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dc.contributor.authorChen, P.C.en_US
dc.contributor.authorLiu, S.C.en_US
dc.contributor.authorLiu, H.I.en_US
dc.contributor.authorChen, T.W.en_US
dc.contributor.authorChiang, K.S.en_US
dc.description.abstractFor quarantine sampling, it is of fundamental importance to determine the probability of finding an infestation when a specified number of units are inspected. In general, current sampling procedures assume 100% probability (perfect) of detecting a pest if it is present within a unit. Ideally, a nematode extraction method should remove all stages of all species with 100% efficiency regardless of season, temperature, or other environmental conditions; in practice however, no method approaches these criteria. In this study we determined the probability of detecting nematode infestations for quarantine sampling with imperfect extraction efficacy. Also, the required sample and the risk involved in detecting nematode infestations with imperfect extraction efficacy are presented. Moreover, we developed a computer program to calculate confidence levels for different scenarios with varying proportions of infestation and efficacy of detection. In addition, a case study, presenting the extraction efficacy of the modified Baermann's Funnel method on Aphelenchoides besseyi, is used to exemplify the use of our program to calculate the probability of detecting nematode infestations in quarantine sampling with imperfect extraction efficacy. The result has important implications for quarantine programs and highlights the need for a very large number of samples if perfect extraction efficacy is not achieved in such programs. We believe that the results of the study will be useful for the determination of realistic goals in the implementation of quarantine sampling.en_US
dc.relationJournal of Nematologyen_US
dc.relation.ispartofseriesJournal of Nematology, Volume 43, Issue 1, Page(s) 16-24.en_US
dc.titleProbability of detecting nematode infestations for quarantine sampling with imperfect extraction efficacyen_US
dc.typeJournal Articlezh_TW
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