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Relative potency in nonsimilar dose–response curves

Published online by Cambridge University Press:  20 January 2017

Christian Ritz ,
Nina Cedergreen ,
Jens Erik Jensen  and
Jens Carl Streibig
Christian Ritz
Affiliation:
Department of Natural Sciences (Statistics Unit), The Royal Veterinary and Agricultural University, 40 Thorvaldsensvej, DK-1871 Frederiksberg C, Denmark
Jens Erik Jensen
Affiliation:
The Danish Agricultural Advisory Service, National Centre, Department of Crop Production, 15 Udkaersvej, DK-8200 Aarhus N, Denmark
Jens Carl Streibig
Affiliation:
Department of Agricultural Sciences (Crop Science), The Royal Veterinary and Agricultural University, 13 Højbakkegård Allé, DK-2630 Taastrup, Denmark
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Abstract

This article discusses the concept of relative potency of herbicides in bioassays where individual dose–response curves can be similar or nonsimilar, often denoted parallel and nonparallel curves, and have different upper and lower limits. The relative potency is constant for similar dose–response curves and measures the relative horizontal displacement of curves of a similar shape along the dose axis on a logarithmic scale. The concept of similar dose–response curves has been used extensively to assess results from herbicide experiments, for example, with the purpose of adjusting herbicide doses to environmental conditions, formulations, and adjuvants. However, deeming dose–response curves similar when they are not may greatly affect the calculation of the relative potency at response levels such as effective dosage (ED)90, which is relevant for effective weed control, or ED 10, which is used in crop tolerance studies. We present a method for calculating relative potencies between nonsimilar dose–response curves at any response level. It also is demonstrated that if the upper, lower, or both limits among response curves are substantially different, then the ED 50 or any other ED level cannot be used indiscriminately to compute the relative potency. Rather, the relative potency should be viewed as a function of the response level.

Keywords

Dose–response curvesexchange rationonsimilar curvesrelative responsespesticidetoxicity
Type
Physiology, Chemistry, and Biochemistry
Information
Weed Science , Volume 54 , Issue 3 , June 2006 , pp. 407 - 412
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Bartley, M. R. 1993. Assessment of herbicide selectivity. Pages 57–73 in Streibig, J. C. and Kudsk, P. eds. Herbicide Bioassays. Boca Raton, FL: CRC Press.Google Scholar
Bates, D. M. and Watts, D. G. 1988. Nonlinear Regression Analysis and Its Applications. New York: J. Wiley.Google Scholar
Brain, P. and Cousens, R. 1989. An equation to describe dose responses where there is stimulation of growth at low doses. Weed Res 29:93–96.CrossRefGoogle Scholar
Cabanne, F., Gaudry, J. C., and Streibig, J. C. 1999. Influence of alkyl oleates on efficacy of phenmedipham applied as an acetone:water solution on Galium aparine . Weed Res 39:57–67.Google Scholar
Cedergreen, N., Ritz, C., and Streibig, J. C. 2005. Improved empirical models describing hormesis. Environ. Toxicol. Chem 24:3166–3172.Google Scholar
Cedergreen, N. and Streibig, J. C. 2005. Can the choice of endpoint lead to contradicting results of mixture toxicity experiments? Environ. Toxicol. Chem 24:1676–1683.CrossRefGoogle Scholar
Christensen, M. G., Teicher, H. B., and Streibig, J. C. 2003. Linking fluorescence induction curve and biomass in herbicide screening. Pest Manag. Sci 59:1303–1310.Google Scholar
Finney, D. J. 1965. The meaning of bioassay. Biometrics 21:785–810.Google Scholar
Finney, D. J. 1978. Statistical Method in Biological Assay. London: Charles Griffin & Company.Google Scholar
Follak, S. and Hurle, K. 2003. Effect of airborne bromoxynil-octanoate and metribuzin on non-target plants. Environ. Pollut 126:139–146.Google Scholar
Harring, T., Streibig, J. C., and Husted, S. 1998. Accumulation of shikimic acid: a technique for screening glyphosate efficacy. J. Agric. Food Chem 46:4406–4412.Google Scholar
Hewlett, P. S. and Plackett, R. L. 1979. The Interpretation of Quantal Responses in Biology. London: Edward Arnold. Pp. 1–80.Google Scholar
Jerne, N. K. and Wood, E. C. 1949. The validity and meaning of the results of biological assays. Biometrics 5:273–299.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Kudsk, P. and Streibig, J. C. 1993. Formulations and adjuvants. Pages 99–116 in Streibig, J. C. and Kudsk, P. eds. Herbicide Bioassays. Boca Raton, FL: CRC Press.Google Scholar
Murali, N. S., Secher, B. J. M., Rydahl, P., and Andreasen, F. M. 1999. Application of information technology in plant protection in Denmark: from vision to reality. Comput. Electron. Agric 22:109–115.Google Scholar
Olofsdotter, M., Andersen, S. B., Olesen, A., and Streibig, J. C. 1994. A comparison of herbicide bioassays in cell cultures and whole plants. Weed Res 34:387–394.CrossRefGoogle Scholar
Rimando, A. M., Dayan, F. E., and Streibig, J. C. 2003. PSII inhibitory activity of resorcinolic lipids from Sorghum bicolor . J. Nat. Prod 66:42–45.Google Scholar
Ritz, C. and Streibig, J. C. 2005. Bioassay analyses using R. J. Stat. Softw 12:1–22.Google Scholar
Rydahl, P. 1995. Computer assisted decision making. Pages 29–37 in Challenges for Weed Science in a Changing Europe. Budapest, Hungary: EWRS.Google Scholar
Schmidt, R. R. 1993. Development of herbicides—role of bioassays. Pages 7–28 in Streibig, J. C. and Kudsk, P. eds. Herbicide Bioassays. Boca Raton, FL: CRC Press.Google Scholar
Scholze, M., Boedeker, W., Faust, M., Backhaus, T., and Altenburger, R. 2001. A general best-fit method for concentration response curves and the estimation of low-effect concentrations. Environ. Toxicol. Chem 20:448–457.Google ScholarPubMed
Seefeldt, S. S., Jensen, J. E., and Fuerst, E. P. 1995. Log-logistic analysis of dose-response relationships. Weed Technol 9:218–227.CrossRefGoogle Scholar
Seiden, P., Kappel, D., and Streibig, J. C. 1998. Response of Brassica napus in tissue culture to metsulfuron-methyl and chlorsulfuron. Weed Res 38:221–228.Google Scholar
Streibig, J. C. 1988. Herbicide bioassay. Weed Res 28:479–484.Google Scholar
Streibig, J. C., Dayan, F. E., Rimando, A. M., and Duke, S. O. 1999. Joint action of natural and synthetic photosystem II inhibitors. Pestic. Sci 55:137–146.3.0.CO;2-D>CrossRefGoogle Scholar
Streibig, J. C. and Jensen, J. E. 2000. Action of herbicides in mixtures. Pages 153–180 in Cobb, A. H. and Kirkwood, R. C. eds. Herbicides and Their Mechanisms of Action. Sheffield: Sheffield Academic Press.Google Scholar
Streibig, J. C., Kudsk, P., and Jensen, J. E. 1998. A general joint action model for herbicide mixtures. Pestic. Sci 53:21–28.Google Scholar
Streibig, J. C., Rudemo, M., and Jensen, J. E. 1993. Dose-response curves and statistical models. Pages 29–55 in Streibig, J. C. and Kudsk, P. eds. Herbicide Bioassays. Boca Raton, FL: CRC Press.Google Scholar
Streibig, J. C., Walker, A., and Blair, A. M. et al. 1995. Variability of bioassays with metsulfuron-methyl in soil. Weed Res 35:215–224.Google Scholar