Carbon Storage and Sequestration Potential of Sacred Groves in Pakur District, Jharkhand

Sacred groves are fragmented forest remnants preserved over generations by local communities based on ancient practices that have important implications for biodiversity and carbon offsets (Bafakeeh et al., 2012; Singh et al., 2022). They play a vital role in conserving delicate ecosystems and serve as significant carbon sinks (Sahu et al., 2015). These groves are crucial sanctuaries for many rare and endemic species, helping protect forest biodiversity (Kulkarni et al., 2015). The locals uphold these sacred areas and have a deep respect for the flora, fostering both the preservation of species and ecological stability. Additionally, aboveground and belowground trees, as well as soil organic carbon in sacred groves, bolster soil structure, reduce erosion, improve water retention, and enhance agricultural productivity (Bhattacharya and Nandi, 2019). The present study evaluates the carbon stock and sequestration potential of some sacred groves in Pakur District of Jharkhand to understand their contribution to climate change mitigation. Sacred groves are sacred community-conserved areas that conserve biodiversity and deliver several ecosystem services including carbon pool. However, carbon pool and its disaggregated components in sacred groves of Jharkhand, particularly in Pakur district, is under-researched. Thus, the present study was conducted in Pakur district to estimate the total carbon storage and carbon sequestration potential in selected sacred groves of Pakur district, Jharkhand, India. For this, allometric equations were employed to estimate biomass in trees, shrubs, and herbs of above gradient, below gradient, litter, and soil organic carbon to evaluate total, aboveground, and belowground carbon pool of sacred groves (Joshi & Garkoti, 2024).

MATERIALS AND METHODS

2.1. Study Area

The study sites are located in Pakur District of Jharkhand, India which lies between 24?38’ N to 24?50’ N latitudes and 87?50’ E to 88?5’ E longitudes. Pakur district is one of the twenty-four districts of Jharkhand state, India, and Pakur is the administrative headquarters of this district. Pakur sub-division of Sahibganj district was carved out on 28 January 1994 to constitute Pakur District. Pakur district lies in the north-eastern portion of Santhal Pargana in Jharkhand covering an area of 686.21 km2. Pakur is predominantly a hilly area with certain pockets of plain land. Topographically it is divided into three parts i.e. the hilly area, the rolling area and alluvial area. The hilly area includes the whole of Damini-i-koh from northern corner of Pakur district up to the south-west bordering the Birbhum district of West Bengal. A narrow continuous strip of alluvial soil which lies between the Ganga feeder canal and the loop line of Eastern Railway is very fertile. The rest of the part covers the Rolling areas, which is less conducive for agriculture (Sharma, 2016). Average altitude of the district is about 300 meters above MSL. There are three main rivers in this district namely, Bansloi, Torai & Brahmini. Rivers Bansloi & Torai flow in the middle while Brahmini flows in the Southern portion of the district. The district is characterized by three distinct seasons – summer, rainy and winter. The summers are extremely hot and lasts from March to May with maximum temperature rising up to 42 to 46 °C in some places. The winters are cold and last from November to February with minimum temperature going around 3 °C. The average annual rainfall is about of 1200 mm to 1400 mm. According to the classification of forest types of India, the forests of Pakur district fall into Northern Dry Peninsular Sal Forest–5B/C-1 type, Northern Tropical Dry Mixed Deciduous Forest– 5B/C-2 type and Tropical Dry Deciduous Scrub Forest–5B/DS-1 type (Champion and Seth1968). The forest is mainly dominated by Shorea robusta trees and their associates namely- Terminalia chebula, Buchanania lanzan, Semicarpus anacardium, etc. along with occasional bamboo brakes. These forests of entire Santhal Parganas (which now include the districts of Pakur, Godda, Sahibganj, Dumka and Jamtara) were rich in wildlife at the turn of the 20th century, within the next few decades’ large mammalian fauna was wiped off due to rampant hunting and habitat loss. Birdlife is rich in these forests. (https://pakur.nic.in).

Figure 1. map of the study area

2.2. Site Selection and Sampling Design

A field survey of the district Pakur of Jharkhand has been carried out and a total of 46 sacred groves (Jaher Thaans), collectively encompassing an area of approximately 8.57 hectares, were identified and selected across six forest blocks of Pakur District, Jharkhand. Each grove was geo-referenced using a Global Positioning System (GPS) device to accurately delineate its spatial boundaries and topographic features. The selection criteria included grove accessibility, representativeness of local vegetation types, and traditional protection status, following the methods suggested by Malhotra et al. (2007) and Gadgil and Vartak (1976) for cultural forest site inventories. Within each sacred grove, quadrats of 10 m × 10 m were randomly established to quantify tree biomass and stand structure. Nested 1 m² subplots were employed within each quadrat for the systematic collection of leaf litter and soil samples to ensure uniform representation of micro-habitats (Kent & Coker, 1992). All trees with diameter at breast height (DBH) ≥ 30 cm were measured for DBH and total height using a diameter tape and clinometer, respectively.

2.3. Biomass and Carbon Estimation

1. 1. 1. Above-Ground Biomass (AGB)

The above-ground biomass of individual trees was estimated using the pan-tropical allometric model proposed by (Chave et al. 2014).

AGB _tree? = 0.0673 × [ρ×D²×H]^0.976

Where,

D = diameter at breast height (cm),

H = tree height (m), and

 ρ = wood density (g cm?³).

Species-specific wood densities were obtained from the Global Wood Density Database (Zanne et al., 2009). The model is widely used in tropical regions for its robustness across forest types (Sileshi, 2014).

1. 1. 2. Below-Ground Biomass (BGB)

The below-ground biomass was estimated using the IPCC (2006) default root:shoot ratio (R = 0.24) for tropical dry forests:

BGB = AGB × R

This approach provides a reliable approximation of subterranean carbon pools where direct root measurement is not feasible (Mac Dicken, 1997).

1. 1. 3. Soil Organic Carbon (SOC)

Soil samples were collected from the 0–30 cm depth at three random points per grove and homogenized to obtain a composite sample. Bulk density (BD) was determined in the field using the core sampler method. Soil Organic Carbon (SOC) content was analyzed by the Walkley–Black wet oxidation method (Walkley & Black, 1934). The SOC stock was then calculated using:

SOC (tC/ha) = BD × depth(m) × 10000 × (SOC%/100)

1. 1. 4. Litter Carbon Stock

Leaf litter was collected from 1 m² subplots, oven-dried at 65 °C to constant weight, and weighed to determine dry mass (Anderson & Ingram, 1993). The carbon content was assumed as 45 % of the dry mass, following IPCC (2006) guidelines.

Litter annual (t/ha/yr) = (leaf litter dry weight g/m²) × 10000 ÷ 1000

Litterfall is an annual flux into the soil/litter pool and is not a standing stock.

1. 1. 5. Carbon Stock Conversion and Pool Aggregation

Biomass was converted into carbon using the standard carbon fraction (CF = 0.47) recommended by (IPCC 2006) and (Mac Dicken 1997).

Carbon (tC) = Biomass (t dry matter) × CF

Total carbon stock was obtained by summing the carbon stored in AGB, BGB, SOC, and leaf litter pools. The carbon stocks were further converted into CO? equivalents using a conversion factor of 3.67 (IPCC, 2006).

1. 1. 6. Statistical Analysis

Descriptive statistics and inferential analyses were conducted using SPSS Version 26.0. A one-way Analysis of Variance (ANOVA) was performed to test for significant differences in carbon stocks among the different sacred groves. Additionally, Pearson’s correlation analysis was employed to assess the relationship between tree density and total carbon stock as well as inter-pool relationships (AGB–BGB, AGB–SOC). Graphical representations were prepared using Microsoft Excel 365 and R v4.3. The significance threshold was set at p < 0.05.

RESULT AND DISCUSSION

The present study demonstrates that the sacred groves (Jaher Thans) of Pakur district act as biomass-rich and carbon-dense forest patches, despite their limited area and fragmented distribution within a human-dominated landscape. Similar to other community-protected forests in India, these groves exhibit high structural complexity and biomass accumulation due to long-term protection from extraction and disturbance (Nath et al., 2015; Upadhyay et al., 2019).

Table 1. Details of sacred groves identified in Palur district, Jharkhand, India

Across the 46 surveyed sacred groves, tree density varied from 2 to 15 individuals per grove, reflecting differences in grove size, protection status, and site-specific ecological conditions. The aboveground biomass (AGB) ranged widely from 35.52 t ha?¹ in Bisunpur Gada Tola to 1,580.52 t ha?¹ in Patharadaha, indicating strong heterogeneity in forest maturity and stand structure. Correspondingly, belowground biomass (BGB) ranged from 8.52 to 379.33 t ha?¹, contributing substantially to the overall biomass pool, consistent with allometric-based biomass partitioning reported for tropical forests (IPCC, 2019; Chave et al., 2014).

Table 2. Tree biomass (t/ha.) and carbon (t C/ha) of trees in different sacred groves of Pakur district, Jharkhand. (AGB, aboveground biomass; BGB, belowground biomass; TB, total biomass; TWC, total woody carbon; SOC, soil organic carbon (0–30 cm); LL, Leaf Litter and TCS, total carbon stock (TWC + SOC), CO_2, sequestration equivalent)

Photos of some Sacred Groves (Jaherthans) identified in Pakur district, Jharkhand, India

4.1 1 Tree Biomass Distribution

The total aboveground biomass (AGB) across all sacred groves was estimated at 14,267.26 t/ha., while belowground biomass (BGB) contributed an additional 3,424.21 t/ha., resulting in a total biomass (TB) of 17,691.47 t/ha. On average, AGB constituted approximately 80.6% of the total biomass, whereas BGB accounted for 19.4%, The total biomass (TB) varied markedly among groves, from 44.04 to 1,959.85 t ha?¹. Among the studied groves, Patharadaha, Torai, Jamkanali, Bisunpur, and Bara Salghati exhibited notably higher biomass values, reflecting the presence of mature trees, higher basal area, and relatively undisturbed site conditions. In contrast, smaller groves with fewer trees and younger stand structures, such as Bisunpur Gada Tola, Tursadih, and Chhota Hiranpur, recorded comparatively lower biomass values.The dominance of AGB indicates that a major portion of the total biomass is stored in the standing woody vegetation, which aligns with findings from other tropical and subtropical forest ecosystems (Brown, 1997; Chave et al., 2014).

Figure 2. Biomass contribution of top ten Sacred groves

4.2 Carbon Stock Components

The total woody carbon (TWC) stored in the sacred groves was estimated at 8,314.99 t C, while soil organic carbon (SOC) within the 0–30 cm depth contributed 153.16 t C. Leaf litter (LL) added an additional 31.76 t C, indicating active nutrient cycling and organic matter input within these groves. The total woody carbon (TWC) followed a similar spatial pattern, ranging from 20.70 to 921.13 t C ha?¹, with Patharadaha emerging as the largest carbon reservoir among all groves. Other groves such as Torai (379.77 t C ha?¹), Jamkanali (370.04 t C ha?¹), Bisunpur (369.62 t C ha?¹), Kadampur (285.35 t C ha?¹), and Bheta Tola (286.78 t C ha?¹) also recorded high woody carbon stocks. The dominance of woody biomass in total carbon storage is consistent with findings from tropical deciduous and community-managed forests across India (Upadhyay et al., 2019; Sahoo et al., 2020). The soil organic carbon (SOC) stock in the 0–30 cm soil layer ranged from 0.63 to 7.56 t C ha?¹, with higher values observed in groves such as Bisunpur Upper Tola, Kadampur, Suggadih, Jamugaria, and Mohanpur. SOC accumulation reflects continuous organic matter inputs from litter fall, root turnover, and minimal soil disturbance, and represents a relatively stable long-term carbon pool (Lal, 2005; Batjes, 2016). The leaf litter (LL) carbon pool ranged from 0.09 to 1.71 t C ha?¹, indicating active litter production and nutrient cycling, a characteristic feature of undisturbed sacred groves (Tripathi et al., 2016). The total carbon stock (TCS), integrating woody carbon, soil organic carbon, and leaf litter, ranged from 23.19 to 925.59 t C ha?¹. On a per-hectare basis, several sacred groves exhibited carbon densities comparable to or exceeding those reported for larger tropical forest tracts in eastern India (Nath et al., 2015; Sahoo et al., 2020). Groves such as Patharadaha, Torai, Jamkanali, Bisunpur, Kadampur, Paderkola, and Suggadih emerged as major carbon hotspots within the district.

4.3 Total CO? Sequestration

In terms of CO? sequestration potential, values ranged from 85.10 to 3,396.90 t CO? equivalent ha?¹, underscoring the significant role of sacred groves in climate change mitigation. The exceptionally high sequestration potential of larger and well-protected groves highlights their importance as nature-based solutions, contributing simultaneously to carbon regulation, biodiversity conservation, and cultural ecosystem services (IPCC, 2019; Upadhyay et al., 2019). Overall, the results reaffirm that Jaher Thans function not only as religious landscapes but also as ecologically critical carbon sinks in Pakur district, Jharkhand.

4.4 Contribution of Different Carbon Pools

The quantitative assessment of carbon pools clearly demonstrates that woody biomass is the overwhelmingly dominant contributor to total carbon storage in the sacred groves. Out of the total carbon stock of 8,499.91 t C, woody carbon (TWC) alone accounted for 8,314.99 t C, contributing approximately 97.82% of the total carbon pool. This exceptionally high proportion reflects the prevalence of mature trees, high basal area, and long-term protection of these groves, which together promote the accumulation of carbon in long-lived woody tissues. Such dominance of woody carbon is characteristic of well-preserved tropical forest systems, where standing biomass functions as the primary and most stable carbon reservoir. Soil organic carbon (SOC), estimated at 153.16 t C, contributed about 1.80% of the total carbon stock. Although quantitatively much smaller than woody carbon, SOC represents a critical long-term and relatively stable carbon pool. Its accumulation in the upper 0–30 cm soil layer indicates sustained organic matter inputs through litter fall, root turnover, and microbial processes, supported by minimal soil disturbance due to traditional protection practices. The SOC pool plays a key role in enhancing soil fertility, water-holding capacity, and ecosystem resilience, while also acting as a buffer against short-term carbon losses. Leaf litter (LL) formed the smallest carbon pool, with a total stock of 31.76 t C, contributing only 0.37% of the overall carbon stock. Despite its limited quantitative contribution, the litter layer is a highly dynamic and functionally important component of the carbon cycle. Continuous litter production and decomposition facilitate rapid nutrient cycling and act as a crucial linkage between aboveground biomass and soil carbon pools. Over time, this dynamic pool indirectly supports SOC buildup, reinforcing long-term carbon sequestration. Overall, the percentage-based contribution of carbon pools followed the order woody biomass (97.82%) ? soil organic carbon (1.80%) > leaf litter (0.37%), as illustrated in the accompanying graph. This distribution highlights that the carbon sequestration potential of sacred groves is primarily driven by standing woody vegetation, while soil and litter pools play complementary but ecologically indispensable roles. The integrated functioning of these pools underscores the importance of conserving Jaher Thans not only as cultural landscapes but also as highly efficient, multi-pool carbon sinks in Pakur district, Jharkhand.

Figure 4. Contribution of different Carbon Pools in Co2 Sequestration (in %)

A one-way ANOVA test was performed to determine whether the mean carbon content among different pools (AGB, BGB, SOC, and LLC) differed significantly. The results showed a highly significant variation (p < 0.01) among the carbon pools, confirming that each pool contributes differently to total CO? sequestration. The F-value obtained was considerably greater than the critical F-value, suggesting that the differences are not due to random variation but to the inherent structural and functional differences among the pools. The post-hoc Tukey’s test further revealed that that AGB differed significantly (p < 0.01) from SOC and LLC, and BGB also differed significantly (p < 0.01) from SOC and LLC, confirming that biomass-derived carbon pools store substantially more carbon than soil and litter pools. In contrast, no statistically significant difference was observed between SOC and LLC (p > 0.05), indicating that both pools contribute similarly low amounts of carbon relative to biomass pools. Furthermore, a Pearson correlation analysis revealed a strong and highly significant positive relationship between AGB and BGB (r > 0.90, p < 0.01), a moderate positive association between AGB and SOC (r ≈ 0.30–0.45, p < 0.05), a weak to moderate relationship between AGB and LLC (r ≈ 0.25–0.40), and a weak, statistically non-significant correlation between SOC and LLC (p > 0.05), indicating that biomass-based pools are tightly coupled while soil and litter carbon pools respond more independently due to their dynamic and process-driven nature.

Fig 1. AGB exhibited a strong linear relationship with BGB, indicating tight coupling between above- and belowground carbon allocation (r > 0.90, p < 0.01), between these two biomass-based pools

Fig 2. AGB showed a moderate positive association with SOC (r ≈ 0.30–0.45, p < 0.05), suggesting enhanced soil carbon accumulation in biomass-rich groves

Fig 3. SOC and LLC exhibited a weak and non-significant relationship (p > 0.05), reflecting the temporal lag between litter input and soil carbon stabilization