%0 Research article
%T Procedure for managing large-scale progeny test data: a case study of Scots pine in Finland
%A Venäläinen, Martti
%A Ruotsalainen, Seppo
%D 2002
%J Silva Fennica
%V 36
%N 2
%R doi:10.14214/sf.539
%U https://silvafennica.fi/article/539
%X Large progeny test networks are typical for conventional forest tree  breeding programmes. The individual progeny tests differ with respect to  age, composition and ability to screen the breeding values of the  parent trees. Several approaches have been introduced to manage the  unbalanced and diverse nature of the data generated by progeny tests.  This report presents a procedure for ranking breeding material on the  basis of ‘messy’ data. Plot means were used as input values and missing  plots were estimated from least squares. The differences between test  means and variances were standardised by the performance level method.  The different precision of the tests was quantified through the  reliability coefficient. In order to facilitate the selection of plus  trees for different purposes, all the available test results were  combined into a single variable that was used for ranking. Three  different kinds of ranking variable were calculated and each of them  proved to be more useful for the selection of plus trees than an  arithmetic or weighted mean. One of them, WMEAN, relied on the  reliability and number of the progeny tests, while the others, WCONF0.50  and WCONF0.10, relied on the standard error of the plus tree mean, thus  emphasising the precision of the values obtained. The analyses were  carried out with SAS® procedures, which require only moderate skills in  statistics, programming and data processing technology. The procedure  has functioned well throughout an eight-year development phase. Nearly  three thousand Scots pine (Pinus sylvestris) plus trees have been ranked  for various characters, and the results have been used for roguing the  seed orchards, to establish new ones, and to select plus trees for  breeding populations.