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SAMPLING PLANT BUGS, LYGUS SPP. (HETEROPTERA: MIRIDAE), IN CANOLA TO MAKE CONTROL DECISIONS1

Published online by Cambridge University Press:  31 May 2012

I.L. Wise
Affiliation:
Cereal Research Centre, Agriculture and Agri-Food Canada, 195 Dafoe Road, Winnipeg, Manitoba, Canada R3T 2M9
R.J. Lamb
Affiliation:
Cereal Research Centre, Agriculture and Agri-Food Canada, 195 Dafoe Road, Winnipeg, Manitoba, Canada R3T 2M9

Abstract

Plant bugs in the genus Lygus are pests of canola (Brassica napus L. and Brassica rapa L.) in western Canada and may require insecticidal control. Sweep-net sampling of field plots and commercial fields in southern Manitoba between 1988 and 1995 was used to develop sequential sampling plans for plant bugs in canola to facilitate control. The variance–mean relationships for plant bug catches were defined by Taylor’s power law, and the parameters of the relationships were the same for field plots and commercial fields. Sampling units of 10, 20, 50, and 100 sweeps per sample had variance–mean relationships with the same slope but different intercepts and required different minimal sample sizes. Samples taken at two crop stages had similar variance–mean relationships, but at a later crop stage the intercept of the relationship differed and the parameters were estimated with less precision. Samples taken in two ways along the edges of commercial fields and at various distances into the fields all gave similar estimates of plant bug density, justifying the use of edge sampling. Experienced samplers caught more plant bugs than inexperienced ones, although the difference was primarily due to the number of nymphs rather than adults that were collected, and this difference was less pronounced in the edge samples. Sweep-net sampling collected less than 10% of the plant bugs present in the sampling area. Sequential decision plans are presented for four sampling units and three crop stages. Sampling commercial canola with a sweep net to make decisions on the need to control plant bugs can be completed in as little as 28–35 min. The sampling is most efficiently conducted with a sampling unit of 10 or 20 sweeps taken along the edge of a field. In an independent test of the sampling method, plant bug densities were classified correctly in relation to the need for control in 20 fields using the minimum sample size.

Résumé

Dans l’ouest canadien, les punaises des plantes du genre Lygus sont des insectes ravageurs du colza (Brassica napus L. et Brassica rapa L.) qui requièrent parfois la mise sur pied de programmes de lutte au moyen d’insecticides. L’échantillonnage au filet fauchoir dans des carrés échantillons et dans des champs commerciaux du sud du Manitoba entre 1988 et 1995 a servi à établir des plans d’échantillonnages en série des punaises dans le colza pour en faciliter la lutte. Les relations variance-moyenne des captures de punaises ont été définies selon la loi de puissance de Taylor et les paramètres de la relation se sont avérés semblables dans les carrés échantillons et les champs commerciaux. Les unités d’échantillonnage de 10, 20, 50 et 100 coups de filet par échantillon avaient des relations variance-moyenne de même pente mais d’ordonnées à l’origine différentes et la taille minimum de l’échantillon devait varier d’une unité à l’autre. Les échantillons recueillis à deux stades de développement de la plante avaient des relations variance-moyenne semblables, mais, à un stade ultérieur de développement, l’ordonnée à l’origine de la relation n’était plus la même et les paramètres ont été estimés avec moins de précision. Les échantillons recueillis de deux façons le long des bordures de champs commerciaux et à diverses distances dans les champs ont tous donné des estimations semblables de la densité des punaises, ce qui justifie l’échantillonnage en bordure. Les échantillonneurs expérimentés ont récolté plus de punaises que les échantillonneurs inexpérimentés, mais les différences se manifestaient surtout par le nombre de larves recueillies plutôt que par le nombre d’adultes, et la différence était moins marquée dans les échantillons recueillis en bordure. L’échantillonnage au filet fauchoir a recueilli moins de 10% des punaises présentes dans la zone d’échantillonnage. Les plans ébauchés à la suite des échantillonnages sont présentés pour quatre des unités d’échantillonnage à trois stades de développement de la plante. L’échantillonnage au filet fauchoir des cultures commerciales de colza pour prendre des décisions quant à la pertinence d’une lutte organisée contre les punaises peut prendre aussi peu que 28–35 minutes. L’échantillonnage le plus efficace consiste en une unité d’échantillonnage de 10 ou 20 coups de filet en bordure du champ. Au cours d’un test indépendant de l’efficacité de la méthode, les densités de punaises ont été déterminées correctement en relation avec la nécessité d’une lutte dans 20 champs, en utilisant des échantillons de taille minimale.

[Traduit par la Rédaction]

Type
Articles
Copyright
Copyright © Entomological Society of Canada 1998

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References

Binns, M.R. 1994. Sequential sampling for classifying pest status. pp. 137–134 in Pedigo, L.P., and Buntin, G.D. (Eds.), Handbook of Sampling Methods for Arthropods in Agriculture. CRC Press, Boca Raton, FL.Google Scholar
Binns, M.R., and Nyrop, J.P.. 1992. Sampling insect populations for the purpose of IPM decision making. Annual Review of Entomology 37: 427453.CrossRefGoogle Scholar
Boivin, G., LeBlanc, J.-P.R., and Adams, J.A.. 1991. Spatial dispersion and sequential sampling plan for the tarnished plant bug (Hemiptera: Miridae) on celery. Journal of Economic Entomology 84: 158164.CrossRefGoogle Scholar
Butts, R.A., and Lamb, R.J.. 1990. Injury to oilseed rape caused by mirid bugs (Lygus) (Heteroptera: Miridae) and its effect on seed production. Annals of Applied Biology 117: 253266.CrossRefGoogle Scholar
Butts, R.A., and Lamb, R.J.. 1991 a. Pest status of Lygus bugs (Heteroptera: Miridae) in oilseed Brassica crops. Journal of Economic Entomology 84: 15911596.CrossRefGoogle Scholar
Butts, R.A., and Lamb, R.J.. 1991 b. Seasonal abundance of three Lygus species (Heteroptera: Miridae) in oilseed rape and alfalfa in Alberta. Journal of Economic Entomology 84: 450456.CrossRefGoogle Scholar
Byerly, K.F., Gutierrez, A.P., Jones, R., and Luck, R.F.. 1978. Comparison of sampling methods for some arthropod populations in cotton. Hilgardia 46: 257282.CrossRefGoogle Scholar
Garcia, A., Gonzalez, D., and Leigh, T.F., 1982. Three methods for sampling arthropod numbers on California cotton. Environmental Entomology 11: 565572.CrossRefGoogle Scholar
Green, R.H. 1970. On fixed precision level sequential sampling. Researches on Population Ecology (Kyoto) 12: 249251.CrossRefGoogle Scholar
Harper, A.M., Schaber, B.D., Entz, T., and Story, T.P.. 1993. Assessment of sweepnet and suction sampling for evaluating pest insect populations in hay alfalfa. Journal of the Entomological Society of British Columbia 90: 6676.Google 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.CrossRefGoogle Scholar
Henry, T.J., and Lattin, J.D.. 1987. Taxonomic status, biological attributes, and recommendations for future work on the genus Lygus (Heteroptera: Miridae). pp. 5468in Hedlund, R.C., and Graham, H.M. (Eds.), Economic Importance and Biological Control of Lygus and Adelphocoris in North America. United States Department of Agriculture, Agricultural Research Service ARS–64.Google Scholar
Hutchison, W.D. 1994. Sequential sampling to determine population density. pp. 207243in Pedigo, L.P., and Buntin, G.D. (Eds.), Handbook of Sampling Methods for Arthropods in Agriculture. CRC Press, Boca Raton, FL.Google Scholar
Iwao, S. 1975. A new method of sequential sampling to classify populations relative to a critical density. Researches on Population Ecology (Kyoto) 16: 281288.CrossRefGoogle Scholar
Kuno, E. 1969. A new method of sequential sampling to obtain the population estimates with a fixed level of precision. Researches on Population Ecology (Kyoto) 11: 127136.CrossRefGoogle Scholar
Leferink, J.H.M., and Gerber, G.H.. 1997. Development of adult and nymphal populations of Lygus lineolaris (Palisot de Beauvois), L. elisus Van Duzee, and L. borealis (Kelton) (Heteroptera: Miridae) in relation to seeding date and stage of plant development on canola (Brassicaceae) in southern Manitoba. The Canadian Entomologist 129: 777787.CrossRefGoogle Scholar
Mailloux, G., and Bostanian, N.J.. 1989. Presence-absence sequential decision plans for management of Lygus lineolaris (Hemiptera: Miridae) on strawberry. Environmental Entomology 18: 829834.CrossRefGoogle Scholar
Maiteki, G.A., and Lamb, R.J., 1987. Sequential decision plan for control of pea aphid, Acyrthosiphon pisum (Homoptera: Aphididae), on field peas in Manitoba. Journal of Economic Entomology 80: 605607.CrossRefGoogle 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.CrossRefGoogle Scholar
SAS Institute Inc. 1990. SAS/STAT user's guide, version 6 edition. SAS Institute Inc., Cary, NC.Google Scholar
Schotzko, D.J., and O'Keefe, L.E.. 1989. Lygus hesperus distribution and sampling procedures in lentils. Environmental Entomology 18: 308314.CrossRefGoogle Scholar
Schwartz, M.D., and Foottit, R.G.. 1992. Lygus species on oilseed rape, mustard, and weeds. A survey across the Prairie Provinces of Canada. The Canadian Entomologist 124: 151158.CrossRefGoogle Scholar
Sevacherian, V., and Stern, V.M.. 1972. Sequential sampling plans for lygus bugs in California cotton fields. Environmental Entomology 1: 704710.CrossRefGoogle Scholar
Snodgrass, G.L. 1993. Estimating absolute density of nymphs of Lygus lineolaris (Heteroptera: Miridae) in cotton using drop cloth and sweep-net sampling methods. Journal of Economic Entomology 86: 11161123.CrossRefGoogle Scholar
Taylor, L.R. 1961. Aggregation, variance and the mean. Nature (London) 189: 732735.CrossRefGoogle Scholar
Timlick, B.H., Turnock, W.J., and Wise, I.. 1993. Distribution and abundance of Lygus spp. (Heteroptera: Miridae) on alfalfa and canola in Manitoba. The Canadian Entomologist 125: 10331041.CrossRefGoogle Scholar
Turnock, W.J., Gerber, G.H., Timlick, B.H., and Lamb, R.J.. 1995. Losses of canola seeds from feeding by Lygus species (Heteroptera: Miridae) in Manitoba. Canadian Journal of Plant Science 75: 731736.CrossRefGoogle Scholar
Wise, I.L., and Lamb, R.J.. 1995. Spatial distribution and sequential sampling methods for the potato aphid, Macrosiphum euphorbiae (Thomas) (Homoptera: Aphididae), in oilseed flax. The Canadian Entomologist 127: 967976.CrossRefGoogle Scholar
Wise, I.L., and Lamb, R.J.. 1998. Economic threshold for plant bugs, Lygus spp. (Heteroptera: Miridae), in canola. The Canadian Entomologist 130: 825836.CrossRefGoogle Scholar