The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Applications: The Analysis of Crop Variety Evaluation Data in Australia
Abstract
The major aim of crop variety evaluation is to predict the future performance of varieties. This paper presents the routine statistical analysis of data from late‐stage testing of crop varieties in Australia. It uses a two‐stage approach for analysis. The data from individual trials from the current year are analysed using spatial techniques. The resultant table of variety‐by‐trial means is combined with tables from previous years to form the data for an overall mixed model analysis. Weights allow for the data being estimates with varying accuracy. In view of the predictive aim of the analysis, variety effects and interactions are regarded as random effects. Appropriate inferential tools have been developed to assist with interpretation of the results. Analyses must be conducted in a timely manner so that variety predictions can be published and disseminated to growers immediately after harvest each year. Factors which facilitate this include easy access to historic data and the use of specialist mixed model software.
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Cites
Accounting for natural and extraneous variation in the analysis of field experiments
Gilmour, A. R., Cullis, B. R., Verbyla, A. P., & Verbyla, A. P. (1997) · Journal of Agricultural, Biological, and Environmental Statistics · doi:10.2307/1400446 · Cited by 752
Self-citationCooper, M., & DeLacy, I. H. (1994) · Theoretical and Applied Genetics · doi:10.1007/bf01240919 · Cited by 349
Spatial analysis of multi-environment early generation variety trials
Cullis, B., Gogel, B., Verbyla, A., & Thompson, R. (1998) · Biometrics · doi:10.2307/2533991 · Cited by 168 · cited 2×
“This paper presents several key enhancements (see Sections 3.3 and 3.4) aimed at more closely approximating the efficient one-stage analysis of Cullis et al. (1998).”
BackgroundMethodInfluentialSelf-citation
Cited by
SMITH, A. B., CULLIS, B. R., & THOMPSON, R. (2005) · The Journal of Agricultural Science · doi:10.1017/s0021859605005587 · Cited by 623
Self-citationSmith, A., Cullis, B., & Thompson, R. (2001) · Biometrics · doi:10.1111/j.0006-341x.2001.01138.x · Cited by 566
Self-citationIn Posidonia oceanica cadmium induces changes in DNA methylation and chromatin patterning
Greco, M., Chiappetta, A., Bruno, L., & Bitonti, M. B. (2012) · Journal of Experimental Botany · doi:10.1093/jxb/err313 · Cited by 538
Genebank genomics highlights the diversity of a global barley collection
Milner, S. G., Jost, M., Taketa, S., Mazón, E. R., Himmelbach, A., Oppermann, M., Weise, S., Knüpffer, H., Basterrechea, M., König, P., et al. (2019) · Nature Genetics · doi:10.1038/s41588-018-0266-x · Cited by 420
A stage‐wise approach for the analysis of multi‐environment trials
Piepho, H., Möhring, J., Schulz‐Streeck, T., & Ogutu, J. O. (2012) · Biometrical Journal · doi:10.1002/bimj.201100219 · Cited by 193
Metadata
- Authors
- Smith, A., Cullis, B., & Gilmour, A.
- Venue
- Australian & New Zealand Journal of Statistics
- Publisher
- Wiley
- Published
- 2001-06
- Volume
- 43
- Issue
- 2
- Pages
- 129-145
- ISSN
- 1369-1473 (ISSN-L)
- 1467-842X
- OpenAlex
- W1548456683
- Cited by
- 217
- References
- 3
- FWCI
- 1.32
- Type
- journal article
- Language
- en
- Open access
- closed
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