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Selection of a Barley Yield Model Using Information–Theoretic Criteria

Published online by Cambridge University Press:  20 January 2017

Marie Jasieniuk ,
Mark L. Taper ,
Nicole C. Wagner ,
Robert N. Stougaard ,
Monica Brelsford  and
Bruce D. Maxwell
Marie Jasieniuk*
Affiliation:
Department of Plant Sciences, Mail Stop 4, University of California, Davis, CA 95616-8780
Mark L. Taper
Affiliation:
Department of Ecology, Montana State University, Bozeman, MT 59717-3460
Nicole C. Wagner
Affiliation:
USDA Foreign Agricultural Service, 1400 Independence Ave. SW, Washington, DC 20250
Robert N. Stougaard
Affiliation:
Montana State University, Northwestern Agricultural Research Center, Kalispell, MT 59901
Monica Brelsford
Affiliation:
Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, MT 59717-3120
Bruce D. Maxwell
Affiliation:
Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, MT 59717-3120
*
Corresponding author's E-mail: [email protected]
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Abstract

Empirical models of crop–weed competition are integral components of bioeconomic models, which depend on predictions of the impact of weeds on crop yields to make cost-effective weed management recommendations. Selection of the best empirical model for a specific crop–weed system is not straightforward, however. We used information–theoretic criteria to identify the model that best describes barley yield based on data from barley–wild oat competition experiments conducted at three locations in Montana over 2 yr. Each experiment consisted of a complete addition series arranged as a randomized complete block design with three replications. Barley was planted at 0, 0.5, 1, and 2 times the locally recommended seeding rate. Wild oat was planted at target infestation densities of 0, 10, 40, 160, and 400 plants m−2. Twenty-five candidate yield models were used to describe the data from each location and year using maximum likelihood estimation. Based on Akaike's Information Criterion (AIC), a second-order small-sample version of AIC (AICc), and the Bayesian Information Criterion (BIC), most data sets supported yield models with crop density (Dc), weed density (Dw), and the relative time of emergence of the two species (T) as variables, indicating that all variables affected barley yield in most locations. AIC, AICc, and BIC selected identical best models for all but one data set. In contrast, the Information Complexity criterion, ICOMP, generally selected simpler best models with fewer parameters. For data pooled over years and locations, AIC, AICc, and BIC strongly supported a single best model with variables Dc, Dw, T, and a functional form specifying both intraspecific and interspecific competition. ICOMP selected a simpler model with Dc and Dw only, and a functional form specifying interspecific, but no intraspecific, competition. The information–theoretic approach offers a rigorous, objective method for choosing crop yield and yield loss equations for bioeconomic models.

Keywords

Wild oat, Avena fatua L. AVEFAbarley, Hordeum vulgare L.Crop–weed competitionbioeconomic modelsmodel selectioninformation criteriaAICAICcBICICOMPwild oatbarleyyieldyield lossrelative time of emergence
Type
Special Topics
Information
Weed Science , Volume 56 , Issue 4 , August 2008 , pp. 628 - 636
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Akaike, H. 1973. Information theory and an extension of the maximum likelihood principle. Pages 267–281. in Petrov, B. N. and Csaki, F. 2nd International Symposium on Information Theory. Budapest Academiai Kiado.Google Scholar
Bozdogan, H. 1990. On the information-based measure of covariance complexity and its application to the evaluation of multivariate linear models. Comm. Stat.–Theory and Methods. 19:221–278.CrossRefGoogle Scholar
Bozdogan, H. 2000. Akaike's Information Criteria and recent developments in Information Complexity. J. Math. Psych. 44:62–91.CrossRefGoogle Scholar
Bozdogan, H. and Haughton, D. M. A. 1998. Information complexity criteria for regression models. Comp. Stat. & Data Analysis. 28:51–76.CrossRefGoogle Scholar
Brown, D. and Rothery, P. 1993. Models in Biology: Mathematics, Statistics and Computing. Chichester John Wiley & Sons. 688.Google Scholar
Burnham, K. P. and Anderson, D. R. 1998. Model Selection and Inference: A Practical Information–Theoretic Approach. New York Springer-Verlag. 353.Google Scholar
Burnham, K. P. and Anderson, D. R. 2002. Model Selection and Multimodel Inference: a Practical Information–theoretic Approach. New York Springer-Verlag. 488.Google Scholar
Chatfield, C. 1995. Model uncertainty, data mining and statistical inference. J. Roy. Stat. Soc., ser. A. 158:419–466.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
Cox, D. R. 1990. Role of models in statistical analysis. Stat. Sci. 5:169–174.Google Scholar
Firbank, L. G. and Watkinson, A. R. 1990. On the effects of competition from monocultures to mixtures. Pages 165–193. in Grace, J. B. and Tilman, D. Perspectives on Plant Competition. Caldwell, NJ Blackburn.Google Scholar
Fischer, D. W., Harvey, R. G., Bauman, T. T., Phillips, S., Hart, S. E., Johnson, G. A., Kells, J. J., Westra, P., and Lindquist, J. 2004. Common lambsquarters (Chenopodium album) interference with corn across the northcentral United States. Weed Sci. 52:1034–1038.Google Scholar
Franklin, A. B., Shenk, T. M., Anderson, D. R., and Burnham, K. P. 2001. Statistical model selection: an alternative to null hypothesis testing. Pages 75–90. in Shenk, T. M. and Franklin, A. B. Modeling in Natural Resources Management. Washington, DC Island.Google Scholar
Hurvich, C. M. and Tsai, C. L. 1989. Regression and time series model selection in small samples. Biometrika. 76:297–307.Google Scholar
Jasieniuk, M., Maxwell, B. D., Anderson, R. L., Evans, J. O., Lyon, D. J., Miller, S. D., Morishita, D. W., Ogg, A. G. Jr., Seefeldt, S. S., Stahlman, P. W., Northam, F. E., Westra, P., Kebede, Z., and Wicks, G. A. 1999. Site-to-site and year-to-year variation in Triticum aestivum–Aegilops cylindrica interference relationships. Weed Sci. 47:529–537.Google Scholar
Jasieniuk, M., Maxwell, B. D., Anderson, R. L., Evans, J. O., Lyon, D. J., Miller, S. D., Morishita, D. W., Ogg, A. G. Jr., Seefeldt, S. S., Stahlman, P. W., Northam, F. E., Westra, P., Kebede, Z., and Wicks, G. A. 2001. Evaluation of models predicting winter wheat yield as a function of winter wheat and jointed goatgrass densities. Weed Sci. 49:48–60.Google Scholar
Johnson, J. B. and Omland, K. S. 2004. Model selection in ecology and evolution. Trends Ecol. Evol. 19:101–108.Google Scholar
Lindquist, J. L., Mortensen, D. A., Westra, P., Lambert, W. J., Bauman, T. T., Fausey, J. C., Kells, J. J., Langton, S. J., Harvey, R. G., Bussler, B. H., Banken, K., Clay, S., and Forcella, F. 1999. Stability of corn (Zea mays)–foxtail (Setaria spp.) interference relationships. Weed Sci. 47:195–200.Google Scholar
Martin, R. J., Cullis, B. R., and McNamara, D. W. 1987. Prediction of wheat loss due to competition by wild oats (Avena spp.). Aust. J. Agric. Res. 38:487–499.Google Scholar
O'Donovan, J. T., Blackshaw, R. E., Harker, K. N., Clayton, G. W., and Maurice, D. C. 2005. Field evaluation of regression equations to estimate crop yield losses due to weeds. Can. J. Plant Sci. 85:955–962.CrossRefGoogle Scholar
Saguira, N. 1978. Further analysis of the data by Akaike's information criterion and the finite corrections. Comm. Stat. A7. 13–26.Google Scholar
Schwarz, G. 1978. Estimating the dimension of a model. Ann. Stat. 6:461–464.Google Scholar
Taper, M. L. 2004. Model identification from many candidates. Pages 488–524. in Taper, M. L. and Lele, S. R. The Nature of Scientific Evidence. Chicago University of Chicago Press.CrossRefGoogle Scholar
Wagner, N. C., Maxwell, B. D., Taper, M. L., and Rew, L. J. 2007. Developing an empirical yield prediction model based on wheat and wild oat (Avena fatua) density, nitrogen and herbicide rate, and growing season precipitation. Weed Sci. 55:652–664.Google Scholar
Weiner, J. 1982. A neighborhood model of annual-plant interference. Ecology. 63:1237–1241.CrossRefGoogle Scholar
Willey, R. W. and Heath, S. B. 1969. The quantitative relationships between plant population and crop yield. Adv. Agron. 21:281–321.Google Scholar