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

Published online by Cambridge University Press:  12 June 2017

Marie Jasieniuk ,
Bruce D. Maxwell ,
Randy L. Anderson ,
John O. Evans ,
Drew J. Lyon ,
Stephen D. Miller ,
Don W. Morishita ,
Alex G. Ogg Jr. ,
Steven Seefeldt  and
Phillip W. Stahlman
...Show all authors
Bruce D. Maxwell
Affiliation:
Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, MT 59717
Randy L. Anderson
Affiliation:
Central Plains Research Center, USDA-ARS, Akron, CO 80720
John O. Evans
Affiliation:
Department of Plant, Soils, and Biometeorology, Utah State University, Logan, UT 84322
Drew J. Lyon
Affiliation:
Panhandle Research and Extension Center, University of Nebraska, Scotsbluff, NE 69361
Stephen D. Miller
Affiliation:
Department of Plant, Soil, and Insect Sciences, University of Wyoming, Laramie, WY 82071
Don W. Morishita
Affiliation:
Twin Falls Research and Extension Center, University of Idaho, Twin Falls, ID 83303
Alex G. Ogg Jr.
Affiliation:
National A. cylindrica Research Program, P.O. Box 53, Ten Sleep, WY 82442
Steven Seefeldt
Affiliation:
USDA-ARS, Washington State University, Pullman, WA 99164
Phillip W. Stahlman
Affiliation:
Agricultural Research Center, Kansas State University, Hays, KS 67601
Francis E. Northam
Affiliation:
Agricultural Research Center, Kansas State University, Hays, KS 67601
Philip Westra
Affiliation:
Department of Bioagricultural Science and Pest Management, Colorado State University, Fort Collins, CO 80523
Zewdu Kebede
Affiliation:
Department of Bioagricultural Science and Pest Management, Colorado State University, Fort Collins, CO 80523
Gail A. Wicks
Affiliation:
West Central Research and Extension Center, University of Nebraska, North Platte, NE 69101
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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.

Keywords

Aegilops cylindrica Host. AEGCY, jointed goatgrassTriticum aestivum L., winter wheatDecision aideconomic thresholdnonlinear regressionparameter estimationrectangular hyperbolic modelyieldcrop–weed interferenceAEGCY
Type
Weed Biology and Ecology
Information
Weed Science , Volume 47 , Issue 5 , October 1999 , pp. 529 - 537
Copyright
Copyright © 1999 by the Weed Science Society of America 

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References

Literature Cited

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. 39:564–569.Google Scholar
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. 45:276–282.Google Scholar
Cousens, R. 1985a. A simple model relating yield loss to weed density. Ann. Appl. Biol. 107:239–252.Google Scholar
Cousens, R. 1985b. An empirical model relating crop yield to weed and crop density and a statistical comparison with other models. J. Agric. Sci. 105:513–521.Google Scholar
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. 35:720–725.Google Scholar
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. 46:533–539.Google Scholar
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. 43:612–618.Google Scholar
Draper, N. R. and Smith, H. 1981. Applied Regression Analysis. New York: J. Wiley, pp. 33–42, 484.Google Scholar
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. 40:296–301.Google Scholar
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. 44:309–313.Google Scholar
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. 47:74–76.Google Scholar
Milliken, G. A. and Milliken-MacKinnon, A. J. 1998. Analysis of repeated measures data using nonlinear models. Pages 32–61 in Koch, A. L., Robinson, J. A., and Milliken, G. A., eds. Mathematical Modeling in Microbial Ecology. New York: Chapman and Hall.Google Scholar
Ogg, A. G. 1993. Jointed goatgrass survey—1993. Magnitude and scope of the problem. Pages 6–12 in Westra, P. and Anderson, R. L., eds. Jointed Goatgrass: A Threat to U.S. Winter Wheat. Fort Collins, CO: Colorado State University.Google Scholar
Ratkowsky, D. A. 1983. Nonlinear Regression Modeling: A Unified Practical Approach. New York: Marcel Dekker, pp. 135–154.Google Scholar
[SAS] Statistical Analysis Systems. 1988. SAS/STAT∗ User's Guide. Release 6.03. Gary, NC: Statistical Analysis Systems Institute. 1028 p.Google Scholar
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. 46:632–634.Google Scholar
Smith, R. J. 1968. Weed competition in rice. Weed Sci. 16:252–255.Google Scholar
Snedecor, G. W. and Cochran, W. G. 1989. Statistical Methods. 8th ed. Ames, IA: Iowa State University Press, pp. 251–252.Google Scholar
Spitters, C.J.T. 1989. Weeds: population dynamics, germination and competition. Pages 182–216 in Rabingge, R., Ward, S. A., and van Laar, H. H., eds. Simulation and Systems Management in Crop Protection. Wageningen, The Netherlands: Pudoc.Google Scholar
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. 1:97–106.Google Scholar
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. 46:313–317.Google Scholar