The size of knots is negatively correlated with bending strength in sawn timber and it is therefore used as a quality grading criterion in national roundwood grading standards. Some standards even use the size of the largest knot as the sole estimate for individual log knottiness. The size of knots is determined by crown horizontal extension, which in turn is dependent on the impact of competing trees. Thus, with knot size models that are competition-dependent, roundwood quality due to knottiness can be simulated for different management alternatives. However, these types of models, calibrated on uneven-sized Norway spruce in Fennoscandia, are currently not available. Therefore, the objective of this study is to develop a competition-dependent model framework for prediction of the largest knot size per stem height section, for application within uneven-sized Norway spruce stands. Data from terrestrial laser scanning of an uneven-sized stand in southern Sweden are used to calibrate a modular prediction framework, consisting of interlinked allometric statistical models. Alternative framework sub-models are presented and the preferred model combination can be selected according to context and available input data. The flexible modular format enables further development of separate sub-components for adaptation to growing conditions not covered by the current calibration range
