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Use of Ecophysiological Models for Crop-Weed Interference: Relations Amongst Weed Density, Relative Time of Weed Emergence, Relative Leaf Area, and Yield Loss

Published online by Cambridge University Press:  12 June 2017

M. J. Kropff
Affiliation:
Dep. Theor. Prod. Eco., Agric. Univ., P.O.B. 430, 6700 AK Wageningen, The Netherlands
S. E. Weaver
Affiliation:
Res. Sci. Agric. Canada, Res. Stn., Harrow, Ontario, Canada NOR 1G0
M. A. Smits
Affiliation:
Dep. Theor. Prod. Ecol., Agric. Univ., P.O.B. 430, 6700 AK Wageningen, The Netherlands

Abstract

The performance of a mechanistic simulation model of crop-weed competition was evaluated with data on the effects of weed density, relative time of weed emergence, and environmental conditions on crop yield for three different crop-weed combinations. Reductions in crop yields due to weed competition were simulated accurately for all experiments, except for one case in which severe water stress combined with weed competition altered crop morphological development (height and leaf area). The mechanistic model was then used to assess the potential and constraints of two empirical models of crop-weed competition, one based upon weed density and relative time of emergence, and the other on relative leaf area. The empirical model describing the relationship between relative leaf area of the weeds shortly after crop emergence and yield loss appeared to have several advantages for management applications, whereas the mechanistic model is more suited for research purposes.

Type
Special Topics
Copyright
Copyright © 1992 by the Weed Science Society of America 

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References

Literature Cited

1. Atherton, J. G. and Rudich, J. 1986. The Tomato Crop: A Scientific Basis for Improvement. Chapman and Hall, London.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.Google Scholar
3. Cousens, R., Brain, P., O'Donovan, J. T., and O'Sullivan, 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. 35:720725.Google Scholar
4. Häkansson, S. 1983. Competition and production in short-lived crop-weed stands. Sveriges Landbruks Univ. Rep. 127. 85 pp.Google Scholar
5. Kropff, M. J. 1988a. Modelling the effects of weeds on crop production. Weed Res. 28:465471.Google Scholar
6. Kropff, M. J. 1988b. Simulation of crop weed competition. Pages 177186 in Miglietta, F., ed. Models in Agriculture and Forest Research. Proceedings of a workshop held at San Miniato, Italy, June 1–3, 1987.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. 31:97105.Google Scholar
8. Kropff, M. J., Vossen, F.J.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
9. Penning de Vries, F.W.T. and van Laar, H. H., eds. 1982. Simulation of plant growth and crop production Simulation Monographs, Pudoc, Wageningen.Google Scholar
10. 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
11. Spitters, C.J.T. 1984. A simple simulation model for crop weed competition. Pages 353366 in Proc. 7th International Symposium on Weed Biology, Ecology and Systematics. COLUMA-EWRS, Paris.Google Scholar
12. Spitters, C.J.T. 1989. Weeds: population dynamics, germination and competition. Pages 182216 in Ragginge, R., Ward, S. A., and van Laar, H. H., eds. Simulation and Systems Management in Crop Protection Simulation Monographs, Pudoc, Wageningen.Google Scholar
13. 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
14. Spitters, C.J.T., Kropff, M. J., and de Groot, W. 1989. Competition between maize and Echinochloa crus-galli analyzed by a hyperbolic regression model. Ann Appl. Biol. 115:541551.Google Scholar
15. Weaver, S. E., Smits, N., and Tan, C. S. 1987. Estimating yield losses of tomato (Lycopersicon esculentum) caused by nightshade (Solanum spp.) interference. Weed Sci. 35:163168.Google Scholar
16. Weaver, S. E., Tan, C. S., and Brain, P. 1988. Effect of temperature and soil moisture on time of emergence of tomatoes and four weed species. Can. J. Plant Sci. 68:877886.Google Scholar
17. Wit, C. T. de. 1978. Simulation of assimilation respiration and transpiration of crops. Simulation Monographs, Pudoc, Wageningen.Google Scholar