**7. Appreciation of the seasonal variability of pest population dynamics**

insecticides (examples listed above). Another important aspect of insecticide rotation is that during the last 50 years, it has been a successful but short-term strategy to rely on a continuous development of new pesticides, so the steady increase in insecticides losing their performance has been less of an issue. However, there seem to be a trend of chemi‐ cal companies registering fewer new insecticides, and at the same time older chemistries are being faced out. So the total number and the diversity of commercially available in‐ secticides are decreasing. And with less available options to choose from, there is obvi‐ ously an increased overall risk of resistance development. The declining number of new insecticide registrations is very interesting and likely explained by a complex of factors. But it is clear that, in recent years, regulatory bodies have increased the amount of risk assessment studies required for a successful registration, and many of these quite expen‐ sive. Thus, chemical companies are less inclined to register new insecticides unless they target very large commercial markets. So risks of insecticide resistance, due to few insec‐ ticide alternatives to choose from, may be of particular concern to comparatively smaller

In this scenario with varying survival rates of Wrs and Wss, the population density after the initial knock-down was, on average, 95%, but there were simulations in which it was below 80%. It should be expected, that increased survival due to low and inconsistent pesticide applications increased the pest population growth during 20 generations, but, in comparison with scenario 1, the effect on pest population was actually quite modest (Fig. 7a). As an example, in scenario 1 (with no survival of homozygous susceptible individuals) the average population density was about 6,000 individuals after 15 generations, while it was about 7,000 individuals in scenario 2. Thus, with half of the simulations allowing 1-20% survival of homozygous susceptible individuals there was only a modest increase in average pest population density. However as indicated by the maximum curve, there were indeed scenarios in which high pest populations were achieved within about 11 generations. With fixed variables and assumption about

**Generations 0 5 10 15 20**

**5 10 15 20**

**Average**

**Minimum and maximum Fixed variables**

Figure 7. Effects of varying spray application performance

**Pest population**

**0**

**b**

**2000**

**R allele frequency in pest population**

**0.0**

**Figure 7.** Effects of varying spray application performance

**0.2**

**0.4**

**0.6**

**0.8**

**1.0**

**4000**

**6000**

**8000**

**10000**

**12000**

**14000**

214 Insecticides - Development of Safer and More Effective Technologies

**a**

Scenario 2: Effect of varying pesticide spray performance. In this scenario, varying reproductive fitness rates and carrying capacity were maintained as described in scenario 1. The survival rate due to pesticide applications was kept constant for the homozygous resistant (Wrr = 1), but this scenario was conducted with varying survival rates of Wrs and Wss. That is, it was assumed that low and inconsistent spray coverage would in some generations increase the survival of Wrs and Wss. The basis for investigating this scenario with varying survival rates of Wrs and Wss was supported by the field spray data presented in Fig. 3: Out of the 91 insecticide spray data sets, several data sets showed spray coverage ranges above 50 times (difference between minimum and maximum). It therefore seems reasonable to assume that there is considerable variation in insecticide dosages and therefore survival rates of subsequent pest population generations. Consequently, a random number function was used to generate survival rates from 0.3 to 0.7 for Wrs, and survival rates below 0.5 were considered to be equal to 0.5. In other words, the random function generated one of five outcomes (0.3, 0.4, 0.5, 0.6, or 0.7) with equal probability, and three of these (0.3, 0.4, and 0.5 or 60% of the outcomes) were equal to 0.5, and there was a 50% chance [(0.7-0.5)×100/(0.7-0.3)] of increased survival due to low and inconsistent spray coverage for Wrs genotypes. Regarding genotype Wss, the same random function approach was applied to generate random survival rates from -0.2 to 0.2, and all rates equal to or below 0 denoted no survival. In other words, there was about 50% chance of Wss genotypes contributing at least some offspring to the next generation. As in scenario 1, a 15.5% fitness cost was maintained for each resistant allele, which meant that the reproductive fitness of rrs = rrr × 1.155 and that of rss = rrs × 1.155. In other words, the survival of Wss genotypes had the potential of contributing substantially to subsequent generations in simulations with Wss > 0.

> Integrated pest management (IPM) has been an applied research discipline since it was first defined by [55]. One of the initial drivers for development of IPM was the recognition of pest populations developing resistance to pesticides [56]. Many definitions and in-depth descriptions of IPM have been provided [4, 57-59]. Broadly speaking, IPM involves integra‐ tion of different tactics such as pesticides, biological control, measures to prevent initial pest establishment, use of plant resistance and cultural control. Consequently, IPM requires in-depth understanding of a given target pest's biology and ecology so that cropping sys‐ tems can be established and managed in ways that minimize risk of pest infestations and subsequent yield losses. IPM is expected to reduce dependence on pesticides, and [60] ar‐ gued that in several respects IPM may be viewed as "IIM", or integrated insecticide man‐ agement. However, the most important difference between IPM and other crop management systems is that IPM is based on two fundamental assumptions about yield loss: 1) that it is correlated with pest density and 2) predictable and therefore can be model‐ led and/or forecasted. Thus, an IPM approach implies that if the pest population density can be accurately estimated, it is possible to determine when and where deployment of re‐ sponsive management options (such as pesticide applications and/or releases of natural en‐ emies) are needed. Reliable and practically feasible sampling or monitoring plans are therefore needed to estimate the pest population density. The pest density estimate is con‐ verted into a decision based on an "economic threshold" (ET), which represents the pest

density at which the value of estimated yield loss equals the cost of responsive interven‐ tion. If it is assumed that yield loss can be predicted or forecasted based on a given pest population density, then the economic injury level (EIL) can be used as benchmark for when to take action. Consequently, responsive intervention, such as applying an insecti‐ cide, should only be deployed, when/if the pest density estimate is expected to exceed the EIL or the ET. The "textbook" concept of EIL (i.e. [4] includes the following variables (Equation 2):

$$\text{EIL} = \mathbf{C} \mid \left(\mathbf{V} \times \mathbf{D} \times \mathbf{K}\right) \tag{2}$$

dividual growers face "low risk" and "high risk" growing seasons, and this is tightly linked to the often opportunistic character of arthropod pest populations – that they are able to take advantage of certain combinations of environmental and agronomic condi‐ tions; but they also suffer under other combinations, and in those years insecticide appli‐ cations may not be warranted. As an example, the diamondback moth is among the most important pests on growing canola in Australia (winter crop). [62] summarized the widely accepted hypothesis regarding the ecological mechanisms driving diamondback moths outbreaks in winter rainfall regions of Australia: in years with good summer rainfall, sup‐ porting various cruciferous plants, including wild radish, turnip weeds and volunteer can‐ ola before the growing season. These host plants provide a "green bridge" during the summer months and enable early establishment of diamondback moth populations. At the same time, good summer and autumn rainfall means that canola is planted comparatively early and therefore establishes well under those growing conditions. Canola is a highly preferred host by diamondback moth [63], so populations developing in weeds and nonagricultural habitats may migrate into canola and cause economic damage. The risk of se‐ vere diamondback moth infestations seem to be further increased if, after early rains, the canola becomes slightly drought stressed. During the last 10 years, seasonal weather pat‐ terns characterized by good summer and autumn rainfall seem to explain a couple of growing seasons with high losses in large canola growing regions due to diamondback moth infestations. However in most years, diamondback moth is not considered a major pest on a wide geographical scale. As already mentioned, diamondback moth is one of the most adaptable arthropod pests regarding insecticide treatments, as it was the first pest to develop resistance to DDT and Bt, and, as a species, it is considered resistant to at least 82 active ingredients (may vary among local populations). Thus, for long-term sus‐ tainable management of diamondback moth, it is highly important that its somewhat sporadic pest status is taken into account and that insecticides are only applied when and where they are deemed necessary. The important aspect of arthropod pest densities only occasionally leading to significant economic losses is that it provides justification for some times (in some growing seasons and/or in some cropping systems) NOT to apply insecti‐ cides, when pest populations are below a given threshold. However, diamondback moth being a sporadic pest in canola in Western Australia is by no means a unique pest–crop system, as most insect pests vary economic importance across seasons. For a wide range of orchards pests [including a moth pest complex of peach [64] and *Acrobasis nuxvorella* Nuenzig (Lepidoptera: Pyralidae) in pecan [65], and field pests [including *Hypera postica* (Gyllenhal) (Coleoptera: Curculionidae) in alfalfa [66], and *Cylindrocopturus adspersus* (Le‐ Conte) (Coleoptera: Curculionidae) in sunflower [67], *Diabrotica virgifera* virgifera LeConte (Coleoptera: Chrysomelidae) adults in maize [68], and *Sitodiplosis mosellana* (Géhin) (Dip‐ tera: Cecidomyiidae) in wheat [69], there are well-established degree-day models to pre‐ dict "low risk" and "high risk" growing seasons. Such degree day models represent two important notions: 1) that the economic importance of a given pest shows considerable re‐ gional and seasonal variation, and 2) that the considerable spatio-temporal variation in economic importance can be predicted/forecasted based on quantitative models. Such

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217

In which "C" denotes the cost of action (i.e. application of an insecticide), "V" denotes the market value of the crop, "D" denotes the relationships between pest density and dam‐ age, and "K" denotes a coefficient of unavoidable loss (between 0-1). With IPM being an approach based on knowledge about the pest density and the relationship between pest density and economic loss, we argue that costs associated with sampling and the willing‐ ness to accept risk should also be included in the calculation of EIL. In development of se‐ quential sampling plans, it is inherently assumed that there is a positive correlation between the precision of the population density estimate and the required sampling ef‐ fort. In most cases, this relationship is probably asymptotic – so the question becomes how much is gained by collecting, for instance, 25 leaf samples compared to 20, or 47 in‐ stead of 42? The answer to this question is not necessarily straight forward, because the "cost" or effort associated with sampling should be taken into account, and the relative cost or effort per data point is not necessarily linear. In other words, most of the sampling costs may be associated with actually driving to the field, and once you are there, it may cost almost the same to take 20 or 25 samples. However for simplicity, we have added two variables to the calculation of the EIL (Equation 3)

$$\rm EIL = \left\lfloor \rm C \times \left( \rm S \,/ \left( 1 \text{ - } P \right) \right) \right\rfloor \left( \rm V \times D \times K \right) \tag{3}$$

With "S" denoting the cost of collecting one sample (i.e. counting number of nymphs on a crop leaf) and "P" denotes the required precision of the sampling effort (0 < P <1). Thus with these additions to Equation 2, it is acknowledged that "expensive" or labour inten‐ sive sampling will increase the pest population density which triggers action, and that re‐ quirements of high precision of pest population estimates will increase the needed sampling effort and therefore the the EIL. The concept of adding precision or tolerance of error to sampling methods is expanded further in sequential sampling [4, 61].

The concept of IPM – or only taking action on a when-needed basis- is supported by the fact that in most cropping systems, densities of pest species and their economic impor‐ tance (expressed in crop damage or yield loss) are markedly influenced by weather and agronomic factors and therefore not constant across growing seasons or regions. Thus, in‐ dividual growers face "low risk" and "high risk" growing seasons, and this is tightly linked to the often opportunistic character of arthropod pest populations – that they are able to take advantage of certain combinations of environmental and agronomic condi‐ tions; but they also suffer under other combinations, and in those years insecticide appli‐ cations may not be warranted. As an example, the diamondback moth is among the most important pests on growing canola in Australia (winter crop). [62] summarized the widely accepted hypothesis regarding the ecological mechanisms driving diamondback moths outbreaks in winter rainfall regions of Australia: in years with good summer rainfall, sup‐ porting various cruciferous plants, including wild radish, turnip weeds and volunteer can‐ ola before the growing season. These host plants provide a "green bridge" during the summer months and enable early establishment of diamondback moth populations. At the same time, good summer and autumn rainfall means that canola is planted comparatively early and therefore establishes well under those growing conditions. Canola is a highly preferred host by diamondback moth [63], so populations developing in weeds and nonagricultural habitats may migrate into canola and cause economic damage. The risk of se‐ vere diamondback moth infestations seem to be further increased if, after early rains, the canola becomes slightly drought stressed. During the last 10 years, seasonal weather pat‐ terns characterized by good summer and autumn rainfall seem to explain a couple of growing seasons with high losses in large canola growing regions due to diamondback moth infestations. However in most years, diamondback moth is not considered a major pest on a wide geographical scale. As already mentioned, diamondback moth is one of the most adaptable arthropod pests regarding insecticide treatments, as it was the first pest to develop resistance to DDT and Bt, and, as a species, it is considered resistant to at least 82 active ingredients (may vary among local populations). Thus, for long-term sus‐ tainable management of diamondback moth, it is highly important that its somewhat sporadic pest status is taken into account and that insecticides are only applied when and where they are deemed necessary. The important aspect of arthropod pest densities only occasionally leading to significant economic losses is that it provides justification for some times (in some growing seasons and/or in some cropping systems) NOT to apply insecti‐ cides, when pest populations are below a given threshold. However, diamondback moth being a sporadic pest in canola in Western Australia is by no means a unique pest–crop system, as most insect pests vary economic importance across seasons. For a wide range of orchards pests [including a moth pest complex of peach [64] and *Acrobasis nuxvorella* Nuenzig (Lepidoptera: Pyralidae) in pecan [65], and field pests [including *Hypera postica* (Gyllenhal) (Coleoptera: Curculionidae) in alfalfa [66], and *Cylindrocopturus adspersus* (Le‐ Conte) (Coleoptera: Curculionidae) in sunflower [67], *Diabrotica virgifera* virgifera LeConte (Coleoptera: Chrysomelidae) adults in maize [68], and *Sitodiplosis mosellana* (Géhin) (Dip‐ tera: Cecidomyiidae) in wheat [69], there are well-established degree-day models to pre‐ dict "low risk" and "high risk" growing seasons. Such degree day models represent two important notions: 1) that the economic importance of a given pest shows considerable re‐ gional and seasonal variation, and 2) that the considerable spatio-temporal variation in economic importance can be predicted/forecasted based on quantitative models. Such

density at which the value of estimated yield loss equals the cost of responsive interven‐ tion. If it is assumed that yield loss can be predicted or forecasted based on a given pest population density, then the economic injury level (EIL) can be used as benchmark for when to take action. Consequently, responsive intervention, such as applying an insecti‐ cide, should only be deployed, when/if the pest density estimate is expected to exceed the EIL or the ET. The "textbook" concept of EIL (i.e. [4] includes the following variables

In which "C" denotes the cost of action (i.e. application of an insecticide), "V" denotes the market value of the crop, "D" denotes the relationships between pest density and dam‐ age, and "K" denotes a coefficient of unavoidable loss (between 0-1). With IPM being an approach based on knowledge about the pest density and the relationship between pest density and economic loss, we argue that costs associated with sampling and the willing‐ ness to accept risk should also be included in the calculation of EIL. In development of se‐ quential sampling plans, it is inherently assumed that there is a positive correlation between the precision of the population density estimate and the required sampling ef‐ fort. In most cases, this relationship is probably asymptotic – so the question becomes how much is gained by collecting, for instance, 25 leaf samples compared to 20, or 47 in‐ stead of 42? The answer to this question is not necessarily straight forward, because the "cost" or effort associated with sampling should be taken into account, and the relative cost or effort per data point is not necessarily linear. In other words, most of the sampling costs may be associated with actually driving to the field, and once you are there, it may cost almost the same to take 20 or 25 samples. However for simplicity, we have added

EIL = C × S / 1 - P / V × D × K ( ( )) ( ) é ù

error to sampling methods is expanded further in sequential sampling [4, 61].

With "S" denoting the cost of collecting one sample (i.e. counting number of nymphs on a crop leaf) and "P" denotes the required precision of the sampling effort (0 < P <1). Thus with these additions to Equation 2, it is acknowledged that "expensive" or labour inten‐ sive sampling will increase the pest population density which triggers action, and that re‐ quirements of high precision of pest population estimates will increase the needed sampling effort and therefore the the EIL. The concept of adding precision or tolerance of

The concept of IPM – or only taking action on a when-needed basis- is supported by the fact that in most cropping systems, densities of pest species and their economic impor‐ tance (expressed in crop damage or yield loss) are markedly influenced by weather and agronomic factors and therefore not constant across growing seasons or regions. Thus, in‐

two variables to the calculation of the EIL (Equation 3)

216 Insecticides - Development of Safer and More Effective Technologies

EIL = C / V × D × K ( ) (2)

ë û (3)

(Equation 2):

models can be used very effectively to estimate whether a particular arthropod pest in a given growing season poses a threat to a certain crop system and provide strong founda‐ tion for only using insecticides on a when-needed basis. Only applying insecticides when needed may save growers money, and it will undoubtedly reduce the risk of insecticide resistance.

thropod resistance in transgenic crops (see [45] for review). Of course, the potential of tak‐ ing advantage of benefits from reduced insecticide application is based on the assumption that a combination of detection/monitoring and degree-day modelling can be converted into accurate and reliable decision support tools. Thus, it is paramount to envision the de‐ velopment of arthropod pest population growth models under field conditions as an es‐ sential part of optimizing use of insecticides – both in terms of when application is

and "high risk" growing seasons. Such degree day models represent two important notions: 1) that the economic importance of a given pest shows considerable regional and seasonal variation, and 2) that the considerable spatio-temporal variation in economic importance can be predicted/forecasted based on quantitative models. Such models can be used very effectively to estimate whether a particular arthropod pest in a given growing season poses a threat to a certain crop system and provide strong foundation for only using insecticides on a when-needed basis. Only applying insecticides when needed may save growers money,

Above, it was established that, mainly as "peace of mind" or because of operational conven‐ ience and less as a response to actual emerging pest infestations, insecticides are being

**Generation 0 5 10 15 20**

**0 5 10 15 20**

**Threshold = 2% of carrying capacity Threshold = 5% of carrying capacity Threshold = 10% of carrying capacity**

**Threshold = 0**

becomes slightly drought stressed. During the last 10 years, seasonal weather patterns characterized by good summer and autumn rainfall seem to explain a couple of growing seasons with high losses in large canola growing regions due to diamondback moth infestations. However in most years, diamondback moth is not considered a major pest on a wide geographical scale. As already mentioned, diamondback moth is one of the most adaptable arthropod pests regarding insecticide treatments, as it was the first pest to develop resistance to DDT and Bt, and, as a species, it is considered resistant to at least 82 active ingredients (may vary among local populations). Thus, for long-term sustainable management of diamondback moth, it is highly important that its somewhat sporadic pest status is taken into account and that insecticides are only applied when and where they are deemed necessary. The important aspect of arthropod pest densities only occasionally leading to significant economic losses is that it provides justification for some times (in some growing seasons and/or in some cropping systems) NOT to apply insecticides, when pest populations are below a given threshold. However, diamondback moth being a sporadic pest in canola in Western Australia is by no means a unique pest–crop system, as most insect pests vary economic importance across seasons. A wide range of orchards pests [including a moth pest complex of peach [64] and *Acrobasis nuxvorella* Nuenzig (Lepidoptera: Pyralidae) in pecan [65], and field pests [including *Hypera postica* (Gyllenhal) (Coleoptera: Curculionidae) in alfalfa [66], and *Cylindrocopturus adspersus* (LeConte) (Coleoptera: Curculionidae) in sunflower [67], *Diabrotica virgifera* virgifera LeConte (Coleoptera: Chrysomelidae) adults in maize [68], and *Sitodiplosis mosellana* (Géhin) (Diptera: Cecidomyiidae) in wheat [69], there are well-established degree-day models to

The Performance of Insecticides – A Critical Review

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219

needed and as part of resistance management.

**Pest population**

**0**

**2000**

**R allele frequency**

**0.2**

**9. Pest infestations – symptoms rather than problems**

**0.3**

**0.4**

**0.5**

**0.6**

**0.7**

**b**

**0.8**

**4000**

**6000**

**8000**

**10000**

**a**

Figure 8. Effects of incorporating an action threshold

and it will undoubtedly reduce the risk of insecticide resistance.

**Figure 8.** Effects of incorporating an action threshold

predict "low risk"
