Pesticide Acute Toxicity Reference Values for Birds - …training.fws.gov/resources/course-resources/pesticides/Terrestrial... · Rev Environ Contam Toxicol 170:13–74 Springer-Verlag

Rev Environ Contam Toxicol 170:13–74 Springer-Verlag 2001

Pesticide Acute Toxicity Reference Values for Birds

Pierre Mineau, Alain Baril, Brian T. Collins, Jason Duffe,Gerhard Joerman, and Robert Luttik

Contents

I. Introduction .......................................................................................................... 13II. Data Selection ...................................................................................................... 15

A. Data Acquisition and Appraisal ..................................................................... 15B. Selection and Processing of Toxicity Data ................................................... 16C. Scaling of Toxicity Data to Body Weight .................................................... 18D. Modification of the Distribution Approach to Incorporate

Body-Weight Scaling ................................................................................ 19E. Choice of an Appropriate Percentile in the Acute Toxicity Distribution .... 20F. Choice of the Appropriate Bird Weights to Model ..................................... 21G. Derivation of Extrapolation Factors ............................................................. 22H. Procedure for Pesticides Having Low Acute Toxicity ................................. 25

III. Results and Discussion ........................................................................................ 71Acknowledgments ...................................................................................................... 71References .................................................................................................................. 72

I. Introduction

Avian risk assessment of pesticides depends for the most part on two laboratory-derived measures of lethality. First, the median lethal dose (LD50), a statisticallyderived single oral dose of a compound that will cause 50% mortality of the testpopulation, and second, the median lethal concentration (LC50), which similarlyderives the concentration of a substance in the diet that is expected to lead to50% mortality of the test population. Mineau et al. (1994) have argued againstthe continued use of the LC50 endpoint in avian risk assessment of pesticides.The test as currently designed was found to provide unreliable results, in partbecause of the difficulty of properly determining exposure during the test. TheLC50 test, conducted on very young birds, is greatly influenced also by the exactage and condition of the test population. Also, the correlation of LC50 valuesamong test species is weak, thus casting further doubt on the value of the end-points and limiting our ability to extrapolate from test species to wild bird spe-

Communicated by George W. Ware.

P. Mineau ( ), A. Baril, B.T. Collins, J. DuffeNational Wildlife Research Centre, Canadian Wildlife Service, Environment Canada, Hull, Quebec,Canada, K1A OH3.

G. JoermanBiologische Bundesanstalt fur Land-und Forstwirtschaft, Braunschweig, Germany.

R. LuttikRIVM, 3720 BA Bilthoven, The Netherlands.

13

14 P. Mineau et al.

cies. Finally, comparison of test results with field evidence suggests that lab-derived LC50s are poor predictors of risk. Until the LC50 test is redesigned toaddress these weaknesses, avian risk assessment will depend almost entirely onthe results of the LD50 test.

Avian risk is difficult to estimate in an absolute sense from laboratory-derived data. Field studies are generally needed to provide “ground truthing” ofa risk model and, to date, a number of such field studies have been carried outand can be used to “calibrate” laboratory-derived predictions of risk based onacute endpoints. Laboratory data are most useful in providing a comparativeassessment of the risk posed by different pesticides. Such comparative assess-ments have also proved useful in the various measurement systems designed toassess the environmental consequences of choosing different agrochemicals oragricultural management systems (Benbrook et al. 1996).

When carrying out comparative risk assessments for pesticides, it is essentialto use the most unbiased data possible. Pesticides are customarily tested againstno more than 1 to 3 bird species, yet there are an estimated 10,000 species inthe world. More than 800 species occur in Canada and the United States alone.In this review, we present acute toxicity values that can be used as referencevalues in pesticide risk assessments. These values are only useful for protectingbirds from pesticides if matched by adequate measures of exposure. Also, theyonly address acute lethal toxicity and not reproductive or chronic health effectsor even sublethal effects that may give rise to delayed mortality or a reductionin biological fitness. Different strategies have been used over the years to com-pare the toxicity of different pesticides to birds. (1) Restricting among-chemicalcomparisons to a commonly tested group of species: This strategy quickly runsinto data gaps and leads to an arbitrary ranking of relative toxicity dependingon the species chosen. Test species can be inconsistent in their relative sensitiv-ity rankings among pesticides (Tucker and Haegele 1971). (2) Several speciesas phylogenetically close as possible to a species of interest are used: Unfortu-nately, toxicological susceptibility does not always follow phylogenetic lines(Schafer and Brunton 1979; Mineau 1991; Joermann 1991; Baril et al. 1994),and it is notoriously difficult to predict which species are most at risk from agiven pesticide treatment. (3) Finally, using the lowest value available from allspecies tested: This approach, however, introduces a systematic bias related tothe amount of test data available and reported for each pesticide.

Baril et al. (1994) and then Luttik and Aldenberg (1995, 1997) suggestedthat a distribution-based method should be employed for birds much as had beenproposed for soil and aquatic organisms (Stephan and Rogers 1985; Kooijman1987; Van Straalen and Denneman 1989). A distribution-based approach usesthe pesticide-specific data available to define the shape of the distributionthrough the estimation of a mean and variance for the distribution. Fitting toxic-ity data to distributions has been criticized, most recently by Newman et al.(2000). However, their main criticism concerns the pooling of toxicity data forvery different phylogenetic groups and the resulting lack of fit to commonlyused distributions. This result would be expected, for instance, when pooling

Pesticide Toxicity to Birds 15

the response of both algae and aquatic invertebrates to a herbicide. We havefound that both the log-logistic and log-normal distributions are adequate whendealing with the toxicity of pesticides to birds. The alternative proposed byNewman and colleagues is to use bootstrapping (distribution-free resampling ofthe data) to arrive at an effect threshold. The main disadvantage of this tech-nique is that any biases inherent in the initial data, a likely problem if fewdata are available, will be preserved and emphasized. We therefore opted for adistribution-based method. However, two modifications of this technique werenecessary: (1) introducing a scaling factor for body weight to improve cross-species comparisons of toxicological susceptibility (Mineau et al. 1996); and (2)developing a strategy to consider chemicals for which there are insufficient datafrom which to derive a distribution. Slightly different approaches were describedby Baril et al. (1994) and by Luttik and Aldenberg (1995) to meet this secondobjective. We present here the relative merits of both these approaches, andprovide what we believe to be the most scientifically defensible reference valuesthat can be used for assessing the relative acute risk of different pesticides tobirds.

II. Data SelectionA. Data Acquisition and Appraisal

Data acquisition procedures were modified slightly from those described in Barilet al. (1994) and Mineau et al. (1996). Under the auspices of the OECD (Organi-sation for Economic Co-operation and Development) and following the recom-mendations of a 1994 workshop on avian toxicity testing (SETAC 1996), theCanadian Wildlife Service’s existing database of LD50 values was expanded withthe assistance of several collaborators worldwide. A recent (Brian J. Montague30 April 98 pers. comm.) version of the U.S. EPA ‘one liner’ database wasobtained and amalgamated with our existing database. Between 1996 and 1997,various data were also obtained from Germany (Federal Biological ResearchCentre for Agriculture and Forestry), the Netherlands (National Institute of Pub-lic Health and the Environment), France (Institut National de la RechercheAgronomique), and the United Kingdom (Pesticide Disclosure Documents fromthe Pesticide Safety Directorate). Many of those data consisted of studies spon-sored by pesticide manufacturers in support of the registration of their pest con-trol products. The confidentiality afforded to these data varies greatly betweencountries. At one extreme (Canada), all data endpoints (e.g., LD50 values) areconsidered to be proprietary and confidential unless specifically marked for pub-lic release by the manufacturer; at the other extreme (U.S.), endpoints are freelyavailable and complete LD50 studies can be requested through “Freedom of In-formation Act” provisions. Some jurisdictions make endpoints available for afee, either in hard copy form (United Kingdom) or through a dial-up database(France). Furthermore, several companies release data endpoints in summaryform (e.g., Material Safety Data Sheets) and these data are picked up by publicsources such as the British Crop Protection Council’s Pesticide Manual (see

16 P. Mineau et al.

following). Because of this confusion, we decided that no species-specific datawould be released nor could any be back-calculated from the information pre-sented here.

Known sources of data from the open literature were also searched. Thesesources included existing compendia of avian acute toxicity data assembled bygovernmental agencies in the U.S. and elsewhere (Schafer et al. 1983; Hudsonet al. 1984; Grolleau and Caritez 1986; Smith 1987), as well as scientific publi-cations containing one or a few values. An exhaustive search of the literaturewas carried out by means of the Terretox database of the U.S. government aswell as the commercially available Medline and Biological Abstracts databases.A final source of data consisted of various editions of the Pesticide Manual (the4th, 9th, and 11th editions; the latter one containing most of the avian data).Before accepting the data from these editions of the Pesticide Manual, a samplewas checked against our existing database. Although the data presented in thePesticide Manual (Tomlin 1997) were often biased toward species of lessersensitivity, the data themselves were almost invariably accurate (a calculatedaccuracy rate of 99% for 108 data points for which the original data source wasalready available to us). The 11th edition of the Pesticide Manual was also usedto indicate which pesticides are currently being commercialized worldwide. Alldata were carefully vetted for errors and duplicates and, where possible, checkedto the original source.

B. Selection and Processing of Toxicity Data

The distribution approach to handling interspecies differences in sensitivity tochemicals requires that a single value be available for each pesticide-speciescombination. In many cases, however, more than one value was available forany combination. It was therefore necessary to establish criteria that provided auniform process for the selection of values. The criteria were chosen so as tominimize bias and variability introduced by the formulation of the pesticide, theage of the birds, and numerous other factors. Our data selection criteria weremodified to agree with those used by Luttik and Aldenberg (1995, 1997) follow-ing several rounds of consultation. This method explains some of the smalldiscrepancies between the values reported here and the few that were presentedin Mineau et al. (1996). The criteria were as follows.

1. Only Data for Adult-Sized Birds Were Used (typically >1 month for passerineand gallinaceous birds; >3 months for waterfowl). In some cases, age wasunspecified but the data, often generated for pesticide submissions, were as-sumed to refer to adults as specified in current and former EPA protocols.Tests on passerine species were generally carried out on wild-caught individu-als which, we assumed, had fledged at that point. The notable exception wasthe domestic chicken, for which it is customary to test young chicks or pul-lets. Values for domestic fowl were therefore excluded unless age was speci-fied.


2. Studies of formulated products or of technical products with very low percent-ages of active ingredient were not used. We did not correct for the percentageof active ingredient found in the technical grade of the pesticide.

3. If more than one LD50 value was available for a species and a given pesticide,a geometric mean value was calculated. No one study was given preferenceover another.

4. If there were multiple LD50 values for a certain species and a given pesticide,and one of those values was a “greater than” (>) or “lower than” (<) value,this value was not used if it lay inside the range of the other available values.However, this value was used as a point estimate (the < or > having beenremoved) when it lay outside the range of other available values for thatspecies and pesticide.

5. If, in a set of available LD50s for a given pesticide, a particular species onlyhad a greater or lower than value, this value was not used if it lay within therange of values available for the other species, but it was used as a pointestimate (the < or > having been removed) if it were outside the range ofvalues available for the other species.

6. Where the majority of available species LD50s were greater than a certainvalue, such as is often the case with nontoxic pesticides where limit values aregiven (e.g., all species >2000 mg/kg), the data were not fitted to a distributionregardless of how many such values were available. Instead, an extrapolationfactor approach was employed, as described next.

7. When separate values were given for each sex, the geometric mean of thetwo values was calculated.

8. When the value given by a single source was a range, the geometric mean ofthe range was calculated. This criterion applied mainly to the studies reportedby Schafer and coauthors (1983) in which the ranges given correspond tovalues obtained from separate studies (Ed Shafer, personal communication).

9. When compendia of values were published by any given laboratory andwhere there were discrepancies between different editions or publications, themost recently published value was accepted. This choice assumes that previ-ous errors were corrected by the laboratory in question.

Unfortunately, we were not able to take into account the method of dosing(e.g., by gavage needle or gelatin capsule) nor were we able to account for theuse of vehicles or diluents (e.g., corn oil), this information seldom being avail-able. We recognize this is an important source of variation as is the differentialpropensity of different bird species to regurgitate (Hart and Thompson 1995).Also, we were unable to ensure that the technical pesticide material was identi-cal from test to test. The data span several decades, and it is likely that the levelof purity and proportion of degradation products and contaminants changed overthe years and across different manufacturers. Some pesticides are known to beracemic mixtures and may have been marketed first as the mixture and later asthe active isomer. Often, this is reflected by the fact that there are several CASnumbers available for any one pesticide (Tomlin 1997). We cannot be certain

18 P. Mineau et al.

that all toxicity tests reported here are specific to the CAS number given for thepesticide in question, nor can we venture a guess as to the contribution of thisfactor to the overall within-chemical variance in toxic response.

As with any such compendium, cholinesterase-inhibiting pesticides are wellrepresented (at least in having higher numbers of species tested per pesticide)because of their relatively high toxicity to birds and the fact that they accountfor the majority of wildlife poisoning incidents. Because of our desire to developmethods that are representative of all classes of pesticides, we carried out someanalyses (at least initially) on cholinesterase inhibitors separately from other pestcontrol products. The data for cholinesterase inhibitors are therefore presentedseparately in this review (see tables 2 and 3). The database thus compiled forcholinesterase inhibitors consists of 147 pesticides and 837 acceptable LD50 de-terminations. For noncholinesterase inhibitors, we were able to compile 1601acceptable LD50 values for 733 pesticides.

C. Scaling of Toxicity Data to Body Weight

It is customary to extrapolate between species on the basis of acute toxicitymeasurements expressed in mg/kg body weight, yet Mineau et al. (1996)showed that for a group of 36 pesticides chosen for the high number of LD50

data points available, the appropriate scaling factor in birds (with toxicity inmg/animal regressed against body weight) is usually > 1 and can be as high as1.55. These authors showed that, when fitting a distribution to LD50 data ex-pressed as mg/kg body weight (i.e., forcing the data to a slope of 1), the result-ing distribution overestimated LD50 values for small-bodied birds and resultedin wider confidence intervals for the usual distribution-based toxicity bench-marks, e.g., the 5% and 95% bounds of the distribution. For the present analysis,and for all pesticides with n ≥ 4, ln LD50 values (in mg/kg rather than mg/ani-mal) were regressed against ln weight in grams. Mean species weights wereobtained from Dunning (1993). As described by Mineau et al. (1996), the slopeor “scaling factor” for the majority of pesticides is positive [corresponding to aslope greater than 1 in Mineau et al. (1996) when toxicity values were expressedas mg/animal]. For the 130 pesticides with n ≥ 4, the regressions were positivein 99 cases and the overall mean slope was 0.239; this was slightly more ex-treme than the value of 1.15 (corresponding to 0.15 when expressed on the basisof mg/kg) reported by Mineau et al. for a subset of 36 pesticides. A similarproportion of slopes was positive for cholinesterase inhibitors (54/68) than forother pesticides combined (45/62). As determined by an F-test, 14 pesticides ofthe 130 had a slope significantly different from 0 at the p < 0.05 probabilitylevel (11 of which were positive). Allowing the p to rise to 0.1, a total of 30slopes were significantly different from null, 24 of them positive. As argued byMineau et al. (1996), failure to achieve statistical significance in a majority ofthe slopes does not remove the biological significance of the finding. Severalslopes may miss being significantly different from 0 either because of inade-quate sample size, or because they are only slightly different from 0, or a combi-nation of both. The fact that the large majority are greater than 0 indicates a


phenomenon that is biologically significant. Therefore, even for pesticides forwhich statistical significance is not achieved, the observed slope should be re-garded as a better estimate of the true value rather than assuming the slope is0. Not using a scaling factor when susceptibility does scale to weight wouldresult in potentially serious underprotection at one end (usually the small one)of avian size ranges. On the other hand, scaling toxicity data to a completelyspurious slope factor might mislead. Fortunately, inspection of the data revealedthat many of the very extreme slopes measured, both positive and negative,were in fact significant, at least at the p < 0.1 level, thus lessening the concernthat we may be fitting data to spurious slopes.

The reason for avian acute toxicity values scaling to weight raised to thevalue of 1.2 or even slightly higher on average is unclear. In mammals, thecommon wisdom is that toxicity tends to scale to 0.67 or 0.75 (reviewed inMineau et al. 1996) although a recent reassessment of acute toxicity data inmammals and birds (Sample and Arenal 1999) calculated an average scalingfactor of 0.94 for mammals and confirmed an average of 1.2 in birds. It wassuggested (Fischer and Hancock 1997) that these scaling factors may be a conse-quence of taxonomic differences. Songbirds (order Passeriformes) constitute themajority of the small-bodied species in the database, and they may be moresusceptible toxicologically. A simpler, and more compelling possibility, is thatsmall birds in any taxonomic group are less able to withstand the rigors of thephysiological disruption brought about by acute dosing, especially reduced foodintake. Regardless of the reason, the end result is the same: the use of theappropriate scaling factor results in the least estimation error for the toxicity ofa pesticide to a bird of a given weight and in reduced variance in the distributionof LD50 values. One, however, needs to exercise caution in estimating LD50

values for very large or very small bird species, or birds from taxonomic groupsthat are poorly represented in the data set. For example, as reviewed by Mineauet al. (1999) there is some evidence that hawk and owl species (orders Falconi-formes and Strigiformes) are more sensitive, at least to cholinesterase inhibitors,than other birds of similar body weight.

D. Modification of the Distribution Approach toIncorporate Body-Weight Scaling

Until now the best method available to derive a set level of protection with agiven level of certainty based on the distribution of toxicity data was that devel-oped by Aldenberg and Slob (1993). These authors modified existing methods(Kooijman 1987; Van Straalen and Denneman 1989; Wagner and Lokke 1991),which aimed to determine environmental concentrations or doses of chemicalsthat were protective of 95% of the species in the wild. The modifications con-sisted of deriving extrapolation constants, Kn, that account for the uncertainty inthe estimates of the distribution parameters when dealing with small sets oflaboratory-derived toxicity data. The extrapolation constants are determinedsuch that a one-sided left confidence limit L for log(HD5) (for Hazardous Doseat the 5% tail of the distribution) is given by

20 P. Mineau et al.

L = yn−[Kn*Sn] (1)

where yn and Sn are mean and standard deviation, respectively, of a sample log(LD50) test data of size n.

This approach does not, however, incorporate body weight as a covariate.Mineau et al. (1996) were able to describe the relationship between the LD50

and body weight using the equation

yi = β0 + β1xi + ei (2)

where, yi = log(LD50), xi = log(Weight), β0 and β1 denote the intercept and slopeof the regression line, and ei denotes the random deviation of the data from themodel.

The distributional method as described by Aldenberg and Slob (1993) doesnot incorporate a body weight covariate and requires an estimate of the meanand the standard deviation or variance. The equivalent terms can be defined fora model with a covariate, but in this case one must choose a value for x0 (thelog of the weight of the bird that one wants to protect). The estimators of thesetwo quantities with and without a covariate are compared in Appendix 1. Forthe Aldenberg and Slob approach, the precision of the estimates only dependson n whereas for the covariate approach it depends on n and another term thatreflects the precision of the predicted LD50 at the designated weight. The pre-dicted LD50 becomes less precise as one moves away from the average of thexi. To compensate for this, a value for the extrapolation constant K must bedeveloped separately for each data set and designated weight of bird to be pro-tected; this is done through a simulation in the manner of Aldenberg and Slob(1993).

E. Choice of an Appropriate Percentile in the Acute Toxicity Distribution

Working with a distribution allows one to set a desired percentile, or a thresholdLD50 value sufficiently protective for an arbitrarily chosen proportion of theentire population of bird species. For risk assessment purposes, it is customaryto choose a value in the left tail, e.g., at the 1% or 5% tail of the distribution.This custom was born of convenience and practicality and has no scientificbasis. Some may take issue with the fact that 5% or even 1% of the speciesinhabiting the exposed ecosystem can be summarily “written off.” Choosing apercentile does not mean that this percentage of species will necessarily beimpacted. The final level of protection afforded to birds will depend on theinterplay of all the components of the risk assessment and regulatory scheme.For comparative risk assessment, using an LD50 value representative of the lefttail of the distribution is preferable to using a measure of central tendency (me-dian, arithmetic mean or geometric mean) because the latter parameters do nottake into account information regarding the variance of the distribution. For thepurpose of this exercise, the 5th percentile of the log-logistic distribution ofspecies LD50s was determined (the point on the tail of the log-logistic distribu-


tion that excludes 5% of the lower LD50 values). It is useful to also rememberthat we are fitting LD50 values to a distribution; these are not no-effect or mini-mal-effect concentrations. At the predicted threshold, half the exposed individu-als from a species at the 5% tail of susceptibility are expected to die. Riskassessors may wish to apply another factor to cover intraspecific differences insusceptibility. Typically, this would be done through consideration of the probitslope of the dose–mortality relationship.

Having arbitrarily fixed the protection level at the 5th percentile of the spe-cies distribution, (termed HD5 (Hazardous Dose 5%) by Aldenberg and col-leagues as well as in this review, or TLD5 (Threshold Lethal Dose 5%) by Bariland colleagues), we still need to fix the level of certainty we wish to attach tothe determination of this value. As argued by Aldenberg and Slob (1993) andconfirmed by Baril et al. (1994), median estimates of the HD5 (calculated with a50% probability of overestimation) may not be sufficiently protective, especiallywhere rare or valuable focal species of unknown susceptibility must be pro-tected. In other words, far fewer than 95% of bird species may actually beprotected. However, thresholds that account for a higher (90% or 95% are com-monly used) certainty that the estimate of the 5th percentile is not overestimatedare extremely conservative (likely overprotective), especially when sample sizesare small. Our intent here was to present threshold values that would allow forthe “fairest” comparison possible between pesticides having data sets of vastlydifferent size and quality. Indeed, one of our goals was to permit comparison ofolder pesticides (often with large data sets) with that of newer products (withtypically few data) without allowing sample size to have an overwhelming influ-ence on the result. We opted therefore to report median threshold values (the5% tail of the distribution calculated with 50% probability of overestimation),recognizing that some of these values carry with them a very high risk of under-protection, which may render them unsuitable for product by product risk as-sessment. Of course, if probabilistic risk assessments are to be carried out, onemay wish to enter the entire estimated distribution of LD50 values rather than anarbitrary 5% threshold.

F. Choice of the Appropriate Bird Weights to Model

Because of the nature of regression, the real advantage of using the new distribu-tion model that incorporates body weight as a covariate is predicting the sensi-tivity of birds having body weights that deviate from the tested average. Forchemicals for which the regression between body weight and toxicity is highlystatistically significant, the confidence in the estimated HD5(50%) for birds ofany chosen weight is high. For those chemicals for which the slope is differentfrom zero but not statistically significant, we opted to apply the covariate aswell. Using a distribution model without a covariate can lead to the erroneousunderestimation of the sensitivity of birds at either extreme of the weight axisif the scaling relationship is real. Because our intent was to provide referencevalues that are sufficiently protective of most birds regardless of size, we as-sumed that the fitted scaling relationship was biologically real in all cases.

22 P. Mineau et al.

To generate a single LD50 value protective of birds in general regardless oftheir weight, we first calculated HD5 values for birds of 20, 100, 200, or 1000g. Those weights were chosen based on typical bird weights recorded fromcasualties in pesticide field studies carried out in North America, primarily theU.S. (CWS unpublished analysis). Although this weight range does not encom-pass all bird species, it does account for the vast majority. The reference valuereported in this review is the lesser of the 5% threshold values calculated forthese four body weights. Most often, this was the value determined for birds of20 g.

G. Derivation of Extrapolation Factors

As proposed by Luttik and Aldenberg (1997), we decided that a minimum offour LD50 values were needed for determining the mean and standard deviationparameters of the distribution. For pesticides with fewer data points, a differentapproach is needed because the data are insufficient to fit to a distribution with-out the parameters being estimated with unusually large errors. We must there-fore predict the fifth percentile of the distribution through extrapolation fromthe small data set using some predetermined factor or set of factors. Luttik andAldenberg (1995) presented one approach to derive such extrapolation factors.1

Their method assumes the following: (1) the mean of the logarithm of the avail-able toxicity data is the best estimate of the mean of the distribution; (2) thestandard deviation is equal to a “generic” standard deviation that is calculatedfrom the pooled historical data sets for a large number of chemicals tested onmany species; and (3) species sensitivities are random across chemicals. Thus,Luttik and Aldenberg determined that, if a single test species is available, anextrapolation factor of 5.7 should be applied to the LD50 (unadjusted for bodyweight scaling) to obtain the median estimated HD5 (to be indicated asHD′5(50%)). This factor stays constant regardless of N, the number of speciesfor which test data are available.

There is, however, a serious impediment to the use of such a strategy forregulatory or comparative purposes. Data available for any given pesticide (es-pecially if the data are limited to one or a few species) are not necessarily arandom sample. As discussed earlier, we have found that only a few data pointsare typically released, and these data may be biased to put a product in the bestlight possible regarding its toxicity to nontarget species. Also, species com-monly used for testing (e.g., the mallard duck and northern bobwhite) tend tobe at the “insensitive” side of the distribution already. We believe that to adoptthe strategy of a single “universal” extrapolation factor would be a strong incen-tive to biased reporting. Several authors have found that species tend to differ

1The word extrapolation factor is used here in place of the frequently used safety factor or assess-ment factor. In contrast to the latter, which often are arbitrarily set at 10 or 100 to reflect a poten-tially error-prone extrapolation, the extrapolation factors presented here are based on sound empiri-cal data reflecting variability in interspecies susceptibility to chemicals.


in their sensitivity to pesticides in a predictable manner; some species are, onaverage, more sensitive than others to pesticides (Baril et al. 1994; Joermann1991; Schafer and Brunton 1979). To make use of these known relationships,Baril and colleagues proposed that extrapolation factors should be tailored tothe actual species for which data are available. For example, a different extrapo-lation factor would be applied to a single mallard LD50 value than to a northernbobwhite or Japanese quail LD50. A simulation exercise (Baril and Mineau 1996;this is an abstract only; data not published) showed that to ignore sensitivityrelationships in favor of a single (universal) extrapolation factor resulted in moreestimation errors.

We therefore opted to generate species-specific extrapolation factors fromour available data set. The calculated HD5(50%) values for all pesticides withN greater or equal to 6 were used to derive extrapolation factors. All computedHD5(50%) values were used regardless of the significance of the regressionstatistics. As outlined earlier, the smaller of the HD5 values computed forweights ranging between 20 and 1000 g was retained as our reference value,and those reference values were used to calculate species- and pesticide-specificextrapolation factors. A sample size of 6 or more ensured that both the parame-ters of the distribution and the slope of the regression between body weight andLD50 were relatively well characterized. Thus, the extrapolation factor specificto a particular species or combination of test species is simply the ratios of thecomputed HD5(50%) to the LD50s of the test species (or geometric mean of LD50

values in the case of a combination of species), averaged over all chemicals inthe database.

We propose that these extrapolation factors be used to estimate HD5(50%)values (HD5(50%)) where N, the number of species tested is, 1–3. Two differentmethods of combining the two or three available LD50 values were tried: takingthe smallest value, and taking the geometric mean of the values. Deriving ex-trapolation factors from the geometric means of available LD50s was found tolead to less estimation error (data not shown). The geometric mean was thenretained as the best way to combine two or three available data points to com-pute extrapolation factors. We computed extrapolation factors for most of thecommonly tested species or combinations of those species (Table 1). Other fac-tors computed for more rarely-tested species were derived as needed but are notgiven here because of the smaller sample sizes used in their derivation.

The use of an extrapolation factor introduces another potentially significantsource of error in the estimation of the HD5(50%); this is the error resultingfrom an individual species’ varying sensitivity relative to the population 5% tail.Whereas some species appear to be reasonably “well behaved” by showing adegree of sensitivity relative to the population tail that is consistent across chem-icals (red-winged blackbird, red-billed quelea, Japanese quail), other speciestended to move extensively within the sensitivity distributions established foreach pesticide (chicken, mallard, European starling). For the latter species,sometimes they were very sensitive and sometimes they were very insensitiverelative to the 5% percentile. The extrapolation factors were therefore ranked

24 P. Mineau et al.

Tab

le1.

Ext

rapo

latio

nfa

ctor

sor

dere

dby

incr

easi

ngco

effi

cien

tsof

vari

atio

n.

Ext

rapo

latio

nA

ppro

xim

ate

95th

5th

nfa

ctor

C.V

.pe

rcen

tile

perc

entil

e

Red

-win

ged

blac

kbir

d67

.00

3.95

2.32

10.6

21.

47R

ed-b

illed

quel

ea22

.00

3.63

2.68

10.9

21.

20B

obw

hite

quai

lan

dJa

pane

sequ

ail

36.0

08.

053.

1827

.97

2.32

Bob

whi

tequ

ail

and

Japa

nese

quai

lan

dm

alla

rddu

ck32

.00

8.94

3.64

34.8

52.

29Ja

pane

sequ

ail

61.0

010

.36

4.11

44.9

62.

39Ja

pane

sequ

ail

and

mal

lard

duck

and

hous

esp

arro

w46

.00

8.37

4.26

37.5

11.

87Ja

pane

sequ

ail

and

mal

lard

duck

56.0

010

.41

5.48

58.9

31.

84E

urop

ean

star

ling

and

red-

win

ged

blac

kbir

d57

.00

5.63

5.57

32.3

70.

98B

obw

hite

quai

lan

dm

alla

rddu

ck40

.00

9.61

6.10

60.1

21.

54B

obw

hite

quai

l42

.00

8.61

7.19

63.0

81.

17R

ing-

neck

edph

easa

nt66

.00

9.41

7.42

71.0

51.

25E

urop

ean

star

ling

59.0

011

.82

7.60

91.3

21.

53M

alla

rddu

ck67

.00

10.3

810

.89

114.

000.

95C

hick

en37

.00

19.7

513

.82

274.

311.

42

App

rox.

C.V

.=(9

5th

perc

entil

e−

5th

perc

entil

e)/e

xtra

pola

tion

fact

or.

95th

perc

entil

e=

extr

apol

atio

nfa

ctor

+1.

645

*SD

.5t

hpe

rcen

tile

=ex

trap

olat

ion

fact

or−

1.64

5*

SD.


on the basis of their approximate coefficient of variation, the lowest coefficientbeing indicative of the lowest likelihood of making a large estimation error.Given the possibility of applying more than one extrapolation factor, we chosethe one with the lowest approximate coefficient of variation. For example, al-though an extrapolation factor based on the geometric mean of several speciesis usually more “stable” and therefore less prone to serious error than a factorbased on a single species, it can be seen from Table 1 that extrapolating theHD′5(50%) from a single Japanese quail LD50 value results in less error, onaverage, than extrapolating from a combination of bobwhite and mallard data.Nevertheless, using an extrapolation factor is clearly a “second-best” alternativeto curve-fitting LD50 data and deriving an actual HD5. In the example just given,testing two species such as the northern bobwhite and mallard increases theprobability that unexpected chance variations in susceptibility will be uncov-ered.

We believe that the approach of using reference values based on species-specific extrapolation factors represents the most unbiased attempt to date tocompare the toxicity of pesticides for which many data points are availablewith those about which we know very little. Because cholinesterase-inhibitingpesticides represent a large group of chemicals with a uniform mode of toxicaction, they were analyzed separately from other pesticides, and specific extrap-olation factors were derived for that group of compounds. However, when thecomparison was made between those compounds and a sample of noncholines-terase inhibitors, we found that the computed mean extrapolation factors werenot significantly different (not shown) and a single set of factors for all pesti-cides in the sample was therefore generated (see Table 1).

H. Procedure for Pesticides Having Low Acute Toxicity

In the case of pesticides of lower toxicity, limit values are often provided; e.g.,LD50 > 2000 mg/kg. Ideally, one would like to take into consideration the condi-tion of the test birds and any mortality at the limit dose. In trying to set a valuethat is protective of 95% of bird species, it matters whether birds at the limitdose were moribund, with possibly some mortality being seen already, orwhether there were no visible signs of toxicity in any of the individuals tested.Unfortunately, this information was not uniformly available. The followingcompromise procedure was therefore developed. (1) If, for any pesticide dataset, a toxicity data point with an exact value existed (rather than a limit), thisdata point was used with the relevant species-specific extrapolation factor wherepossible. (2) Otherwise, the relevant species-specific extrapolation factors wereapplied separately to each available data point (treated as point estimates, ignor-ing the > symbol) and the highest resulting HD′5 value was retained. Even then,it is clear that the resulting HD′5 is probably an underestimate and that thepesticide is likely less toxic than shown. However, given that the HD′5 thuscalculated is already very high, this underestimation is unimportant in the con-text of a relative risk assessment. The acute toxicity of these pesticides is notlikely to be a concern.

26 P. Mineau et al.

Tab

le2.

Ref

eren

ceL

D50

valu

esfo

rC

holin

este

rase

-inh

ibiti

ngpe

stic

ides

orde

red

alph

abet

ical

lyby

com

mon

chem

ical

nam

e.

Com

poun

dC

AS_

RN

nM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Ace

phat

e30

560-

19-1

714

6.00

0.18

094.

7716

0.66

18.5

2—

EA

KT

ON

TM

1757

-18-

22

1037

.50

nana

na18

.99

1U

Ald

icar

b11

6-06

-310

2.82

0.29

55−0

.655

90.

120.

43—

EA

llyxy

carb

6392

-46-

72

12.4

2na

nana

3.37

1S

Am

inoc

arb

2032

-59-

94

46.2

0−0

.361

86.

0268

0.33

6.59

—S

Aza

met

hiph

os35

575-

96-3

239

.30

nana

na3.

982

EA

zinp

hos-

ethy

l26

42-7

1-9

1—

nana

na1.

531

EA

zinp

hos-

met

hyl

86-5

0-0

744

.69

0.78

06−0

.679

30.

002.

28—

EB

endi

ocar

b22

781-

23-3

416

.24

−0.9

475

8.37

240.

370.

72—

EB

enfu

raca

rb82

560-

54-1

1—

nana

na4.

231

EB

OM

YL

TM

122-

10-1

25.

36na

nana

0.25

1U

Bro

mop

hos

2104

-96-

31

—na

nana

491.

141

SB

rom

opho

s-et

hyl

4824

-78-

65

300.

000.

8503

0.80

50.

0112

.88

—E

Buf

enca

rb80

65-3

6-9

832

.95

0.11

652.

7226

0.71

3.09

—S

But

ocar

boxi

m34

681-

10-2

1—

nana

na6.

171

EB

uton

ate

126-

22-7

315

8.00

nana

na40

.00

1S

But

oxyc

arbo

xim

3468

1-23

-71

—na

nana

18.5

81

EC

adus

afos

9546

5-99

-92

123.

05na

nana

6.33

2E

Car

bano

late

671-

04-5

124.

220.

1412

0.89

360.

530.

75—

SC

arba

ryl

63-2

5-2

718

70.5

00.

8026

2.51

470.

0630

.05

—E

Car

bofu

ran

1563

-66-

218

1.65

0.04

230.

257

0.82

0.21

—E

Car

boph

enot

hion

786-

19-6

956

.80

0.40

541.

431

0.12

2.00

—S

Car

bosu

lfan

5528

5-14

-82

51.0

0na

nana

9.52

1E

Als

ota

bled

are

the

CA

Sre

gist

ratio

nnu

mbe

rs,

the

num

ber

ofsp

ecie

sva

lues

avai

labl

e,th

em

edia

nL

D50

,th

esl

ope,

inte

rcep

tan

dp

valu

eof

the

LD

50*w

eigh

tre

gres

sion

,th

eca

lcul

ated

ores

timat

edH

D5(

50%

)an

d,w

here

used

,th

enu

mbe

rof

spec

ies

from

whi

chan

extr

apol

atio

nfa

ctor

was

deve

lope

d(s

eeT

able

1).

The

stat

usfo

llow

sth

eno

men

clat

ure

ofth

eP

esti

cide

Man

ual,

11th

Ed.

;E

,in

use;

S,su

pers

eded

;U

,un

know

n.


Tab

le2.

(Con

tinue

d).

Com

poun

dC

AS_

RN

nM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Chl

oret

hoxy

fos

5459

3-83

-81

—na

nana

3.25

1E

Chl

orfe

nvin

phos

470-

90-6

1523

.70

0.36

351.

8073

0.10

2.73

—E

Chl

orm

epho

s24

934-

91-6

310

0.00

nana

na25

.10

1E

Chl

orph

oxim

1481

6-20

-72

100.

00na

nana

11.6

11

SC

hlor

pyri

fos

2921

-88-

218

27.3

60.

226

2.09

410.

093.

76—

EC

hlor

pyri

fos

met

hyl

5598

-13-

02

845.

00na

nana

25.3

21

EC

hlor

thio

n50

0-28

-73

280.

00na

nana

70.8

91

SC

loet

hoca

rb(B

as26

31)

5148

7-69

-51

—na

nana

0.43

1S

Cou

map

hos

56-7

2-4

126.

780.

2179

0.75

790.

360.

69—

EC

roto

xyph

os77

00-1

7-6

242

3.10

nana

na14

.23

1S

Cru

fom

ate

299-

86-5

218

2.50

nana

na25

.32

1S

Cya

noph

os26

36-2

6-2

1—

nana

na0.

831

ED

emet

on80

65-4

8-3

137.

670.

2357

0.68

810.

151.

04—

SD

emet

on-S

-met

hyl

867-

27-6

749

.00

0.13

963.

0132

0.47

7.24

—E

Dem

eton

-S-

1704

0-19

-62

50.0

6na

nana

8.14

1S

met

hyls

ulph

onD

imid

afos

1754

-58-

12

44.1

5na

nana

3.37

1S

Dia

zino

n33

3-41

-514

5.25

−0.2

608

3.58

830.

290.

59—

ED

icap

thon

2463

-84-

51

—na

nana

4.13

1S

Dic

hlof

enth

ion

97-1

7-6

775

.00

0.26

853.

327

0.57

7.54

—S

Dic

hlor

vos

(DD

VP)

62-7

3-7

1114

.75

0.04

932.

2876

0.61

5.18

—E

Dic

roto

phos

141-

66-2

152.

830.

1787

0.36

450.

320.

42—

ED

ieth

ofen

carb

8713

0-20

-92

2250

.00

nana

na23

4.13

2E

Dim

etho

ate

60-5

1-5

1029

.50

0.17

732.

5296

0.37

5.78

—E

Dim

etila

n64

4-64

-44

27.2

00.

1601

2.80

080.

830.

92—

SD

ioxa

carb

6988

-21-

25

115.

000.

7849

0.41

730.

093.

36—

SD

ioxa

thio

n78

-34-

22

258.

50na

nana

25.5

01

SD

isul

foto

n29

8-04

-47

11.9

00.

2019

1.21

040.

600.

81—

E

28 P. Mineau et al.

Tab

le2.

(Con

tinue

d).

Com

poun

dC

AS_

RN

nM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

EPN

2104

-64-

514

6.43

0.36

240.

604

0.33

0.53

—E

Eth

amph

os(e

tham

fen-

27.

93na

nana

0.78

2U

phos

,et

ham

fent

hion

)E

thio

fenc

arb

2997

3-13

-53

196.

00na

nana

14.9

61

EE

thio

n(d

ieth

ion)

563-

12-2

512

7.80

0.91

69−0

.155

10.

181.

06—

EE

thop

rop

1319

4-48

-49

7.50

0.10

861.

3764

0.40

2.41

—E

Etr

imfo

s38

260-

54-7

1—

nana

na23

.65

1E

Fam

phur

52-8

5-7

32.

70na

nana

0.45

1E

Fena

mip

hos

2222

4-92

-65

1.10

−0.0

863

0.54

440.

660.

43—

EFe

nchl

orph

os29

9-84

-34

487.

420.

6983

2.15

570.

1512

.23

—S

Feni

trot

hion

122-

14-5

1263

.43

0.27

073.

0869

0.39

3.37

—E

Feno

buca

rb37

66-8

1-2

1—

nana

na31

.12

1E

Fens

ulfo

thio

n11

5-90

-214

0.73

0.29

03−1

.827

80.

050.

13—

SFe

nthi

on55

-38-

923

5.62

0.25

810.

4784

0.07

0.87

—E

Fono

fos

944-

22-9

1023

.50

0.34

421.

293

0.07

3.86

—E

Form

etan

ate

2225

9-30

-94

31.7

5−0

.515

6.24

180.

068.

77—

EFo

spir

ate

5598

-52-

72

34.7

5na

nana

3.37

1S

Fost

hiaz

ate

9888

6-44

-32

15.0

0na

nana

1.47

2E

Fura

thio

carb

6590

7-30

-41

—na

nana

2.41

1E

Hep

teno

phos

2356

0-59

-02

52.7

4na

nana

3.04

1E

Isaz

ofos

4250

9-80

-83

11.1

0na

nana

0.51

2E

Isoc

arbo

phos

2435

3-61

-55

1.00

0.25

4−1

.129

10.

148

0.26

—U

Isof

enph

os25

311-

71-1

610

.96

0.09

942.

7255

0.86

0.44

—E

Isop

roca

rb26

31-4

0-5

1—

nana

na14

.23

1E

Lep

toph

os21

609-

90-5

526

8.84

1.22

59−1

.555

30.

410.

09—

SM

alat

hion

121-

75-5

846

6.50

0.03

236.

0138

0.85

139.

10—

EM

epho

sfol

an95

0-10

-71

—na

nana

0.14

1E


Tab

le2.

(Con

tinue

d).

Com

poun

dC

AS_

RN

nM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Met

hacr

ifos

6261

0-77

-91

—na

nana

11.2

01

EM

etha

mid

opho

s10

265-

92-6

315

.82

nana

na1.

702

EM

ethi

dath

ion

950-

37-8

734

.64

nana

na3.

531

EM

ethi

ocar

b20

32-6

5-7

337.

500.

4708

0.03

080.

001.

06—

EM

etho

crot

opho

s25

601-

84-7

23.

12na

nana

0.25

1S

Met

hom

yl16

752-

77-5

1323

.69

0.08

522.

7336

0.50

8.46

—E

Met

hyl

para

thio

n29

8-00

-010

10.8

10.

2021

1.32

540.

272.

13—

EM

ethy

ltr

ithio

n95

3-17

-31

—na

nana

4.51

1S

Mev

inph

os77

86-3

4-7

133.

800.

0254

0.94

090.

880.

70—

EM

exac

arba

te31

5-18

-416

5.64

−0.2

586

3.24

290.

031.

39—

SM

obam

1079

3-30

-18

255.

000.

2555

4.18

030.

3430

.31

—S

Mon

ocro

toph

os69

23-2

2-4

232.

51−0

.031

21.

0218

0.79

0.42

—E

Nal

ed30

0-76

-57

64.9

0na

nana

1.72

1E

Om

etho

ate

1113

-02-

62

28.5

2na

nana

4.14

1E

Oxa

myl

2313

5-22

-03

4.18

nana

na0.

782

EO

xyde

met

on-m

ethy

l30

1-12

-211

53.9

0−0

.045

24.

1453

0.82

13.9

6—

EPa

rath

ion

56-3

8-2

195.

620.

0797

1.22

280.

760.

40—

EPh

enth

oate

2597

-03-

72

259.

00na

nana

23.1

71

EPh

orat

e29

8-02

-28

7.06

0.18

170.

4833

0.65

0.34

—E

Phos

alon

e23

10-1

7-0

1—

nana

na10

6.27

1E

Phos

fola

n94

7-02

-47

2.37

0.10

340.

8054

0.62

0.69

—S

Phos

met

732-

11-6

543

5.80

1.18

54−1

.539

40.

061.

24—

EPh

osph

amid

on29

7-99

-415

4.24

0.15

280.

7174

0.32

1.08

—E

Phox

im14

816-

18-3

1232

.21

0.60

570.

1756

0.01

1.71

—E

Piri

mic

arb

2310

3-98

-28

20.5

20.

1118

2.42

850.

506.

78—

EPi

rim

ipho

s-et

hyl

2350

5-41

-12

6.00

nana

na1.

901

E

30 P. Mineau et al.

Tab

le2.

(Con

tinue

d).

Com

poun

dC

AS_

RN

nM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Piri

mip

hos-

met

hyl

2923

2-93

-72

470.

00na

nana

13.5

11

EPr

omec

arb

2631

-37-

04

13.5

01.

3541

−2.6

021

0.15

0.94

—E

Prop

apho

s72

92-1

6-2

1—

nana

na0.

181

EPr

opet

amph

os31

218-

83-4

378

.00

nana

na7.

091

EPr

opox

ur11

4-26

-123

11.7

60.

344

0.83

440.

021.

31—

EPr

othi

ofos

3464

3-46

-41

—na

nana

13.6

51

EPr

otho

ate

(tri

met

hoat

e)22

75-1

8-5

255

.95

nana

na5.

521

SPy

rida

phen

tion

119-

12-0

1—

nana

na7.

941

EPy

rola

n87

-47-

81

—na

nana

3.27

1S

Qui

nalp

hos

1359

3-03

-82

20.6

5na

nana

0.42

1E

Schr

adan

152-

16-9

319

.00

nana

na2.

021

SSu

lfot

ep36

89-2

4-5

215

0.00

nana

na50

.63

1E

Sulp

rofo

s35

400-

43-2

547

.00

−0.0

883

4.88

540.

896.

85—

ET

ebup

irim

fos

9618

2-53

-51

—na

nana

2.36

1E

Tem

epho

s33

83-9

6-8

1465

.60

0.31

632.

6217

0.10

8.68

—E

Ter

bufo

s13

071-

79-9

59.

481.

0277

−1.5

328

0.32

0.16

—E

Tet

rach

lorv

inph

os96

1-11

-53

4750

.00

nana

na25

.32

1E

Thi

ocar

boxi

me

2911

8-87

-45

12.0

0−0

.414

15.

2618

0.08

5.04

—S

Thi

odic

arb

5966

9-26

-01

—na

nana

234.

961

ET

hiof

anox

3919

6-18

-43

1.20

nana

na0.

121

ET

hiom

eton

640-

15-3

261

.75

nana

na5.

071

ET

hion

azin

297-

97-2

82.

77−0

.225

42.

2454

0.07

1.02

—S

Tri

azam

ate

1121

43-8

2-5

1—

nana

na0.

931

ET

riaz

opho

s24

017-

47-8

59.

470.

1991

1.01

130.

551.

68—

ET

rich

lorf

on52

-68-

612

60.7

30.

3189

2.47

570.

0613

.36

—E

Tri

chlo

rona

t32

7-98

-010

18.5

00.

4069

0.79

890.

300.

73—

S


Tab

le2.

(Con

tinue

d).

Com

poun

dC

AS_

RN

nM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Tri

met

haca

rb(L

andr

inT

M)

1240

7-86

-28

69.0

0−0

.138

25.

0938

0.56

16.2

8—

EV

amid

othi

on22

75-2

3-2

1—

nana

na3.

721

EX

ylyl

carb

2425

-10-

71

—na

nana

6.20

1E

NU

MB

ER

ED

EX

PE

RIM

EN

TA

LP

RO

DU

CT

S:P

RO

BA

BL

YN

EV

ER

MA

RK

ET

ED

2-Pr

opar

gylo

xyph

enyl

N-

3279

-46-

72

45.0

0na

nana

11.3

91

met

hylc

arba

mat

e(H

ER

CU

LE

S96

99)

3-(2

-But

yl)-

phen

ylN

-25

474-

41-3

257

.90

nana

na1.

231

met

hyl

carb

amat

eN

-ben

zene

sulf

oate

(RE

-117

75)

3,4,

5-T

rim

ethy

lphe

nyl

2686

-99-

91

—na

nana

4.50

1m

ethy

lcar

bam

ate

(Lan

drin

TM

isom

er1)

3,5-

Diis

opro

pylp

heny

l33

0-64

-32

55.0

0na

nana

2.53

1N

-met

hylc

arba

mat

e(H

RS

1422

)3,

5X

ylyl

N-m

ethy

lcar

-26

55-1

4-3

1—

nana

na19

.61

1ba

mat

e3-

Prop

argy

loxy

phen

yl36

92-9

0-8

240

.48

nana

na3.

801

N-m

ethy

lcar

bam

ate

(HE

RC

UL

ES

8717

)

32 P. Mineau et al.

Tab

le2.

(Con

tinue

d).

Com

poun

dC

AS_

RN

nM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Die

thyl

5-m

ethy

lpyr

azol

-10

8-34

-91

—na

nana

3.38

13y

lph

osph

ate

(Pyr

azox

on)

m-I

sopr

opyl

phen

yl64

-00-

62

11.3

1na

nana

1.42

1N

-met

hylc

arba

mat

e(H

ER

CU

LE

S57

27)

O,O

-Die

thyl

naph

tale

ne-

2668

-92-

02

261.

86na

nana

6.00

11,

8-di

carb

oxim

ido-

oxyp

hosp

hono

thio

ate

(BA

Y22

408)

O,O

-Dim

ethy

lO

-(3,

5-1

—na

nana

3.64

1di

met

hyl-

4-m

ethy

lthio

-ph

enyl

)pho

spho

ro-

thio

ate

(Bay

er37

342)

O,O

-Dim

ethy

lO

-(4-

3254

-63-

52

0.90

nana

na0.

071

met

hyl-

mer

capt

ophe

nyl)

phos

phat

e(G

C-6

506)

O-E

thyl

S-p-

toly

let

hyl

333-

43-7

23.

34na

nana

0.43

1ph

osph

onod

ithio

ate

(BA

Y38

156)

Phen

ol,

3-(1

-met

hyl-

713

.30

0.03

292.

6034

0.94

1.20

—et

hyl)

-4-(

met

hylth

io)-

met

hyl

carb

amat

e(A

CD

7029

)


Tab

le2.

(Con

tinue

d).

Com

poun

dC

AS_

RN

nM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Phos

phon

amid

othi

oic

95.

62−0

.226

62.

5104

0.41

0.51

—ac

id,

p-et

hyl-

O-[

3-m

ethy

l-4-

(met

hylth

io)-

phen

yl]e

ster

(BA

YH

OL

0574

)Ph

osph

oram

idic

acid

,9

13.3

00.

0151

2.80

430.

304.

33—

ethy

l-,2

,4-d

ichl

oro-

phen

yles

ter

(DO

WC

O16

1)Ph

osph

orot

hioi

cac

id,

O-

7682

-90-

87

4.22

0.34

150.

035

0.00

0.91

—(3

-bro

mo-

5,7-

dim

ethy

l-py

razo

lo[1

,5-a

]-pi

rim

idin

-2-y

l)O

,O-

diet

hyl

este

r(B

AY

7554

6)I N

SUF

FIC

IEN

TP

RO

DU

CT

DA

TA

Dim

ethy

lvin

phos

2274

-67-

1—

EIs

oxat

hion

1885

4-01

-8—

EM

ecar

bam

2595

-54-

2—

EM

etol

carb

1129

-41-

5—

EPr

ofen

ofos

4119

8-08

-7—

EPy

racl

ofos

7745

8-01

-6—

ED

ialif

os10

311-

84-9

275

2.55

SFo

rmot

hion

2540

-82-

12

238.

85E

Piri

mip

hos-

met

hyl

2923

2-93

-71

—E

34 P. Mineau et al.

Tab

le3.

Ref

eren

ceL

D50

valu

esfo

rpe

stic

ides

that

dono

tin

hibi

tch

olin

este

rase

;th

eva

lues

are

orde

red

bypr

inci

pal

targ

etpe

stan

dal

phab

etic

ally

byco

mm

onch

emic

alna

me.

Che

mic

alC

AS_

RN

NM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Use

CH

EM

ICA

LS

PR

IMA

RIL

YA

CT

IVE

AG

AIN

ST

INSE

CT

SA

ND

OT

HE

RIN

VE

RT

EB

RA

TE

S

2-(O

ctyl

thio

)eth

anol

3547

-33-

92

2250

.00

——

—23

4.13

2E

Inse

ctic

ide

Aba

mec

tin71

751-

41-2

210

42.3

0—

——

42.8

02

EA

cari

cide

Ace

quin

ocyl

(AK

D-2

023)

5796

0-19

-72

2000

.00

——

—19

3.05

1E

Aca

rici

deA

ceta

mpi

rid

1354

10-2

0-7

1—

——

—20

.91

1E

Inse

ctic

ide

Ace

tate

sof

Z/E

8-28

079-

04-1

1—

——

—23

2.29

1E

Inse

ctph

erom

one

dode

ceny

lan

dZ

(Z8-

dode

ceny

l)8-

dode

ceno

lA

crin

athr

in10

1007

-06-

12

1625

.00

——

—15

6.09

2E

Aca

rici

dein

sect

icid

eA

ldri

n30

9-00

-212

19.8

30.

440.

800.

151.

15—

SIn

sect

icid

eA

lleth

rin

584-

79-2

1—

——

—19

2.68

1E

Inse

ctic

ide

Alp

ha-C

yper

met

hrin

6737

5-30

-81

——

——

9633

.91

1E

Inse

ctic

ide

Am

itraz

3308

9-61

-11

——

——

41.8

31

EIn

sect

icid

eac

aric

ide

Aza

dira

chtin

992-

20-1

1—

——

—26

1.32

1U

Miti

cide

Azo

cycl

otin

4108

3-11

-81

——

——

17.4

21

EA

cari

cide

Ben

sulta

p17

606-

31-4

419

2.00

1.35

−2.1

10.

230.

41—

EIn

sect

icid

eB

enze

neH

exac

hlor

ide

608-

73-1

1—

——

—12

.54

1E

Inse

ctic

ide

Ben

zyl

benz

oate

120-

51-4

1—

——

—23

2.29

1U

Miti

cide

Bet

a-C

yflu

thri

n68

359-

37-5

4—

——

——

EIn

sect

icid

eB

ifen

azat

e(D

2341

)14

9877

-41-

81

——

——

132.

641

EA

cari

cide

Bif

enth

rin

8265

7-04

-32

1975

.00

——

—20

4.71

2E

Inse

ctic

ide

Als

ota

bled

are

the

CA

Sre

gist

ratio

nnu

mbe

rs,

the

num

ber

ofsp

ecie

sva

lues

avai

labl

e,th

em

edia

nL

D50

,th

esl

ope,

inte

rcep

tan

dp

valu

eof

the

LD

50*w

eigh

tre

gres

sion

,th

eca

lcul

ated

ores

timat

edH

D5(

50%

)an

d,w

here

used

,th

enu

mbe

rof

spec

ies

from

whi

chan

extr

apol

atio

nfa

ctor

was

deve

lope

d(s

eeT

able

1).

The

stat

usfo

llow

sth

eno

men

clat

ure

ofth

eP

esti

cide

Man

ual,

11th

Ed.

;E

,in

use;

S,su

pers

eded

;U

,un

know

n.


Tab

le3.

(Con

tinue

d).

Che

mic

alC

AS_

RN

NM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Use

Bin

apac

ryl

485-

31-4

271

7.50

——

——

—S

Aca

rici

deB

ioal

leth

rin

548-

79-2

1—

——

—23

5.77

1E

Inse

ctic

ide

(Dep

alle

thri

n)B

ioal

leth

ron

S-cy

clo-

2843

4-00

-63

5000

.00

——

—52

0.29

2E

Inse

ctic

ide

pent

enyl

isom

erB

ioet

hano

met

hrin

(RU

-22

431-

62-5

1—

——

——

UIn

sect

icid

e11

-679

)B

iore

smet

hrin

2843

4-01

-71

——

——

506.

331

EIn

sect

icid

eB

rom

opro

pyla

te18

181-

80-1

222

55.0

0—

——

193.

051

EA

cari

cide

BT

6803

8-71

-11

——

——

481.

701

UIn

sect

icid

eB

upro

fezi

n69

327-

76-0

320

00.0

0—

——

680.

402

EIn

sect

icid

eB

utox

ypol

ypro

pyle

ne90

03-1

3-8

1—

——

—26

1.32

1S

Inse

ctic

ide

glyc

olC

alci

umpo

lysu

lfid

e13

44-8

1-6

1—

——

—65

.04

1E

Inse

ctic

ide

Cal

cium

tetr

athi

ocar

ba-

8151

0-83

-01

——

——

137.

051

UIn

sect

icid

em

ate

CG

A50

439

6167

6-87

-71

——

——

78.0

91

EA

cari

cide

,ix

odic

ide

Chl

orda

ne57

-74-

94

62.2

81.

00−1

.63

0.44

0.09

—E

Inse

ctic

ide

Chl

orde

cone

143-

50-0

222

0.33

——

—26

.42

1S

Inse

ctic

ide,

fung

icid

eC

hlor

fena

pyr

1224

53-7

3-0

28.

30—

——

0.56

1E

Inse

ctic

ide,

acar

icid

eC

hlor

flua

zuro

n71

422-

67-8

1—

——

—24

1.81

1E

Inse

ctic

ide

Chl

orof

eniz

on80

-33-

11

——

——

444.

021

SA

cari

cide

Citr

onel

laoi

l80

00-2

9-1

1—

——

—26

1.32

1E

Inse

ctic

ide

Clo

fent

ezin

e74

115-

24-5

252

50.0

0—

——

493.

592

EA

cari

cide

Cod

iem

one

3395

6-49

-91

——

——

249.

711

EIn

sect

pher

omon

e

36 P. Mineau et al.

Tab

le3.

(Con

tinue

d).

Che

mic

alC

AS_

RN

NM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Use

Cop

per

salts

offa

tty90

07-3

9-0

1—

——

—20

9.06

1U

Inse

ctic

ide

acid

s&

rosi

nac

ids

Cry

olite

1509

6-52

-31

——

——

249.

711

EIn

sect

icid

eC

yclo

prot

hrin

6393

5-38

-62

3500

.00

——

—48

2.63

1E

Inse

ctic

ide

Cyf

luth

rin

6835

9-37

-54

——

——

485.

441

EIn

sect

icid

eC

yhal

othr

in68

085-

85-8

1—

——

—48

1.70

1E

Inse

ctic

ide

Cyh

exat

in13

121-

70-5

1—

——

—57

.43

1E

Aca

rici

deC

yper

met

hrin

5231

5-07

-83

1000

0.00

——

—57

9.15

1E

Inse

ctic

ide

Cyr

omaz

ine

6621

5-27

-84

2061

.50

−0.1

38.

250.

6360

4.60

—E

Inse

ctgr

owth

regu

lato

rD

DT

50-2

9-3

513

34.0

00.

365.

120.

3712

2.90

—E

Inse

ctic

ide

Del

tam

ethr

in52

918-

63-5

510

00.0

0—

——

97.0

91

EIn

sect

icid

eD

iafe

nthi

uron

8006

0-09

-92

1500

.00

——

—15

6.09

2E

Inse

ctic

ide,

acar

icid

eD

icof

ol11

5-32

-23

680.

99—

——

72.3

71

EA

cari

cide

Die

ldri

n60

-57-

116

35.1

50.

083.

020.

714.

15—

SIn

sect

icid

eD

ieno

chlo

r22

27-1

7-0

230

87.9

5—

——

243.

972

EIn

sect

icid

eD

iflu

benz

uron

3536

7-38

-53

5000

.00

——

—95

2.56

1E

Inse

ctic

ide

Din

itro-

O-c

reso

l53

4-52

-12

23.0

0—

——

2.22

1U

Inse

ctic

ide

Dip

ropy

lis

ocin

chom

ero-

136-

45-8

1—

——

—15

6.79

1U

Inse

ctic

ide

nate

DU

OM

EE

NT

-E-9

210

19.0

0—

——

37.8

31

UM

osqu

ito(N

-tal

low

-tri

met

hyle

neco

ntro

lag

ent

diam

ines

)E

ndos

ulfa

n11

5-29

-76

52.4

20.

342.

230.

179.

53—

EIn

sect

icid

eE

ndri

n72

-20-

811

1.78

0.03

0.59

0.83

0.75

—S

Inse

ctic

ide

Esf

enva

lera

te66

230-

04-4

214

78.5

1—

——

131.

242

EIn

sect

icid

e


Tab

le3.

(Con

tinue

d).

Che

mic

alC

AS_

RN

NM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Use

Eto

fenp

rox

8084

4-07

-11

——

——

192.

681

EIn

sect

icid

eE

toxa

zole

1532

33-9

1-1

1—

——

—19

2.68

1E

Aca

rici

deFa

rnes

ol46

02-8

4-0

221

50.0

0—

——

223.

732

EM

itici

deFe

naza

quin

1209

28-0

9-8

218

73.5

0—

——

194.

512

EA

cari

cide

Fenb

utat

inox

ide

1335

6-08

-65

——

——

291.

521

EA

cari

cide

Feno

thio

carb

6285

0-32

-22

1471

.54

——

—14

2.91

2E

Aca

rici

deFe

noxy

carb

7912

7-80

-31

——

——

675.

681

EIn

sect

icid

eFe

npyr

oxim

ate

1118

12-5

8-9

220

00.0

0—

——

208.

122

EA

cari

cide

Fenv

aler

ate

5163

0-58

-12

5447

.36

——

—32

1.77

2E

Inse

ctic

ide

Fipr

onil

1200

68-3

7-3

739

.19

−0.3

77.

220.

651.

47—

EIn

sect

icid

eFl

uazu

ron

8681

1-58

-72

2000

.00

——

—20

8.12

2E

Ixod

icid

eFl

uben

zim

ine

3789

3-02

-01

——

——

431.

671

SA

cari

cide

Fluc

yclo

xuro

n94

050-

52-9

1—

——

—19

2.68

1E

Aca

rici

de,

inse

ctic

ide

Fluc

ythr

inat

e70

124-

77-5

226

45.0

0—

——

274.

882

EIn

sect

icid

eFl

ufen

oxur

on10

1463

-69-

81

——

——

232.

291

EIn

sect

icid

eFl

uval

inat

e69

409-

94-5

1—

——

—29

1.52

1E

Inse

ctic

ide

Gos

sypl

ure

(Hex

adec

a-53

042-

79-8

1—

——

—96

3.39

1E

Inse

ctph

erom

one

dien

ylac

etat

e)H

alfe

npro

x11

1872

-58-

31

——

——

218.

821

EA

cari

cide

Hep

tach

lor

76-4

4-8

712

5.00

−0.0

95.

370.

893.

47—

EIn

sect

icid

eH

exaf

lum

uron

8647

9-06

-32

2000

.00

——

—20

8.12

2E

Inse

ctic

ide

Hex

ythi

azox

7858

7-05

-02

3620

.27

——

—48

2.63

1E

Aca

rici

deH

ydra

met

hyln

on67

485-

29-4

221

69.0

0—

——

222.

902

EIn

sect

icid

eIm

azet

habe

nz-m

ethy

l81

405-

85-8

221

50.0

0—

——

223.

732

EIn

sect

icid

eIm

idac

lopr

id10

5827

-78-

97

35.3

6−0

.06

3.90

0.79

8.43

—U

Inse

ctic

ide

38 P. Mineau et al.

Tab

le3.

(Con

tinue

d).

Che

mic

alC

AS_

RN

NM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Use

Isob

enza

n29

7-78

-97

3.16

0.41

−0.9

00.

120.

43—

SIn

sect

icid

eIs

olan

119-

38-0

1—

——

—6.

991

SIn

sect

icid

eIs

omat

e-M

2807

9-84

-11

——

——

232.

291

UPh

erom

one

Lam

bda-

Cyh

alot

hrin

9146

5-08

-61

——

——

428.

141

EIn

sect

icid

eL

imon

ene

138-

86-3

1—

——

—23

2.29

1U

Inse

ctic

ide

Lin

dane

58-8

9-9

1190

.83

0.42

2.86

0.14

10.5

0—

EIn

sect

icid

eL

ufen

uron

1030

55-0

7-8

220

00.0

0—

——

208.

122

EIn

sect

icid

e,ac

aric

ide

Met

hopr

ene

4059

6-69

-81

——

——

192.

681

EIn

sect

grow

thre

gula

tor

Met

hoxy

chlo

r72

-43-

54

——

——

291.

521

EIn

sect

icid

eM

ethy

lch

loro

form

71-5

5-6

1—

——

—29

1.52

1U

Inse

ctic

ide

Met

hyl

nony

lke

tone

112-

12-9

222

50.0

0—

——

234.

132

UIn

sect

icid

eM

usca

lure

2751

9-02

-41

——

——

232.

291

EIn

sect

pher

omon

eN

,N-D

ieth

yl-M

-13

4-62

-31

——

——

159.

701

UIn

sect

icid

eT

olua

mid

eN

apth

alen

e91

-20-

31

——

——

312.

431

SIn

sect

icid

eN

euro

lidol

7212

-44-

42

2150

.00

——

—22

3.73

2E

Miti

cide

Nic

otin

e54

-11-

55

247.

401.

40−1

.89

0.06

1.04

—E

Inse

ctic

ide

Nic

otin

esu

lfat

e65

-30-

510

75.0

00.

372.

820.

196.

94—

UIn

sect

icid

eN

itenp

yram

1207

38-8

9-8

216

87.0

0—

——

165.

482

EIn

sect

icid

eN

oval

uron

1167

14-4

6-6

1—

——

—19

2.68

1E

Inse

ctic

ide

Para

dich

loro

benz

ene

106-

46-7

1—

——

—18

6.76

1U

Inse

ctic

ide

Perm

ethr

in52

645-

53-1

5—

——

—31

27.5

31

EIn

sect

icid

ePh

enol

231

0.20

——

——

—U

Inse

ctic

ide

Phen

othr

in[(

1R)-

tran

s-26

002-

80-2

1—

——

—29

1.52

1E

Inse

ctic

ide

isom

er]


Tab

le3.

(Con

tinue

d).

Che

mic

alC

AS_

RN

NM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Use

Pipe

rony

lbu

toxi

de51

-03-

61

——

——

261.

321

EIn

sect

icid

ePO

EIs

ooct

adec

anol

5229

2-17

-81

——

——

192.

681

UIn

sect

icid

ePo

lybu

tene

9003

-29-

61

——

——

249.

711

UIn

sect

icid

ePo

lych

loro

cam

phan

es2

53.0

0—

——

——

UIn

sect

icid

ePo

tass

ium

salt

ofol

eic

143-

18-0

1—

——

—24

6.38

1U

Inse

ctic

ide

acid

Pota

ssiu

msa

ltsof

fatty

1012

4-65

-91

——

——

291.

521

UIn

sect

icid

eac

ids

Pral

leth

rin

2303

1-36

-92

1085

.50

——

—11

2.60

2E

Inse

ctic

ide

Pym

etro

zine

1233

12-8

9-0

220

00.0

0—

——

208.

122

EIn

sect

icid

ePy

reth

rin

8003

-34-

71

——

——

963.

391

EIn

sect

icid

ePy

rida

ben

9648

9-71

-33

2250

.00

——

—27

9.50

2E

Aca

rici

de,

inse

ctic

ide

Pyri

mid

ifen

1057

79-7

8-0

1—

——

—42

.87

1E

Aca

rici

de,

inse

ctic

ide

Pyri

prox

yfen

9573

7-68

-12

2000

.00

——

—20

8.12

2E

Inse

ctic

ide

Res

met

hrin

1045

3-86

-84

——

——

60.3

21

EIn

sect

icid

eR

oten

one

83-7

9-4

1—

——

—21

1.95

1E

Inse

ctic

ide

Rya

nodi

ne15

662-

33-6

62.

370.

060.

850.

910.

59—

SIn

sect

icid

eSD

-168

984

6.77

−0.2

33.

300.

363.

14—

UIn

sect

icid

e,ac

aric

ide

Sila

fluo

fen

1050

24-6

6-6

220

00.0

0—

——

193.

051

EIn

sect

icid

eSo

dium

cyan

ide

143-

33-9

68.

99−0

.08

2.64

0.77

2.13

—E

Inse

ctic

ide,

acar

icid

eSu

lcof

uron

-sod

ium

3567

-25-

72

1558

.00

——

—14

9.96

2E

Inse

ctic

ide

Sulf

lura

mid

4151

-50-

21

——

——

16.9

61

EIn

sect

icid

eSZ

I-12

116

2320

-67-

41

——

——

193.

051

EA

cari

cide

Tau

-Flu

valin

ate

1028

51-0

6-9

1—

——

—29

1.52

1E

Inse

ctic

ide,

acar

icid

e

40 P. Mineau et al.

Tab

le3.

(Con

tinue

d).

Che

mic

alC

AS_

RN

NM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Use

TD

E72

-54-

81

——

——

41.0

21

SIn

sect

icid

e,m

osqu

itola

rvic

ide

and

adul

ticid

eT

ebuf

enoz

ide

1124

10-2

3-8

1—

——

—24

9.71

1E

Inse

ctic

ide

Teb

ufen

pyra

d11

9168

-77-

32

2000

.00

——

—20

8.12

2E

Aca

rici

deT

eflu

benz

uron

8312

1-18

-02

2125

.00

——

—22

0.74

2E

Inse

ctic

ide

Tef

luth

rin

7953

8-32

-23

734.

00—

——

178.

632

EIn

sect

icid

eT

EPA

545-

55-1

729

.90

−0.2

44.

710.

264.

45—

UIn

sect

icid

e,ch

emo-

ster

ilant

Tet

radi

fon

116-

29-0

4—

——

—58

0.72

1E

Aca

rici

deT

etra

met

hrin

7696

-12-

01

——

——

276.

011

EIn

sect

icid

eT

hioc

ycla

m31

895-

21-3

1—

——

—33

3.01

1E

Inse

ctic

ide

Tob

acco

dust

8037

-19-

21

——

——

249.

711

UIn

sect

icid

eT

oxap

hene

8001

-35-

211

70.7

0−0

.32

6.26

0.21

10.4

0—

SIn

sect

icid

eT

ralo

met

hrin

6684

1-25

-61

——

——

291.

521

EIn

sect

icid

eT

rans

flut

hrin

1187

12-8

9-3

1—

——

——

EIn

sect

icid

eT

ride

c-4-

en-1

-yl

acet

ate

6595

4-19

-02

2060

.66

——

—21

4.34

2E

Inse

ctic

ide

Tri

flum

uron

6462

8-44

-02

2780

.50

——

—20

8.05

2E

Inse

ctic

ide

Z-1

1-H

exad

ecan

ol53

939-

28-9

1—

——

—23

2.29

1U

Inse

ctic

ide

ZX

I89

0116

0791

-64-

01

——

——

32.5

31

EIn

sect

icid

eC

HE

MIC

AL

SP

RIM

AR

ILY

AC

TIV

E

AG

AIN

STB

AC

TE

RIA

AN

DA

LG

AE

OR

AP

PL

IED

AS

FU

MIG

AN

TS

1,2-

Ben

zene

dica

rbox

-64

3-79

-81

——

——

71.1

01

UB

acte

rici

deal

dehy

de


Tab

le3.

(Con

tinue

d).

Che

mic

alC

AS_

RN

NM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Use

1,3-

dibr

omo-

5,5-

77-4

8-5

1—

——

—29

1.52

1U

Bac

teri

cide

dim

ethy

lhyd

anto

in(D

BD

MH

)2-

(Hyd

roxy

met

hyl)

3437

5-28

-51

——

——

202.

441

UB

acte

rici

deam

ino)

etha

nol

2-B

enzy

l-4-

chlo

roph

enol

120-

32-1

1—

——

—29

1.52

1U

Bac

teri

cide

4,4-

Dim

ethy

loxa

zolid

ine

5120

0-87

-42

905.

00—

——

91.8

42

UB

acte

rici

de4-

Chl

oro-

3,5-

xyle

nol

88-0

4-0

1—

——

—27

6.01

1U

Bac

teri

cide

Acr

olei

n10

7-02

-82

14.0

6—

——

1.37

2E

Her

bici

de,

fung

icid

e,m

icro

bici

deA

DB

AC

6842

4-85

-12

810.

04—

——

49.6

12

UB

acte

rici

deA

lkyl

amin

ehy

dro-

9174

5-52

-71

——

——

114.

871

UB

acte

rici

dech

lori

deA

lkyl

amin

o-3-

amin

opro

-61

791-

64-8

1—

——

—8.

561

UB

acte

rici

depa

neA

lkyl

trim

ethy

lam

mo-

6178

9-18

-21

——

——

62.9

51

UB

acte

rici

deni

umch

lori

deA

zadi

oxab

icyc

looc

tane

5972

0-42

-21

——

——

241.

811

UB

acte

rici

deB

CD

MH

1607

9-88

-21

——

——

187.

841

UB

acte

rici

deB

enzi

soth

iazo

lin-3

-one

2634

-33-

51

——

——

71.6

61

UB

acte

rici

deB

HA

P(B

rom

ohyd

roxy

a-24

91-3

8-5

1—

——

—76

.89

1U

Bac

teri

cide

ceto

phen

one)

Bio

ban

P-14

8722

24-4

4-4

1—

——

—96

.34

1U

Bac

teri

cide

Bis

(bro

moa

ceto

xy)-

2-20

679-

58-7

1—

——

—18

.88

1U

Bac

teri

cide

bute

ne

42 P. Mineau et al.

Tab

le3.

(Con

tinue

d).

Che

mic

alC

AS_

RN

NM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Use

Bis

(tri

chlo

rom

ethy

l)30

64-7

0-8

1—

——

—21

6.76

1U

Bac

teri

cide

sulf

one

Bor

icac

id10

043-

35-3

1—

——

—29

1.52

1U

Pest

icid

eB

rom

onitr

osty

rene

7166

-19-

01

——

——

48.1

71

UB

acte

rici

deB

rono

pol

52-5

1-7

1—

——

—49

.04

1E

Bac

teri

cide

Bus

an77

3151

2-74

-01

——

——

47.8

81

UB

acte

rici

deC

alci

umhy

poch

lori

te77

78-5

4-3

280

3.50

——

—41

.32

2U

Alg

icid

eC

hlor

hexi

dine

diac

etat

e56

-95-

11

——

——

233.

801

UB

acte

rici

deC

hrom

icac

id77

38-9

4-5

1—

——

—19

.05

1U

Pres

erva

tive

Cop

per

sulf

ate

(bas

ic)

1332

-14-

51

——

——

47.1

21

UA

ntif

oula

ntC

oppe

rsu

lfat

epe

ntah

y-77

58-9

9-8

1—

——

—43

.89

1E

Alg

icid

edr

ate

Cop

per

trie

than

olam

ine

8202

7-59

-61

——

——

192.

681

UB

acte

rici

deC

osan

145

9755

3-90

-71

——

——

156.

791

UPr

eser

vativ

eC

upro

usth

iocy

anat

e11

11-6

7-7

1—

——

—23

2.29

1E

Bac

teri

cide

Daz

omet

533-

74-4

338

3.50

——

—53

.33

1E

Fum

igan

tD

BN

PA10

222-

01-2

222

0.43

——

—22

.91

2U

Bac

teri

cide

DC

DIC

138-

93-2

1—

——

—30

9.34

1U

Bac

teri

cide

DC

DM

H(1

,3-D

ichl

oro-

118-

52-5

1—

——

—29

1.52

1U

Bac

teri

cide

5,5-

dim

ethy

lhyd

anto

in)

DD

AC

7173

-51-

51

——

——

12.6

21

UB

acte

rici

deD

ichl

orop

rope

ne/

542-

75-6

,55

6-61

-61

——

——

36.8

01

Fum

igan

t,fu

ngic

ide,

met

hylis

othi

ocya

nate

nem

atic

ide

Diio

dom

ethy

lp-

toly

l20

018-

09-1

1—

——

—29

1.52

1U

Pres

erva

tive

sulf

one

Dim

etho

xane

828-

00-2

1—

——

—18

4.09

1U

Bac

teri

cide


Tab

le3.

(Con

tinue

d).

Che

mic

alC

AS_

RN

NM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Use

Dio

ctyl

dim

ethy

lam

mo-

5538

-94-

51

——

——

20.3

51

UA

lgic

ide

nium

chlo

ride

Dith

io-3

-one

,4,5

-dic

hlor

o11

92-5

2-5

1—

——

—28

.69

1U

Bac

teri

cide

Dow

icil

4080

-31-

32

1795

.00

——

—18

3.10

2U

Bac

teri

cide

DT

EA

3636

2-09

-11

——

——

216.

761

UB

acte

rici

deE

riog

lauc

ine/

tart

razi

ne38

44-4

5-9

222

50.0

0—

——

234.

132

UA

lgic

ide

Form

alde

hyde

50-0

0-0

1—

——

—91

.75

1E

Fung

icid

e,ba

cter

icid

eG

luta

rald

ehyd

e11

1-30

-81

——

——

167.

891

UB

acte

rici

deG

rota

n47

19-0

4-4

1—

——

—17

6.54

1U

Bac

teri

cide

Gua

nidi

ne(d

odin

efr

ee11

2-65

-22

1391

.87

——

—14

1.62

2E

Bac

teri

cide

base

)H

ydro

xypr

opyl

met

hane

3038

8-01

-31

——

——

45.6

61

UB

acte

rici

deth

iosu

lfon

ate

Iodi

ne75

53-5

6-2

1—

——

—23

2.29

1U

Bac

teri

cide

Iodi

neco

mpl

ex11

096-

42-7

1—

——

—19

7.44

1U

Bac

teri

cide

Irga

rol

2815

9-98

-01

——

——

261.

321

UB

acte

rici

deIs

obar

dac

1386

98-3

6-9

1—

——

—3.

951

UB

acte

rici

deIs

ocya

nuri

cac

id10

8-80

-51

——

——

184.

491

UB

acte

rici

deL

ithiu

mhy

poch

lori

te13

840-

33-0

1—

——

—54

.62

1U

Bac

teri

cide

Met

alde

hyde

108-

62-3

319

6.00

——

—16

.93

1E

Mol

lusc

icid

eM

ethy

lbr

omid

e74

-83-

91

——

——

8.48

1E

Soil

ster

ilant

,fu

mig

ant

Met

hyl

isoc

yana

te55

6-61

-61

——

——

13.1

01

EFu

ngic

ide,

nem

ati-

cide

,fu

mig

ant

44 P. Mineau et al.

Tab

le3.

(Con

tinue

d).

Che

mic

alC

AS_

RN

NM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Use

Met

hylis

othi

azol

inon

e26

172-

55-4

1—

——

—7.

911

UB

acte

rici

de(A

ctic

ide

14)

Met

roni

dazo

le44

3-48

-11

——

——

481.

701

UB

acte

rici

deM

iner

aloi

l(i

nclu

ding

8012

-95-

11

——

——

261.

321

UB

acte

rici

depa

rafi

noi

l)M

TI

8263

3-79

-21

——

——

17.6

51

UPr

eser

vativ

eN

emag

on96

-12-

82

111.

40—

——

16.5

81

SN

emat

icid

eN

icos

amid

e-ol

amin

e14

20-0

4-8

3—

——

—23

2.29

1E

Mol

lusc

icid

eN

itrap

yrin

1929

-82-

41

——

——

260.

891

EB

acte

rici

de,

nitr

ific

a-tio

nin

hibi

tor

OB

PA(D

ID47

)58

-36-

62

5050

.00

——

—96

3.39

1U

Bac

teri

cide

Oct

hilin

one

2653

0-20

-11

——

——

55.5

01

EB

acte

rici

deO

xazo

lidin

eE

7747

-35-

51

——

——

116.

141

UB

acte

rici

dePa

rach

loro

met

acre

sol

59-5

0-7

1—

——

—17

8.86

1U

Bac

teri

cide

PHM

B32

289-

58-0

1—

——

—24

1.81

1E

Bac

teri

cide

PNM

DC

/DC

DM

C13

7-41

-71

——

——

114.

861

UB

acte

rici

dePo

tass

ium

azid

e12

136-

44-6

320

.80

——

—1.

601

UM

ISC

Pota

ssiu

mbr

omid

e77

58-0

2-3

1—

——

—29

0.36

1U

Bac

teri

cide

Pota

ssiu

mdi

met

hylth

io-

128-

03-0

1—

——

—14

5.76

1U

Bac

teri

cide

carb

amat

ePy

razo

le85

264-

33-1

1—

——

—88

.15

1U

Bac

teri

cide

SDD

C12

8-04

-11

——

——

115.

101

UB

acte

rici

deSi

lver

7440

-22-

41

——

——

261.

321

UB

acte

rici

deSo

dium

2-m

erca

ptob

en-

2492

-26-

41

——

——

249.

711

UB

acte

rici

dezo

thio

late

Sodi

um2-

phen

ylph

enat

e13

2-27

-41

——

——

116.

141

UB

acte

rici

de


Tab

le3.

(Con

tinue

d).

Che

mic

alC

AS_

RN

NM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Use

Sodi

umbr

omid

e76

47-1

5-6

222

00.0

0—

——

228.

872

UB

acte

rici

deSo

dium

chlo

rite

7758

-19-

22

792.

86—

——

79.6

42

UB

acte

rici

deSo

dium

dich

loro

-S-t

ri-

2893

-78-

92

1954

.68

——

—20

3.36

2U

Bac

teri

cide

azin

etri

one

Sodi

umdi

chlo

rois

ocya

n-51

580-

86-0

1—

——

—20

6.27

1U

Bac

teri

cide

urat

edih

ydra

teSo

dium

dode

cylb

enze

ne-

2515

5-30

-01

——

——

157.

491

UB

acte

rici

desu

lfon

ate

Sodi

umhy

poch

lori

te76

81-5

2-9

227

10.1

4—

——

272.

162

UB

acte

rici

deSo

dium

omad

ine

1592

2-78

-82

194.

49—

——

17.2

02

UB

acte

rici

deSt

rept

omyc

in38

10-7

4-0

1—

——

—23

2.29

1E

Bac

teri

cide

TB

Tm

etha

cryl

ate

2635

4-18

-71

——

——

192.

681

UA

ntif

oula

ntT

CM

TB

2156

4-17

-01

——

——

76.7

51

UB

acte

rici

deT

etra

glyc

ine

hydr

oper

i-70

97-6

0-1

1—

——

—29

.04

1U

Bac

teri

cide

odid

eT

HPS

5556

6-30

-81

——

——

29.5

81

UB

acte

rici

deT

ribu

tylti

nm

etha

cryl

ate

2155

-70-

61

——

——

81.0

71

UB

acte

rici

deT

rich

loro

-s-t

riaz

inet

rion

e87

-90-

12

1480

.87

——

—14

6.48

2U

Bac

teri

cide

Tri

chlo

rom

elam

ine

7673

-09-

81

——

——

246.

381

UB

acte

rici

deT

ricl

osan

3380

-34-

52

1487

.50

——

—13

8.59

2U

Bac

teri

cide

Tri

ethy

lhex

ahyd

ro-s

-77

79-2

7-3

247

2.52

——

—47

.49

2U

Pres

erva

tive

tria

zine

Tri

met

hoxy

sily

lqu

ats

2766

8-52

-61

——

——

153.

181

UB

acte

rici

deZ

inc

naph

then

ate

1200

1-85

-31

——

——

261.

321

UPr

eser

vativ

eZ

inc

oxid

e13

14-1

3-2

1—

——

—65

.74

1U

Pres

erva

tive

46 P. Mineau et al.

Tab

le3.

(Con

tinue

d).

Che

mic

alC

AS_

RN

NM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Use

CH

EM

ICA

LS

PR

IMA

RIL

YA

CT

IVE

AG

AIN

STF

UN

GI

2-Ph

enyl

phen

ol13

2-27

-41

——

——

116.

141

EFu

ngic

ide

3-lo

do-2

-pro

pyny

lbu

tyl-

5540

6-53

-61

——

——

99.0

01

UFu

ngic

ide

carb

amat

eA

nila

zine

101-

05-3

520

00.0

00.

196.

730.

2161

4.90

—S

Fung

icid

eA

zaco

nazo

le60

207-

31-0

273

1.00

——

—48

.26

1E

Fung

icid

eA

zoxy

stro

bin

1318

60-3

3-8

1—

——

—23

2.29

1E

Fung

icid

eB

ariu

mm

etab

orat

e13

701-

59-2

1—

——

—14

5.64

1U

Fung

icid

eB

enal

axyl

7162

6-11

-42

6850

.00

——

—81

9.31

2E

Fung

icid

eB

enom

yl17

804-

35-2

310

0.00

——

—25

.32

1E

Fung

icid

eB

istr

ibut

lytin

oxid

e56

-35-

91

——

——

68.8

01

SFu

ngic

ide

Bite

rtan

ol55

179-

31-2

320

00.0

0—

——

420.

741

EFu

ngic

ide

Bro

muc

onaz

ol11

6255

-48-

22

2150

.00

——

—22

3.73

2E

Fung

icid

eB

upir

imat

e41

483-

43-6

238

73.5

0—

——

482.

631

EFu

ngic

ide

Cap

tafo

l24

25-0

6-1

1—

——

—29

1.52

1E

Fung

icid

eC

apta

n13

3-06

-23

100.

00—

——

25.3

21

EFu

ngic

ide

Car

bend

azim

1060

5-21

-72

4681

.99

——

—49

1.11

2E

Fung

icid

eC

arbo

xin

5234

-68-

45

2000

.00

1.64

−1.9

50.

043.

44—

EFu

ngic

ide

Cer

esan

M51

7-16

-81

——

——

33.4

81

SFu

ngic

ide

Chi

nom

ethi

onat

(oxy

thio

-24

39-0

1-2

350

0.00

——

—12

6.58

1E

Aca

rici

de,

fung

icid

equ

inox

)C

hlor

oneb

2675

-77-

61

——

——

481.

701

SFu

ngic

ide

Chl

orot

halo

nil

1897

-45-

61

——

——

193.

051

EFu

ngic

ide

Chl

ozol

inat

e84

332-

86-5

345

00.0

0—

——

790.

552

EFu

ngic

ide

Cop

per

hydr

oxid

e20

427-

59-2

334

00.0

0—

——

219.

112

EFu

ngic

ide

Cop

per

napt

hena

te13

38-0

2-9

1—

——

—26

1.32

1E

Fung

icid

e


Tab

le3.

(Con

tinue

d).

Che

mic

alC

AS_

RN

NM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Use

Cop

per

oxyc

hlor

ide

1332

-40-

71

——

——

45.9

51

EFu

ngic

ide

Cup

roba

me

7076

-63-

31

——

——

—S

Fung

icid

eC

upro

usox

ide

1317

-39-

12

406.

00—

——

33.1

61

EFu

ngic

ide

Cyc

lohe

xim

ide

66-8

1-9

39.

38—

——

1.85

2S

Fung

icid

eC

ymox

anil

5796

6-95

-72

2250

.00

——

—23

4.13

2E

Fung

icid

eC

ypro

cona

zole

1130

96-9

9-4

1—

——

—14

.22

1E

Fung

icid

eC

ypro

dini

l12

1552

-61-

22

2000

.00

——

—20

8.12

2E

Fung

icid

eD

ebac

arb/

carb

enda

zim

6273

2-91

-61

——

——

96.3

41

EFu

ngic

ide

Dic

hlof

luan

id10

85-9

8-9

250

00.0

0—

——

482.

631

EFu

ngic

ide

Dic

hlon

e11

7-80

-61

——

——

192.

681

SFu

ngic

ide

Dic

hlor

opro

pene

542-

75-6

1—

——

—17

.65

1E

Fung

icid

eD

ichl

obut

razo

l75

736-

33-3

1—

——

—92

8.81

1E

Fung

icid

eD

iclo

ran

99-3

0-9

390

0.00

——

—13

9.61

2E

Fung

icid

eD

ieth

ofen

carb

8713

0-20

-92

2250

.00

——

—23

4.13

2E

Fung

icid

eD

ifen

ocon

azol

1194

46-6

8-3

1—

——

—20

7.13

1E

Fung

icid

eD

iflu

met

orim

1303

39-0

7-0

214

30.0

0—

——

85.0

41

EFu

ngic

ide

Dim

ethi

rim

ol52

21-5

3-4

1—

——

—20

2.53

1E

Fung

icid

eD

imet

hom

orph

1104

88-7

0-5

220

00.0

0—

——

208.

122

EFu

ngic

ide

Din

icon

azol

e83

657-

24-3

217

45.1

0—

——

179.

642

EFu

ngic

ide

Din

obut

on97

3-21

-71

——

——

7.59

1E

Aca

rici

de,

fung

icid

eD

inoc

ap39

300-

45-3

1—

——

—24

9.71

1E

Fung

icid

e,ac

aric

ide

Dith

iano

n33

47-2

2-6

629

4.50

0.50

2.66

0.51

5.29

—E

Fung

icid

eD

MD

Mhy

dant

oin

6440

-58-

01

——

——

141.

621

UFu

ngic

ide

Dod

ine

(dog

uadi

ne)

2439

-10-

31

——

——

110.

021

EFu

ngic

ide

Edi

fenp

hos

1710

9-49

-81

——

——

75.1

41

EFu

ngic

ide

48 P. Mineau et al.

Tab

le3.

(Con

tinue

d).

Che

mic

alC

AS_

RN

NM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Use

Epo

xico

nazo

l10

6325

-08-

01

——

——

232.

291

EFu

ngic

ide

Eth

irim

ol23

947-

60-6

1—

——

—20

2.53

1E

Fung

icid

eE

trid

iazo

le25

93-1

5-9

1—

——

—65

.04

1E

Fung

icid

eFe

nam

inos

ulf

140-

56-7

317

.80

——

—4.

521

SFu

ngic

ide

Fena

rim

ol60

168-

88-9

263

2.46

——

—65

.81

2E

Fung

icid

eFe

nbuc

onaz

ol11

4369

-43-

62

2174

.72

——

—27

0.13

2E

Fung

icid

eFe

nfur

am24

691-

80-3

1—

——

—0.

691

EFu

ngic

ide

Fenp

iclo

nil

7473

8-17

-32

1305

.00

——

—52

.13

2E

Fung

icid

eFe

npro

pidi

n67

306-

00-7

1—

——

—39

.21

1E

Fung

icid

eFe

npro

pim

orph

6730

6-03

-03

3949

.68

——

—41

9.73

1E

Fung

icid

eFe

ntin

acet

ate

900-

95-8

410

7.00

0.38

2.90

0.05

38.2

5—

EFu

ngic

ide

Fent

inhy

drox

ide

76-8

7-9

376

.07

——

—7.

392

EFu

ngic

ide

Feri

mzo

ne89

269-

64-7

1—

——

—94

.14

1E

Fung

icid

eFl

uazi

nam

7962

2-59

-62

2986

.00

——

—28

4.34

2E

Fung

icid

eFl

udio

xoni

l13

1341

-86-

12

2000

.00

——

—20

8.12

2E

Fung

icid

eFl

uqui

ncon

azol

e13

6426

-54-

52

2000

.00

——

—20

8.12

2E

Fung

icid

eFl

usila

zole

8550

9-19

-91

——

——

153.

181

EFu

ngic

ide

Flus

ulfa

mid

e10

6917

-52-

61

——

——

7.67

1E

Fung

icid

eFl

utol

anil

6633

2-96

-52

2000

.00

——

—20

8.12

2E

Fung

icid

eFl

utri

afol

7667

4-21

-02

2808

.00

——

—48

1.70

1E

Fung

icid

eFo

lpet

133-

07-3

214

05.7

8—

——

118.

282

EFu

ngic

ide

Fose

tyl-

alum

iniu

m39

148-

24-8

264

98.5

0—

——

785.

422

EFu

ngic

ide

Fube

rida

zole

3878

-19-

12

424.

28—

——

47.7

81

EFu

ngic

ide

Fura

laxy

l57

646-

30-7

1—

——

—57

9.15

1E

Fung

icid

eG

uaza

tine

1351

6-27

-33

216.

00—

——

41.9

91

EFu

ngic

ide

Gua

zatin

e(t

riac

etat

e)57

520-

17-9

312

0.00

——

—32

.65

1U

Fung

icid

e


Tab

le3.

(Con

tinue

d).

Che

mic

alC

AS_

RN

NM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Use

Hex

acon

azol

e79

983-

71-4

1—

——

—39

1.14

1E

Fung

icid

eH

ymex

azol

1000

4-44

-13

1479

.00

——

—17

5.77

2E

Fung

icid

eIm

azal

il35

554-

44-0

320

00.0

0—

——

49.2

31

EFu

ngic

ide

Imib

enco

nazo

le86

598-

92-7

222

50.0

0—

——

234.

132

EFu

ngic

ide

Imin

octa

dine

tria

ceta

te39

202-

40-9

1—

——

—94

.89

1E

Fung

icid

eIp

rodi

one

3673

4-19

-71

——

——

158.

401

EFu

ngic

ide

Isop

roth

iola

ne50

512-

35-1

1—

——

—42

8.29

1E

Fung

icid

eK

asug

amyc

in69

80-1

8-3

1—

——

—38

6.10

1E

Fung

icid

e,ba

cter

icid

eL

igna

san

BL

P52

316-

55-9

1—

——

—44

7.01

1U

Fung

icid

eM

anco

zeb

8018

-01-

73

6861

.49

——

—71

0.95

1E

Fung

icid

eM

aneb

1242

7-38

-24

——

——

345.

341

EFu

ngic

ide

Mep

anip

yrim

1102

35-4

7-7

222

50.0

0—

——

234.

132

EFu

ngic

ide

Mep

roni

l55

814-

41-0

220

00.0

0—

——

208.

122

EFu

ngic

ide

Mer

curi

cch

lori

de74

87-9

4-7

1—

——

—4.

101

EFu

ngic

ide

Met

alax

yl57

837-

19-1

1—

——

—89

.09

1E

Fung

icid

eM

etal

axyl

-M70

630-

17-0

1—

——

—13

7.03

1E

Fung

icid

eM

etco

nazo

le12

5116

-23-

61

——

——

91.7

51

EFu

ngic

ide

Met

hylm

ercu

rydi

cyan

-50

2-39

-62

35.0

0—

——

5.11

1S

Fung

icid

edi

amid

eM

etir

am90

06-4

2-2

1—

——

—24

9.71

1E

Fung

icid

eM

yclo

buta

nil

8867

1-89

-01

——

——

59.2

31

EFu

ngic

ide

Nab

am14

2-59

-63

2120

.00

——

—20

4.63

1E

Fung

icid

eN

uari

mol

6328

4-71

-91

——

——

73.4

61

EFu

ngic

ide

Ofu

race

5881

0-48

-31

——

——

—E

Fung

icid

eO

xadi

xyl

7773

2-09

-33

2000

.00

——

—14

9.07

2E

Fung

icid

e

50 P. Mineau et al.

Tab

le3.

(Con

tinue

d).

Che

mic

alC

AS_

RN

NM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Use

Oxi

ne-c

oppe

r10

380-

28-6

361

8.00

——

—11

5.69

2E

Fung

icid

eO

xyte

trac

yclin

e79

-57-

21

——

——

232.

291

SFu

ngic

ide

Para

nitr

ophe

nol

100-

02-7

1—

——

—67

.02

1U

Fung

icid

ePe

fura

zoat

e10

1903

-30-

42

3300

.00

——

—22

9.73

1E

Fung

icid

ePe

ncon

azol

e66

246-

88-6

324

24.0

0—

——

193.

412

EFu

ngic

ide

Penc

ycur

on66

063-

05-6

320

00.0

0—

——

277.

772

EFu

ngic

ide

Pent

achl

orop

heno

l(P

CP)

87-8

6-5

350

4.00

——

—50

.79

2U

Inse

ctic

ide,

fung

i-ci

de,

herb

icid

ePh

enyl

mer

curi

cac

etat

e62

-38-

44

145.

862.

19−9

.43

0.16

0.01

—E

Fung

icid

e(P

MA

)Pr

ochl

oraz

6774

7-09

-53

707.

00—

——

74.2

02

EFu

ngic

ide

Proc

hlor

az-m

anga

nese

1—

——

—20

2.31

1U

Fung

icid

ePr

ocym

idon

e32

809-

16-8

253

00.0

0—

——

637.

071

EFu

ngic

ide

Prop

amoc

arb

2457

9-73

-52

2910

.00

——

—32

1.72

1E

Fung

icid

ePr

opic

onaz

ole

6020

7-90

-14

2667

.50

0.33

5.69

0.29

296.

80—

EFu

ngic

ide

Prop

ineb

1207

1-83

-92

3000

.00

——

—48

2.63

1E

Fung

icid

ePy

razo

phos

1345

7-18

-62

295.

53—

——

26.5

31

EFu

ngic

ide

Pyri

feno

x88

283-

41-4

220

00.0

0—

——

208.

122

EFu

ngic

ide

Pyri

met

hani

l53

112-

28-0

220

00.0

0—

——

208.

122

EFu

ngic

ide

Pyri

thio

ne11

21-3

0-8

212

6.93

——

—25

.32

1S

Fung

icid

eQ

uint

ozen

e82

-68-

81

——

——

255.

451

EFu

ngic

ide

Sec-

buty

lam

ine

1395

2-84

-61

——

——

12.6

61

EFu

ngic

ide

Silic

ate

met

hoxy

ethy

l3

18.0

0—

——

1.91

1U

Fung

icid

em

ercu

rySo

dium

tetr

athi

oper

oxo-

7345

-69-

91

——

——

137.

051

EFu

ngic

ide,

inse

cti-

carb

onat

e(G

Y-8

1)ci

de,

nem

atic

ide


Tab

le3.

(Con

tinue

d).

Che

mic

alC

AS_

RN

NM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Use

Teb

ucon

azol

e10

7534

-96-

34

1494

.00

0.32

5.97

0.19

347.

30—

EFu

ngic

ide

Ter

razo

le25

93-1

5-9

211

00.0

0—

——

99.7

22

EFu

ngic

ide

Thi

aben

dazo

le14

8-79

-81

——

——

261.

321

EFu

ngic

ide

Thi

opha

nate

-met

hyl

2356

4-05

-81

——

——

482.

631

EFu

ngic

ide

Thi

ram

137-

26-8

750

0.00

0.55

3.31

0.06

36.8

1—

EFu

ngic

ide

Tol

clof

os-m

ethy

l57

018-

04-9

350

00.0

0—

——

485.

062

EFu

ngic

ide

Tol

ylfl

uani

d73

1-27

-14

——

——

482.

631

EFu

ngic

ide

Tra

ns-1

,2-b

is(n

-11

13-1

4-0

215

60.0

0—

——

192.

681

UFu

ngic

ide

prop

ylsu

lfon

yl)e

then

e(C

HE

1843

)T

riad

imef

on43

121-

43-3

4—

——

—38

5.36

EFu

ngic

ide

Tri

adim

enol

5521

9-65

-33

2000

.00

——

—55

5.54

2E

Fung

icid

eT

riaz

oxid

e72

459-

58-6

310

8.43

——

—10

.47

1E

Fung

icid

eT

rich

ogra

mm

aha

rzia

num

220

00.0

0—

——

208.

122

EFu

ngic

ide

Tri

cycl

azol

e41

814-

78-2

210

0.00

——

—10

.41

2E

Fung

icid

eT

ride

mor

ph81

412-

43-3

216

94.0

0—

——

173.

372

EFu

ngic

ide

Tri

flum

izol

e68

694-

11-1

1—

——

—29

1.52

1E

Fung

icid

eT

rifo

rine

2664

4-46

-25

——

——

776.

701

EFu

ngic

ide

Tri

ticon

azol

e13

1983

-72-

76

——

——

232.

291

EFu

ngic

ide

Vin

cloz

olin

5047

1-44

-81

——

——

291.

521

EFu

ngic

ide

Zin

cbo

rate

1244

7-61

-91

——

——

261.

321

UFu

ngic

ide

Zin

eb12

122-

67-7

1—

——

—21

2.54

1E

Fung

icid

eZ

iram

e13

7-30

-44

88.7

3−0

.25

5.94

0.36

29.4

5—

EFu

ngic

ide

CH

EM

ICA

LS

PR

IMA

RIL

YA

CT

IVE

AG

AIN

STP

LA

NT

S

2,3,

6-T

BA

50-3

1-7

213

53.5

5—

——

75.1

41

EH

erbi

cide

2,4,

5-T

93-7

6-5

1—

——

—56

.52

1S

Her

bici

de

52 P. Mineau et al.

Tab

le3.

(Con

tinue

d).

Che

mic

alC

AS_

RN

NM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Use

2,4-

D94

-75-

76

531.

740.

135.

570.

6813

2.90

—E

Her

bici

de2,

4-D

But

otyl

1929

-73-

31

——

——

232.

291

EH

erbi

cide

2,4-

DD

imet

hyla

m-

2008

-39-

11

——

——

58.0

71

EH

erbi

cide

mon

ium

2,4-

Ddi

olam

ine

5742

-19-

81

——

——

69.1

11

UH

erbi

cide

2,4-

DIs

ooct

yles

ter

1928

-43-

41

——

——

63.8

71

EH

erbi

cide

2,4-

Dso

dium

2702

-72-

91

——

——

195.

091

EH

erbi

cide

2,4-

DT

ri-i

sopr

opyl

amin

e32

341-

80-3

1—

——

—47

.04

1U

Her

bici

desa

lt2,

4-D

B94

-82-

61

——

——

178.

401

EH

erbi

cide

2,4-

DP-

pD

MA

salt

1047

86-8

7-0

1—

——

—32

.40

1U

Her

bici

de2,

4-D

PD

imet

hyla

min

e53

404-

32-3

1—

——

—32

.40

1U

Her

bici

desa

lt6-

Ben

zyla

min

opur

ine

1214

-39-

71

——

——

185.

711

EPl

ant

grow

th(N

6-B

enzu

lade

nine

)re

gula

tor

Ace

toch

lor

3425

6-82

-12

1133

.37

——

—96

.27

2E

Her

bici

deA

cibe

nzol

at(C

GA

1351

58-5

4-2

1—

——

—23

2.29

1E

Plan

tac

tivat

or24

5704

)A

cifl

uorf

en-s

odiu

m62

476-

59-9

215

73.0

0—

——

99.6

42

EH

erbi

cide

Acl

onif

en74

070-

46-5

1—

——

—14

47.8

81

EH

erbi

cide

Ala

chlo

r15

972-

60-8

1—

——

—33

0.42

1E

Her

bici

deA

lloxy

dim

-sod

ium

5563

4-91

-81

——

——

286.

201

EH

erbi

cide

Am

etry

n83

4-12

-82

3445

.00

——

—33

6.22

2E

Her

bici

deA

mid

osul

furo

n12

0923

-37-

73

2000

.00

——

—24

8.45

2E

Her

bici

deA

mitr

ole

61-8

2-5

235

75.0

0—

——

407.

292

EH

erbi

cide

Am

mon

ium

sulf

amat

e77

73-0

6-0

1—

——

—28

9.58

1E

Her

bici

deA

ncym

idol

1277

1-68

-51

——

——

25.3

21

EPl

ant

grow

thre

gula

tor


Tab

le3.

(Con

tinue

d).

Che

mic

alC

AS_

RN

NM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Use

Ani

lofo

s64

249-

01-0

1—

——

—27

0.60

1E

Her

bici

deA

rsen

icac

id77

78-3

9-4

1—

——

—4.

231

UH

erbi

cide

Asu

lam

3337

-71-

14

——

——

1668

.64

1E

Her

bici

deA

sula

mso

dium

2302

-17-

24

——

——

425.

801

EH

erbi

cide

Atr

azin

e19

19-2

4-9

271

18.5

0—

——

408.

981

EH

erbi

cide

Aza

feni

din

6804

9-83

-22

2250

.00

——

—23

4.13

2E

Her

bici

deA

zim

sulf

uron

1201

62-5

5-2

222

50.0

0—

——

234.

132

EH

erbi

cide

Ben

azol

in-e

thyl

2505

9-80

-72

4500

.00

——

—44

1.48

2E

Her

bici

deB

enaz

olin

3813

-05-

61

——

——

984.

561

EH

erbi

cide

Ben

flur

alin

1861

-40-

12

2000

.00

——

—20

8.12

2E

Her

bici

deB

enfu

resa

te68

505-

69-1

221

136.

00—

——

1869

.35

2E

Her

bici

deB

enox

acor

9873

0-04

-22

2075

.00

——

—21

5.78

2E

Her

bici

desa

fene

rB

ensu

lfur

on-m

ethy

l83

055-

99-6

1—

——

—20

7.13

1E

Her

bici

deB

ensu

lide

741-

58-2

1—

——

—16

0.98

1E

Her

bici

deB

enta

zon

5072

3-80

-34

2029

.00

0.91

2.41

0.14

32.4

0—

EH

erbi

cide

Bif

enox

4257

6-02

-33

5000

.00

——

—40

7.29

2E

Her

bici

deB

ilana

fos

3559

7-43

-41

——

——

253.

161

EH

erbi

cide

Bis

pyri

bac-

sodi

um12

5401

-75-

41

——

——

261.

321

EH

erbi

cide

Bro

mac

il31

4-40

-91

——

——

261.

321

EH

erbi

cide

Bro

mox

ynil

1689

-84-

52

208.

50—

——

21.6

82

EH

erbi

cide

Bro

mox

ynil-

pota

ssiu

m29

61-6

8-4

1—

——

—5.

311

EH

erbi

cide

Bro

mox

ynil

(but

yrat

e)38

61-4

1-4

282

5.00

——

—74

.79

2U

Her

bici

deB

rom

oxyn

ilhe

ptan

oate

5663

4-95

-81

——

——

41.7

01

UH

erbi

cide

Bro

mox

ynil

octa

noat

e16

89-9

9-2

317

0.00

——

—16

.20

2E

Her

bici

deB

rom

oxyn

ilPh

enol

1689

-84-

53

200.

00—

——

21.6

82

UH

erbi

cide

But

achl

or23

184-

66-9

1—

——

—44

7.01

1E

Her

bici

de

54 P. Mineau et al.

Tab

le3.

(Con

tinue

d).

Che

mic

alC

AS_

RN

NM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Use

But

ralin

3362

9-47

-92

3625

.00

——

—41

6.66

2E

Her

bici

deB

utro

xydi

m13

8164

-12-

22

1610

.50

——

—16

2.61

2E

Her

bici

deC

afen

stro

le12

5306

-83-

41

——

——

481.

701

EH

erbi

cide

Cap

ric

acid

/pel

argo

nic

143-

07-7

1—

——

—26

1.32

1U

Her

bici

deac

idC

arbe

tam

ide

1611

8-49

-32

2000

.00

——

—21

2.54

1E

Her

bici

deC

hlom

etho

xyfe

n32

861-

85-1

1—

——

——

EH

erbi

cide

Chl

oram

ben

133-

90-4

1—

——

—15

9.40

1E

Her

bici

deC

hlor

ambe

n-am

mon

ium

1076

-46-

61

——

——

192.

681

UH

erbi

cide

Chl

oram

ben-

sodi

um19

54-8

1-0

220

00.0

0—

——

208.

122

UH

erbi

cide

Chl

orfl

uren

ol25

36-3

1-4

1—

——

—14

37.8

61

SG

row

thre

gula

tor

Chl

orid

azon

1698

-60-

82

3000

.00

——

—35

1.36

2E

Her

bici

deC

hlor

imur

on-e

thyl

9098

2-32

-41

——

——

241.

811

EH

erbi

cide

Chl

orm

equa

t70

03-8

9-6

240

8.00

——

—53

.57

1E

Plan

tgr

owth

regu

lato

rC

hlor

meq

uat

chlo

ride

999-

81-5

555

5.00

−0.4

39.

040.

1615

5.00

—E

Her

bici

deC

hlor

opro

p-so

dium

5340

4-22

-12

2334

.00

——

—21

8.55

2U

Gro

wth

regu

lato

rC

hlor

oxur

on19

82-4

7-4

245

19.0

0—

——

293.

241

SH

erbi

cide

Chl

orpr

opha

m10

1-21

-32

2000

.00

——

—19

3.05

1E

Her

bici

deC

hlor

sulf

uron

6490

2-72

-31

——

——

481.

701

EH

erbi

cide

Chl

orth

al-d

imet

hyl

1861

-32-

11

——

——

261.

321

EH

erbi

cide

Chl

orth

iam

id19

18-1

3-4

1—

——

—25

.32

1E

Her

bici

deC

inm

ethy

lin87

818-

31-3

1—

——

—24

9.71

1E

Her

bici

deC

inos

ulfu

ron

9459

3-91

-62

2000

.00

——

—19

3.05

1E

Her

bici

deC

leth

odim

9912

9-21

-21

——

——

232.

291

EH

erbi

cide

Clo

dina

fop-

prop

argy

l10

5512

-06-

92

1727

.50

——

—17

7.51

2E

Her

bici

de


Tab

le3.

(Con

tinue

d).

Che

mic

alC

AS_

RN

NM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Use

Clo

fenc

et12

9025

-54-

32

1707

.00

——

—17

4.99

2E

Plan

tgr

owth

regu

lato

rC

lom

azon

e81

777-

89-1

225

10.0

0—

——

261.

192

EH

erbi

cide

Clo

pyra

lid17

02-1

7-6

218

55.8

6—

——

192.

532

EH

erbi

cide

Clo

quin

toce

t-m

exyl

9960

7-70

-22

2000

.00

——

—20

8.12

2E

Her

bici

desa

fend

erC

yana

zine

2172

5-46

-23

445.

00—

——

52.4

12

EH

erbi

cide

Cyc

lani

lide

1131

36-7

7-9

221

5.50

——

—22

.42

2E

Plan

tgr

owth

regu

lato

rC

yclo

ate

1134

-23-

24

——

——

249.

711

EH

erbi

cide

Cyc

loxy

dim

1012

05-0

2-1

220

00.0

0—

——

208.

122

EH

erbi

cide

Cyt

okin

in52

5-79

-11

——

——

291.

521

UG

row

thre

gula

tor

Dai

mur

on42

609-

52-9

1—

——

—23

2.29

1E

Her

bici

deD

alap

on-s

odiu

m12

7-20

-81

——

——

286.

581

EH

erbi

cide

Dam

inoz

ide

1596

-84-

51

——

——

311.

281

EPl

ant

grow

thre

gula

tor

Des

med

ipha

m13

684-

56-5

1—

——

—25

8.67

1E

Her

bici

deD

icam

ba19

18-0

0-9

293

6.55

——

—62

.26

2E

Her

bici

deD

icam

ba-a

lum

inum

1—

——

—24

1.81

1U

Her

bici

deD

icam

ba-d

igly

coam

ine

1040

40-7

9-1

1—

——

—44

.97

1U

Her

bici

deD

icam

ba-

2300

-66-

51

——

——

241.

811

EH

erbi

cide

dim

ethy

lam

mon

ium

Dic

amba

-pot

assi

um10

007-

85-9

1—

——

—31

.94

1E

Her

bici

deD

ichl

oben

il11

94-6

5-6

449

9.61

0.54

2.75

0.39

9.33

—E

Her

bici

deD

ichl

orpr

op-P

1516

5-67

-01

——

——

41.0

61

EH

erbi

cide

Dic

lofo

p-m

ethy

l51

338-

27-3

279

71.8

5—

——

788.

262

EH

erbi

cide

Dif

lufe

nica

n83

164-

33-4

230

75.0

0—

——

305.

162

EH

erbi

cide

56 P. Mineau et al.

Tab

le3.

(Con

tinue

d).

Che

mic

alC

AS_

RN

NM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Use

Dif

lufe

nzop

yr(B

AS

654

1092

93-9

8-3

1—

——

—54

1.43

1E

Her

bici

de00

H)

Dik

egul

ac-s

odiu

m52

508-

35-7

1—

——

—37

4.86

1E

Her

bici

deD

imep

iper

ate

6143

2-55

-12

3500

.00

——

—19

3.05

1E

Her

bici

deD

imet

hena

mid

(SA

N87

674-

68-8

1—

——

—22

1.60

1E

Her

bici

de58

2)D

imet

hipi

n55

290-

64-7

1—

——

—84

.78

1E

Her

bici

de,

plan

tgr

owth

regu

lato

rD

initr

amin

e29

091-

05-2

256

00.0

0—

——

360.

472

EH

erbi

cide

Din

oseb

88-8

5-7

519

.90

0.42

0.31

0.01

3.18

—S

Her

bici

deD

inos

ebac

etat

e28

13-9

5-8

216

.66

——

—2.

291

SH

erbi

cide

Din

oter

b14

20-0

7-1

225

.00

——

——

—E

Her

bici

deD

iqua

t(d

ibro

mid

e)85

-00-

73

184.

87—

——

17.8

11

EH

erbi

cide

Dis

odiu

mm

etha

ne-

144-

21-8

1—

——

—72

.82

1E

Her

bici

dear

sona

teD

ithio

pyr

9788

6-45

-81

——

——

261.

321

EH

erbi

cide

Diu

ron

330-

54-1

320

00.0

0—

——

193.

042

EH

erbi

cide

DM

PA29

9-85

-41

——

——

25.3

21

SH

erbi

cide

DN

OC

534-

52-1

521

.15

0.15

1.97

0.36

6.67

—E

Her

bici

deE

ndot

hall

145-

73-3

232

6.72

——

—29

.20

2E

Her

bici

de,

algi

cide

,pl

ant

grow

thre

gula

tor

End

otha

ll(d

imet

hyla

l-66

330-

88-9

1—

——

—37

.48

1U

Her

bici

de,

algi

cide

,ky

lam

ine)

plan

tgr

owth

regu

lato

r


Tab

le3.

(Con

tinue

d).

Che

mic

alC

AS_

RN

NM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Use

End

otha

ll(d

ipot

assi

um21

64-0

7-0

1—

——

—31

.60

1U

Her

bici

de,

algi

cide

,sa

lt)pl

ant

grow

thre

gula

tor

EPT

C75

9-94

-43

1000

.00

——

—25

.32

1E

Her

bici

deE

spro

carb

8578

5-20

-21

——

——

193.

051

EH

erbi

cide

Eth

alfl

ural

in55

283-

68-6

1—

——

—23

2.29

1E

Her

bici

deE

tham

etsu

lfur

on-m

ethy

l97

780-

06-8

1—

——

—26

1.32

1E

Her

bici

deE

thep

hon

1667

2-87

-04

1924

.00

0.21

6.24

0.48

372.

20—

EH

erbi

cide

Eth

idim

uron

3004

3-49

-34

513.

210.

782.

690.

0359

.64

—S

Her

bici

deE

thof

umes

ate

2625

5-79

-63

3464

.10

——

—47

2.79

2E

Her

bici

deFe

nchl

oraz

ole

1031

12-3

6-3

213

33.8

4—

——

25.8

41

SH

erbi

cide

Fenc

lori

m37

40-9

2-9

227

50.0

0—

——

48.2

61

EH

erbi

cide

safe

ner

Feno

prop

93-7

2-1

293

20.0

0—

——

1091

.75

1S

Her

bici

deFe

noxa

prop

9561

7-09

-73

2510

.00

——

—44

0.07

2S

Her

bici

deFe

noxa

prop

-eth

yl66

441-

23-4

325

10.0

0—

——

440.

072

SH

erbi

cide

Feno

xapr

op-P

1131

58-4

0-0

3—

——

—23

2.29

2E

Her

bici

deFe

noxa

prop

-P-e

thyl

7128

3-80

-21

——

——

232.

291

EH

erbi

cide

Fenr

idaz

one-

sodi

um83

588-

43-6

1—

——

—49

5.70

1U

Her

bici

deFe

nuro

n10

1-42

-84

——

——

232.

291

EH

erbi

cide

Ferr

icsu

lfat

e(s

eeFe

r-10

028-

22-5

1—

——

—26

1.32

1U

Her

bici

dero

ussu

lfat

e)Fe

rrou

ssu

lfat

ehe

ptah

y-77

82-6

3-0

1—

——

—26

1.32

1E

Her

bici

dedr

ate

Ferr

ous

sulf

ate

mon

ohy-

1737

5-41

-61

——

——

249.

711

UH

erbi

cide

drat

eFl

ampr

op-M

-met

hyl

6372

9-98

-61

——

——

447.

881

EH

erbi

cide

58 P. Mineau et al.

Tab

le3.

(Con

tinue

d).

Che

mic

alC

AS_

RN

NM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Use

Flam

prop

-met

hyl

5275

6-25

-93

1000

.00

——

—10

6.27

1S

Her

bici

deFl

ampr

opis

opro

pyl

5275

6-22

-62

1500

.00

——

—10

6.27

1S

Her

bici

deFl

azas

ulfu

ron

1040

40-7

8-0

1—

——

—19

3.05

1E

Her

bici

deFl

uazi

fop-

buty

l69

806-

50-4

1—

——

—74

6.09

1E

Her

bici

deFl

uazi

fop-

P-b

utyl

7924

1-46

-61

——

——

339.

881

EH

erbi

cide

Flum

etra

lin62

924-

70-3

221

50.0

0—

——

223.

732

EG

row

thre

gula

tor

Flum

etsu

lam

9896

7-40

-91

——

——

261.

321

EH

erbi

cide

Flum

iclo

rac-

pent

yl87

546-

18-7

1—

——

—26

1.32

1E

Her

bici

deFl

uom

etur

on21

64-1

7-2

1—

——

—19

2.68

1E

Her

bici

deFl

uoro

glyc

ofen

7750

1-60

-11

——

——

214.

071

EH

erbi

cide

Fluo

rogl

ycof

en-e

thyl

7750

1-90

-71

——

——

367.

021

EH

erbi

cide

Fluo

xypy

r-m

epty

l81

406-

37-3

220

00.0

0—

——

208.

122

EH

erbi

cide

Flup

oxam

1191

26-1

5-7

222

50.0

0—

——

234.

132

EH

erbi

cide

Flup

yrsu

lfur

on-m

ethy

l-14

4740

-54-

51

——

——

216.

761

EH

erbi

cide

sodi

umFl

uraz

ole

7285

0-64

-71

——

——

291.

521

EH

erbi

cide

safe

nder

Flur

idon

e59

756-

60-4

1—

——

—23

2.29

1E

Her

bici

deFl

uroc

hlor

idon

e61

213-

25-0

1—

——

—24

9.71

1E

Her

bici

deFl

urox

ypyr

6937

7-81

-72

2000

.00

——

—20

8.12

2E

Her

bici

deFl

urpr

imid

ol56

425-

91-3

1—

——

—23

2.29

1E

Plan

tgr

owth

regu

lato

rFl

uxof

enim

8848

5-37

-41

——

——

232.

291

EH

erbi

cide

safe

nder

Fom

esaf

en72

178-

02-0

1—

——

—48

1.70

1E

Her

bici

deFo

rchl

orfe

nuro

n68

157-

60-8

1—

——

—26

1.32

1E

Plan

tgr

owth

regu

lato

r


Tab

le3.

(Con

tinue

d).

Che

mic

alC

AS_

RN

NM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Use

Fosa

min

eam

mon

ium

2595

4-13

-62

4791

.29

——

—49

8.10

2E

Her

bici

deFu

rila

zole

1217

76-3

3-8

1—

——

—23

.23

1E

Her

bici

desa

fend

erG

ibbe

relli

cac

id77

-06-

51

——

——

261.

321

EG

row

thre

gula

tor

Glu

fosi

nate

-am

mon

ium

5127

6-47

-24

——

——

232.

291

EH

erbi

cide

Gly

phos

ate

1071

-83-

61

——

——

232.

291

EH

erbi

cide

Gly

phos

ate-

trim

esiu

m81

591-

81-3

214

87.5

0—

——

144.

332

EH

erbi

cide

(sul

fosa

te)

Hal

osul

furo

n-m

ethy

l10

0784

-20-

11

——

——

261.

321

EH

erbi

cide

Hal

oxyf

op69

806-

34-4

1—

——

—20

7.13

1E

Her

bici

deH

alox

yfop

-P-m

ethy

l72

619-

32-0

1—

——

—13

4.61

1E

Her

bici

deH

alox

yfop

etho

xyet

hyl

8723

7-48

-71

——

——

207.

131

EH

erbi

cide

(eto

tyl)

Hex

aflu

rate

1702

9-22

-03

193.

00—

——

18.5

91

SH

erbi

cide

Hex

azin

one

5123

5-04

-21

——

——

261.

441

EH

erbi

cide

Hyd

roge

ncy

anam

ide

420-

04-2

1—

——

—28

.74

1E

Her

bici

de,

plan

tgr

owth

regu

lato

rIC

IS-0

748

8105

2-29

-12

2150

.00

——

—22

3.73

2E

Gro

wth

regu

lato

rIm

azam

etha

benz

1007

28-8

4-5

221

50.0

0—

——

223.

732

EH

erbi

cide

Imaz

amet

habe

nz-m

ethy

l81

405-

85-8

221

50.0

0—

——

223.

732

EH

erbi

cide

Imaz

amox

1143

11-3

2-9

1—

——

—21

4.40

1E

Her

bici

deIm

azap

ic(A

C26

3,22

2)10

4098

-49-

92

2150

.00

——

—22

3.73

2E

Her

bici

deIm

azap

yr81

334-

34-1

221

50.0

0—

——

223.

732

EH

erbi

cide

Imaz

aqui

ne81

335-

37-7

221

50.0

0—

——

223.

732

EH

erbi

cide

Imaz

etha

pyr

8133

5-77

-52

2150

.00

——

—22

3.73

2E

Her

bici

deIm

azos

ulfu

ron

1225

48-3

3-8

222

50.0

0—

——

234.

132

EH

erbi

cide

60 P. Mineau et al.

Tab

le3.

(Con

tinue

d).

Che

mic

alC

AS_

RN

NM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Use

Indo

le-3

-but

yric

acid

133-

32-4

1—

——

—24

9.71

1E

Plan

tgr

owth

regu

-la

tor

Ioxy

nil

1689

-83-

44

68.5

00.

103.

680.

5433

.28

—E

Her

bici

deIo

xyni

loc

tano

ate

3861

-47-

04

385.

420.

294.

190.

6419

.41

—E

Her

bici

deIs

opro

palin

3382

0-53

-01

——

——

232.

291

SH

erbi

cide

Isop

rotu

ron

3412

3-59

-63

4543

.04

——

—31

3.40

2E

Her

bici

deIs

ouro

n55

861-

78-4

1—

——

—23

2.29

1E

Her

bici

deIs

oxab

en82

558-

50-7

1—

——

—23

2.29

1E

Her

bici

deIs

oxaf

luto

le14

1112

-29-

01

——

——

207.

131

EH

erbi

cide

Kar

butil

ate

4849

-32-

51

——

——

579.

151

SH

erbi

cide

L-L

actic

acid

50-2

1-5

1—

——

—26

1.32

1U

Gro

wth

regu

lato

rL

acto

fen

7750

1-63

-41

——

——

291.

521

EH

erbi

cide

Len

acil

2164

-08-

11

——

——

—E

Her

bici

deL

inur

on33

0-55

-23

505.

00—

——

65.0

72

EH

erbi

cide

Mal

eic

hydr

azid

epo

tas-

5154

2-52

-01

——

——

216.

761

EPl

ant

grow

thsi

umsa

ltre

gula

tor

MC

PA94

-74-

62

377.

00—

——

39.2

32

EH

erbi

cide

MC

PA-t

hioe

thyl

2531

9-90

-81

——

——

289.

581

EH

erbi

cide

MC

PAdi

met

hyla

min

e20

39-4

6-5

1—

——

—55

.54

1U

Her

bici

desa

ltM

CPB

-sod

ium

6062

-26-

61

——

——

32.7

51

EH

erbi

cide

MC

PPIs

ooct

yles

ter

2847

3-03

-21

——

——

261.

321

UH

erbi

cide

Mec

opro

p70

85-1

9-0

1—

——

—82

.11

1E

Her

bici

deM

ecop

rop-

P16

484-

77-8

223

61.0

0—

——

151.

192

EH

erbi

cide

Mec

opro

pdi

met

hyla

min

e32

351-

70-5

1—

——

—69

.92

1U

Her

bici

desa

lt


Tab

le3.

(Con

tinue

d).

Che

mic

alC

AS_

RN

NM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Use

Mef

enpy

r-di

ethy

l13

5590

-91-

91

——

——

193.

051

EH

erbi

cide

safe

ner

Mef

luid

ide

5378

0-36

-21

——

——

353.

051

EPl

ant

grow

thre

gula

-to

r,he

rbic

ide

Mep

iqua

tch

lori

de24

307-

26-4

1—

——

—23

2.29

1E

Plan

tgr

owth

regu

lato

rM

etam

-sod

ium

6734

-80-

11

——

——

58.0

71

EH

erbi

cide

Met

amitr

on41

394-

05-2

210

27.7

2—

——

176.

821

EH

erbi

cide

Met

azac

hlor

6712

9-08

-22

2255

.00

——

—23

3.15

2E

Her

bici

deM

etha

benz

thia

zuro

n18

691-

97-9

1—

——

—96

.53

1E

Her

bici

deM

ethy

lars

onic

acid

124-

58-3

1—

——

—49

.36

1E

Her

bici

deM

etob

rom

uron

3060

-89-

72

4036

.00

——

—13

7.93

1E

Her

bici

deM

etol

achl

or51

218-

45-2

1—

——

—24

1.81

1E

Her

bici

deM

etos

ulam

1395

28-8

5-1

221

25.0

0—

——

220.

742

EH

erbi

cide

Met

ribu

zin

2108

7-64

-92

459.

86—

——

42.0

12

EH

erbi

cide

Met

sulf

uron

7951

0-48

-81

——

——

241.

811

EH

erbi

cide

Met

sulf

uron

-met

hyl

7422

3-64

-62

2510

.00

——

—26

1.19

2E

Her

bici

deM

olin

ate

2212

-67-

11

——

——

214.

161

EH

erbi

cide

Mon

olin

uron

1746

-81-

23

1690

.00

——

—18

1.27

2E

Her

bici

deM

onos

odiu

mm

ethy

l-21

63-8

0-6

1—

——

—96

.86

1E

Her

bici

dear

sona

teN

apro

pam

ide

1529

9-99

-71

——

——

78.0

31

EH

erbi

cide

Nap

thal

enea

cetic

acid

2122

-70-

52

2302

.92

——

—23

8.67

2E

Gro

wth

regu

lato

rN

icos

ulfu

ron

1119

91-0

9-4

220

00.0

0—

——

208.

122

EH

erbi

cide

Non

anoi

cac

id11

2-05

-01

——

——

261.

321

EH

erbi

cide

,pl

ant

grow

thre

gula

tor

Nor

flur

azon

2731

4-13

-22

1292

.15

——

—13

0.98

2E

Her

bici

de

62 P. Mineau et al.

Tab

le3.

(Con

tinue

d).

Che

mic

alC

AS_

RN

NM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Use

Orb

enca

rb34

622-

58-7

220

00.0

0—

——

208.

122

EH

erbi

cide

Ory

zalin

1904

4-88

-32

503.

35—

——

52.3

82

EH

erbi

cide

Oxa

bent

rini

l74

782-

23-3

325

00.0

0—

——

439.

202

EH

erbi

cide

safe

ner

Oxa

diaz

on19

666-

30-9

222

74.1

6—

——

192.

892

EH

erbi

cide

Oxa

sulf

uron

1446

51-0

6-9

1—

——

—21

6.76

1E

Her

bici

deO

xyfl

uorf

en42

874-

03-3

1—

——

—61

4.58

1E

Her

bici

dePa

clob

utra

zol

7673

8-62

-02

4953

.25

——

—19

3.05

1E

Plan

tgr

owth

regu

lato

rPa

raqu

atdi

chlo

ride

1910

-42-

54

243.

320.

124.

650.

3788

.50

—E

Her

bici

dePe

bula

te11

14-7

1-2

1—

——

—19

2.68

1E

Her

bici

dePe

ntox

azon

e11

0956

-75-

71

——

——

261.

321

EH

erbi

cide

Phen

med

ipha

m13

684-

63-4

331

07.2

3—

——

299.

931

EH

erbi

cide

Phen

yl-i

ndol

e-3-

8597

7-73

-71

——

——

216.

761

UH

erbi

cide

thio

buty

rate

Phth

alan

ilic

acid

4727

-29-

12

1070

0.00

——

—10

32.8

21

EPl

ant

grow

thre

gula

tor

Picl

oram

1918

-02-

13

2927

.91

——

—52

8.69

1E

Her

bici

dePi

clor

am-p

otas

sium

2545

-60-

03

2121

.32

——

—48

2.63

1E

Her

bici

dePi

clor

amT

IPA

6753

-47-

51

——

——

216.

761

UH

erbi

cide

Pret

ilach

lor

5121

8-49

-61

——

——

965.

251

EH

erbi

cide

Prim

isul

furo

n-m

ethy

l86

209-

51-0

221

50.0

0—

——

223.

732

EH

erbi

cide

Prod

iam

ine

2909

1-21

-21

——

——

261.

321

EH

erbi

cide

Proh

exad

ione

-cal

cium

1272

77-5

3-6

220

00.0

0—

——

208.

122

EPl

ant

grow

thre

gula

tor

Prom

eton

1610

-18-

01

——

——

262.

951

EH

erbi

cide

Prop

achl

or19

18-1

6-7

1—

——

—10

.57

1E

Her

bici

de


Tab

le3.

(Con

tinue

d).

Che

mic

alC

AS_

RN

NM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Use

Prop

anil

709-

98-8

1—

——

—23

.34

1E

Her

bici

dePr

opaq

uiza

fop

1114

79-0

5-1

220

99.0

0—

——

218.

182

EH

erbi

cide

Prop

ham

122-

42-9

1—

——

—19

3.05

1E

Her

bici

dePr

opis

ochl

or86

763-

47-5

213

44.0

0—

——

66.4

11

EH

erbi

cide

Prop

yzam

ide

2395

0-58

-52

5733

.87

——

—73

3.08

1E

Her

bici

dePr

osul

foca

rb52

888-

80-9

316

50.0

0—

——

232.

821

EH

erbi

cide

Pros

ulfu

ron

9412

5-34

-52

1622

.00

——

—15

9.59

2E

Her

bici

dePy

razo

sulf

uron

-eth

yl93

697-

74-6

1—

——

—26

1.32

1E

Her

bici

dePy

rida

te55

512-

33-9

1—

——

—16

0.51

1E

Her

bici

dePy

rim

inob

ac-m

ethy

l13

6191

-56-

51

——

——

232.

291

EH

erbi

cide

Pyri

thio

bac-

sodi

um12

3343

-16-

81

——

——

185.

711

EH

erbi

cide

Qui

nclo

rac

8408

7-01

-42

1974

.68

——

—20

5.46

2E

Her

bici

deQ

uinm

erac

9071

7-03

-61

——

——

232.

291

EH

erbi

cide

Qui

zalo

fop

7657

8-12

-62

2000

.00

——

—20

8.12

2E

Her

bici

deQ

uiza

lofo

p-et

hyl

7657

8-14

-82

2000

.00

——

—20

8.12

2U

Her

bici

deQ

uiza

lofo

p-P

-eth

yl10

0646

-51-

32

2000

.00

——

—20

8.12

2E

Her

bici

deQ

uiza

lofo

p-P

-tef

uryl

1197

38-0

6-6

221

50.0

0—

——

223.

732

EH

erbi

cide

Rim

sulf

uron

1229

31-4

8-0

216

23.1

6—

——

160.

752

EH

erbi

cide

Sebu

thyl

azin

e72

86-6

9-3

227

32.0

5—

——

334.

371

SH

erbi

cide

Seth

oxyd

im74

051-

80-2

237

55.0

0—

——

482.

631

EH

erbi

cide

Sidu

ron

1982

-49-

61

——

——

2584

.20

1E

Her

bici

deSi

maz

ine

122-

34-9

275

00.0

0—

——

965.

251

EH

erbi

cide

Sodi

umar

seni

te77

84-4

6-5

548

.00

1.05

−1.8

90.

140.

55—

UH

erbi

cide

,in

sect

icid

eSo

dium

dim

ethy

lars

inat

e12

4-65

-21

——

——

261.

321

EH

erbi

cide

Sulc

otri

one

9910

5-77

-82

1800

.00

——

—18

1.36

2E

Her

bici

deSu

lfen

traz

one

1228

36-3

5-5

1—

——

—26

1.32

1E

Her

bici

de

64 P. Mineau et al.

Tab

le3.

(Con

tinue

d).

Che

mic

alC

AS_

RN

NM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Use

Sulf

omet

uron

-met

hyl

7422

2-97

-21

——

——

481.

701

EH

erbi

cide

TC

A-s

odiu

m65

0-51

-11

——

——

216.

711

EH

erbi

cide

Teb

uthi

uron

3401

4-18

-12

750.

00—

——

73.5

82

EH

erbi

cide

Ter

baci

l59

02-5

1-2

1—

——

—26

2.37

1E

Her

bici

deT

erbu

thyl

azin

e59

15-4

1-3

212

92.1

5—

——

130.

982

EH

erbi

cide

Tet

rade

c-11

-en-

1-yl

2071

1-10

-81

——

——

249.

711

EH

erbi

cide

acet

ate

The

nylc

hlor

9649

1-05

-31

——

——

232.

291

EH

erbi

cide

Thi

azaf

luro

n25

366-

23-8

1—

——

—25

.58

1S

Her

bici

deT

hiaz

opyr

1177

18-6

0-2

1—

——

—22

2.18

1E

Her

bici

deT

hidi

azur

on51

707-

55-2

1—

——

—36

7.02

1E

Plan

tgr

owth

regu

lato

rT

hife

nsul

furo

n79

277-

67-1

1—

——

—24

1.81

1E

Her

bici

deT

hife

nsul

furo

n-m

ethy

l79

277-

27-3

225

10.0

0—

——

261.

192

EH

erbi

cide

Thi

oben

carb

2824

9-77

-61

——

——

225.

091

EH

erbi

cide

Tio

carb

azil

3675

6-79

-32

1000

0.00

——

—10

62.7

01

EH

erbi

cide

Tra

lkox

ydim

8782

0-88

-02

3725

.00

——

—29

0.94

1E

Her

bici

deT

ri-a

llate

2303

-17-

51

——

——

261.

441

EH

erbi

cide

Tri

apen

then

ol76

608-

88-3

1—

——

—48

2.63

1S

Plan

tgr

owth

regu

lato

rT

rias

ulfu

ron

8209

7-50

-52

2150

.00

——

—22

3.73

2E

Her

bici

deT

ribe

nuro

n10

6040

-48-

61

——

——

261.

321

EH

erbi

cide

Tri

benu

ron-

met

hyl

1012

00-4

8-0

1—

——

—26

1.32

1E

Her

bici

deT

ribu

fos

78-4

8-8

327

3.00

——

—51

.13

2E

Plan

tgr

owth

regu

lato

rT

ricl

opyr

BE

E64

700-

56-7

1—

——

—91

.76

1U

Her

bici

de


Tab

le3.

(Con

tinue

d).

Che

mic

alC

AS_

RN

NM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Use

Tri

flur

alin

1582

-09-

84

——

——

245.

551

EH

erbi

cide

Tri

flus

ulfu

ron

1359

90-2

9-3

222

50.0

0—

——

234.

132

EH

erbi

cide

Tri

flus

ulfu

ron-

met

hyl

1265

35-1

5-7

222

50.0

0—

——

234.

132

EH

erbi

cide

Tri

nexa

pac-

ethy

l95

266-

40-3

1—

——

—19

2.68

1E

Plan

tgr

owth

regu

lato

rU

nico

nazo

le83

657-

17-4

218

88.0

0—

——

191.

372

EPl

ant

grow

thre

gula

tor

Ver

nola

te19

29-7

7-7

234

45.0

0—

——

336.

222

EH

erbi

cide

CH

EM

ICA

LS

PR

IMA

RIL

YA

CT

IVE

AG

AIN

STV

ER

TE

BR

AT

ES

OR

TE

STE

DA

S

VE

RT

EB

RA

TE

CO

NT

RO

LA

GE

NT

S

[(3-

Am

ino-

2,4,

6-1

——

——

34.0

11

UR

oden

ticid

etr

ichl

oro

phen

yl)

met

hyle

ne]h

ydra

zide

Ben

zene

sulf

onic

acid

3594

4-73

-17

3.16

0.28

−0.3

70.

230.

61—

UR

oden

ticid

e(B

ay98

663)

1,3-

di-

(flu

oros

ulfo

nyl)

cycl

o-pe

ntan

e(P

HIL

LIP

S21

33)

1,3-

Prop

aned

iol,2

,2-

1271

2-28

-66

8.75

0.38

0.11

0.29

0.90

—U

Tes

ted

asve

rteb

rate

bis(

chlo

rom

ethy

l)-

agen

tsu

lfat

e(P

HIL

LIP

S26

05)

3-C

hlor

o-P

-tol

uidi

ne10

22.4

5−0

.02

2.88

0.97

0.30

—U

Avi

cide

4-A

min

opyr

idin

e50

4-24

-533

5.34

−0.0

11.

680.

921.

79—

UA

vici

de(A

vitr

ol)

66 P. Mineau et al.

Tab

le3.

(Con

tinue

d).

Che

mic

alC

AS_

RN

NM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Use

5-(p

-Chl

orop

heny

l)-

48.

521.

14−5

.20

0.05

0.04

—U

Rod

entic

ide

3,7,

10-t

rim

ethy

lsi

la-

tran

e(D

.M.

7537

)6-

Am

inon

icot

inam

ide

1—

——

—0.

771

UR

oden

ticid

eA

lpha

-chl

oral

ose

1587

9-93

-318

69.1

60.

263.

120.

0714

.05

—E

Rod

entic

ide

Ant

hraq

uino

ne84

-65-

13

2000

.00

——

—19

3.05

1E

Bir

dR

epel

lent

BA

YC

OE

3664

3945

7-24

-49

5.62

0.28

0.34

0.15

1.11

—U

Tes

ted

asve

rteb

rate

agen

tB

AY

CO

E36

7539

457-

25-5

92.

370.

69−2

.03

0.00

0.38

—U

Tes

ted

asve

rteb

rate

agen

tB

rodi

faco

um56

073-

10-0

89.

1−0

.12

2.49

0.77

0.81

—E

Rod

entic

ide

Bro

mad

iolo

ne28

772-

56-7

267

6.58

——

—53

.26

2E

Rod

entic

ide

Bro

met

halin

6333

3-35

-71

——

——

0.83

1E

Rod

entic

ide

Chl

orop

haci

none

3691

-35-

86

25.4

5−1

.53

13.2

10.

013.

32—

ER

oden

ticid

eC

hole

calc

ifer

ol(v

itam

in67

-97-

01

——

——

192.

681

ER

oden

ticid

eD

3)C

oum

atet

raly

l58

36-2

9-3

337

.50

——

—19

3.05

1E

Rod

entic

ide

Dif

enac

oum

5607

3-07

-51

——

——

9.32

1E

Rod

entic

ide

Dif

ethi

alon

e10

4653

-34-

13

0.87

——

—0.

312

ER

oden

ticid

eFl

ocou

maf

en90

035-

08-8

451

.25

−0.6

46.

450.

500.

07—

ER

oden

ticid

eFl

uoro

acet

amid

e64

0-19

-72

9.46

——

—1.

421

ER

oden

ticid

eM

ethy

lan

thra

linat

e13

4-20

-32

1216

.17

——

—82

.26

2U

Rep

elle

ntM

etom

idat

e53

77-2

0-8

1174

.97

0.11

3.91

0.52

24.0

5—

UT

este

das

vert

ebra

teag

ent

Met

omid

ate

HC

l35

944-

74-2

856

.20

0.15

3.24

0.03

26.8

5—

UT

este

das

vert

ebra

teag

ent


Tab

le3.

(Con

tinue

d).

Che

mic

alC

AS_

RN

NM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Use

Pent

obar

bita

l-so

dium

57-3

3-0

810

7.66

0.00

4.71

0.97

48.9

3—

USo

pori

fic

Phen

cycl

idin

eH

Cl

956-

90-1

1375

.00

0.14

3.63

0.57

9.32

—U

Tes

ted

asve

rteb

rate

agen

tPh

osac

etim

4104

-14-

712

16.6

10.

192.

000.

680.

41—

SR

oden

ticid

ePi

ndon

e83

-26-

11

——

——

27.9

91

ER

oden

ticid

ePo

lyet

hoxy

late

dal

ipha

tic68

131-

40-8

320

06.0

0—

——

227.

851

UR

epel

lent

alco

hols

Scill

iros

ide

507-

60-8

1—

——

—1.

351

SR

oden

ticid

eSo

dium

fluo

roac

etat

e62

-74-

855

5.46

0.18

0.82

0.01

0.85

—E

Rod

entic

ide

(com

poun

d10

80)

Sodi

umw

afar

in12

9-06

012

1310

.50

——

—11

5.97

2U

Rod

entic

ide

Star

licid

e77

45-8

9-3

315.

62−0

.08

2.82

0.73

0.43

—U

Avi

cide

Stry

chni

ne57

-24-

917

6.00

0.15

1.47

0.36

1.04

—E

Rod

entic

ide

Ter

rtia

rybu

tyls

ulfe

nyl-

di-

1—

——

—10

8.01

1U

Rod

entic

ide

met

hyl

dith

ioca

rbam

ate

TFM

(4-N

itro-

3-[t

rifl

uo-

88-3

0-2

1—

——

—44

.12

1U

Lam

pric

ide

rom

ethy

l]ph

enol

)T

halli

umsu

lfat

e74

46-1

8-6

440

.48

0.10

3.10

0.70

8.93

—U

Rod

entic

ide

War

fari

n81

-81-

23

970.

57—

——

120.

212

ER

oden

ticid

eZ

inc

phos

phid

e13

14-8

4-7

744

.85

−0.3

45.

400.

155.

45—

ER

oden

ticid

e

68 P. Mineau et al.

Tab

le3.

(Con

tinue

d).

Che

mic

alC

AS_

RN

NM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FSt

atus

Use

CH

EM

ICA

LS

WIT

HU

NK

NO

WN

SPE

CT

RU

M

OF

AC

TIV

ITY

Bro

fenp

rox

219

42.0

0—

——

201.

992

EU

nkno

wn

Chl

oret

azat

e2

2150

.00

——

—20

7.53

1U

Unk

now

nC

hlor

omet

hylm

ercu

ry11

5-09

-31

——

——

1.74

1U

Unk

now

nC

loxy

nil-

sodi

um1

——

——

3.72

1U

Unk

now

nD

ibro

moi

trilo

prop

i-2

184.

11—

——

18.9

62

UU

nkno

wn

onam

ide

Dic

hlor

prop

(rac

emic

7547

-66-

21

——

——

48.6

51

EU

nkno

wn

acid

)Fl

umeq

uine

4283

5-25

-62

2500

.00

——

—96

.53

1U

Unk

now

nFl

upri

mid

ol1

——

——

232.

291

UU

nkno

wn

Hex

achl

orbe

nzol

1—

——

—48

2.63

1U

Unk

now

nPr

open

amid

e79

-06-

11

——

——

19.2

61

UU

nkno

wn


Tab

le4.

Lis

tof

pest

icid

escu

rren

tlyin

use

with

HD

5(50

%)

belo

w1

mg/

kg.

Com

poun

dC

AS_

RN

nM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FU

sePa

ttern

CH

OL

INE

STE

RA

SE-I

NH

IBIT

ING

INSE

CT

ICID

ES

Thi

ofan

ox39

196-

18-4

31.

20na

nana

0.12

1T

erbu

fos

1307

1-79

-95

9.48

1.02

77−1

.532

80.

320.

16—

Prop

apho

s72

92-1

6-2

1—

nana

na0.

181

Car

bofu

ran

1563

-66-

218

1.65

0.04

230.

257

0.82

0.21

—Ph

orat

e29

8-02

-28

7.06

0.18

170.

4833

0.65

0.34

—Pa

rath

ion

56-3

8-2

195.

620.

0797

1.22

280.

760.

40—

Qui

nalp

hos

1359

3-03

-82

20.6

5na

nana

0.42

1D

icro

toph

os14

1-66

-215

2.83

0.17

870.

3645

0.32

0.42

—M

onoc

roto

phos

6923

-22-

423

2.51

−0.0

312

1.02

180.

790.

42—

Ald

icar

b11

6-06

-310

2.82

0.29

55−0

.655

90.

120.

43—

Fena

mip

hos

2222

4-92

-65

1.10

−0.0

863

0.54

440.

660.

43—

Isof

enph

os25

311-

71-1

610

.96

0.09

942.

7255

0.86

0.44

—Fa

mph

ur52

-85-

73

2.70

nana

na0.

451

Isaz

ofos

4250

9-80

-83

11.1

0na

nana

0.51

2E

PN21

04-6

4-5

146.

430.

3624

0.60

40.

330.

53—

Dia

zino

n33

3-41

-514

5.25

−0.2

608

3.58

830.

290.

59—

70 P. Mineau et al.

Tab

le4.

(Con

tinue

d).

Com

poun

dC

AS_

RN

nM

edia

nSl

ope

Inte

rcep

tp

HD

5(50

%)

#_E

FU

sePa

ttern

Cou

map

hos

56-7

2-4

126.

780.

2179

0.75

790.

360.

69—

Mev

inph

os77

86-3

4-7

133.

800.

0254

0.94

090.

880.

70—

Ben

dioc

arb

2278

1-23

-34

16.2

4−0

.947

58.

3724

0.37

0.72

—O

xam

yl23

135-

22-0

34.

18na

nana

0.78

2D

isul

foto

n29

8-04

-47

11.9

00.

2019

1.21

040.

600.

81—

Cya

noph

os26

36-2

6-2

1—

nana

na0.

831

Fent

hion

55-3

8-9

235.

620.

2581

0.47

840.

070.

87—

Tri

azam

ate

1121

43-8

2-5

1—

——

—0.

931

NO

NC

HO

LIN

EST

ER

ASE

INH

IBIT

OR

S

Phen

ylm

ercu

ric

acet

ate

(PM

A)

62-3

8-4

414

5.86

2.19

−9.4

30.

160.

01—

Fung

icid

eFl

ocou

maf

en90

035-

08-8

451

.25

−0.6

46.

450.

500.

07—

Rod

entic

ide

Chl

orda

ne57

-74-

94

62.2

81.

00−1

.63

0.44

0.09

—In

sect

icid

eD

ifet

hial

one

1046

53-3

4-1

30.

87—

——

0.31

2R

oden

ticid

eB

ensu

ltap

1760

6-31

-44

192.

001.

35−2

.11

0.23

0.41

—In

sect

icid

eC

hlor

fena

pyr

1224

53-7

3-0

28.

30—

——

0.56

1In

sect

icid

e,ac

aric

ide

Fenf

uram

2469

1-80

-31

——

——

0.69

1Fu

ngic

ide

Bro

difa

coum

5607

3-10

-08

9.1

−0.1

22.

490.

770.

81—

Rod

entic

ide

Bro

met

halin

6333

3-35

-71

——

——

0.83

1R

oden

ticid

eSo

dium

fluo

race

tate

62-7

4-8

555.

460.

180.

820.

010.

85—

Rod

entic

ide


III. Results and DiscussionCholinesterase-inhibiting pesticides are grouped together in Table 2; all othersare in Table 3. Pesticides are ordered alphabetically by their common chemicalnames except when a trade name only is available. A few trade names andsynonyms are given to facilitate identification. All names follow the 11th Edi-tion of the Pesticide Manual (Tomlin 1997) supplemented by the Nanogen In-dex (Packer 1975; Walker 1989). Tomlin (1997) was also used to determinewhether any given product is still in commerce or is thought to no longer bemarketed (superseded entries).

One advantage of this process is that it allows us to identify which pesticidescurrently in use are most toxic to birds and to start using some of the betterknown products for which field studies or incident records exist as possible“benchmarks” for equally toxic but more poorly known chemicals. From Tables2 and 3, we arbitrarily selected those pesticides with an HD5(50%) less than 1mg/kg (Table 4). Of the 34 pesticides identified, 24 are cholinesterase-inhibitinginsecticides. Of the remaining 10 products, 2 are insecticides including the verynew pyrrole insecticide chlorfenapyr, 2 are fungicides, and the rest are rodenti-cides, including 3 of the second-generation coumarin anticoagulant products.Interestingly, the cholinesterase inhibitor thought to be the most toxic to birdsis thiofanox (trade name Dacamox). According to the Pesticide Manual (Tomlin1997), this granular and seed treatment insecticide is of only moderate toxicityto birds with LD50 values of 109 mg/kg and 43 mg/kg in the mallard and thebobwhite, respectively. However, this is one of those few cases where this datasource is in error. The values cited by Tomlin (1997) are in fact dietary LC50

values; the true acute toxicity values are lower by 1.5 to 2 orders of magnitude.In an early review of bird-kill incidents by Grue and colleagues (1983), these

authors found that most incidents could be explained on the basis of pesticidetoxicity and the extent to which the pesticides were used in U.S. agriculture. Ina recent review of raptor incidents (Mineau et al. 1999), the higher proportionof kills resulting from labeled uses of pesticides in Canada and the U.S. relativeto the U.K. was determined to be a result of the more permissive use of pesti-cides highly toxic to birds in North America. Certainly, those individuals famil-iar with pesticide bird-kill incidents throughout the world will recognize a num-ber of compounds from Table 4 that keep coming back with depressingregularity. We believe that we have laid the groundwork for a more comprehen-sive review of those pesticides most hazardous to wild birds and for a faircomparison between older products and newer replacements. Recognizing thatthere are very few uses of pesticides that do not result in exposure to birds, weurge regulatory authorities to consider avian acute toxicity more closely beforemaking regulatory decisions.

AcknowledgmentsWe thank all the individuals, companies, and institutions who volunteered datafor this effort. We also thank Charles Benbrook, and the Consumers Union aswell as the W. Alton Jones Foundation for the support necessary to perform the

72 P. Mineau et al.

time-consuming vetting of the database. A number of individuals demonstratedan infinite degree of patience with this task, particularly Rob Kriz and LynnSchirml. We are grateful to Tom Aldenberg and Rick Bennett for comments onan earlier draft of this paper. Over the years, we benefitted from many a discus-sion with colleagues from around the world on this subject and, without beingable to thank them all, would like to acknowledge at least the OECD, SETAC,and the USEPA for holding many of the forums where these discussions tookplace.

Appendix 1. Comparison of the equations for the expected value and variance from asample with and without a covariate for bird weight. (A Fortran program to compute anHD5 with body weight as a covariate is available by writing to the authors.)

Without covariate With covariate

Estimated expected value y = ∑ yi/n yo = y + b(xo − x)

Variance of estimatedσ2 � 1n � σ2 � 1n + (xo − x)2

∑ (xi − x)2 �expected value

Variance estimate (s2) ∑ (yi − y)2/(n − 1) ∑ (yi − yi)2/(n −2)

Degrees of freedom n − 1 n − 2

b = ∑ yi(xi − x)

∑ (xi − x)2

yi = y + b(xi − x)

x0 is the logarithm of the weight of the bird which you want to protecty0 is the expected value of the logarithm of the LD50 for the species of weight x0

σ is the standard deviation of the original log(LD50) data setσ2 is the unknown true population variance of the original log(LD50) data sets2 is the estimator of the variance

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Manuscript received March 17, 2000; accepted July 10, 2000.