## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multirow{2}{*}{**Monthly Scale**} & \multirow{2}{*}{**Annual Scale**} & \multicolumn{4}{c}{**Meteorological Seasons**} & \multicolumn{2}{c}{**Management Seasons**} \\ \cline{3-10}  & & & & **Spring** & **Summer** & **Autumn** & **Winter** & **Warm** & **Cold** \\ \hline R\({}_{\text{g}}\) & \(-0.8\) & \(0.3\) & \(0.2\) & \(-0.1\) & \(0.2\) & \(-0.2\) & \(-0.3\) & \(-0.3\) (ns) \\ T\({}_{\text{a}}\) & \(-0.6\) & \(-0.1\) & \(-0.1\) & \(-0.4\) & \(0.6\) (ns) & \(-0.7\) (ns) & \(-0.4\) & \(-0.5\) (ns) \\ VPD & \(-0.6\) & \(-0.2\) & \(0.5\) & \(-0.3\) & \(-0.2\) (ns) & \(-0.6\) (ns) & \(0.0\) & \(-0.5\) (ns) \\ T\({}_{\text{s}}\) & \(-0.5\) & \(-0.6\) & \(-0.8\) & \(0.4\) & \(0.8\) (ns) & \(-0.1\) (ns) & \(0.0\) & \(-0.1\) (ns) \\ SWC & \(0.2\) & \(0.2\) & \(0.4\) & \(-0.5\) & \(0.1\) (ns) & \(-0.4\) (ns) & \(-0.3\) & \(-0.3\) (ns) \\ Prec & \(-0.1\) & \(-0.4\) & \(0.1\) & \(0.2\) & \(-0.5\) & \(-0.2\) & \(-0.1\) & \(-0.6\) \\ Pasture height & & - & - & - & - & - & \(-0.6\) & - \\ Total dry mass & & - & - & - & - & - & \(0.4\) & - \\ Weight gain & & - & - & - & - & - & \(-0.3\) & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Pearson’s correlation coefficient (R) between NEE and meteorological, soil, and management variables (Table 1) for different scales in a natural pasture of the Brazilian Pompa biome grazed with cattle from 2015 to 2021 for \(p\)-value \(<\) 0.001 and (ns) for \(p\)-value \(>\) 0.05. \(-141\) g C m\({}^{-2}\) y\({}^{-1}\) over five years (2010-2015) in a permanent pasture in southern Belgium, which was managed with fertilizer application and high stocking rates. [PERSON] et al. [19] investigated the effect of grazing on C fluxes in subtropical Florida (US) grasslands and found that in the grazed area, a sink of \(-136\pm 6\) g C m\({}^{-2}\) y\({}^{-1}\) occurred, while the ungrazed area had an emission of \(83\pm 4\) g C m\({}^{-2}\) y\({}^{-1}\). [PERSON] et al. [53] analyzed an intensively managed, permanent grassland in Switzerland for over 16 years, finding an average NEE of \(-284\pm 115\) g C m\({}^{-2}\)y\({}^{-1}\). By analyzing various ecosystems based on 554 site years of data, [PERSON] et al. [54] revealed that the mean NEE was \(-200\) g C m\({}^{-2}\) y\({}^{-1}\) with a standard deviation of 163 g C m\({}^{-2}\) y\({}^{-1}\). Our findings, with 6 years of uninterrupted data, despite the variations, demonstrated that the absorptions of pasture in the Brazilian Pompa biome might be higher than those observed elsewhere, thereby highlighting the potential of this natural ecosystem as an important CO\({}_{2}\) sink while preserving the original vegetation and producing high-quality meat. [PERSON] et al. [54] identified that grasslands and evergreens (humid conifer forests) are functional types where year-to-year changes in Reco were greater than in GPP. The vulnerability of grassland ecosystems to transition from being carbon sources to sinks could increase due to additional disturbances in carbon fluxes resulting from factors such as climate and environmental changes. Our findings showed a slope of these changes very close to 1, although dGPPdt > dReco/dt (slope = 0.98, Figure 6). The average of the NEE anomalies between the years was positive, being 26.12 g C m\({}^{-2}\) y\({}^{-1}\) (Table 2), indicating that the ecosystem is decreasing its ability to absorb carbon. It appears that climatic anomalies have influenced respiration more than photosynthesis, which has modulated annual NEE variations. Our results are the first obtained in the Brazilian Pompa biome by the EC method and need to be complemented with further studies; nonetheless, our findings indicate that this ecosystem may be susceptible to climate change since we detected a very close relationship between respiration and photosynthesis processes in CO\({}_{2}\) exchange.