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Secale cereale interference and economic thresholds in winter Triticum aestivum

Published online by Cambridge University Press:  20 January 2017

Todd A. Pester ,
Philip Westra ,
Randy L. Anderson ,
Drew J. Lyon ,
Stephen D. Miller ,
Phillip W. Stahlman ,
Francis E. Northam  and
Gail A. Wicks
Philip Westra
Affiliation:
Department of Bioagricultural Sciences and Pest Management, Colorado State University, Fort Collins, CO 80523
Randy L. Anderson
Affiliation:
Central Great Plains Research Station, USDA-ARS, Akron, CO 80720
Drew J. Lyon
Affiliation:
Panhandle Research and Extension Center, University of Nebraska–Lincoln, Scottsbluff, NE 69361
Stephen D. Miller
Affiliation:
Department of Plant Sciences, University of Wyoming, Laramie, WY 82071
Phillip W. Stahlman
Affiliation:
Agricultural Research Center–Hays, Kansas State University, Hays, KS 67601
Francis E. Northam
Affiliation:
Agricultural Research Center–Hays, Kansas State University, Hays, KS 67601
Gail A. Wicks
Affiliation:
West Central Research and Extension Center, University of Nebraska–Lincoln, North Platte, NE 69101
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Abstract

Secale cereale is a serious weed problem in winter Triticum aestivum–producing regions. The interference relationships and economic thresholds of S. cereale in winter T. aestivum in Colorado, Kansas, Nebraska, and Wyoming were determined over 4 yr. Winter T. aestivum density was held constant at recommended planting densities for each site. Target S. cereale densities were 0, 5, 10, 25, 50, or 100 plants m−2. Secale cereale–winter T. aestivum interference relationships across locations and years were determined using a negative hyperbolic yield loss function. Two parameters—I, which represents the percent yield loss as S. cereale density approaches zero, and A, the maximum percent yield loss as S. cereale density increases—were estimated for each data set using nonlinear regression. Parameter I was more stable among years within locations than among locations within years, whereas maximum percentage yield loss was more stable across locations and years. Environmental conditions appeared to have a role in the stability of these relationships. Parameter estimates for I and A were incorporated into a second model to determine economic thresholds. On average, threshold values were between 4 and 5 S. cereale plants m−2; however, the large variation in these threshold values signifies considerable risk in making economic weed management decisions based upon these values.

Keywords

Secale cereale L. SECCE, ryeTriticum aestivum L., wheatCrop–weed competitiondecision aidsferal ryewinter wheatyield loss model
Type
Research Article
Information
Weed Science , Volume 48 , Issue 6 , December 2000 , pp. 720 - 727
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Anderson, R. L. 1997. Cultural systems can reduce reproductive potential of winter annual grasses. Weed Technol. 11:608–613.Google Scholar
Anderson, R. L. 1998. Growth characteristics of winter annual grasses in winter wheat. Weed Technol. 12:478–483.Google Scholar
Barnes, J. P. and Putnam, A. R. 1987. Role of benzoxazinones in allelopathy by rye (Secale cereale L.) J. Chem. Ecol. 13:889–906.Google Scholar
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
Blackshaw, R. E. 1993. Downy brome (Bromus tectorum) interference in winter rye. Weed Sci. 41:557–562.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
Chase, W. R., Nair, M. G., Putnam, A. R., and Mishra, S. K. 1991. 2,2'-oxo-1,1'-azobenzene: microbial tranformation of rye (Secale cereale L.) allelochemical in field soils by Acinetobacter calcoaceticus . III. J. Chem. Ecol. 17:1575–1584.Google Scholar
Cousens, R. 1985. A simple model relating yield loss to weed density. Ann. Appl. Biol. 107:239–252.Google Scholar
Daugovish, O., Lyon, D. J., and Baltensperger, D. D. 1999. Cropping systems to control winter annual grasses in winter wheat (Triticum aestivum). Weed Technol. 13:120–126.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.CrossRefGoogle Scholar
Fleming, G. L., Young, F. L., and Ogg, A. G. Jr. 1988. Competitive relationships among winter wheat (Triticum aestivum), jointed goatgrass (Aegilops cylindrica), and downy brome (Bromus tectorum). Weed Sci. 36:479–486.Google Scholar
Gomez, K. A. and Gomez, A. A. 1984. Pages 467–471 In Statistical Procedures for Agricultural Research. 2nd ed. New York: John Wiley.Google Scholar
Jasieniuk, M., Maxwell, B. D., Anderson, R. L., et al. 1999. Site-to site and year-to-year variation in Triticum aestivum-Aegilops cylindrica interference relationships. Weed Sci. 47:529–537.Google Scholar
Knezevic, S. Z., Horak, M. J., and Vanderlip, R. L. 1997. Relative time of redroot pigweed (Amaranthus retroflexus L.) emergence is critical in pigweed-sorghum [Sorghum bicolor (L.) Moench] competition. Weed Sci. 45:502–508.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
Lindquist, J. L., Mortensen, D. A., Westra, P., et al. 1999. Stability of corn (Zea mays)-foxtail (Setaria spp.) interference relationships. Weed Sci. 47:195–200.Google Scholar
Mesbah, A. O. and Miller, S. D. 1999. Fertilizer placement affects jointed goatgrass (Aegilops cylindrica) competition in winter wheat (Triticum aestivum). Weed Technol. 13:374–377.CrossRefGoogle 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
Pester, T. A., Burnside, O. C., and Orf, J. H. 1999. Increasing crop competitiveness to weeds through crop breeding. J. Crop Prod. 2:59–76.Google Scholar
Ratkowsky, D. A. 1983. Pages 135–153 In Nonlinear Regression Modeling: A Unified Practical Approach. New York: Marcel Dekker.Google Scholar
[SAS] Statistical Analysis Systems. 1988. SAS/STAT User's Guide. Version 6.03. Cary, NC: Statistical Analysis Systems Institute. 1028 p.Google Scholar
Stump, W. L. and Westra, P. 1994. Population dynamics of three winter annual grasses. Res. Prog. Rep. West. Soc. Weed Sci. pp. 56–57.Google Scholar
Stump, W. L. and Westra, P. 2000. The seedbank dynamics of feral rye (Secale cereale). Weed Technol. 14:7–14.Google Scholar
Suneson, C. A., Rachie, K. O., and Khush, G. S. 1969. A dynamic population of weedy rye. Crop Sci. 9:121–124.Google Scholar