Dose, Drift, and Non-Target Organisms - ACS ... - ACS Publications

Chapter 3

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Dose, Drift, and Non-Target Organisms Jens C. Streibig*,1 and Jerry M. Green2 1Department of Plant and Environmental Sciences, University of Copenhagen, Hoejbakkegaard Allé 13, DK-2630 Taastrup, Copenhagen, Denmark 2Green Ways Consulting LLC, Landenberg, Pennsylvania 19350, United States *E-mail: [email protected].

This chapter deals with dose-response models to describe the relationship between a dose and its effect on target and non-target organisms, often after the dose has been diluted by drift. We define pesticide drift and describe the endeavor to link effects to an arbitrary point outside the field. Lastly, we analyze data from published papers on non-target plants to determine how they contribute to understand the biological effect of herbicide drift. The research bottleneck is in the drift model and the way non-target plants are affected. The variation in determining EDx (Effective Dose at a response level x) among species is often so large that sensitivity of species cannot be unraveled. In particular, when herbicides with large potency differences are included in a study, the effect of herbicides usually stands out, while other factors are not significant. An aspect currently in the news is the novel use of auxin herbicides on genetically modified auxin herbicide-tolerant crops and the problems that can occur when large areas are sprayed nearby very sensitive non-target plants. One additional aspect is that when using “wild species” as test plants, EDx levels will vary much more than when using crops and weed species that are genetically more uniform.

© 2017 American Chemical Society Duke et al.; Pesticide Dose: Effects on the Environment and Target and Non-Target Organisms ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


Introduction The most common reason for off-target pesticide movement is the drift of very small or fine spray particles caused by wind and poorly calibrated application equipment, but drift can also occur because of chemical volatility. Environmental conditions can influence both types of off-target movement and must always be taken into account when applying pesticides. The effect of a certain dose at origin of spray is the basis for a diluted dose at a given drift distance on non-target plants (Figure 1). It requires knowledge of spraying technique, the wind direction, the behavior of the drift fog and the action of the effective dilution on non-target plant at a distance of interest. According to Figure 1, the horizontal displacement of a dose is a function of the travel distance. Eventually, at some distances the effect of a diluted dose would be so small that it would reach the No Observable Effect Level (NOEL) or Lowest Observable Effect Level (LOEL).

Figure 1. A presumed schematic relationship between a spraying at the origin (dotted line) and the dilutions of the dose-response curve caused by drift. Arrows show wind direction. This chapter deals with the dose-response models to describe the relationship between a dose and its effect on target and non-target organisms. Subsequently, we define pesticide drift and link the effect to an arbitrary point outside the field. Lastly, we review published papers on non-target plants to illustrate how they are affected by herbicide dose. 26 Duke et al.; Pesticide Dose: Effects on the Environment and Target and Non-Target Organisms ACS Symposium Series; American Chemical Society: Washington, DC, 2017.

Dose-Responses


Pesticides are designed to control insects, fungi, and weeds. The level of effect or potency can be defined by ED50, the dose that is required to affect the organism 50% relative to a maximum value. In principle, at very high doses, herbicides will severely injure crops, weeds, and non-target plants, while small doses can have no effect (1). The old axiom of Paracelsus does apply:

At recommended doses, insecticides and fungicides generally do not directly affect plants. Basic studies of susceptibility/tolerance require knowledge of the relationship between dose and plant response from no effect levels at low doses to complete kill at high doses. In this context, the study of herbicides with doseresponse curves is the very same as in general toxicology and pharmacology. General dose response curves are shown in Figure 2. With a wide dose-range, the response goes from zero effect to complete kill. Mathematically the response, y, is a function of the dose, x, described by the log-logistic curve below:

Figure 2. Log-logistic dose-response curves. The continuous biomass response has no finite upper limit whilst that of quantal response could be alive/dead, affected/not affected etc and has a range between 0 to 1. 27 Duke et al.; Pesticide Dose: Effects on the Environment and Target and Non-Target Organisms ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


For continuous responses (e.g. biomass, seed yield, height, enzyme) the upper limit, d, is a parameter close to the untreated control (Figure 2). The lower limit at high doses, c, can be different from zero. The ED50 is the dose that halves the responses between the upper and lower limit. The log-logistic model is just one of numerous models that can be used (1, 2). With quantal responses that are defined by either dead/alive or affected/ nonaffected, the upper limit cannot exceed all organisms being affected, which make the proportion 1.0, and the lower limit no organisms affected (usually defined as zero). Dose-response can be described by two parameters, relative slope, b, and Lethal Dose (LD50) (Figure 2). This kind of data are denoted quantal response and requires a regression fit by assuming a binomial distribution of data (3, 4). Working with quantal response data makes interpretation easy, because the restriction of the response range is between 0 and 1. Quantal responses are often converted to response as a percent of control. It may not affect the parameter estimates, but may have a huge effect on the standard errors of parameters, say LD50; therefore, this scaling should be discouraged. There are cases where the curves in Figure 2 do not apply; if very low doses increase, say biomass relative to the untreated control. This phenomenon, called hormesis, can be difficult to reproduce when experiments are done independently (5). The log-logistic dose-response is a symmetric curve around the inflection point, which coincides with the ED50. Other curves could be asymmetric (1), where the ED50 is not a “natural” parameter, but can be derived from the curve fit (2). In toxicology, the World Health Organization classifies a compound by its LD50 on the basis of mortality studies of feeding rats, guinea pigs or hamsters (3, 6). One of the reasons for choosing the 50% level is, it is the most precise estimate of any LDx level (Figure 2) mortality or seedling demise; and it is perhaps a “best way” of identifying resistance, compared to biomass. However, in ecotoxicology and non-target species identification, ED50 or LD50 is not the best response level to operate on, as the ED10 or LD10 is much more informative (Figure 2), but the variation is wide compared to LD50/ED50, as seen in Figure 3. Whatever the analytical problems, the fitting of a dose-response curve should be the first step when defining drift hazards.

Experinental Design Knowing the activity of a herbicide is helpful when designing an experiment. Box et al. (6) stated, “The choice of experimental design depends on the current knowledge hypothesis. The chosen design should explore the shadowed region of the present knowledge, whose illumination is currently believed to be important to progress.” The effect of experimental error can be reduced by proper experimental design. We want to have small standard errors that give narrow confidence intervals for regression parameters and steep relative slopes of the curves. The higher the relative slope, b, the more precise would be the ED50. 28 Duke et al.; Pesticide Dose: Effects on the Environment and Target and Non-Target Organisms ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


Figure 3. Dose-response fit and 95% confidence interval for a four parameter log-logistic model, The most precise effective dose level is ED50 as the confidence band is narrower than at other effective dose-levels.

In bioassay, the slope, b, of a dose-response curve can be more complex. Knowing the mode of action of a herbicide can help. For example ALS inhibitors often have shallow slopes whilst photosystem I and photosystem II inhibitors have steep slopes. Contact herbicides may have more variable slopes among otherwise similar experiments (see Table 2 in the section Non-Target Plants). Fitting a four parameter log-logistic regression model to continuous data requires at least four doses (7). Two doses to characterize the middle part of the curve to estimate ED50 and relative slope, b. The other two doses are necessary to estimate the upper limit, d, and the lower limit, c. However, to get proper regression fit we need more than four doses. With biomass assays or other complex endpoints, assay to assay 29 Duke et al.; Pesticide Dose: Effects on the Environment and Target and Non-Target Organisms ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


variation in ED50 sometimes make it virtually impossible to delimit dose range within the steepest part of the curve with so few doses. Intuitively, the number of doses for a single response curve should be around six to eight. The most straight forward way to do an assay is to find the ED50 in preliminary experiments and then reduce and increase the doses on either side (Table 1).

Table 1. Four Herbicides Are Being Tested in an Assay. Herbicide # 1 and #2 Have Approximately the Same ED50 while Herbicide #3 Is More Potent and Herbicide #4 Is Even More Potent than Herbicide #3. The Knowledge of ED50 Could Be Based upon Either Preliminary Experiments or Literature (3) Herbicide Dose

1

2

3

4

0

X

X

X

X

1/236

X

1/128

X

X

1/64

X

X

X

X

1/32

X

X

X

X

1/16

X

X

X

X

1/8

X

X

X

X

¼

X

X

X

½

X

X

X

1

X

X

By distributing the doses within the respective herbicides, the ED50 would be determined with high precision. In Table 1, the doses are being increased or diluted by a factor 2 giving the same distance between doses. However, this rather straight forward way of choosing dose-ranges on the basis of assumed ED50 does not apply when it comes to ED10 or ED90, because the variation of the upper and lower part of the regression is larger than the one at ED50 (Figure 3).

Drift Spray drift is defined as the movement of pesticide dust or droplets through the air at the time of application or soon after, to any site other than the area intended. Pesticide drift is most commonly the result of very small or fine droplets produced by spray nozzles moving off-target by wind. Other factors including chemical volatility can also cause pesticide drift under certain conditions. Pesticide drift can contaminate non-target areas and negatively affect human health, the environment and contaminate organic produce. With herbicides, the 30 Duke et al.; Pesticide Dose: Effects on the Environment and Target and Non-Target Organisms ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


most common drift problem is contamination and subsequent injury to non-target plants, which include endangered species, crops, and garden plants. The following factors influence chemical spray drift: 1. 2. 3. 4. 5. 6. 7. 8.

Droplet size (nozzle choice) Boom height Speed of application Air-assistance, shielding Dose Crop development, adjacent crops, shelter buffer zones Wind speed Temperature and humidity

The first five factors relate to the technique. Droplet size is the most influential factor and is influenced by the choice of nozzle, spray pressure and spray mixture. Drift is increased when speed is increased due to turbulence. Concerning biological efficacy, a neutral or positive influence of air-assistance is seen dependent on the type of application. There are a number of ways to mitigate spray drift including adjusting spray application parameters, using anti-drift nozzles and drift mitigation adjuvants. Regulatory agencies such as the U.S. Environmental Protection Agency (EPA) are getting more involved in mitigating spray drift. The EPA is mandating that new herbicide labels including the use of 2,4-D and dicamba in auxin herbicide-tolerant crops have very specific application requirements and EPA has initiated a star rating program to evaluate and rank spray Drift Reduction Technologies (DRT). The star rating DRT program is already being used in the United Kingdom (8). The EPA and the public are increasingly intolerant of any herbicide contamination on any non-target organism. Historically, visual plant damage was the most common way to identify herbicide spray drift with follow-up chemical detection in the laboratory to confirm presence of unwanted pesticides. Chemical analysis techniques continue to improve and are now often more sensitive than are bioassays. Depending of the agricultural area, drift becomes a problem when the arable land makes up a large proportion of the landmass. In Denmark, 65% of the area is cultivated, and drift is a serious problem for sensitive crops or habitats that are receiving pesticide load on an annual basis. This is problematic for organic growers, scattered within the conventional farming area. Auxin herbicides are already used on over 200 million ha globally, but the use of auxin herbicide-resistant crops will likely greatly expand their use in new application scenarios more vulnerable to causing drift problems. The increased use of the auxinic herbicides dicamba and 2,4-D in their respective resistant crops has the potential of injuring other non-target crops and reducing biodiversity in field margins and nearby non-crop habitat if unmanaged (9). Off-target movement of auxin herbicides can occur via spray particle and vapor drift. Particle drift is usually more problematic and should be managed with application techniques, drift control adjuvants, and correct decisions as to when, where, and how to apply. 31 Duke et al.; Pesticide Dose: Effects on the Environment and Target and Non-Target Organisms ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


Particular troublesome for auxin herbicides would be drift onto highly sensitive crops such as soybeans (Glycine max L. Merr), cotton (Gossypium hirsutum L.), and grapes (Vitis vinifera L.). Interestingly, 2,4-D is safer than dicamba on soybeans, and dicamba is safer than 2,4-D on cotton (10). As little as 0.01% of labeled rate of dicamba can injure soybeans, and 0.001% of labeled rate of 2,4-D butyl ester can injure tomatoes (Lycopersicon esculentum Mill.) and lettuce (Lactuca sativa L.) (11). Of more concern to environmentalists will be any drift onto potentially more sensitive species that are considered endangered or threatened (12). New formulations with less volatile salts and drift control adjuvants will help to reduce off-target movement and could even help to reinvent auxin herbicide technology (13, 14). For this technology to be successful, growers will need to follow label directions and make correct decisions on when and how to apply, based on temperature, wind velocity, droplet size, release height, buffer zones and drift reduction technologies. The illegal use of dicamba and widespread drift complains in 2016 casts serious doubt that growers will follow such directions (15).

Figure 4. Data from Ganzelmeiriers (1995) being subject to the model y= A*xB (19), It is virtually the same as given by De Jong who used inverse polynomials (18). 32 Duke et al.; Pesticide Dose: Effects on the Environment and Target and Non-Target Organisms ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


Numerous models have been suggested to predict the effect and extend of pesticide drifts. One of the classical models in Europe has been developed for various crops. Ganzelmeire (cited by Rautmann et al. (16, 17)) has collected data and developed models to predict deposition as function of distances given the wind speed. This has been somewhat extended by de Jong (18). A closer look at the models seems as if the models are virtually identical. The graph in Figure 4 shows a rather good relationship between observed and predicted percent drift. EFSA (European Food Safety Authority) has given model parameters for crops including vegetable below the height of 50 cm. On the basis of data and model fits, recommendations were given, e.g. 75% reduction was already achieved at 0.7 -3 m. Taking into consideration the confidence intervals of the regression the safety margin is in fact close to 3 m.

Non-Target Plants According to the US EPA: “Non-target organism is any organism for which the pesticide was not intended to control. On pesticide labels the intended target organism must be specified. When pesticides can affect other than intended targets, warning must appear of possible contamination of these non-target organism.”. EPA makes rather detailed description of how to test the ecotoxicology of nontarget plants, and suggests species to be used, which include off field species, as well as weed and crop species (20). In contrast to EPA, EFSA has not gone into that much detail but have recently published an opinion paper on “the state of the science on risk assessment of plant protection products for Non-Target Terrestrial Plants (NTTPs) (21). This paper tries defining off-field, in-field, and endangered species. Specific Protection Goals (SPGs) are included and closely linked to ecosystem services and functions, and include maintaining provision of water regulation, food web support, aesthetic values, genetic resources and biodiversity. However, within fields there are rather few plant species competing with crops that are considered non-target for farmers, and, therefore, the EFSA opinion paper is not very operational in practice because too many SPGs are included. The paper also includes almost the same suggestions as to analysis of data and how to present the results, as do the documents from EPA. The Organisation for Economic Co-operation and Development (OECD) also has defined non-target plants. In contrast to EPA and EFSA, non-target plants are outside the target area for crop protection products. Because of this explicitly expressed limitation of what constitutes a non-target plant, it seems operational in practice. All three institutions operate with NOEC and LOEC that often are derived from ANOVA. There are also detailed description of species to be used and the statistical model used. The definition of non-target plants depends upon where they grow. Sometime non-target plants in natural habitats are acting as weeds on arable land and the same applies to specific weeds, because in some situations they are crops. However, EPA defines non-target organism as any organism for which the pesticide was not intended to control. A literature search for the effect of herbicides on non-target 33 Duke et al.; Pesticide Dose: Effects on the Environment and Target and Non-Target Organisms ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


plants yielded about 300 references (August 2016, ISI Web of Knowledge). The major part of those was on aquatic plant species. The desired control levels of weeds in the field are currently beyond ED90 and, as mentioned before, the uncertainty at this response level is rather high. In some special cases such as cultivation of GMO crops there is a decided but unapproachable zero weed tolerance, ED100. The same applies for say ED10, which is an effective dose but more variable than is the ED50. Researchers can create conditions in greenhouse and growth chamber bioassays that make herbicides less potent, but usually indoor studies will show more sensitive response levels when carried out under field conditions. Pesticide companies strive at a recommended rate to ensure “necessary” control levels on target pests under a range of conditions so as to be protected against the uncertainties of stage of development of weeds, weather and temperature. Non-target plants growing adjacent to the target area are not expected to ever receive the full recommended rate (Figure 4).

Log Logistic Regression Slope and EDx The majority of papers for the last 10 to15 years have used dose-response analyses to give EDx-levels and sometimes together with associated standard errors or confidence intervals. On the basis of six papers on terrestrial species, the selection criteria were that EDx was associated with standard error or confidence interval; and the regression model was defined with documentation of experimental design. The stage of development at spraying, duration of the experiment either in days or at defined stage of development at harvest time must be stated, as well as defined environment, greenhouse or field. Lastly, the research questions must be explicitly explained. The table data, extracted from the six papers, were subjected to weighted ANOVA of EDx values by using either the standard errors of the EDx or deriving the standard error from 95% confidence intervals. The ANOVA of the table data from the papers were only considered significant if the p-value was below 1%. It was a precaution of not reporting significance by chance. All papers, except one, have listed ED50. In some instances one can adjust to ED10 by using the assumed relative slope of the log-logistic curves on the basis of the mode of action of the herbicides. Table 2 shows ranges of relative regression slopes from various herbicide bioassays with biomass as endpoints. There is no systematic published analysis of the distribution of the relative slopes around the ED50. However, there is some research in the differences among relative slopes in the Danish Crop Protection Online (CPO) (22, 23). The relative slopes in CPO range between 1 and 3. This was based upon the log-logistic model defined by Finney (4), whilst we use the log-logistic model defined by Ritz et al. (24) It means the CPO slopes have to be multiplied with 2 and the relative slopes would be approximately 1-6. In our own lab we have experienced relative slopes below 1, particularly for ALS inhibitors. If the relative slope exceeds 8 it indicates that the responses may not be optimally distributed. 34 Duke et al.; Pesticide Dose: Effects on the Environment and Target and Non-Target Organisms ACS Symposium Series; American Chemical Society: Washington, DC, 2017.

Table 2. Experience of Range of Relative Slope of Log-Logistic Dose-Response Curve of Biomass for Various Herbicide Mode of Action Groups. For the Two Photosystem Inhibitors the Slopes Are General Steeper for Contact than for Systemic Herbicides, but Uncertainty for Contact Herbicides Is Much Wider When Repeating Experiments.


Mode of action

Relative slope, b

Photosystem I and II

4-6

ALS-inhibitors

1-2

Auxins

1-3

Glyphosate

2-4

Irrespective of estimates of relative slope, b, the upper limit, d, and lower limit, c, of response curves are just a matter of scale range, when comparing various EDx given in the literature (25). On the basis of the slope of the curves and the specific ED50, defined as 1.00, it is possible to predict any other EDx values as shown in Figure 5. The shallower the curves the larger difference between EDx. The slopes, as mentioned earlier, are in fact often the most variable due to environmental variation (26). ANOVA is still being used to find the species sensitivity to herbicides (27–29). The problem with ANOVA is that it does not capture the dose-response relationship so one only can determine a NOEL at pre-defined dose in the ANOVA. It means that the NOEL cannot be compared among papers when different preset doses have been used.

Data Analysis from Published Papers Fletcher et al. (28) studied the effect of chlorsulfuron on non-target crops. The results were analyzed by ANOVA and the lowest dose yielding responses significantly lower than the control was identified. It means that the pre-defined doses, next to the significant dose is determining NOEL. The experiments were done in the greenhouse, and the canola and soybeans were sprayed at various stages of development with chlorsulfuron. By using dose-response curves, the differences at say ED50 and at any other EDx were determined by the entire dose-response curve and not only at a preset dose relative to an untreated control. Obviously, the pre-flower stage of development would, by visual estimate, stand out as significantly different from the rest, but was not in Figure 6, because observations at the lower part of the curve were missing. The use of the ED50 is a well-defined response level and even though it does not give the NOEL; ED25 or ED10 would be more appropriate. But no ED10s were significantly different from zero. The ED10 were in both instances about 0.03-0.05 g ai/ha. The ANOVA NOEL was around 0.1g ai/ha. The ED25 values for canola were all significantly different from zero, but not significantly different from each other. 35 Duke et al.; Pesticide Dose: Effects on the Environment and Target and Non-Target Organisms ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


Figure 5. The influence of the relative slope, b, on the differences in EDx values, when the ED50 is 1.00. The most common relative slopes are between 2 to 6.

The purpose of herbicide efficacy trials is to demonstrate effective control on a wide range of weed species, i.e. the effect level observed in the trials with the recommended rate tends to be close to 100%. Inclusion of the two lower dosages (25 and 50% of the recommended dosage) makes it possible to classify weed species according to sensitivity. Even the lowest rate very often produces effects in the range between 75 and 100%. In Denmark, efficacy experiments are carried out in different years and at different locations, i.e. experimental conditions can vary significantly. Consequently, the standard errors of the ED90 values make comparison among herbicide and weeds difficult. However, due to the high number of efficacy trials the results can give indications of differences in sensitivity of weeds and non-target plants between and within plant families (22). 36 Duke et al.; Pesticide Dose: Effects on the Environment and Target and Non-Target Organisms ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


Figure 6. Canola sprayed at various stages of development. Neither ED50 nor ED10 were significant different form each other. For Soybean ED50 for Preflower and Preflower +13d were significantly different form each other. At ED10 there were no difference among stage of development. Data from Fletcher et al. (28) Strandberg et al. (30) presented bioassay results of 38 annual weed species that occurred in one or more of the Danish field efficacy trials. ED90 values could be estimated for 5 and 10 weed species, respectively, for metsulfuron-methyl and mecoprop-P applied to winter cereals in the autumn or in the spring (Tables 4.1 and 4.2 (30)). An ANOVA of ED90 for the two herbicides in Figure 7 shows that neither species nor cereal type were significant different, only the effect of herbicides was significant (Figure 7). It is notable that the ED90 seems to be realistic from an efficacy point of view. For metsulfuron-methyl the recommend rate was well above the ED90. For mecoprop-P the recommended rate was not enough to keep the efficacy control level above 90%. In greenhouse and growth chambers, the EDx values are usually smaller than in the field, but greenhouse and growth chamber conditions can be managed to make species less responsive. When the change in potency is separated in different locations, there is usually a correlation (31, 32). Strandberg et al. (30) also published ED50 values for three annual and three perennial species in greenhouses. Biomass was the end point and plants were sprayed at either the vegetative or reproductive stage of development (Figure 8). The ED50 is much lower than the recommended field rates, and there was no difference among species (Figure 8). As some of the species are important to the flora outside the arable land, we could look at the sensitivity if the species were exposed to 1% of the recommended field by using the ED50 to estimate the ED10 (Table 2 and Figure 5). On the basis of Table 2 and the relationship between the EDx and ED50 at different slopes in Figure 4, we have calculated the ED10 and also the 1% of recommended rate. For metsulfuron-methyl, the species in Table 3 were not protected from a 1% of recommended rate. 1% is a fair reduction close to the sprayed field (Table 3). 37 Duke et al.; Pesticide Dose: Effects on the Environment and Target and Non-Target Organisms ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


Figure 7. Field assays with two herbicides. Note the y-axis is on a logarithmic scale in order to make a proper separation of box-plots. The horizontal lines with the same color as the boxplot denote recommended rate of the two herbicides (data from Strandberg et al (30), Tables 4.1 and 4.2). The recommended rates seemed to be able to control most of the weed at 90%.

38 Duke et al.; Pesticide Dose: Effects on the Environment and Target and Non-Target Organisms ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


Figure 8. Boxplot of ED50 of various herbicides. Note the y-axis is on a logarithmic scale in order to make a proper separation of box-plots. The horizontal lines with the same color as the boxplot denote recommended rate of the three herbicides in Denmark (Strandberg et al. (30) Table 4.4)

Table 3. Theoretical ED10 and 1% of Recommended Rate Herbicide

ED10 g ai/ha

1% of Recommended rate

Glyphosate

50

13

Mecoprop-P

111

30

Metsulfuron-methyl

0.05

0.1

Carpenter and Boutin (33) published greenhouse dose response analyses of glufosinate ammonium and derived the ED50 for biomass. They used the common log-logistic curve, Gompertz curve, and a linear interpolation method for sublethal toxicity built into the program, ICPIN.exe, which is basically an ANOVA and thus not compatible with the log-logistic and Gompertz curve. 39 Duke et al.; Pesticide Dose: Effects on the Environment and Target and Non-Target Organisms ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


Species were classified as monocots or dicots and as crops or wild species. The species were harvested early, 21 days after spraying. At the late harvest time, plants were transplanted into larger pots to prevent stress in the small pots. The ED50 was derived from the dose- response curves. We used a factorial ANOVA model to analyze the differences of the treatments. The differences between monocots and dicots were of course significant due to selectivity of the compound controlling monocots in dicot crops, as was the choice of regression model as well as their interactions (Figure 9).

Figure 9. Interactions between treatments, (LT= harvesterd three weeks after application and ST=harvested later, depending of stage of development) and regression models. The broken line is the recommended field rate (data from Table 3) (33)

The late harvested plants (LT) had a higher ED50 than did early harvested plants (ST). What is interesting is that different models estimated different ED50 values (Figure 9). The Gompertz curve gave significantly lower ED50 than did the log-logistic; and the lowest was that of ICPIN. However, for some of the Gompertz regression runs, the biomass was square root transformed and thus one cannot rule out that the Gompertz curve was not the proper regression, because transformation changes the functional relationship (34, 35). The ICPIN approximation is basically an ANOVA and the resulting ED50 values were the smallest. Generally, it shows that it is difficult to relate EDx derived from different models to each other, if there is not a proper test for the model fit. 40 Duke et al.; Pesticide Dose: Effects on the Environment and Target and Non-Target Organisms ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


Rotchés-Ribalta et al. (36) tested tribenuron-methyl and 2,4-D in a greenhouse study on species being classified as rare or common. The duration of tests was either called short or long term as mentioned before (The late harvest was around 48 days after treatment) (33). The specific dose-response model was a log-logistic model. On the basis of an ANOVA on ED25, there were no differences between duration of the stage of development or the classification of common and rare species. All variation was overruled by the two herbicides with an ED25 of 195 g ai/ha for 2.4-D and 3 g ai/ha for tribenuron-methyl, respectively. One of the interesting results of the two last papers is comparinson between short term and long term bioassays in a confined environment. There were no differences in EDxs by Rotchés-Ribalta et al. (36), a non-obvious result. This is against common experience in the field where younger plants are more sensitive than older plants. The recommended rate for 2,4-D in common crops is around 1000 g ai/ha, whilst that of tribenuron-methyl is 24 g ai/ha. On the basis of the predicted slopes (Figure 4 and Table 2) and ED25, the predicted ED10 would be 330g ai/ha and 3g ia/ha for 2,4-D and tribenuron-methyl, respectively. For 2,4-D this seems to be a fair protection with a ED10 of 330 g, but not so convincingly for tribenuron-methyl.

Figure 10. The classification on the x-axis is a conglomerate of various other treatments that could not be analyzed (Table 2 (37)). Horizontal line is the recommended field rate of tepraloxydim. 41 Duke et al.; Pesticide Dose: Effects on the Environment and Target and Non-Target Organisms ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


Riemens et al. (37) published a rather complicated experimental design to assess the effect of tepraloxydim on biomass of non-crop grasses (Table 2 of ref. (37)).They used a log-logistic dose-response model with tepraloxidim on four grasses exposed to eight levels of treatments of which some were in the field and some in the greenhouse. The grasses are both serious weeds, but also growing in natural habitats outside the arable land. Unfortunately, it was not possible to analyze the data on the basis of the treatments, which confounded field or greenhouse with spraying time and harvest time. Redefining the treatment as either a field or a greenhouse experiment showed that there was a pronounced interaction between species and treatment (Figure 10). The results in the field and greenhouse were similar except for Poa annua, which stood out in the greenhouse. In fact getting consistent dose-response results with P. annua is difficult due to the plasticity of the species.

Figure 11. Boxplot for the various EDx estimates. Note very few of the EDxs were significantly different form zero. The recommended rates of the herbicides have the same color as the boxplots (Egan et al. (38))

Egan et al. (38) compared herbicide tolerance of rare and common species in the agricultural landscape of Pennsylvania to atrazine, dicamba and glyphosate over two years in the greenhouse. On the basis of surveys, five genera representing both common and rare species were selected. The dose range for the herbicides constituted of 5 doses. A three parameter log-logistic model was fitted and the ED05, ED25 and ED50 were derived. Whatever the EDx of species, the confidence intervals were mostly overlapping zero and thus the EDx values were 42 Duke et al.; Pesticide Dose: Effects on the Environment and Target and Non-Target Organisms ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


not significantly different from zero. Consequently, there was no way to do a weighted ANOVA, and as anticipated the ordinary ANOVA did not find any significance between abundance (rare or common) and the herbicides. Figure 11 illustrates the ED05, ED25, ED50. In the Supplement to the paper, the dose response curves were shown and it appears strange that so many of the EDx values (81% for ED05, 86% for ED25, 64% for ED50) were not significantly different from zero. The results perhaps show an experience encountered by many researchers. The “wild species” selected in natural habitats are much more variable than are weeds and crops being selected for generations to adapt to a rather homogenous arable environment.

Concluding Remarks The study of non-target organism response begins with the Paracelsus principle: size of dose determines if a herbicide has an effect or not. The dose at the origin of spray will sometimes drift and and cause unwanted effects on non target species. Our understanding of factors that influence drift and the effect of dose needs to be utilized to mitigate any off-target effects of pesticide application. The use of dose-response curves and EDx values are are indispensable in this process.

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