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An assessment of degree-day models to predict the phenology of alfalfa weevil (Coleoptera: Curculionidae) on the Canadian Prairies

Published online by Cambridge University Press:  21 December 2019

Juliana Soroka Open the ORCID record for Juliana Soroka [Opens in a new window] ,
Larry Grenkow ,
Héctor Cárcamo ,
Scott Meers ,
Shelley Barkley  and
John Gavloski
Juliana Soroka*
Affiliation:
Saskatoon Research and Development Centre, Agriculture and Agri-Food Canada, 107 Science Place, Saskatoon, Saskatchewan, S7N 0X2, Canada
Larry Grenkow
Affiliation:
Saskatoon Research and Development Centre, Agriculture and Agri-Food Canada, 107 Science Place, Saskatoon, Saskatchewan, S7N 0X2, Canada
Héctor Cárcamo
Affiliation:
Lethbridge Research and Development Centre, Agriculture and Agri-Food Canada, 5403 1st Avenue S, Lethbridge, Alberta, T1J 4B1, Canada
Scott Meers
Affiliation:
Crop Diversification Centre South, Alberta Agriculture and Forestry, 301 Horticultural Station Road East, Brooks, Alberta, T1R 1E6, Canada
Shelley Barkley
Affiliation:
Crop Diversification Centre South, Alberta Agriculture and Forestry, 301 Horticultural Station Road East, Brooks, Alberta, T1R 1E6, Canada
John Gavloski
Affiliation:
Manitoba Agriculture, Food and Rural Development, P.O. Box 1149, 65 3rd Avenue NE, Carman, Manitoba, R0G 0J0, Canada
*
*Corresponding author. Email: julie.soroka@canada.ca
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Abstract

This study examined the use of degree-day models to predict alfalfa weevil Hypera postica (Gyllenhal) (Coleoptera: Curculionidae) population development on the Canadian prairies. Air temperatures, alfalfa weevil abundance, and instar data were collected in 2013 and 2014 from 13 alfalfa (Medicago sativa Linnaeus; Fabaceae) fields across Alberta, Saskatchewan, and Manitoba. We coupled three alfalfa weevil population prediction models with three temperature data sources to determine which combination most closely aligned with results observed. Our objective was to find the best prediction of peak occurrence of second instar alfalfa weevils, the optimum time for management decisions. Of the parameters analysed, prediction model had the greatest effect on the accuracy of peak instar prediction, with Harcourt and North Dakota models better at predicting population peaks than the Guppy–Mukerji model. Interactions between temperature source and prediction model significantly affected prediction accuracy. The probability of accurate prediction of population peaks to within 3.5 days of actual occurrence using in-field and multiple-site temperature data sets, combined with Harcourt and North Dakota development models, was 0.45–0.70. Lower predictability was found from fields in the Mixed Grass Ecoregion than in other ecoregions. The use of the recommended models can assist growers in timing their monitoring activities and deciding if pest management action is warranted.

Information

Type
Research Papers
Information
The Canadian Entomologist , Volume 152 , Issue 1 , February 2020 , pp. 110 - 129
Copyright
© 2019 Entomological Society of Canada. Parts of this are a work of Her Majesty the Queen in Right of Canada 

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Footnotes

Subject editor: Cécile Le Lann

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