Skip to main content Accessibility help
×
Home

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

  • Marie Jasieniuk (a1), 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 S. Seefeldt (a8), Phillip W. Stahlman (a9), Francis E. Northam (a9), Philip Westra (a10), Zewdu Kebede (a10) and Gail A. Wicks (a11)...

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.

Copyright

Corresponding author

Corresponding author. mariej@montana.edu

References

Hide All
Baeumer, K. and de Wit, C. T. 1968. Competitive interference of plant species in monocultures and mixed stands. Neth. J. Agric. Sci. 16:103122.
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. 1991. Aspects of the design and interpretation of competition (interference) experiments. Weed Technol. 5:664673.
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.
Crowley, P. H. 1992. Resampling methods for computation-intensive data analysis in ecology and evolution. Annu. Rev. Ecol. Syst. 23:405447.
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. 2nd ed. New York: J Wiley, pp. 3352.
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. 47:529537.
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. 40:296301.
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.
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. 38:487499.
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.
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.
[SAS] Statistical Analysis Systems. 1988. SAS/STAT® User's Guide. Release 6.03. Cary, 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) × wheat (Triticum aestivum) hybrids in the field by natural hybridization. Weed Sci. 46:632634.
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 7:3572.
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 1:97106.
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.
Weiner, J. 1982. A neighbourhood model of annual plant interference. Ecology 63:12371241.
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

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

  • Marie Jasieniuk (a1), 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 S. 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.