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Related Experiment Videos

Accounting for cluster sampling in constructing enumerative sequential sampling plans.

A J Hamilton1, G Hepworth

  • 1Department of Primary Industries (Knoxfield), Private Bag 15, Ferntree Gully Delivery Centre, Victoria 3156, Australia. Andrew.Hamilton@dpi.vic.gov.au

Journal of Economic Entomology
|July 29, 2004
PubMed
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This study adapts Green's sequential sampling plan for cluster sampling in entomology. It provides methods to adjust plans for hierarchical designs, improving pest surveys like those for diamondback moths.

Area of Science:

  • Applied Entomology
  • Quantitative Ecology
  • Agricultural Pest Management

Background:

  • Green's sequential sampling plan is a standard tool in entomology for constructing sampling stop charts.
  • Traditional application often assumes simple random sampling (SRS), which is rarely used in practice for crop surveys.
  • Hierarchical sampling designs, such as cluster sampling, are more commonly employed in real-world crop monitoring.

Purpose of the Study:

  • To adapt Green's sequential sampling plan for cluster sampling designs.
  • To provide methodologies for adjusting sampling plans when hierarchical designs are used instead of SRS.
  • To offer practical guidance for pest surveys in agricultural settings.

Main Methods:

  • The study explains adjustments to Green's sequential sampling plan to accommodate cluster sampling.

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  • Methodologies are developed for constructing sampling stop charts suitable for hierarchical sampling.
  • The effectiveness of the adjusted plan is illustrated with a case study.

    • Main Results:

    • The research provides a framework for modifying sequential sampling plans for cluster sampling.
    • Adjustments allow for the application of Green's plan in non-SRS survey designs.
    • The developed methods are validated using data from diamondback moth surveys.

    Conclusions:

    • Sequential sampling plans can be effectively modified for cluster sampling in entomological surveys.
    • These adjustments enhance the practical applicability of established sampling methodologies in agriculture.
    • The findings support more efficient and accurate pest monitoring using hierarchical sampling designs.