# Dendrometric data from the silvicultural scenarios developed by Office National des Forêts (ONF) in France: a tool for applied research and carbon storage estimates

Salomé Fournier, Thierry Sardin, Philippe Dreyfus, Didier Francois, Xavier Mandret, Marion Simeoni, Jean-Pierre Renaud, Emila Akroume, Alain Bouvet, Alain Berthelot, et al.

► To cite this version:

Salomé Fournier, Thierry Sardin, Philippe Dreyfus, Didier Francois, Xavier Mandret, et al. Dendrometric data from the silvicultural scenarios developed by Office National des Forêts (ONF) in France: a tool for applied research and carbon storage estimates. Annals of Forest Science, 2022, 79, pp.48. 10.1186/s13595-022-01171-7. hal-03919731

HAL Id: hal-03919731

https://agroparistech.hal.science/hal-03919731

Submitted on 4 Jul 2023

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

DATA PAPER

Open Access

<figure><img src="image_3.png" /></figure>

Check for
updates

# Dendrometric data from the silvicultural scenarios developed by Office National des Forêts (ONF) in France: a tool for applied research and carbon storage estimates

Salomé Fournier<fnref n="1" />*, Thierry Sardin², Philippe Dreyfus<fnref n="1" />, Didier Francois<fnref n="1" />, Xavier Mandret³, Marion Simeoni⁴, Jean-Pierre Renaud<fnref n="1" />, Emila Akroume<fnref n="1" />, Alain Bouvet⁵, Alain Berthelot⁵, Holger Wernsdörfer⁶, Miguel Riviere⁷,⁸, Julien Sainte-Marie⁶, Sandrine Breteau-Amores⁸, François De Coligny⁹ and Christine Deleuze<fnref n="1" />

## Abstract

**Key message:** We provide a database of 52 silvicultural scenarios recommended in French public forests including relevant dendrometric variables and metrics for carbon accounting. The dataset is available at https://doi.org/10.57745/QARRFS. Associated metadata are available at https://metadata-afs.nancy.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/f76ed27f-325d-493b-8731-0995dcaa7805. Special attention was paid to offer carbon metrics required for the French *Label Bas Carbone* offset projects.

**Keywords:** Forest management, Silviculture, Simulation, Forest dynamics, Carbon, Mitigation

## 1 Background

Forests are a source of multiple ecosystem services that benefit society (e.g. wood production, global climate regulation, habitat for biodiversity, landscape amenities, etc.; Brockerhoff et al. 2017), most of which are influenced by forest management. In this regard, European forests are characterised by their wide variety of forest types (Babati et al. 2007), therefore maintaining the important range of associated forest ecosystem services in a changing climate might require to explore a range of alternative forest management and assess their impact to adapt European forests (Mina et al. 2017; Biber et al. 2020).

Some of the issues at stake are easily and directly linked to usual dendrometric variables manipulated by foresters to assess their actions. Accordingly, simulations of silvicultural scenarios, which are common management tools to plan silvicultural interventions, offer a realistic basis for applied studies that aim at researching the impact of forest management on forest ecosystem features or services.

*Office National des Forêts* (ONF) is a French government agency responsible for the sustainable management of publicly owned forests, representing 4.6 million hectares (25% of the forest area of metropolitan France). The core of its activity lies in timber production, and it marketed 11.4 million m³ of timber (40% of national production) in 2020 (ONF 2021). The agency is also responsible for preserving the biodiversity and landscapes of public forests, welcoming public and raising awareness among forest users on environmental issues.

Handling editor: Véronique Lesage

*Correspondence: salome.fournier@onf.fr

<figure><img src="image_4.png" /></figure>

BMC

© The Author(s) 2022. **Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

<footnote n="1">¹ Département Recherche Développement Innovation, Office National des Forêts, 77300 Fontainebleau, France
Full list of author information is available at the end of the article</footnote>
The agency being active since 1964, the historical technical expertise of its agents has been compiled throughout the years in silvicultural guides i.e. sets of possible silvicultural scenarios relevant for a homogenous silvicultural context (a set of environmental and economic conditions), including both planned programs of silvicultural interventions and growth dynamics associated with them (Sardin and Deleuze 2020).

Each guide covers a large regional and contextual domain, providing a basis on which technical recommendations can be made. They describe in figures, the possible interventions, the rates and intensities of harvesting as well as the regeneration processes to produce an adequate quality of roundwood specific for a primary wood product (lumber, plywood, pulp, fuel). ONF experts in charge of writing silvicultural guides, in partnership with academic researchers, have adopted growth and yield models and acquired simulation skills through intensive use, providing more accurate information on stand growth for their management decisions. However, even if a guide illustrates a silvicultural scenario with a large number of usual dendrometric variables, the level of details provided in the different guides can be heterogeneous, making it impossible to extract all figures from the written document and compare them objectively. Furthermore, the few officially published silvicultural guides are no longer available for external purchase from the publisher, while new guides or revisions have now been limited to internal use altogether. Although they can be made available upon request for educational and research purposes, this represents a massive obstacle to their distribution outside of ONF both in France and abroad.

The evolution of silvicultural guides along with the development of growth and yield models offer opportunities for applied forestry research. For instance, quantifying forest carbon stocks and fluxes is mandatory to assess the impact of forest mitigation measures at a forest stand level. Therefore, the collection of ONF silvicultural guides have increasingly been used to calculate carbon sequestration with the development of carbon offset certifications like the *Label Bas Carbone* in France (i.e. government-run carbon offset program created in 2018 and involving forest activities among other sectors : Ministère de la transition écologique et solidaire, I4CE 2020; Griment and Gleizes 2019).

On the one hand, ONF silvicultural recommendations can be carefully expanded outside of French borders to shared European ecoregions, making them relevant for a wider use. On the other hand, the development of generic and comparative modelling approaches at European scale (Mahnken et al. 2022) offers additional purpose to ONF silvicultural simulations to provide realistic reference scenarios as a baseline for comparison in a constant climate.

Consequently, our objective was to provide a consolidated database of standard ONF silvicultural scenarios for major tree species and silvicultural contexts throughout the French metropolitan territory, including relevant dendrometric variables and carbon storage as an example of valorisation. The database will be completed with new silvicultural developments (for mixed and uneven stands for example) and wider range of species and silvicultural contexts in the future. Moreover, silvicultural scenarios will be updated in the wake of potential future improvements in growth and yield models' predictions, specifically to account for changes in forest productivity related to climate change.

# 2 Methods

## 2.1 Description of the silvicultural guides provided in the database

Ten silvicultural guides were used to build the current version of the database provided here, which represent silvicultural scenarios for 12 French major species in pure even-aged systems (Fournier et al. 2022). They are listed in Table 1 and their domains of applicability are shown in Fig. 1.

## 2.2 Growth and yield models used to simulate silvicultural scenarios

All the growth and yield models used here to build silvicultural scenarios simulate the evolution of individual trees without taking into account their spatial position (distance-independent tree-based models). They are listed in Table 1. These growth and yield models have been designed to build and simulate silvicultural scenarios and partly rely on similar construction principles originating from the *Fagacées* model (Le Moguédec and Dhôte 2012).

## 2.3 Construction of silvicultural scenarios on the Capsis platform

All growth and yield models used to simulate silvicultural scenarios in the database are hosted on the *Capsis* platform (Dreyfus and Bonnet 1995; Dufour-Kowalski et al. 2012). A large amount of the original simulations used for the guides developments using the *Capsis* platform are not available anymore, related to the successive updates of the original growth and yield models. Therefore, most of the silvicultural scenarios in the database are from new simulations following the references of the guides.

New silvicultural alternatives have also been added and flagged in the database such as silvicultural scenarios for planting densities (e.g. for sessile oak in the Loire Basin), while others have been updated to better take into account recent silvicultural recommendations such as modification of the minimum exploitable diameter
<table><caption>**Table 1** List of silvicultural guides provided in the database for each species, silvicultural context, and growth and yield models used for simulating stand dynamics</caption><thead><tr><th>Original ONF guide</th><th>Current guide revision</th><th>Geographical area</th><th>Estimated forest area / ×10<sup>3</sup>ha)</th><th>Studied species</th><th>Used growth and yield model</th><th>Model developer</th><th>Model reference</th></tr></thead><tbody><tr><td>(Sardin 2008)</td><td>(Sardin et al. 2018)</td><td>North-east of France</td><td>659</td><td>Quercus petraea (Sessile Oak)</td><td>Fagacées</td><td>J-F. Dhôte, P.Vallet, G. Le Moguédec, F. Mothe</td><td>(Le Moguédec and Dhôte 2012)</td></tr><tr><td>(Jarret 2004)</td><td>(Delord and Mandret 2018)</td><td>Loire basin</td><td>217</td><td>Quercus robur (Pedoncupolate Oak)</td><td>Fagacées</td><td>J-F. Dhôte, P.Vallet, G. Le Moguédec, F. Mothe</td><td>(Le Moguédec and Dhôte 2012)</td></tr><tr><td>(Ladier et al. 2012)</td><td></td><td>Southern Alps mountains</td><td>129<sup>a</sup></td><td>Quercus petraea (Sessile Oak)</td><td>Fagacées</td><td>J-F. Dhôte, P.Vallet, G. Le Moguédec, F. Mothe</td><td>(Le Moguédec and Dhôte 2012)</td></tr><tr><td></td><td></td><td></td><td></td><td>Abies alba (Silver fir)</td><td>Sydy</td><td>P. Dreyfus</td><td>https://sydycapsis.blogspot.com/</td></tr><tr><td></td><td></td><td></td><td></td><td>Pinus nigra ssp nigra (Aus-trian black pine)</td><td>Sydy</td><td>P. Dreyfus</td><td>https://sydycapsis.blogspot.com/</td></tr><tr><td></td><td></td><td></td><td></td><td>Pinus sylvestris (Scots pine)</td><td>Sydy</td><td>P. Dreyfus</td><td>https://sydycapsis.blogspot.com/</td></tr><tr><td>(ONF 1997)</td><td>(Sardin 2013b)</td><td>Pyrenees mountains</td><td>107</td><td>Fagus sylvatica (European beech)</td><td>Fagacées</td><td>J-F. Dhôte, P.Vallet, G. Le Moguédec, F. Mothe</td><td>(Le Moguédec and Dhôte 2012)</td></tr><tr><td>(Abt 2014)</td><td></td><td>Jura mountains</td><td>103</td><td>Abies alba (Silver fir)</td><td>Abial</td><td>J-D. Bontemps, F. Longuetaud, F. Mothe, V. Pérez, D. Rittié, L. Saint-André, J. Seynave</td><td>(Bontemps et al. 2009)</td></tr><tr><td></td><td></td><td></td><td></td><td>Picea abies (Norway spruce)</td><td>FCBA Picea abies</td><td>P. Cailly, S. Cavaignac</td><td>(Pain and Boyer 1996)</td></tr><tr><td>(Chabaud and Nicolas 2009)</td><td></td><td>Plains of central and northern France</td><td>69</td><td>Pinus nigra ssp laricio var corsicana, Pinus nigra ssp laricio var calabrica (Laricio pine)</td><td>Laricio</td><td>C. Meredieu</td><td>(Meredieu 1998)</td></tr><tr><td></td><td></td><td></td><td></td><td>Pinus pinaster (Maritime pine)</td><td>PP3</td><td>B. Lemoine, P. Dreyfus, C. Meredieu</td><td>(Meredieu 2002)</td></tr><tr><td></td><td></td><td></td><td></td><td>Pinus sylvestris (Scots pine)</td><td>Silvestris</td><td>T. Perot, S. Perret, C. Meredieu</td><td>(Pérot et al. 2007)</td></tr><tr><td>(Angelier 2007)</td><td>(Sardin 2013a) (ONF 2017)</td><td>France</td><td>65</td><td>Pseudotsuga menziesii (Douglas fir)</td><td>FCBA Pseudotsuga menziesii</td><td>P. Cailly, S. Cavaignac</td><td>(Bailly and Bigot 1997)</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>Gymnos</td><td>G. Ligot, J. Perin, A. de Pierpont, S. Quevauviller</td><td>(Perin et al. 2016)</td></tr><tr><td>(Tresmontant and Quesney 2015) (ONF 2003)</td><td></td><td>Mediterranean coast</td><td>50</td><td>Pinus halepensis (Aleppo pine)</td><td>NRG-Sydy</td><td>P. Dreyfus</td><td>(Chomel et al. 2016)</td></tr><tr><td></td><td></td><td>Forest of Landes de Gascogne</td><td>45</td><td>Pinus pinaster (Maritime pine)</td><td>PP3</td><td>B. Lemoine, P. Dreyfus, C. Meredieu</td><td>(Meredieu 2002)</td></tr><tr><td>(Sardin et al. 2022)</td><td></td><td>France</td><td>n/a</td><td>Cedrus atlantica (Atlas cedar)</td><td>CA1 - Sydy</td><td>F. Courbet, P. Dreyfus</td><td>(Courbet 2002)</td></tr></tbody></table>

Forest area was estimated for 2019 using the free access OCRE online platform which computes data from French NFI plots (https://ocre-gp.ign.fr/ocre, a the studied species could not be isolated from other non-relevant species, n/a no data was found on the species); the Sydy interface offer features to simplify silvicultural simulations in Capsis with existing growth and yield models as well as offering original models designed by P. Dreyfus
<figure><img src="image_5.png" /><figcaption>**Fig. 1** Areas of applicability for each silvicultural guide represented in the database</figcaption></figure>

to adapt to new market constraints (e.g. for Douglas-fir stands in 2007, exploitable diameter were 55 to 65 cm depending on site fertility and was fixed to 55 cm after 2017, regardless of site fertility; ONF 2017).

## 2.4 Simulation outputs from Capsis and volume calculations

Dendrometric data at stand-level was extracted and stored directly in the database from *Capsis* outputs: age, density, basal area (BA), dominant height, quadratic mean diameter at breast height (DBH), and dominant DBH. The minimum DBH threshold for inventory was set to 7.5 cm. Moreover, for each simulation tree-level data was used for volume calculation but was not available as an output for each simulated age depending on the growth and yield model. Therefore, tree-level data was extracted when available: DBH, total height and tree status (i.e. alive, cut, dead). As a result, some volume data is not available for given ages in the database.

Tree-level data was then treated using the R software version 4.0.2 (2020-06-22).

Even though each growth and yield model supplies their own stand volume outputs based on different volume tables, in the database, we computed every volume at tree-level based on volume tables from Deleuze et al. (2014) for standardisation purposes. During this process, two merchantable volumes following Gschwantner et al. (2019) definitions and a whole stem volume were calculated:

* Merchantable stem volume (over-bark volume of the bole, diameter > 7 cm)

* Merchantable stem and branches volume (over-bark volume of the bole and big branches, diameter > 7 cm)

* Whole stem volume (over-bark volume of every aerial biomass compartment of the tree)

Input variables were: tree species, DBH, total height and the height at which stem diameter decreases of more than 10% (Hdec). In the database, Hdec was fitted as a linear function of individual total height using the data from the National Forest Inventory (NFI)
plots freely available (https://inventaire-forestier.ign.fr/datalfN/).

Volumes of each individual were then aggregated at stand-level and stored in the database.

Mean annual increment and current annual increment (for BA, merchantable stem volume, and whole stem volume) at stand-level were also calculated for each available date (time intervals depend on the models). For the computation of total production, living, cut and dead individuals were considered, except for the *Sylvestris* and *Laricio* models for which dead tree's diameters and heights were not individually available, so it may be marginally underestimated for these particular silvicultural scenarios.

## 2.5 Methodology to calculate carbon storage for each silvicultural scenario

The steps to calculate carbon variables for the database are as follows:

For most growth and yield model, simulations could not start from seed but only from a somewhat later stand age after the juvenile phase. Thus, in order to compute the mean carbon storage on the revolution length volume data was completed before the initial date of the simulation, an exponential model was fitted and defined as

$$ stand_vtot_c0(stand_age) = ini_juv_alpha \times (\exp(ini_juv Beta \times stand_age) - 1) \quad (1) $$

Where ini_juv alpha and ini_juv beta are constraint with the initial growth dynamics (ensuring continuity and continuity of derivative) and provided in the database for each silvicultural scenario.

Next, aboveground carbon storage is calculated as

$$ carb_aer(stand_age) = stand_vtot_c0(stand_age) \times \text{biom\_dens\_Dupouey}_i \times t_C \times \frac{44}{12} \quad (2) $$

Then, root carbon storage is estimated as a function of aerial biomass for temperate forests based on (Cairns et al. 1997):

erential thinning in dominated or dominant individuals, calculated with the ratio: $K_g = (\text{harvestedDBH} / \text{initialDBH})^2$);

$$ carb_root(stand_age) = \exp(-1.0587 + 0.8836 \times \ln(stand_vtot_c0(stand_age) \times \text{biom\_dens\_dupouey}_i + 0.2840)) \times t_C \times \frac{44}{12} \quad (3) $$

Thereby, total carbon storage is calculated as

• Carbon storage: basic specific gravity of the species,

$$ carb_stor(stand_age) = carb_aer(stand_age) + carb_root(stand_age) \quad (4) $$

Finally, mean carbon storage on the rotation length is calculated as

aerial carbon storage, root carbon storage, total carbon storage in the forest stand, harvested carbon

$$ carb_stor_mean = \frac{1}{N} \int_{0}^{N} \text{carb\_stor}(\text{stand\_age}) \cdot \text{d} \text{stand\_age} \quad (5) $$

Where N the rotation length, biom_dens_dupouey, i the basic specific gravity of the species' wood i according to Dupouey's database (unpublished but used in Loustau 2004) and t$_{C}$ the carbon fraction in dry biomass components based on the work of Longuetaud et al. (2013).

## 3 Access to the data and metadata description

The dataset is available at Recherche Data Gouv repository: https://doi.org/10.57745/QARRFS. Associated metadata are available at https://metadata-afs.nancy.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/f76ed27f-325d-493b-8731-0995dcaa7805. The database contains two tables. The first table contains the dendrometric and carbon variables over the years of a silvicultural scenario:

• An identity card of the silvicultural scenario : tree species, original ONF silvicultural guide, current or newer version of a guide, description of the given alternative silvicultural scenario, fertility, dominant height at 50 years old, minimum exploitable diameter, origin of the stand (plantation, natural regeneration);

• Temporal evolution of dendrometric variables (den-
content, mean carbon storage during the silvicultural scenario, carbon capture flow.

The second table records the information needed to replicate the simulations for a database user:

* General information on the model used for simulation: name of the model, version, option of initialisation, ONF agents involved in the simulation and processing chain;

* A characterisation of the initial stand: type of stand (real or virtual), origin, possible modification of the original variables, dendrometric characteristics (age, dominant height, N, BA, diameter class distribution), model parameters to calculate whole stem volume for juvenile stands prior to simulation.

Dendrometric variables are described in Table 2 and each variable definition is available in the metadata file of the database as well. Each row of the database represents the age of a forest stand within a silvicultural scenario.

# 4 Technical validation

A technical validation of the results was performed. Field experts were asked to evaluate simulations in the database to ensure their technical feasibility on field. Table 3 sums up the pairs of simulation model author and silvicultural expert for each silvicultural context provided in the database.

Dendrometric data available in the database are given for fully stocked stands and do not account for usual gaps found in real forest stands (e.g. due to the heterogeneous nature of the ecosystem). Furthermore, growth and yield models are highly sensitive to initial stands and thinning algorithms. That is why, even though we tried to follow as closely as possible the silvicultural scenarios, changing simulation tools from the ones originally used can create some dissimilarities between the new simulations and data of a silvicultural guide. However, using homogeneous simulation tools for the silvicultural scenarios of the database was part of our goal of standardising them. Therefore, to make sure the new simulations represented correctly the original work behind the silvicultural scenarios, experts were also asked to validate the acceptability of those changes.

Moreover, silvicultural scenarios in the database are tightly related to each specific model's weaknesses linked to their construction principles and domain of validity, and models were calibrated with data collected in past climatic conditions. We recommend that the creation of alternative silvicultural scenarios based on ONF ones from the database should be restricted to

* The geographic areas where the growth and yield model's calibration or validation data was collected;

* The growth and yield model's range of validated age or DBH;

* The growth and yield model's range of validated density or BA.

Nonetheless, some ONF silvicultural guides are made using growth and yield models used in extrapolated conditions after validation by experts of the silvicultural context. For example, the PP3 model has never been calibrated on maritime pines from northwestern and central France but was used by the experts to simulate the silvicultural scenarios in this geographic area. Therefore, it leaves the possibility for other database users to extrapolate, with caution, the results of silvicultural scenarios to areas outside of France with similar environmental conditions (e.g. Swiss Jura Mountains, Western Germany).

# 5 Reuse potential and limits

## 5.1 Potential use

Based on the richness of silvicultural contexts, the diversity of species and types of silvicultural scenarios provided in the present database, comparisons of several metrics are easily done to guide forest managers and provide educational elements. Figure 2 shows the mean annual increment (MAI) in whole stem volume for each simulated silvicultural scenario and illustrates how management decisions coupled with environmental characteristics affect stand growth for a given species. Indeed, these silvicultural scenarios are relevant for several types of studies that assess the impact of forest management on the forest ecosystem features or services (Sing et al. 2018; Lundholm et al. 2020). Another illustrated example of potential use can be the differential between harvested whole stem (variable *stand_vtot_c0*) and merchantable stem and branches volumes (variable *stand_vtot_c7*), which can be interesting to simulate the impact of harvest scenarios for small branches under a 7-cm-diameter threshold like in the RESPIRE project (Saint-André et al. 2019).

Furthermore, the description of initial stand characteristics and detailed temporal dendrometric data provided here should allow new users to simulate ONF silvicultural scenarios to the closest, freely calculate unavailable dendrometric variables in the database (e.g. mean total height, relative density index) or access available *Capsis* extensions to offer additional features.

Besides, to face global environmental changes, the diversification of silvicultural scenarios is a key component of future forest management. Therefore, potential model users may experiment, through model simulation, new silvicultural scenarios to address various issues and
<table><caption>**Table 2** Overview of the variables from the dendrometric, thinning and carbon segments of the database with the definition of the variable</caption><thead><tr><th>Type</th><th>Variable</th><th>Description of the variable</th><th>Unit</th></tr></thead><tbody><tr><td rowspan="16">Dendrometric variables</td><td>stand_age</td><td>Age from seed</td><td>Year</td></tr><tr><td>stand_cut</td><td>Stand before or after thinning</td><td></td></tr><tr><td>stand_hdom</td><td>Dominant height of the stand</td><td>m</td></tr><tr><td>stand_n</td><td>Density of the stand</td><td>N.ha<sup>-1</sup></td></tr><tr><td>stand_ba</td><td>Basal area of the stand</td><td>m<sup>2</sup>. ha<sup>-1</sup></td></tr><tr><td>stand_dbh_mean</td><td>Mean quadratic diameter of the stand</td><td>cm</td></tr><tr><td>stand_dbhisation</td><td>Mean dominant diameter of the stand</td><td>cm</td></tr><tr><td>stand_vstem_c7</td><td>Merchantable stem volume of the stand (overbark volume of the bole with a stem top diameter > 7 cm)</td><td>m<sup>3</sup>. ha<sup>-1</sup></td></tr><tr><td>stand_vtot_c7</td><td>Merchantable stem and branches volume of the stand (overbark volume of the bole with a stem top diameter > 7 cm and large branches of a diameter > 7 cm)</td><td>m<sup>3</sup>. ha<sup>-1</sup></td></tr><tr><td>stand_vtot_c0</td><td>Whole stem volume of the stand (overbark volume of the stump, bole and branches without conditions of minimum diameter)</td><td>m<sup>3</sup>. ha<sup>-1</sup></td></tr><tr><td>standProd_ba</td><td>Total basal area production of the stand</td><td>m<sup>2</sup>. ha<sup>-1</sup></td></tr><tr><td>stand_mai_ba</td><td>Mean annual basal area increment of the stand</td><td>m<sup>2</sup>. ha<sup>-1</sup>.an<sup>-1</sup></td></tr><tr><td>stand_cai_ba</td><td>Current annual basal area increment of the stand</td><td>m<sup>2</sup>. ha<sup>-1</sup>.an<sup>-1</sup></td></tr><tr><td>standProd_vstem_c7</td><td>Total merchantable stem volume production of the stand</td><td>m<sup>3</sup>. ha<sup>-1</sup></td></tr><tr><td>stand_mai_vstem_c7</td><td>Mean annual merchantable stem volume increment</td><td>m<sup>3</sup>. ha<sup>-1</sup>.an<sup>-1</sup></td></tr><tr><td>stand_cai_vstem_c7</td><td>Current annual merchantable stem volume increment of the stand</td><td>m<sup>3</sup>. ha<sup>-1</sup>.an<sup>-1</sup></td></tr><tr><td>standProd_vtot_c0</td><td>Total whole stem volume production of the stand</td><td>m<sup>3</sup>. ha<sup>-1</sup></td></tr><tr><td>stand_mai_vtot_c0</td><td>Mean annual whole stem volume increment of the stand</td><td>m<sup>3</sup>. ha<sup>-1</sup>.an<sup>-1</sup></td></tr><tr><td>stand_cai_vtot_c0</td><td>Current annual whole stem volume increment of the stand</td><td>m<sup>3</sup>. ha<sup>-1</sup>.an<sup>-1</sup></td></tr><tr><td rowspan="6">Thinning variables</td><td>harv_n</td><td>Harvested density in the stand</td><td>N. ha<sup>-1</sup></td></tr><tr><td>harv_ba</td><td>Harvested basal area in the stand</td><td>m<sup>2</sup>. ha<sup>-1</sup></td></tr><tr><td>harv_dbh</td><td>Mean quadratic diameter of the harvested stems</td><td>cm</td></tr><tr><td>harv_vstem_c7</td><td>Harvested merchantable stem volume in the stand</td><td>m<sup>3</sup>. ha<sup>-1</sup></td></tr><tr><td>harv_vtot_c0</td><td>Harvested whole stem volume in the stand</td><td>m<sup>3</sup>. ha<sup>-1</sup></td></tr><tr><td>harv_vstem_c7_unit</td><td>Mean harvested merchantable stem volume per tree</td><td>m<sup>3</sup>. stem<sup>-1</sup></td></tr><tr><td>harv_kg</td><td>Thinning qualifier coefficient</td><td></td></tr><tr><td rowspan="7">Carbon accounting variables</td><td>biom_dens_Dupouey</td><td>Basic specific gravity of the species according to the Dupouey database</td><td></td></tr><tr><td>carb_aer</td><td>Carbon storage in aerial biomass of the stand</td><td>tCO<sub>2</sub>. ha<sup>-1</sup></td></tr><tr><td>carb_root</td><td>Carbon storage in root biomass of the stand</td><td>tCO<sub>2</sub>. ha<sup>-1</sup></td></tr><tr><td>carb_stor</td><td>Carbon storage in total biomass of the stand</td><td>tCO<sub>2</sub>. ha<sup>-1</sup></td></tr><tr><td>harv_carb_aer</td><td>Exported carbon content from harvested biomass</td><td>tCO<sub>2</sub>. ha<sup>-1</sup></td></tr><tr><td>carb_stor_mean</td><td>Mean carbon storage on a rotation length</td><td>tCO<sub>2</sub>. ha<sup>-1</sup></td></tr><tr><td>carb_cai</td><td>Annual flow of carbon converted in biomass (current annual carbon increment)</td><td>tCO<sub>2</sub>. ha<sup>-1</sup>.an<sup>-1</sup></td></tr></tbody></table>

unexplored silvicultural contexts. ONF silvicultural scenarios of the present database can serve as references in comparison to these newly simulated alternatives. Furthermore, potentially improved growth and yield models are expected to appear in *Capsis*, which is continuously updated. Therefore, following these new developments, the present database could be used to facilitate the publication of new and updated silvicultural scenarios both in and out of ONF. A new release of *Capsis-ONF* (for ONF internal use) is compiled every year, and past versions of

it are now saved to facilitate the transmission of simulations through the years.

## 5.2 Carbon accounting: an example of further valorisation

The French carbon offset certifications program called *Label Bas Carbone* (LBC) has devoted 3 methodologies designed for forest activities at the moment (i.e. afforestation activities, reconstitutions of degraded forest stands by plantation, coppice conversion into high forests). The first two methodologies require an assessment of
<table><caption>**Table 3** Synthesis of people involved in performing models for simulation on the one hand and in expertise for validation of simulation on the other hand, for each studied species. Authors of simulation models contributed to add silvicultural scenarios (SSCs) of the ONF guides in the database</caption><thead><tr><th>Species</th><th>Geographical area</th><th>Original ONF guide</th><th>Simulation authors</th><th>ONF Experts</th></tr></thead><tbody><tr><td>Abies alba (Silver fir)</td><td>Jura mountains<br>Southern Alps mountains</td><td>(Abt 2014)<br>(Ladier et al. 2012)</td><td>T. Sardin <sup>a</sup><br>P. Dreyfus <sup>a</sup></td><td></td></tr><tr><td>Cedrus atlantica (Atlas cedar)</td><td>France</td><td>(Sardin et al. 2022)</td><td>T. Sardin <sup>a</sup></td><td></td></tr><tr><td>Fagus sylvatica (European beech)</td><td>Pyrenees mountains</td><td>(Sardin 2013b)</td><td>S. Fournier</td><td>T. Sardin</td></tr><tr><td>Picea abies (Norway spruce)</td><td>Jura mountains</td><td>(Abt 2014)</td><td>S. Fournier</td><td>T. Sardin</td></tr><tr><td>Pinus halepensis (Aleppo pine)</td><td>Mediterranean coast</td><td>(Tresmontant and Quesney 2015)</td><td>M. Simeoni<br>S. Fournier</td><td>P. Dreyfus</td></tr><tr><td>Pinus nigra ssp laricio var corsicana,<br>Pinus nigra ssp laricio var calabrica<br>(Laricio pine)</td><td>Plains of central and northern France</td><td>(Chabaud and Nicolas 2009)</td><td>S. Fournier</td><td>X. Mandret</td></tr><tr><td>Pinus nigra ssp nigra (Austrian black pine)</td><td>Southern Alps mountains</td><td>(Ladier et al. 2012)</td><td>P. Dreyfus <sup>a</sup></td><td></td></tr><tr><td>Pinus pinaster (Maritime pine)</td><td>Plains of central and northern France</td><td>(Chabaud and Nicolas 2009)</td><td>S. Fournier</td><td>T. Sardin</td></tr><tr><td>Pinus sylvestris (Scots pine)</td><td>Forest of Landes de Gascogne</td><td>(ONF 2003)</td><td>S. Fournier</td><td>T. Sardin</td></tr><tr><td>Pinus sylvestris (Scots pine)</td><td>Plains of central and northern France</td><td>(Chabaud and Nicolas 2009)</td><td>S. Fournier</td><td>X. Mandret</td></tr><tr><td>Pseudotsuga menziesii (Douglas fir)</td><td>Southern Alps mountains</td><td>(Ladier et al. 2012)</td><td>P. Dreyfus <sup>a</sup></td><td></td></tr><tr><td>Quercus petraea (Sessile Oak)</td><td>France<br>North-east of France<br>Loire basin</td><td>(Angelier 2007)<br>(Sardin 2008)<br>(Jarret 2004)</td><td>S. Fournier<br>S. Fournier<br>S. Fournier</td><td>T. Sardin<br>D. François<br>X. Mandret</td></tr><tr><td>Quercus robur (Pedonculate Oak)</td><td>North-east of France</td><td>(Sardin 2008)</td><td>S. Fournier</td><td>D. François</td></tr></tbody></table>

biomass carbon storage at 30 years old and the mean carbon storage on the complete revolution length. These two values have been added in the database to answer these two LBC's specific requirements. If the simulation data did not provide any value at 30 years old, a linear approximation was made between the values of the two closest ages available. Furthermore, because the volumes of the database are all calculated based on the same equations, they ensure consistency and comparability in calculation. On the other hand, because one of the provided volumes of the database is the whole stem volume, biomass calculations do not need a volume expansion factor (Loustau 2004; Longuetaud et al. 2013) to convert merchantable stem volumes to whole stem volumes like it is presently done in the LBC.

As described in the potential uses of the database, graphical comparisons (Fig. 3) for different species or silvicultural contexts are valuable illustrations for forest managers to understand temporal dynamics of forest carbon storage.

The present version of the database does not yet provide any data for storage in harvested wood products nor for the carbon emissions avoided by their use. Some of the silvicultural scenarios from the database have been used in the *Carbon Accounting Tool* (CAT: Pichancourt et al. 2018) to assess these two carbon pools in *Capsis*.

However, the generalisation of this method to every silvicultural scenarios of the database is currently impossible; as CAT operates with volume equations specific to each growth and yield model, carbon outputs do not match the ones provided in our database using standardized volume equations. Moreover, only a few species-specific processing chains and harvested wood products greenhouse gas emissions assessments were formalised in research projects for the French territory (Cornillier et al. 2017; Vallet et al. 2019) and with different degrees of precision. Thus, further work would be required for being able to complete the database in this respect.

### 5.3 Limits of silvicultural simulations in a changing climate

One of the most important weaknesses of current growth and yield models is their limited ability to predict stand dynamics in the face of global productivity changes. Current forest management may thus experience difficulties to respond to climate-related disturbances that are actually not represented in silvicultural scenarios like those presented in the database (postponement of thinnings, troubles of regeneration). Nevertheless, forest managers will continue to rely, at least in part, on current silvicultural scenarios (among other management tools) to adapt their decisions to a changing environment. These scenarios are indeed solid enough to be applied in low

<footnote><sup>a</sup> Corresponds to the original author of simulations, so no additional experts were involved</footnote>
<figure><img src="image_6.png" /><figcaption>**Fig. 2** Temporal evolution of simulated MAI in whole stem volume for each silvicultural scenario of the database. Silvicultural scenarios are discriminated by fertility index (specific for a species and silvicultural context, a same index does not correspond to equivalent environmental conditions for two different silvicultural guides), the different silvicultural guides and alternatives, or stand origins are not discriminated</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>**Fig. 3** Temporal evolution of total carbon storage (above and underground carbon) for silvicultural scenarios in stands of contrasted fertility indexes for four common French species</figcaption></figure>
vulnerable stands to guarantee the production of quality roundwood in the future, and flexible enough to allow building alternatives pathways in case of significant changes.

Moreover, these silvicultural scenarios still represent good references for the evaluation of climate-sensitive growth and yield models to assess their consistency with usual dendrometric relationships in a constant climate. For instance, comparisons of different variables (density, basal area, DBH) have been made for oak stands between simulations from the GO+ model (Moreaux et al. 2020) and two scenarios from the database using the Fagacées model under RCP 2.6 scenario as baseline silvicultural scenarios in the Forets21 project (https://forets21.inra.fr/pelican3.1/itk-quercus.html).

Finally, species distributions in Europe may go through significant changes in future years (Buras and Menzel 2019). That is why the European forest managers and researchers community may benefit from sharing years of technical knowledge and past silvicultural experiments to anticipate management of migrating species for which silvicultural guidelines are lacking in their new projected areas.

### Acknowledgements

We thank all writers who have taken part in any guide of ONF collection and more precisely : Jean Ladier, Freddie Rey, David Tresmontant, Thierry Quesney, Ludovic Chabaud, Loïc Nicolas, Pascal Jarret, Stéphane Claude, Pascal Debailleul, Guillaume Declochez, Olivier Lanter, Jean-Pierre Polmard, Eric Toussaint, James Wright, Bruno Chopard, Jean-François Cerf, Pauline Delord, Lilian Pauziès, Didier Canteloup who have contributed to the silvicultural guides found in the database. We also thank Pierre Duplat, Mien Tran-Ha, Jean-Hugues Bartet, Jérôme Piat, and François Connard who initiated and greatly contributed to the writing of numerous growth tables and dendrometric improvements at ONF. We also thank everyone who contributed in collecting dendrometric data on field experiments or in forest management which helped build these silvicultural scenarios, and Antoine Fergeau who designed the silvicultural guides map. Still, this work is based on the knowledge of countless anonymous ONF forest managers who shared their experience and contributed to the silvicultural innovations on which these guides are built.
We thank all the academic partners and model developers who share their models and associated libraries with ONF and allow us to write, revise and explore the silvicultural scenarios and especially those whose models were used in this work : Jean-François Dhôte, Patrick Vallet, Gilles Le Moguédec, Frédéric Mothe, Mathieu Fortin, Thomas Pérot, Sandrine Perret, Gauthier Ligot, Jérôme Perin, Astrid de Pierpont, Samuel Quevauvillers, Bernard Lemoine, Céline Meredieu, Thierry Labbé, Priscilla Cailly, Sébastien Cavaignac, Jean-Daniel Bontemps and François Courbet. We thank the Cooperative of data on forest stand growth (GIS COOP) for the data, which contributes both to the models we used, and the simulation process.

Finally, we thank Myriam Legay who first saw the scientific interest of sharing this database with a wider audience.

### Code availability

Code is not available.

### Authors' contributions

Salomé Fournier, Thierry Sardin, Philippe Dreyfus, Marion Simeoni contributed to the dataset with simulations of silvicultural scenarios. Philippe Dreyfus and François de Coligny added growth and yield models to the *Capsis* platform. Thierry Sardin, Philippe Dreyfus, Didier François, Xavier Mandret reviewed the realism of silvicultural scenarios as silvicultural experts. Alain Bouvet and Alain Berthelot provided dendrometric data. Salomé Fournier computed additional

dendrometric data and wrote the datapaper, all co-authors reviewed it. Christine Deleuze supervised the work. All authors read and approved the final manuscript.

### Funding

The National Forests Office funded this work and the previous publications on which it is based.

### Availability of data and materials

Dataset access is at https://doi.org/10.57745/QARRFS. Associated metadata are available at https://metadata-afs.nancy.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/f76ed27f-325d-493b-8731-0995dcaa7805.

### Declarations

#### Ethics approval and consent to participate

Not applicable.

#### Competing interests

The authors declare that they have no conflict of interest.

#### Author details

¹Département Recherche Développement Innovation, Office National des Forêts, 77300 Fontainebleau, France. ²National expert in silviculture, Office National des Forêts, 31000 Toulouse, France. ³Direction Territoriale Centre-Ouest-Aquitaine, Office National des Forêts, 45760 Boigny-sur Bionne, France. ⁴Agence Bouches du Rhône-Vaucluse, Office National des Forêts, 84000 Avignon, France. ⁵FCBA, 21170 Charrey-sur-Saône, France. ⁶Université de Lorraine, AgroParisTech, INRAE, Silva, 54000 Nancy, France. ⁷CIRED, 94736 Nogent-sur-Marne Cedex, France. ⁸Université de Lorraine, Université de Strasbourg, AgroParisTech, CNRS, INRA, BETA, 54000 Nancy, France. ⁹AMAP, Univ Montpellier, IRD, CIRAD, CNRS, INRAE, Montpellier, France.

Received: 9 June 2022 Accepted: 9 December 2022

Published online: 28 December 2022

### References

Abt D (2014) Arc jurassien : sapin et épicéa. ONF Office national des forêts, Paris ISBN : 978-2-84207-378-7

Angelier A (2007) Douglasaies françaises. Office national des forêts, Paris ISBN : 978-2-84207-315-2

Bailly A, Bigot M (1997) Un modèle de croissance pour les plantations équennes de douglas. Inf Forêt 2:549

Barbati A, Corona P, Marchetti M (2007) A forest typology for monitoring sustainable forest management: The case of European Forest Types. Plant Biosyst - An Int J Dealing Aspects Plant Biol 141:93-103. https://doi.org/10.1080/11263500601153842

Biber P, Felton A, Nieuwenhuis M et al (2020) Forest biodiversity, carbon sequestration, and wood production: modeling synergies and trade-offs for ten forest landscapes across Europe. Front Ecol Evol 8:547696. https://doi.org/10.3389/fevo.2020.547696

Bontemps J-D, Longuetaud F, Mothe F, et al (2009) AbiAl v1.0 - un simulateur de croissance-production de type arbre indépendant des distances (MAID) pour les peuplements purs et réguliers de Sapin pectiné (Abies alba Mill.) dans les massifs des Vosges et du Jura. hal-00820917

Brockerhoff EG, Barbaro L, Castagneyrol B et al (2017) Forest biodiversity, ecosystem functioning and the provision of ecosystem services. Biodivers Conserv 26:3005-3035. https://doi.org/10.1007/s10531-017-1453-2

Buras A, Menzel A (2019) Projecting tree species composition changes of European forests for 2061-2090 under RCP 4.5 and RCP 8.5 Scenarios. Front Plant Sci 9:1986. https://doi.org/10.3389/fpls.2018.01986

Cairns MA, Brown S, Helmer EH, Baumgardner GA (1997) Root biomass allocation in the world's upland forests. Oecologia 111:1-11. https://doi.org/10.1007/s004420050201

Chabaud L, Nicolas L (2009) Pineraies des plaines du Centre et du Nord-Ouest. ONF Office national des forêts, Paris ISBN : 978-2-84207-330-5
Chomel F, Chandioux O, Le Courbe A (2016) La validation d'un modèle de croissance au service d'une réflexion sur la sylviculture du Pin d'Alep. *Rev Forest Française* 245. https://doi.org/10.4267/2042/62006

Cornillier C, Vial E, Deroubaix G, et al (2017) GESFOR - Bilan environnemental des systèmes forestiers vis à vis du changement climatique et des autres enjeux : pour une optimisation des pratiques sylvicoles et des politiques territoriales. ADEME, N° de contrat : 1260C0055. https://librairie.ademe.fr/ changement-climatique-et-energie/1465-ges-for-bilan-environnemental- des-systemes-forestiers-vis-a-vis-du-changement-climatique-et-des-autres- enjeux.html

Courbet F (2002) Modélisation et intégration logicielle: croissance, branchaison, qualité des bois Aide à la décision pour la sylviculture et l'utilisation du bois des essences forestières françaises (5ème tranche). Convention MAPA-DERF / INRA. hal-03654591

Deleuze C, Longuetaud F, Rivoire M, et al (2014) EMERGE : Elaboration de Modèles pour une Estimation Robuste et Générique du Bois Energie (2e partie) (dossier). Rendez-vous techniques de l'ONF 17-81. ISSN : 1763-6442

Delord P, Mandret X (2018) Chênaie atlantique : futaie régulière et conversion en futaie régulière : mémento sylvicole de conduite des peuplements. ONF. Direction territoriale Centre-Ouest-Aquitaine, Boigny-sur-Bonne

Dreyfus P, Bonnet F (1995) Capsis : logiciel de simulation de conduites sylvicoles. Rev Forest Française 111. https://doi.org/10.4267/2042/26700

Dufour-Kowalski S, Courbaud B, Dreyfus P et al (2012) Capsis : an open software framework and community for forest growth modelling. Ann Forest Sci 69:221-233. https://doi.org/10.1007/s13595-011-0140-9

Fournier S, Sardin T, Dreyfus P, François D, Mandret X, Simeoni M, Renaud JP, Akroume E, Bouvet A, Berthelot A, Wernsdörfer H, Rivière M, Sainte-Marie J, Brêteau-Amores S, de Coligny F, Deleuze C (2022) Dendrometric data of silvicultural scenarios from the French National Forests Office's (ONF). [Dataset]. Recherche Data Gouv, V4. https://doi.org/10.57745/QARRFS

Grimault J, Gleizes O (2019) Du projet Vocal au label Bas-Carbone... : la certification de projets carbone en forêt française. Forêt Entreprise 245:24-29 https://www.cnpf.fr/sites/socle/files/2022-07/FE245_0.pdf

Gschwantner T, Alberdi I, Balázs A et al (2019) Harmonisation of stem volume estimates in European National Forest Inventories. Ann Forest Sci 76:24. https://doi.org/10.1007/s13595-019-0800-8

Jarret P (2004) Chênaie atlantique. ONF Office national des forêts, Paris ISBN : 978-2-84207-294-0

Ladier J, Rey F, Dreyfus P (2012) Guide des sylvicultures de montagne : Alpes du Sud françaises. ONF Office national des forêts, Aix-en-Provence http://www1.onf.fr/outils/medias/20121108-163304-597960/+files++/1

Le Moguédec G, Dhôte J-F (2012) Fagacées: a tree-centered growth and yield model for sessile oak (Quercus petraea L.) and common beech (Fagus sylvatica L.). Ann Forest Sci 69:257-269. https://doi.org/10.1007/s13595-011-0157-0

Longuetaud F, Mothe F, Senatoise P et al (2013) Les coefficients d'expansion pour déduire différents volumes de branches à partir de volumes de tige. Rendez-vous Techn de l'ONF 39-40:48-59 https://www.onf.fr/outils/ressources/f7ac1507-f1c2-4d24-9eeb-54b82ef1cd7f++versions++/1/++paras++/2/++ass++/1/++i18n++datafr?_=_1544436714.790457&download=1

Loustaud D (2004) Rapport final du projet CARBOFOR : séquestration de carbone dans les grands écosystèmes forestiers en France : quantification, spatialisation, vulnérabilité et impacts de différents scénarios climatiques et sylvicoles. INRA Institut national de la recherche agronomique. Unité de recherches EPHYSE, Cestas http://www.gip-ecofor.org/doc/drupal/gicc/7-01LousteauCraboforRF.pdf

Lundholm A, Black K, Corrigan E, Nieuwenhuis M (2020) Evaluating the impact of future global climate change and bioeconomy scenarios on ecosystem services using a strategic forest management decision support system. Front Ecol Evol 8:200. https://doi.org/10.3389/fevo.2020.00200

Mahnken M, Cailleret M, Collalti A et al (2022) Accuracy, realism and general applicability of European forest models. Glob Chang Biol n/a. https://doi.org/10.1111/gcb.16384

Meredieu C (1998) Croissance et branchaison du Pin Laricio (Pinus nigra Arnold ssp. laricio (poiret) Maire) : élaboration et évaluation d'un système de modèles pour la prévision de caractéristiques des arbres et du bois. tel-02578047

Meredieu C (2002) Intégration dans Capsis d'un modèle de croissance du Pin maritime développé par l'INRA. Auclair D, Bordeaux

Mina M, Bugmann H, Cordonnier T et al (2017) Future ecosystem services from European mountain forests under climate change. J Appl Ecol 54:389-401. https://doi.org/10.1111/1365-2664.12772

Ministère de la transition écologique et solidaire, I4CE (2020) Label Bas Carbone - Guide Pédagogique. Ministère de la transition écologique et solidaire, Paris https://www.i4ce.org/download/guide-pedagogique-label-bas-carbone/?wpdmdl=26155&refresh=5eeb4579726c21592477049

Moreaux V, Martel S, Bosc A et al (2020) Energy, water and carbon exchanges in managed forest ecosystems: description, sensitivity analysis and evaluation of the INRAE GO+ model, version 3.0. Geosci Model Dev. https://doi.org/10.5194/gmd-13-5973-2020

ONF (1997) Guide des sylvicultures du hêtre dans les Pyrénées. ONF. Direction régionale Aquitaine, Bordeaux

ONF (2003) Guide de sylviculture du pin maritime de Lande. ONF. Direction territoriale Sud-Ouest, Toulouse

ONF (2021) L'ONF en chiffres. Office national des forêts, Paris https://www.onf.fr/outils/ressources/208efd30-102c-491f-82e6-2c5b025b4198/++versions++/16/++paras++/3/++ass++/4/++i18n++datafr?_=_1649073474.094339&download=1

ONF (2017) Douglasiaes françaises : additif récolte des gros bois : mémento sylvicole - coupes. ONF Office national des forêts, Paris

Pain O, Boyer E (1996) A whole individual tree growth model for Norwayspruce. In: Proceedings of the second Workshop "Connection between silviculture and wood quality through modelling approaches and simulation software." South Africa

Perin J, Claessens H, Lejeune P et al (2016) Distance-independent tree basal area growth models for Norway spruce, Douglas-fir and Japanese larch in South- ern Belgium. Eur J For Res 136. https://doi.org/10.1007/s10342-016-1019-y

Pérot T, Perret S, Meredieu C, GINISTY C (2007) Prévoir la croissance et la production du Pin sylvestre : le module Sylvestris sous Capsis 4. Rev Forest Française https://doi.org/10.4267/2042/8483

Pichancourt J-B, Manso R, Ningre F, Fortin M (2018) A carbon accounting tool for complex and uncertain greenhouse gas emission life cycles. Environ Model Software 107:158-174. https://doi.org/10.1016/j.envsoft.2018.06.005

Saint-André L, Buée M, Aubert M et al (2019) RESPIRE - Récolte des menus bois en forêt : potentiel, impact, indicateurs et remédiation par épandage de cendres : rapport final. ADEME Agence de l'environnement et de la maîtrise de l'énergie, Angers https://librairie.ademe.fr/cadic/854/rapport-respirercolte-bois-foret-2019.pdf

Sardin T (2008) Chênaies continentales. ONF Office national des forêts, Paris ISBN : 978-2-84207-321-3

Sardin T (2013a) Douglasiaes françaises. 1, Référentiels sylvicoles - FR correctif 2012. ONF Office national des forêts, Paris ISBN : 978-2-84207-359-6

Sardin T (2013b) Hétraies et hétraies sapinières des Pyrénées. ONF Office national des forêts, Paris

Sardin T, Claude S, Debailleul S et al (2018) Chênaies continentales : mémento sylvicole coupes. ONF Office national des forêts, Paris

Sardin T, Deleuze C (2020) Les itinéraires sylvicoles à l'Office National des Forêts : peut-on les optimiser pour le carbone ? Marseille: Forêt Méditerranéenne t. XLI, p 4 https://www.foret-mediterraneenne.org/fr/catalogue/id-2200-les-itineraires-sylvicoles-a-l-office-national-des-forets-peut-on-les-optimiser-pour-le-carbone-

Sardin T, Dreyfus P, Ladier J (2022) Sylviculture du cèdre : mémento pratique - coupes. ONF Office national des forêts, Paris

Sing L, Metzger MJ, Paterson JS, Ray D (2018) A review of the effects of forest management intensity on ecosystem services for northern European temperate forests with a focus on the UK. Forestry: Int J Forest Res 91:151-164. https://doi.org/10.1093/forestry/cpx042

Tresmontant D, Quesney T (2015) Pinèdes de pin d'Alep : mémento sylvicole de conduite des peuplements et mise en oeuvre des travaux sylvicoles. ONF Office national des forêts, Paris

Vallet P, Arnaudet L, Bello J, et al (2019) Rapport final du projet SATAN : Synergie entre ATénuation et AdaptatioN : Approche à l'échelle du territoire. Report, IRSTEA. hal-02609670

**Publisher's note**

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.