**4. Arthropod pests and insecticide resistance**

ing sub-lethal treatment levels. Of the 91 spray trials, 66 (73%) produced spray coverages, in which the lowest spray coverage on a single spray card was below 10% (Fig. 4). At the same time, the spray range (maximum/minimum) was above 110 in two of the spray applications with airplane and was above 5-fold in 17 (19%) of the spray trials. Low and less uniform spray coverage, especially with airplane applications, is most likely attributed to using smaller spray volumes and nozzles, which deliver smaller spray droplets and therefore increases the risk of spray drift [12]. Among the spray trial data obtained from Western Australia, the highest spray coverage obtained from a single spray card was about 40%, which is an indicator of the "maxi‐ mum spray potential". That is, bare ground was sprayed with up to 130 liter per ha, and most growers in this region do not apply more than 90 liter per ha. Consequently, the data collected suggest that it will be very difficult to exceed this level of spray coverage of a growing crop.

Acknowledging the magnitude of resources spent on insecticide applications, and the possible risk of low insecticide performance due to low and inconsistent insecticide applications - it is somewhat noteworthy that there are no widely used quality control measures available. As discussed by [13] and many others, there are numerous factors which can contribute to low performance of a given insecticide application, including: incorrect storage, water pH, wrong concentration of insecticide, nozzles not being turned on, and incorrect application volume. An interesting, but under-utilized resource for assessment of spray coverage, is water sensitive spray cards, which enable growers, consultants, and pesticide applicators to quantify the spray coverage obtained. Water sensitive spray cards are coated with bromoethyl blue, which reacts with water and turn blue-purple depending on dosage of water [14] (Fig. 2b). Although mainly used in applied research projects, they are commercially available through a number of companies and can be used quite effectively to make quantitative assessments of spray applications in response to agronomic variables and weather conditions. [1] used water sensitive spray cards to analyse spray coverage during commercial spray applications in potato fields, of which eight were applied with fixed-wing airplane (spray volume of 194 L per ha) and six with ground rig (spray volume of 48 L per ha). During each spray application, 10 water sensitive spray cards were deployed at the top of the canopy in different parts of the field, and both average and range of spray coverages were analysed (N = 140). Canopy penetration data were also obtained from nine of the 14 commercial spray treatments by having additional spray cards placed about 15 cm from the bottom of the potato canopy. In a recent study conducted in Western Australia, we quantified the "potential spray coverage" of commercial spray rigs by placing water sensitive spray cards at the ground level in a bare field (Fig. 2c). Thus,

Weather conditions were recorded, and spray volume (30-90 liter per ha), tractor speed (15-25 km/h) and nozzles type (various types tested) were experimentally manipulated to obtain spray data from a wide range of commercial spray scenarios. Spray data for this study were collected in three combinations of fields and locations, and we obtained data from 77 unique combinations of spray conditions (location, date, spray volume, tractor speed, and nozzle types) and with four replicated spray cards for each combination (N = 308). Fig. 3a shows average spray coverage at the top of the canopy or above bare ground in response to spray

**Spray range**

The final aspect of spray applications discussed here is "canopy penetration" – or the level of insecticide being deposited in the lower portion of a given crop canopy. The spray data presented so far were all collected either from the top of the canopy or above bare ground. Based on analysis of nine of the spray trials from Texas, it was possible to compare spray coverages at the top of the canopy with in the lower portion of a potato canopy above 35 cm tall. On average, the bottom portion of the canopy received about half the spray coverage of the top portion, and only one of the nine applications provided over 10% average spray coverage in the bottom portion of the canopy. Published spray coverage studies using water sensitive spray cards have shown that it is not

**Spray trial (N = 91) 0 10 20 30 40 50 60 70 80 90**

These spray results obtained across a wide range of operational conditions clearly highlight that, although spray volume is the most important variable, other variables need to be taken into account if the goal is to predict the obtained spray coverage. Furthermore, these results underscore that most insecticide spray coverages are likely quite low and highly influenced by weather variables and spray application settings. Thus, it is paramount to develop decision support tools to optimize timing of applications in accordance to weather variables, so that farmers are in a position to apply insecticides with highest likelihood of obtaining good coverage and therefore high performance. Otherwise, it is possible that spray applications of low and inconsistent insecticide

[13] pointed out that insecticide resistance is among the most significant challenges to food production systems and to public health through management of insect vector born diseases. There are clear indications that many major pests are able to develop physiological and or behavioural insecticide resistance to a large number of insecticides. In this context, physiological insecticide resistance is defined as genotypes being able to tolerate high dosages of neurotoxic ingredients, which are lethal to most

uncommon, especially with aerial spray applications, to obtain spray coverages below 1% [15-17].

dosages contribute to resistance development in target pest populations [10, 18].

**4. Arthropod pests and insecticide resistance** 

**Average spray coverage (%)**

volume, and, as expected, there was a highly positive correlation (df = 1,90, adjusted R2 = 0.790, F = 340.48, P < 0.001). Thus, despite high variability in spraying conditions, spray coverage is clearly driven by volume and reached about 40%, when the equivalent of 200 L/ha was applied. Average spray coverages for the three data sets (aerial and ground rig applications in Texas and ground rig applications in Western Australia) were examined, and spray coverage was divided by the spray volume applied as a measure of spray performance (Fig. 3b). When applying spray formulations with airplanes, the spray coverage performance was about 0.15 (meaning that for each extra liter per ha, the spray coverage increased, on average, by 0.15%), while it was about 0.17 in experimental studies conducted in Western Australia and about 0.24 in ground rig applications in Texas. Thus in terms of "conversion efficiency" (converting spray volume into spray coverage), the ground rig applications in Texas appeared to be most efficient. In addition to comparison of averages, it is important to examine the range of consistency (difference between minimum and maximum) within a given spray application. This information is important, because it may be used to assess the risk of certain portions of treated fields receiving sub-lethal treatment levels. Of the 91 spray trials, 66 (73%) produced spray coverages, in which the lowest spray coverage on a single spray card was below 10% (Fig. 4). At the same time, the spray range (maximum/minimum) was above 110 in two of the spray applications with airplane and was above 5-fold in 17 (19%) of the spray trials. Low and less uniform spray coverage, especially with airplane applications, is most likely attributed to using smaller spray volumes and nozzles, which deliver smaller spray droplets and therefore increases the risk of spray drift [12]. Among the spray trial data obtained from Western Australia, the highest spray coverage obtained from a single spray card was about 40%, which is an indicator of the "maximum spray potential". That is, bare ground was sprayed with up to 130 L/ha, and most growers in this region do not apply more than 90 liter per ha. Consequently, the data collected suggest that it will be very difficult to exceed this level of spray

**<sup>100</sup> Minimum spray coverage (%)**

**b**

**Spray data Aerial Experimental Ground**

**Spray range (max / min)**

**0**

**20**

**40**

**60**

**80**

**Spray coverage / spray volume**

**0.30 Spray coverage (%)**

**Spray coverage / Spray volume**

**0.10**

**0.15**

**0.20**

**0.25**

there was no crop, so the obtained spray coverage represented the highest possible under the given conditions.

volatilization as a possible mode of action in dense crop canopies, and it is unknown whether volatilization plays a major role

across insecticide classes.

**3. Control measures of insecticide applications** 

Figure 2. Ground spray rig applications and use of water sensitive spray cards

202 Insecticides - Development of Safer and More Effective Technologies

Figure 3. Spray coverage in response to spray volume

Figure 4. Minimum and range of spray applications

**Figure 4.** Minimum and range of spray applications

Spray coverage (%)

0

coverage of a growing crop.

20

40

60

**a**

Spray volume applied (Liter per ha) 20 40 60 80 100 120 140 160 180 200

**Figure 3.** Spray coverage in response to spray volume

**Minimum spray coverage per trial (%)**

Commercial ground rig applications (Texas) (N = 6) Commercial aerial applications (Texas) (N = 8)

Linear regression of all data

Experimental ground rig applications (Western Australia) (N = 77)

[13] pointed out that insecticide resistance is among the most significant challenges to food production systems and to public health through management of insect vector born diseas‐ es. There are clear indications that many major pests are able to develop physiological and or behavioural insecticide resistance to a large number of insecticides. In this context, phys‐ iological insecticide resistance is defined as genotypes being able to tolerate high dosages of neurotoxic ingredients, which are lethal to most individuals of the same species. The most common physiological resistance mechanisms are [19]: 1) catabolic processing of the active ingredient, 2) changes in binding sites that are targeted with a given toxin, 3) decreased up‐ take rate, and 4) binding of toxin to sites with no toxic effect. Behavioural resistance [20] has been documented for the past 40 years, and it is interpreted as a behavioural adaptation, which reduces the likelihood of target pests acquiring a lethal dosage of insecticide. Behav‐ ioural insecticide resistance has mainly been discussed in the context of "bait aversion", in which, for instance glucose based bait for control of cockroaches [21-23] no longer works, because the cockroaches avoid the bait. However as discussed below, it also seems plausible that behavioural insecticide resistance may develop in response to low and incomplete spray coverage. Concerns about behavioural insecticide resistance may be of particular con‐ cern when target pests predominantly occur on the abaxial (lower) side of crop leaves and insecticides are not translaminar or systemic. For instance, in a simple study in which either one or both sides of potato leaflets were treated, [24] showed that for some insecticides pota‐ to psyllid mortality was much lower when only one side was treated compared to when both sides of the leaflet were treated. These findings were interpreted as potato psyllids [*Bactericera cockerelli* [Sulc] (Homoptera: Psyllidae)] moving away from (avoiding) the treat‐ ed leaflet side when given a choice between treated and untreated sides.

ticide applications, pest populations would be under less insecticide-induced selection pres‐ sure, which would decrease the risk of pest populations developing insecticide resistance.

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205

When addressing concerns about risk of insect pest populations developing physiological resistance and how management practices can be developed under commercial settings to reduce this risk, it is worthwhile setting the general context. Firstly, we wish to emphasize that there are only two extreme scenarios, which do not potentially lead to development of physiological resistance in target pest populations: 1) always applying an insecticide dosage low enough so that all genotypes survive, 2) applying a high enough dosage to ensure that individuals of all genotypes die. Obviously, the first option is of no interest to growers, as it means zero pest control, and therefore represents waste of resources. As already described in detail based on the analysis of water sensitive spray cards, the second option is in most cases unfeasible from a practical standpoint, and it may also imply very high economical costs. This means that under real-world conditions, applications of insecticides are always

The important point is that the mortality of a given pest individual is NOT random within a pest population: an individual will only succumb to an insecticide application, if the individual is actually susceptible to the pesticide and exposed to a dosage above a certain level (minimum lethal dosage). Moreover, pest individuals within a population vary in their ability to tolerate an insecticide, and – based on their behaviour – vary in likelihood of getting exposed to the insecticide. The intraspecific variation in tolerance to an insecticide is linked to the fact that the mode of action of the vast majority of insecticides is very specific and associated with allelic variation at one (monogenic) or two loci. That is, the insecticide operates by interfering with a very specific metabolic function, but even the slightest change in binding site (induced by mutation at a single locus) may compromise the performance of the insecticide, so pest individuals possessing such changes will have a higher chance of survival, while individuals without the specific allele will be eliminated. If the insecticide resistance is monogenic, and only two alleles exist (r = resistant and s = susceptible) - dosage response curves for the three genotypes are typically presented with mortality increasing along a logarithmic dosage scale [27]. In a theoretical example of a pest population of 10,000 individuals (Fig. 5), individuals of genotype ss may be expected to succumb when the insecticide dosage ranges between 0.3-0.6 ppm, sr individuals when the insecticide dosage ranges between 0.6-1.2 ppm, and rr individuals when the insecticide dosage is above 30 ppm. If p = 0.001 is the allele frequency of r and q= 0.999 is the allele frequency of s, and the genotypes occur in Hardy-Weinberg proportions, then the demographic composition of the pest population in response to insecticide dosage is as outlined in Table 1. It is seen that subjecting a pest population to a dosage above 0.5 ppm causes a >99% reduction of the overall population, but if it less than 60 ppm it also increases the proportion of resistant individuals in the remaining pest population. And although this fairly simple relationship between survival of genotypes and insecticide dosage has been investigated intensively over the last 3-4 decades and been greatly expended upon – it illustrates the core challenge that insecticide based pest control is faced with: Growers want to suppress as large a proportion of the pest population as possible to minimize the economic loss they incur, and therefore apply high dosages of insecticides. However, they are not able to apply a high enough dosage to completely suppress all pest individuals, so a selection pressure is imposed on the pest populations and the end result may be that the pest population develops physiological resistance

**Insecticide dosage (ppm)**

**0.01 0.1 1 10**

**Insecticide dosage Genotype Total PPM ss sr rr Population** 0.00 9980.01 19.98 0.01 10000.00 0.10 9980.01 19.98 0.01 10000.00 0.20 9980.01 19.98 0.01 10000.00 0.30 8982.01 19.98 0.01 9002.00 0.40 5988.01 19.98 0.01 6008.00 0.50 2994.00 19.98 0.01 3013.99 0.60 0.00 19.98 0.01 19.99 0.70 0.00 17.98 0.01 17.99 0.80 0.00 11.99 0.01 12.00 0.90 0.00 5.99 0.01 6.00 1.00 0.00 0.00 0.01 0.01

The important point is that the mortality of a given pest individual is NOT random within a pest population: an individual will only succumb to an insecticide application, if the indi‐ vidual is actually susceptible to the pesticide and exposed to a dosage above a certain level (minimum lethal dosage). Moreover, pest individuals within a population vary in their abili‐ ty to tolerate an insecticide, and – based on their behaviour – vary in likelihood of getting exposed to the insecticide. The intraspecific variation in tolerance to an insecticide is linked to the fact that the mode of action of the vast majority of insecticides is very specific and as‐ sociated with allelic variation at one (monogenic) or two loci. That is, the insecticide oper‐ ates by interfering with a very specific metabolic function, but even the slightest change in binding site (induced by mutation at a single locus) may compromise the performance of the

**Genotype ss Genotype sr Genotype rr**

imposing a selection pressure on target pest populations.

**Pest mortality (%)**

**0.2**

**0.4**

**0.6**

**0.8**

Figure 5. Dosage response

**Figure 5.** Dosage response

Table 1. Dosage response

because it is practically impossible to the homozygous resistant genotypes.

The first reported incidence of physiological pesticide resistance was of San Jose scale [*Quad‐ rispidiotus perniciosus* (Comstock) (Homoptera: Diaspididae)] to lime sulphur in 1914 [25]. Since then, more than 550 arthropod species have been reported as being resistant to one or more pesticides [13]. However already in 1977, more than 364 species of arthropods were reported to show physiological pesticide resistance [26], 447 species in 1984 [27], and [28] 503 species in 1991. A few examples of documented physiological resistance against active ingredients are presented here and are based on data from the Arthropod Pesticide Resist‐ ance Database (APRD, http://www.pesticideresistance.org/): 1) two-spotted spider mite (*Tet‐ ranychus urticae* Koch, Acari: Tetranychidae) has developed resistance to 93 active ingredients, 2) diamondback moth (*Plutella xylostella* L, Lepidoptera: Plutellidae) has devel‐ oped resistance to 82 active ingredients, 3) green peach aphid *Myzus persicae* (Sulzer) (Ho‐ moptera: Aphididae) has developed resistance to 74 active ingredients, 4) Colorado potato beetle (*Leptinotarsa decemlineata* (Say), Coleoptera: Chrysomelidae) has developed resistance to 51 active ingredients, 5) silverleaf whitefly (*Bemisia tabaci* Gennadius, Homoptera: Aleyro‐ didae) has developed resistance to 46 active ingredients, 6) cotton bollworm / corn earworm (*Helicoverpa armigera* Hübner, Lepidoptera: Noctuidae) has developed resistance to 44 active ingredients, and 7) beet armyworm (*Spodoptera exigua* Hübner, Lepidoptera: Noctuidae), has developed resistance to 29 active ingredients. Diamondback moth was the first pest to be‐ come resistant to DDT (dichlorodiphenyltrichloroethane) [29, 30]. From the mid-1990s, the use of formulations of toxins derived from strains of the soil borne bacterium, *Bacillus thur‐ ingiensis* (denoted Bt toxins) have been promoted to control key lepidopteran and coleopter‐ an pests and at the same time preserve natural enemy populations within crops [31]. Due to its high efficiency, low cost and simple application, Bt-based pesticides rapidly became used for control of many pests, and diamondback moth was the first insect pest to become resist‐ ant to Bt toxins [32-34]. Thus, certain characteristics in the diamondback moth genome, its biology, and its interactions with food cropping systems seem to expose an incredible adapt‐ ability and responsiveness to imposed pesticide-induced selection pressures. Consequently, [13] made the important point that while there is a steady increase in reported cases of re‐ sistance, the number of new species with documented resistance is not increasing nearly as fast. It is therefore important to consider that the most important insect pests will likely con‐ tinue to develop resistance to the insecticide pressures that are imposed upon them, and that the ability to develop physiological resistance to insecticides may be one of the driving selec‐ tion pressures for species to become pests. That is, the economically most important arthro‐ pod pest species may share certain common denominators, which enable them to be successful under commercial/agricultural conditions with high levels of selection pressure imposed by insecticide treatments. It may be argued that insight into such denominators is critically important for development of future pest management programs, as it may open avenues for management strategies that rely less on insecticides. With less reliance on insec‐ ticide applications, pest populations would be under less insecticide-induced selection pres‐ sure, which would decrease the risk of pest populations developing insecticide resistance.

When addressing concerns about risk of insect pest populations developing physiological resistance and how management practices can be developed under commercial settings to reduce this risk, it is worthwhile setting the general context. Firstly, we wish to emphasize that there are only two extreme scenarios, which do not potentially lead to development of physiological resistance in target pest populations: 1) always applying an insecticide dosage low enough so that all genotypes survive, 2) applying a high enough dosage to ensure that individuals of all genotypes die. Obviously, the first option is of no interest to growers, as it means zero pest control, and therefore represents waste of resources. As already described in detail based on the analysis of water sensitive spray cards, the second option is in most cases unfeasible from a practical standpoint, and it may also imply very high economical costs. This means that under real-world conditions, applications of insecticides are always imposing a selection pressure on target pest populations.

Figure 5. Dosage response **Figure 5.** Dosage response

to psyllid mortality was much lower when only one side was treated compared to when both sides of the leaflet were treated. These findings were interpreted as potato psyllids [*Bactericera cockerelli* [Sulc] (Homoptera: Psyllidae)] moving away from (avoiding) the treat‐

The first reported incidence of physiological pesticide resistance was of San Jose scale [*Quad‐ rispidiotus perniciosus* (Comstock) (Homoptera: Diaspididae)] to lime sulphur in 1914 [25]. Since then, more than 550 arthropod species have been reported as being resistant to one or more pesticides [13]. However already in 1977, more than 364 species of arthropods were reported to show physiological pesticide resistance [26], 447 species in 1984 [27], and [28] 503 species in 1991. A few examples of documented physiological resistance against active ingredients are presented here and are based on data from the Arthropod Pesticide Resist‐ ance Database (APRD, http://www.pesticideresistance.org/): 1) two-spotted spider mite (*Tet‐ ranychus urticae* Koch, Acari: Tetranychidae) has developed resistance to 93 active ingredients, 2) diamondback moth (*Plutella xylostella* L, Lepidoptera: Plutellidae) has devel‐ oped resistance to 82 active ingredients, 3) green peach aphid *Myzus persicae* (Sulzer) (Ho‐ moptera: Aphididae) has developed resistance to 74 active ingredients, 4) Colorado potato beetle (*Leptinotarsa decemlineata* (Say), Coleoptera: Chrysomelidae) has developed resistance to 51 active ingredients, 5) silverleaf whitefly (*Bemisia tabaci* Gennadius, Homoptera: Aleyro‐ didae) has developed resistance to 46 active ingredients, 6) cotton bollworm / corn earworm (*Helicoverpa armigera* Hübner, Lepidoptera: Noctuidae) has developed resistance to 44 active ingredients, and 7) beet armyworm (*Spodoptera exigua* Hübner, Lepidoptera: Noctuidae), has developed resistance to 29 active ingredients. Diamondback moth was the first pest to be‐ come resistant to DDT (dichlorodiphenyltrichloroethane) [29, 30]. From the mid-1990s, the use of formulations of toxins derived from strains of the soil borne bacterium, *Bacillus thur‐ ingiensis* (denoted Bt toxins) have been promoted to control key lepidopteran and coleopter‐ an pests and at the same time preserve natural enemy populations within crops [31]. Due to its high efficiency, low cost and simple application, Bt-based pesticides rapidly became used for control of many pests, and diamondback moth was the first insect pest to become resist‐ ant to Bt toxins [32-34]. Thus, certain characteristics in the diamondback moth genome, its biology, and its interactions with food cropping systems seem to expose an incredible adapt‐ ability and responsiveness to imposed pesticide-induced selection pressures. Consequently, [13] made the important point that while there is a steady increase in reported cases of re‐ sistance, the number of new species with documented resistance is not increasing nearly as fast. It is therefore important to consider that the most important insect pests will likely con‐ tinue to develop resistance to the insecticide pressures that are imposed upon them, and that the ability to develop physiological resistance to insecticides may be one of the driving selec‐ tion pressures for species to become pests. That is, the economically most important arthro‐ pod pest species may share certain common denominators, which enable them to be successful under commercial/agricultural conditions with high levels of selection pressure imposed by insecticide treatments. It may be argued that insight into such denominators is critically important for development of future pest management programs, as it may open avenues for management strategies that rely less on insecticides. With less reliance on insec‐

ed leaflet side when given a choice between treated and untreated sides.

204 Insecticides - Development of Safer and More Effective Technologies

Table 1. Dosage response **PPM ss sr rr Population** 0.00 9980.01 19.98 0.01 10000.00 0.10 9980.01 19.98 0.01 10000.00 0.20 9980.01 19.98 0.01 10000.00 0.30 8982.01 19.98 0.01 9002.00 0.40 5988.01 19.98 0.01 6008.00 0.50 2994.00 19.98 0.01 3013.99 0.60 0.00 19.98 0.01 19.99 0.70 0.00 17.98 0.01 17.99 0.80 0.00 11.99 0.01 12.00 0.90 0.00 5.99 0.01 6.00 1.00 0.00 0.00 0.01 0.01 The important point is that the mortality of a given pest individual is NOT random within a pest population: an individual will only succumb to an insecticide application, if the indi‐ vidual is actually susceptible to the pesticide and exposed to a dosage above a certain level (minimum lethal dosage). Moreover, pest individuals within a population vary in their abili‐ ty to tolerate an insecticide, and – based on their behaviour – vary in likelihood of getting exposed to the insecticide. The intraspecific variation in tolerance to an insecticide is linked to the fact that the mode of action of the vast majority of insecticides is very specific and as‐ sociated with allelic variation at one (monogenic) or two loci. That is, the insecticide oper‐ ates by interfering with a very specific metabolic function, but even the slightest change in binding site (induced by mutation at a single locus) may compromise the performance of the

**Insecticide dosage Genotype Total**

The important point is that the mortality of a given pest individual is NOT random within a pest population: an individual will only succumb to an insecticide application, if the individual is actually susceptible to the pesticide and exposed to a dosage above a certain level (minimum lethal dosage). Moreover, pest individuals within a population vary in their ability to tolerate an insecticide, and – based on their behaviour – vary in likelihood of getting exposed to the insecticide. The intraspecific variation in tolerance to an insecticide is linked to the fact that the mode of action of the vast majority of insecticides is very specific and associated with allelic variation at one (monogenic) or two loci. That is, the insecticide operates by interfering with a very specific metabolic function, but even the slightest change in binding site (induced by mutation at a single locus) may compromise the performance of the insecticide, so pest individuals possessing such changes will have a higher chance of survival, while individuals without the specific allele will be eliminated. If the insecticide resistance is monogenic, and only two alleles exist (r = resistant and s = susceptible) - dosage response curves for the three genotypes are typically presented with mortality increasing along a logarithmic dosage scale [27]. In a theoretical example of a pest population of 10,000 individuals (Fig. 5), individuals of genotype ss may be expected to succumb when the insecticide dosage ranges between 0.3-0.6 ppm, sr individuals when the insecticide dosage ranges between 0.6-1.2 ppm, and rr individuals when the insecticide dosage is above 30 ppm. If p = 0.001 is the allele frequency of r and q= 0.999 is the allele frequency of s, and the genotypes occur in Hardy-Weinberg proportions, then the demographic composition of the pest population in response to insecticide dosage is as outlined in Table 1. It is seen that subjecting a pest population to a dosage above 0.5 ppm causes a >99% reduction of the overall population, but if it less than 60 ppm it also increases the proportion of resistant individuals in the remaining pest population. And although this fairly simple relationship between survival of genotypes and insecticide dosage has been investigated intensively over the last 3-4 decades and been greatly expended upon – it illustrates the core challenge that insecticide based pest control is faced with: Growers want to suppress as large a proportion of the pest population as possible to minimize the economic loss they incur, and therefore apply high dosages of insecticides. However, they are not able to apply a high enough dosage to completely suppress all pest individuals, so a selection pressure is imposed on the pest populations and the end result may be that the pest population develops physiological resistance

because it is practically impossible to the homozygous resistant genotypes.


of Bt toxins in transgenic Bt crops would be an exception) will an insecticide have a residual effect after 7-10 days. In a study of abamectin, [24] conducted experimental sprays of potato leaflets in different vertical portions of a potato canopy under field conditions. During time intervals after spraying, treated and untreated leaflets were collected and used in bioassays with potato psyllids to determine the adult psyllid mortality over time. Based on this study, [24] concluded that the residual effect of abamectin is less than 48 hours. Although most in‐ secticides have longer residual effect than abamectin, the example highlights the challenge that the effective dosage applied will decline over time, so pest individuals that are not af‐ fected immediately after application may not be exposed to a lethal dosage. For instance at the time of application, pest individuals may not be present at a vulnerable stage (for exam‐ ple mite eggs are not killed by systematic sprays whereas active adult mites will be killed), or the life stage may not be exposed to contact with chemicals (this is especially important for larvae of insects feeding on roots of plants in the soil). This problem or challenge, with not all life stages being equally susceptible to an insecticides application, becomes an even larger issue, if multiple pest species are present, and these different species occur in differ‐ ent parts of the crop canopy, have different movement patterns within the canopy, show dif‐ ference in terms of seasonal population dynamics, and also have different migration patterns between the given crop and neighbouring alternative hosts. Immigration by pest populations deserves to be highlighted as a serious constraint: if a pasture or field is treated and all present pest individuals are killed but high immigration means that a new popula‐ tion of pest individuals move into the given field or pasture a shortly after. If so, a grower might think that the insecticide application "didn't work" – but the reality is that the immi‐ gration rate of the pest needs to be taken into account when assessing what insecticide to apply and when to apply it. It is not practically feasible to apply insecticide specifically for each pest present separately and so inevitably each application event may effectively control some species or life stage, while other pest individuals will be exposed to sublethal dosages.

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In addition to concerns associated with physiological resistance of target pest populations, behavioural resistance may possibly develop in response to incomplete pesticide coverage, as target pests are given a "choice" between treated and untreated surfaces. If the target pest is able to discriminate between treated from untreated surfaces and eventually avoid treated surfaces, the pest will be less exposed to the insecticide. Consequently, the ability to avoid treated surface becomes a strong selection pressure, which can lead to development of be‐ havioural-based resistance, and it has been demonstrated in diamondback moth populations [35, 36], German cockroaches (*Blatella germanica* L. [Blattodea: Blattellidae] [23, 37], and maize weevils (*Sitophilus zeamais* Motschulsky [Coleoptera: Curculionidae]) [38]. [39] dem‐ onstrated that spider mites are repelled by the contact miticide, propargite. In a recent study of spider mites on cotton plants, [40] quantified the consequences of behavioural avoidance and based on theoretical modelling showed that behavioural avoidance can have significant

Summarizing this section, the ability to develop physiological resistance to insecticides is one of the key characteristics of the most economically important arthropod pests. There are widespread examples of pests developing behavioural resistance by avoiding treated leaf

impact on population dynamics.

#### **Table 1.** Dosage response

**Fig. 5** Dosage response **Table 1.** Dosage response

**0.01 0.1 1 10**

**Genotype ss Genotype sr Genotype rr**

**Pest mortality (%)**

**0.2**

**0.4**

**0.6**

**0.8**

insecticide, so pest individuals possessing such changes will have a higher chance of surviv‐ al, while individuals without the specific allele will be eliminated. If the insecticide resist‐ ance is monogenic, and only two alleles exist (r = resistant and s = susceptible) - dosage response curves for the three genotypes are typically presented with mortality increasing along a logarithmic dosage scale [27]. In a theoretical example of a pest population of 10,000 individuals (Fig. 5), individuals of genotype ss may be expected to succumb when the insec‐ ticide dosage ranges between 0.3-0.6 ppm, sr individuals when the insecticide dosage ranges between 0.6-1.2 ppm, and rr individuals when the insecticide dosage is above 30 ppm. If p = 0.001 is the allele frequency of r and q= 0.999 is the allele frequency of s, and the genotypes occur in Hardy-Weinberg proportions, then the demographic composition of the pest popu‐ lation in response to insecticide dosage is as outlined in Table 1. It is seen that subjecting a pest population to a dosage above 0.5 ppm causes a >99% reduction of the overall popula‐ tion, but if it less than 60 ppm it also increases the proportion of resistant individuals in the remaining pest population. And although this fairly simple relationship between survival of genotypes and insecticide dosage has been investigated intensively over the last 3-4 decades and been greatly expended upon – it illustrates the core challenge that insecticide based pest control is faced with: Growers want to suppress as large a proportion of the pest population as possible to minimize the economic loss they incur, and therefore apply high dosages of insecticides. However, they are not able to apply a high enough dosage to completely sup‐ press all pest individuals, so a selection pressure is imposed on the pest populations and the end result may be that the pest population develops physiological resistance because it is practically impossible to kill all the homozygous resistant genotypes.

In this brief and very general discussion of the importance of insecticide dosages, it is im‐ portant also to mention that the efficiency or performance of an applied insecticide declines over time. The term "residual effect" is used to describe the longevity of the time period in which a given insecticide provides effective pest control, and rarely (continuous expression of Bt toxins in transgenic Bt crops would be an exception) will an insecticide have a residual effect after 7-10 days. In a study of abamectin, [24] conducted experimental sprays of potato leaflets in different vertical portions of a potato canopy under field conditions. During time intervals after spraying, treated and untreated leaflets were collected and used in bioassays with potato psyllids to determine the adult psyllid mortality over time. Based on this study, [24] concluded that the residual effect of abamectin is less than 48 hours. Although most in‐ secticides have longer residual effect than abamectin, the example highlights the challenge that the effective dosage applied will decline over time, so pest individuals that are not af‐ fected immediately after application may not be exposed to a lethal dosage. For instance at the time of application, pest individuals may not be present at a vulnerable stage (for exam‐ ple mite eggs are not killed by systematic sprays whereas active adult mites will be killed), or the life stage may not be exposed to contact with chemicals (this is especially important for larvae of insects feeding on roots of plants in the soil). This problem or challenge, with not all life stages being equally susceptible to an insecticides application, becomes an even larger issue, if multiple pest species are present, and these different species occur in differ‐ ent parts of the crop canopy, have different movement patterns within the canopy, show dif‐ ference in terms of seasonal population dynamics, and also have different migration patterns between the given crop and neighbouring alternative hosts. Immigration by pest populations deserves to be highlighted as a serious constraint: if a pasture or field is treated and all present pest individuals are killed but high immigration means that a new popula‐ tion of pest individuals move into the given field or pasture a shortly after. If so, a grower might think that the insecticide application "didn't work" – but the reality is that the immi‐ gration rate of the pest needs to be taken into account when assessing what insecticide to apply and when to apply it. It is not practically feasible to apply insecticide specifically for each pest present separately and so inevitably each application event may effectively control some species or life stage, while other pest individuals will be exposed to sublethal dosages.

insecticide, so pest individuals possessing such changes will have a higher chance of surviv‐ al, while individuals without the specific allele will be eliminated. If the insecticide resist‐ ance is monogenic, and only two alleles exist (r = resistant and s = susceptible) - dosage response curves for the three genotypes are typically presented with mortality increasing along a logarithmic dosage scale [27]. In a theoretical example of a pest population of 10,000 individuals (Fig. 5), individuals of genotype ss may be expected to succumb when the insec‐ ticide dosage ranges between 0.3-0.6 ppm, sr individuals when the insecticide dosage ranges between 0.6-1.2 ppm, and rr individuals when the insecticide dosage is above 30 ppm. If p = 0.001 is the allele frequency of r and q= 0.999 is the allele frequency of s, and the genotypes occur in Hardy-Weinberg proportions, then the demographic composition of the pest popu‐ lation in response to insecticide dosage is as outlined in Table 1. It is seen that subjecting a pest population to a dosage above 0.5 ppm causes a >99% reduction of the overall popula‐ tion, but if it less than 60 ppm it also increases the proportion of resistant individuals in the remaining pest population. And although this fairly simple relationship between survival of genotypes and insecticide dosage has been investigated intensively over the last 3-4 decades and been greatly expended upon – it illustrates the core challenge that insecticide based pest control is faced with: Growers want to suppress as large a proportion of the pest population as possible to minimize the economic loss they incur, and therefore apply high dosages of insecticides. However, they are not able to apply a high enough dosage to completely sup‐ press all pest individuals, so a selection pressure is imposed on the pest populations and the end result may be that the pest population develops physiological resistance because it is

**Insecticide dosage (ppm)** 1.00 0.00 0.00 0.01 0.01

206 Insecticides - Development of Safer and More Effective Technologies

**Fig. 5** Dosage response **Table 1.** Dosage response

**Table 1.** Dosage response

**0.01 0.1 1 10**

**Genotype ss Genotype sr Genotype rr**

**Pest mortality (%)**

**0.2**

**0.4**

**0.6**

**0.8**

**Insecticide dosage Genotype Total**

**PPM ss sr rr Population** 0.00 9980.01 19.98 0.01 10000.00 0.10 9980.01 19.98 0.01 10000.00 0.20 9980.01 19.98 0.01 10000.00 0.30 8982.01 19.98 0.01 9002.00 0.40 5988.01 19.98 0.01 6008.00 0.50 2994.00 19.98 0.01 3013.99 0.60 0.00 19.98 0.01 19.99 0.70 0.00 17.98 0.01 17.99 0.80 0.00 11.99 0.01 12.00 0.90 0.00 5.99 0.01 6.00

practically impossible to kill all the homozygous resistant genotypes.

In this brief and very general discussion of the importance of insecticide dosages, it is im‐ portant also to mention that the efficiency or performance of an applied insecticide declines over time. The term "residual effect" is used to describe the longevity of the time period in which a given insecticide provides effective pest control, and rarely (continuous expression

In addition to concerns associated with physiological resistance of target pest populations, behavioural resistance may possibly develop in response to incomplete pesticide coverage, as target pests are given a "choice" between treated and untreated surfaces. If the target pest is able to discriminate between treated from untreated surfaces and eventually avoid treated surfaces, the pest will be less exposed to the insecticide. Consequently, the ability to avoid treated surface becomes a strong selection pressure, which can lead to development of be‐ havioural-based resistance, and it has been demonstrated in diamondback moth populations [35, 36], German cockroaches (*Blatella germanica* L. [Blattodea: Blattellidae] [23, 37], and maize weevils (*Sitophilus zeamais* Motschulsky [Coleoptera: Curculionidae]) [38]. [39] dem‐ onstrated that spider mites are repelled by the contact miticide, propargite. In a recent study of spider mites on cotton plants, [40] quantified the consequences of behavioural avoidance and based on theoretical modelling showed that behavioural avoidance can have significant impact on population dynamics.

Summarizing this section, the ability to develop physiological resistance to insecticides is one of the key characteristics of the most economically important arthropod pests. There are widespread examples of pests developing behavioural resistance by avoiding treated leaf surfaces or baits containing the active ingredient. With regards to contact insecticides, it is possible that a combination of frequent and low performing pesticide applications creates a selection pressure which favours pest individuals avoiding treated portions of crop leaves, as individuals: 1) have ample opportunity to recover after sub-lethal exposures and there‐ fore "learn" to avoid insecticide treated surfaces, and 2) will be under a directional selection pressure for non-feeding on treated surfaces. However, we are unaware of experimental studies actually addressing the possible relationship between insecticide spray coverage in agricultural field pest populations and behavioural resistance in target pest populations. It is likely that the most important pests will continue to develop resistance to insecticides, as certain traits in their biology and/or ecology appear to enable them to adapt to these severe selection pressures. Thus, continued emphasis on almost exclusive insecticide-based pest control may be a strategy that deserves serious revision, as it seems to play to one of the key "strengths" (their adaptability) of the most important pests. The fundamental challenge is therefore to develop management practices, which minimize the risk of resistance develop‐ ment, and theoretical modelling is critically important in this context, because it can be used as a working tool to examine changes in population genetics over time and under different selection pressures. That is, instead of waiting until growers actually face the severe conse‐ quences of insecticide resistance, we can use theoretical modelling to predict its progress and hopefully find ways to slow it down.

haviour, level of polyphagy, migration/dispersal and mobility, fitness costs of resistance de‐ velopment, and feeding biology), 3) operational (mode of action of insecticide, residual effect of the insecticide, adjuvants added to sprayed formulations, timing of applications in relation to pest population development (which life stages are targeted), dosage applied, crop density at the time of application, type of spray nozzles used, height of spray boom, and 4) weather conditions (which are known to greatly affect spray depositions, see above). With such complexity of factors involved, it is not surprising that much of the current un‐ derstanding of pesticide resistance development in pest populations is based on genetic population modelling and theoretical sensitivity analyses [10, 42-45]. Such modelling ef‐ forts are in many ways constructive and can be used to develop strong justifications for specific research projects and management practices. However at the same time, their val‐

The Performance of Insecticides – A Critical Review

http://dx.doi.org/10.5772/53987

209

The following section is a sensitivity analysis based on genetic population modelling, which expands on work presented in two theoretical modelling papers [26, 41]. Although publish‐ ed almost 40 years ago, these studies present the basic modelling framework needed to ex‐ amine fairly simple/basic questions about resistance development. Results presented here are based on a theoretical arthropod pest population "X" with an initial population of 11,000 individuals followed over 20 subsequent generations, and it is assumed that: 1) adults only give offspring in one generation, 2) each generation was exposed to a single insecticide ap‐ plication, 3) resistance development occurs in a single locus with two alleles, r (resistant) and s (susceptible), 4) p = 0.0001 is the gene frequency of r and q= 0.9999 is the gene frequen‐ cy of s, 5) genotypes occur in Hardy-Weinberg proportions, 6) dominance is assumed to be intermediate, so that, under insecticide based selection pressure, the survival of genotypes is rr > rs > ss, and 7) resistance was associated with a "fitness cost", which is defined as resist‐ ant genotypes having lower fitness than susceptible genotypes in the absence of the particu‐ lar insecticide [45]. Based on a review by [45] of 77 studies of Bt resistance, it was assumed that physiological insecticide resistance was associated with a "fitness cost" of 15.5% for each allele. Although the possible importance of "incomplete resistance" [42] and "hybrid vigor" [45] have been highlighted, these factors were not included in this analysis. The fol‐ lowing sensitivity analysis of r allele frequency and pest population density is based on 1,000 simulations of different scenarios with random variables. Similar to [26], the popula‐ tion density after each discrete generation, N', was assumed to be density-dependent and

( ( )


ù û

(1)

( ( ) ( ( )

/ .

In which W denotes the survival of each genotype, N denotes the number of adults in the previous generation, K denotes the carrying capacity, and Na denotes the initial population.

´ ´- ù <sup>û</sup> ´

' /

*N W N exp r K N K W N exp r K N K W N exp r K N K*

*rs rs rs a ss ss ss a*

´´ ´

/

*rr rr rr a*

= ´´ ´ - +

idity depends on the assumptions used in their construction [46, 47].

described by the following equation 1:

[

[ [
