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Canopy Measurements as Predictors of Weed-Crop Competition

Published online by Cambridge University Press:  12 June 2017

J. I. Vitta  and
C. Fernandez Quintanilla
J. I. Vitta
Affiliation:
Dep. de Cereales y Leguminosas, S.I.A. de la Com. de Madrid, Aptdo. 127, 28800 Alcala de Henares (Spain)
C. Fernandez Quintanilla
Affiliation:
Centro de de Ciencias Medioambientales, Consejo Superior de Investigaciones Científicas (CSIC), Serrano 115 dpdo, 28006 Madrid, Spain
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Abstract

The development of weed management systems requires accurate prediction of weed-crop competition. In this paper, simple regression models of crop yield losses based on weed density and weed leaf area are compared. In weed leaf area models, variations in the relative damage coefficient (q) were also analyzed. Finally, three simple methods to assess weed cover were compared: visual, photographic, and optic device assessment. Leaf area models were at least as accurate as weed density models. However, the generality of the leaf area models was restricted by changes in q, according to the date of leaf area evaluation and the year. Although all methods to assess weed cover correlated adequately with weed leaf area, visual estimates were the best to predict crop yield losses perhaps because very low levels of weed leaf area could be distinguished visually better than by other methods.

Keywords

White mustard, Brassica hirta Moench ♯ SINALwheat Triticum aestivum L.Competitionmathematical modelsweed management
Type
Weed Biology and Ecology
Information
Weed Science , Volume 44 , Issue 3 , September 1996 , pp. 511 - 516
Copyright
Copyright © 1996 by the Weed Science Society of America 

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References

Literature Cited

1. Baeumer, D. and de Wit, C. T. 1968. Competitive interference of plant species in monocultures and mixed stands. Neth. J. Agric. Sci. 16: 103–122.Google Scholar
2. 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.Google Scholar
3. Christiensen, S. 1993. Electronic weed cover assessment. Pages 63–70 in Proc. European Weed Res. Soc. Symp., Quantitative Approaches in Weed and Herbicide Research and their Practical Application.Google Scholar
4. Cousens, R. 1985. A simple model relating yield loss to weed density. Ann. Appl. Biol. 107: 239–252.CrossRefGoogle Scholar
5. Firbank, L. G., Cousens, R., Mortimer, A. M., and Smith, R.G.R. 1990. Effects of soil type on crop yield-weed density relationships between winter wheat and Bromus sterilis . J. Appl. Ecol. 27: 308–318.Google Scholar
6. Ghersa, C. M. and Martinez Ghersa, M. A. 1991. A field method for predicting yield losses in maize caused by johnsongrass (Sorghum halepense). Weed Technol. 5: 279–285.Google Scholar
7. 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: 97–105.Google Scholar
8. Kropff, M. J. and Lotz, L.A.P. 1992. Optimization of weed management systems: the role of ecological models of interplant competition. Weed Technol. 6: 462–467.Google Scholar
9. Kropff, M. J., Weaver, S. E., and Smits, M. A. 1992. Use of ecophysiological models for crop-weed interference: relation amongst weed density, relative time of weed emergence, relative leaf area, and yield loss. Weed Sci. 40: 296–301.CrossRefGoogle Scholar
10. Lotz, L.A.P., Kropff, M. J., and Groeneveld, R.M.W. 1993. The relative leaf cover model tested for practice. Pages 793–798 in Proc. European Weed Res. Soc. Symp., Quantitative Approaches in Weed and Herbicide Research and their Practical Application.Google Scholar
11. Lotz, L.A.P., Kropff, M. J., Wallinga, J., Bos, H. J., and Groeneveld, R.M.W. 1994. Techniques to estimate relative leaf area and cover of weeds in crops for yield prediction. Weed Res. 34: 167–175.Google Scholar
12. Lutman, P.J.W. 1992. Prediction of the competitive effects of weeds on the yields of several spring-sown arable crops. Pages 337–344 in Proc. IXème Colloque International sur la Biologie dès Mauvaises Herbes.Google Scholar
13. Pike, D. R., Stoller, E. W., and Wax, L. M. 1990. Modeling soybean growth and canopy apportionment in weed-soybean (Glycine max) competition. Weed Sci. 38: 522–527.Google Scholar
14. Streibig, J. C., Combellack, J. H., Pritchard, G. H., and Richardson, R. G. 1989. Estimation of thresholds for weed control in Australian cereals. Weed Res. 29: 117–126.Google Scholar
15. Vitta, J. I., Satorre, E. H., and Leguizamón, E. S. 1994. Using canopy attributes to evaluate competition between Sorghum halepense (L.) Pers. and soybean. Weed Res. 34: 89–97.Google Scholar