Plant litter decomposition governs how much carbon soils store and emit, yet the microbial traits that shape ecosystem-scale decay remain unresolved. Metagenomes can quantify genes encoding plant cell-wall-degrading enzymes, but it is unclear whether ecosystem differences in decay reflect distinct enzymatic repertoires, and whether these data improve prediction beyond climate and soil properties. We paired standardized green and rooibos tea-bag decomposition assays across 3–24 months with 295 soil metagenomes from 264 global sites. Using 196 European plots for primary inference, we built a stage-resolved catalogue of 17.6 million carbohydrate-active enzyme (CAZyme) genes. Forest microbiomes decomposed tea faster than grasslands, but this was not explained by greater CAZyme family richness. Instead, ecosystems differed in CAZyme abundance, subfamily and protein-sequence variation, and allocation across biochemical stages of plant cell-wall decay, with evidence of ecosystem-specific selection. CAZyme profiles added explanatory power for 24-month mass loss and improved within-ecosystem prediction but generalized poorly across ecosystems and continents. By showing that ecosystem differences in decomposition arise from the stage-specific distribution of shared enzymatic functions rather than their presence alone, this work shifts microbial trait inference beyond gene inventories and provides a mechanistic genomic framework for carbon-cycle modelling within defined environmental limits.

Growing evidence has shown that, apart from local environmental factors, changes in landscape-level factors by accelerated land-use change can also shape soil pathogenic fungal diversity. However, the global representativeness of such patterns remains unclear. Here, we assess how pathogenic fungal diversity in 511 soil samples worldwide responds to landscape factors, including landscape complexity index based on eight landscape metrics and quantity of different land cover types across six spatial scales (i.e., surrounding landscape, 250 m to 10,000 m radii from the sampling coordinate). We find that while soil variables explain over half of the variance, pathogenic fungal alpha diversity increases with landscape complexity and crop cover proportion, but decreases with grass and tree cover proportion, together explaining 23.4% of the total variance. Landscape factors have weaker impacts on beta diversity, explaining 13.0% of the variance. Across spatial scales, grassland ecosystems exhibit increasingly stronger responses to landscape variables compared to forest ecosystems. Landscape factors have a higher relative contribution to root-associated fungi than leaf/fruit/seed-associated fungi. Our results emphasize the importance of local factors and the complementary role of landscape patterns in shaping global soil pathogenic fungal distributions, highlighting scale-dependent effects across ecosystems and fungal functional groups.

Predicting soil organic carbon (SOC) stocks and their dynamics in forest ecosystems is crucial for assessing forest C balance, but the relative importance of key controls - litter inputs, climate, and soil properties - remains uncertain. Here, we linked SOC stocks at 556 old-growth Swiss forest sites from 350 to 2000 m a.s.l. to a comprehensive set of environmental variables, encompassing climate (mean annual precipitation, MAP: 700-2100 mm, mean annual temperature, MAT: 0-12 degrees C), soil properties, and forest types. In addition, we compared measured SOC stocks with stocks simulated by the Yasso20 model, which is widely used for reporting SOC stock changes. Since Yasso20 is driven solely by litter inputs and climate, deviations between modeled and measured stocks can reveal the significance of additional factors such as organo-mineral interactions that we hypothesized to be crucial for SOC stocks. Total SOC stocks exhibited distinct regional patterns, with the highest values in the Southern Alps, where soils are rich in Fe and Al oxides and receive high MAP. On average, total SOC stocks simulated by Yasso20 aligned well with measured SOC stocks (13.7 vs. 13.2 kg C m(-2)). However, the model did not capture regional SOC variability, underestimating SOC stocks by up to 7 kg C m(-2) in the Southern Alps. The underestimation was primarily explained by soil mineral properties, with their influence depending on soil pH. In soils with pH <= 5, exchangeable Fe had the strongest effect on Yasso20 deviations from measured stocks, while in soils with pH > 5, exchangeable Ca had the strongest effect on model deviations. Beyond Fe and Ca, MAP emerged as an important driver of total SOC stocks, with SOC stocks increasing with MAP. At higher elevations, this coincided with low MAT and a high share of conifers. While Yasso20 accounted for MAT, Yasso20 underestimated SOC stocks for MAP > 1400 mm. Overall, our results indicate that mineral-driven SOC stabilization and climate are the primary drivers of Yasso20 deviations from measured SOC stocks. Incorporating mineral-driven soil organic matter stabilization and coupling to a soil water model can improve the modeling of SOC stocks. However, further studies are needed to verify how C stabilization mechanisms and soil moisture can be included in model-based estimates of SOC stock changes, which is the primary application of Yasso in greenhouse gas inventories.

Forests form a major organic carbon reservoir, both above- and belowground. In the course of global change, predicting possible changes in these carbon reservoirs is essential. To this end, the Horizon Europe PathFinder project aims to develop an innovative forest monitoring system allowing consistent EU greenhouse gas reporting of LULUCF (Land Use, Land Use Change & Forestry) in combination with advanced policy pathway assessments. Greenhouse gas reporting of soil organic carbon (SOC) stock changes in forests commonly relies on simulations by soil carbon cycling models, such as Yasso (Y20), which uses only climate data and soil carbon inputs that can be derived by country-specific approaches from National Forest Inventories. However, the agreement between measured versus simulated carbon stocks and changes at the European scale has not yet been established. Within the framework of this project, this study aims to derive European-wide harmonised soil carbon inputs and stock estimates since the 1990s and further develop the current estimation methodology. After exploration of the available data sets, the ICP Forests Level II forest condition monitoring database was found the most suitable to set the initial modelling conditions. It is the only harmonised data set at the European scale that comprises above- and belowground compartments and contains repeated assessments on a subset of about 200 plots across Europe. The pre-processing of the observed data on soil carbon stock, growth and litterfall from the central ICP Forests database was very labour-intensive. As part of the ICP Forests monitoring programme, carbon concentrations and bulk densities are measured down to a depth of 80 cm. Using mass-preserving splines, soil carbon stocks were estimated down to a depth of 100 m to make them comparable with Y20. Regression models were developed to estimate litterfall inputs based on forest inventory data. We simulated SOC stocks by Y20 in ICP Forests Level II plots with available stand inventory data and soil characterization. Soil carbon inputs were obtained using two approaches: an inventory approach, with litterfall estimated by the above-mentioned regression models, and root and coarse-woody inputs by allometric functions, and a satellite approach, with net primary production (NPP) from MODIS at 500 m resolution. The Y20-simulated SOC stocks were compared with the SOC stocks to 100 cm depth based on the soil inventory data. an inventory approach, with litterfall estimated by the above-mentioned regression models, and root and coarse-woody inputs by allometric functions, and a satellite approach, with net primary production (NPP) from MODIS at 500 m resolution. The Y20-simulated SOC stocks were compared with the SOC stocks to 100 cm depth based on the soil inventory data. On average, the satellite approach estimated higher soil carbon inputs than the inventory approach (+20%). The SOC stocks simulated by Y20 were overall in line with observed SOC stocks. The simulations for broadleaf-dominated stands agreed well with SOC measurements, with average deviations below 1 kg C m -2 using the satellite approach. In coniferous stands, Y20-simulated SOC stocks were lower than observed by 3-5 kg C m -2 . This is likely due to the intrinsic soil properties driving SOC storage and stabilization in highly acidic, coniferous forests (i.e. Podzols and Umbrisols), which are not accounted for in Y20.

Forest soils have significant potential to mitigate climate change through their ability to store large amounts of organic carbon. However, forests are increasingly subject to natural disturbances such as windthrow, wildfire or disease outbreaks, which threaten the permanence of this large carbon stock. In response to increasing disturbances and ongoing climate change, forests are expected to lose their ability to return to pre-disturbance conditions involving a reorganization of tree species composition and stand structure. If tipping points are crossed, even a complete vegetation shift and conversion to non-forest ecosystems is possible. Here we aimed to assess the sensitivity of forest soil carbon to disturbance and its recovery with contrasting successional trajectories by combining two field studies on soil carbon stocks in windthrown forest stands and a global meta-analysis on the effects of different disturbance agents. Our results along an altitudinal gradient in Switzerland show that mountain forests with high carbon stocks in thick organic layers were particularly sensitive to disturbance by windthrow, losing up to 90% of their carbon stored belowground. In contrast, low-elevation forest soils with thin organic layers and smaller carbon stocks were barely affected. These results are consistent with our meta-analysis, which shows that disturbance-induced carbon losses increase with the size of initial carbon stocks. Boreal and high-elevation forests with large soil carbon stocks are highly sensitive to severe and long-lasting carbon losses due to damage from storms, wildfire, insects, and harvesting, while in most temperate and tropical forests soil carbon stocks recover more rapidly and losses are smaller. Results from a disturbance chronosequence in Austria also suggest that vegetation shifts following forest damage can strongly influence the recovery of soil carbon stocks after disturbance. Disturbed sites that remained in a non-forest, grass-dominated state for three decades accumulated about a third more soil carbon than sites that regenerated with trees. In addition to high litter inputs from herbaceous fine roots at grass-dominated sites, we relate this difference to changes in microbial community structure and function. In conclusion, our results underline that the magnitude and duration of soil carbon losses after disturbance depend on the forest type and site specific soil properties. Moreover, vegetation shifts during succession significantly modify the re-accumulation of soil carbon after disturbance.

Estimating growing stock is one of the main objectives of forest inventories. It refers to the stem volume of individual trees which is typically derived by models as it cannot be easily measured directly. These models are thus based on measurable tree dimensions and their parameterization depends on the available empirical data. Historically, such data were collected by measurements of tree stem sizes, which is very time- and cost-intensive. Here, we present an exceptionally large dataset with section-wise stem measurements on 40’349 felled individual trees collected on plots of the Experimental Forest Management project. It is a revised and expanded version of previously unpublished data and contains the empirically derived coarse (diameter ≥7 cm) and fine branch volume of 27’297 and 18’980, respectively, individual trees. The data were collected between 1888 and 1974 across Switzerland covering a large topographic gradient and a diverse species range and can thus support estimations and verification of volume functions also outside Switzerland including the derivation of whole tree volume in a consistent manner.

Storms represent a major disturbance factor in forest ecosystems, but the effects of windthrows on soil organic carbon (SOC) stocks are poorly quantified. Here, we assessed the SOC stocks of windthrown forests at 19 sites across Switzerland spanning an elevation gradient from 420 to 1550 m, encompassing a strong climatic gradient. Results show that the effect size of disturbance on SOC stocks increases with the size of the initial SOC stocks. The largest windthrow-induced SOC losses of up to 29 t C ha-1 occurred in high-elevation forests with a harsh climate developing thick organic layers. In contrast, SOC stocks of low-elevation forests with thin organic layers were hardly affected. A mineralization study further revealed high elevation forests to store higher amounts of easily mineralizable C in thick organic layers that got lost following windthrow. These findings are supported by a meta-analysis of available windthrow studies, showing an increase of storm-induced SOC losses with the size of the initial SOC stocks. Modelling simulations further indicate longer-lasting SOC losses and a slower recovery of SOC stocks after windthrow at high compared to low elevations, due to a slower regeneration of mountain forests and associated lower C inputs into soils in a harsh climate. Upscaling the experimental findings/observed patterns by linking them to a data base of Swiss forest soils shows a total SOC loss of ∼0.4 Mt. C for the whole forested area of Switzerland after two major storm events, counteracting the forest net carbon sink of decades. Our study provides strong evidence that the vulnerability of SOC stocks to windthrow is particularly high in forests featuring thick and slowly forming organic layers, such as mountain soils. Thus, the risk of losing SOC to more frequent windthrows in mountain forests strongly limits their potential to mitigate climate change.

Gegitterte Klimadaten mit einer regelmässigen räumlichen Auflösung sind eine wichtige Grundlage für Umweltstudien. Sie werden mithilfe statistischer Methoden aus Punktdaten von Messstationen erzeugt und haben insbesondere in topografisch komplexen Gebirgsregionen wie den Schweizer Alpen eine limitierte Genauigkeit. Dieser Artikel vergleicht vier für die Schweiz entwickelte gegitterte Datensätze unterschiedlicher Temperatur- und Niederschlagsvariablen mit unabhängigen Messdaten von 14 Dauerbeobachtungsflächen der Langfristigen Waldökosystemforschung (LWF) über einen Zeitraum von 20 Jahren. Die Resultate dienen als Entscheidungshilfe zur Wahl eines für eine grossflächige Anwendung zuverlässigen und homogenen Datensatzes. Die untersuchten Datensätze mit individuellen Stärken und Schwächen kommen für Anwendungen in der Schweiz infrage.

AbstractKey messageDead wood in forests is an important resource due to its role for nutrient cycles, carbon budgets, and biodiversity, among other. While standing and downed dead wood are typically monitored in National Forest Inventories (NFI), stumps have not received comparable attention. Based on the detailed stump inventory in the current Swiss NFI, this study demonstrates the important contribution of stumps to the dead wood pool.ContextDead wood (DW) in forests is an important resource due to its role for nutrient cycles, carbon budgets, and biodiversity, among other. NFIs provide representative DW estimates focusing primarily on standing and downed DW. Little is known on stumps as a DW pool.AimsThe aim of this study is to obtain an accurate assessment of the stump volume and biomass in the Swiss NFI to identify its significance for the DW pool, to evaluate the development over the last 30 years, and to examine the need for additional measurements for improving estimates compared to commonly applied assumptions for stump height such as a constant stump height or a fraction of tree height.MethodsThe current NFI includes a detailed stump inventory to improve accuracy and completeness of the aboveground DW pool estimate. Based on available data, stump volume estimates were derived at different accuracies to evaluate the contribution to the total DW pool over time.ResultsBased on the extended stump inventory in the NFI5, the contribution of stumps to the total DW pool is approximately 25%. The effect of simplifying assumptions or limited measurements to estimate stump volume can result in a significant underestimation of up to$$2/3$$2/3of the more accurate and comprehensive assessment of this pool.ConclusionThis study demonstrates that stumps can be a significant proportion of DW in forests, which should be accounted for in order to improve accuracy and completeness of NFI estimates and derived data such as C stocks for greenhouse gas reporting.

Wälder sind zunehmend von Sturmschäden betroffen. In einem Projekt der Eidgenössischen Forschungsanstalt WSL wurde untersucht, wie sich Windwürfe auf die Kohlenstoffspeicherung im Waldboden auswirken. Hoch gelegene Nadelwälder sind besonders anfällig für Kohlenstoffverluste aus dem Boden. Im Unterschied zu Wäldern tieferer Lagen werden dort grössere Mengen an Kohlenstoff in rein organischen, mächtigen Humusauflagen gespeichert. Eine Stabilisierung durch mineralische Interaktionen fehlt weitgehend, was die Humusauflagen empfindlich macht für Kohlenstoffverluste durch störungsbedingte Veränderungen des Bestandesklimas. Die langsame Baumverjüngung und die geringen Streueinträge in den Boden bremsen den Humusaufbau nach einer Störung zusätzlich. Eine weitere Zunahme von Windwürfen könnte die Bodenkohlenstoffvorräte in Bergwäldern drastisch reduzieren, was negative Auswirkungen auf das Klima hätte. Darüber hinaus könnten störungsbedingte Humusverluste die Bodenqualität nachhaltig verschlechtern.

&lt;p&gt;Storms represent a major disturbance factor in forest ecosystems, but the effects of windthrows on soil organic carbon (SOC) stocks are quantitatively poorly known. Here we present a comprehensive analysis of windthrow-induced changes in SOC stocks in Swiss forests by combining field-based measurements and modelling simulations. We measured the SOC stocks of 19 windthrown forests across Switzerland, about 10 and 20 years after they were disturbed by the storms &amp;#8216;Lothar&amp;#8217; and &amp;#8216;Vivian&amp;#8217; and compared them to the stocks of adjacent intact forests. We also calibrated the process-based model Yasso07 for additional 77 windthrown forests. Our results show that the effect of windthrow on SOC is strongly related to the size of the initial SOC stocks in the organic layer. In absolute and relative terms, the largest SOC losses occurred in high-elevation forests with thick organic layers, where initial SOC stocks decreased by up to 90% (or 30 t C ha&lt;sup&gt;-1&lt;/sup&gt;). In contrast, SOC stocks of low-elevation forests with thin organic layers were hardly affected. The likely reason for this pattern is the high stocks of easily mineralizable organic matter in thick organic layers of mountain forests, while at low elevations a greater SOC fraction is stabilized by mineral interactions. Modelling simulations further show longer-lasting SOC losses and a slower recovery of SOC stocks after windthrow at high-elevations compared to low-elevations, due to a slower regeneration of mountain forests and associated lower C inputs into soils. We also upscaled the SOC changes after windthrow to the whole forested area of Switzerland and estimated a total SOC loss of ~0.3 Mt C after the storms &amp;#8216;Lothar&amp;#8217; and &amp;#8216;Vivian&amp;#8217;. Our results provide strong empirical evidence that windthrows can reduce the SOC stocks of forest ecosystems, with mountain forests being hotspots for SOC losses.&lt;/p&gt;

Introduction: Among terrestrial ecosystems, forests represent large carbon stocks threatened by changing climatic conditions, deforestation, overexploitation, and forest degradation. Close to nature forestry may help forests to continue to acting as carbon sinks by promoting their resilience against disturbances. The EU decided to carry out carbon accounting of emissions and removals from managed forests under the Paris Agreement (PA) by using a projected Forest Reference Level (FRL) based on the continuation of recent management practices. Methods: We developed four conceptual scenarios that could build the Swiss Forest Reference Level and performed simulations over 50 years using Swiss National Forest Inventory (NFI) data and the empirical forest model MASSIMO. To improve MASSIMO, we further developed a new tree species-specific model for small scale mortality that accounts for the Swiss NFI design. Then, using projected biomass and mortality from MASSIMO, carbon budgets of mineral soil, litter, and dead wood were estimated using the Yasso07 model. Results: The U-shaped mortality model performed well (AUC 0.7). Small as well as large trees had the highest mortality probabilities, reflecting both young trees dying due to self-thinning and old trees from age, pests or abiotic influences. All scenarios matched their given harvesting and growing stock targets, whereby the share of broadleaves increased in all regions of Switzerland. This resulted in decreasing biomass growth, possibly due to a species shift from typically fast growing and more shade tolerant conifers to broadleaves. The CO 2 -balance of the conceptual scenarios ranged from 1.06 to −3.3 Mt CO 2 a –1 under Increased Harvesting and Recent Management Practices (RMP), respectively. Rotation periods are shortened under Increased Harvesting , which is an important climate adaptive management strategy, but forests were predicted to become a net carbon source. In contrast, RMP resulted in similar harvesting amounts and forests as carbon sinks, as reported in the past. Further, the RMP scenario does not involve political assumptions and reflects the idea of the CMP approach used by the EU member states, which makes it comparable to other countries. Therefore, we propose the scenario RMP as a suitable and ideal candidate for the Swiss FRL.

Background:National forest inventories (NFI) have a long history providing data to obtain nationally representative and accurate estimates of growing stock. Today, in most NFIs additional data are collected to provide information on a range of forest ecosystem functions such as biodiversity, habitat, nutrient and carbon dynamics. An important driver of nutrient and C cycling is decomposing biomass produced by forest vegetation. Several studies have demonstrated that understory vegetation, particularly annual plant litter of the herb layer can contribute significantly to nutrient and C cycling in forests. A methodology to obtain comprehensive, consistent and nationally representative estimates of herb layer biomass on NFI plots could provide added value to NFIs by complementing the existing strong basis of biomass estimates of the tree and tall shrub layer. The study was based on data from the Swiss NFI since it covers a large environmental gradient, which extends its applicability to other NFIs. Results: Based on data from 405 measurements in nine forest strata, a parsimonious model formulation was identified to predict total and non-ligneous herb layer biomass. Besides herb layer cover, elevation was the main statistically significant explanatory variable for biomass. The regression models accurately predicted biomass based on absolute percentage cover (for total biomass: R2=0.65, p=0; for non-ligneous biomass:R2=0.76; p=0) as well as on cover classes (R2=0.83; p=0; and R2=0.79, p=0), which are typically used in NFIs. The good performance was supported by the verification with data from repeated samples. For the 2nd, 3rd, and 4th Swiss NFI estimates of non-ligneous above-ground herb layer biomass 586.6 ± 7.7, 575.2 ± 7.6, and 586.7 ± 7.9 kg·ha?1, respectively. Conclusions: The study presents a methodology to obtain herb layer biomass estimates based on a harmonized and standardized attribute available in many NFIs. The result of this study was a parsimonious model requiring only elevation data of sample plots in addition to NFI cover estimates to provide unbiased estimates at the national scale. These qualities are particularly important as they ensure accurate, consistent, and comparable results.

Background Forests are an important component of the global carbon (C) cycle and can be net sources or sinks of CO 2 , thus mitigating or exacerbating the effects of anthropogenic greenhouse gas emissions. While forest productivity is often inferred from national-scale yield tables or from satellite products, forest C emissions resulting from dead organic matter decay are usually simulated, therefore it is important to ensure the accuracy and reliability of a model used to simulate organic matter decay at an appropriate scale. National Forest Inventories (NFIs) provide a record of carbon pools in ecosystem components, and these measurements are essential for evaluating rates and controls of C dynamics in forest ecosystems. In this study we combine the observations from the Swiss NFIs and machine learning techniques to quantify the decay rates of the standing snags and downed logs and identify the main controls of dead wood decay. Results We found that wood decay rate was affected by tree species, temperature, and precipitation. Dead wood originating from Fagus sylvatica decayed the fastest, with the residence times ranging from 27 to 54 years at the warmest and coldest Swiss sites, respectively. Hardwoods at wetter sites tended to decompose faster compared to hardwoods at drier sites, with residence times 45–92 and 62–95 years for the wetter and drier sites, respectively. Dead wood originating from softwood species had the longest residence times ranging from 58 to 191 years at wetter sites and from 78 to 286 years at drier sites. Conclusions This study illustrates how long-term dead wood observations collected and remeasured during several NFI campaigns can be used to estimate dead wood decay parameters, as well as gain understanding about controls of dead wood dynamics. The wood decay parameters quantified in this study can be used in carbon budget models to simulate the decay dynamics of dead wood, however more measurements (e.g. of soil C dynamics at the same plots) are needed to estimate what fraction of dead wood is converted to CO 2 , and what fraction is incorporated into soil.

Forest development models have been used to predict future harvesting potentials and forest management reference levels under the Kyoto guidelines. This contribution aims at presenting the individual-tree simulator MASSIMO (MAnagement Scenario SImulation Model) and demonstrating its scope of applications with simulations of two possible forest management reference levels (base or business as usual) in an example application. MASSIMO is a suitable tool to predict timber harvesting potentials and forest management reference levels to assess future carbon budgets of Swiss forests. While the current version of MASSIMO accurately accounts for legacy effects and management scenarios, effects of climate and nitrogen deposition on growth, mortality, and regeneration are not yet included. In addition to including climate sensitivity, the software may be further improved by including effects of species mixture on tree growth and assessing ecosystem service provision based on indicators.

While the Swiss NFI (NFI) delivers detailed information on the state of forest resources at the time of the field visit, data on the annual carbon (C) balance in dead organic matter (DOM) and soil are beyond the scope of the NFI. The annual C balance of DOM and soil on NFI sample plots is thus estimated with the C cycling model YASSO07.

Forest development models have been used to predict growth and yield for a long time. Yield tables, for example, have been optimised to predict the yield of pure even-aged stands on the basis of long-term observations from experimental forest plots. However, forests in Switzerland cover a broad ecological gradient and forest structures are diverse, with several even- and uneven-aged stand types that can include various species mixtures. Given such diverse forest ecosystems, we predict harvesting potentials and forest management reference levels using the Swiss National Forest Inventory-based individual-tree growth simulator MASSIMO.

National forest inventory based growth simulators are an important tool to assess long-term consequences of forest management in many European countries. MASSIMO is the empirically-based growth simulator used in Switzerland. This individual-tree model has been developed to simulate the growth of trees using the spatial grid of the Swiss National Forest Inventory (NFI) and its repeated measurements. MASSIMO has been used at the national scale to predict timber harvesting potentials, to assess the CO2 effects of Swiss forests and their potential for carbon sequestration, especially regarding the full timber chain, and for simulating the forest management reference level (FMRL) under the Kyoto protocol. Further, MASSIMO has been used to evaluate different timber mobilisation scenarios in a mountainous landscape and to evaluate timber-mobilisation strategies and habitat-tree retention in low-elevation Swiss forests.

Information on tree biomass and carbon (C) stock has become more relevant in the context of renewable energy production and reporting obligations, such as the Global Forest Resources Assessment of the FAO or the Greenhouse Gas Inventories under the UNFCCC and the Kyoto Protocol. Biomass and C stock cannot be measured easily in the field, and they are typically derived from existing estimates of volume or relationships to measured attributes such as tree diameter. In the Swiss NFI (NFI), whole tree biomass and its respective C content are derived separately for each individual tree element, including the aboveground elements stemwood, foliage, and large and small branches, and belowground coarse roots, based on volume estimates of stemwood and branchwood, and on foliage and root biomass correlations with tree diameter. Biomass and C stock are estimated at the individual tree level, thereby ensuring accuracy and consistency of the estimates.

In the Swiss National Forest Inventory (NFI), wood volume and changes in wood volume are estimated based on the stem volume of individual trees using various models: stem volume models and tariff models, models estimating volumes of large and small branches and growth models. Many of the models applied in the fourth NFI were described in previous publications, but some of them have subsequently been completed or adjusted based on methodological developments. This chapter mainly updates descriptions published previously.