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Analyzing the performance of corn in China using a factor‐analytic variance‐covariance structure with multiple factors
Author(s) -
Zhang Renhe,
Han Dejun,
Hu Xiyuan
Publication year - 2020
Publication title -
crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.76
H-Index - 147
eISSN - 1435-0653
pISSN - 0011-183X
DOI - 10.1002/csc2.20090
Subject(s) - contrast (vision) , ranking (information retrieval) , covariance , statistics , variance (accounting) , econometrics , biology , mathematics , variance components , computer science , artificial intelligence , economics , accounting
For effective use of the factor‐analytic (FA) model in analyzing data from multi‐environmental trials (METs), we undertook an empirical study of data sets from the corn ( Zea mays L.) performance trails in China to compare all FA models with possible number of factors. We found that for the variety main effects, the estimate, ranking, and test efficiency of contrast were compatible among the FA models with various numbers of factors. For the variety simple effects, the effect estimate, and hence the ranking, changed; the test efficiency of contrast and discrimination power increased with increasing number of factors as the number of factors was smaller than a specific number. With consideration of both test efficiency of contrast and discrimination power of varieties for the trials analyzed, about 10 could be chosen as the optimal number of factors of the FA model approach.