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Sampling for Estimating Frankliniella Species Flower Thrips and Orius Species Predators in Field Experiments
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Sampling for plant disease incidence.

L V Madden, G Hughes

    Phytopathology
    |October 24, 2008
    PubMed
    Summary
    This summary is machine-generated.

    Efficient plant disease sampling requires understanding disease distribution and variance-mean relationships. This study details cluster sampling methods to precisely estimate disease incidence, accounting for spatial heterogeneity and informing sample size calculations.

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    Area of Science:

    • Plant Pathology
    • Statistical Ecology
    • Agricultural Science

    Background:

    • Accurate disease incidence estimation is crucial for effective plant disease management.
    • Understanding disease distribution patterns and variance-mean relationships informs efficient sampling strategies.
    • Cluster sampling is a common method for assessing plant disease, but its precision depends on various factors.

    Purpose of the Study:

    • To evaluate the precision of cluster sampling for estimating plant disease incidence.
    • To determine the relationship between sampling design parameters and the accuracy of disease incidence estimates.
    • To provide methods for calculating the necessary sample size (N) for desired precision in disease surveys.

    Main Methods:

    • Utilized cluster sampling with N sampling units of n individuals each.
    • Investigated binomial and beta-binomial distributions for disease data.
    • Employed the binary power law to model disease incidence and heterogeneity.
    • Applied sequential sampling methods and the sequential probability ratio test.

    Main Results:

    • Precision of disease incidence estimates is directly related to sample size (N) and inversely related to spatial heterogeneity (intracluster correlation, rho).
    • Methods were developed to calculate N *a priori* based on estimated or modeled rho.
    • Sequential sampling methods allow for real-time adjustment of sample size during surveys.
    • The sequential probability ratio test effectively classifies incidence relative to thresholds under beta-binomial distributions.

    Conclusions:

    • Knowledge of disease distribution and heterogeneity is essential for efficient plant disease sampling.
    • The proposed methods enable calculation of optimal sample sizes for precise disease incidence estimation.
    • Sequential sampling offers flexibility and efficiency in disease surveys.
    • Statistical models, particularly the beta-binomial distribution, are valuable tools for analyzing plant disease data.