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Evaluation of models predicting winter wheat yield as a function of winter wheat and jointed goatgrass densities

Published online by Cambridge University Press:  20 January 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 S. Seefeldt  and
Phillip W. Stahlman
...Show all authors
Marie Jasieniuk*
Affiliation:
Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, MT 59717
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, Scottsbluff, 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 S. Seefeldt
Affiliation:
AgResearch, Ruakura Agricultural Research Centre, PB 3123, Hamilton, New Zealand
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
*
Corresponding author. [email protected]
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Abstract

Three models that empirically predict crop yield from crop and weed density were evaluated for their fit to 30 data sets from multistate, multiyear winter wheat–jointed goatgrass interference experiments. The purpose of the evaluation was to identify which model would generally perform best for the prediction of yield (damage function) in a bioeconomic model and which model would best fulfill criteria for hypothesis testing with limited amounts of data. Seven criteria were used to assess the fit of the models to the data. Overall, Model 2 provided the best statistical description of the data. Model 2 regressions were most often statistically significant, as indicated by approximate F tests, explained the largest proportion of total variation about the mean, gave the smallest residual sum of squares, and returned residuals with random distribution more often than Models 1 and 3. Model 2 performed less well based on the remaining criteria. Model 3 outperformed Models 1 and 2 in the number of parameters estimated that were statistically significant. Model 1 outperformed Models 2 and 3 in the proportion of regressions that converged on a solution and more readily exhibited an asymptotic relationship between winter wheat yield and both winter wheat and jointed goatgrass density under the constraint of limited data. In contrast, Model 2 exhibited a relatively linear relationship between yield and crop density and little effect of increasing jointed goatgrass density on yield, thus overpredicting yield at high weed densities when data were scarce. Model 2 had statistical properties that made it superior for hypothesis testing; however, Model 1's properties were determined superior for the damage function in the winter wheat–jointed goatgrass bioeconomic model because it was less likely to cause bias in yield predictions based on data sets of minimum size.

Keywords

Jointed goatgrass, Aegilops cylindrica Host. AEGCYwinter wheat, Triticum aestivum L.Crop–weed interferencedecision aideconomic thresholdintegrated weed managementIPMnonlinear regressionparameter estimationAEGCY
Type
Research Article
Information
Weed Science , Volume 49 , Issue 1 , February 2001 , pp. 48 - 60
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Baeumer, K. and de Wit, C. T. 1968. Competitive interference of plant species in monocultures and mixed stands. Neth. J. Agric. Sci. 16:103–122.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. 1991. Aspects of the design and interpretation of competition (interference) experiments. Weed Technol. 5:664–673.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.CrossRefGoogle 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.CrossRefGoogle Scholar
Crowley, P. H. 1992. Resampling methods for computation-intensive data analysis in ecology and evolution. Annu. Rev. Ecol. Syst. 23:405–447.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. 2nd ed. New York: J Wiley, pp. 33–52.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
Kropff, M. J., Weaver, S. E., and Smith, 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
Martin, R. J., Cullis, B. R., and McNamara, D. W. 1987. Prediction of wheat yield loss due to competition by wild oats (Avena spp.). Aust. J. Agric. Res. 38:487–499.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
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
[SAS] Statistical Analysis Systems. 1988. SAS/STAT® User's Guide. Release 6.03. Cary, 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) × wheat (Triticum aestivum) hybrids in the field by natural hybridization. Weed Sci. 46:632–634.Google Scholar
Shinozaki, K. and Kira, T. 1956. Intraspecific competition among higher plants. VII. Logistic theory of the C-D effect. J. Inst. Polytech., Osaka City Univ. Ser. D 7:35–72.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. Statistics 1:97–106.Google Scholar
Swinton, S., Sterns, J., Renner, K., and Kells, J. 1994. Estimating weed-crop interference parameters for weed management models. East Lansing, MI: Michigan State University, Michigan Agricultural Experiment Station Research Rep. 538. 20 p.Google Scholar
Weiner, J. 1982. A neighbourhood model of annual plant interference. Ecology 63:1237–1241.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