| 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.
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.