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Confidence Intervals for Area of Influence Experiments and Derived Yield Loss Estimates

Published online by Cambridge University Press:  12 June 2017

Roger D. Cousens  and
Michael E. O'Neill
Roger D. Cousens
Affiliation:
School of Crop Sciences, Univ. Sydney, NSW 2006, Australia
Michael E. O'Neill
Affiliation:
School of Crop Sciences, Univ. Sydney, NSW 2006, Australia
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Abstract

A method of calculating confidence intervals of the “area of influence” of a weed plant, and of yield losses calculated from it, was developed. In a worked example using published data, the confidence intervals of the area of influence were found to be large. Yield losses calculated from this method were less precisely estimated than those from a more traditional additive density experiment. This limited evidence suggests that to give similar precision, the area of influence experiments may need to be at least double their present size. If this is indeed the case, published statements on the space, time, and effort advantages of the area of influence design will need to be treated with caution.

Keywords

Additivecompetitionyield loss
Type
Soil, Air, and Water
Information
Weed Science , Volume 41 , Issue 2 , June 1993 , pp. 288 - 290
Copyright
Copyright © 1993 by the Weed Science Society of America 

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References

Literature Cited

1. Bloomberg, J. R., Kirkpatrick, B. L., and Wax, L. M. 1982. Competition of common cocklebur (Xanthium pensylvanicum) with soybean (Glycine max). Weed Sci. 30:507–513.Google Scholar
2. Causton, D. R. and Venus, J. C. 1981. Page 261 in The Biometry of Plant Growth. Edward Arnold, London.Google Scholar
3. Cousens, R. 1985. A simple model relating yield loss to weed density. Ann. Appl. Biol. 107:239–252.Google Scholar
4. Cousens, R. 1987. The theory and the reality of weed control thresholds. Plant Prot. Quart. 2:13–20.Google Scholar
5. Cousens, R. and Marshall, C. J. 1987. Dangers in testing statistical hypotheses. Ann. Appl. Biol. 111:469–476.Google Scholar
6. Henry, W. T. and Bauman, T. T. 1989. Interference between soybeans (Glycine max) and common cocklebur (Xanthium strumarium) under Indiana field conditions. Weed Sci. 37:753–760.CrossRefGoogle Scholar
7. Jordan, N. 1989. A statistical analysis for area-of-influence experiments. Weed Technol. 3:114–121.Google Scholar
8. Mortensen, D. A. and Coble, H. D. 1989. The influence of soil water content on common cocklebur (Xanthium strumarium) interference in soybeans (Glycine max). Weed Sci. 37:76–83.Google Scholar
9. Oliver, L. R. 1987. Principles of threshold research. Abstr. Weed Sci. Soc. Am. 27:93.Google Scholar
10. Ross, G.J.S. 1987. MLP: Maximum Likelihood Program. Rothamsted Exp. Sta., U.K. Google Scholar
11. Shurtleff, J. L. and Coble, H. D. 1985. Interference of certain broadleaf weed species in soybeans (Glycine max). Weed Sci. 33:654–657.Google Scholar