## 2 Materials and Methods
### Experimental Details
#### 2.1.2 Eddy Covariance (EC) Measurements and Processing  
We used paired eddy covariance systems for the 2006-2010 period because it provided EC data measurements for both mowed (P2) and grazed (P4) paddocks. The two EC systems recorded raw data at 20 Hz, and EddyPro@software (LI-COR Inc.) was used for postprocessing and the calculation at 30-minute intervals for fluxes of CO\({}_{2}\), momentum, and sensible and latent heat. Each EC unit included a fast response sonic anemometer (Solent R3-50; Gill Instrument, Lymington, UK) and an open-path CO\({}_{2}\)-H\({}_{2}\)O infrared gas analyzer (LI-7500; LI-Cor Inc., Lincoln, NE, USA) placed at 1.55 m above the ground. In this study, the micrometeorological sign convention is followed, with negative net CO\({}_{2}\) fluxes (NEE) representing the transport from the atmosphere towards the surface (assimilation of CO  
Figure 2: Temporal variation of mean daily air temperature and precipitation over the experimental period (2006–2010) monthly (**a**) and annual (**b**) time scales in Lusignan, France.  
through photosynthesis) and positive ones indicate that the system is a source of carbon (release of CO\({}_{2}\) through respiration).  
Based on previous work using these EC datasets [17; 26], flux measurements, and quality checks were done according to the CarboEurope-IP guidelines [63]. The flux footprint distribution and random uncertainty were analyzed. High-frequency loss corrections [18; 64] were not considered in flux processing process [26]. The Webb-Pearman-Leuning (WPL) correction [65] was applied except for the self-heating of the IRGA because of the sensor orientation [66]. All years of flux measurements for the two EC towers were quality checked and filtered with a custom R program. The quality check led to the rejection of half-hourly flux observations based on nine criteria:  
1. NEE values lower than \(-35\) or higher than \(25\) umol m\({}^{-2}\) s\({}^{-1}\)
2. NEE values higher than \(3.5\) umol m\({}^{-2}\) s\({}^{-1}\) when PAR was above \(400\) umol m\({}^{-2}\) s\({}^{-1}\)
3. NEE values lower than \(-2\) umol m\({}^{-2}\) s\({}^{-1}\) when PAR was below \(25\) umol m\({}^{-2}\) s\({}^{-1}\)
4. Rn \(>300\) W m\({}^{-2}\) and LE \(<0\) W m\({}^{-2}\)
5. If precipitation \(>0\) mm
6. If u* \(<0.1\) m s\({}^{-1}\)
7. \(\lambda\)E values higher than 750 or lower than \(-100\) W m\({}^{-2}\)
8. H values higher than 750 or lower than \(-100\) W m\({}^{-2}\)
9. Atmospheric CO\({}_{2}\) concentration higher than 650 or lower than 320 ppm, respectively.  
Common time series of eddy covariance measurements unavoidably include missing data due to power failures, instrumental malfunctions, or unfavorable micrometeorological conditions that cause the rejection of observations through the filtering process of data. However, complete time series of EC data at the half-hourly timescale are required to be summed to daily, monthly, or annual values [67; 68]. Over the five years of EC measurements used in this study, there were gaps for 39.7% and 40.9% of NEE observations in the dataset for the moved and grazed paddocks, respectively.  
Gaps in 30 minutes NEE were filled and NEE was partitioned between GPP and total ecosystem respiration rate (ER) using the online gap-filling and flux partitioning procedure described by [PERSON] et al. (2005) [69], hereafter referred to the Reichstein algorithm. This gap-filling method uses an improved, running-window look-up table that utilizes both the covariation of NEE with meteorological conditions and temporal autocorrelation of NEE [70]. In the Reichstein algorithm, ER was modeled using the Lloyd and Taylor equation [71] fitted to air temperature. Following this approach, nighttime ER was first regressed against nighttime air temperature, and this relationship was then used to estimate ER for both nighttime and daytime. GPP was determined by subtracting the parameterized ER from NEE.