**6. Some realities associated with rotation of insecticides**

Scenario 2: Effect of varying pesticide spray performance. In this scenario, varying repro‐ ductive 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 in‐ crease the survival of Wrs and Wss. The basis for investigating this scenario with varying sur‐ vival 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 as‐ sume 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 con‐ sidered to be equal to 0.5. In other words, the random function generated one of five out‐ comes (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. Regard‐ ing genotype Wss, the same random function approach was applied to generate random sur‐ vival 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

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 individu‐ als 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 ach‐ ieved within about 11 generations. With fixed variables and assumption about Hardy-Wein‐ berg allele frequencies, the r allele frequency obviously stayed above 50% and increased as the homozygous resistant genotype increased in relative proportion. Fig. 7b showed, as expected, that the varying survival of homozygous susceptible individuals (when Wss > 0) led to a decrease in r allele frequency. In fact after 20 generations, none of the 1,000 simulations led to a higher r al‐ lele frequency than 93%, while with fixed variables it was >96%. In other words, this simple exer‐ cise suggested that by allowing susceptible genotypes some level of survival, low and inconsistent pesticide applications appear to postpone development of complete resistance. However, low and inconsistent pesticide applications also lead to higher risk of high pest popu‐ lation densities (comparing Fig. 6 a and 7 a) and therefore crop damage and corresponding yield

to subsequent generations in simulations with Wss > 0.

212 Insecticides - Development of Safer and More Effective Technologies

It may be argued that the scenarios outlined above are far too simplistic and do not take into account that growers are rotating insecticides as part of resistance management prac‐ tices. The core of resistance management programs is to rotate between active ingredi‐ ents, as cross-resistance to multiple insecticides is much less likely to develop. Regarding transgenic crops expressing Bt toxins, incorporation of non-treated refuges in cropping systems is also being advocated [see [45] for review). We are unaware of recommenda‐ tions about non-treated refuges for any other insecticide treatments. Consequently rota‐ tion of classes of active ingredients is the only widespread resistance management strategy, but there are crop-pest systems in which only a few active ingredients are regis‐ tered for use. For instance in Western Australia, there are three species of aphids [The cabbage aphid, *Brevicoryne brassicae* (L.), the turnip aphid, *Lipaphis erysimi* Kalt, and the green peach aphid, *Myzus persicae* (Sulzer) (Hemiptera: Aphidae)] attacking canola dur‐ ing the flowering/podding period – yet only ONE insecticide (Pirimicarb 500) is regis‐ tered for use against these pests! In addition, active ingredients are increasingly being faced out (banned) - so growers are left with only a few options. And if one particular pest is under a single insecticide selection pressure in one cropping system, then this may be the source for a resistant pest population to emerge. In addition, rotation of in‐ secticides is only an effective option as long as cross-resistance is close to negligible, al‐ though there are ample examples of arthropod pests developing resistance to a many

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

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.

> markets (cropping systems). In addition to chemical companies being less inclined to reg‐ ister new insecticides due to registration costs, it also means that newly registered insec‐ ticides are often considerably more expensive than older insecticides, because the registration costs are passed on to end-users. And a stark difference in price between old and new insecticide obviously creates an economic incentive for continuing insecticide treatment programs based almost exclusively on old/less-expensive insecticides. Thus, re‐ sistance in target pest populations may continue to develop due to lack of rotation of in‐ secticides, because growers are unwilling to incorporate newer and more expensive insecticides into their insecticide application regime even though alternative products are commercially available. In short, development of resistance to one active ingredient is a serious concern, because it may initiate a "snowball effect", as loss of one active ingredi‐ ent, and effectively an entire insecticide class, means that growers can only rotate among pesticides with a few other modes of action, and that increases the risk of resistance de‐ velopment to those alternative pesticides. Thus, for a range of economical, biological/ genetic reasons - growers and other stakeholders associated with the food industry should be profoundly concerned about the long-term sustainability of pest management programs relying almost exclusively on pesticide applications. There needs to be far greater awareness of the risk of resistance developing and its likely long-term cost, so that better decisions can be made on the benefits to rotating with more expensive com‐

The Performance of Insecticides – A Critical Review

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

215

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

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

pounds.

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

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 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

In this scenario with varying survival rates of Wrs and Wss, the population density after the initial knock-down was, on average,

markets (cropping systems). In addition to chemical companies being less inclined to reg‐ ister new insecticides due to registration costs, it also means that newly registered insec‐ ticides are often considerably more expensive than older insecticides, because the registration costs are passed on to end-users. And a stark difference in price between old and new insecticide obviously creates an economic incentive for continuing insecticide treatment programs based almost exclusively on old/less-expensive insecticides. Thus, re‐ sistance in target pest populations may continue to develop due to lack of rotation of in‐ secticides, because growers are unwilling to incorporate newer and more expensive insecticides into their insecticide application regime even though alternative products are commercially available. In short, development of resistance to one active ingredient is a serious concern, because it may initiate a "snowball effect", as loss of one active ingredi‐ ent, and effectively an entire insecticide class, means that growers can only rotate among pesticides with a few other modes of action, and that increases the risk of resistance de‐ velopment to those alternative pesticides. Thus, for a range of economical, biological/ genetic reasons - growers and other stakeholders associated with the food industry should be profoundly concerned about the long-term sustainability of pest management programs relying almost exclusively on pesticide applications. There needs to be far greater awareness of the risk of resistance developing and its likely long-term cost, so that better decisions can be made on the benefits to rotating with more expensive com‐ pounds.
