Literature DB >> 26861068

Sampling method evaluation and empirical model fitting for count data to estimate densities of Oligonychus perseae (Acari: Tetranychidae) on 'Hass' avocado leaves in southern California.

Jesús R Lara1, Naseem T Saremi2, Martin J Castillo2, Mark S Hoddle2.   

Abstract

Oligonychus perseae (Acari: Tetranychidae) is an important foliar spider mite pest of 'Hass' avocados in several commercial production areas of the world. In California (USA), O. perseae densities in orchards can exceed more than 100 mites per leaf and this makes enumerative counting prohibitive for field sampling. In this study, partial enumerative mite counts along half a vein on an avocado leaf, an industry recommended practice known as the "half-vein method", was evaluated for accuracy using four data sets with a combined total of more than 485,913 motile O. perseae counted on 3849 leaves. Sampling simulations indicated that the half-vein method underestimated mite densities in a range of 15-60 %. This problem may adversely affect management of this pest in orchards and potentially compromise the results of field research requiring accurate mite density estimation. To address this limitation, four negative binomial regression models were fit to count data in an attempt to rescue the half-vein method for estimating mite densities. These models were incorporated into sampling plans and evaluated for their ability to estimate mite densities on whole leaves within 30-tree blocks of avocados. Model 3, a revised version of the original half-vein model, showed improvement in providing reliable estimates of O. perseae densities for making assessments of general leaf infestation densities across orchards in southern California. The implications of these results for customizing the revised half-vein method as a potential field sampling tool and for experimental research in avocado production in California are discussed.

Entities:  

Keywords:  Count data; GLMMs; Hass avocado; Negative binomial regression; Oligonychus perseae; Pest management; Sampling

Mesh:

Year:  2016        PMID: 26861068     DOI: 10.1007/s10493-016-0018-5

Source DB:  PubMed          Journal:  Exp Appl Acarol        ISSN: 0168-8162            Impact factor:   2.132


  9 in total

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2.  Photographic sampling: a photographic sampling method for mites on plants.

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3.  Fitting and using growth curves.

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4.  Sequential hypothesis testing with spatially correlated presence-absence data.

Authors:  Elijah DePalma; Daniel R Jeske; Jesus R Lara; Mark Hoddle
Journal:  J Econ Entomol       Date:  2012-06       Impact factor: 2.381

5.  Comparison and Field Validation of Binomial Sampling Plans for Oligonychus perseae (Acari: Tetranychidae) on Hass Avocado in Southern California.

Authors:  Jesus R Lara; Mark S Hoddle
Journal:  J Econ Entomol       Date:  2015-06-11       Impact factor: 2.381

6.  Baseline susceptibility of persea mite (Acari: Tetranychidae) to abamectin and milbemectin in avocado groves in Southern California.

Authors:  Eduardo C Humeres; Joseph G Morse
Journal:  Exp Appl Acarol       Date:  2005       Impact factor: 2.132

7.  Alternative food improves the combined effect of an omnivore and a predator on biological pest control. A case study in avocado orchards.

Authors:  J J González-Fernández; F de la Peña; J I Hormaza; J R Boyero; J M Vela; E Wong; M M Trigo; M Montserrat
Journal:  Bull Entomol Res       Date:  2008-12-08       Impact factor: 1.750

8.  EpiCollect: linking smartphones to web applications for epidemiology, ecology and community data collection.

Authors:  David M Aanensen; Derek M Huntley; Edward J Feil; Fada'a al-Own; Brian G Spratt
Journal:  PLoS One       Date:  2009-09-16       Impact factor: 3.240

9.  Monitoring wildlife-vehicle collisions in the information age: how smartphones can improve data collection.

Authors:  Daniel D Olson; John A Bissonette; Patricia C Cramer; Ashley D Green; Scott T Davis; Patrick J Jackson; Daniel C Coster
Journal:  PLoS One       Date:  2014-06-04       Impact factor: 3.240

  9 in total

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