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Empirical Models of Pigweed (Amaranthus spp.) Interference in Soybean (Glycine max)

Published online by Cambridge University Press:  12 June 2017

Anita Dieleman
Affiliation:
Crop Sci. Dept., Univ. of Guelph, Guelph, ON Can. N1G 2W1
Allan S. Hamill
Affiliation:
Agric. Can. Res. Stn., Harrow, ON Can. NOR 1G0
Stephan F. Weise
Affiliation:
Crop Sci. Dept., Univ. of Guelph, Guelph, ON Can. N1G 2W1
Clarence J. Swanton
Affiliation:
Crop Sci. Dept., Univ. of Guelph, Guelph, ON Can. N1G 2W1

Abstract

Three empirical crop yield loss models were used to describe the interference of redroot pigweed and Powell amaranth populations with soybean. Data were obtained from field experiments conducted in 1992 and 1993. Pigweed densities of 0 to eight plants m−1 were established within the soybean row. Pigweed sowing dates were selected so that weed seedling emergence coincided with VE, VC, and V2 soybean growth stages within the time frame of the critical weed-free period. The model incorporating pigweed density and time of emergence gave the best description of soybean yield loss in comparison to the two relative leaf area models. This model was fit to a combined data set of percent yield loss because parameter estimates did not differ among locations and years. Estimated soybean yield losses decreased from 16.4 to 0.5% with delayed pigweed emergence from 0 to 20 degree days. Leaf area of pigweed relative to soybean encompassed pigweed density and time of emergence. Relationship between relative leaf area and soybean yield loss was best described by the one-parameter model estimating a relative damage coefficient ‘q’ than the two-parameter model that also estimated maximum expected yield loss. The relative damage coefficient ‘q’ decreased with later times of leaf area assessment but could be predicted with one leaf area observation. Empirical models that incorporate time of weed emergence represent a step toward improving predictions of yield loss. This is important for the selection of cost-effective weed control strategies.

Type
Weed Biology and Ecology
Copyright
Copyright © 1995 by the Weed Science Society of America 

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References

LITERATURE CITED

1. Anonymous. 1992. 1993–1994 Field crop recommendations. Publication 296. Ont. Min. Agric. Fd., Toronto, ON. 96 pp.Google Scholar
2. Brown, D. M. 1978. Heat units for corn in southern Ontario. Factsheet. Agdex 111/31, Ont. Min. Agric. Fd., Toronto, ON. 4 pp.Google Scholar
3. Chikoye, D. and Swanton, C. J. 1995. Evaluation of three empirical models depicting common ragweed (Ambrosia artemissifolia L.) competition in white bean (Phaseolus vulgaris L.). Weed Res. (in press).Google Scholar
4. Chikoye, D., Weise, S. F., and Swanton, C. J. 1995. Influence of common ragweed (Ambrosia artemisiifolia) time of emergence and density on white bean (Phaseolus vulgaris). Weed Sci. (in press).Google Scholar
5. Chism, W. J., Birch, J. B., and Bingham, S. W. 1992. Nonlinear regressions for analyzing growth stage and quinclorac interactions. Weed Technol. 6:898903.Google Scholar
6. 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
7. Cousens, R. 1985. A simple model relating yield loss to weed density. Ann. App. Biol. 107:239252.Google Scholar
8. Cousens, R., 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. 35:720725.CrossRefGoogle Scholar
9. Draper, N. R. and Smith, H. 1981. Applied Regression Analysis. John Wiley and Sons, Inc. New York, NY. pages 458517.Google Scholar
10. Fehr, W. R. and Caviness, C. E. 1977. Stages of soybean development. Spec. Rep. 80. Cooperative Ext. Serv., Iowa State Univ., Ames, IA. 12 pp.Google Scholar
11. Frick, B. and Thomas, A. G. 1992. Weed surveys in different tillage systems in southwestern Ontario field crops. Can. J. Plant Sci. 72:13371347.Google Scholar
12. Hamill, A. S. and Thomas, A. G. 1985. Survey for weeds and their competitive effect in corn and soybean fields of Essex and Kent counties in Ontario. Publication 85-2. Weed Survey Series, Agric. Can., Ottawa, ON. 54 pp.Google Scholar
13. Knezevic, S. Z., Weise, S. F., and Swanton, C. J. 1995. A comparison of models depicting yield loss in corn (Zea mays). Weed Res. (in press).Google Scholar
14. Knezevic, S. Z., Weise, S. F., and Swanton, C. J. 1994. Interference of redroot pigweed (Amaranthus retroflexus) in corn (Zea mays). Weed Sci. 42:568573.Google Scholar
15. Kropff, M. J. and Lotz, L. A. P. 1992. Systems approaches to quantify crop-weed interactions and their application in weed management. Agric. Sys. 40:265282.Google Scholar
16. 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.CrossRefGoogle Scholar
17. Lotz, L.A.P., Kropff, M.J., Bos, B., and Wallinga, J. 1992. Prediction of yield loss based on relative leaf cover of weeds. Page 290292 in Proc. First International Weed Control Congress, Melbourne, Australia.Google Scholar
18. Monks, D. W. and Oliver, L. R. 1988. Interactions between soybean (Glycine max) cultivars and selected weeds. Weed Sci. 36:770774.Google Scholar
19. Moolani, M. K., Knake, E. L., and Slife, F. W. 1964. Competition of smooth pigweed with corn and soybeans. Weeds 12:126128.Google Scholar
20. O'Donovan, J. T. 1991. Quackgrass (Elytrigia repens) interference in canola (Brassica campestris). Weed Sci. 39:397401.Google Scholar
21. 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. 33:498503.Google Scholar
22. Shurtleff, J. L. and Coble, H. D. 1985. Interference of certain broadleaf weed species in soybeans (Glycine max). Weed Sci. 33:654657.Google Scholar
23. Stoller, E. W., Harrison, S. K., Wax, L. M., Regnier, E. E., and Nafziger, E. D. 1987. Weed interference in soybeans (Glycine max). Rev. Weed Sci. 3:155181.Google Scholar
24. Swanton, C. J. and Weise, S. F. 1991. Integrated weed management: the rationale and approach. Weed Technol. 5:657663.Google Scholar
25. Thornton, P. K., Fawcett, R. H., Dent, J. B., and Perkins, T. J. 1990. Spatial weed distribution and economic thresholds for weed control. Crop Prot. 9:337342.Google Scholar
26. Van Acker, R. C., Swanton, C. J., and Weise, S. F. 1993. The critical period of weed control in soybean (Glycine max (L.) Merr.). Weed Sci. 41:194220.CrossRefGoogle Scholar
27. Van Acker, R. C., Weise, S. F., and Swanton, C. J. 1993. Influence of interference from a mixed weed species stand on soybean (Glycine max (L.) Merr.) growth. Can. J. Plant Sci. 73:12931304.Google Scholar
28. Weaver, S. E. 1991. Size-dependent economic thresholds for three broadleaf weed species in soybeans. Weed Technol. 5:674679.Google Scholar
29. Weaver, S. E. and McWilliams, E. L. 1980. The biology of Canadian weeds. 44. Amaranthus retroflexus L., A. powellii S. Wats., and A. hybridus L. Can. J. Plant Sci. 60:12151234.Google Scholar
30. Wiles, L. J., Wilkerson, G. G., Gold, H. J., and Coble, H. D. 1992. Modeling weed distribution for improved postemergence control decisions. Weed Sci. 40:546553.Google Scholar