SEQUENTIAL SAMPLING OF ADULT NORTHERN AND WESTERN CORN ROOTWORMS (COLEOPTERA: CHRYSOMELIDAE) IN SOUTHERN ONTARIO

1987 ◽  
Vol 119 (6) ◽  
pp. 577-585 ◽  
Author(s):  
H.J. McAuslane ◽  
C.R. Ellis ◽  
O.B. Allen
Keyword(s):  
Negative Binomial ◽  
Sequential Sampling ◽  
Ratio Test ◽  
Economic Threshold ◽  
Diabrotica Barberi ◽  
Southern Ontario ◽  
Sampling Plans ◽  
Field Corn ◽  
Corn Rootworms ◽  
Sequential Probability

AbstractThree sequential-sampling plans were developed for adult Diabrotica barberi Smith and Lawrence and D. virgifera virgifera LeConte in field corn in southern Ontario. The distribution of both species was well approximated by the negative binomial distribution and obeyed Taylor’s power law. Two plans, following the methods of Kuno (1969) and Green (1970), estimated populations with predetermined precision. The third plan, based on Wald’s (1947) sequential probability ratio test, categorized populations relative to an economic threshold. Different sequential-sampling plans were constructed for 1st-year fields and for all other fields because the parameter k, measuring the extent of aggregation of the beetles, differed between these two classes of fields. Decision equations for Wald’s plan in 1st-year fields were: D1 = 0.98n − 17.75 and D2 = 0.98n + 13.82. Decision equations for 2nd-year or older fields were: D1 = 0.99n − 13.42 and D2 = 0.99n + 10.45. The use of Wald’s plan reduced the required sample size by 55% in 1st-year fields, and by 48% in all other fields. The savings associated with the other sequential-sampling plans were not significant.

The use of Wald's sequential probability ratio test to develop composite three-decision sampling plans

1985 ◽  
Vol 15 (2) ◽  
pp. 326-330
Author(s):  
Gary W. Fowler
Keyword(s):  
Monte Carlo ◽  
Negative Binomial ◽  
Sequential Sampling ◽  
Ratio Test ◽  
Sample Number ◽  
Sampling Plans ◽  
Probability Ratio ◽  
Sequential Probability ◽  
Error Probabilities ◽  
Decision Boundaries

Many sequential sampling plans used in forest sampling are composite three-decision plans based on the simultaneous use of two of Wald's sequential probability ratio tests (SPRTs). Wald's operating characteristic (OC) and average sample number (ASN) equations for each SPRT are used to describe the properties of the composite sampling plan. Wald's equations are only approximate because of "overshooting" of the decision boundaries of the SPRTs and the two SPRTs operate simultaneously in the composite plan. Wald's and Monte Carlo OC and ASN functions were developed for (i) two SPRTs used to develop a three-decision composite plan and (ii) the three-decision composite plan based on the negative binomial distribution. Wald's equations, in general, overestimate the true error probabilities and underestimate the true ASN for a given SPRT. Wald's equations are less accurate in describing the properties of the three-decision plan. Monte Carlo functions are more accurate than Wald's functions. Recommendations are made regarding the choice between Wald's and Monte Carlo functions. A Monte Carlo procedure to modify the decision boundaries of the plan to yield actual error probabilities approximately equal to the desired error probabilities is suggested.

scholarly journals Spatial Distribution and Development of Sequential Sampling Plans for Diaphorina citri Kuwayama (Hemiptera: Liviidae)

Agronomy ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1434
Author(s):  
Gabriel Díaz-Padilla ◽  
J. Isabel López-Arroyo ◽  
Rafael A. Guajardo-Panes ◽  
Ignacio Sánchez-Cohen
Keyword(s):  
Spatial Distribution ◽  
Sequential Sampling ◽  
Field Evaluation ◽  
Production Costs ◽  
Ratio Test ◽  
Operational Characteristic ◽  
Diaphorina Citri ◽  
Sample Number ◽  
Sampling Plans ◽  
Sequential Probability

Vector control in huanglongbing management has been conducted on a calendar basis resulting in high production costs. We addressed this issue and proposed a sequential sampling plan to support decision making for intervention against Diaphorina citri Kuwayama, which is involved in the transmission of the bacteria Candidatus Liberibacter asiaticus, associated with such lethal disease. We analyzed 3,264,660 records from samples gathered from the Mexican trapping program for the monitoring of D. citri; it included weekly inspection of 86,004 yellow sticky traps distributed in the country. Spatial distribution of the insect, estimation of a common k (kc), and sequential sampling plans based on Sequential Probability Ratio Test (SPRT) were determined. Taylor’s power law coefficients were ≥1 indicating aggregation in the spatial distribution of the insect. Common k ranged from 0.0183 to 0.2253 and varied independently of geographic zone or citrus species. We obtained 18 sequential sampling plans, one for each state. In the Average Sample Number (ASN) function, the minimal number of samples to make a decision ranged from 17 to 65. In the Operational Characteristic (OC) function, probabilities for a correct intervention at the threshold of 0.2 D. citri adults/trap in most cases were above 80%. In a field evaluation, the application of sampling plans yielded savings obtained by reduction in the number of interventions for insect control.

Accuracy of sequential sampling plans based on Wald's sequential probability ratio test

1983 ◽  
Vol 13 (6) ◽  
pp. 1197-1203 ◽  
Author(s):  
Gary W. Fowler
Keyword(s):  
Monte Carlo ◽  
Sequential Sampling ◽  
Sequential Probability Ratio Test ◽  
Ratio Test ◽  
Type I ◽  
Sample Number ◽  
Sampling Plans ◽  
Probability Ratio ◽  
Sequential Probability ◽  
Relative Errors

Monte Carlo operating characteristic (OC) and average sample number (ASN) functions were compared with Wald's OC and ASN equations for sequential sampling plans based on Wald's sequential probability ratio test (SPRT) using the binomial, negative binomial, normal, and Poisson distributions. This comparison showed that the errors inherent in Wald's equations as a result of "overshooting" the decision boundaries of the SPRT can be large. Relative errors increased for the OC and ASN equations as the difference between the null (θ0)) and alternative (θ1) test parameter values increased. Relative errors also increased for the ASN equation as the probabilities of type I (α) and type II (β) errors increased. For discrete distributions, the relative errors also increased as θ0 increased with θ1/θ0 fixed. Wald's equations, in general, overestimate the true error probabilities and underestimate the true ASN. For the values of θ0, θ1, α, and β used in many sequential sampling plans in forestry, Wald's equations may not be adequate. For those cases where the errors in Wald's equations are important compared with the other errors associated with the sampling plan, two alternative Monte Carlo OC and ASN functions are proposed.

The Stability of Weed Seedling Population Models and Parameters in Eastern Nebraska Corn (Zea mays) and Soybean (Glycine max) Fields

Weed Science ◽  
1995 ◽  
Vol 43 (4) ◽  
pp. 604-611 ◽  
Author(s):  
Gregg A. Johnson ◽  
David A. Mortensen ◽  
Linda J. Young ◽  
Alex R. Martin
Keyword(s):  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Negative Binomial ◽  
Sequential Sampling ◽  
Mean Value ◽  
Distribution Model ◽  
Ratio Test ◽  
Weed Species ◽  
Sampling Procedures ◽  
Grass Weed

Intensive field surveys were conducted in eastern Nebraska to determine the frequency distribution model and associated parameters of broadleaf and grass weed seedling populations. The negative binomial distribution consistently fit the data over time (1992 to 1993) and space (fields) for both the inter and intrarow broadleaf and grass weed seedling populations. The other distributions tested (Poisson with zeros, Neyman type A, logarithmic with zeros, and Poisson-binomial) did not fit the data as consistently as the negative binomial distribution. Associated with the negative binomial distribution is akparameter.kis a nonspatial aggregation parameter related to the variance at a given mean value. Thekparameter of the negative binomial distribution was consistent across weed density for individual weed species in a given field except for foxtail spp. populations. Stability of thekparameter across field sites was assessed using the likelihood ratio test There was no stable or commonkvalue across field sites and years for all weed species populations. The lack of stability inkacross field sites is of concern, because this parameter is used extensively in the development of parametric sequential sampling procedures. Becausekis not stable across field sites,kmust be estimated at the time of sampling. Understanding the variability in it is critical to the development of parametric sequential sampling strategies and understanding the dynamics of weed species in the field.

Sequential Sampling Plans for Adult Corn Rootworms (Coleoptera: Chrysomelidae)1

1982 ◽  
Vol 75 (5) ◽  
pp. 791-793 ◽  
Author(s):  
R. E. Foster ◽  
J. J. Tollefson ◽  
K. L. Steffey
Keyword(s):  
Sequential Sampling ◽  
Sampling Plans ◽  
Corn Rootworms

scholarly journals Sampling for Plant Disease Incidence

1999 ◽  
Vol 89 (11) ◽  
pp. 1088-1103 ◽  
Author(s):  
L. V. Madden ◽  
G. Hughes
Keyword(s):  
Power Law ◽  
Binomial Distribution ◽  
Disease Incidence ◽  
Sequential Sampling ◽  
Cluster Sampling ◽  
Diseased Plant ◽  
Ratio Test ◽  
Wide Range ◽  
Sequential Probability ◽  
The Relationship

Knowledge of the distribution of diseased plant units (such as leaves, plants, or roots) or of the relationship between the variance and mean incidence is essential to efficiently sample for diseased plant units. Cluster sampling, consisting of N sampling units of n individuals each, is needed to determine whether the binomial or beta-binomial distribution describes the data or to estimate parameters of the binary power law for disease incidence. The precision of estimated disease incidence can then be evaluated under a wide range of settings including the hierarchical sampling of groups of individuals, the various levels of spatial heterogeneity of disease, and the situation when all individuals are disease free. Precision, quantified with the standard error or the width of the confidence interval for incidence, is directly related to N and inversely related to the degree of heterogeneity (characterized by the intracluster correlation, ρ). Based on direct estimates of ρ (determined from the θ parameter of the beta-binomial distribution or from the observed variance) or a model predicting ρ as a function of incidence (derived from the binary power law), one can calculate, before a sampling bout, the value of N needed to achieve a desired level of precision. The value of N can also be determined during a sampling bout using sequential sampling methods, either to estimate incidence with desired precision or to test a hypothesis about true disease incidence. In the latter case, the sequential probability ratio test is shown here to be useful for classifying incidence relative to a hypothesized threshold when the data follows the beta-binomial distribution with either a fixed ρ or a ρ that depends on incidence.

Development of Binomial Sequential Sampling Plans for Sugarcane Aphid (Hemiptera: Aphididae) in Commercial Grain Sorghum

2020 ◽  
Vol 113 (4) ◽  
pp. 1990-1998 ◽  
Author(s):  
Jessica C Lindenmayer ◽  
Kristopher L Giles ◽  
Norman C Elliott ◽  
Allen E Knutson ◽  
Robert Bowling ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Sequential Sampling ◽  
Grain Sorghum ◽  
Ratio Test ◽  
Test Model ◽  
Leaf Sample ◽  
Melanaphis Sacchari ◽  
Sampling Plans ◽  
Sugarcane Aphid ◽  
Economic Thresholds

Abstract The sugarcane aphid (Melanaphis sacchari Zehntner) is a significant economic pest of grain sorghum (Sorghum bicolor (L.) Moench) in the Southern United States. Current nominal and research-based economic thresholds are based on estimates of mean aphids per leaf. Because enumerating aphids per leaf is potentially time consuming, binomial sequential sampling plans for M. sacchari were developed that allow users to quickly classify the economic status of field populations and determine when an economic threshold has been exceeded. During 2016 and 2017, counts of M. sacchari were recorded from 281 sampling events in 140 sorghum fields located in six states (Oklahoma, Kansas, Texas, Arkansas, Louisiana, Mississippi) . Regression analysis was used to describe the relationships between the mean M. sacchari density per two-leaf sample and proportion of plants infested with one or more aphids. Tally thresholds of T50 and T100 aphids per two-leaf sample were selected based on goodness of fit and practicality. Stop lines for both tally thresholds were developed for selected economic thresholds using Wald’s sequential probability ratio test. Model validations using an additional 48 fields demonstrated that reliable classification decisions could be made with an average of 11 samples regardless of location. This sampling system, when adopted, can allow users to easily and rapidly determine when M. sacchari infestations need to be treated.

The impact of misspecification of the negative binomial shape parameter in sequential sampling plans

1986 ◽  
Vol 16 (3) ◽  
pp. 608-611 ◽  
Author(s):  
W. G. Warren ◽  
Pin Whei Chen
Keyword(s):  
Operating Characteristic ◽  
Shape Parameter ◽  
Negative Binomial ◽  
Sequential Sampling ◽  
Forest Pests ◽  
Sample Number ◽  
Sampling Plans ◽  
Average Sample Number ◽  
The Impact ◽  
Average Sample

Standard sequential sampling plans for determining whether infestations of forest pests have attained critical levels are commonly based on the assumption that the counts follow a negative binomial distribution for which the shape parameter, k, which must be specified, may be difficult to estimate and may well be unstable. This paper studies the effect of misspecification of this parameter on the operating characteristic and average sample number functions of a sequential sampling plan. It appears that slight underestimation of the shape parameter can improve the operating characteristic at little cost, i.e., with only small increase in the average sample number.

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