Advanced statistical tools and their applications to agricultural research: An introduction

Canadian Journal of Plant Science

Volumn 91, Issue 4, 2011, Pages 599-601

  Gan, Yantai ^a   Yang, Rong Cai ^b  

^a AGRICULTURE AND AGRI FOOD CANADA   (Canada)

^b ALBERTA AGRICULTURE AND RURAL DEVELOPMENT   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 79960406054     PISSN: 00084220     EISSN: 19181833     Source Type: Journal    
DOI: 10.4141/cjps11505     Document Type: Note

References (8)

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2. Crossa, J., Vargas, M. and Joshi, A. K. 2010. Linear, bilinear, and linear-bilinear fixed and mixed models for analyzing genotype x environment interaction in plant breedingand agronomy. Can. J. Plant Sci. 90: 561-574.
3. Crow, G. H. 2011. Multi-environment experiments in agriculture. Webinar presentation. [Online] Available: http://agronomycanada.com/10stats_Crow.pdf.
4. Fisher, R. A. 1925. Statistical methods for research workers. Oliver and Boyd, Edinburgh, UK. 239 pp.
5. Lamb, E. G., Shirtliffe, S. J. and May, W. E. 2011. Structural equation modelingin the plant sciences: An example using yield components in oat. Can. J. Plant Sci. 91: 603-619.
6. Spilke, J., Piepho, H. P. and Hu, X. 2005. Analysis of unbalanced data by mixed linear models usingthe Mixed procedure of the SAS system. J. Agron. Crop Sci. 191: 47-54.
7. Yang, R.-C. 2010. Towards understandingand use of mixedmodel analysis of agricultural experiments. Can. J. Plant Sci. 90: 605-627.
8. Yang, R.-C. and Juskiw, P. 2011. Analysis of covariance in agronomy and crop research. Can. J. Plant Sci. 91: 621-641.