Using degree-days to predict cereal leaf beetle (Coleoptera: Chrysomelidae) egg and larval population peaks

C. R. Philips, D. A. Herbert, T. P. Kuhar, D. D. Reisig, E. A. Roberts

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

To improve cereal leaf beetle scouting efficiency and encourage the use of thresholds, temperature-based degree-day models were developed and tested to determine their accuracy to predict the date of egg and larval peaks. Previously published cereal leaf beetle temperature development data were used to create the degree-day model. This model of 182 DD using a base development temperature of 8°C was validated using cereal leaf beetle sampling data from four locations in Virginia and North Carolina in 2010, and six locations in 2011. In both years, the degree-day model predicted the average egg peak within 3 d of the observed calendar date. There was also a consistent period between egg and larval peaks averaging 17.5 d. Given the accuracy of this model, historical high and low temperature data were used to create a predictive map of the calendar week that different areas of Virginia and North Carolina would exceed 182 DD, and was validated using survey data from 60 field sites in 2010 and 65 sites in 2011 throughout Virginia and North Carolina. Finally, correlation and linear regression analyses were performed using data from all cereal leaf beetle study populations in 2010 and 2011, as well as previously collected data to determine if the number of eggs at peak could be used to predict larval peak numbers. There was a significant positive linear relationship between egg peak density and larval peak density, explaining 94% of the variation seen in larval peaks, indicating that egg peaks could reliably predict larval infestation levels.

Original languageEnglish (US)
Pages (from-to)761-767
Number of pages7
JournalEnvironmental entomology
Volume41
Issue number4
DOIs
StatePublished - Aug 1 2012

Keywords

  • Oulema melanopus
  • degree-days
  • integrated pest management
  • predictive model
  • sampling

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