scholarly journals Sequential sampling plans and economic injury levels forEmpoasca kraemerion common bean crops at different technological levels

2017 ◽  
Vol 74 (2) ◽  
pp. 398-405 ◽  
Author(s):  
Marcelo Fialho de Moura ◽  
Mayara Cristina Lopes ◽  
Renata Ramos Pereira ◽  
Jorgiane Benevenute Parish ◽  
Mateus Chediak ◽  
...  
Keyword(s):  
Common Bean ◽  
Sequential Sampling ◽  
Sampling Plans ◽  
Economic Injury

Economic injury levels and sequential sampling plans forFrankliniella schultzeiin watermelon crops

2016 ◽  
Vol 73 (7) ◽  
pp. 1438-1445 ◽  
Author(s):  
Poliana S Pereira ◽  
Renato A Sarmento ◽  
Tarcísio VS Galdino ◽  
Carlos HO Lima ◽  
Fábio A dos Santos ◽  
...  
Keyword(s):  
Sequential Sampling ◽  
Sampling Plans ◽  
Economic Injury

Economic injury levels and sequential sampling plans for control decision-making systems ofBemisia tabacibiotype B adults in watermelon crops

2018 ◽  
Vol 75 (4) ◽  
pp. 998-1005 ◽  
Author(s):  
Carlos H de O Lima ◽  
Renato A Sarmento ◽  
Poliana S Pereira ◽  
Arthur V Ribeiro ◽  
Danival J Souza ◽  
...  
Keyword(s):  
Decision Making ◽  
Sequential Sampling ◽  
Sampling Plans ◽  
Economic Injury ◽  
Control Decision

Economic injury levels and sequential sampling plans for Bemisia tabaci (Hemiptera: Aleyrodidae) biotype B on open-field melon crops

2019 ◽  
Vol 125 ◽  
pp. 104887 ◽  
Author(s):  
Thiago L. Costa ◽  
Renato A. Sarmento ◽  
Tamíris A. de Araújo ◽  
Poliana S. Pereira ◽  
Ricardo S. Silva ◽  
...  
Keyword(s):  
Open Field ◽  
Bemisia Tabaci ◽  
Sequential Sampling ◽  
Sampling Plans ◽  
Economic Injury ◽  
Biotype B

scholarly journals Sequential Sampling for Estimation and Classification of the Incidence of Hop Powdery Mildew II: Cone Sampling

Plant Disease ◽  
2007 ◽  
Vol 91 (8) ◽  
pp. 1013-1020 ◽  
Author(s):  
David H. Gent ◽  
William W. Turechek ◽  
Walter F. Mahaffee
Keyword(s):  
Powdery Mildew ◽  
Binomial Distribution ◽  
Disease Incidence ◽  
Sequential Sampling ◽  
Model Construction ◽  
Data Sets ◽  
Data Set ◽  
Sampling Plans ◽  
Simulated Sampling

Sequential sampling models for estimation and classification of the incidence of powdery mildew (caused by Podosphaera macularis) on hop (Humulus lupulus) cones were developed using parameter estimates of the binary power law derived from the analysis of 221 transect data sets (model construction data set) collected from 41 hop yards sampled in Oregon and Washington from 2000 to 2005. Stop lines, models that determine when sufficient information has been collected to estimate mean disease incidence and stop sampling, for sequential estimation were validated by bootstrap simulation using a subset of 21 model construction data sets and simulated sampling of an additional 13 model construction data sets. Achieved coefficient of variation (C) approached the prespecified C as the estimated disease incidence, [Formula: see text], increased, although achieving a C of 0.1 was not possible for data sets in which [Formula: see text] < 0.03 with the number of sampling units evaluated in this study. The 95% confidence interval of the median difference between [Formula: see text] of each yard (achieved by sequential sampling) and the true p of the original data set included 0 for all 21 data sets evaluated at levels of C of 0.1 and 0.2. For sequential classification, operating characteristic (OC) and average sample number (ASN) curves of the sequential sampling plans obtained by bootstrap analysis and simulated sampling were similar to the OC and ASN values determined by Monte Carlo simulation. Correct decisions of whether disease incidence was above or below prespecified thresholds (pt) were made for 84.6 or 100% of the data sets during simulated sampling when stop lines were determined assuming a binomial or beta-binomial distribution of disease incidence, respectively. However, the higher proportion of correct decisions obtained by assuming a beta-binomial distribution of disease incidence required, on average, sampling 3.9 more plants per sampling round to classify disease incidence compared with the binomial distribution. Use of these sequential sampling plans may aid growers in deciding the order in which to harvest hop yards to minimize the risk of a condition called “cone early maturity” caused by late-season infection of cones by P. macularis. Also, sequential sampling could aid in research efforts, such as efficacy trials, where many hop cones are assessed to determine disease incidence.

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