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https://doi.org/10.5194/egusphere-2025-6380
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/egusphere-2025-6380
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
20 Jan 2026
| 20 Jan 2026
Contribution of fine roots on carbon allocation patterns in Norwegian forests
Abstract. Boreal forests play a key role in the global carbon (C) cycle, with fine root production and turnover contributing to belowground C fluxes and inputs to soil organic carbon (SOC) stocks. However, fine root biomass and production are difficult to quantify and introduce uncertainty in estimates of net primary production (NPP), limiting the assessment of C allocation patterns in boreal forests.
The objective of the present study was to assess how different fine root modelling approaches influence estimates of NPP, gross primary production (GPP), carbon use efficiency (CUE), and belowground C allocation to fine roots in Norwegian boreal and nemo-boreal forests. Using Norway’s national forest resource map together with MODIS remote sensing data, we estimated fine root biomass and production using three approaches that differed in how foliage biomass was derived: two based on leaf area index (from MODIS or the forest resource map) and one based on allometric biomass equations. These approaches were combined with alternative fine-root turnover rates and foliage-to-fine-root biomass ratios.
Mean NPP derived from the forest resource map ranged from 318 to 243 g C m⁻² yr⁻¹ across young to mature forests. In Norway spruce and Scots pine forests, NPP increased during early stand development, peaked between 50 and 80 years, and declined with increasing age. Comparison against European MODIS NPP revealed that tree growth alone accounted for 16 % of MODIS NPP, while including fine root and understory NPP contributions increased total NPP by roughly 3–5 times, depending on the fine root estimation method. CUE ranged from 0.30 to 0.63, showing age-related declines and species-level variation, with the lowest values in mature Norway spruce forests.
Our analysis sheds light on the role of fine root biomass and production in forest C budgeting and their influence on NPP, CUE, and belowground C allocation. Excluding fine roots creates a major gap in forest C analyses and we conclude that the choice of method for fine root estimation has a strong impact on regional NPP and its component fluxes. Estimates of forest CUE may guide management by identifying areas with low efficiency, where interventions may enhance C sequestration.
How to cite. Hagenbo, A., Bright, R. M., Mohr, C. W., Hauglin, M., Weldon, S., and Dalsgaard, L.: Contribution of fine roots on carbon allocation patterns in Norwegian forests, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-6380, 2026.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
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- RC1: 'Comment on egusphere-2025-6380', Christoph Pucher, 17 Mar 2026
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RC2: 'Comment on egusphere-2025-6380', Anonymous Referee #2, 22 Apr 2026
I apologise to the editor and to the authors for my delayed response. I read the manuscript titled "Contribution of fine roots on carbon allocation patterns in Norwegian forests" by Hagenbo et al., and I have the following comments and suggestions for improvement. Overall, I find the study to be a valuable contribution to our understanding of fine-root contributions to boreal forest carbon budgets, and I appreciate the authors' comprehensive approach. However, there are some areas where clarification and additional analysis could strengthen the manuscript.## Major points1 - Given the complexity of the methods, I suggest adding a clear diagram (e.g., a flowchart) that maps the different pathways for estimating foliage biomass and fine root production across the three methods. This would help readers grasp the workflow and method-specific differences more quickly. In the same direction, I also suggest restructuring the Methods section to reduce repetition of shared steps and emphasise the differences among the three approaches. For example, the manuscript could include a main workflow subsection for shared components (tree NPP calculation, understory NPP, and GPP/CUE calculation), followed by a separate subsection detailing how foliage biomass and fine root production are estimated in each method. This would reduce redundancy and improve clarity.2 - Temporal consistency of remote sensing inputs and implications of missing respiration acclimation:I suggest a dedicated clarification paragraph in Methods (and a short caveat in Discussion) on the temporal representativeness of all remotely sensed/model inputs. As currently described, the workflow appears to combine products from different temporal frames:- MODIS EURO NPP comes from Neumann et al. (2016), which is a temporally continuous product for 2000-2012.- MODIS land cover is from MCD12Q1 (2000), and LAI/FPAR is from MOD15 Collection 5.- SR16 is based on national airborne laser scanning initiated in 2015.This suggests the analysis is primarily a cross-sectional/fused estimate rather than a strictly year-specific carbon budget. Please state explicitly whether outputs should be interpreted as: (i) representative of a specific year, (ii) a multi-year climatological synthesis, or (iii) a structural comparison among methods independent of a single target year.Respiration acclimation concern:The GPP framework uses fixed/instantaneous temperature responses (e.g., Q10 = 2 for live wood and fine roots; foliage Q10 from the Tjoelker et al. equation), but does not appear to include explicit longer-term thermal acclimation of respiration parameters. In a rapidly warming high-latitude system, this could bias respiration upward relative to acclimated responses. The literature indicates that acclimation can dampen expected increases in respiration under warming, including in boreal/temperate trees (Reich et al., 2016; Huntingford et al., 2017; Zhang et al., 2025). Some potential implications for this study are: Maintenance respiration (Rm) may be overestimated under warmer conditions. Because GPP is computed as NPP + respiration terms, GPP may be biased high if Rm is biased high. CUE (NPP/GPP) may therefore be biased low, especially in warmer subsets and potentially in age/species groups that differ in temperature regime. Apparent age/species gradients in CUE and inferred fine-root allocation fractions may partly reflect differences in respiration parameterisation rather than solely biological allocation differences.Suggested improvement:- Add a sensitivity analysis with an acclimated respiration scenario (for example, temperature-dependent downregulation of reference respiration and/or acclimated Q10) and compare impacts on GPP, CUE, and fine-root C allocation.- At minimum, quantify likely bias direction and uncertainty range introduced by non-acclimating respiration assumptions.3 - In Section 4.4, the discussion about remote-sensing GPP products is somewhat too general. It is correct that many widely used products are linked, directly or indirectly, to model formulations that include respiration and/or NPP closure, but this is not true of all current GPP products. For example, GOSIF-GPP is a SIF-based product derived from OCO-2 solar-induced fluorescence and empirically calibrated against eddy-covariance GPP, rather than being calculated as NPP plus modelled autotrophic respiration (Li & Xiao, 2019). In addition, Wang et al. (2026) proposed GPP-net, a Sentinel-2-based deep-learning framework that estimates GPP from surface reflectance and PAR, trained against flux-tower GPP, and designed to minimise dependence on land-cover parameterisation and coarse meteorological inputs. This distinction matters here because one of the key limitations identified by the authors is the circularity introduced when GPP is reconstructed from NPP and respiration terms using a MOD17-like framework. Using external products such as GOSIF-GPP and GPP-net could therefore substantially simplify the analysis: they allow benchmarking of modelled GPP without explicitly prescribing maintenance respiration and growth respiration in the GPP calculation, reducing dependence on fixed Q10 assumptions and the associated acclimation problem raised above. In that sense, comparison with one or both products would provide a more independent test of whether the reported CUE and age-related patterns are driven by the fine-root/NPP formulation or by the respiration parameterisation used to back-calculate GPP. At the same time, I would not present these products as complete solutions, because they introduce their own uncertainties (for example, SIF-GPP transferability and gap-filling for GOSIF, and model transfer/generalisation uncertainty for data-driven products), and GPP product uncertainty can differ in northern ecosystems (Li & Xiao, 2019; Marsh & Zhang, 2022). Still, I think the manuscript would be stronger if the authors explicitly discuss these alternative GPP product classes and, if feasible, compare their MOD17-like GPP estimates against GOSIF-GPP and/or GPP-net as an additional sensitivity test.## Minor pointsLine 121: I suggest reporting the resolution in arcseconds, which in this case would be 30 arcseconds, right?Line 273: There appears to be a semantic inconsistency in the description of the residual sign. The text states that the foliage-based method underestimated production in both pine and deciduous forests, yet the reported median residual for deciduous forests is positive (+65.1). If residuals are defined as the difference between the study estimate and the reference/meta-analysis value, then a positive residual would indicate overestimation rather than underestimation. Please check the wording or the residual sign convention.## ReferencesHuntingford, C., Atkin, O. K., Martínez-de la Torre, A., Mercado, L. M., Heskel, M. A., Harper, A. B., Bloomfield, K. J., O'Sullivan, O. S., Reich, P. B., Wythers, K. R., Butler, E. E., Chen, M., Griffin, K. L., Meir, P., Tjoelker, M. G., Turnbull, M. H., & Hurry, V. (2017). Implications of improved representations of plant respiration in a changing climate. Nature Communications, 8, 1602. https://doi.org/10.1038/s41467-017-01774-zNeumann, M., Moreno, A., Thurnher, C., Mues, V., Härkönen, S., Mura, M., Bouriaud, O., Lang, M., Cardellini, G., Thivolle-Cazat, A., Bronisz, K., Merganič, J., Alberdi, I., Astrup, R., Mohren, F., Zhao, M., & Hasenauer, H. (2016). Creating a regional MODIS satellite-driven net primary production dataset for European forests. Remote Sensing, 8(7), 554. https://doi.org/10.3390/rs8070554Reich, P. B., Sendall, K. M., Stefański, A., Wei, X., Rich, R. L., & Montgomery, R. A. (2016). Boreal and temperate trees show strong acclimation of respiration to warming. Nature, 531(7596), 633-636. https://doi.org/10.1038/nature17142Zhang, H., Wang, H., Wright, I. J., Bloomfield, K. J., Gallagher, R. V., Li, G., Drake, J. E., Ma, X., Watanabe, M., Atkin, O. K., Dong, N., Crous, K. Y., Han, Q., Wang, L., Wang, M., Togashi, H. F., Xu, H., Weng, E., Sun, Y., ... Peng, C. (2025). Thermal acclimation of stem respiration implies a weaker carbon-climate feedback. Science, 388(6750), 984-988. https://doi.org/10.1126/science.adr9978Li, X., & Xiao, J. (2019). Mapping photosynthesis solely from solar-induced chlorophyll fluorescence: A global, fine-resolution dataset of gross primary production derived from OCO-2. Remote Sensing, 11(21), 2563. https://doi.org/10.3390/rs11212563Wang, S., Ryu, Y., Dechant, B., Zhang, H., Feng, H., Lee, J., & Choi, C. (2026). GPP-net: A robust high-resolution GPP estimation network for Sentinel-2 using only surface reflectance and photosynthetically active radiation. Remote Sensing of Environment, 334, 115198. https://doi.org/10.1016/j.rse.2025.115198Marsh, H., & Zhang, W. (2022). Direct and legacy effects of spring temperature anomalies on seasonal productivity in northern ecosystems. Remote Sensing, 14(9), 2007. https://doi.org/10.3390/rs14092007Citation: https://doi.org/
10.5194/egusphere-2025-6380-RC2
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Andreas Hagenbo
CORRESPONDING AUTHOR
Norwegian Institute of Bioeconomy Research (NIBIO), Postboks 115, 1431 Ås, Norway
Ryan M. Bright
Norwegian Institute of Bioeconomy Research (NIBIO), Postboks 115, 1431 Ås, Norway
Christian Wilhelm Mohr
Norwegian Institute of Bioeconomy Research (NIBIO), Postboks 115, 1431 Ås, Norway
Marius Hauglin
Norwegian Institute of Bioeconomy Research (NIBIO), Postboks 115, 1431 Ås, Norway
Simon Weldon
Norwegian Institute of Bioeconomy Research (NIBIO), Postboks 115, 1431 Ås, Norway
Lise Dalsgaard
Norwegian Institute of Bioeconomy Research (NIBIO), Postboks 115, 1431 Ås, Norway
Short summary
Boreal forests are a major global carbon (C) sink, yet much productivity occurs belowground in fine roots, which are hard to measure and introduce uncertainty in regional C estimates. Using national forest maps and satellite data, we show that including fine root and understory production increases total net primary production 3 to 5-fold relative to tree growth alone and alters carbon use efficiency, highlighting the importance of fine root dynamics for accurate regional C accounting.
Boreal forests are a major global carbon (C) sink, yet much productivity occurs belowground in...
General comments:
As nicely stated in the Introduction section, the study addresses a very relevant topic, as fine-roots are an important component of total net primary production (NPP) and input for soil organic carbon (SOC). As direct measurements are challenging, commonly models are used to derive fine root biomass production and turnover. The authors examine how the varying assumptions underlying the different models influence estimates of NPP and carbon use efficiency (CUE). Again, this is a very relevant topic in terms of carbon budgeting such as greenhouse gas accounting. The authors present ways to calculate and consequently compare fine-root biomass and production and CUE from different sources (MODIS NPP, Norwegian forest resource map), which is a challenging task. This might also serve as a guide for similar studies in other countries. Overall, the study is well presented and limitations are clearly stated. The study is not always easy to read/follow, which is however more related to the complexity of the topic rather than the writing itself. There are some minor concerns regarding the calculation of MODIS GPP and some clarifications are needed for some other calculation steps in addition to some minor technical corrections. Overall I congratulate the authors on a well-designed study on a very relevant and challenging topic!
Specific comments:
Methods
Lines 127-129: When aggregating the SR16 grid cells, how was the dominant tree species identified?
Lines 132-134: Maybe some more detail regarding the stand-level growth and yield models could be added, i.e. which predictors (height, diameter?) are used to predict the annual changes in height, volume, etc.
Lines 142: LAI from MODIS. MODIS provides 8-day LAI values. Which value was used or how was the 8-day data aggregated, i.e. was just the maximum LAI used or the LAI on a specific day or the mean LAI over the whole year? In case of SR16 it is LAImax, correct?
Lines 148-150: The SLA used in the MOD17 algorithm to arrive at NPP from GPP for EvergreenNeedleLeaf ENF, DecideousBroadLeaf DBF, and MixedForest MF are 21.1, 26.2 and 21.5 m2 kg-1 C respectively. Especially in case of ENF (reflecting Pine and Spruce) this value is way higher than the ones used by the authors based on literature. I.e. the authors will arrive at a different foliage biomass compared to the one that was used in the calculation of MODIS NPP (this also relates to the comment below regarding the calculation of GPP). The same, but probably to a lesser extent, is also true for the used turnover rates.
Lines 198-247: Calculation of GPP. As the MODIS Europe dataset does not provide GPP but only NPP, the authors “employ a similar approach to calculate GPP as the MOD17 algorithm, but with some modifications”. However, they give the impression that the MOD17 algorithm first calculates NPP and then adds Ra to arrive at GPP but it is the other way round. MOD17 first calculates 8-day GPP based on on the radiation conversion efficiency concept using fraction of photosynthetic active radiation (FPAR) and climatic information (but not using LAI for instance). Finally, from the annual GPP (the sum of the 8-day GPP) the respiration Ra is removed to arrive at annual NPP. So rather than GPP = NPP + Ra it is NPP = GPP – Ra (later in section 4.4 Methodological limitations they also state that “Remote-sensing derived GPP often relies on NPP and autotrophic respiration, which again is at least not true for MODIS GPP). I think this detail is important, as by modifying the way how Ra is calculated (e.g. using a different SLA), the authors will consequently arrive at different MODIS GPP values than the ones that were used in the actual calculation of MODIS NPP which in turn will affect the MODIS CUE values.
Again, as MODIS Europe only provides NPP and not GPP there is a good reason for back-calculating GPP, but I think the author’s should be aware of this and that they should mention it in the text.
Results
Figures 4 and 5: Site index seems not to be equally distributed over forest age, i.e. forests on more productive sites (higher Site index) seem to be younger than forests on less productive sites (probably due to forests on productive sites being more likely managed for timber production). Is this true and if so, do the authors think that the found relationships also reflect a Site index effect in addition to an age effect?
Technical corrections:
Abstract
Lines 17 to 21: first “Mean NPP ... from 318 to 243 g C m-2 yr -1 across young to mature forests” is reported, but then for “CUE ... from 0.30 to 0.63 ... with lowest values in mature”. Probably change the second part to “CUE ... from 0.63 to 0.30 ...” for consistency.
Introduction
Line 33: “soil organic (SOC)” ... should this be “soil organic carbon (SOC)”?
Line 68: maybe add (RA) after “autotrophic (plant) respiration”
Line 91: “However, growth predictions are sensitive to CUE, ...”. The However seems to not really fit here, as this sentence is not contradicting or contrasting the previous one. Maybe just remove it and simply start with “Growth predictions are sensitive ...” or “Further, growth predictions are sensitive to CUE, as for instance ... “
Methods
Lines 148-150: First SLA in m2 kg-1 is reported the order spruce-pine-deciduous, but then SLA in m2 kg-1 C is reported in the order pine-spruce-deciduous. Same order should be used for consistency.
Results
Figure 1: The same scale on the vertical axis should be used for all 15 panels (or at least for the 5 panels within one column) as otherwise the visual comparison of bars can be quite misleading. Maybe the color palette could be improved to be color-blind friendly.