# A new experimental platform connecting the rhizosphere priming effect with CO2 fluxes of plant-soil systems

Camille Cros, Gaël Alvarez, Frida Keuper, Sébastien Fontaine

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Camille Cros, Gaël Alvarez, Frida Keuper, Sébastien Fontaine. A new experimental platform connecting the rhizosphere priming effect with CO2 fluxes of plant-soil systems. Soil Biology and Biochemistry, 2019, 130, pp.12-22. 10.1016/j.soilbio.2018.11.022. hal-02628132

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1 A new experimental platform connecting the rhizosphere priming effect with CO₂ fluxes
of plant-soil systems.

3 Camille Crosª, Gaël Alvarezª, Frida Keuperᵇ, Sébastien Fontaineª

4 ªUCA, INRA, VetAgro Sup, UMR Ecosystème Prairial, 5, Chemin de Beaulieu, 63000,
5 Clermont-Ferrand, France

6 ᵇINRA, UR AgroImpact, 180, rue Pierre-Gilles de Gennes, 02000, Barenton-Bugny, France

7 Email addresses: CC camille.cros.sci@gmail.com GA gael.alvarez@inra.fr, FK
8 frida.keuper@inra.fr, SF sebastien fontaine@inra.fr

9 Type of paper: Regular paper,

10 Number of Text pages: 37, Number of tables: 1, Number of figures: 5, Supplementary files: 3

11 *Author for correspondence

12 Camille Cros, UCA, INRA, VetAgro Sup, UMR Ecosystème Prairial, 5, Chemin de Beaulieu,
13 63000, Clermont-Ferrand, France

14 Email: camille.cros.sci@gmail.com or camille.cros@inra.fr

15 Declarations of interest: none

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**Abstract**

Forty years of research on the rhizosphere priming effect (RPE) has demonstrated the potentially large increase (up to a factor 3) of soil organic matter mineralization induced by plant roots, but failed to directly quantify its contribution to the carbon (C) balance. Combining continuous CO₂ flux measurements with RPE measurements has thus far been technically challenging. Here, we present an experimental platform of 40 mesocosms (volume = 88L; surface = 0.049m²), including a ¹³C-labeled CO₂ air-production system with a maximum capacity of 4m³ min⁻¹ and customizable labeling intensity. For this study, ¹³C depleted fossil C was used as source of labeled CO₂ and the experiment was run for 250 days.

Continuous net CO₂-exchange measurements allowed us to estimate net ecosystem productivity, gross primary production and ecosystem respiration of the studied plant-soil systems. The RPE was regularly (bi-monthly to monthly) quantified by measuring the accumulation and isotopic composition of CO₂ in dark chambers placed over the mesocosms.

Our results show a good relationship between night plant-soil respiration (from continuous CO₂-exchange measurements) and dark plant-soil respiration (from CO₂ accumulation in dark chambers). This result suggests that our estimates of RPE and plant-soil fluxes based on the different methods are comparable. Preliminary results obtained in spring with grasses cultivated under ambient or elevated CO₂ indicate that the RPE represents 1.22 ± 0.16% of gross primary production and 4.64 ± 1.12% of ecosystem respiration. The RPE estimates may have an uncertainty linked to the possible deviation in delta ¹³C between C sources (soil or plant) and released CO₂ from these sources. We performed a sensitivity analysis on how the variation in intensity of isotopic labeling (difference in delta ¹³C between plant and soil) affects the uncertainty of RPE estimates considering 1‰ delta ¹³C deviation. Estimation of the RPE with an uncertainty lower than 10% of the estimated value requires a labeling intensity higher than 60‰. The developed platform will help to scale up the study of the RPE control on C cycling to the ecosystem level.
59 **Keywords:**
60 Rhizosphere priming effect; plant-soil CO₂ fluxes; ¹³C labeling, experimental platform;
61 priming uncertainty.
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# 1. Introduction

Globally, the soil C pool is about three times larger than the atmospheric carbon pool (Ciais et al., 2013). This means that small changes in soil respiration in response to global warming could have a large effect on atmospheric CO₂ concentration with a positive feedback on climate warming (Kuzyakov, 2006; Lal, 2016). CO₂ efflux from soil includes the rhizosphere priming effect (RPE), a change of soil organic matter (SOM) decomposition due to the presence of living roots supplying soil microorganisms with energy-rich compounds (rhizodeposition, litter) (Liljeroth et al., 1994; Kuzyakov, 2002; Huo et al., 2017). RPE estimates vary between a decrease of -50% to an increase of 380% in soil respiration measured under experimental conditions (Cheng et al., 2014). Hence, at the ecosystem level, the RPE could play an important role in C soil stock fluctuation through C released by SOM mineralization (Fontaine et al., 2007; Heimann and Reichstein, 2008). However, methodological constraints prevent large-scale estimates of the contribution of the RPE to the ecosystem CO₂ balance.

Current experimental studies on the RPE are mostly short-term (days to months) pot-experiments with annual species (Cheng et al., 2003; Dijkstra et al., 2006; Pausch et al., 2016; Shahzad et al., 2015; Zhu et al., 2014). Thus far, the seasonal variations in RPE are most likely underestimated by omitting the low plant-growth seasons such as fall and winter. Moreover, interactions between plants (individuals and species) are not often taken into account (but see: Dijkstra et al., 2010; Pausch et al., 2013). In addition, the plants are often cultivated in short pots (max 40 cm depth) under artificial light and temperature, making extrapolation of measured RPE to ecosystem scale difficult (Shahzad et al., 2012; Zhu and Cheng, 2012; Pausch et al., 2013; Carrillo et al., 2014). Furthermore, the RPE is often coupled with sporadic measurements of night ecosystem respiration ($R_{E_{night}}$) (Shahzad et al., 2012; Moinet et al., 2016) but are less often coupled with photosynthetic fixation (Kuzyakov and Cheng, 2001; Shahzad et al., 2015), while net CO₂ balance has never been measured along
with the RPE. Such measurements linked to CO₂ fluxes are difficult to set up due to
constraints imposed by RPE measurements techniques, especially isotope labeling. In
summary, there is a necessity to develop set ups allowing RPE quantification and its
contribution to ecosystem CO₂ fluxes over longer time scales at higher complexity (multiple
species, individuals) and thus better mimicking natural environmental conditions.

The estimation of the RPE is based on the comparison of SOM-derived CO₂ efflux from
planted and unplanted soil (Dijkstra and Cheng, 2007; Pausch et al., 2016; Shahzad et al.,
2012). However, the CO₂ efflux from planted soil is mixture of two C sources: the plant-
derived CO₂ (root respiration and microbial decomposition of rhizodeposits) and SOM-
derived CO₂ (microbial respiration of SOM decomposition) (Kuzyakov, 2002, 2006). The
only reliable way to separate these two CO₂ sources is the use of different C isotope
techniques in order to trace the origin of carbon (plant or soil) in CO₂ emissions.

The pulse labeling method, which consists of a single short-term exposure of plants to a
labeled ¹³/¹⁴CO₂ atmosphere, is not adequate for RPE measurements. This technique leads to
heterogenous distribution of labeled C in the different plant compartments, and the amount of
labeled C in a plant decreases with time (Keith et al., 1986; Jensen, 1993; Kuzyakov, 2001).
Methods using the difference in isotope composition between C₃ and C₄ plants allow for long
term RPE measurements but are limited by the weak difference in isotope composition (δ¹³C
= -14 ‰) and by the choice of species used (Cheng, 1996; Kuzyakov, 2002). Compared to
pulse labeling or the C₃C₄ methods, continuous plant ¹³C labeling is by far a better method of
labeling to assess the RPE. Continuous plant ¹³C labeling is based on a continuous injection of
labeled CO₂ in a greenhouse allowing a homogeneous distribution of labeled C in plant
compartments and a stable plant isotopic composition over time. Due to the cost of ¹³C-
labeled CO₂, all recent RPE studies with living plants used depleted-¹³CO₂ injection from
fossil fuel combustion (δ¹³Cfossil ≈ -38 ‰; δ¹³Cair ≈ -8 ‰) (Cheng and Dijkstra, 2007; Shahzad
et al., 2012; Pausch et al., 2013; Zhu et al., 2014). These studies differ by two types of methodology. The first method consists of adding fossil ¹³CO₂ in a flow of ambient air (Cheng and Dijkstra, 2007; Pausch et al., 2013; Carrillo et al., 2014). The limitation of which is that the intensity of ¹³C labeling inside the greenhouse is less strong (δ¹³Cgreenhouse ≈ -18‰ to 24‰) and can fluctuate over time due to non-stability of atmospheric ¹³C because of anthropogenic activities (Peterson and Fry, 1987). The second method, generation of CO₂ free-air followed by a ¹³C-labeled CO₂ injection, avoids ambient air pollution and ensures stable ¹³C labeling inside the greenhouse over time (δ¹³Cgreenhouse = -38.55‰) (Shahzad et al., 2012). This method (Shahzad et al., 2012) was used in a greenhouse with relatively little pots (40 cm high, 9.8 cm diameter) and could thus be improved by using larger mesocosms and growth conditions closer to natural growing conditions.

Sampling SOM-derived CO₂ in the presence of a plant requires avoiding plant uptake of CO₂ by photosynthesis. The historical method of sampling SOM-respired CO₂ is to separate aboveground and belowground plant parts, allowing soil respiration measurements but limiting the measurements to species with a big stem most of the time (Cheng et al., 2003; Cheng and Dijkstra, 2007; Pausch et al., 2013; Zhu et al., 2014). Another recent method to sample SOM-respired CO₂, was adapted to herbaceous species by incubation of the whole pot combining above and belowground parts (Shahzad et al., 2012). This method requires dark incubation to stop photosynthesis and to avoid plant absorption of SOM-respired CO₂. Due to the relatively small size and weight of the pots used by Shahzad et al. (2012), this dark incubation could take place outside the greenhouse in a laboratory. If larger mesocosms and more natural environmental conditions are required, the Shahzad et al. (2012, 2015) methodology would need to be adapted to perform direct RPE measurements.

In order to quantify the contribution of the RPE to ecosystem CO₂ fluxes, a method for measuring the ecosystem CO₂ exchange must be developed, taking into account all the
constraints linked to the RPE measurements presented above. The eddy covariance method traditionally used for quantifying ecosystem CO₂ exchange requires a large flat area (Baldocchi et al., 1998; Luyssaert et al., 2009) making plant labeling unfeasible. Another method that could make continuous plant labeling and CO₂ exchange measurements compatible is the quantification of the amount of CO₂ entering and leaving closed mesocosms (Schnyder et al., 2003). However, this method must combine several technologies in order to maintain the quality of plant labeling over time; to adapt the airflow to plant photosynthetic activity; and to limit a greenhouse effect.

The aim of this study is to present an innovative experimental platform, which combines RPE and CO₂ flux measurements. This platform resulted from deep modifications of the method developed by (Shahzad et al., 2012) that allowed plant labelling and RPE measurements. The novelties are: the building of forty individual mesocosms with interchangeable transparent and dark aboveground chambers, a system distributing homogeneously the ¹³C-labelled air to mesocosms, a system of air sampling coupled to a multiplexer connected to differential CO₂ analyzers, and different sensors to control soil and air temperature and moisture. The overall platform was designed in order to face the following technical challenges (each challenge has a specific label, which is used throughout the manuscript):

• Technical challenges specific to the RPE measurements:

o Strong ( > 14 ‰ difference between soil and plant) and stable plant labeling (Shahzad et al., 2012) (C_RPE_1);

o Ecosystem dark incubation for recovery of SOM respired-CO₂ (C_RPE_2);

o The estimation of the RPE requires comparison of soil C mineralization in planted and in unplanted soil. In order to focus on factors that led to the RPE (rhizodepositions and plant litters), the other environmental factors such as soil moisture and temperature have to be similar (C_RPE_3) (Schimel et al., 2007; Chen et al., 2015).
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* Technical challenges specific to plant-soil CO₂ exchange measurements:

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* Detecting the difference in CO₂ concentration between inlet and outlet of each mesocosm while adapting to seasonal variation due to plant/soil activities (Schnyder et al., 2003) (C FLUX 1);

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* Quantification of the airflow through the mesocosms (C FLUX 2).

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* General technical challenge: conditions of light, temperature and soil column similar to natural conditions (GC).

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If the above listed technical requirements are met by our platform, the continuous measurements of CO₂ exchange measurements will permit to quantify net ecosystem productivity (NEP), growth primary production (GPP) and ecosystem respiration (ER) of the studied plant-soil systems. Moreover, regular measurements using dark chambers would allow for quantification of dark respiration and RPE. In order to validate our data, we compared night plant-soil respiration (continuous CO₂-exchange measurements) and dark plant-soil respiration (CO₂ accumulation in dark chamber). If the data provided by the two methods are comparable, we can compare and link estimates of RPE with plant-soil fluxes (NEP, GPP and ER). A sensitivity analysis of RPE to uncertainties of plant and soil isotopic composition at different labeling intensities was done to determine the labeled intensity to use depending on RPE uncertainty chosen.

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## 2. Materials & Methods

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### 2.1 Description of mesocosms and current experiment

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The experimental platform consists of 40 independent mesocosms with a buried belowground part (PVC, 100 cm height, Ø 25 cm) and two aboveground chambers (90 cm height, Ø 25 cm) (Fig. 1, Fig. 2A). A perforated plate covered by a mesh preventing root penetration was placed at 90 cm depth in the belowground part building a tank (10 cm high) collecting leachates (Supplementary Fig. S1). For the transparent aboveground chamber poly-methyl methacrylate (PMMA) was used, securing ambient light conditions in the mesocosms
(General challenge, GC). The transparent chamber was fixed to the belowground pot with clips permitting its removal in order to replace it by a dark chamber (opaque PVC) for RPE and dark respiration measurements (Fig. 2D). Air-tightness was ensured by a rubber seal on the belowground pot.

The current experiment involved ten treatments that do not follow a factorial design (Table S1). These treatments included three different plant species (Trifolium repens, Lolium perenne, Triticum aestivum var. lennox), four plant assemblages and one unplanted soil used to calculate the RPE. Two CO2 atmospheric concentrations and two levels of nitrogen (N) were applied on several assemblages. There were four replicates per treatment.

The soil was a drained Cambisol sampled from an upland grassland in central France (Theix 45° 43' N, 03° 01'E). The five first centimeters corresponding to the root mat were removed and the soil from three distinct layers (5-20cm, 20-50cm, 50-90cm) was sampled. The fresh soil of the tree layers was separately sieved at one cm in order to remove the biggest stones.
Then, the soil was transferred in the mesocosms by reconstituting the natural layering of the sampled soil column (5-20 cm; 20-50 cm; 50-90 cm, respectively) (GC). The soil properties were pH 5-20 cm layer: 6.29 ± 0.14 (mean ± 1 se), 20-50 cm layer: 6.63 ± 0.04, and 50-90 cm layer: 6.68 ± 0.09; soil organic carbon (g kg⁻¹ soil) 5-20 cm layer: 30.28 ± 0.32, 20-50 cm layer: 25.27 ± 0.51, and 50-90 cm layer: 17.62 ± 0.21. Ten treatments with four replicates were installed in September 2016 (Table S1), and for this article, we used the data from November 2016 to July 2017. September and October 2016 were excluded because they corresponded to the establishment of the plant cover. Since the aim of this paper is to present the experimental platform and its performance, only a selection of data and treatments is presented.

## 2.2 System of labeled air production and distribution
The labeling system was based on generation of CO₂-free air and subsequent injection of labeled CO₂ (C_RPE_1) (Fig. 1). The labeled air production system was adapted from Shahzad et al. (2012). Ambient air was injected into the system by a compressor and was distributed to a molecular sieve removing CO₂, H₂O and all other particles. The produced air was stored in a tank at a constant pressure of 6.2 bars thanks to a regulator of pressure. This constant pressure combined to a valve allowed to deliver a constant flow of decarbonized air.

The decarbonized air passed through a humidifier before labeled CO₂ was injected. For economic reasons, ¹³C-depleted CO₂ from fossil fuel origin was used (δ¹³C: -35.23 ± 0.02).

Two successive injection points of CO₂ injection were placed along the air distribution system allowing for a custom atmospheric CO₂ concentration in the mesocosms (Fig. 1). The current experiment included 32 mesocosms under ambient and eight mesocosms under elevated CO₂ concentrations: 400 ± 20 ppm (ambient) and 700 ± 20 ppm (elevated) (Fig. 1).

The quality of the produced air (CO₂ concentration and isotopic ¹³C composition) was continuously controlled by analyzing the residual CO₂ concentration after decarbonation and the CO₂ concentration after injections of labeled air (Fig. 1). Previous studies showed that the isotope composition of such produced CO₂ is stable if the residual CO₂ concentration is maintained below 20 ppm and if the CO₂ concentration after injection is fixed (Shahzad, 2012; Shahzad et al., 2012). The ¹³C isotope composition of the labeled plants was measured on the aboveground material of two species following five biomass harvests (November 2016, December 2016, February 2017, May 2017 and July 2017). The first three harvests were pooled in order to reach a sufficient amount of plant material for analysis. These chosen species, *Lolium perenne*) and *Trifolium repens*, were selected based on their perennial character, thus allowing harvest of material throughout the experiment. Aboveground plant material was ground and analyzed using an elemental analyzer (EA; Carlo Erba, Rodana,
Italy) coupled to an isotope-ratio mass spectrometer (IRMS; Elementar, Langenselbold, Hesse, Germany) in order to determine plant C content and delta ¹³C.

To ensure stable plant labeling and to quantify the airflow through the mesocosms, the air has to be homogeneously distributed in each mesocosm (C_RPE_1, C_FLUX_2). Several air distribution systems were tested (details are available in Sup. Mat. 1.1) and the selected system had an ‘octopus’ shape (Fig. 2A, B, and C). Although the air production and distribution system was designed to ensure a stable airflow across mesocosms over time, the pipes could be obstructed (e.g. by spider webs) disturbing air distribution among mescososms.

Therefore, we checked the airflow crossing all mesoscosms initially and regularly (three times per month on average) by measuring the wind speed at the outlet of each chamber with an anemometer. The airflow rate was calculated by multiplying the wind speed by the pipe cross-section.

The air production was adjusted across seasons to permit the detection of CO₂ exchanges and to limit CO₂ depletion inside the mesocosms. A depletion of 70 ppm of CO₂ induced by plant photosynthesis was fixed as the threshold to adapt the air production. Grass intercropped with legumes under elevated CO₂ was chosen to illustrate the adaptation of airflow to intensity of CO₂ exchange.

## 2.3. Monitoring and management of environmental conditions

Soil moisture and temperature were continuously measured with sensors (two replicates per treatment) (Decagon ECH₂O 5TM ®, Pullman, USA) inserted at 15 cm depth (surface) and 60 cm depth (deep) and connected to a data logger (Campbell CR1000 ®, Utah, USA). Based on these measurements, an irrigation system equipped with ten independent electro-valves (one per treatment) was programmed to maintain soil moisture at about 60% of field capacity in surface of all mesocosms. Soil moisture variation was accepted as long as the soil water potential stayed between field capacity and -8×10⁵ Pa. Therefore, the supply of water was
adapted to each treatment depending on their drying rates in order to allow aerobic microbial activity and comparable moisture conditions between mesocosms (C\_RPE\_3).

Several specific measures were set up in order to limit temperature increase due to a greenhouse effect of the mesocosms (Sup. Mat. 1.2), (C\_RPE\_3, GC). To quantify the potential greenhouse effect, the air temperature inside five randomly selected mesocosms (one per octopus) was recorded at 2-min intervals with sensors (Pt 100 SI 1108, Thermo Est, Maizières-Les-Metz, France). The ambient air temperature, photosynthetic active radiation (PAR) and atmospheric pressure were continuously recorded by the meteorological station '0874-eco-env' located at 100 meters from our experimental platform. To check the potential greenhouse effect of the mesocosms, we calculated the difference of temperature between ambient and mesocosm air.

## 2.4. Continuous CO₂-exchange measurements

Continuous CO₂-exchange was quantified by comparing the CO₂ concentration of the air entering and leaving the mesocosms (C\_FLUX\_1). The air entering the mesocosms was collected at two locations on the air pipes to capture both the ambient and elevated CO₂ concentrations, and was pumped to three analyzers (Licor LI-700 CO₂/H₂O, Lincoln, USA) (Fig. 1). One analyzer was dedicated to the elevated CO₂ treatments and the two others to ambient CO₂ treatments. The air leaving the mesocosms was individually collected and continuously pumped to the multiplexer at a rate of approx. 1 L min⁻¹. The multiplexer sequentially injected the collected air in the dedicated analyzer at 2-min intervals for CO₂ analysis. After a flushing period of 30 secs, the electric signal delivered by the CO₂ analyzers was converted to CO₂ concentration and recorded on a computer (Schnyder et al., 2003; Madsen et al., 2010). For ambient CO₂, each analyzer monitored the CO₂-exchange of 16 mesocosms corresponding to a frequency of measurements of 2 hour⁻¹. For elevated CO₂,
because only eight mesocosms were connected to the analyzer the frequency of measurements was 4.3 hour⁻¹.

Data recorded during platform interventions (maintenance, plant-soil incubations measurements, mesocosms aboveground part removal) were removed from the dataset.

Differential CO₂ concentrations (dCO₂) were corrected with respect to the analyzers drifts between two calibrations. The analyzer drift was considered linear over time.

The net ecosystem productivity (NEP) was calculated as follows. First, dCO₂ (ppm) is transformed in a flux of CO₂ (FₐCO₂, m³ min⁻¹) using the airflow crossing the mesocosm (Fₐir, m³):

$$F_{CO_2}(m^3) = F_{air} \times dCO_2 \qquad (1)$$

This flux of CO₂ expressed in m³ min⁻¹ is converted in mole mn⁻¹ FₐCO₂(mole) using the ideal gas equation:

$$F_{CO_2}(\text{mole}) = (P \times F_{CO_2}(m^3)) / (R \times T) \qquad (2)$$

R is a gas constant (8.314 J·K⁻¹·mol⁻¹) and P (Pa) and T (°K) the pressure and temperature of mesocosm air, respectively. It was assumed that P corresponded to the atmospheric pressure.

Finally, the net ecosystem productivity (NEP, g C min⁻¹ m⁻²) was obtained with the following equation

$$\text{NEP} = F_{\text{CO}_2}(\text{mole}) \times 12 \times (1/0.049) \qquad (3)$$

where 12 is the C molar mass and 0.049 the mesocosm surface in m². Daytime NEP (PAR > 0) and nighttime NEP (PAR = 0) were separated and will from now on be referred to as ‘net photosynthesis’ (gross primary production minus ecosystem respiration) and ‘night respiration’ respectively.

In order to distinguish the NEP into GPP and ER, the method of partitioning proposed by Reichstein et al. (2005) was adapted to address our dataset. This partitioning is based on the regression of nighttime respiration with temperature in order to estimate ER (daytime
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respiration and nighttime respiration) according to Lloyd and Taylor (1994) equations. These
estimates permitted to calculate GPP using NEP = GPP – RE. The sign convention for NEP is
positive for net CO₂ uptake and negative for the release of CO₂ by the plant-soil system.

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**2.5. Rhizosphere priming effect and dark respiration measurements**

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The RPE and dark respiration of the plant-soil system were measured bimonthly or monthly
depending on plant activity, using an adapted version of the incubation method developed by
Shahzad et al. (2012) (C_RPE_2). The evening before each plant-soil incubation, the air
production system was stopped and ambient air was run through the system. The transparent
chambers were replaced the next morning, by dark PVC chambers and left in place for an
incubation of 24 hours (Fig. 2D, Fig. S2). The absence of light stopped the photosynthesis and
prevented re-assimilation of soil-derived CO₂ by plants, thus permitting RPE estimation
(C_RPE_2). Kuzyakov and Cheng (2001) showed that prolonged dark conditions decrease
root activity and soil respiration only when the darkness exceeds two days.

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At the beginning and after 24 hours of incubation, gas samples from each chamber were
pumped and stored in an airtight glass bottle (120 mL) (Fig. S2) in order to quantify the
accumulation of CO₂ corresponding to plant-soil respiration. The CO₂ concentration in gas
sample was determined by gas chromatography (Gas Chromatograph Clarus 480,
PerkinElmer, Waltham, MA, USA). The ¹³C isotopic composition of CO₂ was measured with
a CRDS Analyzer for Isotopic CO₂ (Picarro, Santa Clara, CA, USA) coupled to the small
sample isotope module (SSIM). Preliminary dilutions of gas samples could be made to respect
the Picarro specification for CO₂ range.

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The change in CO₂ concentrations within 24 hours was transformed in quantity of C-CO₂
released by the plant-soil system by using the ideal gas equation and volume, temperature and
pressure of chambers. The total released C-CO₂ corresponds to dark ecosystem respiration

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337 (Rt, g C d⁻¹ m⁻²). Using isotope mass balance equations, the soil-derived CO₂-C (Rs, g C d⁻¹ m⁻²) was separated from plant-derived CO₂-C (Rp, g C d⁻¹ m⁻²):

$$339 \quad R_s + R_p = R_t \qquad (4)$$

$$340 \quad R_s \times A_s^{13} + R_p \times A_p^{13} = R_t \times A_t^{13} \qquad (5)$$

341 where A_s¹³ is the ¹³C abundance of soil-derived carbon, A_p¹³ is the ¹³C abundance of plant 342 C, A_t¹³ is the ¹³C abundance of total CO₂ released from the plant-soil system. We used the ¹³C 343 abundance of aboveground plant biomass for A_p¹³ and the ¹³C abundance of soil organic 344 carbon at the beginning of the experiment for A_s¹³. To this end, plant biomass corresponding 345 to the five harvests (see section 2.2) and initial soil were analyzed with an elemental analyzer 346 (EA; Carlo Erba, Rodana, Italy) coupled to an isotope-ratio mass spectrometer (IRMS; 347 Elementar, Langenselbold, Hesse, Germany), after material drying (60°C for 24h) and 348 grinding.

349 Equations 4 and 5 were solved to calculate Rs as follows:

$$350 \quad R_s = R_t \times (A_t^{13} - A_p^{13}) / (A_s^{13} - A_p^{13}) \qquad (6)$$

351 The rhizosphere priming effect (RPE, g C d⁻¹ m⁻²) was calculated as:

$$352 \quad RPE = R_s - R_{control} \qquad (7)$$

353 where R_control is the CO₂ released by the unplanted control soil (g C d⁻¹ m⁻²).

## 2.6. Uncertainty of rhizosphere priming effect estimates

354 The accuracy of the partitioning of Rt into Rp and Rs depends on the accuracy of isotopic 355 composition estimates of the CO₂ released from soil and plant. These estimates are generally 356 obtained by measuring the isotopic composition of plant material and soil organic matter. 357 However, deviation in isotopic composition between these sources and the measured CO₂ 358 might occur due to 1) isotopic fractionation during respiration and 2) contribution of the 359 different plant- and soil chemical compounds with distinct isotopic composition, to the 360 released CO₂ (Mary et al., 1992; Balesdent et al., 1993; Werth and Kuzyakov, 2010; Zhu and 361
Cheng, 2011). According to these studies, isotopic composition deviation rarely exceeds 1‰
delta ¹³C except in some cases where it can reach 2-3 ‰. In order to understand how such a
deviation might affect the partitioning of Rp and Rs and thereby the calculation of RPE, we
performed a sensitivity analysis. In this analysis, we quantified the effect of an amplitude of
1‰ change in delta ¹³C of either soil-derived or plant-derived CO₂ on the value of the RPE.
Given that the deviation in delta ¹³C can be positive or negative, we tested a deviation of
+0.5‰ and -0.5‰ in delta ¹³C values corresponding to the amplitude of 1‰. Simulations
were done using data from one date of RPE measurements (2017-04-12) and for one
treatment (grass in ambient CO₂). The change in RPE value induced by the isotopic deviation
was calculated as ((RPEₙew – RPEᵢinitial) / RPEᵢinitial) x 100 where RPEᵢinitial and RPEₙew are the
values of RPE before and after the delta ¹³C change (+0.5‰ or -0.5‰). For each C source
/plant or soil), we calculated an RPE uncertainty as the sum of the two RPE changes
consecutive to +0.5‰ and -0.5‰.

The labeling intensity, i.e. the difference in delta ¹³C between plant-C and soil-C, most likely
influences the uncertainty in RPE estimation because the weight of a 0.5‰ deviation in delta
¹³C (noise) is relative to the labeling intensity. Therefore, we performed our analysis of the
sensitivity of the RPE to an amplitude of 1‰ deviations (+0.5‰ and -0.5‰) in delta ¹³C of
soil-derived or plant-derived CO₂ for different fictive labeling intensities: 5‰, 7‰, 14‰,
40‰, 60‰, 80‰ and 100‰. For these simulations, we modified A<sub>s</sub><sup>13</sup> or A<sub>p</sub><sup>13</sup> in order to reach
the targeted labeling intensities. Considering that the labeling intensity does not affect R<sub>t</sub> and
its partitioning in R<sub>s</sub> and R<sub>p</sub>, we calculated a new value of A<sub>t</sub><sup>13</sup> using Eq. 5. With the new
values of A<sub>s</sub><sup>13</sup> , A<sub>p</sub><sup>13</sup> and A<sub>t</sub><sup>13</sup>, we calculated RPE before and after the 0.5‰ delta ¹³C
deviations.

2.7. Linking rhizosphere priming effect with CO₂ fluxes of the plant-soil systems
Different methods, which did not take place at the same time, were used for the plant-soil incubations (RPE, dark respiration) and CO₂ exchange measurements (NEP, GPP and ER).

During plant-soil incubations, CO₂ exchange was not recorded for two days corresponding to the 24-hour dark incubation plus the time of the manipulations. The comparison of RPE with CO₂ fluxes (NEP, GPP and ER) is, however, only valid if the two methods give comparable results. To check this, we compared the dark respiration to the night respiration. For the comparison, we used the night respiration measured before the installation of dark chambers.

The night respiration recorded at each time interval was integrated on the nighttime length and then expressed for 24 hours to be comparable with the dark respiration.

In order to provide a preliminary comparison of RPE to NEP, RE and GPP, two periods with contrasted plant-soil activities were chosen (from 2017-03-12 to 2017-03-14 and from 2017-04-09 to 2017-04-12). NEP, ER and GPP were integrated for 24 hours (g C d⁻¹ m⁻²). Values of RPE were given as absolute values (g C d⁻¹ m⁻²), and in % of unplanted soil respiration, NEP, GPP and ER.

## 2.8. Data analysis

The C flux partitioning (GPP and ER estimates) was implemented by building an algorithm adapted from the R code available at github.com/bgctw/REddyProc dedicated to eddy covariance CO₂ data analysis. The algorithm was mainly modified in order to address the higher time resolution of our measurements than those of the eddy covariance method (half-hourly). Statistical analysis were done using a significance level of *P* < 0.05. A two-way analysis of variance (ANOVA) was used on plant ¹³C isotopic composition to test for an effect of time and treatments. The deviation of soil moisture from the fixed optimum (60% of field capacity) and difference of soil moisture between planted and unplanted treatments were tested using a non-parametric compliance test (Wilcoxon Mann Whitney). The same procedure was applied to soil temperature. The effect of soil temperature and moisture on
411 unplanted soil respiration was investigated using a linear mixed model (R, package nlme,
412 Sup. Mat. 2.1). Comparison of monthly temperatures (Wilcoxon Mann Whitney) between
413 ambient and mesocosm air was made to test for a greenhouse effect. A one-way analysis of
414 variance (ANOVA) was conducted to test significant differences in distributed airflow
415 between the octopuses. The relationships between night respiration (CO2 exchanges) and dark
416 respiration (plant-soil incubation), and between net photosynthesis and PAR were assessed by
417 simple linear regression. The effect of time and treatment on NEP, GPP, RE and their
418 contribution to RPE was analyzed by a two-way analysis of variance (ANOVA). Normality of
419 the residuals and homogeneity of variance were respected for plant isotopic 13C composition,
420 airflow, NEP, GPP, RE and RPE data. For temperature and moisture data, these assumptions
421 were not met and non-parametric Wilcoxon Mann Whitney tests were applied. All data
422 cleaning and calculations were performed with R software (R Core Team, 2013, R version 3.
423 4. 3).

424 3. Results

425 3.1. Environmental conditions

426 Chamber air temperature was significantly different from ambient air temperature (*P < 0.05,
427 Table S2). An overall cooling of -0.67 ± 0.01°C was observed in the chambers compared to
428 ambient air (daytime and nighttime pooled). When the data were separated for daytime and
429 nighttime, we observed a warming of 0.47 ± 0.01°C and a cooling of -2.24 ± 0.01°C,
430 respectively (Table S2).

431 Volumetric soil moisture was not significantly different from the optimum for plant growth
432 (at 28%; 60% of field capacity) in any of the treatments, with a treatment average of 28.05 ±
433 0.90% (*P < 0.05, Table S3). Moreover, soil water potential was within the selected deviation
434 range of the field capacity at -8 × 10⁵ Pa (Fig. S3A). The soil moisture was lower and the soil
435 temperature was higher in unplanted compared to planted treatments (*P < 0.05, Table S3).
The differences were 2.2% for soil moisture and 0.74°C for soil temperature. The difference in soil temperature between unplanted and planted soil was maximum in summer (Fig. S3B).

During incubations of plant-soil systems, differences in soil temperature and moisture remained significant (Data not shown). A statistical analysis of unplanted soil respiration showed that respiration was significantly affected by soil temperature but not soil moisture (Sup. Mat. 2.1).

## 3.2. Plant ¹³C labeling and stability over time

Plant isotopic composition varied from -55.30‰ to -48.63‰ depending on species and was highly different to the isotopic composition of native soil organic carbon (SOC) (at -26.82 ± 0.13‰) (*P* < 0.05, Fig. 3A). Moreover, the isotopic composition of plants did not significantly vary with time in any of the treatments, except for one treatment (grass under elevated CO₂) which showed a small increase in delta ¹³C, from -55.30 ± 0.92‰ to -54.16 ± 0.33‰ (*P* < 0.05, Fig. 3A).

Plant species and CO₂ treatments (ambient *versus* elevated) had a significant effect on plant isotopic composition (*P* < 0.05, Fig. 3A). Specifically, the delta ¹³C of grass was lower than of the legume, and the elevated CO₂ induced an overall decrease of plant delta ¹³C compared to ambient CO₂ (grass C⁺: -54.82 ± 0.22‰ *versus* grass C⁻: -52.63 ± 0.16‰; legumes C⁺: -51.25 ± 0.14‰ *versus* legumes C⁻: -49.99 ± 0.18‰).

## 3.3 Airflow and plant-soil CO₂ exchange

The air distribution into mesocosms did not significantly differ between the five octopuses (air distribution system) at the beginning and throughout the experimentation (Fig. S4). Moreover, the coefficients of variation of airflow distributed by the octopuses are low and not significantly different (3.87% to 5.21%, *P* > 0.05). The airflow was stable over time except if an electric cut happened or a modification was made to adjust airflow to seasonal change of
460

plant photosynthetic activity. The airflow was maintained at about 45 L min⁻¹ mesocosm⁻¹ (1 air renewal min⁻¹; Fig. 4A).

461

462

Continuous CO₂ exchange measurements were able to detect small changes of net ecosystem productivity (NEP) in response to light in planted soils (Fig. S5). NEP showed the classical diurnal cycles with negative values during the night (night respiration) and positive values during the day (Net photosynthesis, Fig. 4B). It is interesting to note that unplanted soil also displayed small but detectable diurnal cycles of NEP.

463

464

465

466

467

**3.4. Estimate of rhizosphere priming effect, gross primary production and ecosystem respiration**

468

469

Plants significantly increased the release of unlabeled CO₂ compared to unplanted soil for the two measurement dates, that is, induced a positive RPE (Table 1). The RPE amounted for 0.09 to 0.41 g C m⁻² d⁻¹, which corresponded to 50% to 149% of unplanted soil respiration depending on treatments and measurement time. The RPE significantly increased from March to April, and the elevated CO₂ had a significant positive effect on RPE only in March (Table 1).

470

471

472

473

474

475

The GPP did not significantly respond to the CO₂ treatment whereas it increased by a factor of 3.5 from March to April (10.25 and 35.4 g C m⁻² d⁻¹, respectively) (Table 1). The ER amounted to 3.5 to 7.1 g C m² d⁻¹ and slightly increased from March to April. The elevated CO₂ had a significant positive effect on ER only in March (Table 1). The GPP was on average between 2.3 and 5.5 times higher than ER in March and April, respectively, indicating that plant-soil systems induced a net fixation of CO₂ (positive NEP value).

476

477

478

479

480

481

Dark respiration (plant-soil incubation) and night respiration (CO₂ exchanges) were strongly correlated (R² = 0.67) (Fig. 5). However, small deviations between results of the two methods were observed for the smallest respiration values. Overall, this correlation indicated that these two methods provide comparable results making a comparison between RPE and plant-soil CO₂ fluxes possible. Overall, the RPE represented 1.22% of GPP and this percentage did not

482

483

484

485
vary significantly among dates and treatments, except for grass under elevated CO₂, which
showed a significantly higher value for one date (1.66%). The contribution of RPE to ER
significantly increased from 2.8% in March to 6.5% in April.

**3.5. Sensitivity analysis of the rhizosphere priming effect**

The sensitivity analysis made on the experimental data showed that the uncertainty of RPE
estimates is higher when the isotopic deviation of delta ¹³C concerned the plant-derived CO₂
rather than the soil-derived CO₂. The -0.5 ‰ and +0.5 ‰ deviations in delta ¹³C of plant-
derived CO₂ respectively led to 13.1% and -13.7% change in RPE value corresponding to a
total uncertainty of 26.8%. In contrast, the -0.5 ‰ and +0.5 ‰ deviations in delta ¹³C of soil-
derived CO₂ respectively led to 3.37% and -3.25% change in RPE value corresponding to a
total uncertainty of 6.6%. (Fig. 3B and Fig. S5). These results also showed that the sign of the
isotopic deviation (+0.5 or -0.5 ‰) affected the direction and magnitude of the RPE value
change.

The different fictive labeling intensities showed that the uncertainty of RPE rapidly decreased
with labeling intensity until 40‰ (Fig. 3B, RPE uncertainty decreased from 141 to 17.4% in
average when labeling intensities increased from 5‰ to 40‰). Beyond this limit, an increase
in labeling intensity only slowly decreased the RPE uncertainty, with in average 0.17%
uncertainty decrease per 1‰ increase in labeling intensity. Estimation of the RPE with an
uncertainty lower than 10% of the estimated value requires a labeling intensity higher than
60‰.

**4. Discussion**

**4.1. The contribution of RPE to plant-soil CO₂ balance can be quantified with the
presented platform**

The close positive correlation found between dark respiration (plant-soil incubation) and night respiration (CO₂ exchanges) suggests that these two independent methods provide reliable and comparable results (Fig. 5). These findings also mean that estimates of RPE and NEP,
GPP, RE measured by these two methods can be linked. Our preliminary results obtained on two-measurement dates show that RPE could represent 1.2% of GPP and between 2.8 and 6.5% of ER. The absolute values of RPE and GPP seem to evolve similarly with time (Table 1) emphasizing the role of photosynthetic activity and associated rhizodepositions on the RPE (Kuzyakov and Cheng, 2001; Kuzyakov, 2002; Shahzad et al., 2015; Huo et al., 2017). As far as we are aware, no other experimental study has produced similar ratios of RPE/NEP, RPE/GPP and RPE/ER for comparisons. Nevertheless, our estimates of RPE expressed as a % of unplanted soil respiration can be compared to numerous previously-published studies on RPE. Our estimates varying between 50 and 150% fall in the range of values provided by the meta-analysis of Huo et al. (2017) for 7-8 months after planting. Collectively, our findings show that the technology developed on the present platform could help to scale up the study of the RPE control on C cycling to the ecosystem level.

## 4.2 Uncertainty of RPE estimates due to possible deviation in ¹³C composition between C source and CO₂

Considering an amplitude of 1‰ delta ¹³C deviation between C source and CO₂, we quantified that the uncertainty of our RPE estimates reach up to 26.8% of the mean value when the deviation concerns the plant C. The higher sensitivity of RPE estimates to the deviation of delta ¹³C of plant rather soil C can be explained by the higher contribution of plant C to the total CO₂ emissions (82% of the total in the studied example). These findings indicate that a special attention has to be paid to plant processes leading to isotopic ¹³C deviation (heterogeneous ¹³C distribution in plant compartments, isotope fractionation during plant respiration) when quantifying the RPE. Moreover, we recommend to regularly measure the ¹³C isotopic composition of the plant material and CO₂ released by plants, and to estimate the related RPE uncertainty.
537 Our results also show the strong nonlinear dependence of RPE uncertainty to labeling
538 intensity (the difference in delta ¹³C between plant-C and soil-C). A quantification of the RPE
539 with an uncertainty lower than 10% requires a labeling intensity higher than 60‰ (Fig. 3B).
540 To our knowledge, all published studies show lower labeling intensity with values varying
541 from 14‰ to 30.5‰ (Cheng and Dijkstra, 2007; Shahzad et al., 2012; Pausch et al., 2013;
542 Carrillo et al., 2014; Zhu et al., 2014). Considering the lowest values of labeling intensity
543 (14‰), the uncertainty can be very high exceeding 50% of the estimated RPE (Fig. 3B). In
544 line with our findings, Dijkstra et al. (2010) calculated an RPE uncertainty of up to 40% for a
545 1‰ deviation in delta ¹³C of plant derived-CO₂. Therefore, the results provided in RPE
546 studies must be analyzed with caution, through careful consideration of the possible delta ¹³C
547 deviation between C source and CO₂, a deviation that might be affected by treatments (e.g.
548 plant species, CO₂ concentration). Our findings also underline the importance of developing
549 new technologies to increase the plant labeling and thereby reduce uncertainty in RPE
550 estimates.

551 In this study, the plant isotopic composition varied between species (2.5‰ to 4.4‰; Fig. 3A),
552 which may be due to different ¹³C isotopic fractionation during photosynthesis and
553 contrasting ecophysiological functioning across species (Marshall et al., 2007). This
554 difference between species is likely to complicate the calculation of the delta ¹³C of the plant
555 derived-CO₂ in crop mixtures because the relative contribution of each species to the whole
556 plants respiration (i.e. exudation) is not known. Dijkstra et al. (2010) and Pausch et al. (2013)
557 overcame this problem by calculating plant delta ¹³C based on the root proportion of each
558 species harvested at the end of the experiment, under the assumption that this proportion
559 remained constant during the experiment. Given the difference in plant delta ¹³C observed in
560 this study and in the studies of Dijkstra et al. (2010) and Pausch et al. (2013) (2.4‰ to 14‰
difference among species), the uncertainty of RPE estimates related to the estimation of a

561
562

global plant isotopic composition could be especially important in plant species mixtures.

### 4.3. Air decarbonation coupled to injection of customizable-labeled CO₂: an efficient but costly method of plant labeling

The technology of plant labeling based on the generation of CO₂-free air with molecular sieves and subsequent injection of customizable labeled CO₂ allows stable plant labeling over time (Fig. 3A) (C_RPE_1). Moreover, the platform supplied with CO₂ from fossil fuel origin allowed us to reach a labeling intensity between 21.8 and 28.5‰ depending on plant species and CO₂ level (Fig. 3A). These labeling intensities are in line with previous results using a similar technology (28.4‰ and 30.5‰; Shahzad et al. 2012). Globally, the capacity of air production (4m³min⁻¹) and labeling intensity offered by the here presented technology (air decarbonation + injection of CO₂ from fossil origin) are relatively strong compared to other systems (reaching a labeling intensity between 14‰ and 23‰; Cheng and Dijkstra, 2007; Pausch et al., 2013; Carrillo et al., 2014; Zhu et al., 2014). The main drawback of this technology is the running cost (at ~36,000 € year⁻¹; details in Sup. Mat. 1.3).

### 4.4. Air distribution: a simple and efficient system

The regulator of pressure in the air tank combined to a valve was efficient to deliver a constant airflow to the air distribution system (Fig. 4A). Moreover, the “octopus-shaped” system distributed air homogeneously among mesocosms (Fig. S3). Hence, this relatively simple system of air distribution guaranteed: 1) a limited difference in temperature between mesocosms (C_RPE_3) (Fig. S2B), and 2) accurate estimates of airflow across the mesocosms. This accurate estimation is essential for quantifying CO₂ exchange of the plant-soil systems (C_FLUX_2) (Fig 4A). The system of air distribution avoids using a complex network of sensors for continuous airflow measurement in each mesocosm.

### 4.5. Combining continuous measurements of CO₂ exchange and plant labeling requires a close surveillance of CO₂ concentrations in mesocosms
Continuous measurements of CO₂ exchange based on the difference between CO₂ entering and leaving the mesocosm allowed the detection of daily variation in NEP, as well as calculations of GPP and ER (C FLUX 1) (Fig. 4B). Previous studies have showed diurnal patterns of NEP values in the same order of magnitude (Frank et al., 2006; Pumpanen et al., 2003). Our system of CO₂ exchange measurements was sensitive enough to detect the fixation of a small quantity of CO₂ in unplanted soil linked to the development of algae (Fig. 4B). However, this performance depends on an adjustment of airflow to the plants’ photosynthetic activity and a close surveillance of CO₂ concentration in mesocosms. The adjustment of airflow is constrained by a trade-off between (1) minimizing airflow in order to detect a change in CO₂ concentration due to photosynthesis or respiration (C FLUX 1) and (2) maximizing airflow in order to limit greenhouse effects (GC) and the depletion of CO₂ concentrations in mesocosms. The depletion of CO₂ concentration may alter the quality of plant labeling by modifying the ¹³C isotopic fractionation during photosynthesis (C RPE 1) (Park and Epstein, 1960).

## 4.6. The multiple measures to control the environmental conditions partly met their technical challenges

The multiple measures (listed in the subsection 2.3 Monitoring and management of environmental conditions) set up to limit greenhouse effects in the mesocosms were efficient since we observed a slight overall cooling of 0.67 °C of mesocosm air compared to ambient air. Moreover, the experimental platform allows the cultivation of plants under natural light on a larger soil surface than before (Shahzad et al., 2012; Zhu and Cheng, 2012; Carrillo et al., 2014; Pausch et al., 2016) and with an entirely-reconstituted soil column. Therefore, estimates of RPE and its contribution to plant-soil CO₂ fluxes were obtained in semi-natural environmental conditions (GC).

Several measures were set up to limit the difference in soil temperature and moisture between planted and unplanted treatments. The soil moisture was maintained within the fixed range of
soil water potential between field capacity and -8x10⁵ Pa and in average close to the optimal conditions for aerobic microbial activity. Small differences in soil moisture were observed between planted and unplanted treatments, but soil moisture was not a limiting factor for soil respiration (Sup. Mat. 2.1). In contrast, the warming observed in unplanted compared to planted treatments (+0.74 °C on average) could increase unplanted soil respiration since temperature had a significant positive effect on soil respiration (Sup. Mat. 2.1). Given that RPE is calculated as the difference in SOC-derived respiration between planted and unplanted soil (Eq. 7), the RPE could be underestimated. In order to quantify this underestimation, we re-analyzed the data corresponding to the measurement of April 2017 (Table 1). Using an Arrhenius equation (Sup. Mat. 2.2), the unplanted soil respiration was corrected to fit with the temperature of planted treatments. This temperature-normalized respiration was then used to calculate a new RPE. Results showed that the unplanted soil warming increased soil respiration by 16.2 - 21.2% and decreased RPE estimates by 11.7 - 14.6% depending on treatments (Sup. Mat. 2.3). Consequently, we recommend for a precise estimation of RPE to correct the respiration data with soil temperature.

## 4.7. Further improvements

The coupling of RPE measurements with CO₂ flux measurements was made with a time lag of two days. During these two days, the GPP might have changed due to meteorological fluctuations modifying root exudations, plant-soil respiration and RPE. Indeed, GPP responds to light intensity in seconds and the reduction of plant-soil respiration and RPE following a reduction of photosynthesis can occur within 24 hours (Shahzad et al., 2012). Overall, the effect of the time lag on our results is likely limited since the dark and night respirations measured with the two interval days were well correlated (Fig. 5). Nevertheless, a simultaneous measurement of RPE and CO₂ fluxes can lead to more accurate estimates. Moreover, the measurement of RPE was time-consuming due to replacement of chambers,
638 gas sampling and analysis. This time-constraint strongly limited the frequency of the RPE
639 measurements (bi-monthly to monthly). All these limits could be levered if the RPE is daily
640 measured by using 1) the nighttime to stop photosynthesis and 2) an isotopic analyzer coupled
641 to the differential CO₂ concentration analyzers in order to analyze the CO₂ concentration and
642 delta ¹³C of air across mesocosms. To improve RPE estimates, the unplanted soil should be
643 covered to protect soil from sunbeams preventing soil warming and algae development.

644 Despite the technology set up to ensure a high labeling intensity, the uncertainty of RPE
645 estimates remains elevated (26.8%) due to the source of ¹³CO₂ used (fossil fuel origin).

646 Depending on studied factors and expected effect on RPE, the accuracy of the RPE estimates
647 must be improved by reaching higher plant labeling intensities. The combustion of methane
648 might be an interesting source of ¹³C labeled CO₂ since there are numerous sources of
649 methane with delta ¹³C varying between -55 to -70‰ corresponding to a labeling intensity of
650 47 to 62‰ (Sakata et al., 1997; Lopez et al., 2017). Finally, future studies could use Fig. 3B
651 to perform an *a priori* power analysis on planned experiments to determine the intensity of
652 isotopic labeling necessary to accurately address specific research questions.

653
654 **5. Acknowledgment**

655 The study has been funded by the l'Agence Nationale de la Recherche (ANR, Projet Basil)

656 and Fondation pour la Recherche sur la Biodiversité (FRB, Call BioDiversa, Basil). We thank

657 R. Falcimagne, D. Colosse, A. Salcedo, V. Guillot, E. Viallard, P. Pichon and S. Revaillot for
658 technical help with the setup of the experimental platform.

659
660 **Author Contribution**

661 The experiment was designed by CC, SF, GA and FK. The setup of the experimental platform
662 was done by CC, SF, GA and RF. CC and GA performed analysis. CC, SF, GA and FK
663 worked together on the paper's skeleton. CC and SF wrote the initial draft, all the authors
664 contributed to improve the manuscript.

665
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821
<table><caption>**Table 1.** Quantification of net ecosystem productivity (NEP), gross primary production (GPP), ecosystem respiration (ER), rhizosphere priming effect (RPE) and the relative contribution of RPE to NEP, GPP and ER during two measurement periods across two treatments: ryegrass under ambient and elevated CO₂, n=4. SOM stands for soil organic matter. Mean values are coupled with standard error (SE). Letters indicate significant differences between treatments and periods (two-way ANOVA following by Tukey's posthoc test, *P* < 0.05).</caption><thead><tr><th rowspan="3">Periods</th><th rowspan="3">Treatments</th><th colspan="6">CO₂ fluxes of the plant-soil system</th><th colspan="6">RPE relative to CO₂ fluxes</th></tr><tr><th colspan="2">NEP</th><th colspan="2">GPP</th><th colspan="2">ER</th><th colspan="2">RPE</th><th colspan="2">% of unplanted soil</th><th colspan="2">% of NEP</th><th colspan="2">% of GPP</th><th colspan="2">% of ER</th></tr><tr><th>mean</th><th>SE</th><th>mean</th><th>SE</th><th>mean</th><th>SE</th><th>mean</th><th>SE</th><th>mean</th><th>SE</th><th>mean</th><th>SE</th><th>mean</th><th>SE</th><th>mean</th><th>SE</th></tr></thead><tbody><tr><td rowspan="2">2017-03-15</td><td>GN-C-</td><td><b>b</b></td><td>6.9</td><td>0.3</td><td><b>b</b></td><td>10.4</td><td>0.44</td><td><b>c</b></td><td>3.5</td><td>0.2</td><td><b>c</b></td><td>0.09</td><td>0.01</td><td><b>c</b></td><td>50.25</td><td>5.79</td><td><b>b</b></td><td>1.38</td><td>0.22</td><td><b>b</b></td><td>0.92</td><td>0.14</td><td><b>b</b></td><td>2.72</td><td>0.44</td></tr><tr><td>GN-C+</td><td><b>c</b></td><td>4.2</td><td>0.6</td><td><b>b</b></td><td>10.1</td><td>1.00</td><td><b>b</b></td><td>5.9</td><td>0.5</td><td><b>b</b></td><td>0.16</td><td>0.01</td><td><b>b</b></td><td>87.75</td><td>7.52</td><td><b>a</b></td><td>4.15</td><td>0.45</td><td><b>a</b></td><td>1.66</td><td>0.10</td><td><b>b</b></td><td>2.82</td><td>0.13</td></tr><tr><td rowspan="2">2017-04-12</td><td>GN-C-</td><td><b>a</b></td><td>29.2</td><td>1.5</td><td><b>a</b></td><td>35.3</td><td>1.26</td><td><b>a</b></td><td>6.1</td><td>0.8</td><td><b>a</b></td><td>0.40</td><td>0.05</td><td><b>a</b></td><td>147.3</td><td>18.64</td><td><b>b</b></td><td>1.37</td><td>0.16</td><td><b>b</b></td><td>1.14</td><td>0.13</td><td><b>a</b></td><td>7.24</td><td>1.78</td></tr><tr><td>GN-C+</td><td><b>a</b></td><td>28.4</td><td>0.3</td><td><b>a</b></td><td>35.5</td><td>0.53</td><td><b>a</b></td><td>7.1</td><td>0.3</td><td><b>a</b></td><td>0.41</td><td>0.03</td><td><b>a</b></td><td>148.8</td><td>11.91</td><td><b>b</b></td><td>1.43</td><td>0.12</td><td><b>b</b></td><td>1.15</td><td>0.10</td><td><b>a</b></td><td>5.76</td><td>0.65</td></tr></tbody></table>
827 **Figure Captions**

828 **Fig. 1.** Schematic representation of the experimental platform: the production of labeled air
829 consisted of a compressor (Worthington Creyssensac, Rollair 50), filters for oil and water
830 (F1) and for oil, water and particles (F2), two molecular sieves (MS1, 2; Adsorption dryers
831 Zander ecodry KEN-3100MSTE), an air tank, a valve regulating the airflow, an humidifier, a
832 tank containing 2.4 tons of CO₂ of fossil fuel origin and two points of CO₂ injection in the
833 airflow. The first injection point allowed increasing CO₂ concentration from about 0 to 400
834 ppm. The second injection point placed on the tube supplying the octopus distributing the air
835 to mesocosms with treatments “elevated CO₂” increased CO₂ concentration from 400 to 700
836 ppm. The CO₂-exchange measurements included a multiplexer composed of primary and
837 secondary pumps and multiple electrovalves for gas sampling, three differential gas-analyzers
838 (Licor LI-700 CO₂/H₂O Analyzer) and a PC for data acquisition. Air is distributed over five
839 independent octopus, each of them connected to eight mesocosms. For the present
840 experiment, the CO₂ injections were adjusted to set up CO₂ concentrations of 400 ± 20 ppm in
841 32 mesocosms (Ambient CO₂ treatment) and 700 ± 20 ppm in 8 mesocosms (Elevated CO₂
842 treatment).

843

844 **Fig. 2.** Pictures of the experimental platform: (A) an overview of the entire platform; (B) a
845 focus on an octopus built with PVC rigid and flexible tubes and joined to the transparent
846 chamber of the mesocosm by a screw union ; (C) a focus on a mesocosm showing the air flow
847 circulation system and the gas sampling for plant-soil CO2 exchange analysis ; (D) an
848 overview of the platform after transparent chambers were replaced by dark chambers for the
849 RPE measurements.

850
**Fig. 3.** (A) Aboveground plant ¹³C signature over time (February 2017-July 2017) of ryegrass and white clover grown under elevated or ambient CO₂ and initial ¹³C signature of soil organic carbon, n= 4. The February time point combines the aboveground plant material from three cuts due to low dry matter harvested (November 2016, December 2016, and February 2017). Letters indicate significant differences between treatments at each time point (two-way ANOVA following by Tukey's *posthoc* test, *P* < 0.05); (B) Uncertainty of rhizosphere priming effect (RPE) estimation related to 1‰ change (+0.5‰ and -0.5‰) in delta ¹³C of plant-derived CO₂ at multiple labeling intensities. The change of RPE value induced by the isotopic deviation was calculated as ((RPEₘₙ - RPEᵢₘₙ) / RPEᵢₘₙ) where RPEᵢₘₙ and RPEₘₙ are the values of RPE before and after the delta ¹³C change (+0.5‰ or -0.5‰). The uncertainty of RPE estimate corresponded to the addition of two RPE changes consecutive to +0.5‰ and -0.5‰. The labelling intensity is the difference in delta ¹³C between labelled plant material and soil organic C. Uncertainty analysis was made on results from the treatment "grass monoculture (ryegrass) under ambient CO₂ concentration" measured the 12th April 2017. The asterisk represents the labeling intensity obtained in this experiment (26 ‰). The uncertainties of RPE estimation for the labeling intensities 5‰, 7‰, 14‰, 40‰, 60‰, 80‰ and 100‰ were obtained by modeling (see Methods).

**Fig. 4.** (A) Airflow and net photosynthesis over time (November 2016-July 2017) for one planted treatments: intercrop of ryegrass and white clover under elevated CO₂, n=4. Black arrows depict partial removal of the aboveground plant material and stars indicate an adjustment of the airflow; (B) Net ecosystem productivity (NEP) over two days in February 2017 (2017-02-18 and 2017-02-19) for three treatments: ryegrass under ambient and elevated CO₂, and unplanted soil. Grey shading indicates nighttime (photosynthetic active radiation (PAR = 0)) and white indicates daytime (PAR > 0).
**Fig. 5.** Relationship between night respiration estimated by continuous CO₂ exchange measurements and dark respiration estimated by regular ecosystem incubation for the study period (November 2016 - July 2017), N=320. The dataset corresponds to measurements made during six periods of 36 hours on the 40 mesocosms. The night and dark respirations were made with a time lag of two days. Dashed line represents the simple linear regression between night and dark respiration. Black line represents the regression line y=x.
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y = 0.89x + 2.07
p-value = 2.2*10-16
R² = 0.67