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A new stand-level growth and yield model, consisting of component equations for stand volume, basal area, survival, and dominant stand height, was developed from a dataset of long-term trials for managed thinned and unthinned even-aged Norway spruce (Picea abies (L.) Karst.) forests in Norway. The developed models predict considerably faster growth rates than the existing Norwegian models. Further, it was found that the existing Norwegian stand-level models do not match the data from the thinning trails. The significance of thinning response functions indicated that thinning increases basal area growth while reducing competition related mortality. No significant effects of thinning were found in the dominant stand height growth. Model examination by means of cross-validation indicated that the models were unbiased and performed well within the data range. An application of the developed stand-level model highlights the potential use for these models in comparing different management scenarios.

Four methodologies to project future trees per acre by diameter class were compared to develop a new modified stand table projection growth model for unmanaged loblolly pine (Pinus taeda L.) and slash pine (Pinus elliottii Engelm.) plantations in East Texas. The new models were fit to 92,882 observations from 153 permanent plots located in loblolly pine plantations and 33,792 observations from 71 permanent plots located in slash pine plantations throughout East Texas. The new models were validated with 12,750 observations from 22 permanent plots and 3,724 observations from 9 permanent plots located in loblolly and slash pine plantations, respectively. The validation data were used to select between the four methodologies. The results indicated which of the new models produced the best results, based on error indexes calculated for trees per acre and basal area per acre at the stand table and diameter class levels across a range of projection lengths. We recommend that this new model be used by forest managers for projecting stand tables in East Texas loblolly and slash pine plantations. An example is also provided to show users how to use the new modified stand table projection model.

Forest structural properties largely govern surface fluxes of moisture, energy, and momentum that strongly affect regional climate and hydrology. Forest structural properties are greatly shaped by forest management activities, especially in the Fennoscandia (Norway, Sweden, and Finland). Insight into transient developments in forest structure in response to management intervention is therefore essential to understanding the role of forest management in mitigating regional climate change. The aim of this study is to present a simple grid-based framework – the Fennoscandic Forest State Simulator (F2S2) -- for predicting time-dependent forest structural trajectories in a manner compatible with land models employed in offline or asynchronously coupled climate and hydrological research. F2S2 enables the prescription of future regional forest structure as a function of: i) exogenously defined scenarios of forest harvest intensity; ii) forest management intensity; iii) climate forcing. We demonstrate its application when applied as a stand-alone tool for forecasting three alternative future forest states in Norway that differ with respect to background climate forcing, forest harvest intensity (linked to two Shared Socio-economic Pathways (SSPs)), and forest management intensity. F2S2 captures impacts of climate forcing and forest management on general trends in forest structural development over time, and while climate is the main driver of longer-term forest structural dynamics, the role of harvests and other management-driven effects cannot be overlooked. To our knowledge this is the first paper presenting a method to map forest structure in space and time in a way that is compatible with land surface or hydrological models employing sub-grid tiling.

An understanding of the relationship between volume increment and stand density (basal area, stand density index, etc.) is of utmost importance for properly managing stand density to achieve specific management objectives. There are two main approaches to analyse growth–density relationships. The first relates volume increment to stand density through a basic relationship, which can vary with site productivity, age, and potentially incorporates treatment effects. The second is to relate the volume increment and density of thinned experimental plots relative to that of an unthinned experimental plot on the same site. Using a dataset of 229 thinned and unthinned experimental plots of Norway spruce, a growth model is developed describing the relationship between gross or net volume increment and basal area. The models indicate that gross volume increases with increasing basal area up to 50 m2 and thereafter becomes constant out to the maximum basal area. Alternatively, net volume increment was maximized at a basal area of 43 m2 and decreased with further increases in basal area. However, the models indicated a wide range where net volume increment was essentially constant, varying by less than 1 m3 ha−1 year−1. An analysis of different thinning scenarios indicated that the relative relationship between volume increment and stand density was dynamic and changed over the course of a rotation.

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 ( L.), Norway spruce ( (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.Pinus sylvestrisPicea abies

<p>As a carbon dioxide removal measure, the Norwegian government is currently considering a policy of large-scale planting of spruce (<em>Picea abies</em> (L) H. Karst) on non-forested lands (i.e., aff-/reforestation) and secondary forested lands dominated by early successional broadleaved tree species (i.e., improved forest management).  Given the need to achieve net zero emissions in the latter half of the 21<sup>st</sup> century in effort to limit the global mean temperature rise to “well below” 2 °C, the mitigation potential of such a policy is unclear given relatively slow tree growth rates in the region.  Further convoluting the picture is the magnitude and relevance of surface albedo changes linked to such projects, which typically counter the benefits of an enhanced forest CO<sub>2</sub> sink in high latitude regions.  Here, we carry out a rigorous empirical assessment of the terrestrial carbon dioxide removal (tCDR) potential of large-scale aff-/reforestation (AR) and improved forest management (IFM) projects in Norway, taking into account transient developments in both terrestrial carbon sinks and surface albedo over the 21<sup>st</sup> century and beyond.  We find that surface albedo changes would likely play a negligible role in counteracting the carbon cycle benefit of tCDR, yet given slow forest growth rates in the region, meaningful tCDR benefits from AR and IFM projects would not be realized until the end of the 21<sup>st</sup> century, with maximum benefits occurring around 2150.  We estimate Norway’s total accumulated tCDR potential at 2100 and 2150 (including surface albedo changes) to be 447 (± 240) and 852 (± 295) Mt CO<sub>2</sub>-eq. at mean costs of US$ 29 (± 18) and US$ 26 (± 14) per ton CDR, respectively.  For perspective, the accumulated tCDR potential at 2100 represents around 8 years of Norway’s total current annual production-based (i.e., territorial) CO<sub>2</sub>-eq. emissions.</p>