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Selecting a Sampling Method for Weed Densities: The Case of Weed Removal in Strips

Published online by Cambridge University Press:  12 June 2017

Karen A. Garrett
Karen A. Garrett*
Affiliation:
Univ. Georgia's Savannah River Ecology Laboratory, P.O. Drawer E, Aiken, SC 29802
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Abstract

When a range of weed densities is needed for competition experiments, one method of reducing existing populations is to remove all weeds in strips of specified length down the crop row. This technique also can be used to create different sizes of weed dusters. Two methods of sampling weed densities after such thinning were compared: unrestricted sampling (under which quadrats are placed randomly within the row) and restricted sampling (under which quadrats are randomly placed within a row under the restriction that they coincide with the beginning of an infested strip). The bias of estimators of weed density under each of these two approaches was derived and bias-corrected estimators of weed density from the two methods were compared on the basis of their variance. The variance under restricted sampling is less than or equal to the variance under unrestricted sampling so that, by a minimum variance criterion, restricted sampling using a bias correction is the better technique.

Keywords

Aggregationcompetitionspatial distributionstatisticsthinning
Type
Weed biology and Ecology
Information
Weed Science , Volume 43 , Issue 3 , September 1995 , pp. 394 - 401
Copyright
Copyright © 1995 by the Weed Science Society of America 

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References

LITERATURE CITED

1. Cochran, W. G. 1977. Sampling Techniques, Third Edition. Wiley, New York. 428 pp.Google Scholar
2. Fuller, W. A. 1987. Measurement Error Models. Wiley, New York. 440 pp.Google Scholar
3. Garrett, K. A. 1993. The spatial pattern and area of influence of a plant competitor: a model of their impact on plant populations with implications for competition experiments. Bull. Ecol. Soc. Amer. 74:244–245.Google Scholar
4. Lybecker, D. W., Schweizer, E. E., and King, R. P. 1991. Weed management decisions in corn based on bioeconomic modeling. Weed Sci. 39:124–129.Google Scholar
5. Mood, A. M., Graybill, F. A., and Boes, D. C. 1974. Introduction to the Theory of Statistics. McGraw-Hill, New York. 564 pp.Google Scholar
6. Taylor, H. M. and Karlin, S. 1984. An Introduction to Stochastic Modeling. Academic Press, Orlando, Florida. 399 pp.Google Scholar
7. VanGessel, M. J., Schweizer, E. E., Garrett, K. A., and Westra, P. 1995. Influence of weed densities and spatial patterns on corn yield. Weed Sci., in press.Google Scholar
8. VanGroenendael, J.M. 1988. Patchy distribution of weeds and some implications for modelling population dynamics: a short literature review. Weed Res. 28:437–441.Google Scholar
9. Westoby, M. 1984. The Self-Thinning Rule. Pages 167–225 in MacFadyen, A. and Ford, E. D., eds. Advances in Ecological Research, 14. Academic Press, New York.Google Scholar
10. Wiles, L. J., Wilkerson, G. G., Gold, H. J., and Coble, H. D. 1992. Modeling weed distribution for improved postemergence control decisions. Weed Sci. 40:546–553.CrossRefGoogle Scholar
11. Yoda, K., Kira, T., Ogawa, H., and Hozumi, K. 1963. Self-thinning in overcrowded pure stands under cultivated and natural conditions. Journal of Biology, Osaka City University. 14:107–129.Google Scholar