Forest Floor Biomass in Relation with Soil  Attributes under Pinus roxburghii Sarg.Plantation, North-East India

Forest ecosystems function as dynamic systems where vegetation, soil, and microbial processes interact to regulate productivity, nutrient cycling, and carbon sequestration [1]. Among these, coniferous species such as Pinus roxburghii dominate large areas of subtropical and temperate regions and play a significant role in biomass accumulation and ecosystem functioning [2]. These systems are particularly important in hilly landscapes, where they influence soil stability, hydrology, and long-term carbon storage [1, 3]. Biomass, defined as the total quantity of living and dead organic matter, is a fundamental ecological parameter used to assess ecosystem productivity and energy flow [4]. It integrates both structural and functional aspects of ecosystems and is widely used in estimating carbon stocks and nutrient dynamics. Biomass distribution within forest ecosystems varies across components such as stem, branch, foliage, and roots, and is strongly influenced by stand age, species composition, and environmental conditions. Studies across different forest systems have demonstrated that biomass accumulation generally increases with plantation age and varies significantly along ecological gradients [5, 6]. Furthermore, component-wise allocation typically shows dominance of stem biomass followed by roots, branches, and foliage, reflecting structural prioritization in tree growth [7].

The forest floor, comprising litter, woody debris, and partially decomposed organic matter, represents a critical interface between vegetation and soil. It acts as a temporary reservoir of nutrients and plays a key role in regulating decomposition processes, soil moisture, and nutrient cycling [8]. Litterfall and its subsequent decomposition provide essential inputs for soil organic matter formation and influence overall ecosystem productivity. The accumulation and turnover of forest floor biomass are governed by climatic factors, litter quality, and microbial activity, leading to spatial and temporal heterogeneity in nutrient cycling processes [9]. Soil serves as both a medium for plant growth and a major sink for carbon storage, particularly in the form of soil organic carbon (SOC). Soil physico-chemical properties such as texture, bulk density, porosity, moisture content, and pH directly influence root development, microbial activity, and nutrient availability. Previous studies have shown that soil properties under forest plantations are significantly modified by vegetation type and litter inputs [10], while soil pH in forest ecosystems typically ranges from acidic to slightly acidic conditions and may vary with depth [11].

The interaction between forest floor biomass and soil properties forms a critical feedback mechanism controlling ecosystem functioning. Litter decomposition contributes to soil organic matter, enhances aggregation, and supports microbial processes, while soil conditions regulate decomposition rates and biomass turnover. Studies have demonstrated a strong relationship between vegetation characteristics and soil carbon storage, indicating that changes in forest structure can significantly influence soil properties and carbon dynamics [12]. Despite extensive research on biomass and soil properties in various forest ecosystems, there remains a lack of site-specific studies focusing on pine plantation systems in Northeast India, particularly in Nagaland. The region is characterized by complex terrain, high rainfall variability, and evolving land-use patterns, which may significantly influence ecosystem processes. However, quantitative data on forest floor biomass and associated soil physico-chemical characteristics in these systems remain limited. Therefore, the present was undertaken with an objective  of estimating the forest floor biomass across different components and analyze few selected parameters of soil in the study area.

MATERIALS AND METHODS

Study Area

The study was conducted in a pine (Pinus roxburghii Sarg.) plantation located at Lumami, Akluto sub-division, Zunheboto district (Fig.1), Nagaland, India (26°18′–27°27′ N; 94°15′–94°40′ E). The region is characterized by a humid subtropical monsoon climate with an average temperature ranging from 27–32°C. Altitude varies between 600 and 2400 m above mean sea level. The climate can be broadly categorized into post-monsoon (October–December), winter (January–February), and pre-monsoon/summer (March–April) periods. The selected plantation was approximately 15 years old and represents a typical pine-dominated system of the region.

Estimation of Forest Floor Biomass

Forest floor biomass was quantified using five randomly placed quadrats (1 m × 1 m) at monthly intervals from October, 2017 to April, 2018. Within each quadrat, all aboveground herbaceous biomass was first harvested. The remaining forest floor material was carefully collected and categorized into: (i) fresh pine leaf litter, (ii) partially decomposed pine litter, (iii) woody pine litter (twigs, bark, cones, and branches), and (iv) herbaceous components (above and belowground). All collected samples were oven-dried at 105°C to constant weight and expressed on a dry weight basis (g m⁻²).

Soil Sampling and Analysis

Soil samples were collected from the study site during autumn and spring seasons at three depth intervals: 0–10 cm, 10–20 cm, and 20–30 cm. Samples were air-dried, ground, and sieved prior to laboratory analysis. Soil temperature was measured in situ at depths of 0–10 cm, 10–20 cm, and 20–30 cm using a soil thermometer. Soil pH was determined using a digital pH meter in a 1:10 soil–water suspension. Bulk density was determined using the core sampler method [13]. Soil moisture content was determined gravimetrically by oven-drying fresh soil samples at 105°C for 48 hours. SOC was estimated using the Walkley and Black [14]. Soil texture was determined using the International Pipette method [15] to estimate the relative proportions of sand, silt and clay. Particle density was determined by the volume displacement method and soil porosity was calculated using bulk density and particle density values:

RESULTS AND DISCUSSION

Fresh Pine Leaf Litter

Fresh leaf litter of pine showed marked temporal variation, ranging from 1.41 ± 0.27 to 12.58 ± 3.21 g m⁻², with peak accumulation observed during March and minimum during November (Fig. 2). The higher values during late winter–early summer reflect increased litterfall associated with phenological cycles of P. roxburghii, while lower values indicate reduced litter input during early post-monsoon periods [16].

Partially Decomposed Pine Leaf Litter

Partially decomposed pine leaf litter of pine ranged from 12.06 ± 2.12 to 15.80 ± 1.99 g m⁻², with a total accumulation of 97.54 g m⁻² (Fig. 3). Maximum accumulation during October and minimum during February indicate a dynamic balance between litter input and decomposition processes. Higher values during post-monsoon may be attributed to carry-over of fresh litter inputs, while subsequent decline reflects enhanced decomposition rates. Similar trends have been reported in Himalayan forest systems, where decomposition dynamics are closely linked to seasonal climatic variability [17, 18]. 

Woody Pine Litter

Woody pine litter constituted the dominant biomass component, ranging from 20.19 ± 10.36 to 45.20 ± 4.63 g m⁻², with a total contribution of 224.24 g m⁻² (Fig. 4). Peak values during March suggest increased accumulation of recalcitrant materials such as twigs, bark, and cones. The dominance of woody litter reflects slower decomposition rates due to lignified structure, consistent with earlier observations in coniferous forest ecosystems [19].

Above Ground Herbaceous Biomass

Aboveground herbaceous biomass ranged from 2.15 ± 0.84 to 5.73 ± 0.87 g m⁻², with maximum values during October and minimum during January (Fig. 5). Seasonal variation indicates strong dependence on favourable moisture and temperature conditions during post-monsoon periods, while reduced winter growth limits biomass accumulation [20].

Below Ground Herbaceous Biomass

Belowground herbaceous biomass ranged from 0.83 ± 0.34 to 3.11 ± 0.81 g m⁻², with highest values in October and lowest in January (Fig. 6). The relatively low contribution of herbaceous biomass compared to litter components reflects limited understory development under dense pine canopy conditions [21].

Total Forest Floor Biomass and Litter Components Contribution

The total forest floor biomass was estimated at 406.77 g m⁻². Among all litter components, woody litter contributed the highest proportion, followed by partially decomposed pine leaf litter, fresh pine leaf litter and herbaceous components. The dominance of woody pine litter indicates slower decomposition and accumulation of recalcitrant organic matter under pine systems. The estimated forest floor biomass (~12.6 t ha⁻¹) falls within the range reported for subtropical Himalayan forests but is lower than temperate systems. This intermediate status reflects climatic conditions that allow moderate microbial activity without extreme limitations or acceleration of decomposition processes.

Seasonal Variation in Soil Physico-Chemical Properties

Bulk density ranged from 0.63 to 0.91 g cm⁻³, with higher values recorded during the spring season and lower values during autumn. The overall low bulk density indicates relatively porous soil conditions typical of forest ecosystems, which favour root penetration and microbial activity. Soil pH showed a consistent increase with depth (20–30 cm) across both seasons. During autumn, pH ranged from 4.97 to 5.25, while in spring it ranged from 4.10 to 5.15 (Fig. 7), indicating acidic soil conditions under the pine plantation. This acidity may be attributed to the decomposition of organic matter, which releases organic acids and lowers soil pH [22]. The observed increase in pH with depth is consistent with typical forest soil profiles. SOC was highest in the surface layer (0–10 cm), with values of 2.76% in autumn and 2.57% in spring (Fig. 8). The maximum SOC was recorded during autumn (2.76%), while lower values were observed during spring (2.25%), with an overall mean of 2.49%. The higher SOC during autumn may be associated with favourable moisture conditions and enhanced litter input and decomposition [23]. A clear decline in SOC with depth was observed, reflecting reduced organic matter inputs and lower biological activity in subsurface layers [24]. Soil temperature exhibited seasonal variation, with slightly higher values during spring (20.3°C) compared to autumn (19.8°C). Temperature decreased with increasing soil depth, likely due to reduced influence of atmospheric conditions and higher thermal stability in deeper layers. Soil moisture content was higher during autumn (29.09%) and lower during spring (20.86%), with an average of 25.31% (Fig. 9). Moisture content was consistently higher in the surface layer (0–10 cm) and decreased with depth. The higher surface moisture can be attributed to the presence of litter cover, which reduces evaporation and enhances water retention, as well as the influence of soil texture [25]. Particle density increased with soil depth, reflecting decreasing organic matter content in deeper layers. Since organic matter has lower density than mineral particles, soils with higher organic content exhibit lower particle density, whereas deeper layers with reduced organic inputs show higher values [26]. Soil porosity ranged from 39% to 69%, with maximum values observed during autumn and minimum during spring (Fig. 10). Porosity increased with soil depth, likely due to reduced compaction compared to the surface layer. The inverse relationship between bulk density and porosity was evident, where lower bulk density corresponded to higher porosity, indicating better soil structure and aeration. Percentage of sand (89%) was higher during autumn followed by clay (8%) and silt (3%) (Fig.11a) and also during spring, percentage of sand (85%) was higher followed by silt(9%) and clay(6% ) (Fig.11b)  indicating the texture of soil in pine plantation site was sandy loam. Soils with coarse (sand) textures tend to have a higher bulk density than soils with fine (clay) textures [27].

CONCLUSION

The present study estimated that forest floor biomass under pine plantation was moderate (406.77 g m⁻²; ~12.6 t ha⁻¹) and dominated by woody pine litter, indicating slower decomposition due to lignified material. Herbaceous biomass contributed minimally, reflecting limited understory development. Seasonal variation in biomass components highlights a dynamic balance between litter input and decomposition driven by climatic and phenological factors. Soil properties showed clear vertical and seasonal patterns. The soil was consistently acidic (4.10–5.25), typical of pine ecosystems. SOC and moisture were higher in the surface layer and decreased with depth, emphasizing the importance of litter inputs in maintaining soil fertility. Low bulk density and higher porosity indicate favourable soil structure for root growth and microbial activity. The study demonstrates a strong linkage between litterfall dynamics and soil properties, forming a feedback system that regulates nutrient cycling and carbon storage. However, the concentration of SOC in surface layers makes the system vulnerable to disturbance, particularly under fire-prone pine conditions. Overall, the pine plantation represents a moderately productive system with active litter–soil interactions, but slower nutrient turnover due to dominance of recalcitrant litter.

References

[1] Costa, J. R. S., Brancalion, P. H. S., Joly, F. X., et al. (2026). Forest ecosystem multifunctionality: A systematic review of measures and drivers. Current Forestry Reports, 12(2). https://doi.org/10.1007/s40725-025-00266-4

[2] Pimoli, M., Joshi, V. C., Arya, S., Sundriyal, R. C., & Yadava, A. K. (2024). Impact of forest management on structure, composition, biomass and carbon stock in Chir-pine (Pinus roxburghii) forest, Western Himalaya. Environmental Challenges, 16, 100964. https://doi.org/10.1016/j.envc.2024.100964

[3] Pan, J., Cai, F., Yi, Z., Zhang, W., Yan, B., Xue, C., Yu, B., & Li, R. (2025). Landscape connectivity significantly influences the spatial spillover effects of soil erosion: Based on examples from typical karst watersheds. Ecological Indicators, 173, 113373. https://doi.org/10.1016/j.ecolind.2025.113373

[4] Osman, A., Lai, Z. Y., Farghali, M., et al. (2023). Optimizing biomass pathways to bioenergy and biochar application in electricity generation, biodiesel production, and biohydrogen production. Environmental Chemistry Letters, 21, 2639–2705. https://doi.org/10.1007/s10311-023-01613-2

[5] Ekka, P., & Saikia, P. (2026). Ecological attributes and tree biomass along urbanisation and elevation gradients in Ranchi, eastern India. Discover Cities, 3, 30. https://doi.org/10.1007/s44327-026-00212-4

[6] He, C., Li, Y., Dai, X., Liu, N., Wu, F., Yan, J., Gao, M., Liang, Y., Yuan, Z., Hao, Z., & Yin, Q. (2025). Species richness is not a good predictor for above-ground biomass in a warm temperate deciduous broadleaf forest. Ecological Processes, 4(6), 1–11. https://doi.org/10.1186/s13717-024-00569-7

[7] Marčiš, P., Pajtík, J., Konôpka, B., & Lukac, M. (2026). Height-based biomass models differ for naturally regenerated and planted young trees. Forest Ecosystems, 15, 100406. https://doi.org/10.1016/j.fecs.2025.100406

[8] Turrión, M. B., Martín-Sanz, R. C., Getino-Álvarez, M., Pando, V., & Lafuente, F. (2025). Interactions between deciduous and coniferous litter in decomposition processes: Insights from ATR-FTIR spectroscopy and elemental analyses. Forest Ecology and Management, 595, 123038. https://doi.org/10.1016/j.foreco.2025.123038

[9] Zhang, L., Yadav, V., Zhu, H., Shi, Y., Hu, X., Wang, X., Zhou, X., Babu, S., & Kang, Y. (2024). Climate drivers of litterfall biomass dynamics in three types of forest stands on the Loess Plateau. Ecological Indicators, 163, 112088. https://doi.org/10.1016/j.ecolind.2024.112088

[10] Temjen, W., Singh, M. R., Ajungla, T., Chophi, K. Z., & Semy, K. (2022). Fungal diversity and physico-chemical parameters of rhizospheric soil from banana plantation sites at Nagaland, India. Agricultural Science Digest, 42(2). doi: 10.18805/ag.D-5274.

[11] Duffy, P. J., Kabrick, J. M., Thornton, C. D., et al. (2025). Response of soil chemistry to long-term occupancy by introduced Pinus species in hardwood forests. Forest Science. https://doi.org/10.1007/s44391-025-00050-8

[12] Polman, E. M. N., Rijsdijk, K. F., Cornelissen, P., et al. (2026). Relationships between soil and vegetation properties and soil carbon stocks differ between grasslands with different water table management. Ecosystems, 29, 27. https://doi.org/10.1007/s10021-026-01046-2

[13] Blake, G. R., & Hartge, K. H. (1986). Bulk density. In A. Klute (Ed.), Methods of Soil Analysis. https://doi.org/10.2136/sssabookser5.1.2ed.c13

[14] Walkley, A., & Black, I. A. (1934). An examination of the Degtjareff method for determining soil organic matter and a proposed modification of the chromic acid titration method. Soil Science, 37, 29–38.

[15] Piper, C. S. (1942). Soil and plant analysis: Laboratory manual of methods for the examination of soils and the determination of the inorganic constituents of plants. Hassell Press.

[16] Bellingham, P. J., Morse, C. W., Buxton, R. P., Bonner, K. I., Mason, N. W. H., & Wardle, D. A. (2013). Litterfall, nutrient concentrations and decomposability of litter in a New Zealand temperate montane rain forest. New Zealand Journal of Ecology, 37(2), 162–171.

[17] Sivaranjani, S., Sarkar, M. S., Panwar, V. P., Pandey, R., Mishra, A. P., & Rathnayake, U. (2025). Modeling soil respiration: Seasonal variability and drivers in pine and broad-leaved forests of the lower Himalayas. Trees, Forests and People, 20, 100804. https://doi.org/10.1016/j.tfp.2025.100804

[18] Fernandes, S., Bernoux, M., Cerri, C., Feigl, B., & Piccolo, M. (2002). Seasonal variation of soil chemical properties and CO₂ and CH₄ fluxes in unfertilized and P-fertilized pastures in an Ultisol of the Brazilian Amazon. Geoderma, 107(3–4), 227–241.

[19] Zhang, D., Hui, D., Luo, Y., & Zhou, G. (2008). Rates of litter decomposition in terrestrial ecosystems: Global patterns and controlling factors. Journal of Plant Ecology, 1, 85–93. https://doi.org/10.1093/jpe/rtn002

[20] Rawlik, K., Nowiński, M., & Jagodziński, A. M. (2021). Short life–fast death: Decomposition rates of woody plants leaf- and herb-litter. Annals of Forest Science, 78, 6. https://doi.org/10.1007/s13595-020-01019-y

[21] Rawlik, K., Rawlik, M., & Jagodziński, A. M. (2023). Seasonal dynamics of herbaceous layer biomass and its contribution to annual net primary production in an oak–hornbeam forest. Forest Ecology and Management, 544, 121195. https://doi.org/10.1016/j.foreco.2023.121195

[22] Zhou, J. M. (2024). The relationship between soil pH and geochemical components. Environmental Earth Sciences, 83, 402. https://doi.org/10.1007/s12665-024-11711-1

[23] de Oliveira, J. D. C., de Freitas, I., Ferreira, E., et al. (2026). Spatial pattern of litter decomposition and soil carbon and nitrogen stocks in an agrosilvopastoral system in the Cerrado biome. Journal of Soil Science and Plant Nutrition. https://doi.org/10.1007/s42729-025-02985-w

[24] Piccolo, A., & Drosos, M. (2025). The essential role of humified organic matter in preserving soil health. Chemical and Biological Technologies in Agriculture, 12, 21. https://doi.org/10.1186/s40538-025-00730-0

[25] Hewelke, E., Zaniewski, P. T., Pędziwiatr, A., et al. (2025). The relations between soil hydrophobicity and vegetation in abandoned arable fields on sandy soil. Biologia, 80, 1115–1123. https://doi.org/10.1007/s11756-024-01776-5

[26] Rajkai, K., Tóth, B., Barna, G., et al. (2015). Particle-size and organic matter effects on structure and water retention of soils. Biologia, 70, 1456–1461. https://doi.org/10.1515/biolog-2015-0176

[27] Yosef, B. A., Gomi, T., Ohira, M., et al. (2025). Spatial variability of soil characteristics for estimation of soil erodibility in headwater catchments. Environmental Earth Sciences, 84, 581. https://doi.org/10.1007/s12665-025-12530-8