Feasibility of aeration for management of maize weevil populations in corn stored in the southern United States: Model simulations based on recorded weather data

Frank H. Arthur, James E. Throne, Dirk E. Maier, Michael D. Montross

Research output: Contribution to journalArticlepeer-review

36 Scopus citations

Abstract

Recorded data from weather stations in 11 southern states were used to determine the optimum initial activation temperatures for immediate cooling of corn that is stored after harvest. Hours of temperature accumulation in September and October that were below 12.8 and 15.6°C (55 and 60 °F),levels that often are recommended for fall cooling of hard red winter wheat stored in the midwestern United States, were calculated and plotted, along with hours of accumulation below 18.3 °C (65°F). Five geographic zones were delineated based on weather data from 53 stations; and a bin-cooling model, which was integrated with a model for maize weevil development, was used to predict weevil population growth in unaerated corn and corn aerated at 12.3, 15.6, and 18.3°C with airflow rates of 0.0013, 0.0026, and 0.0039 m 3 /s/m 3 (0.1, 0.2, 0.3 cubic feet per minute per bushel). The time required to compete a cooling cycle within each region was estimated for each of the 9 temperature-airflow rate combinations. Aeration at 15.6 or 18.3°C, depending on the geographic zone, and an airflow rate of 0.0013 m 3 /s/m 3 resulted in the lowest number of maize weevils. In all zones, aeration dramatically reduced the predicted number of maize weevils compared to population levels in una erated corn.

Original languageEnglish
Pages (from-to)118-123
Number of pages6
JournalAmerican Entomologist
Volume44
Issue number2
DOIs
StatePublished - Jun 1 1998

Bibliographical note

Publisher Copyright:
© 2018 Entomological Society of America.

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Insect Science

Fingerprint

Dive into the research topics of 'Feasibility of aeration for management of maize weevil populations in corn stored in the southern United States: Model simulations based on recorded weather data'. Together they form a unique fingerprint.

Cite this