Cost-effective binomial sequential sampling of western bean cutworm, striacosta albicosta (Lepidoptera: Noctuidae), egg masses in corn

S. Paula-Moraes, E. C. Burkness, T. E. Hunt, R. J. Wright, G. L. Hein, W. D. Hutchison

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Striacosta albicosta (Smith) (Lepidoptera: Noctuidae), is a native pest of dry beans (Phaseolus vulgaris L.) and corn (Zea mays L.). As a result of larval feeding damage on corn ears, S. albicosta has a narrow treatment window; thus, early detection of the pest in the field is essential, and egg mass sampling has become a popular monitoring tool. Three action thresholds for field and sweet corn currently are used by crop consultants, including 4% of plants infested with egg masses on sweet corn in the silkingtasseling stage, 8% of plants infested with egg masses on field corn with ≈95% tasseled, and 20% of plants infested with egg masses on field corn during mid-milk-stage corn. The current monitoring recommendation is to sample 20 plants at each of five locations per field (100 plants total). In an effort to develop a more cost-effective sampling plan for S. albicosta egg masses, several alternative binomial sampling plans were developed using Wald's sequential probability ratio test, and validated using Resampling for Validation of Sampling Plans (RVSP) software. The benefit-cost ratio also was calculated and used to determine the final selection of sampling plans. Based on final sampling plans selected for each action threshold, the average sample number required to reach a treat or no-treat decision ranged from 38 to 41 plants per field. This represents a significant savings in sampling cost over the current recommendation of 100 plants.

Original languageEnglish (US)
Pages (from-to)1900-1908
Number of pages9
JournalJournal of economic entomology
Volume104
Issue number6
DOIs
StatePublished - Dec 1 2011

Keywords

  • Binomial sampling
  • beneθt-cost ratio
  • resampling software
  • western bean cutworm

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