Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-24T16:48:11.825Z Has data issue: false hasContentIssue false
  • English
  • Français

Log-Logistic Analysis of Herbicide Dose-Response Relationships

Published online by Cambridge University Press:  12 June 2017

Steven S. Seefeldt ,
Jens Erik Jensen  and
E. Patrick Fuerst
Steven S. Seefeldt
Affiliation:
USDA-ARS, Pullman, WA 99164
Jens Erik Jensen
Affiliation:
Dept. of Agric. Sci., The Royal Vet. & Agric. Univ., 40 Thorvaldsensvej, DK-1871 Frederiksberg C, Copenhagen, Denmark
E. Patrick Fuerst
Affiliation:
Dep. of Crop and Soil Sci., Washington State Univ., Pullman WA 99164-6420
Get access Rights & Permissions [Opens in a new window]

Abstract

Dose-response studies are an important tool in weed science. The use of such studies has become especially prevalent following the widespread development of herbicide resistant weeds. In the past, analyses of dose-response studies have utilized various types of transformations and equations which can be validated with several statistical techniques. Most dose-response analysis methods 1) do not accurately describe data at the extremes of doses and 2) do not provide a proper statistical test for the difference(s) between two or more dose-response curves. Consequently, results of dose-response studies are analyzed and reported in a great variety of ways, and comparison of results among various researchers is not possible. The objective of this paper is to review the principles involved in dose-response research and explain the log-logistic analysis of herbicide dose-response relationships. In this paper the log-logistic model is illustrated using a nonlinear computer analysis of experimental data. The log-logistic model is an appropriate method for analyzing most dose-response studies. This model has been used widely and successfully in weed science for many years in Europe. The log-logistic model possesses several clear advantages over other analysis methods and the authors suggest that it should be widely adopted as a standard herbicide dose-response analysis method.

Keywords

Lack-of-fit testlogistic curvemodelsnon-linear regression
Type
Feature
Information
Weed Technology , Volume 9 , Issue 2 , June 1995 , pp. 218 - 227
Copyright
Copyright © 1995 by the Weed Science Society of America 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Literature Cited

1. Alcocer-Ruthling, M., Thill, D. C., and Shafii, B. 1992. Seed biology of sulfonylurea-resistant and -susceptible biotypes of prickly lettuce (Lactuca serriola). Weed Technol. 6:858–864.CrossRefGoogle Scholar
2. Ascard, J. 1994. Dose-response models for flame weeding in relation to plant size and density. Weed Res. 34:377–385.CrossRefGoogle Scholar
3. Berkson, J. 1951. Why I prefer logits to probits. Biometrics. 7:327.Google Scholar
4. Biediger, D. L., Baumann, P. A., Weaver, D. N., Chandler, J. M., and Merkle, M. G. 1992. Interactions between primisulfuron and selected soil-applied insecticides in corn (Zea mays). Weed Technol. 6:807–812.Google Scholar
5. Brain, P. and Cousens, R. 1989. An equation to describe dose responses where there is stimulation of the growth at low doses. Weed Res. 29:93–96.CrossRefGoogle Scholar
6. Chism, W. J., Birch, J. B., and Bingham, S. W. 1992. Nonlinear regressions for analyzing growth stage and quinclorac interactions. Weed Technol. 6:898–903.Google Scholar
7. Cox, D. R. and Oakes, D. 1984. Analysis of Survival Data, Chapman and Hall, London. 201 p.Google Scholar
8. Finney, D. J. 1971. Probit Analysis, 3rd ed. Charles Griffin & Company, Ltd., London. 333 p.Google Scholar
9. Hall, J. C. and Carey, C. K. 1992. Control of annual bluegrass (Poa annua) in Kentucky bluegrass (Poa pratensis) turf with linuron. Weed Technol. 6:852–857.Google Scholar
10. Ivany, J. A., MacLeod, J. A., and Sanderson, J. B. 1992. Response of four soybean cultivars to metribuzin. Weed Technol. 6:934–937.Google Scholar
11. Jerne, N. K. and Wood, E. C. 1949. The validity and meaning of the results of biological assays. Biometrics 5:273–299.CrossRefGoogle ScholarPubMed
12. Kudsk, P. 1988. The influence of volume rates on the activity of glyphosate and difenzoquat assessed by a parallel-line assay technique. Pestic. Sci. 24:21–29.Google Scholar
13. Kudsk, P., Olesen, T., and Thonke, K. E. 1990. The influence of temperature, humidity and simulated rainfall on the performance of thiameturon-methyl. Weed Res. 30:261–269.Google Scholar
14. Lanfranconi, L. E., Bellinder, R. R., and Wallace, R. W. 1992. Grain rye (Secale cereale) residues and weed control strategies in reduced tillage potatoes (Solanum tuberosum). Weed Technol. 6:1021–1026.Google Scholar
15. McCullagh, P. and Nelder, J. A. 1989. Generalized Linear Models, 2nd ed. Chapman and Hall, New York. 511 p.CrossRefGoogle Scholar
16. Motulsky, H. J. and Ransnas, L. A. 1987. Fitting curves to data using non-linear regression: a practical and nonmathematical review. FASEB J. 1:365–374.Google Scholar
17. Nyffeler, A., Gerber, H. R., Hurle, K., Pestemer, W., and Schmidt, R. R. 1982. Collaborative studies of dose-response curves obtained with different bioassay methods for soil-applied herbicides. Weed Res. 22:213–222.CrossRefGoogle Scholar
18. Pantone, D. J. and Baker, J. B. 1992. Varietal tolerance of rice (Oryza sativa) to bromoxynil and triclopyr at different growth stages. Weed Technol. 6:968–974.Google Scholar
19. Patterson, D. T. 1985. Comparative ecophysiology of weeds and crops. p. 101–129 in Duke, S. O., ed. Weed Physiology, Vol. I, Reproduction and Ecophysiology, Duke, S. O., ed. CRC Press, Boca Raton, FL.Google Scholar
20. Petersen, J. L. and Uecket, D. N. 1992. Nolina texana control with soil-applied herbicides. Weed Technol. 6:904–908.CrossRefGoogle Scholar
21. Poston, D. H., Murdock, E. C., and Toler, J. E. 1992. Cost-efficient weed control in soybean (Glycine max) with cultivation and banded herbicide applications. Weed Technol. 6:990–995.CrossRefGoogle Scholar
22. Seber, G.A.F. and Wild, C. J. 1989. Nonlinear Regression. John Wiley & Sons, Inc., New York. 768 p.Google Scholar
23. Seefeldt, S. S., Gealy, D. R., Brewster, B. D., and Fuerst, E. P. 1994. Cross-resistance of several diclofop resistant wild oat (Avena fatua) biotypes from the Willamette Valley of Oregon. Weed Sci. 42:430–437.Google Scholar
24. Stamps, R. H. 1992. Prodiamine controlled Florida betony (Stachys floridana) in leatherleaf fern (Rumohra adiantiformis). Weed Technol. 6:961–967.CrossRefGoogle Scholar
25. Streibig, J. C. 1987. Joint action of root-absorbed mixtures of auxin herbicides in Sinapis alba L. and barley (Hordeum vulgare L.). Weed Res. 27:337–347.CrossRefGoogle Scholar
26. Streibig, J. C. and Kudsk, P., eds. 1993. Herbicide Bioassays. CRC Press, Boca Raton, FL. 270 p.Google Scholar
27. Streibig, J. C., Rudemo, M., and Jensen, J. E. 1993. Dose-response curves and statistical models. p. 30–55 in Streibig, J. C. and Kudsk, P., eds. Herbicide Bioassays. CRC Press, Boca Raton, FL.Google Scholar
28. Streibig, J. C. and Thonke, K. E. 1985. The effect of a surfactant on alloxydim-sodium and sethoxydim potency. 1985 Br. Crop Prot. Counc. Monog. No. 28, Symp. on Application and Biology. p. 147–154.Google Scholar
29. Wall, D. A. 1992. Flurtamone for wild mustard (Sinapis arvensis) control in canola (Brassica napus and B. campestris). Weed Technol. 6:878–883.CrossRefGoogle Scholar
30. Weisberg, S. 1985. Applied Linear Regression. 2nd ed. John Wiley & Sons. New York. p. 96–97.Google Scholar