Hostname: page-component-78c5997874-lj6df Total loading time: 0 Render date: 2024-11-19T04:08:30.052Z Has data issue: false hasContentIssue false
  • English
  • Français

Investigations into Alternative Methods to Predict the Competitive Effects of Weeds on Crop Yields

Published online by Cambridge University Press:  12 June 2017

Peter J.W. Lutman ,
Ruth Risiott  and
H. Peter Ostermann
Peter J.W. Lutman
Affiliation:
IACR Rothamsted, Harpenden, Herts AL5 2JQ, UK
Ruth Risiott
Affiliation:
IACR Rothamsted, Harpenden, Herts AL5 2JQ, UK
H. Peter Ostermann
Affiliation:
IACR Rothamsted, Harpenden, Herts AL5 2JQ, UK
Get access Rights & Permissions [Opens in a new window]

Abstract

Sixteen experiments have investigated alternative methods of predicting the competitive effects of a simulated weed (oats) on the yields of spring barley, spring oilseed rape (canola), peas, spring field (faba) beans and flax. The experiments were designed to discover whether early postemergence assessments of crop and weed vigor would achieve more reliable prediction of yield loss than weed density. Weed density (plants m−2) was a very variable predictor of yield loss. The standardized ranges (range/mean) of values over 3 to 4 years of data for the five crops, in the densities causing 5% yield loss, were between 1.14 and 2.59. Predictions based on the relative dry weight of crop and oats (oat dwt/(oat dwt + crop dwt)), assessed while the plants were still small, achieved more reliable predictions, as the standardized ranges were between 0.10 and 1.86. In three of the experiments, predictions based on relative dry weights were compared to similarly timed predictions based on measurements of relative leaf area and of ground cover, assessed subjectively (by eye) and photographically. Subjective and objective (photographic) assessments of cover achieved similar predictions of yield loss, indicating that visual assessments could be a viable tool to assess the potential competitive effects of weeds.

Keywords

Barley, Hordeum vulgare L. ‘Doublet’; field beans, Vicia faba L. ‘Troy, Alfred’flax, Linum usitatissimum L. ‘Antares’oats, Avena sativa L. ‘Dula,’ ‘Rollo’peas, Pisum sativum L. ‘Solara’rape, Brassica napus L. ‘Topas.’Competitionweed biomasscrop biomassrelative dry weightrelative ground coverweed density
Type
Weed Biology and Ecology
Information
Weed Science , Volume 44 , Issue 2 , June 1996 , pp. 290 - 297
Copyright
Copyright © 1996 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. Bauer, T. A., Mortensen, D. A., Wicks, G. A., Hayden, T. A., and Martin, A. R. 1991. Environmental variability associated with economic thresholds for soybeans. Weed Sci. 39: 564–569.CrossRefGoogle Scholar
2. Cousens, R. D. 1985. A simple model relating yield loss to weed density. Ann. Appl. Biol. 107: 239–252.Google Scholar
3. Cousens, R. D., Brain, P., O'Donovan, J. T., and O'Sullivan, P. A. 1987. The use of biologically realistic equations to describe the effects of weed density and relative time of emergence on crop yield. Weed Sci. 35: 720–725.Google Scholar
4. Kropff, M. J. and Spitters, C.J.T. 1991. A simple model of crop loss by weed competition from early observations on relative leaf area of the weeds. Weed Res. 31: 97–106.Google Scholar
5. Lotz, L.A.P., Kropff, M. J., Bos, B., and Wallinga, J. 1992. Prediction of yield loss based on relative leaf cover of weeds. Proc. 1st Int. Weed Control Cong. (Melbourne), 2: 290–292.Google Scholar
6. Lotz, L.A.P., Kropff, M. J., and Groeneveld, R.W.M. 1993. The relative leaf cover model tested in practice. Proc. 8th Eur. Weed Res. Soc. Symposium: Quantitative approaches in weed and herbicide research and their practical application. Pages 793–798.Google Scholar
7. Lotz, L.A.P., Kropff, M. J., Wallinga, J., Bos, B., and Groeneveld, R.W.M. 1994. Techniques to estimate relative leaf area and cover of weeds in crops for yield loss prediction. Weed Res. 34: 167–175.CrossRefGoogle Scholar
8. Lutman, P.J.W. 1992. Prediction of the competitive effects of weeds on the yields of several spring-sown arable crops. IXeme Colloque Int. sur la Biologie des Mauvaises Herbes, Dijon. Pages 337–345.Google Scholar
9. Lutman, P.J.W., Dixon, F. L., and Risiott, R. 1994. The response of four spring-sown arable crops to weed competition. Weed Res. 34: 137–146.Google Scholar
10. Payne, R. W., Lane, P. W., Ainsley, A. E., Bicknell, K. E., Digby, P.G.N., Harding, S. A., Leech, P. K., Simpson, H. R., Todd, A. D., Verrier, P. J., White, R. P., Gower, J. C., Tunnicliffe Wilson, G., and Paterson, L. J. 1987. Payne, G., Lane, R. W. P. W., Ainsley, A. E., Bicknell, K. E., Digby, P.G.N., Harding, S. A., Leech, P. K., Simpson, H. R., Todd, A. D., Verrier, P. J., White, R. P., Gower, J. C., Tunnicliffe Wilson, G., and Paterson, L. J. 1987. Lane, G, R. W. P. W., Ainsley, A. E., Bicknell, K. E., Digby, P.G.N., Harding, S. A., Leech, P. K., Simpson, H. R., Todd, A. D., Verrier, P. J., White, R. P., Gower, J. C., Tunnicliffe Wilson, G., and Paterson, L. J. 1987. G1987. G. Genstat 5 Reference Manual, Clarendon Press, Oxford.Google Scholar
11. Ross, G.J.S. 1978. Curve fitting using the Rothamsted Maximum Likelihood Programme in Edwards, D.A.H. ed. Numerical Software Needs and Availability, Pages 292–323. Academic Press, New York.Google Scholar
12. Wilson, B.J. and Wright, K. J. 1987. Variability in the growth of cleavers (Galium aparine) and their effect on wheat yield. Proc. 1987 Brit. Crop Prot. Conf.—Weeds, 1051–1058.Google Scholar
13. Wilson, B.J. and Wright, K. J. 1990. Predicting the growth and competitive effects of annual weeds in wheat. Weed Res. 30: 201–211.Google Scholar