This thesis consists of six papers and a summary comprising statistical considerations of topics related to bucking optimization in cut-to-length forest harvesting. The topics addressed are: (1) the stem prediction problem in a harvesting situation and (2) measuring the fit between the demand and outcome distributions of logs. Since optimal tree bucking inevitably presumes accurate stem predictions, the choice of a proper stem prediction method is of crucial importance for the properties of all end products. Proper assessment of the bucking result has become relevant as the trend in the sawmill industry has been towards customer-oriented production of well-defined products.

The first article presents a cubic smoothing spline-based stem curve prediction and performs comparisons of this method with two other stem prediction techniques. In the second paper the use of a cubic smoothing spline is studied in the analysis of complete and balanced data. The basic idea was to replace the within-individual part of the Potthof and Roy GMANOVA model by cubic smoothing splines. It is shown how the mean splines can be estimated using a penalized log-likelihood function, and further, that the analysis can be greatly simplified under a certain special class of covariance structures. A rough testing of group profiles is also developed and illustrated.

The third paper studies the traditional ÷2-statistic in the context of measuring the bucking outcome and shows its relation to the Apportionment Index (AI) commonly used in harvesting in Scandinavia. The paper also presents price-weighted versions of both measures. The fourth paper examines the asymptotic sampling distribution of the AI by assuming a multinomial distribution for the bucking outcome. The paper provides approximate expressions for the first two moments of the measure and constructs the lower tolerance limit with a desired confidence level. In the first of the two remaining articles the definition of the AI and its price-weighted version are extended. The paper discusses the proper standardization of the measures and examines their limiting properties. The last article initiates a statistical analysis of the AI based on the joint distribution of random components in the outcome matrix. Dirichlet distribution is adopted to describe the joint distribution of the random components in the cases of two and three log categories. It is then proposed that the distribution parameters be chosen so that the AI is maximized in the averaged sense.