Skip to main content Accessibility help
×
Home

Site-to-site and year-to-year variation in Triticum aestivum–Aegilops cylindrica interference relationships

  • Marie Jasieniuk, Bruce D. Maxwell (a1), Randy L. Anderson (a2), John O. Evans (a3), Drew J. Lyon (a4), Stephen D. Miller (a5), Don W. Morishita (a6), Alex G. Ogg (a7), Steven Seefeldt (a8), Phillip W. Stahlman (a9), Francis E. Northam (a9), Philip Westra (a10), Zewdu Kebede (a10) and Gail A. Wicks (a11)...

Abstract

Crop yield loss–weed density relationships critically influence calculation of economic thresholds and the resulting management recommendations made by a bioeconomic model. To examine site-to-site and year-to-year variation in winter Triticum aestivum L. (winter wheat)–Aegilops cylindrica Host. (jointed goatgrass) interference relationships, the rectangular hyperbolic yield loss function was fit to data sets from multiyear field experiments conducted at Colorado, Idaho, Kansas, Montana, Nebraska, Utah, Washington, and Wyoming. The model was fit to three measures of A. cylindrica density: fall seedling, spring seedling, and reproductive tiller densities. Two parameters: i, the slope of the yield loss curve as A. cylindrica density approaches zero, and a, the maximum percentage yield loss as A. cylindrica density becomes very large, were estimated for each data set using nonlinear regression. Fit of the model to the data was better using spring seedling densities than fall seedling densities, but it was similar for spring seedling and reproductive tiller densities based on the residual mean square (RMS) values. Yield loss functions were less variable among years within a site than among sites for all measures of weed density. For the one site where year-to-year variation was observed (Archer, WY), parameter a varied significantly among years, but parameter i did not. Yield loss functions differed significantly among sites for 7 of 10 comparisons. Site-to-site statistical differences were generally due to variation in estimates of parameter i. Site-to-site and year-to-year variation in winter T. aestivum–A. cylindrica yield loss parameter estimates indicated that management recommendations made by a bioeconomic model cannot be based on a single yield loss function with the same parameter values for the winter T. aestivum-producing region. The predictive ability of a bioeconomic model is likely to be improved when yield loss functions incorporating time of emergence and crop density are built into the model's structure.

Copyright

Corresponding author

Corresponding author. Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, MT 59717; mariej@montana.edu

References

Hide All
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. 39:564569.
Bosnic, A. C. and Swanton, C. J. 1997. Influence of barnyardgrass (Echinochloa crus-galli) time of emergence and density on corn (Zea mays). Weed Sci. 45:276282.
Cousens, R. 1985a. A simple model relating yield loss to weed density. Ann. Appl. Biol. 107:239252.
Cousens, R. 1985b. An empirical model relating crop yield to weed and crop density and a statistical comparison with other models. J. Agric. Sci. 105:513521.
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.
Cowan, P., Weaver, S. E., and Swanton, C. J. 1998. Interference between pigweed (Amaranthus spp.), barnyardgrass (Echinochloa crus-galli), and soybean (Glycine max). Weed Sci. 46:533539.
Dieleman, A., Hamill, A. S., Weise, S. F., and Swanton, C. J. 1995. Empirical models of pigweed (Amaranthus spp.) interference in soybean (Glycine max). Weed Sci. 43:612618.
Draper, N. R. and Smith, H. 1981. Applied Regression Analysis. New York: J. Wiley, pp. 3342, 484.
Kropff, M. J., Weaver, S. E., and Smiths, M. A. 1992. Use of ecophysiological models for weed–crop interference: relations amongst weed density, relative time of emergence, relative leaf area, and yield loss. Weed Sci. 40:296301.
Lindquist, J. L., Mortensen, D. A., Clay, S. A., Schmenk, R., Kells, J. J., Howatt, K., and Westra, P. 1996. Stability of corn (Zea mays)–velvetleaf (Abutilon theophrasti) interference relationships. Weed Sci. 44:309313.
Maxwell, B. D., Stougaard, R. N., and Davis, E. S. 1994. Bioeconomic model for optimizing wild oat management in barley. Proc. West. Soc. Weed Sci. 47:7476.
Milliken, G. A. and Milliken-MacKinnon, A. J. 1998. Analysis of repeated measures data using nonlinear models. Pages 3261 in Koch, A. L., Robinson, J. A., and Milliken, G. A., eds. Mathematical Modeling in Microbial Ecology. New York: Chapman and Hall.
Ogg, A. G. 1993. Jointed goatgrass survey—1993. Magnitude and scope of the problem. Pages 612 in Westra, P. and Anderson, R. L., eds. Jointed Goatgrass: A Threat to U.S. Winter Wheat. Fort Collins, CO: Colorado State University.
Ratkowsky, D. A. 1983. Nonlinear Regression Modeling: A Unified Practical Approach. New York: Marcel Dekker, pp. 135154.
[SAS] Statistical Analysis Systems. 1988. SAS/STAT User's Guide. Release 6.03. Gary, NC: Statistical Analysis Systems Institute. 1028 p.
Seefeldt, S. S., Zemetra, R., Young, F. L., and Jones, S. S. 1998. Production of herbicide-resistant jointed goatgrass (Aegilops cylindrica) X wheat (Triticum aestivum) hybrids in the field by natural hybridization. Weed Sci. 46:632634.
Smith, R. J. 1968. Weed competition in rice. Weed Sci. 16:252255.
Snedecor, G. W. and Cochran, W. G. 1989. Statistical Methods. 8th ed. Ames, IA: Iowa State University Press, pp. 251252.
Spitters, C.J.T. 1989. Weeds: population dynamics, germination and competition. Pages 182216 in Rabingge, R., Ward, S. A., and van Laar, H. H., eds. Simulation and Systems Management in Crop Protection. Wageningen, The Netherlands: Pudoc.
Swinton, S. M. and Lyford, C. P. 1996. A test for choice between hyperbolic and sigmoidal models of crop yield response to weed density, J. Agric. Biol. Environ. Stat. 1:97106.
Zemetra, R. S., Hansen, J., and Mallory-Smith, C. A. 1998. Potential for gene transfer between wheat (Triticum aestivum) and jointed goatgrass (Aegilops cylindrica). Weed Sci. 46:313317.

Keywords

Site-to-site and year-to-year variation in Triticum aestivum–Aegilops cylindrica interference relationships

  • Marie Jasieniuk, Bruce D. Maxwell (a1), Randy L. Anderson (a2), John O. Evans (a3), Drew J. Lyon (a4), Stephen D. Miller (a5), Don W. Morishita (a6), Alex G. Ogg (a7), Steven Seefeldt (a8), Phillip W. Stahlman (a9), Francis E. Northam (a9), Philip Westra (a10), Zewdu Kebede (a10) and Gail A. Wicks (a11)...

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed