The Performance of Insecticides – A Critical Review

Nansen, Christian; Ridsdill-Smith, T. J.

doi:10.5772/53987

Cited by 17 publications

(16 citation statements)

References 92 publications

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“…Targeted application of insecticide has the potential to reduce pesticide usage in conventional farming systems yet maintain an effective control over agricultural pests. Focusing spray along field borders can also potentially address the issue of spray coverage, one of the main problems faced in field-wide treatment of pesticides that severely reduce the efficacy of applied chemicals (Nansen and Ridsdill-Smith 2013). An example of successful targeted application of insecticides would be the recent efforts to control brown marmorated stink bug, Halyomorpha halys Stål (Hemiptera: Pentatomidae), in Northeastern USA by focus spraying along edges in peach orchards instead of orchard-wide application (Blaauw et al 2015).…”

Section: Potential Applications Of Edge-biased Distributions Into Insmentioning

confidence: 99%

Edge-biased distributions of insects. A review

Nguyen

Nansen

2018

Agron. Sustain. Dev.

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Spatial ecology includes research into factors responsible for observed distribution patterns of organisms. Moreover, the spatial distribution of an animal at a given spatial scale and in a given landscape may provide valuable insight into its host preference, fitness, evolutionary adaptation potential, and response to resource limitations. In agro-ecology, in-depth understanding of spatial distributions of insects is of particular importance when the goals are to (1) promote establishment and persistence of certain food webs, (2) maximize performance of pollinators and natural enemies, and (3) develop precision-targeted monitoring and detection of emerging outbreaks of herbivorous pests. In this article, we review and discuss a spatial phenomenon that is widespread among insect species across agricultural systems and across spatial scales-they tend to show "edge-biased distributions" (spatial distribution patterns show distinct "edge effects"). In the conservation and biodiversity literature, this phenomenon has been studied and reviewed intensively in the context of how landscape fragmentation affects species diversity. However, possible explanations of, and also implications of, edge-biased distributions of insects in agricultural systems have not received the same attention. Our review suggests that (1) mathematical modeling approaches can partially explain edge-biased distributions and (2) abiotic factors, crop vegetation traits, and environmental parameters are factors that are likely responsible for this phenomenon. However, we argue that more research, especially experimental research, is needed to increase the current understanding of how and why edge-biased distributions of insects are so widespread. We argue that the fact that many insect pests show edge-biased distribution patterns may be used to optimize both pest monitoring practices and precision targeting of pesticide applications and releases of natural enemies.

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Section: Potential Applications Of Edge-biased Distributions Into Insmentioning

confidence: 99%

Edge-biased distributions of insects. A review

Nguyen

Nansen

2018

Agron. Sustain. Dev.

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“…As discussed by recent authors (Whalon et al 2008;Nansen and Ridsdill-Smith 2013;Renton et al 2014) and publicly available databases (the Arthropod Pesticide Resistance Database (APRD, http://www.pesticideresistance.org/), a main characteristic of most known pests is their ability to develop resistance to pesticides. With the continuously growing list of pesticides becoming ineffective due to resistance, insecticides being phased out due to concerns about their adverse environmental effects, and with chemical companies having to spend increasing amounts of resources on getting new active ingredients registered for commercial use, it seems reasonable to reflect on the longterm sustainability of the current pesticide application practices.…”

Section: Snapcard Predictions and Spray Coverages Published Elsewherementioning

confidence: 99%

“…Thus, applications of pesticides should be considered a selection pressure imposed by humans, and the sustainability of a particular pesticide becomes a matter of resistance management: how to kill enough pests to avoid economic losses but at the same time not impose a strong and persistent selection pressure leading to resistance in the target pest population and shortening the useful life of the pesticide. Many factors influence the actual level of pesticide resistance development in a pest population, including the following (Nansen and Ridsdill-Smith 2013;Gassmann et al 2009;Georghiou and Taylor 1976;Renton et al 2011Renton et al , 2014: (1) genetic factors (i.e. frequency, dominance, and expressivity of resistant alleles and their interactions with other alleles, fitness costs associated with resistance, past selection pressures in pest population, and whether the resistance is monogenic or polygenic), and (2) biological factors (fecundity, generation and development times, mating behavior, level of polyphagy, migration/dispersal and mobility, fitness costs of resistance development, and feeding biology).…”

Section: Snapcard Predictions and Spray Coverages Published Elsewherementioning

confidence: 99%

“…Based on reviews of global trends affecting current food production systems worldwide (Nansen and Ridsdill-Smith 2013;Bon et al 2014;Liu et al 2014;Popp et al 2013), it is predicted that a wide range of agricultural and horticultural production systems will face an increase in pest pressures and therefore also in numbers of pesticide spray applications (Bon et al 2014;Liu et al 2014;Martínez-Blanco et al 2013) (Fig. 1a).…”

Section: Introductionmentioning

confidence: 99%

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Optimizing pesticide spray coverage using a novel web and smartphone tool, SnapCard

Nansen

Ferguson

Moore

et al. 2015

Agron. Sustain. Dev.

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The overuse of pesticides leads to contamination of water and food. Therefore, there is a need for tools and strategies to optimize pesticide application. Here we present SnapCard, a user-friendly and freely available decision support tool for farmers and agricultural consultants, available at snapcard.agric.wa.gov.au. SnapCard allows to predict, measure, and archive pesticide spray coverage quantified from water-sensitive spray cards. Variables include spray settings such as nozzle orifice size, sprayer speed, water carrier rate and adjuvant, and weather variables such as barometric pressure, relative humidity, temperature, and wind speed at ground level. We use separate regression models for four nozzles types. Our results showed that there are strong and positive correlations between water carrier rate and spray coverage for all four nozzle types. Moreover, sprayer speed is highly negatively correlated with obtained spray coverage. In addition, there is no consistent effect of either nozzle type or use of a particular adjuvant, across water carrier intervals. We conclude that varying combinations of spray settings and weather conditions caused marked ranges of spray coverages among the four nozzle types, thus highlighting the importance of selecting the right nozzle orifice size and type. We demonstrate that realistic scenarios of environmental conditions and spray settings can lead to predictions of very low spray coverage with at least one of the four nozzle types. We discuss how the novel and freely available smartphone app, SnapCard, can be used to optimize spray coverage, reduce spray drift, and minimize the risk of resistance development in target pest populations.

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“…This concept was developed further (Brewer and Goodell 2012) and is now also being referred to as "threshold-based pest management" (Nansen and Ridsdill-Smith 2013). As part of developing threshold-based pest management, it is important to be able to estimate, by sampling, the population densities of arthropod pest species in a reliable and practically feasible manner.…”

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confidence: 99%

Spatially-Optimized Sequential Sampling Plan for Cabbage AphidsBrevicoryne brassicaeL. (Hemiptera: Aphididae) in Canola Fields

2016

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The cabbage aphid is a significant pest worldwide in brassica crops, including canola. This pest has shown considerable ability to develop resistance to insecticides, so these should only be applied on a "when and where needed" basis. Thus, optimized sampling plans to accurately assess cabbage aphid densities are critically important to determine the potential need for pesticide applications. In this study, we developed a spatially optimized binomial sequential sampling plan for cabbage aphids in canola fields. Based on five sampled canola fields, sampling plans were developed using 0.1, 0.2, and 0.3 proportions of plants infested as action thresholds. Average sample numbers required to make a decision ranged from 10 to 25 plants. Decreasing acceptable error from 10 to 5% was not considered practically feasible, as it substantially increased the number of samples required to reach a decision. We determined the relationship between the proportions of canola plants infested and cabbage aphid densities per plant, and proposed a spatially optimized sequential sampling plan for cabbage aphids in canola fields, in which spatial features (i.e., edge effects) and optimization of sampling effort (i.e., sequential sampling) are combined. Two forms of stratification were performed to reduce spatial variability caused by edge effects and large field sizes. Spatially optimized sampling, starting at the edge of fields, reduced spatial variability and therefore increased the accuracy of infested plant density estimates. The proposed spatially optimized sampling plan may be used to spatially target insecticide applications, resulting in cost savings, insecticide resistance mitigation, conservation of natural enemies, and reduced environmental impact.

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The Performance of Insecticides – A Critical Review

Cited by 17 publications

References 92 publications

Edge-biased distributions of insects. A review

Edge-biased distributions of insects. A review

Optimizing pesticide spray coverage using a novel web and smartphone tool, SnapCard

Spatially-Optimized Sequential Sampling Plan for Cabbage AphidsBrevicoryne brassicaeL. (Hemiptera: Aphididae) in Canola Fields

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