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EFFICIENT MONITORING OF THE DENSITY OF ADULT NORTHERN CORN ROOTWORM (COLEOPTERA: CHRYSOMELIDAE) IN HELD CORN

Published online by Cambridge University Press:  31 May 2012

Alan J. Sawyer
Alan J. Sawyer
Affiliation:
Department of Entomology, Cornell University, Ithaca, New York 14853
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Abstract

Sampling statistics for adult northern corn rootworms in New York field corn are reported, with particular reference to efficient monitoring for decision-making in pest management. The most efficient sample unit of those examined was a single, entire plant. Sample sizes and associated costs required to achieve fixed levels of precision for estimates of density are reported. Because required sample sizes are density dependent, a sequential-sampling plan providing final estimates of density at preset levels of precision is developed. The method makes no assumption about the frequency distribution of insect counts, which was found to vary from field to field and with time. Disturbance caused by sampling activity may introduce a bias of unknown direction and magnitude in estimates of beetle density. Care in approaching and examining plants; should reduce this bias.

Résumé

On présente des statistiques d'échantillonnage pour les adultes de la chrysomèle du maïs dans le maïs fourrage, en particulier en ce qui concerne l'efficacité de la surveillance pour la prise des décisions en lutte intégrée. L'unité d'échantillonnage la plus efficace parmi celles qui ont été examinées est un seul plant entier. On mentionne les tailles d'échantillon nécessaires à l'obtention de niveaux donnés de précision, de même que les coûts correspondants. Puisque les tailles d'échantillons requises sont dépendantes de la densité, on a développé un plan d'échantillonnage séquentiel permettant d'obtenir des estimés finals de la densité à des niveaux donnés de précision. La méthode ne requiert pas de suppositions quant à la distribution de fréquence des décomptes d'insectes, laquelle variait d'un champ à l'autre et en fonction du temps. Les perturbations dûes à l'échantillonnage peuvent fausser de façon imprévisible les estimés de la densité de la chrysomèle. Il est recommandé d'approcher et d'examiner les plants avec prudence de façon à éviter ce biais.

Type
Articles
Information
The Canadian Entomologist , Volume 117 , Issue 2 , February 1985 , pp. 171 - 183
Copyright
Copyright © Entomological Society of Canada 1985

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References

Chiang, H. C. and Flaskerd, R. G.. 1965. Sampling methods of adult populations of the corn rootworms. Proc. N. cent. Brch ent. Soc. Am. 20: 67–68.Google Scholar
Cochran, W. G. 1963. Sampling Techniques, 2nd ed. Wiley, NY. 413 pp.Google Scholar
Foster, R. E., Ruesink, W. G., and Luckmann, W. H.. 1979. Northern corn rootworm egg sampling. J. econ. Ent. 72: 659–663.CrossRefGoogle Scholar
Foster, R. E., Tollefson, J. J., and Steffey, K. L.. 1982. Sequential sampling plans for adult corn rootworms (Coleoptera: Chrysomelidae). J. econ. Ent. 75: 791–793.CrossRefGoogle Scholar
Green, R. H. 1970. On fixed precision sequential sampling. Researches Popul. Ecol. Kyoto Univ. 12: 249–251.Google Scholar
Hein, G. L. and Tollefson, J. J.. 1984. Comparison of adult corn rootworm (Coleoptera: Chrysomelidae) trapping techniques as population estimators. Environ. Ent. 13: 266–271.CrossRefGoogle Scholar
Kuno, E. 1969. A new method of sequential sampling to obtain the population estimates with a fixed level of precision. Researches Popul. Ecol. Kyoto Univ. 11: 127–136.Google Scholar
Lovett, O. L. 1975. Wisconsin corn rootworm surveys. Proc. N. cent. Brch ent. Soc. Am. 30: 30–36.Google Scholar
Onsager, J. A. 1976. The rationale of sequential sampling, with emphasis on its use in pest management. U.S. Dep. Agric. Tech. Bull. 1526. 19 pp.Google Scholar
Peters, D. C. 1969. Some results from a “rootworm scouting” program. Proc. N. cent. Brch ent. Soc. Am. 24: 142–143.Google Scholar
Pieters, E. P. 1978. Bibliography of sequential sampling plans for insects. Bull. ent. Soc. Am. 24: 372–374.Google Scholar
Ritchie, S. W. and Hanway, J. J.. 1982. How a corn plant develops. Iowa State University, Cooperative Extension Service, Agric. Home Econ. Exp. Stn. Spec. Rep. 48 (rev.). 21 pp.Google Scholar
Ruppel, R. F. and Bird, G. W.. 1981. Chemical control of insects and nematodes in field and forage crops. Ext. Bull. E-1582, Michigan State Univ. Coop. Ext. Service, East Lansing, MI. 35 pp.Google Scholar
Steffey, K. L. and Tollefson, J. J.. 1982. Spatial dispersion patterns of northern and western corn rootworm adults in Iowa cornfields. Environ. Ent. 11: 283–286.CrossRefGoogle Scholar
Steffey, K. L., Tollefson, J. J., and Hinz, P. N.. 1982. Sampling plan for population estimation of northern and western corn rootworm adults in Iowa cornfields. Environ. Ent. 11: 287–291.CrossRefGoogle Scholar
Taylor, L. R. 1961. Aggregation, variance and the mean. Nature 189(4766): 732–735.CrossRefGoogle Scholar
Taylor, L. R. 1984. Assessing and interpreting the spatial distributions of insect populations. A. Rev. Ent. 29: 321–357.CrossRefGoogle Scholar
Tollefson, J. J. and Owens, J. C.. 1976. Corn rootworm adult and egg sampling techniques as predictors of larval damage. Proc. N. cent. Brch Ent. Soc. Am. 31: 30–31.Google Scholar
Wald, A. 1945. Sequential tests of statistical hypotheses. Annals Math. Stat. 16: 117–186.CrossRefGoogle Scholar
Welch, S. M., Croft, B. A., Brunner, J. F., and Michels, M. F.. 1978. PETE: an extension phenology modeling system for management of multi-species pest complex. Environ. Ent. 7: 487–494.CrossRefGoogle Scholar