Current issue: 56(2)
Under compilation: 56(3)
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
18 permanent sample subplots of the Swedish and the Hassian Forestry Institute, each measured in equal intervals for several decades, were divided into subplots of different size. An analysis of variance was calculated for every set of subplot size. The development of intraclass-correlations over years and over different sizes of subplots could be explained if three different correlations were assumed: soil-correlation, correlations from irregular distribution of the trees, and correlation resulting from competition. Intraclass-correlations were positive or negative depending on dominance of one or two of these correlations.
An explanatory simultation study of competitional variance showed the effect of the degree of competitional correlations on the variance of means of subplots of different sizes. If this coefficient was small, all variances of subplots means within the range investigated became larger than expected in experiments without competition, with larger coefficients the variances of means of the smaller subplots became smaller, those of larger subplots larger than expected.
Plots of medium or large size are probably optimal for long term experiments with forest trees, if all sources of costs in such experiments are taken into account.
The PDF includes a summary in English and Finnish.
Emphasis was laid on the finding of regression equations to indicate the dependence of standard error upon various variables in systematic sampling. As a result, the size of sample for a given precision could be computed, under varying alternatives of sample plot size and type. Another task was that of examining inventory costs by means of time studies. On combination of the results in regard to the sample size and survey time, the relative efficiency of different alternatives could be discussed, with a view to the precision of the total volume of growing stock.
The PDF includes a summary in Finnish.
New mortality models were developed for the purpose of improving long-term growth and yield simulations in Finland, Norway, and Sweden and were based on permanent national forest inventory plots from Sweden and Norway. Mortality was modelled in two steps. The first model predicts the probability of survival, while the second model predicts the proportion of basal area in surviving trees for plots where mortality has occurred. In both models, the logistic function was used. The models incorporate the variation in prediction period length and in plot size. Validation of both models indicated unbiased mortality rates with respect to various stand characteristics such as stand density, average tree diameter, stand age, and the proportion of different tree species, Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst.), and broadleaves. When testing against an independent dataset of unmanaged spruce-dominated stands in Finland, the models provided unbiased prediction with respect to stand age.