Hostname: page-component-76fb5796d-45l2p Total loading time: 0 Render date: 2024-04-29T11:22:33.828Z Has data issue: false hasContentIssue false
  • English
  • Français

The use of Biologically Realistic Equations to Describe the Effects of Weed Density and Relative Time of Emergence on Crop Yield

Published online by Cambridge University Press:  12 June 2017

Roger Cousens ,
Philip Brain ,
John T. O'Donovan  and
P. Ashley O'Sullivan
Roger Cousens
Affiliation:
Weed Sci. and Biometrician, Dep. Agric. Sci., Bristol Univ., Long Ashton Res. Stn., Bristol, England BS18 9AF
Philip Brain
Affiliation:
Weed Sci. and Biometrician, Dep. Agric. Sci., Bristol Univ., Long Ashton Res. Stn., Bristol, England BS18 9AF
John T. O'Donovan
Affiliation:
Weed Sci., Alberta Env. Centre, Vegreville, Alberta TOB 4LO
P. Ashley O'Sullivan
Affiliation:
Weed Sci., Agric. Can. Res. Stn. Lacombe, Alberta TOC ISO
Get access Rights & Permissions [Opens in a new window]

Abstract

A model, based on a rectangular hyperbola, has been developed to describe the relationship between population density and relative time of seedling emergence of wild oat (Avena fatua L. # AVEFA) and yield of barley (Hordeum vulgare L.) and wheat (Triticum aestivum L.). The equation is where yL is percent yield loss, D is weed density, T is relative time of emergence of weed and crop, and a, b, and c are nonlinear regression coefficients. Significant differences in fitted equations were found between years. From the values of regression coefficients it was concluded that barley is a better competitor than wheat and is less affected by late-emerging wild oat. The model was tested on previously published data. It provided only a slightly better description of the data than a multiple-regression model, but avoided a number of undesirable, implausible properties inherent in the more frequently used approach. In particular, the model does not predict a loss in yield when no weeds are present or a yield increase from late-emerging weeds.

Keywords

Nonlinear modelscompetitionAvena fatuaAVEFA
Type
Special Topics
Information
Weed Science , Volume 35 , Issue 5 , September 1987 , pp. 720 - 725
Copyright
Copyright © 1987 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. Cousens, R. 1985a. A simple model relating yield loss to weed density. Ann. Appl. Biol. 107:239–252.CrossRefGoogle Scholar
2. Cousens, R. 1985b. An empirical model relating crop yield to weed and crop density and a statistical comparison with other models. J. Agric. Sci. 105:513–521.CrossRefGoogle Scholar
3. Cousens, R. 1987. Theory and reality of weed control thresholds. Plant Prot. Quart. 2:13–20.Google Scholar
4. Cousens, R., Peters, N.C.B., and Marshall, C. J. 1984. Models of yield loss – weed density relationships. Pages 367–374 in Proceedings of the 7th International Symposium on Weed Biology, Ecology and Systematics. COLUMA, Paris.Google Scholar
5. Cousens, R., Pollard, F., and Denner, R.A.P. 1985. Competition between Bromus sterilis and winter cereals. Pages 67–74 in Aspects of Applied Biology 9, The Biology and Control of Weeds in Cereals.Google Scholar
6. Dew, D. A. 1972. An index of competition for estimating crop loss due to weeds. Can. J. Plant Sci. 52:921–927.CrossRefGoogle Scholar
7. de Wit, C. T. and Baeumer, K. 1967. Pages 233–247 in Principles of Measuring Crop Losses in Competitive Situations with Particular Reference to Weeds. Proc. F.A.O. Symposium on Crop Losses. F.A.O., Rome.Google Scholar
8. Mead, R. 1971. A note on the use and misuse of regression models in ecology. J. Ecol. 59:214–219.Google Scholar
9. O'Donovan, J. T., de St. Remy, E. A., O'Sullivan, P. A., Dew, D. A., and Sharma, A. K. 1985. Influence of the relative time of emergence of wild oat (Avena fatua) on yield loss of barley (Hordeum vulgare) and wheat (Triticum aestivum). Weed Sci. 33:498–503.Google Scholar
10. O'Sullivan, P. A., Kossatz, V. C., Weiss, G. M., and Dew, D. A. 1982. An approach to estimating yield loss of barley due to Canada thistle. Can. J. Plant Sci. 62:725–731.CrossRefGoogle Scholar
11. O'Sullivan, P. A., Weiss, G. M., and Kossatz, V. C. 1985. Indices of competition for estimating rapeseed yield loss due to Canada thistle. Can. J. Plant Sci. 65:145–149.Google Scholar
12. Schweizer, E. E. 1973. Predicting sugarbeet root losses based on kochia densities. Weed Sci. 21:565–567.Google Scholar
13. Spitters, C.J.T. 1983. An alternative approach to the analysis of mixed cropping experiments. 1. Estimation of competition effects. Neth. J. Agric. Sci. 31:1–11.Google Scholar
14. Suehiro, K. and Ogawa, H. 1980. Competition between two annual herbs, Atriplex gmelini C. A. Mey and Chenopodium album L., in mixed cultures irrigated with seawater of various concentrations. Oecologia (Berl.) 45:167–177.Google Scholar
15. Wells, G. J. 1979. Annual weed competition in wheat crops: the effect of weed density and applied nitrogen. Weed Res. 19:185–191.Google Scholar
16. Wright, A. J. 1981. The analysis of yield-density relationships in binary mixtures using inverse polynomials. J. Agric. Sci. 96:561–567.CrossRefGoogle Scholar