## 4 Discussion
### Comparison of Biometric and Eddy Covariance NPP Estimate  
In studies that are similar to this one, it has been common to compare _NEP_ estimates from EC and BM methods [8; 18; 19; 20; 21; 22; 91; 92; 93], because such estimates are methodologically different and have independent measurement errors [19]. The biometric method of the _NEP_ assessment requires the assessment of \(R_{h}\). The \(R_{h}\) is usually assessed from soil respiration measurements, using methods such as root exclusion by trenching, tree girdling, or isotope labelling [94; 95], or assuming, e.g., based on the work of [PERSON] et al. [94], that \(R_{h}\) is usually 50% of the soil respiration flux [19; 38]. Alternatively, \(R_{h}\) can be calculated from the \(R_{ECO}\) if the share of \(R_{h}\) (or \(R_{a}\)) in \(R_{ECO}\) is known [6]. In our case, \(R_{h}\) is estimated from \(R_{ECO}\) and a fixed \(R_{h}\):\(R_{ECO}\) ratio, where \(R_{ECO}\) is obtained with EC method by the partitioning of \(NEE\) fluxes. This rendered that the comparison of _NEP_s would no longer be valid. Therefore, in order to preserve the independence of two measurements, we had to compare estimates of _NPPs_. A similar approach of comparing _NPPs_s was already used in other studies [6; 22; 23]. Comparison of annual \(NPP_{EC}\) and \(NPP_{BM}\) showed good correlation (\(R\) = 0.680, adj. \(R^{2}\) = 0.396, \(p\) = 0.03). However, the observed correlation is obtained only when including the last year of the experiment (year 2017) when unfavorable meteorological conditions negatively affected both estimates. Our results are in line with previous studies stating that only long-term monitoring of C fluxes can reveal a correlation between biometric and EC estimates [18; 22]. EC estimate was always higher than the biometric one (Figure 12), with an average difference of 140 gC m\({}^{-2}\) year\({}^{-1}\), i.e., 17%. One of the possible reasons for the observed bias is a fact that \(NPP_{EC}\) includes the production of whole ecosystem (trees, understory and ground vegetation) with all of its above- and belowground carbon pools, while \(NPP_{BM}\) (in our study) includes only the production of trees, excluding fine roots and grasses. Consequently, we can assume that the higher \(NPP_{EC}\), as compared to \(NPP_{BM}\), is due to the estimate from which productions of fine roots and grasses are missing. Productions of fine roots and grasses can be significant contributors to total biometric NPP estimate, ranging from 10% [6] to 41% [22] for fine roots or 45% for fine roots and grasses together [96]. Underestimation of biometric _NPP_ has been identified as key potential source of bias in similar studies [93]. One of the main assumption, which we made for the purpose of \(NPP_{EC}\) calculation, is that the ratio of \(R_{h}\):\(R_{ECO}\) is fixed in time because \(R_{h}\) estimates were available only for the years 2008 and 2009 [38]. The fixed \(R_{h}\):\(R_{ECO}\) ratio assumption is questionable, and probably not true [97; 98], but we think that it is not fully unjustified. Namely, the forest in the footprint was thinned in 2006 and 2007 with an average intensity of 8% by volume (see caption of Figure 15). Thinning residues constituted a one-time addition to the litter pool probably causing slight increase in \(R_{h}\) in several years following thinning. In addition, [PERSON] et al. [98] recently provided evidence that global \(R_{h}\) is rising due to shifts in soil organic carbon (SOC) forms and enhanced SOC mineralization driven by rising global temperatures. At the same time, according to [PERSON] et al. [99], continuous accumulation of living biomass during the study period should have resulted with an increase in \(R_{a}\). When considering both studies [98; 99], \(R_{ECO}\) should have experience an increase with time. At our site, \(R_{ECO}\) exhibited a small, positive, but not statistically significant trend (\(p\) = 0.140).