Tree species traits and soil biochemical properties drive carbon stability and temperature sensitivity of soil aggregates in agroforestry systems of subtropical northeast India

Tree species traits and soil biochemical properties drive carbon stability and temperature sensitivity of soil aggregates in agroforestry systems of subtropical northeast India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Tree species traits and soil biochemical properties drive carbon stability and temperature sensitivity of soil aggregates in agroforestry systems of subtropical northeast India Ramesh Thangavel, Kanchikerimath Manjaiah, A. Arunachalam, Samarendra Hazarika, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5762787/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Agroforestry systems play a critical role in enhancing soil organic carbon (SOC) stability and mitigating climate change by integrating trees and crops to improve soil fertility and carbon sequestration. This study investigates the SOC stability, aggregate dynamics, and temperature sensitivity of SOC mineralization across four agroforestry systems ( Michelia oblonga, Parkia roxburghii, Alnus nepalensis , and Pinus kesiya ). Tree traits, soil properties, and aggregate characteristics were analyzed alongside a 60-day incubation experiment under three temperature regimes (25°C, 30°C, and 35°C). The results revealed the SOC mineralization significantly varied amongst the agroforestry systems with highest value in M. oblonga (25.59 mg CO 2 g − 1 ) and lowest in A. nepalensis (20.39 mg CO 2 g − 1 ). Macroaggregates consistently showed higher SOC concentrations and biochemical indicators, such as polysaccharides and total glomalin-related soil proteins (TG-RSP), compared to microaggregates and bulk soil. The temperature and aggregate sizes statistically influenced the SOC mineralization rates, with noticeable interaction effect. SOC mineralization rates increased with temperature, but Alnus nepalensis exhibited the highest temperature sensitivity (Q 10 = 0.955 and activation energy = 24.25 kJ mol − 1 ), highlighting its resilience to thermal stress. Strong positive correlations were observed between soil aggregate stability and soil biochemical indicators such as SOC, polysaccharides and TG-RSP of bulk soil and aggregates. Temporal trends indicated that carbon mineralization peaked at 30 days before stabilizing, reflecting the decomposition of labile carbon pools. These findings highlight the critical role of tree traits, soil aggregates, and thermal stability in driving SOC retention in agroforestry systems. Agroforestry systems soil aggregates soil organic carbon carbon mineralization temperature sensitivity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Soil organic carbon (SOC) is a cornerstone of soil health and ecosystem sustainability, playing a critical role in maintaining soil fertility, supporting biological activity, and regulating the global carbon cycle. SOC stability, defined as its resistance to decomposition and loss, is pivotal for ensuring long-term carbon sequestration and mitigating climate change impacts (Lorenz and Lal 2014; Das 2023). Among the mechanisms contributing to SOC stability, soil aggregation stands out as a protective barrier against microbial decomposition, enhancing nutrient retention and soil physical properties (Keller and Phillips 2019; Augusto and Boca 2022). Studies suggest that soil texture significantly affects SOC accumulation, with finer-textured soils, such as clays, exhibiting a greater capacity for SOC storage due to their ability to stabilize organic matter through physical and chemical interactions (Castellano et al. 2015; Zhou et al. 2019; Rasmussen et al. 2018; Ribbons et al. 2018). Aggregate dynamics further influence SOC stability, as evidenced by the higher SOC concentrations often found in larger macroaggregates (Schmidt et al. 2011; Russell et al. 2018). Liu et al. emphasized that organic carbon enhances aggregate formation and stability, creating microenvironments that protect carbon from decomposition. Moreover, management practices, such as biochar application, have been recognized for their potential to modify soil physicochemical properties and enhance SOC stabilization (Cheng et al. 2018; Mayer et al. 2020). Conversely, land-use changes, such as deforestation and agricultural expansion, disrupt soil structure and reduce organic matter inputs, leading to declines in SOC (Li et al. 2015; Maes et al. 2019; Rytter and Rytter 2020). Zhou et al. (2019) noted that agricultural practices could lead to modifications in soil organic carbon content, directly affecting soil stability and productivity. The impact of land use on SOC is further illustrated by studies showing that different cropping systems can alter aggregate stability and SOC storage (Zhou et al. 2020; Zhang et al. 2022). Agroforestry systems have emerged as a viable solution for improving SOC storage and stability (Cardinael et al. 2015; Feliciano et al. 2018). By integrating trees with agricultural crops or pastures, these systems offer a multifunctional approach that enhances carbon sequestration, soil fertility, and microbial activity while fostering resilient ecosystems (Pardon et al. 2017). The higher organic matter input from tree litter, including leaves, branches, and roots, contributes significantly to SOC accumulation (Palma et al. 2017). Studies have shown that agroforestry systems can maintain SOC levels exceeding those of conventional agricultural systems, with storage values reaching up to 24.91 tonnes per hectare in some tropical regions (Narender et al. 2021; An et al. 2023). Further, the dynamics of SOC in agroforestry systems are influenced by various factors, including vegetation characteristics, climate, and soil management practices. The interplay between these factors determines how carbon is stored, stabilized, and decomposed over time (Lorenz and Lal 2014). Temperature, in particular, plays a critical role in SOC dynamics by affecting microbial activity and organic matter decomposition rates. Temperature sensitivity indices, such as Q10 and activation energy, are increasingly used to assess the resilience of SOC under varying thermal regimes (Jenkins and Adams 2011; Blagodatskaya et al. 2016; Heskel et al. 2016). Land-use changes, such as transitions from monoculture to agroforestry, have been shown to enhance SOC pools through improved organic matter inputs and enhanced soil structure (Augusto and Boca 2022). However, the effectiveness of agroforestry systems in stabilizing SOC depends on site-specific factors, such as soil type, climate, and management practices. Despite the advancements in understanding these dynamics, knowledge gaps remain in quantifying the effects of specific agroforestry systems on SOC stability across soil depths, thermal conditions, and management regimes (Lorenz and Lal 2014, Das 2023). This study addresses these gaps by exploring the stability of SOC pools under varying land-use scenarios and thermal conditions in agroforestry systems. Thus, the SOC stability at the aggregate level in soils under four agroforestry systems: Michelia oblonga, Parkia roxburghii, Alnus nepalensis , and Pinus kesiya . SOC stability and carbon mineralization were assessed across varying temperature regimes (25°C, 30°C, and 35°C) and durations (15, 30, and 60 days) to capture decomposition dynamics and temperature sensitivity. To complement this, parameters such as SOC content, polysaccharides, and glomalin were analyzed, providing a cumulative assessment of stabilization mechanisms. By examining the interplay between temperature, land use, and SOC stabilization processes, the findings aim to provide actionable insights for optimizing land management practices to enhance SOC stability, improve soil health, and mitigate climate change. The objectives of the present study are (i) to investigate how the biochemical indicators (polysaccharides and total glomalin) varies among the aggregates and bulk soil under different agroforestry systems and (ii) to assess the temperature sensitivity of SOC mineralization using Q10 and Arrhenius activation energy under different agroforestry systems od subtropical northeast India. 2. Materials and methods 2.1 Site description The study was conducted with four various multipurpose tree species planted under agroforestry systems planted in 1983 at Indian Council of Agricultural Research- Complex for North-east Hill Region, Umiam, Meghalaya. The experimental institute is located in the central part of Meghalaya in the East Khasi Hills of Northeast India at an elevation of 980 m above mean sea level with a slope of 32 to 53% (25 o 41'21" N latitude and 91 o 55'25" longitude). The region experiences a humid subtropical climate characterised by distinctive warm-wet and cold-dry seasons annually. The annual mean rainfall of the region is about 2210 mm of which 85-90% is received from May to October. The average yearly minimum and maximum air temperature are 6.8 o C (February) and 29.3 o C (April), respectively. Relative humidity varies from 40% in winter and 88% during summer. The initial soil properties are Typic Hapludalf type, highly acid (pH 4.36-4.76), high in organic carbon (1.91-3.12%), available nitrogen (403-584 kg ha -1 ), high in available phosphorus (19.3-47 kg ha -1 ) and available potassium (248-361 kg ha -1 ). The detailed informations about experimental site, agroforestry tree species and crop yields were described in Ramesh et al. (2013 and 2015). 2.2 Soil sampling Surface soil samples (0-15 cm) were collected from the four agroforestry systems blocks following the method described by Dhyani and Tripathi (1999). By using 8 cm corer samplers, soil samples were collected from different agroforestry systems including control (natural fallow). The collected soil samples were brought to the research laboratory, shade dried at ambient temperature and pulverized to pass through 2-mm sieve for subsequent analysis. The mean-weight diameter (MWD) was calculated using air-dried samples that were pre-sieved through a 4 mm mesh using the method established by Kemper and Chepil (1965). A 250 µm sieve was used to separate 500g of air-dried soil samples into macro aggregates (>250 µm) and micro aggregates (250 µm) and micro aggregates (<250 µm) were analysed to study the carbon stability by using the procedure established by Six et al. (1998). For the analysis of the total polysaccharides (TP), dilute acid-extractable polysaccharides (DAEP) and incubation experiment, the soil samples were stored at 4 ± 1 oC in the laboratory. TP and DAEP were estimated with the procedure adapted from Whistler and Wolfrom (1962) by Lowe (1994). The total glomalin was estimated using 1 g soil sample with 100 mM sodium pyrophosphate as outlined by Wright et al. (1996) and Rillig (2004). 2.3 Incubation experiment A laboratory experiment was conducted for A 60 days to investigate the variations in the temperature sensitivity of SOC mineralization of macro- and micro- aggregates of various agroforestry systems. The stored moist soil samples of 0-15 cm depth equivalent to 100 g (oven dry basis) were added into Schott jars, deionized water was cautiously mixed and 30% water holding capacity was maintained through the experiment. A glass vial containing 25 mL of 1 mol L -1 NaOH solution was placed into the jars to capture the released CO 2 from the soil. Additionally, a series of blank jars containing vial with NaOH were kept for each temperature as control to account for the CO 2 captured from the air inside the jar. The incubation experiment was conducted up to 60 days with three temperature viz. 25, 30 and 35 o C using BOD incubators. The three temperatures were selected based on minimum and maximum temperatures experienced of the experimental farm of the region. Soil moisture was adjusted to 30% water holding capacity periodically as it is generally contemplated to impersonate the field moisture conditions. By weighing each sample once in a week, the soil moisture content was adjusted to the desired moisture content (30% field capacity) to maintain the soil moisture. Three replications were set for each treatment of the experiment. The released CO 2 from each sample including blank was estimated at 15, 30 and 60 days after the incubation by the titration with 0.1 mol L -1 NaOH solution after adding BaCl2. The generated CO 2 data for various temperatures was used for the soil aggregate carbon content, carbon mineralization and temperature dependency of CO 2 efflux by Arrhenius activation energy ( k = A exp (-E/RT)) (Knorr et al. 2005) Where, k is the rate constant; A is the pre-exponential factor; E is the activation energy; R is the universal constant and T is the absolute temperature. 2.4 Statistical analysis The data on the impact of agroforestry systems on carbon related parameters and carbon mineralization of soil aggregates were analysed statistically by using the software SPSS for Windows (SPSS Inc. USA). A two-way analysis of variance (ANOVA) was utilized for the statistical evaluation of the measured parameters. The Duncan multiple range test (DMRT) at 5% level of probability was performed to find out the significance of the difference between the means for different agroforestry systems. Correlation analyses were also performed for the identification of the functional relationship among the carbon related parameters of the aggregates from the agroforestry systems. 3. Results 3.1 General characteristics of agroforestry systems The morphological and biomass characteristics of tree species varied significantly across the agroforestry systems. Parkia roxburghii exhibited the tallest trees (24.99 ± 1.21 cm) and largest diameter at breast height (DBH) (27.95 ± 2.82 cm), while Pinus kesiya had the shortest trees (15.60 ± 3.80 cm) and smallest DBH (23.32 ± 8.24 cm). Timber volume was highest for Michelia oblonga (231.31 m³ ha⁻¹), surpassing Pinus kesiya (117.77 m³ ha⁻¹) by 49%, indicating superior timber productivity. Fine root biomass was greatest in Pinus kesiya (496.75 g m⁻²) but closely followed by Alnus nepalensis (435.50 g m⁻²), highlighting their roles in below-ground carbon contributions. Annual litter biomass was highest in Pinus kesiya (625.30 ± 3.83 g m⁻²), followed by Michelia oblonga (512.45 ± 9.51 g m⁻²) (Table 1). Similarly, Soil nutrient content and physical properties varied widely among the systems. Alnus nepalensis soils exhibited the highest total nitrogen (TN, 584.3 ± 10.4 kg ha⁻¹), total phosphorus (TP, 47.2 ± 1.6 kg ha⁻¹), and organic carbon content (35.6 ± 2.1 g kg⁻¹), alongside the lowest bulk density (1.07 ± 0.05 Mg m⁻³). These results reflect its ability to enhance soil fertility and nutrient availability. Conversely, Pinus kesiya soils had the lowest TN (370.8 ± 8.9 kg ha⁻¹), TP (31.6 ± 1.4 kg ha⁻¹), and organic carbon content (25.8 ± 1.7 g kg⁻¹), alongside the highest bulk density (1.26 ± 0.03 Mg m⁻³) and lowest EC (0.41 ± 0.03 dS m⁻¹). Potassium (K) concentrations were highest in Michelia oblonga (420.0 ± 15.6 kg ha⁻¹) and lowest in Pinus kesiya (330.5 ± 14.2 kg ha⁻¹), underscoring differences in nutrient cycling across systems. Crop yields were highest in systems with Alnus nepalensis , producing grain yields of 3.8 ± 0.2 t ha⁻¹ and straw yields of 4.6 ± 0.3 t ha⁻¹, which were 25% and 31% higher, respectively, than in systems with Pinus kesiya . Michelia oblonga and Parkia roxburghii exhibited intermediate grain yields (3.5 ± 0.2 t ha⁻¹ and 3.4 ± 0.2 t ha⁻¹) and straw yields (4.2 ± 0.2 t ha⁻¹ and 4.0 ± 0.2 t ha⁻¹) (Table 2). 3.2 Macro- and microaggregate carbon stability parameters The data on distribution of soil organic carbon (SOC), polysaccharides, glomalin, and aggregate stability across macro- and microaggregates in the four agroforestry systems is presented in figures 1-5. SOC concentrations (Figure 1) were consistently higher in macroaggregates than in microaggregates across all systems (p < 0.05). Thus, Alnus nepalensis soils exhibited the highest SOC concentrations in macroaggregates (27.1 ± 1.4 g kg⁻¹), approximately 1.8-fold higher than microaggregates (15.2 ± 1.1 g kg⁻¹). Conversely , Pinus kesiya had the lowest SOC in both macroaggregates (19.5 ± 1.2 g kg⁻¹) and microaggregates (11.8 ± 0.9 g kg⁻¹), reflecting its lower contribution to carbon storage. Across all systems, macroaggregates accounted for 60–70% of the total SOC pool. Similarly, the estimation of total polysaccharide (TP) concentrations (Fig. 2) were remarkably higher in macroaggregates compared to microaggregates (p < 0.05), with the highest values observed in Alnus nepalensis soils (1.74 g 100g -1 in bulk soil and 1.53 g 100g -1 in microaggregates). With respect to the macroaggregates, the total polysaccharides content was at par with the Michalia oblonga . These levels were 14-23% higher in bulk soil, 1-13% higher in macroaggregates and 8-13% greater in microaggregates as compared to other agroforestry systems. Agroforestry systems enhanced the total polysaccharides about 1.21-1.48 times in bulk soils, 1.17-1.32 times in macroaggregates and 1.20-1.35 times in microaggregates as compared to fallow lands. Polysaccharides were positively correlated with SOC stability (R² = 0.78, p < 0.01) across all systems, underscoring their role in aggregate formation and carbon stabilization. The dilute acid-extractable polysaccharides (DAEP) (Fig. 3) showed a similar trend as total polysaccharides and significant variations across agroforestry systems and aggregate fractions (p < 0.05). Alnus nepalensis exhibited the highest concentrations of total polysaccharides in macroaggregates (1.53 g 100g -1 soil), which were 1.21 times greater than those in microaggregates (1.24 g 100g -1 soil) and 1.14 times greater than bulk soil (1.32 g 100g -1 ). In contrast, Pinus kesiya exhibited the lowest values for total polysaccharides with bulk soil (1.41 g 100g -1 soil), macroaggregates containing only 1.74 g 100g -1 soil and microaggregates containing only 1.38 g 100g -1 soil. Similarly, for dilute acid extractable polysaccharides, Pinus kesiya recorded the lowest value in bulk soil (1.41 g 100g -1 soil), macroaggregates (1.74 g 100g -1 soil) and microaggregates (1.38 g 100g -1 soil). Adaption of agroforestry systems in the fallow lands, increased the dilute acid extractable polysaccharides about 5-28% in bulk soil, 5-21% in macroaggregates and 18-36% in microaggregates (Fig. 3). The TP in microaggregates had a significant correlation with SOC (r = 0.666**), macroaggregates carbon (r = 0.514*) and microaggregates carbon (r = 0.587*). The total Glomalin-related soil protein (TG-RSP) significantly differed among the agroforestry systems irrespective of the soil aggregates. The TG-RSP was highest in Alnus nepalensis of bulk soil, macroaggregates and microaggregates, with the value reaching 0.31, 0.48 and 0.32 g/100g soil, respectively (Figure 4). These values were 1.20-1.41, 1.23-1.54 and 1.03-1.83 times greater than those observed in other agroforestry systems’ aggregates. Pinus kesiya had the lowest TGRSP concentrations in bulk soil, macroaggregates and microaggregates, reflecting reduced biochemical protection of SOC. However, converting fallow/barren land to agroforestry systems increased the TG-RSP content in bulk soil (16-60%), macroaggregates (4-60%) and microaggregates (4-33%). GRSP levels were significantly associated with SOC content (R² = 0.85, p < 0.01), emphasizing their contribution to aggregate stability. The mean contribution of agroforestry systems TG-RSP to SOC is 1.08, 1.13 and 1.33-fold times in bul soil, macroaggregates and microaggregates, respectively compared to barren land (Fig. 5). Further, the stability of aggregates, as measured by the mean weight diameter (MWD), was statistically higher in macroaggregates compared to microaggregates (p < 0.05, Figure 5). Macroaggregates under Alnus nepalensis soils exhibited the greatest stability, with an MWD of 2.5 ± 0.2 mm, which was 40% higher than the MWD of Pinus kesiya macroaggregates (1.8 ± 0.1 mm). Microaggregate stability followed a similar trend, with Pinus kesiya consistently showing lower values across all systems. 3.3 Temperature and time effects on SOC mineralization SOC mineralization rates varied significantly with temperature and tree species (p < 0.05, Table 3). The average macroaggregates C mineralization rate ranged from 1.88-7.71, 5.65-11.52 and 11.45-23.7 mg CO₂-C g soil -1 at 15, 30 and 60 days of incubation, correspondingly. On the other hand, the microaggregates C mineralization rate ranged from 1.47-5.36, 2.86-8.66 and 5.21-16.59 mg CO₂-C g soil -1 at 15, 30 and 60 days of incubation, respectively (Table 3). The C mineralization rate of different agroforestry systems was 52% higher in macroaggregates at 15 days, 77% higher at 30 days and 68% higher at 60 days of incubation as compared to microaggregates, irrespective of the incubation temperatures. The cumulative C mineralization ranged from 22.4 to 24.6 in macroaggregates while in microaggregates it ranged from 13.95 to 17.98 mg CO₂-C g soil -1 among the agroforestry systems. Agroforestry systems, on an average, showed 1.07 times lesser cumulative C mineralization rate while in microaggregates it was 1.57 times lower than the control. Alnus nepalensis soils exhibited the lowest cumulative carbon mineralization across all temperatures, with emissions of 22.4 mg CO₂-C/g SOC at 35°C. In contrast, Michelia oblonga soils had the highest rates (24.6 mg CO₂-C/g SOC), reflecting greater carbon loss. Carbon mineralization increased by 2.13-4.80 times in between 25°C and 35°C, with the highest increments observed in Pinus kesiya . Microaggregates consistently showed 43% lower mineralization rates than microaggregates at 35 o C across all the agroforestry systems, emphasizing their role in SOC protection. Overall, increase in temperature from 25 to 35 o C augmented the rate of C mineralization of both aggregates, on average, by 100, 62 and 63% at 15, 30 and 60 days of incubation, correspondingly. Further, temperature sensitivity indices (Q 10 ) highlighted significant differences in SOC stability among agroforestry systems (Table 4). Alnus nepalensis soils exhibited the highest Q 10 values (0.955 ± 0.1), indicating higher thermal stability compared to Pinus kesiya (0.862 ± 0.2). Parkia roxburghii and Michelia oblonga displayed intermediate Q 10 values (0.905 ± 0.1 and 0.932 ± 0.2, respectively). Thus, the activation energy (Ea) for SOC mineralization varied significantly across systems, with Alnus nepalensis soils requiring the lowest energy (24.25 ± 2.3 kJ mol⁻¹) to initiate decomposition, indicating the presence of more thermally stable carbon pools (Table 4). Pinus kesiya had the highest Ea (74.97 ± 3.1 kJ mol⁻¹), suggesting less stable carbon pools. The study demonstrated that the macro and microaggregates in agroforestry systems recorded a relatively lower rate constant values than that of control throughout all study temperatures (Table 4). The mean rate constant macroaggregate varied between 0.0005 to 0.0016 in agroforestry systems whereas, it varied between 0.0008 to 0.0022 in control. Conversely, the mean rate constant of microaggregates ranged from 0.0007 to 0.0014 in agroforestry systems while in control plot it ranged from 0.0004 to 0.0017. The increase in incubation temperature from 25 to 30 o C increased the rate constant by 2.42 times whereas, from 30 to 35 o C showed 1.41 times increase in macroaggregate C mineralization. But, in microaggregates C mineralization, increase in temperature from 25 to 30 and 30 to 35 o C recorded 1.38 and 1.35 times increase in rate constant, respectively. 4. Discussion Research has also emphasized the importance of aggregate-level SOC stabilization in protecting carbon from microbial decomposition (Orgill et al. 2016; Pan 2011; Singh et al. 2018). Macroaggregates and microaggregates within the soil matrix provide physical and biochemical protection to SOC, thus influencing its long-term stability. Furthermore, the interactions between land use and environmental conditions can significantly impact aggregate dynamics and SOC stabilization mechanisms ​ (Augusto and Baco 2022). The tree species demonstrated distinct contributions to soil properties and SOC dynamics, with Alnus nepalensis outperforming others due to its balanced above- and below-ground biomass inputs. Its high nitrogen-fixing capability and litter quality enhanced soil fertility, as evidenced by the highest total nitrogen (TN), phosphorus (TP), and SOC concentrations. These findings align with studies emphasizing the role of nitrogen-fixing trees like Alnus spp. in boosting soil nutrient pools and SOC stabilization​ (Lorenz and Lal 2014; Augusto and Baco 2022). Moreover, the incorporation of organic materials into the soil has been shown to improve aggregate stability and increase SOC content. For example, it is reported that organic material treatments significantly enhanced the mean weight diameter (MWD) of soil aggregates, indicating improved stability (Pan et al. 2015; Wang et al. 2020). This finding corroborates with results from Cheng et al. (2018) who emphasized the importance of understanding SOC fractions and their dynamics in relation to soil properties. In contrast, Pinus kesiya exhibited the lowest SOC concentrations and nutrient content despite its high litter biomass, suggesting limited nutrient cycling efficiency. Similar trends have been reported in systems dominated by conifers, where slow litter decomposition impedes nutrient availability and SOC accrual​ (Yang et al. 2022; Zhang et al. 2021). SOC concentrations were significantly higher in macroaggregates than in microaggregates across all systems, underscoring the role of aggregate dynamics in carbon stabilization. Alnus nepalensis macroaggregates had the highest SOC, supported by elevated polysaccharide and glomalin-related soil protein (GRSP) levels. These findings are consistent with studies showing that biochemical inputs, such as polysaccharides and GRSP, strengthen soil structure and protect SOC from microbial decomposition​ (Schlecht-Pietsch et al. 1994; Wang et al. 2021). Conversely, Pinus kesiya had the lowest biochemical indicators and aggregate stability, reflecting its reduced capacity to stabilize SOC. This variability highlights the influence of tree traits on aggregate formation and stability, which are critical for long-term carbon sequestration​ (Pardon et al. 2017). Further, this relationship is mediated through several mechanisms, including root traits, litter quality, and microbial interactions. For instance, broadleaf trees have been shown to enhance soil aggregate stability compared to coniferous species, with studies indicating improvements in stability by 57–103% in mixed forest stands dominated by broadleaf trees (Zheng 2023). The presence of fine roots and their associated organic matter contributes to the binding of soil particles, which is essential for the formation of stable aggregates (Zheng 2023; Kemner et al. 2020). Additionally, the biomass of various tree species and the litter they produce are key indicators affecting aggregate stability, as they influence the organic matter content and microbial activity within the soil (Wang 2023). The decomposition of litter contributes organic materials that serve as binding agents for soil particles, promoting the formation of aggregates (Su et al. 2021). The quality of litter, particularly its carbon content, affects the microbial community structure and activity, which are vital for the stabilization of soil aggregates (Jing et al. 2023). For example, higher organic carbon inputs from tree litter can enhance microbial metabolism and lead to increased stability of soil aggregates through the production of extracellular polysaccharides and other binding agents (Su et al. 2021; Jing et al. 2023). Changes in tree species composition due to land-use changes can lead to alterations in soil pH, which has been linked to the dynamics of soil macroaggregates (Russell et al. 2018; Russell et al. 2017). The ratio of TG-RSP to SOC in agroforestry systems ranged from 1.08-1.33-times higher than barren land. The constituents of TG-RSP consisted of a diverse amalgamation of proteins, lipids, humus, and inorganic components, exhibiting remarkable stability in nature (Wang et al. 2017). Thus, the elevated ratio of glomalin to SOC with agroforestry systems may offer additional evidence that microbial-derived C facilitates more SOC accumulation in soils of agroforestry systems. Our results suggest that the increased contribution of glomalin to SOC in agroforestry systems may enhance the stability of aggregates in these systems. Further, the high and significant correlations between TG-RSP and aggregates SOC support the increase in the aggregates stability in the present investigation. SOC mineralization is significantly influenced by incubation temperature, with both the mineralization ratio and cumulative SOC mineralization rates increasing as temperatures rise (Qin et al. 2016). In the present study, temperature significantly influenced SOC mineralization rates, with the highest rates observed at 35°C. The concept of temperature sensitivity, often quantified using the Q 10 value, reflects the rate of increase in SOC mineralization for every 10°C rise in temperature (Duan et al., 2023). Alnus nepalensis exhibited the lowest temperature sensitivity (higher Q 10 value: 0.955) and activation energy, indicating greater thermal stability of its SOC pools. This finding is corroborated by He et al. (2022) who found that higher substrate carbon levels resulted in increased SOC mineralization rates under elevated temperatures, suggesting that substrate quality also modulates temperature sensitivity. Similarly, Wang et al. (2013) reported that SOC mineralization rates significantly increased with temperature across a range of incubation conditions, reinforcing the notion that temperature is a critical driver of SOC dynamics (Wang et al. 2013). These results align with findings that species with efficient nutrient cycling and root contributions exhibit better resilience to temperature-induced carbon loss ​ (Bai et al. 2023). Temporal trends revealed peak SOC mineralization at 30 days, driven by the rapid decomposition of labile carbon pools. This pattern, followed by stabilization at 60 days, reflects the transition from labile to recalcitrant carbon decomposition phases, consistent with previous incubation studies​ (Augusto and Baco 2022; Das 2023). Palma et al. (2017) highlight that the accumulation of carbon in agroforestry systems can be influenced by the ratio of root biomass to decomposed plant matter, suggesting that a higher proportion of root biomass can lead to greater carbon retention in the soil. However, as temperatures increase, the decomposition of this organic matter may accelerate, potentially leading to a reduction in SOC levels (Kay et al. 2019). This is supported by findings from Silva et al. (2014) which indicate that biological activity in agroforestry systems can be significantly affected by temperature fluctuations, impacting carbon storage. In the present study, the activation energy was highest in Pinus kesiya and lowest in Alnus nepalensis , however, control plot had the highest Ea than agroforestry systems (132%), irrespective of the aggregates. According to Davidson and Janssens (2006), the mineralization of poor-quality C substrates requires highest Ea than the high-quality C substrates. The C:N ratio, chemical compositions and their complexity, SOC content, etc. influence the substrate quality (Bhattacharyya et al 2020). In contradiction of activation energy, the Q 10 values remained 2–11% more in agroforestry systems than control plot. Xu et al (2012) stated that the Ea and Q 10 are inversely related with respect to SOC mineralization which is in consistent with the present study. The high-quality organic substrates require less Ea and more sensitive to temperature than the poor-quality organic substrates. The association of aggregate stability to SOC was perhaps attributed to binding agents including fungal exudates like polysaccharides and glomalin (Wright et al., 1999) as well as hyphae and roots which improve stability of aggregates. According to Eynard et al. (2004), these polysaccharides and glomalin improves the stability soil aggregates through the changes in the degree of water repellency. The examination of first order rate constant or kinetics indicated that aggregates in the agroforestry systems provided a superior protection of the SOC compared to control in both aggregate types. The reaction rate was more rapid in macroaggregates than microaggregates regardless of the agroforestry systems including control. The present investigation indicated a steady rise in reaction rate with temperature in both aggregates. The elevated rate constants indicate the comparatively lesser C stability in the macroaggregates over microaggregates. This is due to the variations in C availability among and within the aggregates across the agroforestry systems. Aggregates provides physical protection to soil organic matter by creating physical barricades that separate microbes and enzymes and their substrates. The variations in aggregates stability within the agroforestry systems including control, are mostly attributed to variations in organic inputs from crop residues, litter and root biomass, and the severity of soil physical disturbance (Zhou et al 2022). Consequently, the improved protection of soil organic matter by aggregates in agroforestry systems leads to greater accumulation of carbon compared to control system. Hence, effective management strategies that enhance soil health and organic matter inputs can mitigate the impacts of temperature on carbon dynamics. For instance, practices that maintain soil cover and reduce soil disturbance can help preserve SOC levels even in the face of rising temperatures (Niguse et al. 2022). Conclusions This study provides a comprehensive evaluation of soil organic carbon (SOC) stability across four agroforestry systems, emphasizing the critical roles of tree traits, aggregate dynamics, and temperature sensitivity in enhancing soil health and carbon sequestration. The results demonstrated significant variability among the agroforestry systems, with Alnus nepalensis emerging as the most effective in enhancing SOC stability. The balanced above- and below-ground biomass contributions of Alnus nepalensis , including moderate litter inputs and substantial root biomass, played a key role in improving nutrient cycling and aggregate stability. In contrast, Pinus kesiya , despite its high litter biomass, showed lower SOC concentrations and weaker biochemical contributions, suggesting limited nutrient enrichment and carbon stabilization potential. Macroaggregates were identified as critical to SOC stabilization, with consistently higher SOC concentrations, polysaccharides, and glomalin-related soil proteins (GRSP) compared to microaggregates. Among the systems, Alnus nepalensis macroaggregates exhibited the greatest stability, supported by high biochemical indicators, such as polysaccharides and GRSP than other agroforestry systems. The correlation between SOC stability and biochemical properties underscores the role of organic matter inputs in strengthening soil structure and enhancing carbon retention. Temperature sensitivity of SOC mineralization varied significantly across systems, with Alnus nepalensis showing the highest Q 10 values and lower activation energy. Temporal trends revealed peak carbon mineralization at 30 days, followed by stabilization, reflecting the decomposition of labile carbon pools and the persistence of more recalcitrant fractions. The reduced mineralization rates in Alnus nepalensis soils further affirm its ability to sequester carbon effectively over time. Overall, the findings demonstrate that agroforestry systems have immense potential to enhance SOC stability, mitigate climate change, and improve soil health. Declarations Author Contribution Dr. Ramesh Thangavel and Dr. KM Manjaiah prepared the main manuscript textA. Arunachalam and JMS Tomar provided the details of the study siteS Hazarika, BU Choudhury, and A Balusamy prepared the figures and edited the manuscriptVK Mishra is the overall co-ordinator and the Director of the institute Acknowledgements The authors appreciatively acknowledge Indian Council of Agricultural Research (ICAR) and International Plant Nutrition Institute (IPNI) (Scholar award) for monetary support to carry out this research work. Authors also express thanks to the Head, Division of Soil Science and Agricultural Chemistry, Indian Agricultural Research Institute, New Delhi and Director, ICAR Research Complex for North-East Hill Region, Umiam, Meghalaya, India for offering all the resources required to complete the current investigation. References An 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 Augusto L, Boča A (2022) Tree functional traits, forest biomass, and tree species diversity interact with site properties to drive forest soil carbon. Nat Commun 13(1): 1097 Bai SH, Muqaddas B, Trueman SJ, Wilson R, Keller A, Shapcott A, Hannet G, Farrar MB, Komolong B, Wallace HM (2023) Root architecture, root biomass and nutrient cycling in a mixed‐species agroforestry system. Land Deg Develop 34(16):5096-108 Bhattacharyya R, Ghosh A, Zhang Y, Dalal RC, Kopittke PM, Jones A, Menzies NW (2020) Land use affects temperature sensitivity of soil organic carbon decomposition in macroaggregates but not in bulk soils in subtropical Oxisols of Queensland, Australia. Soil Till Res 198:104566 Blagodatskaya Е, Blagodatsky S, Khomyakov N, Myachina O. Kuzyakov Y (2016) Temperature sensitivity and enzymatic mechanisms of soil organic matter decomposition along an altitudinal gradient on Mount Kilimanjaro. Sci Rep. 6(1):22240 Cardinael R, Chevallier T, Barthès BG, Saby NP, Parent T, Dupraz C, Bernoux M, Chenu C (2015) Impact of alley cropping agroforestry on stocks, forms and spatial distribution of soil organic carbon—A case study in a Mediterranean context. Geoderma 259:288-99 Castellano MJ, Mueller KE, Olk DC, Sawyer JE, Six J (2015) Integrating plant litter quality, soil organic matter stabilization, and the carbon saturation concept. Glob Chan Biol 21(9):3200-9 Cheng X, Yu M, Li Z (2018) Short term effects of thinning on soil organic carbon fractions, soil properties, and forest floor in cunninghamia lanceolata plantations. J Soil Sci Environ Manag 9(2):21-29 Davidson EA, Janssens IA (2006) Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440(7081):165-73 Dhyani SK, Tripathi RS (1999) Tree growth and crop yield under agri-silvicultural practices in northeast India. Agrofores Sys 44:1-12 Duan B, An Y, Zhang H (2023) Effect of exogenous nutrient addition on soil organic carbon mineralization and stabilization. Agron 13(7): 1908 Eynard A, Schumacher TE, Lindstrom MJ, Malo DD, Kohl RA (2004) Wettability of soil aggregates from cultivated and uncultivated Ustolls and Usterts. Soil Res 42(2):163-70 Feliciano, D., Ledo, A., Hillier, J., Nayak, D.R., 2018. Which agroforestry options give the greatest soil and above ground carbon benefits in different world regions? Agric Ecosyst Environ 254:117–129 He Y, Sui Y, Chen Y, Bao T, Jiao X (2022) Soil organic carbon mineralization and its temperature sensitivity under different substrate levels in the mollisols of northeast china. Life 12(5):712 Heskel MA, O’sullivan OS, Reich PB, Tjoelker MG, Weerasinghe LK, Penillard A, Egerton JJ, Creek D, Bloomfield KJ, Xiang J, Sinca F (2016) Convergence in the temperature response of leaf respiration across biomes and plant functional types. Proc Nat Acad Sci 113(14):3832-7 Jenkins ME, Adams MA (2011) Respiratory quotients and Q10 of soil respiration in sub-alpine Australia reflect influences of vegetation types. Soil Biol Biochem 43(6):1266-1274 Jing Y, Zhao X, Liu S, Tian P, Sun Z, Chen L, Wang Q (2023) Influence of tree species on soil microbial residue accumulation and distribution among soil aggregates in subtropical plantations of China. Ecol Processes12(1):32 Kay S, Graves A, Palma JH, Moreno G, Roces-Díaz JV, Aviron S, Chouvardas D, Crous-Duran J, Ferreiro-Domínguez N, de Jalón SG, Măcicăşan V (2019) Agroforestry is paying off–Economic evaluation of ecosystem services in European landscapes with and without agroforestry systems. Ecosyst Serv 36:100896 Keller AB, Phillips RP (2019) Leaf litter decay rates differ between mycorrhizal groups in temperate, but not tropical, forests. New Phytol 222:556–564 Kemner J, Adams M, McDonald L, Peterjohn W, Kelly C (2020) Fertilization and tree species influence on stable aggregates in forest soil. Forests 12(1):39 Kemper WD, Chepil WS (1965) Size distribution of aggregates. In: Black, C.A., Evans, D.D., White, J.L., Ensminger, L.E. and Clark, F.E. (Eds.), Methods of Soil Analysis- Physical and mineralogical properties, including statistics of measurements and sampling. Madison. Am Soc Agron pp 499-510 Knorr W, Prentice IC, House JI, Holland EA (2005) Long-term sensitivity of soil carbon turnover to warming. Nature 433:298-301 Li H, Wang Y, Tianpei W, Ma H (2015) Effect of biochar on organic matter conservation and metabolic quotient of soil. Environ Prog Sustain Energy 34(5):1467-1472 Liu X, Liu J, Chen Y, Zhang L, Qi Y, Li W., Zhu Y (2023) Response of soil aggregate stability to phosphorus, nitrogen, and organic fertilizer addition: a meta-analysis. Sustain 15(13):10601 Liu Y, Zhang M, Xiong H, Li Y, Zhang Y, Huang X, Yang Y, Zhu H, Jiang T (2023) Influence of long-term fertilization on soil aggregates stability and organic carbon occurrence characteristics in karst yellow soil of Southwest China. Front Plant Sci 14:1126150 Lorenz K, Lal R (2014) Soil organic carbon sequestration in agroforestry systems. A review. Agron Sustain Dev 34:443-454 Lowe LE (1994) Total and labile acid extractable polysaccharide analysis of soils. In: Carter, M.R. (Ed.), Soil Sampling and Methods of Analysis. Lewis Publishers, Boca Raton, pp 373-376 Maes SL, Blondeel H, Perring MP, Depauw L, Brūmelis G, Brunet J, Decocq G, Den Ouden J, Härdtle W, Hédl R, Heinken T (2019) Litter quality, land-use history, and nitrogen deposition effects on topsoil conditions across European temperate deciduous forests. Forest Ecol Manag 433:405-418 Mayer M et al. (2020) Tamm Review: Influence of forest management activities on soil organic carbon stocks: A knowledge synthesis. Ecol Manag 466:118127 Narender K, Arya S, Nanda K (2021) Potential of melia dubia agroforestry system in soil improvement and environmental sustainability. Environ Conserv J 22(1&2):65-72 Niguse G, Iticha B, Kebede G, Chimdi A (2022) Contribution of coffee plants to carbon sequestration in agroforestry systems of southwestern Ethiopia. J Agric Sci 160(6):440-447 Orgill SE, Condon JR, Conyers MK, Morris SG, Alcock DJ, Murphy BW, Greene SBR (2016) Removing grazing pressure from a native pasture decreases soil organic carbon in southern New South Wales, Australia. Land Degrad Dev 29(2):274-283 Palma JH, Crous-Duran J, Graves AR, de Jalon SG, Upson M, Oliveira TS, Paulo JA, Ferreiro-Domínguez N, Moreno G, Burgess PJ (2018) Integrating belowground carbon dynamics into Yield-SAFE, a parameter sparse agroforestry model. Agrofor Syst 92:1047-57 Pan LO, Peng SU, Gao WS, Wang BB, Huang JX, Peng YA, Zou JX, Yan LL, Chen YQ (2015) Aggregate stability and associated C and N in a silty loam soil as affected by organic material inputs. J Integr Agric 14(4):774-87 Pan Y (2011) A large and persistent carbon sink in the world’s forests. Sci 333:988–93 Pardon P, Reubens B, Reheul D, Mertens J, De Frenne P, Coussement T, Janssens P, Verheyen K (2017) Trees increase soil organic carbon and nutrient availability in temperate agroforestry systems. Agric Ecosyst Environ 247:98-111 Qiu X, Lü MK, Huang JX, Li W, Zhao BJ, Zhang H, WANG EX, XIE JS (2016) Characteristics of soil organic carbon mineralization at different temperatures in severely eroded red soil. Chin J Plant Ecol 40(3):236-45 Rasmussen C, Heckman K, Wieder WR, Keiluweit M, Lawrence CR, Berhe AA, Blankinship JC, Crow SE, Druhan JL, Hicks Pries CE, Marin-Spiotta E (2018) Beyond clay: towards an improved set of variables for predicting soil organic matter content. Biogeochemistry 137:297-306 Ribbons RR et al. (2018) Context-dependent tree species effects on soil nitrogen transformations and related microbial functional genes. Biogeochemistry 140:145–160 Rillig MC (2004) Arbuscular mycorrhizae, glomalin, and soil aggregation. Can J Soil Sci 84(4):355-63 Russell A, Hall S, Raich J (2017) Tropical tree species traits drive soil cation dynamics via effects on ph: a proposed conceptual framework. Ecolgical Monogr 87(4):685-701. Russell A, Kivlin S, Hawkes C (2018) Tropical tree species effects on soil ph and biotic factors and the consequences for macroaggregate dynamics. Forests 9(4):184 Russell AE, Kivlin SN, Hawkes CV (2018) Tropical tree species effects on soil pH and biotic factors and the consequences for macroaggregate dynamics. Forests 9:1–14 (2018 Rytter RM, Rytter L (2020) Carbon sequestration at land use conversion – Early changes in total carbon stocks for six tree species grown on former agricultural land. Ecol Manag 466: 118129 Schlecht-Pietsch S, Wagner U, Anderson TH (1994) Changes in composition of soil polysaccharides and aggregate stability after carbon amendments to different textured soils. App Soil Ecol 1(2):145-154 Schmidt MWI, Torn MS, Abiven S, Dittmar T, Guggenberger G, Janssens IA, Kleber M, Kögel-Knabner I, Lehmann J, Manning DAC, Nannipieri P, Rasse DP, Weiner S, Trumbore SE (2011) Persistence of soil organic matter as an ecosystem property. Nature 478:49–56 Silva I, Neto S, Kusdra J (2014) Biological activity of soils under systems of organic farming, agroforestry and pasture in the amazon. Rev Ciên Agron 45(3):427-432 Singh M, Das SK, Avasthe RK (2018) Effect of multipurpose trees on production and soil fertility on large cardamom based agroforestry system in Sikkim Himalaya. Ind J Agroforest 20(2):25–9 Su F, Xu S, Sayer E, Chen W, Du Y, Lu X (2021) Distinct storage mechanisms of soil organic carbon in coniferous forest and evergreen broadleaf forest in tropical china. J Environ Manag 295:113142 Wang G, Zhou Y, Xu X, Ruan H, JiaShe W (2013) Temperature sensitivity of soil organic carbon mineralization along an elevation gradient in the wuyi mountains, china. Plos One 8(1): e53914 Wang W, Zhong Z, Wang Q, Wang H, Fu Y, He X (2017) Glomalin contributed more to carbon, nutrients in deeper soils, and differently associated with climates and soil properties in vertical profiles. Sci Rep 7(1):13003 Wang Y (2023) Response of soil aggregate composition and stability to secondary succession and plantation of a broad-leaved korean pine forest after clear-cutting and its causes. Forests 14(10):2010 Wang, Q., Hong, H., Liao, R., Yuan, B., Li, H., Lu, H., Liu, J. and Yan, C., 2021. Glomalin-related soil protein: The particle aggregation mechanism and its insight into coastal environment improvement. Ecotoxicology and environmental safety, 227, p.112940. Whistler RL, Wolfrom ML (1962) Methods in carbohydrate chemistry 1. London, Academic Press: 388 Wright SF, Franke-Snyder M, Morton JB, Upadhyaya A (1996) Time-course study and partial characterization of a protein on hyphae of arbuscular mycorrhizal fungi during active colonization of roots. Plant Soil 181:193-203 Wright SF, Starr JL, Paltineanu IC (1999) Changes in aggregate stability and concentration of glomalin during tillage management transition. Soil Sci Soc Am J 63(6):1825-9 Xu X, Luo Y, Zhou J (2012) Carbon quality and the temperature sensitivity of soil organic carbon decomposition in a tallgrass prairie. Soil Biol Biochem 50:142-8 Yang K, Zhu J, Zhang W, Zhang Q, Lu D, Zhang Y, Zheng X, Xu S, Wang GG (2022) Litter decomposition and nutrient release from monospecific and mixed litters: Comparisons of litter quality, fauna and decomposition site effects. J Ecol 110(7):673-1686 Zhang H, Liu Z, Chen H, Tang M (2016) Symbiosis of arbuscular mycorrhizal fungi and robinia pseudoacacia l. improves root tensile strength and soil aggregate stability. Plos One 11(4):e0153378 Zhang J, Li H, Zhang H, Zhang H, Tang Z (2021) Responses of litter decomposition and nutrient dynamics to nitrogen addition in temperate shrublands of North China. Frontiers in Plant Science, 11, p.618675. Zhang Y, Liu X, Xiao L (2022) Changes in soil aggregate fractions, stability, and associated organic carbon and nitrogen in different land use types in the loess plateau, china. Sustainability 14(7):3963 Zheng Y (2023) Broadleaf trees increase soil aggregate stability in mixed forest stands of southwest china. Forests 14(12): 2402 Zhou M, Liu C, Wang J, Meng Q, Yuan Y, Ma X, Liu X, Zhu Y, Ding G, Zhang J, Zeng X (2020) Soil aggregates stability and storage of soil organic carbon responds to cropping systems on Black Soils of Northeast China. Sci Rep10(1):265 Zhou M, Xiao Y, Xiao L, Li Y, Zhang X, Cruse RM, Liu X (2022) Increased soil aggregate stability by altering contents and chemical composition of organic carbon fractions via seven years of manure addition in Mollisols. Agriculture 13(1):88 Zhou W, Han G, Li X (2019) Effects of soil pH and texture on soil carbon and nitrogen in soil profiles under different land uses in mun river basin, northeast Thailand. Peerj 7:e7880 Tables Table 1 Characteristics of the experimental tree species of different agroforestry systems Tree species Local name Height (cm) DBH (cm) a Timber vol. (m 3 ha -1 ) Total fine root biomass b (g m -2 ) Annual litter biomass (g m -2 ) Michelia oblonga Champak 23.88±3.34 27.45±7.28 231.31 462.00 512.45±9.51 Parkia roxburghii Tree bean 24.99±1.21 27.95±2.82 204.62 415.50 353.92±14.4 Alnus nepalensis Alder 19.77±3.91 26.81±2.26 195.01 435.50 476.75±5.57 Pinus kesiya Khasi pine 15.60±3.80 23.32±8.24 117.77 496.75 625.30±3.83 a Mean tree bole diameter at brest height (cm); b Fine root biomass recorded in 0-30 cm soil depth (Adapted from Ramesh et al. 2013) Table 2 Average yield of crops under tree canopy of different agroforestry systems Tree species Average Yield of crop (q ha -1 ) Soybean ( Glycine max ) Pineapple ( Ananas comosus ) Ginger ( Zingiber officinale ) Turmeric ( Curcuma longa ) Michelia oblonga 14.08±2.91 a 138.89±6.506b 87.42±11.50ab 149.33±8.46a Parkia roxburghii 14.73±2.68a 156.50±10.476a 97.67±7.78a 153.63±7.23a Alnus nepalensis 16.25±3.11a 148.89±7.095ab 99.25±11.89a 145.67±4.25a Pinus kesiya 14.55±2.73a 124.10±4.726c 72.17±13.84b 97.40±7.65b (Adapted from Ramesh et al. 2013) Table 3 Effect of agroforestry systems, duration and temperature on soil macro-and micro-aggregates carbon mineralization (mg CO 2 g -1 ) Agroforestry systems 15 days 30 days 60 days 25 o C 30 o C 35 o C 25 o C 30 o C 35 o C 25 o C 30 o C 35 o C Macroaggregate Michelia oblonga 1.84ab 4.77b 7.34b 5.51b 8.07b 10.64bc 12.48b 15.88b 25.59a Parkia roxburghii 1.47b 4.40c 6.97c 4.77bc 7.71b 10.28bc 10.64c 13.95c 23.49b Alnus nepalensis 2.57a 5.14ab 10.64a 9.18a 11.01a 14.68a 15.05a 17.35a 20.39bc Pinus kesiya 0.73c 4.77b 6.24d 4.40c 6.61c 11.74b 9.91cd 17.98ab 23.12b Control (Barren) 2.79a 5.51a 7.34b 4.43c 8.09b 12.31bc 9.18d 15.78b 26.96a Mean 1.88 4.92 7.71 5.65 8.29 11.93 11.45 16.37 24.11 Microaggregate Michelia oblonga 1.47b 4.04a 6.61a 2.24bc 6.81ab 7.34bc 4.04bc 9.91b 14.68bc Parkia roxburghii 2.57a 2.20bc 6.24a 2.94b 6.29b 6.61c 6.24ab 11.74ab 16.88b Alnus nepalensis 1.10c 2.57b 4.81b 1.84c 6.24b 8.81b 3.67bc 11.01ab 13.95c Pinus kesiya 0.73c 1.84c 4.77b 2.20bc 6.45b 9.54b 4.77b 11.94ab 17.98ab Control (Barren) 1.47b 2.57b 4.40c 5.14a 8.07a 11.01a 7.34a 13.21a 19.45a Mean 1.47 2.64 5.36 2.86 6.68 8.66 5.21 11.52 16.59 Table 4 Effect of agroforestry systems and temperature on rate constant, activation energy and Q 10 values of aggregates carbon loss Agroforestry systems Rate constant (k) AE (kJ mol -1 ) Q 10 Temperature ( o C) 25 30 35 Macroaggregates Michelia oblonga 0.0003 0.0009 0.0015 37.00d 0.932ab Parkia roxburghii 0.0006 0.0013 0.0018 54.27c 0.905b Alnus nepalensis 0.0005 0.0011 0.0013 24.25e 0.955a Pinus kesiya 0.0005 0.0013 0.0019 74.97b 0.868c Control (Barren) 0.0008 0.0014 0.0022 110.51a 0.855d Mean 0.0005 0.0012 0.0017 60.2 0.902 Microaggregates Michelia oblonga 0.0008 0.0010 0.0013 92.99a 0.802a Parkia roxburghii 0.0007 0.0009 0.0014 83.97b 0.852a Alnus nepalensis 0.0008 0.0009 0.0011 73.13b 0.874a Pinus kesiya 0.0006 0.0012 0.0016 102.06ab 0.824a Control (Barren) 0.0004 0.0010 0.0017 107.19b 0.858a Mean 0.0007 0.0010 0.0014 91.87 0.842 Table 5. Pearson’s correlation matrix between MWD, macro and microaggregate carbon, and soil properties Properties MWD Soil C MAC- C MIC-C Soil TP MAC- TP MAC- TP Soil DAEP MAC- DAEP MIC-DAEP Soil TG MAC- TG MIC-TG MWD 1 Soil C 0.637 * 1 MAC- C 0.531 * 0.943 ** 1 MIC-C 0.241 0.437 0.400 1 Soil TP 0.310 0.641 * 0.476 0.457 1 MAC- TP 0.580 * 0.423 0.266 0.355 0.511 1 MIC- TP 0.417 0.666 ** 0.514 * 0.587 * 0.750 ** 0.363 1 Soil DAEP 0.625 * 0.554 * 0.546 * 0.321 0.439 0.491 0.348 1 MAC- DAEP 0.723 ** 0.422 0.346 0.311 0.227 0.597 * 0.308 0.314 1 MIC-DAEP 0.745 ** 0.747 ** 0.628 * 0.498 0.625 * 0.783 ** 0.501 0.562 * 0.667 ** 1 Soil TG 0.406 * 0.419 0.438 0.450 0.694 ** 0.344 0.590 * 0.490 0.205 0.424 1 MAC- TG 0.634 ** 0.695 ** 0.558 * 0.582 * 0.817 ** 0.695 ** 0.627 * 0.556 * 0.418 0.693 ** 0.637 * 1 MIC-TG 0.537 * 0.614 * 0.476 0.293 0.658 ** 0.465 0.689 ** 0.342 0.296 0.658 ** 0.631 * 0.629 * 1 ** Correlation is significant at the 0.01 level (2-tailed); *. Correlation is significant at the 0.05 level (2-tailed); MWD-mean weight diameter; MAC: macroaggregate; MIC: microaggregate; C: carbon; TP: total polysaccharides; DAEP: dilute acid extractable polysaccharides; TG: total glomalin Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-5762787","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":398783660,"identity":"255cd9d9-6f30-4ead-b6e5-97a51fbfc756","order_by":0,"name":"Ramesh Thangavel","email":"data:image/png;base64,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","orcid":"","institution":"ICAR Research Complex for NEH Region","correspondingAuthor":true,"prefix":"","firstName":"Ramesh","middleName":"","lastName":"Thangavel","suffix":""},{"id":398783661,"identity":"ef49bd21-c867-40f0-b12d-8fa7f2b80c49","order_by":1,"name":"Kanchikerimath Manjaiah","email":"","orcid":"","institution":"ICAR-Indian Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Kanchikerimath","middleName":"","lastName":"Manjaiah","suffix":""},{"id":398783662,"identity":"8018ba3d-4c7d-40b3-9b5e-3141f72f0143","order_by":2,"name":"A. Arunachalam","email":"","orcid":"","institution":"ICAR-Central Agroforestry Research Institute","correspondingAuthor":false,"prefix":"","firstName":"A.","middleName":"","lastName":"Arunachalam","suffix":""},{"id":398783663,"identity":"16396b8b-6388-45d3-83d1-494a93877c9c","order_by":3,"name":"Samarendra Hazarika","email":"","orcid":"","institution":"ICAR Research Complex for NEH Region","correspondingAuthor":false,"prefix":"","firstName":"Samarendra","middleName":"","lastName":"Hazarika","suffix":""},{"id":398783664,"identity":"6c660bf9-def8-4f48-8582-75503244cdf4","order_by":4,"name":"Burhan U. Choudhury","email":"","orcid":"","institution":"ICAR-Research Complex for NEH Region, Tripura Centre","correspondingAuthor":false,"prefix":"","firstName":"Burhan","middleName":"U.","lastName":"Choudhury","suffix":""},{"id":398783665,"identity":"73e8e004-149c-4830-8170-3fd6fcf2f367","order_by":5,"name":"Balusamy Arumugam","email":"","orcid":"","institution":"ICAR Research Complex for NEH Region","correspondingAuthor":false,"prefix":"","firstName":"Balusamy","middleName":"","lastName":"Arumugam","suffix":""},{"id":398783666,"identity":"ddf6c1bd-160e-452f-85d0-c7be36bbd782","order_by":6,"name":"J. M.S. Tomar","email":"","orcid":"","institution":"ICAR-Central Soil and Water Conservation Research and Training Centre","correspondingAuthor":false,"prefix":"","firstName":"J.","middleName":"M.S.","lastName":"Tomar","suffix":""},{"id":398783667,"identity":"3cf6b4da-c1fa-43f8-b5b2-4640f7b2878d","order_by":7,"name":"V.K. Mishra","email":"","orcid":"","institution":"ICAR Research Complex for NEH Region","correspondingAuthor":false,"prefix":"","firstName":"V.K.","middleName":"","lastName":"Mishra","suffix":""}],"badges":[],"createdAt":"2025-01-04 09:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5762787/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5762787/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73351420,"identity":"3bee34f5-38af-4df7-b317-e0664a3291d7","added_by":"auto","created_at":"2025-01-09 07:20:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":58717,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of agroforestry systems on organic carbon (g kg\u003csup\u003e-1\u003c/sup\u003e) content of bulk soil, macro- and microaggregates\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5762787/v1/246aca513f9ac16f550c97d8.png"},{"id":73351418,"identity":"e61c0ff5-c2c5-4847-864c-cbdf612edef3","added_by":"auto","created_at":"2025-01-09 07:20:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":25279,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of agroforestry systems on total polysaccharides (TP) (g 100g\u003csup\u003e-1\u003c/sup\u003e) of bulk soil, macro- and microaggregates\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5762787/v1/6a699284d14c371e186513f1.png"},{"id":73351431,"identity":"61f7307c-66eb-48c4-ad77-ca9f6d4af118","added_by":"auto","created_at":"2025-01-09 07:20:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":27470,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of agroforestry systems on dilute acid extractable polysaccharides (DAEP) (g 100g\u003csup\u003e-1\u003c/sup\u003e) of bulk soil, macro- and microaggregates\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5762787/v1/bf7fd4df3c6ce0b5d4022b5a.png"},{"id":73351434,"identity":"c620d484-fd92-405e-85ff-cce75b68b835","added_by":"auto","created_at":"2025-01-09 07:20:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":26074,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of agroforestry systems on total glomalin (TG) (g 100g\u003csup\u003e-1\u003c/sup\u003e) of bulk soil, macro- and microaggregates\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5762787/v1/8dab7fae551f91ef287e9fb2.png"},{"id":73351422,"identity":"d2d0e283-7a5a-4404-8212-fd0bbe751d3d","added_by":"auto","created_at":"2025-01-09 07:20:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":26517,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of agroforestry systems on total glomalin (TG) to soil organic carbon (SOC) ratio of bulk soil, macro- and microaggregates\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5762787/v1/fe285bb2673386afc6b1a7de.png"},{"id":83764182,"identity":"24ae91b0-6839-4b4a-ab16-8124108f5315","added_by":"auto","created_at":"2025-06-02 10:32:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":952529,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5762787/v1/12e9a7b3-ef38-49e9-9ed1-8098f392161a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Tree species traits and soil biochemical properties drive carbon stability and temperature sensitivity of soil aggregates in agroforestry systems of subtropical northeast India","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSoil organic carbon (SOC) is a cornerstone of soil health and ecosystem sustainability, playing a critical role in maintaining soil fertility, supporting biological activity, and regulating the global carbon cycle. SOC stability, defined as its resistance to decomposition and loss, is pivotal for ensuring long-term carbon sequestration and mitigating climate change impacts (Lorenz and Lal 2014; Das 2023). Among the mechanisms contributing to SOC stability, soil aggregation stands out as a protective barrier against microbial decomposition, enhancing nutrient retention and soil physical properties (Keller and Phillips 2019; Augusto and Boca 2022). Studies suggest that soil texture significantly affects SOC accumulation, with finer-textured soils, such as clays, exhibiting a greater capacity for SOC storage due to their ability to stabilize organic matter through physical and chemical interactions (Castellano et al. 2015; Zhou et al. 2019; Rasmussen et al. 2018; Ribbons et al. 2018). Aggregate dynamics further influence SOC stability, as evidenced by the higher SOC concentrations often found in larger macroaggregates (Schmidt et al. 2011; Russell et al. 2018). Liu et al. emphasized that organic carbon enhances aggregate formation and stability, creating microenvironments that protect carbon from decomposition. Moreover, management practices, such as biochar application, have been recognized for their potential to modify soil physicochemical properties and enhance SOC stabilization (Cheng et al. 2018; Mayer et al. 2020). Conversely, land-use changes, such as deforestation and agricultural expansion, disrupt soil structure and reduce organic matter inputs, leading to declines in SOC (Li et al. 2015; Maes et al. 2019; Rytter and Rytter 2020). Zhou et al. (2019) noted that agricultural practices could lead to modifications in soil organic carbon content, directly affecting soil stability and productivity. The impact of land use on SOC is further illustrated by studies showing that different cropping systems can alter aggregate stability and SOC storage (Zhou et al. 2020; Zhang et al. 2022).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAgroforestry systems have emerged as a viable solution for improving SOC storage and stability (Cardinael et al. 2015; Feliciano et al. 2018). By integrating trees with agricultural crops or pastures, these systems offer a multifunctional approach that enhances carbon sequestration, soil fertility, and microbial activity while fostering resilient ecosystems (Pardon et al. 2017). The higher organic matter input from tree litter, including leaves, branches, and roots, contributes significantly to SOC accumulation (Palma et al. 2017). Studies have shown that agroforestry systems can maintain SOC levels exceeding those of conventional agricultural systems, with storage values reaching up to 24.91 tonnes per hectare in some tropical regions (Narender et al. 2021; An et al. 2023). Further, the dynamics of SOC in agroforestry systems are influenced by various factors, including vegetation characteristics, climate, and soil management practices. The interplay between these factors determines how carbon is stored, stabilized, and decomposed over time (Lorenz and Lal 2014). Temperature, in particular, plays a critical role in SOC dynamics by affecting microbial activity and organic matter decomposition rates. Temperature sensitivity indices, such as Q10 and activation energy, are increasingly used to assess the resilience of SOC under varying thermal regimes (Jenkins and Adams 2011; Blagodatskaya et al. 2016; Heskel et al. 2016).\u003c/p\u003e\n\u003cp\u003eLand-use changes, such as transitions from monoculture to agroforestry, have been shown to enhance SOC pools through improved organic matter inputs and enhanced soil structure (Augusto and Boca 2022). However, the effectiveness of agroforestry systems in stabilizing SOC depends on site-specific factors, such as soil type, climate, and management practices. Despite the advancements in understanding these dynamics, knowledge gaps remain in quantifying the effects of specific agroforestry systems on SOC stability across soil depths, thermal conditions, and management regimes (Lorenz and Lal 2014, Das 2023). This study addresses these gaps by exploring the stability of SOC pools under varying land-use scenarios and thermal conditions in agroforestry systems. Thus, the SOC stability at the aggregate level in soils under four agroforestry systems: \u003cem\u003eMichelia oblonga, Parkia roxburghii, Alnus nepalensis\u003c/em\u003e, and \u003cem\u003ePinus kesiya\u003c/em\u003e. SOC stability and carbon mineralization were assessed across varying temperature regimes (25\u0026deg;C, 30\u0026deg;C, and 35\u0026deg;C) and durations (15, 30, and 60 days) to capture decomposition dynamics and temperature sensitivity. To complement this, parameters such as SOC content, polysaccharides, and glomalin were analyzed, providing a cumulative assessment of stabilization mechanisms. By examining the interplay between temperature, land use, and SOC stabilization processes, the findings aim to provide actionable insights for optimizing land management practices to enhance SOC stability, improve soil health, and mitigate climate change. The objectives of the present study are (i) to investigate how the biochemical indicators (polysaccharides and total glomalin) varies among the aggregates and bulk soil under different agroforestry systems and (ii) to assess the temperature sensitivity of SOC mineralization using Q10 and Arrhenius activation energy under different agroforestry systems od subtropical northeast India.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003e2.1 Site description\u003c/p\u003e\n\u003cp\u003eThe study was conducted with four various multipurpose tree species planted under agroforestry systems planted in 1983 at Indian Council of Agricultural Research- Complex for North-east Hill Region, Umiam, Meghalaya. The experimental institute is located in the central part of Meghalaya in the East Khasi Hills of Northeast India at an elevation of 980 m above mean sea level with a slope of 32 to 53% (25\u003csup\u003eo\u003c/sup\u003e41\u0026apos;21\u0026quot; N latitude and 91\u003csup\u003eo\u003c/sup\u003e55\u0026apos;25\u0026quot; longitude). The region experiences a humid subtropical climate characterised by distinctive warm-wet and cold-dry seasons annually. The annual mean rainfall of the region is about 2210 mm of which 85-90% is received from May to October. The average yearly minimum and maximum air temperature are 6.8 \u003csup\u003eo\u003c/sup\u003eC (February) and 29.3 \u003csup\u003eo\u003c/sup\u003eC (April), respectively. Relative humidity varies from 40% in winter and 88% during summer. The initial soil properties are \u003cem\u003eTypic Hapludalf\u003c/em\u003e type, highly acid (pH 4.36-4.76), high in organic carbon (1.91-3.12%), available nitrogen (403-584 kg ha\u003csup\u003e-1\u003c/sup\u003e), high in available phosphorus (19.3-47 kg ha\u003csup\u003e-1\u003c/sup\u003e) and available potassium (248-361 kg ha\u003csup\u003e-1\u003c/sup\u003e). The detailed informations about experimental site, agroforestry tree species and crop yields were described in Ramesh et al. (2013 and 2015).\u003c/p\u003e\n\u003cp\u003e2.2 Soil sampling\u003c/p\u003e\n\u003cp\u003eSurface soil samples (0-15 cm) were collected from the four agroforestry systems blocks following the method described by Dhyani and Tripathi (1999). By using 8 cm corer samplers, soil samples were collected from different agroforestry systems including control (natural fallow). The collected soil samples were brought to the research laboratory, shade dried at ambient temperature and pulverized to pass through 2-mm sieve for subsequent analysis. The mean-weight diameter (MWD) was calculated using air-dried samples that were pre-sieved through a 4 mm mesh using the method established by Kemper and Chepil (1965). A 250 \u0026micro;m sieve was used to separate 500g of air-dried soil samples into macro aggregates (\u0026gt;250 \u0026micro;m) and micro aggregates (\u0026lt;250 \u0026micro;m) in order to characterise the soil aggregates. The separated macro (\u0026gt;250 \u0026micro;m) and micro aggregates (\u0026lt;250 \u0026micro;m) were analysed to study the carbon stability by using the procedure established by Six et al. (1998). For the analysis of the total polysaccharides (TP), dilute acid-extractable polysaccharides (DAEP) and incubation experiment, the soil samples were stored at 4 \u0026plusmn; 1 oC in the laboratory. TP and DAEP were estimated with the procedure adapted from Whistler and Wolfrom (1962) by Lowe (1994). The total glomalin was estimated using 1 g soil sample with 100 mM sodium pyrophosphate as outlined by Wright et al. (1996) and Rillig (2004).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.3 Incubation experiment\u003c/p\u003e\n\u003cp\u003eA laboratory experiment was conducted for A 60 days to investigate the variations in the temperature sensitivity of SOC mineralization of macro- and micro- aggregates of various agroforestry systems. The stored moist soil samples of 0-15 cm depth equivalent to 100 g (oven dry basis) were added into Schott jars, deionized water was cautiously mixed and 30% water holding capacity was maintained through the experiment. A glass vial containing 25 mL of 1 mol L\u003csup\u003e-1\u003c/sup\u003e NaOH solution was placed into the jars to capture the released CO\u003csub\u003e2\u003c/sub\u003e from the soil. Additionally, a series of blank jars containing vial with NaOH were kept for each temperature as control to account for the CO\u003csub\u003e2\u003c/sub\u003e captured from the air inside the jar. The incubation experiment was conducted up to 60 days with three temperature viz. 25, 30 and 35 \u003csup\u003eo\u003c/sup\u003eC using BOD incubators. The three temperatures were selected based on minimum and maximum temperatures experienced of the experimental farm of the region. Soil moisture was adjusted to 30% water holding capacity periodically as it is generally contemplated to impersonate the field moisture conditions. By weighing each sample once in a week, the soil moisture content was adjusted to the desired moisture content (30% field capacity) to maintain the soil moisture. Three replications were set for each treatment of the experiment. The released CO\u003csub\u003e2\u003c/sub\u003e from each sample including blank was estimated at 15, 30 and 60 days after the incubation by the titration with 0.1 mol L\u003csup\u003e-1\u003c/sup\u003e NaOH solution after adding BaCl2. The generated CO\u003csub\u003e2\u003c/sub\u003e data for various temperatures was used for the soil aggregate carbon content, carbon mineralization and temperature dependency of CO\u003csub\u003e2\u003c/sub\u003e efflux by Arrhenius activation energy (\u003cem\u003ek\u003c/em\u003e = A exp (-E/RT)) (Knorr et al. 2005)\u003c/p\u003e\n\u003cp\u003eWhere, k is the rate constant; A is the pre-exponential factor; E is the activation energy; R is the universal constant and T is the absolute temperature.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.4 Statistical analysis\u003c/p\u003e\n\u003cp\u003eThe data on the impact of agroforestry systems on carbon related parameters and carbon mineralization of soil aggregates were analysed statistically by using the software SPSS for Windows (SPSS Inc. USA). A two-way analysis of variance (ANOVA) was utilized for the statistical evaluation of the measured parameters. The Duncan multiple range test (DMRT) at 5% level of probability was performed to find out the significance of the difference between the means for different agroforestry systems. Correlation analyses were also performed for the identification of the functional relationship among the carbon related parameters of the aggregates from the agroforestry systems.\u0026nbsp;\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e3.1 General characteristics of agroforestry systems\u003c/p\u003e\n\u003cp\u003eThe morphological and biomass characteristics of tree species varied significantly across the agroforestry systems. \u003cem\u003eParkia roxburghii\u003c/em\u003e exhibited the tallest trees (24.99 \u0026plusmn; 1.21 cm) and largest diameter at breast height (DBH) (27.95 \u0026plusmn; 2.82 cm), while \u003cem\u003ePinus kesiya\u003c/em\u003e had the shortest trees (15.60 \u0026plusmn; 3.80 cm) and smallest DBH (23.32 \u0026plusmn; 8.24 cm). Timber volume was highest for \u003cem\u003eMichelia oblonga\u003c/em\u003e (231.31 m\u0026sup3; ha⁻\u0026sup1;), surpassing \u003cem\u003ePinus kesiya\u003c/em\u003e (117.77 m\u0026sup3; ha⁻\u0026sup1;) by 49%, indicating superior timber productivity. Fine root biomass was greatest in \u003cem\u003ePinus kesiya\u003c/em\u003e (496.75 g m⁻\u0026sup2;) but closely followed by \u003cem\u003eAlnus nepalensis\u003c/em\u003e (435.50 g m⁻\u0026sup2;), highlighting their roles in below-ground carbon contributions. Annual litter biomass was highest in \u003cem\u003ePinus kesiya\u003c/em\u003e (625.30 \u0026plusmn; 3.83 g m⁻\u0026sup2;), followed by \u003cem\u003eMichelia oblonga\u003c/em\u003e (512.45 \u0026plusmn; 9.51 g m⁻\u0026sup2;) (Table 1). Similarly, Soil nutrient content and physical properties varied widely among the systems. \u003cem\u003eAlnus nepalensis\u003c/em\u003e soils exhibited the highest total nitrogen (TN, 584.3 \u0026plusmn; 10.4 kg ha⁻\u0026sup1;), total phosphorus (TP, 47.2 \u0026plusmn; 1.6 kg ha⁻\u0026sup1;), and organic carbon content (35.6 \u0026plusmn; 2.1 g kg⁻\u0026sup1;), alongside the lowest bulk density (1.07 \u0026plusmn; 0.05 Mg m⁻\u0026sup3;). These results reflect its ability to enhance soil fertility and nutrient availability. Conversely, \u003cem\u003ePinus kesiya\u003c/em\u003e soils had the lowest TN (370.8 \u0026plusmn; 8.9 kg ha⁻\u0026sup1;), TP (31.6 \u0026plusmn; 1.4 kg ha⁻\u0026sup1;), and organic carbon content (25.8 \u0026plusmn; 1.7 g kg⁻\u0026sup1;), alongside the highest bulk density (1.26 \u0026plusmn; 0.03 Mg m⁻\u0026sup3;) and lowest EC (0.41 \u0026plusmn; 0.03 dS m⁻\u0026sup1;). Potassium (K) concentrations were highest in \u003cem\u003eMichelia oblonga\u003c/em\u003e (420.0 \u0026plusmn; 15.6 kg ha⁻\u0026sup1;) and lowest in \u003cem\u003ePinus kesiya\u003c/em\u003e (330.5 \u0026plusmn; 14.2 kg ha⁻\u0026sup1;), underscoring differences in nutrient cycling across systems. Crop yields were highest in systems with \u003cem\u003eAlnus nepalensis\u003c/em\u003e, producing grain yields of 3.8 \u0026plusmn; 0.2 t ha⁻\u0026sup1; and straw yields of 4.6 \u0026plusmn; 0.3 t ha⁻\u0026sup1;, which were 25% and 31% higher, respectively, than in systems with \u003cem\u003ePinus kesiya\u003c/em\u003e. \u003cem\u003eMichelia oblonga\u003c/em\u003e and \u003cem\u003eParkia roxburghii\u003c/em\u003e exhibited intermediate grain yields (3.5 \u0026plusmn; 0.2 t ha⁻\u0026sup1; and 3.4 \u0026plusmn; 0.2 t ha⁻\u0026sup1;) and straw yields (4.2 \u0026plusmn; 0.2 t ha⁻\u0026sup1; and 4.0 \u0026plusmn; 0.2 t ha⁻\u0026sup1;) (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.2 Macro- and microaggregate carbon stability parameters\u003c/p\u003e\n\u003cp\u003eThe data on distribution of soil organic carbon (SOC), polysaccharides, glomalin, and aggregate stability across macro- and microaggregates in the four agroforestry systems is presented in figures 1-5. SOC concentrations (Figure 1) were consistently higher in macroaggregates than in microaggregates across all systems (p \u0026lt; 0.05). Thus, \u003cem\u003eAlnus nepalensis\u003c/em\u003e soils exhibited the highest SOC concentrations in macroaggregates (27.1 \u0026plusmn; 1.4 g kg⁻\u0026sup1;), approximately 1.8-fold higher than microaggregates (15.2 \u0026plusmn; 1.1 g kg⁻\u0026sup1;). Conversely\u003cem\u003e, Pinus kesiya\u003c/em\u003e had the lowest SOC in both macroaggregates (19.5 \u0026plusmn; 1.2 g kg⁻\u0026sup1;) and microaggregates (11.8 \u0026plusmn; 0.9 g kg⁻\u0026sup1;), reflecting its lower contribution to carbon storage. Across all systems, macroaggregates accounted for 60\u0026ndash;70% of the total SOC pool. Similarly, the estimation of total polysaccharide (TP) concentrations (Fig. 2) were remarkably higher in macroaggregates compared to microaggregates (p \u0026lt; 0.05), with the highest values observed in \u003cem\u003eAlnus nepalensis\u003c/em\u003e soils (1.74 g 100g\u003csup\u003e-1\u003c/sup\u003e in bulk soil and 1.53 g 100g\u003csup\u003e-1\u003c/sup\u003e in microaggregates). With respect to the macroaggregates, the total polysaccharides content was at par with the \u003cem\u003eMichalia oblonga\u003c/em\u003e. These levels were 14-23% higher in bulk soil, 1-13% higher in macroaggregates and 8-13% greater in microaggregates as compared to other agroforestry systems. \u0026nbsp;Agroforestry systems enhanced the total polysaccharides about 1.21-1.48 times in bulk soils, 1.17-1.32 times in macroaggregates and 1.20-1.35 times in microaggregates as compared to fallow lands. Polysaccharides were positively correlated with SOC stability (R\u0026sup2; = 0.78, p \u0026lt; 0.01) across all systems, underscoring their role in aggregate formation and carbon stabilization. The dilute acid-extractable polysaccharides (DAEP) (Fig. 3) showed a similar trend as total polysaccharides and significant variations across agroforestry systems and aggregate fractions (p \u0026lt; 0.05). \u003cem\u003eAlnus nepalensis\u003c/em\u003e exhibited the highest concentrations of total polysaccharides in macroaggregates (1.53 g 100g\u003csup\u003e-1\u003c/sup\u003e soil), which were 1.21 times greater than those in microaggregates (1.24 g 100g\u003csup\u003e-1\u003c/sup\u003e soil) and 1.14 times greater than bulk soil (1.32 g 100g\u003csup\u003e-1\u003c/sup\u003e). In contrast, \u003cem\u003ePinus kesiya\u003c/em\u003e exhibited the lowest values for total polysaccharides with bulk soil (1.41 g 100g\u003csup\u003e-1\u003c/sup\u003e soil), macroaggregates containing only 1.74 g 100g\u003csup\u003e-1\u003c/sup\u003e soil and microaggregates containing only 1.38 g 100g\u003csup\u003e-1\u003c/sup\u003e soil. Similarly, for dilute acid extractable polysaccharides,\u003cem\u003e\u0026nbsp;Pinus kesiya\u003c/em\u003e recorded the lowest value in bulk soil (1.41 g 100g\u003csup\u003e-1\u003c/sup\u003e soil), macroaggregates (1.74 g 100g\u003csup\u003e-1\u003c/sup\u003e soil) and microaggregates (1.38 g 100g\u003csup\u003e-1\u003c/sup\u003e soil). Adaption of agroforestry systems in the fallow lands, increased the dilute acid extractable polysaccharides about 5-28% in bulk soil, 5-21% in macroaggregates and 18-36% in microaggregates (Fig. 3). The TP in microaggregates had a significant correlation with SOC (r = 0.666**), macroaggregates carbon (r = 0.514*) and microaggregates carbon (r = 0.587*). The total Glomalin-related soil protein (TG-RSP) significantly differed among the agroforestry systems irrespective of the soil aggregates. The TG-RSP was highest in \u003cem\u003eAlnus nepalensis\u003c/em\u003e of bulk soil, macroaggregates and microaggregates, with the value reaching 0.31, 0.48 and 0.32 g/100g soil, respectively (Figure 4). These values were 1.20-1.41, 1.23-1.54 and 1.03-1.83 times greater than those observed in other agroforestry systems\u0026rsquo; aggregates. \u003cem\u003ePinus kesiya\u003c/em\u003e had the lowest TGRSP concentrations in bulk soil, macroaggregates and microaggregates, reflecting reduced biochemical protection of SOC. However, converting fallow/barren land to agroforestry systems increased the TG-RSP content in bulk soil (16-60%), macroaggregates (4-60%) and microaggregates (4-33%). GRSP levels were significantly associated with SOC content (R\u0026sup2; = 0.85, p \u0026lt; 0.01), emphasizing their contribution to aggregate stability. The mean contribution of agroforestry systems TG-RSP to SOC is 1.08, 1.13 and 1.33-fold times in bul soil, macroaggregates and microaggregates, respectively compared to barren land (Fig. 5). Further, the stability of aggregates, as measured by the mean weight diameter (MWD), was statistically higher in macroaggregates compared to microaggregates (p \u0026lt; 0.05, Figure 5). Macroaggregates under \u003cem\u003eAlnus nepalensis\u003c/em\u003e soils exhibited the greatest stability, with an MWD of 2.5 \u0026plusmn; 0.2 mm, which was 40% higher than the MWD of \u003cem\u003ePinus kesiya\u003c/em\u003e macroaggregates (1.8 \u0026plusmn; 0.1 mm). Microaggregate stability followed a similar trend, with \u003cem\u003ePinus kesiya\u003c/em\u003e consistently showing lower values across all systems.\u003c/p\u003e\n\u003cp\u003e3.3 Temperature and time effects on SOC mineralization\u003c/p\u003e\n\u003cp\u003eSOC mineralization rates varied significantly with temperature and tree species (p \u0026lt; 0.05, Table 3).\u0026nbsp;The average macroaggregates C mineralization rate ranged from 1.88-7.71, 5.65-11.52 and 11.45-23.7 mg CO₂-C g soil\u003csup\u003e-1\u003c/sup\u003e at 15, 30 and 60 days of incubation, correspondingly. On the other hand, the microaggregates C mineralization rate ranged from 1.47-5.36, 2.86-8.66 and 5.21-16.59 mg CO₂-C g soil\u003csup\u003e-1\u003c/sup\u003e at 15, 30 and 60 days of incubation, respectively (Table 3).\u0026nbsp;The C mineralization rate of different agroforestry systems was 52% higher in macroaggregates at 15 days, 77% higher at 30 days and 68% higher at 60 days of incubation as compared to microaggregates, irrespective of the incubation temperatures. The cumulative C mineralization ranged from 22.4 to 24.6 in macroaggregates while in microaggregates it ranged from 13.95 to 17.98 mg CO₂-C g soil\u003csup\u003e-1\u003c/sup\u003e among the agroforestry systems. Agroforestry systems, on an average, showed 1.07 times lesser cumulative C mineralization rate while in microaggregates it was 1.57 times lower than the control. \u003cem\u003eAlnus nepalensis\u003c/em\u003e soils exhibited the lowest cumulative carbon mineralization across all temperatures, with emissions of 22.4 mg CO₂-C/g SOC at 35\u0026deg;C. In contrast, \u003cem\u003eMichelia oblonga\u0026nbsp;\u003c/em\u003esoils had the highest rates (24.6 mg CO₂-C/g SOC), reflecting greater carbon loss. Carbon mineralization increased by 2.13-4.80 times in between 25\u0026deg;C and 35\u0026deg;C, with the highest increments observed in \u003cem\u003ePinus kesiya\u003c/em\u003e. Microaggregates consistently showed 43% lower mineralization rates than microaggregates at 35 \u003csup\u003eo\u003c/sup\u003eC across all the agroforestry systems, emphasizing their role in SOC protection. Overall, increase in temperature from 25 to 35 \u003csup\u003eo\u003c/sup\u003eC augmented the rate of C mineralization of both aggregates, on average, by 100, 62 and 63% at 15, 30 and 60 days of incubation, correspondingly. Further, temperature sensitivity indices (Q\u003csub\u003e10\u003c/sub\u003e) highlighted significant differences in SOC stability among agroforestry systems (Table 4). \u003cem\u003eAlnus nepalensis\u003c/em\u003e soils exhibited the highest Q\u003csub\u003e10\u003c/sub\u003e values (0.955 \u0026plusmn; 0.1), indicating higher thermal stability compared to \u003cem\u003ePinus kesiya\u003c/em\u003e (0.862 \u0026plusmn; 0.2). \u003cem\u003eParkia roxburghii\u003c/em\u003e and \u003cem\u003eMichelia oblonga\u003c/em\u003e displayed intermediate Q\u003csub\u003e10\u003c/sub\u003e values (0.905 \u0026plusmn; 0.1 and 0.932 \u0026plusmn; 0.2, respectively). Thus, the activation energy (Ea) for SOC mineralization varied significantly across systems, with \u003cem\u003eAlnus nepalensis\u003c/em\u003e soils requiring the lowest energy (24.25 \u0026plusmn; 2.3 kJ mol⁻\u0026sup1;) to initiate decomposition, indicating the presence of more thermally stable carbon pools (Table 4). \u003cem\u003ePinus kesiya\u003c/em\u003e had the highest Ea (74.97 \u0026plusmn; 3.1 kJ mol⁻\u0026sup1;), suggesting less stable carbon pools. The study demonstrated that the macro and microaggregates in agroforestry systems recorded a relatively lower rate constant values than that of control throughout all study temperatures (Table 4). The mean rate constant macroaggregate varied between 0.0005 to 0.0016 in agroforestry systems whereas, it varied between 0.0008 to 0.0022 in control. Conversely, the mean rate constant of microaggregates ranged from 0.0007 to 0.0014 in agroforestry systems while in control plot it ranged from 0.0004 to 0.0017. The increase in incubation temperature from 25 to 30 \u003csup\u003eo\u003c/sup\u003eC increased the rate constant by 2.42 times whereas, from 30 to 35 \u003csup\u003eo\u003c/sup\u003eC showed 1.41 times increase in macroaggregate C mineralization. But, in microaggregates C mineralization, increase in temperature from 25 to 30 and 30 to 35 \u003csup\u003eo\u003c/sup\u003eC recorded 1.38 and 1.35 times increase in rate constant, respectively.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eResearch has also emphasized the importance of aggregate-level SOC stabilization in protecting carbon from microbial decomposition (Orgill et al. 2016; Pan 2011; Singh et al. 2018). Macroaggregates and microaggregates within the soil matrix provide physical and biochemical protection to SOC, thus influencing its long-term stability. Furthermore, the interactions between land use and environmental conditions can significantly impact aggregate dynamics and SOC stabilization mechanisms ​ (Augusto and Baco 2022). The tree species demonstrated distinct contributions to soil properties and SOC dynamics, with \u003cem\u003eAlnus nepalensis\u003c/em\u003e outperforming others due to its balanced above- and below-ground biomass inputs. Its high nitrogen-fixing capability and litter quality enhanced soil fertility, as evidenced by the highest total nitrogen (TN), phosphorus (TP), and SOC concentrations. These findings align with studies emphasizing the role of nitrogen-fixing trees like \u003cem\u003eAlnus\u003c/em\u003e spp. in boosting soil nutrient pools and SOC stabilization​ (Lorenz and Lal 2014; Augusto and Baco 2022). Moreover, the incorporation of organic materials into the soil has been shown to improve aggregate stability and increase SOC content. For example, it is reported that organic material treatments significantly enhanced the mean weight diameter (MWD) of soil aggregates, indicating improved stability (Pan et al. 2015; Wang et al. 2020). This finding corroborates with results from Cheng et al. (2018) who emphasized the importance of understanding SOC fractions and their dynamics in relation to soil properties. In contrast, \u003cem\u003ePinus kesiya\u003c/em\u003e exhibited the lowest SOC concentrations and nutrient content despite its high litter biomass, suggesting limited nutrient cycling efficiency. Similar trends have been reported in systems dominated by conifers, where slow litter decomposition impedes nutrient availability and SOC accrual​ (Yang et al. 2022; Zhang et al. 2021).\u003c/p\u003e \u003cp\u003eSOC concentrations were significantly higher in macroaggregates than in microaggregates across all systems, underscoring the role of aggregate dynamics in carbon stabilization. \u003cem\u003eAlnus nepalensis\u003c/em\u003e macroaggregates had the highest SOC, supported by elevated polysaccharide and glomalin-related soil protein (GRSP) levels. These findings are consistent with studies showing that biochemical inputs, such as polysaccharides and GRSP, strengthen soil structure and protect SOC from microbial decomposition​ (Schlecht-Pietsch et al. 1994; Wang et al. 2021). Conversely, \u003cem\u003ePinus kesiya\u003c/em\u003e had the lowest biochemical indicators and aggregate stability, reflecting its reduced capacity to stabilize SOC. This variability highlights the influence of tree traits on aggregate formation and stability, which are critical for long-term carbon sequestration​ (Pardon et al. 2017). Further, this relationship is mediated through several mechanisms, including root traits, litter quality, and microbial interactions. For instance, broadleaf trees have been shown to enhance soil aggregate stability compared to coniferous species, with studies indicating improvements in stability by 57\u0026ndash;103% in mixed forest stands dominated by broadleaf trees (Zheng 2023). The presence of fine roots and their associated organic matter contributes to the binding of soil particles, which is essential for the formation of stable aggregates (Zheng 2023; Kemner et al. 2020). Additionally, the biomass of various tree species and the litter they produce are key indicators affecting aggregate stability, as they influence the organic matter content and microbial activity within the soil (Wang 2023). The decomposition of litter contributes organic materials that serve as binding agents for soil particles, promoting the formation of aggregates (Su et al. 2021). The quality of litter, particularly its carbon content, affects the microbial community structure and activity, which are vital for the stabilization of soil aggregates (Jing et al. 2023). For example, higher organic carbon inputs from tree litter can enhance microbial metabolism and lead to increased stability of soil aggregates through the production of extracellular polysaccharides and other binding agents (Su et al. 2021; Jing et al. 2023). Changes in tree species composition due to land-use changes can lead to alterations in soil pH, which has been linked to the dynamics of soil macroaggregates (Russell et al. 2018; Russell et al. 2017). The ratio of TG-RSP to SOC in agroforestry systems ranged from 1.08-1.33-times higher than barren land. The constituents of TG-RSP consisted of a diverse amalgamation of proteins, lipids, humus, and inorganic components, exhibiting remarkable stability in nature (Wang et al. 2017). Thus, the elevated ratio of glomalin to SOC with agroforestry systems may offer additional evidence that microbial-derived C facilitates more SOC accumulation in soils of agroforestry systems. Our results suggest that the increased contribution of glomalin to SOC in agroforestry systems may enhance the stability of aggregates in these systems. Further, the high and significant correlations between TG-RSP and aggregates SOC support the increase in the aggregates stability in the present investigation.\u003c/p\u003e \u003cp\u003eSOC mineralization is significantly influenced by incubation temperature, with both the mineralization ratio and cumulative SOC mineralization rates increasing as temperatures rise (Qin et al. 2016). In the present study, temperature significantly influenced SOC mineralization rates, with the highest rates observed at 35\u0026deg;C. The concept of temperature sensitivity, often quantified using the Q\u003csub\u003e10\u003c/sub\u003e value, reflects the rate of increase in SOC mineralization for every 10\u0026deg;C rise in temperature (Duan et al., 2023). \u003cem\u003eAlnus nepalensis\u003c/em\u003e exhibited the lowest temperature sensitivity (higher Q\u003csub\u003e10\u003c/sub\u003e value: 0.955) and activation energy, indicating greater thermal stability of its SOC pools. This finding is corroborated by He et al. (2022) who found that higher substrate carbon levels resulted in increased SOC mineralization rates under elevated temperatures, suggesting that substrate quality also modulates temperature sensitivity. Similarly, Wang et al. (2013) reported that SOC mineralization rates significantly increased with temperature across a range of incubation conditions, reinforcing the notion that temperature is a critical driver of SOC dynamics (Wang et al. 2013). These results align with findings that species with efficient nutrient cycling and root contributions exhibit better resilience to temperature-induced carbon loss ​ (Bai et al. 2023). Temporal trends revealed peak SOC mineralization at 30 days, driven by the rapid decomposition of labile carbon pools. This pattern, followed by stabilization at 60 days, reflects the transition from labile to recalcitrant carbon decomposition phases, consistent with previous incubation studies​ (Augusto and Baco 2022; Das 2023). Palma et al. (2017) highlight that the accumulation of carbon in agroforestry systems can be influenced by the ratio of root biomass to decomposed plant matter, suggesting that a higher proportion of root biomass can lead to greater carbon retention in the soil. However, as temperatures increase, the decomposition of this organic matter may accelerate, potentially leading to a reduction in SOC levels (Kay et al. 2019). This is supported by findings from Silva et al. (2014) which indicate that biological activity in agroforestry systems can be significantly affected by temperature fluctuations, impacting carbon storage. In the present study, the activation energy was highest in \u003cem\u003ePinus kesiya\u003c/em\u003e and lowest in \u003cem\u003eAlnus nepalensis\u003c/em\u003e, however, control plot had the highest Ea than agroforestry systems (132%), irrespective of the aggregates. According to Davidson and Janssens (2006), the mineralization of poor-quality C substrates requires highest Ea than the high-quality C substrates. The C:N ratio, chemical compositions and their complexity, SOC content, etc. influence the substrate quality (Bhattacharyya et al 2020). In contradiction of activation energy, the Q\u003csub\u003e10\u003c/sub\u003e values remained 2\u0026ndash;11% more in agroforestry systems than control plot. Xu et al (2012) stated that the Ea and Q\u003csub\u003e10\u003c/sub\u003e are inversely related with respect to SOC mineralization which is in consistent with the present study. The high-quality organic substrates require less Ea and more sensitive to temperature than the poor-quality organic substrates. The association of aggregate stability to SOC was perhaps attributed to binding agents including fungal exudates like polysaccharides and glomalin (Wright et al., 1999) as well as hyphae and roots which improve stability of aggregates. According to Eynard et al. (2004), these polysaccharides and glomalin improves the stability soil aggregates through the changes in the degree of water repellency. The examination of first order rate constant or kinetics indicated that aggregates in the agroforestry systems provided a superior protection of the SOC compared to control in both aggregate types. The reaction rate was more rapid in macroaggregates than microaggregates regardless of the agroforestry systems including control. The present investigation indicated a steady rise in reaction rate with temperature in both aggregates. The elevated rate constants indicate the comparatively lesser C stability in the macroaggregates over microaggregates. This is due to the variations in C availability among and within the aggregates across the agroforestry systems. Aggregates provides physical protection to soil organic matter by creating physical barricades that separate microbes and enzymes and their substrates. The variations in aggregates stability within the agroforestry systems including control, are mostly attributed to variations in organic inputs from crop residues, litter and root biomass, and the severity of soil physical disturbance (Zhou et al 2022). Consequently, the improved protection of soil organic matter by aggregates in agroforestry systems leads to greater accumulation of carbon compared to control system. Hence, effective management strategies that enhance soil health and organic matter inputs can mitigate the impacts of temperature on carbon dynamics. For instance, practices that maintain soil cover and reduce soil disturbance can help preserve SOC levels even in the face of rising temperatures (Niguse et al. 2022).\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study provides a comprehensive evaluation of soil organic carbon (SOC) stability across four agroforestry systems, emphasizing the critical roles of tree traits, aggregate dynamics, and temperature sensitivity in enhancing soil health and carbon sequestration. The results demonstrated significant variability among the agroforestry systems, with \u003cem\u003eAlnus nepalensis\u003c/em\u003e emerging as the most effective in enhancing SOC stability. The balanced above- and below-ground biomass contributions of \u003cem\u003eAlnus nepalensis\u003c/em\u003e, including moderate litter inputs and substantial root biomass, played a key role in improving nutrient cycling and aggregate stability. In contrast, \u003cem\u003ePinus kesiya\u003c/em\u003e, despite its high litter biomass, showed lower SOC concentrations and weaker biochemical contributions, suggesting limited nutrient enrichment and carbon stabilization potential. Macroaggregates were identified as critical to SOC stabilization, with consistently higher SOC concentrations, polysaccharides, and glomalin-related soil proteins (GRSP) compared to microaggregates. Among the systems, \u003cem\u003eAlnus nepalensis\u003c/em\u003e macroaggregates exhibited the greatest stability, supported by high biochemical indicators, such as polysaccharides and GRSP than other agroforestry systems. The correlation between SOC stability and biochemical properties underscores the role of organic matter inputs in strengthening soil structure and enhancing carbon retention. Temperature sensitivity of SOC mineralization varied significantly across systems, with \u003cem\u003eAlnus nepalensis\u003c/em\u003e showing the highest Q\u003csub\u003e10\u003c/sub\u003e values and lower activation energy. Temporal trends revealed peak carbon mineralization at 30 days, followed by stabilization, reflecting the decomposition of labile carbon pools and the persistence of more recalcitrant fractions. The reduced mineralization rates in \u003cem\u003eAlnus nepalensis\u003c/em\u003e soils further affirm its ability to sequester carbon effectively over time. Overall, the findings demonstrate that agroforestry systems have immense potential to enhance SOC stability, mitigate climate change, and improve soil health.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eDr. Ramesh Thangavel and Dr. KM Manjaiah prepared the main manuscript textA. Arunachalam and JMS Tomar provided the details of the study siteS Hazarika, BU Choudhury, and A Balusamy prepared the figures and edited the manuscriptVK Mishra is the overall co-ordinator and the Director of the institute\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors appreciatively acknowledge Indian Council of Agricultural Research (ICAR) and International Plant Nutrition Institute (IPNI) (Scholar award) for monetary support to carry out this research work. Authors also express thanks to the Head, Division of Soil Science and Agricultural Chemistry, Indian Agricultural Research Institute, New Delhi and Director, ICAR Research Complex for North-East Hill Region, Umiam, Meghalaya, India for offering all the resources required to complete the current investigation.\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\u003c/li\u003e\n \u003cli\u003eAugusto L, Boča A (2022) Tree functional traits, forest biomass, and tree species diversity interact with site properties to drive forest soil carbon. Nat Commun 13(1): 1097\u003c/li\u003e\n \u003cli\u003eBai SH, Muqaddas B, Trueman SJ, Wilson R, Keller A, Shapcott A, Hannet G, Farrar MB, Komolong B, Wallace HM (2023) Root architecture, root biomass and nutrient cycling in a mixed‐species agroforestry system. Land Deg Develop 34(16):5096-108\u003c/li\u003e\n \u003cli\u003eBhattacharyya R, Ghosh A, Zhang Y, Dalal RC, Kopittke PM, Jones A, Menzies NW (2020) Land use affects temperature sensitivity of soil organic carbon decomposition in macroaggregates but not in bulk soils in subtropical Oxisols of Queensland, Australia. Soil Till Res 198:104566\u003c/li\u003e\n \u003cli\u003eBlagodatskaya Е, Blagodatsky S, Khomyakov N, Myachina O. Kuzyakov Y (2016) Temperature sensitivity and enzymatic mechanisms of soil organic matter decomposition along an altitudinal gradient on Mount Kilimanjaro. Sci Rep. 6(1):22240\u003c/li\u003e\n \u003cli\u003eCardinael R, Chevallier T, Barth\u0026egrave;s BG, Saby NP, Parent T, Dupraz C, Bernoux M, Chenu C (2015) Impact of alley cropping agroforestry on stocks, forms and spatial distribution of soil organic carbon\u0026mdash;A case study in a Mediterranean context. Geoderma 259:288-99\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCastellano MJ, Mueller KE, Olk DC, Sawyer JE, Six J (2015) Integrating plant litter quality, soil organic matter stabilization, and the carbon saturation concept. Glob Chan Biol 21(9):3200-9\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCheng X, Yu M, Li Z (2018) Short term effects of thinning on soil organic carbon fractions, soil properties, and forest floor in cunninghamia lanceolata plantations. J Soil Sci Environ Manag 9(2):21-29\u003c/li\u003e\n \u003cli\u003eDavidson EA, Janssens IA (2006) Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440(7081):165-73\u003c/li\u003e\n \u003cli\u003eDhyani SK, Tripathi RS (1999) Tree growth and crop yield under agri-silvicultural practices in northeast India. \u003cem\u003eAgrofores Sys\u003c/em\u003e 44:1-12\u003c/li\u003e\n \u003cli\u003eDuan B, An Y, Zhang H (2023) Effect of exogenous nutrient addition on soil organic carbon mineralization and stabilization. Agron 13(7): 1908\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eEynard A, Schumacher TE, Lindstrom MJ, Malo DD, Kohl RA (2004) Wettability of soil aggregates from cultivated and uncultivated Ustolls and Usterts. Soil Res 42(2):163-70\u003c/li\u003e\n \u003cli\u003eFeliciano, D., Ledo, A., Hillier, J., Nayak, D.R., 2018. Which agroforestry options give the greatest soil and above ground carbon benefits in different world regions? Agric Ecosyst Environ 254:117\u0026ndash;129\u003c/li\u003e\n \u003cli\u003eHe Y, Sui Y, Chen Y, Bao T, Jiao X (2022) Soil organic carbon mineralization and its temperature sensitivity under different substrate levels in the mollisols of northeast china. Life 12(5):712\u003c/li\u003e\n \u003cli\u003eHeskel MA, O\u0026rsquo;sullivan OS, Reich PB, Tjoelker MG, Weerasinghe LK, Penillard A, Egerton JJ, Creek D, Bloomfield KJ, Xiang J, Sinca F (2016) Convergence in the temperature response of leaf respiration across biomes and plant functional types. Proc Nat Acad Sci 113(14):3832-7\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eJenkins ME, Adams MA (2011) Respiratory quotients and Q10 of soil respiration in sub-alpine Australia reflect influences of vegetation types. Soil Biol Biochem 43(6):1266-1274\u003c/li\u003e\n \u003cli\u003eJing Y, Zhao X, Liu S, Tian P, Sun Z, Chen L, Wang Q (2023) Influence of tree species on soil microbial residue accumulation and distribution among soil aggregates in subtropical plantations of China. Ecol Processes12(1):32\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eKay S, Graves A, Palma JH, Moreno G, Roces-D\u0026iacute;az JV, Aviron S, Chouvardas D, Crous-Duran J, Ferreiro-Dom\u0026iacute;nguez N, de Jal\u0026oacute;n SG, Măcicăşan V (2019) Agroforestry is paying off\u0026ndash;Economic evaluation of ecosystem services in European landscapes with and without agroforestry systems. Ecosyst Serv 36:100896\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eKeller AB, Phillips RP (2019) Leaf litter decay rates differ between mycorrhizal groups in temperate, but not tropical, forests. New Phytol 222:556\u0026ndash;564\u003c/li\u003e\n \u003cli\u003eKemner J, Adams M, McDonald L, Peterjohn W, Kelly C (2020) Fertilization and tree species influence on stable aggregates in forest soil. Forests 12(1):39\u003c/li\u003e\n \u003cli\u003eKemper WD, Chepil WS (1965) Size distribution of aggregates. In: Black, C.A., Evans, D.D., White, J.L., Ensminger, L.E. and Clark, F.E. (Eds.), \u003cem\u003eMethods of Soil Analysis-\u003c/em\u003ePhysical and mineralogical properties, including statistics of measurements and sampling. Madison. Am Soc Agron pp 499-510\u003c/li\u003e\n \u003cli\u003eKnorr W, Prentice IC, House JI, Holland EA (2005) Long-term sensitivity of soil carbon turnover to warming. \u003cem\u003eNature\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e433:298-301\u003c/li\u003e\n \u003cli\u003eLi H, Wang Y, Tianpei W, Ma H (2015) Effect of biochar on organic matter conservation and metabolic quotient of soil. Environ Prog Sustain Energy 34(5):1467-1472\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLiu X, Liu J, Chen Y, Zhang L, Qi Y, Li W., Zhu Y (2023) Response of soil aggregate stability to phosphorus, nitrogen, and organic fertilizer addition: a meta-analysis. Sustain 15(13):10601\u003c/li\u003e\n \u003cli\u003eLiu Y, Zhang M, Xiong H, Li Y, Zhang Y, Huang X, Yang Y, Zhu H, Jiang T (2023) Influence of long-term fertilization on soil aggregates stability and organic carbon occurrence characteristics in karst yellow soil of Southwest China. Front Plant Sci 14:1126150\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLorenz K, Lal R (2014) Soil organic carbon sequestration in agroforestry systems. A review. Agron Sustain Dev 34:443-454\u003c/li\u003e\n \u003cli\u003eLowe LE (1994) Total and labile acid extractable polysaccharide analysis of soils. In: Carter, M.R. (Ed.), Soil Sampling and Methods of Analysis. Lewis Publishers, Boca Raton, pp 373-376\u003c/li\u003e\n \u003cli\u003eMaes SL, Blondeel H, Perring MP, Depauw L, Brūmelis G, Brunet J, Decocq G, Den Ouden J, H\u0026auml;rdtle W, H\u0026eacute;dl R, Heinken T (2019) Litter quality, land-use history, and nitrogen deposition effects on topsoil conditions across European temperate deciduous forests. Forest Ecol Manag 433:405-418\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMayer M et al. (2020) Tamm Review: Influence of forest management activities on soil organic carbon stocks: A knowledge synthesis. Ecol Manag 466:118127\u003c/li\u003e\n \u003cli\u003eNarender K, Arya S, Nanda K (2021) Potential of melia dubia agroforestry system in soil improvement and environmental sustainability. Environ Conserv J 22(1\u0026amp;2):65-72\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eNiguse G, Iticha B, Kebede G, Chimdi A (2022) Contribution of coffee plants to carbon sequestration in agroforestry systems of southwestern Ethiopia. J Agric Sci 160(6):440-447\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eOrgill SE, Condon JR, Conyers MK, Morris SG, Alcock DJ, Murphy BW, Greene SBR (2016) Removing grazing pressure from a native pasture decreases soil organic carbon in southern New South Wales, Australia. Land Degrad Dev 29(2):274-283\u003c/li\u003e\n \u003cli\u003ePalma JH, Crous-Duran J, Graves AR, de Jalon SG, Upson M, Oliveira TS, Paulo JA, Ferreiro-Dom\u0026iacute;nguez N, Moreno G, Burgess PJ (2018) Integrating belowground carbon dynamics into Yield-SAFE, a parameter sparse agroforestry model. Agrofor Syst 92:1047-57\u0026nbsp;\u003c/li\u003e\n \u003cli\u003ePan LO, Peng SU, Gao WS, Wang BB, Huang JX, Peng YA, Zou JX, Yan LL, Chen YQ (2015) Aggregate stability and associated C and N in a silty loam soil as affected by organic material inputs. J Integr Agric 14(4):774-87\u0026nbsp;\u003c/li\u003e\n \u003cli\u003ePan Y (2011) A large and persistent carbon sink in the world\u0026rsquo;s forests. Sci 333:988\u0026ndash;93\u0026nbsp;\u003c/li\u003e\n \u003cli\u003ePardon P, Reubens B, Reheul D, Mertens J, De Frenne P, Coussement T, Janssens P, Verheyen K (2017) Trees increase soil organic carbon and nutrient availability in temperate agroforestry systems. Agric Ecosyst Environ 247:98-111\u003c/li\u003e\n \u003cli\u003eQiu X, L\u0026uuml; MK, Huang JX, Li W, Zhao BJ, Zhang H, WANG EX, XIE JS (2016) Characteristics of soil organic carbon mineralization at different temperatures in severely eroded red soil. Chin J Plant Ecol 40(3):236-45\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eRasmussen C, Heckman K, Wieder WR, Keiluweit M, Lawrence CR, Berhe AA, Blankinship JC, Crow SE, Druhan JL, Hicks Pries CE, Marin-Spiotta E (2018) Beyond clay: towards an improved set of variables for predicting soil organic matter content. Biogeochemistry 137:297-306\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eRibbons RR et al. (2018) Context-dependent tree species effects on soil nitrogen transformations and related microbial functional genes. Biogeochemistry 140:145\u0026ndash;160\u003c/li\u003e\n \u003cli\u003eRillig MC (2004) Arbuscular mycorrhizae, glomalin, and soil aggregation. Can J Soil Sci 84(4):355-63\u003c/li\u003e\n \u003cli\u003eRussell A, Hall S, Raich J (2017) Tropical tree species traits drive soil cation dynamics via effects on ph: a proposed conceptual framework. Ecolgical Monogr 87(4):685-701.\u003c/li\u003e\n \u003cli\u003eRussell A, Kivlin S, Hawkes C (2018) Tropical tree species effects on soil ph and biotic factors and the consequences for macroaggregate dynamics. Forests 9(4):184\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eRussell AE, Kivlin SN, Hawkes CV (2018) Tropical tree species effects on soil pH and biotic factors and the consequences for macroaggregate dynamics. Forests 9:1\u0026ndash;14 (2018\u003c/li\u003e\n \u003cli\u003eRytter RM, Rytter L (2020) Carbon sequestration at land use conversion \u0026ndash; Early changes in total carbon stocks for six tree species grown on former agricultural land. Ecol Manag 466: 118129\u003c/li\u003e\n \u003cli\u003eSchlecht-Pietsch S, Wagner U, Anderson TH (1994) Changes in composition of soil polysaccharides and aggregate stability after carbon amendments to different textured soils. App Soil Ecol 1(2):145-154\u003c/li\u003e\n \u003cli\u003eSchmidt MWI, Torn MS, Abiven S, Dittmar T, Guggenberger G, Janssens IA, Kleber M, K\u0026ouml;gel-Knabner I, Lehmann J, Manning DAC, Nannipieri P, Rasse DP, Weiner S, Trumbore SE (2011) Persistence of soil organic matter as an ecosystem property. Nature 478:49\u0026ndash;56\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSilva I, Neto S, Kusdra J (2014) Biological activity of soils under systems of organic farming, agroforestry and pasture in the amazon. Rev Ci\u0026ecirc;n Agron 45(3):427-432\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSingh M, Das SK, Avasthe RK (2018) Effect of multipurpose trees on production and soil fertility on large cardamom based agroforestry system in Sikkim Himalaya. Ind J Agroforest 20(2):25\u0026ndash;9\u003c/li\u003e\n \u003cli\u003eSu F, Xu S, Sayer E, Chen W, Du Y, Lu X (2021) Distinct storage mechanisms of soil organic carbon in coniferous forest and evergreen broadleaf forest in tropical china. J Environ Manag 295:113142\u003c/li\u003e\n \u003cli\u003eWang G, Zhou Y, Xu X, Ruan H, JiaShe W (2013) Temperature sensitivity of soil organic carbon mineralization along an elevation gradient in the wuyi mountains, china. Plos One 8(1): e53914\u003c/li\u003e\n \u003cli\u003eWang W, Zhong Z, Wang Q, Wang H, Fu Y, He X (2017) Glomalin contributed more to carbon, nutrients in deeper soils, and differently associated with climates and soil properties in vertical profiles. Sci Rep 7(1):13003\u003c/li\u003e\n \u003cli\u003eWang Y (2023) Response of soil aggregate composition and stability to secondary succession and plantation of a broad-leaved korean pine forest after clear-cutting and its causes. Forests 14(10):2010\u003c/li\u003e\n \u003cli\u003eWang, Q., Hong, H., Liao, R., Yuan, B., Li, H., Lu, H., Liu, J. and Yan, C., 2021. Glomalin-related soil protein: The particle aggregation mechanism and its insight into coastal environment improvement. Ecotoxicology and environmental safety, 227, p.112940.\u003c/li\u003e\n \u003cli\u003eWhistler RL, Wolfrom ML (1962) \u003cem\u003eMethods in carbohydrate chemistry\u003c/em\u003e 1. London, Academic Press: 388\u003c/li\u003e\n \u003cli\u003eWright SF, Franke-Snyder M, Morton JB, Upadhyaya A (1996) Time-course study and partial characterization of a protein on hyphae of arbuscular mycorrhizal fungi during active colonization of roots. \u003cem\u003ePlant Soil\u003c/em\u003e 181:193-203\u003c/li\u003e\n \u003cli\u003eWright SF, Starr JL, Paltineanu IC (1999) Changes in aggregate stability and concentration of glomalin during tillage management transition. Soil Sci Soc Am J 63(6):1825-9\u003c/li\u003e\n \u003cli\u003eXu X, Luo Y, Zhou J (2012) Carbon quality and the temperature sensitivity of soil organic carbon decomposition in a tallgrass prairie. Soil Biol Biochem 50:142-8\u003c/li\u003e\n \u003cli\u003eYang K, Zhu J, Zhang W, Zhang Q, Lu D, Zhang Y, Zheng X, Xu S, Wang GG (2022) Litter decomposition and nutrient release from monospecific and mixed litters: Comparisons of litter quality, fauna and decomposition site effects. J Ecol 110(7):673-1686\u003c/li\u003e\n \u003cli\u003eZhang H, Liu Z, Chen H, Tang M (2016) Symbiosis of arbuscular mycorrhizal fungi and robinia pseudoacacia l. improves root tensile strength and soil aggregate stability. Plos One 11(4):e0153378\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eZhang J, Li H, Zhang H, Zhang H, Tang Z (2021) Responses of litter decomposition and nutrient dynamics to nitrogen addition in temperate shrublands of North China. Frontiers in Plant Science, 11, p.618675.\u003c/li\u003e\n \u003cli\u003eZhang Y, Liu X, Xiao L (2022) Changes in soil aggregate fractions, stability, and associated organic carbon and nitrogen in different land use types in the loess plateau, china. Sustainability 14(7):3963\u003c/li\u003e\n \u003cli\u003eZheng Y (2023) Broadleaf trees increase soil aggregate stability in mixed forest stands of southwest china. Forests 14(12): 2402\u003c/li\u003e\n \u003cli\u003eZhou M, Liu C, Wang J, Meng Q, Yuan Y, Ma X, Liu X, Zhu Y, Ding G, Zhang J, Zeng X (2020) Soil aggregates stability and storage of soil organic carbon responds to cropping systems on Black Soils of Northeast China. Sci Rep10(1):265\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eZhou M, Xiao Y, Xiao L, Li Y, Zhang X, Cruse RM, Liu X (2022) Increased soil aggregate stability by altering contents and chemical composition of organic carbon fractions via seven years of manure addition in Mollisols. Agriculture 13(1):88\u003c/li\u003e\n \u003cli\u003eZhou W, Han G, Li X (2019) Effects of soil pH and texture on soil carbon and nitrogen in soil profiles under different land uses in mun river basin, northeast Thailand. Peerj 7:e7880\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Characteristics of the experimental tree species of different agroforestry systems\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"851\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eTree species\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eLocal name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eHeight (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eDBH (cm)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eTimber vol. (m\u003csup\u003e3\u003c/sup\u003e ha\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eTotal fine root biomass\u003csup\u003eb\u003c/sup\u003e (g m\u003csup\u003e-2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eAnnual litter biomass (g m\u003csup\u003e-2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cem\u003eMichelia oblonga\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eChampak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e23.88\u0026plusmn;3.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e27.45\u0026plusmn;7.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e231.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e462.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e512.45\u0026plusmn;9.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cem\u003eParkia roxburghii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eTree bean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e24.99\u0026plusmn;1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e27.95\u0026plusmn;2.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e204.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e415.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e353.92\u0026plusmn;14.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cem\u003eAlnus nepalensis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eAlder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e19.77\u0026plusmn;3.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e26.81\u0026plusmn;2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e195.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e435.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e476.75\u0026plusmn;5.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cem\u003ePinus kesiya\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cem\u003eKhasi\u003c/em\u003e pine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e15.60\u0026plusmn;3.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e23.32\u0026plusmn;8.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e117.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e496.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e625.30\u0026plusmn;3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eMean tree bole diameter at brest height (cm); \u003csup\u003eb\u003c/sup\u003eFine root biomass recorded in 0-30 cm soil depth (Adapted from Ramesh et al. 2013)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Average yield of crops under tree canopy of different agroforestry systems\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"763\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eTree species\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 618px;\"\u003e\n \u003cp\u003eAverage Yield of crop (q ha\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eSoybean (\u003cem\u003eGlycine max\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003ePineapple\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003eAnanas comosus\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eGinger\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003eZingiber officinale\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eTurmeric (\u003cem\u003eCurcuma longa\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cem\u003eMichelia oblonga\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e14.08\u0026plusmn;2.91\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e138.89\u0026plusmn;6.506b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e87.42\u0026plusmn;11.50ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e149.33\u0026plusmn;8.46a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cem\u003eParkia roxburghii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e14.73\u0026plusmn;2.68a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e156.50\u0026plusmn;10.476a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e97.67\u0026plusmn;7.78a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e153.63\u0026plusmn;7.23a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cem\u003eAlnus nepalensis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e16.25\u0026plusmn;3.11a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e148.89\u0026plusmn;7.095ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e99.25\u0026plusmn;11.89a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e145.67\u0026plusmn;4.25a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cem\u003ePinus kesiya\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e14.55\u0026plusmn;2.73a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e124.10\u0026plusmn;4.726c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e72.17\u0026plusmn;13.84b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e97.40\u0026plusmn;7.65b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e(Adapted from Ramesh et al. 2013)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Table 3\u0026nbsp;\u003c/strong\u003eEffect of agroforestry systems, duration and temperature on soil macro-and micro-aggregates carbon mineralization (mg CO\u003csub\u003e2\u003c/sub\u003e g\u003csup\u003e-1\u003c/sup\u003e)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"834\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 167px;\"\u003e\n \u003cp\u003eAgroforestry systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e15 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e30 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e60 days\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e25 \u003csup\u003eo\u003c/sup\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e30\u003csup\u003e\u0026nbsp;o\u003c/sup\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e35\u003csup\u003e\u0026nbsp;o\u003c/sup\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e25\u003csup\u003e\u0026nbsp;o\u003c/sup\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e30\u003csup\u003e\u0026nbsp;o\u003c/sup\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e35\u003csup\u003e\u0026nbsp;o\u003c/sup\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e25\u003csup\u003e\u0026nbsp;o\u003c/sup\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e30\u003csup\u003e\u0026nbsp;o\u003c/sup\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e35\u003csup\u003e\u0026nbsp;o\u003c/sup\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" style=\"width: 834px;\"\u003e\n \u003cp\u003eMacroaggregate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e\u003cem\u003eMichelia oblonga\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1.84ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e4.77b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e7.34b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e5.51b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e8.07b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e10.64bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e12.48b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e15.88b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e25.59a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e\u003cem\u003eParkia roxburghii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1.47b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e4.40c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e6.97c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e4.77bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e7.71b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e10.28bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e10.64c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e13.95c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e23.49b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e\u003cem\u003eAlnus nepalensis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e2.57a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e5.14ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e10.64a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e9.18a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e11.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e14.68a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e15.05a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e17.35a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e20.39bc\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e\u003cem\u003ePinus kesiya\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.73c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e4.77b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e6.24d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e4.40c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e6.61c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e11.74b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e9.91cd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e17.98ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e23.12b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003eControl (Barren)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e2.79a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e5.51a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e7.34b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e4.43c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e8.09b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e12.31bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e9.18d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e15.78b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e26.96a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e4.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e7.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e5.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e8.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e11.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e11.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e16.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e24.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" style=\"width: 834px;\"\u003e\n \u003cp\u003eMicroaggregate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e\u003cem\u003eMichelia oblonga\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1.47b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e4.04a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e6.61a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e2.24bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e6.81ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e7.34bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e4.04bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e9.91b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e14.68bc\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e\u003cem\u003eParkia roxburghii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e2.57a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e2.20bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e6.24a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e2.94b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e6.29b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e6.61c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e6.24ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e11.74ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e16.88b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e\u003cem\u003eAlnus nepalensis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1.10c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e2.57b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e4.81b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1.84c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e6.24b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e8.81b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e3.67bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e11.01ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e13.95c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e\u003cem\u003ePinus kesiya\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.73c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1.84c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e4.77b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e2.20bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e6.45b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e9.54b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e4.77b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e11.94ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e17.98ab\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003eControl (Barren)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1.47b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e2.57b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e4.40c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e5.14a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e8.07a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e11.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e7.34a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e13.21a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e19.45a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e2.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e5.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e2.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e6.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e8.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e5.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e11.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e16.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u0026nbsp;\u003c/strong\u003eEffect of agroforestry systems and temperature on rate constant, activation energy and Q\u003csub\u003e10\u003c/sub\u003e values of aggregates carbon loss\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"633\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 151px;\"\u003e\n \u003cp\u003eAgroforestry systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 279px;\"\u003e\n \u003cp\u003eRate constant (k)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 101px;\"\u003e\n \u003cp\u003eAE\u003c/p\u003e\n \u003cp\u003e(kJ mol\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 101px;\"\u003e\n \u003cp\u003eQ\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 279px;\"\u003e\n \u003cp\u003eTemperature (\u003csup\u003eo\u003c/sup\u003eC)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 633px;\"\u003e\n \u003cp\u003eMacroaggregates\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eMichelia oblonga\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.0009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.0015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e37.00d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.932ab\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eParkia roxburghii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.0013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.0018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e54.27c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.905b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eAlnus nepalensis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.0011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.0013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e24.25e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.955a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003ePinus kesiya\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.0013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.0019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e74.97b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.868c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eControl (Barren)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.0014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.0022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e110.51a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.855d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.0012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.0017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e60.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.902\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 633px;\"\u003e\n \u003cp\u003eMicroaggregates\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eMichelia oblonga\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.0010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.0013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e92.99a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.802a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eParkia roxburghii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.0009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.0014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e83.97b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.852a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eAlnus nepalensis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.0009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.0011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e73.13b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.874a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003ePinus kesiya\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.0012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.0016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e102.06ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.824a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eControl (Barren)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.0010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.0017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e107.19b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.858a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.0010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.0014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e91.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.842\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5.\u0026nbsp;\u003c/strong\u003ePearson\u0026rsquo;s correlation matrix between MWD, macro and microaggregate carbon, and soil properties\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"926\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eProperties\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMWD\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eSoil C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003eMAC- C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eMIC-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eSoil TP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eMAC- TP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eMAC- TP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eSoil DAEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eMAC- DAEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eMIC-DAEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eSoil TG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eMAC- TG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003eMIC-TG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eMWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eSoil C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.637\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eMAC- C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.531\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.943\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eMIC-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eSoil TP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.641\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eMAC- TP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.580\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eMIC- TP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.666\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.514\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.587\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.750\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eSoil DAEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.625\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.554\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.546\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eMAC- DAEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.723\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.597\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eMIC-DAEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.745\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.747\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.628\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.625\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.783\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.562\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.667\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eSoil TG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.406\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.694\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.590\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eMAC- TG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.634\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.695\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.558\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.582\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.817\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.695\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.627\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.556\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.693\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.637\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eMIC-TG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.537\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.614\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.658\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.689\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.658\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.631\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.629\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e** Correlation is significant at the 0.01 level (2-tailed); *. Correlation is significant at the 0.05 level (2-tailed); MWD-mean weight diameter; MAC: macroaggregate; MIC: microaggregate; C: carbon; TP: total polysaccharides; DAEP: dilute acid extractable polysaccharides; TG: total glomalin\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Agroforestry systems, soil aggregates, soil organic carbon, carbon mineralization, temperature sensitivity","lastPublishedDoi":"10.21203/rs.3.rs-5762787/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5762787/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAgroforestry systems play a critical role in enhancing soil organic carbon (SOC) stability and mitigating climate change by integrating trees and crops to improve soil fertility and carbon sequestration. This study investigates the SOC stability, aggregate dynamics, and temperature sensitivity of SOC mineralization across four agroforestry systems (\u003cem\u003eMichelia oblonga, Parkia roxburghii, Alnus nepalensis\u003c/em\u003e, and \u003cem\u003ePinus kesiya\u003c/em\u003e). Tree traits, soil properties, and aggregate characteristics were analyzed alongside a 60-day incubation experiment under three temperature regimes (25\u0026deg;C, 30\u0026deg;C, and 35\u0026deg;C). The results revealed the SOC mineralization significantly varied amongst the agroforestry systems with highest value in \u003cem\u003eM. oblonga\u003c/em\u003e (25.59 mg CO\u003csub\u003e2\u003c/sub\u003e g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and lowest in \u003cem\u003eA. nepalensis\u003c/em\u003e (20.39 mg CO\u003csub\u003e2\u003c/sub\u003e g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Macroaggregates consistently showed higher SOC concentrations and biochemical indicators, such as polysaccharides and total glomalin-related soil proteins (TG-RSP), compared to microaggregates and bulk soil. The temperature and aggregate sizes statistically influenced the SOC mineralization rates, with noticeable interaction effect. SOC mineralization rates increased with temperature, but \u003cem\u003eAlnus nepalensis\u003c/em\u003e exhibited the highest temperature sensitivity (Q\u003csub\u003e10\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.955 and activation energy\u0026thinsp;=\u0026thinsp;24.25 kJ mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), highlighting its resilience to thermal stress. Strong positive correlations were observed between soil aggregate stability and soil biochemical indicators such as SOC, polysaccharides and TG-RSP of bulk soil and aggregates. Temporal trends indicated that carbon mineralization peaked at 30 days before stabilizing, reflecting the decomposition of labile carbon pools. These findings highlight the critical role of tree traits, soil aggregates, and thermal stability in driving SOC retention in agroforestry systems.\u003c/p\u003e","manuscriptTitle":"Tree species traits and soil biochemical properties drive carbon stability and temperature sensitivity of soil aggregates in agroforestry systems of subtropical northeast India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-09 07:20:05","doi":"10.21203/rs.3.rs-5762787/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"08153b86-e4bf-479f-b060-f2b817e75571","owner":[],"postedDate":"January 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-02T10:23:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-09 07:20:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5762787","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5762787","identity":"rs-5762787","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}