I
reductions may be imuortant
I
Walter W. Heck USDAIARS North Carolina State University Raleigh, N.C. 27650 Richard M. Adams Oregon State University Corvallis, Ore. 97331 William W. Cure North Carolina State University Raleigh, N.C. 27650 Allen S. Heagle USDAIARS North Carolina State University Raleigh, N.C. 27650 Howard E. Heggestad USDAIARS Beltsville Agricultural Research Center Beltsville, Md. 20705 Robert J. Kohut Boyce Thompson Institute Cornell University Ithaca, N.Y. 14853 Lance W. Kress Argonne National Laboratory Argonne, Ill. 60439 John 0. Rawlings North Carolina State University Raleigh, N.C. 27650
0. Clifton Taylor University of California Riverside, Calif, 92502
Early estimates of direct crop losses using the limited data available suggest that about $3 billion annually, or about 5.6% of the gross value of farm commodities, would be lost if the country experienced a seasonal 7-h/d concentration of 0.06 mean ozone (03) ppm ( I ) . The current secondary National Ambient Air Quality Standard (NAAQS) for O3 is based on information about vegetation effects presented in the criteria document, “Air Quality Criteria for Ozone and Other Photochemical Oxidants” ( 2 ) . A review of the document reveals a lack of field studies in which economically important crops are exposed to O3 at concentrations experienced in the U S . The National Crop Loss Assessment Network (NCLAN) was initiated to address the significance of crop losses caused by air pollution, with an initial emphasis on 03. The NCLAN approach was discussed in the first NCLAN assessment ( 3 ) . The Research Management Committee (RMC) coordinates the planning, management, and execution 0013-936X/83/0916-0572A$01.50/0
of the NCLAN program. The program has three primary objectives: to define the relationships between yields of major agricultural crops and doses of 0 3 , S02, NO2, and their mixtures as required to satisfy the needs of the economic assessment and to support the development of the NAAQS; to assess the primary effects on the nation’s economy that result from exposing major agricultural crcys to 0 3 , SOz, NO2, and their mixtures; and to aid further understanding of the cause and effect relationships that determine crop responses to pollutant exposures. The NCLAN program includes six field sites that produce experimental data needed to develop dose-response functions. Research at each site is the responsibility of the senior scientists located at the site (Table 1). Detailed descriptions of methodology, results, and interpretation for the experiments at each site will be published separately ( 4 , 5 ) . Open-top chambers were used to expose plants to a range of 0 3 concentrations, with daily variations determined by changes in ambient O3 concentrations at each site (6-9). In addition, each experiment included a plot, without a chamber, exposed to ambient air. All plots (chambered or nonchambered) were 3 m in diameter. The experimental designs, replications, and treatments differed at each site. However, the basic design at each site included the following O3 treatments: AA-plot, without a chamber, exposed to ambient air; CF-plot, with a chamber, exposed to charcoal-filtered air; this treatment resulted in O3 concentrations ranging from 20 to 50% of ambient levels because O3 entered through the open top; NF-1-plot, with a chamber, exposed to nonfiltered air to which sufficient 0 3 was added (about 0.005 to 0.01 5 ppm) for 7 h/d to compensate for system losses; and NF-2, NF-3, and NF-4-treatments that were the same as NF-1 except that additional amounts of O3 were added to the ambient air for 7 h/d. Each day ozone additions were made during the same 7-h period (0900-1600 h standard time). At this time, ambient 0 3 concentrations are normally the highest, and plants are generally more sensitive than they are during the remaining hours of the day. To allow dew formation. the chamber
@ 1983 American Chemical Society
fans were not run between 2100 and 0500 h. Also, exposures were not conducted on rainy days. At each site, the plots that received the AA treatment were used to evaluate the effects of the chambers. Ozone-dispensing and time-sharing monitoring systems were similar to those described by Heagle et al. ( 8 ) . This assessment presents a summary of NCLAN field research and addresses several issues of importance to the program. Specifically,it includes the development of crop loss functions using the two years of NCLAN data and the data that were collected in several previous studies that employed similar techniques and an economic assessment using both linear and Weibull dose-response functions to determine the effect of model specifications on estimates of economic loss.
Crop loss functions In the first NCLAN assessment, relationships between the seasonal 7-h/d mean O3concentration and crop yield were developed for individual crop cultivars at specific sites ( 3 ) .The assessment used a seasonal 7-h/d mean O3 concentration of 0.025 ppm as the value from which yield losses were calculated. Although linear, quadratic, logistic, and plateau-linear regression models were used to develop yield loss functions for each of 19 available data sets, the linear model was selected for the first NCLAN assessment since only five of the 19 data sets were significantlyimproved by the three-parameter models (that is, the quadratic, logistic, and plateaulinear). Because the relative response of the cultivars may be homogeneous, it is worthwhile to find a way to consolidate response curves among cultivars to produce a common crop model for each species as the number of cultivars evaluated increases. The use of a common mathematical model would facilitate this consolidation. Such a function should be flexible enough to accommodate the range of observed responses that may be suggested by the biological behavior. For comparing responses among cultivars and species, it would be useful to express the parameters of the function in biologically meaningful units such as concentration or yield. From a set of alternative functions, we have chosen to reevaluate the linear, quadratic, and plateau-linear functions and to seriously consider the Weibull function. The seasonal 7-h/d mean O3 concentration of 0.025 ppm is a reasonable Environ. Scl. Technol., Vol. 17, No. 12, 1983
573A
1for the assessment of crop loss from 0 3
Robert Amundson, I
&, N.Y. Swmeasl
a Walter W. Heck. USDA,
Raleigh, N.C. Beltsville, M. Central Stales Argonne, 111. Southwest Shalt= and Tracey, Calif. NorUlwesl Corvallis. Ore.
Ncdh Carolina Slate Unlversily. Raleigh. N.C. Joseph E. Miller, Argonrm N a l i m l Lab.. Argonne. Ill. a 0. ClittM Taylor, Univefsil) of California. Riverside. Calif. Eric M. Preston. EPA. Corvallis. Ore
RobartKOhut. J wence
William W. Cure, Allen S. Heagle. Howard E. -st& Lance W. Kress OGail Eingham
a Richard Adams,
ate?.% of slallstt*i, Noah Carolina Slate ReSBarh Managewent Cornnunee wrdinated data arialys1s and presenialioo with JOhn R a w l i m
olady Neely t-
for several reasons: It is flexible enough to cover the range of biological responses observed; the parameters are easily interpreted; since effects are expressed as a proportion of maximum yield, data representing the effects on different cultivars can readily be combined to estimate a common . proportional re. sponse; the Weibull form oermits testing of homogeneity among’the individuz cultivars in the common proportional response; and where homogeneity is found, the common proportional response models can be used to represent the response of the crop as a species.
The WeibuU function The Weibull model is given as
common control concentration from which to calculate yield losses because it is close to the values found in the C F treatments at NCLAN field sites. During three seasons of NCLAN operations. the seasonal 7-h/d (09001600 h) mean 0 3 concentiations for the C F treatments have ranged from 0.012 to 0.026 ppm with a median of 0.020 ppm. In our first report, we also suggested that an ozone level of 0.025 ppm is probably a reasonable “natural” background 0 3 concentration (3). A review of two references (IO,11) and discussions with A. P. Altshuller (12) suggest that no definitive statement can be made about the natural background of 0 3 . There are no long-term studies of natural 0 3 ; most measurements of natural 0 3 were made at an altitude above 5 km. The season of the year, the particular year, the latitude, the altitude, and local meteorological conditions all influence the natural O3 at any location. However, available data suggest that a seasonal 7-h/d mean 0 3 concentration range of 0.025 f 0.01 ppm is a reasonable estimate of natural 0 3 . We anticipate that this range will be found throughout the crop-growing regions of the US.Undoubtedly, there are certain locations where using a value of 0.025 ppm will inflate the calculated effects and other locations where it will deflate the calculated effects. But using this value for a nationwide assessment that spans a number of years should give a reasonable interpretation of O3 effects on crop production. It should be recognized that neither the experimental design nor the analysis of crop loss models requires a 5741
specific knowledge of the natural O3 Y = a exp[-(x/u)’],+ E (1) concentration. This article and our first report (3) use data from NCLAN where Y is the yield and x is the O3 and similar data sets to illustrate the dose (seasonal 7-h/d mean 0 1 condevelopment of crop loss functions. centration in ppm).’The three paramThose ushe the model can choose anv eters to be estimated are a. the hvmnatural O&ncentration and calculaik thetical maximum yield at zerd-03 yield losses. Alternatively, one can use concentration; u, the O3concentration the current ambient 0 3 concentrations when yield is 0.3701;e, a dimensionless and calculate yield gains as the con- shape parameter ( c = 1 gives the excentrations decrease or yield losses as ponential loss function whereas a the concentrations increase. larger c [e.g., 4.51 gives a region of This section shows how the indi- almost no loss [a threshold] before the vidual data sets were analyzed, quali- curve starts to drop); and E, the rantatively compares the four models dom error associated with each obidentified for possible use, gives a brief servation. Curves showing the effects overview of the Weibull model, shows of c illustrate that both nearly linear how the Weibull model was used to and threshold responses are covered combine data sets, and predicts yield over the relevant range of 0 3 expolosses using the Weibull model. sures. The a (yield) will vary with inherent differences in crop cultivar Single data set analyses yields. The change in response with O3 In fitting the four models (linear, concentration is shown by the expoquadratic, plateau-linear, Weibull) to nential part of the model, exp[-(x/ each data set, including data obtained u ) ~ ]which , gives the yield response at in the AA treatment, an analysis of dose x as a proportion of the yield at variance (AOV) was used to obtain an zero dose, a.A priori one expects a to estimate of experimental error for differ among studies, species, and hypothesis testing. For each data set cultivars. However, similarities among the AOV-mean square error (AOV- proportional responses of crop cultivars MSE) term and the reduction in sums are of particular interest and can of squares were calculated for the readily be determined by homogeneity three-parameter models relative to the tests on u and c. (If u and c show holinear model. Comparisons among the mogeneity, the data sets show similar reduction in sums of squares, for the proportional responses to 03.) three-parameter models relative to the The yields from plants given the A A linear model, suggest that in many treatment were compared with those cases one of the three-parameter from plants growing in chambers. The models (quadratic, plateau-linear, or Weibull function was extended to inWeibull) is preferable to the linear clude a parameter, a*,to account for model but in terms of the “goodness of chamber effects. The value of a2 is a fit,” the three-parameter models are measure of the deviation of the nonindistinguishable from each other. chambered plot yield from the chamThe Weibull function was selected bered plot yield as shown in the dose-
Envlron. SCI. Technol.. VOI. 17. No. 12. 1983
response curve; in effect, a2 causes the proportional yield response to be based only on the chambered plots. The significance of a2 was evaluated with a t-test. Results are shown in Table 2 (see box). The estimates of Weibull parameters (shown with the ) for individual data sets are also shown in Table 2. A more thorough description of the Weibull approach for use with the NCLAN data is found in Rawlings and Cure (13).
FIGURE 1
The relativeresponseof five majorcrop species to 01as predicted by the Weibull model'
-
Combinationof data sets The Weibull model was used as the basis for testing the homogeneity of response over several data sets. If a common response model is shown for a species in Table 2, this means that the data sets show common B's and s's but that each data set has a different &. (That is, the homogeneity of theproportional yield responses are tested.) The combined parameters were estimated by weighting so that each study contributed nearly equally to the estimates. The data sets obtained from experiments on different cultivars of cotton (irrigated, droughted) gave a positive test for homogeneity. However, the F statistic was large (3.2), and the analysis of variance showed an ozone-soil moisture interaction. Thus, these data sets were not combined.
Yield losses with the Weihull model The Weibull function parameters (Table 2) were used to calculate predicted yields for seasonal 7-h/d mean 0 3 concentrations for 17 of the 19 data sets used in the 1982 report (3) plus the nine new data sets (Table 3). The ozone concentrations chosen were 0.025,0.04,0.05,0.06, and 0.10 ppm. The 0.10-ppm concentration was arbitrarily used as a maximum. The 0.04, 0.05, and 0.06 concentrations were chosen because they cover the range of seasonal mean concentrations found in many parts of the U S . (Concentrations within the eastern US.vary from 0.04-0.07 ppm.) Percentage yield losses for these 0 3 concentrations of 0.04, 0.05, 0.06, and 0.10 ppm were then calculated relative to the predicted yield at a seasonal 7-h/d mean 0 3 concentration of 0.025 ppm (Table 3). In the economic analyses, comparisons were made to measured ambient 0 3 levels, not to a level of 0.025 ppm. When the common proportional responses were homogeneous, the Weibull function parameters for the combined models were used to calculate a percentage yield reduction for the combined data sets (Table 3).
These combination models permit the development of yield loss estimates for some of our major crop species, five of which are shown in Figure 1. The data for soybeans are especially meaningful because data from different cultivars, locations, and years were sufficiently homogeneous that they could be combined to give a single proportional response function. This could not be done with corn since the cultivar Coker 16 data were not homogeneous with the data for the other two cultivars. Comparison of economic losses One objective of the NCLAN program is to assess the primary economic consequences of O3 on agriculture, using yield response data such as that developed in the preceding section. Since the four response models discussed in the section about crop loss functions predict different yield responses, the choice of response model affects the economic assessment. The primary purpose of this section is to test the sensitivity of economic estimates to yield responses predicted by the linear (plateau-linear for corn) and
4 bivariate Weibull function 'or predicting the effects on crop yield caused by two pollutants By using a second response term, Weibull function can be extended
B
to a bivariate function that is useful r predicting the effects on yield that 3uld be caused by treating plants with combinations of O3and SO2 (4). The bivariate function is given by: a . exp[-(x,/u,)ci] X exp[--(xd@dC4 (2) where the symbols have the same meaning they had in Equation 1 (subscript 1 is for Os, 2 is for SO2).Model fitting with the bivariate function requires that five as opposed to three parameters be estimated. The function is based on the assumption that the two pollutants act independently. hut multiDlicatively.to reduce yield. Y=
Environ. Sci. Technol.. Vol. 17, Na. 12. 1983
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Envlron. Sci. Technd., Vol. 17, NO. 12. 1983
TABLE 3
Predicted yield and relative yield losses for 26 ozone dose-plant response data sets using the Weibull function for several seasonal 7-hld mean ozone concentrations
G397 mneer 3780 Common response d Wheal Bluebov II COker i7-2-1 Holly Oasis Cotta, (Acala 55-2) * irrigated Oroughted Kidmy Bean California light r(
Peanut W 6 LeHuce Empire
I
133
125
115
67
7.0
12.6
890
802
718
447
13.7
22.2
:
Turnip Just right Purple to; Shogoin Tokvo cross
I Envirm. Sci. Tedmol.. Vol. 17. No. 12, 1883
577A
Weibull response models. (The linear was used in our first report 131, and the Weibull is used in this report.) An economic model is employed that accounts for the effect of yield reductions-calculated from the alternative response functions-on consumer and producer well-being. Economic estimates of losses or benefits of control for reductions in O3 below current ambient levels are then derived for a specific region using the potential yield and production adjustments that could occur at alternative 03 levels. Yield adjustments and economic estimates are measured against ambient 0 3 concentrations found in the production region; the “background” level of 0.025 ppm is not used. The emphasis here is on demonstrating general procedures and issues and not on calculating definitive measures of the benefits of control. The validity of these economic estimates depends on the incorporated economic, biological, and air quality data. Therefore, the resultant loss estimates should be considered preliminary. More detailed discussion of the assumptions and of the various economic, biological, and air quality data are presented elsewhere ( 3 , 2 1 ) . Procedure. Two steps were used to calculate economic effects. First, physical adjustments or changes in regional production were predicted for three agricultural commodities (corn, soybeans, wheat) under three alternative ozone doses using NCLAN data and the linear and Weibull response models discussed earlier. Second, these regional production losses were then used to drive the economic model, which provides estimates of the direction and magnitude of economic benefits or losses associated with the alternative 0 3 levels. Thus, the pre570A
Envlmn. Sci. Technol.. VoI. 17. No. 12, 1983
dicted effects of 03 on plant yield and on total production are critical inputs in this economic assessment. The region used in the analysis is the corn belt-Ohio, Indiana, Illinois, Iowa, and Missouri. For corn, soybeans, and wheat, this is a major production area, which is subject to relatively high 0 3 concentrations. It accounts for approximately 35% of corn and soybean production and 10% of wheat production in the U S . (22). The five states were divided into 13 subregions intended to represent homogeneous production areas. Cropping pattern alternatives ’(the alternative sets of crops that might be grown in a given region), resource availability (the amount of arable land by soil type as well as average farm size), and air quality were assumed constant within each subregion. These subregional data, when combined with NCLAN data obtained from both linear and Weibull dose-response models, provided yield losses and production adjustments for use in the economic model. The assessment measured economic effects at three 0 3 concentrations (seasonal 7-h/d means): 0.04, 0.05, and 0.08 ppm. When compared to current (1980) ambient 0 3 levels as reported in the US.EPA, SAROAD (Storage and Retrieval of Aerometric Data) data, the first level (0.04 ppm) represents an improvement in air quality for all states and subregions in the corn belt. On average, the level of 0.05 ppm represents a slight improvement in regional 0 3 concentrations. The extreme level of 0.08 ppm depicts a severe worsening of air quality. Current ambient levels of 0 3 and actual production data for the 13 subregions were used to calculate a base case against which theeffects of these sea-
sonal doses could be calculated. Economic model. The common procedure for assessing dollar losses due to 0 3 is to develop assumed yield or production losses and then multiply losses by an invariant price. (Stanford Research Institute (231 and Shriner et al. [ 2 4 ] ) .While providing loss estimates, the conceptual basis of such an approach when applied to largescale assessments has been challenged by economists (25, 26). and such estimates may differ sharply from those calculated from more rigorous economic models (21.27). The economic framework used here addresses some, but not all, economically important issues associated with 0 3 pollution. Specifically, the economic model predicts price adjustments that may arise with changes in crop production due to 0 3 and estimates the effect of such price changes on consumer and producer well-being (see box). This measure of well-being, known as economic surplus (an economic efficiency measure that ignores distribution issues), is used to ap-
proximate the net dollar gain or loss associated with 03-induced changes in production and consumption of the commodities in question. Economic surplus provides a dollar measure of the benefits and costs to society of alternative policies. While this concept is somewhat abstract, its use in policy analysis is well documented (28.29). By comparing the economic surplus at respective supply-demand intersections (for each 0 3 level) with the base case (current 03 levels), the potential changes in net benefits for each assumed O3 level may be assessed. Estimates of changes in economic surplus were used to indicate the benefits of pollution control in the corn belt, assuming production in other regions is held constant. The intersection of the crop supply and demand equations in the economic model defines the equilibrium price associated with alternative supply quantities for each commodity. The integration of the area bounded by these supply and demand equations also measures the economic surplus for each price-quantity pairing. This integral was approximated geometrically using the linear supply and demand parameters and the corresponding equilibrium quantity of each crop to determine the economic surplus at each assumed 0 3 level. Response estimation. For soybeans, corn, and wheat, more than one cultivar was available from the multiple years of the NCLAN or other field programs-specifically, five cultivars of soybeans, four of wheat, and three of corn. For each subregion a representative cultivar, for each crop commonly grown in the subregion, was selected. In cases where the available response function for this cultivar was not available, the most widely planted cultivar in the corn belt was selected as representative.
The statistical results of alternative models reported earlier indicate that forms other than the linear may provide better predictors of yield response to O3 dose for some crops. If the predicted yield responses differ sharply across models, the economic estimates may also change. To test this, the economic awsments were performed using both linear or plateau-linear (for corn) and the Weibull models. Even though the Weibull model can he more broadly applied to dose-response relationships, comparing these models is of interest because the last assessment used the linear model. Air quality data. All yield adjustments and aggregate production responses for each of the subregions were expressed as changes from the actual production in 1980 with the ambient 0 3 levels existing then. Thus, ambient O3concentrations, as measured by the 7-h/d seasonal mean, are needed for each subregion for 1980. Since the economic assessments (for each 0 3 level) are based on comparisons with average crop production under 1980 ambient O3levels, it is important that the base case data represent the relative O3levels over the 13 regions used in this analysis. The 0,data for ambient regional concentrations are derived from the U.S. EPA SAROAD data, as modified by EPA researchers ($. Reagan, personal communication). It was necessary to estimate rural 0 3 levels from measurements at monitoring sites, most of which were urban. Estimated levels of ambient 7-h/d seasonal mean O3 varied from 0.045 ppm to 0.057 ppm across the 13 subregions. The validity of these estimates (their resemblance to actual rural 0 3 levels) is critical to the accuracy of the physical loss estimates. Results. Using O3 data for the 13 subregions in the corn belt and the
linear and Weibull response models, potential changes in crop production associated with varying 0,levels were calculated. The average production (for each crop) for the period 1978-80 was used as the base level. The actual and estimated production levels for each commodity for each 0,level are presented in Table 4. The base economic surplus levels are calculated by integrating the economic model using the average production associated with regional ambient 0 3 levels. By then using the assumed production levels (equilibrium quantities) from Table 4 for the alternative O3 levels (0.04, 0.05, and 0.08 ppm), new values for economic surplus are calculated. The differences between the 0.04 and 0.05 values and the base case values give approximationsof the economic benefits of control at these two O3levels. The values obtained for an O3level of 0.08 ppm represent the economic costs of a degradation in O3 levels. The aggregate economic surplus values between the base case and the alternatives are shown in Table 5. As is evident from Table 5, the economic benefits increase as 0 3 concentrations decline. At a concentration of 0.04 ppm, the benefits of an O3reduction below 1980 ambient levels for these three corn belt commodities are $0.73 billion with the linear models and $1.19 billion with the Weibull model (5-8% of the corn belt gross value of farm commodities). The 0.05-ppm concentration is relatively close to ambient for most regions and results in a benefit of approximately $0.14 billion with the linear and $0.23 billion with the Weibull model. For these O3levels, the use of the Weibull model results in substantially greater estimates of the benefits of control than does the linear model. However, the opposite case is noted for the 0.08-ppm analysis. Here, total
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economic surplus is reduced by $3.13 billion according to the linear model and $2.99 billion with the Weibull (approximately 20% of the corn belt values). This reversal in relative effects in the linear case arises primarily from corn, which, in keeping with the linear-plateau model, is insensitive to O3 at current ambient or lower levels but is moderately sensitive at higher O3 concentrations. Conversely, with the Weibull, modest yield changes are recorded at all the 0 3 levels. Had a strictly linear response function been used for corn, the difference in economic surplus between the linear and Weibull functions would be much more dramatic. The implication is that, for some crops, the choice of resoonse functions for economic assessments should not be based on statistical tests alone, but should also depend on biological considerations. The results also imply that response experiments need to be designed to adequately cover the response surface so that statistical discrimination will be enhanced. However, as explained earlier in this article, the choice be tween these two models is obvious, as the Weibull function is both mathematically and biologically superior to the linear functions. The results of this type of economic assessment need to be placed in perspective. Besides being unable to account for adjustments in regions outside the corn belt, it should be noted that this economic assessment is unable to account for adjustments the producer makes to compensate for losses, such as substitution of cultivars or crops, locational adjustments, and management strategies that may reduce or increase economic losses, depending on the direction of movement in air quality. Such adjustments are included in the forthcoming NCLAN national economic assessment, which
3 concenlrations are 588 ed with the estimated rur response models were
3.1% soft red winter wheat, 0.08 to 3.690; California irrigated cotton, 6.6 to 15.2% and peanuts, 7.0 to 19.7%. The data presented are valid for the particular cultivars and conditions under which they were obtained. Care should be taken in extrapolating the data beyond the conditions under which they were generated. This article represents the second NCLAN assessment of yield losses. It is a limited assessment and uses an empirical modeling approach, but it is Summary the first NCLAN report to include an NCLAN (30) is a group of gov- economic assessment. This initial ernment and nongovernment organi- economic assessment was limited to a zations cooperating in field research, five-state region to test an economic crop production modeling, and eco- model that used selected crop loss nomic studies to assess the immediate functions for soybeans, corn, and and lone-term economicconseauences wheat. the available 0 2 air aualitv of the affects of air pollution i n crop data, and crop production figures. Thk production. The nationally coordi- comparison of the linear and Weibull nated field studies are designed to crop functions clearly shows that the provide crop dose-response data 4hat functional form selected has a major are as free of artifacts as is possible influence on the economic assessment. using state-of-the-art technology. In this report, we recommend the Several three-parameter functions Weibull as the more appropriate re(Weibull, quadratic, plateau-linear) sponse function for general assesswere compared with the linear func- ments. The NCLAN program will continue tion used in the first NCLAN report (3). These three-parameter functions to develop yearly assessments of the generally fit the data better than the effect of 0 3 on crops using improved linear and give a good statistical in- data bases and crop loss functions. The terpretation of the data. The Weibull program recognizes the need to inwas considered the most useful of the corporate the effects of cultivar susceptibility and soil moisture into the three-parameter functions, Weibull parameters were used to functional relationships. Research inpredict yields at several seasonal 7-h/d volving these factors has been initiated. mean O3 concentrations for 26 data Both of these factors will be taken into sets. The Weibull model also was used account as the program begins to into combine data sets and to predict corporate additional concepts into the percentage yield reductions for those response functions. For assessment combined data sets. Estimated pro- purposes, NCLAN does not anticipate duction losses to major crops from O3 the development of a detailed mechaat seasonal 7-h/d mean 0 3 concen- nistic modeling approach. It is expected that the data in this trations of 0.04 to 0.06 ppm (when compared to a base concentration of and other NCLAN publications will 0.025 ppm) were: soybeans, 7.9 to be used by EPA in the development of 18.6% field corn (2 cultivars)... 0.6 to criteria documents and for cost-ben-
uses a method that incorporates mathematical programming. In this, as well as in most other economic procedures, the effects on yield obtained with response models serve to start the economic mechanisms in motion. The ultimate economic effect of these alterations in crop yield may be less apparent than the triggering yield changes, as the economic losses or benefits may not parallel these changes.
beans and wheat, plateau-linear for corn.
J 58QA
Envlron. Scl. Tedlml.. VOl. 17. No. 12, 1983
efit analysis. These data will also be useful to the US. Department of Ag-
(4) Hcagle. A. S.: Heck. W. W.; Rawlings, J. 0.; Philbeck, R. B. Crop Scicncc. in press.
riculture in assessing the importance of 0,to agricultural production and in the development of protective measures such as using resistant cultivars.
.~ in press.
( 5 ,) K r s s L. W.: Miller. J. E. J. Enuimn. Oual.. .
Acknowledgment The authors give special acknowledgment l o the NCLAN Research Management Committee, which represents the major laboratories and produced the first report, for its help and su port on this article. We also acknowle ge the primary support of the EPA through agreements with the participating laboratories. Although the research described in this article has been funded in part by the U.S. EPA through the Interagency Agreement 7001-1282 to the US. Department of grtcullure. it has not been subjected to the EPA’s required peer and policy review and thus does not necessarily reflect the views of the agency. Before publication, this article was read and commented on for suitability as an ES&T feature b y Dr. Samuel P. McLaughlin, Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tenn. 37830.
B
f.
Allen S. Heagle (1.) is a research plan1 pathologist with the US.Departmenr oJ Agriculture in Raleigh, N.C., and an associate member of the Plant Pathology and Crop Science Departments at North Carolina State Uniuersity.
Howard E. Heggestad (1.) is a plant pathologist wirh the US. De arfment of Agriculture in Beltsuille, MdlSince 1957 he has srudied the eflects ofair pollutants on plants
(6) Davis, J. M.; Roger, H. H. J. Air Polluf. Conrrol Assoc. 1980.30.905-8. (7) Heagle, A. S.; Body, D. E.; Heck, W. W. J. Enuiron. Qual. 1973,2,358-65. (8) Heagle, A. S.; Philbeck. R. B.: Rogers, H. H.; Letchworth, M. B. Phyropafkologs 1979.69, 15-20, (9) Mandl. R. H.; Weinstein. L. H.; McCune, D. C.; Keveny, M. J. Emiron. Qual. 1973,Z.
_,. -
271 -76
(IO) Environmental Protection Agency. “Air Qualit) Criteria for Orone and Other Photochemical Oaidants.” FPA-60018-78-004. Office of Research and Develodment, Re: search Triangle Park, N.C.. 1978. (Ill V w c c . W..Smgh. H. B.“Contributionof rlrslarnhcric oione to mound-level ozone Mnieniraiions. a KientitL review of existing evidence.”Techn. Repon Task 3.SRI Projeci 3643: Environmental Protection Agency, Research Triangle Park, N.C.. 1982. (12) Altshuller. A. P.. Environmental Protec6oon Agency. personal communication.
. ,OR1
(lji-i&ings,J. O.:Cure. W. W..rubmitted for publicationin Crop Science. 1141 Hcanlc. A. S.:Lctchuorth. M. B. J . En’ uiron. Qual. 198% I i , 690-94. ( I S ) Heagle. A. S.; Letchworth, M. B.; Mitchell. C. A. Phyfoporhology, in press. (16) Heagle. A. S.; Letchworth. M. B.; Mitchell. C. Phyfopofhology 1983, 73.
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