analyses, a few cultivars are not
included because their response is statistically different from the majority
of other cultivars of that crop. An analysis based on these more extreme
responses can serve to place some bounds on the assumed "more representative"
responses captured in Analyses I and II. Analysis III thus uses the single
cultivars available for barley and sorghum in combination with the
nonheterogeneous cultivars of corn, soybeans, wheat and cotton.
The assessment also requires a base ozone level for use in the calculation
of yield changes. Since the economic model is calibrated to 1980, 1980 ozone
levels are a logical benchmark for use in the hypothetical ozone analyses.
These 1980 values underlie Analyses I, II and III. However, annual variation
in seasonal ozone levels is quite high for some areas, amounting to 40 percent
in some cases. As an alternative, the years from 1978 to 1982 are averaged
and the average values are used as a second characterization of base ozone
levels. These new ambient ozone levels for each region are then used to
evaluate the four ozone situations, i.e., the 10, 25, and 40 percent changes
are now calculated relative to the alternative ozone base level. The response
functions used in the evaluation are those for the first analysis (the
national pooled responses). This set of four model solutions is identified as
Analysis IV.
The presence of drought or moisture stress within some NCLAN field plots
has shown an ambiguous relationship with respect to the effects of ozone on
xi
-------
crop yield. However, for a few replicated studies, drought stress reduced the
effect of ozone on yields; i.e., had an antagonistic effect. Thus, if plants
are drought stressed during high ozone levels, the effects of ozone, as
predicted by controlled experiments when plants are typically well-watered,
may overstate the effects of ozone. This possibility has important
implications in a national assessment, where from 50 to 90 percent of the
acreage of major crops are grown under nonirrigated conditions. Ideally,
response functions that reflect the ozone-water stress interactions could be
used in combination with some rneso-scale drought data to assess more
accurately the economic effects of ozone under real world conditions.
Unfortunately, such a complete data set currently does not exist. However,
limited data are available on ozone-water stress interactions from three NCLAN
experiments. In addition, an NCLAN crop simulation model provides some
information on the influence of water stress on ozone effects (King and Snow,
1984). The results of these studies can be used to provide some preliminary
quantitative adjustments to those non-water stressed response functions
generated from NCLAN data. Using these limited observations, a fifth general
analysis (Analysis V) examines the economic impact of ozone effects on crops
in the presence of water stress.
These five analyses, combined with the four ozone levels and the base
case, result in 21 distinct evaluations using the economic sector model
formulation. Analysis I generates an overall impression of ozone effects.
Analyses II through V are analyses of the sensitivity of the economic
estimates to varying yield assumptions. The results of these analyses,
including aggregate economic effects on producers and consumers provide a
detailed prediction of the probable economic effects of ozone on agriculture
under a range of conditions.
The results obtained with the economic base model are evaluated by
comparison with actual values realized in 1980. The base model outputs of
primary concern are the equilibrium prices and quantities produced for
included crops and livestock products. The values of these variables, derived
endogenously in the model solutions, are those that optimize the mathematical
problem inherent in the model specification. Thus, validation of these values
against actual 1980 levels establishes the general performance of the model.
Overall, the modeled prices and quantities are quite close to actual
values. All model prices fall within 3 percent of actual prices. Predicted
quantities are generally within 10 percent of actual. Other model features,
including the net social benefit estimates (objective function value) and
regional production patterns are also close to actual values for the base
year. This validation of the general equilibrium model indicates that the
base model can serve as an accurate benchmark against which to gauge the
economic effects of ozone-induced yield changes.
The economic effects generated within the model are estimates of the
changes in consumers' plus producers' surplus of those involved with the
agricultural sector, as stimulated by a change in ozone. This quantity is
equivalent to the sum of the agricultural sector-related income lost or gained
by producers and consumers as a consequence of air pollution changes. The
term "benefit" is used to indicate a gain in agriculturally-related consumers'
xi i
-------
and producers' surplus. Similarily "costs" (negative benefits) will be used
to indicate losses. The calculation of consumers' and producers' surplus does
not include compliance costs required to achieve the alternative ozone
concentrations evaluated in this analysis. Also, the effect of ozone changes
on non-agricultural goods and services are not evaluated. Thus, the benefits
reported here should not necessarily be interpreted as net benefits to society
associated with the ozone changes evaluated in this study.
The results of Analysis I, as measured against the base case, indicate
that the annual benefits (in 1980 dollars) to society from ozone adjustments
are substantial. The 10, 25 and 40 percent changes in ozone below ambient
ozone levels result in benefits of $.756, SL.937, and $2.859 billion,
respectively. A 25 percent increase in ozone results in an annual loss
(negative benefit) to society of $2.363 billion. These economic estimates are
0.7, 1.4, 2.0 and 1.7 percent of the economic value of the included crops and
livestock commodities predicted in the base model. The average yield changes
giving rise to these economic changes are adjustments of 1.0, 2.5, 3.5, and
3.0 percent, respectively.
These estimates include yield adjustments for hay, based on the average
yield response of the other crops. Hay is an important crop in a national
assessment because of its role in the feed-livestock balance. An analysis
using Analysis I ozone yield effects was also performed holding hay yields
constant. If hay yields are not varied as ozone changes, the corresponding
results are $0.669, $1.712, $2.518 and $-2.096 billion. Thus, the hay effect
is approximately 13 percent of the economic estimates.
Both consumers and producers benefit at ozone levels lower than ambient
in this assessment, with approximately 60 to 70 percent of the total benefits
accruing to consumers. Consumers benefit from reduced prices arising from
increased supplies. About 60 percent of the consumer benefits accrue to
foreign consumers as manifested in export markets. Producers' benefits arise
from the complex nature of supply curve shifts and the inclusion of the export
sectors in the model; i.e., non-parallel shifts of the supply curves result in
producer benefits in the face of varying demand elasticities for some
commodities. On a regional level, the producers in the Pacific (California),
Delta, Northeast and Southeast regions experience the greatest relative gains
from ozone reductions. This is due to a combination of high ambient ozone
levels and a mix of ozone-sensitive crops (soybeans, cotton) in those regions,
as well as some shifting of regional market shares at alternative ozone
levels.
Analyses II through V provide some measure of the sensitivity of the
economic estimates to assumptions concerning response and ozone data.
Analysis II indicates that the effect of pooling cultivar data regionally,
rather than across all cultivars, as in Analysis I, is trivial. This
observation suggests that the response bias arising from aggregating all
statistically similar cultivars into one common response is minimal. Analysis
III shows a much more dramatic difference in economic estimates when compared
with Analysis I (approximately a 60 percent difference). Analysis III uses
the most extreme cultivar response available for soybeans, corn, wheat and
cotton. Statistically and agronomically, however, these cultivar (yield)
xiii
-------
responses behave unlike the other NCLAN cultivars for those crops. As such,
these economic estimates are upper bounds on potential impacts as manifested
if the current NCLAN response data capture the extremes in ozone sensitivity
within the entire set of commercial cultivars. Analysis IV demonstrates that
the annual ambient ozone levels used in the assessment do affect economic
estimates but, like Analysis II, the difference is small (5 to 14 percent)
between 1980 levels and a five year average. A more important issue may be
the measure of dose (e.g., seven-hour or twelve-hour) rather than variability
in the measure. Finally, Analysis V represents an attempt to include an
antagonistic hypothesis concerning drought stress-ozone effects in the
assessment. Compared with Analysis I, the economic estimates are about twenty
percent lower. This difference is important, in that water stress and other
environmental interactions occur commonly under commercial agricultural
production conditions. There are currently insufficient data to fully
incorporate such interactive stresses in economic assessments.
A number of limitations or caveats need to be attached to these
estimates. While the estimates are derived from a conceptually sound economic
model implemented with the most recent supporting data, there are several
sources of uncertainty or error. These include the issue of exposure dynamics
(seven-hour vs. twelve-hour), and the lack of environmental interactions,
particularly ozone-moisture stress interactions, in many of the response
experiments. Each of these response issues is being addressed in current
NCLAN research. Also, the ozone data are based on a limited set of USEPA's
Storage and Retrieval of Aerometric Data (SAROAD) monitoring sites, mainly in
urban and suburban areas. While the spatial interpolation process (Kriging)
results in a fairly close correspondence between predicted and actual ozone
levels at a few validation points, there is a need for more monitoring sites
in rural areas. The large scale economic model, with its many variables,
parameters and the underlying data used to derive these values, is a potential
source of error. The model is a long-run equilibrium model and assumes that
supply will be consumed at some price. The model was validated against
historical values, and hence its policy utility is greatest when addressing
changes bounded by historical levels, rather than quantum adjustments. The
modest adjustments portrayed in the ozone analyses fall within these
historical levels.
xiv
-------
THE ECONOMIC EFFECTS OF OZONE ON AGRICULTURE
I. INTRODUCTION
The harmful effects of ozone and other air pollutants on vegetation have
been documented for at least 35 years (Middleton et al., 1950; Brisley and
Jones, 1950; Beasdale, 1952). Such damages are now one criteria by which
Secondary National Ambient Air Quality Standards (SNAAQS) are evaluated, as
established by the Clean Air Act and its Amendments. Within the vegetative
effects category, primary research emphasis has been placed on measuring
effects on agricultural crops, given the importance of food and fiber to human
welfare.
Attempts to assess the monetary impact of these agricultural effects
followed (e.g., Benedict et al., 1971). While assessments of air pollution
costs (or benefits of control) have not been used in setting secondary
standards, such information may be used for this purpose in the future as well
as in support of regulatory policy relating to secondary standards as required
by Executive Order 12291 (Padgett and Richmond, 1983). Until recently
however, economic assessments of ozone control to agriculture have been
plagued by sparse or even contradictory biological information on which to
base an assessment. Where data did exist, the procedures by which the data
were generated did not readily lend themselves to economic analysis, as
described in Adams and Crocker (1982). As a result, the validity of many
assessments is questionable.
Conducting economic assessments on agriculture or other ecosystems
requires specific biological response data linking pollutant levels and
performance parameters of the ecosystem in question. For agricultural
assessments, this response is represented by a relationship linking crop yield
to air quality (or pollutant exposure levels) and other causal factors. The
relationship may be quantified directly using data generated from biological
experimentation, indirectly from observed producer output and behavioral data
or from some combination of data sources. From the standpoint of economic
analysis, procedures that are based on observed producer data, such as
typically used in production function estimation or dual cost function
techniques, are preferred (Crocker et al., 1981).
While some success has been achieved in applying both primal and dual cost
procedures to agricultural assessments at the state level (e.g., see Mjelde et
al., 1984), assessors in general have had little success in directly applying
such techniques across large geographical areas, due to both data and
statistical difficulties (e.g., see Adams et al., 1982; Manuel et al., 1981).
Even for those regional studies where plausible estimates are obtained,
related experimental data are required to formulate testable hypotheses as
1
-------
well as to establish the credibility of estimates derived from behavioral data
(Mjelde et al., 1984, Rowe et ar., 1984). As a result, national level
assessments of pollutant damages to agriculture currently rely on
supplemental data from some form of biological experimentation to define the
relationship between exposure and plant yield response. The closer these
experimental procedures are to real world or commercial production conditions,
the more useful they become in economic assessments.
National Crop Loss Assessment Network (NCLAN) scientists recently have
attempted to improve the state of knowledge regarding crop response to air
pollutants (Heck et al., 1982). Conceived in 1980, this program involves
field experiments on major agricultural crops to provide information on crop
response to ozone, the most pervasive ambient air pollutant. The output from
these experiments will ultimately form the basis for policy-making by
supporting assessments of the national consequences of ozone on agriculture.
Although some controls are imposed on the structure of the field experiments,
the NCLAN data are generated so as to be at least minimally compatible with
most economic assessment techniques. Performance of a preliminary economic
assessment using these data is one objective of the NCLAN program.
This manuscript is a report on the preliminary NCLAN national economic
assessment of ozone effects on agriculture. The analysis, data and results
underlying this assessment represent the collective biological, meterological
and economic knowledge gained from the NCLAN program through 1983. This
knowledge is reflected in the biological response models and the economic
methodologies used to derive the economic estimates. These data are the most
defensible currently available for assessing the effects of ozone on
agriculture.
The overall objective of this paper is thus to provide background,
documentation and resultant estimates of the economic consequences of ozone on
major U.S. agricultural crops; under alternative levels of ambient ozone. The
economic analysis is limited to those ozone effects directly associated with
the production and consumption of a set of agricultural commodities. Effects
on non-agricultural goods, as, well as compliance costs of achieving any
changes in ambient ozone levels are not evaluated here, hence the estimates
should not necessarily be interpreted as net economic effects. The results
are derived from an economic analysis based on a U.S. agricultural sector
model (adapted from Chattin et al., 1983). Empirical emphasis is on six major
crops (corn, soybeans, wheat., cotton, grain sorghum, and barley) which account
for over 75 percent of U.S. cropped acreage (USDA, 1982). Potential ozone
effects on hay are evaluated using the average yield response of other crops
as a surrogate. In addition, the assessment accounts for the derived-demand
relationship between feed grains, such as corn, and livestock by including a
livestock production and feeding component.
The specific objectives of the analysis are:
1. to provide assessments of the economic consequences of alternative
ambient ozone levels within the agricultural sector, including those
accruing to both producers and consumers of agricultural commodities;
compliance costs will not be examined.
-------
2. to test the sensitivity of these economic estimates to varying yield
forecasts reflecting sources of uncertainty within the NCLAN response
data; and
3. to use these results to provide insight on the importance of improved
information for possible inclusion in subsequent economic assessments
of ozone effects on agriculture.
The next section briefly discusses biological and economic issues
concerning the conduct of economic assessments and the use of dose-response
information. Following this, the economic methodology and model are
presented, along with the procedures and data sources underlying that
analysis. Next, the results of the application of the economic model to the
U.S. agricultural sector under alternative assumptions regarding ozone levels,
response data and environmental interactions (ozone-moisture stress) are
presented. Finally, policy implications and areas for further research are
discussed.
II. A REVIEW OF ASSESSMENT ISSUES AND PAST STUDIES
In view of the importance of U.S. agriculture to both domestic and world
consumption of food and fiber, major reductions in supply due to air pollution
could have a substantial effect on human welfare. Biological research, such
as that coming out of NCLAN and earlier studies, indicate that crop yields
may be reduced substantially under current ambient ozone levels.
Given that ambient ozone levels during the growing season are sufficient
to reduce yields of important crops in many agricultural production areas
(e.g., the Corn Belt, the Southeast), the potential for substantial economic
effects exists. For example, the five state Corn Belt region (Iowa, Illinois,
Indiana, Missouri and Ohio) produces over 50 percent of the two principal
crops in the U.S., corn and soybeans. As noted in Table 1, even modest
changes in crop yields could translate into a sizeable economic effect, in
view of the $33 billion farm value of just these two crops. Crops for which
NCLAN data are available amounted to over $50 billion in farm value in 1981.
An economic assessment of ozone on only these six major commodities thus can
capture a major portion of the national economic consequences of this
pollutant.
The potential for major economic effects from ozone has resulted in
numerous attempts to assess dollar losses (or the benefits of pollution
control) attributable to ambient ozone and other pollutants. Most studies
predate the availability of NCLAN data for major agricultural commodities.
Identification of critical assessment issues and an evaluation of how well
these are addressed in past assessments can serve to strengthen the conceptual
and empirical basis of the current NCLAN assessment, as well as provide some
perspective on the general magnitudes of the economic consequences of ozone
damages to agriculture.
-------
Biological and Practical Issues
The need for plant response information measured in terms of yield units
(rather than foliar injury) has, been noted by most analysts doing assessment
research (e.g., Leung et al., 1978; Adams and Crocker, 1982). Plant
scientists also recognized the need to report response in terms of yield if
economic losses are eventually to be estimated. For example, Oshima and
coworkers (Oshima, 1973; Oshima et al., 1976; Oshima and Gallavan, 1980) have
reported crop losses in terms of potential or actual yield reduction for a few
crops. However, the NCLAN program (Heck et al., 1982) appears to be among the
first coordinated national plant science research efforts to provide response
information in a usable form for economic assessments. The NCLAN data and
yield projections are discussed in more detail subsequently.
Table 1. 1981 acreage and value of major U.S. commodities included in
NCLAN economic assessment
Acres Planted Value of Production
Crop (millions) ($ billion)
Corn
Soybeans
Wheat, winter
Wheat, spring
Alfalfa hay
Other hay
Grain Sorghum
Cotton
Barley
Total, NCLAN Crops
Total, All Crops
84.106
70.087
57.425
23.005
26.269
33.168
15.894
14.461
8.283
332.698
356.881
20.001
12.943
7.692
2.534
a /
9.212-a/
2.032
4.109
1.169
59.602
74.984
NCLAN Crops as Percentage
of total 93.22 79.60
— Includes all hay.
SOURCE: USDA, Agricultural Statistics. 1982.
4
-------
Past attempts to assess the agricultural value of pollution control
typically did not involve adequate plant science information. As a result
assessors used a wide range of data sources and this lead to some highly
divergent dollar-loss estimates of pollution damage. Such divergences may be
attributable to some specific biological and air quality data issues or
problems:
1. The effect of sparse data on pollution-induced crop losses. A lack
of data caused assessments to be based on extrapolations from
available foliar injury estimates resulting in often unreliable
yield-reduction estimates.
2. Selection of alternative cultivar and crop mixes, regions, and time
periods in the analysis. Crop prices, production levels, and ozone
exposure vary geographically and temporally, with resultant changes
in dollar loss estimates.
3. Selection or definition of alternative background ambient levels to
portray "clean air" (absence of anthropogenic ozone) in the analysis.
When used in combination with a standardized dose-response function,
the use of different background ozone levels provides different yield
reduction estimates and ultimately different economic estimates.
4. The difficulty of extrapolating from controlled chamber experiments
to agronomic regions with all the required assumptions regarding soil
type, precipitation regimes, pollution exposure patterns, solar
radiation levels, and interactions among these edaphic and climatic
variables.
In addition, alternative assessment techniques have been employed. It is
not possible to sort out the relative contribution of better economic
methodologies vis-a-vis better technical data to assessments of environmental
regulation. However, recent empirical work of Adams et al. (1982) suggests
that adequate representation of both economic and biological process
contribute equally to the measurement of net benefits. The implication of
this observation is that knowing both biological and economic responses is
important in performing economic assessments.
Economic Issues
Decision making related to the formulation of public policy centers on
perceived changes in "public welfare." There are alternative criteria by
which to judge changes in aggregate public welfare arising from policy actions
(Harberger). However, in a benefit-cost analysis of ambient air quality
changes, regulatory actions typically do not lead to regulations from which
all parties benefit. Virtually any air pollution control action or regulation
will disadvantage someone (polluters, consumers, agricultural producers) in
terms of perceived welfare. A measurable or quantitative definition of
welfare is needed as a basis for judging socially desirable changes associated
with alternative pollution control actions. This also implies the need for
common or consistent measures of value for the various welfare components.
-------
The economically accepted measures of welfare are compensating and
equivalent variation as reflected in economic surplus measured through
consumers' and producers' surplus (see Just et al., 1982, or Willig, 1976 for
definitions and more extensive discussions). Consumers' and producers'
surplus represent the utility gained by individuals when: (1) in consuming
goods, they obtain goods at a price less than the maximum they would be
willing to pay; and (2) in producing goods, they sell at a price above the
price at which they would have been willing to supply. Most economic
assessments of environmental controls now measure benefits in terms of
economic surplus. A graphical presentation of these surpluses under a wide
range of air pollution situations is presented in Adams et al. (1984). In
addition to using consistent benefits or welfare measures in an environmental
assessment, the forces or processes that give rise to changes in welfare need
to be identified and modeled.
For example, the physical effect of air pollution on vegetation reflects
the natural or physiological processes associated with the toxin in question.
However, the varying economic impacts of air pollution on agricultural
producers may be a function of such factors as: 1) edaphic factors and
endowments; 2) production systems and alternatives; 3) geographical location;
4) types of agricultural crops planted; 5) availability of substitute crops;
and 6) managerial ability. Consumers of these agricultural products can be
classified into strata or groups based upon such factors as: 1) income level;
2) regional location; 3) percentage of income spent on food; and 4) food and
other preferences. While consumer surplus is an accepted measure of aggregate
welfare, the nature and distribution of impacts within the consumer category
is another dimension of welfare. Several distributional issues which then
arise in an assessment of air pollution policies include:
1. How are gains and/or losses distributed between various classes of
people (for example, tradeoffs between consumers and producers)?
2. How are gains and/or losses distributed regionally, commodity-wise,
or among factor owners?
3. What might be the impact of an action in terms of strata within a
class (e.g., farm size or income distribution)?
4. How do the distributional effects change with time?
The latter two distributional concerns are difficult to address due to
insufficient data. However, the use of techniques which ignore or are
incapable of addressing the first two distributional consequences will result
in misleading estimates whenever: 1) prices in the output or input markets in
the agricultural sector are sensitive to changes in yield and input usage from
varying levels of air quality; and 2) producers and consumers adopt different
means for adjusting to changes in air quality.
In the first case, if the percentage yield increases caused by air quality
improvements are less than the resultant percentage price reductions, an
assessment that ignored such price effects would attribute benefits to the
-------
producer when in reality there would be producer revenue losses (and consumer
gains). The second concern relates to producer (or consumer) adaptation
strategies. For example, if producers can adopt different production patterns
(or utilize more resistant cultivars or adopt other compensatory input
changes, such as adding lime and fertilizer) and thereby reduce their
potential losses (or increase gains) from an air pollution increase, they will
do so. Failure to account for this adaptive producer behavior will result in
overestimates of losses experienced by producers in the face of air quality
degradation. Similarly, consumers may substitute certain agricultural
commodities in the face of relative price changes, so that the net effect of a
rise in the price of a commodity due to air pollution-induced supply changes
may not be as severe as first indicated.
Assessment Methodologies Applied to Agriculture
Air pollution-agricultural assessments found in the literature fall
within three broad methodological categories. Only one of these categories
features methodologies that are capable of addressing the above issues. The
first type uses damage functions to report crop losses in physical units such
as the reduction in actual or potential crop production in a given
geographical unit (e.g., a state or region). Examples include the recent work
by Loucks and Armentano (1982) and the "DAMAGE" model defined in Moskowitz et
al. (1982). This type of assessment makes no claim to report economic losses.
The second (and most prevalent type of assessment of those reporting dollar
losses) translates the physical losses obtained from damage functions into a
dollar value by multiplying estimated yield losses by an assumed constant crop
price. For the purposes of this section, this ad hoc approach is defined as
the "traditional" procedure. As an economic assessment methodology, it
suffers from serious conceptual weaknesses, which limit the validity of the
estimates to some very restrictive cases. Thus, not all dollar loss estimates
(such as those obtained from this approach) should be viewed as valid economic
estimates. The third assessment type features the use of theoretically
justified economic methodologies that address some of the economic issues
raised in the preceeding section. Such studies provide estimates of benefits
of control in dollar terms which account for producer-consumer decision-making
processes and hence distributional consequences of alternative environmental
states. These estimates will more accurately reflect the economic effects of
air pollution in those situations where economic processes and markets are
known to operate, as in the case of agricultural production. Dollar estimates
arising from traditional and economic assessment methodologies are seldom
distinguished in the popular press. However, economists generally discount
the monetary estimates obtained from the traditional or damage function type
of assessment (critiques of this approach may be found in Leung et al., 1978,
and Crocker, 1982).
The advantage of the traditional procedure is the relative ease with which
dollar values may be obtained. More comprehensive economic assessments, as
exemplified by Leung et al. (1982), Benson et al. (1982), Adams et al. (1982),
Mjelde et al. (1984), Kopp et al. (1983) and Adams and McCarl (1983) attempt
to account for market impacts of air pollution-induced yield reductions and
producer behavioral responses. These studies involve somewhat different
-------
techniques as determined by the structure of the particular economic problem
(i.e., duality, mathematical programming, econometric-simulation). However,
they all explicitly deal with price adjustments, providing estimates of the
economic effects on various economic agents, such as producers and consumers.
In the Benson et al. (1982) and Adams et al. (1982) studies, the results
obtained from these comprehensive economic analyses were compared with
estimates obtained from the traditional procedure,using the same data. The
differences were moderate to large, with the traditional procedure
overestimating losses from air pollution when a "clean air" and ambient ozone
condition (an environmental degradation) were compared. Also the traditional
procedure provides estimates which at best can only address producers' effects
with no attention paid to the fate of consumers. Thus, there are fundamental,
conceptual and empirical differences between losses measured by the
traditional procedure and those obtained from more comprehensive economic
assessments. A detailed review of alternative economic techniques and their
suitability to environmental economic assessments is presented in Freeman
(1979).
A Review of Regional Assessments
Most of the economic assessments in the literature focus on regional
effects. This regional emphasis may be attributed to the relative abundance
of data on crop response and air quality for selected regions, as well as the
national importance of some agricultural regions (such as the Corn Belt and
California). While regional estimates are not sufficient for evaluating
alternative national air pollution standards, such studies can provide useful
comparative information on alternative economic methodologies for assessing
environmental damages. Also, regional estimates may be indicative of the
potential magnitudes of national effects, if that region (e.g., the Corn Belt)
produces a dominant share of major commodities such as corn and soybeans.
Economic estimates of pollution effects for selected regions are presented in
Table 2.
Of the regional studies reported in the literature, several have focused
on southern California, a region with both high pollution (ozone) levels and
an important agricultural economy. Adams et al. (1982) assessed the impact of
ozone on 14 annual vegetable and field crops in four agricultural subregions
of central and southern California for 1976 using a price-endogenous,
quadratic-programming approach. Their model solution predicted the effects of
changed ozone levels and associated yield and price changes on the welfare of
producers and consumers. Ozone-induced reductions in yield were derived for
most crops from the Larsen-Heck (1976) foliar injury models. The authors then
estimated what crop production and price would have been if the 1976 federal
secondary ambient air quality standard (80 ppb not to be exceeded more than
once a year) had been achieved.
The estimated losses from current ambient ozone were found to be
relatively small when compared with total agricultural value in the region —
approximately $45 million compared to a total agricultural value of nearly $2
billion. Elimination of 1976 oxidant pollution would have increased 1976
producer surplus by $35 million and ordinary consumer surplus by $10 million.
8
-------
Table 2. Summary of recent regional air pollution control benefits estimates
Region
Reference
Annual
benefits or
loss estimate
($ millions)
Comments
Southern
California
South Coast
Air Basin
(California)
Ohio River
Basin
Minnesota
Corn Belt
II1i no i s
California
California
Adams et al
1982
Leung et al.
1982
Page et al.
1982
Benson et al.
1982
Adams and McCarl
1984
Mjelde et al.
1984
Howitt et al,
1983
Rowe et al.
1984
43-45 Estimated as economic surplus in 1976 dollars
for 14 annual crops. Employs mathematical
programming model to evaluate benefits of
reducing current ambient levels to seasonal
seven-hour ave. of 40 ppb.
93-103 Losses estimated as economic surplus in 1975
(300)a dollars for citrus, avacados and selected
annual crops. Employs econometric procedures
to compare "clean air case" (no oxidant pollu-
tion) with ambient levels.
278 Losses estimated as producer losses for corn,
(6,960) soybeans, and wheat in 1976 dollars. Region
includes Illinois, Indiana, Ohio, Kentucky,
West Virginia, and Pennsylvania.
30.5 Losses estimated in 1980 dollars for corn,
alfalfa, and wheat under alternative ozone
assumptions. Farm level dollar losses
obtained from econometric model of national
commodity markets.
668 Uses a sectoral model of U.S. agriculture to
record economic effects of changes in yields
of corn, soybeans, and wheat due to alterna-
tive oxidant standards. Benefits include
effects on both consumer and producer of a
more stringent federal standard (80 ppb).
55-2006 Uses profit functions to measure effect of
ozone on producers' profits. Aggregate
effect over corn, soybeans, and wheat assum-
ing a 25 percent reduction in ambient ozone.
37 Benefits measured as an increase in economic
surplus from a reduction in ambient ozone to
40 ppb seasonal 7-hr average. Analysis based
on a mathematical programming model of Cali-
fornia annual crops.
46-117 Benefits measured as increase in economic
surplus arising from meeting three alternative
California oxidant standards. Uses mathemati-
cal programming model of major California
annual and perennial crops.
Estimate of direct and indirect losses for entire state.
Estimated annual equivalent loss due to oxidants.
c Present value of losses due to oxidants for 25 year period (1976-2000).
"Worst case" ozone situation, ignores production effects outside Minnesota. If other regions
included in analysis, worsening of ozone increases total gross returns to Minnesota producers
by $67 million due to inelastic nature of commodity demand.
Range of economic benefits due to a 25 percent reduction in ozone from ambient levels over a
four-year period (1978-1981).
-------
By way of comparison, Adams et al. applied the traditional approach to
estimating losses (multiplying the estimated difference between actual and
potential yield for 1976 by market price) and derived a total estimated loss
of $52.5 million. While the difference appears relatively small, the
traditional method only measures effects on producers. If the latter figure
is compared with the producer loss from the economic analysis, the result is
an approximately 50 percent greater loss estimate for the traditional
procedure, largely because of the inability to accommodate offsetting price
changes and producer mitigative adjustments.
Leung et al. (1982) estimated ozone damage to nine annual and perennial
crops in the California South Coast Air Basin, representing some 40 percent by
value of crop production in the region. Rather than use an experimentally
based measure of dose-response, Leung et al. estimated yield loss with yield
functions derived from secondary data. Crop-yield reductions were estimated
for 1975 by calculating the differences between actual yields with 1975 levels
of ozone and yields predicted by the linear response functions at zero ozone
concentration. Using the predicted yield adjustments from these yield
functions to drive the analyses, Leung et al. (1982) then calculated changes
in consumers' and producers' surplus to approximate the welfare effects of
changes in agricultural supply brought about by ozone. Demand and supply
curves were estimated with data from 1958 to 1977 and then used to calculate
1976 losses of consumer and producer surplus from ozone exposure of $103
million.
There are two more recent studies of the effects of air pollutants on
California agriculture. In the first, Howitt et al. (1984) assess the impact
of alternative ozone levels on the statewide production of fourteen annual
crops. The economic model is similar to that employed in Adams et al. (i.e.,
a price-endogenous, quadratic programming model), updated to 1978 values. The
response data are derived from NCLAN experiments, with the exception of
alfalfa, which is taken from Oshima et al. (1976).
The analysis examines three ozone scenarios, reflecting hypothetical
changes in ambient ozone to 40, 50 and 80 ppb seasonal seven-hour averages.
The first is a reduction approximately equivalent to meeting a Federal
standard of 80 ppb hourly maximum (about a 25 percent reduction in ambient
ozone assuming the hourly maximum is about 2 times the seasonal seven-hour
mean). The second portrays a slight degradation (increase) in ozone. The 80
ppb analysis portrays an increase of about 33 percent in ambient ozone. The
economic effects of meeting the two extreme ozone actions are a $36 million
net benefit (from the 25 percent reduction in ozone) and a $157 million loss.
The 50 ppb analysis amounts to a slight degradation in air quality and hence a
very small economic loss. These effects are in line with those observed in
Adams et al.
The second study, by Rowe et al. (1984), focuses on both annual and
perennial crops in the San Joaquin Valley of California, the major production
area of the state. Using both field and experimental data, Rowe et al.
estimate a series of yield functions for the major crops grown in the San
Joaquin Valley for both ozone and sulfur dioxide ($02). With the exception of
potatoes, no yield changes arise from the ambient S02 levels. The yield
10
-------
adjustments for ozone are then used to drive the same economic programming
model used in Howitt et al. (1984).
The ozone analyses of Rowe et al. feature greater ozone reduction and
hence greater yield adjustments than those of Howitt et al. As a result (and
perhaps because of the broader crop coverage) the benefits of control are
larger, amounting to from $48 to $117 million, depending on the ozone
assumption. These results, in combination with earlier California studies,
provide evidence of some substantial air pollution effects, but the
distribution of these impacts appears to be felt most heavily by the producers
of the commodities. The Rowe et al. study also provides some support for use
of combined secondary (survey) and experimental data as a means to overcome a
lack of response effects information for a broad range of crops.
Losses within the Ohio River Basin (Illinois, Indiana, Ohio, Kentucky,
West Virginia, and Pennsylvania) were estimated by Page, et al. (1982). The
region is a major producer of corn, soybeans, and wheat; it also experiences
oxidant levels sufficiently high to depress crop yields. Although the study
examined two pollutants, S02 and ozone, the largest losses (approximately 98
percent) were attributed to ozone levels. The yield reduction data were
derived from Loucks and Armentano (1982), who synthesized experimentally
determined crop loss data and extrapolated the results to air quality
concentrations in the Ohio River Basin. Losses were measured at the producer
level as changes in producers' income (quasi-rents) between clean air and
ambient ozone levels and corresponding losses (minimal, maximum, and probable)
were projected over the 1976 to 2000 period, and the estimated loss expressed
in 1976 dollars. The net present value of ozone-induced losses across the
various loss scenarios for the 25-year period is approximately $6.8 billion,
or an annual equivalent of $278 million. Not surprisingly, the bulk of these
losses is estimated to accrue to the states with the largest agricultural
production, Illinois and Indiana.
The Page et al. and some other economic assessments (Manuel et al., 1981;
Leung et al., 1982) utilize a crop by crop approach, so that substitution
between crops is not considered. Such substitution is contained in the Adams
et al. programming-based study. Smith and Brown (1982) assess in detail the
importance of substitution between differentially sensitive crops (acreage
shifts between crops) in response to ozone-induced yield changes or to
relative crop price changes with a farm level linear programming model. Yield
changes are predicted with the Loucks and Armentano (1982) ozone-response
model. For the profit maximizing producer, such substitution should occur if
ozone levels change, because soybeans are considerably more sensitive to ozone
than corn. Such relative sensitivity and its potential effects on economic
estimates has been noted earlier by Adams and Crocker (1979), Adams et al.
(1979), and Leung, et al. (1978).
Studies which fail to consider cross-crop substitution may lead to biased
estimates of changes in consumer and producer surplus from reduced ozone.
Such studies will tend, ceteris paribus, to underestimate the increase in
consumer surplus for crops which are more sensitive to ozone than other crops,
and tend to overestimate the increase in consumer surplus for crops that are
less sensitive. If the economic benefits from pollution control standards are
11
-------
estimated in terms of changes in consumer and producer surplus caused by
shifts in supply, then the effect of crop substitution on the assessment of
the benefits to be gained by reducing ozone levels needs to be determined.
The linear programming model utilized by Smith and Brown assessed the
impact of four different yield improvement scenarios on acreage shifts among
corn, soybeans, and wheat. Comparison of each crop's acreage under different
yield improvement scenarios reveals that as ozone levels fall, there is a
shift from corn and wheat —- crops assumed to be more resistant to ozone
damage — to soybeans. Damage estimates equal to or greater than the Loucks
and Armentano (1982) minimum yield loss estimate result in major shifts in
cropping patterns as well as significant loss in net farm income. Allowing
crop substitution increases the estimated economic gain to farmers (from
reduced ozone) by up to 20 percent over the estimates from the
non-substitution analysis.
A major limitation of this study for policy purposes is that only
benefits accruing to producers are assessed. Crop demand was assumed totally
elastic so that output price would not change when supplies shifted. Although
such an assumption is valid at the farm level, the pervasive nature of ozone
pollution in the Corn Belt and that region's market share of corn and soybeans
suggests that the effects of price responses arising from aggregate supply
shifts should be considered.
In a model of crop losses induced by ozone stress in Minnesota, Benson et
al., (1982) also provide estimates of the economic consequences of ambient
ozone. Yield loss functions for alfalfa, wheat and corn are evaluated at the
county level with actual or simulated 1980 ozone data.
Benson et al. aggregate the estimated potential yield losses occurring at
ambient ozone concentrations for each county to provide a statewide crop loss.
A national econometric model was then used to convert yield (production)
adjustments for each crop into dollar losses, under alternative supply
assumptions: (1) assuming ozone, and hence production, is unchanged in areas
outside of Minnesota (analytically the same as the traditional procedure); and
(2) assuming that ozone levels change nationwide and then accounting for
supply and demand relationships for each crop as affected by production
changes in all regions. The assumptions gave highly divergent estimates of
effects on Minnesota producers. For example, the estimated dollar loss
attributable to a worst case ozone level obtained from the first assumption is
approximately $30 million in 1980 dollars. Conversely, when the econometric
model accounted for price changes (measures) resulting from probable changes
in production to all regions, it indicated that there would be a gain to
producers of $67 million in the short run if ozone levels increased (in
Minnesota as well as other production areas for these crops). These results,
when combined with similar observations from Adams et al. (1982) and
Leung et al. (1982) suggest the importance of assessment methodologies that
account for regional market linkages and resultant price effects in performing
economic assessments of a pervasive environmental problem.
Mjelde et al. (1984) employ duality concepts in their analysis of the
effects of ozone on Illinois cash grain farms. In addition to measuring the
12
-------
direct economic consequences of ozone on farmers' income, this analysis
demonstrates the methodological utility of the duality approach. One of the
primary objectives of their effort is to test whether a meaningful link can be
established between the physical loss estimates generated under controlled
experimentation and the physical response information inherent in observed
economic behavior (i.e., farmers' cost data). To explore this, Mjelde et al.
(1984) invoke duality concepts in developing profit functions for Illinois
grain farms.
Profit functions are estimated from a large sample of detailed cost and
production data for Illinois farms and incorporate environmental variables
(i.e., ozone, temperature, and rainfall) as well as traditional economic
variables. In most specifications, ozone is revealed to have a negative and
significant (at the 5% level) impact on profit. When direct production
effects (elasticity) of ozone are compared with NCLAN results obtained at
Argonne National Laboratory in Illinois (Heck et al., 1983a), the production
responses appear to be reasonably close. Specifically, the aggregate (across
three crops — corn, soybeans, and wheat) output elasticity from Mjelde et al.
is -0.132. For a 25% increase in ozone, it is estimated that aggregate output
for the three crops would decline 3.3 percent. The same 25 percent increase
using the Argonne data indicates an 11.7 percent and 3.7 percent decrease in
output for Corsoy and Williams cultivars of soybean, respectively. For two
corn cultivars (Pioneer 3780 and PAG 397), output would decline between 1.4
and 0.6 percent. The Mjelde et al. aggregate production estimate (of 3.3)
lies between these estimates.
Finally Mjelde et al. calculate that ozone resulted in an aggregate loss
in profits to Illinois farmers of approximately $50 million in 1980. This
estimate is based on the ozone elasticity with respect to profit -0.43.
Again, this result seems consistent with some previous loss estimates (Heck et
al., 1983; Page, 1982).
A study of ozone effects on Corn Belt agriculture by Adams and McCarl
(1984) uses a large-scale micro-macro economic model specification to measure
effects of alternative oxidant standards on producers and consumers. Detailed
farm level models designed to capture producer responses are interfaced with a
national sector model to measure aggregate effects. The model is an earlier
version of the economic model used in the current NCLAN national assessment.
The analysis is driven by NCLAN response functions for the Corn Belt generated
through the 1982 crop year and includes region-specific response data for
corn, soybeans, and wheat. Changes in yields associated with hypothetical
federal secondary oxidant standards as suggested by the NCLAN data provide the
basis for the analysis. The results of the analysis suggest that reduction in
oxidants (lowering the present standard to 80 ppb from 120 ppb) would
provide a net benefit of $668 million. Conversely, relaxing the standard to
160 ppb would result in a loss to consumers and producers of approximately
$2.0 billion. In terms of distributional consequences, this analysis follows
shifts associated with changes in supply in the face of inelastic demand.
That is, the 80 ppb scenario (of increased production) benefits consumers
at the expense of producers, whereas the 160 ppb assumption results in the
opposite situation.
13
-------
A Review of National Assessments
Properly structured national analyses overcome a fundamental limitation
of regional analyses by accounting for economic linkages between groups and
regions. However, detailed national assessments also tend to be more costly
to perform. As a result, there are fewer estimates of pollution effects at
the national than the regional level. Of the national assessments performed
since 1980, several use the traditional, ad hoc approach to quantify dollar
effects. Analyses of this type tend to ignore the economic concept of
benefits, as discussed earlier. However, some of the most recent national
assessments included in this review do use more defensible measures of
economic effects. National level estimates of air pollution damages are
summarized in Table 3. As indicated in the table, the range of estimates is
relatively small. Caution should be used in making comparisons between these
estimates, however, as the analyses employ somewhat different crops, response
information and assessment approaches, as detailed below.
The recent national level economic estimates of ozone effect to
agriculture include an assessment by the Stanford Research Institute (SRI) for
the National Commission on Air Quality (1981). This is an updated version of
the Benedict et al. (1971) study. The intent was to estimate the benefits of
meeting the Secondary National Ambient Air Quality Standards for ozone and
sulfur dioxide accruing to 15 agricultural crops. The principal
methodological differences between the earlier Benedict et al. approach and
the SRI approach include the use of a wider range of dose response functions
drawn from the plant science literature through 1980, updated production data
from the 1974 Census of Agriculture and updated air quality and price
information.
SRI estimated the decline in total production from oxidants and sulfur
dioxide emissions using the updated response functions and county-level data
on air quality for non-attainment counties (531 counties). These physical
loss estimates were then translated to a dollar value by the ad hoc procedure
of multiplying the reduction in production by the 1980 crop price for each
commodity. The resultant dollar loss estimate (or potential benefit of
meeting the secondary national ambient air quality standards for ozone and
sulfur dioxide) was reported to be $1.8 billion (1980 dollars) for
agricultural crops. Note that this entire amount is assumed to accrue to
producers, as the method is incapable of measuring consumer effects. Of this
total, the benefits of meeting the oxidant standard are far greater than the
direct benefits of meeting SO^ standards ($1747 million compared to $34
million), because the number of nonattainment counties for oxidants is nearly
six times the number of nonattainment counties for S0~.
Another study assessing the benefits to be gained by reducing air
pollution was conducted by Manuel et al. (1981). Their investigation centered
on estimating the potential benefits for cotton and soybean production of
achieving alternative secondary national ambient air quality standards for
sulfur dioxide (SO^) and total suspended particles (TSP). The estimated
demand equation included the prices of soybeans, corn and substitutes. On the
supply side, cross sectional data were utilized to estimate crop yields;
14
-------
Table 3. Summary of recent estimates of national economic consequences of
pollution
Study
Crops
Annual
Benefits of
Control
Comments
Stanford Research corn, soybeans, alfalfa $1.8 billion
Institute (1981) and 13 other annual crops
Shriner et al.
(1982)
corn, soybeans, wheat,
peanuts
$3.0 billion
Adams and Crocker
(1984)
corn, soybeans, cotton
$2.2 billion
Adams, Crocker, and corn, soybeans, wheat,
Katz (1984) cotton
$2.4 billion
Kopp et al. (1983) corn, soybeans, wheat,
cotton, peanuts
$1.2 billion
Updated version of Benedict-SRI
model. Loss measured in 1980
dollars for 531 counties.
Effects estimated in 1978
dollars, measured at producer
level. Control assumes a back-
ground or "clean air" oxidant
level of 25 ppb ozone. Uses
NCLAN response information for
1980.
Benefits measured in 1980
dollars using economic surplus.
Benefit represents difference
between current production and
production if an ambient ozone
level of 40 ppb had been achieved
Uses NCLAN response information
for 1980.
Benefits neasured as economic
surplus in 1980 dollars.
Benefits arise from the in-
crease in production due to a
reduction in ozone from 53 ppb
to approximately 40 ppb.
Response information from 1980,
1981, and 1982 NCLAN data.
Benefits measured as economic
surplus in 1978 dollars.
Benefits due reduction in
Federal standard from 120 ppb
to 80 ppb hourly-maximum. Uses
NCLAN response information from
1980 and 1981.
15
-------
variables included fertilizer per acre, lime per acre and S02 level. An
acreage response for each crop is estimated as a function of the crop's
expected price, the expected price of a substitute crop and government support
programs for that crop. Supply was subsequently estimated as the product of
the yield equation and the acreage response function. Estimated changes in
producer and consumer surplus are attained by a simply dynamic model.
The Manuel et al. (1981) econometric model improved on the traditional
procedures used in the SRI analysis in that it includes price effects across
crops, and hence accounts for crop substitution in response to changes in
output prices. The model framework also does not require the direct use of
experimentally derived dose-response functions; instead, production function
concepts are used to account for ozone and S02 impacts on production.
However, the statistical robustness and stability of the
SOp yield-coefficients are disappointing. Further, as with any aggregate
econometric approach, the model is unable to reflect compensatory input
adjustments or the substitution which may occur as farmers choose to plant
more of some crops and less of others because of relative yield changes.
A national assessment by Shriner et al. (1982) for the Office of
Technology Assessment estimated the losses associated with ambient ozone
levels for four crops ~ corn, soybeans, wheat, and peanuts. The study
employed dose-response information from early NCLAN experiments and simulated
county-level ozone data generated from available USEPA Storage and Retrieval
of Atmospheric Data System (SAROAD) monitoring sites. With the crop-response
information and county-level ozone data in combination with 1978 Census of
Agriculture data on yields, percentage reductions were measured against a base
or "background" ozone level of 25 ppb ambient concentration. As in the SRI
study, the estimated physical reduction in county production levels for each
crop were then converted to a dollar value by the traditional ad hoc procedure
of multiplying by the county-level price. The aggregate loss (or difference
between value of production at ambient levels and those at 25 ppb) for the
United States was estimated at approximately $3 billion. The benefits
estimates suffer from the same problems as those associated with the SRI and
other approaches that abstract from economic factors. The principal
improvement of this study over the SRI assessment is in the use of 1980 NCLAN
data.
Another economic estimate of ozone pollution effects was developed by
Adams and Crocker (1984), who used information on ozone-induced plant effects
to determine the sensitivity of resultant economic estimates to additional
plant response information. However, the study also presents a numerical
estimate of ozone damage to three crops, representing about 60% of the value
of U.S. crop output (corn, soybeans, and cotton), using response data derived
from 1980 and 1981 NCLAN experiments (Heck et al., 1982).
In developing these estimates, Adams and Crocker used linear ozone
dose-response functions (derived from NCLAN results), and estimated farm-level
demand and supply relationships with relatively simple general equilibrium
functions. The response data, combined with the structure of the commodity
markets in questions, were used to compare the benefits to consumers and
producers of progressively more stringent control schemes. The estimated
16
-------
difference in economic surplus between ambient ozone levels and a seasonal
seven hour average of 40 ppb was approximately $2.2 billion.
Another national level estimate of the economic consequences of ozone on
corn, soybeans, cotton and wheat is reported in Adams et al. (1984). This
study builds on some of the methodology used in Adams and Crocker (1984).
While the primary purpose of the analysis was to measure the adequacy of
natural science information in performing economic assessments, the derivation
of a series of benefits functions also provides an approximation of the
benefits of changing ozone levels on the four crops included in the study.
Benefits are calculated in terms of Marshallian surplus estimated from the
integration of supply and demand curves for each crop under alternative ozone
levels. Yield effects are generated with both linear and quadratic response
functions.
The benefits of an assumed reduction in ambient ozone (to 40 ppb) across
the entire U.S. is estimated to be approximately $2.4 billion in 1980 dollars.
A 25 percent increase in ozone (to 66 ppb) results in a net loss of $3.0
billion. These benefits and costs are probably upper bounds, due to producer
micro-response that may mitigate for changes in ozone and the use of an
assumed current ambient concentration of 53 ppb (associated with the present
Federal standard) for all production regions. This ambient level is the upper
bound seasonal seven-hour concentration for major production areas as reported
in the SAROAD data. To the extent that all regions are equal to or less than
this amount, the benefits of reductions in ozone are overestimated and the
losses of ozone increases are underestimated.
Finally, a recent assessment by Kopp et al. (1983) measures the national
economic effects of changes in ambient ozone level on the production of corn,
soybeans, cotton, wheat and peanuts. The study is notable in that the
assessment methodology is based on development of a series of detailed cost
structures for "representative farms" which are then aggregated to regional
and national supply responses. Like some of the programming based studies,
this methodology places emphasis on developing reasonable micro or
producer-level responses to yield changes. The effects of ozone on the yields
of the included crops are based on NCLAN data through 1981. Response
functions of the Box-Tidwell type are fit to these data. Predicted yield
changes associated with alternative secondary standards are then used to shift
the regional supply response relationships. The price and consumption effects
are measured through a set of demand relationships for each commodity,
reflecting a range of elasticity assumptions.
The results of the analysis indicate that a reduction in ozone from
regional ambient levels to an approximate 40 ppb seasonal seven-hour average
would result in a $1.1 billion net benefit. Conversely, an increase in ozone
to an assumed ambient concentration of 80 ppb across all regions would produce
a net loss of approximately $3.0 billion. These values of ozone reductions
are slightly less than those reported in Adams and Crocker (1984), and Adams
et al. (1984). The differences may be attributable to the finer regional and
farm scale resolution in Kopp, et al., as well as different ambient base ozone
levels (lower than the 53 ppb in Adams et al., 1983), and different elasticity
assumptions vis-a-vis these other studies. While the Kopp et al. methodology
17
-------
appears to force the entire economic adjustment process onto what is perceived
to be the high cost production region and does not address such issues as crop
substitution, it reinforces the need for detailed representation of the
economic processes underlying agricultural production in performing policy
analyses of ozone pollution.
While these national assessments represent continued improvement over
time, some common limitations should be noted. For example, most of these
benefits estimates from decreasing ambient pollution levels are conditional on
the assumption that those levels across a given region would not exceed the
assumed level; i.e., assumes that pollution levels are uniform across all
crop-production areas. If the actual concentrations are lower or vary across
agricultural areas, then the benefits accruing to the implementation of
national standards would be different, with the direction of change dependentf
on the relative share of consumer and producer benefits in total benefits.
Also, potential producer adjustments to changes in pollution levels beyond
those observed in historical patterns are not addressed in any of these
national studies. Thus, if past adjustments serve to define the range of
responses, effects from as yet unrealized ozone levels may be biased.
This review of past and current studies of air pollution effects on
agriculture indicates that quantitative techniques are available to address
the important conceptual dimensions of the assessment problem. To date, these
techniques have focused primarily on regional effects, due in part to a lack
of defensible plant science and meteorological data across major crops and
regions. With the availability of data from NCLAN, credible national economic
assessments are now feasible. The next section discusses the economic
methodology to be used in this preliminary assessment. Among the set of
theoretically defensible techniques, it appears most compatible with the data
and the nature of this assessment problem. Some of the other assessment
methodologies discussed in this review are also capable of pro viding
theoretically defensible benefits estimates, but it is our belief that the
employed methodology provides a broader range of policy infor mation on ozone
control. The reasoning behind this assertion is presented in the methodology
discussions.
III. METHODOLOGY
Economists have devoted considerable effort to assessing the consequences
of policy or technically induced change on participants within the
agricultural sector. Many types of changes have been examined. For example,
Freeman (1979) reviews approaches examining the consequences of environmental
change; Feder, Just and Zilberman (1983) examine the consequences of technical
changes on agriculture, and the preceeding discussion has reviewed the effects
of alternative air pollution levels or policies on agriculture. The
assessments of such changes have been performed at different levels of
generality within the agricultural sector. In particular, assessments of the
benefits of change have been performed at both the farm and sectoral levels.
Analysis at each of these levels potentially leads to quite different results.
For example, it is not difficult to imagine a case in which the analysis on a
18
-------
single Corn Belt farm would lead to the conclusion that a ban on pesticides
without effective substitutes would lead to Corn Belt farmers receiving less
income. But conversely, when considering a similar ban where the pesticide is
widely used throughout the Corn Belt on commodities subject to inelastic
demand, it is possible that an increase in farm sector gross revenue would
occur while consumers would lose. Ultimately, changes in producer welfare
will be a function of the characteristics of demand and supply. Thus, there
are potentially major differences in the results of such evaluations depending
upon the level at which the evaluations are performed. This suggests the need
to consider broader implications in evaluation of induced change rather than
simply isolating the farm level effects as economists have traditionally done
(Heady and Srivistava, 1975; McCarl and Spreen, 1980; and Norton and Schiefer,
1980 review such larger efforts).
Focusing on broader effects typically involves research tradeoffs. The
evaluation of changes at the sectoral level often require one to sacrifice
microeconomic detail in order to keep the problem tractable. This can have
serious consequences. For example, when doing appraisals of induced change
with aggregate programming models (i.e., models such as those used in Heady
and Srivistava, 1975; Baumes, 1978; or Burton, 1982) one often finds
extreme specialization in production. That is, one gets solutions where
whole regions are devoted to a single crop. This situation usually leads to
the imposition of inflexible "flexibility" constraints (Sahi and Craddock,
1974). However, McCarl (1982a) recently argued that a way to avoid this
specialization in production and thereby generate more plausible results
within a sectoral analysis was to link microeconomic considerations with the
sector model through a Dantzig-Wolfe decomposition scheme using heuristic
procedures. The analysis used in this assessment incorporates the McCarl
methodological proposal in the context of the induced change analysis due to
ozone. Implementing the methodology requires both detailed farm-level models
and a macro or sector model.
In this study the agricultural sector model component is a
price-endogenous mathematical programming model of the agricultural sector;
i.e., an activity analysis spatial equilibrium model (Takayama and Judge,
1971). Such sector models have been used extensively by agricultural
economists to simulate the effect of alternative agricultural policies or
technological change (Heady and Srivistava, 1975; Duloy and Norton, 1973).
Among the various methodologies available to formulate policy models,
mathematical programming has proven to be a particularly useful tool given its
ability to predict potential consequence of as yet unrealized policies. This
general methodology has been applied to air pollution effects in the
Adams et al., 1982; Adams and McCarl, 1984; Howitt et al., 1984; and Rowe
et al., 1984 assessments reported in the previous section.
In addition to the sector model, a series of farm models based on
(1) linear programming (LP) models and (2) analyses of historical data were
used. The LP-based farm model (REPFARM, McCarl, 1982b) was implemented for
representative farms in the Corn Belt. These LP-based representative farms
were developed by Brown and Pheasant (1983). Each REPFARM model was run under
19
-------
a large set of alternative crop prices to develop a set of whole farm plans
which consisted of mixtures of corn, soybeans, wheat, oats and double crop
soybean acreage. For areas outside the Corn Belt, historical data were used
to generate whole farm plans. Crop records by state for 1970-1981 were used
both to develop representative state level crop mixes and to derive
econometrically estimates of crop yield changes associated with crop mix
changes. These econometric procedures were intended to model regional
acreage-yield relationships, as discussed below. In turn, the whole farm
plans from both sources were used to generate activities for the sector model
(originally developed by Baumes, 1978 and documented in Chattin et al., 1983)
utilizing the USDA FEDS budgets. The sector model was then solved under the
alternative ambient ozone scenarios. Thus, the informal procedure proposed by
McCarl (1982a) was utilized, in which the representative LP farm models for
the Corn Belt and the crop mixes and regional acreage-yield relations for
other regions were used to generate a number of activities for use in the
sector model. Such detailed specification and estimation of crop-mix
activities was to overcome the aggregation problems identified in McCarl
(1982a).
We now turn our attention to a detailed discussion of the farm model, the
sector model and the linkage of these models to assess the economic impacts of
ozone on agriculture.
The Farm Model
As noted above, the mathematical programming model applied here consists
of both a micro or producer level component and a macro component. The
features of this particular model are discussed in more detail elsewhere
(McCarl et al., 1983). The mathematical programming sector model contains
activities which represent production and consumption of various commodities.
The production and consumption sectors are assumed to be made up of a large
number of individuals, each of whom maximizes some objective function under
competitive market conditions. Specifically, each producer is expected to
maximize profits subject to a set of technical and behavioral constraints.
That is:
Profits
n m
I ( Z P
k=l h=i
..
hk
r. x.. )
where:
Ph is the market price per unit of the
output (h=l,..., m); and
is the yield of the h — output from the k — production process
(h=l,. . . , m; k=l,.. . , n) ;
20
-------
r- is the market price per unit of the i— purchased factor
(i=l,..., s).
x.. is the use of the i— purchased factor in the k— production
1 process (i=l,..., s; k=l,..., n);
This constrained profit maximization problem is then specified as a
mathematical (linear) programming problem (McCarl and Spreen, 1980).
Necessary and sufficient conditions for solution of a constrained maximum
problem are defined by the Kuhn-Tucker conditions, conditions easily traced to
standard microeconomic marginal conditions for profit maximization. The
analytics of this linkage are presented in Appendix A.
In this analysis, producer level behavior is portrayed by the series of
linear programming representative farms for the Corn Belt region. The
representative farms for the other production regions are based on FEDS
budgets which define input and output relationships to represent crop
production. Specifically, 12 rep-farms are modeled within the five-state
region designated as the Corn Belt by the USDA. These rep-farms are assumed
to mimic the input-output or technical and economic environment of the
producers in a given region. The farms are modeled with considerable detail
on cropping activities (twelve annual crops plus hay and beef, pork, and milk
production), input use, and environmental and other fixed constraints.
Multiple activities are defined for a given crop in a given region to cover
the wide range of production technologies available to producers. Such detail
is needed to model adequately potential producer mitigative behavior in the
face of environmental changes. As noted by Crocker (1982), failure to account
for these adaptive opportunities tends to overstate potential damages and
understate benefits.
The multiple activities (within crops) generate the set of primary
agricultural commodities that interface with the sector models. These primary
commodities are listed in Table 4. In addition to crops, the analysis also
includes livestock products. An endogenous livestock component is needed,
given that livestock are the "consumers" of most of the primary crop
commodities, as well as accounting for approximately 50 percent of economic
activity in the agricultural sector.
The farm model used was the REPFARM programming model (McCarl, 1982b;
McCarl and Pheasant, 1982; Brown and Pheasant, 1983). This model was used to
develop representative farms for a series of substate regions in the Corn
Belt. The REPFARM model facilitated a definition of various possibilities
involving continuous corn, rotation corn (rotated with soybeans), rotation
soybeans, oats, single crop soybeans, wheat and double crop soybeans in these
areas utilizing location specific timing of planting and harvesting, along
with different sequences of other crop cultural operations and different
yields for each crop by type (continuous, etc.), planting, and harvesting
date. The data used in the representative farms were based on agricultural
statistics, extension reports, existing models and expert opinion (see Brown
and Pheasant, 1983; for details). The representative farms for the thirteen
subregions were run with different corn prices and twelve different soybean to
21
-------
Table 4. Primary commodities included in the economic model
Commodity Units
Field Crop Commodities
Cotton 1000 bales
Corn 1000 bushels
Soybeans 1000 bushels
Wheat 1000 bushels
Sorghum 1000 bushels
Oats 1000 bushels
Barley 1000 bushels
Rice 1000 cwt
Sugar cane 1000 tons
Sugar beets 1000 tons
Silage 1000 tons
Hay 1000 tons
Livestock Commodities
Milk 1000 cwt
Culled dairy cows 1000 head
Culled dairy calves 1000 head
Culled beef cows 1000 head
Live heifers 1000 cwt
Live calves 1000 cwt
Non fed beef available for slaughter 1000 cwt
Fed beef available for slaughter 1000 cwt
Calves available for slaughter 1000 cwt
Feeder Pigs 1000 cwt
Hogs available for slaughter 1000 cwt
corn ratios as well as five different wheat prices. This results in 300
different price ratios for each of the representative farms. These data were
summarized to yield a series of unique crop mixes (i.e., land use patterns
including all crops following the procedure in McCarl, 1982a) and accompanying
yields for each of the representative farms in the Corn Belt. To develop
representative farms for the states outside the Corn Belt, crop mixes and
corresponding base yields were derived from acreage and yield response
relationships estimated from historical data, as discussed below. These whole
farm crop mixes and yield adjustments were then used in combination with the
FEDS crop budgets for each state or region to generate a range of farm level
activities for the crops in the analysis.
22
-------
Generation of Other State Activities and Crop Mixes
The yield-acreage response relationships observed over time in regions
outside the Corn Belt are used to develop a mix of crop activities from the
FEDS budgets for these regions. This procedure, in combination with the Corn
Belt individual LP models, is intended to provide an economically and
technically realistic portrayal of producer's behavioral responses. These
relationships are derived econometrically from historical patterns on relative
crop acreage (percentage of a unit land area devoted to each crop). Each crop
mix implies a given cost structure and corresponding yield. Since yields are
expected to change with crop mix, these yield adjustments are predicted by the
yield-acreage response functions. Specifically, the yield of a crop can vary,
not only in response to the acreage planted of that crop, but also the the
acreage planted of other crops.
The rationale and procedure used to derive econometrically the
yield-acreage relationships is described in detail in Appendix B. In general,
however, the procedure involves estimating a system of yield equations
expressed as a function of crop acreages ("own" and "other") and other
variables based on historical data from 1970 to 1981. These relationships
were then "normalized" to a 1980 base value. This gave rise to a series of
annual crop mix-yield adjustment ratios. The ratios are used in combination
with the FEDS crop budgets for each crop to generate a set of farm level
activities that portray various cropping alternatives for each state
representative farm.
The alternative to explicitly accounting for these relationships would
have been to simply use the FEDS budgets and a given annual crop mix to define
crop alternatives (activities) for each representative farm. While
analytically simpler than trying to account for crop mix-yield changes, this
was not done because it ignores potentially important yield response
information. Specifically, since expected profit is assumed to be a primary
factor in the producers planting decision (acreage and crop mix) and since
these acreage and crop mix decisions can affect yields, it is important to
account for these responses in the modeling of farm level production. If the
yield response relationship is ignored, the acreage response arising from
changes in expected profit would likely be overstated. This would then bias a
subsequent ozone analysis where changes in relative crop yields are used to
portray the effects of alternative ozone levels.
The Sector Model
The producer level responses generated through the REPFARM model and the
historical data analyses are interfaced with the macro component of the sector
model to obtain a measure of social benefits (McCarl et al., 1983).
Consistent aggregation is achieved by building on the above micro conditions,
which allows the aggregate and micro processes to be linked. These linkages
are demonstrated analytically in Appendix A. The macro model features
constant elasticity of demand relationships for the outputs (commodities) of
23
-------
the micro models. The elasticities vary with end use and across domestic and
export markets. Given the long-run nature of the model, export demand assumes
major importance. Export demand elasticities for corn, sorghum, wheat,
soybeans, soybean oil and soybean meal are derived from Burton (1982) who
cites the USDA Forecast Support Group as the source. The export elasticities
vary, from -.15 for corn, to -1.53 and -1.98 for soybeans (whole) and sorghum,
respectively. Assuming supply and demand functions which are integrable and
independent of sector activity, first order conditions are then achieved in
macro model specification. The objective function of this specification is:
Maximize n = Z g^Z.) - z e.(X.) - z C Y
i j J J m
where n is the sum of ordinary consumers and producers' surplus and the
integrals are evaluated from zero to Z.*, the amount of the i— commodity
produced and sold to consumers; and from zero to X.*, the amount of the
j— factor used. The parameters are as follows: J
9-j(Z.) is the area under the demand function for the i— product;
e.(X.) is the area under the supply function for the j— factor;
J J
C is the miscellaneous cost of production,
subject to a set of technical and behavioral constraints. Given the micro and
macro structure of a model, the sector model solution then simulates a
long-run, perfectly competitive equilibrium. The full empirical detail of the
objective function is provided in Equation (14) of Appendix A.
Following Samuelson (1952), the objective function (n) may be interpreted
as a measure of ordinary consumers1 and producers' surplus (quasi-rents) or
net social benefit. Analytically, this is defined as the area between the
demand and supply curves to the left of their intersection. The demand
functions are specified at the national level, as are aggregate production
responses. Thus, the solution of the sectoral model provides objective
function values at the national level.
The linking of the detailed producer behavioral model with a macro model
measured in (changes in) consumers' and producers' surplus provides a useful
policy model. Justification for the use of economic surplus in policy
analysis is well documented in the literature (Willig, 1976; Just et al.,
1981) and is particularly relevant to agricultural uses where aggregate
distributional consequences are of concern. By imposing alternative ozone
levels on the rep-farm behavior, as manifested in yields predicted by the
NCLAN response functions, changes in production and consumption and ultimately
economic surplus may be measured. Comparisons of changes in consumer and
producer surplus between the alternative ozone levels and current ambient
concentration indicates the benefits for these alternative ozone levels.
24
-------
The sector model was solved under a mix of demand curves (constant
elasticity, stepped demand, each displaying a range of elasticities) using the
MINOS software package (Murtaugh and Saunders). It is structured (in a
two-region example) as in Figure 1. The model encompasses production,
processing, domestic demand, export demand, and imports. A simplified tableau
appears in Figure 2. The basic data utilized in implementing the model are
United States Department of Agriculture Firm Enterprise Data System (FEDS)
budgets. The United States is disaggregated into ten regions consisting of
the 48 states but with the Corn Belt (Iowa, Indiana, Ohio, Illinois, Missouri)
disaggregated into 12 subregions. This results in a total of 55 production
regions. The primary crop coverage (commodities) were presented in Table 4.
Table 5 summarizes the secondary commodities arising from these primary items.
For the purpose of this study, the FEDS budgets for 1978, as reformatted and
expanded by Burton (1982) were updated to 1980, using 1980 yields, acreages,
and prices. The modified budgets include transportation costs, chemicals,
machinery, fuel and repairs and interest. Labor and land availability depends
on endogeneous prices. The miscellaneous production costs were altered
following the procedures outlined in Fajardo, McCarl, and Thompson (1981); or
Baumes (1978). (These procedures involve calculating miscellaneous costs so
that the miscellaneous costs exactly equaled the difference between the value
of production and the cost of the endogenously priced inputs within the
model.)
Definition of crop production alternatives was handled in two ways,
depending on region. Crop production activities for regions outside the Corn
Belt were defined using the adjustments reflected in historic yield and
acreage trends over the period 1970-1981 as explained above and in Appendix B.
Within the Corn Belt, activities were formed by aggregating the corn, soybean,
oats, and wheat activities according to the whole farm crop mix plans
generated by the representative farm models. Specifically, the REPFARM model
when, run for a particular price ratio, yielded a particular combination of
crops; for example, 45 percent corn, 35 percent soybeans, and 20 percent wheat
with associated yields outside the Corn Belt. Under the historical data
procedure, crop mixes were constructed which consisted of the percentage of
each crop (e.g., 45 percent corn, 15 percent wheat, 35 percent cotton and 5
percent soybeans) during a particular year. Associated relationships were
estimated which gave crop yield as a function of own crop acreage, other
acreage, and other variables, as noted above. Eleven historic alternative
crop mixes and associated yields were developed (1971-1981). The crop mixes
for both the Corn Belt and other regions were then aggregated into budgets for
whole farm multiple crop plans by taking the budget item for each crop times
the percentage of that crop grown. In addition, yields within these composite
budgets were developed as follows. A base ratio was selected from the
microeconomic model or the data, giving the typical cropping pattern which led
to the 1980 yield statistics. Then the yield for a particular cropping
pattern was divided by the base yield, to obtain the proportional relationship
between yield under this crop mix and base yields. Subsequently, the FEDS
crop budget yield was multiplied by this relationship and by the proportion of
corn to generate corn yield for the multiple crop budget. Thus, if the crop
mix represented 90 percent corn, whereas in the base situation (1980) there
was 50 percent corn, then the yield procedure above would account for a
25
-------
d)
-o
o
u
O)
c/l
O)
M-
O
to
CD
S-
O)
(O
O
3
s_
+J
26
-------
> o
+ + o
> o
1
3
ro
01
O)
T3
o
T3
QJ
CL
CVJ
CD
C7)
27
-------
Table 5. Secondary commodities in the economic model
Commodity Units
Soybean meal 1000 Ibs.
Soybean oil 1000 cwt.
Poultry Feed 1000 Ibs.
Feed grains 1000 Ibs.
Protein supplement dairy feed 1000 Ibs.
High protein livestock feed 1000 Ibs.
High protein swine feed 1000 Ibs.
Low protein swine feed 1000 Ibs.
Veal 1000 cwt.
Non-fed beef 1000 cwt.
Fed beef 1000 cwt.
Pork 1000 cwt.
reduction in corn yields experienced in the micro model due to expanded corn
acreage, i.e., less favorable corn planting conditions.
Finally, demand levels for products and the supply prices are drawn from
1980 agricultural statistics. As discussed earlier, elasticities used are
from Burton (1982), and vary according to end use, for both domestic and
export markets.
IV. PROCEDURE
The economic model, in combination with yield changes predicted by the
NCLAN response function at alternative ozone levels, is used to generate
estimates of economic effects due to ozone changes. Each of a series of
hypothetical ozone scenarios is judged against a base case analysis. The
difference between economic surplus in the base case solution and the
hypothetical ozone analyses represents the benefits or costs of the ozone
change. The base case is intended to reflect 1980 actual agronomic,
meterological and economic conditions, i.e., the base case analysis is one
28
-------
where the economic model is run (solved) using 1980 actual parameters,
including yields. This year was selected as a base year given the absence of
any apparent disequilibrium conditions relative to other recent years. The
credibility of the base modeling solution is established by comparing the
predicted values of key variables, such as equilibrium prices and farm
revenues with actual values for the base year. Establishment of a close
correspondence between model output and actual values is needed to confirm the
general validity of the model.
Once a plausible solution is established for a base case, the model is
then solved using alternative yield values (modified from actual 1980 yields)
that reflect hypothetical ozone levels in each production area (state or
sub-state levels). The yield adjustments, measured as percentage or
proportionate changes, are derived by use of an appropriate mix of NCLAN
response functions with the respective hypothetical ozone levels. In this
analysis four alternative ozone levels are evaluated: a 10 percent, 25
percent and 40 percent reduction in ambient ozone concentrations and a 25
percent increase in ozone (from 1980 and 1978-82 mean levels).
The effects of these changes in ozone on crop yields in each production
area are estimated with selected NCLAN response functions. For crops where no
NCLAN data exist (i.e., oats, rice, sugar beets, sugar cane), the yields are
held constant (no change) over the various ozone levels. The exception is
hay, as described subsequently. It should be noted that the use of NCLAN (or
any other experimentally derived) response functions in an economic assessment
assumes that the imposition of ozone is analogous to neutral technological
change, i.e., efficient input combinations do not change in response to ozone
and that other environmental stress factors (in addition to ozone) also remain
unchanged (from the levels observed in the ambient environment of the field
chambers). The NCLAN functions used here are estimated from data generated
through the 1983 crop year. Given the large number of response functions
available for some crops, a series of assumptions are made as to which are
most appropriate for use in a broad scale assessment of this type. Following
Rawlings and Cure (1984), a Weibull density model specification of the
relationship between ozone and yields is adopted. Among the set of functional
forms tested by NCLAN statisticians, the Weibull has been selected as the most
reasonable (and flexible), based on biological and statistical considerations
(Rawlings and Cure, 1984, Heck et al., I983b). Within this functional form,
however, there are still eleven cultivars of soybeans, three of corn, eight of
wheat, and two of cotton from which to choose, as well as individual cultivars
for grain sorghum and barley. In addition, some cultivars are replicated over
several years, resulting in more data sets than cultivars for some crops;
e.g., there are 17 data sets for the nine soybean cultivars.
Initial screening of these cultivar responses with the Weibull model
indicates that a common or "pooled" response for soybeans, wheat, and corn,
estimated using data from multiple cultivars of each crop may be appropriate,
given the statistically homogeneous proportionate response displayed by most
cultivars. Thus, the pooled Weibull response models for corn, soybeans and
wheat, in combination with Weibull models for irrigated (western U.S.) and
non-irrigated (southeast) cotton cultivars and the single cultivars available
for grain sorghum and barley constitute one possible cultivar mix. Using this
29
-------
set of response functions, the four ozone alternatives are then translated
into corresponding proportionate yield changes. The economic effects of these
four yield change situations (ire evaluated with four corresponding model
solutions. This set of four evaluations constitute Analysis I. Through the
use of the pooled data, this analysis incorporates nearly all the response
information available from four years of NCLAN experiments.
One important commodity for which NCLAN data are not available is hay.
Hay, in the form of alfalfa or grass-legume mixtures, is a dominant crop in
several regions. It also plays a role in the feed-livestock balance and the
resulting spatial characteristics of livestock feeding. While data are not
currently available to directly gauge the effects of ozone on hay yields, one
study of alfalfa hay response in California established a statistically
significant relationship between yields and ozone (Oshima, 1976). While the
Oshima study approach is unlike the NCLAN protocals, it does suggest that
alfalfa hay yields are affected by ambient ozone. In view of this possible
ozone sensitivity and the economic importance of hay, a "with" and "without"
hay response evaluation is included in Analysis I. (Pasture and rangeland are
also important in the production of livestock; they are not addressed in this
hay analysis.) Specifically, the four ozone alternatives are first performed
on the six major crops for which NCLAN data are available, holding the yield
of hay constant at 1980 levels. Then, a hay response is included in the
analysis by using the average response of the six NCLAN crops as a surrogate.
This average response is used to adjust the yields of hay. The "with" and
"without" hay analysis can suggest the importance of including hay in future
assessments and hence the need for more hay response data.
The use of a pooled response for soybeans, corn, and wheat is based on
the statistical homogeneity of response (slope) across the set of cultivars
for each crop. This test of homogeneity (an "F" test on the estimated
intercept and slope parameters of the respective Wei bull models) implies that
the proportionate response (or slope) of an individual cultivar cannot be
shown to be statistically different from the response portrayed by another
cultivar when fit with the Weibull density model. However, this does not
imply that the cultivars predict identical yield changes for the same ozone
level. Also, within a given crop, a few cultivars were shown to be
non-homogeneous. For example, of the eight soybean cultivars, two did not
show statistically homogeneous yield changes. Thus, it is possible that at
the regional level, some subset of these individual cultivars may more
accurately predict yield response. For example, of the eight soybean
cultivars, some are restricted to different latitudes or maturity zones, such
as Hodgson in the Lake States, Williams, Corsoy, Amsoy and Pella in the Corn
Belt and Davis, Essex and Forrest in the south and southeastern
states (Caldwell, 1973). These differences may then affect the economic
estimates. To cover this possibility, a second general analysis is performed,
where more region-specific cultivar responses are used for corn, wheat, and
soybeans, in addition to the cultivars of cotton, grain sorghum, and barley
used in the first analysis. The hay response as defined in Analysis I is also
included. This next set of four ozone-yield solutions is identified as
Analysis II.
30
-------
Analyses I and II use various combinations of pooled responses for
certain crops. In the pooled analyses, a few cultivars are ignored because of
their heterogeneous nature. However plausible it may be to view these as
atypical responses, it is nonetheless remotely possible that their response
may characterize some regional response patterns. An analysis based on these
more extreme responses can also serve to place some bounds on the assumed
"more representative" responses captured in Analyses I and II. Analysis III
thus uses the single cultivars available for barley and sorghum in combination
with the most sensitive non-heterogeneous cultivars of corn, soybeans, wheat
and cotton, along with the surrogate hay response.
The assessment procedure also requires judgments with respect to base
level air quality used in the calculation of yield changes. Given that the
projected yield changes are measured against an actual or base level of ozone,
the value used to express the actual level is important. Since the economic
model is calibrated to 1980, 1980 ozone levels are a logical benchmark for use
in the hypothetical ozone analyses. These 1980 values underlie Analyses I, II
and III. However, annual variation in ozone levels is quite high for some
areas. For example, the range in seasonal ozone concentrations (as averaged
over the April through September period) is in excess of 40 percent for the
1978-82 time period (Heck et al., 1983a). To smooth out some of this
variability and ease the problems of using a given or "representative" year,
alternative base years (from 1978 to 1982) are averaged and the average values
are used as a second characterization of base ozone levels. (While not
addressed here, the variability in ozone may have implications in terms of
yield variability and hence producer risk.) These new ambient ozone levels for
each region are then used to evaluate the four ozone situations, i.e. the 10,
25, and 40 percent changes are now calculated relative to the alternative
ozone base level. The response functions used in the evaluation are those for
the first analysis (the national pooled responses, with hay). This set of
four model solutions or runs is identified as Analysis IV.
The pairing of a detailed economic model and the full set of NCLAN data
is intended to provide economic estimates of ozone effects occuring under
"real world" conditions. However, the NCLAN data do not specifically address
the interactions between ozone effects as manifested in the field chambers and
other environmental stress factors. For example, meterological records
indicate that ambient ozone and temperature are positively correlated. This
suggests that ambient ozone may be positively correlated with drought or
moisture stress. The presence of drought or moisture stress within several
NCLAN field experiments has been shown to reduce the effects of ozone on crop
yields i.e., have an antagonistic effect. (One study displayed a slight
synergistic relationship but the results were not replicated at the time this
assessment was prepared.) If the antagonistic assumption is correct and if
plants are drought stressed during high ozone levels, the effects of ozone, as
predicted by controlled experiments when plants are typically well-watered,
may overstate the effects of ozone. This possibility has important
implications in a national assessment, where from 50 to 90 percent of the
acreage of major crops within some subregions of the model are grown under
non-irrigated conditions. Even if drought is not identified as a major
31
-------
meteorological event in a given growing season, non-irrigated crops probably
still experience less than optimal moisture conditions at some point in the
growing season.
Ideally, response functions that reflect the ozone-water stress
interactions should be used in combination with some micro or meso-scale
drought data to assess more accurately the economic effects of ozone levels on
agriculture. Unfortunately, such a complete data set does not currently
exist. However, limited data are available on ozone-water stress interactions
from three NCLAN experiments, one on cotton and two on soybeans. In addition,
an NCLAN crop simulation model also estimates the effect of water stress on
ozone effects (D. King, personal communication). The results of these studies
can be used to provide some crude quantitative adjustments to the fully
watered response functions generated from NCLAN data. Using these limited
observations, the fifth general analysis (Analysis V) examines the effect of
ozone on crops in the presence of water stress. The cultivar mix again is
that used in the first analysis (pooled or common response using 1980 ozone
levels with a surrogate hay response).
These five analyses, in combination with the four ozone levels and the
base case, result in 21 distinct economic assessments (model solutions) using
the farm level-sectoral model formulation. The general assumptions underlying
the 5 analyses are sunmarized in Table 6. Analyses II through V can be viewed
as analyses of the sensitivity of the economic estimates to varying yield
assumptions. The results of all these analyses, including aggregate economic
effects on producers and consumers (the net economic effect) as well as the
regional level effects on producer incomes, cropping patterns and land use are
discussed in detail in the results section. However, to implement those
procedures, considerable data, both from the NCLAN field experiments and from
USEPA the air quality and meteorological sources, are needed. These data sets
and modifications, where required, are discussed in detail in the following
section.
V. THE DATA: SOURCES AND ASSUMPTIONS
Implementing the economic model and performing the five analyses requires
biological and meterological information. This section discusses the sources
of these data and assumptions inherent in their use.
Plant Response
A major goal of the NCLAN program is to develop a base of plant science
information on the response of crops to ozone and other stresses. To
accomplish this task, plant scientists working with the NCLAN program measure
the yield response of crops to alternative ozone levels using open-top field
chambers (Heck et al., 1982). Plants are exposed for seven hours per day
(0900 to 1600) to ambient and alternative ozone levels. The 7-hour daily
exposure is assumed to occur during the period of stomatal activity.
32
-------
Currently, these data cover four years of crop experiments (1980-1983),
conducted on multiple crops and cultivars at five sites in the U.S. The
procedures and results of these analyses have been documented in literature
prepared by the NCLAN Research Management Committee and the individual
researchers (e.g., see Heck et al., 1982a; Heck et al., 1982b; Heck et al.,
1983a; Heck et al., 1983b). In addition, Heck et al. (1984b) summarized all
the NCLAN response information, in exposure-response form, for the four years
of the program. This summary publication is the source of the individual crop
response functions used in the economic analysis reported subsequently.
The response functions reported by Heck et al. (1984b) encompass both
individual cultivars for each of the six crops and a set of common or "pooled"
responses for each crop using multiple cultivars. The cultivars selected for
experimentation at each NCLAN site are those commonly planted by producers in
that region. However, many other regionally important cultivars are not
tested. The pooled analysis of cultivars is one means of testing for
potential errors in yield effects due to a small sample. Specifically, the
pooled responses are estimated using data from sets of cultivars demonstrated
to be statistically homogeneous (display the same proportionate response).
Such pooling is one means by which the potential variability across individual
sites can be tested. If responses generated at different geographical
locations are deemed statistically homogeneous, then some of the acknowledged
problems of extrapolation from specific sites to broader regional scales can
be minimized. For some crops (barley and sorghum) only one cultivar has been
tested and hence a pooled response is not possible.
The response functions (from Heck et al., 1983, 1984b) that are used in
this economic analysis are summarized in Table 7. Note that all functions are
reported in the Weibull form. Among the large number of functional forms
(linear, linear plateau, quadratic, cubic) fit to the raw NCLAN data, the
Weibull is assumed to be the most defensible based upon statistical and plant
science considerations (Heck et al., 1983; Rawlings and Cure, 1984). The
Weibull functions reported in Table 7 are used to project ozone-induced yield
adjustments for each crop under the various analyses contained in the economic
assessment.
The common or pooled responses, in combinations with the single cultivar
available for barley and sorghum, are used to develop the first set of crop
yield adjustments (reported in Appendix Tables D.3 to D.ll) for use in
Analysis I. Then, the more "regionalized" pooled response functions estimated
from subsets of cultivars, in combination with the single cultivar crops, are
used to develop the yield adjustments in a second general analysis (Analysis
II). By using cultivar combinations showing statistically homogeneous
response patterns, Analysis I and II are intended to minimize problems of
extrapolation from data generated at specific sites to large scale regional
effects. Finally, a set of response functions composed of individual
cultivars that are not statistically homogeneous in response (as compared with
the cultivars in the "pooled" response analyses) are used to predict yields.
These responses are representative of the more extreme response possibilities
for each crop. This set constitutes Analysis III.
33
-------
c
o
•r-
4->
D-
3
«J
in
c
LU
01
fc. i/t
*J Ol
I-
o£
^
c
4^
3
o
.£
U1
>)
'«j
C
. " C 0.
§•£ -2 "« 1! a! o *
.C >,*"' b> * si "*" S
.- .2 i = _ «
*~ C 0. ^3 4-1 C *O O
O *f" X C QJ U
"O t— C OJ E O i/i
QJ QJ fO "O 4-> O l_
fO • -»- (/I r— 3 >
§-r—
_ -»->
tn -i— E X QJ (J •
,__ r— Q QJ yi t~ *Q l/l O
W 3 4^ QJ *J f^ 3 -^
CO 3E C = O CTt
(O ^3 4-) QJ 4-J (^ QJ QJ
(/1QJQJE3 '*- C I.
"So"c«/J.c Xo>c
^— QJ QJ i/l QJ 4^ »^OQJ
O QJ C 'r~ ^1 X CO •'~
QJ E
QJ * O- */i £} l/l -f™
Ql C •'"• 4J X
Ol-t-QJQJ OC3 •— «
JC ._ >> O QJ (*,. 4J
<— C 3 O •OW1U33QJ
QJ • o> »— «5 -a -t- CT> c
»*- ^ f— E CTi O3»c>UO
O 3 -C U. O»4-i "Oi/ltrt
l^ T— L» > S 3 T3 ^— ' "O W
cu>oro -oCjo; c
OO-»-trt QJ.C
«•* OJ C C L. •>- b. L.
QJf-OQJ3 "-CntJ>>^3
,— 4-> 4-> -— Wl CT»C) •— t/1
O •— 4-> t ^ QJEI'O'Ofc-
^- »— .
»-«
t O 1- JC
i/t >• 14. O
C V -f~ O O
Si S ^ tS *
^0 3 U O
5 £ e t j; °
4-» QJ 4-> 'rO
4% .^ .t^ • £ -!= c
E r~~ c -*-* *o
•*-• C .O. — to QJ c
O "O 4-> QJ T-
•- « 'Z 1 ^ 0 >,
>» QJ > O £ — ^ ."^
*— JC i*- t/l ^~
C 3 **~ *^~ L. ^Q
*O t^. E E O X QJ > i"
fc. ••- O. •— C QJ
X'»~JCQJ CN-3QJ
QJ QJ
O t) O TS
Z QJ Z QJ
U i.
t— (—
^_
c
O QJ <+-
t/l CT> O CNJ
tJ (Q CO *"*•
QJ k. QJ CTl «T3 QJ
§ ** QJ 5 ^ aj
S <2 tS <
•
a. & *
O Q) • C
u >» *j a) a.
O X i/l (/*
^— l/l QJ Q;
i — (T3 t_
*J c ra a>
^ fQ QJ ^3 4^ t/>
2 U <£ O >>
%- • 0) o l- us
« c 1- Z 3 C
•^ O c C
^~ 4-> (O '^
3 1- 4-> l/l
i— O) 1- -O t+_ 3 3
•o ID re •*»/» -o £ ^
••- 4-" C I— 0)
•a i— 10 a) >i E
c 3 a> •*- *0 ^
— <_» ^3 >>3: i^
i— i :»
^ ""*
Ol5
o 3 ^
"*" o al
4-> 4-1 ^1
•?> >"^^
*fl QJ ^ •*-*
4-» C •«-**-
O. (^ yi
§(^ QJ
• 'I-* t-
ta ol QJ ^
>•> *J jO Qj
^ t/l «f— ^7
<0 4->
C QJ V O
•f- I. O
•H 3 °* 2!
r— t/1 4-> O
QJ •»— t/i C
QJ CTl
c ••-
O ^1 C^
O. 3 O. C 00
VI O O CTl
*" 5 U la *
C 3 ^C 3 QJ
O ••-> _J E "O
0 « Z */i §
^^
t/>
!o '!/i
E "io
5 <
34
-------
Ozone Data
To utilize the NCLAN response functions, two types of air quality data
are needed. First, some measures of actual ambient ozone concentrations in
rural production areas are necessary as a starting point. The ozone data must
be presented in an exposure measure consistent with that used in the NCLAN
experiments (seasonal average of the daily seven-hour concentrations) at a
geographical scale sufficient to be compatible with the 55 production regions
in the sectoral model. Ideally, actual ambient ozone data for all
agricultural production areas in the economic model should be used.
Unfortunately, such complete data do not exist, as the available data from
USEPA's Storage and Retrieval of Aerometric Data (SAROAD) system are taken
from predominantly urban monitors. However, a surrogate data set has been
prepared (J. Reagan, USEPA, RTP). The procedures and county level results
giving rise to this data set for five years (five growing seasons encompassing
April through September measurements) covering the period 1978-1982 are
discussed by Heck et al. (1983a).
The surrogate county-level ambient ozone concentrations are derived by a
spatial interpolation of the USEPA SAROAD monitoring data. This interpolation
between monitoring sites is based on a "Kriging" procedure that implicitly
gives greater weight to nearest monitoring sites. It should be noted that
some states have as few as three monitoring sites. Even where sites are more
numerous (over 100 in California, for example), most tend to be in urban or
suburban areas. Thus, this geographical interpolation of the national SAROAD
data set is subject to some uncertainties. These potential sources of error
are summarized in Heck et al. (1983a). However, certain meteorological
aspects of ozone make this pollutant amenable to interpolation procedures;
e.g., its pervasive nature and the existence of smooth gradients of
concentrations rather than abrupt spikes or plumes. Further, the nature of
the exposure measure (seasonal seven-hour average) is also assumed to "smooth"
out some of the variability in the ozone events. A comparison of "Kriged"
ozone levels in with some actual ambient concentrations recorded at NCLAN
sites (Heck et al., 1983a) reveal a fairly close correspondence.
Specifically, the Kriged estimates are generally within 5 percent of the
measured means (the exception is California). While these characteristics
perhaps make this procedure a defensible approach to fill in gaps in the rural
ambient air quality data, its use is based ultimately on the absence of any
better data. The resulting state level estimates of seasonal (April through
September) seven-hour ambient ozone concentrations data for each year and for
the period 1978-1982 are presented in Table 8. A broad regional breakdown of
these data with annual variability is presented graphically in Figure 3.
The Kriged ozone concentrations for 1980 are used as the base ozone
levels in Analyses I, II, and III. As an alternative base ozone assumption,
the 1978-1982 average reported in Table 8 is used with the cultivar mix
identified in Analysis I. This new base ozone-cultivar mix pairing is
Analysis IV. Other annual ozone levels (from the 1978-1982 period) could have
also been used to test the effect of base ozone assumptions on economic
estimates. The average level was used because there was no consistent trend
or pattern in the annual ozone levels when expressed at a state level. Thus,
some regions displayed highest ozone levels (for the five year period) in
35
-------
Table 7. Weibull model parameter estimates, by crop and cultivar
combinations a/
Parameters
Crop/Cultivar
Barley
(Poco cv.)
Corn
(Pioneer-3780 cv.)
Corn ,
(2 pooled cuKivars)— '
Cotton
(Acala SJ-2 cv.)
Cotton
(Stoneville-213 cv.)
Cotton .,
(2 pooled cultivars)-'
Grain Sorghum
(Dekalb-28 cv.)
Soybeans
(Davis cv.-1981)
Soybeans (all data) ,
(16 pooled data sets)-7
Soybeans (Corn Belt)f,
(5 pooled cultivars)-'
Soybeans (Southeast) ,
(5 pooled cultivars)^-'
Wheat, winter
(Roland cv.)
Wheat, winter . ,
(2 pooled cultivars)-7
Wheat, spring .,
(4 pooled cultivars)-7
ct
1 . 988 , .
(0.051P
12533
(323)
--
5351
(310)
3686
(140)
_..
8137
(218)
5593
(863)
...
_..
__
5479
(312)
--
4.480
(0.200)
a
0.205
(0.669)
0.155
(0.004)
0.158
0.092
(0.005)
0.112
(0.004)
0.226
0.296
(0.019)
0.128
(0.019)
0.165
0.144
0.144
0.113
(0.005)
0.146
0.186
(0.040)
c
4.278
(17.15)
3.091
(0.461)
3.530
2.530
(0.731)
2.577
(0.416)
1.445
2.217
(1.229)
0.872
(0.284)
1.303
1.307
1.576
1.633
(0.288)
2.235
3.200
(1.860)
Degrees
of
Freedom
21
17
--
21
21
--
17
43
--
--
__
17
--
--
36
-------
- Includes Pioneer 3780 and PAG 397 cultivars.
— The general form of the Weibull model is:
Y = a exp[(X/a)c] + e
where Y is yield and X is the ozone dose in seasonal seven-hour per day
mean concentration in ppm. The parameters are a, the hypothetical maxi-
mum yield at zero ozone concentration; a, the ozone concentration when
yield is 0.37; c, a dimensionless shape parameter (e.g., c=l is the
exponential loss function and larger c's imply less repsonse). If e is
zero, then yield goes to zero as ozone -*°°.
- Values in parentheses are standard errors. Standard errors for pooled
models are not reported in sources cited below.
— Includes Acala SJ-2, irrigated and dry cultivar experiments.
e/
- Includes Amsoy, Corsoy 1980, 1983; Davis 1982, 1983; Essex, Forrest 1982,
1983 - irrigated and dry; Hodgson and Williams 1981, 1982 - irrigated and
dry; and 1983-irrigated and dry cultivar experiments.
— Includes Amsoy, Corsoy 1980, 1983(2) and Hodgson cultivar experiments.
%t Includes Davis 1982, 1983; Essex and Forrest 1981, 1983(2) cultivar
experiments.
— Includes Abe and Arthur 71 cultivars.
— Includes Blueboy II; Coker 47-27; Holly and Oasis cultivars.
SOURCE: Heck et al., 1984b, Tables 1 and 3; Heck et al., 1983, Table 2,
and Kress and Miller, 1983. Response functions incorporating
1983 data (the pooled soybean models) are from W.W. Cure (personal
communication).
37
-------
years when other regions were below the five-year average. In the absence of
any consistent pattern, the five year average was selected as a point of
comparison with the 1980 values.
The only exception to this direct application of the Kriged seasonal data
pertains to ozone levels used for winter wheat, which is grown in the fall
through spring period when ozone levels are typically less than observed in
the April-September period. To approximate these lower winter levels, the
April values for each region are used as the base ozone levels for use with
the winter wheat response functions. These April values may be slightly
higher than ambient winter season averages. However, given that the most
active growth period for winter wheat occurs in the spring, the use of April
values for ozone is acceptable.
The second piece of ozone information needed to develop yield adjustments
are the hypothetical ozone levels to be examined in the economic analysis.
For the purposes of this assessment effort, these are identified as 10, 25,
and 40 percent reductions in ozone from ambient (as portrayed in Table 8) and
a 25 percent increase in ambient ozone. These adjustments in ambient ozone
are portrayed in Appendix Table D.I for each of the five years and for the
1978-1982 average concentrations. Each hypothetical ambient ozone level is
then used in conjunction with the Weibull response functions for each crop to
predict the yield changes associated with these changes in ambient ozone
concentrations.
The 10 and 25 percent adjustments in ambient ozone are considered
plausible changes, in that changes of this magnitude are encompassed in the
temporal variability displayed in the five year ozone data set. Also, the 10
and 25 percent improvements in ozone levels are of policy importance in that
these adjustments parallel changes in ambient ozone likely to be associated
with alternative Federal secondary ambient air quality standards of 100 and 80
ppb (not to be exceeded more than once per year). Assuming the imposition of
a more strict Federal standard, the ambient seasonal seven hour concentrations
should decline. If one assumes a log-normal distribution of ozone events over
the season, then achieving a hypothetical Federal hourly standard of 80 ppb
would translate into a seasonal seven-hour average of about 40 ppb. Such an
ambient ozone level is consistent with those achieved with the 25 percent
ozone reduction scenario (see Appendix Table D.I). Similarly, the 25 percent
ozone increase results in ozone levels somewhat similar to what would be
realized if a Federal secondary standard of 140 ppb were just achieved.
The 40 percent ozone reduction is an extreme analysis in that such a
reduction in ozone would bring actual ambient concentrations down to or below
what is generally thought to be background or natural ozone levels (Heck et
al., 1984a). Thus, the economic benefits measured in the 40 percent analysis
may not be relevant from a policy standpoint. It does, however, represent the
maximum benefits of ozone control, assuming all anthropogenic sources of ozone
precursors could be controlled.
38
-------
Table 8. Seasonal 7-hour average ozone levels by state, 1978-1982-
^\Year
State ^\
Alabama
Arizona
Arkansas
Cal ifornia
Colorado
Connecticut
Delaware
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
1978
50.47
45.62
56.62
46.82
50.36
45.50
43.00
42.98
54.65
N.A.
49.92
48.58
47.25
47.53
48.73
43.25
42.82
44.91
41.23
38.05
43.98
52.35
52.55
34.00
49.03
49.47
42.25
42.10
49.96
42.13
1979
41.22
42.48
50.14
51.14
51.32
49.50
45.00
34.69
43.40
51.00
45.30
39.87
40.72
43.76
39.10
35.14
37.66
41.82
44.17
35.60
36.49
40.50
48.51
49.43
42.30
53.31
40.75
43.60
47.90
40.05
1980
- (parts
46.98
51.63
51.47
52.04
50.92
58.83
36.00
35.33
46.82
45.71
46.92
46.72
42.11
45.47
44.88
42.22
34.98
48.72
46.83
37.56
39.25
49.25
49.20
45.00
45.17
50.84
42.58
53.50
45.87
40.91
1981
per billion)
47.28
47.93
47.62
50.44
48.10
51.00
54.00
42.51
45.87
47.60
41.69
45.10
32.88
40.98
39.16
41.94
31.41
49.73
44.92
37.93
32.79
47.20
37.78
46.00
38.10
48.21
34.58
50.30
48.40
37.14
1982
50.43
46.07
45.56
46.29
50.42
51.33
49.00
38.87
45.65
47.88
45.52
47.33
35.34
41.62
45.68
39.78
35.57
50.73
44.25
39.40
33.92
44.38
40.24
46.17
33.45
48.53
37.50
51.30
47.02
36.74
1978-1982
Average
47.3
46.7
50.3
49.3
50.2
51.2
45.4
38.9
47.3
38.4
45.9
45.5
39.7
43.9
43.5
40.5
36.5
47.2
44.3
37.7
37.3
46.7
45.7
44.1
41.6
50.1
39.5
48.2
47.8
39.4
39
-------
Table 8. (conti
Xx Year
\
State\
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
nued)
1978
50.83
37.00
45.98
52.37
27.32
43.65
41.33
63.52
42.27
50.32
42.23
49.05
43.07
52.90
27.63
41.77
45.81
46.31
1979
40.81
2:6.00
38.42
48.53
30.11
38.31
47.00
46.50
33.74
35.27
46.16
48.39
42.86
40.71
31.34
37.91
43.59
50.75
1980
- (parts
54.66
38.20
42.77
47.44
32.06
45.11
53.33
53.91
42.00
46.66
42.86
52.96
43.14
54.80
26.49
44.14
40.46
47.56
1981
per bill ion)
47.94
37.25
41 . 04
46.54
35.20
38.20
51.66
49.10
37.38
43.22
43.64
49.90
35.36
48.16
28.78
39.05
38.19
48.83
1982
46.10
34.21
43.88
44.40
38.18
40.85
50.67
48.33
33.39
48.12
45.57
52.40
37.29
49.33
28.65
41.10
38.55
49.71
1978-1982
Average
49.7
36.5
42.4
47.9
32.6
41.2
48.8
52.3
38.8
44.7
44.1
50.5
40.3
49.2
28.6
40.8
41.3
48.6
Source: J. Reagan (1984) and 1982 NCLAN Annual Report (Heck et al., 1983).
- Averaging time is 0900 through 1600 hours, April through September.
40
------- An error occurred while trying to OCR this image.
-------
Moisture Stress - Ozone Interactions
The NCLAN crop response functions presented in Table 7 provide the
mechanism for quantifying the biological link between ambient ozone levels and
crop yield response. In addition to providing this quantitative information
about plant response for use in economic assessments, the NCLAN crop
experiments also provide information of a more qualitative nature concerning
environmental stresses that may interact with ozone. Specifically, the
possible interactive effects of ozone with water stress and with other
pollutants (SOp) have been addressed in a few experiments (see, for example,
Heck et al., 1983). While these experiments and the resultant data are not
sufficient to document and quantify such interactions adequately, they provide
some plausible grounds for modifying and perhaps bounding the economic
estimates generated from the response information in Table 7.
The information on interactive stresses can suggest whether the NCLAN
response functions (which are estimated holding other inputs and stresses
constant at "normal" or ambient levels) over- or under-state the real or
"true" effect of ozone on commercially grown crops. For moisture stress,
several NCLAN experiments conducted in 1982 and 1983 suggest that
water-stressed plants are less prone to suffer ozone yield reductions than
fully watered plants. (The one synergistic observation noted earlier is the
exception.) Ozone yield effects for water-stressed plants are up to 30
percent less than well-watered plants for these experiments. Thus, data
generated under fully watered or optimal water conditions may overstate
potential ozone yield depression when compared with crops grown under less
than optimal water conditions. Such an expectation is consistent with general
production theory concepts as used in economic analysis; namely that the
marginal product (or effect) of inputs such as water will be greater in an
unconstrained environment. This manifestation of the Le Chatlier principal
means that when all inputs are set at physical product maximizing levels, the
marginal effect on yields will be greater than at levels observed under
suboptimal but more realistic production conditions (Silberberg, 1973).
The NCLAN requirements on drought stress have focused on only two crops,
cotton and soybeans. While the preliminary cotton results demonstrate that
drought stress reduces ozone effects, the soybean results are ambiguous, in
that one experiment demonstrated that drought stress increased yield response
to ozone at near ambient levels. As noted earlier, however, this
contradiction with the cotton and other soybean results is not yet replicated
and hence the bulk of the available evidence points to an antagonistic
relationship between drought stress and ozone-yield effects.
In addition to conducting ozone-water stress experiments, NCLAN has
constructed a general crop model designed to estimate the influence of drought
on ozone-caused crop loss (King and Snow, 1984). The model attempts to
estimate both the direct influence of water stress on the ozone damage process
and the influence of ozone-caused damagef on subsequent crop water use.
Preliminary ozone-water stress simulations (King, personal communication)
provide the basis of a moisture stress analysis which is defined in Analysis
V. The crop model and associated assumptions used to derive the adjustment
42
-------
factors is described in Appendix C. The resulting adjustment curve is
presented in Figure 4.
The crop model estimates are used to compare the potential difference in
ozone effects between non-irrigated and adequately watered crops. This
differential for varying degrees of water stress is then used to modify the
well-watered yield adjustments reported in Appendix Tables D.3 to D.ll
(representing the pooled responses used in Analysis I). The plausibility of
the adjustment factors in Figure 4 is supported by the general results of the
few NCLAN experiments that compare well-watered and drought stressed ozone
responses. Note that the adjustment factors are expressed in terms of the
yield reductions due to moisture stress. Thus two types of information are
needed to incorporate the effects of drought stress pictured in Figure 4 into
the ozone assessment. First, information on the spatial distribution of
rainfall in the U.S. is required to gauge the extent and severity of drought
for a particular growing season. Given that summer rainfall patterns
associated with continental climate zones tend to vary even within a
relatively small geographical area, detailed spatial resolution in rainfall
data is desirable. In this analysis, rainfall as measured on a state-by-state
basis for 1980 served to suggest the potential for drought stress.
Specifically, the departure from normal July rainfall for each state, as
reported in the Weekly Weather and Crop Summary (USDC, NOAA, 1980), is used to
define potential crop drought stress. The use of July deviations in rainfall
as a drought measure is based on the results of Huff and Neill (1980, 1982)
which demonstrate that July rainfall is the most critical environmental
determinant of seasonal yields for corn and soybeans. These July rainfall
departures are reported in Table 9.
A second piece of information is needed to translate the deviations from
normal July rainfall into drought stress yield reductions. This information
is derived from studies by Thompson (1969, 1970) for corn and soybean yields
in the Corn Belt. Specifically, regression equations are derived from
Thompson's results that predict percent yield reduction for these crops
associated with deviations in July precipatation. The effects across the two
crops are averaged to arrive at a surrogate crop drought stress yield
reduction. This reduction is then used in combination with the ozone-water
stress trade-off curve depicted in Figure 4 to obtain an adjustment factor to
be applied to the fully watered NCLAN response functions as used in the
previous analyses. These are reported in the last column of Table 9.
A number of points need to be made concerning this procedure and the
resultant adjustment factors. First, the July data used here are statewide.
To the extent that drought may be more localized, these averages introduced
potential biases that can only be addressed with detailed rainfall data.
Second, rainfall in months other than July obviously influence yields. The
extent of these other rainfall influences is not analyzed. Third, the drought
stress procedure attempts to key on those states where crops typically are not
irrigated. Thus, only states in the eastern half of the U.S. are included
(non-irrigated wheat production in the western U.S. was not included). Some
states in this set, however, do feature a percentage of irrigated acreage. An
attempt was made to account for states with both irrigated and non-irrigated
acreage (Nebraska, Oklahoma, Kansas, Texas and Arkansas) by reducing the
43
-------
Table 9. July drought, yield
factors, by state
reductions and moisture stress adjustment
Departure from
, normal July . ,
State—' precipitation^'
Alabama
Arkansas
Connecticut
Florida
Georgia
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Nebraska
New Hampshire
New Jersey
New York
North Carolina
North Dakota
Ohio
Oklahoma
Pennsylvania
Rhode Island
South Carolina
(cm)
6.17
5.33
0
2.06
8.26
2.74
0.25
3.43
3.20
2.79
0
0
2.35
2.03
0
3.05
3.30
4.70
4.88
1.78
5.21
0.89
6.15
2.46
0
4.06
0
0
2.54
Percent yield reduction from , Moisture
normal due to moisture stress— stress
Soybeans
14.0
10.9
0
3.8
18.5
5.0
1.0
6.6
5.0
5.2
0
0
4.5
4.0
0
5.8
6.3
9.5
5.0
3.3
10.8
2.0
13.0
4.5
0
8.1
0
0
4.7
Corn
27.7
25.0
0
16.5
35.0
17.9
12.6
19.7
13.0
18.1
0
0
16.8
16.2
0
18.8
19.4
23.3
12.5
15.6
24.8
13.6
27.7
17.2
0
21.5
0
0
17.5
Average
20.9
18.0
0
10.1
26.8
11.5
6.8
13.2
9.0
11.7
0
0
10.6
10.1
0
12.3
12.9
16.4
8.8
9.5
17.8
7.8
20.4
10.9
0
14.8
0
0
11.1
adjustment
factor d_/
.65
.68
1.0
.74
.61
.72
.87
.70
.77
.72
1.0
1.0
.73
.74
1.0
.72
.70
.68
.76
.75
.68
.80
.66
.73
1.0
.69
1.0
1.0
.73
44
-------
Table 9. (continued)
Departure from
Percent yield reduction from , Moisture
normal due to moisture stress— stress
1 normal July ,,
State- precipitation-' Soybeans Corn Average
South Dakota
Tennessee
Texas
Vermont
Virginia
West Virginia
Wisconsin
(cm)
1.40
1.02
6.40
0
0
0
3.80
2.5 14.7 8.6
2.0 13.9 8.0
12.5 24.5 18.5
000
000
000
7.4 20.8 14.1
adjustment
factor c/
.78
.80
.66
1.0
1.0
1.0
.69
a/
b/
Includes those states where crop production occurs primarily under non-
irrigated conditions. For Nebraska, Kansas, Oklahoma, and Texas, the
adjustment factors are weighted to reflect a percentage of irrigated
cropland that would not experience drought stress.
— Includes only departures below normal precipitation. A zero value
corresponds to normal or above normal rainfall for July.
— Percent yield reductions estimated from quadratic yield regressions
derived by Thompson (1969, 1970). The estimated reduction for corn
assumes that a certain yield reduction is associated with normal July
rainfall. The soybean reduction is based on the assumption that no
yield reduction occurs for normal or above normal July rainfall.
- Adjustments derived from Figure 4.
45
-------
Factor for adjusting
fully watered ozone
response functions
1.0
0.9 -
0.8
0.7 -
0.6 -
0.5
0.0
20%
Source: King (1984)
30% 40%
Moisture stress
yield reductions
Figure 4. The modelled effect of moisture stress on
predicted ozone yield reductions
46
-------
drought effect by the percentage of irrigated acreage. Still, it is likely
that some irrigated acreage in other states may not be reflected in the
adjustment factors. This again may bias the adjustment factors for such
states. Fourth, the adjustment factor is based on the averaged response of
corn and soybeans. It is assumed that this drought-yield relationship holds
for all the crops in the assessment. While corn and soybeans are the dominant
crops in the assessment, the use of this average may not accurately portray
the response of other crops. Finally, the curve depicted in Figure 4 is based
on a newly developed model of the drought-ozone interaction as it affects
crops. While the parameters and data underlying the model appear plausible
and the results conform to the available NCLAN drought stress experiments, the
application of the model should be considered tentative. Thus, the results of
the moisture stress-ozone analysis captured in Analysis V need to be used with
caution. As noted above, the primary purpose of the analysis is to suggest
the importance of accounting for moisture-stress and other interactions in
economic assessments of environmental stress.
VI RESULTS AND IMPLICATIONS
An empirical model of an economic sector as diverse and dynamic as U.S.
agriculture not only requires a conceptual framework capable of addressing the
important economic dimensions, but also substantial physical and economic data
to implement the model and an algorithm for solving the analytical problem
inherent in the conceptual model specification. The conceptual model, data and
solution procedure used in this assessment have been described earlier. Given
the number of production activities, regions and final products, the dimensions
of the analytical problem are substantial. Specifically, the 55 production
regions, ten primary crop commodities, 16 secondary crop and livestock com-
modities and the consumption-export balance considerations for each give rise
to a matrix with approximately 200 rows and 1500 columns. The problem is then
solved on a Cyber 170 computer using the MINOS software package.
The aggregate model is a revision of the Baumes model which has been used in
a number of previous studies (Baumes, 1978; Burton, 1982; House, 1983; and Adams
and McCarl, 1984). These previous solutions were considered adequate represen-
tations of agricultural conditions, as established by comparison of model pre-
dictions of equilibrium prices and quantities with actual values for a given
time period. However, the current assessment reflects model modifications and
data that differ from earlier analyses. Thus, the plausibility of the results
from the current model and data need to be established.
The plausibility of the results generated by this or any conditional nor-
mative model rest partially on the validity of the base model. The resemblance
of base model outputs to actual prices and production (as reported in USDA,
Agricultural Statistics, 1981) is one means of establishing the credibility of
the model. Close correspondence between actual and predicted values of key
endogenous variables can then lend support to the validity of subsequent changes
in economic values arising in the ozone-yield reduction evaluations.
47
-------
This section first reports the results of the base model and compares
these outputs with 1980 values. Specifically, equilibrium prices and quan-
tities, objective function values (welfare measures) and regional production
patterns are compared. Each of these features has direct importance in
demonstrating the plausibility of the model as well as evaluating the economic
effects of ozone. The results and implications of the five major ozone-yield
evaluations (Analyses I through V) as measured against the base situation are
then presented. Additional information, such as input use and consumer
surplus estimates by commodity, is contained in the model solutions. While
not discussed here, the validity of these latter estimates is linked to the
price and quantity relationships that underlie the model solution process.
Establishing the plausibility of these key model variables is thus sufficient
to document the performance of the overall model.
Base Model Results
The economic model applied to the ozone assessment problem simulates a
long-run equilibrium. To the extent that agriculture is usually in a state of
disequilibrium with respect to production and prices in any given year, exact
correspondence between a short-run situation, even with parameters and data
calibrated to that situation,, is not expected. What is expected, however, is
for the model to predict the general magnitudes of important variables, e.g.,
prices. More importantly, the model should consistently account for and pre-
dict directions in the movements of these variables when the model is per-
turbed. This is crucial for the assessment of potential effects arising from
technical or policy changes, since it is the direction of change in these
values, perhaps more than the values themselves, that is of relevance in
gauging policy efficiency. These same considerations apply to the assessment
of environmental change, such as the potential alteration in ozone levels
addressed here.
To demonstrate the performance of a general equilibrium model of agri-
culture, the model outputs of primary concern are predicted prices and quan-
tities of the included crops and commodities. The level of these variables
represent the optimal solution to the mathematical problem contained in the
conceptual model. Since changes in ozone levels will alter crop yields (from
actual 1980 yields used in the base model) and relative profitability in the
subsequent analyses, it is important that the base model prices and quantities
provide a reasonable portrayal of commodity mixes and prices. The actual and
model prices and quantities in 1980 for 12 crop commodities are presented in
Table 10.
As is evident from Table 10, the model predicts equilibrium prices that
are generally within five percent or less of the actual prices observed in
1980. For most crops (11 of the 12) the model price levels are equal to or
slightly higher than actual. In terms of quantities, the model results are
within 10 percent of actual for most crops (the exceptions are sorghum, 17
percent, and silage, 20 percent). For 7 of the 12 commodities, model produc-
tion exceeds actual. The relatively large oversupply of sorghum and silage
48
-------
Table 10. Actual and model 1980 prices and quantities for primary crops
and commodities
1980 Prices
Commodity
Cotton
Corn
Soybeans
Wheat
Sorghum
Rice
Barley
Oats
Silage
Hay
Soybean Meal
Soybean Oil
-1 Units are
Model
$
366.72
3.25
7.74
3.71
3.00
12.79
2.91
1.93
19.46
70.90
0.11
0.24
as follows:
Actual
/ \Vf\ 1 +• '
358.00
3.11
7.57
3.91
2.94
12.80
2.85
1.79
NA
71.00
0.11
0.23
500 pound bales for
1980
Model
(mill
17.45
7,339.85
1,778.07
2,633.94
700.88
164.78
335.50
472.91
91.24
141.58
46,180.80
10,755.81
Quantities
Actual
ion units)
15.65
6,645.84
1,792.06
2,374.31
579.20
146.15
360.96
458.26
110.97
131.03
50,624.00
11,270.00
cotton; bushels for corn,
soybeans, wheat, barley, oats, and sorghum; hundredweight for rice;
tons for hay and silage; pounds for soybean meal and oil.
49
-------
depicted in the base model is due to the presence of a drought in the Corn
Belt and Southern Plains regions in 1980 that lowered yields which then lead
to a shift of corn planted for grain into silage use. However, when the
silage and sorghum quantities are compared with 1979 and 1981 actual levels,
the model supply projections are within 3 percent. Given the long-run nature
of the assessment model, sharp divergences from historical levels in a given
year are difficult to simulate.
A similar comparison of livestock and livestock products is provided in
Table 11. The inclusion of a livestock component is an important feature of
an agricultural assessment, since livestock account for approximately 50 per-
cent of total economic activity in agriculture. Further, most of the grain
and oil seed production in the U.S. is produced for livestock consumption, not
directly for consumers. A model of agriculture should thus represent the con-
sumption link between primary commodities (e.g., corn for grain), intermediate
products (e.g., oil or meal) and final products (e.g., fresh meat products).
Reductions in corn or soybean yields due to ozone can then be traced through
the feed grain-oil seed-livestock balance to the final consumer.
The comparison in Table 11 indicates that model and actual livestock pri-
ces and quantities display strong similarities. Specifically, model prices
fall within 3 percent of actual, while quantities are again within 10 percent
of actual levels observed in 1980. Overall then, the model prices and quan-
tities for both crop and livestock commodities appear to capture the relative
magnitudes of equilibrium prices and quantities observed in recent years.
The prices and quantities, endogenously derived in the model are those
that maximize the model objective function. As noted in the methodology
discussion and Appendix A, optimizing the objective function is analogous to
maximizing the sum of consumers' and producers' surplus (as measured through
commodity and factor markets). While it is important to establish the plausi-
bility of the equilibrium prices and quantities, it is the change in objective
function value that is used to measure the economic effects of ozone on agri-
culture. Specifically, changes in the objective function value (economic
surplus) between the base case and the ozone analyses represent the social
benefits or costs of changes in ozone levels. Further, comparisons of shifts
in consumers' and producers' relative shares of the objective function across
different analyses can suggest the equity consequences of such changes. For
these reasons, the objective function value assumes major importance in the
assessment.
The objective function (or total social benefits) value for the base
solution are as follows:
Consumers' Surplus: $114.96 billion
Producers' Surplus: 26.02 billion
Total Social Benefit: $140.98 billion
50
-------
Table 11. Actual and model 1980 prices and quantities for dairy and
livestock products
1980 Prices
Product
Model
Actual-7'
$/unit
Milk
Pork
Fed Beef
Veal
Non-Fed Beef
12.95
139.00
237.50
310.30
150.20
13.00
139.50
237.60
309.50
149.76
1980 Quantities
Model
million
1,282.24
141.68
138.20
3.66
64.40
Actual-''
units
1,286.20
165.77
159.36
4.11
73.22
Source: USDA, Statistical Reporting Service, Statistical Bulletin 552.
Units are hundredweight
finished meat products.
— Units are hundredweight. Meat prices are average retail prices for
These values are measured in 1980 dollars and capture the economic surpluses
associated with the production and consumption (domestic and foreign) of the
major crop and livestock commodities in the U.S. The reasonableness or
validity of these estimates is not as easily demonstrated as prices and
quantities, given that actual measures of such welfare effects are not
available. However, some information, such as the farm gate value of these
commodities, can be used to establish reasonableness. According to USDA
Agricultural Statistics, the 1980 farm value of the crop and livestock
commodities included in the model was approximately $111 billion ($60 billion
for crops, $51 billion for livestock products). The comparable gross farm
level revenue calculated in the model is $107 billion, an error of less than
four percent. This indicates that the demand specification underlying the
prices and quantities is realistic, which provides some evidence that the
associated surplus values, measured as integrals under those curves, are also
reasonable. Ultimately, however, the validity of the objective function value
is based on the endogenously derived prices and quantities which give rise to
the objective function values. The credibility of these values has already
been established.
Modeling the U.S. agricultural sector is an exercise in comparative
advantage in that the historical regional mix and acreages of crops reflects
that region's resource endowments and proximity to markets. While particular
regions normally specialize in the production of given crops because of
51
-------
comparative advantage, market, shares will change due to weather and other
environmental changes. This is one rationalization for the use of spatial
equilibrium models in assessing the effects of pervasive induced changes on
agriculture, such as those associated with technical change or Federal
policies. Regional definition is an important feature of the model, as it
allows for the comparison of differential effects across regions in the face
of environmental change. The capacity of a region to respond to changes in
crop yields due to ozone or technical change by adjusting acreage or switching
crops will have an effect on the economic consequences experienced by that
region. Given different resource endowments and ambient ozone levels, this
implies that the economic effects of changes in ozone levels will not be
uniform across all regions. Like the tradeoff between producer and consumer
well-being, distributional effects in regional welfare are another dimension
of the equity issue in regulatory policy.
As noted above, the aggregate crop production levels reported in Tables 10
and 11 are close to actual production. However, the performance of sector
models of the type used here frequently suffers when regional or finer levels
of disaggregation are examined. To ensure that the spatial dimensions of the
model are consistent with actual patterns at a less aggregated level, the
cropped acreage predicted by the model are compared with actual acreages for
the ten major USDA production regions (Figure 5). These values are presented
in Table 12. As with the other model outputs, there is a close correspondence
between model regional acreages and actual. The model results are slightly
below actual national acreage of all crops. This is due to the exclusion of
some minor specialty crops from the model specification. Despite a slight
understatement nationally, the base model acreages are quite similar to the
regional production patterns realized in the U.S. in 1980.
This evaluation of the model indicates that the central features of the
1980 base solution accurately simulate the actual environment for that time
period. The comparison of equilibrium prices and quantities, revenues and
regional acreage responses serve to validate the model and establish the base
analysis as a suitable benchmark to gauge the economic effects of the ozone-
yield perturbations. The next section presents the results of applying the
five ozone-yield reduction analyses to the base model. Analysis I through V,
each with the four ambient ozone level adjustments, are presented and
discussed. The key features to be evaluated are the changes in net social
benefits (between the base value and each ozone assumption), the changes in
crop prices and the regional effects.
Analysis I
As summarized previously (in Table 6), Analysis I uses pooled response
functions based on all statistically homogeneous experiments (across cultivars
and years) of corn, soybean, and wheat (spring and winter). In addition, the
single cultivars of barley, sorghum, and cotton (irrigated and dry) are used.
A surrogate hay response, based on the average yield response of the six other
crops, is also included and used to perform "with" and "without" hay adjust-
ment analyses; i.e., in one set of runs, hay yields are held constant through
52
-------
to
2
O
O
UJ
C*
2
O
h-
U
z>
O
O
01
-l->
CO
co
O)
_c
4J
C
cr.
O)
s-
U
3
-a
o
J_
Q.
O)
3
cn
-------
Table 12. Model and actual 1980 regional and total U.S. cropped acreages^
Region
Northeast
Lake States
Corn Belt
Northern Plains
Appalachian
Southeast
Delta
Southern Plains
Mountain
Pacific
Total
Source: USDA, Agricultural
Acreage
Model
1,000 Acres
13,157
39,637
84,740
75,533
20,439
15,234
20,938
35,779
22,709
11,938
340,104
Statistics, 1981.
Actual
12,949
37,770
90,064
77,956
20,797
14,944
22,078
37,805
27,393
14,728
356,484
— Cropped acreage refers to land planted to crops. It excludes pasture
and grazing land.
the ozone changes; in the other analysis hay yields change with changing
ozone. The ozone assumptions are 10, 25, and 40 percent reductions and a 25
percent increase in ambient levels, as measured by the 1980 seasonal seven-
hour acreage in each of the 55 production regions. Together, these ozone
levels and cultivar response functions (as defined earlier) give rise to the
yield adjustment coefficients that are used to modify the base model yields
(reported in Appendix 0). The resulting changes in objective function values
(from the base case) define the economic benefits or costs to society for each
ozone adjustment.
54
-------
The model output of greatest interest is the objective function value
generated under each model solution. The values of the objective function for
Analysis I are presented in Table 13. The table includes both the "with" and
"without" hay yield adjustment evaluations. Also presented are the changes in
objective function values from the base case, as well as producer and consumer
surplus components of each total. This table thus provides estimates of the
preliminary national economic consequences of ozone on agriculture utilizing
NCLAN data through 1983. As such, these estimates satisfy one of the major
objectives of the NCLAN program.
The economic effects reported in Table 13 are estimates of the changes in
consumers' plus producers' surplus of those involved with the agricultural
sector, as stimulated by a change in ozone. This quantity is equivalent to
the sum of the agricultural sector-related income lost or gained by producers
and consumers as a consequence of air pollution changes. The term "benefit"
is used to indicate a gain in agriculturally-related consumers' and producers'
surplus. Similarily "costs" (negative benefits) will be used to indicate
losses. The calculation of consumers' and producers' surplus does not include
compliance costs required to achieve the alternative ozone concentrations
evaluated in this analysis. Also, the effect of ozone changes on
non-agricultural goods and services are not evaluated. Thus, the benefits
reported here should not necessarily be interpreted as net benefits to society
associated with the ozone changes evaluated in this study.
A considerable amount of information can be gleaned from Table 13.
First, the general magnitudes of the changes in economic surplus are
noteworthy as they represent the benefits to society from alternative ozone
levels under the assumptions of this assessment. Specifically, the simulated
improvements in air quality (reductions in ozone from 1980 ambient levels)
result in substantial annual benefits to society. The benefits of a 10
percent reduction in rural ozone range from $669 to $756 million, depending on
whether hay yields are allowed to vary in the assessment. A 25 percent
reduction in ozone produces benefits of $1.712 to $1.937 billion across the
same hay assumptions. The more extreme 40 percent reduction in ozone would
yield benefits of $2.52 to $2.86 billion. Finally, an increase in ozone
levels (air quality degradation) by 25 percent produces a cost (negative
benefit) to society of approximately $2.10 to $2.36 billion. All values are
annual equivalents measured in 1980 dollars and exclude any consumption or
production subsidies not directly embedded in the 1980 data.
The economic estimates associated with the four ozone adjustments amount
to changes of approximately 1.3, 3.2, 4.6, and -4.0 percent in the gross farm
value of the included crops. As a percentage change of the objective function
values, the estimates are substantially less (0.5, 1.4, 2.0 and -1.7) percent,
respectively. These changes are triggered by corresponding average yield
changes (for the NCLAN crops) of 1.1, 2.5, 3.8, and -3.0 percent. Thus, the
economic effects tend to be substantially less than the original physical
effect suggested by the response functions. This observation is expected,
given the general equilibrium nature of the economic model that allows for
compensatory production, price and input adjustments. This relationship
between physical effects and economic consequences is also consistent with
results reported by other researchers (e.g., Adams et al., 1982, Howitt et
al., 1984).
55
-------
The importance of including a hay response can be partially addressed by
examining the numbers in Table 13. Specifically, the difference between the
two analyses is the approximately 13 percent higher benefits estimates when
hay yields are assumed to vary (as the average of other NCLAN crops). This
translates into differences of over $300 million in the extreme ozone changes.
While the hay yield adjustments used in the "with hay" analysis are a
surrogate response, they are similar to the results of Oshima et al. (1976)
that indicate a moderate sensitivity for hay (alfalfa). Thus, the inclusion
of these hay adjustments may provide a reasonable approximation of the effects
of ozone on hay. Current NCLAN experiments are producing a series of hay
response estimates that should refine future assessments.
Table 13. Annual economic effects of ozone in 1980 dollars: Analysis I
Changes in
Economic surplus economic surplus
Ozone Producer Consumer Total Producer Consumer Total
Assumption Surplus Surplus Surplus Surplus Surplus Surplus
$ billion
Without Hay
Adjustment
Base 26.015 114.957 HO.971
10* Reduction 26.250 115.390 141.640 0.235 0.433 0.669
25* Reduction 26.567 116.116 142.683 0.552 1.159 1.712
40% Reduction 26.788 116.701 143.489 0.773 1.744 2.518
25% Increase 25.413 113.462 138.875 -0.607 -1.495 -2.096
With Hay
Adjustment
Base 26.015 114.957 140.971
10% Reduction 26.337 115.389 141.727 0.322 0.432 0.756
25% Reduction 26.807 116.101 142.908 0.792 1.144 1.937
40% Reduction 27.271 116.559 143.830 1.256 1.602 2.859
25% Increase 25.122 113.486 138.608 -0.893 -1.471 -2.363
56
-------
Another aspect of Table 13 includes the distributional effects across
producers and consumers. As is evident, both producers and consumers share in
these gains from increased supply. That consumers benefit from falling prices
is an expected outcome. In absolute terms, consumers benefit more than produ-
cers i.e., over 60 percent of the gains accrue to consumers in most analyses.
However, the observation that there are aggregate gains to producers with
increased supplies is not generally expected, given slightly inelastic demands
for most domestic commodities and a mix of elasticities for export markets.
The change (increase) in producer surplus resulting from an increase in
supply is due to the complex interaction of the demand and supply
relationships within the model. As noted, domestic and foreign demand is
characterized by varying elasticities (including elastic responses for some
exports). Further, it is the change in intercept and slope of the supply
curve that partially determines net changes in producer surplus (i.e., the
shifts in supply within the model are not always characterized by parallel
shifts). In addition, primary (e.g., feed grains) and intermediate (e.g.,
livestock) commodities are included in the model, with corresponding derived
demand implications vis a vis producers effects. Under these conditions,
increases in producer surplus with increased supply are realized. Given the
open nature of the economy in the model, it should also be noted that the
analysis and results are based on the assumption that changes in ozone
standards within the U.S. will not affect supplies in other exporting or
importing countries. While some "spillover" of ozone changes may occur in
southern Canadian wheat-producing regions, the meteorology of ozone formation
and transport suggests that transboundary effects should be minimal,
particularly with respect to transcontinental effects. Note also that ozone
levels in the Northern Plains region of the U.S., a major wheat producing
region, are relatively low compared to the Corn Belt and are unlikely to
experience substantial ambient ozone due to changes in Federal standards.
While no data are available on ozone levels in southern Canadian wheat
producing regions, it is likely that ambient ozone concentrations in that area
are also low.
Before proceeding to discuss other economic effects associated with
Analysis I, the above economic estimates need to be placed in perspective.
The 10 and 25 percent changes in ambient ozone levels are plausible changes,
in that temporal variation of this magnitude are observed in the seasonal
averages over the periods 1978-1982. However, the 40 percent reduction in
ambient levels is approaching what some researchers suggest to be background
or natural levels (Heck et al., 1984a). While the attainment of such a change
may not be feasible, the benefits value imputed to this level of control are
an estimate of the total economic consequences of all anthroprogenic ozone.
The 25 percent ozone reductions can also serve as a useful comparison
with some recent economic estimates derived by other researchers, given that
it closely simulates the ozone standards or levels used in these recent
national assessments. Specifically, recent national analyses by Kopp et al.
(1983) and Adams et al. (1984) use NCLAN data to derive yield adjustments. In
the Kopp et al. analysis, improvement in air quality from the present ozone
secondary standard of 120 ppb hourly maximum (not to be exceeded more than
once per year) to a 80 ppb standard are estimated to result in benefits to
57
-------
society of approximately $1.10 billion. Such an assumed improvement in
ambient levels is thus close to the 25 percent ozone reduction alternative in
this study, assuming a log-normal distribution of ozone events. The Kopp et
al. analysis does not include hay, sorghum, barley or livestock. Empirically
it is perhaps closest to the $1.71 billion generated in the "without hay"
analysis presented in Table 13. The difference in benefits recorded in the
present study ($1.71 vs. $1.10 billion) may be due to more complete crop
coverage, inclusion of an endogenous livestock sector and use of more recent
NCLAN data (through 1983) in the present study. Also, the assessment
methodologies differ, particularly with respect to the aggregation of regional
supply and the regional adjustment process to ozone-induced supply shifts.
The Adams et al. study uses a comparable 25 percent ozone reduction and
estimates the benefits from increased yields of corn, cotton, soybeans and
wheat to be approximately $2.4 billion. The higher value in that study is due
to use of higher ambient levels (uses the upper bound of regional ozone levels
for 1980), the use of national supply functions for each crop (no micro
detail) and a different benefits calculation procedure. The present study
appears to overcome some conceptual and empirical shortcomings of these
previous national assessments that use NCLAN data. The relative positioning
of the estimates (higher than Kopp et al., lower than Adams et al.) seems
consistent with the respective model structures and empirical focus.
Other dimensions of the model solutions in Analysis I are important to
further assess the economic effects of these ozone levels. The changes in
welfare across producers and consumers reported in Table 13 are an aggregate
measure of distributional effects. Another distributional aspect is the
relationship between domestic and foreign consumers of the commodities in the
model. To examine this latter type of distributional effect, Table 14
presents a comparison of the consumer surpluses accruing to domestic and
export use of the commodities. As the table indicates, the majority of the
total consumer surplus arises from domestic consumption. This is because
domestic use of the aggregate of these commodities is much greater than
quantities exported. However, when the changes in consumer surplus associated
with the increment in supply due to ozone are calculated, the gain through
export markets is greater. This implies that the benefits of increases in
supplies (beyond historical averages) of these commodities will accrue to
foreign consumers. While domestic consumers may benefit from reduced prices
due to increased supplies, the benefits to foreign consumers (and their
intermediaries in the U.S.) are also substantial.
To understand how the changes in supply affect domestic prices and hence,
consumer well-being, the prices of selected primary commodities and livestock
products are presented in Table 15. The primary commodities in the table are
those for which yields were adjusted in the ozone analysis. They are also
ones that are used primarily as feedgrain or feed concentrate (protein
supplement). The livestock commodities are final products consumed directly
by the consumer as fresh meat. As is evident from the table, reductions in
ozone have a differential effect on prices. For example, soybeans and to a
lesser extent, wheat, show rather dramatic changes in equilibrium prices due
to increases in yield (supply). This is due to their relative sensitivity to
ozone, which results in large increases in supply relative to the much more
58
-------
ozone resistant crops, such as barley and sorghum, which have very limited
supply increases, even at large ozone reductions.
On the livestock side, poultry and pork show the greatest price changes
associated with the slight increases in supply of feedgrain under the ozone
analysis. Milk also shows a modest change in price, consistent with the
increase or decrease in feedgrain supplies in each analyses. Fed beef prices
do not change much, showing only a limited price change at the more extreme
ozone levels. The greater sensitivity of pork and poultry prices is due to
the percent that feed contributes to total product value. Nearly the entire
marketable product of pork and poultry is produced during the feed grain and
concentrate feeding phase. Further, this feeding phase tends to be for a
relatively short period (approximately thirty days for broilers). Conversely,
less than half of the total marketable weight of beef is produced under the
finishing or grain-feeding phase, with the bulk of body weight gain arising
from consumption of roughage or pasture. As a result, feedgrains make up
about 95 percent of the total variable cost of poultry and 80 percent for
pork. For fed cattle, feedgrains are less than 40 percent (USDA, 1983).
Table 14. Effect of ozone on distribution of consumer surplus between
domestic and export markets
Consumer Surplus
Change in Consumer Surplus
Ozone Assumption Domestic Export Total Domestic Export Total
Base Case (1980)
10% Reduction
25% Reduction
40% Reduction
25% Increase
100
101
101
101
100
.940
.059
.383
.516
.296
14
14
14
15
13
.016
.330
.717
.043
.189
114
115
116
116
113
.956
.389
.101
.559
.486
-
0.
0.
0.
-0.
--
119
443
576
644
-
0.
0.
1.
-0.
--
314
701
027
827
—
0.433
1.145
1.603
-1.470
59
-------
-o
c
-a
CU
cu
r—
•r—
O
c
• r —
fO
s-
O>
•o
cu
cu
**•
O)
4_)
U
r—
CU
CO
II
0
oo
O)
O
S- co
Q. CU
• r-
C i %
O ••-
-o
cu o
c E
O E
r-4 o
0 0
*4— Sf
0 (J
o
•4"^ 4—^
O CO
cu cu
C|- >
LU i—
•
LO
t-l
cu
f»
ITS
c
o
1 »
Q_
g
3
CO
to
CU
C
o
N
0
CU CU
C CO
O f^3
N CU
O S-
o
^^ c
LO l—i
CM
C
cu o
c •<-
O 4->
N U
O 3
•o
X CU
O Qi
«*
CU O
c •<-
O -t->
N u
O 3
•o
X CU
LO C£
CM
C
CU 0
c •<-
O •(->
N 0
O 3
•o
^^ CU
O Qi
0)
VI
rO
CO
i/>
.(_>
• r-
g"
n^
>^
-M
•r—
^3
o
s
5
r^ «* oo r— i
C\J CM OO CT>
• • • •
CO CO CO CM
^3f ^f i£i CO
CVJ rM «Ct CT>
1 * t • • •
•r- OO ^^ CO CM
c
3
£_
CU
a.
«A
<* LO "Sj" r— 1
CM CO LO O>
• • • •
CO P^ CO CM
LO O O r-H
CM CD CD CD
• • • •
co r*» co CM
LO «* i— 1 T— 1
CM P*^ ^^ Cn
• • • •
CO f^» CO CM
• • • •
3333
.0 _a _a .a
CO
c
CU 4-> CU
C jQ a} i—
oo 1- >> CU 1-
QJ O O -C «5
O| (_> CO 3 OO
V.
CM cn
O CM
• •
CO O
O t-l
O CM
* *
CO O
O CM
O CM
• •
CO O
i-t co
O CM
• •
CO O
8^-
CM
• •
CO O
• •
3 -O
-Q i—
,
E *r~ xy
3 O 0
J= O
CT C 4->
^ Hj t/>
O CU CU
oo ca >
J
LO O
^- CD
co h^
O co
i— t CM
S«*
LO
• •
CM t^»
O CO
r-l CM
O LO
CO LO
• •
CM r^
O CO
,— 1 CM
O LO
en LO
• •
CM r~
O CO
«-H CM
§1^
LO
• •
co r^
O CO
r-l CM
• •
+•* ^-^
X f
o u
^_
>> cu
i- CU
•I-* CO
r —
3 TO
o cu
O- Lu
O
LO
•
cn
co
t— 4
CT>
•
OO
CO
I— 1
CM
LO
•
00
co
t— 4
cn
o
•
cn
CO
i— t
LO
r— 1
•
cn
CO
i— i
•
+j
^
o
v^
C_
O
Cu
•
oo
(— i
CO
CM
.—1
cn
00
CM
t— 1
CM
•
CM
.— 1
LO
cn
CM
t— (
•
4_j
^
O
NX
t—
• r*
^^
60
-------
Thus, slight changes in feedgrain prices are more likely to be felt more in
the prices of pork and poultry than for beef.
A final feature of the economic assessment results concern the regional
effects. Regions display different ambient ozone levels and hence the changes
in ozone under each analysis would lead to different yield responses for each
region. Table 16 contains a breakdown of producers surplus values for each of
the ten major regions in the assessment model for the base model and each
ozone alternative. As is evident from the table, almost all regions benefit
from reduced ozone and suffer losses from increased ozone. The greatest
absolute benefits generally occur to those regions with greatest agricultural
value of included crops; e.g., the Corn Belt and Lakes states. A few smaller
regions, such as the Delta, also record large changes in surplus values.
In relative terms, the distribution of gains is somewhat different. As
the "percent change" columns suggest, the regions that tend to gain the most
from reduced ozone are those areas with fairly high ambient levels and a crop
mix dominated by sensitive crops; i.e., soybeans and cotton. Regions that
display some or all of these characteristics are the Pacific (including
California), Delta, Northeast, and Southeast. Similarly, increases in ozone
also hit these same regions the hardest. Other regions, like the Northern
plains, receive almost no benefits (or losses) from adjustments in ozone in
either direction due to relatively low ambient ozone levels.
Overall, the results of Analysis I indicate that the benefits of moderate
ozone reductions are substantial in absolute terms but a relatively small
percentage of total agricultural value (approximately 3 percent of gross crop
value). The benefits of ozone reductions accrue to both producers and
consumers, with about 60 percent of the consumer benefits accruing to foreign
consumers. Domestic consumers benefit from slightly lower prices of livestock
products, due to increased supplies of feedgrains and oil seed. Regionally,
the major beneficiaries are those areas with high relative levels of ozone and
ozone-sensitive crops.
The remaining analyses (Analyses II through V) are discussed below. Only
the direct welfare effects of each analysis will be presented. The values
generated in Analyses II through IV can be viewed as sensitivity analyses of
the importance of response function and ozone assumptions used in the economic
assessment. Analysis V provides some indication of the need to account for
environmental interactions in future assessments. All the analyses include
the hay yield adjustments.
Analysis II
This analysis represents an attempt to introduce regionalization of
cultivars into the mix used in the assessment. The issue of extrapolating
from site-specific experiments to meso or macro scale assessments is an uneasy
one. Analysis I, by using the full set of available homogeneous data for
several crops, represents one approach to smoothing out the problems of
61
-------
>fc
ra
-C
0)
c
•r™
^2
3
r—
C_>
U
3 6-8
73
CU to
C CC 3
0 i—
•r- S3 Q.
-!-> O i-
i" "* oo
3
CO
cC CU
O)
QJ C C
c o ro
O -r- JC
IM +•> C_3
O U
3 ^
^j
CU to
a: 3
i—
^5 Q.
ID i-
CM 3
OO
CU
o>
c c:
o ro
4-* C_)
U
3 &S
"0
CU i/J
rv* 3
^—>
frS Q.
O S-
i — 3
OO
CU
l/l
ro
CO
c
o
• n-
Ol
cu
a:
ID
co
•
10
1
^o
r—
LD
•
o
fs^
o
oi
^_
c o
o to
•— o
1 —
•n™
JD
b*^-
^O
OO
•
^J"
un
t^>.
LO
•
o
r--.
CM
CO
Oi
10
ID
•
o
^-~
LO
LO
•
0
4->
to
ro
cu
.c
-4-J
i.
O
"Z.
to
vo
•
l™~
1
r~—
00
•
co
^^
CM
CM
00
CM
OO
CO
^j-
O
•
f—
CXJ
CO
•
OO
o
OJ
•
^—
00
00
•
CO
CO
^*
r-.
•
CO
l/l
cu
ro
_j
CO
^—<
•'
CM!
1
^O
o
•
r*x
CM
"-;
ID
CO
CO
ID
l~^
00
•
oo
CM
O">
^^
•
f*^.
r**«.
CTi
•
i—
^-
0
•
f"*^
^~
^O
CM
•
r**..
4-)
^—
cu
oa
c~
S_
O
o
0
ID
•
O
1
en
ID
ID
•
CO
o
ID
o
Oi
Oi
ID
CO
1
1
00
ID
•
CO
1
1
o
r-.
ID
•
CO
to
f^
ID
•
CO
I/)
c
•r—
fO
r-~
Q.
_f
^_>
^_
o
CVJ
CM
«
co
r
^^
CM
CM
•
CM
co
ID
^
CM
O
CM
r^
00
CM
•51-
UD
CO
•
CM
«^-
•=1-
f—
r—
co
CO
•
CM
00
O*t
CM
•
CM
ro
• r~
f~
U
ro
i —
ro
Q.
a.
CO
^O
cu
_c
^ ^
3
O
OO
o
CM
•
a\
i
*&
C\J
CM
•
P—
r—
Oi
o
^~
ID
Oi
^f
, —
CM
^~
•
f>^
CO
*st*
^J-
•
r~~
CTt
^^
•
00
ID
OI
CO
•
r—
00
^-
oo
•
r—
03
^ _>
r—
cu
0
r^.
^~
•
CM
1
ID
00
•
CM
00
CM
CO
O
UD
CM
O
CO
•
f
CO
1 —
ID
•
CM
ID
CO
•
0
CTI
ID
•
CM
O
^J-
ID
•
CM
to
c
•r—
ro
r—
D-
.E
1 '
3
O
oo
ID
p^»
•
CM
1
01
CO
^j-
•
CM
r—
1*^
CM
to
f**^.
ID
CM
r*^
^j-
•
CM
O
ID
«
CM
1
1
to
CD
ID
•
CM
oo
o
ID
•
CM
£=
•r—
ro
4J
C
3
O
00 CO
^J- «^
• •
*i- co
CM 1
1
^f CM
i— CM
tO r"" •
• •
O LD
CM
to co
CM 00
O «d-
CO
01 r—
ID r»~
O CM
r — r--
CM
CO •=!•
O O
• •
r- OO
CM
•?3" r*^»
CO O
Oi OO
o to
CM
to co
00 CM
O i—
o r*^
CM OO
co co
• •
o to
CM
CO LD
r~- ^—
00 O
• •
o ^o
CM
U _J
•f— ^£
*4- \—
•r- O
0 1—
ro
D-
62
-------
extrapolation. To the extent that "proportionate" responses are observed to
be statistically homogeneous, then site-to-site variability may be minimized.
The alternative approach is to pool only those data (cultivars) from given
regions. Unfortunately, only with soybeans have sufficient experiments been
conducted (15 experiments covering 8 cultivars) to perform an effective
regionalization. However, as observed in Analysis I, soybeans are one of the
"driving" crops in the economic assessment; their ozone sensitivity leads to
pronounced changes in own price and quantity. Analysis II uses these
regionalized pooled soybean cultivars, in combination with the other response
functions used in Analysis I. Again, the four ozone adjustments from 1980
ambient levels are used to generate yield changes.
The economic effects of ozone as captured by Analysis II are presented in
Table 17. As is evident from the table, the effects of regionalizing the
soybean responses is small; in general, the change in surplus estimates from
the "with hay" analysis in Analysis I (Table 13) are less than 2 percent. The
other general observations in Table 17 parallel those of Table 13; benefits
increase with less ambient ozone, with consumers capturing the greatest total
share of benefits but producers benefiting in relative terms. This latter
occurrence again derives primarily from the open nature of the model
associated with exports and production of livestock commodities.
The implication of this analysis is that the improvements in estimates from
regionalizing the response functions for soybeans, corn and wheat are rather
trivial. This does not mean that more cultivar information is unimportant.
Rather, the analysis tends to reinforce the use of aggregate pooled responses
as an acceptable measure of response. This observation is also a function of
the available data sets, in that for some crops, regionalization was not
possible, simply because there are insufficient cultivars. Also, only
homogeneous data sets are used here; thus, it is unlikely that the yield
differences would be great. The more extreme case is presented in Analysis
III, where heterogeneous cultivars are used to project yield adjustments.
Analysis III
Analyses I and II attempt to deal with site or cultivar variability by
pooling across homogeneous data sets. For most crops with multiple cultivar
experiments, the majority of the cultivars are found to be statistically
homogeneous, leading to similar relative yield changes. However, within each
crop, there are one or more nonhomogeneous cultivars that display more extreme
behavior. As noted earlier, it is unlikely that these extreme cultivar
responses represent the "true" relationship between ozone and yields.
However, their use in the current assessment can serve to define potential
bounds on the economic assessments generated in Analyses I and II. Thus, in
Analysis III, the soybeans, corn, wheat and cotton cultivars showing the
maximum yield response (sensitivity) to ozone are used, in combination with
the barley, sorghum and surrogate hay response, to define the next set of
yield adjustments. The ozone assumptions are as defined in Analyses I and
II.
63
-------
Table 17. Economic effects of ozone: Analysis II
Economic Surplus
Changes in Economic Surplus
Ozone Producers'
Assumption Surplus
Base
10%
25%
40%
25%
Reduction
Reduction
Reduction
Increase
26
26
26
27
25
.015
.350
.844
.253
.037
Consumers'
Surplus
114
115
116
116
113
.957
.379
.090
.520
.512
Total
Surplus
t
140.
141.
142.
143.
138.
bill
971
729
934
744
549
Producers
Surplus
0.335
0.829
1.238
-0.978
1 Consumers' Total
Surplus Surplus
0
1
1
-1
.422
.133
.563
.445
0.758
1.963
2.803
-2.422
The economic effects of ozone yields predicted by these extreme cultivars
are reported in Table 18. As expected, the estimates are substantially
greater than those produced in Analysis I. These estimates are approximately
50 percent higher over the range of ozone levels than those of Analysis I.
Specifically, the 25 percent ozone reduction now translates into benefits of
$2.9 billion, rather than $1.9 billion as in Analysis I. This benefit
estimate amounts to nearly 5 percent of the farm value of the primary crops.
The greater economic estimates arise from the much greater yield adjustments
associated with the extreme cultivar response.
The distribution of the effects across producers and consumers is similar
to those observed in previous analyses, that is, the consumers benefit most
from ozone reductions in absolute terms, but producers have a larger relative
gain. Compared with Analysis I, however, the gain to consumers is slightly
larger across all ozone levels, due in part to the larger shift in supply.
The importance of the 50 percent difference between the results of
Analysis III and those in Analysis I depends on the willingness to believe
that these cultivars more accurately portray the true (commercial) response of
all cultivars of each crop. The statistical and plant science evidence
reported in Heck et al. (1984b) and Rawlings and Cure (1984) suggests that the
pooled responses are more likely to capture the true response. The results of
Analysis III do imply that the estimates in Analysis I may be slightly low,
given that these more sensitive cultivars are not represented in that average
response but are part of the commercial mix of cultivars that make up total
production of each crop. The percent of actual cropped acreage made up of the
extreme cultivars (e.g., Davis for soybeans) however, is a small percent of
the acreage percentage captured in the pooled cultivars.
64
-------
Analysis IV
Ambient ozone levels display substantial variation from year to year,
even when measured as a seasonal average. Given that seasonal ambient ozone
levels are used in combination with a hypothetical ozone level to calculate
the yield reductions, the base year used in an assessment may influence the
yield changes and hence the economic effects. The previous three analyses are
keyed to the 1980 ambient levels. A rank ordering of 1980 ozone levels among
the production regions used in the analysis indicates that these 1980 values
span the range from highest to lowest within the five year period 1978-1982.
As an alternative base level, the average of the seasonal average ambient
ozone levels is used to define the new ozone base. (Given the temporal
variability displayed across regions, it was not possible to pick any year in
the five year period that was uniformly high or low across all regions.) The
effects of changing the ozone base are moderated in this study by the way
alternative ozone levels are calculated (percent changes, rather than absolute
departures, from ambient). Thus, the effects of changing ozone base are not
as severe as may occur under a different analysis. Nonetheless, Analysis IV
can provide some suggestions as to the role of ozone data in the assessment.
The response function mix is that used in Analysis I.
Table 18. Economic effects of ozone: Analysis III
Economic Surplus Changes in Economic Surplus
Ozone Producers' Consumers' Total Producers' Consumers' Total
Assumption Surplus Surplus Surplus Surplus Surplus Surplus
$ billion -
Base 26.015 114.957 140.971
10% Reduction 26.390 115.713 142.104 0.375 0.756 1.133
25% Reduction 26.972 116.920 143.892 0.958 1.963 2.921
40% Reduction 27.212 118.243 145.455 1.197 3.287 4.484
25% Increase 24.844 112.867 137.711 -1.171 -2.090 -3.261
65
-------
The differences in ozone levels between 1980 and the five year average
ranges from only two to 26 percent among the 55 regions defined in the
economic model. It is unlikely then that there will be major changes in
economic effects. As Table 19 indicates, the values are rather close to those
of Analysis I. For example, the 25 percent ozone reduction produces a $1.67
billion benefit as opposed to 1.93 in the same ozone scenario in Analysis I, a
difference of 14 percent. A comparison of the other estimates with Analysis I
reveals an even closer correspondence.
The particular year chosen for the base ozone value in the analysis thus
seems to have little effect on the economic estimates so long as the extreme
years are avoided. This is partly due to the use of a seasonally averaged
exposure measure, which may smooth out the extremes in ozone events temporally
and spatially, and the fact that the ozone alternatives are measured in terms
of constant percent ozone adjustments from the base, rather than as absolute
departures. The use of different exposure measures, as reported in Heck et
al. (1984a), would probably increase the sensitivity of the assessment to the
base ozone levels as would the use of ambient ozone level forecasts based on
meterological modeling. In the current analysis, however, the choice of any
other year from the available ozone data does not. appear to be a critical
issue.
Analysis V
The last permutation of Analysis I undertaken in this assessment effort
is potentially the most important. Ozone effects, like any environmental
stress, do not occur in isolation. Other yield-moderating influences occur
simultaneously with the presence of ozone; some naturally, such as weather
phenomenon, and others man-made, such as cultural and management practices.
The previous four analyses (and all other economic assessments) have assumed
that the effect of ozone on the yield or production response of a crop is
analogous to neutral technological change, shifting the position of the crop
response (or production) function upward or downward. In reality,
interactions between ozone and other factors may also change the shape of the
response surface. As discussed earlier, a few interactive NCLAN studies have
examined ozone effects in the presence of moisture stress. These preliminary
experiments (two on cotton and two on soybeans) indicate that water stress may
dampen the response of ozone (lessen the slope) and hence result in a lower
yield adjustment than expected from a non-water stressed plant. As discussed
earlier and in Appendix C, the fact that much commercial production occurs
under non-irrigated conditions suggests that response experiments based on
adequately watered data may overstate yield losses.
Analysis V represents a preliminary attempt to account for this potential
antagonistic interaction between water stress and ozone by using regional
drought estimates for 1980, July rainfall yield regressions and a crop model
to arrive at adjustment factors for the fully watered response functions.
Although simplistic, this analysis can serve to reinforce the need for more
information on the broad range of interactive processes. The cultivar mixes
and ozone assumptions are those used in Analysis I.
66
-------
Table 19. Economic effects of ozone: Analysis IV
Economic Surplus
Changes in Economic Surplus
Ozone Producers'
Assumption Surplus
Base
10% Reduction
25* Reduction
40% Reduction
25/6 Increase
26.015
26.451
26.542
27.056
25.282
Consumers'
Surplus
114.957
115.404
116.100
116.634
113.466
1 Total
Surplus
-------
VII. LIMITATIONS OF THE ANALYSIS
The environmental assessment performed in this study is an attempt to
model a highly complex bioeconomic system. Not only must the important
dimensions of the agricultural economic system be adequately portrayed, but
the biological and technical possibilities associated with environmental
stress on that system need to be addressed. Thus, it is possible that
conceptual and empirical errors have been committed in implementing the
assessment, including the uncertainty embedded in some of the biological and
meteorological relationships that lie at the heart of the ozone assessment.
Each of these sources of uncertainty need to be recognized in any use of the
economic estimates derived in this study.
Three general areas of the assessment can be identified as contributing
to the uncertainty in the estimates: the response data, the ozone data, and
the economic model. Each has been developed and applied using best available
information and theoretically consistent procedures. However, the assumptions
and abstractions inherent in each can be viewed as caveats on the assessment
Table 20. Economic effects of ozone: Analysis V
Economic Surplus Changes in Economic Surplus
Ozone Producers' Consumers' Total Producers' Consumers' Total
Assumption Surplus Surplus Surplus Surplus Surplus Surplus
_____ ______ $ billion
Base 26.015 114.957 140.971
10% Reduction 26.359 115.210 141.569 0.344 0.253 0.598
25% Reduction 26.659 115.874 142.532 0.644 0.917 1.561
40% Reduction 26.909 116.314 143.223 0.894 1.357 2.252
25% Increase 25.460 113.664 139.124 -0.555 -1.293 -1.847
68
-------
results. These limitations can serve to suggest logical areas for further
research. These three areas are discussed in more detail below.
The NCLAN response information represent the most complete available data
set on crop yield response to ozone. The use of standard protocols over time
and space in the NCLAN experiments facilitate the use of these data in
assessments of this type. While the data provide strong evidence of the
negative effects of ozone on crop yields, the procedures used to generate the
data are not perfect. Several limitations of the experimental design and
procedures are apparent. First is the failure to account for a range of
environmental interactions in the experiments, such as moisture stress. This
issue is currently being addressed, but only preliminary data are available
for this assessment. Second, the data are also generated at a relatively few
sites, requiring extrapolation to broad regional scales. Third, while data
are generated under field conditions, the plants are in controlled chambers,
which introduce biases, although the available evidence suggests that chamber
effects are slight. Fourth, the exposure dynamics to date are confined to a
seven-hour (0900 - 1600) per day exposure over the growing season. Longer
daily exposures (e.g., 12 hour) may result in higher yield losses if stomatal
activity occurs outside the 0900 to 1600 time window. Plants grown under
natural conditions would then be exposed for a longer period than the seven
hours from 0900 to 1600. In future NCLAN experiments, alternative exposure
regimes will be employed to test this hypothesis.
In addition to experimental design and protocol questions, the crop mix
and cultivars tested to date are limited to major annual field crops and a
relatively few cultivars for each crop. Given limited resources, NCLAN
management has focused on major annual crops. Crops such as rice, sugar beets
and perennials have not been addressed. Also, with the exception of corn,
soybeans and wheat, only one or two cultivars of each crop have been tested.
Other cultivars may respond differently (though the soybean data suggest a
rather common response across cultivars). Thus, while the current assessment
covers a large percentage of annual field crops, the area of perennials and
specialty crops is ignored. This exclusion of perennials and other crops is
also a source of negative bias in the economic estimates, given that some of
these crops are known to be sensitive to ozone.
The ambient ozone data used in the assessment are also open to question.
Those data, based on the Kriging extrapolation procedure, again represent the
best data currently available. The Kriging procedures used to derive the
regional rural level estimates have been described in Heck et al. (1983a).
One of the primary problems in calculating these ozone values is that the
underlying SAROAD data are generally measured at urban or suburban monitoring
sites and hence may not reflect rural concentrations. Further, for many
states, there are fewer than ten monitoring sites from which to extrapolate to
the entire state. These available data can establish temporal and spatial
trends but the accuracy of the ambient ozone data for each agricultural
production area may be limited by the available SAROAD data. However, a
comparison of the Kriged ozone levels with actual values for a few NCLAN sites
did reveal close correspondence (within 5 percent). It should also be noted
that SAROAD data are collected at a height of three meters, whereas the crop
canopy in the NCLAN chambers is lower (about one meter). Evidence
69
-------
suggests that ambient ozone levels are lower at the crop canopy than at three
meters, implying an upward, bias in yield effects predicted by SAROAD data.
The absence of available ozone models to project rural regional levels
for use in formulating alternative ozone levels tends to diminish the utility
of most assessments. That is, it is difficult to link alternative secondary
ozone standards to probable rural ozone concentrations. This requires the use
of either tenuous assumptions concerning the distribution of ozone levels or
the use of constant percentage adjustments in ambient ozone, as employed here.
While the constant adjustments are not a source of uncertainty per se, these
assumptions may not correspond to levels that would be realized under
alternative standards.
Finally, the economic model, with its many parameters and assumptions is
a possible source of error in the economic estimates. While the validation of
the model demonstrated its ability to simulate agriculture accurately, the
estimates may be affected by the structure of the model. For example, as with
most models of this type, a conceptual limitation is the absence of cross
price effects between commodities. If the magnitude of such effects is
sufficiently strong, then the consumer welfare impact of direct price effects
(as included in this analysis) may be misstated. The direction of the bias
will depend on the mix of substitutes and complementary commodities in the
analysis. Also, the influence of income as a demand shifter has been omitted.
The model does not include some mitigative adjustments, such as changes in
fertilizer use rates, that may accompany yield changes due to ozone. Further,
input usage and costs could decrease in the long-run if producers adjust
output levels to the increased input productivity associated with reduced
ozone. Finally, measurement errors in the economic data, statistical errors
in the parameter estimates and algorithmic errors in the model solution
procedure may also introduce biases. To the extent that these conceptual and
empirical limitations will be constant across all model analyses, the
potential biases should be minimized as the economic estimates are measured as
deviations from the base solution, not as the total value of the objective
function. The sensitivity of the model can lend some evidence as to the
importance of these error sources but extensive sensitivity analysis on all
parameters would require hundreds of costly model solutions. Limited
examination of specific parameters indicated a fairly stable objective
function value. However, like some of the other sources of uncertainty, the
effects of assessment model uncertainty on the economic estimates are not
fully quantifiable. In spite of these possible uncertainties associated with
the analysis, the estimates generated here are considered to be more accurate
and complete than any previously available.
VIII. SUMMARY
This manuscript is a report of an assessment of the economic consequences
of ozone pollution on U.S. agriculture. The economic analysis is limited to
those ozone effects directly associated with the production .and consumption of
a set of agricultural commodities. Effects on non-agricultural commodities as
well as compliance costs of achieving any changes in ambient ozone levels are
70
-------
not evaluated here, hence the estimates are not net economic effects. The
assessment is based on a large scale spatial equilibrium model of the U.S.
agricultural sector. The ozone analyses are driven by ozone-yield response
functions derived from four years of NCLAN data. The assessment covers the
major annual crops in the U.S. and traces these crop yield effects through the
livestock sector to final consumption, both domestic and foreign. It is the
most comprehensive economic assessment of air pollution effects on agriculture
undertaken to date.
The study involves multiple ozone analyses. Both alternative ozone
levels and alternative yield response relationships are used to generate five
distinct analyses (Analyses I-V). Each of the five major analyses includes
four hypothetical adjustments in rural ambient ozone levels in the 55
production areas in the model. Until addition information is obtained on the
factors or issues addresed in Analyses II - V, Analysis I is viewed as the
most defensible set of economic estimates. Thus, analyses II - V are intended
to provide evidence of the stability of the economic estimates in Analysis I
to the underlying biological data base and assumption. The magnitude of the
differences in estimates across assumptions can suggest areas for further
research.
The economic model was validated by comparison with actual performance of
the agriculture sector in 1980. Following the establishment of a credible
base model, the model was perturbed according to the yield adjustments
predicted in each of the ozone analyses. The resultant economic estimates
indicate that benefits of reduced ozone will accrue to both agricultural
producers and consumers. Specifically, a 25 percent change in ozone below
current ambient levels results in a benefit to society of from $1^6 to $1.9
billion, depending on the underlying response assumption. Conversely, a 25%
increase in ozone produces a cost (negative benefit) to society of $1.9 to
$2.3 billion. Ozone alterations of 10 percent and 40 percent show lower or
greater benefits, as expected.
The analysis of the effects of yield assumptions on economic estimates
suggests that the incomplete treatment of other stress interactions in the
design of the NCLAN experiments introduces some biases in the economic
estimates. For example, introduction of moisture stress along with ozone,
reduced the economic estimates by about 20 percent when compared with the
ozone-response data that are derived from adequately watered field plots.
Conversely, the use of a daily seven-hour exposure period rather than longer
periods in the NCLAN experiments is probably imparting a downward bias to the
economic effects. Other potential sources of positive or negative biases have
been noted.
The ongoing research within NCLAN on moisture stress and alternative
exposure dynamics can serve to improve on this area of uncertainty. In
addition to better response data, future agricultural assessments can benefit
from more SAROAD monitoring data in rural areas and the use of meteorological
models to relate hypothetical Federal secondary ozone standards to rural
ambient ozone concentrations that may result from those standards.
71
-------
As is the case in virtually any economic study there are a number of
things which could be done to improve, extend, clarify, or test the
sensitivity of results of this analysis. These areas for further research
include:
1. The analytical model could be extended to incorporate uncertainty
(both as it occurs naturally in agriculture and as it is influenced by
ozone) and farmers' attitudes toward uncertainty.
2. A more detailed validation exercise could be done on the model
studying its ability to predict changes in the agricultural sector as
a result of exogenous changes such as those induced by air pollution.
3. A study could be done on the impact of the crop mix assumptions as
they influence the assessment. This would involve questions regarding
both the influence of crop mixes vis-a-vis conventional single crop
models and the influence of historical versus representative farm
generated results.
4. An analysis could be done on the consequences of the level of spatial
aggregation used in the model. In particular, the influence of the
extra detail contained in the Corn Belt representative farms (relative
to the micro-detail in other parts of the country) could be
investigated. This would provide guidance on the desirability of more
detailed disaggregation in the rest of the country in future analyses.
5. Analyses could be conducted on the factor neutrality-nonneutrality of
ozone-induced technical change. The sensitivity of the assessment to
alternative assumptions could be investigated.
6. The importance of cross elasticities of demand on the economic
estimates could be studied, both in terms of developing relative
estimates and examining the effect of alternative assumptions on the
assessment.
Better data and refined assessment methodologies can lead to improved
benefits forecasts. These in turn can assist regulatory agencies in setting
environmental policies. The results of this national assessment, in
combination with other recent economic assessments of ozone damage clearly
establish that there are economic benefits to be attained from changes in
ozone below current ambient levels. However, no attempt was made to estimate
the cost of reducing ozone concentrations to the three levels for which
benefits were estimated. Thus, these agricultural benefits, in combination
with benefits accruing to other receptor classes and compliance costs of
reducing ozone, can be used to gauge the net economic efficiency of potential
ozone controls.
72
-------
REFERENCES
Adams, R.M., and T.D. Crocker. 1980. "Analytical Issues in Economic
Assessments of Vegetation Damages," Crop Loss Assessment, P.S. Teng
and S.V. Krupa (eds.), Proceedings, E.G. Stakman Commemorative
Symposium, Misc. Publication No. 7, Agricultural Experiment
Station, University of Minnesota, pp. 1980-209.
. 1982. "Dose-Response Information and Environmental Damage
Assessments: An Economic Perspective." Journal of the Air
Pollution Control Association, 32:1062-1067.
. 1984. "Economically Relevant Response Estimation and the Value
of Information: The Case of Acid Deposition." Economic
Perspective on Acid Deposition Control, T.D. Crocker (ed.), Ann
Arbor Science, Butterworth Publishers, pp. 35-64.
Adams, R.M., T.D. Crocker, and R.W. Katz. 1984. The Adequacy of
Natural Science Information in Economic Assessments of Pollution
Control: A Bayesian Methodology. Review of Economics and
Statistics (in press).
Adams, R.M., T.D. Crocker, and N. Thanavibulchai. 1982. "An Economic
Assessment of Air Pollution Damages to Selected Annual Crops in
Southern California." Journal of Environmental Economics and
Management, 9:42-58.
Adams, R.M., M.V. Ledeboer and B.A. McCarl. 1984b. The Economic Effects
of Air Pollution on Agriculture: An Interpretive Review of the
Literature. Oregon Agricultural Experiment Station Special Report
No. 702.February.
Adams, R.M. and B.A. McCarl. 1984. "Assessing the Benefits of
Alternative Oxidant Standards on Agriculture: The Role of Response
Information." Journal of Environmental Economics and Management
(in press).
Adams, R.M., N. Thanavibulchai, and T.D. Crocker. 1979. "Methods
Development for Assessing Air Pollution Control Benefits ~ Volume
III — A Preliminary Assessment of Air Pollution Damages for
Selected Crops within Southern California." EPA-600/5-79-DDIC.
U.S. Environmental Protection Agency, Washington, D.C.
73
-------
Baumes, H. 1978. A Partial Equilibrium Sector Model of U.S.
Agriculture Open to Trade: A Domestic Agricultural and
Agricultural Trade Policy Analysis. Unpublished Ph.D. thesis,
Purdue University.
Benedict, H.M., C.O. Miller, and J.S. Smith. 1971. "Assessment of
Economic Impact of Air Pollutants on Vegetation in the United
States: 1969-1971." EPA-650/5-78-002. Stanford Research
Institute, Menlo Park, California.
Benson, E.J., S. Krupa, P.S. Teng, and P.E. Welsch. 1982. "Economic
Assessment of Air Pollution Damages to Agricultural and
Silviculatural Crops in Minnesota." Final Report to Minnesota
Pollution Control Agency.
Bleasedale, O.K.A. "Atmospheric Pollution and Plant Growth." 1952.
Nature, 169:376-377.
Brisley, H.R., and W.W. Jones. 1950. "Sulfur Dioxide Fumigation of
Wheat with Special Reference to Effect on Yield." Plant
Physiology, 25:666-681.
Brown, D. and J. Pheasant. 1983. A Linear Programming Assessment of
Economic Damages to Midwest Agriculture Due to Ozone. Final Report
to Corvallis Environmental Research Laboratory, U.S. Environmental
Protection Agency.
Burton, R. 1982. Reduced Herbicide Availability: An Analysis of the
Economic Impacts on Agriculture. Unpublished Ph.D. thesis. Purdue
University.
Caldwell, B.E. 1973. Soybeans: Improvement, Production and Uses.
Agronomy Monograph No. 16!Edited by B.E. Caldwell.American
Society of Agronomy, Madison, Wisconsin.
Chattin, B., B.A. McCarl, and H. Baumes, Jr. 1983. Users Guide and
Documentation for a Partial Equilibrium Sector Model of U.S.
Agriculture. Agricultural Experiment Station Bulletin No. 313,
Purdue University.
Crocker, T.D. 1982. "Pollution Damage to Managed Ecosystems."
Economic Assessments in Effects of Air Pollution on Farm
Commodities, J.S. Jacobson and A.A. Miller (eds.), Izaak Walton
League of America.
Crocker, T.D., B.L. Dixon, R.E. Howitt, and R. Oliviera. 1981. A_
Program for Assessing the Economic Benefits of Preventing Air
Pollution Damages to Agriculture. Discussion paper prepared for
the National Crop Loss Assessment Network. USEPA, Corvallis
Environmental Research Laboratory.
74
-------
Day, R.H. 1963. "On Aggregating Linear Programming Models of
Production." Journal of Farm Economics, 45:797-813.
Duloy, J.H. and R.D. Norton. 1973. "CHAC: A Programming Model of
Mexican Agriculture," Multilevel Planning: Case Studies in Mexico.
L. Goreux and A. Manne (eds.). North Holland Publishing Company,
pp. 291-312.
Fajardo, D., B.A. McCarl, and R. Thompson. 1981. "A Multicommodity
Analysis of Trade Policy Effects: The Case of Nicaragua
Agriculture." American Journal of Agricultural Economics,
63:23-31.
Feder, 6., R. Just, and D. Zilberman. 1983. Adoption of Agricultural
Innovations in Developing Countries: A Survey. World Bank Staff
Working Paper No. 542.
Freeman, A.M., III. 1979. The Benefits of Environmental Improvement.
Baltimore; the Johns Hopkins University Press.
Frick, G.E. and R.A. Andrews. 1965. "Aggregation Bias and Four Methods of
Summing Farm Supply Functions." Journal of Farm Economics, 47:696-700.
Hanks, R.J. and R.W. Hill. 1980. "Modeling Crop Responses to Irrigation
in Relation to Soils, Climate, and Salinity." International
Irrigation Information Center. IIIC Publ. #6. Ottawa, Canada.
(Distr. by Perganon Press.)
Harberger, A.C. 1971. "Three Basic Postulates for Applied Welfare
Economics: An Interpretative Essay." Journal of Economic
Literature. 9:785-797.
Heady, E.O., and U.K. Srivastava. 1975. Spatial Sector Programming
Models in Agriculture. Iowa State University Press, Ames, Iowa.
Heck, W.W., O.C. Taylor, R.M. Adams, G. Bingham, J. Miller, E. Preston,
and L. Weinstein. 1982. "Assessment of Crop Loss from Ozone."
Journal of the Air Pollution Control Association, 32:353-361.
Heck, W.W., O.C. Taylor, R.M. Adams, J.E. Miller and L. Weinstein.
1983a. National Crop Loss Assessment Network (NCLAN) 1982 Annual
Report. Report to USEPA, Corvallis Environmental Research
Laboratory, June.
Heck, W.W., R.M. Adams, W.W. Cure, R. Kohut, L. Kress, and P. Temple.
1983b. "A Reassessment of Crop Loss from Ozone." Environmental
Science and Technology, 17:572A-581A.
Heck, W.W., W.W. Cure, J.O. Rawlings, L.J. Zaragoza, A.S. Heagle, H.E.
Heggested, R.J. Kohut, L.W. Kress and P.J. Temple. 1984a.
"Assessing Impacts of Ozone on Agricultural Crops: I. An
Overview." Journal of the Air Pollution Control Association 34:729-735.
75
-------
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. 1984b. "Ozone
Crop Yield Functions for Loss Assessment." Journal of the Air
Pollution Control Association (in press).
House, R.M. 1983. USMP: A Mathematical Programming Model for Agriculture
Sector Policy Analysis. USDA, Economic Research Service, Washington,
D.C. Draft Report.
Howitt, R.E., I.E. Gossard, and R.M. Adams. 1984. "Effects of
Alternative Ozone Levels and Response Data on Economic Assessments:
The Case of California Crops." Journal of the Air Pollution
Control Association (in press).
Huff, F.A. and J.C. Neill. 1980. Assessment of Effects and Predictability
of Climate Fluctuations as Related to Agricultural Production.
Illinois Institute of Natural Resources. Final report to Natural
Science Foundation. May.
Huff, F.A. and O.C. Neill. 1982. "Effects of Natural Climate Fluctuations
on the Temporal and Spatial Variation in Crop Yields." Journal of
Applied Meterology, 21:540-550.
Just, R.E., D.L. Hueth, and A, Schmitz. 1982. Applied Welfare
Economics and Public Policy. New York: Prentice-Hall.
King, D. 1984. "Modeling the Effect of Drought on Crop Sensitivity to
Ozone." 1983 NCLAN annual report.
Kopp, R.J., W.T. Vaughan and M. Hazilla. 1983. Agricultural Benefits
Analysis: Alternative Ozone and Photchemical Oxidant Standards.
Final Report to Economic Analysis Branch, U.S. Environmental
Protection Agency, Research Triangle Park, North Carolina, June.
Kress, L.W. and J.E. Miller. 1983. "Impact of Ozone on Soybean Yield."
Journal of Environmental Quality, 12:276-281.
Larson, R.I. and W.W. Heck. 1976. "An Air Quality Data Analysis System
for Interrelating Effects, Standards and Needed Source Reductions:
Part 3, Vegetation Injury." Journal of the Air Pollution Control
Association, 26:325-333.
Leung, S., W. Reed, S. Cauchois, and R. Howitt. 1978. "Methodologies
for Evaluation of Agricultural Crop Yield Changes: A Review."
EPA-600/5-78-018, Corvallis.
Leung, S.K., W. Reed, and S. Geng. 1982. "Estimations of Ozone Damage
to Selected Crops Grown in Southern California." Journal of the
Air Pollution Control Association, 32:160-164.
76
-------
Leung, S., W. Carson, S. Geng, M. Noorbakhsh, and W. Reed. 1981. "The
Economic Effects of Air Pollution on Agricultural Crops:
Application and Evaluation of Methodologies, A Case Study." U.S.
Environmental Protection Agency, Corvallis, Oregon.
Loucks, O.L., and T.V. Armentano. 1982. "Estimating Crop Yield Effects
from Ambient Air Pollutants in the Ohio River Valley." Journal of
the Air Pollution Control Association, 32:146-150.
Manuel, E.H., R.L. Horst, K.M. Brennan, W.N. Laner, M.C. Duff, and J.K.
Tapiero. 1981. "Benefit Analysis of Alternative Secondary
National Ambient Air Quality Standards for Sulfur Dioxide and Total
Suspended Particulates," Vol. IV. EPA-68-D2-3392. Math Tech.,
Inc.; Office of Air Quality Planning and Standards; U.S.
Environmental Protection Agency. Final Review Draft.
McCarl, B.A., D. Brown, R.M. Adams, and J. Pheasant. "Linking Farm and
Sectoral Models in Spatial Equilibrium Analyses: An Application of
Ozone Standards as They Effect Corn Belt Agriculture." In
Quantitative Methods for Market Oriented Economic Analysis Over Space
and Time. T. Takayama and N. Uri, eds, JAI Press, Greenwich,
Connecticut (in press).
McCarl, B.A. 1982a. "Cropping Activities in Agricultural Sector
Models: A Methodological Proposal." American Journal of
Agricultural Economics, 64:768-772.
McCarl, B.A., 1982b. "REPFARM: Design, Calculation and Interpretation
of the Linear Programming Model." Agricultural Experiment Station
Bulletin No. 385, Purdue University.
McCarl, B.A., and T.H. Spreen. 1980. "Price Endogeneous Mathematical
Programming as a Tool for Sector Analysis." American Journal of
Agricultural Economics, 62:87-102.
McCarl, B.A. and J. Pheasant. 1983. "REPFARM: Documentation of the
Computer Model." Agricultural Experiment Station Bulletin No. 409,
Purdue University.
Middleton, O.T., J.B. Kendrick, Jr., and H.W. Schiwalm. 1950. "Injury to
Herbaceous Plants by Smog or Air Pollution." Plant Disease Reporter,
34:245-252.
Miller, T.A. 1966. "Sufficient Conditions for Exact Aggregation in Linear
Programming Models." Agricultural Economics Research, 18:52-27.
Mjelde, J.W., R.M. Adams, B.L. Dixon, and P. Garcia. 1984. "Using
Farmers' Actions to Measure Crop Loss Due to Air Pollution."
Journal of the Air Pollution Control Association 34:360-364.
77
-------
Moskowitz, P.O., E.A. Coveney, W.H. Medeiros, and S.C. Morris. 1982.
"Oxidant Air Pollution: A Model for Estimating Effects on U.S.
Vegetation." Journal of the Air Pollution Control Association,
32:155-160.
Murtaugh B. and M. Saunders. 1977. MINOS: Users Guide. Operations
Research Technical Report, No. 77-9. Systems Operations Laboratory,
Stanford University.
Norton, R.D. and G. Schiefer., 1980. "Agricultural Sector Programming
Models: A Review. European Review of Agricultural Economics,
7:229-264.
Norton, R.D. and L.M. Solis. 1981. Programming Studies for Mexican
Agricultural Policy. International Bank for Reconstruction and
Development. Washington, D.C.
Oshima, R.J. 1973. "Development of a System for Evaluation and
Reporting Economic Crop Losses Caused by Air Pollution in
California: I. Quality Study." University of California final
report to the California Air Resources Board under Agreement
ARB-287, Riverside, California.
Oshima, R.J., M.P. Poe, P.K. Braegelmann, D.W. Baldwin, and V. VanWay.
1976. "Ozone Dosage-Crop Loss Function for Alfalfa: A
Standardized Method for Assessing Crop Losses from Air Pollutants."
Journal of the Air Pollution Control Association, 26:861-865.
Oshima, R.J. and R. Gallavan. 1980. "Experimental Designs for the
Quantification of Crop Growth and Yield Response from Air
Pollutants." Crop Loss Assessment, P.S. Teng and S.V. Drupa
(eds.). Proceedings E.G. Stakman Commemorative Symposium,
Miscellaneous Publication No. 7, Agricultural Experiment Station,
University of Minnesota, pp. 62-70.
Padgett, J., and H. Richmond. 1983. "The Process of Establishing and
Revising National Air Quality Standards." Journal of the Air
Pollution Control Association, 33:13-16.
Page, W.P., G. Arbogast, R.G. Fabian, and J. Ciecka. 1982. "Estimation
of Economic Losses to the Agricultural Sector from Air Borne
Residuals in the Ohio River Basin." Journal of the Air Pollution
Cont r o 1 Association, 32:151-154.
Paris, Q. and G.C. Rausser. 1973. "Sufficient Conditions for Aggregation
of Linear Programming Models." American Journal of Agricultural
Economics, 55:659-666.
78
-------
Rowe, R.P., L.G. Chestnut, C. Miller, R.M. Adams, M. Thresher, H.O.
Mason, R.E. Howitt, and J. Trijonis. 1984. Economic Assessment of
the Effect of Air Pollution in the San Joaquin Valley. Draft Report
to the Research Division, California Air Resources Board. Energy and
Resource Consultants, Inc., Boulder, Colorado.
Rawlings, J.D. and W.W. Cure. 1984. "The Weibull Function as a
Dose-Response Model for Studying Air Pollution Effects." Crop
Science (in press).
Sahi, R. and W.C. Craddock. 1974. "Estimation of Flexibility
Coefficients for Recurvsive Programming Models: Alternative
Approaches." American Journal of Agricultural Economics,
56:344-350.
Samuelson, P.A. 1952. "Spatial Price Equilibrium and Linear Programming."
American Economic Review, 42:283-303.
Sheehy, S.J. and R.H. McAlexander. 1965. "Selection of Representative
Benchmark Farms for Supply Estimation." American Journal of
Agricultural Economics, 41:681-695.
Shriner, D.S., W. W. Cure, A.S. Heagle, W.W. Heck, S.W. Johnson, R.J.
Olson and J.M. Skelly. 1982. "An Analysis of Potential
Agriculture and Forestry Impacts of Long-Range Transport Air
Pollutants." ORNL-5910. Tennessee: Oak Ridge National
Laboratory.
Silberberg, E. 1978. The Structure of Economics: A Mathematical
Analysis. New York: McGraw-Hill Book Company.
Smith, M. and D. Brown. 1982. "Crop Production Benefits from Ozone
Reduction: An Economic Analysis." Department of Agricultural
Economics, Agricultural Experiment Station, Purdue University
Station Bulletin No. 388.
Spreen, T.H. and T. Takayama. 1980. "A Theoretical Note on Aggregation of
Linear Programming Models of Production." American Journal of
Agricultural Economics, 62:146-151.
Stanford Research Institute. 1981. "An Estimate of the Nonhealth
Benefits of Meeting the Secondary National Ambient Air Quality
Standards." Prepared for the National Commission on Air Quality.
Washington, D.C.
Takayama, T. and G. Judge. 1971. Spatial and Temporal Price and
Allocation Models. North Holland Publishing Company, Amsterdam.
Thompson, L.M. 1969. "Weather and Technology in the Production of Corn in
the U.S. Corn Belt." Agronomy Journal, 61:453-456.
79
-------
Thompson, L.M. 1970. "Weather and Technology in the Production of
Soybeans in the Central United States." Agronomy Journal, 62:232-236.
U.S. Department of Agriculture. 1982. Agricultural Statistics. 1981.
U.S. Government Printing Office, Washington, D.C.
U.S. Department of Agriculture. Economic Research Service. 1982.
Livestock and Meat Statistics: Supplement for 1981. Statistical
Bulletin No. 522. Washington, O.C.
U.S. Department of Agriculture, Economic Research Service. 1983. Economic
Indicators of the Farm Sector: Costs of Production 1982. EC IPS.2-3.
U.S. Government Printing Office. Washington, D.C.
U.S. Department of Commerce, National Oceanographic and Atmospheric
Administration. 1980. Weekly Weather and Crop Bulletin. August 5.
U.S. Environmental Protection Agency. 1978. Air Quality Criteria for
Ozone and Other Photochemical Oxidants. ECAO, EPA-600/8-78-004.
Research Triangle Park, M.C.
U.S. Environmental Protection Agency. 1984. Air Quality Criteria for Ozone
and Other Photochemical Oxidants, Vol. III. ECAO, Research Triangle
Parck, N.C. June.
Willig, R.D. 1976. "Consumers' Surplus Without Apology." American
Economic Review, 66:589-597.
Zellner, A. 1962. "An Efficient Method of Estimating Seemingly Unrelated
Regressions and Tests for Aggregation Bias." Journal of the American
Statistical Association, 57:348-368.
80
-------
APPENDIX A
The Analytics of the Assessment Model
The purpose of this appendix is to provide a description of the economic
model and procedures to be used in the national assessment. This model is a
modified version of that used in the Corn Belt assessment described above.
The analytics of the model, including the link between micro or producer
behavior and aggregate measures of welfare, are discussed in some detail.
This is to allow the reader to grasp the essential features and assumptions of
the model and to document the procedures that will be used in applying the
model to the assessment of the national consequences of ozone. This
presentation and discussion is also motivated in part by questions raised at
the 1984 NCLAN peer review.
Description of the Aggregate Sector Model
The aggregate model used in the national assessment is a mathematical
programming model based on the Takayama and Judge activity analysis spatial
equilibrium model, the CHAC model of Ouloy and Norton as explained in Norton and
Solis (1981) and the agricultural sector models of Heady and associates (as
reported in Heady and Srivistava, 1975). The particular model used here was
developed by Baumes (1978), improved in data specification by Burton (1982),
documented in Chattin et a!., (1983), updated in the EPA Corn Belt study
(Adams and McCarl, 1983) and updated for this study. The basic model has been
used in the past in other studies by USDA and the Office of Technology
Assessment. It has currently been adopted for routine use by USDA (House,
1983). The revisions inherent in this current model are motivated by the
methodological suggestions of McCarl (1982a).
A complete description of the model is beyond the scope of this document
(the reader interested in such should refer to any of the following: Chattin
et al., 1983; House, 1983; Baumes, 1978 or Burton, 1982). In addition related
conceptual material is given in McCarl and Spreen (1980); Norton and Schiefer
(1980); Takayama and Judge (1971); Norton and Solis (1981); McCarl (1982); and
Heady and Srivistava (1975). Nevertheless, we will briefly cover five topics
of discussion. These topics regard the conceptual model including all
producers, aggregation to a representative set of producers with less
microeconomically detailed models, the overall model structure, calculation of
economic surplus and the incorporation of air pollution (ozone)-induced yield
changes.
81
-------
A Conceptual Model Involving All Producers
Conceptually, the model development starts with the farm (this development
is adapted from the more detailed presentation, of McCarl and Spreen, 1980): A
multi-product farm is assumed to maximize its profits (income minus cost).
Income arises from product price times quantity of output produced summed over
all products. Cost arises from the use of purchased inputs times their prices
summed over all inputs. Total farm input usage is linked to farm activity as is
farm output. It is further assumed that the time dimension is long run; thus
fixed factors do not exist and all inputs are priced. Under these conditions
the farm's profit maximization problem may be written as:
maximize - Ed^. + ZPQYo - ZC^
jo i
subject to Za,.X. - Z, <_ 0 for all i
|J J
-Ze-X. + YQ _< 0 for all o
J
tfXj 0
where
j refers to types of farm processes
o refers to types of farm outputs
i refers to types of farm variables
m refers to types of farm fixed inputs
d. is miscellaneous costs of employing one unit of activity X.
J J
a— is the use of variable input i by one unit of activity X.
' J J
e . is the production of output o by one unit of activity X.
*^J J
f . is the use of the firm's mth fixed inputs
bm is the endowment of the mth fixed inputs
82
-------
X. is the number of units of farm activity j undertaken
J
P is the per unit price of the oth output
Y is the number of units of the oth output sold
Ci is the per unit price of the ith variable input
Zi is the number of units of the ith input bought.
Given that the objective is to maximize this function then the conditions
for optimality may be found through the application of the Kuhn-Tucker theorem.
Thus for this problem to have an optimum we know that
= -dj - f iaij + f oeoj + ^ ~
2) y- = PQ - UQ < 0 for all 0
3) |t- = -C- + X, < 0 for all i
0 for all j
J
8) YQ > 0 for all o
9) Zi > 0 for all i
10) \. >_ o for all i
83
-------
11) U > 0
o —
12) am _> 0
13) Ea..X. - Z. < 0
ij J
for all o
for all m
for all i
14) -£e.X. + YQ < 0
J
for all o
15) Zf^X., 0) then, by conditions 5 and 6, conditions 2 and 3 would be strict
equalities. This leads to a solution in which the optimum value of U and
equal the price of output and price of input, respectively. Thus, for a pro-
it maximizing farm, the marginal revenue from producing more of an output is
price and the firm equates prices to its marginal value of output. Similarly,
the marginal cost of inputs is equated to their price. Thus the conditions for
production are given by substituting into 1 which becomes
84
-------
- f 1«U + fo'oj
or
which implies at optimality that the profit maximizing farm produces output (X.)
until the marginal revenue of producing that output (summed price times outputj
quantity) is less than or equal to the marginal cost of production (summed input
cost times usage plus miscellaneous cost).
Given these profit maximizing conditions for an individual farm, one can
form conditions for the agricultural sector. Implicit in the above discussion
is the assumption that the price of outputs and inputs are constant regardless
of the level of activity by the farm. This is hardly true of the aggregate.
Clearly as the quantity of a crop produced decreases then the price to consumers
increases. Similarly the prices of inputs such as labor and land would be
related to quantity used. Thus, it is reasonable to adopt demand and supply
functions for products and inputs. Let the price of these items be a linear
function of the volume produced:
po - v v
C, - 9i(V
where
f TY ) is the inverse demand function for the Oth output
T is total production summed across all farms
g^(T-) is the inverse supply function for the ith input
Z. is total use of the ith input.
Here we assume that the price of an item is dependent on own quantity only
and that all other factors can be collapsed into the curve a priori.
Given these functions we may now write a set of first order conditions
which includes the aggregate, but maintains the individual farm profit maxi-
85
-------
mizing conditions. These conditions would involve a) the addition of a
subscript for farm (k) on all the parameters and variables above; b) the inclu-
sion of equations to form the aggregates
Yok - Yo
Zik -
c) the inclusion of aggregate dual variables (U ,\.) which equal the firm shadow
prices (U k,X-k) for all firms; and d) the inclusion of a relationship between
the aggregate price and the shadow price
f (Y ) - U < o
oo o -
-X1 + g^Z^ < 0;
These equations describe a situation under which all the firms maximize
profits in a perfectly competitive agricultural sector. The conditions are
duplicated by the following optimization model which is a generalization of
equation (9):
Ti
maximize I J Q f (Y)dY
o
L z dX
k j
subject to z a^,-uX.u - Z.. < 0 for all k and i
k lk
Z an,-bX..|. + Y . < 0 for all k and o
"~
zZik - Z. £ 0 for all i
l\
86
-------
Yok - Yo 1 ° for
Xik>VZi'Yok>Ziki°
This forms the base conceptual framework underlying this assessment
(see McCarl and Spreen for a more detailed development).
Aggregation
The above model explicitly works with individual farms. Such microeconomic
detail for every farm is unrealistic in an empirical study of the scope
undertaken here. Consequently, following the procedures of all previous spatial
equilibrium studies we use geographically "homogeneous" producers. The
assumption is adopted therefore that within each geographical region the model
for the region is adequately representative of all producers in the region.
Hueristically, this would occur, following the exact aggregation literature, if
all producers had the same possibilities and resources, and all technical
coefficients were proportional across farms (see Day, 1963, Miller, 1964, Paris
and Rausser, 1983, and Spreen and Takayama, 1980, for elaboration). Sheehy and
McAlexander, 1965, and Frick and Andrews, 1965, discuss procedures for
minimizing bias.
However, we have yet a further aggregation difficulty concerning
microeconomic detail. At a minimum there are 55 representative farm models
within the national model. The Corn Belt models each could have as many as 220
constraints and 400 activities (following Brown and Pheasant, 1983).
Extrapolating to all regions the farm production component could have more than
12,000 constraints and 22,000 activities. This is unrealistic given data and
computer cost limitations. Consequently the decision was made to construct more
aggregate microeconomic representations. This was done following the
suggestions of McCarl in which crop mixes were used to specify activities. The
crop mixes were generated using the representative farm models of Brown and
Pheasant (1983) for the Corn Belt and historical information in combination with
USDA Farm Enterprise Data System (FEDS) data for the regions outside the Corn
Belt.
Model Structure
The above material has presented a view of the model which does not fully
document its empirical characterization of the agricultural sector. Here we
will present a summation notation representation of the model. This
representation is not exhaustive as the actual empirical model has in excess
of 1,400 columns and 200 rows with numerous bounds.
87
-------
The model contains the following symbols and associated definitions:
Definition _of Variables
X. - the t agricultural production activity in region r.
XP. - t processing activity.
NI - quantity of national input p supplied.
Y£ - domestic consumption of the H primary agricultural
commodity.
n ^ - n stepson the excess supply function facing the U.S.
for the a agricultural commodity.
r - domestic consumption of the c processed commodity.
i U 4. L.
J - n step on the excess demand curve for the c
processed commodity facing the U.S.
9 - supply of the q land type in region r.
Xr - supply of hired labor for the r region.
FAMLB - use of farm family labor in the r region.
Technical Coefficient Definitions
a. . - per unit yield of the £ agricultural commodity in the
t production activity in the r region.
a2at - per unit usage of the c processed commodity in the
t production activity in the r region.
ht. - per unit use of the H agricultural commodity in the
t processing activity.
nnp - the quantity of the & agricultural commodity imported
at the n step of the excess supply curve facing the
U.S.
th
a2t - per unit yield of the c processed commodity from the
t processing activity.
-------
112
.th
- quantity of the c processed commodity imported at the
n step on the excess supply curve facing the U.S.
kk - quantity of the 5. agricultural commodity exported at
the n step on the excess demand curve facing the U.S.
ww - quantity of the c processed commodity exported at the
n step of the excess demand curve facing the U.S.
lb. - per unit use of agricultural labor in the t.
agricultural production activity for the r region.
ntrq - quantity of the q land type, used per unit of the t
production activity in the r region.
InS. - quantity of the q land type used in the S state by
Xtr'
I. - per unit use of the p national input in the t
p agricultural production activity in region r.
NI2. - per unit use of the p national input in the t
p processing production activity.
d. . - per unit use of the.ji agricultural production ..
J regulation by the t production activity in the r
regon
per un
processing activity.
- per unit use of the w processing regulation by the t
±,L. +• U\
d3 - per unit use of the s input regulation by the p
p national input.
d4 - per unit use of the s input regulation by the q land
type in the r region.
d5 - per unit use of.the s input regulation by agricultural
labor in the r region.
Objective Function Cost Coefficients
b. - per unit cost of the t agricultural production
activity in the r region.
qfU ) - area generating function for labor supply curve for the
r region at quantity X .
89
-------
n «(e „) ~ area 9en?Katin9 function for land supply of the q type
rq rq in the r™ region.
Z - cost of supplying one unit of the p national input.
g. - per unit cost of the t processing production activity.
dd g - area under the excess supply curve facing the U.S. for
the H agricultural commodity up to the n step.
xx - area under the excess supply curve facing the U.S. for
the c processed commodity up to the n step.
bbp - area generating function for the domestic demand curve
for the H agricultural commodity.
rwr - reservation wage for farm family labor in the r
region.
gg (r ) - area generating function under the domestic demand curve
facing the U.S. for the c processed commodity.
rim . - area under the excess demand curve fcing the U.S. for
the £ agricultural commodity up to the n step.
yy - area under the excess demand curve facing the U.S. for
the c processed commodity up to the n step.
Right Hand Side Definitions
cccr - quantity of farm family labor in the r region.
ddd - initial land endowment of the q type in the r
region.
eee - maximum land use in state s.
fff - initial stock of national input p.
The formal linear programming model is given below.
90
-------
Agricultural Commodity - Marketing Clearing
I i K I o IN
(1 \ r»-*- r* _ v i T-I£ i- vn *-• T
Ay "" Zj Zj
£ = 1, 2, ... L
Equation (1) states that production of the t agricultural commodities by
all technologies employed in all regions less the total quantity demanded as
inputs by the processing sector, plus imports of the commodity, less domestic
consumption, less foreign demand or exports, must be greater than or equal to
zero. Or, more simply stated, quantity supplied is greater than or equal to
quantity demanded.
Secondary Commodity - Market Clearing
a2tcXPt - n2nc Tnc + rc
Tl R
Z1 Z a2a, X. < 0
=l r=l trc tr
C *"" -L j Cy •••)
Equation (2) is the same relationship as equation (1), except it refers to
processed commodities. Production plus imports, less domestic demand, less
exports, is greater than or equal to zero.
Regional Agricultural Labor - Market Clearing
(3) FAMLBr -i- Z1 lbtrXtr - \
This equation represents the supply and demand interaction in the
agricultural labor market by region. The amount of labor demanded by all
technologies must be less than or equal to the hired labor supplied plus the
91
-------
family labor used, where family labor used must not exceed the stock of family
labor.
Agricultural Land - Marketing Clearing
lntrqXtr
q = 1, 2, ..., Q r = 1, 2, ..., R
The land required by each technology or production activity employed cannot
exceed the land supplied in the region.
Xtr
s = 1, 2, ..., NS
The land required in a state cannot exceed the maximum tillable acres.
National Inputs - Market Clearing
(6) J! Jl JtrpXtr + j' NI VPt - NIP - fffP ^ °
Equation (6) introduces the interrelationship between the processing and
agricultural sector. The use of national inputs in all of agriculture plus the
use of national inputs in processing must be less than the quantity purchased
plus the quantity already owned.
Foreign Demand - Agricultural Commodities
N
(7> r nn£ < 1 A = 1, 2, ..., L
92
-------
Foreign Demand - Processed Commodities
N
(8) E Yn < 1 c = 1, 2, ..., C
n=l nc ~
Equations (7) and (8) are constraints to insure that a convex combination
of adjacent linear segments on the specified price responsive supply (factors)
and demand (outputs) curves are chosen in solution following Duloy and Norton
(1973).
Objective Function
maximize
TI R R R Q
(9) - E E b. X. - E qr(Xr) - E E h (ern)
t=l r=l tr tr r=l r T r=l q=l rq rq
P T9 L
- E ZD NI - I* gt XP + E bb
p=l P P t=1 t t £=1
L N
~ nr
E ggc(rc) + E E m
:=1 c £=1 n=1
Jl nl W«Y« + jl nl
Equation (9) is the total sum of producers' plus consumers' surpluses
realized for all markets; factor market and output or product market.
When given the appropriate technical coefficients, objective function cost
coefficients and right hand side values, the maximization of equation (14)
subject to constraints (1) - (8) leads to a competitive equilibrium.
Equilibrium price and quantity levels for factors of production, agricultural
and nonagricultural commodities are determined endogenously subject to the
constraints imposed on the model.
93
-------
The area generating functions are formed as followed. The curves are
basically either infinitely or finitely elastic. The infinite elasticity cases
for some inputs reflect a constant price (times quantity). The finite
elasticity curves for outputs are put in as a kinked curve, which is infinitely
elastic to a point, then display a constant elasticity (to avoid unrealistic
solutions and enhance analytical tractability). The join point occurs at a
different point for supply and demand curves. The floor price for supply curves
is the price which is the larger of one-third the 1980 equilibrium price or the
price which would generate one-third the 1980 equilibrium quantity on the
constant elasticity curve which would pass through the 1980 equilibrium price
and quantity. The ceiling price for the demand curve is the smaller of three
times the 1980 price or that price which would generate one-third the 1980
quantity. Thus, the area under these curves is calculated as in the attached
figures (Figures la, Ib and Ic). The term PMIN is the floor or ceiling price.
The area to that point is PMIN times quantity; the additional area from the
intersection of PMIN and the constant elasticity curve is calculated by
integration.
The reader should note that the product supply functions implicit within
this model are not constant elasticity but rather are curves which are
projected through the production process.
As with any attempt to model a complex system, the mathematical model above
has its limitations. The first of these is the assumed independency of the
factor supply and product demand schedules. The cross price effects for supply
(of inputs) and demand (for outputs) is omitted. Secondly, the influence of
income generated as a demand shifter has been omitted. Thus, demand curves
supplied to the model may over or under estimate the true demand if income is
different than the base year.
Welfare Measures: Economic Surplus
The objective function of the sector model is used to obtain measures of
social benefits from changes in air quality. The sector model features demand
relationships for sectoral outputs (commodities) of various types. The
elasticity, and equation forms vary with end use; export demand is elastic.
Assuming supply and demand functions which are integrable and independent of
sector activity, first order conditions are then met in the sector model
specification. The objective function of this specification is empirically
specified in equation (9) but can be conceptually represented as
maximize
where n is the sum of ordinary consumers' surplus and producers' surplus and
the integrals are evaluated from zero to Z*, the amount of i^h commodity
produced and sold to consumers; and from zero to Xj, the amount of the jth
factor used. The parameters are as follows:
94
-------
Price
PMIN
long-run
equilibriun
price
Consumer
Surplus
QMIN
•Demand
Quantity
long-run
q* equilibriun
quantity
(la) Consumer surplus for modified constant elasticity demand curve
Price
q* Quantity
(Id) Consumer surlpus for stepped demand curve
Price
PMIN
Supply
Producer
Surplus.
QMIN q* Quantity
(ic) Producer surplus for modified constant elasticity supply curve
Figure A.l Calculation of economic surplus
95
-------
g.(Z.) is the area under the demand function for the i product;
e.(X.) is the area under the supply function for the j factor;
J 0
c (Y ) is the miscellaneous cost of production;
subject to the technical and behavioral constraints defined above. Given the
micro and macro structure of a model, the sector model solution then simulates
a long-run, perfectly competitive equilibrium.
Following Samuelson (1952), the objective function ( H) may be
interpreted as a measure of ordinary consumers' and producers' surplus
(quasi-rents) or net social benefit. Analytically, this is defined as the
area between the demand and supply curves to the left of their intersection.
The demand functions are specified at the national level, as are aggregate
production responses. The producer quasi-rents are measured through the
factor markets (land and labor). The use of these measures has been
demonstrated in Just et al. (1981) and Just and Hueth (1980) to be analogous
to measurement of producers surplus through the aggregate supply function.
Justification for the use of economic surplus in policy analysis is well
documented in the literature (Willig, 1976; Just et al., 1981) and is
particularly relevant to agricultural uses where distributional consequences
are of concern. By imposing alternative ozone levels on the producer
behavior, as manifested in yields predicted by the NCLAN response functions,
changes in production and consumption and ultimately economic surplus may be
measured. Comparisons of changes in surplus between the alternative ozone
levels and current ambient concentration indicates the benefits for these
alternative ozone levels.
The mechanics for calculating producer and consumer surplus is a straight
forward application of integration and optimization procedures. The
mathematical program used to solve the sector model calculates the value of
the nonlinear objective function. This objective function is calculated by
integrating the area under the demand curves less the area under the supply
curves less other costs.
In the model, demand curves are of three main types: constant elasticity,
perfectly elastic, and step-like curves. The consumer surplus is calculated as
the area between the demand curve and price line. However, the area below a
constant elasticity demand curve and above the price line does not sum to a
limit. Consequently, the constant elasticity demand curves have been truncated
at a level equal to one-third the initial equilibrium price, or one-third the
initial equilibrium quantity, whichever is associated with the smaller quantity.
The truncated demand curve is then joined to a perfectly elastic demand curve at
that level (see Figure Al).
Supply curves for land and labor are also of the constant elasticity type.
A similar truncation occurs for these curves since it is unrealistic to expect
these curves to pass through the origin. The producer surplus is calculated by
commodity. However, producer and consumer surplus can also accrue when a
production or consumption constraint is active. In this case, there will be an
96
-------
addition to the surplus as measured by the area below the price line and above
the supply curve if an upper limit on production is reached, or a subtraction
from that area if a lower limit is active. This area is calculated by
multiplying the constraint shadow price times its right-hand side. No
constraints on consumption were imposed in the model.
Thus, only two deviations from the standard calculation of consumer and
producer surplus occurred. The first related to the modification of constant
elasticity supply and demand curves. The second deviation incorporated the
welfare effect of constraints on production.
Incorporation of Ozone Effects
The ozone induced yield effects were included in the model through a
modification of the atr term in the agricultural commodity-market clearing
row. This modification involved the multiplication of the coeficient by a
yield adjustment parameter which was state and crop specific. These yield
adjustment parameters are derived from the NCLAN data and resultant response
functions reported in Table 7. The respective yield adjustments for each
crop, region and ozone alternative are presented in Appendix D. This type of
adjustment requires the assumption that ozone induces a neutral technological
change; i.e., that other resources (labor, fertilizer) will not adjust
independently in response to an adjustment in ozone. Thus, the same fixed
input proportions are assumed before and after ozone. Other assumptions could
have been used, but there was little empirical basis for changing the resource
mix.
97
-------
Appendix B
Estimation of the Yield-Acreage Response
for Use in Generating Crop Alternatives
Agronomic and economic factors suggest that own crop acreage can
affect yield in several ways.
(i) It is assumed that crops are grown on 'land that has a comparative
advantage for the production of that crop. If the acreage planted to a
certain crop is a dominant land use, increases in acreage can only be onto
land less suited for the production of that crop. This implies that average
yields will decline. The effect in a given region will depend on total land
availability and the magnitude of the increase in acreage. For crops of minor
importance in a region, the effect of acreage increases on yield is
uncertain. However, if the effect is a positive one, one would expect that as
more land is planted to that crop, the yield increase will occur at a
diminishing rate after a point.
(ii) Farms are often equipped to handle a maximum acreage of a crop.
Once this crop acreage is exceeded, factors relating to the timeliness of
production have an effect. Even if no other crops were grown, timeliness may
be a problem, for example, if all the crop cannot be harvested at the "optimal
time". Thus, expansion past this maximum level will lead to negative yield
effects if the expansion is not associated with a corresponding increase in
inputs.
(iii) Continuous planting of a given crop can have an important effect on
yield if a build-up in disease or parasite population is likely to occur.
This will reduce yields.
Acreage of other crops can affect yield of a particular crop in two main
ways:
(i) Yield can be affected by the crop that was planted on that land the
previous year. Alterations in crop patterns can be beneficial to yield, if
the previous crop was a legume or if the land had been fallow. Conversely,
the previous use pattern can be detrimental to yield if that crop had a large
nutrient demand or deteriorated the soil structure.
(ii) Other crops planted in the same year affect yield by competing for
resources such as labor, machinery, or management time.
98
-------
Since expected crop price is a primary factor in determining acreage
(planting decisions), and acreage can affect yield, it is important to
accurately model the crop yield-acreage relationship. If the response
relationship between yields and acreage is ignored, the acreage response
arising from changes in expected price would likely be overstated. This would
then bias a subsequent ozone analysis where changes in relative crop yields
are used to portray the effects of alternative ozone levels.
The General Approach
The steps involved in adjusting expected yield to account for acreage
changes were:
1. Collection of aggregate state production and acreage data on the
10 principal crops in the U.S.;
2. aggregation of state data to conform to the regional definitions.
3. a system of equations expressing yield as a function of crop
acreage and other variables were specified and estimated for each
region;
4. the system of equations was used to provide a yield prediction for
each of the historical crop average mixes used in the sector
model holding all variables except acreage at the 1980 levels.
5. the predicted yield for each year was divided by the 1980
predicted yield to form a yield modifying ratio.
In the linear program (farm level component) of the economic model,
activities were not represented as production units of individual crops, but
as the combination of crop production arising from the relative acreage of
each crop planted in a particular year. The 1980 yields for each crop mix in
each region were adjusted to account for the acreage mix using the yield
modifying ratio prior to inserting the activity into the LP matrix. Thus,
each crop mix will have been corrected for the acreage effect of the crop mix
on yield, and will be at a realistic level because the crop mix included in
the LP solution was an historical observation.
Crop yields were modeled for: corn for grain, corn for silage, soybeans,
wheat, oats, barley, sorghum for grain, hay, cotton and rice, in regions where
these crops were grown. The data were for years 1960-1981 and were taken from
"Agricultural Statistics." The systems of equations were estimated using
Zellner's seemingly unrelated regression method (Zellner, 1962) because the
disturbances between equations within the system were expected to be
correlated.
Once the model was estimated econometrically, the yields were predicted
for the years 1970-1981 holding the durrniy intercept and time variables at the
1980 level. The subsequent predictions were then divided by the 1980
prediction to obtain the yield modifying ratios.
99
-------
Model Results
Assessing the quality of the results for such a model was made more
difficult because the correct sign of some variables could not be determined _a_
priori. The other acreage effect (AC. .), for example, could be either
positive or negative depending on thejcrop; being more or less competitive for
resources than at the average level. Interpretation of coefficients was
complicated because of the multicollinearity introduced by expressing acreage
variables as a percentage of the regional crop acreage in that year. Thus,
one acreage variable could not increase without another decreasing. However,
the structure of the model was not considered as important as the ability to
predict acreage response effects.
For the model to be successful, the explanatory variables needed to be
uncorrelated with weather. The more the explanatory variables were correlated
with weather, the more the weather effect, rather than the yield effect, was
being modeled.
A priori knowledge provided two possible checks. First, because acreage
effects were thought to have relatively small effects on yield, the yield
acreage effects were expected to show a small variance. Second, if only the
acreage effect had been modeled, it would be unreasonable for the yield
modifying ratios in a particular year to be all above, or all below the 1980
base value of one, since such a result is more likely to be to the consequence
of weather, not acreage changes.
The model, as estimated, appeared successful. The variation in the yield
ratios were generally small, and it was not obvious that excluded weather
effects had been captured by the explanatory variables. Trends on acreages
reflected increases in the yield of one crop countered by decreases in the
yields of other crops.
For the most part, signs of the major crops between regions were
consistent. In addition, with the exception of cotton, the major crops that
showed a positive own acreage effect had a negative effect of the margin. The
net effect of any biases in the estimates is dampened by the procedure used to
incorporate them in the analysis. The adjustments, measured as a ratio
relative to base yields are quite small, and distributed about the 1.0 value.
A value greater than one for a given crop tends to be offset by values of less
than one for other crops. Both the effect of changes in acreage on yield and
the countervailing effects in own and competing crop prices tend to constrain
the model to historically consistent crop mixes.
100
-------
APPENDIX C
Use of a Crop Model
to Generate Moisture Stress Adjustment Factors
The quantitative factors used to adjust the well-watered ozone response
functions are derived from a general crop model that includes ozone and water
effects on crop yields. This model is described by King and Snow (1984). To
simulate the drought-ozone interaction in this model, marketable yield Y is
expressed as the product of seasonal transpiration (ST) and transpiration
efficiency (TE), i.e.,
Y = ST • TE
This assumes that drought acting alone affects ST but not TE. Thus, drought
reduces yield in proportion to its effect on ST. Seasonal transpiration (ST)
is calculated with a simple soil water balance submodel, similar to that
described by Hanks and Hill (1980). The transpiration model reduced
transpiration on days when the plant available soil moisture falls below a
threshold value.
The model assumes that ozone reduces both ST and TE, but that less ozone
damage occurs on days of drought stress than would occur for well-watered
crops. Also, ozone is assumed to reduce the rate of root zone development.
Both of the above ozone effects influence soil moisture status. Thus, the
model accounts for ozone effects on water use plus drought effects on ozone
damage.
The model is used to generate a curve (Figure 4) depicting probable
adjustment factors to the well-watered ozone response functions. This was
done by running the model with and without ozone over a range of drought
severities. The model was run using Indianapolis, Indiana hourly ozone values
for 1978, and with a representative 1978 rainfall record from an Indiana
weather station. Daily potential evapotranspiration was derived from Indiana
weather records. To estimate a drought stress factor, the model was adjusted
to produce a 10 percent ozone loss for the well-watered case. The model was
then run with the above rainfall input, with and without ozone. In this case,
ozone produced a 6.9 percent yield loss and drought produced a 16.5 percent
loss in the presence of ambient ozone. Thus, the model predicts that a
drought which reduces yield by 16.5 percent (from the well-watered case)
modifies the well-watered ozone effects (expressed as a percent) by a factor
of 0.69. (For this prediction to be generally true, all combinations of
rainfall and evapotranspiration producing a given drought effect must also
101
-------
have the same effect on ozone-caused losses. Simulations to date indicate
that the timing of rainfall ozone episodes also influences the drought-ozone
interaction.)
To estimate the correction factor for other than the 16.5 percent drought
associated with the above rainfall record, the rainfall record was multiplied
by a constant which varied from .6 to 1.6. The model was then run with the
proportionally adjusted rainfall records to produce a plot of the correction
factor as a function of drought severity. This plot was approximated with the
three line segments of Figure 4. The middle line segment of that curve is a
least squares fit to the 6 points in the 9 to 33 percent drought interval.
The drought effect on ozone-caused loss was quite nonlinear for drought
effects of 0 to 8 percent. Because drought is patchily distributed across a
region (varying with microtopography) this nonlinearity will bias the
correction factor if it is calculated from average drought effects for a
region. To correct for this bias (in a crude fashion) it is assumed that the
correction factor was linear from 0 to 10 percent drought as shown in Figure
4. (This assumption would be strictly correct if regions with average drought
reduction of 0 to 10 percent were composed only of fields with no drought
stress and fields with a 10 percent yield reduction due to drought.) For
drought effects greater than 33 percent, there appeared to be little change in
the ozone response to drought.
It should be noted that the figure was produced assuming a crop cover
development and season length comparable to that of soybeans. It is believed
that crops with different season lengths may show a rather similar
drought-ozone interaction when the interaction is expressed in terms of
percent yield reduction due to drought. However, this has not been verified.
The modeled correction factor may also vary somewhat for ozone scenarios other
than the zero vs. 10 percent well-watered ozone effect used to construct
Figure 4.
102
-------
Table D.I.
APPENDIX D
Supporting Response and Ozone Data
Actual and adjusted seasonal average ozone levels by state,
1978-1982 a/
Year
State
Alabama
10% reduction
25% reduction
40% reduction
25% increase
Arizona
10% reduction
25% reduction
40% reduction
25% increase
Arkansas
10% reduction
25% reduction
40% reduction
25% increase
California
10% reduction
25% reduction
40% reduction
25% increase
Colorado
10% reduction
25% reduction
40% reduction
25% increase
Connecticut
10% reduction
25% reduction
40% reduction
25% increase
Delaware
10% reduction
25% reduction
40% reduction
25% increase
1978
50.47
45.40
37.90
30.30
63.10
45.62
41.10
34.20
27.40
57.00
56.62
51.00
42.50
34.00
70.80
46.82
42.10
35.10
28.10
58.50
50.36
45.30
37.80
30.20
63.00
45.50
41.00
34.10
27.30
56.90
43.00
38.70
32.30
25.80
53.80
1979
41.22
37.10
30.90
24.70
51.50
42.48
38.20
31.90
25.50
53.10
50.14
45.10
37.60
30.10
62.70
51.14
46.00
38.40
30.70
63.90
51.32
46.20
38.50
30.80
64.20
49.50
44.60
37.10
29.70
61.90
45.00
40.50
33.80
27.00
56.30
1980
46.98
42.30
35.20
28.20
58.70
51.63
46.50
38.70
31.00
64.50
51.47
46.30
38.60
30.90
64.30
52.04
46.80
39.00
31.20
65.10
50.92
45.80
38.20
30.60
63.70
58.83
52.90
44.10
35.30
73.50
36.00
32.40
27.00
21.60*
45.00
1981
per bil
47.28
42.60
35.50
28.40
59.10
47.93
43.10
35.90
28.80
59.90
47.62
42.90
35.70
28.60
59.60
50.44
45.40
37.80
30.30
63,10
48.10
43.30
36.10
28.90
60.10
51.00
45.90
38.30
30.60
63.80
54.00
48.60
40.50
32.40
67.50
1982
1 -J /IT)') — _
50.43
45.40
37.80
30.30
63.00
46.07
41.50
34.60
27.60
57.60
45.56
41.00
34.20
27.30
57.00
46.29
41.70
34.70
27.80
57.90
50.42
45.40
37.80
30.30
63.00
51.33
46.20
38.50
30.80
64.20
49.00
44.10
36.80
29.40
61.30
1978-1982
Average
47.3
46.7
50.3
49.3
50.2
51.2
45.4
103
-------
Table D.I. (continued)
Year
State
Florida
10% reduction
25% reduction
40% reduction
25% increase
Georgia
10% reduction
25% reduction
40% reduction
25% increase
Idaho
10% reduction
25% reduction
40% reduction
25% increase
Illinois
10% reduction
25% reduction
40% reduction
25% increase
Indiana
10% reduction
25% reduction
40% reduction
25% increase
Iowa
10% reduction
25% reduction
40% reduction
25% increase
Kansas
10% reduction
25% reduction
40% reduction
25% increase
1978
42.98
38.20
31.90
25.50
53.10
54.65
49.20
41.00
32.80
68.30
N.A.
N.A.
N.A.
N.A.
N.A.
49.92
44.90
37.40
30.00
62.40
48.58
43.70
36.40
29.10
60.70
47.25
42.50
35.40
28.40
59.10
47.53
42.80
35.60
28.50
59.40
1979
34.69
31.20
26.00
20.80*
43 . 40
43.40
39.10
32.60
26.00
54.30
51.00
45.90
38.30
30.60
63 . 80
45 . 30
40.80
34.00
27.20
56.60
39.87
3S.90
29.90
23.90*
49.80
40.72
36.60
30.50
24.40*
50.90
43.76
39.40
32.80
26.30
54.70
1980
f Y\ O "¥*•+• C
— ^pdl Uo
35.33
31.80
26.50
21.20*
44.20
46.82
42.10
35.10
28.10
58.50
45.71
41.10
34.30
27.40
57.10
46.92
42.20
35.20
28.20
58.70
46.72
42.00
35.00
28.00
58.40
42.11
37.90
31.60
25.30
52.60
45.57
40.90
34.10
27.30
56.80
1981
per billion)
42,51
38.30
31.90
25.50
53.10
45.87
41.30
34.40
27.50
57.50
47.60
42.80
35.70
28.60
59.50
41.69
37.50
31.30
25.00
52.10
45.10
40.60
33.80
27.10
56.40
32.88
29.10
24.30
19.40*
40.50
40.98
36.90
30.70
24.60
57.20
1982
38.87
35.00
29.20
23.30*
48.60
45.65
41.10
34.20
27.40
57.10
47.88
43.10
35.90
28.70
59.90
45.52
41.00
34.10
27.30
56.90
47.33
42.60
35.50
28.40
59.20
35.34
31.80
26.50
21.20*
44.20
41.62
37.50
31.20
25.00
52.00
1978-1982
Average
38.9
47.3
38.4
45.9
45.5
39.7
43.9
104
-------
Table D.I, (continued)
Year
State
Kentucky
10% reduction
25% reduction
40% reduction
25% increase
Louisiana
10% reduction
25% reduction
40% reduction
25% increase
Maine
10% reduction
25% reduction
40% reduction
25% increase
Maryland
10% reduction
25% reduction
40% reduction
25% increase
Massachusetts
10% reduction
25% reduction
40% reduction
25% increase
Michigan
10% reduction
25% reduction
40% reduction
25% increase
Minnesota
10% reduction
25% reduction
40% reduction
25% increase
1978
48.73
43.90
36.50
29.20
60.90
43.25
38.90
32.40
26.00
54.10
42.82
38.50
32.10
25.70
53.50
44.91
40.40
33.70
26.90
56.10
41.23
37.10
30.90
24.70
51.50
38.05
34.20
28.50
22.80*
47.60
43.98
39.60
33.00
26.40
55.00
1979
39.10
35.20
29.30
23.50*
48.89
35.14
31.60
26.40
21.10*
43.90
37.66
33.90
28.20
22.60*
47.10
41.82
37.60
31.40
25.10
52.30
44.17
39.80
33.10
26.50
55.20
35.60
32.00
26.70
21.40*
44.50
36.49
32.80
27.40
21.90*
45.60
1980
— f ni5T%t'C
(.parrs
44.88
40.40
33.70
26.90
56.10
42.22
38.00
31.70
25.30
52.80
34.98
31.50
26.20
21.00*
43.70
48.72
43.80
36.50
29.20
60.90
46.83
42.10
35.10
28.10
58.50
37.56
33.80
28.20
22.50*
47.00
39.25
35.30
29.40
23.60
49.10
1981
per billion)
39.16
35.20
29.40
23.50*
49.00
41.94
37.70
31.50
25.20
52.40
31.41
28.30
23.60
18.80*
39.30
49.73
44.80
37.30
29.80
62.20
44.92
40.40
33.70
27.00
56.20
37.93
34.10
28.40
22.80*
47.40
32.79
29.50
24.60
19.70*
41.00
1982
45.68
41.10
34.30
27.40
57.10
49.78
35.80
29.80
23.90*
49.70
35.57
32.00
26.60
21.30*
44.50
50.73
45.70
38.00
30.40
63.40
44.25
39.80
33.20
26.60
55.30
39.40
35.50
29.60
23.60*
49.30
33.92
30.50
25.40
20.40*
42.40
1978-1982
Average
43.5
40.5
36.5
47.2
44.3
37.7
37.3
105
-------
Table D.I. (continued)
Year
State
Mississippi
10% reduction
25% reduction
40% reduction
25% increase
Missouri
10% reduction
25% reduction
40% reduction
25% increase
Montana
10% reduction
25% reduction
40% reduction
25% increase
Nebraska
10% reduction
25% reduction
40% reduction
25% increase
Nevada
10% reduction
25% reduction
40% reduction
25% increase
New Hampshire
10% reduction
25% reduction
40% reduction
25% increase
New Jersey
10% reduction
25% reduction
40% reduction
25% increase
New Mexico
10% reduction
25% reduction
40% reduction
25% increase
1978
52.35
47.10
39.30
31.40
65.40
52.55
47.30
39.40
31.50
65,70
34.00
30.60
25.50
20.40*
42.50
49.03
44.10
36.80
29.40
61.30
49.47
44.50
37.10
29.70
61.80
42.25
38.00
31.70
25.40
52.80
42.10
37.90
31.60
25.30
52.60
49.96
45.00
37.50
30.00
62.50
1979
40.50
36.50
30.40
24.30*
50.60
48.51
43.70
36.40
29.10
60 . 60
49.43
44.50
37.10
29.70
61.80
42.30
38.10
31.70
25.40
52.90
53.31
48.00
40.00
32.00
66.60
40.75
36.70
30.60
24.50
50.90
43 . 60
39.20
32.70
26.20
54.50
47.90
43.10
35.90
28.70
59.90
1980
49.25
44.30
36.90
29.60
61.60
49.20
44.30
36.90
29.50
61.50
45.00
40.50
33.80
27.00
56.30
45.17
49.70
33.90
27.10
56.50
50.84
45.80
38.10
30.50
63.60
42.58
38.30
31.90
25.50
53.20
53.50
48.20
40.10
32.10
66.90
45.87
41.30
34.40
27.50
57.30
1981
per billion)
47.20
42.50
35.40
28.30
59.00
37.78
34.00
28.30
22.70*
47.20
46.00
41.40
34.50
27.60
57.50
38.10
34.30
28.60
22.90*
47.60
48.21
43.40
36.20
28.90
60.30
34.58
31.10
25.90^
20.70
43.20
50.30
45.30
37.70
30.20
62.90
48.40
43.60
36.30
29.00
60.50
1982
44.38
39.90
33.30
26.60
55.50
40.24
36.20
30.20
24.10*
50.30
46.17
41.60
34.60
27.70
57.70
33.45
30.10
25.10
20.10*
41.80
48.53
43.70
36.40
29.10
60.70
37.50
33.80
28.10
22.50*
46.90
51.30
46.20
38.50
30.80
64.10
47.02
42.30
35.30
28.20
58.80
1978-1982
Average
46.7
45.7
44.1
41.6
50.1
39.5
48.2
47.8
106
-------
Table D.I, (continued)
Year
State
New York
10% reduction
25% reduction
40% reduction
25% increase
North Carolina
10% reduction
25% reduction
40% reduction
25% increase
North Dakota
10% reduction
25% reduction
40% reduction
25% increase
Ohio
10% reduction
25% reduction
40% reduction
25% increase
Oklahoma
10% reduction
25% reduction
40% reduction
25% increase
Oregon
10% reduction
25% reduction
40% reduction
25% increase
Pennsylvania
10% reduction
25% reduction
40% reduction
25% increase
Rhode Island
10% reduction
25% reduction
40% reduction
25% increase
1978
42.13
37.90
31.60
25.30
52.70
50.83
52.90
44.10
35.30
73.50
37.00
33.30
27.80
22.20*
46.30
45.98
41.40
34.50
27.60
57.50
52.37
47.10
39.30
31.40
65.50
27.32
24.60
20.50
16.40*
34.20
43.65
39.30
32.70
26.20
54.60
41.33
37.20
31.00
24.80
51.70
1979
40.05
36.00
30.00
24.00*
50.10
40.81
36.70
30.60
24.50
51.00
36.00
32.40
27.00
21.60*
45.00
38.42
34.60
28.80
23.10*
48.00
48.53
43.70
36.40
29.10
60.70
30.11
27.10
22.60
18.10*
37.60
38.31
34.50
28.70
23.00*
47.90
47.00
42.30
35.30
28.20
58.80
1980
40.91
36.80
30.70
24.50
51.10
54.66
49.20
41.00
32.80
68.30
38.20
34.40
28.70
22.90*
47.80
42.77
38.50
32.10
25.70
53.50
47.44
42.70
35.60
28.50
59.30
32.06
28.90
24.00
19.20*
40.10
45.11
40.60
33.80
27.10
56.40
53.33
48.00
40.00
32.00
66.70
1981
per bil
37.14
33.40
27.90
22.30*
46.40
47.94
43.10
36.00
28.80
59.90
37.25
33.50
27.90
22.40*
46.60
41.04
36.90
30.80
24.60
51.30
46.54
41.90
34.90
27.90
58.20
35.20
31.70
26.40
21.10*
44.00
38.20
34.40
28.70
22.90*
47.80
51.66
46.50
38.70
31.00
64.60
1982
11 on "\ — —
36.74
33.10
27.60
22.00*
45.90
46.10
41.50
34.60
27.70
57.60
34.21
30.80
25.70
20.50*
42.80
43.88
39.50
32.90
26.30
54.90
44.40
40.00
33.30
26.60
55.50
38.18
34.40
28.60
22.90*
44.70
40.85
36.80
30.60
24.50
51.10
50.67
45.60
38.00
30.40
63.30
1978-1982
Average
39.4
49.7
36.5
42.4
47.9
32.6
41.2
48.8
107
-------
Table D.I. (continued)
Year
State
South Carolina
10% reduction
25% reduction
40% reduction
25% increase
South Dakota
10% reduction
25% reduction
40% reduction
25% increase
Tennessee
10% reduction
25% reduction
40% reduction
25% increase
Texas
10% reduction
25% reduction
40% reduction
25% increase
Utah
10% reduction
25% reduction
40% reduction
25% increase
Vermont
10% reduction
25% reduction
40% reduction
25% increase
Virginia
10% reduction
25% reduction
40% reduction
25% increase
Washington
10% reduction
25% reduction
40% reduction
25% increase
1978
63.52
57.20
47.60
38.10
79.40
42.27
38.50
32.10
25.70
53.50
50.32
45.30
37.70
30.20
62.90
42.23
38.10
31.70
25.30
52.80
49.05
44.10
36.80
29.40
61.30
43.07
38.80
32.30
25.80
53.80
52.90
47.60
39.70
31.70
66.10
27.63
24.90
20.70
16.60*
34.50
1979
46.50
41.90
34.90
27.90
58.10
38.74
34.90
29.10
23.20*
48.40
35.27
31.70
26.50
21.20*
44.10
46.16
41.50
34.60
27.70
57.70
48.39
43 . 60
36.30
29.00
60.50
42 . 86
38.60
32.10
25.70
53- . 60
40.71
36.60
30.50
24.40*
50.90
31.34
28,20
23.50
18.80*
39.20
1980
53.91
48.50
40.40
32.30
67.40
42.00
37.80
31.50
25.20
52.50
46.66
42.00
35.00
28.00
58.30
42.86
38.60
32.10
25.70
53.60
52.96
47.70
39.70
31.80
66.20
43.14
38.80
32.40
25.90
53.90
54.80
49.30
41.10
32.90
68.50
26.49
23.80
19.90
15.90*
33.10
1981
per billi
49.10
44.20
36.80
29.50
61.40
37.38
33.60
28.00
22.40*
46.70
43.22
38.90
32.40
25.90
54.00
43.64
39.30
32.70
26.20
54.60
49.90
44.90
37.40
29.90
62.40
35.36
31.80
26.50
21.20*
44.20
48.16
43.30
36.10
28.90
60.20
28.78
25.90
21.60
17.30*
36.00
1982
48.33
43.50
36.20
29.00
60.40
33.30
30.10
25.00
20.00*
41.70
48.12
43.30
36.10
28.90
60.20
45.57
41.00
34.20
27.30
57.00
52.40
47.20
39.30
31.40
65.50
37.29
33.60
28.00
22.40*
46.60
49.33
44.40
37.00
29.60
61.70
28.65
25.80
21.50
17.20*
35.80
1978-1982
Average
52.3
38.8
44.7
44.1
50.5
40.3
49.2
28.6
108
-------
Table D.I. (continued)
Year
State
West Virginia
10% reduction
25% reduction
40% reduction
25% increase
Wisconsin
10% reduction
25% reduction
40% reduction
25% increase
Wyoming
10% reduction
25% reduction
40% reduction
25% increase
1978
41.77
37.60
31.30
25.10
52.20
45.81
41.20
34.40
27.50
57.30
46.31
41.70
34.70
27.80
57.90
1979
37.91
34.10
28.40
22.70*
47.40
43.50
39.20
32.70
26.20
54.50
50.75
45.70
38.10
30.50
63.40
1980
f r\r\T 1" <
(.pal L.
44.14
39.70
33.10
26.50
55.20
40.46
36.40
30.30
24.30
50.60
47.56
42.80
35.70
28.50
59.50
1981
; per billion)
39.05
35.10
29.30
23.40* •
48.80
38.19
34.40
28.60
22.90*
47.70
48.83
43.90
36.60
29.30
61.00
1982
41.10
37.00
30.80
24.70
51.40
38.55
34.70
28.90
23.10*
48.20
49.71
44.70
37.30
29.80
62.10
1978-1982
Average
40.8
41.3
48.6
* Indicates that the reduced ozone level falls below the 25 ppb background
level reported in Heck et al. (1984a).
-' Source: J. Reagan (1984) and 1982 NCLAN Annual Report (Heck et al. 1983).
109
-------
Table D.2. April and May average ozone levels for winter wheat
producing states _§/
^•x. Year
Arizona
Ambient
10% reduction
25% reduction
40% reduction
25% increase
Arkansas
Ambient
10% reduction
25% reduction
40% reduction
25% increase
California
Ambient
10% reduction
25% reduction
40% reduction
25% increase
Colorado
Ambient
10% reduction
25% reduction
40% reduction
25% increase
Idaho
Ambient
10% reduction
25% reduction
40% reduction
25% increase
Illinois
Ambient
10% reduction
25% reduction
40% reduction
25% increase
Indiana
Ambient
10% reduction
25% reduction
40% reduction
25% increase
1978
N.A.
N.A.
N.A.
N.A.
N.A.
42.31
38.10
37.10
25.40
52.90
N.A.
N.A.
N.A.
N.A,,
N.A.,
47.44
42.70
35.60
28.50
59.30
N.A.
N.A.
N.A.
N.A.
N.A.
42.92
38.60
32.20
25.80
53.70
40.36
36.30
30.30
24.20
50.50
1979
— /"TIT
ipt
47.90
43.10
35.90
28.70
59.90
50.85
45.80
38.10
30.50
63.60
45.64
44.10
34.20
27.40
57.10
48.23
43.40
36.20
28.90
60.30
45.63
41.10
34.20
27.40
57.00
34.66
31.20
26.00
20.80
43.30
32.68
29.40
24.50
19.60
40.90
1980
ihl
JUJ
44.00
39.60
29.70
26.40
55.00
44.74
40.30
33.60
26.80
55.90
43.39
39.10
32.50
26.00
54.20
46.48
41.80
34.90
27.90
58.10
46.69
42.00
35.00
28.00
58.40
41.35
37.50
31.00
24.80
51.70
33.55
30.20
25.20
20.10
41.90
1978-80
Average
46.00
46.00
41.40
36.30
27.60
57.50
44.50
41.60
33.40
26.70
55.70
47.40
46.20
39.60
35.50
110
-------
Table D.2. (continued)
s^\ar
Kansas
Ambient
10% reduction
25% reduction
40% reduction
25% increase
Kentucky
Ambient
10% reduction
25% reduction
40% reduction
25% increase
Michigan
Ambient
10% reduction
25% reduction
40% reduction
25% increase
Missouri
Ambient
10% reduction
25% reduction
40% reduction
25% increase
Montana
Ambient
10% reduction
25% reduction
40% reduction
25% increase
Nebraska
Ambient
10% reduction
25% reduction
40% reduction
25% increase
New Mexico
Ambient
10% reduction
25% reduction
40% reduction
25% increase
1978
39.00
35.10
29.30
23.40
48.80
39.60
35.60
29.70
23.80
49.50
37.00
33.30
27.80
22.20
46.30
38.39
34.60
28.80
23.00
48.00
28.00
25.20
21.00
16.80
35.00
42.38
38.10
31.80
25.40
53.00
47.00
42.30
35.30
28.20
58.80
1979
frml
ippi
42.04
37.80
31.50
25.20
52.60
37.07
33.40
27.80
22.20
46.30
30.10
27.10
20.30
18.10
37.60
47.27
42.50
35.50
28.40
59.10
41.13
37.00
30.80
24.70
51.40
42.83
38.50
32.10
25.70
53.50
41.60
37.40
31.20
25.00
52.00
1980
-,1
') ~
38.32
34.40
28.70
22.90
47.80
38.88
35.00
29.20
23.30
48.60
34.30
30.90
23.20
20.60
42.90
46.56
41.90
34.90
27.90
58.20
46.50
41.90
34.90
27.90
58.10
41.72
37.50
31.30
25.00
52.20
41.70
37.50
31.30
25.00
52.10
1978-80
Average
39.80
38.50
33.80
44.00
38.50
42.30
43.40 '
111
-------
Table D.2. (continued)
^\^ Year
State\
New York
Ambient
10% reduction
25% reduction
40% reduction
25% increase
North Carolina
Ambient
10% reduction
25% reduction
40% reduction
25% increase
Ohio
Ambient
10% reduction
25% reduction
40% reduction
25% increase
Oklahoma
Ambient
10% reduction
25% reduction
40% reduction
25% increase
Pennsylvania
Ambient
10% reduction
25% reduction
40% reduction
25% increase
South Carolina
Ambient
10% reduction
25% reduction
40% reduction
25% increase
South Dakota
Ambient
10% reduction
25% reduction
40% reduction
25% increase
1978
42.13
37.90
31.60
25.30
52.70
45.68
41.10
34.30
27.40
57.10
39 . 89
35.90
29.90
23.90
49.90
40.0]
36.00
30.00
24.00
50.00
42.14
37.90
31.60
25.30
52.70
48.47
43.60
36.40
29.10
60.60
37.10
33.40
27.80
22.30
46.40
1979
_ fnr>}
IPPC
36.68
33.00
27.50
22.00
45.90
42.37
38.10
31.80
25.40
53.00
36.32
32.70
27.20
21.80
45.40
42.76
38.50
32.10
25.70
53.50
34.20
30.80
25.70
20.50
42.80
46.59
41.90
34.90
28.00
58.20
37.20
33.50
27.90
22.30
46.50
1980
•.•\
')
38.72
34.80
29.00
23.20
48.40
50.08
45.10
37.60
30.00
62.60
32.30
29.10
24.20
19.40
40.40
32.55
29.30
24.40
19.50
40.70
39.43
35.50
29.60
23.70
49.30
52.31
47.10
39.20
31.40
65.40
39.80
35.80
29.90
23.90
49.80
1978-80
Average
39.20
46.00
36.20
38.40
38.50
49.10
38.00
112
-------
Table D.2. (continued)
Year
State
1978
1979
1980
1978-80
Average
Tennessee
Ambient
10% reduction
25% reduction
40% reduction
25% increase
Texas
Ambient
10% reduction
25% reduction
40% reduction
25% increase
Wyoming
Ambient
10% reduction
25% reduction
40% reduction
25% increase
(ppb)
42.22
38.00
31.70
25.30
52.80
41.80
37.60
31.40
25.10
52.30
45.74
41.20
34.30
27.40
57.20
36.05
32.40
27.00
21.60
45.10
39.61
35.60
29.70
23.80
49.50
47.63
42.90
35.70
28.60
59.50
44.05
39.60
33.00
26.40
55.10
39.60
35.60
29.70
23.80
49.50
46.74
42.10
35.10
28.00
58.40
40.80
40.30
46.70
a/
Winter wheat producing states (e.g., Washington, Oregon) with
winter-spring ambient ozone concentrations near or below the
25 parts per billion reported as background by Heck et al.
(1982) are not listed in the table. Reductions below such
levels are not feasible. These states are included in the
economic assessment, however, as shown in the yield adjust-
ments portrayed in Tables D.16 through D.35, by using a
minimum ozone value of 22 ppb.
113
-------
Table D.3. Predicted soybean yield adjustments by state, year and
ozone assumption a/
State and Ozone
Assumption
Alabama
10% reduction
25% reduction
40% reduction
25% increase
Arkansas
10% reduction
25% reduction
40% reduction
25% increase
Delaware
10% reduction
25% reduction
40% reduction
25% increase
Florida
10% reduction
25% reduction
40% reduction
25% increase
Georgia
10% reduction
25% reduction
40% reduction
25% increase
Illinois
10% reduction
25% reduction
40% reduction
25% increase
Indiana
10% reduction
25% reduction
40% reduction
25% increase
Iowa
10% reduction
25% reduction
40% reduction
25% increase
1978
2.80
7.02
11.22
- 6.92
3.23
8.12
13.01
- 7.91
2.30
5.74
9.15
- 5.73
2.27
5.66
9.01
- 5.65
3.09
7.76
12.43
- 7.59
2.77
6.93
11.07
- 6.83
2.67
6.69
10.69
- 6.61
2.58
6.46
10.31
- 6.40
1979
2.18
5.45
8.67
- 5.45
2.78
6.96
11.13
- 6.86
2.43
6.08
9.69
- 6.04
1.77
4.39
6.97
- 4.44
2.33
5.81
9.26
- 5.79
2.45
6.13
9.77
- 6.09
2.10
5.23
8.31
- 5.24
2.15
5.37
8.54
- 5.37
1980
2.57
6.42
10.42
- 6.36
2.87
7.20
11.51
- 7.08
1.85
4.60
7.31
- 4.64
1.81
4.49
7.13
- 4.54
2.56
6.39
10.19
- 6.33
2.56
6.41
10.22
- 6.35
2.55
6.37
10.17
- 6.32
2.24
5.60
8.91
- 5.59
1981
2.59
6.47
10.32
- 6.41
2.61
6.53
10.42
- 6.46
3.05
7.65
12.24
- 7.49
2.27
5.56
0.01
- 5.65
2.49
6.23
9.93
- 6.18
2.22
5.53
8.79
- 5.52
2.44
6.10
9.72
- 6.06
1.62
4.03
6.39
- 4.09
1982
2.80
7.02
11.21
- 6.91
2.47
6.17
9.85
- 6.13
2.70
6.77
10.81
- 6.68
2.03
5.06
8.05
- 5.08
2.48
6.19
9.87
- 6.15
2.47
6.17
9.83
- 6.13
2.59
6.48
10.34
- 6.41
1.81
4.50
7.14
- 4.54
1978-82
5 year Ave.
2.59
6.48
10.37
- 6.41
2.79
7.00
11.18
- 6.89
2.47
6.17
9.84
- 6.12
2.03
5.03
8.03
- 5.07
2.59
6.48
10.34
- 6.41
2.49
6.23
9.94
- 6.18
2.47
6.17
9.85
- 6.13
2.08
5.19
8.26
- 5.20
114
-------
Table 0.3. (continued)
State and Ozone
Assumption
Kansas
10% reduction
25% reduction
40% redution
25% increase
Kentucky
10% reduction
25% reduction
40% reduction
25% increase
Louisiana
10% reduction
25% reduction
40% reduction
25% increase
Maryland
10% reduction
25% reduction
40% reduction
25% increase
Michigan
10% reduction
25% reduction
40% reduction
25% increase
Minnesota
10% reduction
25% reduction
40% reduction
25% increase
Mississippi
10% reduction
25% reduction
40% reduction
25% increase
Missouri
10% reduction
25% reduction
40% reduction
25% increase
1978
2.60
6.51
10.39
- 6.45
2.68
6.72
10.73
- 6.64
2.32
5.79
9.21
- 5.77
2.43
6.06
9.67
- 6.03
1.98
4.93
7.83
- 4.96
2.37
5.91
9.41
- 5.88
2.93
7.35
11.76
- 7.22
2.95
7.39
11.82
- 7.25
1979
2.35
5.87
9.35
- 5.85
2.05
5.10
8.11
- 5.12
1.80
4.46
7.09
- 4.51
2.22
5.55
8.83
- 5.54
1.82
4.54
7.20
- 4.58
1.88
4.68
7.43
- 4.72
2.14
5.33
8.48
- 5.34
2.67
6.68
10.67
- 6.60
1980
2.47
6.16
9.82
- 6.12
2.43
6.06
9.66
- 6.03
2.25
5.61
8.94
- 5.61
2.68
6.72
10.73
- 6.64
1.95
4.85
7.71
- 4.88
2.06
5.12
8.15
- 5.14
2.72
6.81
10.88
- 6.72
2.72
6.80
10.86
- 6.71
1981
2.17
5.41
8.60
- 5.41
2.05
5.11
8.12
- 5.13
2.23
5.57
8.86
- 5.56
2.75
6,89
11.01
- 6.80
1.97
4.91
7.80
- 4.94
1.65
4.10
6.50
- 4.15
2.58
6.46
10.30
- 6.39
1.96
4.89
7.76
- 4.91
1982
2.21
5.51
8.78
- 5.51
2.48
6.20
9.88
- 6.15
2.09
5.21
8.29
- 5.22
2.82
7.07
11.30
- 6.96
2.07
5.15
8.19
- 5.17
1.72
4.27
6.78
- 4.32
2.39
5.98
9.52
- 5.95
2.12
5.29
8.41
- 5.30
1978-82
5 Year Ave.
2.36
5.89
9.39
- 5.87
2.34
5.84
9.30
- 5.81
2.14
5.33
8.48
- 5.33
2.58
6.46
10.31
- 6.39
1.96
4.88
7.75
- 4.91
1.94
4.82
7.65
- 4.82
2.55
6.41
10.19
- 6.32
2.48
6.21
9.90
- 6.15
115
-------
Table D.3. (continued)
State and Ozone
Assumption
Nebraska
10% reduction
25% reduction
40% reduction
25% increase
New Jersey
10% reduction
25% reduction
40% reduction
25% increase
New York
10% reduction
25% reduction
40% reduction
25% increase
North Carolina
10% reduction
25% reduction
40% reduction
25% increase
North Dakota
10% reduction
25% reduction
40% reduction
25% increase
Ohio
10% reduction
25% reduction
40% reduction
25% increase
Oklahoma
10% reduction
25% reduction
40% reduction
25% increase
Pennsylvania
10% reduction
25% reduction
40% reduction
25% increase
1978
2.71
6.77
10.81
- 6.69
2.24
5.59
8.90
- 5.59
2.24
5.60
8.91
- 5.59
3.39
8.52
13.68
- 8.28
1.91
4.76
7.56
- 4.79
2.50
6.25
9.96
- 6.20
2.93
7.36
11.77
- 7.22
2.34
5.85
9.32
- 5.83
1979
2.26
5.63
8.96
- 5.62
2.34
5.84
9.31
- 5.82
2.11
5.26
8.36
- 5.27
2.16
5.38
8.56
- 5.38
1.85
4.60
7.31
- 4.64
2.00
4.99
7 . 93
- 5.01
2.67
6.68
10.67
- 6.61
2.00
4.97
7.90
- 5.00
1980
2.45
6.11
9.74
- 6.07
3.01
7.56
12.10
- 7.41
2.16
5.40
8.59
- 5.40
3.09
7.75
12.43
- 7.60
1.99
4.95
7.87
- 4.98
2.29
5.71
9.08
- 5.69
2.60
6.50
10.37
- 6.43
2.44
6.10
9.72
- 6.06
1981
1.98
4.94
7.85
- 4.96
2.79
6.99
11.17
- 6.89
1.92
4.78
7.60
- 4.82
2.63
6.58
10.51
- 6.51
1.93
4.80
7.63
- 4.83
2.17
5.42
8.62
- 5.42
2.54
6.34
10.12
- 6.29
1.99
4.95
7.87
- 4.98
1982
1.69
4.20
6.66
- 4.25
2.86
7,17
11.46
- 7.05
1.90
4.72
7.49
- 4.75
2.51
6.27
9.99
- 6.22
1.74
4.32
6.85
- 4.37
2.36
5.89
9.39
- 5.87
2.39
5.98
9.53
- 5.95
2.16
5.39
8.57
- 5.39
1978-82
5 Year Ave.
2.22
5.53
8.80
- 5.52
2.65
6.63
10.59
- 6.55
2.07
5.15
8.19
- 5.17
2.76
6.90
11.03
- 6.80
1.88
4.69
7.44
- 4.72
2.26
5.65
9.00
- 5.64
2.63
6.57
10.49
- 6.50
2.19
5.45
8.68
- 5.45
116
-------
Table D.3. (continued)
State and Ozone
Assumption
South Carolina
10% reduction
25% reduction
40% reduction
25% increase
South Dakota
10% reduction
25% reduction
40% reduction
25% increase
Tennessee
10% reduction
25% reduction
40% reduction
25% increase
Texas
10% reduction
25% reduction
40% reduction
25% increase
Virgina
10% reduction
25% reduction
40% reduction
25% increase
Wisconsin
10% reduction
25% reduction
40% reduction
25% increase
1978
3.73
9.39
15.11
- 9.05
2.25
5.62
8.95
- 5.61
2.79
7.00
11.18
- 6.89
2.25
5.62
8.94
- 5.61
2.97
7.45
11.92
- 7.31
2.49
6.22
9.91
- 6.16
1979
2.53
6.34
10.11
- 6.28
2.02
5.04
8.01
- 5.06
1.80
4.49
7.12
- 4.53
2.51
6.28
10.01
- 6.23
2.15
5.36
8.53
- 5.37
2.34
5.84
9.31
- 5.82
1980
3.04
7.63
12.22
- 7.47
2.24
5.58
8.88
- 5.57
2.54
6.36
10.15
- 6.31
2.29
5.72
9.11
- 5.71
3.10
7.79
12.48
- 7.62
2.14
5.32
8.47
- 5.33
1981
2.71
6.78
10.83
- 6.70
1.94
4.82
7.66
- 4.85
2.32
5.78
9.21
- 5.76
2.34
5.85
9.32
- 5.83
2.65
6.62
10.57
- 6.55
1.99
4.95
7.87
- 4.98
1982
2.66
6.65
10.62
- 6.57
1.69
4.19
6.64
- 4.24
2.64
6.61
10.56
- 6.54
2.47
6.18
9.85
- 6.13
2.73
6.82
10.90
- 6.73
2.01
5.01
7.96
- 5.03
1978-82
Average
2.93
7.36
11.78
- 7.21
2.03
5.05
8.03
- 5.07
2.42
6.05
9.64
- 6.01
2.37
5.92
9.45
- 5.90
2.72
6.81
10.88
- 6.72
2.20
5.47
8.70
- 5.47
a/
— Predicted yield adjustments based on Weibull response model estimated
from a pooled set of 16 cultivar experiments (Heck et al., 1984b;
W.W. Cure, personal communication).
117
-------
Table D.4. Predicted corn yield adjustments by state, year and ozone
assumption aj
State and Ozone
Assumption
Alabama
10% reduction
25% reduction
40% reduction
25% increase
Arizona
10% reduction
25% reduction
40% reduction
25% increase
Arkansas
10% reduction
25% reduction
40% reduction
25% increase
California
10% reduction
25% reduction
40% reduction
25% increase
Colorado
10% reduction
25% reduction
40% reduction
25% increase
Connecticut
10% reduction
25% reduction
40% reduction
25% increase
Delaware
10% reduction
25% reduction
40% reduction
25% increase
Florida
10% reduction
25% reduction
40% reduction
25% increase
1978
.55
1.13
1.50
-2.12
.39
.79
1.04
-1.50
.83
1.69
2.21
-3.14
.42
.87
1.13
-1.65
.55
1.12
1.46
-2.14
.38
.78
1.03
-1.49
.31
.64
.84
-1.22
.30
.62
.81
-1.17
1979
.27
.55
.74
-1.05
.30
.62
.81
-1.17
.54
1.10
1.44
-2.07
.58
1.18
1.55
-2.26
.58
1.20
1.56
-2.29
.51
1.05
1..38
-2. ,01
.37
.75
.99
-1.43
.15
.30
.40
- .57
Year
1980
.43
.88
1.15
-1.64
.60
1.22
1.60
-2.28
.59
1.21
1.58
-2.26
.61
1.26
1.64
-2.41
.57
1.16
1.52
-2.23
.95
1.93
2.52
-3.60
.17
.34
.45
- .65
.16
.32
.42
- .61
1981
.44
.90
1.19
-1.68
.46
.94
1.23
-1.79
.45
.92
1.20
-1.72
.55
1.13
1.47
-2.15
.47
.95
] .25
-].82
.57
1.17
1.53
-2.24
.70
1.43
1.87
-2.68
.30
.62
.81
-1.17
1982
.55
1.13
1.50
-2.11
.40
.81
1.07
-1.56
.38
.78
1.03
-1.50
.41
.83
1.09
-1.58
.55
1.12
1.47
-2.15
.59
1.20
1.57
-2.29
.50
1.02
1.33
-1.90
.22
.45
.59
- .85
1978-82
Average
.45
.92
1.22
-1.72
.43
.88
1.15
-1.66
.56
1.14
1.49
-2.14
.51
1.05
1.38
-2,01
.54
1.11
1.45
-2.12
.60
1.23
1.60
-2.33
.41
.84
1.10
-1.58
.23
.46
.60
- .87
118
-------
Table D.4. (continued)
State and Ozone
Assumption
Georgia
10% reduction
25% reduction
40% reduction
25% increase
Idaho
10% reduction
25% reduction
40% reduction
25% increase
Illinois
10% reduction
25% reduction
40% reduction
25% increase
Indiana
10% reduction
25% reduction
40% reduction
25% increase
Iowa
10% reduction
25% reduction
40% reduction
25% increase
Kansas
10% reduction
25% reduction
40% reduction
25% increase
Kentucky
10% reduction
25% reduction
40% reduction
25% increase
Louisana
10% reduction
25% reduction
40% reduction
25% increase
Maine
10% reduction
25% reduction
40% reduction
25% reduction
1978
.73
1.49
1.05
-2.78
N.A.
N.A.
N.A.
N.A.
.53
1.09
1.42
-2.07
.48
.99
1.29
-1.88
.44
.90
1.17
-1.70
.45
.91
1.20
-1.74
.49
1.00
1.31
1.90
.32
.66
.86
1.24
.31
.63
.83
-1.20
1979
.32
.66
.87
-1.26
.57
1.17
1.53
-2.19
.38
.77
1.01
-1.47
.24
.49
.64
- .93
.26
.53
.69
-1.00
.33
.68
.89
-1.30
.22
.46
.60
.87
.15
.32
.41
.59
.20
.40
.53
- .76
Year
1980
.42
.87
1.13
-1.65
.39
.80
1.04
-1.52
.43
.87
1.14
-1.66
.42
.86
1.13
-1.61
.29
.60
.78
-1.12
.38
.78
1.02
-1.49
.36
.75
.98
1.42
.29
.60
.79
1.13
.15
.31
.41
- .59
1981
.39
.81
1.06
-1.53
.45
.92
1.20
-1.71
.28
.58
.75
-1.09
.37
.76
.99
-1.43
.12
.24
.31
- .45
.26
.54
.71
-1.03
.23
.46
.61
.87
.29
.59
.77
1.11
.10
.21
.28
- .40
1982
.39
.79
1.04
-1.51
.46
.94
1.23
-1.73
.38
.79
1.03
-1.49
.44
.90
1.18
-1.68
.16
.32
.42
- .61
.28
.57
.75
-1.09
.39
.80
1.04
1.51
.24
.49
.64
.92
.16
.33
.43
- .62
1978-82
Average
.45
.92
1.21
- 1.75
.47
.96
1.25
-1.79
.40
.82
1.07
-1.56
.39
.80
1.05
-1.51
.25
.52
.68
- .98
.34
.70
.91
-1.33
.34
.69
.91
1.32
.26
.53
.69
1.00
.18
.38
.49
- .71
119
-------
Table D.4. (continued)
State and Ozone
Assumption
Maryland
10% reduction
25% reduction
40% reduction
25% increase
Massachusetts
10% reduction
25% reduction
40% reduction
25% increase
Michigan
10% reduction
25% reduction
40% reduction
25% increase
Minnesota
10% reduction
25% reduction
40% reduction
25% increase
Mississippi
10% reduction
25% reduction
40% reduction
25% increase
Missouri
10% reduction
25% reduction
40% reduction
25% increase
Montana
10% reduction
25% reduction
40% reduction
25% increase
Nebraska
10% reduction
25% reduction
40% reduction
25% increase
1978
.37
.75
.98
-1.42
.27
.55
.73
-1.05
.20
.42
.55
- .79
.34
.70
.91
-1.32
.63
1.28
1.68
-2.39
.64
1.30
1.70
-2.43
.14
.28
.37
- .53
.50
1.02
1.33
-1.94
1979
.28
.58
.76
-1.10
.34
.71
.92
-1.34
.16
.33
.43
- .62
.18
.36
.47
- ,68
.25
.52
.68
- .99
.48
.98
1.28
-1.87
.51
1.05
1.37
-2.00
.30
.61
.79
-1.15
Year
1980
.49
1.00
1.30
-1.90
.42
.87
1.14
-1.65
.19
.40
.52
- .75
.23
.47
.61
- .88
.51
1.04
1.35
-1.98
.50
1.03
1.35
-1.97
.37
.75
.99
-1.43
.37
.76
1.00
-1.45
120
1981
.52
1.07
1.40
-2.00
.37
.75
.98
-1.42
.20
.41
.54
- .78
.12
.25
.32
- .47
.44
.89
1.17
-1.70
.20
.62
.62
- .77
.40
.82
1.07
-1.55
.20
.42
.55
- .79
1982
.56
1.15
1.50
-2.15
.35
.71
.93
-1.35
.23
.47
.62
- .89
.14
.28
.36
- .53
.35
.72
.94
-1.36
.25
.78
.78
- .96
.40
.83
1.08
-1.57
.13
.27
.35
- .50
1978-82
Average
.44
.91
1.99
-1.71
.35
.72
.94
-1.36
.20
.41
.53
- .77
.20
.41
.54
- .78
.43
.89
1.16
-1.68
.41
1.15
-1.60
.36
.75
.97
-1.42
.30
.62
.80
-1.17
-------
Table D.4. (continued)
State and Ozone
Assumption
Nevada
10% reduction
25% reduction
40% reduction
25% increase
New Hampshire
10% reduction
25% reduction
40% reduction
25% increase
New Jersey
10% reduction
25% reduction
40% reduction
25% increase
New Mexico
10% reduction
25% reduction
40% reduction
25% increase
New York
10% reduction
25% reduction
40% reduction
25% increase
North Carolina
10% reduction
25% reduction
40% reduction
25% increase
North Dakota
10% reduction
25% reduction
40% reduction
25% increase
Ohio
10% reduction
25% reduction
40% reduction
25% increase
1978
.51
1.05
1.38
-1.96
.29
.60
.79
-1.55
.29
.60
.78
-1.13
.53
1.09
1.42
-2.08
.29
.60
.78
-1.13
.95
1.93
2.52
-3.60
.18
.38
.50
- .72
.40
.81
1.06
-1.55
1979
.67
1.37
1.79
-2.55
.26
.53
.70
-1.01
.33
.68
.88
-1.28
.46
.94
1.23
-1.79
.24
.50
.66
- .95
.26
.53
.70
-1.01
.17
.34
.45
- .65
.21
.43
.57
- .82
Year
1980
.57
1.16
1.51
-2.17
.30
.62
.81
-1.18
.68
1.39
1.81
-2.59
.39
.81
1.06
-1.53
.26
.54
.71
-1.02
.73
1.49
1.95
-2.78
.21
.42
.55
- .80
.31
.63
.83
-1.20
1981
.47
.96
1.26
-1.83
.15
.30
.39
- .56
.54
1.12
1.46
-2.09
.48
.97
1.27
-1.86
.19
.38
.50
- .73
.46
.94
1.23
-1.79
.19
.39
.51
- .73
.27
.55
.71
-1.03
1982
.48
.98
1.29
-1.87
.19
.40
.52
- .75
.58
1.20
1.56
-2.23
.43
.88
1.15
-1.68
.18
.37
.48
- .70
.40
.82
1.07
-1.56
.14
.29
.38
- .54
.34
.69
.90
-1.31
1978-82
Average
.54
1.10
1.44
-2.08
.24
.49
.64
- .93
.49
.99
1.30
-1.86
.46
.94
1.23
-1.79
.23
.48
.63
- .91
.56
1.14
1.50
-2.15
.18
.36
.48
- .69
.30
.62
.81
-1.18
121
-------
Table D.4. (continued)
State and Ozone
Assumption
Oklahoma
10% reduction
25% reduction
40% reduction
25% increase
Oregon
10% reduction
25% reduction
40% reduction
25% increase
Pennsylvania
10% reduction
25% reduction
40% reduction
25% increase
Rhode Island
10% reduction
25% reduction
40% reduction
25% increase
South Carolina
10% reduction
25% reduction
40% reduction
25% increase
South Dakota
10% reduction
25% reduction
40% reduction
25% increase
Tennessee
10% reduction
25% reduction
40% reduction
25% increase
Texas
10% reduction
25% reduction
40% reduction
25% increase
1978
.63
1.29
1.68
-2.46
.06
.13
.17
- .24
.33
.68
.89
-1.29
.27
.56
.73
-1.06
1.24
2.52
3.29
-4.69
.30
.61
.79
-1.15
.55
1.12
1.46
-2.13
.29
.60
.79
-1.14
1979
.48
.98
1.29
-1.87
.09
.18
.24
- .34
.21
.43
.56
- .81
.43
.88
1.15
-1.67
.41
.85
1.11
-1.59
.22
.,45
..58
- .84
.16
.32
.42
- .60
.40
.83
1.08
-1.57
Year
1980
.44
.91
1.19
-1.73
.11
.23
.30
- .43
.37
.76
1.00
-1.45
.67
1.37
1.79
-2.55
.70
1.42
1.86
-2.66
.29
.59
.77
-1.12
.42
.86
1.12
-1.63
.31
.64
.83
-1.21
1981
.41
.85
1.11
-1.62
.15
.32
.42
- .60
.21
.42
.55
- .80
.60
1.23
1.60
-2.29
.50
1.02
1.34
-1.95
.19
.39
.51
- .74
.32
.65
.86
-1.24
.33
.68
.89
-1.28
1982
.35
.72
.94
-1.37
.21
.42
.55
- .80
.26
.54
.70
-1.02
.56
1.14
1.50
-2.14
.47
.97
1.27
-1.85
.13
.26
.35
- .50
.47
.95
1.25
-1.82
.38
.79
1.03
-1.50
1978-82
Average
.46
.95
1.24
-1.81
.13
.26
.34
- .48
.28
.57
.74
-1.07
.51
1.04
1.35
-1.94
.66
1.36
1.77
-2.55
.22
.46
.60
- .87
.38
.78
1.02
-1.49
.34
.71
.92
-1.34
122
-------
Table D.4. (continued)
State and Ozone
Assumption
Utah
10% reduction
25% reduction
40% reduction
25% increase
Vermont
10% reduction
25% reduction
40% reduction
25% increase
Virginia
10% reduction
25% reduction
40% reduction
25% increase
Washington
10% reduction
25% reduction
40% reduction
25% increase
West Virginia
10% reduction
25% reduction
40% reduction
25% increase
Wisconsin
10% reduction
25% reduction
40% reduction
25% increase
Wyoming
10% reduction
25% reduction
40% reduction
25% increase
1978
.50
1.02
1.34
-1.95
.32
.65
.85
-1.23
.65
1.33
1.74
-2.48
.07
.14
.18
- .25
.28
.58
.76
-1.10
.39
.80
1.05
-1.53
.41
.83
1.09
-1.59
1979
.48
.97
1.27
-1.86
.31
.64
.83
-1.21
.26
.53
.69
-1.00
.10
.21
.28
- .40
.20
.41
.54
- .78
.33
.67
.88
-1.28
.56
1.15
1.50
-2.15
Year
1980
.65
1.34
1.75
-2.49
.32
.65
.85
-1.23
.74
1.51
1.97
-2.82
.06
.12
.15
- .22
.34
.70
.92
-1.34
.25
.52
.68
- .98
.45
.92
1.20
-1.74
1981
.53
1.08
1.42
-2.02
.16
.32
.42
- .61
.47
.96
1.25
-1.82
.08
.16
.20
- .29
.22
.46
.60
- .87
.21
.42
.55
- .80
.49
1.01
1.31
-1.88
1982
.63
1.29
1.68
- 2.41
.19
.39
.51
- .74
.51
1.04
1.36
-1.99
.07
.15
.20
- .29
.27
.55
.72
-1.04
.21
.44
.57
- .83
.52
1.07
1.40
-2.00
1978-82
Average
.56
1.14
1.49
-2.15
.26
.53
.69
-1.00
.52
1.07
1.40
-2.02
.08
.15
.20
- .29
.26
.54
.71
-1.02
.28
.57
.75
-1.08
.49
1.00
1.30
-1.87
— Predicted yield adjustments based on Weibull response model of three
pooled corn cultivars.
123
-------
Table D.5. Predicted southeast cotton (nonirrigated) yield adjustments
by state, year and ozone assumptions a/
State and Ozone
Assumption
Alabama
10% reduction
25% reduction
40% reduction
25% increase
Arkansas
10% reduction
25% reduction
40% reduction
25% indrease
Florida
10% reduction
25% reduction
40% reduction
25% increase
Georgia
10% reduction
25% reduction
40% reduction
25% increase
Kentucky
10% reduction
25% reduction
40% reduction
25% increase
Louisiana
10% reduction
25% reduction
40% reduction
25% increase
Mississippi
10% reduction
25% reduction
40% reduction
25% increase
Missouri
10% reduction
25% reduction
40% reduction
25% increase
1978
3.09
6.93
9.84
-9.48
4.19
9.45
13.45
-12,54
1.98
4.40
6.20
-6.20
3.82
8.59
12.21
-11.52
2.82
6.32
8.95
-8.70
2.07
4.61
6.51
-6.47
3.41
7.66
10.86
-10.37
3.44
7.74
10.97
-10.47
1979
1.83
4.07
5 . 73
-S.55
3.04
6.82
9.66
-9.34
1.17
2.58
3.63
-3.72
2.08
4. .65
6. .57
-6.53
1.58
3.54
4.98
-5.03
1.21
2.68
3.76
-3.84
1.74
3.88
5.47
-5.50
2.78
6.24
8 . 84
-8.60
1980
2.57
5.74
8.11
-8.25
3.26
7.32
10.37
-9.95
1.23
2.72
3.81
-3.89
2.55
5.59
8.04
-7.88
2.28
5.09
7.18
-7.10
1.94
4.33
6.10
-6.10
2.90
6.51
9.21
-8.93
2.90
6.48
9.18
-8.91
1981
2.61
5.84
8.25
-8.07
2.66
5.95
8.41
-8.22
1.98
4.41
6.21
-6.20
2.42
5.39
7.61
-7.48
1.60
3.55
5.00
-5.05
1.92
4.26
6.00
-5.99
2.61
5.81
8.22
-8.04
1.46
3.24
4.55
-4.61
1982
3.09
6.93
9.82
-9.47
2.37
5.30
7.47
-7.37
1.57
3.48
4.90
-4.95
2.37
5.31
7.51
-7.41
2.39
5.33
7.53
-7.42
1.67
3.71
5.21
-5.25
2.21
4.93
6.97
-6.91
1.72
3.81
5.37
-5.40
1978-82
Average
2.64
5.90
8.35
- 8.16
3.10
6.97
9.87
-9.48
1.59
3.52
4.95
-4.99
3.65
5.91
8.39
-8.16
2.13
4.77
6.73
-6.66
1.76
3.92
5.52
-5.53
2.57
5.76
8.15
-7.95
2.46
5.50
7.78
7.60
124
-------
Table D.5. (continued)
State and Ozone
Assumption
North Carolina
10% reduction
25% reduction
40% reduction
25% increase
South Carolina
10% reduction
25% reduction
40% reduction
25% increase
Tennessee
10% reduction
25% reduction
40% reduction
25% increase
Virginia
10% reduction
25% reduction
40% reduction
25% increase
West Virginia
10% reduction
25% reduction
40% reduction
25% increase
1978
4.63
10.48
14.94
-13.74
5.67
12.91
18.28
-16.48
3.08
6.89
9.76
-10.39
3.50
7.87
11.17
-10.64
1.89
4.21
5.93
-5.94
1979
1.78
3.96
5.58
-5.60
2.50
5.58
7.89
-7.75
1.21
2.69
3.80
-3.88
1.77
3.93
5.54
-5.57
1.47
3.26
4.59
-4.66
1980
3.82
8.59
12.21
-11.52
3.68
8.29
11.76
-11.13
2.52
5.63
7.97
-7.82
3.84
8.55
12.30
-11.59
2.19
4.87
6.87
-6.81
1981
2.71
6.05
8.57
-8.36
2.88
6.46
9.31
-8.86
2.07
4.61
6.49
-6.45
2.73
6.13
8.67
-8.45
1.58
3.53
4.96
-5.02
1982
2.43
5.46
7.71
-7.59
2.76
6.18
8.75
-8.52
2.73
6.12
8.65
-8.43
2.91
6.52
9.25
-8.97
1.81
4.03
5.68
-5.70
1978-82
Average
3.07
6.91
9.80
-9.36
3.50
7.88
11.20
-10.45
2.32
5.19
7.33
-7.39
2.95
6.60
9.39
-9.04
1.79
3.98
5.61
-5.63
a/
— Predicted yield adjustments based on Weibull response model of
Stoneville 213 cultivars.
125
-------
Table D.6. Predicted cotton yield adjustments by state, year and ozone
assumption for irrigated cotton (Acala SJ-2, 1981)
State and Ozone
Assumption
Arizona
10% reduction
25% reduction
40% reduction
25% increase
California
10% reduction
25% reduction
40% reduction
25% increase
Nevada
10% reduction
25% reduction
40% reduction
25% increase
New Mexico
10% reduction
25% reduction
40% reduction
25% increase
Oklahoma
10% reduction
25% reduction
40% reduction
25% increase
Texas
10% reduction
25% reduction
40% reduction
25% increase
1978
1.41
3.42
5.31
-3.70
1.46
3.56
5.51
-3.83
1.59
3.87
5.99
-4.15
1.61
3.92
6.07
-4.21
1.73
4.20
6.52
-4.49
1.27
3.06
4.73
-3.31
1979
1.27
3.09
4.78
-3.34
1.66
4.05
6.28
-4.35
1.78
4.31
6.69
-4.61
1.51
3.68
5.71
-3.96
1.54
3.74
5.82
-4.05
1.43
3.48
5.40
-3.76
Year
1980
1.69
4.11
6.38
-4.40
1.78
4.16
6.45
-4.45
1.65
4.02
6.23
-4.31
1.41
3.46
5.35
-3.72
1.49
3.33
5.62
-3.91
1.28
3.12
4.84
-3.38
1981
1.51
3.68
5.71
-3.97
1.63
3.97
6.16
-4.26
1.53
3.72
5.76
-4.00
1.54
3.74
5.79
-4.02
1.45
3.52
5.46
-3.81
1.32
3.20
4.97
-3.47
1982
1.44
3.48
5.38
-3.75
1.44
3.50
5.42
-3.77
1.54
3.74
5.82
-4.05
1.47
3.58
5.56
-3.86
1.35
3.29
5.09
-3.56
1.40
3.41
5.29
-3.70
1978-82
Average
1.46
3.56
5.51
-3.83
1.59
3.85
5.96
-4.13
1.62
3.93
6.10
-4.22
1.51
3.68
5.70
-3.95
1.51
3.62
5.65
-3.96
1.34
3.25
5.05
-3.52
126
-------
Table D.7. Predicted cotton yield adjustments by state, year and ozone
assumption for irrigated cotton (Acala SJ-2, 1982)
State and Ozone
Assumption
Arizona
10% reduction
25% reduction
40% reduction
25% increase
California
10% reduction
25% reduction
40% reduction
25% increase
Nevada
10% reduction
25% reduction
40% reduction
25% increase
New Mexico
10% reduction
25% reduction
40% reduction
25% increase
1978
3.89
8.73
12.35
-11.86
4.18
9.38
13.29
-12.63
4.85
10.93
15.52
-14.45
4.97
11.23
15.95
-14.79
1979
3.22
7.19
10.14
- 9.91
5.30
11.98
17.05
-15.66
5.93
13.45
19.21
-17.31
4.44
9.99
14.17
-13.35
Year
1980
5.44
12.30
13.04
-16.02
5.55
12.57
17.92
-16.34
5.20
11.78
16.76
-15.45
3.95
8.87
12.55
-12.00
1981
4.44
10.00
14.19
-13.38
5.10
11.52
16.39
-15.15
4.51
10.16
14.42
-13.57
4.56
10.28
14.58
-13.70
1982
4.00
8.97
12.69
-12.14
4.05
9.09
12.87
-12.27
4.60
10.35
14.70
-13.79
4.22
9.49
13.45
-12.76
1978-82
Average
4.20
9.44
12.48
-12.66
4.84
10.91
15.50
-14.41
5.02
11.42
16.12
-14.91
4.43
9.97
14.14
-13.32
127
-------
Table D.8. Predicted winter wheat (pooled cultivars) yield adjustments,
by state, year and April-May ozone concentrations
State and Ozone
Assumption
Arizona
10% reduction
25% reduction
40% reduction
25% increase
Arkansas
10% reduction
25% reduction
40% reduction
25% increase
California
10% reduction
25% reduction
40% reduction
25% increase
Colorado
10% reduction
25% reduction
40% reduction
25% increase
Idaho
10% reduction
25% reduction
40% reduction
25% increase
Illinois
10% reduction
25% reduction
40% reduction
25% increase
Indiana
10% reduction
25% reduction
40% reduction
25% increase
Kansas
10% reduction
25% reduction
40% reduction
25% increase
1978
N.A.
N.A.
N.A.
N.A.
1,33
3. ,02
4,37
-3, .98
N.A.
N.A.
N.A.
N.A.
1.72
3.92
5.67
5.11
N.A.
N.A.
N.\.
N.A.
1.37
3.12
4.51
-4.10
1.19
2.72
3.92
-3.59
1.10
2.51
3.63
-3.33
1979
1.75
4.01
5.80
-5.22
2.01
4.59
6.66
-5.94
1.57
3.59
5.19
-4.69
1.78
4.07
5.89
5.29
1.57
3.59
5.19
-4.69
.85
1.92
2.77
-2.57
.74
1.69
2.43
-2.25
1.31
2.98
4.30
-3.92
128
1980
1.45
3.30
4.77
-4.33
1.50
3.43
4.96
-4.49
1.40
3.20
4.62
-4.20
1.64
3.74
5.41
4.88
1.65
3.78
5.47
-4.93
1.26
2.87
4.14
-3.78
.79
1.79
2.58
-2.39
1.06
2.41
3.48
-3.20
1978-80
Average
1.60
3.65
5.29
-4.78
1.61
3.68
5.33
-4.80
1.48
3.39
4.90
-4.45
1.71
3.91
5.66
5.09
1.61
3.68
5.33
-4.81
1.16
2.63
3.80
-3.48
.91
2.07
2.97
-2.74
1.57
2.63
3.80
-3.48
-------
Table D.8. (continued)
State and Ozone
Assumption
Kentucky
10% reduction
25% reduction
40% reduction
25% increase
Michigan
10% reduction
25% reduction
40% reduction
25% increase
Missouri
10% reduction
25% reduction
40% reduction
25% increase
Montana
10% reduction
25% reduction
40% reduction
25% increase
Nebraska
10% reduction
25% reduction
40% reduction
25% increase
New Mexico
10% reduction
25% reduction
40% reduction
25% increase
New York
10% reduction
25% reduction
40% reduction
25% increase
North Carolina
10% reduction
25% reduction
40% reduction
25% increase
1978
1.14
2.60
3.75
-3.44
.98
2.23
3.22
-2.96
1.06
2.42
3.50
-3.21
.52
1.19
1.71
-1.60
1.33
3.03
4.38
-3.99
1.68
3.84
5.55
-5.00
1.31
2.99
4.32
-3.94
1.58
3.60
5.20
-4.70
1979
.98
2.24
3.23
-2.98
.62
1.40
2.02
-1.88
1.70
3.89
5.63
-5.07
1.24
2.83
4.09
-3.74
1.36
3.41
4.49
-4.09
1.28
2.91
4.20
-3.83
.96
2.19
3.15
-2.91
1.33
3.03
4.38
-3.99
1980
T\f^if*r* i^iri"
1.10
2.50
3.60
-3.30
.83
1.88
2.71
-2.50
1.64
3.76
5.43
-4.90
1.64
3.75
5.42
-4.89
1.28
2.93
4.23
-3.86
1.28
2.92
4.22
-3.85
1.09
2.47
3.57
-3.27
1.94
4.44
6.43
-5.74
1978-80
Average
1.07
2.45
3.52
-3.24
.81
1.84
2.65
-2.45
1.47
3.36
4.85
-4.39
1.13
2.59
3.74
-3.41
1.32
3.12
4.37
-3.98
1.41
3.22
4.66
-4.23
1.12
2.55
3.68
-3.37
1.62
3.69
5.34
-4.81
129
-------
Table D.8. (continued)
State and Ozone
Assumption
Ohio
10% reduction
25% reduction
40% reduction
25% increase
Oklahoma
10% reduction
25% reduction
40% reduction
25% increase
Pennsylvania
10% reduction
25% reduction
40% reduction
25% increase
South Carolina
10% reduction
25% reduction
40% reduction
25% increase
South Dakota
10% reduction
25% reduction
40% reduction
25% increase
Tennessee
10% reduction
25% reduction
40% reduction
25% increase
Texas
10% reduction
25% reduction
40% reduction
25% increase
Wyoming
10% reduction
25% reduction
40% reduction
25% increase
1978
1.16
2.64
3.82
-3.50
1.17
2 . 66
3.84
-3.52
1.31
2.99
4.33
-3.94
1.80
4.12
5.96
-5.35
.99
2.24
3.24
-2.98
1.32
3. .01
4, .34
-3.96
1.29
2.94
4.25
-3.87
1.58
3.61
5.22
-4.72
1979
.94
2.14
3.08
-2.84
1.36
3.10
4.47
-4.07
.82
1.87
2.69
-2.49
1.65
3.76
5.44
-4.91
.99
2.26
3.26
-3.00
.93
2.10
3.03
-2.80
1.14
2.60
3.75
-3.44
1.73
3.96
5.73
-5.15
1980
nf*T*/~" f^TTt"
.72
1.64
2.36
-2.20
.74
1.67
2.41
-2.23
1.13
2.58
3.72
-3.41
2.14
4.90
7.11
-6.31
1.16
2.63
3.80
-3.48
1.45
3.31
4.79
-4.34
1.14
2.60
3.75
-3.44
1.66
3.79
5.48
-4.94
1978-80
Average
.94
2.14
3.08
-2.85
1.09
2.47
3.57
-3.27
1.09
2.48
3.58
-3.28
1.86
4,26
6.17
-5.52
1.05
2.38
3.43
-3.15
1.23
2.81
4.05
-3.70
1.19
2.71
3.97
-3.58
1.66
3.79
5.48
-4.94
130
-------
Table D.9. Predicted spring wheat yield adjustments by state, year
and ozone assumption a/
State and Ozone
Assumption
Arizona
10% reduction
25% reduction
40% reduction
25% increase
California
10% reduction
25% reduction
40% reduction
25% increase
Idaho
10% reduction
25% reduction
40% reduction
25% increase
Minnesota
10% reduction
25% reduction
40% reduction
25% increase
Montana
10% reduction
25% reduction
40% reduction
25% increase
North Dakota
10% reduction
25% reduction
40% reduction
25% increase
Oregon
10% reduction
25% reduction
40% reduction
25% increase
South Dakota
10% reduction'
25% reduction
40% reduction
25% increase
1978
.54
1.17
1.60
-1.86
.59
1.26
1.73
-2.00
N.A.
N.A.
N.A.
N.A.
.49
1.05
1.44
-1.67
.23
.50
.68
- .80
.30
.64
.87
-1.02
.12
.26
.36
- .42
.44
.94
1.28
-1.49
1979
.44
.95
1.30
-1.51
.76
1.64
2.24
-2.58
.75
1.62
2.22
-2.56
.28
.61
.84
- .98
.69
1.48
2.03
-2.34
.27
.59
.80
- .94
.16
.35
.48
- .56
.34
.73
1.00
-1.16
1980
.78
1.68
2.31
-2.65
.80
1.72
2.36
-2.71
.55
1.18
1.61
-1.87
.35
.76
1.03
-1.20
.52
1.13
1.54
-1.79
.32
.70
.96
-1.11
.20
.42
.57
- .67
.43
.92
1.26
-1.46
1981
.63
1.35
1.85
-2.14
.73
1.57
2.15
-2.48
.62
1.33
1.82
-2.10
.21
.45
.61
- .72
.56
1.20
1.64
-1.90
.30
.65
.89
-1.04
.26
.55
.75
- .88
.30
.66
.90
-1.05
1982
.56
1.21
1.65
-1.91
.57
1.22
1.67
-1.94
.63
.1.35
1.85
-2.13
.23
.49
.68
- .79
.56
1.21
1.66
-1.92
.24
.51
.69
- .81
.32
.70
.95
-1.11
.22
.47
.65
- .76
1978-82
Average
.59
1.27
1.74
-2.01
.69
1.48
2.03
-2.34
.64
1.37
1.88
-2.05
.31
.67
.92
-1.07
.51
1.10
1.51
-1.75
.29
.62
.84
- .98
.21
.46
.62
- .73
.35
.74
1.02
-1.18
131
-------
Table D.9. (continued)
State and Ozone
Assumption
1978
1979
1980
1981
1982
1978-82
Average
Washington
10% reduction
25% reduction
40% reduction
25% increase
.13
.27
.37
- .44
.18
.39
.54
- .63
.11
.24
.33
- .39
.14
.31
.42
- .49
.14
.30
.41
- .49
.14
.30
.41
- .49
a/
— Predicted yield adjustment based on Weibull response model of Blueboy II,
Coker 47-27, Holly and Oasis cultivars.
132
-------
Table D.10. Predicted barley yield adjustments by state, year and
ozone assumption aj
State and Ozone
Assumption
Arizona
10% reduction
25% reduction
40% reduction
25% increase
California
10% reduction
25% reduction
40% reduction
25% increase
Colorado
10% reduction
25% reduction
40% reduction
25% increase
Delaware
10% reduction
25% reduction
40% reduction
25% increase
Idaho
10% reduction
25% reduction
40% reduction
25% increase
Illinois
10% reduction
25% reduction
40% reduction
25% increase
Kansas
10% reduction
25% reduction
40% reduction
25% increase
Kentucky
10% reduction
25% reduction
40% reduction
25% increase
1978
.06
.11
.14
-.26
.07
.29
.16
-.29
.09
.17
.22
-.39
.05
.09
.11
-.20
N.A.
N.A.
N.A.
N.A.
.09
.17
.21
-.38
.07
.14
.17
-.31
.08
.15
.19
-.34
1979
.04
.08
.11
-.19
.10
.17
.23
-.42
.10
.19
.24
-.43
.06
.11
.14
-.24
.09
.18
.23
-.42
.06
.11
.14
-.25
.05
.10
.12
-.22
.03
.06
.07
-.13
Year
1980
(percent yield
.10
.19
.24
-.44
.10
.20
.25
-.45
.09
.18
.23
-.41
.02
.04
.05
-.09
.06
.12
.14
-.26
.07
.13
.16
-.29
.06
.11
.14
-.25
.05
.11
.13
-.24
1981
change)
.07
.14
. 18
-.32
.09
.18
.22
-.40
.07
.14
.18
-.32
.12
.23
.29
-.53
.07
.14
.17
-.31
.04
.08
.10
-.18
.04
.07
.09
-.16
.03
.06
.07
-.13
1982
.06
.11
.15
-.27
.06
.12
.15
-.28
.09
.18
.22
-.40
.08
.16
.19
-.35
.07
.14
.18
-.32
.06
.11
.14
-.26
.04
.08
.10
-.17
.06
.11
.14
-.26
1978-82
Average
.07
.13
.16
-.30
.08
.16
.20
-.37
.09
.17
.22
-.39
.06
.13
.16
-.28
.07
.15
.18
-.33
.06
.12
.15
-.27
.05
.10
.12
-.22
.05
.10
.12
-.22
133
-------
Table D.10.(continued)
State and Ozone
Assumption
Maryland
10% reduction
25% reduction
40% reduction
25% increase
Michigan
10% reduction
25% reduction
40% reduction
25% increase
Minnesota
10% reduction
25% reduction
40% reduction
25% increase
Montana
10% reduction
25% reduction
40% reduction
25% increase
Nebraska
10% reduction
25% reduction
40% reduction
25% increase
Nevada
10% reduction
25% reduction
40% reduction
25% increase
New Jersey
10% reduction
25% reduction
40% reduction
25% increase
New Mexico
10% reduction
25% reduction
40% reduction
25% increase
1978
.05
.11
.13
-.24
.03
.05
.07
-.12
.05
.10
.12
-.22
.02
.03
.04
-.07
.08
.16
.19
-.35
.08
.16
.20
-.37
.04
.08
.10
-.18
.09
.17
.21
-.38
1979
.04
.08
.10
-.18
.02
.04
.05
-.09
.02
.04
.06
-.10
.08
.16
.20
-.36
.04
.08
.10
-.19
.11
.22
.28
-.50
.05
.09
.12
-.21
.07
.14
.18
-.32
Year
1980
(percent
.08
.15
.19
-.34
.03
.05
.06
-.11
.03
• 06
.08
-.14
.06
.11
.14
-.24
.06
.11
.14
-.25
.09
.18
.23
-.41
.12
.23
.28
-.51
.06
.12
.15
-.26
1981
yield change)
.08
.17
.21
-.37
.03
.05
.07
-.12
.01
.03
.03
-.06
.06
.12
.15
-.27
.03
.05
.07
-.12
.07
.14
.18
-.33
.09
.17
.22
-.39
.08
.15
.18
-.33
1982
.09
.18
.23
-.41
.03
.06
.08
-.14
.02
•03
• 04
-.07
.06
.12
.15
-.27
.02
.03
.04
-.07
.08
.15
.19
-.34
.10
.19
.24
-.43
.07
.13
.16
-.29
1978-82
Average
.07
.14
.17
-.31
.03
.05
.06
-.12
.03
.05
• 07
-.12
.06
.11
.14
-.24
.04
.09
.11
-.19
.09
.17
.22
-.39
.08
.15
.19
-.35
.07
.14
.18
-.32
134
-------
Table D. 10. (continued)
State and Ozone
Assumption
New York
10% reduction
25% reduction
40% reduction
25% increase
North Carolina
10% reduction
25% reduction
40% reduction
25% increase
North Dakota
10% reduction
25% reduction
40% reduction
25% increase
Ohio
10% reduction
25% reduction
40% reduction
25% increase
Oklahoma
10% reduction
25% reduction
40% reduction
25% increase
Oregon
10% reduction
25% reduction
40% reduction
25% reduction
Pennsylvania
10% reduction
25% reduction
40% reduction
25% increase
South Carolina
10% reduction
25% reduction
40% reduction
25% increase
1978
.04
.08
.10
-.18
.17
.34
.42
-.77
.02
.05
.06
-.11
.06
.12
.15
-.27
.11
.21
.26
-.47
.01
.01
.02
-.03
.05
.09
.12
-.21
.24
.47
.59
-1.07
1979
.03
.07
.08
-.15
.04
.07
.09
-.16
.02
.04
.05
-.09
.03
.05
.07
-.12
.08
.15
.19
-.34
.01
.02
.02
-.04
.03
.05
.07
-.12
.06
.12
.16
-.28
Year
1980
(percent
.04
.07
.09
-.16
.13
.25
.31
-.56
.03
.05
.07
-.12
.04
.09
.11
-.20
.07
.14
.17
-.31
.01
.03
.03
-.06
.06
.11
.14
-.25
.12
.23
.29
-.53
1981
yield change)
.02
.05
.06
-.11
.07
.14
.18
-.32
.02
.05
.06
-.11
.04
.07
.09
-.16
.06
.12
.16
-.28
.02
.04
.05
-.09
.03
.05
.07
-.12
.08
.16
.20
-.35
1982
.02
.05
.06
-.10
.06
.12
.15
-.27
.02
.03
.04
-.08
.05
.10
.12
-.22
.05
.10
.13
-.23
.03
.05
.07
-.12
.04
.07
.09
-.16
.07
.15
.18
-.33
1978-82
Average
.03
.06
.08
-.14
.09
.18
.23
-.42
.02
.04
.06
-.10
.04
.09
.11
-.19
.07
.14
.18
-.32
.02
.03
.04
-.07
.04
.08
.10
-.17
.12
.23
.28
-.51
135
-------
Table p. 10.(continued)
State and Ozone
Assumption
South Dakota
10% reduction
25% reduction
40% reduction
25% increase
Tennessee
10% reduction
25% reduction
40% reduction
25% increase
Texas
10% reduction
25% reduction
40% reduction
25% increase
Utah
10% reduction
25% reduction
40% reduction
25% increase
Virginia
10% reduction
25% reduction
40% reduction
25% increase
Washington
10% reduction
25% reduction
40% reduction
25% increase
West Virginia
10% reduction
25% reduction
40% reduction
25% increase
Wisconsin
10% reduction
25% reduction
40% reduction
25% increase
Year
1978
.04
.08
.10
-.19
.09
.17
.22
-.39
.04
.08
.10
-.19
.08
.16
.20
-.35
.11
.22
.27
-.49
.01
.01
.02
-.03
.04
.08
.10
-.18
.06
.12
.15
-.26
1979
.03
.06
.07
-.13
.02
.04
.04
-.09
.06
.12
.15
-.27
.08
.15
.18
-.33
.04
.07
.09
-.16
.01
.02
.03
-.05
.03
.05
.06
-.12
.05
.09
.12
-.21
1980
(percent
.04
.08
.10
-.18
.06
.13
.16
-.28
.04
.09
.11
-.20
.11
.22
.27
-.49
.13
.25
.31
-.57
.01
.01
.01
-.03
.05
.10
.12
-.22
.04
.07
.09
-.15
1981
yield change)
.02
.05
.06
-.11
.05
.09
.11
-.20
.05
.09
.12
-.21
.09
.17
.21
-.38
.07
.14
.18
-.33
.01
.02
.02
-.04
.03
.06
.07
-.13
.03
.05
.07
-.12
1982
.02
.03
.04
-.07
.07
.14
.18
-.32
.06
.11
.14
-.26
.11
.21
.26
-.47
.08
.16
.20
-.36
.01
.02
.02
-.04
.04
.07
.09
-.17
.03
.06
.07
-.13
1978-82
Average
.03
.06
•07
-•13
.06
.11
.14
-.26
.05
.10
.12
-.23
.09
.18
.22
-.40
.09
.17
.21
-.38
.01
.02
.02
-.04
.04
.07
.09
-.16
.04
.08
.10
-.18
136
-------
Table D.10. (continued)
State and Ozone
Assumption
Year
1978
1979
1980
1981
1982
1978-82
Average
(percent yield change)
Wyoming
10% reduction
25% reduction
40% reduction
25% increase
.06
.12
.15
-.28
.09
.18
.23
-.41
.07
.14
.17
-.31
.08
.15
.19
-.35
.08
.16
.21
-.37
.08
.15
.19
-.34
a/
— Predicted yield changes based on Weibull response model for Poco cultivar*
137
-------
Table D.ll. Predicted sorghum yield adjustments by year, state and
ozone assumption a/
State and Ozone
Assumption
Alabama
10% reduction
25% reduction
40% reduction
25% increase
Arizona
10% reduction
25% reduction
40% reduction
25% increase
California
10% reduction
25% reduction
40% reduction
25% increase
Colorado
10% reduction
25% reduction
40% reduction
25% increase
Georgia
10% reduction
25% reduction
40% reduction
25% increase
Illinois
10% reduction
25% reduction
40% reduction
25% increase
Indiana
10% reduction
25% reduction
40% reduction
25% increase
Iowa
10% reduction
25% reduction
40% reduction
25% increase
1978
.41
.94
1.35
-1.26
.33
.75
1.08
-1.01
.35
.79
1.14
-1.07
.41
.93
1.34
-1.25
.49
1.12
1.61
-1.50
.40
.92
1.32
-1.23
.38
.86
1.24
-1.16
.36
.81
1.17
-1.09
1979
.26
.60
.86
- .81
.28
.64
.92
- .86
.43
.97
1.39
-1.30
.43
.97
1.49
-1.31
.30
.67
.97
- .90
.33
.74
1.06
.99
.24
.56
.80
- .75
.26
.58
.84
- .78
1980
.35
.80
1.15
-1.08
.43
.99
1.42
-1.32
.44
1.00
1.45
-1.35
.42
.96
1.38
-1.28
.35
.79
1.14
-1.07
.35
.80
1.15
-1.07
.35
.79
1.14
-1.06
.28
.63
.90
- .%*
1981
.36
.81
1.17
-1.09
.37
.84
1.20
-1.12
.41
.94
1.35
-1.26
.37
.84
1.21
-1.31
.33
.76
1.09
-1.02
.27
.61
.88
- .83
.32
.73
1.05
- .98
.16
.36
.52
- .49
1982
.41
.94
1.35
-1.26
.34
.77
1.10
-1.03
.34
.77
1.11
-1.04
.41
.94
1.35
-1.26
.33
.75
1.08
-1.01
.33
.75
1.07
-1.00
.36
.81
1.17
-1.09
.19
.42
.61
- .57
1978-82
Average
.36
.82
1.18
-1.10
.35
.80
1.14
-1.07
.39
.89
1.29
-1.20
.41
.93
1.34
-1.28
.36
.82
1.18
- 1.10
.34
.76
1.10
-1.02
.33
.75
1.08
-l.Oi
.25
.56
.81
- .75
138
-------
Table D.ll. (continued)
State and Ozone
Assumption
Kansas
10% reduction
25% reduction
40% reduction
25% increase
Kentucky
10% reduction
25% reduction
40% reduction
25% increase
Louisiana
10% reduction
25% reduction
40% reduction
25% increase
Mississippi
10% reduction
25% reduction
40% reduction
25% increase
Missouri
10% reduction
25% reduction
40% reduction
25% increase
Nebraska
10% reduction
25% reduction
40% reduction
25% increase
New Mexico
10% reduction
25% reduction
40% reduction
25% increase
North Carolina
10% reduction
25% reduction
40% reduction
25% increase
1978
.36
.82
1.18
-1.10
.38
.87
1.25
-1.17
.29
.67
.96
- .90
.45
1.02
1.47
-1.37
.45
1.03
1.48
-1.38
.39
.88
1.27
-1.18
.40
.92
1.32
-1.23
.58
1.32
1.90
-1.76
1979
.30
.68
.98
- .92
.23
.53
.77
- .72
.19
.42
.60
- .57
.25
.57
.83
- .78
.38
.86
1.24
-1.15
.28
.63
.91
- .85
.37
.84
1.20
-1.12
.26
.58
.84
- .79
1980
.33
.74
1.07
-1.00
.32
.72
1.04
- .97
.28
.63
.91
- .85
.39
.89
1.28
-1.19
.39
.89
1.28
-1.19
.32
.73
1.06
- .99
.33
.76
1.09
-1.02
.49
1.12
1.61
-1.50
1981
.26
.59
.85
- .80
.24
.53
.77
- .72
.27
.62
.89
- .84
.36
.81
1.16
-1.09
.22
.49
.71
- .66
.22
.50
.72
- .68
.38
.85
1.23
-1.15
.37
.84
1.20
-1.12
1982
.27
.61
.88
- .82
.33
.75
1.08
-1.01
.24
.55
.80
- .75
.31
.70
1.01
- .95
.25
.57
.82
- .76
.17
.38
.54
- .51
.35
.80
1.15
-1.08
.34
.77
1.10
-1.03
1978-82
Average
.30
.69
.99
- .93
.30
.68
.98
- .92
.25
.58
.83
- .78
.35
.80
1.15
-1.08
.34
.77
1.11
-1.03
.28
.62
.90
- .84
.37
.83
1.20
-1.12
.41
.93
1.33
-1.24
139
-------
Table D.ll. (continued)
State and Ozone
Assumption
Oklahoma
10% reduction
25% reduction
40% reduction
25% increase
Pennsylvania
10% reduction
25% reduction
40% reduction
25% increase
South Carolina
10% reduction
25% reduction
40% reduction
25% increase
South Dakota
10% reduction
25% reduction
40% reduction
25% increase
Tennessee
10% reduction
25% reduction
40% reduction
25% increase
Texas
10% reduction
25% reduction
40% reduction
25% increase
Virginia
10% reduction
25% reduction
40% reduction
25% increase
1978
.45
1.02
1.47
-1.37
.30
.68
.98
- .91
.69
1.57
2.26
-2.09
.28
.63
.91
- .85
.41
.93
1.34
-1.25
.28
.63
.91
- .85
.46
1.04
1.50
-1.40
1979
.38
.86
L.24
-1.16
.22
.51
.73
- .69
.34
.78
1.13
-1.05
.23
.52
.75
- ..70
.19
.42
.61
- .57
.34
.77
1.11
-1.03
.26
.58
.84
- .78
1980
.36
.82
1.18
-1.10
.32
.73
1.05
- .98
.48
1.09
1.57
-1.46
.27
.62
.90
- .84
.35
.79
1.13
-1.06
.29
.65
.94
- .88
.50
1.13
1.62
-1.51
1981
.35
.78
1.13
-1.05
.22
.50
.73
- .68
.39
.88
1.27
-1.19
.21
.48
.69
- .65
.29
.66
.96
- .89
.30
.68
.98
- .91
.37
.85
1.22
-1.14
1982
.30
.69
.99
- .93
.26
.59
.84
- .79
.38
.85
1.23
-1.14
.17
.37
.54
- .51
.37
.84
1.21
-1.13
.33
.75
1.08
-1.01
.39
.89
1.28
-1.20
1978-82
Average
.37
.83
1.20
-1.12
.26
.60
.87
- .81
.46
1.03
1.49
-1.39
.23
.52
.76
- .71
.32
.73
1.05
- .98
.31
.70
1.00
- .94
.40
.90
1.29
-1.21
a/
— Predicted yield changes based on Weibull response model for
Dekalb-28 cultivar.
140
-------
Table D.12. Predicted soybean yield adjustments using pooled regional
response functions
Ozone Assumption
State 10%
Alabama—
a/
Arkansas-
Delaware—
Florida—
a/
Georgia—
Illinois^7
Indiana—
b/
Iowa-
Kansas—
b/
Kentucky-
Louis i ana-
Mary land—
Michigan—
* b/
Minnesota—
».- • • -a/
Mississippi-
Missouri—
Nebraska-
New Jersey-
New York—
a/
North Carolina-
North Dakota^7
Ohio^7
Oklahoma-
Pennsylvania—
South Carolina^7
South Dakota—7
a/
Tennessee-
Texas-7
,.. . . a/
Virginia—
. b/
Wisconsin-
Reduction
2.7
2.9
1.7
1.7
2.6
2.7
2.6
2.2
2.5
2.5
2.2
2.8
1.9
2.0
2.9
2.9
2.5
3.3
2.1
3.4
1.9
2.3
2.7
2.5
3.3
2.2
2.6
2.3
3.4
2.1
25% Reduction 40%
6.4
7.5
4.2
4.1
6.4
6.4
6.4
5.4
6.1
6.0
5.4
6.8
4.5
4.8
7.0
6.9
6.0
8.0
5.1
8.2
4.6
5.5
6.5
6.0
8.1
5.4
6.4
5.6
8.3
5.1
Reduction
9.9
11.6
6.4
6.2
9.9
10.0
9.8
8.3
9.4
9.2
8.3
10.5
6.9
7.4
10.7
10.7
9.3
12.3
7.9
12.8
7.1
8.5
10.1
9.3
12.5
8.3
9.8
8.5
2.8
7.8
25% Increase
7.0
8.0
4.6
4.5
6.9
6.9
6.9
5.9
6.6
6.5
5.9
7.3
4.9
5.3
7.5
7.5
6.5
8.5
5.6
8.7
5.1
6.0
7.1
6.5
8.6
5.9
6.9
6.0
8.8
5.5
141
(continued)
-------
a/
— Includes Southeast and Southern cultivars of soybeans (Davis, Essex
and Forrest).
Includes Corn
and Hodgson).
— Includes Corn Belt and Lake states cultivars (Amsoy, Corsoy, Williams
142
-------
Table D.13. Predicted soybean (Davis1 81 cv.) yield adjustments by
state, year and ozone assumption
State and Ozone
Assumption
Alabama
10% reduction
25% reduction
40% reduction
25% increase
Arkansas
10% reduction
25% reduction
40% reduction
25% increase
Delware
10% reduction
25% reduction
40% reduction
25% increase
Florida
10% reduction
25% reduction
40% reduction
25% increase
Georgia
10% reduction
25% reduction
40% reduction
25% increase
Illinois
10% reduction
25% reduction
40% reduction
25% increase
Indiana
10% reduction
25% reduction
40% reduction
25% increase
Iowa
10% reduction
25% reduction
40% reduction
25% increase
1978
3.98
10.36
17.31
- 9.01
4.40
11.51
19.30
-10.01
3.45
8.95
14.90
- 7.96
3.41
8.85
14.73
- 7.88
4.27
11.14
18.67
- 9.72
3.94
10.25
17.13
- 9.02
3.84
10.00
16.70
- 8.82
3.75
9.75
16.27
- 8.61
1979
3.32
8.61
14.32
- 7.69
3.95
10.29
17.20
- 9.05
3.59
9.33
15.54
- 8.27
2.85
7.37
12.20
- 6.65
3.48
9.02
15.02
- 8.02
3.61
9.38
15.64
- 8.32
3.23
8.35
13.88
- 7.47
3.29
8.52
14.16
- 7.61
1980
3.73
9.70
16.18
- 8.57
4.05
10.54
17.64
- 9.25
2.95
7.62
12.63
- 6.86
2.90
7.49
12.41
- 6.75
3.72
9.67
16.13
- 8.55
3.73
9.69
16.16
- 8.56
3.71
9.65
16.10
- 8.53
3.39
8.78
14.61
- 7.82
143
1981
3.75
9.76
16.28
- 8.62
3.78
9.82
16.39
- 8.67
4.22
11.02
18.45
- 9.63
3.41
8.86
14.74
- 7.89
3.65
9.49
15.82
- 8.40
3.36
8.70
14.47
- 7.76
3.60
9.35
15.57
- 8.29
2.68
6.92
11.45
- 6.27
1982
3.97
10.35
17.30
- 9.09
3.63
9.43
15.72
- 8.36
3.87
10.08
16.84
- 8.88
3.15
8.16
13.56
- 7.32
3.64
9.45
15.75
- 8.37
3.63
9.42
15.71
- 8.35
3.76
9.77
16.30
- 8.63
2.90
7.49
12.41
- 6.75
1978-82
Average
3.75
9.76
16.28
- 8.60
3.96
10.32
17.25
- 9.07
3.62
9.39
15.67
- 8.32
3.14
8.15
13.53
- 7.30
3.75
9.75
16.28
- 8.61
3.65
9.48
15.82
- 8.40
3.63
9.42
15.71
- 8.35
3.20
8.29
13.78
- 7.41
-------
Table D.I 5. [continued)
State and Ozone
Assumption
Kansas
10% reduction
25% reduction
40% reduction
25% increase
Kentucky
10% reduction
25% reduction
40% reduction
25% increase
Louisiana
10% reduction
25% reduction
40% reduction
25% increase
Maryland
10% reduction
25% reduction
40% reduction
25% increase
Michigan
10% reduction
25% reduction
40% reduction
25% increase
Minnesota
10% reduction
25% reduction
40% reduction
25% increase
Mississippi
10% reduction
25% reduction
40% reduction
25% increase
Missouri
10% reduction
25% reduction
40% reduction
25% increase
1978
3.77
9.80
16.36
- 8.66
3.85
10.03
16.75
- 8.84
3.47
9.00
14.98
- 8.00
3.58
9.31
15.51
- 8.26
3.09
8.01
13.29
- 7.19
3.52
9.13
15.21
- 8.11
4.11
10.71
17.92
- 9.38
4.12
10.75
17.98
- 9.41
1979
3.50
9.09
15.14
- 8.08
3.17
8.21
13.63
- 7.35
2.88
7.45
12.35
- 6.72
3.36
8.72
14.51
- 7.78
2.92
7.54
12.50
- 6.80
3.27
8.47
14.09
- 7.57
3.27
8.47
14.09
- 7.57
3 . 84
9.99
16.68
- 8.81
1980
3.62
9.42
15.69
- 8.34
3.58
9.30
15.50
- 8.25
3.39
8.80
14.64
- 7.84
3.85
10.03
16.75
- 8.84
3.06
7.91
13.13
- 7.11
3.18
8.24
13.68
- 7.38
3.89
10.13
16.92
- 8.92
3.89
10.12
16.90
- 8.91
1981
3.30
8.57
14.24
- 7.65
3.17
8.22
13.65
- 7.36
3.37
8.75
14.55
- 7.80
3.92
10.22
17.07
- 8.99
3.09
7.98
13.25
- 7.17
2.71
7.00
11.59
- 6.34
3.75
9.74
16.25
- 8.61
3.08
7.96
13.21
- 7.14
1982
3.35
8.69
14.45
- 7.75
3.64
9.45
15.76
- 8.37
3.22
8.34
13.85
- 7.46
3.99
10.41
17.40
- 9.14
3.19
8.26
13.73
- 7.40
2.80
7.22
11.95
- 6.52
3.55
9.21
15.34
- 8.18
3.25
8.42
14,00
- 7.53
1978-82
Average
3.51
9.11
15.18
- 8.10
3.48
9.04
15.06
- 8.03
3.27
8.47
14.07
- 7.56
3.74
9.74
16.25
- 8.60
3.07
7.94
13.18
- 7.13
3.10
8.01
13.30
- 7.18
3.71
9.65
16.10
- 8.53
3.64
9.45
15.75
- 8.36
144
-------
Table D.1.5. (continued)
State and Ozone
Assumption
Nebraska
10% reduction
25% reduction
40% reduction
25% increase
New Jersey
10% reduction
25% reduction
40% reduction
25% increase
New York
10% reduction
25% reduction
40% reduction
25% increase
North Carolina
10% reduction
25% reduction
40% reduction
25% increase
North Dakota
10% reduction
25% reduction
40% reduction
25% increase
Ohio
10% reduction
25% reduction
40% reduction
25% increase
Oklahoma
10% reduction
25% reduction
40% reduction
25% increase
Pennsylvania
10% reduction
25% reduction
40% reduction
25% increase
1978
3.88
10.09
16.85
- 8.88
3.38
8.78
14.60
- 7.82
3.39
8.78
14.61
- 7.83
4.56
11.92
20.02
-10.33
3.02
7.81
12.95
- 7.02
3.66
9.51
15.86
- 8.42
4.11
10.71
17.93
- 9.38
3.50
9.07
15.11
- 8.06
1979
3.40
8.82
14.67
- 7.85
3.49
9.06
15.09
- 8.06
3.24
8.39
13.94
- 7.50
3.29
8.53
14.19
- 7.62
2.95
7.62
12.63
- 6.86
3.12
8.08
13.41
- 7.25
3.84
9.99
16.68
- 8.81
3.11
8.06
13.38
- 7.23
1980
3.60
9.36
15.60
- 8.30
4.19
10.93
18.29
- 9.55
3.30
8.55
14.22
- 7.64
4.27
11.14
18.67
- 9.72
3.11
8.04
13.34
- 7.21
3.43
8.90
14.82
- 7.93
3.76
9.79
16.33
- 8.64
3.60
9.35
15.58
- 8.29
1981
3.10
8.02
13.31
- 7.19
3.96
10.32
17.26
- 9.07
3.03
7.83
13.00
- 7.04
3.80
9.88
16.49
- 8.72
3.04
7.86
13.03
- 7.06
3.31
8.58
14.26
- 7.66
3.70
9.62
16.04
- 8.51
3.11
8.04
13.34
- 7.21
1982
2.76
7.13
11.80
- 6.45
4.03
10.51
17.58
- 9.22
3.00
7.76
12.87
- 6.98
3.67
9.53
15.90
- 8.44
2.82
7.27
12.05
- 6.57
3.51
9.11
15.18
- 8.10
3.55
9.21
15.35
- 8.18
3.30
8.54
14.20
- 7.63
1978-82
Average
3.35
8.68
14.45
- 7.73
3.81
9.92
16.56
- 8.74
3.19
8.26
13.73
- 7.40
3.92
10.20
17.05
- 8.97
2.99
7.72
12.80
- 6.94
3.41
8.84
14.71
- 7.87
3.79
9.86
16.47
- 8.70
3.32
8.61
14.32
- 7.68
145
-------
Table D. l.i. (continued)
State and Ozone
Assumption
South Carolina
10% reduction
25% reduction
40% reduction
25% increase
South Dakota
10% reduction
25% reduction
40% reduction
25% increase
Tennessee
10% reduction
25% reduction
40% reduction
25% increase
Texas
10% reduction
25% reduction
40% reduction
25% increase
Virginia
10% reduction
251. reduction
40% reduction
25% increase
Wisconsin
10% reduction
25% reduction
40% reduction
25% increase
1978
4.88
12.80
21.55
-11.01
3.40
8.81
14.66
- 7.85
3.97
10.33
17.26
- 9.08
3.39
8.80
14.65
- 7.84
4.15
10.81
18.10
- 9.46
3.65
9.48
15.80
- 8.39
1979
3.70
9.61
16.03
- 8.50
3.14
8.14
13.52
- 7.30
2.89
7.48
12.39
- 6.74
3.67
9.55
15.92
- 8.45
3.29
8.51
]4.15
- 7.61
3.49
9.06
15.09
- 8.05
1980
4.22
11.00
18.43
- 9.61
3.38
8.76
14.57
- 7.81
3.71
9.64
16.08
- 8.52
3.44
8.92
14.85
- 7.94
4.28
11.1"
18.71
- 9. "4
3.27
8.47
14.07
- 7.57
1981
3.88
10.10
16.87
- 8.89
3.05
7.88
13.08
- 7.08
3.46
8.99
14.97
- 8.00
3.49
9.07
15.10
- 8.06
3.81
9.92
16.56
- 8.75
3.10
8.03
13.34
- 7.21
1982
3.83
9.95
16.62
- 8.78
2.76
7.12
11.78
- 6.44
3.81
9.91
16.55
- 8.75
3.63
9.43
15.73
- 8.36
3.90
10.14
16.94
- 8.93
3.13
8.10
13.45
- 7.27
1978-82
Average
4.10
10.69
17.90
- 9.36
3.15
8.14
13.52
- 7.30
3.57
9.27
15.45
- 8.22
3.52
9.15
15.25
- 8.13
3.89
10.11
16.89
- 8.90
3.33
8.63
14.35
- 7.70
146
-------
Table D.14.
Predicted corn (Pioneer, cv.) yield adjustments by state,
year and ozone assumption
State and Ozone
Assumption
Alabama
10% reduction
25% reduction
40% reduction
25% increase
Arizona
10% reduction
25% reduction
40% reduction
25% increase
Arkansas
10% reduction
25% reduction
40% reduction
25% increase
California
10% reduction
25% reduction
40% reduction
25% increase
Colorado
10% reduction
25% reduction
40% reduction
25% increase
Connecticut
10% reduction
25% reduction
40% reduction
25% increase
Delaware
10% reduction
25% reduction
40% reduction
25% increase
Florida
10% reduction
25% reduction
40% reduction
25% increase
1978
.87
1.85
2.51
-3.05
.64
1.35
1.83
-2.24
1.24
2.65
3.59
-4.32
.69
1.47
1.98
-2.42
.86
1.84
2.49
-3.03
.63
1.34
1.81
-2.22
.53
1.13
1.52
-1.87
.51
1.08
1.46
-1.80
1979
1
-1
1
1
-1
1
2
-2
1
2
-3
1
2
-3
1
2
-2
1
1
-2
-
.46
.99
.33
.64
.51
.08
.46
.80
.85
.82
.45
.99
.91
.93
.61
.17
.92
.95
.64
.21
.82
.74
.36
.87
.61
.30
.75
.15
.27
.58
.78
.97
1980
.
1.
2.
-2.
m
1.
2.
-3.
f
I.
2.
-3.
,
2.
2.
-3.
,
1.
2.
-3.
1.
2.
4.
-4.
.
.
.
-1.
,
.
.
-1.
70
48
00
45
93
99
69
27
92
97
66
24
96
04
76
35
89
91
58
13
40
99
05
85
31
65
87
08
29
61
83
02
1981
1
2
-2
1
2
-2
1
2
-2
1
2
-3
1
2
-2
1
2
-3
1
1
-1
1
1
-1
.71
.51
.04
.50
.74
.58
.13
.60
.73
.55
.09
.55
.87
.85
.50
.04
.75
.60
.16
.63
.90
.91
.59
.15
.51
.08
.46
.80
.51
.09
.47
.80
1982
1
2
-3
1
1
-2
1
1
-2
1
1
-2
1
2
-3
1
2
-3
1
2
-2
1
-1
.87
.85
.50
.04
.66
.39
.88
.31
.63
.35
.82
.23
.67
.42
.91
.34
.87
.85
.50
.04
.92
.95
.64
.21
.79
.69
.28
.79
.39
.82
.11
.37
1978-82
Average
1
2
-2
1
2
-2
1
2
-3
1
2
-2
1
2
-3
1
2
-3
1
1
-1
1
-1
.72
.54
.08
.54
.70
.48
.00
.44
.87
.87
.52
.07
.82
.74
.35
.86
.86
.83
.48
.01
.93
.99
.69
.26
.55
.17
.58
.94
.39
.84
.13
.39
147
-------
Table D.14. (continued)
State and Ozone
Assumption
Georgia
10% reduction
25% reduction
40% reduction
25% increase
Idaho
10% reduction
25% reduction
40% reduction
25% increase
Illinois
10% reduction
25% reduction
40% reduction
25% increase
Indiana
10% reduction
25% reduction
40% reduction
25% increase
Iowa
10% reduction
25% reduction
40% reduction
25% increase
Kansas
10% reduction
25% reduction
40% reduction
25% increase
Kentucky
10% reduction
25% reduction
40% reduction
25% increase
Louisiana
10% reduction
25% reduction
40% reduction
25% increase
1978
1.11
2.38
3.22
-3.88
N.A.
N.A.
N.A.
N.A.
.77
1.65
2.22
-2.71
.77
1.65
2.22
-2.71
.71
1.51
2.04
-2.49
.72
1.54
2.08
-2.54
.78
1.66
2.24
-2.74
.54
1.15
1.55
-1.90
1979
1
1
-1
1
2
-3
1
1
-2
1
-1
1
-1
1
1
-1
1
-1
-1
.54
.16
.56
.92
.90
.91
.59
.15
.62
.32
.79
.19
.42
.89
.20
.48
.45
.95
.28
.58
.56
.19
.60
,97
.39
.84
.13
.40
.28
.60
.81
.01
1980
1
1
-2
1
1
-2
1
1
-2
1
1
-2
1
1
-1
1
1
-2
1
1
-2
1
1
-1
.69
.47
.98
.42
.64
.36
.84
.25
.69
.48
.99
.44
.68
.46
.97
.41
.50
.05
.42
.75
.63
.34
.81
.22
.60
.29
.74
.13
.50
.06
.44
.77
1981
.
1.
1.
-2.
t
1.
2.
-2.
m
1.
1.
-1.
.
1.
1.
-2.
.
n
.
- .
.
.
1.
-1.
,
.
1.
-1.
9
1.
1.
-1.
65
38
86
28
73
54
09
55
48
02
38
70
61
31
76
16
22
47
63
78
46
97
31
61
40
84
14
40
49
04
41
73
1982
1
1
-2
1
2
-2
1
1
-2
1
2
-2
-1
1
1
-1
1
1
-2
1
-1
.64
.36
.83
.24
.74
.57
.12
.60
.63
.34
.81
.23
.71
.52
.05
.51
.29
.61
.83
.02
.48
.02
.37
.69
.64
.36
.83
.25
.42
.88
.19
.47
1978-82
Average
1
2
-2
1
2
-2
1
1
-2
1
1
-2
1
-1
1
1
-2
1
1
-1
1
-1
.73
.55
.09
.55
.75
.60
.15
.11
.64
.36
.83
.25
.64
.37
.84
.25
.43
.92
.24
.52
.57
.21
.63
.01
.56
.20
.62
.98
.45
.95
.28
.58
148
-------
Table D.14. (continued)
State and Ozone
Assumption
Maine
10% reduction
25% reduction
40% reduction
25% increase
Maryland
10% reduction
25% reduction
40% reduction
25% increase
Massachusetts
10% reduction
25% reduction
40% reduction
25% increase
Michigan
10% reduction
25% reduction
40% reduction
25% increase
Minnesota
10% reduction
25% reduction
40% reduction
25% increase
Mississippi
10% reduction
25% reduction
40% reduction
25% increase
Missouri
10% reduction
25% reduction
40% reduction
25% increase
Montana
10% reduction
25% reduction
40% reduction
25% increase
1978
.52
1.11
1.50
1.85
.61
1.29
1.74
-2.14
.46
.99
1.33
-1.64
.36
.77
1.04
-1.28
.57
1.21
1.63
-2.00
.97
2.08
2.81
-3.41
.99
2.10
2.84
-3.45
.26
.54
.73
- .91
1979
.35
.75
1.01
1.24
.49
1.03
1.39
-1.72
.58
1.22
1.65
-2.03
.30
.63
.84
-1.05
.32
.68
.91
-1.13
.44
.93
1.26
-1.56
.77
1.64
2.21
-2.70
.82
1.74
2.35
-2.86
1980
.28
.59
.80
- .99
.78
1.66
2.24
-2.74
.69
1.47
1.98
-2.43
.35
.74
1.00
-1.23
.40
.85
1.14
-1.41
.81
1.72
2.32
-2.83
.80
1.71
2.31
-2.82
.61
1.30
1.75
-2.15
1981
.20
.42
.57
- .71
.83
1.77
2.39
-2.91
.61
1.29
1.74
-2.14
.36
.76
1.03
-1.27
.23
.49
.65
- .81
.71
1.50
2.03
-2.49
.35
.75
1.02
-1.26
.65
1.39
1.88
-2.30
1982
.29
.62
.84
-1.04
.88
1.88
2.55
-3.10
.58
1.23
1.66
-2.04
.40
.86
1.16
-1.43
.25
.54
.73
- .90
.58
1.24
1.68
-2.06
.43
.92
1.24
-1.53
.66
1.40
1.90
-2.32
1978-82
Average
.33
.70
.94
-1.17
.72
1.53
2.06
-2.52
.58
1.24
1.67
-2.06
.35
.75
1.01
-1.25
.35
.75
1.01
-1.25
.70
1.49
2.02
-2.47
.67
1.42
1.92
-2.35
.60
1.27
1.72
-2.11
149
-------
Table D.14. (continued)
State and Ozone
Assumption
Nebraska
10% reduction
25% reduction
40% reduction
25% increase
Nevada
10% reduction
25% reduction
40% reduction
25% increase
New Hampshire
10% reduction
25% reduction
40% reduction
25% increase
New Jersey
10% reduction
25% reduction
40% reduction
25% increase
New Mexico
10% reduction
25% reduction
40% reduction
25% increase
New York
10% reduction
25% reduction
40% reduction
25% increase
North Carolina
10% reduction
25% reduction
40% reduction
25% increase
North Dakota
10% reduction
25% reduction
40% reduction
25% increase
1978
.80
1.69
2.29
-2.79
.82
1.74
2.35
-2.87
.50
1.07
1.44
-1.77
.50
1.05
1.42
-1.75
.84
1.80
2.43
-2.96
.50
1.06
1.43
-1.76
1.40
2.99
4.05
-4.85
.33
.71
.95
-1.18
1979
.50
1.07
1.44
-1.78
1.03
2.20
2.97
-3.60
.45
.95
1.29
-1.59
.55
1.77
1.59
-1.95
.74
1.57
2.13
-2.60
.42
.90
1.22
-1.50
.45
.96
1.29
-1.59
.31
.65
.87
-1.08
1980
.62
1.31
1.77
-2.17
.89
1.90
2.56
-3.12
.51
1.09
1.47
-1.81
1.04
2.22
3.01
-3.64
.65
1.38
1.86
-2.28
.45
.96
1.30
-1.60
1.11
2.38
3.22
-3.88
.37
.78
1.05
-1.30
1981
.36
.77
1.04
-1.29
.75
1.60
2.16
-2.64
.27
.57
.77
- .96
.86
1.83
2.48
-3.02
.76
1.63
2.20
-2.68
.34
.71
.96
-1.19
.74
1.58
2.13
-2.61
.34
.72
.97
-1.20
1982
.24
.52
.70
- .86
.77
1.64
2.22
-2.71
.35
.74
.99
-1.23
.92
1.95
2.64
-3.20
.70
1.49
2.01
-2.46
.33
.69
.93
-1.15
.66
1.40
1.89
-2.31
.26
.55
.75
- .93
1978-82
Average
.50
1.07
1.45
-1.78
.85
1.82
2.45
-2.99
.42
.88
1.19
-1.37
.77
1.76
2.23
-2.71
.74
1.57
2.13
-2.60
.41
.86
1.17
-1.44
.87
1.86
2.52
-3.05
.32
.68
.92
-1.14
150
-------
Tablet). 14. (continued)
State and Ozone
Assumption
Ohio
10% reduction
25% reduction
40% reduction
25% increase
Oklahoma
10% reduction
25% reduction
40% reduction
25% increase
Oregon
10% reduction
25% reduction
40% reduction
25% increase
Pennsylvania
10% reduction
25% reduction
40% reduction
25% increase
Rhode Island
10% reduction
25% reduction
40% reduction
25% increase
South Carolina
10% reduction
25% reduction
40% reduction
25% increase
South Dakota
10% reduction
25% reduction
40% reduction
25% increase
Tennessee
10% reduction
25% reduction
40% reduction
25% increase
1978 .
.65
1.39
1.87
-2.29
.13
.28
.37
- .46
.12
.28
.37
- .46
.55
1.18
1.59
-1.
.47
1.00
1.34
-1.66
1.78
3.81
5.71
-6.11
.50
1.07
1.44
-1.77
.86
1.84
2.48
-3.02
1979
1
-1
1
2
-2
-
1
-1
1
2
-2
1
1
-2
1
-1
-1
.37
.79
.07
.32
.77
.64
.22
.71
,18
.37
.50
.63
.37
.79
.06
.31
.70
.48
.01
.45
.67
.44
.94
.37
.38
.81
.10
.36
.29
.61
.82
.02
1980
1
1
_ i
1
2
-2
-
1
1
-2
1
2
2
-3
1
2
3
-3
1
1
-1
1
1
-2
.52
.11
.49
.84
.72
.53
.06
.52
.21
.45
.61
.76
.61
.31
.76
.16
.03
.20
.98
.60
.07
.28
.08
.72
.49
.05
.41
.74
.68
.45
.96
.40
1981
1
-1
1
1
-2
-1
1
-1
1
2
-3
1
2
-2
-1
1
1
-1
.46
.97
.31
.62
.68
.44
.94
.38
.28
.60
.82
.01
.37
.78
.05
.30
.94
.99
.69
.27
.80
.70
.30
.80
.34
.73
.98
.22
.54
.14
.54
.90
1982
1
1
-1
1
1
-2
1
-1
1
-1
1
2
-3
1
2
-2
-
1
2
-2
.56
.20
.62
.99
.58
.24
.68
.06
.37
.78
.05
.30
.45
.96
.30
.60
.88
.88
.54
.09
.76
.62
.19
.67
.24
.51
.69
.86
.75
.60
.16
.64
1978-82
Average
1
1
-1
1
1
-2
-
1
1
-1
1
2
-2
1
2
3
-3
1
-1
1
1
-2
.51
.09
.47
.81
.58
.23
.65
.03
.23
.50
.67
.83
.47
.00
.35
.67
.80
.71
.31
.81
.02
.17
.04
.53
.39
.83
.12
.39
.62
.33
.79
.20
151
-------
Table D.14. (continued)
State and Ozone
Assumption
Texas
10% reduction
25% reduction
40% reduction
25% increase
Utah
10% reduction
25% reduction
40% reduction
25% increase
Vermont
10% reduction
25% reduction
40% reduction
25% increase
Virginia
10% reduction
25% reduction
40% reduction
25% increase
Washington
10% reduction
25% reduction
40% reduction
25% increase
West Virginia
10% reduction
25% reduction
40% reduction
25% increase
Wisconsin
10% reduction
25% reduction
40% reduction
25% increase
Wyoming
10% reduction
25% reduction
40% reduction
25% increase
1978
.50
1.06
1.44
-1.77
.80
1.70
2.29
-2.79
.53
1.13
1.53
-1.88
1.01
2.15
2.90
-3.52
.13
.29
.39
- .48
.48
1.03
1.39
-1.71
.64
1.37
1.85
-2.27
.67
1.42
1.91
-2.35
1979
.66
1.40
1.90
-2.32
.76
1 . 63
2.20
-2.68
.52
1.11
1.50
-1.85
.45
.95
1.28
-1.58
,20
,42
.57
- .71
.36
.76
1.03
-1.27
.55
1.17
1.59
-1.95
.89
1.89
2.55
-3.10
1980
.52
1.11
1.50
-1.85
1.01
2.15
2.91
-3.53
.53
1.14
1.54
-1.89
1.12
2.40
3.24
-3.91
.12
.25
.34
- .42
.57
1.22
1.65
-2.03
.44
.93
1.26
-1.55
.72
1.54
2.08
-2.54
1981
.55
1.18
1.59
-1.96
.84
1.79
2.42
-2.94
.29
.61
.83
-1.03
.75
1.60
2.16
-2.64
.15
.32
.44
- .54
.39
.83
1.13
-1.39
.37
.78
1.05
-1.30
.79
1.67
2.26
-2.76
1982
.63
1.35
1.82
-2.23
.98
2.08
2.82
-3.42
.34
.72
.98
-1.21
.81
1.73
2.33
-2.84
.15
.32
.43
- .54
.46
.98
1.32
-1.63
.38
.80
1.08
-1.34
.83
1.77
2.39
-2.91
1978-82
Average
.57
1.22
1.65
-2.03
.87
1.87
2.53
-3.07
.44
.94
1.28
-1.57
.83
1.77
2.38
-2.90
.15
.32
.43
- .54
.45
.96
1.30
-1.61
.48
1.01
1.37
1.68
.78
1.66
2.24
-2.73
152
-------
Table D.1S. Predicted winter wheat (Roland, cv.) yield adjustment by
state, year and April-May ozone concentrations
State and Ozone
Assumption
Arizona
10% reduction
25% reduction
40% reduction
25% increase
Arkansas
10% reduction
25% reduction
40% reduction
25% increase
California
10% reduction
25% reduction
40% reduction
25% increase
Colorado
10% reduction
25% reduction
40% reduction
25% increase
Idaho
10% reduction
25% reduction
40% reduction
25% increase
I llinois
10% reduction
25% reduction
40% reduction
25% increase
Indiana
10% reduction
25% reduction
40% reduction
25% increase
Kansas
10% reduction
25% reduction
40% reduction
25% increase
1978
N.A.
N.A.
N.A.
N.A.
3.23
7.83
12.05
- 8.46
N.A.
N.A.
N.A.
N.A.
3.91
9.51
14.70
-10.11
N.A.
N.A.
N.A.
N.A.
3.31
8.02
12.35
- 8.65
2.99
7.23
11.11
- 7.86
2.82
6.82
10.47
- 7.45
1979
3.97
9.67
14.95
-10.26
4.38
10.71
16.60
-11.25
3.66
8.90
13.74
- 9.52
4.01
9.78
15.13
-10.37
3.66
8.90
13.73
- 9.52
2.32
5.59
8.56
- 6.18
2.11
5.07
7.75
- 5.63
3.19
7.74
11.91
- 8.38
1980
3.45
8.37
12.89
- 8.99
3.54
8.61
13.27
- 9.23
3.37
8.17
12.58
- 8.80
3.77
9.18
14.18
- 9.79
3.80
9.26
14.29
- 9.86
3.11
7.53
11.58
- 8.16
2.20
5.30
8.10
- 5.87
2.74
6.62
10.16
- 7.24
1978-80
Average
3.71
9.02
13.92
- 9.62
3.71
9.05
13.97
- 9.65
3.51
8.53
13.16
- 9.16
3.89
9.49
14.67
-10.09
3.73
9.08
14.01
- 9.69
2.91
7.05
10.83
- 7.66
2.43
5.86
8.99
- 6.45
2.91
7.06
10.84
- 7.69
153
-------
Table D.15. (continued)
State and Ozone
Assumption
Kentucky
10% reduction
25% reduction
40% reduction
25% increase
Michigan
10% reduction
25% reduction
40% reduction
25% increase
Missouri
10% reduction
25% reduction
40% reduction
25% increase
Montana
10% reduction
25% reduction
40% reduction
25% increase
Nebraska
10% reduction
25% reduction
40% reduction
25% increase
New Mexico
10% reduction
25% reduction
40% reduction
25% increase
New York
10% reduction
25% reduction
40% reduction
25% increase
North Carolina
10% reduction
25% reduction
40% reduction
25% increase
1978
2.89
7.00
10.75
- 7.63
2.59
6.24
9.57
- 6.85
2.75
6.64
10.19
- 7.26
1 . 63
3.92
5.97
- 4.40
3.24
7.85
12.08
- 8.48
3.85
9.36
14.46
- 9.96
3.21
7.77
11.96
- 8.40
3.67
8.92
13.76
- 9.53
1979
2.59
6.26
9.60
- 6.87
1.84
4.42
6.74
- 4.94
3.88
9.45
14.60
-10.05
3.08
7.46
11.47
- 8.09
3.30
7.99
12.30
- 8.62
3.14
7.61
11.70
- 8.24
2.55
6.15
9.43
- 6.76
3.24
7.85
12.08
- 8.48
1980
2.81
6.79
10.42
- 7.41
2.28
5.50
8.41
- 6.08
3.79
9.21
14.22
- 9.82
3.78
9.19
14.19
- 9.80
3.15
7.64
11.76
- 8.28
3.15
7.64
11.75
- 8.27
2.79
6.74
10.34
- 7.36
4.27
10.43
16.16
-10.99
1978-80
Average
2.76
6.68
10.26
- 7.30
2.24
5.39
8.24
- 5.96
3.47
8.43
13.00
- 9.04
2.83
6.86
10.54
- 7.43
3.23
7.83
12.05
- 8.46
3.38
8.20
12.64
- 8.82
2.85
6.89
10.58
- 7.51
3.73
9.07
14.00
- 9.67
154
-------
Table D.15. (continued)
State and Ozone
Assumption
Ohio
10% reduction
25% reduction
40% reduction
25% increase
Oklahoma
10% reduction
25% reduction
40% reduction
25% increase
Pennsylvania
10% reduction
25% reduction
40% reduction
25% increase
South Carolina
10% reduction
25% reduction
40% reduction
25% increase
South Dakota
10% reduction
25% reduction
40% reduction
25% increase
Tennessee
10% reduction
25% reduction
40% reduction
25% increase
Texas
10% reduction
25% reduction
40% reduction
25% increase
Wyoming
10% reduction
25% reduction
40% reduction
25% increase
1978
2.93
7.09
10.88
- 7.71
2.94
7.12
10.94
- 7.75
3.21
7.77
11.96
- 8.41
4.05
9.87
15.26
-10.45
2.60
6.27
9.61
- 6.88
3.22
7.80
12.00
- 8.43
3.16
7.67
11.80
- 8.30
3.68
8.94
13.79
- 9.55
1979
2.51
6.05
9.27
- 6.66
3.29
7.97
12.27
- 8.60
2.27
5.47
8.37
- 6.05
3.79
9.22
14.24
- 9.83
2.61
6.30
9.66
- 6.91
2.48
5.97
9.15
- 6.58
2.89
7.00
10.75
- 7.63
3.93
9.58
14.80
-10.17
1980
2.07
4.97
7.59
- 5.53
2.09
5.03
7.69
- 5.60
2.87
6.95
10.67
- 7.58
4.60
11.25
17.45
-11.75
2.92
7.06
10.84
- 7.69
3.45
8.38
12.92
- 9.01
2.89
7.00
10.75
- 7.63
3.81
9.26
14.23
- 9.88
1978-80
Average
2.50
6.04
9.24
- 6.63
2.77
6.71
10.30
- 7.32
2.78
6.73
10.33
- 7.35
4.14
10.11
15.65
-10.68
2.71
6.54
10.04
- 7.16
3.05
7.38
11.36
- 8.01
2.98
7.22
11.10
- 7.85
3.80
9.26
14.27
- 9.86
155
-------
Table 016. Yield Adjustments for Analysis I:
10% Ozone Reduction
STATE
ALABAHA
ARIZONA
ARKANSAS
CALIFORNIA
COLORADO
CONNECTICUT
DELAWARE
FLORIDA
GEORGIA
IDAHO
ILLINOIS
INDIANA
I QUA
KANSAS
KENTUCKY
LOUISIANA
MAINE
nARYLAND
rtASSACHUSETTS
niCHIGAN
(1INNESOTA
I1ISSISSIPPI
HISSOURI
flONTANA
NEBRASKA
NEVADA
NEU HAMPSHIRE
NEU JERSEY
NEU MEXICO
NEU YORK
NORTH CAROLINA
NORTH DAKOTA
OHIO
OKLAHOMA
OREGON
PENNSYLVANIA
RHODE ISLAND
SOUTH CAROLINA
SOUTH DAKOTA
TENNESSEE
TEXAS
UTAH
VERflONT
VIRGINIA
WASHINGTON
UEST VIRGINIA
UISCONSIN
WYOMING
CORN
1.004
1.006
1.006
1.006
1.006
1.010
1.002
1.002
1.004
1.004
1.004
1.004
1.003
1.004
1.004
1.003
1.002
1.005
1.004
1.002
1.002
1.005
1.005
1.004
1.004
1.006
1.003
1.007
1.004
1.003
1.007
1.002
1.003
1.004
1.001
1.004
1.007
1.007
1.002
1.004
1.003
1.007
1.003
1.007
1.001
1.003
1.003
1.005
SOYBEANS
1.026
.000
1.029
.000
.000
.000
1.019
1.018
1.026
.000
1.026
1.026
1.022
1.025
1.024
1.023
.000
1.027
.000
1.020
1.021
1.027
1.027
.000
1.025
.000
.000
1.030
.000
1.022
1.031
1.020
1.023
1.026
.000
1.024
.000
1.030
1.022
1.025
1.023
.000
.000
1.031
.000
.000
1.021
.000
COTTON
1.026
1.017
1.033
1.018
.000
.000
.000
1.012
1.026
.000
.000
.000
.000
.000
.000
1.014
.000
.000
.000
.000
.000
1.029
1.029
.000
.000
1.017
.000
.000
1.014
.000
1.038
.000
.000
1.015
.000
.000
.000
1.037
.000
1.025
1.013
.000
.000
1.038
.000
.000
.000
.000
SPRING
WHEAT
.000
1.008
.000
1.008
1.008
.000
.000
.000
.000
1.006
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
1.004
.000
.000
1.005
.000
1.008
.000
.000
.000
.000
.000
1.003
.000
.000
1.002
.000
.000
.000
1.004
.000
.000
1.008
.000
.000
1.001
.000
1.004
1.006
WINTER
WHEAT
1.015
1.015
1.015
1.014
1.016
.000
1.012
.000
1.015
1.017
1.013
1.008
1.012
1.011
1.011
1.009
.000
1.012
.000
1.008
1.012
1.016
1.015
1.013
1.013
1,015
.000
1.013
1.041
1.011
1.016
1.013
1.009
1.011
1.006
1.011
.000
1.021
1.012
1.015
1.014
1.020
.000
1.016
1.005
1.010
1.014
1.017
GRAIN
SORGHUM
1.004
1.004
1.004
1.004
1.004
.000
.000
.000
1.004
.000
1.004
1.004
1.003
1.003
1.003
1.003
.000
.000
.000
.000
.000
1.004
1.004
.000
1.003
.000
.000
.000
1.003
.000
1.005
.000
.000
1.004
.000
1.003
.000
1.005
1.003
1.004
1.003
.000
.000
1.005
.000
.000
.000
.000
BARLEY
.000
1.001
.000
1.001
1.001
.000
1.000
.000
.000
1.001
1.001
.000
.000
1.001
1.001
.000
.000
1.001
.000
1.000
1.000
.000
.000
1.001
1.001
1.001
.000
1.001
1.001
1.000
1.001
1.000
1.000
1.001
1.000
1.001
.000
1.001
1.000
1.001
1.000
1.001
.000
1.001
1.000
1.001
1.000
1.001
156
-------
Table 017. Yield Adjustments for Analysis I:
25% ozone Reduction
SPRING WINTER GRAIN
STATE CORN SOYBEANS COTTON UHEAT UHEAT SORCHUrt BARLEY
ALABAHA
ARIZONA
ARKANSAS
CALIFORNIA
COLORADO
CONNECTICUT
DELAWARE
FLORIDA
GEORGIA
IDAHO
ILLINOIS
INDIANA
IOUA
KANSAS
KENTUCKY
LOUISIANA
E1AINE
flARYLANO
MASSACHUSETTS
niCHICAN
HINNESOTA
fllSSISSIPPI
niSSOURI
riONTANA
NEBRASKA
NEVADA
NEU HAMPSHIRE
NEU JERSEY
NEU I1EXICO
NEU YORK
NORTH CAROLINA
NORTH DAKOTA
OHIO
OKLAHCtlA
OREGON
PENNSYLVANIA
RHODE ISLAND
SOUTH CAROLINA
SOUTH DAKOTA
TENNESSEE
TEXAS
UTAH
VERflONT
VIRGINIA
WASHINGTON
UEST VIRGINIA
UISCONSIN
uYCtiiNC
1.009
1.012
1.012
1.013
1.012
1.012
1,003
1.003
1.009
1.008
1.009
1.009
1.006
1.008
1.008
1.006
1.003
1.010
1.009
1.004
1.005
1.010
1.010
1.008
1.008
1.012
1.006
1.014
1.008
1.005
1.015
1.004
1.006
1,009
1.002
1.008
1.014
1.014
1.006
1.009
1.006
1.013
1.007
1.015
1.001
1.007
1.005
1.009
1.064
.000
1.072
.000
.000
.000
1.046
1.045
1.064
.000
1.064
1.064
1.056
1.062
1.061
1.056
.000
1.067
.000
1.049
1.051
1.068
1.068
.000
1.061
.000
.000
1.076
.000
1.054
1.078
1.050
1.057
1.065
.000
1.061
.000
1.076
1.056
1.064
1.057
.000
.000
1.078
.000
.000
1.053
.000
1.057
1.123
1.073
1.126
.000
.000
.000
1.027
1.056
.000
.000
.000
.000
.000
.000
1.043
.000
.000
.000
.000
.000
1.065
1.065
.000
.000
1.118
.000
.000
1.089
.000
1.086
.000
.000
1.033
.000
.000
.000
1.083
.000
1.056
1.031
.000
.000
1.086
.000
.000
.000
.000
1
.000
1.017
.000
1.017
1.016
.000
.000
.000
.000
1.012
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
1.008
.000
.000
1.011
.000
1.016
.000
.000
.000
.000
.000
1.007
.000
.000
1.004
.000
.000
.000
1.009
.000
.000
1.018
.000
.000
1.002
.000
1.008
1.013
57
1.034
1.033
1.034
1.032
1.037
.000
1.027
.000
1.035
1.038
1.029
1.018
1.028
1.024
1.025
1.020
.000
1.027
.000
1.019
1.026
1.037
1.038
1.038
1.029
1.033
.000
1.030
1.029
1.025
1.044
1.029
1.016
1.017
1.014
1.026
.000
1.049
1.026
1.033
1.026
1.047
.000
1.036
1.012
1.023
1.043
1.038
1.008
1.010
1.010
1.010
1.010
.000
.000
.000
1.008
.000
1.008
1.008
1.006
1.007
1.007
1.006
.000
.000
.000
.000
.000
1.009
1.009
.000
1.007
.000
.000
.000
1.008
.000
1.011
.000
.000
1.008
.000
1.007
.000
1.011
1.006
1.008
1.007
.000
.000
1.011
.000
.000
.000
.000
.000
1.002
.000
1.002
1.002
.000
1.000
.000
.000
1.001
1.001
.000
.000
1.001
1.001
.000
.000
1.002
.000
1.001
1.001
.000
.000
1.001
1.001
1.002
.000
1.002
1.001
1.001
1.003
1.001
1.001
1.001
1.000
1.001
.000
1.002
1.001
1.001
1.001
1.002
.000
1.003
1.000
1.001
1.001
1.001
-------
TaDle D18. Yield Adjustments for Analysis I:
40% Ozone Reduction
STATE
ALABAT1A
ARIZONA
ARKANSAS
CALIFORNIA
COLORADO
CONNECTICUT
DELAWARE
FLORIDA
GEORGIA
IDAHO
ILLINOIS
INDIANA
IOUA
KANSAS
KENTUCKY
LOUISIANA
tlAINE
DARYLAND
MASSACHUSETTS
niCHICAN
niNNESOTA
nississippi
nissouRi
nONTANA
NEBRASKA
NEVADA
NEW HAHPSHIRE
NEW JERSEY
NEU HEXICO
NEW YORK
NORTH CAROLINA
NORTH DAKOTA
OHIO
OKLAHOtIA
OREGON
PENNSYLVANIA
RHODE ISLAND
SOUTH CAROLINA
SOUTH DAKOTA
TENNESSEE
TEXAS
UTAH
VEW10NT
VIRGINIA
WASHINGTON
WEST VIRGINIA
WISCONSIN
UYOIING
CORN
1.012
1.012
1.016
1.016
1.015
1.025
1.005
1.004
1.011
1.010
1.011
1.011
1.010
1.010
1.010
1.010
1.004
1.013
1.011
1.005
1.006
1.014
1.014
1.010
1.010
1.015
1.008
1.018
1.011
1.007
1.020
1.010
1.008
1.012
1.003
1.010
1.018
1.019
1.010
1.011
1.008
1.018
1.090
1.020
1.002
1.009
1.007
1.012
SOYBEANS
1.104
.000
1.115
.000
.000
.000
1.073
1.071
1.102
.000
1.102
1.102
1.089
1.098
1.097
1.089
.000
1.107
.000
1.077
1.082
1.109
1,109
.000
1,097
.000
.000
1.121
.000
1.086
1.124
1.079
1.091
1.104
.000
1.097
.000
1.122
1.089
1.102
1.091
.000
.000
1.125
.000
.000
1.085
.000
COTTON
1.081
1.130
1.104
1.179
.000
.000
.000
1.038
1.080
.000
.000
.000
.000
.000
.000
1.061
.000
.000
.000
.000
.000
1.092
1.092
.000
.000
1.168
.000
.000
1.126
.000
1.122
.000
.000
1.056
.000
.000
.000
1.118
.000
1.080
1.048
.000
.000
1.123
.000
.000
.000
.000
SPRING
UHEAT
.000
1.023
.000
1.024
1.022
.000
.000
.000
.000
1.016
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
1.010
.000
.000
1.015
,000
1.022
.000
.000
.000
.000
.000
1.001
.000
.000
1.001
.000
.000
.000
1.013
.000
.000
1,025
.000
.000
1.000
.000
1,011
1.018
WINTER
UHEAT
1.050
1.039
1.050
1.046
1.054
.000
1.040
.000
1.050
1.055
1.041
1.026
1.040
1.035
1.036
1.029
.000
1.039
.000
1.027
1.038
1.054
1.054
1.054
1.042
1.048
.000
1.042
1.042
1.036
1.064
1.041
1.024
1.024
1.020
1.037
,000
1.071
1.038
1.048
1.038
1.068
.000
1.052
1.017
1.033
1.047
1.055
GRAIN
SORGHUM
1.012
1.014
1.014
1.015
1.014
.000
.000
.000
1.011
.000
1.012
1.011
1.009
1.011
1.010
1.009
.000
.000
.000
.000
.000
1.013
1.012
.000
1.011
.000
.000
1,011
1.011
.000
1.016
.000
.000
1.012
.000
1.011
.000
1.016
1.009
1.011
1.009
.000
.000
1.016
.000
.000
.000
.000
BARLEY
.000
1.002
.000
1.003
1.002
.000
1.001
.000
.000
1.001
1.002
.000
.000
1.001
1.001
.000
.000
1.002
.000
1.001
1.001
.000
.000
1.001
1.001
1.002
.000
1.002
1.002
1.001
1.003
1.001
1.001
1.002
1.000
1.001
.000
1.003
1.001
1.002
1.001
1.003
.000
1.003
1.000
1.001
1.001
1.002
158
-------
Table 019. Yield Adjustments for Analysis I:
25% ozone Increase
STATE
ALABAMA
ARIZONA
ARKANSAS
CALIFORNIA
COLORADO
CONNECTICUT
DELAWARE
FLORIDA
GEORGIA
IDAHO
ILLINOIS
INDIANA
IOUA
KANSAS
KENTUCKY
LOUISIANA
MAINE
(1ARYLAND
MASSACHUSETTS
MICHIGAN
MINNESOTA
nississippi
MISSOURI
MONTANA
NEBRASKA
NEVADA
NEU HAMPSHIRE
NEU JERSEY
NEU MEXICO
NEU YORK
NORTH CAROLINA
NORTH DAKOTA
OHIO
OKLAHOMA
OREGON
PENNSYLVANIA
RHODE ISLAND
SOUTH CAROLINA
SOUTH DAKOTA
TENNESSEE
TEXAS
UTAH
VERMONT
VIRGINIA
WASHINGTON
UEST VIRGINIA
WISCONSIN
UYOMINC
CORN
,984
.977
.977
.976
.978
.964
.994
.994
.935
.985
.983
.984
.989
.981
.986
.989
.994
.981
.984
.993
.991
.980
.980
.986
.986
.978
.988
.974
,985
.989
.972
.992
.988
.985
.996
.986
.978
.973
.989
.984
.988
.975
.988
.972
.998
.987
.980
.983
SOYBEANS
.936
.000
.929
.000
.000
.000
.954
.965
.937
.000
.937
.937
.944
.939
.940
.944
.000
.934
.000
.951
.949
.933
.938
.000
.939
.000
.000
.926
.000
.946
.924
.950
.943
.936
.000
.939
.000
.925
.944
.937
.943
.000
.000
.924
.000
.000
.947
.000
COTTON
.918
.840
.901
.837
.000
.000
.000
.961
.921
.000
.000
.000
.000
.000
.000
.939
.000
.000
.000
.000
,000
.911
.911
.000
.000
.846
.000
.000
.880
.000
.885
.000
.000
.961
.000
.000
.000
.889
.000
.922
.966
.000
.000
.884
.000
.000
.000
.000
SPRING
UHEAT
.000
.974
.000
.973
.975
.000
.000
.000
.000
.981
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.988
.000
.000
.982
.000
.975
.000
.000
.000
.000
.000
.989
.000
.000
.993
.000
.000
.000
.985
.000
.000
.972
.000
.000
.996
.000
.987
.979
WINTER
UHEAT
.955
.957
.955
.958
.951
.000
.964
.000
.955
.951
.962
.976
.964
.968
.967
.973
.000
.964
.000
.975
.965
.952
.951
.951
.961
.957
.000
.961
.962
.967
,943
.963
.978
.978
.981
.966
.000
.937
.965
.957
.966
.940
.000
.953
.984
.969
.957
.951
GRAIN
SORGHUM
.989
.967
.967
.987
.987
.000
.000
.000
.989
.000
,989
.989
.992
.990
.990
.992
.000
.000
.000
.000
.000
.988
.988
.000
.990
.000
.000
.000
.990
.000
.985
.000
.000
.989
.000
.990
.000
,985
.992
.989
.990
.000
.000
.985
.000
.000
.000
.000
BARLEY
.000
.996
.000
.996
.996
.000
.999
.000
.000
.997
.997
.000
.000
.998
.998
.000
.000
.997
.000
.999
.999
.000
.000
.998
.998
.996
.000
.995
.997
.998
.994
.999
.998
.997
.999
.998
.000
.995
.998
.997
.998
.995
.000
.994
.999
.998
.999
.997
159
-------
TaDie 020. Yield Adjustments for Analysis II:
Ozone Reduction
STATE
ALABAHA
ARIZONA
ARKANSAS
CALIFORNIA
COLORADO
CONNECTICUT
DELAWARE
FLORIDA
GEORGIA
IDAHO
ILLINOIS
INDIANA
IOUA
KANSAS
KENTUCKY
LOUISIANA
flAINE
I1ARYLANO
MASSACHUSETTS
niCHIGAN
niNNESOTA
nississiPPi
nissouRi
MONTANA
NEBRASKA
NEVADA
NEW HAMPSHIRE
NEU JERSEY
NEU ttEXlCO
NEU YORK
NORTH CAROLINA
NORTH DAKOTA
OHIO
OKLAHOnA
OREGON
PENNSYLVANIA
RHODE ISLAND
SOUTH CAROLINA
SOUTH DAKOTA
TENNESSEE
TEXAS
UTAH
VERMONT
VIRGINIA
WASHINGTON
UEST VIRGINIA
WISCONSIN
UYOniNC
CORN
1.004
1.006
1.006
1.006
1.006
1.010
1.002
1.002
1.004
1.004
1.004
1.004
1.003
1.004
1.004
1.003
1.002
1.005
1.004
1.002
1.002
1.005
1.005
1.004
1.004
1.006
1.003
1.007
1.004
1.003
1 . 007
1.002
1.003
1.004
1.001
1.004
1.007
1.007
1.002
1.004
1.003
1.007
1.003
1.007
1.001
1.003
1.003
1.005
SOYBEANS
1.027
.000
1.029
.000
.000
.000
1.017
1.017
1.026
.000
1.027
1,026
1,022
1,025
1.025
1.022
.000
1.028
.000
1.019
1.020
1.029
1.029
.000
1.025
.000
.000
1.033
.000
1.021
1.034
1.099
1.023
1.027
.000
1.025
.000
1.033
1.022
1.026
1.023
.000
.COO
1.034
.000
.000
1.021
.000
COTTON
1.026
1.017
1.033
1.018
.000
.000
.000
1.012
1.026
.000
.000
.000
.000
.000
.000
1.014
.000
.000
.000
.000
.000
1.029
1.029
.000
.000
1.017
.000
.000
1.014
.000
1.038
.000
.000
1.015
.000
.000
.000
1.037
.000
1.025
1.013
.000
.000
1.038
.000
.000
.000
.000
SPRING
WHEAT
.000
1.008
.000
1.008
1.008
.000
.000
.000
.000
1.006
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
1.004
.000
.000
1.005
.000
1.008
.000
.000
.000
.000
1.003
.000
.000
.000
1.002
.000
.000
.000
1.004
.000
.000
1.008
.000
.000
1.001
.000
1.004
1.006
WINTER
WHEAT
1.015
1.015
1.015
1.014
1.016
.000
1.012
.000
1.015
1.017
1.013
1.008
1.012
1.011
1.011
1.009
.000
1.012
.000
1.008
1.012
1.016
1.015
1.013
1.013
1.015
.000
1.013
1.041
1.011
1.016
1.013
1.009
1.011
1.006
1.011
.000
1.021
1.012
1.015
1.014
1.020
.000
1.016
1.005
1.010
1.014
1.017
GRAIN
SORCHUtl
1.004
1.004
1.004
1.004
1.004
.000
.000
.000
1.004
.000
1.004
1.004
1.003
1.003
1.003
1.003
.000
.000
.000
.000
.000
1.004
1.004
.000
1.003
.000
.000
.000
1.003
.000
1.005
.000
.000
1.004
.000
1.003
.000
1.005
1.003
1.004
1.003
.000
.000
1.005
.000
.000
.000
.000
BARLEY
.000
1.001
.000
1.001
1.001
.000
1.000
.000
.000
1.001
1.001
.000
.000
1.001
1.001
.000
.000
1.001
.000
1.000
1.000
.000
.000
1.001
1.001
1.001
.000
1.001
1.001
1.000
1.001
1.000
1.000
1.001
1.000
1.001
.000
1.001
1.000
1.001
1.000
1.001
.000
1.001
1.000
1.001
1.000
1.001
160
-------
Table 021. Yield Adjustments for Analysis II:
25% Ozone Reduction
STATE
ALABATIA
ARIZONA
ARKANSAS
CALIFORNIA
COLORADO
CONNECTICUT
DELAWARE
FLORIDA
GEORGIA
IDAHO
ILLINOIS
INDIANA
IOWA
KANSAS
KENTUCKY
LOUISIANA
MAINE
MARYLAND
MASSACHUSETTS
MICHIGAN
MINNESOTA
nississippi
MISSOURI
MONTANA
NEBRASKA
NEVADA
NEW HAMPSHIRE
NEW JERSEY
NEW MEXICO
NEU YORK
NORTH CAROLINA
NORTH DAKOTA
OHIO
OKLAHOMA
OREGON
PENNSYLVANIA
RHODE ISLAND
SOUTH CAROLINA
SOUTH DAKOTA
TENNESSEE
TEXAS
UTAH
VERMONT
VIRGINIA
UASHINGTON
WEST VIRGINIA
WISCONSIN
UYOniNG
CORN
1.009
1.012
1.012
1.013
1.012
1.012
1.003
1.003
1.009
1.008
1.009
1.009
1.006
1.008
1.008
1.006
1.003
1.010
1.009
1.004
1.005
1.010
1.010
1.008
1.008
1.012
1.006
1.014
1.008
1.005
1.015
1.004
1.006
1.009
1,002
1.080
1.014
1.014
1.006
1.009
1.006
1.013
1.007
1.015
1.001
1.007
1.005
1.009
SOYBEANS
1.064
.000
1.075
.000
.000
.000
1.042
1.041
1.064
.000
1.064
1.064
1.054
1.061
1.060
1.054
.000
1.068
.000
1.045
1.048
1.070
1.069
.000
1.060
.000
.000
1.080
.000
1.051
1.082
1.046
1.055
1.065
.000
1.060
.000
1.081
1.054
1.064
1.056
.000
.000
1.083
.000
.000
1.051
.000
COTTON
1.057
1.123
1.073
1.126
.000
.000
.000
1.027
1.056
.000
.000
.000
.000
.000
.000
1.043
.000
.000
.000
.000
.000
1.065
1.065
.000
.000
1.118
.000
.000
1.089
.000
1.086
.000
.000
1.033
.000
.000
.000
1.083
.000
1.056
1.031
.000
.000
1.086
.000
.000
.000
.000
SPRING
UHEAT
.000
1.017
.000
1.017
1.016
.000
.000
.000
.000
1.012
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
1.011
.000
1.016
.000
.000
.000
.000
.000
1.007
.000
.000
1.004
.000
.000
.000
1.009
.000
.000
1.018
.000
.000
1.002
.000
1.008
1.013
UINTER
UHEAT
1.034
1.033
1.034
1.032
1.037
.000
1.027
.000
1.035
1.038
1.029
1.018
1.028
1.024
1.025
1.020
.000
1.027
.000
1.019
1.026
1.037
1.038
1.038
1.029
1.033
.000
1.030
1.029
1.025
1.044
1.029
1.016
1.017
1.014
1.026
.000
1.049
1.026
1,033
1.026
1.047
.000
1.036
1.012
1.023
1.043
1.038
GRAIN
SORGHUtl
1.008
1.010
1.010
1.010
1.010
.000
.000
.000
1.008
.000
1.008
1.008
1.006
1.007
1.007
1.006
.000
.000
.000
.000
.000
1.009
1.009
.000
1.007
.000
.000
.000
1.008
.000
1.011
.000
.000
1.008
.000
1.007
.000
1.011
1.006
1.008
1.007
.000
.000
1.011
.000
.000
.000
.000
BARLEY
.000
1.002
.000
1.002
1.002
.000
1.000
.000
.000
1.001
1.001
.000
.000
1.001
1.001
.000
.000
1.002
.000
1.001
1.001
.000
.000
1.001
1.001
1.002
.000
1.002
1.001
1.001
1.003
1.001
1.001
1.001
1.000
1.001
.000
1.002
1.001
1.001
1.001
1.002
.000
1.003
1.000
1.001
1.001
1.001
161
-------
Table D22. Yield Adjustments for Analysis II:
40% Ozone Reduction
STATE
ALABAMA
ARIZONA
ARKANSAS
CALIFORNIA
COLORADO
CONNECTICUT
DELAWARE
FLORIDA
GEORGIA
IDAHO
ILLINOIS
INDIANA
IOUA
KANSAS
KENTUCKY
LOUISIANA
MAINE
MARYLAND
MASSACHUSETTS
niCHIGAN
fllNNESOTA
MISSISSIPPI
nissouRi
nONTANA
NEBRASKA
NEVADA
NEU HAMPSHIRE
NEU JERSEY
NEU (1EXICO
NEU YORK
NORTH CAROLINA
NORTH DAKOTA
OHIO
OKLAHOMA
OREGON
PENNSYLVANIA
RHODE ISLAND
SOUTH CAROLINA
SOUTH DAKOTA
TENNESSEE
TEXAS
UTAH
VERMONT
VIRGINIA
UASHINGTON
UEST VIRGINIA
UISCONSIN
WYOMING
CORN
1.012
1.012
1.016
1.016
1.015
1.025
1.005
1.004
1.011
1.010
1.011
1.011
1.010
1.010
1.010
1.010
1.004
1.013
1.011
1.005
1.006
1.014
1.014
1.010
1.010
1.015
1.008
1.018
1.011
1.007
1.020
1.010
1.008
1.012
1.003
1.010
1.018
1.019
1.010
1.011
1.008
1.018
1.090
1.020
1.002
1.009
1.007
1.012
SOYBEANS
1.099
.000
1.116
.000
.000
.000
1.064
1.062
1.099
.000
.1.100
1.098
1.083
1.094
1.092
1.083
.000
1.105
.000
1.069
1.074
1.107
1.107
.000
1.093
.000
.000
1.123
.000
1.079
1.128
1.071
1.085
1.101
.000
1.093
,000
1,125
1.083
1.098
1.085
.000
.000
1.128
.000
.000
1.078
.000
COTTON
1.081
1.130
1.104
1.179
.000
.000
.000
1.038
1.080
.000
.000
.000
.000
.000
.000
1.061
.000
.000
.000
.000
.000
1.092
1.092
.000
.000
1.168
.000
.000
1.126
.000
1.122
.000
.000
1.056
.000
.000
.000
1.118
.000
1.080
1.048
.000
.000
1.123
.000
.000
.000
.000
SPRING
UHEAT
.000
1.023
.000
1.024
1.022
.000
.000
.000
.000
1.016
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
1.010
.000
.000
1.015
.000
1.022
.000
.000
.000
.000
.000
1.001
.000
.000
1.001
.000
.000
.000
1.013
.000
.000
1.025
.000
.000
1.000
.000
1.011
1.018
UINTER
UHEAT
1.050
1.048
1.050
1.046
1.054
.000
1.040
.000
1.050
1.055
1.041
1.026
1.040
1.035
1.036
1.029
.000
1.039
.000
1.027
1.038
1.054
1.054
1.054
1.042
1.048
.000
1.043
1.042
1.036
1.064
1.450
1.024
1.024
1.020
1.037
.000
1.071
1.038
1.048
1.038
1.068
.000
1.052
1.017
1.033
1.047
1.055
GRAIN
SORGHUT1
1.012
1.014
1.014
1.015
1.014
.000
.000
.000
1.011
.000
1.012
1.009
1.011
1.010
1.009
.000
.000
.000
.000
.000
.000
1.013
1.013
.000
1.011
.000
.000
.000
1.011
.000
1.016
.000
.000
1.012
.000
1.011
.000
1.016
1.009
1.011
1.009
.000
.000
1.016
.000
.000
.000
.000
BARLEY
.000
1.002
.000
1.003
1.002
.000
1.001
.000
.000
1.001
1.002
.000
.000
1.001
1.001
.000
.000
1.002
.000
1.001
1.001
.000
.000
1.001
1.001
1.002
.000
1.003
1.002
1.001
1.003
1.001
1.001
1.002
1.000
1.001
.000
1.003
1.001
1.002
1.001
1.003
.000
1.003
1.000
1.001
1.001
1.002
162
-------
Table D23. Yield Adjustments for Analysis II:
25% Ozone Increase
STATE
ALABAMA
ARIZONA
ARKANSAS
CALIFORNIA
COLORADO
CONNECTICUT
DELAWARE
FLORIDA
GEORGIA
IDAHO
ILLINOIS
INDIANA
IOUA
KANSAS
KENTUCKY
LOUISIANA
MAINE
MARYLAND
MASSACHUSETTS
MICHIGAN
MINNESOTA
MISSISSIPPI
MISSOURI
MONTANA
NEBRASKA
NEVADA
NEU HMPSHIRE
NEW JERSEY
NEU MEXICO
NEU YORK
NORTH CAROLINA
NORTH DAKOTA
OHIO
OKLAHOMA
OREGON
PENNSYLVANIA
RHODE ISLAND
SOUTH CAROLINA
SOUTH DAKOTA
TENNESSEE
TEXAS
UTAH
VEW10NT
VIRGINIA
WASHINGTON
UEST VIRGINIA
UISCONSIN
UYOHINC
CORN
.984
,977
.977
.976
.978
.964
.994
.994
.935
.985
.983
.984
.989
.981
.986
.989
.994
.981
.984
.993
.991
.980
.980
.986
,986
.978
,988
.974
.985
.989
.972
.992
.988
.983
.996
.986
.978
.973
.989
.984
.988
.975
.988
.972
,998
.987
.980
.983
SOYBEANS
.930
.000
.920
.000
.000
.000
.954
.955
.931
.000
.931
.931
.941
.934
.935
.941
.000
.927
.000
.951
.947
.925
.925
.000
.935
.000
.000
.915
.000
.944
.913
.949
.940
.929
.000
.935
.000
.914
.941
.931
.940
.000
.000
.912
.000
.000
.945
.000
COTTON
,918
.840
.901
.837
.000
.000
.000
.961
.921
.000
.000
.000
.000
.000
.000
.929
.000
.000
.000
.000
.000
.911
.911
.000
.000
.846
.000
.000
.880
.000
.885
,000
.000
.961
.000
.000
.000
.889
.000
.992
.966
.000
.000
.884
.000
.000
.000
.000
SPRING
UHEAT
.000
.974
.000
.973
.975
.000
.000
.000
.000
.981
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.988
.000
.000
.982
.000
.975
.000
.000
.000
.000
.000
.989
.000
.000
.993
.000
.000
.000
.985
.000
.000
.972
.000
.000
.996
.000
.987
.979
UINTER
UHEAT
.955
,957
.955
.958
.951
.000
.964
.000
.955
.951
.962
.976
.964
.968
.967
.973
.000
.964
.000
.975
.965
.952
.957
.951
.961
.957
.000
.961
.962
.976
.943
.963
.978
.978
.981
.966
.000
.937
.965
.957
.966
.940
,000
.953
.984
.969
.957
.951
GRAIN
SORGHUM
.939
,967
.967
,987
.987
.000
.000
.000
.989
.000
.989
.989
.992
.990
.990
.992
.000
,000
.000
.000
.000
.988
.988
.000
.990
.000
.000
.000
.990
.000
.985
.000
.000
.989
.000
.990
.000
.985
,992
.989
.990
,000
.000
.985
.000
.000
.000
.000
BARLEY
.000
.996
.000
.996
.996
.000
.999
.000
.000
.997
.997
.000
.000
.998
.998
.000
.000
.997
.000
.999
.999
.000
.000
.998
.998
.996
.000
.995
.997
.998
QQ4
.999
.998
.997
.999
.998
.000
.995
.998
.997
.998
,9QS
,000
.994
.999
.998
.999
.997
163
-------
Table 024. Yield Adjustments for Analysis III:
10% Ozone Reduction
STATE
ALABAMA
ARIZONA
ARKANSAS
CALIFORNIA
COLORADO
CONNECTICUT
DELAWARE
FLORIDA
GEORGIA
IDAHO
ILLINOIS
INDIANA
I QUA
KANSAS
KENTUCKY
LOUISIANA
ftAINE
MARYLAND
MASSACHUSETTS
niCHIGAN
MINNESOTA
MISSISSIPPI
MISSOURI
MONTANA
NEBRASKA
NEVADA
NEW HAMPSHIRE
NEU JERSEY
NEU MEXICO
NEU YORK
N.GSTH CAROLINA
NORTH DAKOTA
OHIO
OKLAHOMA
OREGON
PENNSYLVANIA
RHODE ISLAND
SOUTH CAROLINA
SOUTH DAKOTA
TENNESSEE
TEXAS
UTAH
VERMONT
VIRGINIA
UASHINCTON
UEST VIRGINIA
WISCONSIN
WYOMING
CORN
1.007
1.009
1.009
1.010
1.009
1.014
1.003
1.003
1.007
1.006
1.007
1.007
1.005
1.006
1.006
1.005
1.003
1.008
1.007
1.004
1.004
1.008
1,008
1.006
1.006
1.009
1.005
1.010
1.007
1.005
1.011
1,004
1.005
1.007
1.002
1.006
1.010
1.011
1.005
1.007
1.005
1.010
1.005
1.011
1.001
1.006
1.004
1.007
SOYBEANS
1.037
.000
1.041
.000
.000
.000
1.030
1.029
1.037
.000
1.037
1.037
1.034
1.036
1.036
1.034
.000
1.039
.000
1.031
1.032
1.039
1.039
.000
1.036
.000
.000
1.042
.000
1.033
1.045
1.031
1.034
1.038
.000
1.036
.000
1.042
1.034
1.037
1.034
.000
.000
1.043
.000
.000
1.033
.000
COTTON
1.026
1.017
1.033
1.018
.000
.000
.000
1.012
1.026
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
1.029
1.029
.000
.000
1.017
.000
.000
1.014
.000
1.038
.000
.000
1.015
.000
.000
.000
1.037
.000
1.025
.000
.000
.000
1.038
.000
.000
.000
.000
SPRING
WHEAT
.000
1.008
.000
1.008
1.008
.000
.000
.000
.000
1.006
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
1.004
.000
.000
1.005
.000
1.008
.000
.000
.000
.000
.000
1.003
.000
.000
1.002
.000
.000
.000
1.004
.000
.000
1.008
.000
.000
1.001
.000
1.004
1.006
UINTER
WHEAT
1.036
1.035
1.035
1.034
1.038
.000
1.030
.000
1.036
1.038
1.031
1.022
1.030
1.027
1.028
1.024
.000
1.030
.000
1.023
1.029
1.038
1.038
1.038
1.032
1.035
.000
1.032
1.032
1.028
1.043
1.031
1.021
1.021
1.019
1.029
.000
1.046
1.029
1.035
1.029
1.044
.000
1.037
1.016
1.027
1.034
1.038
GRAIN
SORGHUM
1.004
1.004
1.004
1.004
1.004
.000
.000
.000
1.004
.000
1.004
1.004
1.003
1.003
1.003
1.003
.000
.000
.000
.000
.000
1.004
1.004
.000
1.003
.000
.000
.000
1.003
.000
1.005
.000
.000
1.004
.000
1.003
.000
1.005
1.003
1.004
1.003
.000
.000
1.005
.000
.000
.000
.000
BARLEY
.000
1.001
.000
1.001
1.001
.000
1.000
.000
.000
1.001
1.001
.000
.000
1.001
1.001
.000
.000
1.001
.000
1.000
1.000
.000
.000
1.001
1.001
1.001
.000
1.001
1.001
1.000
1.001
1.000
1.000
1.001
1.000
1.001
.000
1.001
1.000
1,001
1.000
1.001
.000
1.001
1.000
1.001
1.000
1.001
164
-------
Table D25. Yield Adjustments for Analysis ill:
25% ozone Reduction
STATE
ALABAMA
ARIZONA
ARKANSAS
CALIFORNIA
COLORADO
CONNECTICUT
DELAWARE
FLORIDA
GEORGIA
IDAHO
ILLINOIS
INDIANA
IOWA
KANSAS
KENTUCKY
LOUISIANA
HAINE
MARYLAND
HASSACHUSETTS
I1ICHIGAN
MINNESOTA
niSSISSIPPI
nissouRi
HONTANA
NEBRASKA
NEVADA
NEW HAMPSHIRE
NEW JERSEY
NEW MEXICO
NEW YORK
NORTH CAROLINA
NORTH DAKOTA
OHIO
OKLAHOMA
OREGON
PENNSYLVANIA
RHODE ISLAND
SOUTH CAROLINA
SOUTH DAKOTA
TENNESSEE
TEXAS
UTAH
VERHONT
VIRGINIA
WASHINGTON
WEST VIRGINIA
UISCONSIN
WYOMING
CORN
1.015
1.020
1.020
1.020
1.019
1.030
1.007
1.006
1.015
1.014
1.015
1.015
1.011
1.013
1.013
1.011
1.006
1.017
1.015
1.007
1.009
1.017
1.017
1.013
1.013
1.019
1.011
1.022
1.014
1.010
1.024
1.008
1.011
1.015
1.005
1.013
1.022
1.023
1.011
1.015
1.011
1.022
1.011
1.024
1.003
1.012
1.009
1.015
SOYBEANS
1.097
.000
1.041
.000
.000
.000
1.076
1.075
1.097
.000
1.097
1.097
1.088
1.094
1.093
1.088
.000
1.100
.000
1.079
1.082
1.101
1.101
.000
1.094
.000
.000
1.109
.000
1.086
1.111
1.080
1.089
1.098
.000
1.094
.000
1.110
1.088
1.097
1.089
.000
.000
1.112
.000
.000
1.085
.000
COTTON
1.057
1.123
1.073
1.126
.000
.000
.000
1.027
1.056
.000
.000
.000
.000
.000
.000
1.043
.000
.000
.000
.000
.000
1.065
1.065
.000
.000
1.118
.000
.000
1.089
.000
1.086
.000
.000
1.033
.000
.000
.000
1.083
.000
1.056
1.031
.000
.000
1.086
.000
.000
.000
.000
SPRING
WHEAT
.000
1.017
.000
1.017
1.016
.000
.000
.000
.000
1.012
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
1.008
.000
.000
1.011
.000
1.016
.000
.000
.000
.000
.000
1.007
.000
.000
1.004
.000
.000
.000
1.009
.000
.000
1.018
.000
.000
1.002
.000
1.008
1.013
WINTER
WHEAT
1.086
1.084
1.086
1.082
1.092
.000
1.073
.000
1.087
1.093
1.075
1.053
1.073
1.066
1.068
1.058
.000
1.072
.000
1.055
1.070
1.091
1.092
1.092
1.076
1.084
.000
1.078
1.076
1.067
1.104
1.075
1.050
1.050
1.044
1.070
.000
1.113
1.071
1.084
1.070
1.110
.000
1.089
1.039
1.064
1.083
1.093
GRAIN
SORGHUM
1.008
1.010
1.010
1.010
1.010
.000
.000
.000
1.008
.000
1.008
1.008
1.006
1.007
1.007
1.006
.000
.000
.000
.000
.000
1.009
1.009
.000
1.007
.000
.000
.000
1.008
.000
1.011
.000
.000
1.008
.000
1.007
.000
1.011
1.006
1.008
1.007
.000
.000
1.011
.000
.000
.000
.000
BARLEY
.000
1.002
.000
1.002
1.002
.000
1.000
.000
.000
1.001
1.001
.000
.000
1.001
1.001
.000
.000
1.002
.000
1.001
1.001
.000
.000
1.001
1.001
1,002
.000
1.002
1.001
1.001
1.003
1.001
1.001
1.001
1.000
1.001
.000
1.002
1.001
1.001
1.001
1.002
.000
1.003
1.000
1.001
1.001
1.001
165
-------
Table D26. Yield Adjustments for Analysis III:
40% Ozone Reduction
STATE
ALABAHA
ARIZONA
ARKANSAS
CALIFORNIA
COLORADO
CONNECTICUT
DELAWARE
FLORIDA
GEORGIA
IDAHO
ILLINOIS
INDIANA
IOUA
KANSAS
KENTUCKY
LOUISIANA
ttAINE
MARYLAND
MASSACHUSETTS
HICHICAN
MINNESOTA
HISSISSIPPI
MISSOURI
MONTANA
NEBRASKA
NEVADA
NEU HAMPSHIRE
NEU JERSEY
NEU 11EXICO
NEU YORK
NORTH CAROLINA
NORTH DAKOTA
OHIO
OKLAHOMA
OREGON
PENNSYLVANIA
RHODE ISLAND
SOUTH CAROLINA
SOUTH DAKOTA
TENNESSEE
TEXAS
UTAH
VERHONT
VIRGINIA
WASHINGTON
WEST VIRGINIA
UISCONSIN
WYOMING
CORN
1.020
1.027
1.027
1.028
1.026
1.041
1.009
1.008
1.020
1.018
1.020
1.020
1.014
1.018
1.017
1.041
1.008
1.022
1.010
1.010
1.011
1.023
1.023
1.018
1.018
1.026
1.015
1,030
1.019
1.013
1.072
1.011
1,015
1,021
1.006
1.018
i,o3o
1.0.71
1.014
1.020
1.015
1.029
1.015
1.032
1.003
1.017
1,013
1.021
SOYBEANS
1.162
.000
1.176
.000
.000
.000
1.126
1.124
1.161
.000
1.162
1.161
1.146
1.157
1.155
1.145
.000
1.167
.000
1.131
1.137
1.169
1.169
.000
1.1'>6
.000
.000
1.133
.000
1.142
1.187
1.1";3
1.148
1.163
.000
1.156
.000
1.184
1.146
1.161
1.149
.000
.000
1.187
.000
.000
1.141
.000
COTTON
1.081
1.130
1.104
1.179
.000
.000
.000
1.038
1.080
.000
.000
.000
.000
.000
.000
1.061
.000
.000
.000
.000
.000
1.092
1.092
.000
.000
1.168
.000
.000
1.1L'6
.000
1.122
.'JOC
.000
1.056
.000
.000
.000
1.118
.000
1.080
1.048
.000
.000
1.123
.000
.000
.000
.000
SPRING
UHEAT
.000
1.023
.000
1.024
1.022
.000
.000
.000
.000
1.016
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
1.010
.000
.000
1.015
.000
1.022
.000
.000
.000
.000
.000
1.001
.000
.000
1.001
.000
.000
.000
1.013
.000
.000
1.025
.000
.000
1.000
.000
1.011
1.018
UINTER
UHEAT
1.133
1.129
1.133
1.126
1.142
.000
1.112
.000
1.134
1.143
1.116
1.081
1.112
1.102
1.104
1.088
.000
1.111
.000
1.084
1.108
1.141
1.142
1.142
1.118
1.129
.000
1.119
1.113
1.103
1.162
1.115
1.076
1.077
1.068
1.107
.000
1.175
1.108
1.129
1.108
1.168
.000
1.138
1.059
1.098
1.128
1.142
GRAIN
SORGHUfl
1.012
1.014
1.014
1.015
1.014
.000
.000
.000
1.011
.000
1.011
1.009
1.011
1.010
1.009
.000
.000
.000
.000
.000
.000
1.013
1.013
.000
1.011
.000
.000
.000
1.011
.000
1.016
.000
.000
1.012
.000
1.011
.000
1.016
1.009
1.011
1.009
.000
.000
1.016
.000
,000
.000
.000
BARLEY
.000
1.002
.000
1.003
1.002
.000
1.001
.000
.000
1.001
1.002
.000
.000
1.001
1.001
.000
.000
1.002
.000
.000
1.001
.000
.000
1.001
1.001
1.002
.000
1.00?
1.002
1.001
1.003
1.001
1.001
1.002
1.000
1.001
.000
1.003
1.001
1.002
1.001
1.003
.000
1.003
1.000
1.001
1.001
1.002
166
-------
TaDle D27. Yield Adjustments for Analysis III:
25% Ozone Increase
STATE
ALABAftA
ARIZONA
ARKANSAS
CALIFORNIA
COLORADO
CONNECTICUT
DELAWARE
FLORIDA
GEORGIA
IDAHO
ILLINOIS
INDIANA
IOUA
KANSAS
KENTUCKY
LOUISIANA
tIAINE
MARYLAND
MASSACHUSETTS
MICHIGAN
MINNESOTA
HISSISSIPPI
MISSOURI
nONTANA
NEBRASKA
NEVADA
NEU HAMPSHIRE
NEU JERSEY
NEU DEXICG
NEU YORK
NORTH CAROLINA
NORTH DAKOTA
OHIO
OKLAHOMA
OREGON
PENNSYLVANIA
RHODE ISLAND
SOUTH CAROLINA
SOUTH DAKOTA
TENNESSEE
TEXAS
UTAH
VERMONT
VIRGINIA
UASHINGTON
WEST VIRGINIA
WISCONSIN
UY01INC
CORN
.976
.967
.968
.967
.969
.952
.989
.990
.976
.978
.976
.976
.983
.978
.979
.982
.990
.97?
,076
.488
.989
.972
.972
.979
.978
.969
.982
.964
,977
.984
.qiJl
.987
.^8°
!§75
.992
.973
.%4
.963
.983
.976
.082
.965.
.981
.961
.996
.980
.985
.975
SOYBEANS
.914
.000
.908
.000
.000
.000
.931
.933
.914
.000
.914
.915
.922
.917
.918
.922
.000
.912
."!'"/:)
.929
.926
.911
.911
.000
.917
.000
.000
004
.000
.924
OPT
.m
/~!-*1
.914
.000
.917
,000
.904
.922
.915
.921
.000
.000
.903
.000
.000
.924
.000
COTTON
.918
.840
.901
.337
.000
.000
.000
.961
.921
.000
.000
,000
.000
.000
.000
.939
.000
.000
. xH'i"1
.uOC
.000
.911
.911
.000
.000
.846
.000
.000
.880
.000
.885
,000
. 000
.961
.000
.000
,000
.889
.000
.992
.966
.000
.000
.884
.000
.000
.000
.000
SPRING
WHEAT
.000
.974
.000
.973
.975
.000
.000
.000
.000
.981
.000
.000
.000
.000
.000
.000
,000
.000
.000
.000
.988
.000
.000
.982
.000
.975
.000
.000
.000
,000
.000
.989
.000
,,.oo
.993
.000
.000
.000
.985
,000
.000
.972
.000
.000
.996
.000
,987
.979
WINTER
'JHEAT
.910
.910
.908
.912
.902
.000
.921
.000
.907
.901
.918
.941
.921
.928
.926
.936
.000
.921
.000
.939
.923
.903
.902
.902
.917
.910
.000
,916
.917
.924
.890
.919
.945
.944
.950
.924
.000
.883
.923
.910
.924
.886
.000
.904
.956
.930
.911
.901
GRAIN
SORGHUM
.989
.967
.967
.987
.987
.000
.000
.000
.989
.000
.989
,989
.992
.990
.990
.992
.000
.000
.000
.000
.000
.988
.988
.000
.990
.000
.000
000
.990
.000
.985
.000
.000
.989
.000
.990
.000
.985
.992
,989
.990
.000
.000
.985
.000
.000
.000
.000
BARLEY
.000
.996
.000
.996
.996
.000
.999
.000
.000
.997
.997
,000
.000
.998
.998
.000
.000
.997
,000
.999
.999
.000
.000
.998
.998
.996
.000
.90S
.997
.998
.994
.999
.998
.997
.999
.998
000
.995
.998
.997
.998
.995
.000
.994
.999
.998
.999
.997
167
-------
Table D28. Yield Adjustments for Analysis IV:
10% Ozone Reduction
STATE
ALABAMA
ARIZONA
ARKANSAS
CALIFORNIA
COLORADO
CONNECTICUT
DELAWARE
FLORIDA
GEORGIA
IDAHO
ILLINOIS
INDIANA
IOUA
KANSAS
KENTUCKY
LOUISIANA
MAINE
MARYLAND
MASSACHUSETTS
MICHIGAN
MINNESOTA
MISSISSIPPI
MISSOURI
MONTANA
NEBRASKA
NEVADA
NEU HAMPSHIRE
NEU JERSEY
NEU MEXICO
NEU YORK
NORTH CAROLINA
NORTH DAKOTA
OHIO
OKLAHOMA
OREGON
PENNSYLVANIA
RHODE ISLAND
SOUTH CAROLINA
SOUTH DAKOTA
TENNESSEE
TEXAS
UTAH
VERMONT
VIRGINIA
WASHINGTON
WEST VIRGINIA
UISCONSIN
UYOflINC
CORN
1.005
1.004
1.006
1.005
1.005
1.006
1.004
1.002
1.005
1.005
1.004
1.004
1.003
1.003
1.003
1.003
1.002
1.004
1.004
1.002
1.002
1.004
1.004
1.004
1.003
1.005
1.002
1.005
1.005
1.002
1.007
1.002
1.003
1.005
1.001
1.003
1.005
1.007
1.002
1.004
1.003
1.006
1.003
1.005
1.001
1.003
1.003
1.005
SOYBEANS
1.026
.000
1.028
.000
.000
.000
1.025
1.020
1.026
.000
1.025
1.025
1.021
1.024
1.023
1.021
.000
1.026
.000
1.020
1.094
1.026
1.025
.000
1.022
.000
.000
1.027
.000
1 .021
1.028
1.019
1.023
1.026
.000
1.022
.000
1.029
1.020
1.024
1.024
.000
.000
1.027
.000
.000
1.022
.000
COTTON
1.026
1.042
1.031
1.048
.000
.000
.000
1.016
1.037
.000
.000
.000
.000
.000
.000
1.018
.000
.000
.000
.000
.000
1.026
1.025
.000
.000
1.050
.000
.000
1.044
.000
1.031
.000
.000
1.015
.000
.000
.000
1.035
.000
1.023
1.013
.000
.000
1.030
.000
.000
.000
.000
SPRING
UHEAT
.000
1.006
.000
1.007
1.007
.000
.000
.000
.000
1.006
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
1.003
.000
.000
1.005
.000
1.007
.000
.000
.000
.000
.000
1.003
.000
.000
1.002
.000
.000
.000
1.004
.000
.000
1.007
.000
.000
1.001
.000
1.004
1.007
UINTER
UHEAT
1.013
1.016
1.016
1.015
1.017
.000
1.009
.000
1.014
1.016
1.012
1.001
1.012
1.016
1.011
1.010
.000
1.008
.000
1.008
1.011
1.016
1.015
1.011
1.013
1.013
.000
1.010
1.014
1.011
1.016
1.010
1.009
1.011
1.005
1.011
.000
1.019
1.011
1.012
1.012
1.019
.000
1.012
1.005
1.010
1.013
1.017
GRAIN
SORGHUM
1.004
1.004
1.004
1.004
1.004
.000
.000
.000
1.004
.000
1.003
1.003
1.003
1.003
1.003
1.003
.000
.000
.000
.000
.000
1.004
1.004
.000
1.003
.000
,000
.000
1.004
.000
1.004
.000
.000
1.004
.000
1.003
.000
1.005
1.002
1.003
1,003
.000
.000
1.004
.000
.000
.000
.000
BARLEY
.000
1.001
.000
1.001
1.001
.000
1.001
.000
.000
1.001
1.001
.000
.000
1.001
1.001
.000
.000
1.001
.000
1.000
1.000
.000
.000
1.001
1.000
1.001-
.000
1.001
1.001
1.000
1.001
1.000
1.000
1.001
1.000
1.000
.000
1.001
1.000
1.001
1.001
1.001
.000
1.001
1.000
1.000
1.000
1.001
168
-------
Table 029. Yield Adjustments for Analysis IV:
25% Ozone Reduction
STATE
ALABAMA
ARIZONA
ARKANSAS
CALIFORNIA
COLORADO
CONNECTICUT
DELAUARE
FLORIDA
GEORGIA
IDAHO
ILLINOIS
INDIANA
IOUA
KANSAS
KENTUCKY
LOUISIANA
flAINE
HARYLAND
MASSACHUSETTS
MICHIGAN
MINNESOTA
MISSISSIPPI
MISSOURI
MONTANA
NEBRASKA
NEVADA
NEU HAMPSHIRE
NEW JERSEY
NEU MEXICO
NEU YORK
NORTH CAROLINA
NORTH DAKOTA
OHIO
OKLAHOMA
OREGON
PENNSYLVANIA
RHODE ISLAND
SOUTH CAROLINA
SOUTH DAKOTA
TENNESSEE
TEXAS
UTAH
VERMONT
VIRGINIA
WASHINGTON
UEST VIRGINIA
UISCONSIN
UYOttINC
CORN
1.009
1.009
1.011
1.011
1.011
1.012
1.008
1.005
1.009
1.010
1.008
1.008
1.005
1.007
1.007
1.005
1.004
1.009
1.007
1.004
1.004
1.009
1.009
1.008
1.006
1.011
1.005
1.010
1.009
1.005
1.011
1.004
1.006
1.010
1.003
1.006
1.010
1.034
1.005
1.008
1.007
1.011
1.005
1.011
1.002
1.005
1.006
1.010
SOYBEANS
1.065
.000
1.070
.000
.000
.000
1.062
1.050
1.065
.000
1.062
1.062
1.052
1.059
1.058
1.053
.000
1.065
.000
1.049
1.048
1.064
1.062
.000
1.055
.000
.000
1.066
.000
1.052
1.069
1.047
1.057
1.066
.000
1.045
.000
1.074
1.051
1,061
1.059
.000
.000
1.068
.000
.000
1.055
.000
COTTON
1.059
1.094
1.031
1.048
.000
.000
.000
1.016
1.037
.000
.000
.000
.000
.000
.000
1.018
.000
.000
.000
.000
.000
1.026
1.025
.000
.000
1.050
.000
.000
1.044
.000
1.069
.000
.000
1.036
.000
.000
.000
1.079
.000
1.023
1.033
.000
.000
1.066
.000
.000
.000
.000
SPRING
UHEAT
.000
1.013
.000
1.015
1.016
.000
.000
.000
.000
1.014
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
1.007
.000
.000
1.011
.000
1.015
.000
.000
.000
.000
.000
1.006
.000
.000
1.005
.000
.000
.000
1.007
.000
.000
1.016
.000
.000
1.003
.000
1.009
1.014
UINTER
UHEAT
1.030
1.037
1.037
1.034
1.039
.000
1.020
.000
1.032
1.037
1.026
1.021
1.026
1.026
1.025
1.023
.000
1.018
.000
1.018
1.025
1.035
1.034
1.026
1.031
1.040
.000
1.024
1.032
1.026
1.037
1,023
1.021
1.025
1.012
1.025
.000
1.043
1.024
1.028
1.027
1.044
.000
1.026
1.012
1.023
1.030
1.038
GRAIN
SORGHUI1
1.008
1.008
1.009
1.009
1.009
.000
.000
.000
1.008
.000
1.008
1.008
1.006
1.007
1.007
1.006
.000
.000
.000
.000
.000
1.008
1.008
.000
1.006
.000
.000
.000
1.008
.000
1.009
.000
.000
1.008
.000
1.006
.000
1.010
1.005
1.007
1.007
.000
.000
1.009
.000
.000
.000
.000
BARLEY
.000
1.001
.000
1.002
1.002
.000
1.001
.000
.000
1.002
1.001
.000
.000
1.001
1.001
.000
.000
1.001
.000
1.001
1.001
.000
.000
1.001
1.001
1.002
.000
1.002
1.001
1.001
1.002
1.000
1.001
1.002
1.000
1.001
.000
1.002
1.001
1.001
1.001
1.002
.000
1.002
1.002
1.001
1.001
1.002
169
-------
TaDle D30. Yield Adjustments for Analysis IV:
40% ozone Reduction
ARE
STATE
ALABAI1A
ARIZONA
ARKANSAS
CALIFORNIA
COLORADO
CONNECTICUT
1.011 1
FLORIDA
GEORGIA
IDAHO
ILLINOIS
INDIANA
IOUA
KANSAS
KENTUCKY
LOUISIANA
MAINE
MARYLAND
MASSACHUSETTS
niCHIGAN
MINNESOTA
HISSISSIPPI
MISSOURI
MONTANA
NEBRASKA
NEVADA
NEW HAMPSHIRE
NEW JERSEY
NEW MEXICO
NEU YORK
NORTH CAROLINA
NORTH DAKOTA
OHIO
OKLAHOMA
OREGON
PENNSYLVANIA
RHODE ISLAND
SOUTH CAROLINA
SOUTH DAKOTA
TENNESSEE
TEXAS
UTAH
VERMONT
VIRGINIA
UASHINGTON
UEST VIRGINIA
WISCONSIN
WYOMING
CORN
1.012
1.012
1.015
1.014
1.015
1.016
.098
1.010
1.012
1.015
1.011
1.011
1.007
1.009
1.009
1.007
1.005
1.020
1.009
1.005
1.005
1.012
1.012
1.010
1.008
1.014
1.006
1.013
1.012
1.006
1.015
1.005
1.008
1.012
1.003
1.007
1.014
1.018
1.006
1.010
1.009
1.015
1.007
1.014
1.002
1.007
1.008
1.013
SOYBEANS
1.104
.000
1.112
.000
.000
.000
.000
1.080
1.103
.000
1.099
1.099
1.083
1.094
1.093
1.085
.000
1.103
.000
1.078
1.077
1.102
1.099
.000
1.088
.000
000
1.106
.000
1.082
^ J.J U
1.074
1.090
1.105
.000
1.087
.000
1.118
1.080
1.096
1.095
.000
.000
1.109
.000
.000
1.087
.000
COTTON
1.084
1.125
1.099
1.155
.000
.000
.000
1.050
1.084
,000
.000
.000
.000
.000
.000
1.055
.000
.000
.000
.000
.000
1.082
1.078
.000
,000
1.161
.Ono
.000
1.141
.000
i . u<38
.000
.000
1.057
.000
.000
.000
1.112
.000
1.073
1.051
.000
.000
1.094
.000
.000
.000
.000
SPRING
UHEAT
.000
1.017
.000
1.020
1.021
.000
1.029
.000
.000
1.019
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
1.009
.000
.000
1.015
.000
1.021
.000
.000
.000
.000
, 000
1.008
.000
.000
1.006
.000
.000
.000
1.010
.000
.000
1.022
.000
.000
1.004
.000
1.012
1.019
UINTER
UHEAT
1.043
1.053
1.053
1.049
1.057
.000
.000
.000
1.046
1.053
1.038
1.030
1.038
1.038
1.035
1.037
.000
1.026
.000
1.027
1.037
1.051
1.049
1.037
1.044
1.058
.000
1.034
1.047
1.037
1.053
1.033
1.031
1.036
1.017
1.036
.000
1.062
1.034
1.041
1.040
1.064
.000
1.038
1.017
1.033
1.043
1.055
GRAIN
SORGHUM
1.012
1.011
1.014
1.013
1.013
.000
1.002
.000
1.012
.000
1.011
1.011
1.008
1.010
1.010
1.008
.000
.000
.000
.000
.000
1.012
1.011
.000
1.009
.000
.000
.000
1.012
.000
1 .013
.000
.000
1.012
.000
1.009
.000
1.015
1.008
l.Oll
1,010
.000
.000
1.013
,000
.000
.000
.000
BARLEY
.000
1.002
.000
1.002
1.002
.000
1.035
.000
.000
1.002
1.002
.000
.000
1.001
1.001
.000
.000
1.002
.000
1.001
1.001
.000
.000
1.001
1.001
1.002
.000
1.002
1.002
1.001
1.002
1.001
1.001
1.002
1.000
1.001
.000
1.003
1.001
1.001
1.001
1.002
.000
1.002
1.002
1.001
1.001
1.002
170
-------
Table 031. Yield Adjustments for Analysis IV:
25% Ozone Increase
STATE
ALABAMA
ARIZONA
ARKANSAS
CALIFORNIA
COLORADO
CONNECTICUT
DELAWARE
FLORIDA
GEORGIA
IDAHO
ILLINOIS
INDIANA
IOWA
KANSAS
KENTUCKY
LOUISIANA
MAINE
MARYLAND
MASSACHUSETTS
MICHIGAN
MINNESOTA
MISSISSIPPI
MISSOURI
MONTANA
NEBRASKA
NEVADA
NEW HAMPSHIRE
NEW JERSEY
NEW MEXICO
NEW YORK
NORTH CAROLINA
NORTH DAKOTA
OHIO
OKLAHOMA
OREGON
PENNSYLVANIA
RHODE ISLAND
SOUTH CAROLINA
SOUTH DAKOTA
TENNESSEE
TEXAS
UTAH
VERMONT
VIRGINIA
WASHINGTON
WEST VIRGINIA
WISCONSIN
WYOMING
CORN
.983
.983
.979
.980
.979
.977
.984
.991
.983
.982
.984
.985
.990
.987
.987
.990
.99?
,983
.986
.992
.992
.983
.984
.986
.988
.979
.991
.981
.982
.991
.979
.•>-}3
.988
.982
.995
.989
.981
.975
.991
.985
.987
.979
.990
.980
.997
.990
.989
.981
SOYBEANS
.936
.000
.931
.000
.000
.000
.939
.949
.936
.000
.938
.939
.948
.941
.942
.947
.000
.-j?fi
.000
.951
.952
.937
.939
.000
.945
.000
000
,y34
.000
.948
,932
.y.-i>
.944
.935
.000
.946
.000
.928
.949
.939
.941
.000
.000
.933
.000
.000
.945
.000
COTTON
.918
.873
.905
.856
.000
.000
.000
.950
.918
.000
.000
.000
,000
.000
.000
.945
.000
.JOG
.000
.000
.000
.921
.924
.000
.000
.851
.000
.000
.867
.000
. 906
.000
.000
.960
.000
.000
.000
.896
.000
.926
.965
.000
.000
.910
.000
.000
.000
.000
SPRING
WHEAT
.000
.980
.000
.977
.976
.000
.000
.000
.000
.980
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.989
.000
.000
.983
.000
.976
.000
.000
.000
.000
.000
.990
.000
.000
.993
.000
.000
.000
.988
.000
.000
.975
.000
.000
.995
.000
.986
.978
UINTER
WHEAT
.961
.952
.952
.956
.949
.000
.973
.000
.958
.952
.965
.973
.965
.965
.968
.969
.000
.976
.000
.976
.966
.954
.956
.966
.960
.948
.000
.969
.958
.966
.952
.969
.972
.967
.985
.967
.000
.945
.969
.963
.964
.943
.000
.966
.984
.970
.961
.951
GRAIN
SORGHUM
.989
.989
.988
.988
.987
.000
.000
.000
.989
.000
.989
.990
.993
.991
.991
.990
.000
.000
.000
.000
.000
.989
.990
.000
.992
.000
.000
.000
.990
.000
.988
.000
.000
.989
.000
.992
.000
.986
.993
.990
.991
.000
.000
.988
.000
.000
.000
.000
BARLEY
.000
.997
.000
.996
.996
.000
.997
.000
.000
.997
.997
.000
.000
.998
.998
.000
.000
.997
.000
.999
.999
.000
.000
.998
.998
.996
.000
.997
.997
.999
.996
.999
.998
.997
.999
.998
.000
.995
.999
.997
.998
.996
.000
.996
.999
.998
.998
.997
171
-------
Table D32. Yield Adjustments for Analysis v1:
10% Ozone Reduction
STATE
ALABAtIA
ARIZONA
ARKANSAS
CALIFORNIA
COLORADO
CONNECTICUT
DELAWARE
FLORIDA
GEORGIA
IDAHO
ILLINOIS
INDIANA
IOWA
KANSAS
KENTUCKY
LOUISIANA
HAINE
I1ARYLAND
HASSACHUSETTS
HICHIGAN
MINNESOTA
MISSISSIPPI
MISSOURI
nONTANA
NEBRASKA
NEVADA
NEU HAMPSHIRE
NEW JERSEY
NEU ffEXICO
NEU YORK
NORTH CAROLINA
NORTH DAKOTA
OHIO
OKLAHOMA
OREGON
PENNSYLVANIA
RHODE ISLAND
SOUTH CAROLINA
SOUTH DAKOTA
TENNESSEE
TEXAS
UTAH
VERflONT
VIRGINIA
UASHINGTON
UEST VIRGINIA
WISCONSIN
UYOMINC
CORN
1.003
1.006
1.004
1.006
1.006
1.010
1.002
1.002
1.002
1.004
1.003
1.004
1.002
1.003
1.003
1.003
1.002
1.004
1.003
1.002
1.001
1.004
1.003
1.004
1.003
1.006
1.002
1.005
1.004
1.002
1.005
1.002
1.003
1.003
1.001
1.004
1.007
1.005
1.002
1.003
1.002
1.007
1.003
1.007
1.001
1.003
1.002
1.005
SOYBEANS
1.017
.000
1.020
.000
.000
.000
1.019
1.013
1.016
.000
1.019
1.023
1.015
1.019
1.017
1.023
.000
1.020
.000
1.020
1.015
1.019
1.018
.000
1.019
.000
.000
1.020
.000
1.018
1.021
1.015.
1.023
1.018
.000
1.024
.000
1.022
1.017
1.020
1.015
.000
.000
1.031
.000
.000
1.015
.000
COTTON
1.017
1,017
1.024
1.018
.000
.000
.000
1.009
1.016
.000
.000
.000
.000
.000
.000
1.014
.000
.000
.000
.000
.000
1.020
1.020
.000
.000
1.017
.000
.000
1.014
.000
1.025
.000
.000
1.010
.000
.000
.000
1.027
.000
1.020
1.009
.000
.000
1.038
.000
.000
.000
.000
SPRING
UHEAT
.000
1.008
.000
1.008
1.008
.000
.000
.000
.000
1.006
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
1.003
.000
.000
1.005
.000
1.008
.000
.000
.000
.000
.000
1.002
.000
.000
1.002
.000
.000
.000
1.00?
.000
.000
1.008
.000
.000
1.001
.000
1.003
1.006
UINTER
UHEAT
1.010
1.015
1.010
1.014
1.016
.000
1.012
.000
1.009
1.017
1.009
1.007
1.008
1.009
1.008
1.009
.000
1.009
.000
1.008
1.009
1.112
1.010
1.013
1.010
1.015
.000
1.009
1.041
1.009
1.011
1.010
1.009
1.008
1.006
1.011
.000
1.015
1.009
1.012
1.009
1.020
.000
1.016
1.005
1.010
1.010
1.017
GRAIN
SORCHUtl
1.003
1.004
1.003
1.004
1.004
.000
.000
.000
1.002
.000
1.003
1.004
1.002
1.002
1.002
1.003
.000
.000
.000
.000
.000
1.003
1.003
.000
1.002
.000
.000
.000
1.003
.000
1.003
.000
.000
1.003
.000
1.003
.000
1.004
1.002
1.003
1.002
.000
.000
1.005
.000
.000
.000
.000
BARLEY
.000
1.001
.000
1.001
1.001
.000
1.000
.000
.000
1.001
1.001
.000
.000
1.001
1.001
.000
.000
1.007
.000
1.000
1.000
.000
.000
1.001
1.001
1.001
.000
1.001
1.001
1.000
1.007
1.000
1.000
1.001
1.000
1.001
.000
1.001
1.000
1.001
1.000
1.001
.000
1.001
1.000
1.001
1.000
1.001
172
-------
Table D33. Yield Adjustments for Analysis V:
25% Ozone Reduction
STATE
ALABAMA
ARIZONA
ARKANSAS
CALIFORNIA
COLORADO
CONNECTICUT
DELAWARE
FLORIDA
GEORGIA
IDAHO
ILLINOIS
INDIANA
IOUA
KANSAS
KENTUCKY
LOUISIANA
rtAINE
nARYLAND
HASSACHUSETTS
ftlCHIGAN
RINNESOTA
nississippi
MISSOURI
HONTANA
NEBRASKA
NEVADA
NEU HAtlPSHIRE
NEU JERSEV
NEU flEXICO
NEU YORK
NORTH CAROLINA
NORTH DAKOTA
OHIO
OKLAHOTIA
OREGON
PENNSYLVANIA
RHODE ISLAND
SOUTH CAROLINA
SOUTH DAKOTA
TENNESSEE
TEXAS
UTAH
VERflONT
VIRGINIA
UASHINGTON
UEST VIRGINIA
UISCONSIN
UYOftING
CORN
1.006
1.012
1.008
1.013
1.012
1.012
1.003
1.002
1.006
1.008
1.007
1.008
1.004
1.006
1.006
1.006
1.003
1.007
1.007
1.004
1.004
1.007
1.007
1.008
1.006
1.012
1.005
1.010
1.008
1.004
1.010
1.003
1.006
1.006
1.002
1.008
1.014
1.010
1.005
1.007
1.004
1.013
1.007
1.015
1.001
1.007
1.006
1.009
SOYBEANS
1.042
.000
1.049
.000
.000
.000
1.046
1.03?
1.040
.000
1.046
1.056
1.039
1.048
1.044
1.056
.000
1.050
.000
1.049
1.037
1.048
1.046
.000
1.046
.000
.000
1.052
.000
1.0.43
1.052
1.037
1.057
1.045
.000
1.061
.000
1.056
1.044
1.051
1.038
.000
.000
1.078
.000
.000
1.037
.000
COTTON
1.037
1.123
1.050
1.126
.000
.000
.000
1.020
1.034
.000
.000
.000
.000
.000
.000
1.043
.000
.000
.000
.000
.000
1.046
1.044
.000
.000
1.118
.000
.000
1.089
.000
1.057
.000
.000
1.023
.000
.000
.000
1.061
.000
1.045
1.021
.000
.000
1.086
.000
.000
.000
.000
SPRING
UHEAT
.000
1.017
.000
1.017
1.016
.000
.000
.000
.000
1.012
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
1.006
.000
.000
1.011
.000
1.016
.000
.000
.000
.000.
.000
1.005
.000
.000
1.004
.000
.000
.000
1.007
.000
.000
1.018
.000
.000
1.002
.000
1.006
1.013
UINTER
UHEAT
1.022
1.033
1.023
1.032
1.037
.000
1.027
.000
1.021
1.038
1.021
1.016
1.020
1.019
1.018
1.020
.000
1.020
.000
1.019
1.019
1.026
1.026
1.038
1.022
1.033
.000
1.020
1.029
1.Q2Q
1.029
1.021
1.016
1.012
1.014
1.026
.000
1.036
1.020
1.026
1.017
1.047
.000
1.036
1.012
1.023
1.030
1.038
GRAIN
SORGHUh
1.005
1.010
1.007
1.010
1.010
.000
.000
.000
1.005
.000
1.006
1.007
1.004
1.005
1.005
1.006
.000
.000
.000
.000
.000
1.006
1.006
.000
1.005
.000
.000
.000
1.008
.QGQ
1.007
.000
.000
1.006
.000
1.007
.000
1.008
1.005
1.006
1.005
.000
.000
1.011
.000
.000
.000
.000
BARLEY
.000
1.002
.000
1.002
1.002
.000
1.000
.000
.000
1.001
1.001
.000
.000
1.001
1.001
.000
.000
1.002
.000
1.001
1.001
.000
.000
1.001
1.001
1.002
.000
1.001
1.001
;,
-------
Table D3A. Yield Adjustments for Analysis V:
4-0% Ozone Reduction
STATE
ALABAMA
ARIZONA
ARKANSAS
CALIFORNIA
COLORADO
CONNECTICUT
DELAUARE
FLORIDA
GEORGIA
IDAHO
ILLINOIS
INDIANA
IOUA
KANSAS
KENTUCKY
LOUISIANA
MAINE
MARYLAND
MASSACHUSETTS
MICHIGAN
MINNESOTA
MISSISSIPPI
MISSOURI
MONTANA
NEBRASKA
NEVADA
NEU HAMPSHIRE
NEU JERSEY
NEU MEXICO
NEU YORK
NORTH CAROLINA
•40RTH DAKOTA
OHIO
OKLAHOMA
OREGON
PENNSYLVANIA
RHODE ISLAND
SOUTH CAROLINA
SOUTH DAKOTA
TENNESSEE
TEXAS
UTAH
VERMONT
VIRGINIA
WASHINGTON
UEST VIRGINIA
UISCONSIN
WYOMING
CORN
1.008
1.012
1.011
1.016
1.015
1.025
1.005
1.003
1.007
1.010
1.008
1.010
1.007
1.008
1.007
1.010
1.004
1.010
1.008
1.005
1.004
1.010
1.010
1.010
1.008
1.015
1.006
1.012
1.011
1.006
1.013
1,007
1.008
1.008
1.003
1.010
1.018
1.014
1.008
1.009
1.005
1.018
1.090
1.020
1.002
1.009
1.005
1.012
SOYBEANS
1.068
.000
1.078
.000
.000
.000
1,073
1.053
1.062
.000
1.073
1.089
1.052
1.076
1.070
1.039
,000
1.079
.000
1.077
1.006
1.076
1.074
.000
1.074
.000
.000
1.0S2
.000
1.069
1.082
1.058
1.091
1.072
.000
1.097
.000
1.089
1.069
1.082
1.060
.000
.000
1.125
.000
.000
1.059
.000
COTTON
1.053
1.130
1.071
1.179
.000
.000
.000
1.028
1.049
.000
.000
.000
.000
.000
.000
1.061
.000
.000
.000
.000
.000
1.064
1.063
.000
.000
1.168
.000
.000
1.126
.000
1.081
nrn
i . V J
.000
1.039
.000
.000
.000
1.086
.000
1.064
1.032
.000
.000
1.123
.000
.000
.000
.000
SPRING
UHEAT
.000
1.023
.000
1.024
1.022
.000
.000
.000
.000
1.016
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
1.007
.000
.000
1,015
.000
1.022
.000
.000
.000
.000
.000
1 OC1
.000
.000
1.001
.000
.000
.000
1.010
.000
.000
1.025
.000
.000
1.000
.000
1.008
1.018
WINTER
UHEAT
1.033
1.048
1.034
1.046
1.054
.000
1.040
.000
1.031
1.055
1.030
1.023
1.028
1.027
1.026
1.029
.000
1.029
,000
1.027
1.027
1.038
1.037
1.054
1.032
1.048
.000
1.029
1.042
1.029
1.042
1.030
1.024
1.017
1.020
1.037
.000
1.052
1.030
1.038
1.025
1.068
.000
1.052
1.017
1.033
1.032
1.055
GRAIN
SORGHUM
1.008
1.014
1.010
1.015
1.014
.000
.000
.000
1.007
.000
1.009
1.010
1.006
1.009
1.007
1.009
.000
.000
.000
.000
.000
1.009
1.009
.000
1.008
.000
.000
.000
1.011
.000
1.011
f>pin
!6oo
1.008
.000
1.011
.000
1,012
1.007
1.009
1.006
.000
.000
1.016
.000
.000
.000
.000
BARLEY
.000
1.002
.000
1.003
1.002
.000
1.001
.000
.000
1.001
1.001
.000
.000
1.001
1.001
.000
.000
1.002
.000
1.001
1.001
.000
.000
1.001
1.001
1.002
.000
1.002
1.002
1.001
1.002
1.001
1.001
1.001
1.000
1.001
.000
1.002
1.001
1.002
1.001
1.003
.000
1.003
1.000
1.001
1.001
1.002
174
-------
Table D35. Yield Adjustments for Analysis V:
25% Ozone Increase
STATE
ALABAMA
ARIZONA
ARKANSAS
CALIFORNIA
COLORADO
CONNECTICUT
DELAWARE
FLORIDA
GEORGIA
IDAHO
ILLINOIS
INDIANA
IOUA
KANSAS
KENTUCKY
LOUISIANA
MAINE
MARYLAND
MASSACHUSETTS
MICHIGAN
MINNESOTA
MISSISSIPPI
MISSOURI
MONTANA
NEBRASKA
NEVADA
NEU HAMPSHIRE
NEW JERSEY
NEU MEXICO
NEU YORK
NORTH CAROLINA
NORTH DAKOTA
OHIO
OKLAHOMA
OREGON
PENNSYLVANIA
RHODE ISLAND
SOUTH CAROLINA
SOUTH DAKOTA
TENNESSEE
TEXAS
UTAH
VERMONT
VIRGINIA
WASHINGTON
WEST VIRGINIA
WISCONSIN
WYOMING
CORN
.990
.977
.984
.976
.978
MA
.994
.996
.960
.985
.988
.986
.992
.985
.990
.989
.994
.986
.988
.993
.994
.986
.986
.986
.989
.978
.991
.982
.985
.991
.982
,994
.988
.988
.996
.986
.978
.980
.991
.987
.992
.975
.988
.972
.998
.987
.986
.983
SOYBEANS
.958
.000
.952
.000
.000
.000
.954
.974
.962
.000
.955
.945
.961
.953
.957
.944
.000
.951
.000
.951
.963
.953
.985
.000
.954
.000
.000
,950
.000
.957
.950
.964
.943
.956
.000
.939
.000
.945
.956
.950
.966
.000
.000
.924
.000
,000
.963
.000
COTTON
.947
.840
.933
.837
.000
.000
.000
.971
.952
.000
.000
.000
.000
.000
.000
.939
.000
.000
.000
.000
.000
.938
.940
.000
.000
.846
.000
.000
.880
.000
.924
.000
.000
.973
.000
.000
.000
.919
.000
.938
.978
.000
.000
.884
.000
.000
.000
.000
SPRING
WHEAT
.000
.974
.000
.973
.975
.000
.000
.000
.000
.981
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.991
.000
.000
.982
.000
.975
.000
.000
.000
.000
.000
.992
.000
.000
.993
.000
.000
.000
.988
.000
.000
.972
.000
.000
.996
.000
.991
.979
WINTER
WHEAT
.971
.957
.969
.958
.951
.000
.964
.000
.973
.951
.973
.979
.975
.975
.976
.973
.000
.973
.000
.975
.975
.966
.967
.951
.970
.957
.000
.974
.962
.974
.962
.973
.978
.985
.981
.966
.000
.954
.973
.966
.978
.940
.000
.953
.984
.969
.970
.951
GRAIN
SORGHUM
.993
.967
.978
.987
.987
.000
.000
.000
.993
.000
.992
.990
.994
.992
.993
.992
.000
.000
.000
.000
.000
.992
.992
.000
.992
.000
.000
.000
.990
.000
.990
.000
.000
.992
.000
.990
.000
.989
.994
.991
.993
.000
.000
.985
.000
.000
.000
.000
BARLEY
.000
.996
.000
.996
.996
.000
.999
.000
.000
.997
.998
.000
.000
.999
.999
.000
.000
.998
.000
.999
.999
.000
.000
.998
.999
.996
.000
.907
.997
.998
.996
.993
.998
.998
.999
.998
.000
.996
.998
.998
.999
.995
.000
.994
.999
.998
.999
.997
175
*GPO 593-975 1984
------- |