Md Zulfikar Khan

Abad Chabbi
email: abad.chabbi@inrae.fr

Caitlin E Hicks Pries

Margaret S Torn

Cornelia Rumpel

Pauline Chivenge

Urp3f

Management impacts on whole soil warming responses of CO 2 production and efflux in temperate climate

Keywords:

Although global warming has the potential to increase soil CO 2 efflux, the magnitude of these changes are uncertain, as CO 2 production rates in deep soil are poorly constrained. In particular, management effects on the warming responses at depth are unknown. Here, we conducted an in-situ soil warming experiment down to 2.0-m depth in an agricultural Cambisol to study the warming responses of (1) CO 2 production across different depths and (2) CO 2 efflux from topsoil in different seasons under two management practices. To this end, we measured whole-soil profile water content, CO 2 production and CO 2 efflux under continuous grassland and cropland in response to elevated temperature (+4 • C).

Warming decreased soil water content for both management practices. We found contrasting warming effects on surface CO 2 efflux, depending on season and land management practices. Subsoil CO 2 production was more sensitive to warming than topsoil CO 2 production with grassland subsoil showing a greater warming response than cropland subsoil. Topsoil CO 2 production decreased in response to warming in the cropland but not the grassland. We concluded that warming responses of CO 2 production and efflux are affected by soil management practices. Their effect on biological processes (roots and microbial activity) and factors affecting gas diffusivity, such as soil water availability and soil physical organization need to be assessed to model warming effects on carbon exchange between soil and the atmosphere in agricultural systems.

Introduction

According to the Representative Concentration Pathways (RCPs) 8.5 scenario, the global soil temperature is expected to rise to +4.5 ± 1.1 • C above pre-industrial levels down to 1.0 m depth by the end of the 21st century (worst IPCC scenario) [START_REF] Soong | CMIP5 Models Predict Rapid and Deep Soil Warming Over the 21st Century[END_REF]. These increasing temperatures will affect carbon (C) cycling in terrestrial ecosystems and possibly cause positive or negative feedbacks in terms of CO 2 exchange between the soil and atmospheric reservoirs [START_REF] Carey | Temperature response of soil respiration largely unaltered with experimental warming[END_REF][START_REF] Davidson | Temperature sensitivity of soil carbon decomposition and feedbacks to climate change[END_REF]. One of the most important mechanisms driving feedbacks to climate change is the increase in microbial activity in response to warming, which can increase the decomposition of soil organic carbon (SOC) and the soil CO 2 efflux to the atmosphere [START_REF] Crowther | Quantifying global soil carbon losses in response to warming[END_REF]. SOC is the largest biologically active C pool in terrestrial ecosystems, storing twice as much C as the earth's atmosphere and vegetation combined (up to 2500 Pg C) [START_REF] Friedlingstein | Global Carbon Budget[END_REF]IPCC, 2013). But it is still uncertain how this reservoir will respond to warming because the mechanisms controlling SOC turnover and microbial access to SOC are not fully understood [START_REF] Bradford | Managing uncertainty in soil carbon feedbacks to climate change[END_REF][START_REF] Schmidt | Persistence of soil organic matter as an ecosystem property[END_REF][START_REF] Van Gestel | Predicting soil carbon loss with warming[END_REF].

In view of the international efforts to increase SOC stocks in agricultural systems through the development of sustainable agricultural practices [START_REF] Rumpel | The 4p1000 initiative: Opportunities, limitations and challenges for implementing soil organic carbon sequestration as a sustainable development strategy[END_REF][START_REF] Smith | Biophysical and economic limits to negative CO2 emissions[END_REF], warming effects on C fluxes in managed agricultural systems are of crucial importance. Warming may affect the CO 2 soil efflux through its impact on the production and transport of CO 2 within and between individual soil depths. This was evidenced in recent in-situ whole soil warming (+4 • C) experiments, in temperate and tropical forests, which found that warming caused substantial increases in soil CO 2 efflux due to the stimulation of soil profile CO 2 production [START_REF] Hicks Pries | The whole-soil carbon flux in response to warming[END_REF][START_REF] Nottingham | Soil carbon loss by experimental warming in a tropical forest[END_REF]. This increase was induced by the heterotrophic decomposition of SOC pools rather than autotrophic sources [START_REF] Nottingham | Soil carbon loss by experimental warming in a tropical forest[END_REF][START_REF] Soong | Five years of whole-soil warming led to loss of subsoil carbon stocks and increased CO2 efflux[END_REF].

Rising temperatures are expected to stimulate CO 2 efflux by their effect on the plant-soil system [START_REF] Bond-Lamberty | Temperature-associated increases in the global soil respiration record[END_REF][START_REF] Jochheim | Dynamics of Soil CO2 Efflux and Vertical CO2 Production in a European Beech and a Scots Pine Forest[END_REF][START_REF] Nottingham | Soil carbon loss by experimental warming in a tropical forest[END_REF] and the magnitude of these responses are likely controlled by water availability [START_REF] Védère | How does soil water status influence the fate of soil organic matter? A review of processes across scales[END_REF]. Water regulates plant and microbial activity, substrate availability, and soil gas diffusivity [START_REF] Chen | Effects of precipitation on soil organic carbon fractions in three subtropical forests in southern China[END_REF][START_REF] Mcgrath | Minerals limit the deep soil respiration response to warming in a tropical Andisol[END_REF][START_REF] Wood | Sensitivity of Soil Respiration to Variability in Soil Moisture and Temperature in a Humid Tropical Forest[END_REF]. Also, seasonal climate variations affect soil temperature, soil water content, and plant phenology in temperate ecosystems [START_REF] Vincent | Spatial and seasonal variations in soil respiration in a temperate deciduous forest with fluctuating water table[END_REF], leading to variable soil CO 2 efflux across time [START_REF] Vanhala | Seasonal variation in the soil respiration rate in coniferous forest soils[END_REF][START_REF] Vincent | Spatial and seasonal variations in soil respiration in a temperate deciduous forest with fluctuating water table[END_REF]. The controlling factors of CO 2 production and efflux, such as soil water content, microbial activity, root abundance and activity, which control substrate supply, and soil tortuosity are all influenced by soil management practices [START_REF] Kuzyakov | Sources of CO2 efflux from soil and review of partitioning methods[END_REF][START_REF] Oertel | Greenhouse gas emissions from soils-A review[END_REF][START_REF] Pagenkemper | Investigation of Time Dependent Development of Soil Structure and Formation of Macropore Networks as Affected by Various Precrop Species[END_REF][START_REF] Md | Environmental controls and the influence of vegetation type, fine roots and rhizomorphs on diel and seasonal variation in soil respiration[END_REF]. Insight into how soil warming interacts with management to affect seasonal soil CO 2 efflux and its origin within the soil profile will enhance our ability to predict terrestrial C fluxes and C sink potential under future projected climate change.

The majority of publications addressing the effects of agricultural management on SOC turnover have focused on topsoil, ignoring the amount and climate vulnerability of SOC in deep soil layers [START_REF] Van Der Voort | Dynamics of deep soil carbon -Insights from 14C time series across a climatic gradient[END_REF][START_REF] Yost | How deep is the soil studiedan analysis of four soil science journals[END_REF][START_REF] Moreland | Deep in the Sierra Nevada critical zone: saprock represents a large terrestrial organic carbon stock[END_REF]. It has been assumed that SOC stored in deep soil is relatively stable and unresponsive to agricultural practices. This assumption was mainly based on radiocarbon dating studies showing that subsurface C could be hundreds to thousands of years old [START_REF] Hobley | Land-use contrasts reveal instability of subsoil organic carbon[END_REF][START_REF] Wang | The impact of land use change C turnover in soils[END_REF]. Although decomposition rates in deeper soils are slower than in surface soils, recent studies have shown that part of subsoil C could turn over much faster due to preferential input of labile organic matter which could induce positive priming effects in subsoil [START_REF] Chabbi | Stabilised carbon in subsoil horizons is located in spatially distinct parts of the soil profile[END_REF][START_REF] Min | Differential effects of wetting and drying on soil CO2 concentration and flux in near-surface vs. deep soil layers[END_REF][START_REF] Rumpel | Deep soil organic matter-a key but poorly understood component of terrestrial C cycle[END_REF] and also be more responsive to changing abiotic conditions [START_REF] Hicks Pries | The whole-soil carbon flux in response to warming[END_REF][START_REF] Nottingham | Soil carbon loss by experimental warming in a tropical forest[END_REF][START_REF] Ofiti | Warming promotes loss of subsoil carbon through accelerated degradation of plant-derived organic matter[END_REF][START_REF] Zhang | Rising soil temperature in China and its potential ecological impact[END_REF] and agricultural management practices [START_REF] Harrison | Deep Soil Horizons: Contribution and Importance to Soil Carbon Pools and in Assessing Whole-Ecosystem Response to Management and Global Change[END_REF][START_REF] Osanai | Contrasting agricultural management effects on soil organic carbon dynamics between topsoil and subsoil[END_REF][START_REF] Samson | Response of subsurface C and N stocks dominates the whole-soil profile response to agricultural management practices in a cool, humid climate[END_REF][START_REF] Shahbaz | Decadal Nitrogen Fertilization Decreases Mineral-Associated and Subsoil Carbon: A 32-Year Study[END_REF] than previously assumed. Moreover, the microbial communities in subsoils are also quite different from those in surface soils, owing to lower SOC availability and more favorable conditions for oligotrophs due to nutrient-poor conditions [START_REF] Ekelund | Distribution with depth of protozoa, bacteria and fungi in soil profiles from three Danish forest sites[END_REF][START_REF] Fierer | Variations in microbial community composition through two soil depth profiles[END_REF][START_REF] Oertel | Greenhouse gas emissions from soils-A review[END_REF][START_REF] Scheffers | The broad footprint of climate change from genes to biomes to people[END_REF][START_REF] Stone | Changes in extracellular enzyme activity and microbial community structure with soil depth at the Luquillo Critical Zone Observatory[END_REF][START_REF] Zifcakova | Factors Affecting Soil Microbial Processes[END_REF]. Because of the soil depth profile with gradients of many abiotic and biotic factors affecting the mechanisms that control SOC input, loss, transformation, and stabilization, deep SOC is likely to respond differently to soil warming than surface SOC [START_REF] Hicks Pries | The Deep Soil Organic Carbon Response to Global Change[END_REF][START_REF] Lützow | Temperature sensitivity of soil organic matter decomposition-what do we know?[END_REF]. However, up to now it is unknown how deep soil C under different agricultural practices will respond to warming.

In this study, we thus aimed to provide in-situ experimental evidence on the SOC response to warming in managed agricultural soils down to 1.0 m depth. We used a whole-soil warming experiment established on an agricultural Cambisol to investigate how soil water content, CO 2 production, and CO 2 efflux respond to +4 • C of warming under continuous grassland and temporary grassland in its cropland phase. In particular, we had the following study questions: (1) How does warming affect soil water content under contrasting land management?; (2) How does whole-soil warming affect the CO 2 efflux under contrasting management practices and seasons?; (3) How does the CO 2 production among differ depths respond to warming under contrasting land management? We hypothesized that in response to in-situ warming (H1) water content will decrease across all soil depths, because warming induces higher evapotranspiration, (H2) CO 2 efflux will increase year round in soils under continuous grassland but only seasonally in cropland soils, and (H3) CO 2 production will increase across the whole soil profile in grassland but not cropland soils, because grasslands show more root activity and root-derived C inputs throughout the year.

Materials and methods

Study area description

The experimental field site is part of the long-term Observatory for Environmental Research: Agro-ecosystems, Biogeochemical Cycles and Biodiversity (SOERE ACBB, http://www.soere-acbb.com/index.ph p/fr/), hosted by the INRAE (French National Institute of Agricultural Research and Environment), situated in Lusignan, Nouvelle-Aquitaine (46 • 25′12.91″ N; 0 • 07′29.35″ E), western France. The climate of the experimental site is oceanic with mean annual precipitation of around 760 mm, with most of it occurring from November to March. The mean annual temperature is about 12 • C, with a monthly maximum of 27.9 • C in August and a monthly minimum of -3.1 • C in January (data from Météo France, 2007 to 2021). The site is flat with a slight slope gradient (0.5 percent). The soils are complex because of their particular pedogenetic evolution [START_REF] Ducloux | Les terres rouges à chataigniers de l'ouest de la France[END_REF]. The soil is classified as a Cambisol (FAO soil group) with loamy texture in the upper horizons, and clayey texture at depth with a high content of oxides and kaolinite, classified as a Paleo-Ferralsol. Pedoclimatic characteristics are shown in Table 1.

Land management history

The long-term experiment (SOERE ACBB) started in 2005 on an area previously covered by oak forest and then devoted to conventional agroecosystem management (grassland, grain cropping, or temporary (ley) grassland) for at least the next 100 years. The aim of the experiment is to elucidate the impact of temporary grassland introduction into the cropping cycle (maize (Zea mays L.), winter wheat (Triticum spp.), and winter barley (Hordeum vulgare L.)) on biogeochemical cycling. The experiment is conducted with several treatments and two controls (continuous grassland and continuous cropland). Temporary (ley) grassland treatments differ in duration (3 or 6 years of ley), fertilization strategy (with or without N fertilization) and harvesting regime (cutting or pasture). Croplands are ploughed to a depth of 30 cm (using a mouldboard plough), and crop residues (straw and yield) are returned to the soil after harvesting. For grassland treatments, a mixture of perennial ryegrass (Lolium perenne cv. Milca), cocksfoot (Dactylis glomerata cv. Ludac), and tall fescue (Festuca arundinacea cv. Soni) is used. Grasslands are hayed three to four times per year depending on productivity or grazed by cattle. Plant root biomass is greater in the continuous grassland than in the ley grassland and cropland down to 90 cm depth (Table 2).

In-situ experimental soil warming

In 2019, we established an in-situ soil warming (+4 • C) experiment in the continuous grassland and in the ley grassland that began its cropland phase in 2020 after 6 years of temporary grassland (Table 3 and Fig. 1). The treatments are called 'grassland' and 'cropland' hereafter. The in-situ warming experiment heated the soil to +4 • C above ambient temperature ( • C) across 10 m 2 circular plots to a depth of 1.0 m with 2.0 m long resistance heating cables while retaining the natural temperature gradient within the soil profile based on Hicks [START_REF] Hicks Pries | The whole-soil carbon flux in response to warming[END_REF] and [START_REF] Hanson | A method for experimental heating of intact soil profiles for application to climate change experiments[END_REF]. The warming experiment consists of three (n = 3) replicated plot pairs: control (ambient temperature) and heated (ambient temperature +4 • C). There was approximately a 10 m distance between the control and heated pair plots (Fig. 2b). Briefly, warming was maintained throughout the 3.5 m diameter circular plot and down to 1.0 m depth with twenty-two 2.0-m-long steel pipes, which were installed vertically around 0.50 m apart and 0.10 m beyond the edge of the plots (Fig. 2a). In warmed plots, self-regulating heating cables (TECHNITRACE (CAMT EA 35 W/m 230 V), France) were placed inside the pipes, which were then filled with sand, while control plots did not have that type of heating cable. We installed additional heating cables 5 cm below the soil surface in a horizontal zigzagging pattern across the circular plot, around 0.30 m apart, to compensate for surface heat loss (Fig. 2a). Similar unpowered heated cables were not installed in the control plots, modified from Hicks [START_REF] Hicks Pries | The whole-soil carbon flux in response to warming[END_REF]. The heaters were powered by an electric power supply, which was controlled by a silicon-controlled rectifier (SCRs, Watlow, Missouri, USA) via a currentto-voltage converter with a unipolar multiplexer (1KOhms resistance). The amount of electricity (source of heating) supplied to the deep heaters was based on the temperature difference between control-heated paired thermistors at 50, 75, and 100 cm depths at 0.75 m radial distance from the center of the plot. Similarly, the temperature difference of thermistors at 15 and 30 cm depth at radial distances of 0 and 0.75 m from the center of the plot was used to calculate the power supplied to the surface heaters of each paired plot. We installed this in-situ soil warming experiment in two phases: [START_REF] Védère | How does soil water status influence the fate of soil organic matter? A review of processes across scales[END_REF] initial installation in 2019 and 2020, during which the warming system was turned on, and (2) conditioning in 2020 and 2021 before field data collection started in June 2021 (Fig. 2c). The conditioning phase was due to low soil warming with respect to our target of +4 • C, and we reached our target after 1.5 years of turning on the warming system.

Automated temperature and moisture data measurements

Portable dataloggers (CR1000, Campbell Scientific, Utah, USA) recorded continuous soil profile temperature and moisture data at 10second intervals. In each plot, a PVC tube containing six temperature probes (OMEGA 44,007 thermistor) was placed at different depths (5, 15, 30, 50, 75, and 100 cm) with five replicates at 0.75 m radial distance from each other to monitor temperature, modified from Hicks Pries et al. (2017). The sensors at 15 and 75 cm depth were also used to regulate the surface and depth temperatures in order to ensure that the +4 • C relative to the control is maintained throughout the soil profile. Another tube including five capacitance sensors equipped with enviroSCAN probes (Sentek, Australia) measured soil moisture at different depths (15, 30, 50, 70, and 90 cm) at a radial distance of 0.75 m from the center of each plot. The soil moisture readings were calibrated by comparing the sensor values at each depth to the volumetric water content recorded in the nearest (within 0.30 m) soil samples. We repeated this soil sampling two times over one year, in both dry and wet periods, to minimize the calibration errors.

Evaluation of in-situ soil warming (+4 • C) performance

Our in-situ soil warming experiment shows a homogeneous temperature gradient (+4 ± 0.2 • C for cropland and +4 ± 0.3 • C for grassland) down to 1.0 m depth of the soil profile, except 0-15 cm depth, throughout the whole study period (Figs. 3a and3b). The 0-15 cm depth showed a higher variability (+4 ± 0.5 • C for cropland and +4 ± 0.4 • C for grassland) concerning our target (+4 • C) because there is no surface heating to compensate for the heat loss due to close contact of ambient air temperature and radiation compared to deep soils. However, the relative warming effect was consistent over the entire study period (Fig. 3a) and did not change with weather (seasonal fluctuations) or soil moisture conditions.

Leaf area index

During the growing season (March to June 2022), we measured leaf area (m 2 ) by scanning the leaf blades in a leaf area scanner (LI-2000, Li-

Table 1

Soil properties by horizon of the experimental site [START_REF] Chabbi | Stabilised carbon in subsoil horizons is located in spatially distinct parts of the soil profile[END_REF][START_REF] Moni | Spatial dependance of organic carbon-metal relationships: A multi-scale statistical analysis, from horizon to field[END_REF]. Mean and coefficient of variation expressed as a percentage of the mean. Both grassland and cropland treatments are on the same soil type. *Data collected from temporary grassland in 2019, after that was in its cropland phase after 6 years of ley grassland (see Table 3).

/cm /g cm -3 ---/g kg -1 soil--- /cmol c kg -1 /% ---/g kg -1 soil--- Lp (n = 6) 0-

Table 3

Long-term land-use management history under an experimental site since 2005 in the Nouvelle-Aquitaine (Lusignan) region of western France.

Cor, Lincoln, Nebraska, USA) to estimate the leaf area index (LAI, m 2 m -2 ) based on ground cover (m 2 ).

Soil organic carbon content

In late May 2022, we collected soil samples from control-heated paired plots (n = 3) at different soil depths (0-15 cm, 15-30 cm, 30-50 cm, 50-70 cm, and 70-90 cm) before harvesting the vegetation cover, including cropland and grassland. To elaborate, soils were randomly sampled using a mechanical soil auger (8 cm in diameter) with five replicates (n = 5). Subsequently, all replicates were combined into a single composite sample for each plot and depth. These samples were then air-dried, sieved to 2 mm, and ground into a fine powder. Following this preparation, the concentration of soil organic carbon (SOC) was determined using a CHN auto-analyzer (Flash EA, Thermo Electron Corporation, Bremen, Germany).

Soil CO 2 efflux

Measurements of surface soil respiration (CO 2 efflux) were performed from July 2021 to June 2022 using soil respiration autochambers (8100A, LI-COR, Lincoln, Nebraska), which continuously measured the soil respiration fluxes every 90 min in each plot. During each flux measurement, the air temperature was also recorded using a temperature probe (OMEGA 44,007 thermistor), which was connected to the auto-chamber lid above the soil surface. The soil CO 2 efflux was calculated as follows:

f = dc dt × hMP RT (1)
Where f denotes the surface CO 2 efflux rates (μg m -2 s -1 ), dc/dt is the rate of increase of the CO 2 concentration inside the chamber with time and is determined by the slope of linear regression; h is the height of the chamber; M is the molar mass of CO 2 ; P is the atmospheric pressure; R is the ideal gas constant; and T is the chamber air temperature in Kelvin. After that, the surface CO 2 efflux was converted to g C m -2 d -1 units.

To prevent CO 2 efflux from aboveground plant parts (leaves and stems), living plants inside the auto-chambers were manually eradicated once a week if they grew, and the removed plant material was left inside the chambers to die. Therefore, the CO 2 efflux measured in the soil did not include aboveground respiration from living plants (e.g., leaves and Geoderma 440 (2023) 116725 6 stems) but included belowground root respiration.

Soil profile CO 2 production

Gas measurements were carried out using a set of gas wells at 15, 30, 50, 70, and 90 cm depths of the soil profile, which had been installed at each plot at a distance of 0.20 m from the center. At 70 and 90 cm, the set up included two additional replicates to better account for deep soil heterogeneity. The gas wells were 6.35 mm (diameter) stainless steel tubes inserted at a 45 • angle into the soil to the desired depth and topped with straight swage pipe-fittings with septa at the distal end (TECHNI-TRACE, France). We collected soil gas samples every fifteen days during six months (January 2022 to June 2022). Samples were collected from the gas wells with a 60 ml syringe, after clearing the headspace in each well. For gas analysis, 3, 20 ml samples were immediately injected into a portable infrared gas analyzer (EGM-5, PP Systems, USA) in the field and stored the gas concentration (ppm) reading with a nonvolatile semiconductor memory (TECHNITRACE, France). In addition, the actual CO 2 concentration from the EGM-5 data was calculated using a four-point standard calibration curve ranging from 0 to 20,000 ppm.

We modeled depth-resolved CO 2 production (µmol m -3 s -1 ) from soil CO 2 concentrations (ppm) using Fick's first Law as a flux gradient approach (Davidson et al., 2006;[START_REF] Hicks Pries | The whole-soil carbon flux in response to warming[END_REF][START_REF] Vargas | Looking deeper into the soil: biophysical controls and seasonal lags of soil CO2 production and efflux[END_REF]:

F = -D dC dz ( 2
)
Where F denotes the flux density of CO 2 across a horizontal surface on each depth of soil (µmol m -2 s -1 ), D indicates the diffusion coefficient (mm 2 s -1 ) of air into soil pores, and the change in CO 2 mol fraction at each depth is represented by dC/dz, which was calculated using the first derivative of a curve fit to the depth profile of CO 2 mol fraction. For each depth and time interval, the diffusion coefficient (D) was calculated as follows:

D = D o *ξ ( 3
)
D o = D ao ( T 293.15 ) 1.75 ( 101.3 P ) ( 4
)
Where D o is the diffusion coefficient of CO 2 in the air at the soil temperature (T) and local air pressure (P), D ao is a reference value for CO 2 in the air (15.7 mm 2 s -1 ), and ξ is the dimensionless tortuosity factor. Tortuosity was estimated using six different equations [START_REF] Jassal | Relationship between soil CO2 concentrations and forest-floor CO2 effluxes[END_REF][START_REF] Millington | Gas Diffusion in Porous Media[END_REF][START_REF] Moldrup | Tortuosity, Diffusivity, and Permeability in the Soil Liquid and Gaseous Phases[END_REF][START_REF] Moyes | Interannual variation in seasonal drivers of soil respiration in a semi-arid Rocky Mountain meadow[END_REF], and we picked the equation that resulted in the best match between the measured surface flux and the estimated surface flux from soil profile data. The most effective equation was determined by an empirical relationship [START_REF] Hicks Pries | The whole-soil carbon flux in response to warming[END_REF][START_REF] Moyes | Interannual variation in seasonal drivers of soil respiration in a semi-arid Rocky Mountain meadow[END_REF]: 1.93 (5)

ξ = 0.95ε
Where ε is the calculated air-filled porosity (m 3 m -3 ) from the total soil porosity (based on dry soil bulk density and solid particle density) and volumetric water content at each soil depth and measurement time.

Finally, CO 2 production (µmol m -3 s -1 ) was calculated from a given depth interval by subtracting the difference in flux densities (µmol m -2 s -1 ) across that interval and dividing by the depth (m) increment. After that, the CO 2 production was converted to g C m -3 d -1 units.

Statistical analysis

To evaluate the impacts of the warming treatment on various measured responses, we ran mixed effects models in RStudio (R Core Team, 2022) using the nlme package [START_REF] Pinheiro | Linear and nonlinear mixed effects models (nlme)[END_REF] and restricted maximum likelihood with blocks (n = 3) as a random effect. Using Q-Q plots and residual histograms, we visually examined all models for homoscedasticity and normality of distribution, and we used a log transformation to meet model assumptions when necessary. Afterward, we used slightly different statistical model structures owing to the varied data structures of SOC concentration, soil water content, CO 2 production and CO 2 efflux. Plots were utilized as random effects to examine the fixed effects of treatment, depth, and their interaction on SOC content within the soil profile. This analysis was conducted separately for cropland and grassland.

To test the fixed effects of treatment, depth, and their interaction on soil water content, we used plots crossed with the sampling date as random effects (to account for violations of independence), separately for cropland and grassland. For surface soil CO 2 efflux, we used a different model that included treatment, seasons, and their interaction as fixed effects, and plots crossed with sampling date as random effects, to test the treatment and seasonal effects on surface soil CO 2 efflux, separately for cropland and grassland. To test the fixed effects of treatment, depth, and their interaction on soil CO 2 production from within the soil profile, we used plots crossed with the sampling date as random effects, separately for cropland and grassland. Besides that, we used treatment, field, and their interaction as fixed effects, and plots crossed with sampling date as random effects, to test the treatment and management effects on leaf area index. Also, a pairwise mean comparison test was performed using the emmeans package in RStudio [START_REF] Searle | Population marginal means in the linear model: An alternative to least squares means[END_REF]) when significant differences were observed for any response variables from mixed effect models.

We also calculated the warming response for each block at each sampling date, to test whether warming affected whole-soil profile CO 2 production and surface CO 2 efflux. The warming response was expressed as a percentage difference between warmed and control conditions, which was normalized by the control values of soil CO 2 production and surface CO 2 efflux. A similar warming response was also tested for SOC concentrations.

Warmingresponse(%) =

Warm -Control Control × 100 (6)

Results

Warming effects on soil profile water content

In control plots, annual average soil water contents across the different soil depths ranged from 23.7 to 26.4% in cropland and 24.3 to 28.5% in grassland (Fig. 4). The soil water content significantly (p ≤ 0.05) decreased in most depths with in-situ warming for grassland, and a decreasing trend was noted for cropland, which was only statistically significant at 50 cm (p ≤ 0.05). In addition, we observed a significant interaction between warming treatment and depth (p < 0.001) for both cropland and grassland. Indeed, grassland showed a stronger warming response than cropland, as warming reduced the water content under grassland by 9.7 to 22.9% and by 0.35 to 13.2% under cropland compared to control plots. The greatest differences between the two management practices were found at 30 cm depth with warming, where soil water content was strongly decreased for grassland and slightly decreased for cropland (Fig. 4).

The depth-wise soil water content also depended on the season (significant, p < 0.001) for both land management practices (supplementary Fig. S1). During the winter season, topsoil (0-15 cm) showed higher water content, while during other seasons deeper soils had a higher water content for both cropland and grassland. Despite the seasonal variations, in-situ warming reduced soil water content throughout the year for both land management practices.

Warming effects on seasonal CO 2 efflux

The mean surface CO 2 efflux (g C m -2 d -1 ) in the four seasons is presented in Fig. 5. In control plots, CO 2 efflux ranged from 0.52 to 1.35 g C m -2 d -1 under cropland and 0.50 to 2.47 g C m -2 d -1 under grassland (Fig. 5). The response of CO 2 efflux to warming depended on the season (Fig. 5). Comparing the different seasons for the grassland, our results show that in-situ warming only increased (non-significantly) surface CO 2 efflux in winter, while warming significantly (p ≤ 0.05) reduced CO 2 efflux in other seasons (Fig. 5). In contrast, in-situ warming had no effect on surface CO 2 efflux in cropland during the summer and winter, while in autumn and spring warming reduced CO 2 efflux (nonsignificantly) (Fig. 5). Overall, the whole-soil warming response of annual surface CO 2 efflux was reduced by 21.8% under cropland (from 399 g C m -2 y -1 in control plots to 312 g C m -2 y -1 in heated plots) and by 20.6% under grassland (from 528 g C m -2 y -1 in control plots to 419 g C m -2 y -1 in heated plots).

Warming effects on soil profile CO 2 production

The CO 2 production in the different soil depths, which was measured during six months, is presented in Fig. 6. In control plots, CO 2 production ranged from 0.54 to 4.68 g C m -3 d -1 under cropland and 0.84 to 5.21 g C m -3 d -1 under grassland (Fig. 6). In grassland, the CO 2 production significantly (p ≤ 0.05) increased with in-situ warming in subsoil (below 30 cm), while statistically non-significant differences were observed between the two treatments in topsoil (0-30 cm) (Fig. 6). In contrast, in cropland, the CO 2 production significantly (p ≤ 0.05) decreased with in-situ warming at the 0-15 cm and 30-50 cm depths, but an increasing trend was also noted below 50 cm (Fig. 6). The interaction between treatment and depth was significant (p < 0.001) for cropland but not for grassland (p = 0.084).

Overall, we found that warming-induced CO 2 production in subsoil was higher for both grassland and cropland as compared to their controls, with a larger increase in grassland (42%) than in cropland (10%). In contrast, the warming-induced CO 2 production rate in topsoil (0-30 cm) differed between grassland and cropland: warming increased CO 2 production by 2.4% in grassland but decreased CO 2 production by 4.2% in cropland. However, it should be noted that the depthwise CO 2 production measurements occurred during the winter and spring seasons, while we do not have data for the summer and autumn seasons. The warming response on cumulative CO 2 production from the whole-soil profile during six months was increased by 1.2% under cropland (from 84 g C m -3 half.y -1 in control plots to 85 g C m -3 half.y -1 in heated plots) and by 16.3% under grassland (from 98 g C m -3 half.y -1 in control plots to 114 g C m -3 half.y -1 in heated plots). Geoderma 440 (2023) 116725 9

Warming effects on soil profile carbon content

In the control plots, SOC concentrations varied across different soil depths, ranging from 20.24 to 5.42 kg C m -3 soil in cropland and from 22.59 to 5.20 kg C m -3 soil in grassland (Fig. 7). In-situ warming did not affect the topsoil SOC content under both land management practices. However, a divergent response to warming was observed in the subsoil (>30 cm): it significantly increased under cropland but not under grassland (Fig. 7). The interaction between treatment and depth was found to be significant (p < 0.001) for cropland but not for grassland (p = 0.484).

Overall, we observed that the warming-induced SOC content in subsoil was higher in cropland (12.7%) but lower in grassland (0.09%). In contrast, the warming-induced SOC content in topsoil decreased for both cropland (0.05%) and grassland (2.42%) compared to their respective controls. Over the course of one year, the warming response on SOC content across the entire soil profile increased by 4.9% in cropland (from 60.6 kg C m -3 in control plots to 63.6 kg C m -3 in heated plots) and decreased by 1.6% in grassland (from 57.4 kg C m -3 in control plots to 56.5 kg C m -3 in heated plots).

Warming effects on leaf area index

In control plots, the leaf area index was 3.2 under cropland and 3.4 under grassland (Table 4). It significantly increased with in-situ warming for grassland (p < 0.01) and also showed an increasing, but nonsignificant trend for cropland (Table 4).

Discussion

Negative warming effects on whole-soil profile water content under the two land management practices

Our findings revealed that in-situ warming had a negative effect on soil water content in the whole-soil profile for both land management practices (Fig. 4). These results are in agreement with a recent metaanalysis by [START_REF] Xu | A meta-analysis of the response of soil moisture to experimental warming[END_REF], which showed that warming generally leads to soil drying in agricultural ecosystems. The two main processes of water loss from the soil reservoir include evaporation from the soil surface and transpiration by plants [START_REF] Chapin | Principles of Terrestrial Ecosystem Ecology[END_REF]. Both may be affected by land management practices [START_REF] Liancourt | Temporal and spatial variation in how vegetation alters the soil moisture response to climate manipulation[END_REF]. In this study, we showed that grassland soil was more vulnerable to soil water loss under in-situ warming compared to cropland. Despite the lack of air warming, warming-induced reduction in soil water content could be attributed to increased plant growth and transpiration processes with a higher leaf area index (Table 4), as also found by other authors (Chen et al., 2021a;[START_REF] Wan | Changes in microclimate induced by experimental warming and clipping in tallgrass prairie[END_REF][START_REF] Xia | Impacts of day versus night warming on soil microclimate: Results from a semiarid temperate steppe[END_REF]. However, the plants' effects on soil water might be species-or management-and/or site-specific [START_REF] Wang | Soil moisture and evapotranspiration of different land cover types in the Loess Plateau[END_REF]. In particular, in grassland with a complex root system exploring deeper soil layers it might be stronger than croplands. Indeed in grasslands, perennial plants could respire throughout the year involving (i) transpiration by plant leaves and (ii) water consumption by plant roots for their physiological and metabolic activities [START_REF] Du | Sapflow characteristics and climatic responses in three forest species in the semiarid Loess Plateau region of China[END_REF]. In contrast, cropland soil is characterized by periods of bare soil, plant regrowth and harvest, involving less root production (Table 2) [START_REF] Hu | Does the higher root carbon contribution to soil under cropping cycles following grassland conversion also increase shoot biomass?[END_REF] and consequently probably much less water consumption by plant roots and transpiration. As soil water becomes depleted under in-situ warming, this could alter the plants' C allocation strategies depending on land management practices, thereby affecting the C inputs and turnover in the soil profile [START_REF] Li | Deep soil water extraction by apple sequesters organic carbon via root biomass rather than altering soil organic carbon content[END_REF][START_REF] Mokany | Critical analysis of root : shoot ratios in terrestrial biomes[END_REF][START_REF] Védère | How does soil water status influence the fate of soil organic matter? A review of processes across scales[END_REF].

Contrasting warming response of seasonal CO 2 efflux under the two land management practices

Our data indicated that in-situ warming suppressed surface CO 2 efflux for both land management practices in the spring and autumn, whereas the other seasons (winter and summer) showed a contrasting warming response of surface CO 2 efflux (Fig. 5). In-situ warming could be a constraint for the spring and autumn seasons by slowing the processes that lead to both autotrophic and heterotrophic respirations that are controlled by precipitation and soil moisture determining plant growth [START_REF] Miao | Effects of warming on soil respiration during the non-growing seasons in a semiarid temperate steppe[END_REF][START_REF] Quansah | Carbon dioxide fluxes from contrasting ecosystems in the Sudanian Savanna in West Africa[END_REF]. In contrast, the different warming responses of soil CO 2 efflux in the winter and summer for both land management practices might be related to the continuous rhizodeposition supplied by the perennial root systems in grassland while cropland soil is bare during these periods [START_REF] Hu | Grassland management and integration during crop rotation impact soil carbon changes and grass-crop production[END_REF][START_REF] Mcgowan | Soil Organic Carbon, Aggregation, and Microbial Community Structure in Annual and Perennial Biofuel Crops[END_REF]. In grassland, the warming response was negative in summer but positive in winter, which could be due to the warminginduced lower soil water content and seasonal fluctuation of plant growth and their root activity [START_REF] Du | Seasonal changes and vertical distribution of fine root biomass during vegetation restoration in a Karst area, southwest China[END_REF][START_REF] Hu | Plant community characteristics and their relationships with soil properties in a karst region of southwest China[END_REF]. For example, the combination of high soil temperature and low soil water content might constrain root and microbial activity under summer warming, while in winter adequate moisture fuels greater microbial activity (supplementary Fig. S1) [START_REF] Davidson | Soil water content and temperature as independent or confounded factors controlling soil respiration in a temperate mixed hardwood forest[END_REF][START_REF] Vincent | Spatial and seasonal variations in soil respiration in a temperate deciduous forest with fluctuating water table[END_REF]. In cropland, in-situ warming did not affect surface CO 2 efflux during summer or winter, which could be because the plants are only actively growing in the spring, providing evidence for plant activity as a control (Chen et al., 2021b).

The contrasting warming response of seasonal CO 2 efflux leads to an overall reduction in whole-soil warming annual surface CO 2 efflux, depending on land management practices. Our findings differ from those found in various terrestrial ecosystems where warming stimulated soil CO 2 efflux [START_REF] Hicks Pries | The whole-soil carbon flux in response to warming[END_REF][START_REF] Lin | Response of ecosystem respiration to warming and grazing during the growing seasons in the alpine meadow on the Tibetan plateau[END_REF][START_REF] Luo | Effect of warming and grazing on litter mass loss and temperature sensitivity of litter and dung mass loss on the Tibetan plateau[END_REF][START_REF] Ofiti | Warming promotes loss of subsoil carbon through accelerated degradation of plant-derived organic matter[END_REF][START_REF] Schindlbacher | Carbon losses due to soil warming: Do autotrophic and heterotrophic soil respiration respond equally?[END_REF][START_REF] Yan | Response of soil greenhouse gas fluxes to warming: A global meta-analysis of field studies[END_REF]. Nevertheless, our data are consistent with studies reporting reduced soil and microbial respiration under warming in an old-field grasslands [START_REF] Wan | Responses of soil respiration to elevated CO2, air warming, and changing soil water availability in a model old-field grassland[END_REF], in some seasons in tallgrass prairie [START_REF] Verburg | Impacts of an anomalously warm year on soil CO2 efflux in experimentally manipulated tallgrass prairie ecosystems[END_REF], and in an alpine meadow [START_REF] Saleska | The effect of experimental ecosystem warming on CO2 fluxes in a montane meadow[END_REF]. However, one recent meta-analysis demonstrated that on average, global soil will be a source of C to the atmosphere under a future warmer climate [START_REF] Wang | Global soil profiles indicate depth-dependent soil carbon losses under a warmer climate[END_REF], supporting the expectation of positive SOC loss as a warming feedback [START_REF] García-Palacios | Evidence for large microbial-mediated losses of soil carbon under anthropogenic warming[END_REF]. In our study, warming-induced reductions in soil water content may be strongly influencing surface CO 2 efflux, through its influence on microbial communities and their functionalities, which are temperature-and moisture-dependent [START_REF] Curiel Yuste | Microbial soil respiration and its dependency on carbon inputs, soil temperature and moisture[END_REF][START_REF] Davidson | Temperature sensitivity of soil carbon decomposition and feedbacks to climate change[END_REF]. Water availability is a critical factor that can affect plant and microbial respiration. When water is limited, plants slow down photosynthesis to conserve water [START_REF] Wang | Effects of drought stress on photosynthesis and photosynthetic electron transport chain in young apple tree leaves[END_REF], leading to a decrease in plant contributions to soil respiration [START_REF] Ribas-Carbo | Effects of Water Stress on Respiration in Soybean Leaves[END_REF]. Similarly, water limitation can reduce microbial activity and their contribution to respiration as insufficient water can impede substrate diffusion to microbial cells [START_REF] Lehmann | Persistence of soil organic carbon caused by functional complexity[END_REF][START_REF] Moyano | Responses of soil heterotrophic respiration to moisture availability: An exploration of processes and models[END_REF][START_REF] Védère | How does soil water status influence the fate of soil organic matter? A review of processes across scales[END_REF]. Moreover, in-situ warming may reduce the contribution of seasonal fine root respiration as a source of surface CO 2 efflux. In warmer climates, grassland ecosystems may increase their total root system length but lose a proportion of fine roots growing in the superficial layer of soil [START_REF] Pilon | Grassland root demography responses to multiple climate change drivers depend on root morphology[END_REF]. Our results showed greater positive effects of warming on subsoil CO 2 production than in topsoil for both agricultural practices (Fig. 6). This is consistent with previous whole-soil warming observations in a temperate forest [START_REF] Hicks Pries | The whole-soil carbon flux in response to warming[END_REF], but not in a tropical forest [START_REF] Mcgrath | Minerals limit the deep soil respiration response to warming in a tropical Andisol[END_REF]. The stronger warming effect on CO 2 production in subsoil than in topsoil could be due to a contrasting response of microbial metabolisms and/or root respiration in both parts of the soil profile. In subsoil, microbial activity is generally much lower than in topsoil [START_REF] Rumpel | Deep soil organic matter-a key but poorly understood component of terrestrial C cycle[END_REF], which was explained by the absence of fresh organic matter input [START_REF] Fontaine | Stability of organic carbon in deep soil layers controlled by fresh carbon supply[END_REF]. Under warming, energy input into subsoil through root growth and dissolved organic matter could be increased and stimulate copiotrophic populations at greater depths, leading to CO 2 production through decomposition of fresh root litter [START_REF] Müller | Carbon transfer from maize roots and litter into bacteria and fungi depends on soil depth and time[END_REF][START_REF] Sanaullah | How do microbial communities in top-and subsoil respond to root litter addition under field conditions?[END_REF][START_REF] Soong | Five years of whole-soil warming led to loss of subsoil carbon stocks and increased CO2 efflux[END_REF]. Root growth deeper in the soil profile could be stimulated by lower soil water content in warmed soil, which forces plants to explore deeper soil horizons [START_REF] Joslin | Effects of altered water regimes on forest root systems[END_REF][START_REF] Sotta | Effects of an induced drought on soil carbon dioxide (CO2) efflux and soil CO2 production in an Eastern Amazonian rainforest. Brazil[END_REF].

Warming effects on CO 2 production were contrasting in the two management systems. Subsoil CO 2 production was more pronounced under warming of grassland soil compared to cropland. This might be explained by greater root production and activity under grassland than cropland, as indicated by the greater standing root biomass in this agroecosystem (Table 2). Conversly, we observed that warming-induced subsoil SOC content was significantly higher in cropland but slightly lower in grassland. These findings suggest that the turnover or accumulation of subsoil SOC, as well as the subsequent mechanisms for CO 2 production under warming conditions, may vary based on different management practices. For instance, if warming leads to an increase in above-ground biomass, soil disturbance caused by tillage might be advantageous. This disturbance helps in transferring crop residues, dead roots, and topsoil C into subsoil layers where C accumulates, as shown in studies by [START_REF] Yang | Fungi determine increased soil organic carbon more than bacteria through their necromass inputs in conservation tillage croplands[END_REF]. Consequently, this process results in higher subsoil SOC content in cropland. Additionally, the organic materials added through this process could serve as a food source for microorganisms, potentially leading to increased CO 2 production in the short term.

In contrast, warming might cause a greater allocation of belowground C in grassland compared to cropland. This allocation is primarily derived from rhizodeposition provided by the perennial root system, as indicated in Table 2 [START_REF] Hu | Grassland management and integration during crop rotation impact soil carbon changes and grass-crop production[END_REF][START_REF] Mcgowan | Soil Organic Carbon, Aggregation, and Microbial Community Structure in Annual and Perennial Biofuel Crops[END_REF]. Consequently, continuous interactions between living root systems and subsoil microbes could have contributed to higher CO 2 production in grassland. This is supported by the negligible SOC loss observed under warming conditions.

In topsoil, warming-induced CO 2 production was differently affected by management. Under grassland, increased CO 2 production in topsoil could be attributed to a greater contribution of plant roots and rhizosphere microbes or saprotrophic microorganisms under warming conditions, which are the primary contributors to SOC decomposition [START_REF] Schindlbacher | Carbon losses due to soil warming: Do autotrophic and heterotrophic soil respiration respond equally?[END_REF][START_REF] Zhou | Source components and interannual variability of soil CO2 efflux under experimental warming and clipping in a grassland ecosystem[END_REF], as evidenced by lower SOC content under warming conditions. While decreasing CO 2 production in response to warming in cropland may be attributed to a double effect of both warming and tillage-induced soil disturbance (ploughing horizon at 30 cm depth), which can lead to shifts in soil microbial community and function due to lower plant root biomass (Table 2), resulting in lower CO 2 production and less sensitivity to warminginduced SOC decomposition, as evidenced by negligible SOC loss.

Future prospects

Although an increase in CO 2 production in subsoil is well documented in this study (Fig. 6) and supported by another whole-soil warming study in a temperate forest [START_REF] Hicks Pries | The whole-soil carbon flux in response to warming[END_REF], the biotic fixation and movement of CO 2 in the context of projected global warming under different agricultural management practices are still unknown. Despite a few recent studies confirming that soil microbes can fix CO 2 in temperate soils [START_REF] Nel | Soil microbial anaplerotic CO2 fixation in temperate soils[END_REF][START_REF] Spohn | Dark microbial CO2 fixation in temperate forest soils increases with CO2 concentration[END_REF]. Moreover, warming might change the microbial community composition and in particular increase fungal contribution, leading to the formation of more stable pore structures [START_REF] Miller | Mycorrhizal Fungi Influence Soil Structure[END_REF]. These effects may depending on soil management, as there is lower fungal contribution in cropland than in grassland due to ploughing events [START_REF] Schnoor | Soil disturbance alters plant community composition and decreases mycorrhizal carbon allocation in a sandy grassland[END_REF]. In order to get a complete picture of soil responses, heterotropic respiration should in future studies be distinguished from autotropic respiration.

Additionally, CO 2 flux within the soil profile may also be influenced by its microstructural properties. Indeed, a recent study found that grassland soil has a larger macropore diameter due to the presence of large biopores that are periodically destroyed in cropland [START_REF] Schlüter | Land use impact on carbon mineralization in well aerated soils is mainly explained by variations of particulate organic matter rather than of soil structure[END_REF]. The processes affecting soil structure may have an impact on soil gas diffusivity, which determines whether CO 2 produced at depth is lost to the atmosphere or fixed in situ [START_REF] Liao | Microbial autotrophy explains large-scale soil CO2 fixation[END_REF]. Thus, we propose that for understanding and modeling the effects of soil warming on CO 2 efflux under different agricultural management practices, the parameters influencing CO 2 diffusivity, such as soil architecture need to be taken into account in addition to factors affecting CO 2 production such as root activity and microbial community composition.

Conclusion

In this study we investigated seasonality of whole soil warming responses of CO 2 efflux. Moreover, we compared CO 2 production across soil depths down to 1 m. We conclude that in-situ soil warming led to soil drying, the extent of which depended on soil management. Soil drying was more pronounced in grassland compared to cropland, supporting our initial hypothesis (H1). We also found contrasting seasonal dynamics of surface CO 2 efflux. While our findings indicate that in-situ warming suppressed surface CO 2 efflux for both land management practices in the spring and autumn, winter and summer showed greater increases and decreases in response to warming, respectively, under grassland compared to cropland. These findings reject our second hypothesis (H2), which states that CO 2 efflux would increase year round in response to warming under continuous grassland but only seasonally in cropland soils.

Subsoil (>30 cm) CO 2 production was more vulnerable to warming than in the topsoil, and this increase due to warming was more pronounced in grassland compared to cropland, confirming our last hypothesis (H3). In topsoil (0-30 cm), warming decreased CO 2 production in cropland but not in the grassland. Overall, land management had a significant impact on CO 2 production throughout the soil profile, which may driven by both autotrophic and heterotrophic sources. Thus, we suggest that management influences the warming responses of CO 2 production and efflux by its effects on soil physical organization in addition to plant and microbial activity with root carbon input.

(AgroEcoSystem) and Région Nouvelle-Aquitaine. We are grateful to all the technical staff involved in the international soil warming experiment, in particular Cédric Perrot (URP3F Lusignan), Christophe Deberranger, Patrick Beauclair and the staff of ACBB Experimental unite (FERLUZ, Lusignan). This work is financed by the SOILWARM project ANR, Grand N • 21-CE01-0025 and the ACBB platform.

Fig. 1 .

 1 Fig. 1. In-situ whole-soil warming experiment under contrasting grassland management: (a) continuous grassland; and (b) cropland (cropland phase after 6 years of ley grassland) in a temperate region of western France.

Fig. 2 .

 2 Fig. 2. In-situ soil warming experimental design under contrasting grassland management: (a) whole-soil warming methodology based on self-regulating heating cable down to 2.0 m soil profile (the red zigzag line represents the buried surface heating location at 5 cm depth while the green and gray bar represents the location of the deep heating system down to 2.0 m depth); (b) heated-control paired (replicates, n = 3) plots; and (c) warming experiment work-flow (after 1.5 years, we have +4 • C, which has continued) in a temperate region of western France.

Fig. 3a .

 3a Fig. 3a. The average temperature difference between the control-heated pair plots (n = 3) at five depths over the study period (July 2021 to July 2022). The red line is the +4 • C target. Discontinuities in warming (drops followed by sharp increases) were due to electrical outages.

Fig. 3b .

 3b Fig. 3b. A radial cross-section of the study plot shows the average temperature difference (n = 3 plot pairs) from August to October 2021. The black dots represent the location of the temperature sensors. The horizontal zigzag line (black) is the location of the surface heaters and the vertical grey bar represents the 2.0 m long deep heaters, which are located 25 cm from the edge of the plot at 175 cm from the plot center. The data were interpolated using the automap package in R.

Fig. 4 .

 4 Fig. 4. Mean soil water content (%) in the control and heated plots (mean ± SE, n = 3) throughout the 1.0 m depth of soil profile over the study period (July 2021 to June 2022). In the figure, the p-values denote the statistically significant effects of treatment, depth, and their interaction (treatment × depth). The asterisk indicates any significant treatment effect within depth as determined by post hoc analysis (*p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001).

Fig. 5 .

 5 Fig. 5. Mean seasonal variations of soil CO 2 efflux (g C m -2 d -1 ) in the control and heated plots (mean ± SE, n = 3) from the whole-soil profile over the study period (July 2021 to June 2022). In the figure, the p-values denote the statistically significant effects of treatment, season, and their interaction (treatment × season). The asterisk indicates any significant treatment effect within season as determined by post hoc analysis (*p ≤ 0.05; ***p ≤ 0.001).

Fig. 6 .

 6 Fig. 6. Mean CO 2 production (g C m -3 d -1 ) in the control and heated plots (mean ± SE, n = 3) with depth increment (0-15, 15-30, 30-50, 50-70, and 70-90 cm) over the study period (January 2022 to June 2022). In the figure, the p-values denote the statistically significant effects of treatment, depth, and their interaction (treatment × depth). The asterisk indicates any significant treatment effect within depth as determined by post hoc analysis (*p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001).

Fig. 7 .

 7 Fig. 7. Soil organic carbon (SOC) concentration (kg C m -3 soil) in the control and heated plots (mean ± SE, n = 3) with depth increment (0-15, 15-30, 30-50, 50-70, and 70-90 cm), sampled in May 2022, one year after the onset of in-situ warming. In the figure, the p-values denote the statistically significant effects of treatment, depth, and their interaction (treatment × depth). The asterisk indicates any significant treatment effect within depth as determined by post hoc analysis (*p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001).

  CEC stands for cation exchange capacity; Fe o and Al o standas for amorphous forms of Fe and Al; Fe d and Al d stands for non-crystalline forms of Fe and Al.

		30	1.43 ± 5%	6.3 ±	105 ±	727 ±	168 ±	7.08 ±	0.98 ± 7%	1.20 ±	3.22 ±	20.97 ±	3.08 ±
				3%	14%	3%	87%	11%		19%	22%	25%	25%
	S (n = 6)	30-59	1.45 ± 4%	6.6 ±	95 ± 9%	594 ±	311 ±	8.6 ± 22%	0.94 ± 6%	1.65 ±	1.53 ±	27.19 ±	5.11 ±
				4%		7%	11%			14%	17%	27%	18%
	S2 (n = 5)	59-84	1.60 ± 7%	6.9 ±	103 ±	520 ±	377 ±	10.8 ±	0.94 ± 5%	2.01 ±	1.82 ±	45.27 ±	7.20 ±
				5%	11%	9%	21%	27%		9%	15%	22%	21%
	C1 (n = 5)	84-143	1.59 ± 8%	6.6 ±	98 ± 8%	330 ±	572 ±	11.6 ±	0.93 ± 9%	1.95 ±	2.03 ±	62.79 ±	7.42 ±
				4%		11%	9%	16%		19%	14%	36%	22%
	C2 (n = 6)	>143	1.61 ± 6%	5.2 ±	110 ±	242 ±	642 ±	9.9 ± 17%	0.82 ± 10%	1.87 ±	2.27 ±	67.02 ±	6.16 ±
				3%	10%	13%	12%			15%	11%	32%	27%
	Here,

Table 2

 2 Plant root mass from whole soil profile (mean ± SE, n = 5) of the experimental site in the control plots with two different land management practice.

	Field	Year	Root mass (kg ha -1 ) in different soil depth
			0-30 cm	30-60 cm	60-90 cm
	Cropland	2019*	7160 ± 340	918 ± 26	222 ± 9
		2020	4250 ± 150	643 ± 8	314 ± 6
		2021	3560 ± 190	726 ± 7	258 ± 9
	Grassland	2019	9850 ± 260	1200 ± 45	484 ± 8
		2020	11600 ± 1100	1250 ± 65	496 ± 10
		2021	12800 ± 1600	2000 ± 13	563 ± 9

Table 4

 4 Warming response of leaf area index (mean ± SE, n = 3) under the two different land management practices during the plant growing season (March to June 2022) with different treatments (control: heated).

		Leaf area index (m 2 m -2 )
	Field	Control	Heated
	Cropland Grassland	3.2 ± 0.2 b 3.4 ± 0.2 b	3.6 ± 0.2 ab 4.6 ± 0.2

a

Here, different letters indicate the statistically significant effects of treatment and field interaction, as determined by post hoc analysis (p < 0.01).

Md.Z. Khan et al.

Md.Z. Khan et al.

Acknowledgments

The lead author, Md. Zulfikar Khan (PhD), was funded by the INRAE

Data availability

Data will be made available on request.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi. org/10.1016/j.geoderma.2023.116725.


Anis Ouni

Hervé Rivano

Fabrice Valois

Capacity of wireless mesh networks: determining elements and insensible properties

Keywords: Wireless mesh network, network-wise capacity, flow-wise capacity, routing, network topology Réseaux Radio Maillés, Capacité, Routage, Topologie

The capacity of a multi-hop wireless network is the traffic payload that it can transport. This is a prominent quality of service issue, particularly in the highly constrained settings of 802.11 wireless mesh network. In this paper, we consider two complementary definitions of the capacity. A network-wise capacity is defined as the sum of the upload traffic, and a flow-wise capacity highlighting the unfairness among traffic flows. We study the behavior of these capacities face to several parameters: routing protocols, number and location of gateways bridging the network to the Internet, and the physical network topology. Thorough simulations highlight the insensitivity of the capacity face to these parameters while it is directly related to the congestion around the gateway. Furthermore, we show that if the number of gateways increases, the capacity tends to a maximum boundary.

Introduction

Wireless mesh networks (WMNs) are proliferating as access network infrastructure [START_REF] Akyildiz | A survey on wireless mesh networks[END_REF] for broadband mobile services. In this paper, we consider a wireless mesh network based on collaboration between access points (a.k.a. routers) and organized in a twofold architecture as depicted in Fig. 1. The access points are equipped with a routing functionality and communicate together through radio links, forming a wireless backhaul infrastructure. Their role is, on the one hand, to collect the traffic generated by -potentially mobile-clients and, on the other hand, to forward these traffic through multi-hop communications toward some dedicated routers, called gateways, that bridge the backhauling network to the Internet.

Since the architecture is twofold, one can distinguish two domains of communications: one between clients and access points and another one among the routers of the access points. We assume that these two domains of communications do not interfere together because they independent radio channels. We focus in this paper on the performances of the backhauling infrastructure formed by the network of routers. The traffic considered in the backhauling infrastructure is the aggregated traffic of the clients collected at the access points. Each router of the network has hence to forward a given demand toward the gateways. The performance of the network is then evaluated with respect to the quality of service that it is possible to provide to these traffic flows. In the generic settings of wireless networks, many paper of the literature have pointed out a critical behavior of the capacity [START_REF] Gupta | The capacity of wireless networks[END_REF][START_REF] Jun | The nominal capacity of wireless mesh networks[END_REF][START_REF] Franceschetti | Closing the gap in the capacity of random wireless networks[END_REF][START_REF] Méheut | Evaluation stochastique et simulation des réseaux radio[END_REF][START_REF] Liu | Capacity of a wireless ad hoc network with infrastructure[END_REF]. The capacity of a network is therefore a key issue of quality of service which is necessary to optimize. In this work, we aim at identifying the determining elements and insensible properties of the capacity of WMNs.

We consider two complementary definitions of the capacity. A first one, denoted network-wise capacity, is a measurement of the behavior of the whole network. It is defined as the sum of the traffics that have reached the gateways to the Internet. A second one, denoted flow-wise capacity, measures the capacity of each flow, that is the quantity of bandwidth allocated to the traffic collected by each router. To combine these two notions of capacity allows to highlight the unfairness among flows in network, which is a user-oriented point-of-view, within an operator-oriented look at the average behavior of the infrastructure.

Thorough simulations illustrate the behavior of these capacity notions face to several parameters. We show that the network-wise capacity as well as the flow-wise capacity are roughly insensitive to the choice of routing protocols and the placement of gateways in the network. We also show that the regularity of the network topology has little impact.

On the other hand, it is obvious that the addition of gateways improves the performances of the network in terms of capacity, latency, bandwidth allocated to each node... Nevertheless, we show that there is an upper bound under which the capacity keeps when the density of gateways in the network increases.The question of the impact of fairness on network performance is also addressed. This article is organized as follows. A state of the art is presented in section 2 where we review the basic research that studied the radio network-wise capacity. Section 3 is dedicated to describing the methodology of our work, the assumptions made, and the radio model and routing protocols that we have used. Section 4 is devoted to the study of scenarios and analysis results. The last section concludes our work and presents the prospects and directions for the continuation of these researches.

State of the art

The capacity of a wireless mesh network is one of the most important criteria of quality of service. This metric is directly linked to the available bandwidth to each user of the network, or to the whole network.

Several notions of capacity have been defined in the literature. In [START_REF] Molle | Génération de colonnes pour le routage et l'ordonnancement dans les réseaux radio maillés[END_REF], the capacity represents the quantity of flow that can be fairly split to the users. In [START_REF] Gupta | The capacity of wireless networks[END_REF][START_REF] Jun | The nominal capacity of wireless mesh networks[END_REF][START_REF] Liu | Capacity of a wireless ad hoc network with infrastructure[END_REF], the capacity is defined as the maximum bandwidth that can be allocated to each user. The study of the capacity may have different objectives. For an operator, the objective is to increase the number of served users while ensuring a better quality of service. For the user, improving the capacity is to obtain more bandwidth to increase his end-to-end flow.

The capacity of networks radio became a subject which interests a important number of research works. Several works focused on dedicated radio network topologies [START_REF] Gupta | The capacity of wireless networks[END_REF], confirmed by other studies [START_REF] Franceschetti | Closing the gap in the capacity of random wireless networks[END_REF][START_REF] Méheut | Evaluation stochastique et simulation des réseaux radio[END_REF], underline the highly sensitive behavior of the radio capacity. They showed that the capacity of a node in INRIA an ad-hoc wireless network decreases with 1 √ n when the network cardinality, n, increases. Unlike ad-hoc networks, wireless mesh networks are static and have bottlenecks located around the gateways. Therefore, the available capacity allocated to each node is reduced by a factor of 1 n [START_REF] Jun | The nominal capacity of wireless mesh networks[END_REF]. This result is found and extended in [START_REF] Rivano | Capacity evaluation framework and validation of self-organized routing schemes[END_REF], which evaluates the difference of capacity provided by an ad-hoc network or an hybrid network, using linear programming models.

The capacity of a multi-hop mesh network is greater than the capacity of a single-hop. This is made possible by allowing spatial reuse, i.e., the activation of more than one link at a time [START_REF] Girard | Engineering wireless mesh networks[END_REF]. Moreover, even with an optimal scheduling of calls, the network-wise capacity remains limited due to a bottleneck around the access point (gateway) [START_REF] Girard | Engineering wireless mesh networks[END_REF][START_REF] Molle | Optimal routing and call scheduling in wireless mesh networks with localized information[END_REF].

From these results, the researchers have focused their works on the study and optimization of the capacity. These works are based generally on optimization approaches, and heuristics. The optimization models, mainly using linear programming, have for objective to determine theoretical bounds to the capacity. In [START_REF] Molle | Optimal routing and call scheduling in wireless mesh networks with localized information[END_REF][START_REF] Gomes | Bottleneck analysis for routing and call scheduling in multi-hop wireless networks[END_REF], a linear programming models of wireless mesh networks have been developed in order to compute a joint optimal resource allocation and routing. One of their main results is the identification of a critical area, located around the gateways, and characterized by an heavy congestion. A direct consequence on the gateways location optimization is a constraint on the distance separating the gateways that is enough to satisfy to provide the maximum capacity, while the routing outside the critical areas has little influence.

Capacity evaluation methodology

In all simulations, we consider a wireless mesh network using a single radio channel shared among all routers. These routers are assumed to be static.

The upload traffic in the network is uniformly distributed among the routers. We assume that the network operates in a saturated regime: each router periodically sends a given quantity of traffic which presents the aggregate traffic of its clients. This traffic is routed to gateways through multi-hop paths computed by the different routing protocols that are described in the following.

We assume that the communications in the infrastructure do not interfere with any other communication. We also assume that the interfaces connecting the gateways to the Internet have infinite capacity. Our study hence focuses on the behavior of the backhaul network.

The MAC layer used is a CSMA / CA with the classical Request to Send (RTS) / Clear to Send (CTS) mechanism [START_REF]Ieee standard for information technologytelecommunications and information exchange between systems-local and metropolitan area networks-specific requirements -part 11 : Wireless lan medium access control (mac) and physical layer (phy) specifications[END_REF].

We study two kinds of topologies. On the one hand, most measurements are achieved on grid shaped topologies while, on the other hand, the role played by the regularity of these topologies on the performances is evaluated using topologies generated by a 2D-Poisson process.

Radio Model

In a wireless network, a radio link depends on several parameters. It mainly depends on the relative locations of the transmitter and the receiver. It also depends on other parameters of the physical layer such as modulation, shadow-ing, path loss, fading and interferences [START_REF] Hamida | Impact of the physical layer modeling on the accuracy and scalability of wireless network simulation[END_REF] .

To have more realistic scenarios, a physical layer taking into account the phenomenon of attenuation in free space and interference is simulated. When a node E transmits a signal with a transmission power Pt, the signal power at the reception is of the form Pt * L(d ij ), where d ij is the euclidean distance between the transmitter and receiver and L(.) is the attenuation function, or path-loss.

The interference model that is implemented takes into account the perturbations caused by the transmissions of all other nodes in the network. With all these parameters, it asseses if a packet is correctly received. A signal sent by a node E can be received at a receiver R if and only if the SIN R (Signal to Interference and Noise Ratio) at the receiver is upper a threshold θ. This model is defined as follows:

SIN R = Pt * L(d ij ) W + I(R) > θ
where W ∈ R + represents the thermal noise at the receiver and I(R) is the sum of the signals received from competing communication.

Routing protocols

We consider four routing protocols in order to route the upload traffic from the routers to the gateways.

Shortest path routing

This routing protocol is based on the Dijkstra algorithm [START_REF] Edsger | A note on two problems in connexion with graphs[END_REF]. The goal is to find the shortest path in terms of hops between source and destination. The global knowledge of the whole topology is necessary and obtained using periodic control packets.

Geographic routing protocol

This routing protocol is based on the knowledge of geographic position for each node using GPS-like positioning. The main idea is to compare, at each hop, the euclidean distance between all neighbors and the destination, and choose to forward the packet to the closest neighbor [START_REF] Karp | Greedy perimeter stateless routing for wireless networks[END_REF].

Random routing protocol

This routing is based on a random walk. It means that at each hop, the packet is forwarded to a randomly chosen neighbor. This protocol does not require the knowledge of the whole network, but only the neighborhood of each router using hello packets. Two strategies are used to improve the behavior of this protocol: i) the packet is sent to the destination if it is a neighbor and ii) a packet is never routed to a node which has no other neighbor.

INRIA

Static routing protocol

With this routing protocol, all the paths between source node and destination are manually entered. This protocol does not require the use of control packets.

Performance evaluation criteria

Network capacity

In our work, the network-wise capacity is the quantity of traffic sent by all nodes (N) and forwarded to the Internet through the gateways (K) during the simulation period. It is a view of the global bandwidth of the network shared among all nodes.

This metric represents the maximum quantity of traffic that the network can transmit to the Internet. A better network-wise capacity is necessary for providing a better quality of service to a larger number of users.

This metric is calculated as follows.

C network = k∈K n∈N |Received_packets(n → k)|

Simulation_time

This metric does not illustrate the unfairness problem in the network. A more detailed view is necessary for taking into account the bandwidth allocated to each flow.

Flow capacity

It is defined by the sum of traffics sent by a router and received by the gateway during the observation period. This metric illustrates the bandwidth consumed by each router in the network. Thus, it allows to study the problem of unfairness in the distribution of bandwidth amonog the flows. This is a key point of the quality of service. In fact, an operator must ensure a bandwidth acceptable for each node in the network. This metric is calculated as follows.

C flow(n) = k∈K |Received_packets(n → k)|

Simulation_time

These two metrics are complementary because the first gives a global vision of the network while the secons gives a detailed view.

All the following results are obtained through intensive simulations using the WSNet simulator [START_REF] Ben Hamida | Scalable versus accurate physical layer modeling in wireless network simulations[END_REF] and a confidence interval of 95% is guaranteed.

Clustering algorithm

To study the impact of the increase of the gateways density towards the capacity of mesh networks, we considered two scenarios.

In the first one, presented in the Fig 2(a), the network is divided into N clusters. Each cluster contains a gateway. Subsequently, each node sends its traffic to the closest gateway. The second scenario, presented in 2(b), is similar to the first but in this case, each node chosen randomly and uniformly one of the gateways and sends its traffic. The difference between these two scenarios is that in the first one, if a client wants to connect to the Internet, then his traffic is routed to the nearest gateway. This is in order to reduce the number of hops (to the gateway), reduce the load on the links, reduce the impact of the collision domain, and thus to increase the throughput of each node [START_REF] Jun | The nominal capacity of wireless mesh networks[END_REF]. But if a user wants to access a service not provided by the nearest gateway, or if it is busy or does not work, then his traffic must be routed to the appropriate gateway.

The construction of the clusters and the placement of each gateway in a cluster, are calculated by the algorithm 1. The objective is to group nodes in order to construct clusters with small diameter. Each cluster has almost the same cardinality. To do this, a node is selected then it combines its successive neighborhoods to get the cardinality desired (line 1-9). Then, in each cluster, the node closest to the barycenter is selected to be a gateway (line 10-14).

RESULTS AND ANALYSIS

We measure the evolution of network-wise capacity and flow-wise capacity face several parameters : routing protocols, network size, number and placement of gateways.

The routing independence

The independence of routing is highlighted by comparing the capacity obtained by the shortest paths routing, geographic routing, and the two types of random routing (classical and optimized) detailed in Part 3.2. Figures 3 and4 show the evolution of network-wise capacity with network size in the case of grid topology and random topology, while Figures 5 and6 show the flow-wise capacity.

We will see that there is no routing that provides a capacity significantly better than the others, only the random walk not optimized is less efficient. Create C ∈ H

3:

Select a node x 4:

Candidates = {x} (FIFO list)

5:
while

(|C| ≤ C cluster ) et |Candidates| ≥ 0 do 6:
Choose y ∈ Candidates For both types of topologies, the capacity evolves with the same way. We can note that the average capacity offered to nodes decreases more than 1 n . This is due to the increase of collisions and interference where the network cardinality increases. Adding to all these factors, the presence of an important congestion around the gateway (bottleneck), the flow-wise capacity and the network-wise capacity decrease independently from routing protocols. In fact, when the network size increases, each node will have more neighbors in one and two hops. Thus, the access to the medium becomes increasingly difficult and the interference increases, especially around the gateway.

Placement of gateways

The small influence of the gateway placement is illustrated exhaustively on a 7x7 grid. Figure 7 illustrates the network-wise capacity obtained according to the location of the gateway. We find a classical result of axial symmetry of the grid because all the placements which are situated at the same distance of the grid center have the same capacity. We see that capacity increases slowly from the center to the board where the maximum capacity is reached. Indeed, the nodes that are situated at the center of the grid are characterized by a strong constraint caused by the congestion, interference and collision around these nodes, while nodes in the grid border have a more limited interference.

INRIA

The impact of the gateway density

The impact of the gateway density in the network must be studied taking into account previous measurements. We study here two types of traffic : the first is sent to the nearest gateway (the barycenter of its cluster), the other is to gateway chosen randomly (3.4).

Figure 8 shows the variation of network-wise capacity depending on the gateway density on a 12x12 grid. In the case of routing to the nearest gateway, the contribution of additional gateway is significant when their density is low, then it tends toward a threshold. The linear growth of the capacity is because the placement of the gateways does not matter as long as they are sufficiently spaced from each other gateway. When the interference zone of the gateways have a contact, then it becomes useless to add another.

In the case of random gateway choice, network-wise capacity increases linearly with gateway density. Here, the distance (hops number) between a node and the gateway is more important which increases the collisions and interference on the multi-hops path. We tends towards an asymptotic behavior where the variation of network-wise capacity when a transition from N to (N +1) gateway results mainly of the traffic delivered directly to the Internet by the last added gateway.

Independence of the topology

The independence of the topology is illustrated by figure 9 where we show that the capacity of a random topology evolves similarly to that the grid for routing to the nearest gateway and to a random destination. We can see also this independence of topology by analyzing the figures presented in 4.1, where we found the same results for the grid topology and random topologies.

In this context, a result shows that under certain assumptions, a random network is a finite distance from a grid of equal density [START_REF] Klasing | From balls and bins to points and vertices[END_REF].

Equity problem

Several studies [START_REF] Nandagopal | Achieving mac layer fairness in wireless packet networks[END_REF][START_REF] Bharghavan | Macaw : A media access protocol for wireless lan's[END_REF] have highlighted some issues of equity in access to the radio medium that could occur with the use of 802.11. The existence of this problem is confirmed by Figure 10 which shows the evolution of the average flow-wise capacity by level1 . We see an inequality in the sharing of network bandwidth. Only the first nodes (especially the first) uses a large share of bandwidth, while very few packets of their successors reach the gateway. Most works on the network-wise capacity does not take into account this problem [START_REF] Jun | The nominal capacity of wireless mesh networks[END_REF][START_REF] Franceschetti | Closing the gap in the capacity of random wireless networks[END_REF][START_REF] Méheut | Evaluation stochastique et simulation des réseaux radio[END_REF][START_REF] Rivano | Capacity evaluation framework and validation of self-organized routing schemes[END_REF][START_REF] Liu | Capacity of a wireless ad hoc network with infrastructure[END_REF]. However,the unfairness problem has a big influence on network performance, in particular on the bandwidth allocated to each user. Also a mechanism, ensuring fairness in the radio network, risks to be very expensive in terms of traffic control and signaling, because it requires synchronization and a regulation of bandwidth consumption.

The question arises whether our results are strengthened or limited in the presence of a mechanism of equity.

Conclusion

In this work, we present results of simulations to isolate the determining elements of the capacity of wireless mesh network. We highlight some insensible properties of the capacity. We show that the regularity of the topology, the placement of gateways and the routing protocols have a limited impact on the network-wise and flow-wise capacity. These capacities are bounded to the bottlenecks around gateways. We show that the capacity tends toward a maximal value, when the number of gateways in the network increases. These results can be useful, in particular, during the designing of a wireless mesh network. Furthermore, these results moves us to optimize finely the behavior of the network in zones centered around the gateway.

Figure 1 :

 1 Figure 1: Wireless mesh network architecture: routers collect the traffic from clients (mobile or static) and forward it to the Internet through gateways.

Figure 2 :

 2 Figure 2: Scenarios of gateways sharing : (a) sending to the nearest gateway, (b) sending to a gateway chosen randomly

INRIA Algorithm 1

 1 clusters construction Require: N : set of nodes, H : set of clusters, C cluster : desired cardinality 1: while (N = ∅) do 2:

  end while 12: while (H = ∅) do 13:

Figure 3 :

 3 Figure 3: Network capacity vs routing and cardinality (grid)

Figure 4 :Figure 5 :

 45 Figure 4: Network capacity vs routing and cardinality (random topology)

Figure 6 :Figure 7 :

 67 Figure 6: Flow capacity vs routing and cardinality (random topology)

Figure 8 :

 8 Figure 8: Network capacity vs gateways number (grid 12x12)

Figure 9 :

 9 Figure 9: Network capacity vs gateways number (random topology)

INRIA

Figure 10 :

 10 Figure 10: Flow average capacity by level (grid 11x11)

RR n°7171

It's the set of nodes that have the same distance in terms of number of hops from the gateway.RR n°7171