Errors Incurred When Using Iwao's Sequential Decision Rule in Insect Sampling

Nyrop, Jan P.; Simmons, Gary A.

doi:10.1093/ee/13.6.1459

Cited by 24 publications

(26 citation statements)

References 5 publications

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“…Consequently, the OC function underestimates the ␣ near 2 and overestimates ␤ near 1 (Fowler 1978, Fowler andLynch 1987). Thus, it is suggested that ␣ and ␤ be used as parameters that can be adjusted to provide acceptable OC and ASN functions and that this makes the cost/reliability trade-off a vital part of the sampling program (Nyrop and Simmons 1984, Nyrop et al 1989, Nyrop and Binns 1990. Considering these issues and with adjustments to the sampling plan parameters, the resulting OC functions of each sampling plan under consideration can be used to generate a decision probability analysis and determine the best sampling plan (Tables 1Ð3).…”

Section: Discussionmentioning

confidence: 99%

Implementing Reduced-Risk Integrated Pest Management in Fresh-Market Cabbage: Influence of Sampling Parameters, and Validation of Binomial Sequential Sampling Plans for the Cabbage Looper (Lepidoptera: Noctuidae)

Burkness¹,

Hutchison²

2009

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Populations of cabbage looper, Trichoplusiani (Lepidoptera: Noctuidae), were sampled in experimental plots and commercial fields of cabbage (Brasicca spp.) in Minnesota during 1998-1999 as part of a larger effort to implement an integrated pest management program. Using a resampling approach and the Wald's sequential probability ratio test, sampling plans with different sampling parameters were evaluated using independent presence/absence and enumerative data. Evaluations and comparisons of the different sampling plans were made based on the operating characteristic and average sample number functions generated for each plan and through the use of a decision probability matrix. Values for upper and lower decision boundaries, sequential error rates (alpha, beta), and tally threshold were modified to determine parameter influence on the operating characteristic and average sample number functions. The following parameters resulted in the most desirable operating characteristic and average sample number functions; action threshold of 0.1 proportion of plants infested, tally threshold of 1, alpha = beta = 0.1, upper boundary of 0.15, lower boundary of 0.05, and resampling with replacement. We found that sampling parameters can be modified and evaluated using resampling software to achieve desirable operating characteristic and average sample number functions. Moreover, management of T. ni by using binomial sequential sampling should provide a good balance between cost and reliability by minimizing sample size and maintaining a high level of correct decisions (>95%) to treat or not treat.

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Section: Discussionmentioning

confidence: 99%

Implementing Reduced-Risk Integrated Pest Management in Fresh-Market Cabbage: Influence of Sampling Parameters, and Validation of Binomial Sequential Sampling Plans for the Cabbage Looper (Lepidoptera: Noctuidae)

Burkness¹,

Hutchison²

2009

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“…A decision on whether to spray could readily be reached for 8-10 fields in 1 day, depending on distance between fields. This level of sampling has a 25% probability of yielding an incorrect population assessment, which represents a higher frequency of sequential decision errors than sampling programs at the two higher precision levels (Nyrop and Simmons 1984), a .40% likelihood of having to resample, and a 13% possibility of making an incorrect control decision in fields. The latter two assessments, though, were from fields that were selected because the probability of their larval populations being near or above economic thresholds was higher.…”

Section: Discussionmentioning

confidence: 99%

Sequential decision plan for controlling Mamestra configurata in spring canola

2009

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A sequential decision plan was developed for controlling larvae of the bertha armyworm, Mamestra configurata Walker (Lepidoptera: Noctuidae), in canola (Brassica napus L. and B. rapa L., Brassicaceae), using 0.25 and 0.5 m 2 sampling units. Fields in Manitoba were sampled from 1980 to 1994 to determine minimum sample sizes and upper and lower cumulative larval counts at three economic thresholds. Taylor's power law described most of the variation between mean larval density and variance for 0.25 m 2 (r 2 5 0.926) and 0.5 m 2 (r 2 5 0.924) samples. Larvae were found to have a moderately clumped distribution in canola (b 5 1.42). Levels of precision (D 0 ) varying from 0.15 to 0.25 caused minimum sample sizes to vary between 6 and 21 for the 0.5 m 2 samples to between 9 and 31 for the 0.25 m 2 samples, for an economic threshold of 16-24 larvae/m 2 (P 5 0.20). Mean sampling times ranged from 40-108 for the 0.25 m 2 samples to 49-126 min for the 0.5 m 2 samples. The sampling plan for the 0.25 m 2 samples was verified in 18 fields in 2006 and 2007. A correct decision was made in 87% (D 0 5 0.25), 91% (D 0 5 0.20), and 94% (D 0 5 0.15) of the fields when the recommendation was to spray if a decision could not be reached after a second sampling. The mean number of samples needed for making a decision was 14 (D 0 5 0.25), 19 (D 0 5 0.20), and 32 (D 0 5 0.15).We recommend that growers use a precision level of 0.20 to minimize error rates and sampling effort. In most years, the minimum number of 0.25 m 2 samples per field that growers would need to take is 14-17.

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“…Both the operating characteristics and the average sample size are affected by the statistical assumptions of the particular method chosen. Of particular importance is the assumption that the precision level is fixed, which is not usually valid (Nyrop and Simmons 1984). This assumption is less critical for sweep sampling than for counts on individual plants because the sampling unit consists of many sweeps and the counts are essentially means.…”

Section: Discussionmentioning

confidence: 99%

FOLIAGE SUITABILITY OF SAPLINGS GROWN UNDER CONTRASTING WATER REGIMES TO THE GYPSY MOTH, LYMANTRIA DISPAR

Naidoo

Lechowicz

1998

Can Entomol

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The Canadian Entomologist 130: 837 -851 (1998) Plant bugs in the genus Lygus are pests of canola (Brassica napus L. and Brassica rapa L.) in western Canada and may require insecticidal control. Sweep-net sampling of field plots and commercial fields in southern Manitoba between 1988 and 1995 was used to develop sequential sampling plans for plant bugs in canola to facilitate control. The variance-mean relationships for plant bug catches were defined by Taylor's power law, and the parameters of the relationships were the same for field plots and commercial fields. Sampling units of 10, 20, 50, and 100 sweeps per sample had variance-mean relationships with the same slope but different intercepts and required different minimal sample sizes. Samples taken at two crop stages had similar variance-mean relationships, but at a later crop stage the intercept of the relationship differed and the parameters were estimated with less precision. Samples taken in two ways along the edges of commercial fields and at various distances into the fields all gave similar estimates of plant bug density, justifying the use of edge sampling. Experienced samplers caught more plant bugs than inexperienced ones, although the difference was primarily due to the number of nymphs rather than adults that were collected, and this difference was less pronounced in the edge samples. Sweep-net sampling collected less than 10% of the plant bugs present in the sampling area. Sequential decision plans are presented for four sampling units and three crop stages. Sampling commercial canola with a sweep net to make decisions on the need to control plant bugs can be completed in as little as 28-35 min. The sampling is most efficiently conducted with a sampling unit of 10 or 20 sweeps taken along the edge of a field. In an independent test of the sampling method, plant bug densities were classified correctly in relation to the need for control in 20 fields using the minimum sample size. Wise, I.L., et R.J. Lamb. 1998. L'dchantillonnage des punaises des plautes Lygus spp. (Heteroptera : Miridae) pour dvaluer la n6cessit6 des programmes de lutte dans les cultures de colza. The Canadian Entomologist 130 : 837-851. Dans l'ouest canadien, les punaises des plantes du genre Lygus sont des insectes ravageurs du colza (Brassica napus L. et Brassica rapa L.) qui requibrent parfois la mise sur pied de programmes de lutte au moyen d'insecticides. L'Cchantillonnage au filet fauchoir dans des carrCs Cchantillons et dans des champs commerciaux du sud relation n7Ctait plus la m&me et les parambtres ont Ct C estimCs avec moins de prCcision. Les Cchantillons recueillis de deux fagons le long des bordures de champs commerciaux et B diverses distances dans les champs ont tous donnC des estimations semblables de la densit6 des punaises, ce qui justifie 1'Cchantillonnage en bordure. Les Cchantillonneurs expCrimentCs ont rCcoltC plus de punaises que les Cchantillonneurs inexpCrimentCs, mais les diffkrences se manifestaient surtout par le nombre de larves recueillies plut6t q...

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Errors Incurred When Using Iwao's Sequential Decision Rule in Insect Sampling

Cited by 24 publications

References 5 publications

Implementing Reduced-Risk Integrated Pest Management in Fresh-Market Cabbage: Influence of Sampling Parameters, and Validation of Binomial Sequential Sampling Plans for the Cabbage Looper (Lepidoptera: Noctuidae)

Implementing Reduced-Risk Integrated Pest Management in Fresh-Market Cabbage: Influence of Sampling Parameters, and Validation of Binomial Sequential Sampling Plans for the Cabbage Looper (Lepidoptera: Noctuidae)

Sequential decision plan for controlling Mamestra configurata in spring canola

FOLIAGE SUITABILITY OF SAPLINGS GROWN UNDER CONTRASTING WATER REGIMES TO THE GYPSY MOTH, LYMANTRIA DISPAR

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