Introduction

Soil organic carbon (SOC) plays a crucial role in maintaining carbon (C) budget of the earth. It can seize and preserve atmospheric carbon dioxide, thus act as a carbon sink stockpiling C two to three times greater than the terrestrial plants and atmosphere1,2. C in soil can be organic or inorganic form but it is the soil organic C fraction (SOCF) that improves the soil physiochemical properties by augmenting activities of soil biota, drainage improvement and nutrient availability to plants. Nevertheless, weathering of carbonates and bicarbonates (lithogenic and pedogenic) contributes to the soil inorganic C fraction (SICF). The SICF is responsible for degrading soil quality chemically particularly in the arid and semi-arid tropical biomes. Both the SOCF and SICF regulate many ecosystem functions such as water balance, nutrient mobilization, greenhouse gases concentrations and activities linked to soil biota. Soil C sequestration substantially contributes to the C foot print reduction attracting attentions of academicians, researchers and planners across the globe to understand the mechanism involved in it. Globally soil organic C stock in the top one meter of soil accounts ~ 3700 to ~ 5700 Pg (1 Pg C = 105 g C) of which the SICF constitutes ~ 700 to 1700 Pg and SOCF ~ 3000 to 3700 Pg3 Within this one meter depth it is the top few centimetres of soil (0–30 cm) having larger share in the soil C reservoir4.

Minimal disturbance and continuous addition of organic matter be beneficial to forests over other land use systems to act as a potential carbon sink in global C budget. Climate, soil property and other locality factors influence tree growth and biomass production. In forests litter fall, rhizodeposition and decomposition rate influence soil C stock and its vertical distribution. The amount of C sequestration that the forest soil can have may vary significantly with vegetation type, activities of soil organisms and management interventions besides geoclimatic factors5,6. Among these factors vegetation attributes such as broad leaf or conifer species7, short rotation or long rotation species8, arbuscular or ectomycorrhizae association9 greatly influence soil organic C pools. Apart from this at microlevel litter quality, root architecture, exudates addition and bioturbation of litter by soil fauna also influence soil carbon stocks10,11. At the individual species level one important plant attribute emphasized by many workers is the litter characteristics linked with decomposition rate. It is often hypothesized and acknowledged that slower litter decomposition either due to litter characteristics or cool climatic conditions often is associated with higher soil C stocks than quick litter decomposition12. These conditions are more prevalent in coniferous forests under temperate climate. However, in tropical forests under a hot and humid climate litter decay rate is fast leading to pronounced microbial stabilization and transformation of biomass carbon to recalcitrant soil carbon stock13.

However scant information is available about impacts of a particular tree species on soil organic C stability and its vertical distribution. This is because forest is a dynamic entity and temporal variation in species composition is inevitable especially under a changing climate. Detailed precise information on tree species effect on SOC stability is crucial in predicting carbon budget of a forest ecosystem. Tree species especially those having high rotation age such as sal, teak in tropical forests are engineers of forest soil. It is often hypothesized that long rotation species store C for longer time than short rotation tree species14. They often influence soil properties through canopy architecture, floral litter supply, distribution of organic matter at different horizons via root litter addition and bioturbation of litter by soil fauna8.

Shorea robusta Gaertn.f. (family: Dipterocarpaceae) is a climax tropical hardwood timber species distributed between 20–32° N latitude and 75–95° E longitude. In India, natural sal forests cover nearly about 10 million hectare area ranging from Uttarakhand in the north to Andhra Pradesh in the south and Assam in the east to Haryana in the west15. These forests are source of many ecosystem services, harbor rich biodiversity, provide livelihood to tribal people and inextricably intertwined with the cultural practices of ethnic communities. By virtue of long rotation period sal trees significantly contribute to the carbon stock of tropical forests16. Sal forest upholds a positive carbon balance within their ecosystems, possessing greater capacity to sequester carbon thereby potentially mitigates climate change17. Sal is a prominent species in tropical woodlands of the Indian Eastern Ghats18,19. Many studies have been conducted to assess carbon stock of sal forests in tropical region across the country8,17,20,21 but these inventories are limited to quantify above ground biomass C, SOC stock or both without any emphasis to SOC stability and fractionations. However, information about SOC stock of sal forests and its stability are scanty in tropical moist deciduous woodlands of the Indian Eastern Ghats. The objectives of this study were to (1) quantify the SOC stock, (2) evaluate the impact of sal tree density to the annual litter input and SOC stock and (3) assess the change in different SOC fractions (very labile, labile, less labile and non-labile) in the tropical moist deciduous woodlands of Indian Eastern Ghats. The findings of present work may be helpful in predicting carbon budget of tropical forests dominated by sal in the region, and to find out whether sal forest is able to function as a carbon source or sink. Moreover these forests may have robust species selection mechanism in afforestation programmes.

Material and methods

Study area

This study was conducted in the tropical woodlands of Phulbani Forest Division in Odisha which is a constituent of the steep Eastern Ghats Mountains. It spreads over 3629.37 km2 between 19° 49′ 50′′ to 20° 41′ 20′′ N latitude and 83° 46′ 53′′ to 84° 35′ 15′′ E longitude. The area has undulating topography interspersed with plains, plateaus and hillocks of various elevations ranging from 475 to 1150 m amsl. The climate is subtropical and hot type with average temperature between 35 and 45.5 °C during summer (March–June) and 2 to 11 °C during winter months (November–February). The area receives annual rainfall in the range of 1523–1602 mm of which about 80% was obtained during monsoon (June to September) and the rest in winter months. Geologic mass of the area is dominated by alluvial, laterite, sandstone and archean rocks. Soils are mostly red lateritic group, immature, sandy-loam to loamy textured, acidic reaction and classified as Alfisols, Inceptisols and Entisols22. The vegetation is endowed with bounty forest resources and falls under the Indian tropical moist and dry deciduous forest types23. Sal creates pure stands and sal-dominated mixed forests in the lower hills and lowlands based on the depth and moisture content of the soil, but as altitude rises in the upper hill slopes, a wide variety of species are found in large quantities.

Vegetation sampling

To understand the ecological status of sal in the forests of the region three main types of forests viz. Pure Sal Forest (PSF), Sal Dominated Mixed Deciduous Forest (SDMDF) and Moist Mixed Deciduous Forest without Sal (MDFWS) were selected after repeated preliminary field surveys in Phulbani district’s tropical moist deciduous woodland (Fig. 1). There are four reserved forests (RF) in the study area and in each RF four sample plots (1.0 ha each) were sampled totalling 12.0 ha for the study. Four 31.5 m × 31.5 m sub-plots were demarcated within each sample plot for tree enumeration as per the standard protocol24. Tree species found in each sample plot were recognized, counted and diameter was measured at the breast height (1.37 m from ground). Descriptive variables for community structure such as density and basal area were calculated as per Misra25. Diversity indices viz. Shannon-Weiner diversity index (H′) and Margalef’s species richness index (R) were computed following Shannon and Weaver26 and Margalef27. Within each sub plot four sub-sub plots (1 m × 1 m × 30 cm) were constructed using locally available stones to capture floral litter (senescence leaf, reproductive parts and small branches < 2.5 cm diameter) biomass. Using a steel auger (inn dia 7.5 cm × length 60 cm), the fine root (< 2.0 mm diameter) and litter biomass were estimated using sequential soil core method28 Fine root samples were taken at four random places within the canopy layer up to the depth of 45 cm. Both litter and fine root biomass samples were collected seasonally (Winter-January, Summer-May and Rainy-September) and summed to estimate annual litter input to soil. Litter thus collected were segregated species and plant part wise, oven dried at 80 °C for 72 h for estimating annual litter input to soil. The carbon content of the litter mass was measured by combusting 0.2 mg of oven dried powdered sample using a CHNS analyser (Elementar-UNICUBE having accuracy of 99.70 ± 0.30%).

Fig. 1
figure 1

Map showing location of the sampling sites.

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Soil sampling and analysis

Soil samples were systematically collected at three soil depths (0–15, 15–30 and 30–45 cm) with the help of a metallic core from two arbitrary locations in a given sub-plot (31.5 m × 31.5 m). Thus, 96 soil cores (2 cores × 3 depth × 4 sub plot × 4 major plot) were collected from each forest type. The collected samples were properly tagged (forest and soil depth wise) and brought to the laboratory, and oven-dried at 105 °C for 48 h for soil moisture estimation. Some air-dried samples were separately grinded gently using a wooden hammer, sieved through 2 mm mesh and stored in glass jars for further chemical analysis. Soil pH was determined electrometrically in soil deionized water suspension (1:2.5 ratios). The soil texture was determined using hydrometer29 and the USDA textural triangle.

The total soil carbon and total nitrogen in soil samples were estimated by combusting 0.2 g of sample in CHNS analyzer (Elementar UNICUBE, with accuracy; 99.7 ± 0.3%). The soil inorganic carbon was determined using dilute hydrochloric acid (1 M HCL) in 1:10 ratio30. The total soil organic carbon content was computed by subtracting SIC from TSC. For determining fractionation of soil organic carbon at different lability the modified Walkley and Black31 method was adopted in which the concentration of H2SO4 was used at three various degrees (12N, 18N and 24N) at a stable concentration of K2Cr2O732. The Walkley and Black31 standard quick oxidation method is equivalent to 24N H2SO4 oxidizable carbon. The organic carbon content was separated into four carbon fractionations of lowering lability: (1) very labile soil carbon (VLSC), (2) labile soil carbon (LSC), (3) less labile soil carbon (LLSC), and (4) non-labile soil carbon (NLSC). The very labile and labile pool are referred to as active soil carbon pool and less labile and non-labile pool collectively designated as passive carbon pool.

The bulk density of the fine soil fraction (< 2 mm), soil depth and SOC concentration were used to estimate the soil carbon stock (SOC) at each depth using the following formula.

$${\text{SOC}}\;{\text{ stock }}\left( {{\text{Mg}}\;{\text{ C }}\;{\text{ha}}^{ - 1} } \right) = \mathop \sum \limits_{{{\text{SH}} = 1}}^{{\text{N}}} {\text{SOC }} \times {\text{Bulk}}\;{\text{ density}} \times {\text{Soil }}\;{\text{depth }} \times 100{ }$$

where SH is the soil horizon, bulk density of the fine soil fraction (Mg m−3) is the depth of the soil layer (m), SOC is the concentration of soil organic carbon present, The carbon management index (CMI) was computed in accordance with Blair et al.33 as follows:

$$CMI = CPI \times CLI \times 100$$

where LI is the C lability index and CPI is the C pool index

$$CPI = Total\; C\; in\; the \;test \;sample /Total \;C \;in\; the \;reference \;sample$$
$$CLI = Lability \;of \;C \;in \;the \;test \;sample \;soil /Lability \;of \;C \;in \;the \;reference\; soil\; sample$$
$$C_{L} = Labile \;Carbon/Non \;labile \;Carbon$$

Soil in the PSF was taken as the reference soil because all sites were natural undisturbed/minimally disturbed forests and our motto was to know how fully stocked sal forest soil differs from other forest soil with respect to stability of carbon. The ration of stable fraction of SOC to total SOC was used to calculate the C stability ratio (CSR) 34.

$$CSR \left( \% \right) = ({\text{Stable}}\;{\text{ fraction}}\;{\text{ of }}\;{\text{SOC }}/{\text{Total }}\;{\text{SOC}}) \times 100$$

Statistical analysis of data

The standard errors of means and replicated sample mean were computed using descriptive statistical methods. A one-way analysis of variance (ANOVA) with a 95% confidence level was used to compare the mean values of various analytical parameters. To identify significant differences Tukey’s honest significance difference (HSD) post hoc test was run. MS EXCEL and SPSS statistical package SPSS-20 (SPSS Inc. Chicago, USA) were used for all statistical analysis. The correlation between soil physicochemical parameters, SOC and vegetation attributes of forest types were analyzed using software R vegan and correlogram plot was drawn in corrplot package.

Results

Vegetation characteristics

The species composition and dominance, richness, Shannon diversity Index and Margalef’s richness index varied significantly (P < 0.05) between the forest communities though no significant variation found in tree density and stand basal area (Table 1). The species richness ranged from 17 to 51; tree density from 545 to 745.45 ha−1 and basal area from 17.83 to 27.15 m−2 ha−1. Shorea robusta dominated the upper canopy layer in PSF while in SDMDF it was shared by Buchanania lanzan, Diospyros melanoxylon, Pterocarpus marsupium along with the sal. In MDFWS, however, major species found in the upper canopy layer were Terminalia alata, Anogeissus latifolia and Bridelia retusa. The annual litter (leaf, reproductive parts, small branches and fine roots) input to the soil ranged from 8.93 tonnes ha−1 in MDFWS to 12.77 tonnes ha−1 in SDMDF. The share of sal to annual litter input was high in PSF (56.83%) and low in MDFWS (5.85%) community.

Table 1 Floristic characteristics and annual litter input in three forest types of Eastern Ghats.
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Variations in soil characteristics

Significant differences (P < 0.05) were found in soil physical parameters across the forest communities and depth in terms of particle fractionation, bulk density, pH, organic carbon and total nitrogen content (Table 2). Soils of each forest type were acidic at upper layer (0–15 cm) that ranged from 5.31 to 5.88 but varied from slightly acidic (6.02) to neutral (7.46) at lower layer (30–45 cm) and found significantly (P < 0.05) higher in PSF. Soil textural class showed that PSF and MDFWS communities had loamy sand texture whereas SDMDF had sandy loam texture. In all forest types, there was significant differences (P < 0.05) in the content of clay and sand. The sand content (89.30%) was noticeably greater in MDFWS community while clay content (17.20%) was higher in SDMDF compared to other forest communities. However, no discernable variations in silt percentage were noticed among the forest communities except in the lower layer (30–45 cm). At all depths, the soil bulk density was considerably higher (P < 0.05) in MDFWS while it was the least in PSF at surface layers (1.63) (Table 2). There was no significant differences in the soil moisture content among the communities but showed a higher value in PSF (18.07%) and decreased along the soil depths. At the lower layer of MDFWS, total SOC concentration was lowest (0.59%) and considerably higher (P < 0.05) in PSF. Likewise, PSF had a noticeably higher concentration (1.29%) of soil total nitrogen (TN) at surface layer and lowest in MDFWS at lower layer (0.91%).

Table 2 Soil physico-chemical properties of three forest types in the Indian Eastern Ghats.
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Soil organic carbon pools

SOC pool C fractions stock viz. VLSC and LSC differed significantly (P < 0.03) among the forest types. The proportion of SOC fractions stock at 0–45 cm soil depth followed the order PSF > SDMDF > MDFWS. The active carbon pool fractions were found to be high in PSF (77.62 Mg C ha−1) followed by SDMDF (43.21 Mg C ha−1) and MDFWS (32.87 Mg C ha−1) (Table 4). All forest types had higher active carbon pool than passive carbon pools (Fig. 2). Likewise, the soil total nitrogen (TN) concentration was significantly higher in PSF than other forest types.

Fig. 2
figure 2

Active and passive carbon pools in three different types of forests (i). PSF pure sal forest, (ii) SDMDF-moist deciduous forest with sal predominance and (iii) MDFWS-Moist deciduous forest with no sal predominance.

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Soil organic carbon stock

There were notable differences (P < 0.045) in total SOC stock of three forest types and it was in the order PSF > SDMDF > MDFWS (Table 3). A declining tendency was seen in the distribution of soil organic carbon stock from upper layer to the bottom layer in all forest types (Fig. 2). The soil organic C fractions at varying lability (VLSC, LSC, LLSC and NLSC) have a positive significant (P < 0.05) correlation (Fig. 3). Total SOC in all three forest communities was positively related with annual litter input but negatively related to tree density and basal area distribution (Fig. 3).

Table 3 Soil bulk density (g cm−3) and soil carbon stocks (Mg C ha−1) in different forms across forest types at 0–45 cm depth.
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Fig. 3
figure 3

Pearson’s correlations between SOC fractions, other soil attributes and vegetation traits.

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Litter input

Annual litter input was significantly higher (P < 0.01) in SDMDF (12.77 Mg ha−1) than other forest types and it was the lowest in MDFWS (8.93 Mg ha−1). Reproductive components supplied the least to the overall annual intake while vegetative parts-such as leaf, tiny branches and fine roots- contributed the most Table 4. Among the fractions of leaf litter the share of Shorea robusta was obviously high in PSF (56.83%) followed by SDMDF (29.21%) and MDFWS (5.85%) respectively. Forest type and species composition did not significantly influence the carbon content percentage in litter mass, which ranged from 41.97 to 43.92%. The annual carbon input to the soil through litter fall was significantly higher (P < 0.01) in SDMDF (5.62 Mg C ha−1) and lowest in MDFWS (3.89 Mg C ha−1) (Table 1).

Table 4 Variations in active and passive SOC pool (Mg C ha−1) in different forest types at 0–45 cm depth.
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Carbon management index and stability ratio

The carbon management index (CMI) decreased with reduction in density of Sal trees and it was lower in MDFWS (29.31). On the contrary, carbon stability ratio (CSR) was higher in SDMDF (0.40) and lower in PSF (0.31) (Table 5).

Table 5 Influence of forest types on C management index (CMI) and stability ratio.
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Discussion

Vegetation characteristics and litter input

Species diversity, vegetation structure and ecosystem processes are frequently employed to track a forest’s ecological health35. While vegetation structure gives an insight to habitat suitability and productivity of a forest, the species diversity helps to understand the proportion of native species versus alien intruders, resource sharing and trophic structure. Generally sal trees grow gregariously in its natural habitat. It forms a mono-specific dense canopy in both dry and damp deciduous woods that are found across India’s tropical and subtropical areas36. Among various physico-chemical properties, soil moisture has been attributed as the major dominating edaphic factors controlling growth of sal in its natural zone37. So it is obvious that the species richness in pure sal and sal-dominated forests would be quite low as compared to the sal-forests growing in similar edaphic and climatic conditions elsewhere. This is also evident in our present observations of species richness in the order of PSF (17) < SDMDF (39) < MDFWS (51). The species richness in the present study falls within the range of species documented in sal forests38,39,40 and moist mixed deciduous forests41,42 of the region. These forests varied with respect to plant density and stand basal area and were attributed to variations in edaphic and climatic conditions, and vogue management options (reserve forest managed under selection silvicultural system).

Measure of ecosystem processes such as nutrient cycling via litter input provide information on functioning and stability under various stresses like deforestation, fire, drought and climate change. These ecological principles are also applicable to sal forests. The primary supply of carbon and nutrients in a terrestrial ecosystem is litter input which consists of decomposing tissues and senescent plant components. In forest ecosystem plant litter production constitutes a significant fraction of the total annual biomass production. Litter decomposition via leaching-decay-mineralization regulates nutrient and carbon fluxes. In tropical forest ecosystems the decomposition rate and mineralization process is very fast making the soil nutrient poor. So the quantity of litter input to soil serves as the primary catalyst for nutrient succession and soil fertility maintenance. Litter fall in tropical forests is regulated by species composition, stand age structure, eco-physiology of the littering species, site productivity, slope, aspect and climate particularly temperature and precipitation43,44. Apart from these factors past history (seedling origin or coppice forest), stand density and tree basal area affects annual litter quantity in sal forest45. We observed annual litter fall of 10.58 to 12.77 Mg ha−1y−1 in PSF and SDMDF respectively which is little bit higher than litter fall in sal forests of the region (6.88 Mg ha−1y−1 in coppice sal forest to 7.99 Mg ha−1y−1 in old growth sal forest). This is attributed to high plant density (545 to 746.75 stems ha−1) and inclusion of root litter in the study46. However, lower litter input of 8.93 Mg ha−1y−1 was observed in MDFWS which is lower than the average the litter fall of 9.3 Mg ha−1y−1 for tropical mixed forests43 attributed to phonological behaviour and prevalence (dominance) of low litter producing tree species such as Terminalia alata and Anogeissus acuminate 47.

Total organic carbon distribution

Soil organic matter is the key determinant of soil nutritional status and fertility. Hence maintaining it with desired quantity and quality envisages sustainability of ecological functions. Soil organic C pool is directly influenced by SOM input and regulates stability of entire ecosystem. Forest types along with other site specific attributes have an impact on the amount and quality of SOC. It is commonly known that dynamics of soil carbon stock are impacted by kind of vegetation, climate, land use and management techniques2. Under similar geologic and climatic conditions total organic C content is much higher in a broad leaved forest than other forest types48. In an ecosystem, the total SOC build up is the result of balancing carbon inputs and C losses from microbial respiration and breakdown49. Variation in total SOC content among three forest type was noticed and it was significantly high in PSF (111.45 Mg C ha−1). But, the estimated soil organic carbon in these forest types is lower than the Indian average of 182.94 Mg C ha−150. In terms of percentage, the soil organic carbon content in PSF was significantly higher (1.67%) than other two forest types (SDMDF-0.98% and MDFWS-0.71%) and it is within the range for SOC % reported for natural sal forests in other parts of the country. Banik et al.40 estimated 2.20% SOC for natural sal forests of Tripura having plant density 1160 stems ha−1 and basal area of 22.53 m2 ha−1. Similarly, Thapa et al.51 found that the natural sal forests of Meghalaya which has 788 stems ha−1 and basal area of 40.17 m2 ha−1, had a SOC value of 1.05%. In these forests though sal forms pure crop other species also present at varying degrees. In our studied three forest types contribution of sal to the total litter fall mass varied significantly from 56.83% in PSF to 29.21% in SDMDF and 5.85% in MDFWS community. The annual litter input, ranging from 8.93 to 12.77 Mg ha⁻1, is comparable to the annual litter input in the moist sal forests (11.80 Mg ha⁻1) and plateau sal forests (10.30 Mg ha⁻1) of Eastern Nepal45. This contributes between 3.89 and 5.62 Mg ha⁻1 of carbon to the soil annually. Apart from soil macrofauna who disintegrates the large sized litter to smaller sizes, arbuscular mycorrhizal fungi (AMF), in particular, are microorganisms that are essential for conversion of organic C to different forms52. Plant and AMF communities affect each other’s community structure and are mutually dependent. Tree composition and understory floral community also regulate AMF communities. It has been observed that AMF spore density and population is quite low in sal forest as compared to mixed forests53. Higher clay fraction, acidic soil condition, hard soil surface with thin to nil herbaceous layer and lower AMF population is the reason for slower decomposition of litter mass in sal forests. Slower litter decomposition coupled with cumulative accumulation litter at an average rate 10.58 Mg ha−1y−1 in PSF makes the forest floor rich in undecomposed or partially decomposed humus content and it was noted in the field survey. Stockpile of decomposed or partially decomposed humus and its recalcitrant nature retards microbial activity leading to elevated SOC content in PSF2. Another reason for elevated total SOC in PSF could be due to addition of higher fine root litter and rhizodeposition of carbon. However, SDMDF harboured numerous legume trees belonging to genus Bauhinia, Albizia, Cassia, Dalbergia and Desmodium whose foliar litter decomposes quickly as compared to the sal54. Hence mixing of legume tree litter along with that of sal could have caused quick litter decomposition and C transformation leading to lower SOC stock in SDMDF apart from higher litter accumulation55. Another reason for the lower SOC content in SDMDF than PSF in spite of higher litter input (Fig. 4) is the incidence of periodic fire for non-timber forest product collection and other anthropogenic disturbances leading to removal of forest floor litter mass.

Fig. 4
figure 4

Relationship between total SOC (Mg C ha−1) and annual litter input in PSF, SDMDF and MDFWS.

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A greater SOC stock at the upper soil layer of all forest types (Fig. 2) could be attributed to the accumulation of litter mass facilitating continuous supply of easily hydrolysable and mineralisable C owing to higher microbial activities. Kaushal et al.56 also observed higher SOC and its fractions in upper soil layer of 14 year old bamboo plantation. Very labile fraction of SOC stock was in the order PSF < SDMDF < MDFWS. This infers to the fact that there is a constant supply of raw or partially decomposed humus in PSF ecosystem than other two forest types. Very labile soil carbon fraction is proportionately higher as compared to the non-labile carbon fraction and it has also been reported in natural broadleaf forest2, temperate forest57,58, bamboo plantations56 and orchards 59.

Carbon management index (CMI) provides an insight to SOC rate change (both quantity and quality) with respect to management practices and vegetation cover60. Higher CMI value indicates a healthy soil having proper soil C builds up and good fertility status. Conversely, lower CMI value infers to the soil C degradation for some reason33. The linear decrease in CMI with respect to sal tree density in natural undisturbed forest soil signifies positive influence of sal tree litter on soil organic C build up and stability. The higher CMI values for natural undisturbed forests have been reported earlier60,61.

The total SOC was in a significant and positive correlation with VLSC, LSC, LLSC and NLSC with r > 0.94 (Fig. 4). Litter addition has a positive correlation with total SOC and SOC fractions. Soil bulk density is negatively correlated with total SOC and SOC fractions which are consistent with Ahirwal et al.58. Generally vegetation attributes such as tree density and basal area per hectare positively influences above ground litter fall and fine root litter thereby boosts SOC stock of forest62. In our study we observed tree density and basal area are negatively correlated with SOC stock and its fractions (Fig. 3). A lower total SOC stock in SDMDF and MDFWS occurs despite the higher basal area and more tree density. This is attributed to higher number of trees in lower girth classes, recurrent forest fire and various human activities causing disturbances in the area19.

Conclusions

The dynamics of soil C fractions showed how species composition affected the soil carbon pool. The soil under pure sal (PSF) stand had clearly much greater labile organic carbon than SDMDF and MDFWS. This suggests that the organic matter in soils of this forest type could easily be lost through decay if human induced disturbance is intensified. A significant (P < 0.05) positive correlation was observed between the total SOC and different carbon fractions stock. The finding of our study highlights that long rotation species such as sal has the potential to sequester and store more organic carbon in soil. Hence sal forests have the soil carbon sink capacity and can mitigate climate change impact. The information provided will aid in assessing soil C storage of different forest types on a regional and national level.