Sequential Sampling Plan for Northern and Western Corn Rootworms (Coleoptera: Chrysomelidae) in New York

1991 ◽  
Vol 84 (1) ◽  
pp. 165-169 ◽  
Author(s):  
E. J. Shields ◽  
R. B. Sher ◽  
P. S. Taylor
Keyword(s):  
New York ◽  
Sequential Sampling ◽  
Sampling Plan ◽  
Sequential Sampling Plan ◽  
Corn Rootworms

Development of a Sequential Sampling Plan using Spatial Attributes of Cercospora Leaf Spot Epidemics of Table Beet in New York

Plant Disease ◽  
2021 ◽  
Author(s):  
Daniel Winter Heck ◽  
Julie R Kikkert ◽  
Linda Hanson ◽  
Sarah Jane Pethybridge
Keyword(s):  
New York ◽  
Leaf Spot ◽  
Disease Incidence ◽  
Sequential Sampling ◽  
Sampling Plan ◽  
Classification Models ◽  
Cercospora Leaf Spot ◽  
Sample Number ◽  
Sequential Sampling Plan ◽  
True Value

Sampling strategies that effectively assess disease intensity in the field are important to underpin management decisions. To develop a sequential sampling plan for the incidence of Cercospora leaf spot (CLS), caused by Cercospora beticola, 31 table beet fields were assessed in New York. Assessments of CLS incidence were performed in six leaves arbitrarily selected in 51 sampling locations along each of the three to six linear transects per field. Spatial pattern analyses were performed, and results were used to develop sequential sampling estimation and classification models. CLS incidence (p) ranged from 0.13 to 0.92 with a median of 0.31, and beta-binomial distribution, which is reflective of aggregation, best described the spatial patterns observed. Aggregation was commonly detected (>95%) by methods using the point-process approach, runs analyses, and autocorrelation up to the fourth spatial lag. For SADIE, 45% of the datasets were classified as a random pattern. In the sequential sampling estimation and classification models, disease units are sampled until a prespecified target is achieved. For estimation, the goal was sampling CLS incidence with a preselected coefficient of variation (C). Achieving the C = 0.1 was challenging with less than 51 sampling units, and only observed on datasets with an incidence above 0.3. Reducing the level of precision, i.e. increasing C to 0.2, allowed the preselected C be achieved with a lower number of sampling units and with an estimated incidence (p̂) close to the true value of p. For classification, the goal was to classify the datasets above or below prespecified thresholds (pt) used for CLS management. The average sample number (ASN) was determined by Monte Carlo simulations, and was between 20 and 45 at disease incidence values close to pt, and approximately 11 when far from pt. Correct decisions occurred in over 76% of the validation datasets. Results indicated these sequential sampling plans can be used to effectively assess CLS incidence in table beet fields.

EFFICIENT MONITORING OF THE DENSITY OF ADULT NORTHERN CORN ROOTWORM (COLEOPTERA: CHRYSOMELIDAE) IN HELD CORN

1985 ◽  
Vol 117 (2) ◽  
pp. 171-183 ◽  
Author(s):  
Alan J. Sawyer
Keyword(s):  
New York ◽  
Sequential Sampling ◽  
Plant Sample ◽  
Sample Sizes ◽  
Sequential Sampling Plan ◽  
Field Corn ◽  
Corn Rootworms ◽  
Entire Plant ◽  
Sampling Statistics ◽  
Northern Corn Rootworm

AbstractSampling statistics for adult northern corn rootworms in New York field corn are reported, with particular reference to efficient monitoring for decision-making in pest management. The most efficient sample unit of those examined was a single, entire plant. Sample sizes and associated costs required to achieve fixed levels of precision for estimates of density are reported. Because required sample sizes are density dependent, a sequential-sampling plan providing final estimates of density at preset levels of precision is developed. The method makes no assumption about the frequency distribution of insect counts, which was found to vary from field to field and with time. Disturbance caused by sampling activity may introduce a bias of unknown direction and magnitude in estimates of beetle density. Care in approaching and examining plants; should reduce this bias.

scholarly journals Within-plant distribution and binomial sequential sampling plan for Tetranychus evansi (Acari: Tetranychidae) in greenhouse tomato: implications for management.

2020 ◽  
Author(s):  
Willis Ndeda Ochilo ◽  
Gideon Nyamasyo ◽  
John Agano
Keyword(s):  
Sequential Sampling ◽  
Sampling Plan ◽  
Plant Distribution ◽  
Tetranychus Evansi ◽  
Sequential Sampling Plan ◽  
Red Spider Mite ◽  
Mite Density ◽  
Density Analysis ◽  
The Tropics ◽  
Best Fit

Abstract The red spider mite, Tetranychus evansi is a critical pest of tomato in the tropics. Control of T. evansi has traditionally depended on acaricide treatments. However, it is only in a handful of crops where monitoring techniques for mites, using statistical methods, have been developed to help farmers decide when to spray. The objective of this study, therefore, was to develop a sampling plan that would help farmers increase accuracy, and reduce the labor and time needed to monitor T. evansi on tomato. The distribution of T. evansi within-plant was aggregated, and intermediate leaves (YFL) was the most appropriate sampling unit to evaluate the mite density. Analysis based on Taylor's Power Law showed an aggregated pattern of distribution of T. evansi, while assessment of the fitness of the binomial model indicated that a tally threshold of 5 mites per YFL provided the best fit. Consequently, binomial sequential sampling plans premised on three action thresholds (0.1, 0.2 and 0.3) were developed. The binomial sequential sampling plan for T. evansi developed in this study has the potential to significantly increase the chance for targeted acaricide applications. This judicious use of pesticides is especially crucial within the context of integrated pest management (IPM).

scholarly journals SPATIAL DISTRIBUTION AND SEQUENTIAL SAMPLING OF Brevipalpus phoenicis IN CITRUS

2016 ◽  
Vol 38 (4) ◽  
Author(s):  
WALTER MALDONADO JR ◽  
JOSÉ CARLOS BARBOSA ◽  
MARÍLIA GREGOLIN COSTA ◽  
PAULO CÉSAR TIBURCIO GONÇALVES ◽  
TIAGO ROBERTO DOS SANTOS
Keyword(s):  
Spatial Distribution ◽  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Negative Binomial ◽  
Sequential Sampling ◽  
Sampling Plan ◽  
Goodness Of Fit Test ◽  
Control Plan ◽  
Sequential Sampling Plan ◽  
Brevipalpus Phoenicis

ABSTRACT Among the pests of citrus, one of the most important is the red and black flat mite Brevipalpus phoenicis (Geijskes), which transmits the Citrus leprosis virus C (CiLV-C).When a rational pest control plan is adopted, it is important to determine the correct timing for carrying out the control plan. Making this decision demands constant follow-up of the culture through periodic sampling where knowledge about the spatial distribution of the pest is a fundamental part to improve sampling and control decisions. The objective of this work was to study the spatial distribution pattern and build a sequential sampling plan for the pest. The data used were gathered from two blocks of Valencia sweet orange on a farm in São Paulo State, Brazil, by 40 inspectors trained for the data collection. The following aggregation indices were calculated: variance/ mean ratio, Morisita index, Green’s coefficient, and k parameter of the negative binomial distribution. The data were tested for fit with Poisson and negative binomial distributions using the chi-square goodness of fit test. The sequential sampling was developed using Wald’s Sequential Probability Ratio Test and validated through simulations. We concluded that the spatial distribution of B. phoenicis is aggregated, its behavior best fitted to the negative binomial distribution and we built and validated a sequential sampling plan for control decision-making.

Economic injury level for sequential sampling plan of Frankliniella schultzei in bell pepper crops

2019 ◽  
Vol 123 ◽  
pp. 30-35 ◽  
Author(s):  
Jhersyka da S. Paes ◽  
Tamíris A. de Araújo ◽  
Rodrigo S. Ramos ◽  
João Rafael S. Soares ◽  
Vitor C.R. de Araújo ◽  
...  
Keyword(s):  
Sequential Sampling ◽  
Sampling Plan ◽  
Bell Pepper ◽  
Economic Injury Level ◽  
Frankliniella Schultzei ◽  
Sequential Sampling Plan ◽  
Economic Injury

A sequential sampling plan for exponential distribution

2016 ◽  
Vol 35 (3) ◽  
pp. 331-346
Author(s):  
Yeh Lam ◽  
Boris Choy ◽  
Philip Yu
Keyword(s):  
Exponential Distribution ◽  
Sequential Sampling ◽  
Sampling Plan ◽  
Sequential Sampling Plan
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