The great interest in continuous cover forestry (CCF) in Finland has increased the need for tools to predict tree growth in uneven-aged stands. Growth predictions are commonly obtained with stand simulators like MOTTI, which is primarily targeted at even-aged stands for rotation forestry (RF) in Finland. The aims of this study were: 1) to test how the existing individual tree growth models of MOTTI worked for Norway spruce trees growing in uneven-aged stands in Finland; 2) to identify significant predictors if biases were detected; and 3) to compile calibration models in diameter or height growth. We used data based on 20 CCF Norway spruce permanent sample plots with a 20–25-year monitoring period measured in southern Finland. Simulation with MOTTI revealed obvious diameter and height growth biases in these stands. Growth was especially overpredicted for small trees. The current MOTTI predictions were unsuitable for CCF, and calibration models were therefore compiled using a linear mixed-effects regression approach. They were developed based on variables indicating tree size, tree- and stand-level competition, an uneven-aged stand structure, an asymmetrical competition variable, and dummy variables indicating the time since the last selection cutting in years. The resulting models predicted tree dbh and tree height more accurately than the existing MOTTI. The compiled calibration models will be incorporated into the MOTTI simulator to provide a more reliable prediction of tree diameter and height growth in uneven-aged stands. Moreover, the significant predictors reported in this study were considered as informative variables to develop calibration models for tree growth in CCF in a similar situation, where models are equipped only for RF.

中文翻译：

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芬兰不均匀树龄林中生长的挪威云杉直径和高度生长的校准模型

芬兰对连续覆盖林业 (CCF) 的极大兴趣增加了对预测不均匀林分树木生长的工具的需求。生长预测通常通过 MOTTI 等林分模拟器获得，该模拟器主要针对芬兰轮作林业 (RF) 的同龄林分。本研究的目的是： 1) 测试 MOTTI 现有的单棵树生长模型如何适用于生长在芬兰不均匀树龄林中的挪威云杉树；2) 如果检测到偏差，则确定重要的预测因素；3) 编制直径或高度增长的校准模型。我们使用的数据基于在芬兰南部测量的 20 个 CCF 挪威云杉永久样地，监测期为 20-25 年。MOTTI 模拟揭示了这些林分明显的直径和高度生长偏差。对小树的生长预测尤其过高。当前的 MOTTI 预测不适合 CCF，因此使用线性混合效应回归方法编译校准模型。它们是根据表示树木大小、树木和林分水平竞争、不均匀年龄的林分结构、不对称竞争变量以及表示自上次选择砍伐以来的时间（以年为单位）的虚拟变量来开发的。由此产生的模型比现有的 MOTTI 更准确地预测树木胸径和树高。编译后的校准模型将被纳入 MOTTI 模拟器中，以更可靠地预测树龄不均匀的林分中的树木直径和高度生长。此外，本研究中报告的重要预测变量被视为信息变量，用于在类似情况下开发 CCF 中树木生长的校准模型，其中模型仅配备 RF。