Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-30T23:26:12.858Z Has data issue: false hasContentIssue false

Sequential decision plan for controlling Mamestra configurata in spring canola1

Published online by Cambridge University Press:  02 April 2012

I.L. Wise*
Affiliation:
Cereal Research Centre, Agriculture and Agri-Food Canada, 195 Dafoe Road, Winnipeg, Manitoba, Canada R3T 2M9
W.J. Turnock
Affiliation:
Cereal Research Centre, Agriculture and Agri-Food Canada, 195 Dafoe Road, Winnipeg, Manitoba, Canada R3T 2M9
J. Gavloski
Affiliation:
Manitoba Agriculture and Food, P.O. Box 1149, 6523rd Avenue NE, Carman, Manitoba, Canada R0G 0J0
*
2Corresponding author (e-mail: [email protected]).

Abstract

A sequential decision plan was developed for controlling larvae of the bertha armyworm, Mamestra configurata Walker (Lepidoptera: Noctuidae), in canola (Brassica napus L. and B. rapa L., Brassicaceae), using 0.25 and 0.5 m2 sampling units. Fields in Manitoba were sampled from 1980 to 1994 to determine minimum sample sizes and upper and lower cumulative larval counts at three economic thresholds. Taylor’s power law described most of the variation between mean larval density and variance for 0.25 m2 (r2 = 0.926) and 0.5 m2 (r2 = 0.924) samples. Larvae were found to have a moderately clumped distribution in canola (b = 1.42). Levels of precision (D0) varying from 0.15 to 0.25 caused minimum sample sizes to vary between 6 and 21 for the 0.5 m2 samples to between 9 and 31 for the 0.25 m2 samples, for an economic threshold of 16–24 larvae/m2 (P = 0.20). Mean sampling times ranged from 40–108 for the 0.25 m2 samples to 49–126 min for the 0.5 m2 samples. The sampling plan for the 0.25 m2 samples was verified in 18 fields in 2006 and 2007. A correct decision was made in 87% (D0 = 0.25), 91% (D0 = 0.20), and 94% (D0 = 0.15) of the fields when the recommendation was to spray if a decision could not be reached after a second sampling. The mean number of samples needed for making a decision was 14 (D0 = 0.25), 19 (D0 = 0.20), and 32 (D0 = 0.15). We recommend that growers use a precision level of 0.20 to minimize error rates and sampling effort. In most years, the minimum number of 0.25 m2 samples per field that growers would need to take is 14–17.

Résumé

Nous mettons au point un plan décisionnel séquentiel pour les larves de Mamestra configurata Walker (Lepidoptera: Noctuidae) dans le canola (Brassica napus L. et B. rapa L., Brassicaceae) comprenant des unités d’échantillonnage de 0,25 m2 et de 0,5 m2. Nous avons échantillonné des champs au Manitoba de 1980 à 1994 afin de déterminer la taille minimale des échantillons et les nombres cumulatifs minimaux et maximaux de larves à trois seuils économiques. La plus grande partie de la variation entre la densité larvaire moyenne et la variance dans les échantillons de 0,25 m2 (r2 = 0,926) et 0,5 m2 (r2 = 0,924) peut se décrire par la loi des puissances de Taylor. Les larves ont une répartition moyennement contagieuse dans le canola (b = 1,42). Des niveaux variables de précision ou D0 de 0,15 à 0,25 entraïnent une variation de MSS de 6–21 pour les échantillons de 0,5 m2 et de 9–31 pour les échantillons de 0,25 m2 pour un ET de 16–24 larves par m2 (P = 0,20). La durée moyenne de l’échantillonnage varie respectivement de 40–108 et de 49–126 minutes pour les échantillons de 0,25 et de 0,5 m2. Nous avons vérifié le plan d’échantillonnage pour les échantillons de 0,25 m2 dans 18 champs en 2006 et 2007. Une décision correcte a été prise dans 87 % (D0 = 0,25), 91 % (D0 = 0,20) et 94 % (D0 = 0,15) des champs, lorsqu’il était recommandé de faire un arrosage dans les cas où une décision ne pouvait être prise après un second échantillonnage. Le nombre moyen d’échantillons requis pour en arriver à une décision est de 14 (D0 = 0,25), 19 (D0 = 0,20) et 32 (D0 = 0,15). Nous recommandons aux producteurs d’utiliser un niveau de précision de 0,20 afin de minimiser les taux d’erreur et l’effort d’échantillonnage. La plupart des années, les producteurs auraient à prélever un minimum de 14–17 échantillons de 0,25 m2 par champ.

[Traduit par la Rédaction]

Type
Articles
Copyright
Copyright © Entomological Society of Canada 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Binns, M.R., and Nyrop, J.P. 1992. Sampling insect populations for the purpose of IPM decision making. Annual Review of Entomology, 37: 427453.Google Scholar
Bracken, G.K. 1984. Within plant preferences of larvae of Mamestra configurata (Lepidoptera: Noctuidae) feeding on oilseed rape. The Canadian Entomologist, 116: 4549.Google Scholar
Bracken, G.K. 1987. Relation between pod damage caused by larvae of bertha armyworm, Mamestra configurata Walker (Lepidoptera:Noctuidae), and yield loss, shelling, and seed quality in canola. The Canadian Entomologist, 119: 365369.CrossRefGoogle Scholar
Bracken, G.K., and Bucher, G.E. 1977. An estimate of the relation between density of bertha armyworm and yield loss on rapeseed, based on artificial infestations. Journal of Economic Entomology, 70: 701705.CrossRefGoogle Scholar
Bracken, G.K., and Bucher, G.E. 1984. Measuring the cost–benefit of control measures for bertha armyworm (Lepidoptera: Noctuidae) infestations in rapeseed. The Canadian Entomologist, 116: 591595.CrossRefGoogle Scholar
Canola Council of Canada. 1991. Grow with canola. Canola grower's manual. Canola Council of Canada, Winnipeg, Manitoba.Google Scholar
Green, R.H. 1970. On fixed precision level sequential sampling. Researches in Population Ecology, 12(2): 249251 doi:10.1007/BF02511568.CrossRefGoogle Scholar
Harcourt, D.G. 1965. Spatial pattern of the cabbage looper Trichoplusia ni on crucifers. Annals of the Entomological Society of America, 58: 8994.CrossRefGoogle Scholar
Harper, F.R., and Berkenkamp, B. 1975. Revised growth stage key for Brassica campestris and B. napus. Canadian Journal of Plant Science, 55: 657658.Google Scholar
Iwao, S. 1975. A new method of sequential sampling to classify populations relative to a critical density. Researches in Population Ecology, 16(2): 281288 doi:10.1007/BF02511067.Google Scholar
King, K.M. 1929. The bertha armyworm in the prairie provinces. Pamphlet No. 103, Dominion of Canada Department of Agriculture, Ottawa, Ontario.Google Scholar
Kuno, E. 1969. A new method of sequential sampling to obtain the population estimates with a fixed level of precision. Researches in Population Ecology, 11(2): 127136 doi:10.1007/BF02936264.Google Scholar
Mason, P.G., Arthur, A.P., Olfert, O.O., and Erlandson, M.A. 1998. The bertha armyworm (Mamestra configurata) (Lepidoptera: Noctuidae) in western Canada. The Canadian Entomologist, 130: 321336.Google Scholar
Nyrop, J.P., and Simmons, G.A. 1984. Errors incurred when using Iwao's sequential decision rule in insect sampling. Environmental Entomology, 13: 14591465.Google Scholar
SAS Institute Inc. 1990. SAS/STAT user's guide. Version 6 edition. SAS Institute Inc., Cary, North Carolina.Google Scholar
Shorey, H.H., Anders, L.A., and Hale, R.L. Jr., 1962. The biology of Trichoplusia ni (Lepidoptera: Noctuidae). I. Life history and behavior. Annals of the Entomological Society of America, 55: 591597.Google Scholar
Southwood, T.R.E. 1966. Ecological methods with particular reference to the study of insect populations. Methuen and Company Ltd., London, United Kingdom.Google Scholar
Taylor, L.R. 1961. Aggregation, variance and the mean. Nature (London), 189(4766): 732735 doi:10.1038/189732a0.CrossRefGoogle Scholar
Taylor, L.R., Woiwod, I.P., and Perry, J.N. 1978. The density-dependence of spatial behaviour and the rarity of randomness. Journal of Animal Ecology, 47(2): 383406 doi:10.2307/3790.Google Scholar
Turnock, W.J. 1987. Predicting larval abundance of the bertha armyworm, Mamestra configurata Wlk., in Manitoba from catches of male moths in sex attractant traps. The Canadian Entomologist, 119: 167178.Google Scholar
Turnock, W.J., and Bilodeau, R.J. 1985. A comparison of three methods of examining the density of larvae of the bertha armyworm, Mamestra configurata, in fields of canola (Brassica spp.). The Canadian Entomologist, 117: 10651066.Google Scholar
Turnock, W.J., and Philip, H.G. 1977. The outbreak of bertha armyworm Mamestra configurata (Noctuidae: Lepidoptera), in Alberta, 1971 to 1975. The Manitoba Entomologist, 11: 1021.Google Scholar
Ulmer, B., Gillot, C., and Erlandson, M. 2003. Conspecific eggs and bertha armyworm, Mamestra configurata (Lepidoptera: Noctuidae), oviposition site selection. Environmental Entomology, 32: 529534.Google Scholar