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Optimization of Weed Management Systems: The Role of Ecological Models of Interplant Competition

Published online by Cambridge University Press:  12 June 2017

M. J. Kropff
Affiliation:
International Rice Research Institute, P.O. Box 933, 1099, Manila, The Philippines
L.A.P. Lotz
Affiliation:
Centre for Agrobiological Research, P.O. Box 14, 6700 AA Wageningen, The Netherlands

Abstract

The strategy to optimize weed management systems with a minimum use of herbicides includes both the adaptation of crop management practices and well designed decision making systems, based on postemergence observations of weed infestations. Both strategies require thorough quantitative insight into the crop weed ecosystem, which can be provided by systems analysis, using process based models. These models also can be applied to similar systems like intercropping. For practical application, however, a simple measure of weed infestation and a simple model which relates weed infestation to yield loss are required. Recent progress in model development is discussed. An alternative empirical model that predicts yield loss from the relative leaf area of the weeds shortly after crop emergence, seems to be a useful approach for prediction of yield loss shortly after crop emergence. The use of systems approaches at different levels of detail for bridging the gap between research and practical application is discussed.

Type
Symposium
Copyright
Copyright © 1990 by the Weed Science Society of America 

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References

Literature Cited

1. Berkowitz, A. R. 1988. Competition for resources in weed-crop mixtures. p. 89119 in Altieri, M. A., and Liebman, M. eds. Weed Management in Agroecosystems: Ecological Approaches. CRC Press, Boca Raton, Florida.Google Scholar
2. Cousens, R. 1985. An empirical model relating crop yield to weed and crop density and a statistical comparison with other models. J. Agric. Sci. 105:513521.CrossRefGoogle Scholar
3. Cousens, R., Brain, P., O'Donovan, J. T., O'Sullivan, A. 1987. The use of biologically realistic equations to describe the effects of weed density and relative time of emergence of crop yield. Weed Sci. 35:720725.Google Scholar
4. De Datta, S. K. 1981. Principles and Practices of Rice Production. John Wiley and Sons, New York, 618 p.Google Scholar
5. Hakansson, S. 1983. Competition and production in short-lived crop-weed stands. Sveriges Landbruks Univ. Uppsala, Sweden. Report 127, 85 p.Google Scholar
6. Kropff, M. J. 1988. Modelling the effects of weeds on crop production. Weed Res. 28:465471.Google Scholar
7. Kropff, M. J., Vossen, FJ.H., Spitters, C.J.T., and de Groot, W. 1984. Competition between a maize crop and a natural population of Echinochloa crus-galli (L.). Neth. J. Agric. Sci. 32:324327.Google Scholar
8. Kropff, M. J. and Spitters, C.J.T. 1991. A simple model for crop loss by weed competition on basis of early observation on relative leaf area of the weeds. Weed Res. 31:97105.Google Scholar
9. Kropff, M. J., Weaver, S. E., and Smits, M. A. 1992. Use of ecophysiological models for crop weed interference: Relations amongst weed density, relative time of weed emergence, relative leaf area, and yield loss. Weed Sci. 40:296301.Google Scholar
10. Lotz, L.A.P., Kropff, M. J., and Groenweld, R.M.W. 1990. Herbicide application in winter wheat Experimental results on weed competition analyzed by a mechanistic model. Neth. J. Agric. Sci. 30:711718.Google Scholar
11. Penning de Vries, F.W.T. and van Laar, H. H. eds., 1982. Simulation of plant growth and crop production. Simulation Monographs. Pudoc, Wageningen, 308 p.Google Scholar
12. Rabbinge, R., Ward, S. A., and van Laar, H. H. 1989. Simulation Monographs. Pudoc, Wageningen, 420 p.Google Scholar
13. Spitters, C.J.T. 1983. An alternative approach to the analysis of mixed cropping experiments. I. Estimation of competition effects. Neth. J. Agric. Sci. 31:111.Google Scholar
14. Spitters, C.J.T. 1989. Weeds: population dynamics, gerSmination and competition. p. 182216 in Rabbinge, R., Ward, S. A., and van Laar, H. H. (eds.) Simulation and systems management in crop protection. Simulation monographs. Pudoc, Wageningen, 420 p. Google Scholar
15. Spitters, C.J.T. and Aerts, R. 1983. Simulation of competition for light and water in crop weed associations. Aspects Appl. Biol. 4:467484.Google Scholar
16. Spitters, C.J.T., Kropff, M. J., and de Groot, W. 1989. Competition between maize and Echinochloa crus-galli L. analyzed by a hyperbolic regression model. Ann. Appl. Biol. 115:541551.Google Scholar
17. Weaver, S. E., Smits, N., and Tan, C. S. 1987. Estimating yield losses of tomato (Lycopersion esculentum) caused by nightshade (Solanum spp.) interference. Weed Sci. 35:163168.CrossRefGoogle Scholar
18. Weaver, S. E., Kropff, M. J., and Groeneveld, R.M.W. 1992. Use of ecophysiological models for crop-weed interference: the critical period of weed interference. Weed Sci. 40:302307.Google Scholar