## 2 Materials and Methods
### Eddy Covariance and Net Ecosystem Exchange
As we explore in this study, EC methods require careful analysis and determination of system uncertainty to be used across project sites and ultimately as a foundation for harmonized NEE carbon commercial products. Therefore, the gaps in the NEE time series have to be filled. Lookup tables are commonly used to fill the gaps, which consists of creating a table with mean NEE values over several ranges of temperature and radiation jointly and fill missing values with their corresponding mean in the table [34]. For instance, all the missing NEE data points that are between 100-150 W m\({}^{-2}\) in Rg and 20-21 \({}^{\circ}\)C in \(T_{air}\) are filled with the mean NEE, calculated with measured data, for that interval. Some variations of this methodology include VPD and different time windows to calculate the means [19]. CO\({}_{2}\) budgets estimated with this methodology have a maximum error of around 180 gCO\({}_{2}\) m\({}^{-2}\) year\({}^{-1}\), depending on the ecosystem type and the quantity of missing data [34]. By signs convention, positive NEE values represent an emission of CO\({}_{2}\) from the vegetation to the atmosphere and negative values are CO\({}_{2}\) sequestrations. The first occurs mostly at night and are associated with \(R_{exo}\) while the latter values are present in day-hours due to GPP being higher than \(R_{exo}\). Partitioning NEE (Equation (1)) into \(R_{exo}\) and GPP is useful to better understand how the ecosystem functions and to evaluate hypothetical scenarios, including diagnostic indicators for the sequestration strength of a desired soil carbon commercial product. As the sensor does not distinguish between both fluxes (it only measures NEE), \(R_{exo}\) and GPP have to be calculated with available methodology [19; 20; 32]. For this calculation, the nighttime approach (NT) estimates \(R_{exo}\) fitting the [PERSON] and Taylor [38] model for respiration (Equation (3)) using only nighttime data, because NEE = \(R_{exo}\) at night, and then extrapolating the parameters \(R_{ref}\) and E\({}_{0}\) found in the regression to calculate daytime \(R_{exo}\) (\(T_{ref}\) and \(T_{0}\) are fixed). Then, GPP is calculated by difference with Equation (1) [19]. Recentresearch suggests that the \(R_{coo}\) extrapolation might be biased [32] due to the respiration photo-inhibition of leaves, and it suggests novel methods to partition the flux. However, these methods are under development and most of the references up to date use the NT approach or the daytime approach (DT) that can be found in [20]. \[R_{cco}=R_{ref}\ e^{\frac{E0}{T_{ref}-10}\ -\ \frac{E0}{T_{ref}-10}} \tag{3}\]  
While all living organisms respire and increase \(R_{cco}\), only vegetation contributes to GPP, thus NEE yearly budgets may oscillate between positive and negative values over many years, following changes in the climatic and biological variables. However, data from the longest EC flux measurements tower (Harvard Forest Eddy Covariance Tower) showed that this ecosystem remained as a carbon sink for 15 years [3; 6] without any year being a net emission. Therefore, it is important to determine the causes of this pattern to improve the management over similar ecosystems, which may turn to be profitable commercial projects in the international agreements crediting framework. The Harvard Forest (HF) Environmental Measurement Site tower (42.537755\({}^{\circ}\) N, 72.171478\({}^{\circ}\) W; US-Ha1) is a component of the Harvard Forest Long Term Ecological Research (LTER) site in Petersham, Massachusetts, USA (Harvard Forest Long Term Ecological Research Site, 2019), and a core site in the AmeriFlux network (US-Ha1) and the National Ecological Observatory Network (NEON) (HARV) ([[https://www.neonscience.org/field-sites/harv](https://www.neonscience.org/field-sites/harv)]([https://www.neonscience.org/field-sites/harv](https://www.neonscience.org/field-sites/harv))).