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It is important in both ecotoxicology and agronomy to measure the sensitivity of a plant species to a herbicide and to understand those factors determining variation in the response. Such information is used in the regulatory process, as well as helping to determine recommended application rates under field conditions (Hance & Holly 1990) and in environmental assessment of the long-term effects of pesticides (Boutin, Freemark & Keddy 1995; Pratt et al. 1997).
The most common method used to compare the susceptibilities of a test species to a given herbicide is to produce a dose–response relationship (Seefeldt, Jensen & Fuerst 1995). Test plants, grown under controlled conditions, are exposed to increasing doses of herbicide and some measure of damage is assessed (mortality, mass reduction, colour change, height, etc.). Where applicable a response curve is fitted to these data from which secondary indicator values are derived, usually the LD50 for mortality and ED50 for other performance measures. The LD50 is defined as the dose at which there is 50% mortality of sprayed plants, and the ED50 is defined as the dose at which the plant response is halfway between the response when the dose is zero and the maximum response (i.e. the asymptotic yield as dose becomes increasingly high).
It is, of course, simplistic to use only one parameter to compare sensitivity to herbicide. However, if only one measure of susceptibility is to be used, then the LD50 or ED50 are, in general, the most appropriate because the steepest part of the curve generally lies around the 50% value and, thus the ED50 can be measured more accurately than, say, the ED90 (Seefeldt, Jensen & Fuerst 1995). In the present study, all work was concerned with performance measures (fresh weight) and thus the ED50 was used throughout.
It is well known that dose–response relationships are dependent on various factors, including the characteristics of the target species that govern interception, retention and uptake of a herbicide; the herbicide’s mode of action; the growth stage of the plant at the time of spraying (Garrod 1989; Ascard 1994); and the growing conditions, both edaphic and climatic (Garrod 1989; Fletcher, Johnson & McFarlane 1990; Skuterud et al. 1998; Sharma & Singh 2001). Mechanisms of delivery of foliar applied herbicides, including the median droplet size of the spray and the droplet size distribution spectrum (Enfält et al. 1997), also govern efficacy, as does the density of the target plants (Ascard 1994). Often in mixed communities, high volume rates, small droplet sizes and adjuvants (compounds added to herbicide to improve the droplet spectrum or impaction of droplets) are used to ensure sufficient coverage. There is, however, a lack of information about the effects of plant density on herbicide dose–response relationships. This is important if reduced rates of application are to be promoted and integrated with other forms of weed control through manipulation of crop competition. Boyle & Fairchild (1997) noted that, within plant communities, competitive processes will interact with herbicides in the sense that there may be a primary phytotoxic effect of the herbicide on the target species with a secondary, indirect, effect of the herbicide on the individual plant due to the altered competition from neighbours. At reduced rates of herbicide application, competitive processes may play a key role in determining the outcomes of herbicide application, and an understanding of the interaction is important in recommending reduced rates of application (Haas 1989; Rydahl & Thonke 1993; Christensen 1994).
In this paper we describe a series of experiments using a model system to test whether plant density affects the dose–response relationship, and, having demonstrated a relationship, to examine at what point in the course of the experimental process density has the greatest influence on this relationship.
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Knowledge of dose–response relationships between a pesticide and a range of species is an integral part of the regulatory process (Hance & Holly 1990). For herbicides, the dose–response to the chemical is usually tested on a range of species, including those of economic importance (crops and major weeds) and a range of non-target species that might be affected by it during use (Boutin, Freemark & Keddy 1995).
Dose–response relationships often show high variability, and it is essential to minimize this as far as possible. These relationships have been shown to be affected in the field by wind speed and rainfall (Garrod 1989; Fletcher, Johnson & McFarlane 1990), and in both field and laboratory by temperature, size of plant (Ascard 1994), physiological state of the plant (Garrod 1989), time of spraying (Skuterud et al. 1998) and the density of the plants being tested (Ascard 1994). Clearly, if this is so and the aim is to isolate the effects of the herbicide on plant performance and to produce accurate results, then it is essential to understand how experimental conditions influence the process.
In order to minimize experimental error in this study we used seeds from the same source, carried out our experiments using a precision sprayer and grew our plants in a controlled environment room. However, even with these precautions the variability was high, and there were important block effects noted in some experiments. In experiment 1, for example, block effects within shelf were found although there were no consistent trends found in these block effects, and there were no significant effects of shelf. These results suggest that high replications, coupled with appropriate blocking to take account of the small-scale variations in environmental conditions even within a controlled environment room, are needed to minimize variation and hence increase the signal relative to noise. The high level of variation is of unknown source and may be a result of any combination of influences, e.g. small-scale environmental heterogeneity, genetic variation, variation in plant size for whatever reason, variation between plants in herbicide deposition and competition increasing the magnitude of variation from other sources.
effects of plant density on sensitivity to herbicide
It is surprising that there have been few attempts to assess the effects of plant density on dose–response relationships, given the well-known effects that density has on the outcome of competition (Begon & Mortimer 1986; Firbank & Watkinson 1990; Goldberg & Barton 1992) and the general acceptance of its importance in commercial plant growing. The limited information that is available comes from a study on flame weeding showing that plant density was not as important as the size of plant in determining a plant’s response to flame weeding (Ascard 1994). However, this conclusion cannot be extrapolated directly to herbicides because: (i) size was varied by varying age, which might correlate with variation in other variables such as cuticle thickness or hairiness that, in turn, could affect the response to flame weeding more or less than the response to herbicide; (ii) it is difficult to compare the two different variables (size and density) because the change in plant response depends on the relative range of the two different variables tested, the higher the range tested the greater the effect on the dose–response. Therefore, it is possible that the conclusion of Ascard (1994) results from the range of densities tested being low compared with the range of sizes tested.
However, in this study we have unequivocal evidence (experiment 1) that the density of A. githago plants has a significant effect on the dose–response to the herbicide 2,4-D amine. The data for the high densities (64 plants per pot) were variable but still gave an ED50 of approximately 15 times the ED50 calculated for the low densities (two plants per pot). This suggests that competition is important in determining ED50 values.
isolating the phase in which competition affects dose–response
It is possible that the main effect is due to competition before spraying, where, for example, plants grown at high densities grow with more vertical leaves and hence receive less herbicide than plants grown at low density. The comparison of plants grown at high density prior to spraying but reduced to low density at the time of spraying, with plants grown at low density throughout (experiment 3), showed that there was no significant difference in the ED50 values. Therefore, we conclude that the effects of competition before spraying were not responsible for the observed difference in ED50 values.
Another possibility is that the main effect of competition occurs at the time of spraying itself. For example, it is possible that plants grown at high density shade each other for receipt of herbicide, and consequently on average receive less herbicide per unit leaf area than plants grown at low density. This study shows that the amount of spray per unit leaf area received by plants at low density was approximately 2·5 times the amount received by plants grown at high density and that the amount of spray received per plant at low density was approximately four times the amount received by plants grown at high density (experiment 2).
We also showed that, where the difference in density occurs only at the time of spraying itself, the ED50 is approximately three times higher in plants grown at high density (experiment 3). We therefore conclude that the effects of high density vs. low density at the time of spraying cause a c. threefold difference on the ED50 because either (i) plants at high density receive 2·5 times less herbicide per unit leaf area than plants grown at low density, or (ii) plants at high density receive four times less herbicide per plant than plants grown at low density. It would therefore be interesting to do further work to examine which of the two measures of herbicide receipt (dose per plant, dose per leaf area) is a better predictor of response to herbicide. There was a high level of variation in the amount of spray received per plant and per unit leaf area. The extent to which herbicide uptake and subsequent effect is influenced by deposition on stems as opposed to leaves deserves further attention.
The third period during which competition may have a significant effect on the dose–response to herbicide is competition after spraying and before harvesting. This may have two explanations. (i) There is, in effect, a trade-off between the phytotoxic effect of the herbicide reducing growth and the effect of reduced competition from the neighbours that increases growth. Thus the effect of herbicide is less than where there is no competition, so the ED50 is greater at higher plant density. (ii) Competition after spraying may decrease the rate of growth and thus decrease the effect of the herbicide 2,4-D, which acts as a growth inhibitor (Royal Society of Chemistry 1991).
We have shown that there was a 15-fold difference in ED50 between plants of high and low density (experiment 1), of which most was due to competition during the period after spraying (experiment 5). We did not investigate how much this was due to the two explanations set out above (i and ii). However, we were able to show that application of herbicide at a high concentration in dense populations caused decreased competition (experiment 4). This is the result expected if the first explanation (i) was important, i.e. that at high density competition is reduced by the effect of herbicide and therefore the net effect of herbicide is less than for those plants without competition. However, it is possible that the large effect of competition after spraying demonstrated in experiment 5 may have been altered by the different conditions in that experiment due to equipment failure. Future work should safeguard against changes over time by a fully complete experiment containing all treatments (LLL, HHH, HHL, HLL) with replication over time to achieve sufficient power.
In these experiments we have demonstrated that density affects plants’ responses to herbicide. However, we were unable to explore the relationship between density and ED50. Future work might test a number of different densities and attempt to describe, mathematically, the relationship between ED50 and density.
the agricultural context of these results
The work presented here has used A. githago as a model species because it has many attributes suitable for competition studies. However, this species is no longer of economic importance as a weed as a consequence of improved seed cleaning. The densities tested here differed greatly (32-fold difference) and it is not known whether such densities were found in the field when A. githago was a common weed. It is known, however, that similar densities of A. githago have been used in competition experiments (Firbank & Watkinson 1985). While these results need to be tested on other species, the conclusions are likely to be applicable to crops and weeds used to assess pesticide toxicity.
The regulatory process demands that the toxicity of a pesticide to both weeds and crops be assessed (Hance & Holly 1990). The results presented here demonstrate that not only should a range of doses be applied but that the effects of plant density should be taken into account. For example, it may be that, as in our experiment, weeds at low density are particularly sensitive to a pesticide. This potential positive feedback system might lead to local extinction and this should not be ignored, particularly if the weed is of conservation value. Furthermore, although it is beyond the immediate scope of this study, we believe that our findings suggest that non-target neighbour species may strongly affect the response of a plant to herbicide (Humphry 1999). Therefore future work should include testing combinations of species, e.g. crop and weed.
In summary we have shown that a high density of plants increased the ED50 and that the period during which density had most effect was the period after spraying. It follows that single plant dose–response experiments may not be sufficient to predict the effect of herbicide on a community (either semi-natural or sown; Brain et al. 1999). This is because, within a community, the densities of plants are greater than those found in single-plant dose–response trials. It is also possible that the effect of density on a species’ response to herbicide depends on the species in question. Further work to test this might involve comparing the effect of density on dose–response amongst a range of species.