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Integrating drivers influencing the detection of plant pests carried in the international cut flower trade

Areal, F., Touza, J., MacLeod, A., Dehnen-Schmutz, K., Perrings, C., Palmieri, M. G. and Spence, N. J. (2008) Integrating drivers influencing the detection of plant pests carried in the international cut flower trade. Journal of Environmental Management, 89 (4). pp. 300-307. ISSN 0301-4797

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To link to this article DOI: 10.1016/j.jenvman.2007.06.017

Abstract/Summary

This paper analyses the cut flower market as an example of an invasion pathway along which species of non-indigenous plant pests can travel to reach new areas. The paper examines the probability of pest detection by assessing information on pest detection and detection effort associated with the import of cut flowers. We test the link between the probability of plant pest arrivals as a precursor to potential invasion, and volume of traded flowers using count data regression models. The analysis is applied to the UK import of specific genera of cut flowers form Kenya between 1996 and 2004. There is a link between pest detection and the Genus of cut flower imported. Hence, pest detection efforts should focus on identifying and targeting those imported plants with a high risk of carrying pest species. For most of the plants studied efforts allocated to inspection have a significant influence on the probabilty of pest detction. However, by better targetting inspection efforts, it is shown that plant inspection effort could be reduced without increasing the risk of pest entry. Similarly, for most of the plants analysed, an increase in volume traded will not necessarily lead to an increase in the number of pests entering the UK. For some species, such as conclude that analysis at the rank of plant Genus is important both to understand the effectiveness of plant pest detection efforts and consequently to manage the risk of introduction of non-indigenous species.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Life Sciences > School of Agriculture, Policy and Development
Faculty of Life Sciences > School of Agriculture, Policy and Development > Economic and Social Sciences Division > Food Economics and Marketing (FEM)
ID Code:8257
Uncontrolled Keywords:Alien invasive species, Cut flowers, Plant pest species, Count data, models, Phytosanitary inspection, BIOLOGICAL INVASIONS, REGRESSION, POISSON, OVERDISPERSION, TESTS, MODEL
Publisher:Elsevier

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