# Spatial distribution of soil carbon inputs in two contrasted hedge agroforestry systems

Lucas Lesaint, Valérie Viaud

► To cite this version:

Lucas Lesaint, Valérie Viaud. Spatial distribution of soil carbon inputs in two contrasted hedge agroforestry systems. Agriculture, Ecosystems & Environment, 2026, 397, pp.110104. 10.1016/j.agee.2025.110104. hal-05493340

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# Spatial distribution of soil carbon inputs in two contrasted hedge agroforestry systems

Lucas Lesaint<fnref n="1" /> ID, Valérie Viaud* ,<fnref n="2" /> ID

UMR SAS, INRAE, Institut Agro, Rennes 35000, France

<figure><img src="image_3.png" /></figure>

## ARTICLE INFO

### Keywords:

- Carbon sequestration

- Root carbon input

- Climate change

- Field margins

- Hedgerow

- Temperate climate

## ABSTRACT

Hedge agroforestry systems are known for their potential to store large amounts of carbon (C) in agricultural soils, but there remains a lack of understanding about the spatial distribution of soil organic C (SOC) stocks near hedges. This study thus focused on the spatial organisation of C inputs near hedges associations with two cropping systems typical of mixed farming in Normandy, France. The two sites selected consisted of mixed hedges adjacent to a permanent grassland or a cropland rotation (i.e. annual crops and temporary grassland). At both sites, C inputs from fine roots, hedge leaf litter, applied organic fertilisation, crop/grass residues and grazing cattle were measured from 0 to 30 cm in depth at distances of 1, 3 and 10 m from the hedge. At the same time, SOC stocks and their distribution in the fine and coarse fractions of the soil were measured from 0 to 90 cm in depth at the same distances.

**Results:** showed higher C inputs from fine-root turnover and hedge leaf litter at 1 and 3 m in cropland compared to 10 m, while C inputs from crop residues were higher at 10 m. In grassland, more C inputs came from fine-root turnover and grass residues at 1 and 3 m from the hedge. C input from hedge leaf litter was associated with high SOC storage in the coarse soil fraction, while C inputs from fine roots were associated with high SOC storage in the fine soil fraction. While the results showed that hedges contributed less than expected to soil C inputs, they also showed the need to maintain the current hedge linear layout to avoid a rapid decline in soil carbon stocks derived from hedges, which are largely made up of labile carbon.

## 1. Introduction

Agroforestry systems, in which land management combines trees with crops and/or livestock (Shi et al., 2018), are increasingly recognised for their potential to enhance soil carbon (C) storage in agricultural areas (Lorenz and Lal, 2014). De Stefano and Jacobson (2018) showed that conversion from agriculture to agroforestry increased soil organic C (SOC) stocks by up to 10 % and 40 % from 0 to 30 cm in depth in grassland and cropland, respectively. In Europe, agroforestry systems represent 8.8 % of utilised agricultural area (den Herder et al., 2017). One of the most common traditional forms of agroforestry consists of farmland with a high density of hedges, i.e. rows of trees and/or shrubs along field margins (Moreno et al., 2018). A recent meta-analysis by Drexler et al. (2021) reported that SOC stocks were on average 32 % larger under hedges than under adjacent cropland. The higher SOC storage potential is explained by both higher soil C inputs and lower C

turnover rates in soil compared to those of systems without trees (Cardinael et al., 2020; Shi et al., 2018). SOC stocks are not evenly distributed in agroforestry fields (Cardinael et al., 2017; Viaud and Kunnemann, 2021), mainly due to spatial heterogeneity in C inputs caused by the spatial arrangement of trees and herbaceous species (Guillot et al., 2021). In particular, the contribution of the root compartment (i.e. root litter and rhizodeposition) to C inputs is often emphasised in agroforestry systems. However, direct quantification of C inputs and the relative contribution of above-ground and root compartments have been explored little to date for hedge agroforestry systems in temperate climates.

Studies show that stable SOC is derived mainly from root biomass (Poeplau et al., 2021; Rasse et al., 2005). In agroforestry, tree roots tend to grow in deeper layers, perhaps due to competition with crops or grass (Cardinael et al., 2015; Mulia and Dupraz, 2006). In a temperate climate, Upson and Burgess (2013) reported higher fine-root density

<footnote>* Correspondence to: UMR SAS, INRAE, 65 rue de Saint-Brieuc, Rennes 35054, France
E-mail address: valerie.viaud@inrae.fr (V. Viaud).</footnote>

<footnote n="1">¹ ORCID: 0000-0002-8542-8633</footnote>

<footnote n="2">² 0000-0002-6434-369X</footnote>
from 90 to 150 cm in depth under an agroforestry treatment than under a control without trees, with no differences from 0 to 30 cm in depth. Root biomass under hedges is usually estimated using the shoot:root ratio and ranges from 15 to 56 Mg C. ha⁻¹ from 0 to 100 cm in depth, depending on hedge characteristics and the shoot:root ratio used (Axe et al., 2017; Crossland, 2015). In comparison, cereal crops have mean root biomass of ca. 1.3 Mg C. ha⁻¹ from 0 to 75 cm in depth, and grassland has a root biomass of ca. 9 Mg C. ha⁻¹ from 0 to 100 cm in depth, depending on the management (Burke et al., 2008; Hirte et al., 2018a).

The turnover of fine roots (diameter < 2 mm) is an important mechanism that contributes to SOC storage, as fine roots (FR) have the highest production and turnover rates (Gill and Jackson, 2000). The root turnover rate equals the annual root production divided by the root biomass of the vegetation in place. Several methods exist to measure root production. Sequential soil coring and mini-rhizotrons are widely used, but they remain expensive and require intensive data analysis (Johnson et al., 2001), and they are destructive methods that cannot be easily used everywhere. In contrast, the ingrowth core method is easier to use and allows root production to be compared among treatments. However, it degrades soil structure greatly, which can alter root development (Freschet et al., 2021). Across ecosystems, FR turnover ranges from 0.2 to 3.5 times fine-root biomass per year depending on the species, land use and soil properties (Brunner et al., 2013; Luo et al., 2021; Morales Ruiz et al., 2021). C input from roots is estimated by multiplying root biomass by the root turnover rate and the mean root C content. Brunner and Godbold (2007) reported annual C input from roots of 0.9 Mg C·ha⁻¹·yr⁻¹ in forest ecosystems in temperate climates; however, soil C input from FR turnover is rarely studied in ecosystems other than forests.

Sokol et al. (2019) also emphasised the crucial role of rhizodeposition in increasing SOC stocks compared to those of shoot litter and root residues. Rhizodeposition helps stabilise SOC by releasing mucilage and protein that bind mineral particles and increase soil aggregate stability (Czarnes et al., 2000; Whalley et al., 2005). However, sampling root exudates is a major challenge due to the high diversity of root systems and soil properties, and their interactions with microbial communities (Oburger and Jones, 2018).

The present study aimed to obtain reference values for the contribution of hedge agroforestry systems to C input from roots in the topsoil (0–30 cm) relative to C inputs from above-ground biomass, and to investigate the relationship between these C inputs and the distribution of C stocks in the soil. The study focuses here on a specific type of hedge mixing coppice trees and shrubs, one that is widespread in mixed crop-livestock systems in western France. The specific objectives were (1) to characterise C inputs for a given year in the vicinity of a mixed hedge and (2) to quantify the importance of C inputs from the hedge in relation to other C inputs (e.g. organic fertiliser, C inputs from crops) for a hedge association with two cropping systems typical of mixed farming: (1) permanent grassland and (2) rotation of annual crops and temporary grassland.

# 2. Materials and methods

## 2.1. Study sites

The study was conducted in Normandy (France), where two study sites were selected: a permanent grassland named G_site (48.7368° N, 0.3976° W) and a rotation of cropland and temporary grassland named C_site (48.7252° N, 0.3014° W); each was an agricultural field bordered by a mixed hedge. Both study sites represented the type of cropping systems bordered by hedges associated with mixed crop-livestock farming in western France. Both study sites shared similar hedges, with old mature trees typical of the region, while also differing in land management in the adjacent field and in additional carbon stocks due to the hedge presence. G_site consisted of permanent grassland, whereas

C_site consisted of a rotation of cropland and temporary grassland. Hedges had the same vegetation structure mixing coppice trees and shrubs, with similar tree age and management. G_site and C_site are located in a temperate oceanic climate with mean annual temperature, rainfall and potential evapotranspiration of 11.4°C, 721 mm, and 767 mm, respectively. Soils at both sites are Brunisols. Soil texture is loamy at G_site (18 % sand, 63 % silt, and 19 % clay) and sandy at C_site (55 % sand, 26 % silt, and 19 % clay).

Trees in both hedges were more than 50 years old. The main species in both hedges were the common hazel (Corylus avellana) and sessile oak (Quercus petraea), with approximately 70 trees per 100 m of hedge. They were managed in a similar manner: the hazels had been pruned in 2021, but until then, none of the hedges had been pruned for the previous 20 years. The hedge at G_site was oriented east-west and was 105 m long, whereas the one at C_site was oriented north-south and was 130 m long.

G_site had been used as a permanent grassland for at least 50 years. The grassland is made up of a mixture of different species, predominantly English ryegrass and white clover. However, its exact composition is not fully characterised. The permanent grassland was mowed once per year in June and grazed by dairy heifers from March to April and August to November. G_site was fertilised with 15 Mg. ha⁻¹ of composted cattle manure once every 3 years in November. C_site was cultivated as an 8-year rotation, with 3 years of triticale (X triticosecale) followed by 5 years of temporary grassland. The temporary grassland was mowed once per year in July and grazed by dairy cows from March to September. The triticale was sown in October and harvested in July (with the straw returned to the soil). C_site was fertilised with 20 Mg. ha⁻¹ of composted cattle manure every year in January in 2021, the temporary grassland was in its last year, and thus it was ploughed in October 2021 to sow triticale. When C_site was cultivated with triticale, a 1 m-wide grass strip was left next to the hedge.

To assess impacts of hedge agroforestry systems on soil C inputs and their spatial distribution, multiple parameters were measured at both sites at three distances from the hedge: 1, 3, and 10 m. These distances were chosen to capture the lateral heterogeneity of C inputs next to hedges, as previous studies showed that hedges no longer influence SOC stocks more than 10 m from the hedge (Viaud and Kunnemann, 2021). At C_site, the sampling points 1 m from the hedge were in the grass strip. We then investigated the consistency between C inputs spatial distribution and that of SOC stocks.

## 2.2. Spatial distribution of carbon inputs during a given year

In 2022, at each distance from the hedge, we measured C inputs from roots, hedge leaf litter and crop/grass residues to compare their spatial distribution between the permanent grassland (G_site) and the cropland (C_site), both bordered with a hedge (Figure S1.). Additionally, C inputs from applied organic fertiliser and grazing cattle were estimated from farmers survey and literature data. At G_site, samples were collected in the field to the south of the hedge, whereas at C_site, they were collected in the field to the east of the hedge.

### 2.2.1. Carbon input from fine roots

At both sites, five transects were placed along the hedge, each at the level of a tree: five trees at least 10 m apart were selected randomly along the hedge, and the five transects were positioned perpendicular to the hedge at the level of the selected trees. Along each transect, three sampling points were placed 1, 3 and 10 m from the hedges, respectively (Fig. 1). At each point, the ingrowth core method (Freschet et al., 2021) was used to estimate FR production over six colonisation periods throughout the year (20 Mar-10 Apr, 2–18 May, 12–27 June, 15–30 Aug, 19 Sep-3 Oct and 7–21 Nov), which were chosen to represent a full growing season. At the beginning of each period and at each sampling point, a 30 cm soil core was extracted, weighed and sieved at 2 mm to remove all roots. A 2 mm mesh bag was then placed into the core hole, and filled with sieved soil until it had a bulk density similar to that of the
<figure><img src="image_4.png" /><figcaption>Fig. 1. Experimental design for sampling at both sites in 2022.</figcaption></figure>

extracted soil. To compensate for the soil loss during sieving, extra soil from the same distance from the hedge was collected and sieved to reestablish the initial bulk density. The bag was left in place for the 2–3 weeks colonisation period so that the roots could colonise the soil in it. At the end of the colonisation period, the bag was carefully extracted using a knife to avoid damaging roots. Fine roots in the bag were manually washed, sorted, dried at 60°C and weighed. Fine roots could not be differentiated by species based on colour or texture; thus, tree roots were not separated from grass or crop roots. The temperature at a depth of 15 cm was also measured at the beginning and end of each colonisation period. FR production equalled the density of roots produced during the colonisation period relative to the soil surface area occupied by the ingrowth core (g.m⁻².month⁻¹). Annual FR production was calculated assuming that root production was linear among the colonisation periods and zero from 15 Dec-15 Feb.

Using the same spatial sampling design (Fig. 1), soil was sampled from 0 to 90 cm in depth at each sampling point using an 8 cm diameter root auger in order to measure FR biomass. Samples were collected on 21 June 2022, when FR biomass was theoretically at its annual peak. Soil samples were washed to manually extract the roots, which were dried at 60°C and weighed.

FR turnover (i.e. the amount of FR biomass renewed each year) was calculated as FR production divided by FR biomass (Gill and Jackson, 2000) and expressed as a number of times per year. FR C input was then calculated as FR biomass multiplied by FR turnover multiplied by FR C content, which was estimated as 0.398, a mean value from Legay et al. (2016).

### 2.2.2. Carbon input from hedge leaf litter

Leaf litter (LL) biomass was measured at C_site in autumn and winter of 2022. As G_site was grazed, a litter tray could not be installed, and thus leaves were not sampled there. However, as the hedges at G_site and C_site had the same characteristics and management, C input from leaf litter was considered the same at both sites. Ten transects were placed perpendicular to the hedge, each with three sampling points placed 1, 3 and 10 m from the hedge, respectively (Fig. 1). To assess the heterogeneity of LL biomass along the hedge, five transects began at hazels (Hazel) and five began at sessile oaks (Oaks). At 10 m from the hedge, LL biomass was less than 1 leaf m⁻² and thus considered to be zero. On 21 Nov, a 1 m² quadrat was placed on the soil surface at each sampling point, and all leaves in it were collected. A 0.112 m² tray (28*40 cm) was then placed in the centre of the 1 m² quadrat, and all fallen leaves were collected from it every 2 weeks to prevent them from decomposing. For the transects perpendicular to Oaks, all leaves were washed, dried at 60°C and weighed. Some transects perpendicular to Hazel were closer to Oaks than others. As Oaks were more developed than Hazel, their leaf

litter strongly influenced LL biomass. To consider tree-species distribution along the hedge, a different approach was used for the transects perpendicular to Hazel: leaves were washed, sorted by species, dried at 60°C and weighed. Thus, two measurements were performed: LL biomass of Hazel alone and LL biomass of both Hazel and Oaks (Oaks and Hazel mix). LL C input was then calculated for each point as the LL biomass (Oaks, Hazel or Oaks and Hazel mix) multiplied by the mean C content of leaf litter from woody vegetation (48 g C. 100 g⁻¹ DM (Zribi et al., 2016; Rolando et al., 2025)).

### 2.2.3. Carbon input from cropland and grassland above-ground biomass

Grassland (G_site) and cropland (C_site) above-ground biomass was measured in 2022 to estimate C inputs from crop and grass above-ground residues. Before triticale harvest or grassland mowing, three transects were placed perpendicular to the hedge at each site, each with three sampling points placed 1, 3 and 10 m from the hedge, respectively (Fig. 1). At each point, above-ground biomass was collected in a 25 × 25 cm quadrat, dried at 60°C and weighed.

C inputs from crop and grass residues (CR C inputs) in 2022 were calculated from the total above-ground biomass at each distance from the hedge. C inputs were calculated using high and low estimates to account for uncertainties in C inputs. At G_site, for each distance from the hedge, C inputs were estimated using results of Graux A et al., (2022), who estimated CR C inputs in grassland as 1.5–3.0 times the C exported after mowing:

$$ \text{Grassland CR\_C\_inputs} = C_{\text{CR}} \times B_{\text{ag}} \times \alpha \quad (1) $$

where grassland CR_CInputs is the above-ground C input from grass residues (Mg C.ha⁻¹.yr⁻¹), CCR is the C content of grass residues (0.43 g C.100 g⁻¹) based on unpublished data from Britany, France, Bag is grassland dry above-ground biomass before mowing calculated from the 25 × 25 cm quadrat biomass (Mg.ha⁻¹), and α is a coefficient representing the fraction of biomass retained as residues after mowing. Following Graux A et al., (2022), α was either 1.5 or 3.0 to calculate low and high estimates.

At C_site, as there was a grass strip 1 m from the hedge, C inputs from grass residues at 1 m were calculated in the same way as at G_site. At 3 and 10 m from the hedge, C inputs from crop residues were estimated using results of Boiffin et al. (1986) and Bolinder et al. (1997), who estimated CR C inputs from triticale as 1.0–1.9 times the amount of C exported after harvest if the straw is left in the field (which was the case for C_site):

$$ \text{CR\_C\_inputs} = C_{\text{CR}} \times Y_T \times \alpha \quad (2) $$

where CR_CInputs is the above-ground C input from triticale residues (Mg C.ha⁻¹.yr⁻¹), CCR is the C content of triticale residues
(0.425 g C.100 g⁻¹) taken from Bolinder et al., (1997), Y_T is the triticate yield calculated from the 25 × 25 cm quadrat biomass (Mg.ha⁻¹.yr⁻¹), and α is 1.0 for low estimates and 1.9 for high estimates.

### 2.2.4. Carbon input from organic fertilisation

In 2022, C input from organic fertilisation (OF) represented compost application and excreta from cattle at G_site and cattle manure application at C_site. C input from compost or cattle manure application was calculated from the type and amount of organic fertilisers applied according to the farmers. At each site, the same amount of organic fertilisers was applied at all distances from the hedge right up to the bottom of the hedge. C content for each manure or compost was obtained from Morvan et al., (2006) and other local unpublished dataset. Compost or manure C inputs was calculated as follows:

$$CM\_C\_inputs = C_{OF} \times DM_{OF} \times Q_{OF} \quad (3)$$

where CM_CInputs is the C input from compost or manure application (Mg. ha⁻¹), C_OF is the C content of organic fertilisers (%), DM_OF is the dry matter content of organic fertilisers (%), and Q_OF is the amount of organic fertilisers applied (Mg.ha⁻¹).

C input from cattle excreta came from their excreta and was calculated for dairy cows as follows:

$$CE\_C\_inputs = Nb_{DC} \times EC \times (Nb_{GR}/365.5)/1000/F_{A} \quad (4)$$

where CE_CInputs is the C input from grazing dairy cows excreta (Mg. C.ha⁻¹.yr⁻¹), Nb DC is the number of dairy cows grazing, EC the mean amount of C excreted per cow (kg C.yr⁻¹), Nb GR is the number of days of grazing, and F A is the field area (ha).

OF C inputs was then calculated as the sum of compost or manure C inputs and cattle excreta C inputs for each site.

### 2.3. Soil organic carbon stocks

At both sites, SOC stocks were measured in Feb 2021 to compare their spatial distribution to that of mean annual C inputs at each distance from the hedge. At both sites, three transects were placed perpendicular to the hedge in three of the five transects used for C input measurement, each with three sampling points placed 1, 3 and 10 m from the hedge, respectively. At each point, the soil was sampled using an 8 cm diameter auger at four depths: 0–15, 15–30, 30–60 and 60–90 cm. For each distance and depth, samples from the three transects were bulked, resulting in one measurement for each distance and depth. Each composite sample was then air dried for 72 h and sieved at 2 mm. The resulting samples were decarbonated using HCl before being analysed for SOC concentration using a LECO CHN analyser at the LAS laboratory located in Arras, France (dry combustion, standard NF ISO 10694 (AFNOR, 1995)). At the same sampling points, undisturbed soil cores were also sampled to measure bulk density. These samples were dried at 105°C for 48 h and sieved at 2 mm to determine the volume and mass of the coarse fraction. The bulk density (ρ zg. cm⁻³) of the fine fraction (< 2 mm) was then determined:

$$\rho_z = \frac{Mtot_z - M_{CZ}}{Vtot_z - V_{CZ}} \quad (5)$$

where Mtot z (g) is the total mass of the sample in layer z, Vtot z (cm⁻³) is the total volume of the sample, MC z (g) is the mass of the coarse fraction in layer z, and VC z (cm⁻³) is the volume of the coarse fraction in layer z.

SOC stock (S z, Mg. ha⁻¹) for each soil layer (z) at each distance from the hedge was calculated as follows:

$$s_z = OC_z \times \rho_z \times h_z \times \left( 1 - \frac{V_C}{Vtot_z} \right) \times 10^{-1} \quad (6)$$

where OC z is the SOC concentration (g. kg⁻¹) of the fine fraction, and h z is the thickness of layer z (cm).

SOC stocks were adjusted for an equivalent soil mass (Wendt and Hauser, 2013). At 1 and 10 m from the hedge, the SOC content of particle-size fractions was determined for each depth using a procedure adapted from (Balesdent et al., 1998). A 100 g subsample of air-dried soil, sieved to 5 mm, was taken from the original composite sample for each distance and depth. The soil sample (25 g) was dispersed in 300 mL of deionised water by shaking it with glass beads for 10 h and then wet-sieved to 50 µm to separate the particulate organic matter (POM, > 50 µm) from the mineral associated organic matter (MAOM, < 50 µm). Finally, the C content of both fractions was measured (dry combustion, standard NF ISO 10694 (AFNOR, 1995)).

### 2.4. Data analysis

Statistical analyses were performed using R version 4.1.1 (R Core Team, 2021), with differences considered significant at p < 0.05. All graphs were plotted using the ggplot2 package (Wickham, 2016). Significant differences were analysed for fine roots (i.e. FR biomass, FR production, FR turnover and FR C inputs) among depths, and for each depth, among distances and between sites, and for hedge leaf litter (LL biomass and LL C inputs) among distances. The Shapiro-Wilk test was used to verify the normality of data distribution, followed by analysis of variance (ANOVA) and Tukey's HSD test for the analyses. When normality was not met, the non-parametric Kruskal-Wallis test was performed (De Mendiburu and Yaseen, 2020).

## 3. Results

### 3.1. Spatial distribution of carbon inputs

#### 3.1.1. Carbon input from fine roots

FR biomass, FR production, FR turnover and FR C inputs all varied among distances and between sites (Table 1).

At G_site, cumulative FR biomass from 0 to 90 cm was significantly higher at 10 m than at 1 or 3 m. From 0–30 cm, FR biomass was significantly higher only at 10 m compared to 3 m (Fig. 2A). From 60–75 cm, FR biomass tended to be higher at 1 m than at 3 and 10 m without significant differences. At C_site, both cumulative FR biomass from 0 to 90 cm and FR biomass from 0 to 30 cm were significantly higher at 1 m compared to 3 and 10 m (Fig. 2B). Cumulative FR biomass from 0 to 90 cm did not differ significantly between sites.

At both sites in 2022, FR production was highest in spring and autumn and lowest in summer (Fig. 3). Mean annual rainfall and temperature were 554 mm and 12.0°C, respectively, in 2022 compared to a mean of 721 mm and 11.4°C over the previous 20 years (2000–2020). At G_site, FR production did not differ significantly among distances, but at C_site, FR production was significantly higher at 1 and 3 m than at 10 m from 0 to 15 cm. At both sites, annual FR production was significantly higher from 0 to 15 cm than from 15 to 30 cm. FR production did not differ significantly between G_site and C_site at 1 or 3 m, but it was significantly higher at G_site than at C_site at 10 m.

At G_site, FR turnover was significantly higher at 1 and 3 m than at 10 m from 0 to 15 cm, and at 3 m than at 1 and 10 m from 15 to 30 cm. At C_site, FR turnover did not differ significantly among distances. However, for a given distance, FR turnover did not differ significantly between sites.

FR C inputs ranged from 0.13 to 1.83 Mg.ha⁻¹.yr⁻¹. At G_site, FR C inputs did not differ significantly among distances while at C_site, FR C inputs were significantly higher at 1 and 3 m than at 10 m from 0 to 15 cm, and at 1 m than at 10 m from 15 to 30 cm. At 10 m from the hedge, FR C inputs were significantly higher at G_site than at C_site.

#### 3.1.2. Carbon input from hedge leaf litter

LL C inputs ranged from 0 to 0.53 Mg. C.ha⁻¹.yr⁻¹ among distances and tree species (Table 2). Regardless of the tree species, LL C inputs did not differ significantly between 1 and 3 m; however, no litter was
<caption>**Table 1**</caption>

Means and standard errors of fine-root (FR) biomass, annual production, turnover and C input by depth and distance from the hedge at G_site (permanent grassland) and C_site (cropland rotation). Letters show significant differences for each variable for a given depth and distance from the hedge and for a given study site (p < 0.05).

<table><thead><tr><th>Study site</th><th>Depth (cm)</th><th>Distance</th><th>FR biomass (g.m<sup>-2</sup>)</th><th>FR production (g.m<sup>-2</sup>.yr<sup>-1</sup>)</th><th>FR turnover</th><th>FR C inputs (Mg.ha<sup>-1</sup>.yr<sup>-1</sup>)</th></tr></thead><tbody><tr><td rowspan="6">G_site</td><td rowspan="3">0–15</td><td>1 m</td><td>139 ± 57 (AB)</td><td>126 ± 11 (A)</td><td>1.53 ± 0.49 (A)</td><td>0.50 ± 0.04 (A)</td></tr><tr><td>3 m</td><td>106 ± 12 (A)</td><td>152 ± 13 (A)</td><td>1.52 ± 0.23 (A)</td><td>0.60 ± 0.05 (A)</td></tr><tr><td>10 m</td><td>298 ± 72 (B)</td><td>153 ± 5 (A)</td><td>0.65 ± 0.17 (B)</td><td>0.61 ± 0.02 (A)</td></tr><tr><td rowspan="3">15–30</td><td>1 m</td><td>102 ± 28 (a)</td><td>69 ± 5 (a)</td><td>0.86 ± 0.18 (a)</td><td>0.28 ± 0.02 (a)</td></tr><tr><td>3 m</td><td>51 ± 15 (b)</td><td>74 ± 1 (a)</td><td>1.92 ± 0.49 (b)</td><td>0.30 ± 0.04 (a)</td></tr><tr><td>10 m</td><td>106 ± 7 (a)</td><td>106 ± 2 (a)</td><td>0.55 ± 0.06 (a)</td><td>0.22 ± 0.03 (a)</td></tr><tr><td rowspan="6">C_site</td><td rowspan="3">0–15</td><td>1 m</td><td>201 ± 50 (α)</td><td>160 ± 54 (α)</td><td>0.89 ± 0.19 (α)</td><td>0.64 ± 0.21 (α)</td></tr><tr><td>3 m</td><td>134 ± 28 (αβ)</td><td>171 ± 50 (α)</td><td>2.07 ± 1.24 (α)</td><td>0.68 ± 0.20 (α)</td></tr><tr><td>10 m</td><td>88 ± 11 (β)</td><td>75 ± 15 (β)</td><td>0.87 ± 0.14 (α)</td><td>0.30 ± 0.06 (β)</td></tr><tr><td rowspan="3">15–30</td><td>1 m</td><td>85 ± 28 (x)</td><td>54 ± 10 (x)</td><td>1.03 ± 0.44 (x)</td><td>0.22 ± 0.04 (x)</td></tr><tr><td>3 m</td><td>33 ± 5 (xy)</td><td>49 ± 7 (x)</td><td>1.61 ± 0.29 (x)</td><td>0.19 ± 0.03 (xy)</td></tr><tr><td>10 m</td><td>22 ± 5 (y)</td><td>35 ± 3 (x)</td><td>2.00 ± 0.55 (x)</td><td>0.14 ± 0.01 (y)</td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Fine-root biomass (g.m⁻²) as a function of depth and distance from the hedge at G_site and C_site. All distances at G_site lay in permanent grassland, while those at C_site lay in a grass strip (1 m) or cropland (3 and 10 m). Different letters indicate significant differences (p < 0.05) among distances from the hedge for a given site and soil layer. Whiskers represent 1.5 times the interquartile range.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Monthly cumulative rainfall, mean soil temperature and root production (g.m⁻²month⁻¹) as a function of distance from the hedge and sampling period at site G_site and C_site in 2022. Whiskers represent 1.5 times the interquartile range.</figcaption></figure>

present at 10 m (i.e. LL C inputs = 0). At 1 and 3 m, LL C inputs were significantly higher under Oaks than under Hazel or Oaks/Hazel mix, and under Oaks/Hazel mix than under Hazel alone.

### 3.1.3. Carbon input from cropland and grassland above-ground biomass

Crop/grass residue above-ground biomass was similar among distances at G_site while it was higher at 10 m compared to 1 and 3 m from

the hedge at C_site (Table S2). CR and OF C inputs in 2022 varied among distances and between sites (Table 3). At G_site, CR C inputs were higher at 1 and 3 m compared to 10 m while they were higher at 10 m compared to 1 and 3 m from the hedge at C_site.

### 3.1.4. Carbon input from organic fertilisation

In 2022, farmers surveys showed that OF C inputs was the same at
<caption>**Table 2**</caption>

Means and standard errors of leaf litter biomass (LL biomass) and leaf litter carbon input (LL C inputs) by tree species (n = 5 each) and distance from the hedge at C_site. Different letters show significant differences in either LL biomass or C inputs between tree species for a given distance from the hedge.

<table><thead><tr><th>Distance</th><th>Tree species</th><th>LL biomass (g.m<sup>-2</sup>)</th><th>LL C inputs (Mg.ha<sup>-1</sup>.yr<sup>-1</sup>)</th></tr></thead><tbody><tr><td rowspan="3">1 m</td><td>Oak</td><td>100.3 ± 12.5 (A)</td><td>0.53 ± 0.06 (A)</td></tr><tr><td>Hazel</td><td>7.9 ± 0.4 (B)</td><td>0.04 ± 0.002 (B)</td></tr><tr><td>Mix</td><td>28.7 ± 4.9 (C)</td><td>0.14 ± 0.02 (C)</td></tr><tr><td rowspan="3">3 m</td><td>Oak</td><td>111.5 ± 12.1 (A)</td><td>0.48 ± 0.06 (A)</td></tr><tr><td>Hazel</td><td>5.6 ± 2.2 (B)</td><td>0.03 ± 0.01 (B)</td></tr><tr><td>Mix</td><td>27.0 ± 8.4 (C)</td><td>0.13 ± 0.04 (C)</td></tr></tbody></table>

each site regardless of the distance: 2 Mg C.ha⁻¹.yr⁻¹ at G_site, of which 0.5 Mg C.ha⁻¹.yr⁻¹ came from grazing, and 1.9 Mg C.ha⁻¹.yr⁻¹ at C_site, assuming uniform application of organic fertiliser and uniform cattle excreta in the field.

### 3.1.5. Spatial distribution of carbon inputs in 2022

At G_site, total C input was higher at 1 and 3 m than at 10 m, due mainly to higher LL C inputs and CR C inputs (Fig. 4). At C_site, total C input was lower at 3 m than at 1 and 10 m. Although LL C inputs and FR C inputs were higher at 1 and 3 m, CR C inputs was higher at 10 m, which resulted in little difference in total C input among distances at C_site.

## 3.2. Soil organic carbon stocks

Mean SOC stocks were larger at C_site than at G_site (Table 4). At G_site, at 1 m, the maximum soil depth was 60 cm. At 1, 3 and 10 m, SOC stocks from 0 to 30 cm represented 76 %, 65 % and 63 % of total stocks, respectively, at G_site and 62 %, 69 % and 57 %, respectively, at

C_site. SOC stocks were 28 % and 0.4 % larger at 1 and 3 m, respectively, than at 10 m at G_site from 0 to 30 cm. At C_site, they were 83 % and 6 % larger at 1 and 3 m, respectively, than at 10 m from 0 to 30 cm.

At G_site, the POM and MAOM fractions represented ca. 25 % and 75 % of the soil mass, respectively, in the entire soil profile and at both 1 and 10 m (Table S1). At C_site, the POM and MAOM fractions represented ca. 50 % and 50 % of the soil, respectively, from 0 to 60 cm, and 65 % and 35 % of the soil, respectively, from 60 to 90 cm. The distribution of SOC stocks differed greatly in both fractions between 1 and 10 m at both sites (Fig. 5). At G_site, from 0 to 30 cm, SOC stocks were larger at 1 m than at 10 m, and most of the difference in SOC stocks between the two distances was contained in the POM fraction. At C_site, from 0 to 60 cm, SOC stocks in both fractions were larger at 1 m than at 10 m.

At G_site, the spatial distribution of annual C inputs was not consistent with that of SOC stocks. C inputs were higher at 1 and 3 m than at 10 m, while from 0 to 30 cm, SOC stocks were larger only at 1 m than at 3 and 10 m. At C_site, inter-annual C inputs were also not consistent with SOC stocks. C inputs were nearly the same among distances, with higher C inputs at 10 m for high estimates, while SOC stocks

<caption>**Table 4**</caption>

Soil organic carbon (SOC) stocks by depth and distance from the hedge at sites G_site and C_site.

<table><caption>**Table 3**</caption><thead><tr><th rowspan="2">Study site</th><th rowspan="2">Depth (cm)</th><th colspan="3">SOC stocks (Mg C.ha<sup>-1</sup>)</th></tr><tr><th>1 m</th><th>3 m</th><th>10 m</th></tr></thead><tbody><tr><td rowspan="3">G_site</td><td>0-30</td><td>101.70</td><td>79.64</td><td>79.35</td></tr><tr><td>0-60</td><td>134.30</td><td>105.76</td><td>113.01</td></tr><tr><td>0-90</td><td>NA</td><td>121.71</td><td>125.87</td></tr><tr><td rowspan="3">C_site</td><td>0-30</td><td>157.10</td><td>91.22</td><td>85.82</td></tr><tr><td>0-60</td><td>230.93</td><td>118.08</td><td>131.36</td></tr><tr><td>0-90</td><td>252.78</td><td>130.87</td><td>151.09</td></tr></tbody></table>

Carbon (C) input (Mg.ha⁻¹.yr⁻¹) from fine roots (FR), leaf litter (LL), organic fertilisation (OF), and crop residues (CR) by distance from the hedge at sites G_site and C_site in 2022. Low and High indicate low and high estimates, respectively.

<table><thead><tr><th rowspan="2">Study site</th><th rowspan="2">Distance from the hedge</th><th rowspan="2">FR C inputs</th><th colspan="2">LL C inputs</th><th rowspan="2">OF C inputs</th><th colspan="2">CR C inputs</th><th colspan="2">Total C inputs</th></tr><tr><th>Low</th><th>High</th><th>Low</th><th>High</th><th>Low</th><th>High</th></tr></thead><tbody><tr><td rowspan="3">G_site</td><td>1 m</td><td>0.96</td><td>0.04<sup>*</sup></td><td>0.53<sup>*</sup></td><td>2.0</td><td>1.1</td><td>2.4</td><td>4.10</td><td>5.84</td></tr><tr><td>3 m</td><td>1.10</td><td>0.03<sup>*</sup></td><td>0.48<sup>*</sup></td><td>2.0</td><td>1.3</td><td>2.7</td><td>4.43</td><td>6.33</td></tr><tr><td>10 m</td><td>1.02</td><td>0<sup>*</sup></td><td>0<sup>*</sup></td><td>2.0</td><td>0.9</td><td>1.8</td><td>3.92</td><td>4.82</td></tr><tr><td rowspan="3">C_site</td><td>1 m</td><td>1.04</td><td>0.04</td><td>0.53</td><td>1.9</td><td>1.0</td><td>2.3</td><td>3.98</td><td>5.72</td></tr><tr><td>3 m</td><td>1.07</td><td>0.03</td><td>0.48</td><td>1.9</td><td>0.9</td><td>1.9</td><td>3.90</td><td>5.4</td></tr><tr><td>10 m</td><td>0.53</td><td>0</td><td>0</td><td>1.9</td><td>1.6</td><td>3.4</td><td>4.03</td><td>5.83</td></tr></tbody></table>

<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Leaf litter (LL), organic fertilisation (OF), crop residue (CR), fine root (FR) and Total annual carbon input (Mg.ha⁻¹.yr⁻¹) at 1, 3 and 10 m from the hedge for a permanent grassland (G_site) or a rotation of cropland and temporary grassland with a 1 m grass strip (C_site).</figcaption></figure>

<footnote>* C input from Leaf litter at G_site was considered to be the same as C_site.</footnote>
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Soil organic carbon (SOC) stocks as a function of depth and distance from the hedge for particulate organic matter (> 50 µm) and fine organic matter (< 50 µm) soil fractions at 1 and 10 m from the hedge for G_site and C_site (n = 1).</figcaption></figure>

were larger at 1 m than at 3 and 10 m from 0 to 30 cm.

# 4. Discussion

## 4.1. Spatial distribution of carbon inputs in grassland vs cropland plots

This study focused on the spatial distribution of C inputs in a grassland and a cropland, both bordered by a hedge. Surprisingly, results showed higher C inputs at 10 m from the hedge in the cropland (5.83 Mg. ha⁻¹. yr⁻¹) compared to the grassland (4.82 Mg. ha⁻¹. yr⁻¹). In 2022, at G_site, total C input was higher at 1 and 3 m than at 10 m from the hedge due to higher LL C inputs and CR C inputs. At 10 m, although fine roots had higher productivity and biomass, they also had lower turnover than at 1 m during the year of sampling, resulting in similar total C inputs among distances.

At C_site even if FR and LL C inputs were higher at 1 and 3 m compared to 10 m from the hedge, the lower CR C inputs at 1 and 3 m compared to 10 m from the hedge resulted in little difference in total C inputs among distances. Near the hedge, the increase in LL C inputs is most likely related to the proximity of the trees (McElhinny et al., 2010). Similarly, the higher FR C inputs at 1 and 3 m may be related to the proximity of both trees and grass strip roots. Similar results were reported by Cardinael et al. (2015) who suggested that mixed vegetation systems contribute to greater below-ground C inputs compared to monocultures.

At both G_site and C_site, which are crop-livestock systems, C inputs were high. A large percentage of C inputs came from crop residues, but compared to conventional agriculture, large percentages also came from organic fertilisation (Pellerin et al., 2020) (i.e. 34 % and 41 % at 1 and 10 m, respectively, at G_site and 33 % each at 1 and 10 m, respectively, at C_site). At both sites, surveys showed that organic fertilisation was to the same among distances. In other similar systems, manure application and grazing may not occur near the hedge, which may influence the distribution of total C input among distances from the hedge.

Tree species had a strong local influence on LL C inputs, as it was lower under Hazel than under Oaks. In a hedge composed of diverse tree species, specific characteristics of each species, including above-ground biomass and litter quality, may strongly influence lateral variability in SOC stocks along hedges (Mohr and Topp, 2005). The present study, which focused on lateral variability in C inputs as a function of distance from the hedge, did not capture this lateral variability. These results

show that the species planted in hedges, in interaction with the crop in the adjacent field, can strongly influence the hedge's potential to store C.

The study explored in detail the contribution of roots from 0 to 30 cm to total C input. Although the 0–30 cm layer contained a large percentage of the cumulative root biomass from 0 to 90 cm at G_site and C_site (mean of 60 % and 68 %, respectively), measuring C input from fine roots only from 0 to 30 cm does not fully assess their distribution near hedges. Other studies reported that tree roots in agroforestry systems could explore soil layers down to 4 m (Cardinael et al., 2015; Fernández et al., 2008), so we likely underestimated C input from roots near the hedges at both sites. C input from roots was also underestimated because we did not consider rhizodeposition, which can provide a large percentage of total below-ground C input (Hirte et al., 2018b; Villarino et al., 2021). For leaf litter C inputs results, it should be acknowledged that the assumption of similar LL C inputs between G_site and C_site may be weakened by possible grazing on tree leaves at G_site, which could impact litter deposition.

## 4.2. Contribution of fine roots to soil carbon inputs

The distance from the hedge significantly influenced cumulative FR biomass at both sites. At G_site, total FR biomass was lower at 1 and 3 m than at 10 m. In contract, at C_site, FR biomass was higher at 1 and 3 m than at 10 m, likely due to the presence of roots from hedge trees or from the grass strip. FR production was also higher at 1 and 3 m than at 10 m, as the soil had been left bare for several months between cropping periods. Although perennial grasses already have high root biomass in shallow soil layers (Don et al., 2009; DuPont et al., 2014), we expected similar or higher root biomass near the hedge at G_site.

At 10 m, FR biomass and FR production were higher at G_site than at C_site. Similarly, Frank et al. (2006) reported greater root biomass in the 0–30 cm soil layer in undisturbed grassland compared with continuous wheat or wheat-fallow rotations. Grassland can have higher root biomass than cropland due to differences in plant species, as perennial grasses have higher root biomass than cereal crops do (DuPont et al., 2014). The presence of multiple plant species in grassland may also increase the variety of root architectures, thus increasing the colonisation and use of soil (Mueller et al., 2013). Soil disturbance such as tillage and the fact that crops are harvested every year may also prevent crops from achieving a root biomass similar to that of perennial grasses.

FR turnover did not differ among distances from the hedge at C_site
but was significantly higher at 1 and 3 m than at 10 m at G_site, which indicates that fine roots of hedges can have higher turnover rates than those of grassland from 0 to 30 cm. Contrary to our results, Gill and Jackson (2000) reported a lower mean root turnover rate for tree roots than for grassland in the entire soil profile. These differences between studies may be due to specific characteristics of hedge trees compared to forest trees. Next to a hedge, the presence of trees increases variability in water resources (Caubel et al., 2003), which can increase root turnover (Peek et al., 2006). In a temperate forest, Brunner et al. (2013) reported lower root production, ranging between 37 and 126 g.m².yr⁻¹ and similar root turnover of 0.23–2.92 at a mean soil depth of 44 cm, compared to the present study results (mean FR production of 195 g.m².yr⁻¹ and FR turnover of 1.29 in the first 30 cm of soil.). Another study on temperate grassland, showed values of root turnover in the same range as our study, from 1.58 to 3.41 in the 0–20 cm soil layer (Luo et al., 2021).

FR C inputs decreased as depth increased at both sites, which agrees with results of Hirte et al. (2018b), who compared C inputs at depths of 0–25, 25–50 and 50–75 cm under wheat and maize. Brunner and Godbold (2007) reported mean annual storage of 0.9 Mg C.ha⁻¹.yr⁻¹ from FR turnover for the entire soil profile of temperate forests in Europe, which is also similar to results of the present study (0.78 in the first 30 cm of soil). Additional research is now necessary to dissociate C input from the hedge roots and C inputs from crops or grass roots.

### 4.3. Inconsistency between mean annual carbon inputs and SOC stocks

At both sites, SOC stocks tend to be larger at 1 m than those at 10 m. At 10 m, however, SOC stocks tend to be larger in the sandy cropland than in the loamy grassland, which was surprising, but agreed with a previous study of a larger sample size that included G_site and C_site (Lesaint et al., 2023).

The difference in SOC stocks at 1 and 10 m tends to be mainly in the POM fraction at G_site but in both the POM and MAOM fractions at C_site. Like in soils whose mineral-associated organic matter (MAOM) fraction is saturated with C, any increase in SOC stock will occur mainly in the POM fraction (Cotrufo et al., 2019). At C_site, although SOC stocks at 10 m were larger than those at G_site, the MAOM fraction may not be saturated. Hedge agroforestry systems may be particularly interesting in this context, as C accumulation in the POM fractions does not seem to have an upper limit, provided that sufficient C inputs are maintained (Cotrufo et al., 2019).

At 10 m, C_site tends to have larger total SOC stocks than G_site did. This result is not consistent with those in the literature, which indicate that grassland has larger SOC stocks than cropland does (Guillaume et al., 2021). In the present study, C inputs and SOC stocks were compared only in the 0–30 cm soil layer, based on the assumption that the C inputs would supply SOC stocks mainly in this layer. Although SOC stocks change slowly, as land management had not changed in the previous decade, we hypothesised that SOC was more or less at equilibrium with C inputs and thus remained consistent with annual C inputs, but the latter was not the case. These results may indicate that SOC stocks are influenced not only by C inputs, but also by underlying stabilisation processes near the hedge. In particular, the presence of the hedge may have a significant impact on soil physical, chemical and biological properties, including pH (Biffi et al., 2022; Holden et al., 2019), bulk density (Biffi et al., 2022; Holden et al., 2019; Walter et al., 2003) and soil fauna and microbial activity (Holden et al., 2019; Van Vooren et al., 2018), which in turn may affect C storage. It is also possible that the inconsistency between C inputs and stocks was due to not accounting for rhizosphere deposition C inputs, which may be significant over time, especially for deep soil horizons (Angst et al., 2018). To have a better understanding of the links between SOC stocks and C inputs, SOC stocks were also compared with inter-annual soil C inputs to take account of inter-annual variations in C inputs linked to crop rotation (Appendix 1). At both sites, considering inter-annual C inputs did

not change the trends in the distribution of C inputs near the hedge or its relation to SOC stocks confirming that underlying C stabilisation processes occur. At C_site, ploughing the temporary grassland after its last year may have increased soil C mineralisation, but mineralisation was not considered in the study. To assess the influence of management of these systems on the spatial distribution of C inputs more accurately, simulation modelling of these systems is necessary.

### 4.4. Influence of drought on the distribution of carbon inputs in 2022

While the results showed strong difference in C input distribution between the hedge bordering the cropland and the hedge bordering the permanent grassland, the sampling was done during the year 2022, which was affected by a drought. This drought had an impact on carbon inputs distribution. In 2022, yields at 10 m were 65 % lower at G_site and 5 % lower at C_site than the mean annual yield reported by the farmer, perhaps because annual rainfall was 26 % lower in 2022 than the mean annual rainfall over the previous 20 years, especially during the growing season. At G_site, root:shoot ratios at 10 m were consistent with the mean of 4.2 reported by Mokany et al. (2006) for temperate grassland, but they were lower at 1 and 3 m. At C_site, root:shoot ratios at 10 m were higher than the mean of 0.2 reported by (Bolinder et al., 1997), who focused, however, only on the 0–30 soil layer. The root: shoot ratio at C_site was higher at 1 and 3 m than at 10 m. Frank (2007) reported different results, with no influence of drought on grassland above-ground biomass but a decrease in below-ground biomass.

The fact that 2022 was a dry year may explain the differences in spatial distribution of C inputs from crop or grass residues between G_site and C_site. A year with below-average rainfall can decrease crop production, but the orientation of the hedge may have influenced this effect. At G_site, the hedge's east-west orientation (sampled field to the east) may have kept vegetation near the hedge in the shade, decreasing its radiation balance and thus its mortality due to the dry weather (Guyot and Verbrugghe, 1976). At C_site, however, the hedge's north-south orientation (sampled field to the south) may have increased the radiation balance near the hedge compared to areas further away, thus increasing crop mortality due to the dry weather and in turn reducing C input from crop residues (Guyot and Verbrugghe, 1976). As these interpretations are based on observations from a single year, they should be treated with caution.

The impact of the 2022 drought on the results highlights the sensitivity of C inputs and their distribution to climate. These results emphasize the need for additional research to understand how hedge planting and management can be modified to efficiently take into account future drought periods. In addition, long-term monitoring would be valuable to study crop-hedge interactions during drought using root identification or isotopic tracing to better our understanding of SOC storage near hedges and the impact of hedges on crop resilience.

## 5. Conclusion

This study examined C inputs in the 0–30 cm soil layer next to a mixed hedge in association with two cropping systems typical of mixed farming in France, and how hedges in field margins influence them. At both sites, C inputs from the hedge were significantly higher at 1 and 3 m than at 10 m from the hedge. The mixed hedge influenced C inputs of the cropland and temporary grassland rotation more than those of the permanent grassland. In the cropland, total C input differed little among distances from the hedge; however, C inputs from leaf litter and fine roots were higher at 1 and 3 m, while C input from crop residues was higher at 10 m from the hedge. In the permanent grassland, C inputs at 1 and 3 m differed from those at 10 m mainly due to higher C inputs from leaf litter and grass residues near the hedge. C inputs from the hedge leaf litter was mainly stored in the coarse soil fraction and C inputs from fine-root turnover was mainly associated with the fine soil fraction. Overall, within the 0–30 cm soil layer considered in this study, C inputs from
hedges may not be significant compared to C inputs from crops and grasses and C inputs from organic fertilisers, still, hedges are an interesting landscape component to associate with other land management practices beneficial for soil carbon storage. In the current context of a general decline in the hedge linear length in Europe, this work shows the need to maintain then current hedge agroforestry systems in order to avoid a rapid loss of labile soil C after hedge removal.

Overall, in contexts of mixed livestock farming where C inputs (organic fertilization, crop residues) are already high compared to cash crop systems, the presence of the hedge further increased the estimated total inputs in the vicinity of the hedge for one of the studied sites. This means that hedges can have a significant local impact on C inputs. However, at the field scale, where hedges are only located at the edge of the field, the effect of hedges on total C inputs could be less significant compared to crop/grasses and organic fertilisers C inputs. In the context of climate change, however, the presence of hedges remains relevant, particularly for its potential impact on the micro-climate around the hedge and its value for livestock farming as a source of food and shade. In addition, hedges can contribute to biodiversity conservation by providing habitats for wildlife.

To further understand C inputs, further research needs to be conducted at greater soil depths to be able to consider more fully effects of hedges on the fields they border. Better understanding of impacts of hedges on C inputs and SOC storage in agriculture fields will help define their contribution to climate change mitigation. With the increased frequency of dry and wet years due to climate change, which may affect the carbon inputs from hedges and their spatial distribution, additional work is also needed to understand the effects of climate contrasts on hedge-crop and hedge-grassland associations, particularly through inter-annual monitoring.

## CRediT authorship contribution statement

**Lucas Lesaïnt:** Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Data cura-
tion, Conceptualization. **Valérie Viaud:** Writing – review & editing, Validation, Supervision, Resources, Project administration, Methodol-
ogy, Investigation, Funding acquisition, Conceptualization.

## Declaration of Competing Interest

All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

## Acknowledgements

The authors thank Sylvain Busnot, Yannick Benard, Mikael Fau-
cheux, Florian Gaillard, Clotilde Blancfene, Vincent Stubbe, Virginie
Parnaudeau, and Nolwenn Poulaillon for their assistance in field sam-
pling. They are grateful to the AFAC - agroforestry (French Association
for Rural Trees - agroforestry) and the RMQS team (French national soil
monitoring programme) for the additional data they provided. They are
also grateful to the farmers who allowed sampling and monitoring in
their fields. This research was funded by ADEME (the French Environ-
ment and Energy Management Agency) and CASDAR (Special Allocation
Account for Agricultural and Rural Development) in the RESP'HAIES
project. The two authors contributed to the study conception and design,
the material preparation and the data collection and analysis. The first
draft of the manuscript was written by Lucas Lesaïnt and Valérie Viaud
commented on previous versions of the manuscript. The two authors
read and approved the final manuscript.

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the

online version at doi:10.1016/j.agee.2025.110104.

## Data availability

Data will be made available on request.

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