Hostname: page-component-74656f9884-c52kr Total loading time: 0 Render date: 2026-11-08T19:16:16.356Z Has data issue: false hasContentIssue false
  • English
  • Français

Research Methods in Weed Science: Statistics

Published online by Cambridge University Press:  20 January 2017

Christian Ritz ,
Andrew R. Kniss  and
Jens C. Streibig
Christian Ritz
Affiliation:
Department of Nutrition, Exercise and Sports, University of Copenhagen, Nørre Allé, DK-2200 Copenhagen N, Denmark
Andrew R. Kniss
Affiliation:
Department of Plant Sciences, University of Wyoming, 3354 1000 E. University Avenue, Laramie, WY 82071
Jens C. Streibig*
Affiliation:
Department of Plant and Environmental Sciences, University of Copenhagen, Hoejbakkegaard, DK-2630 Taastrup, Denmark
*
Corresponding author's E-mail: jcs@plen.ku.dk
Rights & Permissions [Opens in a new window]

Extract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

There are various reasons for using statistics, but perhaps the most important is that the biological sciences are empirical sciences. There is always an element of variability that can only be dealt with by applying statistics. Essentially, statistics is a way to summarize the variability of data so that we can confidently say whether there is a difference among treatments or among regression parameters and tell others about the variability of the results. To that end, we must use the most appropriate statistics to get a “correct” picture of the experimental variability, and the best way of doing that is to report the size of the parameters or the means and their associated standard errors or confidence intervals. Simply declaring that the yields were 1 or 2 ton ha−1 does not mean anything without associated standard errors for those yields. Another driving force is that no journal will accept publications without the data having been subjected to some kind of statistical analysis.

Keywords

BromoxynilioxyniloxitrilAnalysis of covarianceanalysis of variancelinear regressionslogistic regressionsnonlinear regressions

Information

Type
Weed Biology and Ecology
Information
Weed Science , Volume 63 , Issue SP1: Special Issue: Research Methods in Weed Science , February 2015 , pp. 166 - 187
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - SA
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike licence (http://creativecommons.org/licenses/by-nc-sa/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the same Creative Commons license is included and the original work is properly cited.
Copyright
Copyright © Weed Science Society of America

References

Literature Cited

Blackman, GE, Templeman, WG, Halliday, DJ (1951) Herbicides and selective phytotoxicity. Annu Rev Plant Physiol 2:199–230Google Scholar
Cedergreen, N, Kudsk, P, Mathiassen, SK, Streibig, JC (2007) Combination effects of herbicides on plants and algae: do species and test systems matter? Pest Manag Sci 63:282–295Google Scholar
Cedergreen, N, Madsen, TV (2002) Nitrogen uptake by the floating macrophyte Lemna minor. New Phytol 155:285–292Google Scholar
Christensen, MG, Teicher, HB, Streibig, JC (2003) Linking fluorescence induction curve and biomass in herbicide screening. Pest Manag Sci 59:1303–1310Google Scholar
Cousens, R (1985) An empirical model relating crop yield to weed and crop density and a statistical comparison with other models. J Agric Sci 105:513–521Google Scholar
Cousens, R (1988) Misinterpretations of results in weed research through inappropriate use of statistics. Weed Res 28:281–289Google Scholar
Finney, DJ (1971) Probit Analysis. 3 edn. LondonGriffin. 333 pGoogle Scholar
Hothorn, T, Bretz, F, Westfall, P (2008) Simultaneous inference in general parametric models. Biometrical J 50:346–363Google Scholar
Kniss, AR, Wilson, RG, Martin, AR, Burgener, PA, Feuz, DM (2004) Economic evaluation of glyphosate-resistant and conventional sugar beet Weed Tech. 18:388–396Google Scholar
Kniss, AR, Vassios, JD, Nissen, SJ, Ritz, C (2011) Nonlinear regression analysis of herbicide absorption studies. Weed Sci 59:601–610Google Scholar
Mennan, H, Streibig, JC, Ngouajio, M, Kaya, E (2012) Tolerance of two Bifora radians Bieb populations to ALS inhibitors in winter wheat. Pest Manag Sci 68:116–122Google Scholar
Nelder, JA (1966) Inverse polynomials, a useful group of multi-factor response functions. Biometrics 22:128–141Google Scholar
Onofri, A, Carbonell, EA, Piepho, HP, Mortimer, AM, Cousens, RD (2010) Current statistical issues in Weed Research. Weed Res 50:5–24Google Scholar
Rasmussen, J, Bibby, BM, Schou, AP (2008) Investigating the selectivity of weed harrowing with new methods. Weed Res 48:523–532Google Scholar
Ritz, C (2010) Toward a unified approach to dose-response modeling in ecotoxicology. Environ Toxicol Chem 29:220–229Google Scholar
Ritz, C, Cedergreen, N, Jensen, JE, Streibig, JC (2006) Relative potency in nonsimilar dose–response curves. Weed Sci 54:407–412Google Scholar
Ritz, C, Pipper, CB, Streibig, JC (2013) Analysis of germination data from agricultural experiments. Eur J Agron 45:1–6Google Scholar
Ritz, C, Streibig, JC (2005) Bioassay analysis using R. J Stat Softw 12:1–22Google Scholar
Ritz, C, Streibig, JC (2008) Nonlinear Regression with R New YorkSpringer. 1:1–144Google Scholar
Samuels, M, Witmer, J, Schaffner, A (2004) Statistics for the Life Sciences. 4th edn. LondonPearson. 672 pGoogle Scholar
Seefeldt, SS, Jensen, JE, Fuerst, EP (1995) Log-logistic analysis of dose–response relationships. Weed Technol 9:218–227Google Scholar
Streibig, JC (1983) Fitting equations to herbicide bioassays: using the methods of parallel line assay for measuring joint action of herbicide mixtures. Ber Fachg Herbol 24:183–193Google Scholar
Streibig, JC (1984) Measurement of phytotoxicity of commercial and unformulated soil-applied herbicides. Weed Res 24:327–331Google Scholar
Streibig, JC (1988) Herbicide bioassay. Weed Res 28:479–484Google Scholar
Streibig, JC, Combellack, JH, Pritchard, GH, Richardson, RG (1989) Estimation of thresholds for weed-control in Australian cereals. Weed Res 29:117–126Google Scholar
Streibig, JC, Jensen, JE (2000) Action of herbicides in mixtures. Pages 153–180in Cobb, AH, Kirkwood, RC, eds. Herbicides and Their Mechanisms of Action. SheffieldSheffield AcademicGoogle Scholar
Streibig, JC, Rudemo, M, Jensen, JE (1993) Dose–response curves and statistical models. Pages 29–55in Streibig, JC, Kudsk, P, eds. Herbicide Bioassays. Boca RatonCRCGoogle Scholar
Stroup, WW (2014) Rethinking the analysis of non-normal data in plant and soil science. Agron J. 106:1–17Google Scholar
Van der Vliet, L, Ritz, C (2013) Statistics for analyzing ecotoxicity test data. Pages 1081–1096in Blaise, C, Férard, JF, eds. Encyclopedia of Aquatic Ecotoxicology. BerlinSpringer. 1221 pGoogle Scholar
Woodford, EK (1950) Experimental techniques for the evaluation of selective herbicides. NAAS Q Rev 9:1–10Google Scholar