# Drivers and CO 2 flux budgets in a Sahelian Faidherbia albida agro-silvo-pastoral parkland: Insights from continuous high-frequency soil chamber measurements and Eddy Covariance

Seydina Mohamad Ba, Olivier Roupsard, Lydie Chapuis-Lardy, Frédéric Bouvery, Yélognissè Agbohessou, Maxime Duthoit, Aleksander Wieckowski, Torbern Tagesson, Mohamed Habibou Assouma, Espoir Koudjo Gaglo, et al.

► To cite this version:

Seydina Mohamad Ba, Olivier Roupsard, Lydie Chapuis-Lardy, Frédéric Bouvery, Yélognissè Agbohessou, et al. Drivers and CO 2 flux budgets in a Sahelian Faidherbia albida agro-silvo-pastoral parkland: Insights from continuous high-frequency soil chamber measurements and Eddy Covariance. 2025. hal-05387486

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1 Drivers and CO₂ flux budgets in a Sahelian *Faidherbia albida* agro-silvo-pastoral parkland:

2 Insights from continuous high-frequency soil chamber measurements and Eddy

3 Covariance.

4 Seydina Mohamad Ba<sup>a d</sup>, Olivier Roupsard<sup>b c d</sup>, Lydie Chapuis-Lardy<sup>c f</sup>, Frédéric Bouvery<sup>g</sup>,

5 Yélognissè Agbohessou<sup>c e</sup>, Maxime Duthoit<sup>c e</sup>, Aleksander Wieckowski<sup>h</sup>, Torbern Tagesson<sup>h</sup>,

6 Mohamed Habibou Assouma<sup>i j k</sup>, Espoir K. Gaglo<sup>a d</sup>, Claire Delon<sup>l</sup>, Bienvenu Sambou<sup>a</sup>, Dominique

7 Serça<sup>l</sup>

8 <sup>a</sup>Faculté des Sciences et Techniques (FST), Institut des Sciences de l'Environnement (ISE), Université

9 Cheikh Anta Diop (UCAD) de Dakar, 5005, Dakar-Fann, Sénégal

10 <sup>b</sup>CIRAD, UMR Eco&Sols, Dakar, Sénégal

11 <sup>c</sup>Eco&Sols, Univ Montpellier, CIRAD, INRAE, Institut Agro, IRD, Montpellier, France

12 <sup>d</sup>LMI IESOL, Centre IRD-ISRA de Bel Air, Route des hydrocarbures, 18524, Dakar, Sénégal

13 <sup>e</sup>CIRAD, UMR Eco&Sols, Université de Montpellier, Cirad, INRAE, IRD, Institut Agro Montpellier, 2 place

14 Viala, Montpellier, France

15 <sup>f</sup>IRD, UMR Eco&Sols, Université de Montpellier, Cirad, INRAE, IRD, Institut Agro Montpellier, 2 place Viala,

16 Montpellier, France

17 <sup>g</sup>INRAE, 147 rue de l'Université, 75338 Paris, France

18 <sup>h</sup>Department of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12, S-223 62 Lund,

19 Sweden

20 <sup>i</sup>CIRAD, UMR SELMET, dP ASAP, Bobo Dioulasso, Burkina Faso

21 <sup>j</sup>SELMET, CIRAD, INRAE, Univ Montpellier, Institut SupAgro, Montpellier, France

22 <sup>k</sup>Centre International de Recherche-Développement sur l'Élevage en zone Subhumide (CIRDES), N°559, rue

23 5-31 Avenue du Gouverneur Louveau, Bobo-Dioulasso, Burkina Faso

24 <sup>l</sup>Laboratoire d'Aérologie, Université de Toulouse, CNRS, IRD, 14 Avenue Edouard Belin, 31400 Toulouse,

25 France

26 Corresponding authors:

27 Seydina Mohamad Ba: seydina.ba@ird.fr

28 Olivier Roupsard: olivier.roupsard@cirad.fr
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29 Highlights:

30
31 Long-term high frequency CO₂ flux measurements using automated static chambers in a Sahelian *F. albida* parkland.

32
33 Empirical gap-filling and flux partitioning methods validated against Eddy Covariance GPP.

34
35 Fluxes peaked during the rainy season in both FS and Sh, driven mainly by soil moisture and leaf area.

36
37 *F. albida* trees enhance CO₂ fluxes under canopies ("fertile island" effect) and account for ~50% of annual ecosystem GPP.
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38 **ABSTRACT:**

39 Agroforestry systems — combining trees with crops and/or livestock — are increasingly 40 promoted as sustainable and climate-resilient land-use strategies. Despite their widespread 41 presence in the Sahel, experimental data on their potential as carbon sinks are scarce. This study 42 presents a full-year, high-frequency dataset of CO₂ fluxes in a Sahelian agro-silvo-pastoral 43 parkland dominated by *F. albida*, located in Senegal's groundnut basin. CO₂ fluxes were 44 continuously measured using automated static chambers, allowing the quantification of soil and 45 crop respiration (Rch), gross primary production (GPPch), and net carbon exchange (FCO₂ch) 46 under both full sun and shaded (under tree canopies) environments.

47 Seasonal patterns of CO₂ fluxes were similar in both environments, with peaks during the rainy 48 season. Rch and GPPch were significantly higher under tree canopies, indicating a 'fertile island' 49 effect. CO₂ flux variability was primarily driven by soil moisture and leaf area index. Chamber- 50 based GPP estimates closely matched those from Eddy Covariance measurements. On an annual 51 scale, *F. albida* trees contributed approximately 50% of total ecosystem GPP, with a carbon use 52 efficiency of 0.48. Net annual CO₂ exchange was estimated at -1.4 ± 0.02 and -1.8 ± 0.01 Mg C- 53 CO₂ ha⁻¹ using chamber and Eddy Covariance methods, respectively. These findings underscore 54 the role of *F. albida*-based agroforestry systems as effective carbon sinks in Sahelian landscapes, 55 supporting their potential contribution to climate change mitigation.

56 **Keywords:** Sahelian agro-silvo-pastoral systems, CO₂ fluxes, automated static chambers, Eddy 57 Covariance, 'fertile island effect' of trees, carbon budgets.
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## 1. Introduction

Plant photosynthesis and respiration—both autotrophic (plant) and heterotrophic (microbial)— are fundamental processes driving carbon dioxide ($CO_2$) fluxes in terrestrial ecosystems (Lambers et al., 2008; Raich et al., 2014; Reichle, 2020). Accurate quantification of these processes is critical for assessing ecosystem carbon (C) sink potential (Baldocchi, 2020), particularly for informing climate-smart land management strategies.

To capture these processes at the ecosystem scale, the Eddy Covariance (EC) technique has emerged as a transformative method, enabling continuous and high-frequency $CO_2$ flux measurements (Baldocchi, 2003, 2008). Extensive EC networks in Europe (Stojanović et al., 2024), Asia (Yu et al., 2011), and the Americas (Chu et al., 2021) have significantly advanced our understanding of the global C cycle. In contrast, sub-Saharan Africa remains critically underrepresented (Bombelli et al., 2009; Houghton & Hackler, 2006; Williams et al., 2007). Although some studies have used EC (Ardö et al., 2008; Brümmer et al., 2008; Merbold et al., 2009; Tagesson et al., 2016), static chambers (Assouma et al., 2017; Owusu et al., 2024; Rosenstock et al., 2016; Wachiye et al., 2020), or modeling approaches (Agbohessou et al., 2023, 2024; Delon et al., 2019; Rahimi et al., 2021), they remain sparse and methodologically heterogeneous, limiting comparability and regional C budget integration.

Among these underrepresented landscapes, agroforestry systems in the Sahel— particularly agro-silvo-pastoral systems (ASPS) that combine trees, crops, and livestock— are increasingly promoted for sustainable land management and climate resilience (Cardinael et al., 2021; Gupta et al., 2023; Mbow et al., 2014; Stetter & Sauer, 2024). However, the structural and functional heterogeneity of these systems poses significant challenges for accurately quantifying and upscaling C fluxes. *Faidherbia albida*, a keystone agroforestry tree species in these ASPS (Leroux et al., 2022; Lu et al., 2022), is of particular interest due to its reverse phenology, capacity to enhance soil fertility and crop yields (Bayala et al., 2020; Roupsard et al., 2020; Sileshi et al., 2016; 2020). Yet, its functional role in modulating both the magnitude and seasonal dynamics of $CO_2$ fluxes remains poorly understood.

Addressing this knowledge gap requires integrated approaches capable of capturing both aggregate and component-specific $CO_2$ fluxes. While EC remains the gold standard method for $CO_2$ flux measurements at the landscape scale (Baldocchi, 2003), it captures net ecosystem exchange (NEE) as an aggregate signal, without separating the contributions from individual compartments such as soil, crops, and trees. This limits its utility for disentangling processes and attributing sources in heterogeneous systems like ASPS. Automatic static chambers provide a valuable complement to EC, as they enable continuous, high-frequency measurements at finer scales and at the level of specific ecosystem components. This approach facilitates component-specific quantification of $CO_2$ fluxes, particularly from soil and crop compartments (Luo & Zhou, 2006;
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94 Denmead, 2008; Zaman et al., 2021). When combined with EC, this dual-method approach
95 strengthens source attribution and improves the upscaling of fluxes across complex agroforestry
96 landscapes.

97 This study presents one of the first integrated quantification of CO₂ fluxes in a Sahelian ASPS
98 dominated by *F. albida*, combining EC and automatic static chambers.

99 Specifically, we aim to (1) conduct year-round, high-frequency *in situ* CO₂ flux measurements
100 from soil and crops using automated static chambers; (2) partition the net CO₂ fluxes (FCO₂ch)
101 into respiration (Rch) and photosynthesis (GPPch); (3) investigate the environmental drivers of
102 fluxes and the spatial variability linked to tree presence; and (4) compare chamber-based flux
103 estimates with ecosystem-scale measurements derived from the EC method.
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## 2. Materials and methods

### 2.1. Site description

The study was conducted in the agroforestry parkland of Sob village (Niakhar municipality, Fatick region), located in the groundnut basin of Senegal, within the Sahelo-Sudanian climatic zone of West Africa (Fig. 1). The climate is characterized by a long dry season (8–9 months) with high temperatures and strong diurnal variations, and a short rainy season from late June to mid-October (Delaunay et al., 2018).

Soils are locally known as "Dior" and classified as Arenosols (IUSS Working Group WRB, 2022). The topsoil has low organic matter (<1%) and phosphorus (<3 mg kg⁻¹), a sandy texture (>85% sand), and an acidic pH (Malou et al., 2021; Siegwart et al., 2022). Rainfed agriculture predominates. The main cropping system includes pearl millet (*Pennisetum glaucum* L.) and groundnut (*Arachis hypogaea* L.) in biennial rotation, with occasional intercropping of cowpea (*Vigna unguiculata* L.).

The site hosts the 'Faidherbia Flux' station (14°29′44.916"N; 16°27′12.851"W; FLUXNET ID: SN-Nkr), a long-term research platform for monitoring ecosystem services in agroforestry systems. It is dominated by *F. albida*, a nitrogen-fixing, reverse-phenology tree with deep roots accessing groundwater (Roupsard et al., 1999). The tree density is ~13 trees ha⁻¹, with canopies covering ~10% of the soil surface (Roupsard et al., 2020). The EC tower is installed at 20 m height, approximately 12.5 m above the canopy. The study field is a typical 'bush field', characterized by low soil fertility, no mineral fertilization, and off-site export of crop residues and manure (Malou et al., 2021).
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<figure><img src="image_11.png" /><figcaption>Fig. 1: Study area.

(a) geographical location of Sob, Groundnut basin, Senegal (Map data © Google Earth, 2025), (b) overview (image from the Eddy Covariance tower located in the same bush-field) of the *Faidherbia albida* parkland during the rainy season, depicting groundnut crops with bare soil in the inter-row, *F. albida* trees (defoliated during the rainy season, average height = 13m) and location of the chambers under the Shade of trees (horizontal black arrows; N=4) and in Full sun (vertical white arrows; N=4); The shelter (red arrow) with solar panels is to fit the analyser, automation and batteries (c) automatic chamber enclosing a groundnut plant (during the rainy season) or bare soil (during the dry season), (d) Eddy Covariance (EC) tower (measurement height = 20 m) during the dry season.</figcaption></figure>
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## 2.2. Experimental setup

### 2.2.1. CO₂ flux measurements in automatic chambers

Continuous net CO₂ fluxes (FCO₂ch) from soil and groundnut plants were measured over a full phenological year (June 17, 2021 – June 17, 2022) using eight automated static chambers (50×50×50 cm), each enclosing one groundnut plant. Four chambers were installed in full sun (FS), at least 20 m from trees, and four under *F. albida* canopy shade (Sh). The chambers were transparent, custom-built (Duthoit et al., 2020), and installed on metal bases embedded 10 cm into the soil one month prior to measurements.

During the rainy season (June–November), groundnut coexisted briefly with spontaneous weeds until weeding (mid-July), after which chambers contained only groundnut. Post-harvest (early November), chambers remained bare while surrounding plots experienced weed regrowth.

CO₂ concentrations were measured at 1 Hz using a Picarro G2508 gas analyser (Picarro Inc., Santa Clara, CA, USA) (Fleck et al., 2013; Reum et al., 2019; Valujeva et al., 2022). A fully automated system was built for sequential half-hour flux measurements (alternating FS and Sh). Measurement duration was 15 min per chamber in the dry season, reduced to 5 min during the rainy season to limit condensation effects.

### 2.2.2. CO₂ flux measurements by Eddy Covariance

The EC system (Li-COR SMARTFLUX®, including a Gill MasterPro 3D sonic anemometer and a LI-7500 RS open path CO₂ and H₂O gas analyser) was mounted at a height of 20 m on a 30m mast, above *F. albida*. It continuously monitored net CO₂ exchange from the ecosystem. Raw data were collected at 20 Hz frequency and post-processed from binary files using the advanced mode of the EddyPro® v7.0, with standard corrections and procedures: sonic tilt correction (double rotation), block averaging, covariance maximisation for time lag, and WPL correction (Webb et al., 1980). Quality control followed Foken et al. (2004) and Vickers & Mahrt (1997); random uncertainty was estimated per Finkelstein & Sims (2001). Spectral corrections were applied according to Moncrieff et al. (1997, 2004). Footprints were computed according to Kormann and Meixner (2001), using the FREddyPro R package (Xenakis, 2016), indicated a ~1 ha source area covering the entire field. Gap-filling and flux partitioning were conducted using ReddyProc (Wutzler et al., 2018), applying the daytime partitioning approach of Lasslop et al. (2010).

### 2.2.3. Ancillary measurements

Environmental and vegetation variables were monitored continuously throughout the study. Global radiation (Rg) was estimated from photosynthetically active radiation (PAR) using a Skye sensor (averaged over 30-min intervals). NDVI of crops under full sun was recorded semi-hourly
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167 by a calibrated downward-facing sensor installed at 20 m height (Pontailler et al., 2003),
168 processed following Soudani et al. (2012), and used to estimate LAI time series for groundnut,
169 weeds, and cowpea based on end-of-season field LAI measurements in six 15 m² plots (as in
170 Roupsard et al., 2020).

171 Rainfall was recorded by an automatic weather station (CR1000 with TE525MM rain gauge,
172 Campbell Scientific), and soil volumetric water content (VWC) and temperature (T<sub>soil</sub>, at 6 cm
173 depth) were monitored using TOMST® TMS-4 sensors, benchmarked prior to field deployment
174 inside and outside the chambers (Wild et al., 2019). Air temperature (T<sub>air</sub>) was recorded inside
175 each chamber at 15 cm above ground, all at 5-min intervals. These measurements contribute to
176 the SoilTemp global database (Lembrechts et al., 2020, 2022).

177 Groundnut development was tracked weekly by counting leaves in each chamber. Total
178 groundnut LAI (LAIch) was then derived from average single-leaf area and chamber surface.

179 A detailed description of the data used in this study is provided in Supplement S1 (Table S1.1).

2.3. *Data processing*

181 **2.3.1. Flux calculation**

182 Net CO₂ fluxes (FCO₂ch, in µmol CO₂ m⁻² s⁻¹) from the chambers were calculated from the linear
183 change in CO₂ concentration over time (ΔC/Δt) using the Eq.1.

$$
184 \quad \mathrm{FCO_2ch} = \left(\frac{P}{RT_k}\right) \left(\frac{V}{A}\right) \left(\frac{\Delta C}{\Delta t}\right) \quad (\mathrm{Eq.~1})
$$

185 where P is atmospheric pressure (101 325 N m⁻²), R is the ideal gas constant (8.31 N m mol⁻¹ K⁻¹),
186 Tₖ is air temperature inside the chamber in Kelvin, V (0.125 m³) is the total system volume
187 (chamber, tubing, analyser cavity, pump, and water trap), and A (0.25 m²) is the chamber
188 footprint. The slope ΔC/Δt was obtained via linear regression (Duthoit et al., 2020).

189 Mean FCO₂ch values were computed separately for the four replicate chambers in full sun (FS)
190 and under *F. albida* shade (Sh). By convention, negative values indicate net CO₂ uptake
191 (photosynthesis), and positive values indicate net CO₂ release (respiration).

192 **2.3.2. Quality control of chamber-based CO₂ flux measurements**

193 The quality of chamber-based CO₂ flux measurements was assessed using the coefficient of
determination (R² ≥ 0.8) of the linear increase in CO₂ concentration during chamber closure. The
194 minimum detectable flux (MDF) was then calculated following Nickerson (2016) (Eq.2). The MDF
195 defines the flux detection threshold, below which data are considered unreliable due to
196 instrument sensitivity and sampling constraints (Zaman et al., 2021). In this study, the MDF was
197 ±0.0004 µmol CO₂ m⁻² s⁻¹.
198

199
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$$200 \quad \mathbf{MDF} = \left( \frac{A_a}{tc(\sqrt{tc/ps})} \right) \left( \frac{VP}{ART} \right) \quad (\mathbf{Eq. 2})$$

where $A_a$ is the analytical precision of the Picarro analyser (0.6 ppm; Picarro Inc., 2015), $tc$ the closure time (s), $ps$ the sampling frequency (1 Hz), $V$ the chamber volume, $P$ the atmospheric pressure (101 325 N m⁻²), $A$ the chamber footprint, $R$ the gas constant (8.3 N m mol⁻¹·K⁻¹), and $T$ the air temperature in Kelvin.

Following this quality control, fluxes were partitioned (Section 2.3.3) and gap-filled (Section 2.3.4).

### 2.3.3. Partitioning of chamber-based CO₂ fluxes

The net CO₂ fluxes (FCO₂ch), averaged from four chambers per environment (FS and Sh), were partitioned into two components according to Eq. 3 (Reichstein et al., 2005).

$$210 \quad \mathbf{FCO_2ch} = \mathbf{Rch} + \mathbf{GPPch} \quad (\mathbf{Eq. 3})$$

$Rch$ includes heterotrophic respiration (Rh) from soil and other autotrophic respiration (Ra) from groundnut plants and roots of *F. albida* (Ra Groundnut + Ra tree below-ground). $Rch$ is always positive ($Rch > 0$). $GPPch$ (Gross Primary Productivity) represents the photosynthetic CO₂ uptake by the groundnut plants and is negative during the day ($GPPch < 0$), and zero at night, when $FCO_2ch = Rch$.

Half-hourly FCO₂ch fluxes were partitioned as follows: (1) an Arrhenius-type function (Lloyd & Taylor, 1994) was fitted between nocturnal $Rch$ and $T_{soil}$ during nighttime periods, for each 5-days throughout the time series (Eq. 4). This empirical formulation is based on several key assumptions. First, the relationship between nocturnal respiration and soil temperature is assumed to follow an exponential response, reflecting the temperature sensitivity of respiration processes. Second, the model assumes temporal stability of the respiration-temperature relationship between night and day, allowing diurnal respiration to be extrapolated from fitted parameters in Eq.4 and daytime $T_{soil}$. Third, we assumed that no abrupt changes in substrate availability or soil moisture occur between day and night — conditions that could otherwise disrupt the temperature-respiration relationship. Third, it is assumed that no abrupt changes in substrate availability or soil moisture occur between night and day — conditions that could otherwise decouple respiration rates from temperature. These assumptions are widely applied in CO₂ flux partitioning approaches (Reichstein et al., 2005; Lasslop et al., 2010). (2) Diurnal $Rch$ was estimated by applying the Lloyd & Taylor function, previously calibrated on nocturnal data, to the corresponding daytime $T_{soil}$ measurements for each 5-day interval. (3) $GPPch$ was subsequently derived as the residual component of the net CO₂ flux during the day, according to:

232
233
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$$234 \quad \text{nocturnal Rch} = \text{R}_{\text{ref}} \cdot \exp \left[ E_0 \left( \frac{1}{T_{\text{ref}} - T_0} - \frac{1}{T_{\text{soil}} - T_0} \right) \right] \quad (\text{Eq. 4})$$

where Rref (µmol CO₂ m⁻² s⁻¹) is a fitted parameter representing the base respiration at the reference temperature [Tref (K), (set at 288.15 K)]. E₀ (K) is the temperature sensitivity (set at 250 K), Tsoil (K) the soil temperature (K), and T₀ (K) is kept constant at 231.13 K, according to Lloyd & Taylor (1994).

$$239 \quad \text{GPPch} = \text{diurnal FCO}_2\text{ch} - \text{diurnal Rch} \quad (\text{Eq. 5})$$

where diurnal FCO₂ch and diurnal Rch represent the daytime net CO₂ fluxes and respiration in µmol CO₂ m⁻² s⁻¹, respectively.

### 2.3.4. Gap-filling procedure

Missing Rch data were gap-filled using the model derived from Eq. 4 (Lloyd & Taylor, 1994). Prior to gap-filling GPPch, raw data were standardised by LAI to reduce variability between chambers due to differences in leaf surface area (Eq. 6). A light-response model was then fitted to the standardised GPPch data, every 5-day period, to gap-fill missing values. The model is based on a rectangular hyperbolic function that describes the relationship between photosynthetic CO₂ uptake and incoming global radiation (Rg) (Eq. 7). It corresponds to a Michaelis–Menten-type light-response curve, commonly used in ecosystem carbon exchange studies (Falge et al., 2001; Lasslop et al., 2010).

$$251 \quad \text{GPPch. stand} = \frac{\text{GPPch}}{\text{LAIch}} * \text{LAI. field} \quad (\text{Eq. 6})$$

where GPPch.stand (µmol CO₂ m⁻² s⁻¹) is the standardised GPPch. LAIch and LAIfield (m² leaves m⁻² soil) represent the groundnut LAI inside the chambers and the groundnut + weeds + cowpea LAI for the whole field, respectively.

$$255 \quad \text{GPP} = \frac{\alpha \beta R_g}{\alpha R_g + \beta} \quad (\text{Eq. 7})$$

where α (µmol CO₂ m⁻² s⁻¹) represents the light use efficiency of the groundnut plants inside the chambers, and refers to the initial slope of the light-response curve, β (µmol CO₂ m⁻² s⁻¹) is the maximum CO₂ uptake rate by the groundnut plants at light saturation, and Rg the global radiation (W m⁻²).

### 2.3.5. Comparing chamber-based (Ch) and Eddy Covariance (EC) methods

Chamber measurements were upscaled to field-level CO₂ fluxes and compared with EC-derived fluxes. Before comparison, a correction was applied (Eq. 6) to account for differences in LAI between chambers (LAIch) and the field (LAIfield), due to the presence of cowpea and weeds in the field but not in the weeded chambers.

Upscaling considered tree cover, with FS and Sh chamber fluxes weighted at 90% and 10%, respectively. Rch.stand and GPPch.stand, representing chamber-based respiration and
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267 photosynthesis at field scale. These fluxes were compared, on a half-hourly basis, to EC-derived
268 Reco.EC and GPP.EC (S3, Table S3.1). The November–December transition period was excluded
269 due to weed-driven uncertainties after groundnut harvest.

270 During the rainy season (F. albida leafless), GPP.EC represented ground vegetation (groundnut,
271 cowpea, weeds), while Reco.EC included autotrophic respiration from all vegetation (including
272 trees), and heterotrophic respiration (Reco.EC = Ra tree below-ground + Ra tree above-ground
273 + Ra groundnut + Ra cowpea + Ra weeds + Rh). Rch.stand could not be fully upscaled to the field
274 due to uncertainty in its partitioning between Ra and Rh. Rch.stand accounted only for Ra tree
275 below-ground, Ra groundnut, and Rh.

276 In the dry season (leafy trees, bare soil), GPP.EC reflected tree photosynthesis only (GPP tree),
277 while GPPch.stand was nil. Reco.EC included Ra tree (above- and below-ground) and Rh.
278 Rch.stand, measured on bare soil represented only Ra tree below-ground + Rh.

279 **2.3.6. Contribution of trees to full ecosystem respiration and photosynthesis**

280 During the dry season, when the trees (F. albida) maintained their foliage, a comparison between
281 chamber and EC measurements allowed for the estimation of the contribution of the above-
282 ground tree compartments to total ecosystem respiration (S3, Table S3.1). Based on this estimate,
283 total tree respiration (Ra tree) was then calculated under the assumption that the tree root
284 systems (Ra tree below-ground) represent 1/3 of the above-ground biomass (Jackson et al. 1996).
285 Given the GPP measured during the dry season was equivalent to GPP of trees (GPP trees) from
286 EC measurements, the carbon use efficiency of the trees (CUE tree) was then calculated (S3, Table
287 S3.1). The resulting CUE value was assessed to determine whether it approximated the typical
288 value of 0.5, which is often used as a default in ecosystem models (Zhou et al., 2019; 2020).

289 **2.3.7. Net annual C budget at the ASPS scale**

290 The annual C budget of CO₂ fluxes was estimated for chambers and EC measurements in Mg C-CO₂
291 ha⁻¹. The chambers CO₂ fluxes budgets were obtained by calculating the annual sum of the net
292 CO₂ flux measurements and then weighting with the tree cover rate (10% for the Sh, 90% for the
293 FS). These annual budgets for the field are considered apparent, as they do not account for the
294 biomass exported from the field after the harvest, the decomposition of which therefore escaped
295 both the chambers and the EC. Additionally, the inputs and the outputs of fecal matter resulting
296 from livestock wandering during the dry season were not quantified and are therefore neglected.
297 The objective here is to compare two approaches at different scales using apparent net C budgets,
298 rather than to provide an absolute C budget.
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299 **2.4. Statistical analyses**

300 Statistical analyses were performed using the R software (R. Core Team, 2023). To compare the
301 mean values of climatic parameters between the FS and Sh situations, a non-parametric Mann-
302 Whitney test was used when both the normality (shapiro.test) and the homogeneity of the
303 variance (Levene Test, R package ‘Car’; Fox et al., 2023) were not confirmed. This approach was
304 similarly applied to compare the seasonal dynamics of CO2 fluxes between FS and Sh, as well as
305 between the chamber-based and Eddy Covariance (EC) methods. Means and standard deviations
306 were computed using the ‘skim’ function from the R package ‘skimr’ (Waring et al., 2022).

307 Respiration (Rch) (Eq. 4) and GPP (GPPch) models (Eq. 7) were fitted using non-linear least
308 squares regression, implemented in the library in R 'nls.multstart' (Padfield et al., 2025). For the
309 GPPch model, parameters α and β with non-significant p-values were removed, and then the
310 remaining values were interpolated and smoothed using a 'spline' function from the 'zoo' library
311 in R (Zeileis et al., 2024). Ordinary least-square linear regressions were fitted between the
312 measured and the modeled values derived from. Model performance of Eq. 4 and Eq. 7 was
313 evaluated by fitting ordinary least-square linear regressions between the measured and the
314 modeled values using R², root mean square error (RMSE), and the bias metrics. Given that the
315 primary objective of these equations was to accurately reproduce the seasonal dynamics of the
316 CO2 fluxes to fill gaps in data, particular emphasis was placed on R², with a higher value reflecting
317 a better fit of the model to the measurements.

318 Correlation analysis was conducted between chamber CO2 fluxes (FCO2ch, Rch, GPPch) and soil
319 temperature (Tsoil, °C), air temperature (Tair, °C), VWC, the leaf area index of groundnut plants in
320 the chambers (LAIch), and the fitted parameters for respiration — Rref — and photosynthesis —
321 α and β. This analysis was performed using the ‘cor.test’ function from the ‘stats’ package in R
322 (Lüdecke et al., 2021), applying the Spearman method.

323 The threshold of the daily mean soil temperature (Tsoil, °C) at which the cumulative daily
324 respiration (Rch, g C-CO2 m⁻² d⁻¹) began to decline was determined using segmented regression
325 from the R package ‘segmented’ (Muggeo, 2003). The associated uncertainty (standard error) of
326 this estimate was evaluated through a bootstrap procedure.
<figure><img src="image_22.png" /></figure>

327 **3. Results**

328 **3.1. Microclimatic conditions**

329 During the experiment, the cumulative rainfall was 550 mm, which was representative of the
330 interannual average. Precipitations were lowest in July and highest between August and
331 September, a period that typically corresponds to the peak of the rainy season (Fig. 2a). Global
332 radiation ranged between 5.8 and 32.4 MJ m⁻² d⁻¹ (data not shown). The daily mean VWC in the
333 chambers showed significant variation, ranging from 1% at the end of the dry season to a
334 maximum of 30% during the rainy season (Fig. 2a). While VWC was similar during the rainy
335 season, it remained consistently higher in FS than in Sh throughout the dry season (p < 0.05),
336 which was unexpected. However, it should be noted that the last rain of October 2021 recharged
337 the FS chambers more effectively, likely due to foliage rainfall interception by *F. albida* which had
338 just put on leaves at that time, potentially explaining this discrepancy in VWC.

339 Within the chamber, the daily mean Tsoil ranged from 26°C in April to 37.5°C at the end of the dry
340 season (Fig. 2b), whileair varied between 23.7°C and 35.5°C (Fig. 2c). However, during
341 instantaneous daily peaks, Tsoil could exceed 45°C in May (data not shown). As expected, both daily
342 mean Tsoil and Tair were significantly higher in FS compared to Sh situations (p < 0.05), with Tsoil
343 and Tair averaging respectively 1°C and 0.5°C lower under the tree canopy.
<figure><img src="image_23.png" /></figure>

<figure><img src="image_24.png" /></figure>

<figure><img src="image_25.png" /><figcaption>344 Fig. 2: One-year time series of daily average microclimatic parameters measured inside chambers.</figcaption></figure>

345 (a) volumetric soil water content (VWC) at a depth of 6 cm (%). (b) soil temperature ($T_{soil}$) at a depth of 6 cm (°C), (C) air temperature ($T_{air}$) at a height of 15 cm (°C). The blue line depicts the daily rainfall (mm d⁻¹) throughout the year. FS: Full sun chambers; Sh: Shaded chambers. Mean and SD represent respectively the mean value and the standard deviation. The p-value indicates the probability associated with the statistical test, assessing the differences in means between FS and Sh with the significance level α set to 0.05.
<figure><img src="image_26.png" /></figure>

<figure><img src="image_27.png" /></figure>

350 **3.2. Modeling the chamber-based total respiration (Rch) and photosynthesis (GPPch)**

351 **3.2.1. Dynamics of references respiration, light use efficiency, and maximum CO₂ uptake rate at**
352 light saturation (R<sub>ref</sub>, α, and β)

353 The reference respiration (R<sub>ref</sub>) showed comparable seasonal dynamics both at a distance from
354 the trees (FS) and under the tree canopies (Sh) (S2, Fig. S2.2). In both situations, R<sub>ref</sub> showed
355 strong variability during the rainy season, peaking in September 2021 at 2.4 µmol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup> for
356 FS and 2.9 µmol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup> for Sh (S2, Table S2.1). In contrast, during the dry season — from
357 November 3, 2021 (after harvest) until the onset of the following rainy season (June 2022) — R<sub>ref</sub>
358 values dropped both for FS and Sh, averaging 0.3 ± 0.5 µmol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup> for FS and 0.5 ± 0.6 µmol
359 CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup> for Sh. This represents a reduction by a factor of 8 for FS and 6 for Sh compared to the
360 rainy season. The mean annual R<sub>ref</sub> values were significantly higher under Sh than in FS, with value
361 approximately 1.5 times greater (S2, Table S2.1).

362 Regarding GPP in chambers, the light use efficiency (α) and the maximum CO₂ uptake by
363 groundnut plants in the chambers (β), also reached their maximum during the peak of the rainy
364 season (S2, Fig. S2.3, a and b). The maximum value of α reached 0.2 µmol CO₂ J⁻¹ in FS and 0.3
365 µmol CO₂ J⁻¹ in Sh (S2, Table S2.1). Similarly, the maximum values of optimum CO₂ uptake rate at
366 light saturation (β) were 40.2 µmol CO₂ m⁻² s⁻¹ for FS and 42.8 µmol CO₂ m⁻² s⁻¹ for Sh (S2, Table
367 S2.1). In the dry season, when photosynthetic activity ceased in the chambers, both α and β were
368 assumed to be nil (S2, Fig. S2.3, a and b). On average, α and β were significantly higher in Sh than
369 in FS, by a factor of 1.7 and 1.2, respectively (S2, Table S2.1). We noted that the decline in
370 photosynthetic activity of the groundnut crop occurred earlier and rapidly at a distance from the
371 trees (FS), as reflected by the sharply observed recession of α and β in FS.

372 **3.2.2. Dynamics of nocturnal respiration in chambers**

373 The averaged nocturnal respiration (nocturnal Rch) calculated from the measurements across
374 each treatment (FS and Sh), showed similar seasonal patterns (Fig. 3, a and c). Following the first
375 rains, Rch values increased dramatically, with a nocturnal 'Birch effect' — a sudden pulse of CO₂
376 release following soil rewetting — observed to be more pronounced under Sh compared to FS,
377 approximately by a factor of 2. At the peak of the rainy season (September), the maximum
378 nocturnal Rch values reached approximately 6.0 µmol CO₂ m⁻² s⁻¹ in FS and 9.0 µmol CO₂ m⁻² s⁻¹
379 in Sh (Fig. 3, a and c). Thereafter, nocturnal Rch declined well before the groundnut harvest along
380 with the rainfall spacing and the groundnut crop senescence (data not shown). During the dry
381 season nocturnal Rch continued to decrease, with maximum values around 1.0 µmol CO₂ m⁻² s⁻¹
382 in FS and 2.0 µmol CO₂ m⁻² s⁻¹ in Sh (Fig. 3, a and c).
<figure><img src="image_28.png" /></figure>

<figure><img src="image_29.png" /></figure>

The modeled nocturnal Rch values closely matched the measured nocturnal Rch values (mean across four chambers per treatment), as indicated by the model performance metrics ($R^2$ = 0.9, with bias and RMSE values of 0.3 and 0.5 µmol CO$_2$ m$^{-2}$ s$^{-1}$, respectively, for FS; $R^2$ = 0.7, with bias and RMSE values of 0.4 and 0.6 µmol CO$_2$ m$^{-2}$ s$^{-1}$, respectively, for Sh) (Fig. 3, b and d). Similarly, the daily mean modeled values also fitted well with the measured values, with FS showing 0.9 ± 0.9 µmol CO$_2$ m$^{-2}$ s$^{-1}$ (modeled) and 1.2 ± 1.2 µmol CO$_2$ m$^{-2}$ s$^{-1}$ (measured), while Sh recorded 1.4 ± 0.9 µmol CO$_2$ m$^{-2}$ s$^{-1}$ (modeled) and 1.5 ± 1.2 µmol CO$_2$ m$^{-2}$ s$^{-1}$ (measured). Given the close match between the measured and modeled values, the fitted model parameters were used subsequently to fill data gaps and estimate diurnal Rch values, as presented in Fig. 4, a and c.
<figure><img src="image_30.png" /></figure>

<figure><img src="image_31.png" /></figure>

<figure><img src="image_32.png" /><figcaption>Fig. 3: Dynamics of instantaneous nocturnal CO₂ fluxes in chambers in Full sun (FS; a and b) and Shaded (Sh; c and d) environments (data filtered based on R² of the CO₂ variation over the time of chamber closure and Minimum Detectable Flux, Eq.2).</figcaption></figure>

395 (a) and (c): measured nocturnal respiration in chambers (Rch: black dots; average of measurements in 4
396 chambers per location) vs. modeled (coloured line). The vertical black line indicates the harvest date of
397 groundnuts inside the chambers. The red arrows indicate the ‘Birch’ effect and the blue line represents the
398 rainfall (mm 30mn⁻¹). Roman numerals (above the black arrows) refer to vegetation conditions prevailing
399 inside the chambers, i.e. (I) bare soil, (II) weeds, (III) weeds + groundnuts, and (IV) groundnuts only.

400 (b) and (d): scatter plot between measured and modeled nocturnal Rch. The solid blue line indicates the
401 regression line and the dashed black one the (1:1) line RMSE and bias are expressed as fluxes (in µmol CO₂
402 m⁻² s⁻¹). Each point represents the mean value from 4 chambers within the FS or Sh environments.
<figure><img src="image_33.png" /></figure>

<figure><img src="image_34.png" /></figure>

### 3.2.3. *Dynamics of daytime fluxes in chambers*

The measured GPPch.stand, as well as GPP modeled with Eq. 6, showed similar seasonal dynamics in FS and Sh (Fig. 4, a and c). The fluxes peaked during the rainy season (Fig. 4a and c), coinciding with periods of vigorous vegetative growth characterised by a high leaf area index (LAIch) of groundnut plants within the chambers (S2, Fig. S2.1). The maximum calculated and standardised GPPch values reached -50 µmol CO₂ m⁻² s⁻¹ for FS and -37 µmol CO₂ m⁻² s⁻¹ for Sh. As expected, these fluxes were nil during the dry season when the soil was bare (Fig. 4, a and c).

The modeled GPPch values closely followed the same trends as the calculated values, although model performance was slightly better for FS (R² = 0.7 with bias and RMSE values of 4.2 and 6.1 µmol CO₂ m⁻² s⁻¹, respectively) compared to Sh (R² = 0.6 with bias and RMSE values of 6.1 and 5.6 µmol CO₂ m⁻² s⁻¹, respectively) (Fig. 4, b and d).

The calculated diurnal respiration values (diurnal Rch calculated) for FS and Sh revealed a 'Birch effect' similar to that observed during the night, though slightly more pronounced under Sh by a factor of 1.2. Diurnal Rch values increased significantly during the rainy season, reaching a maximum of 6.0 µmol CO₂ m⁻² s⁻¹ for both FS and Sh (Fig. 4, a and c). In the dry season, on bare soil, these values declined, with maximum respiration reaching only 0.5 µmol CO₂ m⁻² s⁻¹ for both situations (FS and Sh) (Fig. 4, a and c).
<figure><img src="image_35.png" /></figure>

<figure><img src="image_36.png" /></figure>

<figure><img src="image_37.png" /><figcaption>Fig. 4: Dynamics of instantaneous diurnal CO₂ fluxes in chambers in Full sun (FS; a and b) and Shaded (Sh; b and d) environments (filtered based on R² of the CO₂ variation over the time closure in FS and Sh and Minimum Detectable Flux, Eq.2).</figcaption></figure>

423 (a) and (c): non-gap-filled diurnal Rch calculated (black line, positive values; average of measurements in 424 425 values) and modeled (coloured line, negative values). The vertical black line indicates the harvest date of 426 groundnuts inside the chambers and the blue line represents the rainfall (mm 30mn⁻¹). Roman numerals 427 (above the black arrows) refer to conditions prevailing inside the chambers, i.e., (I) bare soil, (II) weeds, 428 (III) weeds + groundnuts, and (IV) groundnuts.

429 (b) and (d): scatter plot between calculated and modeled GPPch. The solid blue line indicates the regression 430 line and the dashed black one the (1:1) line. RMSE and bias are expressed as fluxes (in µmol CO₂ m⁻² s⁻¹). 431 Each point represents the mean value from 4 chambers within the FS or Sh environments.
<figure><img src="image_38.png" /></figure>

<figure><img src="image_39.png" /></figure>

432 **3.3. Dynamics of daily cumulative CO₂ fluxes in chambers**

433 The seasonality of daily cumulative of GPPch.stand showed similar dynamics between FS and Sh,
434 with higher variability during the rainy season than during the dry season (Fig. 5). Daily total Rch
435 peaked during the rainy season at 5.1 g C-CO₂ m⁻² d⁻¹ for FS and 5.4 g C-CO₂ m⁻² d⁻¹ for Sh, while
436 the maximum GPPch.stand values were comparable at around -15.0 g C-CO₂ m⁻² d⁻¹ for both FS
437 and Sh (Table 1; S2, Fig. S2.4, a, b, c, and d). In the dry season, Rch decreased (Fig. 5), averaging
438 0.5 g C-CO₂ m⁻² d⁻¹ for FS and 1.0 g C-CO₂ m⁻² d⁻¹ for Sh. GPPch declined well before harvest
439 (senescence) and remained nil during the dry season (Fig. 5). During the rainy season FCO₂ch
440 peaked at around 11.0 g C-CO₂ m⁻² d⁻¹ for FS and Sh (Fig. 5) (Table 1; S2, Fig. S2.4, e and f), while
441 FCO₂ch values were the same as Rch during the dry season. In absolute terms, the mean Rch and
442 GPPch were significantly higher under Sh as compared to FS, by factors of 1.3 and 1.2, respectively.
443 Conversely, the mean FCO₂ch was significantly higher under FS (0.4 g C-CO₂ m⁻² d⁻¹) than under
444 Sh (0.2 g C-CO₂ m⁻² d⁻¹) (Table 1).

445 The annual cumulative Rch values were 392.8 g C-CO₂ m⁻² for FS and 574.5 g C-CO₂ m⁻² for Sh.
446 The GPPch fluxes reached -539.5 g C-CO₂ m⁻² for FS and -632.6 g C-CO₂ m⁻² for Sh. Annual net
447 cumulative C exchange (FCO₂ch) were -146.7 g C-CO₂ m⁻² in FS and -58.1 g C-CO₂ m⁻² in Sh.
<figure><img src="image_40.png" /></figure>

<figure><img src="image_41.png" /></figure>

<figure><img src="image_42.png" /><figcaption>Fig. 5: Seasonal dynamics of daily gap-filled cumulative fluxes (in gC-CO₂ m⁻² d⁻¹) in chambers.</figcaption></figure>

449 (a) soil+crop respiration (Rch), (b) photosynthesis (GPPch, standardised for LAI) and (c) net CO₂ exchange
450 (FCO₂ch). The yellow and green solid lines compare the FS and Sh environments, respectively. The vertical
451 black line indicates the harvest date of groundnuts inside the chambers. The blue line depicts the daily
452 cumulative rainfall (mm d⁻¹) throughout the rainy season, and the red arrow indicates the 'Birch'
453 effect. Roman numerals (above the black arrows) in (a) and (c) refer to the prevailing conditions inside the
454 chambers: (I) bare soil, (II) weeds, (III) weeds + groundnuts, (IV) groundnuts.

455
<figure><img src="image_43.png" /><figcaption>Table 1: Comparison of daily cumulative and gap-filled chamber CO₂ fluxes (Rch, GPPch standardised for LAI, and FCO₂ch in g C-CO₂ m⁻²) in the FS and Sh condition.</figcaption></figure>

<table><thead><tr><th></th><th>Annual sum</th><th>Daily Mean ±SD</th><th>Min</th><th>Max</th><th>Mann-Whitney test</th></tr><tr><th>(g C-CO<sub>2</sub> m<sup>-2</sup>)</th><th>.yr<sup>-1</sup></th><th>.d<sup>-1</sup></th><th>.d<sup>-1</sup></th><th>.d<sup>-1</sup></th><th></th></tr></thead><tbody><tr><td><strong>Rch</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>FS</td><td>392.8</td><td>1.1 ± 0.9</td><td>0.4</td><td>5.1</td><td>*</td></tr><tr><td>Sh</td><td>574.5</td><td>1.6 ± 1.1</td><td>0.6</td><td>5.4</td><td></td></tr><tr><td><strong>GPPch</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>FS</td><td>-539.5</td><td>-4.1 ± 4.3</td><td>&lt; -0.1</td><td>-14.9</td><td>*</td></tr><tr><td>Sh</td><td>-632.6</td><td>-4.8 ± 4.6</td><td>&lt; -0.1</td><td>-14.8</td><td></td></tr><tr><td><strong>FCO<sub>2</sub>ch</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>FS</td><td>-146.7</td><td>-0.4 ± 2.4</td><td>-11.0</td><td>1.8</td><td>*</td></tr><tr><td>Sh</td><td>-58.1</td><td>-0.2 ± 2.7</td><td>-10.9</td><td>2.8</td><td></td></tr></tbody></table>

Annual sum corresponds to the annual cumulative fluxes (g C-CO₂ m⁻² yr⁻¹). Mean, SD, Min, and Max represent respectively the mean, standard deviation, minimum, and maximum values at the daily scale (g C-CO₂ m⁻² d⁻¹). Asterisks (*) indicate the p-values from the Mann-Whitney test, used to assess differences in mean between FS and Sh (p < 0.05). Positive values indicate CO₂ emissions, while negative values represent CO₂ uptake.
<figure><img src="image_44.png" /></figure>

<figure><img src="image_45.png" /></figure>

464 **3.4. Drivers of daily respiration and photosynthesis in chambers**

465 The chamber-based daily cumulative respiration (Rch) and GPPch showed significant and positive
466 correlations with the leaf area index (LAIch), both at a distance from the trees (FS) and under the
467 trees (Sh) (Table 2). The influence of LAIch on GPPch was stronger (r = 0.86 for FS and Sh) than
468 its influence on Rch (r = 0.60 for FS; r = 0.69 for Sh). Soil VWC was also positively correlated with
469 Rch and GPPch, both in FS and Sh. However, the influence of soil VWC on Rch was stronger under
470 Sh compared to FS, while its influence on GPPch was similar in both situations (FS and Sh). Soil
471 temperature showed weak negative correlations with Rch (in FS and Sh) and with GPPch (only in
472 Sh). Finally, no significant correlations were found between T<sub>air</sub>, and any of the CO<sub>2</sub> fluxes (Table
473 2).
<figure><img src="image_46.png" /></figure>

<figure><img src="image_47.png" /><figcaption>Table 2: Spearman correlation matrix based on daily cumulative and gap-filled CO₂ fluxes from full year chamber measurements (g C-CO₂ m⁻² d⁻¹) with microclimatic parameters.</figcaption></figure>

<table><thead><tr><th>Parameters</th><th>Condition</th><th>Rch</th><th>GPPch</th></tr></thead><tbody><tr><td rowspan="2">T<sub>soil</sub></td><td>FS</td><td>-0.25 ***</td><td>ns</td></tr><tr><td>Sh</td><td>-0.28 ***</td><td>-0.38 ***</td></tr><tr><td rowspan="2">T<sub>air</sub></td><td>FS</td><td>ns</td><td>ns</td></tr><tr><td>Sh</td><td>ns</td><td>ns</td></tr><tr><td rowspan="2">VWC</td><td>FS</td><td>0.51 ***</td><td>0.75 ***</td></tr><tr><td>Sh</td><td>0.78 ***</td><td>0.75 ***</td></tr><tr><td rowspan="2">LAIch</td><td>FS</td><td>0.60 ***</td><td>0.86 ***</td></tr><tr><td>Sh</td><td>0.69 ***</td><td>0.86 ***</td></tr></tbody></table>

Spearman correlation coefficients between daily cumulative and gap-filled CO₂ flux components (Rch and GPPch, with GPPch in absolute terms) and daily mean microclimatic parameters in full sun (FS) and shaded chambers (Sh). T<sub>soil</sub> (°C) is the daily mean soil temperature at 6 cm depth, T<sub>air</sub> (°C) the daily mean air temperature at 15 cm height, VWC (%) the daily mean volumetric water content (VWC, %), and LAIch (m⁻² leaf m⁻² soil) the chamber leaf area index value for a given day. Significance levels are indicated by (***): for p < 0.001; ns denotes a non-significant correlation (p > 0.05).
<figure><img src="image_48.png" /></figure>

482 **3.5. Comparison of respiration and GPP measurements between chambers (Ch) and Eddy Covariance (EC) methods**

484 The chamber-based daily total CO₂ fluxes, gap-filled and weighted according tree cover were compared with the fluxes obtained using the EC method (Fig. 6).

486 During the rainy season, both total respiration and GPP showed comparable dynamics between the two methods, with synchronised peaks and higher variability compared to the dry season (Fig. 6). The maximum value of Reco.EC, peaked at 13.5 g C-CO₂ m⁻² d⁻¹ (Table 3). The initial value of Rch.stand was comparable to Reco.EC but peaked only at 5.1 g C-CO₂ m⁻² d⁻¹ (Table 3), meaning a third of the peak of Reco.EC. The maximum GPP, was -14.3 g C-CO₂ m⁻² d⁻¹ and -14.6 g C-CO₂ m⁻² d⁻¹ for GPP.EC and GPPch.stand, respectively (Table 3). This indicates that the LAI-based standardisation and upscaling approach were realistic, at least up to the peak of groundnut growth.

494 On average, Reco.EC was significantly higher than Rch.stand, by a factor of 2.3. GPP.EC was also significantly higher than GPPch.stand, but only by a factor of 1.2 (Table 3).

496 During the dry season, Reco.EC and Rch.stand gradually decreased. The values for Reco.EC remained higher than for Rch.stand, which was fairly consistent with the contribution of the Ra tree above-ground compartment, even if this difference seemed to disappear at the end of the dry season (Fig. 6). The measured 'Birch effect' was highest for Rch.stand in 2021, but was the opposite in 2022 due to a system failure at the beginning of the rainy season. The maximum value of GPP.EC reached -2.4 g C-CO₂ m⁻² d⁻¹ when the trees were at their maximum of foliage, after harvest and while weeds were still present in the field. However, after the harvest, chamber photosynthesis (GPPch.stand) was nil (Table 3).
<figure><img src="image_49.png" /></figure>

<figure><img src="image_50.png" /><figcaption>Fig 6: Comparing the seasonal dynamics of CO₂ fluxes between Eddy Covariance (EC) measurements and upscaled chamber measurements (ch.stand).</figcaption></figure>

(a) represent the seasonal dynamics of soil + crop respiration (Rch.stand) and ecosystem respiration (Reco.EC) and (b) photosynthesis (GPP.stand and GPP.EC). The black and dashed grey lines show Ch and EC seasonal dynamics, respectively. The vertical black line indicates the harvest date of groundnuts inside the chambers. The blue line depicts the daily cumulative rainfall (mm d⁻¹), and the red arrow indicates the 'Birch' effect. Roman numerals (above the black arrows) refer to conditions prevailing inside the chambers: (I) bare soil, (II) weeds, (III) weeds + groundnuts, (IV) groundnuts.

504
505

506
507
508
509
510
511
<figure><img src="image_51.png" /><figcaption>512 Table 3: Comparison of gap-filled CO₂ fluxes between Eddy Covariance (EC) and upscaled chamber (Ch.stand) measurements, by season (rainy or dry).</figcaption></figure>

<table><thead><tr><th rowspan="2"></th><th colspan="4">Rainy season</th><th colspan="4">Dry season</th></tr><tr><th>Daily Mean<br/>± SD<br/>d<sup>-1</sup></th><th>Min<br/>d<sup>-1</sup></th><th>Max<br/>d<sup>-1</sup></th><th>Mann-Whitney test</th><th>Daily Mean<br/>± SD<br/>d<sup>-1</sup></th><th>Min<br/>d<sup>-1</sup></th><th>Max<br/>d<sup>-1</sup></th><th>Mann-Whitney test</th></tr></thead><tbody><tr><td colspan="9"><strong>Reco.EC or Rch.stand</strong></td></tr><tr><td>EC</td><td>4.6 ± 3.2</td><td>0.2</td><td>13.5</td><td>*</td><td>1.2 ± 0.4</td><td>0.3</td><td>2.1</td><td>*</td></tr><tr><td>Ch.stand</td><td>2.0 ± 1.1</td><td>0.5</td><td>5.1</td><td></td><td>0.5 ± 0.04</td><td>0.4</td><td>0.6</td><td></td></tr><tr><td colspan="9"><strong>GPP.EC or GPPch.stand</strong></td></tr><tr><td>EC</td><td>-5.1 ± 3.6</td><td>-0.7</td><td>-14.3</td><td>*</td><td>-1.7 ± 0.3</td><td>-0.6</td><td>-2.4</td><td></td></tr><tr><td>Ch.stand</td><td>-4.2 ± 4.3</td><td>&lt;-0.1</td><td>-14.6</td><td></td><td>0</td><td>0</td><td>0</td><td></td></tr></tbody></table>

513 Mean, SD, Min, and Max represent the daily mean fluxes, standard deviation, minimum, and maximum values, respectively (g C - CO₂ m² m⁻² d⁻¹). The Asterisks (*) indicate

514 the p-values from the Mann-Whitney test, used to assess differences in mean between EC and Ch. Positive values indicate CO₂ emissions, while negative values

515 represent CO₂ uptake.

<figure><img src="image_52.png" /></figure>
<figure><img src="image_53.png" /></figure>

## 3.6. The contribution of *F. albida* to Reco and GPP

During the dry season, the cumulative contribution of *F. albida* to ecosystem respiration (Ra tree) was 139.6 g C-CO₂ m⁻². This represent 12% of the total annual cumulative Reco, which was estimated at 1180.0 g C-CO₂ m⁻². The contribution of trees (GPP tree) to total annual GPP was -270.2 g C-CO₂ m⁻², equivalent to ~50% of the total annual cumulative GPP of the ecosystem (550 g C-CO₂ m⁻²).

The ratio between these two components (Ra tree / GPP tree) in absolute terms was 0.52, reflecting a carbon use efficiency (CUE) of 0.48 (S3, Table S3.1).

## 3.7. Carbon budgets at the field-scale

The upscaled chamber-based annual cumulative total respiration flux (Rch.stand) was estimated to be 4.1 ± 0.01 Mg C-CO₂ ha⁻¹ (Table 4). In comparison, the annual budget of Reco.EC was 10.0 ± 0.03 Mg C-CO₂ ha⁻¹ (Table 4), more than two times larger than Rch.stand.

The upscaled GPPch.stand reached an annual cumulative value of -5.5 ± 0.03 Mg C-CO₂ ha⁻¹, whereas the annual cumulative GPP.EC was -11.8 ± 0.03 Mg C-CO₂ ha⁻¹ (Table 4).

The annual net C budget, based on both methods, was estimated at -1.4 ± 0.02 Mg C-CO₂ ha⁻¹ for chambers (FCO₂ch.stand) and -1.8 ± 0.01 Mg C-CO₂ ha⁻¹ for Eddy Covariance (NEE.EC) (Table 4).
<figure><img src="image_54.png" /><figcaption>Table 4: Annual budget of CO₂ fluxes based on Eddy Covariance (EC) and upscaled chamber methods (Ch.stand).</figcaption></figure>

<table><thead><tr><th></th><th>Annual sum<br>(Mg C-CO₂ ha⁻¹)</th><th>Std error<br>(Mg C-CO₂ ha⁻¹)</th></tr></thead><tbody><tr><td><strong>Reco.EC or Rch.stand</strong></td><td></td><td></td></tr><tr><td>EC</td><td>10.0</td><td>0.03</td></tr><tr><td>Ch.stand</td><td>4.1</td><td>0.01</td></tr><tr><td><strong>GPP.EC or GPPch.stand</strong></td><td></td><td></td></tr><tr><td>EC</td><td>-11.8</td><td>0.03</td></tr><tr><td>Ch.stand</td><td>-5.5</td><td>0.03</td></tr><tr><td><strong>NEE.EC or FCO₂ch.stand</strong></td><td></td><td></td></tr><tr><td>EC</td><td>-1.8</td><td>0.01</td></tr><tr><td>Ch.stand</td><td>-1.4</td><td>0.02</td></tr></tbody></table>

Annual sum corresponds to the annual cumulative fluxes for full year measurements (Mg C-CO₂ ha⁻¹). EC refers to fluxes measured by the Eddy Covariance method, and Ch refers to the fluxes measured by chambers, which are then upscaled to the whole field. Rch.stand represents the chamber respiration, while Reco.EC denotes the ecosystem respiration according to the EC method. GPP.EC and GPPch.stand are the gross primary production or photosynthesis flux, measured by EC and Ch methods, respectively. NEE.EC and FCO₂ch.stand represent the net ecosystem exchange for EC and Ch, respectively. The associated standard error is denoted as Std error (Mg C-CO₂ ha⁻¹). Positive values indicate CO₂ emissions, while negative values represent CO₂ uptake.
<figure><img src="image_55.png" /></figure>

542 **4. Discussion**

543 **4.1. Seasonality and drivers of chamber-based CO₂ fluxes**

544 In our agroforestry context, seasonal variability in CO₂ fluxes closely followed rainfall dynamics, peaking during the wet season and declining sharply in the dry season, consistent with soil moisture depletion and crop senescence. This pattern is typical of semi-arid ecosystems (Ago et al., 2016a; Brümmer et al., 2008; Guillen-Cruz et al., 2023; Macharia et al., 2020; Mosongo et al., 2022; Wieckowski et al., 2024).

549 Respiration and photosynthesis were primarily driven by soil moisture and LAI, reflecting the system's sensitivity to water availability and crop dynamics. Soil moisture enhanced both processes by stimulating microbial activity and supporting plant growth (Borken et al., 2002; Conant et al., 2004; Merbold et al., 2009; Yu et al., 2020; Zhao et al., 2016). The stronger correlation between soil moisture and respiration under F. albida canopy (Sh: r = 0.78) compared to full sun (FS: r = 0.51) suggests greater microbial sensitivity to moisture beneath trees. This likely reflects enhanced substrate availability, resulting in stronger post-rainfall respiration pulses (Meisner et al., 2015) and supporting the 'fertile island' effect, where trees improve local soil conditions (Eldridge et al., 2024). Photosynthetic capacity also responded to soil moisture, as shown by positive correlations with LAI and key physiological traits such as light use efficiency (α) and maximum CO₂ uptake rate (β) (Gonsamo et al., 2019; Qiu et al., 2023; Zhang et al., 2024).

560 In contrast, the influence of soil temperature ($T_{soil}$) on respiration was weakly negative in both FS and Sh, indicating a thermal threshold beyond which respiration is suppressed—estimated at 32 ± 1.5 °C in FS and 29.5 ± 1.9 °C in Sh (S2, Fig. S2.6, a and b), similar to findings in Eastern Ghana (Owusu et al., 2024). This inhibition likely results from decreased enzymatic and microbial activity under combined heat and water stress (Liu et al., 2018; Richardson et al., 2012). In semi-arid regions, soil respiration often becomes decoupled from temperature due to seasonal moisture constraints (Jia et al., 2020; Tucker & Reed, 2016; Warren, 2014), with microbial activity limited during dry periods despite favourable temperatures. This decoupling helps explain the weak or absent correlation between $T_{soil}$ and soil moisture (S2, Fig. S2.5, b), particularly under Sh (r = -0.28). Management practices such as organic inputs can also modulate these dynamics, adding further variability to soil respiration responses (Meena et al., 2020; Oyonarte et al., 2012; Rong et al., 2015; Xue & Tang, 2018).

572 **4.2. Magnitude of chamber-based total CO₂ respiration fluxes**

573 Mean total soil respiration values were consistent with those reported in other low-input agricultural systems across sub-Saharan Africa (Mapanda et al., 2010; Pelster et al., 2017; Rosenstock et al., 2016). In full sun (FS), the mean respiration (1.0 ± 0.9 g C-CO₂ m⁻² d⁻¹) closely
<figure><img src="image_56.png" /></figure>

matched values measured by Wachiye et al. (2020) in a semi-arid Kenyan field at 1158 m altitude (1.1 ± 0.1 g C-CO₂ m⁻² d⁻¹). This similarity likely reflects comparable environmental conditions, including moderate rainfall (~550 mm yr⁻¹) and low soil organic carbon and nitrogen contents (<1%) in the 0–20 cm layer of sandy soil. In contrast, respiration under *F. albida* canopy (Sh: 1.6 ± 1.1 g C-CO₂ m⁻² d⁻¹) was higher, likely due to additional autotrophic respiration from tree roots and greater organic inputs beneath the canopy. Nonetheless, this flux remains close to values observed in low-input sorghum fields on sandy loam soils in eastern Ghana (1.7 ± 1.1 g C-CO₂ m⁻² d⁻¹), despite higher rainfall (950–1000 mm yr⁻¹) in that region (Owusu et al., 2024).

Cumulative annual respiration fluxes fell within the range reported for Sahelian croplands (250–450 g C-CO₂ m⁻²) (Brümmer et al., 2009) and other sub-Saharan African agricultural systems (Kim et al., 2016). The cumulative flux under tree cover is similar to that measured in cassava fields in eastern Tanzania (440 g C-CO₂ m⁻² yr⁻¹), despite the latter receiving higher rainfall (~1115 mm yr⁻¹) (Rosenstock et al., 2016). This convergence may stem from comparable soil fertility constraints, with low soil organic carbon (1–1.7%) and nitrogen contents (<0.5%). In contrast, the slightly lower cumulative flux in FS may reflect less favourable microclimatic conditions—such as elevated soil temperatures and increased aridity away from tree cover—limiting microbial activity (see Section 4.1).

Across sub-Saharan Africa, soil respiration fluxes based on static chamber measurements show high spatial variability, largely shaped by climate and land use. For example, Owusu et al. (2024) found higher respiration in woodlands (3.8 ± 0.8 g C-CO₂ m⁻² d⁻¹) and grazed areas (2.7 ± 1.7) than in croplands (1.7 ± 1.1) in humid eastern Ghana. This gradient was linked to differences in soil moisture and organic matter. Similarly, Rosenstock et al. (2016) reported much higher fluxes in highland pastures in Kenya (3.8–4.4 g C-CO₂ m⁻² d⁻¹) compared to cultivated fields in eastern Tanzania (1.2 ± 0.2), highlighting the role of vegetation cover and soil fertility.

### 4.3. *Effect of trees on chamber-based soil respiration and photosynthesis*

A notable increase in respiration and photosynthesis fluxes was observed under *F. albida* trees (Sh) compared at a distance from trees (FS). This increase may indicate the potential role of *F. albida* in modulating CO₂ exchange dynamics (Rch and GPPch) within this agro-silvo-pastoral system. These results are consistent with preliminary findings from similar environments (Duthoit et al., 2020).

Numerous studies have investigated the effect of tree species on greenhouse gas fluxes, particularly CO₂, revealing significant variations across different ecological contexts (Bréchet et al., 2021, 2025; Klaus et al., 2024; Mazza et al., 2021; Ramesh et al., 2013; Rheault et al., 2024). However, the underlying mechanisms by which trees influence these dynamics are not yet fully understood.
<figure><img src="image_57.png" /></figure>

In general, agroforestry systems have been well-documented for their ability to provide a range of ecosystem services (e.g., Assefa et al., 2024; Bado et al., 2021; Kuyah et al., 2019; Rolo et al., 2023). Specifically, *Faidherbia*-based agroforestry systems may play a crucial role in regulating CO₂ exchanges between the soil and atmosphere. *F. albida*-based agroforestry systems are recognized for enhancing both soil organic and mineral fertility (Bayala et al., 2020; Dilla et al., 2019; Sileshi, 2016; Sileshi et al., 2020; Stephen et al., 2020), mainly through litter accumulation and direct inputs from livestock excreta under their canopies. Additionally, the extensive roots system of *F. albida* trees helps concentrate mineral nutrients, contributing to the formation of a 'fertile island' effect under the trees (Siegwart et al., 2022; Eldridge et al., 2024). Moreover, *F. albida* improve water infiltration (Diongue et al., 2023; Faye et al., 2020; Sarr et al., 2023), enhance soil moisture retention (Clermont-Dauphin et al., 2023) and contribute to reduced soil temperatures (de Carvalho et al., 2021; Lopes et al., 2024; Sida et al., 2018). These changes foster a more favourable environment for soil microbial activity and crop development (Diack et al., 2024; Diene et al., 2024; Leroux et al., 2020; Roupsard et al., 2020) under the trees compared to open areas. Consequently, this likely explains the stronger effect of soil moisture and the leaf area index of groundnuts on Rch under the trees, resulting in higher total respiration (Table 2). For photosynthesis, the effect of these parameters was similar in both FS and Sh (Table 2). However, the significantly higher intensity of GPPch under Sh can be explained by greater light use efficiency (α) and a higher maximum CO₂ uptake rate at light saturation (β) in this shaded environment. In agroforestry systems, light use efficiency can at least partially mitigate the reduction in photosynthetically active radiation under tree canopies (Charbonnier et al., 2017).

Similar results have been observed in different climatic conditions and ecosystems. Gomes et al. (2016) investigated soil respiration using mobile chambers (LI-8100-102 model) under trees in coffee-based agroforestry (AF) systems and in open areas (FS) in Minas Gerais, Brazil. These studies were conducted with agroecological management practices, such as weeding, intercropping maize between coffee rows, and mulching. The AF systems exhibited lower air and soil temperatures (at 5 and 10 cm depth) and higher air and soil humidity compared to FS (Gomes et al., 2016). These authors observed greater spatial variability in soil respiration in AF (34.1%) compared to FS (24.2%). This variability was mainly linked with fluctuations in labile carbon and total nitrogen, reflecting more favourable soil microclimate for microbial activity in AF. In contrast, soil temperature (10 cm depth) accounted for most of the variability observed in FS, where the absence of tree canopy resulted in high soil temperatures and low soil moisture (Gomes et al., 2016). Likewise, Haren et al. (2010) reported 38% higher soil respiration near large trees (DBH > 35 cm) in clay-rich Amazonian forests compared to open sites. Interestingly, the magnitude of CO₂ fluxes was independent of tree species, indicating that canopy effects may outweigh species-specific traits in some contexts. In our study, *F. albida*'s influence on CO₂ fluxes
<figure><img src="image_58.png" /></figure>

647 aligns with this general pattern observed in tropical agroforestry. However, the mechanisms
648 linking individual tree species to microbial and physicochemical drivers of CO2 dynamics remain
649 insufficiently understood and warrant further investigation (Jevon et al., 2023).

650 **4.4. Birch Effect**

651 A rapid increase in soil respiration was observed following the first rainfall events, particularly
652 under *F. albida*. This phenomenon can be attributed to the lower bulk density of the soil under the
653 trees (Clermont-Dauphin et al., 2023; Siegwart et al., 2023), which potentially lead to CO2
654 accumulation during the dry season due to higher soil organic matter (SOM) (Siegwart et al.,
655 2023). Additionally, the sensitivity of microbial communities to subtle variations in soil moisture,
656 compounded by the tree effect, may further explain this phenomenon, as outlined in Sections 4.1
657 and 4.3. This phenomenon, known as the ‘Birch effect’ (Birch, 1958), has been reported across
658 various semi-arid ecosystems in sub-Saharan Africa (Ago et al., 2016b; Fan et al., 2015;
659 Wieckowski et al., 2024), as well as other semi-arid ecosystems globally (Roby et al., 2022; Yan et
660 al., 2014; Yu et al., 2020). In these contexts, the ‘Birch effect’ may result from the displacement of
661 soil gas phases by the piston effect generated during water infiltration (Singh et al., 2023).
662 Furthermore, microbial communities in semi-arid environments adopt osmoregulatory
663 mechanisms to withstand water deficit (Warren, 2014), which is particularly pronounced during
664 the dry season. This phenomenon reduces soil microbial metabolism (Schimel et al., 2007). Upon
665 rapid soil rewetting, especially after prolonged dry periods, soil microbial metabolism process is
666 swiftly reactivated, leading to a transient pulse in respiration and a CO2 release (Barnard et al.,
667 2020; Kim et al., 2012; Manzoni et al., 2020; Vargas et al., 2018). Isotopic signatures of soil
668 respiration provide evidence supporting the hypothesis that these pulses result from the rapid
669 mineralisation of necromass or osmolytes excreted by microorganisms under drought stress
670 (Schimel et al., 2007; Unger et al., 2010). Additional factors may amplify the ‘Birch effect’. For
671 instance, drying-rewetting cycles can induce physical disruption of soil aggregates, enhance
672 oxygen penetration and thereby expose previously protected organic matter to microbial
673 decomposition (Rabbi et al., 2024). This increases substrate availability and subsequently boosts
674 soil respiration fluxes.
675

675 The magnitude of the ‘Birch effect’ is modulated by the severity and duration of drought. Thus, at
676 our study site, given the 8- to 9-month-long dry season, the ‘Birch effect’ is particularly intense.
677 Indeed, extended drought periods promote greater accumulation of microbial necromass and
678 intensify hypo-osmotic stress responses upon rewetting (Singh et al., 2023).
<figure><img src="image_59.png" /></figure>

<figure><img src="image_60.png" /></figure>

679 **4.5. Comparing EC and chamber-based methods**

680 Results revealed high seasonal variability, with higher values during the rainy season compared to the dry season. This seasonal pattern aligns with findings from studies in the Sahel using the EC method for CO₂ flux measurements (Brümmer et al., 2008; Tagesson et al., 2015; Agbohessou et al., 2023, Wieckowski et al., 2024). Comparable patterns have been also documented at the ecosystem scale in other semi-arid environments (Ago et al., 2014; Archibald et al., 2009; Ardö et al., 2008; Jia et al., 2020; Quansah et al., 2015; Williams et al., 2009; Zhang, Bi, et al., 2024).

686 Several comparative studies between chamber and EC methods have reported both congruent and divergent CO₂ flux estimates (Bastviken et al., 2022; Poyda et al., 2017; Riederer et al., 2014; J. Tang et al., 2008; Wang et al., 2010). In the present study, ecosystem respiration fluxes during the rainy season exhibited notable discrepancies measurements between EC (Reco.EC) and upscaled chamber-based (Rch.stand). This is attributable to differences in the flux components captured by each method. Specifically, Reco.EC included respiration from below- and above-ground tree parts, crops (groundnuts and cowpeas), weeds, and soil, whereas Rch.stand accounted only respiration from below-ground tree, groundnut crop, and soil. Therefore, as expected, Reco.EC (4.6 ± 3.2 g C-CO₂ m⁻² d⁻¹) were significantly higher than Rch.stand (2.0 ± 1.1 g C-CO₂ m⁻² d⁻¹).

696 For chamber-based GPP measurements, values were standardised (GPP-stand) by the field's leaf area index (LAI.field). This allowed it to improve comparability with GPP.EC when trees were leafless in the rainy season. In both cases, GPP accounted only for crops (groundnut and cowpea) and weeds, as trees were non-photosynthetic in the rainy season. Despite this standardisation, GPP.EC values (-5.1 ± 3.6 g C-CO₂ m⁻² d⁻¹) were significantly higher than GPPch.stand values (-4.2 ± 4.3 g C-CO₂ m⁻² d⁻¹). However, the divergence did not occur on the peak of GPP (which was very similar in both methods), but from the onset of groundnut senescence, when weeds became the dominant photosynthetic contributors. Thus, during the groundnut growth season, with leafless *F. albida* trees and almost no weeds, GPP measurements from EC and chambers generate closely comparable results. Therefore, this provides an initial form of cross-validation between the two methods. It is important to note that the EC method integrates CO₂ fluxes over a larger spatial scale, encompassing all ecosystem components (Baldocchi, 2003), while the chamber method captures fluxes on a smaller scale (i.e., at the 0.25 m² scale). This scale disparity can introduce uncertainties when upscaling chamber-based fluxes to the field, as vegetation composition within chambers does not represent the EC footprint's average vegetation. This makes upscaling chamber-based measurements challenging. Nevertheless, the standardisation we applied on chamber photosynthesis by LAI has been relatively successful.

713 During the dry season, Reco.EC included respiration from below- and above-ground tree parts (with leaves) and bare soil, while Rch.stand measured only below-ground tree and bare soil
<figure><img src="image_61.png" /></figure>

715 respiration. Consequently, the difference between Reco.EC and Rch.stand was solely attributable
716 to above-ground tree respiration (Ra tree above-ground). In terms of GPP, chamber
717 measurements were nil, whereas GPP.EC reflected only GPP trees.

718 The transition period, characterised by groundnut senescence, tree leaf regrowth, and weed
719 proliferation, introduced further complexity, amplifying method-specific discrepancies. Rch.stand
720 measurements facilitated the estimation of tree contribution to Reco.EC (Ra tree) and the
721 verification of the consistency for EC results in terms of carbon use efficiency (CUE), estimated
722 here at 0.48. This value indicates that nearly 50% of the carbon captured by trees is allocated to
723 biomass. The CUE estimate here is well comparable to the global average across diverse
724 ecosystems, climates, and management practices (0.49 ± 0.14) (Tang et al., 2019). Similar CUE
725 values have been reported for semi-arid grasslands (0.46 ± 0.10), but our value is notably lower
726 than those documented for wetlands (0.61 ± 0.13) (Tang et al., 2019). Overall, these findings
727 reinforce the plausibility of our assumptions regarding the compartment's contributions to
728 Reco.EC and Rch.stand, thereby providing a second cross-validation of the EC-Ch comparison.
729 However, despite a frequently assumed CUE of 0.5 in models, global estimates span a broad range
730 (0.20 to 0.82), depending on ecosystem type and management practices (DeLucia et al., 2007;
731 Tang et al., 2019). This underscores the importance of refining carbon flux models to better
732 represent the biophysical processes governing CO₂ exchange in semi-arid agroforestry systems.
733 The combined use of EC and chamber methodologies offers a comprehensive perspective on
734 ecosystem-scale CO₂ flux dynamics, advancing understanding of carbon cycling in these
735 environments.

## 4.6. Net carbon exchange budget

737 The annual net carbon (C) exchange budget was quantified at -1.4 ± 0.02 Mg C-CO₂ ha⁻¹ with the
738 chamber method and -1.8 ± 0.01 Mg C-CO₂ ha⁻¹ by the Eddy Covariance (EC), indicating that the
739 studied agro-silvo-pastoral system functions as a net carbon sink. These findings corroborate the
740 system's potential role in mitigating greenhouse gas emissions, consistent with previous
741 observations in semi-arid ecosystems (Rahimi et al., 2021; Tagesson et al., 2015; Agbohessou et
742 al., 2023, Wieckowski et al., 2024).

743 The estimated net C exchange budget is close to the reported mean for Sahelian ecosystems (-1.6
744 ± 0.5 Mg C-CO₂ ha⁻¹; Tagesson et al., 2016). The EC-based net C exchange budget (-1.8 ± 0.01 Mg
745 C-CO₂ ha⁻¹) is also similar to the value of -1.9 ± 0.4 Mg C-CO₂ ha⁻¹ reported for semi-arid savannas
746 of northeastern Benin, despite higher annual rainfall (1495 mm; Ago et al., 2016b). Furthermore,
747 our EC estimate is close to the average net C exchange reported for West African terrestrial
748 ecosystems (-2.0 ± 1.5 Mg C-CO₂ ha⁻¹; Ago et al., 2016a).
<figure><img src="image_62.png" /></figure>

However, estimates from Tagesson et al. (2015) (-2.7 ± 0.07 Mg C-CO₂ ha⁻¹) for a semi-arid savannah in Dahra, Senegal, located between the 300 mm and 400 mm isohyets, were comparatively higher. This is potentially attributable to specific characteristics of that specific savannah site, such as herbaceous vegetation cover during the rainy season, the presence of evergreen trees, and land management practices linked to pastoral livestock activities (Tagesson et al., 2016).

The net C exchange estimates presented in this study are, in fact, apparent fluxes, given that they exclude organic matter (OM) imports and, more critically, exports, introducing uncertainties. Notably, the export of crop residues and direct inputs from animal excreta —particularly significant in 'bush fields' during the dry season — were not accounted for. In our case of 'bush field', crop residues are exported to feed livestock, while livestock faeces are collected for use as fuel or manure in 'home fields'. Such practices may lead to a significant soil organic carbon stocks depletion (Malou et al, 2021), potentially diminishing the net C budget (-1.4 ± 0.02 Mg C-CO₂ ha⁻¹) over time and shifting the system closer to carbon neutrality (Assouma et al., 2019).

These results should be contextualized within the broader framework of climate change and semi-arid ecosystem management. Although agro-silvo-pastoral systems can function as annual carbon sinks, they remain highly sensitive to interannual rainfall variability and escalating anthropogenic pressures. Sustainable management practices, particularly regarding crop residue exports, are essential for maintaining soil mineral fertility and preserving the system's capacity to act as a carbon sink, thereby contributing to climate change mitigation.

## 4.7. Limitations of the study

This study benefited from the inverse phenology of F. albida, allowing for direct comparison between chamber-based GPP (GPPch.stand) and ecosystem-level GPP (GPP.EC) during the leafless period of the trees. However, the system's spatial heterogeneity —common in agroforestry— posed challenges for accurately partitioning CO₂ fluxes among trees, crops, and soil. A key limitation was the development of weeds during the late rainy season, which complicated the attribution of fluxes, particularly during the transitional period. Additionally, while GPPch was successfully standardised by LAI for upscaling, this was not feasible for respiration. Respiration integrates both autotrophic and heterotrophic components, which respond to different drivers and are not directly linked to LAI, limiting the precision of upscaled Rch.

Future improvements should aim to separately quantify respiration sources —tree roots, crops, and microbial (heterotrophic) respiration— and account explicitly for the weed layer, to refine flux partitioning in such complex agroforestry systems.
<figure><img src="image_63.png" /></figure>

<figure><img src="image_64.png" /></figure>

783 **Conclusion**

784 This study demonstrates the successful application of automated static chambers to quantify CO₂ fluxes in a Sahelian agroforestry system dominated by *F. albida*. The continuous, high-frequency measurements captured key seasonal dynamics and short-lived events (e.g., Birch effect), providing a more accurate assessment of carbon exchange than traditional intermittent sampling.
785
786
787 By integrating crop and soil components and applying dynamic partitioning models, the study quantified both respiration and photosynthesis fluxes at fine temporal resolution. The results revealed a clear 'fertile island' effect under tree canopies, with higher respiration and photosynthetic activity, and highlighted the significant contribution of *F. albida* trees to annual carbon uptake.
788
789
790
791
792

793 The consistency between chamber- and eddy covariance-based estimates reinforces the robustness of the methodology. Overall, this work underscores the role of *F. albida*-based agroforestry systems as effective carbon sinks in semi-arid environments, offering valuable insights for carbon accounting and sustainable land management in the Sahel.
794
795
796
<figure><img src="image_65.png" /></figure>

797 Acknowledgments

798 This research was financially supported by the CaSSECS project (Carbon Sequestration and
799 Greenhouse Gas Emissions in (Agro) Silvopastoral Systems of the CILS-Sahel States
800 (FOOD/2019/410-169), within the framework of the European Union's initiative 'Development
801 of Smart Innovation through Agricultural Research' (DeSIRA-UE-EuropAID). We extend our
802 sincere gratitude to the coordination team of the CaSSECS project, the "Laboratoire Mixte
803 International Intensification Écologique des Sols Cultivés en Afrique de l'Ouest" (LMI IESOL) of
804 the of the French National Institute for Development (IRD) in Dakar (Senegal), as well as to the
805 Faidherbia-Flux platform (https://lped.info/wikiObsSN/?Faidherbia-Flux), its partners, and
806 affiliated projects PEPR FairCarbonN/PC3-RIFT, EU-H2020 [SUSTAIN-SAHEL (Grant N° 861974)]
807 and EU-HORIZON EUROPE [GALILEO (Grant N° 101181623)]. Our deepest appreciation goes to
808 Ibou Diouf, the observer at our experimental site. Tagesson also acknowledged funding from
809 Formas (Dnr 2021-00644).
<figure><img src="image_66.png" /></figure>

810 Author contribution: CRediT

811 **Seydina Mohamad BA**: Conducting in situ experiments, collecting and processing data,

812 writing-original draft, review and editing. **Olivier Roupsard**: Designing experimental

813 apparatus and methodology, writing, review and editing. **Lydie Chapuis-Lardy**: Designing

814 methodology, writing, review and editing. **Yélognissè Agbohessou**: Processing data,

815 review and editing. **Fred Bouvery**: Designing chambers and connection to the instrument,

816 review and editing. **Maxime Duthoit**: Designing experimental set and methodology,

817 review and editing. **Aleksander Wieckowski**: Review and editing. **Mohamed Habibou**

818 **Assouma**: Review and editing. **Espoir Gaglo**: Processing data, review and editing. **Claire**

819 **Delon**: review and editing. **Torbern Tagesson**: Designing methodology, review, and

820 editing. **Bienvenu Sambou**: Review and editing. **Dominique Serça**: Designing methodology,

821 writing, review and editing.
<figure><img src="image_67.png" /></figure>

<figure><img src="image_68.png" /></figure>

822 References

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848 Assefa, A., Muthuri, C. W., Gebrekirstos, A., Hadgu, K., & Fetene, M.: Tree growth and wheat productivity are affected by pollarding Faidherbia albida in semi-arid Ethiopia, Agroforest. Syst., 98(3), 783-796, https://doi.org/10.1007/s10457-023-00948-7, 2024.
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