NDLI: Efficiency of adaptive cluster sampling for estimating plant disease incidence.

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Efficiency of adaptive cluster sampling for estimating plant disease incidence.

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Ojiambo, P. S. Scherm, H.
Description	Country affiliation: United States Author Affiliation: Ojiambo PS ( Department of Plant Pathology, North Carolina State University, Raleigh 27695, USA. peter_ojiambo@ncsu.edu)
Abstract	Conventional sampling designs such as simple random sampling (SRS) tend to be inefficient when assessing rare and highly clustered populations because most of the time is spent evaluating empty quadrats, leading to high error variances and high cost. In previous studies with rare plant and animal populations, adaptive cluster sampling, where sampling occurs preferentially in the neighborhood of quadrats in which the species of interest is detected during the sampling bout, has been shown to estimate population parameters with greater precision at an effort comparable to SRS. Here, we use computer simulations to evaluate the efficiency of adaptive cluster sampling for estimating low levels of disease incidence (0.1, 0.5, 1.0, and 5.0%) at various levels of aggregation of infected plants having variance-to-mean ratios (V/M) of approximately 1, 3, 5, and 10. For each simulation, an initial sample size of 50, 100, and 150 quadrats was evaluated, and the condition to adapt neighborhood sampling (CA), i.e., the minimum number of infected plants per quadrat that triggers a switch from random sampling to sampling in neighboring quadrats, was varied from 1 to 4 (corresponding to 7.7 to 30.8% incidence of infected plants per quadrat). The simulations showed that cluster sampling was consistently more precise than SRS at a field-level disease incidence of 0.1 and 0.5%, especially when diseased plants were highly aggregated (V/M = 5 or 10) and when the most liberal condition to adapt (CA = 1) was used. One drawback of adaptive cluster sampling is that the final sample size is unknown at the beginning of the sampling bout because it depends on how often neighborhood sampling is triggered. In our simulations, the final sample size was close to the initial sample size for disease incidence up to 1.0%, especially when a more conservative condition to adapt (CA > 1) was used. For these conditions, the effect of disease aggregation was minor. In summary, both precision and the sample size required with adaptive cluster sampling responded similarly to disease incidence and aggregation, i.e., both were most favorable at the lowest disease incidence with the highest levels of clustering. However, whereas relative precision was optimized with the most liberal condition to adapt, the ratio of final to initial sample size was best for more conservative CA values, indicating a tradeoff. In our simulations, precision and final sample size were both simultaneously favorable for disease incidence of up to 1.0%, but only when infected plants were most aggregated (V/M = 10).
File Format	HTM / HTML
ISSN	0031949X
e-ISSN	19437684
Journal	Phytopathology
Issue Number	7
Volume Number	100
Language	English
Publisher	The American Phytopathological Society
Publisher Date	2010-07-01
Publisher Place	United States
Access Restriction	Open
Subject Keyword	Discipline Botany Cluster Analysis Plant Diseases Computer Simulation Sample Size
Content Type	Text
Resource Type	Article
Subject	Plant Science Agronomy and Crop Science

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