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Identifying high‐density regions of pests within an orchard

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  • Fei He
  • Daniel R. Jeske
  • Elizabeth Grafton‐Cardwell

Abstract

This paper proposes a statistical method for identifying high‐density regions of pests, so‐called hot spots, within an orchard. Our method uses scanning windows to search for clusters of high counts within the sampled data. The proposed method enables a localized alternative for treatment that could be faster, less costly, and more environmentally friendly. R code that implements the hot spot identification method is provided as online supplementary material. The method is illustrated through simulated examples and a real data on counts of cottony cushion scales from an orchard.

Suggested Citation

  • Fei He & Daniel R. Jeske & Elizabeth Grafton‐Cardwell, 2020. "Identifying high‐density regions of pests within an orchard," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 36(3), pages 417-431, May.
    • Handle: RePEc:wly:apsmbi:v:36:y:2020:i:3:p:417-431
    DOI: 10.1002/asmb.2496
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    References listed on IDEAS

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    1. Kulldorff, Martin & Tango, Toshiro & Park, Peter J., 2003. "Power comparisons for disease clustering tests," Computational Statistics & Data Analysis, Elsevier, vol. 42(4), pages 665-684, April.
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