Positive influence of apple trees on soil chemical and biological activity in an agroecological garden orchard system

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Ramananjatovo, R. Guénon, J. Peugeot, E. Chantoiseau, M. Delaire, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4686563/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Oct, 2024 Read the published version in Agroforestry Systems → Version 1 posted 7 You are reading this latest preprint version Abstract The specific aim of this study was to assess the impact of 20-year-old apple trees on the soil agronomic quality in an agroforestry system consisting of 2 rows of apple trees with 5 rows of vegetable beds in between. The effects of this system were analyzed specifically on soil microbial activity and fertility. Measurements were carried out for 2 years between 2019 and 2021 in apple tree rows (R) and in vegetable rows 1.5 m (B1), 3 m (B2) and 5 m (C) from the apple tree row. Litter quantities and soil organic matter (SOM) content were measured as well as the decomposition rates of apple tree leaf litter. Soil microbial activity was characterized by measuring (1) in-situ soil respiration and (2) basal (BR) and substrate induced respiration (SIR) under controlled conditions. The results showed that proximity to apple trees was linked to higher SOM content. The litter decomposition rate was up to 1.7-times greater under the tree rows than in vegetable beds. The amplitude of insitu soil CO 2 flux variation and the maximum flux were lower under the tree rows than in vegetable beds, mainly due to lower temperature. In the vegetable beds, the maximum in-situ soil CO 2 flux was attained faster in B1 than in C. Under controlled laboratory conditions, we showed that BR was significantly stronger in R, B1 and B2 than in C (5, 5, 4.7 and 3.5 µgC-CO 2 .h − 1 .g − 1 soil DW, respectively). In addition, the soil in the apple tree rows was more sensitive to the addition of glucose (SIR) than the soil in the vegetable beds. Our results suggest that soil microbial activity was more intensive up to 3 m from the apple trees. Globally, the results highlight the complexity of the interactions among the biotic and abiotic factors that are at the origin of the spatial heterogeneity encountered. Agroforestry decomposition mineralization organic matter soil respiration Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction Maintaining a healthy soil is a major challenge in agriculture. Treebased intercropping is a promising solution to ensure high production while at the same time improving soil quality, as shown in agroforestry systems (AFS) (Dupraz and Liagre 2008 ). Litter supply and its decomposition have been reported to be an important contributor to the physical, chemical and microbiological quality of the soil (Suárez et al. 2021 ). Trees can provide input of ligninrich materials that is slowly decomposed and enriches the soil in stable organic C (Montagnini and Nair 2004 ). More specifically, fruit trees provide a further source of litter, namely the wood from pruning. This, along with nutrients released from litter decomposition can stimulate the activity of soil microorganisms (Leigh et al. 2002 ). Although a global positive effect of trees on soil quality has been reported in AFS (Dollinger and Jose 2018 ; Dupraz and Liagre 2008 ), most of the studies carried out on AFS have been limited to a global comparison between AFS and an adjacent cropland (Hergoualc’h et al. 2012 ; Tian et al. 2013 ; Eddy and Yang 2022 ) or forest (Becker et al. 2015 ; Triadiati et al. 2011 ). Only a handful of studies have taken into account the area of influence of adjacent trees (An et al. 2023 ; Das 2024 ). Recent meta-analyses have demonstrated the positive effects of agroforestry systems on soil bio-physico-chemical properties, but without identifying the beneficial effect of distance from tree rows (Mayer et al. 2022 ; Ngaba et al. 2024 ). As highlighted by Cardinael et al. ( 2020 ), there is a lack of knowledge about the influence of the lateral gradient generated by the trees, and their effect on soil functions. In garden-orchard systems (GOS) where vegetables are intercropped with fruit trees, the quantification of soil organic matter (SOM) distribution and the soil microbial activity as well as its biodegradation capacity are crucial to understand how fruit trees influence these processes. In a previous study on GOS, Ramananjatovo et al. ( 2021 ) showed that apple trees created specific microclimatic conditions within 3 m from the tree rows (air temperature was 1.5°C lower near the trees and air relative humidity 5% higher), largely influencing the adjacent soil of vegetable beds in the inter-rows. Within this area, N mineralization rate was nearly doubled. In the present work, we propose to further investigate the influence of fruit trees on the distribution of SOM, as well as leaf litter biodegradation, and to quantify microbial activity through in-situ and laboratory indicators. The originality of this study resides in both studied cropping system and addressed research questions. On one hand, GOS, an agroecological production system of fruits and vegetables, is a very promising cropping system (Ouma and Jeruto 2010 ) but remains poorly studied to date. On the other hand, very little is known about the degree to which trees can influence SOM biodegradation and microbial activity in temperate AFS (Mungai et al. 2005 ; Nii-Annang et al. 2009 ; Mustafa et al. 2022 ) and to our knowledge, there are currently no scientific studies that address this topic in GOS. This study aimed to (1) quantify litterfall and SOM along a lateral gradient of distance from the fruit trees, (2) investigate the link of both the microclimate generated by fruit trees and SOM distribution with the decomposition of litter and (3) determine the amplitude of soil microbial activity. Accordingly, the study is predicated upon three hypotheses. We first hypothesized that litter supply will be greater near the trees and will be associated with higher amount of SOM. Secondly, since the trees can modify both the temperature and the moisture of the soil (Ramananjatovo et al., 2021 ), the decomposition of fresh organic matter will be greater under the tree rows and will decrease with increasing distance from the trees, up to 3 m. Finally, as the accumulation of SOM may lead to a general improvement in soil fertility, we hypothesized that there is a spatial gradient of soil microbial activity with increasing distance from the tree row towards the vegetable beds. 2 Materials and methods 2.1 Experimental design The experiment was carried out at the Institut Agro in Angers (France, longitude 0° 36' W, latitude 47° 28' N, elevation 49 m), from September 2019 until August 2021. The study plot was presented by Ramananjatovo et al. ( 2021 ). The studied soil consisted of 4 layers and had an average depth of about 1.2 m. It belongs to the Luvisol Redoxisol classification (FAO classification). Its main properties are shown in Table 1 . Table 1 Main characteristics of the soil profile (from Ramananjatovo et al. 2021 ) Soil layer Ag Eg BTg Cg (0–30 cm) (30–50 cm) (50–90 cm) (90–120 cm) Texture Clay [%] 15.5 22 36.5 31.3 Silt [%] 42.2 39.6 31.7 28.3 Sand [%] 39.6 37.8 31.1 39.7 Chemical properties pH H2O 6.7 7.2 7.5 5.1 pH KCl 5.9 6.3 6.6 3.8 Organic matter [g.kg -1 ] 35 6 < 6 < 6 Total C [g.kg -1 ] 20.3 3.5 < 3 < 3 Total N [g.kg -1 ] 1.3 0.5 0.4 0.3 C:N ratio 15.6 7.4 < 7.4 < 10.8 P 2 O 5 [mg.kg -1 ] 68 16 10 < 10 K 2 O [mg.kg -1 ] 258 140 111 71 MgO [mg.kg -1 ] 165 157 168 125 Na 2 O [mg.kg -1 ] 14 12 27 21 CaO [mg.kg -1 ] 1 757 1 324 2 528 1 149 CaCO 3 [g.kg -1 ] < 1 < 1 < 1 < 1 CEC [meq.100 g -1 ] 7.5 6.3 7.7 7.2 Hydraulic properties (mean ± SD, n = 3) Bulk density [g.cm -3 ] 1.37 ± 0.11 1.65 ± 0.06 1.55 ± 0.07 1.61 ± 0.06 Field capacity [% volumetric water content] 24.4 ± 1.4 28.7 ± 4.3 30.1 ± 6.3 29.1 ± 5.1 Wilting point [% volumetric water content] 17.7 ± 0.6 23.2 ± 3.7 24.4 ± 7.2 22.4 ± 4.6 Saturated hydraulic conductivity [10 − 4 cm.s -1 ] 4.33 ± 0.47 1.23 ± 0.13 3.03 ± 0.50 2.33 ± 0.13 Briefly, the experimental area (Fig. 1 ) consisted of two rows of apple trees ( Malus × domestica Borkh .) that were planted in 2000. The plot was originally an apple orchard with herbaceous vegetation before the inter-row was converted into vegetable beds in 2016. The studied tree rows were constituted of different varieties of apple trees. The East row was composed of ‘Golden Delicious’ while the West row was composed of three varieties: ‘Gala’, ‘Fuji’ and ‘Red winter’. The two rows were separated by 12m with a north-south orientation, and the average distance between each tree in the row was 1.6m with branches connected to each other and trimmed at a height of 2.5 m. Soil under the apple trees was permanently covered by herbaceous plants. The inter-row was made up of 5 vegetable beds 1.8m wide where unfertilized lettuce ( Lactuca sativa L., var. ‘Olana’) and radish ( Raphanus sativus L., var. ‘Ostergruss’) were successively grown between 2019 and 2021. Faba bean ( Vicia faba L., var. ‘Irena’) was cultivated as a green manure during the winter 2020. The vegetable beds were irrigated using a drip irrigation system (Dripnet PC 390 Ø 16 mm, NETAFIM, Gardanne, France) during the lettuce and radish growing seasons, in summer (June to August). Apple tree rows were not irrigated. 2.2 Quantification of leaf litter Apple tree leaf litter was quantified from September to December, in 2019 and 2020. Senescent leaves were collected in each tree row and each vegetable bed using 1 mwide collection nets, maintained at approximately 40 cm from the soil surface (Fig. 1 B). Leaves were collected daily to prevent loss and decomposition. The leaves were air dried (around 25°C) and inserted into paper bags at each stage. Once the collection period was over (December), all the leaves collected in each zone were weighed and sub-samples were oven-dried at 60°C for 48 hours to calculate their residual water content and deduce their dry mass. 2.3 Measurement of litter decomposition The kinetics of litter degradation were assessed using the litterbag technique. Decomposition experiment was conducted in 2020 and 2021, from January to May. In 2020 the two rows of apple trees (RE vs. RW) were compared since they present different microclimatic environments (Ramananjatovo et al. 2021 ). Eight litterbags were placed in both RE and RW sides under the apple trees. In 2021, litterbags were placed to test the effect of the distance to trees on RW, B1W and C. The litterbags were placed in the western part of the plot because we had found the most contrasting microclimatic conditions in this area in our previous experiments (Ramananjatovo et al. 2021 ). In each case, approximately 6 g of air-dried apple leaves (mixture of leaves collected from all varieties within the two rows) were introduced in 20 cm pockets (Fig. 1 C) (Harmon et al. 1999 ) with a mesh size of 5 mm to allow macrofauna access for the decomposition process (Bradford et al. 2002 ). The litterbags have been placed on the soil. Residual biomass was collected monthly for four months. On each sampling date, the remaining leaves were gently washed and dried at 30°C for 24 hours, then at 60°C for the following 48 hours. The decomposition rate (k) was determined from first-order kinetics (Olson, 1963 ): \(\:{M}_{r}=\:{M}_{0}.{e}^{-kt}\) (Eq. 1) where \(\:{\text{M}}_{\text{r}}\) is the leaf residual dry weight at time t (g) and \(\:{\text{M}}_{0}\) the leaf initial dry weight (g). Small data loggers (iButtons, Dallas Semiconductor, California, USA) were placed between the leaves in the litterbags to continuously monitor the temperature (°C) and humidity (%) of the litter. 2.4 Soil organic matter (SOM), total organic C (TOC) and total N SOM, TOC and soil N contents were determined in each row and each vegetable bed, in 2020 and 2021. Soil samples were collected at 10 cm depth using an auger. To quantify SOM, the water contained in the soil samples was first eliminated by oven drying for 48 hours at 105°C. They were then heated to 550°C for 7h in a muffle furnace. TOC and N were determined by dry combustion using a C and N elemental analyser (Flash EA 112 Series, Thermo Fisher Scientific, Massachusetts, USA; NF ISO 10694) after grinding with a ball mill (MM 400, Retsch, Éragny, France). TOC was assimilated to soil total C since the CaCO 3 content of the soil was very low (< 1 g.kg − 1 soil DW, Table S1 , Supplementary material). 2.5 Characterization of soil microbial activities Measurement of soil respiration is among the most commonly used techniques to characterize soil microbial activity (Phillips and Nickerson 2015 ). It can be determined by quantifying the CO 2 production by the soil (Dilly and Zyakun 2008 ). CO 2 fluxes are primarily derived from decomposition activities of microorganisms, plant roots and soil fauna respiration. In this study, we performed two types of measurements: (1) an in-situ measurement of CO 2 flux to investigate the combined effects of biotic and abiotic factors on the degradation of SOM and (2) a lab-measurement of CO 2 flux at optimal temperature and moisture conditions to estimate the potential microbial activities. 2.5.1 In-situ measurement of CO 2 flux Soil CO 2 fluxes under the apple tree rows and in each vegetable bed, were quantified using an automatic gas chamber associated with an infrared gas analyzer (Cflux-1, PP systems, Arizona, USA) (Fig. 1 D). The volume of the chamber was approximately 2.5 L for a sampling surface of 0.033 m 2 . The device was equipped with a temperature (± 0.3°C) and a soil moisture (± 1%) sensor (HydraProbe, Stevens Water Monitoring Systems, Inc.). Soil collars were installed at each sampling point at least 24 hours before the measurements to avoid any disturbance of the soil. Green foliage was clipped in the area of the CO 2 measurement prior to the soil collar insertion to exclude plant respiration. Measurements in the vegetable beds were conducted on four dates: April 26, May 3, May 28, and July 15, 2021. Soil temperature and water content at 5 cm depth were recorded during each measurement. During the first day (April 26, 2021), CO 2 was quantified from 9:00 am to 6:00 pm to cover the diurnal evolution of the soil respiration. Then, for the next days, we decided to stop the measurements at 4:00 pm. Kaye et al. ( 2005 ) suggested that measurements taken between 9:00 am and 1:00 pm can be considered to be representative of daily fluxes but we decided to perform measurements until 4:00 pm because the maximum CO 2 flux was reached at this time. Under the tree rows, the fluxes were measured only once (June 30, 2021). The duration of flow measurements was 5 min, measurements were performed every 15 min. The daily average flux was calculated by averaging the measurements taken between 9:00 am and 4:00 pm. Two devices were used during the experiment. In the vegetable beds, one device was left permanently in the C bed and the second was moved every 15 minutes between B1 and B2, in order to compare the fluxes emitted in each bed during the same day. Under the apple tree rows, each device was left permanently under the row. 2.5.2 Quantification of basal and substrate induced respiration in the lab This experiment was conducted to evaluate the total microbial activity and the active microbial biomass by measuring basal respiration (BR) (Panikov, 2005 ) and substrate induced respiration (SIR) with glucose (Anderson and Domsch, 1978 ), respectively. SIR can be used as a proxy of the active microbial biomass as the addition of available C to the soil results in a strong increase in respiration rate and thus reveals the group of microorganisms using the substrate (Anderson and Domsch 1978 ). Glucose was used as a substrate because it can be readily used as a C source by most soil microorganisms (Stotzky and Norman 1961 ). Five soil samples were taken at a depth of 10 cm in each tree row and vegetable bed. They were mixed and sieved to 2 mm. We used the Microresp™ method (Campbell et al., 2003 ) to determine the amount of CO 2 emitted. The technique consists of 2 microplates, the microwell plate and the detection plate. An agar gel (30 g.L − 1 ) enriched in KCl (150 mmol.L − 1 ), in NaHCO 3 (2.5 mmol.L − 1 ) and in red cresol (12.5 µg.mL − 1 ) has been prepared for the detection plate. The microwell plate receives soil samples with or without added glucose. Silicone seal was used to bond the 2 plates, which were incubated for 4 hours at 20°C. A spectrophotometer (Epoch, BioTek, Vermont, USA) was used to read the absorbance of the detection gel at 570 nm before and after incubation. This difference in absorbance wa used to deduce the amount of CO 2 emitted by the soil. Wells with no substrate (distilled water only) were used to determine basal respiration (BR), while those with glucose were used to determine substrate-induced respiration (SIR). We calculated the metabolic quotient ( q CO 2 ) as the ratio between BR and SIR (Anderson and Domsc, 1993). This indicator provides information on the respiration rate between growing and potentially active micro-organisms (Anderson and Domsch 1993 ). A rate close to 1 means low growth capacity (Wardle and Ghani 1995 ). 2.6 Data analysis A one-way ANOVA analysis was carried out to assess the significance of differences between each vegetable bed and apple tree rows. This test was performed for each variable studied. Significance was assessed at 5%. R Studio's "xtable" package was used to perform a Pearson correlation analysis (Dahl et al. 2009 ). It was used to assess correlations between climatic variables, soil characteristics and litter decomposition. Three regression models proposed by Tang et al. ( 2006 ) were used to analyze the correlations between soil respiration, soil temperature and soil water content: \(\:SR=\:a\cdot\:{e\:}^{b\cdot\:T}\) (Eq. 2) \(\:SR=\:a\cdot\:\theta\:+b\) (Eq. 3) \(\:SR=\:a\cdot\:{e}^{b\cdot\:T}\cdot\:{\theta\:}^{c}\) (Eq. 4) where SR is the soil respiration (µmolCO 2 .m − 2 .s − 1 ), T the soil temperature (°C) and θ the water saturation (WFPS in %) at 5 cm depth, a, b, and c are the coefficients of the model. Soil water saturation (θ) was calculated as follow (Paul 2014 ) : where SWC is the volumetric soil water content (% v/v), Bd the soil bulk density (g.cm − 3 ) and Pd the soil particle density (= 2.65 g.cm − 3 ). The temperature sensitivity of soil respiration \(\:{\text{Q}}_{\text{10:}}\) was calculated using Van’t Hoff ( 1898 ) equation : \(\:{Q}_{10}={e\:}^{10d}\) (Eq. 6) where d is the model coefficient in (Eq. 2). \(\:{\text{Q}}_{\text{10:}}\) indicates the rate of change of CO 2 emissions for each 10°C increase in soil temperature. All statistical analyses were performed with R Studio (v 3.6.0) (R Studio Team, 2020 ). 3 Results 3.1 Distribution of litterfall, SOM, TOC and soil N The annual quantity of litterfall measured under the apple tree rows and in each vegetable bed is shown in Table 2 . Litterfall ranged from 1.1 to 48 g DW.m − 2 in 2019 and from 2.4 to 32 g DW.m − 2 in 2020. Average litterfall for both years was 40.83, 20.67, 11.59, 2.34, 1.78, 2.82 and 30.48 g DW.m − 2 in RW, B1W, B2W, C, B2E, B1E and RE, respectively. Litter deposition gradually decreased with the distance from the trees. We observed a significant difference between the tree rows and the vegetable beds ( p < 0.001). Table 2 Apple litterfall collected in each tree row and vegetable bed during fall 2019 and 2020 (mean ± SD, n = 3). Values with different letters indicate significant differences ( p < 0.05, Tukey HSD test) Row/Bed Fall 2019 Fall 2020 RW 48.78 ± 14.35 a 32.89 ± 5.55 a B1W 20.24 ± 5.06 bc 21.12 ± 1.25 bc B2W 7.55 ± 1.05 cd 15.64 ± 4.76 c C 1.98 ± 0.96 d 2.72 ± 1.78 d B2E 1.12 ± 0.88 d 2.44 ± 2.26 d B1E 3.2 ± 0.72 cd 2.46 ± 0.91 d RE 34.66 ± 5.25 ab 26.32 ± 4.36 ab In the 0–10 cm layer, and compared to bed C, SOM levels were 2.1 and 1.4 times higher in RW and B1W, respectively ( p < 0.001). These contents were 70, 46, 33, 34, 32, 36 and 61 g.kg − 1 soil DW in RW, B1W, B2W, C, B2E, B1E and RE, respectively. Soil TOC content was the strongest in apple tree rows (p < 0.001, Table 2 ). Focusing on the vegetable beds, the central bed C has the lowest values compared to B1W (-13%) and B1E (-7%). N content followed patterns similar to TOC (r = 0.82, p < 0.001), with RW and RE significantly higher than B2 and C beds. The C/N ratio of the soil was not significantly different among the tree rows and the vegetable beds even if a trend of higher C enrichment appears in RW and RE beds (Table 2 ). Correlation analysis shows that litterfall input and SOM have a strong, positive relationship (r = 0.91, p < 0.001), TOC (r = 0.82, p < 0.001) and N (r = 0.73, p < 0.01) contents of the topsoil (0–10 cm) (Figure S1 , Supplementary material). The differences in TOC between the tree rows and the vegetable beds are quite high, especially between RW/RE and the B2 and C beds where the differences reached 10 g C.kg − 1 soil DW in the 0–10 cm layer, representing a difference of 13.7 t C ha − 1 in terms of SOC stock. 3.2 Litter decomposition In 2020, the remaining litter biomass after 120 days was not significantly different between RW and RE. The litter decomposition kinetics were reproducible from one year to another in RW. In 2021, 30 days after the installation of litterbags, a greater acceleration of litter biomass loss (p < 0.001) was shown in RW, compared to B1W and C. The remaining mass after 120 days of decomposition represents 25, 38 and 40% of the initial litter biomass in RW, B1W and C, respectively. The pattern of decomposition was comparable between B1W and C in the vegetable beds (Fig. 3 ). Modelling of the decrease in biomass over time using the model in Eq. 1 was satisfactory for both tree rows and vegetable beds (R 2 = 0.91, RMSE = 7%, not shown). Modelling was used to extract the decomposition rate (day − 1 ). In 2020 it was 0.013 and 0.010 in RW and RE, respectively. In 2021, it was estimated at 0.012, 0.008, and 0.007 in RW, B1W, and C, respectively. The litter decomposition rate was positively correlated with SOM (r = 0.93, p < 0.001), soil N (r = 0.89, p < 0.001) and soil TOC (r = 0.71, p < 0.01) (Figure S1 , Supplementary material). In 2020, we measured a lower temperature and a higher humidity inside the litterbags installed under RW compared to RE (difference of 1.5°C in temperature and 4% in relative humidity on average between RW and RE, Figure S2, Supplementary material). In 2021, temperatures measured inside the litterbags were on average 1.5°C lower in RW and 0.5°C lower in B1W than in C. As expected, the humidity showed the opposite trend (RH 3% higher on average in RW and 4% higher in B1W compared to C) (Figure S3, Supplementary material). In either the tree rows and the vegetable beds, and for both years, we found a positive correlation between the decomposition rate and the relative humidity (r = 0.75, p < 0.001), but not with temperature (Figure S1 , Supplementary material). 3.3 In-situ soil respiration The diurnal evolution of in-situ CO 2 fluxes was different between the vegetable beds and the apple tree rows, especially in terms of amplitude, maximum value and delay in reaching the maximum value. The amplitude of flux variation and the maximum flux were lower under the tree rows than in the vegetable beds. In the inter-row, the maximum flux was reached faster in the B1 beds than in C (Fig. 4 ). The CO 2 fluxes increased exponentially with soil temperature and decreased with increasing soil water saturation. Temperature and moisture alone explained 38% and 10% of the spatial and temporal variation of measured CO 2 fluxes, respectively, while the temperaturemoisture interaction explained up to 75% of the flux variation (Table 3 ). Table 3 Respiration, temperature, and soil moisture (WFPS) relationships (mean ± SE), *** p < 0.001 Variables Regression model Model coefficients p R 2 Q 10 a b c Temperature (T) \(\:\text{SR}=\:\text{a}.{\text{e}}^{\text{b}\text{T}}\) 2.68 ± 0.20 0.03 ± 0.01 - *** 0.38 1.32 WFPS (θ) \(\:\text{SR}=\text{a}{\theta\:}+\text{b}\) -0.05 ± 0.01 8.3 ± 0.62 - *** 0.10 - Temperature x WFPS \(\:\text{SR}=\:\text{a}.{\text{e}}^{\text{b}\text{T}}.{{\theta\:}}^{\text{c}}\) 11.67 ± 2.83 0.04 ± 0.01 -0.4 ± 0.06 *** 0.75 1.43 3.4 Basal and glucose-induced soil respiration A higher basal respiration (BR) was observed under apple tree rows (particularly in RW), with a progressive decrease away from the trees (the lowest one in C bed, p < 0.001). A part from B1 beds, we have seen an effect between the West and East of the plot, with higher BR values to the West (p < 0.001). The sensitivity induced by the addition of glucose to soil samples resulted in greater SIR in tree rows than in vegetable beds (p < 0.001). Focusing solely on vegetable beds, SIR was twice as strong in B1E (Fig. 5 ). Calculation of the metabolic quotient q CO 2 shows that tree rows and B1E bed have the lowest values (p < 0.001). There was a positive correlation between SIR and soil TOC (r = 0.79, p < 0.001) and SIR and soil N (r = 0.75, p < 0.05) (not shown). 4 Discussion The originality of this work lies first and foremost in the combination of different approaches to characterizing the dynamics of organic matter in an original agroforestry system combining fruit trees and vegetables. Another innovative aspect is the consideration of spatial variability (distance from the fruit tree) in order to assess the extent to which the fruit tree can have a positive impact on soil biological activity. 4.1 Litter inputs, plot configuration and management practices all affect the distribution of SOC in a garden-orchard system The litterfall distribution measured in our study is consistent with Thevathasan and Gordon ( 1997 ) who highlighted that around 80% of leaves fell within 2.5 m from the trees in a 6yearold poplar AFS. Furthermore, we found a difference between the eastern and the western parts of the plot as the litterfalls in B1E or B2E were significantly lower than those in B1W or B2W, although these beds were located at the same distance from the tree rows. This could be due to two main factors. The first explanation is related to the effects of wind on leaf dispersal, as reported by Swieter et al. ( 2021 ). Indeed, we recorded prevailing west winds during autumn, for both years (Figure S4, Supplementary material). Accordingly, the leaves from the western tree rows (RW) fell into the inter-row while those from the eastern rows (RE) were mostly transported out of the plot and were not quantified. Therefore, the prevailing wind direction is an important criterion to consider in GOS design, as it can significantly influence litter distribution and consequently the distribution of SOM. Secondly, the two rows were not composed of the same varieties of apple trees. Trees did not have exactly the same canopy structure, which influences the quantity of litter returned to the soil (Morffi-Mestre et al. 2020 ; Mayer et al. 2022 ). The strong and positive correlation between the litterfall supply and the SOM, TOC and soil N we found in our study suggests that the higher litter contribution close to the tree rows could have been an important contributing factor for the greater accumulation of SOM, TOC and N at these locations, as observed in several agroforestry and forestry context studies (Tesfay et al. 2020 ; Suhaili et al. 2021 ; Eddy and Yang 2022 ; An et al. 2023 , Rinady et al. 2023 ). However, the differences in TOC between the tree rows and the vegetable beds are quite high, especially between RW/RE and the B2 and C beds. Such a difference would take several decades to establish and could not be attributed exclusively to litter inputs. We can also offer some explanations as to the distribution of soil TOC due to the historical use of the plot. Until 2016, this was a classic apple orchard with herbaceous cover in the inter-row. Since then, vegetable beds have been installed. We assume that such a transformation has led to a decrease in SOC in the inter-row, and more contrasting differences in SOC between the apple tree rows and the vegetable beds. Furthermore, since the change in land use, the vegetable beds have been repeatedly ploughed. According to Poeplau and Don ( 2013 ), the conversion from natural vegetation to cropland may lead to a depletion of the SOC stock due to soil disturbance, especially soil tillage. 4.2 SOM enrichment and microclimatic conditions increase litter decomposition rate near the trees, up to 1.5 m We have shown good repeatability of decomposition rates calculated from one year to the next in RW. These values are very similar to those investigated in apple orchards in Italy (0.012 day − 1 , Tagliavini et al., 2007 ). Litter decomposition was faster in apple tree rows. This result is partially linked to the SOM enrichment and the nutrient status of the soil, as we found a strong and positive correlation between the litter decomposition rate and SOM, soil N and soil TOC (Fahad et al. 2022 ). Several studies have shown that the decomposition rate is controlled by the chemical properties of the soil, especially SOM content and nutrient availability (Krishna and Mohan 2017 ; Zhou et al. 2008 ; Ngaba et al. 2024 ). Hobbie and Vitousek ( 2000 ) highlighted that fresh litter may contain insufficient nutrients to meet the growth and maintenance requirements of decomposers (i.e. fauna and microbes). Differences in litter decomposition rate can also be explained by the microclimatic conditions which has been measured between the tree row and the vegetable beds. Air temperature and relative humidity are abiotic factors that play an important part in the rate of litter decomposition. Pandey et al ( 2007 ) showed a variability of 68% in forest ecosystems. Karungi et al. ( 2018 ) point out that such microclimatic conditions play a major role in regulating the microbial population and macrofauna, and hence their activities. The differences in temperature and humidity recorded between RW, B1W and C are related to the shading effects of the trees, as discussed by Ramananjatovo et al. ( 2021 ) and Ngaba et al. ( 2024 ). In either the tree rows and the vegetable beds, and for both years, we did not find positive correlation between decomposition rate and temperature, only with relative humidity, suggesting that decomposition is limited by humidity first. This is what Petraglia et al. ( 2019 ) have also shown in 6 agroecosystems. 4.3 Microbial activities would be more intensive near the trees, up to 3 m, but remain highly dependent on microclimatic conditions Soil respiration is closely correlated with microbial biomass and activities (Jenkinson et al. 1976 ). The patterns of distribution in soil basal respiration in our study indicate that the microbial community is more active near the apple tree rows, suggesting a higher potential capacity for decomposition and mineralization near the trees. Moreover, the strong SIR rates in RW, RE and B1E clearly indicate a higher active microbial biomass response to glucose addition near the tree rows. The weakest q CO2 under the trees, especially in RW, reveals a C limitation for microbes, and indicate that the microbial biomass is more conservative (i.e. lower CO 2 release by microbial biomass unit) than in vegetable beds. Guillot et al. ( 2021 ) also observed twice the microbial activity in the tree rows of a walnut-based agroforestry system. The higher C and N contents in the soil beneath the tree rows partly explain the higher SIR in this area, as we found a positive correlation between SIR and soil TOC, and SIR and soil N (An et al. 2023 ). Bae et al. ( 2013 ) reported the same correlations in agroforestry systems in the Philippines and according to Teklay et al. ( 2006 ), microbial activity is driven primarily by available organic C and total soil N. However, the lack of correlation between BR and soil C and N content in our work suggests that there might be other variables that were not considered in the study, and which would present a gradient of heterogeneity along the distance from the trees (e.g. soil phosphorus, soil pH). A low in-situ respiration rate may indicate that soil conditions (nutrient availability, temperature, moisture, etc.) are limiting the biological activities. The kinetics of in-situ CO 2 fluxes observed in our study are consistent with Gomes et al. ( 2016 ) who compared the hourly evolution of soil respiration between an agroforestry coffee plantation and a monoculture. They found that the fluxes measured during the day were more stable in the agroforestry system (15% increase) than in the monoculture (49% increase) due to a lower amplitude of variation in soil temperature, which is attributed to the shading effects. The temperature and the soil moisture are the most influential abiotic factors for soil respiration (Fang and Moncrieff 2001 ; Fenn et al. 2010 ; Gomes et al. 2020 ; Luo and Zhou 2006 ; Tang et al. 2006 ). In the current study, in-situ CO 2 fluxes increased exponentially with soil temperature. Our results are in agreement with Carey et al. ( 2016 ) who showed that in general, respiration rates increase exponentially with temperature, up to 25°C. In addition, in our experiment, increasing soil water saturation resulted in a decrease in CO 2 fluxes. Previous studies have reported that high moisture can decrease soil CO 2 emissions by preventing CO 2 diffusion to the surface (Melling et al. 2005 ) and by regulating the physiological processes of aerobic microorganisms (Melling et al. 2014 ). We found that the individual effects of temperature and soil moisture on soil respiration were lower than the interaction effects of the two factors. This can be explained by the fact that under uncontrolled conditions, the effects of these two variables may interfere with each other. For example, increases in temperature are often accompanied by decreases in soil water content and vice versa. Our results confirm previous work on the effects of temperature and soil water content interactions on soil respiration (Lellei-Kovács et al. 2011 ; Li et al. 2014 ; Sierra et al. 2017 ; Tucker and Reed 2016 ). 5 Conclusion In this study, we investigated the impact of apple trees on the distribution and dynamics of SOM in a garden orchard system. The distance from the tree resulted in a significant reduction in litterfall supply, and has been associated with a heterogeneous spatial distribution of SOM and soil C and N contents. The decomposition of apple leaf litter was different between the tree row and the vegetable beds, due to contrasting temperature and moisture conditions. When moisture was not limiting, the rate of leaf litter decomposition was higher under the apple tree rows than in vegetable beds. The basal and glucose induced soil respiration were greater under the apple trees and the vegetable beds closest to the trees (within 3 m), indicating a higher SOM mineralization potential at these locations. Our results suggest that proximity to trees stimulates soil microbial activity in GOS, due to SOM enrichment from litter supply. However, soil humidity and temperature conditions have a strong influence on microbial activities. For two main reasons, it seems difficult to generalize our conclusion about the effects of apple trees on soil microbial degradation activities in GOS. On the one hand, the experiments conducted in this study mainly focused on processes that are indicative of soil microbial activities. Direct quantification of microbial biomass by fumigation-extraction or molecular methods would be needed to validate the results observed in the current study. On the other hand, it is difficult to characterize the effects of microclimatic variables on soil microbial activities because of their complex interactions. Understanding such interactions is important as it could eventually lead to a more efficient fertilization management of the vegetable beds, as well as help preventing waste of resources (over-fertilization) and the associated environmental impacts. For instance, the application of fertilizers could be reduced near the trees where SOM is greater and the organic nutrients potentially more available for vegetable crops. This would allow to reduce the overall use of fertilizers in GOS, avoid nutrients losses and, indirectly, greenhouse gas emissions. Declarations Competing Interests: 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. Funding: this study was funded by French Ministry of Agriculture and Food, the regional program “RFI Objectif Végétal” of Pays de la Loire (France), and the “Fondation de France” Author Contribution Conceptualization, T.R., E.C., P.G., R.G., M.D., G.B-S. and P.C.; methodology, T.R., E.C., P.G., R.G., M.D., G.B-S. and P.C.; validation, E.C., P.G., R.G., M.D., G.B-S. and P.C.; formal analysis, T.R., E.C., J.P., P.G., R.G. and P.C.; investigation, T.R., E.C., J.P., P.G., R.G., M.D., G.B-S. and P.C.; writing—original draft preparation, T.R.; writing—review and editing, T.R., E.C., P.G., R.G., M.D., G.B-S. and P.C.; visualization, T.R., E.C., P.G., R.G. and P.C.; supervision, E.C., G.B S. and P.C.; project administration, T.R., E.C., G.B-S. and P.C.; funding acquisition, E.C., P.G., R.G., M.D., G.B-S. and P.C. All authors have read and agreed to the published version of the manuscript. 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Supplementary Files SupplementarymaterialOrganicMatterGOSV1PC.docx Cite Share Download PDF Status: Published Journal Publication published 07 Oct, 2024 Read the published version in Agroforestry Systems → Version 1 posted Reviews received at journal 29 Jul, 2024 Reviewers agreed at journal 28 Jul, 2024 Reviewers agreed at journal 22 Jul, 2024 Reviewers invited by journal 22 Jul, 2024 Editor assigned by journal 16 Jul, 2024 Submission checks completed at journal 06 Jul, 2024 First submitted to journal 04 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4686563","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":336145990,"identity":"7eb5abd2-ca02-4024-a508-9fd6f6177505","order_by":0,"name":"T. Ramananjatovo","email":"","orcid":"","institution":"Institut Agro, EPHOR","correspondingAuthor":false,"prefix":"","firstName":"T.","middleName":"","lastName":"Ramananjatovo","suffix":""},{"id":336145991,"identity":"f780e4cb-c920-40d6-af8a-6263c1fbd59b","order_by":1,"name":"R. Guénon","email":"","orcid":"","institution":"Institut Agro, EPHOR","correspondingAuthor":false,"prefix":"","firstName":"R.","middleName":"","lastName":"Guénon","suffix":""},{"id":336145992,"identity":"88616fd9-5bef-49fb-82cb-9098178c291f","order_by":2,"name":"J. Peugeot","email":"","orcid":"","institution":"Institut Agro, EPHOR","correspondingAuthor":false,"prefix":"","firstName":"J.","middleName":"","lastName":"Peugeot","suffix":""},{"id":336145994,"identity":"f7cb430c-eb7c-40a6-84d1-ca5a7e15ffd2","order_by":3,"name":"E. Chantoiseau","email":"","orcid":"","institution":"Institut Agro, EPHOR","correspondingAuthor":false,"prefix":"","firstName":"E.","middleName":"","lastName":"Chantoiseau","suffix":""},{"id":336145997,"identity":"7d8be69c-772c-40e5-8cbe-ed2fbe9fc2b1","order_by":4,"name":"M. Delaire","email":"","orcid":"","institution":"Université d’Angers, INRAE, IRHS, SFR QuaSaV","correspondingAuthor":false,"prefix":"","firstName":"M.","middleName":"","lastName":"Delaire","suffix":""},{"id":336145998,"identity":"93c7ff21-0ede-4527-9f67-36c01886caba","order_by":5,"name":"G. Buck-Sorlin","email":"","orcid":"","institution":"Université d’Angers, INRAE, IRHS, SFR QuaSaV","correspondingAuthor":false,"prefix":"","firstName":"G.","middleName":"","lastName":"Buck-Sorlin","suffix":""},{"id":336146000,"identity":"197d56ab-33f9-49a9-a1a9-56f2e89b267f","order_by":6,"name":"P. Guillermin","email":"","orcid":"","institution":"Université d’Angers, INRAE, IRHS, SFR QuaSaV","correspondingAuthor":false,"prefix":"","firstName":"P.","middleName":"","lastName":"Guillermin","suffix":""},{"id":336146001,"identity":"ab5d33b2-8d2b-40fa-af0a-1a6023a6356f","order_by":7,"name":"P. Cannavo","email":"data:image/png;base64,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","orcid":"","institution":"Institut Agro, EPHOR","correspondingAuthor":true,"prefix":"","firstName":"P.","middleName":"","lastName":"Cannavo","suffix":""}],"badges":[],"createdAt":"2024-07-04 12:26:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4686563/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4686563/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10457-024-01088-2","type":"published","date":"2024-10-07T15:57:05+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62007836,"identity":"805d71c7-dc0f-41f7-bb9f-33bde26c95de","added_by":"auto","created_at":"2024-08-08 07:06:09","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":94948,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental design (A), litter collection nets (B), litterbag for litter decomposition measurement (C) and in-situ soil respiration measurement device (D). Measurements were taken (1) below the apple tree rows: West row (RW) and East row (RE), and in the five vegetable beds located in the inter-row: (2) at 1.5 m from the West (B1W) or East (B1E) row, (3) at 3.2 m from the West (B2W) or East (B2E) row, and (4) in the middle of the inter-row (C)\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4686563/v1/41951607782f8c414b1a1b4c.jpg"},{"id":62007240,"identity":"ed72a079-8423-42c6-9beb-b4fb2e46e56e","added_by":"auto","created_at":"2024-08-08 06:58:09","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":52855,"visible":true,"origin":"","legend":"\u003cp\u003eTotal organic C, total N and C:N ratio in the upper soil layer (0-10 cm) in each apple tree row and vegetable bed. Error bars represent the standard deviation of the mean (n = 6). Groups with different letters significantly differ from each other (p \u0026lt; 0.05, Tukey HSD test)\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4686563/v1/24a7afe983714d4ddfcecf1a.jpg"},{"id":62007837,"identity":"34c58acb-b8b0-4ef2-8a7c-f52d01e5d9af","added_by":"auto","created_at":"2024-08-08 07:06:09","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":37067,"visible":true,"origin":"","legend":"\u003cp\u003eRemaining apple leaf mass in each apple tree row and in B1W and C beds. For each year, measurements were conducted from mid-January to mid-May. Error bars represent the standard deviation of the mean (n = 3)\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4686563/v1/ada983f872dc82c91da76192.jpg"},{"id":62007244,"identity":"e499c369-06b8-47bc-8440-f0b1f02272cf","added_by":"auto","created_at":"2024-08-08 06:58:09","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":60789,"visible":true,"origin":"","legend":"\u003cp\u003eDiurnal evolution of \u003cem\u003ein-situ\u003c/em\u003e CO\u003csub\u003e2\u003c/sub\u003e fluxes (soil respiration, SR) in each tree row and each vegetable bed. Error bars represent the standard deviation associated with the measurement days (n = 4 days for vegetable beds, n = 1 day for apple tree rows)\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4686563/v1/312b315ce164b03b1a3992a1.jpg"},{"id":62007241,"identity":"cbbe2b66-ff72-4b1a-8958-caaf26350800","added_by":"auto","created_at":"2024-08-08 06:58:09","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":53688,"visible":true,"origin":"","legend":"\u003cp\u003eSoil\u003cstrong\u003e \u003c/strong\u003ebasal respiration (BR), soil glucose-induced respiration (SIR) and metabolic quotient (\u003cem\u003eq\u003c/em\u003eCO2)\u003cem\u003e \u003c/em\u003ein each tree row and vegetable bed. Error bars represent the standard deviation of the mean (n = 48). Groups with different letters are significantly different each other (p \u0026lt; 0.05, Tukey HSD test\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4686563/v1/d60bd6283a352220bcccd67a.jpg"},{"id":66597239,"identity":"f4e86a47-06a5-47bf-8a4c-ec84b0d833ef","added_by":"auto","created_at":"2024-10-14 16:08:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1195511,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4686563/v1/d53be42c-3f7a-464e-a332-51c719330042.pdf"},{"id":62007245,"identity":"e2f0d4f7-d4ac-4e50-a680-6657dcb32796","added_by":"auto","created_at":"2024-08-08 06:58:10","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2873816,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarymaterialOrganicMatterGOSV1PC.docx","url":"https://assets-eu.researchsquare.com/files/rs-4686563/v1/2f4769ad26fe43a48f8835b8.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Positive influence of apple trees on soil chemical and biological activity in an agroecological garden orchard system","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eMaintaining a healthy soil is a major challenge in agriculture. Treebased intercropping is a promising solution to ensure high production while at the same time improving soil quality, as shown in agroforestry systems (AFS) (Dupraz and Liagre \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Litter supply and its decomposition have been reported to be an important contributor to the physical, chemical and microbiological quality of the soil (Su\u0026aacute;rez et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Trees can provide input of ligninrich materials that is slowly decomposed and enriches the soil in stable organic C (Montagnini and Nair \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). More specifically, fruit trees provide a further source of litter, namely the wood from pruning. This, along with nutrients released from litter decomposition can stimulate the activity of soil microorganisms (Leigh et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough a global positive effect of trees on soil quality has been reported in AFS (Dollinger and Jose \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Dupraz and Liagre \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), most of the studies carried out on AFS have been limited to a global comparison between AFS and an adjacent cropland (Hergoualc\u0026rsquo;h et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Tian et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Eddy and Yang \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) or forest (Becker et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Triadiati et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Only a handful of studies have taken into account the area of influence of adjacent trees (An et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Das \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Recent meta-analyses have demonstrated the positive effects of agroforestry systems on soil bio-physico-chemical properties, but without identifying the beneficial effect of distance from tree rows (Mayer et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ngaba et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). As highlighted by Cardinael et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), there is a lack of knowledge about the influence of the lateral gradient generated by the trees, and their effect on soil functions.\u003c/p\u003e \u003cp\u003eIn garden-orchard systems (GOS) where vegetables are intercropped with fruit trees, the quantification of soil organic matter (SOM) distribution and the soil microbial activity as well as its biodegradation capacity are crucial to understand how fruit trees influence these processes. In a previous study on GOS, Ramananjatovo et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) showed that apple trees created specific microclimatic conditions within 3 m from the tree rows (air temperature was 1.5\u0026deg;C lower near the trees and air relative humidity 5% higher), largely influencing the adjacent soil of vegetable beds in the inter-rows. Within this area, N mineralization rate was nearly doubled. In the present work, we propose to further investigate the influence of fruit trees on the distribution of SOM, as well as leaf litter biodegradation, and to quantify microbial activity through \u003cem\u003ein-situ\u003c/em\u003e and laboratory indicators. The originality of this study resides in both studied cropping system and addressed research questions. On one hand, GOS, an agroecological production system of fruits and vegetables, is a very promising cropping system (Ouma and Jeruto \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) but remains poorly studied to date. On the other hand, very little is known about the degree to which trees can influence SOM biodegradation and microbial activity in temperate AFS (Mungai et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Nii-Annang et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Mustafa et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and to our knowledge, there are currently no scientific studies that address this topic in GOS.\u003c/p\u003e \u003cp\u003eThis study aimed to (1) quantify litterfall and SOM along a lateral gradient of distance from the fruit trees, (2) investigate the link of both the microclimate generated by fruit trees and SOM distribution with the decomposition of litter and (3) determine the amplitude of soil microbial activity. Accordingly, the study is predicated upon three hypotheses. We first hypothesized that litter supply will be greater near the trees and will be associated with higher amount of SOM. Secondly, since the trees can modify both the temperature and the moisture of the soil (Ramananjatovo et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the decomposition of fresh organic matter will be greater under the tree rows and will decrease with increasing distance from the trees, up to 3 m. Finally, as the accumulation of SOM may lead to a general improvement in soil fertility, we hypothesized that there is a spatial gradient of soil microbial activity with increasing distance from the tree row towards the vegetable beds.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Experimental design\u003c/h2\u003e \u003cp\u003eThe experiment was carried out at the Institut Agro in Angers (France, longitude 0\u0026deg; 36' W, latitude 47\u0026deg; 28' N, elevation 49 m), from September 2019 until August 2021. The study plot was presented by Ramananjatovo et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The studied soil consisted of 4 layers and had an average depth of about 1.2 m. It belongs to the Luvisol Redoxisol classification (FAO classification). Its main properties are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMain characteristics of the soil profile (from Ramananjatovo et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSoil layer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAg\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEg\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBTg\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCg\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0\u0026ndash;30 cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(30\u0026ndash;50 cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(50\u0026ndash;90 cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(90\u0026ndash;120 cm)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTexture\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClay [%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSilt [%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSand [%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChemical properties\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003csub\u003eH2O\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003csub\u003eKCl\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrganic matter [g.kg\u003csup\u003e-1\u003c/sup\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal C [g.kg\u003csup\u003e-1\u003c/sup\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal N [g.kg\u003csup\u003e-1\u003c/sup\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC:N ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e [mg.kg\u003csup\u003e-1\u003c/sup\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u003csub\u003e2\u003c/sub\u003eO [mg.kg\u003csup\u003e-1\u003c/sup\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMgO [mg.kg\u003csup\u003e-1\u003c/sup\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNa\u003csub\u003e2\u003c/sub\u003eO [mg.kg\u003csup\u003e-1\u003c/sup\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaO [mg.kg\u003csup\u003e-1\u003c/sup\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaCO\u003csub\u003e3\u003c/sub\u003e [g.kg\u003csup\u003e-1\u003c/sup\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEC [meq.100 g\u003csup\u003e-1\u003c/sup\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHydraulic properties (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, n\u0026thinsp;=\u0026thinsp;3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBulk density [g.cm\u003csup\u003e-3\u003c/sup\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eField capacity [% volumetric water content]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWilting point [% volumetric water content]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSaturated hydraulic conductivity [10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u0026nbsp;cm.s\u003csup\u003e-1\u003c/sup\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBriefly, the experimental area (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) consisted of two rows of apple trees (\u003cem\u003eMalus\u003c/em\u003e \u0026times; \u003cem\u003edomestica Borkh\u003c/em\u003e.) that were planted in 2000. The plot was originally an apple orchard with herbaceous vegetation before the inter-row was converted into vegetable beds in 2016. The studied tree rows were constituted of different varieties of apple trees. The East row was composed of \u0026lsquo;Golden Delicious\u0026rsquo; while the West row was composed of three varieties: \u0026lsquo;Gala\u0026rsquo;, \u0026lsquo;Fuji\u0026rsquo; and \u0026lsquo;Red winter\u0026rsquo;. The two rows were separated by 12m with a north-south orientation, and the average distance between each tree in the row was 1.6m with branches connected to each other and trimmed at a height of 2.5 m. Soil under the apple trees was permanently covered by herbaceous plants. The inter-row was made up of 5 vegetable beds 1.8m wide where unfertilized lettuce (\u003cem\u003eLactuca sativa\u003c/em\u003e L., var. \u0026lsquo;Olana\u0026rsquo;) and radish (\u003cem\u003eRaphanus sativus\u003c/em\u003e L., var. \u0026lsquo;Ostergruss\u0026rsquo;) were successively grown between 2019 and 2021. Faba bean (\u003cem\u003eVicia faba\u003c/em\u003e L., var. \u0026lsquo;Irena\u0026rsquo;) was cultivated as a green manure during the winter 2020. The vegetable beds were irrigated using a drip irrigation system (Dripnet PC 390 \u0026Oslash; 16 mm, NETAFIM, Gardanne, France) during the lettuce and radish growing seasons, in summer (June to August). Apple tree rows were not irrigated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Quantification of leaf litter\u003c/h2\u003e \u003cp\u003eApple tree leaf litter was quantified from September to December, in 2019 and 2020. Senescent leaves were collected in each tree row and each vegetable bed using 1 mwide collection nets, maintained at approximately 40 cm from the soil surface (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Leaves were collected daily to prevent loss and decomposition. The leaves were air dried (around 25\u0026deg;C) and inserted into paper bags at each stage.\u003c/p\u003e \u003cp\u003eOnce the collection period was over (December), all the leaves collected in each zone were weighed and sub-samples were oven-dried at 60\u0026deg;C for 48 hours to calculate their residual water content and deduce their dry mass.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Measurement of litter decomposition\u003c/h2\u003e \u003cp\u003eThe kinetics of litter degradation were assessed using the litterbag technique. Decomposition experiment was conducted in 2020 and 2021, from January to May. In 2020 the two rows of apple trees (RE vs. RW) were compared since they present different microclimatic environments (Ramananjatovo et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Eight litterbags were placed in both RE and RW sides under the apple trees.\u003c/p\u003e \u003cp\u003eIn 2021, litterbags were placed to test the effect of the distance to trees on RW, B1W and C. The litterbags were placed in the western part of the plot because we had found the most contrasting microclimatic conditions in this area in our previous experiments (Ramananjatovo et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn each case, approximately 6 g of air-dried apple leaves (mixture of leaves collected from all varieties within the two rows) were introduced in 20 cm pockets (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC) (Harmon et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) with a mesh size of 5 mm to allow macrofauna access for the decomposition process (Bradford et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The litterbags have been placed on the soil. Residual biomass was collected monthly for four months. On each sampling date, the remaining leaves were gently washed and dried at 30\u0026deg;C for 24 hours, then at 60\u0026deg;C for the following 48 hours.\u003c/p\u003e \u003cp\u003eThe decomposition rate (k) was determined from first-order kinetics (Olson, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1963\u003c/span\u003e):\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{M}_{r}=\\:{M}_{0}.{e}^{-kt}\\)\u003c/span\u003e \u003c/span\u003e (Eq.\u0026nbsp;1)\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{M}}_{\\text{r}}\\)\u003c/span\u003e\u003c/span\u003e is the leaf residual dry weight at time t (g) and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{M}}_{0}\\)\u003c/span\u003e\u003c/span\u003e the leaf initial dry weight (g). Small data loggers (iButtons, Dallas Semiconductor, California, USA) were placed between the leaves in the litterbags to continuously monitor the temperature (\u0026deg;C) and humidity (%) of the litter.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Soil organic matter (SOM), total organic C (TOC) and total N\u003c/h2\u003e \u003cp\u003eSOM, TOC and soil N contents were determined in each row and each vegetable bed, in 2020 and 2021. Soil samples were collected at 10 cm depth using an auger. To quantify SOM, the water contained in the soil samples was first eliminated by oven drying for 48 hours at 105\u0026deg;C. They were then heated to 550\u0026deg;C for 7h in a muffle furnace. TOC and N were determined by dry combustion using a C and N elemental analyser (Flash EA 112 Series, Thermo Fisher Scientific, Massachusetts, USA; NF ISO 10694) after grinding with a ball mill (MM 400, Retsch, \u0026Eacute;ragny, France). TOC was assimilated to soil total C since the CaCO\u003csub\u003e3\u003c/sub\u003e content of the soil was very low (\u0026lt;\u0026thinsp;1 g.kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e soil DW, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Supplementary material).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Characterization of soil microbial activities\u003c/h2\u003e \u003cp\u003eMeasurement of soil respiration is among the most commonly used techniques to characterize soil microbial activity (Phillips and Nickerson \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). It can be determined by quantifying the CO\u003csub\u003e2\u003c/sub\u003e production by the soil (Dilly and Zyakun \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). CO\u003csub\u003e2\u003c/sub\u003e fluxes are primarily derived from decomposition activities of microorganisms, plant roots and soil fauna respiration. In this study, we performed two types of measurements: (1) an \u003cem\u003ein-situ\u003c/em\u003e measurement of CO\u003csub\u003e2\u003c/sub\u003e flux to investigate the combined effects of biotic and abiotic factors on the degradation of SOM and (2) a lab-measurement of CO\u003csub\u003e2\u003c/sub\u003e flux at optimal temperature and moisture conditions to estimate the potential microbial activities.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1 In-situ measurement of CO\u003csub\u003e2\u003c/sub\u003e flux\u003c/h2\u003e \u003cp\u003eSoil CO\u003csub\u003e2\u003c/sub\u003e fluxes under the apple tree rows and in each vegetable bed, were quantified using an automatic gas chamber associated with an infrared gas analyzer (Cflux-1, PP systems, Arizona, USA) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). The volume of the chamber was approximately 2.5 L for a sampling surface of 0.033 m\u003csup\u003e2\u003c/sup\u003e. The device was equipped with a temperature (\u0026plusmn;\u0026thinsp;0.3\u0026deg;C) and a soil moisture (\u0026plusmn;\u0026thinsp;1%) sensor (HydraProbe, Stevens Water Monitoring Systems, Inc.). Soil collars were installed at each sampling point at least 24 hours before the measurements to avoid any disturbance of the soil. Green foliage was clipped in the area of the CO\u003csub\u003e2\u003c/sub\u003e measurement prior to the soil collar insertion to exclude plant respiration. Measurements in the vegetable beds were conducted on four dates: April 26, May 3, May 28, and July 15, 2021. Soil temperature and water content at 5 cm depth were recorded during each measurement. During the first day (April 26, 2021), CO\u003csub\u003e2\u003c/sub\u003e was quantified from 9:00 am to 6:00 pm to cover the diurnal evolution of the soil respiration. Then, for the next days, we decided to stop the measurements at 4:00 pm. Kaye et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) suggested that measurements taken between 9:00 am and 1:00 pm can be considered to be representative of daily fluxes but we decided to perform measurements until 4:00 pm because the maximum CO\u003csub\u003e2\u003c/sub\u003e flux was reached at this time. Under the tree rows, the fluxes were measured only once (June 30, 2021).\u003c/p\u003e \u003cp\u003eThe duration of flow measurements was 5 min, measurements were performed every 15 min. The daily average flux was calculated by averaging the measurements taken between 9:00 am and 4:00 pm. Two devices were used during the experiment. In the vegetable beds, one device was left permanently in the C bed and the second was moved every 15 minutes between B1 and B2, in order to compare the fluxes emitted in each bed during the same day. Under the apple tree rows, each device was left permanently under the row.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2 Quantification of basal and substrate induced respiration in the lab\u003c/h2\u003e \u003cp\u003eThis experiment was conducted to evaluate the total microbial activity and the active microbial biomass by measuring basal respiration (BR) (Panikov, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and substrate induced respiration (SIR) with glucose (Anderson and Domsch, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1978\u003c/span\u003e), respectively. SIR can be used as a proxy of the active microbial biomass as the addition of available C to the soil results in a strong increase in respiration rate and thus reveals the group of microorganisms using the substrate (Anderson and Domsch \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1978\u003c/span\u003e). Glucose was used as a substrate because it can be readily used as a C source by most soil microorganisms (Stotzky and Norman \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e1961\u003c/span\u003e). Five soil samples were taken at a depth of 10 cm in each tree row and vegetable bed. They were mixed and sieved to 2 mm. We used the Microresp\u0026trade; method (Campbell et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) to determine the amount of CO\u003csub\u003e2\u003c/sub\u003e emitted. The technique consists of 2 microplates, the microwell plate and the detection plate. An agar gel (30 g.L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) enriched in KCl (150 mmol.L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), in NaHCO\u003csub\u003e3\u003c/sub\u003e (2.5 mmol.L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and in red cresol (12.5 \u0026micro;g.mL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) has been prepared for the detection plate. The microwell plate receives soil samples with or without added glucose. Silicone seal was used to bond the 2 plates, which were incubated for 4 hours at 20\u0026deg;C. A spectrophotometer (Epoch, BioTek, Vermont, USA) was used to read the absorbance of the detection gel at 570 nm before and after incubation. This difference in absorbance wa used to deduce the amount of CO\u003csub\u003e2\u003c/sub\u003e emitted by the soil. Wells with no substrate (distilled water only) were used to determine basal respiration (BR), while those with glucose were used to determine substrate-induced respiration (SIR). We calculated the metabolic quotient (\u003cem\u003eq\u003c/em\u003eCO\u003csub\u003e2\u003c/sub\u003e) as the ratio between BR and SIR (Anderson and Domsc, 1993). This indicator provides information on the respiration rate between growing and potentially active micro-organisms (Anderson and Domsch \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). A rate close to 1 means low growth capacity (Wardle and Ghani \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e1995\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Data analysis\u003c/h2\u003e \u003cp\u003eA one-way ANOVA analysis was carried out to assess the significance of differences between each vegetable bed and apple tree rows. This test was performed for each variable studied. Significance was assessed at 5%. R Studio's \"xtable\" package was used to perform a Pearson correlation analysis (Dahl et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). It was used to assess correlations between climatic variables, soil characteristics and litter decomposition. Three regression models proposed by Tang et al. (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) were used to analyze the correlations between soil respiration, soil temperature and soil water content:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:SR=\\:a\\cdot\\:{e\\:}^{b\\cdot\\:T}\\)\u003c/span\u003e \u003c/span\u003e (Eq.\u0026nbsp;2)\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:SR=\\:a\\cdot\\:\\theta\\:+b\\)\u003c/span\u003e \u003c/span\u003e (Eq.\u0026nbsp;3)\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:SR=\\:a\\cdot\\:{e}^{b\\cdot\\:T}\\cdot\\:{\\theta\\:}^{c}\\)\u003c/span\u003e \u003c/span\u003e (Eq.\u0026nbsp;4)\u003c/p\u003e \u003cp\u003ewhere SR is the soil respiration (\u0026micro;molCO\u003csub\u003e2\u003c/sub\u003e.m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e.s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), T the soil temperature (\u0026deg;C) and θ the water saturation (WFPS in %) at 5 cm depth, a, b, and c are the coefficients of the model. Soil water saturation (θ) was calculated as follow (Paul \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) :\u003c/p\u003e \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"405\" height=\"71\"\u003e\u003c/p\u003e \u003cp\u003ewhere SWC is the volumetric soil water content (% v/v), Bd the soil bulk density (g.cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) and Pd the soil particle density (=\u0026thinsp;2.65 g.cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eThe temperature sensitivity of soil respiration \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Q}}_{\\text{10:}}\\)\u003c/span\u003e\u003c/span\u003ewas calculated using Van\u0026rsquo;t Hoff (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e1898\u003c/span\u003e) equation :\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{Q}_{10}={e\\:}^{10d}\\)\u003c/span\u003e \u003c/span\u003e (Eq.\u0026nbsp;6)\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003ed\u003c/em\u003e is the model coefficient in (Eq.\u0026nbsp;2). \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Q}}_{\\text{10:}}\\)\u003c/span\u003e\u003c/span\u003e indicates the rate of change of CO\u003csub\u003e2\u003c/sub\u003e emissions for each 10\u0026deg;C increase in soil temperature.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed with R Studio (v 3.6.0) (R Studio Team, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Distribution of litterfall, SOM, TOC and soil N\u003c/h2\u003e \u003cp\u003eThe annual quantity of litterfall measured under the apple tree rows and in each vegetable bed is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Litterfall ranged from 1.1 to 48 g DW.m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e in 2019 and from 2.4 to 32 g DW.m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e in 2020. Average litterfall for both years was 40.83, 20.67, 11.59, 2.34, 1.78, 2.82 and 30.48 g DW.m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e in RW, B1W, B2W, C, B2E, B1E and RE, respectively. Litter deposition gradually decreased with the distance from the trees. We observed a significant difference between the tree rows and the vegetable beds (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eApple litterfall collected in each tree row and vegetable bed during fall 2019 and 2020 (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, n\u0026thinsp;=\u0026thinsp;3). Values with different letters indicate significant differences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Tukey HSD test)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRow/Bed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eFall 2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eFall 2020\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e48.78\u0026thinsp;\u0026plusmn;\u0026thinsp;14.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e32.89\u0026thinsp;\u0026plusmn;\u0026thinsp;5.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB1W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e20.24\u0026thinsp;\u0026plusmn;\u0026thinsp;5.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ebc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e21.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ebc\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB2W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.55\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ecd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e15.64\u0026thinsp;\u0026plusmn;\u0026thinsp;4.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ec\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e2.72\u0026thinsp;\u0026plusmn;\u0026thinsp;1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB2E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e2.44\u0026thinsp;\u0026plusmn;\u0026thinsp;2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB1E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ecd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e2.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e34.66\u0026thinsp;\u0026plusmn;\u0026thinsp;5.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e26.32\u0026thinsp;\u0026plusmn;\u0026thinsp;4.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eab\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the 0\u0026ndash;10 cm layer, and compared to bed C, SOM levels were 2.1 and 1.4 times higher in RW and B1W, respectively (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These contents were 70, 46, 33, 34, 32, 36 and 61 g.kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e soil DW in RW, B1W, B2W, C, B2E, B1E and RE, respectively. Soil TOC content was the strongest in apple tree rows (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Focusing on the vegetable beds, the central bed C has the lowest values compared to B1W (-13%) and B1E (-7%). N content followed patterns similar to TOC (r\u0026thinsp;=\u0026thinsp;0.82, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with RW and RE significantly higher than B2 and C beds. The C/N ratio of the soil was not significantly different among the tree rows and the vegetable beds even if a trend of higher C enrichment appears in RW and RE beds (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCorrelation analysis shows that litterfall input and SOM have a strong, positive relationship (r\u0026thinsp;=\u0026thinsp;0.91, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), TOC (r\u0026thinsp;=\u0026thinsp;0.82, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and N (r\u0026thinsp;=\u0026thinsp;0.73, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) contents of the topsoil (0\u0026ndash;10 cm) (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Supplementary material). The differences in TOC between the tree rows and the vegetable beds are quite high, especially between RW/RE and the B2 and C beds where the differences reached 10 g C.kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e soil DW in the 0\u0026ndash;10 cm layer, representing a difference of 13.7 t C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in terms of SOC stock.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Litter decomposition\u003c/h2\u003e \u003cp\u003eIn 2020, the remaining litter biomass after 120 days was not significantly different between RW and RE. The litter decomposition kinetics were reproducible from one year to another in RW. In 2021, 30 days after the installation of litterbags, a greater acceleration of litter biomass loss (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) was shown in RW, compared to B1W and C. The remaining mass after 120 days of decomposition represents 25, 38 and 40% of the initial litter biomass in RW, B1W and C, respectively. The pattern of decomposition was comparable between B1W and C in the vegetable beds (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eModelling of the decrease in biomass over time using the model in Eq.\u0026nbsp;1 was satisfactory for both tree rows and vegetable beds (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.91, RMSE\u0026thinsp;=\u0026thinsp;7%, not shown). Modelling was used to extract the decomposition rate (day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). In 2020 it was 0.013 and 0.010 in RW and RE, respectively. In 2021, it was estimated at 0.012, 0.008, and 0.007 in RW, B1W, and C, respectively. The litter decomposition rate was positively correlated with SOM (r\u0026thinsp;=\u0026thinsp;0.93, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), soil N (r\u0026thinsp;=\u0026thinsp;0.89, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and soil TOC (r\u0026thinsp;=\u0026thinsp;0.71, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Supplementary material).\u003c/p\u003e \u003cp\u003eIn 2020, we measured a lower temperature and a higher humidity inside the litterbags installed under RW compared to RE (difference of 1.5\u0026deg;C in temperature and 4% in relative humidity on average between RW and RE, Figure S2, Supplementary material). In 2021, temperatures measured inside the litterbags were on average 1.5\u0026deg;C lower in RW and 0.5\u0026deg;C lower in B1W than in C. As expected, the humidity showed the opposite trend (RH 3% higher on average in RW and 4% higher in B1W compared to C) (Figure S3, Supplementary material). In either the tree rows and the vegetable beds, and for both years, we found a positive correlation between the decomposition rate and the relative humidity (r\u0026thinsp;=\u0026thinsp;0.75, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but not with temperature (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Supplementary material).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 In-situ soil respiration\u003c/h2\u003e \u003cp\u003eThe diurnal evolution of \u003cem\u003ein-situ\u003c/em\u003e CO\u003csub\u003e2\u003c/sub\u003e fluxes was different between the vegetable beds and the apple tree rows, especially in terms of amplitude, maximum value and delay in reaching the maximum value. The amplitude of flux variation and the maximum flux were lower under the tree rows than in the vegetable beds. In the inter-row, the maximum flux was reached faster in the B1 beds than in C (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe CO\u003csub\u003e2\u003c/sub\u003e fluxes increased exponentially with soil temperature and decreased with increasing soil water saturation. Temperature and moisture alone explained 38% and 10% of the spatial and temporal variation of measured CO\u003csub\u003e2\u003c/sub\u003e fluxes, respectively, while the temperaturemoisture interaction explained up to 75% of the flux variation (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRespiration, temperature, and soil moisture (WFPS) relationships (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE), *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRegression model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eModel coefficients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eQ\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ec\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature (T)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{SR}=\\:\\text{a}.{\\text{e}}^{\\text{b}\\text{T}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWFPS (θ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{SR}=\\text{a}{\\theta\\:}+\\text{b}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e-0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e8.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature x WFPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{SR}=\\:\\text{a}.{\\text{e}}^{\\text{b}\\text{T}}.{{\\theta\\:}}^{\\text{c}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e11.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Basal and glucose-induced soil respiration\u003c/h2\u003e \u003cp\u003eA higher basal respiration (BR) was observed under apple tree rows (particularly in RW), with a progressive decrease away from the trees (the lowest one in C bed, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A part from B1 beds, we have seen an effect between the West and East of the plot, with higher BR values to the West (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The sensitivity induced by the addition of glucose to soil samples resulted in greater SIR in tree rows than in vegetable beds (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Focusing solely on vegetable beds, SIR was twice as strong in B1E (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e Calculation of the metabolic quotient \u003cem\u003eq\u003c/em\u003eCO\u003csub\u003e2\u003c/sub\u003e shows that tree rows and B1E bed have the lowest values (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). There was a positive correlation between SIR and soil TOC (r\u0026thinsp;=\u0026thinsp;0.79, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and SIR and soil N (r\u0026thinsp;=\u0026thinsp;0.75, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (not shown).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe originality of this work lies first and foremost in the combination of different approaches to characterizing the dynamics of organic matter in an original agroforestry system combining fruit trees and vegetables. Another innovative aspect is the consideration of spatial variability (distance from the fruit tree) in order to assess the extent to which the fruit tree can have a positive impact on soil biological activity.\u003c/p\u003e \u003cp\u003e \u003cb\u003e4.1 Litter inputs, plot configuration and management practices all affect the distribution of SOC in a garden-orchard system\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe litterfall distribution measured in our study is consistent with Thevathasan and Gordon (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) who highlighted that around 80% of leaves fell within 2.5 m from the trees in a 6yearold poplar AFS. Furthermore, we found a difference between the eastern and the western parts of the plot as the litterfalls in B1E or B2E were significantly lower than those in B1W or B2W, although these beds were located at the same distance from the tree rows. This could be due to two main factors. The first explanation is related to the effects of wind on leaf dispersal, as reported by Swieter et al. (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Indeed, we recorded prevailing west winds during autumn, for both years (Figure S4, Supplementary material). Accordingly, the leaves from the western tree rows (RW) fell into the inter-row while those from the eastern rows (RE) were mostly transported out of the plot and were not quantified. Therefore, the prevailing wind direction is an important criterion to consider in GOS design, as it can significantly influence litter distribution and consequently the distribution of SOM. Secondly, the two rows were not composed of the same varieties of apple trees. Trees did not have exactly the same canopy structure, which influences the quantity of litter returned to the soil (Morffi-Mestre et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mayer et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The strong and positive correlation between the litterfall supply and the SOM, TOC and soil N we found in our study suggests that the higher litter contribution close to the tree rows could have been an important contributing factor for the greater accumulation of SOM, TOC and N at these locations, as observed in several agroforestry and forestry context studies (Tesfay et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Suhaili et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Eddy and Yang \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; An et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Rinady et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, the differences in TOC between the tree rows and the vegetable beds are quite high, especially between RW/RE and the B2 and C beds. Such a difference would take several decades to establish and could not be attributed exclusively to litter inputs. We can also offer some explanations as to the distribution of soil TOC due to the historical use of the plot.\u003c/p\u003e \u003cp\u003eUntil 2016, this was a classic apple orchard with herbaceous cover in the inter-row. Since then, vegetable beds have been installed. We assume that such a transformation has led to a decrease in SOC in the inter-row, and more contrasting differences in SOC between the apple tree rows and the vegetable beds. Furthermore, since the change in land use, the vegetable beds have been repeatedly ploughed. According to Poeplau and Don (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), the conversion from natural vegetation to cropland may lead to a depletion of the SOC stock due to soil disturbance, especially soil tillage.\u003c/p\u003e \u003cp\u003e \u003cb\u003e4.2 SOM enrichment and microclimatic conditions increase litter decomposition rate near the trees, up to 1.5 m\u003c/b\u003e \u003c/p\u003e\u003cp\u003eWe have shown good repeatability of decomposition rates calculated from one year to the next in RW. These values are very similar to those investigated in apple orchards in Italy (0.012 day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, Tagliavini et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLitter decomposition was faster in apple tree rows. This result is partially linked to the SOM enrichment and the nutrient status of the soil, as we found a strong and positive correlation between the litter decomposition rate and SOM, soil N and soil TOC (Fahad et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Several studies have shown that the decomposition rate is controlled by the chemical properties of the soil, especially SOM content and nutrient availability (Krishna and Mohan \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhou et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Ngaba et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Hobbie and Vitousek (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) highlighted that fresh litter may contain insufficient nutrients to meet the growth and maintenance requirements of decomposers (i.e. fauna and microbes). Differences in litter decomposition rate can also be explained by the microclimatic conditions which has been measured between the tree row and the vegetable beds. Air temperature and relative humidity are abiotic factors that play an important part in the rate of litter decomposition. Pandey et al (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) showed a variability of 68% in forest ecosystems. Karungi et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) point out that such microclimatic conditions play a major role in regulating the microbial population and macrofauna, and hence their activities. The differences in temperature and humidity recorded between RW, B1W and C are related to the shading effects of the trees, as discussed by Ramananjatovo et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Ngaba et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In either the tree rows and the vegetable beds, and for both years, we did not find positive correlation between decomposition rate and temperature, only with relative humidity, suggesting that decomposition is limited by humidity first. This is what Petraglia et al. (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) have also shown in 6 agroecosystems.\u003c/p\u003e \u003cp\u003e \u003cb\u003e4.3 Microbial activities would be more intensive near the trees, up to 3 m, but remain highly dependent on microclimatic conditions\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSoil respiration is closely correlated with microbial biomass and activities (Jenkinson et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1976\u003c/span\u003e). The patterns of distribution in soil basal respiration in our study indicate that the microbial community is more active near the apple tree rows, suggesting a higher potential capacity for decomposition and mineralization near the trees. Moreover, the strong SIR rates in RW, RE and B1E clearly indicate a higher active microbial biomass response to glucose addition near the tree rows. The weakest \u003cem\u003eq\u003c/em\u003eCO2 under the trees, especially in RW, reveals a C limitation for microbes, and indicate that the microbial biomass is more conservative (i.e. lower CO\u003csub\u003e2\u003c/sub\u003e release by microbial biomass unit) than in vegetable beds. Guillot et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) also observed twice the microbial activity in the tree rows of a walnut-based agroforestry system. The higher C and N contents in the soil beneath the tree rows partly explain the higher SIR in this area, as we found a positive correlation between SIR and soil TOC, and SIR and soil N (An et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Bae et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) reported the same correlations in agroforestry systems in the Philippines and according to Teklay et al. (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), microbial activity is driven primarily by available organic C and total soil N. However, the lack of correlation between BR and soil C and N content in our work suggests that there might be other variables that were not considered in the study, and which would present a gradient of heterogeneity along the distance from the trees (e.g. soil phosphorus, soil pH).\u003c/p\u003e \u003cp\u003eA low \u003cem\u003ein-situ\u003c/em\u003e respiration rate may indicate that soil conditions (nutrient availability, temperature, moisture, etc.) are limiting the biological activities. The kinetics of \u003cem\u003ein-situ\u003c/em\u003e CO\u003csub\u003e2\u003c/sub\u003e fluxes observed in our study are consistent with Gomes et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) who compared the hourly evolution of soil respiration between an agroforestry coffee plantation and a monoculture. They found that the fluxes measured during the day were more stable in the agroforestry system (15% increase) than in the monoculture (49% increase) due to a lower amplitude of variation in soil temperature, which is attributed to the shading effects. The temperature and the soil moisture are the most influential abiotic factors for soil respiration (Fang and Moncrieff \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Fenn et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Gomes et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Luo and Zhou \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Tang et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In the current study, \u003cem\u003ein-situ\u003c/em\u003e CO\u003csub\u003e2\u003c/sub\u003e fluxes increased exponentially with soil temperature. Our results are in agreement with Carey et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) who showed that in general, respiration rates increase exponentially with temperature, up to 25\u0026deg;C. In addition, in our experiment, increasing soil water saturation resulted in a decrease in CO\u003csub\u003e2\u003c/sub\u003e fluxes. Previous studies have reported that high moisture can decrease soil CO\u003csub\u003e2\u003c/sub\u003e emissions by preventing CO\u003csub\u003e2\u003c/sub\u003e diffusion to the surface (Melling et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and by regulating the physiological processes of aerobic microorganisms (Melling et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). We found that the individual effects of temperature and soil moisture on soil respiration were lower than the interaction effects of the two factors. This can be explained by the fact that under uncontrolled conditions, the effects of these two variables may interfere with each other. For example, increases in temperature are often accompanied by decreases in soil water content and vice versa. Our results confirm previous work on the effects of temperature and soil water content interactions on soil respiration (Lellei-Kov\u0026aacute;cs et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e ; Li et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e ; Sierra et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2017\u003c/span\u003e ; Tucker and Reed \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn this study, we investigated the impact of apple trees on the distribution and dynamics of SOM in a garden orchard system. The distance from the tree resulted in a significant reduction in litterfall supply,\u003c/p\u003e \u003cp\u003eand has been associated with a heterogeneous spatial distribution of SOM and soil C and N contents. The decomposition of apple leaf litter was different between the tree row and the vegetable beds, due to contrasting temperature and moisture conditions. When moisture was not limiting, the rate of leaf litter decomposition was higher under the apple tree rows than in vegetable beds. The basal and glucose induced soil respiration were greater under the apple trees and the vegetable beds closest to the trees (within 3 m), indicating a higher SOM mineralization potential at these locations. Our results suggest that proximity to trees stimulates soil microbial activity in GOS, due to SOM enrichment from litter supply. However, soil humidity and temperature conditions have a strong influence on microbial activities. For two main reasons, it seems difficult to generalize our conclusion about the effects of apple trees on soil microbial degradation activities in GOS. On the one hand, the experiments conducted in this study mainly focused on processes that are indicative of soil microbial activities. Direct quantification of microbial biomass by fumigation-extraction or molecular methods would be needed to validate the results observed in the current study. On the other hand, it is difficult to characterize the effects of microclimatic variables on soil microbial activities because of their complex interactions. Understanding such interactions is important as it could eventually lead to a more efficient fertilization management of the vegetable beds, as well as help preventing waste of resources (over-fertilization) and the associated environmental impacts. For instance, the application of fertilizers could be reduced near the trees where SOM is greater and the organic nutrients potentially more available for vegetable crops. This would allow to reduce the overall use of fertilizers in GOS, avoid nutrients losses and, indirectly, greenhouse gas emissions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting Interests:\u003c/h2\u003e \u003cp\u003eThe 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.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003ethis study was funded by French Ministry of Agriculture and Food, the regional program \u0026ldquo;RFI Objectif V\u0026eacute;g\u0026eacute;tal\u0026rdquo; of Pays de la Loire (France), and the \u0026ldquo;Fondation de France\u0026rdquo;\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, T.R., E.C., P.G., R.G., M.D., G.B-S. and P.C.; methodology, T.R., E.C., P.G., R.G., M.D., G.B-S. and P.C.; validation, E.C., P.G., R.G., M.D., G.B-S. and P.C.; formal analysis, T.R., E.C., J.P., P.G., R.G. and P.C.; investigation, T.R., E.C., J.P., P.G., R.G., M.D., G.B-S. and P.C.; writing\u0026mdash;original draft preparation, T.R.; writing\u0026mdash;review and editing, T.R., E.C., P.G., R.G., M.D., G.B-S. and P.C.; visualization, T.R., E.C., P.G., R.G. and P.C.; supervision, E.C., G.B S. and P.C.; project administration, T.R., E.C., G.B-S. and P.C.; funding acquisition, E.C., P.G., R.G., M.D., G.B-S. and P.C. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors are very grateful to R\u0026eacute;mi Gardet (PHENOTIC platform) for logistical support, to Dominique Lemesle (EPHor) for the maintenance of all sensors used in the study, and to Yvette Barraud-Roussel (EPHor) for chemical analysis\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAn Z, Pokharel P, Plante AF, Bork EW, Carlyle CN, Williams EK, Chang SX (2023) Soil organic matter stability in forest and cropland components of two agroforestry systems in western Canada. Geoderma 433:116463. https://doi.org/10.1016/j.geoderma.2023.116463\u003c/li\u003e\n\u003cli\u003eAnderson JPE, Domsch KH (1978) A physiological method for the quantitative measurement of microbial biomass in soils. Soil Biol. Biochem. 10:215\u0026ndash;221. https://doi.org/10.1016/0038-0717(78)90099-8\u003c/li\u003e\n\u003cli\u003eAnderson TH, Domsch KH (1993) The metabolic quotient for CO2 (qCO2) as a specific activity parameter to assess the effects of environmental conditions, such as ph, on the microbial biomass of forest soils. Soil Biol. Biochem. 25:393\u0026ndash;395. https://doi.org/10.1016/0038-0717(93)90140-7\u003c/li\u003e\n\u003cli\u003eAnderson TH, Domsch KH (1990) Application of eco-physiological quotients (qCO2 and qD) on microbial biomasses from soils of different cropping histories. Soil Biol. Biochem. 22:251\u0026ndash;255. https://doi.org/10.1016/0038-0717(90)90094-G\u003c/li\u003e\n\u003cli\u003eBae K, Lee DK, Fahey TJ, Woo SY, Quaye AK, Lee YK (2013). Seasonal variation of soil respiration rates in a secondary forest and agroforestry systems. Agrofor. 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Plant Soil 311:61. https://doi.org/10.1007/s11104-008-9658-5\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"agroforestry-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agfo","sideBox":"Learn more about [Agroforestry Systems](http://link.springer.com/journal/10457)","snPcode":"10457","submissionUrl":"https://submission.nature.com/new-submission/10457/3","title":"Agroforestry Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Agroforestry, decomposition, mineralization, organic matter, soil respiration","lastPublishedDoi":"10.21203/rs.3.rs-4686563/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4686563/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe specific aim of this study was to assess the impact of 20-year-old apple trees on the soil agronomic quality in an agroforestry system consisting of 2 rows of apple trees with 5 rows of vegetable beds in between. The effects of this system were analyzed specifically on soil microbial activity and fertility. Measurements were carried out for 2 years between 2019 and 2021 in apple tree rows (R) and in vegetable rows 1.5 m (B1), 3 m (B2) and 5 m (C) from the apple tree row. Litter quantities and soil organic matter (SOM) content were measured as well as the decomposition rates of apple tree leaf litter. Soil microbial activity was characterized by measuring (1) \u003cem\u003ein-situ\u003c/em\u003e soil respiration and (2) basal (BR) and substrate induced respiration (SIR) under controlled conditions. The results showed that proximity to apple trees was linked to higher SOM content. The litter decomposition rate was up to 1.7-times greater under the tree rows than in vegetable beds. The amplitude of \u003cem\u003einsitu\u003c/em\u003e soil CO\u003csub\u003e2\u003c/sub\u003e flux variation and the maximum flux were lower under the tree rows than in vegetable beds, mainly due to lower temperature. In the vegetable beds, the maximum \u003cem\u003ein-situ\u003c/em\u003e soil CO\u003csub\u003e2\u003c/sub\u003e flux was attained faster in B1 than in C. Under controlled laboratory conditions, we showed that BR was significantly stronger in R, B1 and B2 than in C (5, 5, 4.7 and 3.5 \u0026micro;gC-CO\u003csub\u003e2\u003c/sub\u003e.h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e soil DW, respectively). In addition, the soil in the apple tree rows was more sensitive to the addition of glucose (SIR) than the soil in the vegetable beds. Our results suggest that soil microbial activity was more intensive up to 3 m from the apple trees. Globally, the results highlight the complexity of the interactions among the biotic and abiotic factors that are at the origin of the spatial heterogeneity encountered.\u003c/p\u003e","manuscriptTitle":"Positive influence of apple trees on soil chemical and biological activity in an agroecological garden orchard system","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-08 06:58:05","doi":"10.21203/rs.3.rs-4686563/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2024-07-29T10:12:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"272013085451447145889566350417871477829","date":"2024-07-28T18:20:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"37356103931891786524378116964458219551","date":"2024-07-22T16:00:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-22T15:25:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-16T07:21:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-06T15:31:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Agroforestry Systems","date":"2024-07-04T12:24:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"agroforestry-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agfo","sideBox":"Learn more about [Agroforestry Systems](http://link.springer.com/journal/10457)","snPcode":"10457","submissionUrl":"https://submission.nature.com/new-submission/10457/3","title":"Agroforestry Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"83a5ee80-6cdd-4e50-916b-a78a39bca049","owner":[],"postedDate":"August 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-10-14T16:03:18+00:00","versionOfRecord":{"articleIdentity":"rs-4686563","link":"https://doi.org/10.1007/s10457-024-01088-2","journal":{"identity":"agroforestry-systems","isVorOnly":false,"title":"Agroforestry Systems"},"publishedOn":"2024-10-07 15:57:05","publishedOnDateReadable":"October 7th, 2024"},"versionCreatedAt":"2024-08-08 06:58:05","video":"","vorDoi":"10.1007/s10457-024-01088-2","vorDoiUrl":"https://doi.org/10.1007/s10457-024-01088-2","workflowStages":[]},"version":"v1","identity":"rs-4686563","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4686563","identity":"rs-4686563","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}