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Zhang, Hong
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Lundström, N. L. P., Zhang, H. & Brännström, Å. (2017). Pareto-efficient biological pest control enable high efficacy at small costs. Ecological Modelling, 364, 89-97
Open this publication in new window or tab >>Pareto-efficient biological pest control enable high efficacy at small costs
2017 (English)In: Ecological Modelling, ISSN 0304-3800, E-ISSN 1872-7026, Vol. 364, p. 89-97Article in journal (Refereed) Published
Abstract [en]

Biological pest control is increasingly used in agriculture as an alternative to traditional chemical pest control. In many cases, this involves a one-off or periodic release of naturally occurring and/or genetically modified enemies such as predators, parasitoids, or pathogens. As the interaction between these enemies and the pest is complex and the production of natural enemies potentially expensive, it is not surprising that both the efficacy and economic viability of biological pest control are debated. Here, we investigate the performance of very simple control strategies. In particular, we show how Pareto-efficient one-off or periodic release strategies, that optimally trade off between efficacy and economic viability, can be devised and used to enable high efficacy at small economic costs. We demonstrate our method on a pest–pathogen–crop model with a tunable immigration rate of pests. By analyzing this model, we demonstrate that simple Pareto-efficient one-off and periodic release strategies are efficacious and simultaneously have profits that are close to the theoretical maximum obtained by strategies optimizing only the profit. When the immigration rate of pests is low to intermediate, one-off control strategies are sufficient, and when the immigration of pests is high, periodic release strategies are preferable. The methods presented here can be extended to more complex scenarios and be used to identify promising biological pest control strategies in many circumstances.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2017
Pareto frontier, Pest management, Optimization, Dual target, Spodoptera litura
National Category
Bioinformatics and Systems Biology
urn:nbn:se:umu:diva-140512 (URN)10.1016/j.ecolmodel.2017.08.015 (DOI)000414818900008 ()
Available from: 2017-10-12 Created: 2017-10-12 Last updated: 2018-06-09Bibliographically approved

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