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Logistic Analysis for Monitoring and Assessing Herbicide Efficacy

Published online by Cambridge University Press:  12 June 2017

David L. Turner ,
Michael H. Ralphs  and
John O. Evans
David L. Turner
Affiliation:
Intermountain Res. Stn., U.S. Dep. Agric. For. Serv., 324 25th St., Ogden, UT
Michael H. Ralphs
Affiliation:
Agric. Res. Serv. U.S. Dep. Agric. Poisonous Plant Res. Lab., 1150 E. 1400 N., Logan, UT 84321
John O. Evans
Affiliation:
Plant Sci., Utah State Univ., Logan, UT 84322
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Abstract

Two relatively new methods for analyzing herbicide efficacy data are described. Weighted multiple regression using the logit transformation for plant mortality data is illustrated and compared with the more accurate maximum likelihood logistic regression procedure. A partial data set evaluating the effects of increasing application rates of picloram (0, 1.1, 2.2 and 4.5 kg ae ha–1) for control of tall larkspur is used to illustrate the methods. Suggestions are made for using logistic regression to monitor herbicide efficacy over several years.

Keywords

Picloram (4-amino-3,5,6 trichloro-2-pyridinecarboxylic acid)tall larkspur, Delphinium barbeyi (L.) Huth ♯ DELBAStatistical analysislogit transformationweighted regressionlogistic regressionmaximum likelihood estimationmortality analysis
Type
Research
Information
Weed Technology , Volume 6 , Issue 2 , June 1992 , pp. 424 - 430
Copyright
Copyright © 1990 by the Weed Science Society of America 

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References

Literature Cited

1. Agresti, A. 1990. Categorical Data Analysis. John Wiley & Sons, Inc., New York.Google Scholar
2. Ahrens, W. H., Cox, D. J., and Budhwar, G. 1990. Use of the arcsine and square root transformations for subjectively determined percentage data. Weed Sci. 38:452–458.CrossRefGoogle Scholar
3. Chatfield, C., and Collins, A. J. 1980. Introduction to Multivariate Analysis. Chapman and Hall, New York.Google Scholar
4. Cox, D. R., and Snell, E. J. 1989. Analysis of Binary Data. 2nd edition. Chapman and Hall, New York.Google Scholar
5. Hosmer, D. W., and Lemeshow, S. 1989. Applied Logistic Regression. John Wiley & Sons, Inc., New York.Google Scholar
6. McCullagh, P., and Nelder, J. A. 1983. Generalized Linear Models. Chapman and Hall, New York.CrossRefGoogle Scholar
7. Neter, J., Wasserman, W., and Kutner, M. H. 1989. Applied Linear Regression Models. 2nd edition. Richard D. Irwin, Inc., Homewood, Ill. Google Scholar
8. Ralphs, M. H., Turner, D. L., Mickelsen, L. V., Evans, J. O., and Dewey, S. A. 1990. Herbicides for control of tall larkspur (Delphinium barbeyi). Weed Sci. 38:573–577.Google Scholar
9. SAS Institute. 1990. SAS Technical Report P-200, SAS/STAT Software: CALIS and LOGISTIC Procedures, Release 6.04. SAS Inst Inc., Cary, N.C. Google Scholar
10. Snedecor, G. W., and Cochran, W. G. 1980. Statistical Methods. 7th edition. Iowa State University Press, Ames, Iowa.Google Scholar