Raja Waqar Ahmed Khan*, Maryam Quratulain, Hamayun Shaheen*, Karamit Hussain and Abdul Basit Mehmood. J Ecol Environ 2025;49:. https://doi.org/10.5141/jee.24.088

It is essential to quantify the amount of carbon stored in the biomass of forest species to determine the potential for mitigating climate change through forest management. This study aimed to estimate the biomass carbon stock (BCS) of coniferous tree species in 16 temperate (TFs) and 4 subalpine forests (SFs) in the state of Azad Jammu and Kashmir (AJK). BCS was calculated for individual trees using allometric equations. The total BCS was 66.5 ± 6 .8 Mg ha-1, with 42.4 ± 7.3 Mg ha-1 (63.7%) in TFs and 24.2 ± 4.1 Mg ha-1 (36.3%) in SFs. The dominant species, Pinus wallichiana A.B. Jacks. and Picea smithiana (Wall.) Boiss., had corresponding BCS totals of 22.4 ± 4.6 (33.7%) and 21.7 ± 4.8 Mg ha-1 (21.7%), respectively. Abies pindrow Royle had a BCS of 14.1 ± 3.8 Mg ha-1 (21.2%), while the lowest value of 8.3 ± 1.3 Mg ha-1 (15.5%) was found in Cedrus deodara (Roxb. ex D. Don) G. Don. TFs showed healthier structural attributes, with higher tree diameter at breast height (DBH) (155.7 ± 8.2 cm) and density (157.1 ± 4.2 trees ha-1) compared to SFs, which had lower DBH (131 ± 7.4 cm) and density (113.9 ± 7.7 trees ha-1). The forests in this region are facing significant deforestation, with 154.0 ± 6.4 stumps ha-1 in temperate forests and 48.8 ± 2.8 stumps ha-1 in subalpine forests. Statistical analysis revealed a significant correlation between BCS and tree girth, height, and total stem density. This study highlights the allocation trends of BCS among keystone species in a climate-sensitive region and emphasizes the need for forest conservation in the context of climate change.

Understanding soil organic carbon dynamics is crucial for climate change mitigation and developing strategies for sustainable land management. This research aimed to quantify SOC, analyze the effect of physicochemical properties on SOC, and evaluate different statistical models to identify the effective predictors for SOC. A total of 280 soil samples were analyzed for physicochemical properties using standard protocols. The study applied several statistical models, including Linear Mixed, Random Forest, Bayesian Linear, Generalized Additive, Multivariate Regression models, and Principal Component Analysis (PCA). Undisturbed soils exhibited significantly higher SOC stocks, averaging 74.71±8.65 Mg ha⁻1, compared to 53.58±7.13 Mg ha⁻1 in disturbed soils. The LMM and GAM indicated a significant baseline SOC but showed no notable effect of altitude on SOC (p = 0.703 and 0.62-0.93, respectively). RFR identified bulk density as the strongest predictor, with the highest node purity (2269.57). PCA accounted for 78.35% of the variance, showing the critical role of soil texture in stabilizing SOC. Altitude showed a minimal effect; soil bulk density is the key factor in SOC variability, while clay content is crucial for SOC stabilization. Advanced models like RFR provide better SOC predictions, aiding sustainable land management while incorporating additional variables that could further enhance SOC forecasting.

Himalayan soils are highly susceptible to the impacts of climate change and land-use shifts. Soil organic carbon (SOC) and the carbon management index (CMI) are key measures of soil health in response to land use changes. This study assessed total organic carbon (TOC), carbon fractions, carbon pool index (CPI), CMI, and physicochemical properties in forest and agricultural land-use systems in the temperate western Himalayas. Soil samples were collected across 32 distinct sites representing forest (FS), wheat (WS), rice (RS), and maize (MS) systems (n = 32). FS exhibited the highest average TOC (25.6 g kg-1), followed by MS (19.3 g kg-1), WS (15.2 g kg-1), and RS (14 g kg-1). TOC levels differed significantly between land-use categories (ANOVA; F = 13.4, P< 0.001), with all agricultural systems showing significantly lower TOC levels than forests. The maximum concentration of labile carbon (LC) was identified in FS (4.3%), while the lowest was observed in WS (2.8%). MS had the highest CPI (0.75), whereas RS had the highest average CMI (85.5), indicating distinct carbon management dynamics despite lower baseline TOC. TOC was positively correlated with altitude and electrical conductivity but negatively correlated with soil pH. Multiple linear regression (R2 = 0.70) confirmed land-use type as the primary predictor of TOC variation, showing significant reductions for RS (11.6 g kg-1), WS (10.7 g kg-1), and MS (4.9 g kg-1) compared to the forest baseline (P< 0.05). Principal component analysis successfully differentiated agricultural and forest systems based on topography, soil properties, and carbon pools. The primary cause of SOC depletion is the initial conversion of forests to farmland, underscoring the need for climate smart land management to maintain ecosystem services and replenish regional carbon stocks.