6A-1

APPENDIX 6A: METHODS AND DATA USED TO DEVELOP ESTIMATES OF 

OZONE EFFECTS ON CROP AND FOREST PRODUCTIVITY

Incorporating the impacts of different ambient ozone concentration levels into 

FASOMGHG requires determining crop yield and forest productivity impacts associated with 

changes in concentrations. Productivity impacts are required for each crop/region and forest 

type/region combination included within the model. In this section, we describe our methods for 

calculating relative yield losses (RYLs) and relative yield gains (RYGs) of crops and tree species 

under alternative ambient ozone concentration levels.

These data are essential for our market analysis because crop and forest yields play an 

important role in determining the economic returns to agricultural and forest production 

activities. Thus, they affect landowner decisions regarding land use, crop mix, forest rotation 

lengths, production practices, and others. Alterations in ambient ozone concentration levels will 

therefore change the supply curves of U.S. agricultural and forest commodities, resulting in new 

market equilibriums. Because both the changes in ozone concentrations and the distribution of 

ozone-sensitive crops and tree species vary spatially, there may be substantial differences in the 

net impacts across regions. There may also be distributional impacts as commodity production 

shifts between regions in response to changes in relative productivity. 

6A.1 Methodology

There are several alternative metrics used for assessing ozone concentrations (see Lehrer et 

al. [2007] for more information). For this assessment, we are using the W126 metric, which is a 

weighted sum of all ozone concentrations observed from 8 a.m. to 8 p.m available in 2025 and 

2038. More specifically, we are using W126 ozone concentration surfaces generated using 

enhanced Voronoi Neighbor Averaging (eVNA). W126 concentration surfaces based on meeting 

the current ozone standard1 were provided by EPA in the previous iteration of this project (2013) 

served again as the baseline for this analysis. According to information provided by EPA, the 

eVNA W126 ozone surface is built from monitor data fused with Community Multiscale Air 

 

1The current primary and secondary ozone standards are 0.075 parts per million (ppm) (or 75 parts per billion) based 

on the annual fourth-highest daily maximum 8-hr concentration, averaged over 3 years. For the purposes of 

calculating impacts on crop yields and forest growth rates, we used the W126 equivalent of the current standard. 
6A-2

Quality (CMAQ) model-based gradient interpolations. The spatial resolution of the ozone 

surface in ArcGIS Shapefile format is 12 km. 

County-level values were extracted from the eVNA W126 ozone surface using ArcGIS. 

Only the ozone concentrations for the cropland and forestland portions of the W126 ozone 

surface are used to derive the county-level average crop and forest W126 ozone levels, 

respectively. These weighting adjustments were made to better reflect the ozone concentration 

that would affect the specific portions of each county containing forested land or cropland, rather 

than basing county-level exposure on the ozone concentration across the whole county. Data 

from the 2011 USGS National Land Cover Database (NLCD), updated from the previously used 

2006 NLCD data are used to extract the cropland and forestland portions from the ozone surface 

(Jin et al., 2013). The maps below demonstrate the change in forest and cropland area resulting 

from updating the data from 2006 to 2011. 

Table 6A-1. Comparison of Total Cropland and Forestland NLCD Area (sq m)

2006 NLCD 2011 NLCD % Increase

Cropland Area 1,730,787,687,000 1,786,333,937,400 3.21

Forestland Area 1,938,825,202,500 1,963,044,107,100 1.25

Figure 6A-1. Cropland Area (sq m) by County according to NLCD 2006 data
6A-3

Figure 6A-2. Cropland Area (sq m) by County according to NLCD 2011 data

Figure 6A-3. Forest Area (sq m) by County according to NLCD 2006 data
6A-4

Figure 6A-4. Forest Area (sq m) by County according to NLCD 2011 data

6A.1.1 Calculation of Relative Yield Loss

The median W126 ozone concentration response (CR) functions for crops and tree 

seedlings in the 2007 EPA technical report (Lehrer et al., 2007) are used to calculate the RYLs 

for crops and tree species under each ambient ozone concentration scenario used in this analysis. 

Table 6A-1 presents the α and β parameters being used in the W126 ozone CR function for 

different crops and tree species. The W126 ozone CR function is as follows: RYL = 1 −

e

−(W126⁄α)

β

. 
6A-5

Table 6A-2. Parameter Values Used for Crops and Tree Species

α β

Crops

Corn 98.3 2.973

Sorghum 205.9 1.963

Soybean 110.0 1.367

Winter wheat 53.7 2.391

Potato 99.5 1.242

Cotton 94.4 1.572

Tree Species

Ponderosa 159.63 1.1900

Red alder 179.06 1.2377

Black cherry 38.92 0.9921

Tulip poplar 51.38 2.0889

Sugar maple 36.35 5.7785

Eastern white 63.23 1.6582

Red maple 318.12 1.3756

Douglas fir 106.83 5.9631

Quaking aspen 109.81 1.2198

Virginia pine 1,714.64 1.0000

6A.1.1.1 Relative Yield Loss for Crops

Specifically, for crops, we first calculate the FASOMGHG subregion RYLs for crops that 

have W126 ozone CR functions using the subregion-level, cropland-based ozone concentration 

values under each scenario. The FASOMGHG subregion-level ozone concentration values are 

initially calculated for all crops as the simple averages of the county-level ozone concentration 

values. For crops that do not have W126 ozone CR functions, we assign them W126 ozone CR 

functions based on the crop proxy mapping shown in Table 6A-2. This crop mapping was based 

on the authors’ judgment and previous experience.2In addition, for oranges, rice, and tomatoes, 

which have ozone CR functions that are not W126-based (they are defined based on alternative 

measures of ozone levels), we directly used the median RYG values under the 13 ppm-hr ozone 

level reported in Table G-7 of Lehrer et al. (2007). More details on RYG are presented in further 

subsections. 

 

2 Also, note that FASOMGHG defines short-rotation woody trees such as hybrid poplar and willow as crops. Ozone 

impacts on short-rotation woody trees were based on ozone RYLs for aspen. 
6A-6

Table 6A-3. Mapping of Ozone Impacts on Crops to FASOMGHG Crops

Crops Used for 

Estimating Ozone 

Impacts FASOMGHG Crops

W126 Crops

Corn Corn

Cotton Cotton

Potatoes Potatoes

Winter wheat Soft white wheat, hard red winter wheat, soft red winter wheat, durum wheat, 

hard red spring wheat, oats, barley, rye, sugar beet, grazing wheat, and improved 

pasture

Sorghum Sorghum, silage, hay, sugarcane, switchgrass, miscanthus, energy sorghum, and 

sweet sorghum

Soybeans Soybeans and canola

Aspen (tree) Hybrid poplar, willow (FASOMGHG places short-rotation woody biomass 

production in the crop sector rather than in the forest sector)

Non-W126 Crops

Oranges Orange fresh/processed, grapefruit fresh/processed

Rice Rice

Tomatoes Tomato fresh/processed

Moreover, for crops that have county-level production data and W126 ozone CR functions 

(including functions based on proxy crops), we updated the RYLs with production-weighted 

W126 values. The 2012 USDA Census of Agriculture (Ag Census) county-level production data 

are used to derive the weighted FASOMGHG subregion RYLs, following Formula (6A.1). 

wRYLik = Ozone CR Functionk(

∑j Prodijk∗W126ij

∑j Prodijk

),(6A.1)

where i denotes FASOMGHG subregion, j indicates county, and k represents crop. Ozone 

CR Functionk refers to the ozone concentration response function for crop k. Prodijk represents 

the county-level production level of crop k, and W126ij represents the cropland-based ozone 

value for county j in subregion i. Finally, wRYLik stands for the weighted FASOMGHG 

subregion RYL for crop k. RYLs are calculated for each ozone concentration level being 

considered. 
6A-7

6A.1.1.2 Relative Yield Loss for Trees

The ozone CR functions for tree seedlings were used to calculate RYLs for FASOMGHG 

trees over their whole life span. To derive the FASOMGHG region-level RYLs for trees under 

each ozone concentration scenario, we used FASOMGHG region ozone values and the mapping 

in Table 6A-3.

Table 6A-4. Mapping of Ozone Impacts on Forests to FASOMGHG Forest Types

Tree Species Used for Estimating 

Ozone Impacts FASOMGHG Forest Type FASOMGHG Region(s)

Black cherry, tulip poplar Upland hardwood SC, SE

Douglas fir Douglas fir PNWW

Eastern white pine Softwood CB, LS

Ponderosa pine Softwood PNWE, PNWW, PSW, RM

Quaking aspen Hardwood RM

Quaking aspen, black cherry, red maple, 

sugar maple, tulip poplar

Hardwood CB, LS, NE

Red alder Hardwood PNWE, PNWW, PSW

Red maple Bottomland hardwood SC, SE

Virginia pine Natural pine, oak-pine, planted pine SC

Virginia pine, eastern white pine Natural pine, oak-pine, planted pine SE

Virginia pine, eastern white pine Softwood NE

Note: CB = Corn Belt; LS = Lake States; NE = Northeast; PNWE = Pacific Northwest—East side; PNWW = Pacific 

Northwest—West side; PSW = Pacific Southwest; RM = Rocky Mountains; SC = South Central; SE = Southeast.

Specifically, the FASOMGHG region-level RYLs are first calculated for each tree species 

listed in first column of Table 6A-3. Then, a simple average of RYLs for each tree species 

mapped to a FASOMGHG forest type in a given region is calculated. The mapping of tree 

species to FASOMGHG forest types is based on Elbert L. Little, Jr.’s Atlas of United States 

Trees (1971, 1976, 1977, 1978). Note that crop RYLs are generated at the FASOMGHG 

subregion level, whereas forest RYLs are calculated at the FASOMGHG region level, consistent 

with the greatest level of regional disaggregation available for these sectors within 

FASOMGHG. 
6A-8

6A.1.1.3 Calculation of Relative Yield Gain

As described by Lehrer et al. (2007), the RYL is the relative yield loss compared with the 

baseline yield under a “clean air” environment. For implementation within FASOMGHG, we 

calculate the RYG for crops and trees from moving between ambient ozone concentrations (i.e., 

RYG is calculated as a change in RYL when moving between scenarios). 

Thus, to obtain the RYG for crops and trees under alternative ozone concentrations, we 

need the RYLs under each scenario. For example, to derive RYG under the current standard 

75 ppb scenario relative to current conditions “currcond,” we use Formula (6A.2):

RYG75ppb =

1−RYL75ppb

1−RYLcurrcond

− 1 =

RYLcurrcond−RYL75ppb

1−RYLcurrcond

(6A.2)

The FASOMGHG subregion-level crop RYGs and the FASOMGHG region-level tree 

RYGs for changes associated with moving from one scenario to another were calculated for 

additional comparisons in the same way. 

6A.1.2 Conducting Model Scenarios in FASOMGHG

The current crop/forest budgets included in FASOMGHG are assumed to reflect 

input/output relationships under current ambient ozone concentrations as these budgets are based 

on historical data. To model the effects of changing ozone concentrations on the agricultural and 

forest sectors, the following five scenarios were constructed and run through the model:

1. “Current Conditions” scenario, where no RYGs of crops and trees are considered 

(assumed to be consistent with current ambient ozone concentration levels);

2. 75 ppb scenario, where crop and forest yields are assumed to increase by the 

percentages calculated in RYG75ppb, calculated relative to the current scenario;

3. 70 ppb scenario, using RYG70ppb, calculated relative to both current and 75 ppb 

scenarios

4. 65 ppb scenario, using RYG65ppb, calculated relative to both current and 75 ppb 

scenarios

5. 60 ppb scenario, using RYG60ppb, calculated relative to both current and 75 ppb 

scenarios
6A-9

The time scope of the FASOMGHG model scenarios used for these analyses is 2000–

2050, solved in 5-year time steps.3 The crop and tree RYGs are introduced into the model 

starting in 2025. During the time-steps of 2025 and 2030, crop and tree RYGs are assumed to be 

same as the RYGs in 2025. After 2040, RYGs remain constant at the RYGs obtained in 2038. 

The special case is in 2035, for which the weighted average of RYGs in 2025 and 2038 (60% for 

RYGs in 2025 and 40% for RYGs in 2038) are assigned to RYGs in 2035. Figure 6A-5 presents 

the modeling process of simulating ozone scenarios using FASOMGHG. The changes in crop 

and tree yield growth potentially lead to new market equilibriums for agricultural and forestry 

commodities, as well as land use changes between agricultural and forestry uses and 

consequently GHG emissions and sequestration changes. 

By comparing the market equilibriums under different scenarios, we can calculate the 

welfare, land use, and GHG impacts of alternative ozone standards on the U.S. agricultural and 

forest sector, including changes in consumer and producer welfare, land use allocation, and GHG 

mitigation potential over time. 

 

3 Because of terminal period effects, the model is run out to the 2050 time period, but only results through the 2040 

model time period (representative of 2040–2044) are used in our analyses.
6A-10

Figure 6A-5. FASOMGHG Modeling Flowchart

6A.1.3 Data Inputs

In this section, we summarize the input data used in the FASOMGHG scenarios specified 

for this assessment. Following the methods described above, we calculated W126 ozone 

concentration levels by region and crop. Effects on crop yields and forest productivity were 

calculated for each FASOMGHG region. We present the values used as model inputs in tabular 

and map format, with a primary focus here on comparison of the more stringent scenarios to the 

current standard.

6A.1.3.1 Ambient Ozone Concentration Data

The county-level forested and cropland W126 ozone values were aggregated at regional 

and subregional levels, respectively. 
6A-11

Table 6A-5. Forestland W126 Ozone Values under Alternative Scenarios

2025 2038

FASOMGHG Region C.C.a 75 ppb 70 ppb 65 ppb 60 ppb 75 ppb 70 ppb 65 ppb 60 ppb 

CB 11.84 5.24 4.58 3.11 1.99 5.24 4.58 3.10 1.99

GP c

8.95 5.15 4.47 3.73 2.87 5.06 4.37 3.63 2.79

LS 6.33 2.83 2.71 2.23 1.83 2.83 2.71 2.23 1.83

NE 8.48 3.65 3.19 2.29 1.64 3.65 3.19 2.29 1.64

PNWE 5.55 2.24 2.24 2.22 2.19 2.16 2.14 2.10 2.06

PNWW 3.79 1.49 1.49 1.49 1.49 1.49 1.48 1.48 1.48

PSW 17.28 6.65 6.65 6.64 6.54 4.52 3.61 2.91 2.45

RM 13.36 7.32 7.29 6.83 5.75 6.69 6.57 6.08 5.07

SC 11.84 4.12 3.46 2.48 1.75 4.11 3.46 2.48 1.75

SE 13.10 3.03 2.81 2.26 1.82 3.03 2.81 2.26 1.82

SWa

10.03 6.08 4.58 3.43 2.47 6.04 4.55 3.40 2.44

a Current Conditions

b Current Standard

c GP and SW are modeled as agriculture-only regions in FASOMGHG. 

Note: CB = Corn Belt; GP = Great Plains; LS = Lake States; NE = Northeast; PNWE = Pacific Northwest—East 

side; PNWW = Pacific Northwest—West side; PSW = Pacific Southwest; RM = Rocky Mountains; SC = South 

Central; SE = Southeast; SW = Southwest.

Table 6A-5 displays the agricultural W126 ozone values at the subregion level. Similar to 

the forest ozone values, the Pacific Southwest, South Central, Southeast, and Rocky Mountains 

regions experience the greatest agricultural ozone reductions under the 75 ppb scenario 

compared with current conditions. The Corn Belt, Southwest, and Northwest regions also see 

noteworthy agricultural ozone reductions.
6A-12

Table 6A-6. Cropland W126 Ozone Values under Modeled Scenarios

2025 2038

FASOMGHG Region Sub-region C.C.a 75 ppb 70 ppb 65 ppb 60 ppb 75 ppb 70 ppb 65 ppb 60 ppb 

CB Illinois, Northern 7.84 3.90 3.51 2.47 1.65 3.90 3.51 2.46 1.65

Illinois, Southern 11.46 6.54 5.79 3.86 2.38 6.53 5.79 3.86 2.38

Indiana, Northern 10.38 4.65 4.43 3.03 1.99 4.64 4.43 3.02 1.99

Indiana, Southern 13.13 6.66 6.40 4.22 2.54 6.65 6.40 4.22 2.54

Iowa, Central 5.71 2.79 2.46 2.06 1.65 2.79 2.46 2.06 1.65

Iowa, Northeast 6.23 2.69 2.42 2.02 1.67 2.69 2.42 2.02 1.67

Iowa, Southern 6.65 3.31 2.68 2.07 1.53 3.31 2.68 2.06 1.53

Iowa, Western 5.74 3.33 2.90 2.51 2.07 3.31 2.90 2.49 2.07

Missouri 11.50 5.17 4.01 2.76 1.81 5.17 4.01 2.76 1.81

Ohio, Northeast 12.71 5.34 5.08 3.51 2.35 5.34 5.08 3.51 2.35

Ohio, Northwest 12.07 5.89 5.65 3.88 2.54 5.88 5.65 3.87 2.54

Ohio, Southern 13.49 5.93 5.73 3.64 2.12 5.93 5.73 3.64 2.12

GP c Kansas 10.93 8.17 6.88 5.54 4.12 8.02 6.74 5.39 4.03

Nebraska 8.87 5.81 5.27 4.41 3.24 5.65 5.12 4.26 3.13

North Dakota 4.46 3.03 3.00 2.86 2.72 3.03 2.96 2.86 2.69

South Dakota 5.66 3.73 3.50 3.12 2.68 3.67 3.43 3.09 2.62

LS Michigan 9.50 5.20 4.96 3.57 2.50 5.20 4.96 3.57 2.50

Minnesota 4.97 2.63 2.49 2.31 2.12 2.63 2.49 2.31 2.11

Wisconsin 7.02 3.04 2.85 2.25 1.78 3.04 2.85 2.25 1.78

(continued)
6A-13

2025 2038

FASOMGHG Region Sub-region C.C.a 75 ppb 70 ppb 65 ppb 60 ppb 75 ppb 70 ppb 65 ppb 60 ppb 

NE Connecticut 11.90 4.53 3.69 2.71 1.85 4.53 3.69 2.71 1.85

Delaware 17.45 7.88 6.29 4.15 2.58 7.88 6.29 4.15 2.58

Maine 3.70 1.62 1.47 1.30 1.20 1.62 1.47 1.30 1.20

Maryland 17.20 7.37 5.88 3.77 2.29 7.37 5.87 3.77 2.29

Massachusetts 10.23 2.98 2.39 1.69 1.18 2.98 2.39 1.69 1.18

New Hampshire 5.84 1.99 1.73 1.32 1.06 1.99 1.73 1.32 1.06

New Jersey 16.70 6.95 5.42 3.49 2.19 6.93 5.42 3.49 2.19

New York 8.36 3.93 3.53 2.78 2.18 3.93 3.53 2.78 2.18

Pennsylvania 12.14 5.37 4.42 2.98 1.97 5.37 4.42 2.98 1.97

Rhode Island 11.72 4.47 3.71 2.69 1.85 4.47 3.71 2.69 1.85

Vermont 5.51 2.01 1.84 1.55 1.35 2.01 1.84 1.55 1.34

West Virginia 10.74 4.63 4.24 2.73 1.68 4.63 4.24 2.73 1.68

PNWE Oregon 6.66 2.64 2.64 2.62 2.55 2.53 2.48 2.43 2.35

Washington 4.96 1.79 1.79 1.79 1.78 1.78 1.78 1.77 1.77

PNWW 3.59 1.35 1.35 1.36 1.36 1.34 1.34 1.35 1.35

PSW California, Northern 21.86 9.61 9.61 9.58 9.49 6.20 4.54 3.21 2.35

California, Southern 21.46 12.49 12.48 12.43 12.31 6.20 5.52 4.82 4.23

(continued)
6A-14

2025 2038

FASOMGHG Region Sub-region C.C.a 75 ppb 70 ppb 65 ppb 60 ppb 75 ppb 70 ppb 65 ppb 60 ppb 

RM Arizona 13.53 9.63 9.63 9.17 7.91 8.24 8.11 7.51 6.24

Colorado 16.33 10.71 10.41 8.98 6.39 10.23 9.88 8.46 6.06

Idaho 12.75 5.05 5.04 4.79 4.15 4.62 4.57 4.30 3.75

Montana 6.61 3.25 3.25 3.15 3.00 3.16 3.16 3.09 2.89

Nevada 15.48 6.57 6.56 6.29 5.58 5.58 5.25 4.75 4.03

New Mexico 12.58 8.54 8.15 7.40 6.20 8.31 7.89 7.11 5.91

Utah 18.06 8.62 8.61 7.81 5.85 7.53 7.40 6.58 4.95

Wyoming 14.28 7.56 7.50 6.79 5.20 7.02 6.91 6.22 4.76

SC Alabama 13.16 2.89 2.70 2.29 1.91 2.89 2.70 2.29 1.91

Arkansas 12.30 4.81 3.76 2.57 1.71 4.81 3.76 2.57 1.70

Kentucky 13.79 5.59 5.35 3.52 2.16 5.59 5.34 3.52 2.16

Louisiana 9.59 4.62 3.58 2.66 1.95 4.62 3.58 2.66 1.95

Mississippi 11.21 3.32 2.75 2.11 1.55 3.32 2.75 2.11 1.54

Tennessee 15.55 4.57 4.22 2.85 1.83 4.57 4.22 2.85 1.83

Texas, East 9.92 5.67 3.88 2.79 1.99 5.64 3.87 2.77 1.98

SE Florida 9.16 2.95 2.86 2.76 2.60 2.95 2.86 2.76 2.60

Georgia 12.79 2.56 2.45 2.21 1.95 2.56 2.45 2.21 1.95

North Carolina 14.31 3.35 3.08 2.49 2.00 3.35 3.08 2.49 2.00

South Carolina 12.82 2.18 2.07 1.77 1.52 2.18 2.07 1.77 1.51

Virginia 12.62 3.95 3.47 2.35 1.59 3.95 3.47 2.35 1.59

(continued)
6A-15

2025 2038

FASOMGHG Region Subregion C.C.a75 ppb 70 ppb 65 ppb 60 ppb 75 ppb 70 ppb 65 ppb 60 ppb 

SWa Oklahoma 11.52 7.44 5.72 4.31 3.07 7.38 5.67 4.25 3.04

Texas, Central 

Blacklands 9.17 5.45 4.06 3.05 2.18 5.43 4.05 3.03 2.16

Texas, Coastal Bend 7.24 5.23 4.13 3.24 2.44 5.23 4.12 3.23 2.42

Texas, Edwards 

Plateau 9.02 6.38 5.24 4.28 3.38 6.32 5.17 4.20 3.31

Texas, High Plains 11.74 8.49 7.50 6.44 5.18 8.36 7.38 6.27 5.03

Texas, Rolling Plains 10.64 7.13 5.71 4.56 3.45 7.07 5.66 4.48 3.38

Texas, South 4.33 3.45 2.91 2.48 2.09 3.44 2.90 2.47 2.05

Texas, Trans Pecos 11.83 7.49 7.09 6.61 5.86 7.38 6.96 6.44 5.68

a Current Conditions

b Current Standard

c GP and SW are modeled as agriculture-only regions in FASOMGHG.
6A-16

Figure 6A-6 presents the incremental ozone reductions under alternative 2038 ozone 

standards with respect to the current 75 ppb standard. As the standard is tightened from the 70 

ppb scenario to the 60 ppb scenario, the greatest ozone reductions are observed in the Pacific 

Southwest region. Central parts of the Rocky Mountains region extending into northern areas of 

the Southwest and South central regions also see substantial ozone reductions. These ozone 

reductions would affect the production of crops and timber that are susceptible to ground-level 

ozone in these regions. 

Figure 6A-6. Ozone Reductions with Respect to 75 ppb under Alternative Scenarios
6A-17

6A.1.3.2 Changes in Crop and Forest Yields with Respect to 75 ppb Scenario

Figures 6A-7 through 6A-12 display major crops’ RYGs under alternative ozone 

standards scenarios at the FASOMGHG subregion level, with respect to the current 75 ppb 

standard. These are the values that were directly incorporated into FASOMGHG to define the 

scenarios modeled. Figures 6A-13 and 6A-14 display changes in forest RYGs. As discussed 

previously, the Rocky Mountains, Corn Belt, and parts of the southern regions of the United 

States (e.g., within the Pacific Southwest, South Central, and Southeast regions) are shown to 

experience the most significant further ozone reductions under the alternative policy scenarios. 

Hence, one would expect to see the most sizable increases in RYGs for crops and tree species 

grown in those regions. This finding is consistent with our calculations. 
6A-18

Figure 6A-7. Percentage Changes in Corn RYGs with Respect to the 75 ppb Scenario
6A-19

Figure 6A-8. Percentage Changes in Cotton RYGs with Respect to the 75 ppb Scenario
6A-20

Figure 6A-9. Percentage Changes in Potato RYGs with Respect to the 75 ppb Scenario
6A-21

Figure 6A-10.Percentage Changes in Sorghum RYGs with Respect to the 75 ppb Scenario
6A-22

Figure 6A-11.Percentage Changes in Soybean RYGs with Respect to the 75 ppb Scenario
6A-23

Figure 6A-12.Percentage Changes in Winter Wheat RYGs with Respect to the 75 ppb 

Scenario
6A-24

Figure 6A-13.Percentage Changes in Softwood RYGs with Respect to the 75 ppb Scenario
6A-25

Figure 6A-14.Percentage Changes in Hardwood RYGs with Respect to the 75 ppb Scenario
6A-26

6A.2 Model Results

FASOMGHG was used to estimate the projected effects of alternative ozone 

concentration standards on the U.S. agricultural and forestry sectors. As introduced earlier, the 

comparisons considered for this report focus on the differences between a scenario assuming 

compliance with the existing 2008 standards (75 ppb) and scenarios in which three more 

stringent ozone standards are met. Those three scenarios are 70 ppb, 65 ppb, and 60 ppb W126 

values. Our analysis included changes to production, prices, forest inventory, land use, welfare, 

and GHG mitigation potential associated with achieving each of the more stringent standards. 

6A.2.1 Agricultural Sector

Ozone negatively affects growth in many plants, leading to lower crop yields. In addition, 

some crops are more sensitive to ozone than others, so the percentage changes in yield will vary 

by crop and region. However, reducing ambient ozone concentrations would generally increase 

agricultural yields and total production, though the reductions in ozone concentrations that would 

be achieved under a given standard vary across regions. Our analysis began by determining the 

extent to which current yield losses caused by ozone could be reversed by reducing ozone levels. 

Increased crop yields lead to a greater available supply of most agricultural crops, which in turn 

tends to reduce market prices. There is also an overall tendency toward acreage shifting away 

from ozone-sensitive crops. In general, impacts in the agricultural sector are relatively limited, 

especially when compared with the forestry sector. By and large, more stringent standards led to 

increased incremental impacts, but the additional impact in moving to increasingly stringent 

ozone standards was relatively small. 

6A.2.1.1 Production and Prices

Changes in U.S. agricultural production and prices were measured using Fisher indices

(sees Tables 6A-6 and 6A-7).1 Both primary and secondary commodity production levels are 

projected to increase by 2040 as a result of heightened productivity. Agricultural production 

changes were generally relatively small across products, rarely exceeding an increase of 0.50% 

with respect to the current standard and often changing by 0.02% or less.

 

1 The Fisher price index is known as the “ideal” price index. It is calculated as the geometric mean of an index of 

current prices and an index of past prices.
6A-27

Table 6A-7. Agricultural Production Fisher Indices (Current conditions =100)

Sector Policy 2010 2020 2030 2040

Primary 

Commodities

Crops 75 ppb 100.0 99.8 100.7 101.3

70 ppb 100.0 99.8 100.7 101.3

65 ppb 100.0 99.8 100.8 101.4

60 ppb 100.0 99.8 100.9 101.4

Livestock 75 ppb 100.0 99.9 100.2 100.3

70 ppb 100.0 99.9 100.2 100.3

65 ppb 100.0 99.9 100.2 100.4

60 ppb 100.0 99.9 100.2 100.5

Farm productsa 75 ppb 100.0 99.8 100.1 100.3

70 ppb 100.0 99.8 100.4 100.3

65 ppb 100.0 99.8 100.5 100.4

60 ppb 100.0 99.9 100.5 100.5

Secondary 

Commodities

Processed 75 ppb 100.0 100.0 100.1 100.1

70 ppb 100.0 99.9 100.1 100.1

65 ppb 100.0 99.9 100.1 100.1

60 ppb 100.0 100.0 100.1 100.1

Meats 75 ppb 100.0 100.0 100.1 100.3

70 ppb 100.0 100.0 100.1 100.2

65 ppb 100.0 100.0 100.1 100.3

60 ppb 100.0 100.0 100.2 100.3

Mixed feeds 75 ppb 100.0 99.9 100.1 100.5

70 ppb 100.0 99.9 100.1 100.5

65 ppb 100.0 99.9 100.1 100.5

60 ppb 100.0 99.9 100.2 100.5

a Farm Products is the composite of Crops and Livestock.
6A-28

Table 6A-7. Agricultural Price Fisher Indices (Current Conditions = 100)

Sector Policy 2010 2020 2030 2040

Primary Commodities

Crops 75 ppb 100.0 100.2 98.5 97.2

70 ppb 100.0 100.2 98.4 97.0

65 ppb 100.0 100.2 98.3 97.0

60 ppb 100.0 100.2 98.3 97.0

Livestock 75 ppb 100.1 101.4 99.2 99.9

70 ppb 100.6 102.2 100.3 97.0

65 ppb 100.1 102.2 100.6 97.0

60 ppb 100.1 101.8 100.4 97.0

Farm productsa 75 ppb 100.0 100.2 100.1 99.9

70 ppb 100.0 100.2 98.4 97.0

65 ppb 100.0 100.2 98.3 97.0

60 ppb 100.0 100.2 98.3 97.0

Secondary Commodities

Processed 75 ppb 100.1 100.3 98.8 98.3

70 ppb 100.1 100.3 98.6 98.2

65 ppb 100.1 100.3 98.2 98.0

60 ppb 100.1 100.3 98.2 97.9

Meats 75 ppb 100.1 99.8 100.1 99.9

70 ppb 100.1 99.8 100.1 99.9

65 ppb 100.1 99.8 100.1 100.0

60 ppb 100.1 99.8 100.1 100.0

Mixed feeds 75 ppb 100.1 99.7 98.2 99.3

70 ppb 100.1 99.7 98.1 99.5

65 ppb 100.1 99.7 97.4 99.3

60 ppb 100.1 99.7 97.5 99.5

Increased production led to a general decline in market prices because the equilibrium 

price adjusts to higher levels of supply. This result is consistent with expectations because higher 

productivity leads to greater supply, which tends to decrease market prices. Changes in price 

were generally more pronounced than changes in production, with the largest decreases in the 
6A-29

Farm Products, Livestock, and Processed categories. Agricultural prices tend to decline by a 

greater percentage than production increases because the demand for most agricultural 

commodities is inelastic.2 However, almost all declines in price were less than 2.0% of prices at 

the current standards, and most were less than 0.5%. 

6A.2.1.2 Crop Acreage

Crop acreage was projected to decline with the introduction of the ozone standards because

additional productivity per acre reduces the demand for crop acreage. In aggregate, farmers will 

be able to meet the demand for agricultural commodities using less land under scenarios with 

lower ozone concentrations. Consistent with these expectations, the total cropped area is slightly 

smaller for each model year in the alternative standard cases. However, land allocation also 

depends on relative returns across various uses and is influenced by forest harvest timing. 

Changes in land allocation between the agricultural and forestry sectors are discussed later in this 

section.

Table 6A-8 provides projections of acreage in each of the major U.S. crops, as well as 

composites of all remaining crops and total cropland. The absolute change relative to the current 

standard is presented for each alternative standard. Larger changes occurred in sorghum acreage, 

whereas only minor changes occurred in all other crops, leading to almost no net change in crop 

acreage across all crops. This shift occurred largely because of differential crop sensitivity to 

ozone concentrations. Note that the sum of the crop-specific changes will not necessarily equal 

the total changes shown in Table 6A-8 because some double-cropping is reflected in the model 

(e.g., soybeans and winter barley). 

 

2 Demand elasticities are measures of the responsiveness of the quantity demanded to a change in price. 

Commodities with inelastic demands are those where consumers change the quantity of a good they purchase by a 

smaller percentage than the change in market price. Many food products fall into this category because they are 

relatively low-priced necessities. 
6A-30

Table 6A-8. Major Crop Acreage, Million Acres

Crop Policy 2010 2020 2030 2040

Corn

75 ppb 91.2 85.8 77.4 70.9

Change with Respect to Current Standard

70 ppb 0.00 0.02 0.00 -0.05

65 ppb 0.00 0.01 0.06 -0.07

60 ppb 0.00 0.01 0.04 -0.08

Soybeans

75 ppb 4.9 5.2 5.8 6.9

Change with Respect to Current Standard

70 ppb 0.00 0.00 0.00 0.00

65 ppb 0.00 0.00 0.00 -0.03

60 ppb 0.00 0.00 0.00 -0.03

Hay

75 ppb 43.7 41.1 41.7 42.0

Change with Respect to Current Standard

70 ppb 0.00 -0.02 0.03 0.00

65 ppb 0.00 -0.01 0.05 -0.01

60 ppb 0.00 0.01 0.06 -0.07

Hard Red 

Winter Wheat

75 ppb 25.5 23.7 23.2 22.4

Change with Respect to Current Standard

70 ppb 0.00 0.00 0.00 -0.06

65 ppb 0.00 0.00 -0.06 -0.11

60 ppb 0.00 0.00 -0.03 -0.10

Cotton

75 ppb 13.6 13.6 14.2 14.0

Change with Respect to Current Standard

70 ppb 0.00 0.00 -0.04 -0.01

65 ppb 0.00 0.00 -0.05 -0.01

60 ppb 0.00 0.00 -0.04 0.00

Hard Red Spring 

Wheat

75 ppb 13.5 12.9 13.4 13.0

Change with Respect to Current Standard

70 ppb 0.00 0.00 0.01 0.01

65 ppb 0.00 0.01 -0.03 0.00

60 ppb 0.00 0.01 -0.08 -0.01

Sorghum

75 ppb 73.1 71.9 72.0 72.2

Change with Respect to Current Standard

70 ppb 0.00 0.20 0.02 -4.88

65 ppb 0.00 0.24 0.02 -4.90

60 ppb 0.00 0.26 0.09 -4.89

SwitchGrass

75 ppb 0.1 0.1 0.1 0.1

Change with Respect to Current Standard
6A-31

Crop Policy 2010 2020 2030 2040

70 ppb 0.00 0.00 0.00 0.00

65 ppb 0.00 0.00 0.00 0.00

60 ppb 0.00 0.00 0.00 0.00

All Othersa

75 ppb 352.2 366.7 355.8 340.0

Change with Respect to Current Standard

70 ppb 0.00 0.10 0.01 -0.17

65 ppb 0.00 0.13 -0.12 -0.32

60 ppb 0.00 0.19 -0.25 -0.45

Total

75 ppb 5.6 5.4 4.6 4.0

Change with Respect to Current Standard

70 ppb 0.00 0.00 0.01 -0.01

65 ppb 0.00 -0.01 0.02 0.00

60 ppb 0.00 -0.01 0.01 0.00

a Canola, durum wheat, fresh grapefruit, fresh orange, fresh tomato, grazing wheat, hybrid poplar, oats, potato, 

processed grapefruit, processed orange, processed tomato, rice, rye, silage, soft red winter wheat, soft white 

wheat, spring barley, sugar beet, sugarcane, sweet sorghum, winter barley. 

6A.1.3 Forestry Sector

As with agricultural crops, ozone diminishes growth in most tree species, and our analysis 

began by estimating how much of this diminished growth would be reversed under the more 

stringent ozone standards. Impacts are significantly higher in the forestry sector, especially in 

hardwood species and species more prevalent in the southern regions. Impacts are more 

significant for southern regions because of the higher baseline ozone concentrations in the South 

Central and Southeast regions. Higher initial concentrations resulted in higher reductions to meet 

the alternative standards and, thus, higher impacts to tree growth. This relationship also 

contributes to the larger changes in the forestry sector as a whole. 

6A.1.3.1 Production and Prices

Reducing ozone concentrations led to increased forest growth, which was reflected in 

increased production in FASOMGHG. Some of the most substantial ozone standard impacts 

occurred in saw log and pulp log harvest quantities and prices. Compared with the current 

standard, alternative standard cases had consistently higher production except for hardwood pulp 

logs in 2030 and softwood pulp logs in 2040, where production increased only marginally and at 
6A-32

times fell below the baseline estimates, especially in 2040. The most significant impacts 

occurred in hardwood pulp logs, where harvests were projected to be more than 1% higher than 

under the current standard level by 2040 for the 60 ppb concentration. There are some cases 

where production of pulp logs and saw logs moved in opposite directions. There are two primary 

explanations for these trends. The first is that as softwood saw log production expands, the price 

for softwood saw logs drops, allowing processers to substitute saw logs for cases of production 

in which pulp logs are traditionally used. The second is that even when the primary log size 

being harvested is pulp logs, saw logs will generally also be present because of natural variation 

in tree growth rates (and vice versa for harvest of saw logs). With higher growth rates, there 

would tend to be more saw logs in stands harvested primarily for pulp logs over time. 

The largest changes in production occurred at the 60 ppb level. Changes from the current 

standard to 70 ppb were fairly small, but increased in 65 ppb, and changes were even larger in 60 

ppb. Table 6A-9 presents these changes by major product.

The impact of policy intervention on timber market prices was more substantial than the 

change in production in terms of percentage changes compared with the current standard. 

Although increases in production of forest products did not exceed 1.5% compared with the 

current standard, changes in price were as large as 12.8%. As with agricultural products, many 

forest products have relatively inelastic demand so prices tend to change by a larger percentage 

than quantities. Table 6A-10 lists absolute changes with respect to the current standard, whereas 

Table 6A-11 lists the percentage change in forest product prices for each year, alternative 

standard, and forest product. 

Table 6A-9. Forest Products Production, Million Cubic Feet

Product Policy 2010 2020 2030 2040

Hardwood Saw logs 75 ppb 3,588 3,394 3,708 4,246

Change with Respect to Existing Standard

70 ppb 1 1 38 3

65 ppb 1 0 33 -6

60 ppb 3 0 54 -13

Hardwood Pulp logs 75 ppb 2,448 2,162 2,510 2,220

Change with Respect to Existing Standard
6A-33

70 ppb 2 1 -29 3

65 ppb 3 -2 -29 14

60 ppb 10 -1 -40 25

Softwood saw logs 75 ppb 4,568 5,114 5,449 6,542

Change with Respect to Existing Standard

70 ppb -1 10 -2 7

65 ppb 0 22 1 53

60 ppb 5 35 21 61

Softwood pulp logs 75 ppb 3,437 3,878 4,350 4,298

Change with Respect to Existing Standard

70 ppb 1 -2 3 -17

65 ppb 0 1 4 -23

60 ppb 2 1 8 -8

Table 6A-10. Forest Product Prices, U.S. Dollars per Cubic Foot

Product Policy 2010 2020 2030 2040

Hardwood saw logs 75 ppb 0.80 0.89 0.58 0.33

Change with Respect to Existing Standard

70 ppb 0.00 -0.01 -0.01 -0.02

65 ppb 0.00 -0.01 -0.02 -0.03

60 ppb 0.00 -0.01 -0.03 -0.04

Hardwood pulp logs 75 ppb 0.30 0.64 0.45 0.24

Change with Respect to Current Standard

70 ppb 0.00 -0.01 -0.01 -0.01

65 ppb 0.00 -0.01 -0.03 -0.02

60 ppb 0.00 -0.02 -0.04 -0.03

Softwood saw logs 75 ppb 2.46 2.08 1.78 1.50

Change with Respect to Current Standard

70 ppb 0.00 0.00 0.00 0.00

65 ppb 0.00 -0.01 -0.01 -0.02

60 ppb -0.01 -0.02 -0.02 -0.04

Softwood pulp logs 75 ppb 1.49 1.33 1.47 1.14

Change with Respect to Current Standard

70 ppb 0.00 0.00 0.00 0.00
6A-34

Product Policy 2010 2020 2030 2040

65 ppb 0.00 0.00 -0.01 -0.02

60 ppb -0.01 0.00 -0.03 -0.05

Table 6A-11. Forest Product Prices and Percentage Change, U.S. Dollars per Cubic Foot

Product Policy 2010 2020 2030 2040

Hardwood saw logs 75 ppb 0.80 0.89 0.58 0.33

% Change with Respect to Existing Standard

70 ppb -0.41% -0.75% -2.16% -5.07%

65 ppb -0.24% -1.00% -3.89% -9.11%

60 ppb -0.25% -1.22% -5.60% -11.36%

Hardwood pulp logs 75 ppb 0.30 0.64 0.45 0.24

% Change with Respect to Existing Standard

70 ppb 0.07% -0.96% -3.19% -5.15%

65 ppb 0.12% -1.92% -5.57% -10.53%

60 ppb -0.06% -2.87% -8.98% -12.75%

Softwood saw logs 75 ppb 2.46 2.08 1.78 1.50

% Change with Respect to Existing Standard

70 ppb 0.01% -0.17% -0.16% -0.13%

65 ppb 0.03% -0.27% -0.36% -1.08%

60 ppb -0.28% -0.97% -1.20% -2.65%

Softwood pulp logs 75 ppb 1.49 1.33 1.47 1.14

% Change with Respect to Existing Standard

70 ppb -0.05% 0.28% -0.28% -0.13%

65 ppb -0.18% 0.10% -0.76% -1.90%

60 ppb -0.57% -0.27% -1.87% -4.11%

6A.1.3.2 Forest Acres Harvested

Harvested acres are projected to decline in hardwoods as a result of higher productivity in 

the policy cases. Conversely softwood acres harvested increases over time. The difference 

between the hardwood harvested acres in the current standard case and in the alternative 
6A-35

standards widens from 2010 to 2040, increasing to a difference of more than 4% under the 60 

ppb case. The impact to total acres of softwood harvested shows a less uniform pattern, with the 

largest impacts under the 65 ppb scenario, and an increase of up to 6% of harvested acres by 

2040 compared to 2010. Table 6A-12 presents the model results for forest acres harvested.

Table 6A-12. Forest Acres Harvested, Thousand Acres

Product Policy 2010 2020 2030 2040

Total hardwood 75 ppb 14,923 11,701 12,277 13,138 

Change with Respect to Current Standard

70 ppb 4 50 -139 -151

65 ppb 8 37 -265 -352

60 ppb 37 50 -377 -513

Total softwood 75 ppb 17,539 16,158 14,911 19,031

Change with Respect to Current Standard

70 ppb -11 35 47 106 

65 ppb -13 107 223 361 

60 ppb -2 142 328 314 

6A.1.3.3 Forest Inventory

Under FASOMGHG definitions, existing inventory includes only trees that have been 

standing since the initial model year of 2000. All trees planted since then, including both 

reforestation and afforestation, are included in new inventory. The model projected significant 

increases in existing inventory for hardwood species under the current standard, and consistent 

with the increase in the acres harvested of softwood, the existing inventory of softwoods declines 

through 2040 under the current standard. The difference in responses is partially explained by 

differential sensitivity to ozone between species. Hardwood species show a much higher 

sensitivity to ozone levels and are thus modeled to respond more dramatically to reductions in 

ozone concentration. 

Some relatively large differences between ozone standards occurred in the forest inventory 

projections. For example, existing hardwood inventory was projected to be 4.0% small under the 

60 ppb case than the 70 ppb case by 2040. New hardwood inventory is similarly sensitive, with 
6A-36

the model projecting a 2% decrease for this same comparison. For new and existing inventory of 

both hardwoods and softwoods, the largest impacts occurred at the 70 ppb standard. This type of 

nonlinear response can occur because of differences in the relative impacts on alternative forest 

and agricultural products that lead to land reallocation. Table 6A-13 presents the model results 

for forest inventory.

Table 6A-13. Existing and New Forest Inventory, Million Cubic Feet

Product Policy 2010 2020 2030 2040

Existing Hardwood 75 ppb 281,924 273,055 292,685 306,296

Change with Respect to Existing Standard 

70 ppb 0 -64 1,983 4,998

65 ppb 0 -138 5,974 14,937

60 ppb 0 -278 8,896 22,548

Existing Softwood 75 ppb 184,828 153,771 135,137 133,794

Change with Respect to Existing Standard 

70 ppb 0 -19 185 494

65 ppb 0 -38 683 1,583

60 ppb -6 -77 1,059 2,553

New Hardwood 75 ppb 1,932 9,437 18,872 29,732

Change with Respect to Existing Standard 

70 ppb 0 -152 -107 77

65 ppb 0 -150 -63 577

60 ppb 1 -156 0 931

New Softwood 75 ppb 8,837 64,254 114,454 128,581

Change with Respect to Existing Standard 

70 ppb 0 -42 -17 -497

65 ppb 4 6 -128 -1,164

60 ppb 7 -18 -312 -1,798

6A.1.4 Cross-Sectoral Policy Impacts

One of the advantages of a model such as FASOMGHG for analysis of impacts on major 

land-using activities is the ability to account for shifts in land use. Differentiated impacts on 

productivity across products will lead to changes in market prices and in the relative profitability 
6A-37

of alternative land uses. In response, landowners will change their allocation of land across 

different productive activities, which will contribute to market impacts. In addition, these 

changes in land use have implications for GHG emissions and other environmental impacts. In 

this section, we discuss changes in land use, net GHG emissions, and producer and consumer 

welfare across the agricultural and forest sectors. 

6A.1.4.1 Land Use

FASOMGHG projected changes in eight land use categories: existing forest, 

reforestation, afforestation, cropland, pasture, cropland pasture,

3

and lands enrolled in the 

Conservation Research Program (CRP).

4 The largest impacts under the current standard were 

projected in afforestation. The general projected pattern under the alternative standards within 

these categories was a decline in reforested area, afforested area, and cropland in 2030 and 2040, 

coupled with increases in the pasture and cropland pastured areas. There was no change in 

acreage retained in CRP or rangeland.

The incremental impact of more stringent ozone standards appears to be non-linear in 

some cases, especially in existing and reforested areas. Existing forest exhibits a general decline 

in the area under the 70 ppb scenario; in the 60 ppb scenario, however, there are small declines 

through 2020, followed by large increases in 2030 and 2040. This is due to the different trends in 

the hardwood and softwood species. Table 6A-14 presents the model results by major land use 

type.

Table 6A-14. Land Use by Major Category, Thousand Acres

Product Policy 2010 2020 2030 2040

Existing forest 75 ppb 257,565 201,587 161,426 131,210

Change with Respect to Current Standard

70 ppb -7 82 -113 -269

65 ppb 0 0 0 0

60 ppb -2 -84 159 691

Reforested 75 ppb 72,201 117,974 148,837 172,267

Change with Respect to Current Standard

 

3 Cropland pasture is managed land suitable for crop production (i.e., relatively high productivity) that is being used 

as pasture.

4 Rangeland estimates are also included, but rangeland is held fixed in FASOMGHG by assumption because it 

cannot be allocated to any other use.
6A-38

Product Policy 2010 2020 2030 2040

70 ppb -10 -7 -56 -189

65 ppb -16 52 -212 -878

60 ppb 28 107 -267 -1346

Afforested 75 ppb 14,086 10,886 6,404 11,474

Change with Respect to Current Standard

70 ppb 0 0 -134 -134

65 ppb 0 0 -309 -309

60 ppb 0 0 -427 -427

Cropland 75 ppb 311,713 313,325 304,227 292,678

Change with Respect to Current Standard

70 ppb 0 96 -38 -60

65 ppb 0 124 -102 -157

60 ppb 0 180 -157 -200

Pasture 75 ppb 84,280 85,049 86,133 82,571

Change with Respect to Current Standard

70 ppb 3 -7 86 64

65 ppb 10 -11 189 168

60 ppb 10 24 257 263

Cropland pasture 75 ppb 45,381 44,634 55,061 61,042

Change with Respect to Current Standard

70 ppb 0 1 29 51

65 ppb 0 1 161 216

60 ppb 0 6 235 277

Rangeland 75 ppb 302,210 301,104 300,049 299,039

Change with Respect to Current Standard

70 ppb 0 0 0 0

65 ppb 0 0 0 0

60 ppb 0 0 0 0

CRP 75 ppb 36,879 36,659 36,659 36,659

Change with Respect to Current Standard

70 ppb 0 0 0 0

65 ppb 0 0 0 0

60 ppb 0 0 0 0
6A-39

6A.1.4.2 Welfare

Welfare impacts resulting from the implementation of alternative standard levels

followed the same pattern between the agriculture and forestry sectors, although it was more 

pronounced in forestry. Consumer surplus typically increased in both cases as higher 

productivity under reduced ozone conditions tended to increase total production and reduce 

market prices. Because demand for most forestry and agricultural commodities is inelastic, there 

are more instances in which producer surplus declines. In some year/ozone concentration 

combinations, the effect of falling prices on producer profits more than outweighs the effects of 

higher production levels. 

Percentage changes in agricultural sector consumer and producer surplus between the

current standard and the alternative standards were relatively small in many cases, with the 

largest percentage change being a 5.8% decline in producer surplus in the 2040 model period. 

However, the agricultural sector is a very large market, and even small percentage changes in 

welfare can result in annualized values of tens or even hundreds of millions of dollars. Table 6A15 provides consumer and producer surplus for the agricultural sectors under the current 

standard, along with the change in surplus for each alternative standard. There is considerable 

variability in the magnitude of consumer and producer impacts from year to year, which is not 

surprising given the dynamic nature of the model and numerous adjustments taking place over 

time in response to changes in net returns associated with alternative land uses. 

Table 6A-15. Consumer and Producer Surplus in Agriculture, Million 2010 U.S. Dollars

Product Policy 2010 2015 2020 2025 2030 2035 2040

Consumer 

Surplus

75 ppb 1,907,219 1,928,147 1,955,266 1,983,375 2,013,518 2,042,566 2,071,338

Change with Respect to Existing Standard

70 ppb 0 0 0 113 66 58 -10

65 ppb -1 1 4 262 148 289 24

60 ppb 0 47 9 462 100 408 66

Producer 

Surplus

75 ppb 718,105 824,802 815,996 865,123 817,707 874,549 908,352

Change with Respect to Existing Standard

70 ppb 1,202 2,454 1,796 609 -1,144 675 -52,504

65 ppb 216 530 -407 1,174 -2,933 -39,605 -10,211
6A-40

60 ppb -176 1,015 -1,470 1,320 -1,492 -38,788 -8,119

The impacts of the scenarios with more stringent ozone standards were larger in the 

forestry sector, with bigger increases in consumer surplus and greater declines in producer 

surplus. Table 6A-16 presents the model results of the welfare analysis in the forestry sector.

Table 6A-16. Consumer and Producer Surplus in Forestry, Million 2010 U.S. Dollars

Product Policy 2010 2015 2020 2025 2030 2035 2040

Consumer 

surplus

75 ppb 715,634 760,957 801,653 819,407 867,332 885,687 926,837

Change with Respect to Current Standard

70 ppb 3 24,592 -9 -2 70 167 72

65 ppb 12 24,595 4 43 189 263 138

60 ppb 30 24,695 127 257 433 686 245

Producer 

surplus

75 ppb 93,795 147,719 154,137 144,888 147,039 147,797 132,850

Change with Respect to Current Standard

70 ppb 1 -24,614 -28 94 -24 -152 8

65 ppb -5 -24,621 -73 291 -111 -86 -35

60 ppb -57 -24,727 -261 135 -471 -503 37

Because of the complex dynamics of the agriculture and forestry sectors and variability in 

welfare impacts over time, it is often helpful to summarize the impacts in terms of annualized 

values. Table 6A-17 and Table 6A-18 summarizes the annualized impacts of alternative ozone 

standards on consumer and producer surplus in the agricultural and forestry sectors for 2010–

2044 at a discount rate of 3% and 7% respectively.5 The impacts of alternative standards on 

consumer surplus are positive for each of the tighter standards for both agricultural and forestry 

sectors, with the benefits increasing with more stringent requirements. For producer surplus, on 

the other hand, annualized impacts are negative for all of the tighter standards for both 

agriculture and forestry sector, becoming more negative as stringency is increased. Overall, total 

 

5 Each model period in FASOMGHG is representative of the 5-year period starting with that year, so results reported 

for 2040 are representative of 2040–2044. Thus, we use values through 2044 in the annualization calculations. 
6A-41

surplus declines for the agriculture sector while rises slightly for the forest sector across, leading 

to a net decrease for both sectors.

Table 6A-17. Annualized Changes in Consumer and Producer Surplus in Agriculture and 

Forestry, 2010–2044, Million 2010 U.S. Dollars (3% Discount Rate)

Product Policy Agriculture Forestry Total

Consumer surplus 75 ppb 1,969,838 805,563 2,775,401

Change with Respect to Current Standard

70 ppb 28 4,552 4,580

65 ppb 86 4,592 4,678

60 ppb 132 4,744 4,877

Producer surplus 75 ppb 817,744 135,478 953,222

Change with Respect to Current Standard

70 ppb -3,601 -4,534 -8,135

65 ppb -5,035 -4,524 -9,559

60 ppb -4,741 -4,684 -9,425

Total surplus 75 ppb 2,787,582 941,041 3,728,623

70 ppb -3,573 18 -3,555

65 ppb -4,949 68 -4,882

60 ppb -4,608 60 -4,548

Table 6A-18. Annualized Changes in Consumer and Producer Surplus in Agriculture and 

Forestry, 2010–2044, Million 2010 U.S. Dollars (7% Discount Rate)

Product Policy Agriculture Forestry Total

Consumer surplus

75 ppb 1,951,843 782,748 2,734,591

Change with Respect to Existing Standard

70 ppb 21 5,570 5,592

65 ppb 61 5,599 5,660

60 ppb 100 5,721 5,821

Producer surplus

75 ppb 800,022 130,681 930,703

Change with Respect to Existing Standard

70 ppb -945 -5,561 -6,506

65 ppb -2,719 -5,555 -8,273

60 ppb -2,635 -5,694 -8,328

Total surplus 75 ppb 2,751,865 913,429 3,665,294
6A-42

Change with Respect to Existing Standard

70 ppb -923 9 -914

65 ppb -2,658 45 -2,613

60 ppb -2,535 27 -2,508

6A.1.4.3 Greenhouse Mitigation Potential

The capacity for both the agricultural and forest sectors to sequester carbon is enhanced

in each of the alternative standard cases, with increasing magnitude as policy stringency is 

increased. Although FASOMGHG projects fewer acres of forestland and total cropland, the 

accelerated storage of carbon in trees and forestland and cropland soils outweighs any decline 

from reductions in covered area. Carbon storage in both sectors is consistently higher in the 

alternative standard cases, with the gap widening over time (see Figure 6A-15 for change in 

forest carbon stock). By 2040, the agricultural sector sequestered 0.01%~0.05%% more carbon 

under the alternative standard cases and the forestry sector up to 0.5% more, resulting in gains of 

more than 1,500 million metric tons CO2 equivalent (MMtCO2e). Table 6A-19 presents carbon 

sequestration projections under the current standard and changes under each alternative 

standard.

6 Note that negative values in the row for the current standard indicate sequestration or 

carbon storage. Negative values in the change rows indicate that the alternative standard stores 

more carbon than the current standard (and vice versa for positive changes). 

Notice that for the agricultural sector, the overall stock of net GHG would decrease over 

time in the baseline because cropping activities involve fertilizer and chemical usage, fossil 

fuels, running machinery, livestock emissions from enteric fermentation and manure 

management, and so forth—all these GHG emissions are being released each year, while soil 

carbon sequestration moves toward equilibrium within 25 years of a change in tillage. As soil 

carbon reaches equilibrium, little additional sequestration is taking place each year but annual 

emissions from other sources continue. Thus, over time, the annual emissions tend to outweigh 

the increase in carbon stocked in agricultural soils, and net stock of GHG tends to become less 

negative and eventually positive relative to the starting point.7

 

6 These are total stocks of net GHG emissions over time, not annual emissions. If the total stock of GHG is 

becoming more negative over time, more net sequestration is taking place than emissions. If the total stock of GHG 

is becoming less negative or positive over time, emissions are greater than the increase in sequestration. 

7 This change is consistent with the fact that U.S. agriculture is a net source of emissions on an annual basis. The 

value of the total GHG stock associated with agriculture is starting at a negative value because of the FASOMGHG 

convention of accounting for total carbon sequestration present in agricultural soils in the first year of the model run. 

A large stock of carbon is sequestered, but it does not increase by much over time. 
6A-43

Figure 6A-15.Carbon Storage in Forestry Sector, MMtCO2e

Table 6A-19. Carbon Storage, MMtCO2e

Product Policy 2010 2020 2030 2040 2010-2044

Agriculture

75 ppb -18,621 -15,240 -11,772 -8,009 -268,210

Change with Respect to Existing Standard

70 ppb 0 -1 -3 -1 -24

65 ppb 0 -2 -6 -4 -62

60 ppb -1 -4 -11 -11 -132

Forestry

75 ppb -73,321 -74,338 -77,963 -81,063 -1,533,424

Change with Respect to Existing Standard

70 ppb 1 51 -100 -259 -1537

65 ppb 2 53 -323 -774 -5207

60 ppb 12 94 -465 -1189 -7739

Changes in forestry sector carbon sequestration are largely driven by changes in forest 

management, which include the increases in tree yield in the lower ozone environments. The 

increased sequestration in this category outweighs losses in sequestration in the other major 

 (84,000)

 (82,000)

 (80,000)

 (78,000)

 (76,000)

 (74,000)

 (72,000)

 (70,000)

2010 2015 2020 2025 2030 2035 2040

Base 75 ppb 70 ppb 65 ppb 60 ppb
6A-44

forestry categories: afforestation and forest soil. Table 6A-20 presents the detailed changes in 

forestry carbon sequestration.

Table 6A-20. Forestry Carbon Sequestration, MMtCO2e

Product Policy 2010 2020 2030 2040

Afforestation, 

Trees

75 ppb -730 -1594 -963 -1502

Change with Respect to Existing Standard

70 ppb 0 0 22 29

65 ppb 0 0 48 64

60 ppb 0 0 66 88

Afforestion, Soils

75 ppb -767 -598 -550 -1018

Change with Respect to Existing Standard

70 ppb 0 0 11 11

65 ppb 0 0 27 27

60 ppb 0 0 37 37

Forest 

Management

75 ppb -39827 -37995 -40023 -39556

Change with Respect to Existing Standard

70 ppb 1 43 -125 -291

65 ppb 2 44 -387 -852

60 ppb 11 77 -553 -1298

Forest Soils

75 ppb -28320 -27698 -27243 -27474

Change with Respect to Existing Standard

70 ppb 0 10 5 4

65 ppb 1 13 20 19

60 ppb 1 21 27 28

6A.2 Summary

Impacts to both sectors generally mirror one another, although they are more prominent in 

the forestry sector. Not only are tree species more responsive to changes in ozone, but the largest 

reductions to meet the alternative standards will occur in regions with large forestry sectors: 

South Central, Southeast, and Rocky Mountains. Reductions in agricultural regions are 

comparatively moderate. Productivity of both crops and forests is projected to increase at each of 

the alternative standard levels. This increase in supply resulted in decreased prices for forest 

products and agricultural commodities, which benefits consumer welfare while reducing 

producer welfare. Unless there are significant changes to wood products markets in particular, 

producers will be forced to sell at reduced prices to absorb the increased supply. Nonetheless, 
6A-45

gains to consumers become increasingly large with more stringent ozone standards. Gains to 

agricultural producers in the most stringent case are associated with a decline in forestry returns 

that results in a net shift in land use toward agriculture. 

Increased productivity is also projected to affect land use both within and between the 

agricultural and forest sectors. Within sectors, acreage is projected to shift from crops and tree 

species that are more sensitive to ozone to those that are less sensitive because productivity in the 

former will be more substantially affected by reductions in ozone concentrations. For ozonesensitive crops and species, producers are projected to require less land to produce at the same or 

higher levels. Forest acreage in particular is projected to decline sharply, driven by declines in 

both reforestation and afforestation.

Despite reductions in crop and forest area, carbon sequestration is expected to increase 

over time, led almost entirely by increased forest sequestration. Although there is less 

reforestation and afforestation and lower sequestration in new inventory, the change is a result of 

existing inventories becoming so much larger as trees grow faster. Lower sequestration in new 

inventory is outweighed by increased inventory in standing forests, represented in the model as a 

change in forest management. 

Increased stringency in the ozone standard generally produces larger impacts on all of the 

model outputs. However, the additional impact of moving from the current standard to 65 ppb, or 

to 60 ppb was sometimes marginal compared with changes occurring between the current 

standard to 70 ppb. In particular, the impacts to the forestry sector, most notably in forest 

inventories and the forest sector welfare analysis, tended to increase at a decreasing rate after 

meeting the 70 ppb standard. 

The model results are subject to several limitations: First, the ozone concentration response 

functions applied to crops and trees were using “median” parameters in Lehrer et al. (2007)—the 

RYLs and RYGs calculated are thus “median” ones; second, the use of crop proxy mapping and 

the forest-type mapping due to incomplete data specified in Section 6.1 adds to the uncertainty of 

these model results; third, the potential changes in tree species mixes within forest types due to 

ground ozone-level changes were not considered; and last, the international trade component in 

FASOMGHG that assumes USDA-based future projections under current conditions may present 
6A-46

another uncertainty for the model results, especially when soybeans and wheat are among the 

major crop commodities for U.S. exports and have relatively large responses to changed ozone 

environments. 

6A.3 References

Jin, S., Yang, L., Danielson, P., Homer, C., Fry, J., and Xian, G. 2013. A comprehensive change detection method 

for updating the National Land Cover Database to circa 2011. Remote Sensing of Environment, 132: 159 –

175.

Lehrer, J.A., M. Bacou, B. Blankespoor, D. McCubbin, J. Sacks, C.R. Taylor, and D.A. Weinstein. 2007. Technical 

Report on Ozone Exposure, Risk, and Impact Assessments for Vegetation. EPA 452/R-07-002. 

Little, E.L., Jr. 1971. Atlas of United States Trees, Volume 1, Conifers and Important Hardwoods. U.S. Department 

of Agriculture Miscellaneous Publication 1146, 9 p., 200 maps. 

Little, E.L., Jr. 1976. Atlas of United States Trees, Volume 3, Minor Western Hardwoods. U.S. Department of 

Agriculture Miscellaneous Publication 1314, 13 p., 290 maps. 

Little, E.L., Jr. 1977. Atlas of United States Trees, Volume 4, Minor Eastern Hardwoods. U.S. Department of 

Agriculture Miscellaneous Publication 1342, 17 p., 230 maps. 

Little, E.L., Jr. 1978. Atlas of United States Trees, Volume 5, Florida. U.S. Department of Agriculture 

Miscellaneous Publication 1361, 262 maps.

U.S. Department of Agriculture, National Agricultural Statistics Service (USDA NASS). Various years. USDA 

Agricultural Statistics (1990–2012). Available at: http://www.nass.usda.gov/Publications/Ag_Statistics/.

U.S. Department of Agriculture, Natural Resource Conservation Service (USDA NRCS). 2003. Annual NRI—Land 

Use. Available at: http://www.nrcs.usda.gov/technical/NRI.