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Modeling competition between wild oat (Avena fatua L.) and yellow mustard or canola

Published online by Cambridge University Press:  20 January 2017

Oleg Daugovish ,
Donald C. Thill  and
Bahman Shafii
Oleg Daugovish
Affiliation:
University of California Cooperative Extension, 669 County Square Drive, Suite 100, Ventura, CA 93003-5401
Bahman Shafii
Affiliation:
Statistical Programs, College of Agriculture, University of Idaho, Moscow, ID 83844-2337
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Abstract

Wild oat, a troublesome weed in cereals, infests about 11 million hectares of cropland in the United States. Diversifying cereal production with alternative crops, such as yellow mustard and canola, can provide flexibility in cropping systems, decrease production risks, and allow for effective weed suppression. The objective of the study was to quantify the competitive ability of yellow mustard and canola relative to wild oat in addition series field experiments, which were conducted in 1999 and 2000 near Genesee, ID. Biomass and seed production of wild oat were reduced 67 and 80%, respectively, in mixtures with yellow mustard, which was three to four times greater than the reduction in corresponding mixtures with canola. In addition, yellow mustard reduced the biomass and seed production of wild oat equally regardless of wild oat density. In contrast, the competitive effect of canola on wild oat biomass decreased 5 to 10 times when wild oat density increased from 100 to 200 plants m−2. Yellow mustard at all densities and at both biomass harvests suppressed wild oat biomass and seed production similarly. But suppression of wild oat by canola increased as canola density increased, and canola plants were more competitive at the flowering stage than at the rosette stage. Wild oat had little or no effect on yellow mustard seed yield but reduced canola seed yield 37%, when averaged over canola densities. Additionally, the oil content of canola seed was reduced 0.4% for every 1% of wild oat seed in the harvested seed. Models developed in this study accurately predicted plant populations of yellow mustard and canola that provided optimal weed suppression and crop yield for different wild oat populations.

Keywords

Wild oat, Avena fatua L. AVEFAcanola, Brassica napus L. ‘Sunrise’yellow mustard, Sinapis alba L. ‘Idagold’Alternative cropsplant competitionweed–crop interference
Type
Research Article
Information
Weed Science , Volume 51 , Issue 1 , February 2003 , pp. 102 - 109
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Alcocer-Ruthling, M., Thill, D. C., and Shafii, B. 1992. Differential competitiveness of sulfonylurea resistant and susceptible prickly lettuce (Lactuca serriola). Weed Technol. 6:303–309.CrossRefGoogle Scholar
Baker, R. J. 1981. Soil Survey of Latah County Area, Idaho. Washington, D.C.: U.S. Department of Agriculture Soil Conservation Survey, 84 p.Google Scholar
Barbour, M. G., Burk, J. H., and Pitts, W. D. 1987. Terrestrial Plant Ecology. Menlo Park, CA: Benjamin Cummings. 189 p.Google Scholar
Blackshaw, R. E., Anderson, G. W., and Dekker, J. 1987. Interference of Sinapis arvensis L. and Chenopodium album L. in spring rapeseed (Brassica napus L.). Weed Res. 27:207–213.Google Scholar
Brown, P. 1995. Chemical Characteristics and Biological Effects of Brassica Allelochemicals. Ph.D. dissertation. University of Idaho, Moscow, ID. 89 p.Google Scholar
Cousens, R. 1985. An empirical model relating crop yield to weed and crop density and a statistical comparison with other models. J. Agric. Sci. 105:513–521.Google Scholar
Cudney, D. W., Jordan, L. S., and Hall, A. E. 1991. Effect of wild oat (Avena fatua) infestations on light interception and growth rate of wheat (Triticum aestivum). Weed Sci. 39:175–179.CrossRefGoogle Scholar
Daugovish, O. 2001. Competitive Ability of Yellow Mustard or Canola with Wild Oat and Rotational Effects of Yellow Mustard. Ph.D. dissertation. University of Idaho, Moscow, ID. 135 p.Google Scholar
Daugovish, O., Lyon, D. J., and Baltesperger, D. D. 1999. Cropping systems to control winter annual grasses in winter wheat (Triticum aestivum). Weed Technol. 13:120–126.CrossRefGoogle Scholar
Dew, D. A. and Keys, C. H. 1976. An index of competition for estimating loss of rape due to wild oats. Can. J. Plant Sci. 56:1005–1006.Google Scholar
Esser, A. D. 1998. Agronomic and Economic Feasibility of Yellow Mustard (Sinapis alba L.) as an Alternative Crop in the Dryland Region of the Pacific Northwest. . University of Idaho, Moscow, ID. pp. 22–27.Google Scholar
Evans, R. M., Thill, D. C., Tapia, L., Shafii, B., and Lish, J. M. 1991. Wild oat (Avena fatua) and spring barley (Hordeum vulgare) density affect spring barley yield. Weed Technol. 5:33–39.Google Scholar
Hammond, E. G. 1991. Organization of rapid analysis of lipids in many individual plants. Pages 321–330 In Linskens, H. F. and Jackson, J. F., eds. Modern Methods of Plant Analysis. Volume 12. Essential Oils and Waxes. Berlin: Springer-Verlag.CrossRefGoogle Scholar
Howard, H. K. and Daun, J. K. 1991. Oil Content Determination in Oilseeds by NMR, Method of the Canadian Grain Commission Grain Research Laboratory. Winnipeg, Canada: Agriculture Canada. 5 p.Google Scholar
Morishita, D. W. and Thill, D. C. 1988. Wild oat (Avena fatua) and spring barley (Hordeum vulgare) growth and development in monoculture and mixed culture. Weed Sci. 36:43–48.CrossRefGoogle Scholar
Nelson, D. C. and Nylund, R. E. 1962. Competition for peas growing for processing and weeds. Weeds 10:224–229.Google Scholar
O’Donovan, J. T. 1999. Effect of barley plant density on wild oat interference, shoot biomass and seed yield under zero tillage. Can. J. Plant Sci. 79:655–662.Google Scholar
Ogawa, H. and Shinozaki, K. 1953. Intraspecific competition yield interrelationships in regulatory dispersed populations. J. Inst. Polytech. Osaka City Univ. Ser. D. 4:1–16.Google Scholar
Radosevich, S. R. 1987. Methods to study interactions among crops and weeds. Weed Technol. 1:190–198.Google Scholar
Radosevich, S. R. 1988. Methods to study crop and weed interactions. Pages 121–142 In Alteri, M. A. and Liebman, M., eds. Weed Management for Agroecosytems: Ecological Approaches. Boca Raton, FL: CRC.Google Scholar
[SAS] Statistical Analysis Systems. 1991. SAS/STAT™ User's Guide. Version 6, 4th ed., Volume 2. Cary, NC: Statistical Analysis Systems Institute. 943 p.Google Scholar
Spitters, C.J.T. 1983. An alternative approach to the analysis of mixed cropping experiments. I. Estimation of competition effects. Neth. J. Agric. Sci. 31:1–11.Google Scholar
Thompson, C. R., Thill, D. C., and Shafii, B. 1994. Growth and competitiveness of sulfonylurea-resistant and -susceptible kochia (Kochia scoparia). Weed Sci. 42:172–179.CrossRefGoogle Scholar
Vleeshouwers, L. M., Streibig, J. C., and Skovgaard, I. 1989. Assessment of competition between weeds and crops. Weed Res. 29:273–280.CrossRefGoogle Scholar