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Applications: The Analysis of Crop Variety Evaluation Data in Australia
Author(s) -
Smith Alison,
Cullis Brian,
Gilmour Arthur
Publication year - 2001
Publication title -
australian and new zealand journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 1369-1473
DOI - 10.1111/1467-842x.00163
Subject(s) - variety (cybernetics) , computer science , table (database) , data science , data mining , statistics , mathematics , artificial intelligence
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.