Current issue: 53(2)
Under compilation: 53(3)
In this study, we developed models of transfer effects for growth and survival of Scots pine (Pinus sylvestris L.) in Sweden and Finland using a general linear mixed-model approach. For model development, we used 378 provenance and progeny trials with a total of 276 unimproved genetic entries (provenances and stand seed check-lots) distributed over a wide variety of climatic conditions in both countries. In addition, we used 119 progeny trials with 3921 selected genetic entries (open- and control pollinated plus-tree families) for testing model performance. As explanatory variables, both climatic indices derived from high-resolution gridded climate datasets and geographical variables were used. For transfer, latitude (photoperiod) and, for describing the site, temperature sum were found to be main drivers for both survival and growth. In addition, interaction terms (between transfer in latitude and site altitude for survival, and transfer in latitude and temperature sum for growth) entail changed reaction patterns of the models depending on climatic conditions of the growing site. The new models behave in a way that corresponds well to previous studies and recommendations for both countries. The model performance was tested using selected plus-trees from open and control pollinated progeny tests. Results imply that the models are valid for both countries and perform well also for genetically improved material. These models are the first step in developing common deployment recommendations for genetically improved forest regeneration material in both Sweden and Finland.
Estimates of individual heritability and genetic correlation are presented for a set of 10 growth and quality traits based on data from 16 Scots pine (Pinus sylvestris L.) progeny trials in Finland. Seven of the traits (tree height, stem diameter, crown width, Pilodyn value, branch diameter, branch angle and branch number) were objectively measured, whereas three traits (stem straightness, branching score and overall score) were assessed visually. The genetic correlations were mostly moderate or low, and favourable from the tree breeder's point of view. All variables related to tree size correlated relatively strongly and positively. Tree height exhibited a more favourable genetic relationship with the crown form traits than diameter, the latter showing positive correlation with branch diameter. Except for the slight negative correlation between branch angle and branch diameter, the branching traits were not notably correlated. The pilodyn value was positively correlated with stem diameter, reflecting negative correlation between diameter growth and wood density. The highest genetic correlations occurred among the two visually evaluated quality scores and branch diameter. All of the heritabilities were less than 0.4. Overall score, Pilodyn, branch angle, branching score and tree height showed the highest heritability.
Tree height data from 33 progeny trials of Scots pine (Pinus sylvestris L.) were used to determine the effect of within-plot subsampling on the magnitude of statistically detectable differences between families, family heritability and correlation of family means based on different sample sizes. The results indicated that in trials established with a standard plot configuration of 25 trees per plot, measuring only 10–15 trees gives nearly the same precision as with assessment of all the plot trees. Even as few as 4–6 trees assessed per plot may constitute a sufficient sample if families or parental trees of extreme performance are being selected. Trials established with non-contiguous plots were found to be more efficient than those established using multiple-tree contiguous plots.
A simulation approach was applied to study the pattern of environmental variability and the relative statistical efficiency of 14 different plot types. The study material consisted of two nine-year-old field tests of Scots pine (Pinus sylvestris L.). The area of the test sites was 1.57 and 0.67 hectares. The efficiency was measured as the error variance attached to the estimate of family mean and the total size of a test needed to detect a given, least significant difference between two family means. The statistical efficiency tended to decline along with increasing plot size. The importance of plot shape was negligible compared to plot size. The highest efficiency was obtained with single-tree plots. Non-contiguous plots appeared to be considerably more efficient than block plots of equal size. The effects of intergenotypic competition on the choice of plot type are discussed.
The PDF includes an abstract in Finnish