Altitude-dependent Biomass accumulation and Carbon Storage Potential of Agroforestry Systems in Garhwal Region, India

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The present study was conducted across three altitudinal zones (800–2300 m) in the Garhwal Himalaya, Uttarakhand, to assess biomass accumulation, carbon stocks, and soil organic carbon (SOC) under different agroforestry systems. A total of 14 agroforestry models representing agri-silviculture (AS), agri-horticulture (AH), and agri-silvi-horticulture (ASH) systems were evaluated across 12 villages. Results revealed significant variation in aboveground biomass density (AGBD), belowground biomass density (BGBD), and total biomass density (TBD) among systems and altitudes. Mixed systems, particularly ASH (homegarden), consistently recorded higher biomass and carbon stocks, with the highest values observed in HASH6 (162.67 t ha⁻¹ biomass and 73.20 t C ha⁻¹). SOC constituted the largest carbon (C) pool across all systems, yet showed only weak correlation with biomass components, suggesting complex soil carbon dynamics. Biomass and C stocks generally increased with altitude, likely due to favorable climatic conditions and reduced decomposition rates. The study highlights that system composition, particularly the inclusion of diverse tree species, plays a critical role compared to altitude alone in determining C sequestration potential. Promoting diversified agroforestry systems can enhance long-term carbon storage and support climate-resilient land-use strategies in the Himalayan ecosystem. Agroforestry systems Biomass density Carbon stock Soil organic carbon (SOC) Altitudinal gradient Himalayan region Figures Figure 1 Figure 2 Figure 3 Introduction Over the last century, intensified agricultural management has exerted considerable pressure on the environment, resulting in biodiversity loss (IPBES 2019), increased greenhouse gas emissions (IPCC 2023 ), and accelerated nutrient cycling processes (Peñuelas and Sardans 2022 ). Additionally, anthropogenic activities have caused an estimated 1.0°C increase in global temperature above pre-industrial levels, and it is expected that, if current emission trends persist, global temperatures will rise by 1.5°C above pre-industrial levels between 2030 and 2052 (IPCC 2018 ). Carbon capture is now widely considered an important approach for limiting the rise of in the atmosphere (Mangalassery et al. 2014 ). The soil and plant biomass that make up the large terrestrial carbon pool can absorb and store atmospheric carbon through photosynthesis (Kaul et al. 2010 ). Soil supports essential ecosystems functios, with Carbon storage playing a crucial role in climate regulations, influencing primary productivity and nutrient cycling (Wiesmeier et al. 2019 ). Agroforestry, the combination of trees and crops, captures and stores large amounts of atmospheric carbon dioxide (CO2) in biomass and soil, which can improve soil nutrients and combat climate change by enhancing carbon cycling (Branch and Wulfmeyer. 2019), indicating the potential to mitigate climate change through carbon sequestration (Nair et al. 2009 ; Udawatta et al. 2017 ). Soil organic carbon (SOC) is a major carbon pool because carbon remains stored in soil for a long period, and its level is strongly influenced by land-use type (Alemu 2012 ). It is estimated active carbon pools are largely contributed by the plant litter, belowground decomposition and microbial biomass and because of their higher liability, they act as a major source of nutrients in topsoil (Yang et al. 2009 ). At the same time, fine tree roots and root exudates can add organic matter to deeper soil layers, thereby improving soil carbon storage at greater depths in agroforestry systems (Cardinael et al. 2018 ). Trees are among the major C pools that contribute to global carbon cycling. Hence, tree-based land use systems, particularly in non-forested environments such as agricultural land, have an important impact on the process. Planting trees in agroforestry systems enhances the quantity of carbon sequestration with time as the tree’s biomass increases with the increase of tree age (Yasin et al. 2019 ). Among different systems assessed under the Land-Use, Land-Use Change, and Forestry (LULUCF) framework, agroforestry has been reported to have a high potential for carbon sequestration (Nair and Garrity 2012 ). Global assessments indicate that land-use-based interventions can reduce emissions by nearly 30% by enhancing carbon sequestration, making them essential for meeting the emission-reduction targets outlined at the COP-25 summit (Aynekulu et al. 2020 ). Agroforestry systems can store carbon in both vegetation and soils; however, the level of carbon storage differs among regions and system types (Chatterjee et al. 2018 ). This storage potential depends on tree species composition, planting density, management practices, and tree age (Asigbaase et al. 2021). But the structure and functioning of agroforestry systems vary widely with local climatic conditions, including temperature, elevation, soil, and precipitation pattern (Nath et al. 2022 ; Sharma et al. 2022 ). Numerous studies from the Himalayan region have been done on biomass production and carbon sequestration in agroforestry systems, which show that biomass production and carbon stock vary among agroforestry systems; agri-horti-silviculture and horti-silvi-pastoral which show higher biomass accumulation and carbon sequestration than agri-silviculture or boundary plantations (Panwar et al. 2022 ). Altitude also influences carbon storage, as Singh et al. ( 2024 ) observed that the maximum total carbon density was recorded at higher elevations, However, future studies are needed to investigate the effects of particular tree-crop combinations within the agroforestry system on the levels of soil organic carbon (SOC), carbon sequestration potential and biomass accumulation in northwestern Himalayas. Therefore, more studies at regional levels are required to better understand this potential (Das et al. 2020 ) given the significance to this purpose, the study was conducted to explore the effect of altitude variation of different agroforestry system. This study hypothesized that biomass accumulation and carbon storage potential between different agroforestry land-use systems vary with altitudes. Keeping this in view, the present investigation was carried out with the objective of quantifying biomass, carbon stock, and soil organic carbon (SOC) of different agroforestry systems in the Garhwal Himalaya region. Material and methods Site description The study was conducted in the hilly region of Garhwal Himalaya covering district Pauri and Rudraprayag of Uttarakhand state, India. For district Pauri, the altitude ranges from 226m to 3,068 msl, the average rainfall as 1231 mm, and temperature as 35.55℃ to -1.42℃. In Rudraprayag district, the altitude ranges from 507 to 7,061 m, with a average rainfall of about 1,913 mm. The minimum and maximum temperatures are − 5.95°C and 33.24°C, respectively. The land Use and Land Cover (LULC) categories of both districts are presented in Table 1 (NMCG 2023). Table 1 Land use and land cover of Pauri and Rudraprayag district District name Built-up land Cropland Plantation Water bodies Scrubland Open forests Dense forests Snow and glacier Total geographical area Pauri 9.09 km 2 328.44 km 2 654.84 km 2 81.21 km 2 20.59 km 2 2512.7 km 2 1889.71 km 2 - 5496.58 km 2 Rudraprayag 0.85 km² 75.36 km² 218.09 km² - 227.80 km² 342.40 km² 1087.69 km² 58.91 km² 2011.10 km² Selection of study sites and sampling size A preliminary survey was conducted to identify the study area with a dominant agroforestry system and to document respondents' agroforestry practices. The villages are divided into three elevational zone, i.e., 800–1300m (lower elevation), 1300-1800m (middle elevation) and 1800–2300m (upper elevation) making a total of 12 villages (four in each altitude zone) selected for study purposes (Table 2 , Fig. 1 ). The sample villages were dominated by three agroforestry systems comprising of agri-silviculture (trees on bunds), agri-horticulture (scattered fruit trees with agriculture crops) and, agri-silvi-horticulture (homegarden). Initially, 17 agroforestry models were identified, of which 14 were selected to meet the study's objectives. From each model, five farms were randomly selected, resulting in a total of 70 sampled farm fields. Details of the agroforestry systems, associated components, and tree density under each system are provided in Table 3 . The data represent lower, middle, and upper elevation-based agroforestry equivalents, such as LASH, MASH, MAH, HAS, etc. For data collection, 'nali' was selected as the land measuring unit, as it is a traditional unit of land measurement in the hill region of Uttarakhand. One nali is approximately 200.67 m² (≈ 0.020 ha or 0.0496 acres or 2,160 sq ft) (Bist 2024 ). Table 2 Geograpical description of selected village of district Pauri and Rudraprayag, Uttarakhand Elevation District Village Altitude (m) Longitude Latitude Lower (800-1300m) Rudraprayag Fatehpur 823 30°14.080’ 078°56.277’ Rudraprayag Kandai 895 30°12.696’ 078°56.767’ Pauri Bhandhai 1219 30°12.497’ 078°53.862’ Rudraprayag Gahadkhal 1299 30°12.448’ 078º56.046’ Middle (130 1 -1800m) Rudraprayag Kotmalla 1346 30°16.297’ 079°04.535’ Pauri Bughani 1410 30°11.658’ 078°51.110’ Pauri Budeshu 1595 30°11.242’ 078°52.892’ Pauri Markhora 1460 30°10.770’ 078°51.198’ Higher (180 1 -2300m) Pauri Jhinoli 1859 30° 01.534′ 79° 03.123′ Rudraprayag Jundoli 1995 30°12.494’ 078°55.768’ Pauri Naini 2080 30°04.139’ 079°04.991’ Pauri Musaiti 2034 30º01.794’ 079°01.241’ Table 3 Selected agroforestry system and their component across altitudinal ranges (800-2300m) Altitude AF System Code Forest trees spp. Horticulture trees spp. Trees/nali Trees/ha Rabi crops Kharif crops Lower (800–1300 m) AS LAS 1 Grewia optiva – 6 300 Triticum aestivum, Hordeum vulgare Echinochloa frumentacea, Eleusine coracana AH LAH 2 – Citrus sinensis 6 300 Pisum sativum, Lens culinaris Glycine max, Phaseolus vulgaris, Eleusine coracana ASH LASH 3 Grewia optiva, Ficus auriculata Citrus sinensis 3.2/4.4 160/220 Allium cepa, Allium sativum Vigna mungo, Phaseolus vulgaris, Zea mays Middle (1300–1800 m) AS MAS 1 Celtis australis – 4 200 Triticum aestivum, Hordeum vulgare Echinochloa frumentacea, Eleusine coracana AH MAH 2 – Citrus sinensis, Prunus domestica, Pyrus pashia 6.4 320 Allium cepa, Allium sativum, Solanum tuberosum Cajanus cajan, Cucurbita maxima AH MAH 3 – Citrus sinensis 5.6 280 Solanum tuberosum Vigna mungo, Zea mays ASH MASH 4 Grewia optiva, Quercus leucotricophora Citrus sinensis, Prunus domestica 4.8 240 Allium cepa, Allium sativum Cajanus cajan, Cucurbita maxima ASH MASH 5 Grewia optiva Pyrus pashia, Juglan regia, Citrus sinensis 2.8/6.8 140/340 Solanum tuberosum, Allium cepa Vigna mungo, Lagenaria siceraria Higher (1800–2300 m) AS HAS 1 Quercus leucotricophora – 7.2 360 Triticum aestivum, Hordeum vulgare Echinochloa frumentacea, Eleusine coracana AH HAH 2 – Citrus sinensis, Malus domestica, Prunus persica 6.4 320 Solanum tuberosum Vigna mungo, Phaseolus vulgaris AH HAH 3 – Citrus sinensis, Citrus reticulata, Citrus limon 8 400 Solanum tuberosum Zea mays, Cucumis sativus ASH HASH 4 Quercus leucotricophora, Ficus nerifolia Citrus sinensis 6.8/3.2 340/160 Triticum aestivum Zea mays, Curcuma longa ASH HASH 5 Quercus leucotricophora Juglan regia, Pyrus pashia 4.4/2.8 220/140 Solanum tuberosum Phaseolus vulgaris, Lagenaria siceraria ASH HASH 6 Quercus leucotricophora, Ficus nerifolia Citrus reticulata, Prunus persica 6.4/4 320/200 Solanum tuberosum, Allium cepa Colocasia esculenta, Cucurbita maxima Data collection Five sample plots of 10 × 10 m2 were laid down in cropping systems in each site. Inside each of the larger plots, 1 × 1 sub-plots were laid for soil sampling. Diameter at Breast Height (DBH) and tree height were measured in all plots using a diameter tape and Ravi’s multimeter. The volume of the selected tree was calculated from the recorded DBH and height using the individual-tree volume equation and the generic volume equation (Table 4 ). Soil samples were collected from two depths (0–30 and 30–60 cm) to make a composite sample. A total of 140 composite samples were analyzed to determine SOC and bulk density using a 5 cm diameter core sampler and soil auger. Table 4 Volume equation of different tree species Tree species Volume equation Reference Grewia optiva V = − 0.44075 + 7.49221D − 36.09962D2 + 71.91238D F.S.I.1996 Quercus oppositifolia 0.08519–1.24724*D + 7.94479*D 2 F.S.I.1996 Ficus palmata √V = 0.03629 + 3.95389D − 0.84421√D F.S.I.1996 Ficus auriculata √V = 0.03629 + 3.95389D − 0.84421√D F.S.I.1996 Celtis australis V = 0.23781–2.09431*D + 7.78268*D 2 Singh et al. 2012 Prunus spp. V = 0.193297–2.267002 × D + 10.679492 × D 2 Ram et al., 2014 Juglans regia V = 0.23781–2.09431*D + 7.78268*D Singh et al. 2012 Others V = 0.00855 + 0.4432D2 + 0.28813D 2 H F.S.I. 1996 Laboratory analysis The C content of the soil samples was determined using the Walkley–Black method (Rosell et al. 2001 ) while bulk density was determined using oven dry method (Black et al. 1986). Data estimation: The Growing Stock Volume Density (GSVD) was estimated using volume equations (Table 4 ). The estimated GSVD (m3 ha − 1) was then converted into AGBD of the tree by multiplying GSVD by BEF. Above Ground Biomass Density (AGBD) was estimated using the Biomass Expansion Factor (BEF), while Below Ground Biomass Density (BGBD) was calculated using a regression equation based on AGBD. The Total Biomass Density (TBD) was calculated as the sum of AGBD and BGBD. Tree Carbon Density (TCD) was estimated by applying a conversion factor of 0.45, assuming that 45% of total biomass is carbon. Soil Organic Carbon (SOC) and Bulk Density (BD) were calculated using standard procedures. The equations used for estimating biomass, carbon density, and soil parameters are presented in Table 5 . Table 5 Equations for biomass, carbon stock and soil parameters Parameter Equation Reference AGBD BEF Brown et al. 1999 Pine 1.68 (for GSVD 100 m 3 ha − 1 ) Hardwood exp {1.91 − 0.34 × ln (GSVD)} (for GSVD ≤ 200 m 3 ha − 1 ) BGBD exp {−1.059 + 0.884 × ln (AGBD) + 0.284} TBD AGBD + BGBD IPCC 2006 AGC AGBD × Carbon fraction (conifers = 0.46, hardwood = 0.45) Negi et al. 2003 BGC BGBD × Carbon fraction (conifers = 0.46, hardwood = 0.45) TCD AGC + BGC IPCC 2006 SOC (t ha − 1 ) Soil bulk density (gcm − 3 ) × soil depth(cm) × SOC (%) × 100 Pearson et al. 2005 BD (gcm − 3 ) ODS (g)/Volume of core sampler (cm3 Statistical analysis Pearson’s correlation analysis was performed to examine the relationship between biomass and carbon stock. The analysis was conducted using OriginPro 2026. ANOVA was performed to examine the variation in biomass and carbon stock among the agroforestry system. Since soil organic carbon (SOC) did not differ significantly among the agroforestry systems ( p > 0.05 ) , no further tests were performed, whereas Tukey’s HSD test was used to compare biomass and tree carbon among agroforestry systems using OPSTAT. Results Intrazonal biomass estimation The results showed significant differences (p ≤ 0.05) in above-ground biomass density (AGBD t ha-1), below-ground biomass density (BGBD t ha-1), and total biomass density (TBD t ha-1) among agroforestry systems across different altitudinal ranges as depicted in (Table 6 ). In lower elevation, the highest biomass was recorded in LASH 3 (86.87a, 22.51a, and 109.23a) for AGBD, BGBD, and TBD respectively, while the lowest values were observed in LAH 2 (15.45b, 5.13b, and 20.58b. In middle elevation, MASH 4 (82.02a, 22.59a, and 104.61a) and MASH 5 performed best in terms of AGBD, BGBD and TBD (69.08a, 20.25b, and 89.34a). MAS1 and MAH1 produced intermediate values of studied parameters while MAH 3 (17.07b, 5.83b, and 22.89b) showed the lowest biomass across all parameters. Similarly, in the higher elevation, HASH dominated with HASH 6 exhibiting the highest biomass (128.51a, 34.16a, and 162.67a), closely followed by HASH 4 . HAH 3 recorded the lowest values (36.77c, 13.09c, and 49.86c) while intermediate values were produced by HAS 1 and HAH 2 (‘bc’). According to total biomass accumulation of trees was found in the order of HASH 6 > HASH 4 > HASH 5 > LASH 3 (Table 6 ). Overall, the mixed HASH system consistently accumulated carbon 2–3 times more than simple agroforestry systems such as HAH at higher elevations. Table 6 Aboveground biomass (AGB), Belowground biomass density (BGB) and Total biomass (TBD) (t ha − 1 ) production in selected Agroforestry (AF) systems (800-1300m) AGBD BGBD TBD LAS 1 59.82 ab 16.47 a 76.30 ab LAH 2 15.45 b 5.13 b 20.58 b LASH 3 86.87 a 22.51 a 109.23 a (1301-1800m) AGBD BGBD TBD MAS 1 60.78 ab 16.75 b 77.54 ab MAH 2 48.50 ab 14.48 b 62.98 ab MAH 3 17.07 b 5.83 b 22.89 b MASH 4 82.02 a 22.59 a 104.61 a MASH 5 69.08 a 20.25 b 89.34 a (1801-2300m) AGBD BGBD TBD HAS 1 73.13 bc 20.36 bc 93.50 bc HAH 2 66.78 bc 20.79 bc 87.58 bc HAH 3 36.77 c 13.09 c 49.86 c HASH 4 117.37 ab 31.74 ab 149.12 ab HASH 5 95.73 ab 25.75 abc 121.48 ab HASH 6 128.51 a 34.16 a 162.67 a Letters used as subscript represents variance among the systems and same letters depict non-significant variance among two systems. LAS – Lower altitude agri-silviculture system, LAH – Lower altitude agri-horticulture system, LASH – Lower altitude agri-silvi-horticulture system. MAS – Middle altitude agri-silviculture system, MAH – Middle altitude agri-horticulture system, MASH – Middle altitude agri-silvi-horticulture system. HAS – Higher altitude agri-silviculture system, HAH – Higher altitude agri-horticulture system, and HASH – Higher altitude agri-silvi-horticulture system. AGBD – Aboveground Biomass Density, BGBD – Belowground Biomass Density, TBD – Total Biomass Density. Altitude wise carbon density In the lower elevational zone, the LASH 3 had considerably greater AGC t ha − 1 (39.09a), BGC t ha − 1 (10.12a), and TGC t ha − 1 (49.22a) compared to the Citrus-based system (LAH 2 ) with the lowest AGC (6.95b), BGC (2.30b), and TGC (9.26b). LAS 1 occupied an intermediate position as 34.33ab TGC. It proves a clear advantage of mixed agri-silvi-horticulture system adoption over pure systems such as agri-silviculture or agri-horticulture at lower elevation. Significant differences in carbon stocks were observed between agroforestry systems at middle elevation, with the highest AGC (36.77a) and TGC (46.93a) recorded for MASH 4 , demonstrating the significant cumulative contribution of woody species to carbon storage in the Himalayan region. Similarly, MASH 5 showed strong TGC and AGC values (40.20a and 31.08a). On the other hand, MAH 3 had the lowest total carbon values (10.12b). In the higher elevation, HASH 6 had the highest AGC (57.82a), BGC (15.37a), and TGC (73.20a), followed by HASH 4 (67.10ab). HAH 3 (Citrus-based) had the lowest TGC (22.44c) among the carbon stocks. From the results (Table 7 ), carbon stock values across selected agroforestry systems followed a pattern similar to biomass production, with significantly higher carbon stocks recorded in agri-silvi-horticulture (homegarden), followed by agri-horticulture. Table 7 Aboveground carbon stock (AGC), belowground carbon stock (BGC) and total carbon stock (TGC) (t ha − 1 ) in selected agroforestry (AF) systems (800-1300m) AGC BGC TGC LAS 1 26.92 ab 7.41 a 34.34 ab LAH 2 6.95 b 2.31 b 9.26 b LASH 3 39.10 a 10.13 a 49.22 a (1301-1800m) AGC BGC TGC MAS 1 27.35 ab 7.54 b 234.89 ab MAH 2 21.83 ab 6.52 b 28.34 ab MAH 3 7.67 b 2.62 b 10.30 b MASH 4 36.77 a 10.16 a 46.93 a MASH 5 31.08 a 9.11 b 40.20 a (1801-2300m) AGC BGC TGC HAS 1 32.91 bc 9.16 bc 42.07 bc HAH 2 30.05 bc 9.35 bc 39.41 bc HAH 3 16.55 c 5.89 c 22.44 c HASH 4 52.82 ab 14.28 ab 67.10 ab HASH 5 43.08 ab 11.58 ab 54.66 ab HASH 6 57.82 a 15.37 a 73.20 a Soil organic carbon (SOC) Soil organic carbon (SOC, t ha − 1 ) varied across nine agroforestry systems distributed across three elevational zones. At lower elevation, SOC in the surface layer (0–30 cm) ranged from 7.86 in LAH 2 to 85.24 in LASH 3 , while in the sub-surface layer (30–60 cm), it ranged from 27.20 in LAS 1 to 90.23 in LASH 3 . In the middle elevation, SOC in the surface layer ranged from 50.47 in MAS 1 to 92.35 in MASH 5 , whereas in the sub-surface layer, it ranged from 30.02 in MAS 1 to 106.96 in MASH 4 . At higher elevation, comparatively higher SOC values were observed for surface SOC (78.23 in HASH 5 to 129.24 in HAH 2 ) and the sub-surface layer (53.24 in HAH 2 ; 91.84 in HASH 4 ), indicating its significant role in C sequestration in under-reported particular ranges of agroforestry systems. Overall. The higher TGC accumulation in HASH 6 , while BGC values also showed a rise with increasing elevation in these systems (11.25 > 11.29 > 17.08) (Table 8 ). Table 8 Altitude wise soil organic carbon (SOC) (t ha − 1 ) in selected agroforestry (AF) systems (800-1300m) 0-30cm 30-60cm LAS 1 25.74 ± 24.95 27.20 ± 25.75 LAH 2 7.86 ± 2.24 33.64 ± 25.45 LASH 3 85.24 ± 71.76 90.23 ± 76.49 F 1.30 1.39 p 0.43 0.41 (1301-1800m) 0-30cm 30-60cm MAS 1 50.47 ± 33.3 30.02 ± 8.08 MAH 2 72.80 ± 40.59 96.15 ± 33.44 MAH 3 89.58 ± 10.20 54.97 ± 5.29 MASH 4 75.58 ± 28.15 106.96 ± 36.05 MASH 5 92.35 ± 3.07 85.58 ± 10.68 F 0.35 0.82 p 1.52 0.34 (1801-2300m) 0–30 30–60 HAS 1 92.19 ± 37.97 72.45 ± 6.86 HAH 2 129.24 ± 16.74 53.24 ± 10.32 HAH 3 122.53 ± 9.72 64.4 ± 4.36 HASH 4 114.10 ± 50.70 91.84 ± 19.94 HASH 5 78.23 ± 2.52 80.32 ± 2.69 HASH 6 94.77 ± 3.00 64.42 ± 2.01 F 0.46 1.52 p 0.79 0.26 F indicates ANOVA F-value and p indicates level of significance ( p < 0.05) Ecosystem carbon pool The bar diagrams illustrate the relative contribution of SOC (0–60 cm), AGC, and BGC to total ecosystem carbon stock across different agroforestry systems and elevational zones. Across all zones, SOC constituted the largest proportion of total carbon stock, followed by AGBC and BGBC, for which (0–60 cm was the dominant carbon pool, accounting for the majority of total ecosystem carbon. The SOC exceeds the biomass C, mostly under the Q. leucotrichophora, F. nerifolia + C. sinensis-based agri-silvi-horticulture system (Fig. 2 a-c), with the highest proportion of SOC to total biomass C stock at 206.95 (t ha − 1 ). However, the lowest value was reported in the LASH3 (G. optiva, F. auriculata + C. sinensis) agri-horticultural system, with a value of 41.5 (t ha − 1 ). Biomass and carbon stocks interrelationships AGBD, BGBD, and TBD showed perfect positive correlations (r = 1.00) with each other, indicating a very strong linear relationship dominating the correlation matrix of biomass components, indicating that an increase in AGB is accompanied by proportional root biomass and that C stock is directly dependent on overall biomass accumulation. AGC exhibited strong positive correlations with AGBD (r = 0.86), BGBD (r = 0.84), and TBD (r = 0.85), demonstrating that increases in both above- and below-ground biomass substantially enhance above-ground carbon storage. TGC showed correlation with all biomass density variables, suggesting that total carbon storage is primarily controlled by biomass accumulation due to the varied components in selected agroforestry systems. In contrast, BGC showed moderate correlations with biomass variables (r = 0.64–0.66), indicating a comparatively weaker response of below-ground carbon to changes in biomass, where SOC displayed weak to moderate positive relationships with both biomass and vegetation carbon variables (r = 0.39–0.48). Discussion Cross altitudinal trends in terms of Biomass, biomass carbon, SOC and carbon pool Across all elevational zones, mixed systems such as LASH, MASH, and HASH stored a significantly larger amount of carbon than single, less diversified systems such as LAH, MAH, and HAH, elucidating the synergistic effect of mixed tree patterns on the C stocking pattern of agroforestry systems. The agri-silvi-horticulture system (homegarden) showed consistently higher AGB, BGB, and TBD. The increase in biomass with altitude may be influenced by several factors like physiography, site conditions, age, density of perennial components, management practices, etc. (Singh et al. 2020 ). Similar trends of increasing above-ground biomass with elevation have also been reported (Gupta et al. 2017 ; Kumar et al. 2018 ). Overall, the increase in biomass with elevation across different agroforestry systems implies that precipitation plays a major role in biomass variation in the Himalayan region, with peak rainfall occurring between 2000 and 2400 m above mean sea level (Burman et al. 2025). This altitude dependent increase in C sequestration has been supported by a number of previous studies where biomass and C density have been reported to be aligned with increase in altitudinal range even up to 200–2500, possible due to long lived tree species, reduced microbial activity, and decomposition rate and more possibly adaptive shift in resource allocation and utilization by the systems components. If we look into systems wise carbon allocation, the higher carbon stock observed in agri-silvi-horticulture systems may be mainly due to the inclusion of forest tree species, which allow continuous carbon storage in the woody biomass as reported in similar studies (Nadège et al. 2018 ). other possible reasons could be diversified biomass inputs (woody trees-horticulture tree species-conventional cereal crops) while pure systems such as AH, or AS tend to rely more on short rotation trees and crops, leading to reduced C allocation by these systems. the findings compliment earlier work confirming higher biomass in diversified agroforestry systems such as homegarden (Maryo et al. 2023 ). No statistically significant difference among studied systems confirmed overlapping ecological effects and effect of management practices on S sinking rather than altitude alone as the dominant driver. This capacity of agroforestry systems depends on several factors, including species composition, tree age, structure, functional components, and planting density (Feliciano et al. 2018 ). However, farmlands with a higher tree density have been reported to sequester more carbon compared to other land-use systems (Dar et al. 2019 ). In contrast, lower tree density observed in the study may explain the comparatively reduced carbon sequestration potential. The findings therefore, supports promotion of mixed and complex agroforestry systems in climate-vulnerable agro climatic zones, particularly at higher elevation ranges of Himalaya to enhance their climatic adaptability, mitigating effects through more C-sequestration etc. as this can contribute to achieve commendable C sink targets in the region. In the present study, SOC content was higher in the surface soil than in the subsoil. The findings align with recent studies emphasizing on complex soil stabilization rather than altitude only with a gradual decline in SOC with increasing soil depth (Eyasu and Tassew 2019 ; Devi et. 2025). A high intra-site variability in terms of large SE values indicated heterogeneous soil conditions whereas no significant difference we reported among all studied systems. Depth wise also, inconsistent trends were followed showing complex soil conditions. Among the different systems, the agri-silvi-horticulture system recorded the highest variability in SOC values at lower elevation where only slight increase was reported in surface to sub-surface level (LAH2 and LASH3), possibly due to litter accumulation, lower organic matter stabilization and higher temperature effects. This litter accumulation promotes soil carbon build-up and contributes to improved soil structure. A comparable pattern was also reported under the agr-silvi-horticulture system by Singh et al. ( 2018 ). In contrast, a large proportion of biomass in agri-horticulture and agri-silviculture systems (such as boundary plantations) is removed annually through harvesting, pruning, and felling, resulting in lower carbon retention. Higher above and below ground biomass can also contribute a significant amount of C in soils that increase SOC stock (Ahirwal et al. 2022 ). In middle altitude, moderate to high values (30–106) with high sub surface value for MASH 4 (106.96) represented high biomass production, enhanced root biomass conditions to subsoil carbon content, most probably due to favorable climatic conditions especially for mixed systems. at higher elevations, from findings it is confirmed that SOC, and nutrient levels tend to increase with increasing altitude (Meliyo et al. 2016 ; Sierra and Crow 2022 )., especially in surface soils (up to 129.24) with more stable variability compared to lower altitudes, possibly due to less decomposition of organic matter in sub soils and high SOC accumulation in surface layers. One way ANOVA revealed no significant difference in BGBD among reported systems in any single altitudinal zone in the region. Overall, LASH, MASH and HAH/HASH represented highest BGBD while LAH and MAS system indicated comparatively less accumulation particularly in surface layer. The findings proved homogeneity in below ground carbon storage within each elevation band as the accumulated amount of SOC largely depends on agroforestry system type, structure and functions influenced by environmental ad socio-economic factors (Chisanga et al., 2018 ). The findings align with earlier studies where tree density and edaphic factors override compositional differences in root biomass allocation. For example, total biomass and SOC did not vary among different agroforestry systems despite variations in crop components, indicating dominance of micro-environmental variability with strong influence of root dynamics although, latitude alone is insufficient to explain C storage capabilities of soils especially at higher elevations. The findings support to Dar et al. ( 2026 ), demonstrating partial influence of altitude on soil characteristics thereby confirmed non linear altitudinal trends with SOC in Himalayan soils for managing C sinks under future climatic scenarios. The total carbon stock varied considerably among different practices and from region to region. The non-significant differences observed in biomass carbon stock and SOC among the studied systems suggest that higher biomass carbon does not necessarily result in higher SOC. This may be attributed to the influence of several factors, including management practices, biomass extraction, soil management, and land-use history (Nair et al. 2009 ). In all systems, SOC stocks were found to be higher than biomass carbon stocks, indicating that soil serves as a major carbon reservoir. Another possible reason might be slower organic matter decomposition, accumulation of fine-root necromass in the soil profile leading to SOC accumulation in western Himalayan region. Negash et al. (2015) reported that high SOC levels not only improve soil quality and productivity but also represent a more stable and long-term carbon storage pool compared to biomass carbon. However, this pattern supports accumulation of more C stock with altitude despite less tree diversity and basal area under harsh climatic conditions, on SOC storage. Overall, it is emphasized to include more and more tree-based land-use systems such as agroforestry deals with problems related to changing land use patterns and global warming as earlier suggested by Li et al ( 2012 ). In terms of AGB, BGB, SOC relationships, correlation analysis revealed a strong structural and functional linkage between biomass and carbon sink. Moderate correlations of BGB with biomass indicated a comparatively weaker response of below-ground carbon to changes in biomass suggesting that Root C stock can be influenced by overall soil conditions and below-ground microbial conditions. The findings provide evidences to Jackson et al ( 1996 ) that BGC dynamics is more complex and less directly tied to biomass than above-ground pools. A weak to moderate positive relationships of SOC with both biomass and vegetation carbon variables indicated that SOC is not solely governed by biomass inputs as earlier mentioned by Schmidt et al. ( 2011 ). but it can be influenced by the extent of soil disturbance and human interventions, particularly the extraction of biomass and agroforestry products, which may limit soil carbon accumulation (Kirsten et al. 2016 ). This may explain the very weak correlation observed between biomass and SOC in the present study. Previous studies have reported that SOC is primarily governed by soil properties, while the influence of vegetation and topography is comparatively limited (Kinoshita et al. 2016 ). In general, the relationship between the two variables may also be affected by other factors such as tree management, age of the agroforestry system, species composition, tree density, rotation period, elevation, climate, and inherent soil characteristics further regulate this relationship (Sharma et al. 2023 ). The findings of the present study are consistent with earlier studies reporting a weak relationship between biomass carbon stock and SOC (Mathew et al. 2016 ; Bania et al. 2026 ). Collectively, the results aligned a strong linkage between biomass and vegetation carbon pools, whereas soil organic carbon exhibited a more complex and indirect relationship with vegetation attributes possibly due to faster change in biomass than SOC accumulation. The study might be helpful in providing information to decision-makers and farmers for selection of suitable agroforestry systems across different locations for efficient and long-term carbon balancing and mitigation of climate variability in the Himalayan ecosystem of Uttarakhand. Conclusion Among the studied agroforestry systems, mixed tree-based systems such as HASH 6 ) recorded the highest biomass density (162.67 t ha⁻¹), total carbon stock (81.33 t ha⁻¹), and higher soil organic carbon (SOC), indicating its greater carbon sequestration potential compared to other systems, especially as the elevation goes up, possible due to environmental filtering and tree composition. The results demonstrated a strong influence of system composition (forest trees + fruit trees + crops) on C stocking than altitude alone within a zone. The agri-silvi-horticulture system showed consistently better performance among all altitudes. Mixed agri-silvi-horticulture system served as an optimal option for total C storage (up to 81 t C ha − 1 ) at mid to higher elevations, indicating their potential to mitigate climatic challenge through more C stocking. Promoting such agroforestry systems across diverse agro-climatic zones of the Uttarakhand Himalaya can therefore contribute to long-term environmental sustainability a. It is recommended to focus futuristic studies with multi-factor approach on long-term monitoring on intrazonal and intra system performance, root-functional trait analysis, root-mediated carbon dynamics and impact of other climatic variants such as temperature, precipitation on systems components to develop robust empirical evidence on climate-smart agroforestry systems and land use planning for the region. Declarations Funding The authors did not receive support from any organization for the submitted work. Author Contribution Ng Manitombi devi wrote main manuscript, visualization, formal analysis and Himshikha Gusain done supervision, review and editing ans conceptualization. All authors reviewed the manuscript. Acknowledgement The author sincerely thank the Department of Forestry and Natural resources at HNB Garhwal University for providing necessary facilities. We are also grateful to the villagers for their kind cooperation and suppport during the study. References Ahirwal J, Gogoi A, Sahoo UK (2022) Stability of soil organic carbon pools affected by land use and land cover changes in forests of the eastern Himalayan region, India. CATENA 215:106308. https://doi.org/10.1016/j.catena.2022.106308 Alemu B (2012) Carbon stock potentials of woodlands and land use and land cover changes in north western lowlands of Ethiopia. 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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-9328202","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625385186,"identity":"2f51ffbe-65a6-4088-b9ad-a1a73f6c0b35","order_by":0,"name":"Manitombi Devi NG","email":"","orcid":"","institution":"Hemwati Nandan Bahuguna Garhwal University","correspondingAuthor":false,"prefix":"","firstName":"Manitombi","middleName":"Devi","lastName":"NG","suffix":""},{"id":625385187,"identity":"77f8fd86-2f78-4879-ad7c-9413dac39a89","order_by":1,"name":"Himshikha Gusain","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYBACAwkehgMMDAd4JCQYGB//4LEBijE2HiBWC7Mxg0waSEsDQS1AcIABqIVNmMHmMAOEiweYS/cePHTjzx0ZydnNz5gLcs7brW0/DLSlxiYalxbLOecSDue2PeORljlm9njGmdvJ284kArUcS8ttwOWwGzkGh3MbDvPISSSYG/D23E42OwDUwthwGL+WnD8gLenfJHj/nUs2O/+QGC1sh3mkJXLMpHl4DtiZ3SBgi+UMkMPaDvNIzsgpNpzBk5xgdgNoSwIev5hL5Bh/BjrMXuJG+sYHH3js7M3Opz988KHGBqcWDJAIVplArHIQsCdF8SgYBaNgFIwMAADxoWmVP2RBgAAAAABJRU5ErkJggg==","orcid":"","institution":"Hemwati Nandan Bahuguna Garhwal University","correspondingAuthor":true,"prefix":"","firstName":"Himshikha","middleName":"","lastName":"Gusain","suffix":""}],"badges":[],"createdAt":"2026-04-05 19:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9328202/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9328202/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107315510,"identity":"1dd6285a-99c2-40ae-abd7-2d26e0feaeb0","added_by":"auto","created_at":"2026-04-20 09:43:47","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":403293,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLocation of selected sample villages across different altitudinal zones in Pauri and Rudraprayag\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9328202/v1/8f676279f5ba3d023619f149.jpeg"},{"id":107315507,"identity":"73bfd5a3-12df-4a6a-97b7-87b068036629","added_by":"auto","created_at":"2026-04-20 09:43:45","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":555785,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a-c): Total carbon stock (t ha\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e-1\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e) in lower, middle, and higher elevations of different agroforestry system\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9328202/v1/591c53bf86dc530b61af3866.jpeg"},{"id":107315506,"identity":"ce693200-cab6-4892-b458-bdd4adf40f04","added_by":"auto","created_at":"2026-04-20 09:43:45","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":26586,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePearson correlation coefficient (r) between all carbon stocks (AGBD = Aboveground biomass density; BGBD = Belowground biomass density, TBD = Total biomass density; AGC = Aboveground carbon, BGC = Belowground carbon, TGC = total ground carbon; SOC = soil organic carbon)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9328202/v1/75160546060e73b9b4907198.jpeg"},{"id":107315585,"identity":"1f773a79-3aa2-4ae3-8260-2549040cffeb","added_by":"auto","created_at":"2026-04-20 09:43:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1984614,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9328202/v1/18caa115-6259-456d-9f7b-7e3ee2b90cc1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Altitude-dependent Biomass accumulation and Carbon Storage Potential of Agroforestry Systems in Garhwal Region, India","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOver the last century, intensified agricultural management has exerted considerable pressure on the environment, resulting in biodiversity loss (IPBES 2019), increased greenhouse gas emissions (IPCC \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and accelerated nutrient cycling processes (Pe\u0026ntilde;uelas and Sardans \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, anthropogenic activities have caused an estimated 1.0\u0026deg;C increase in global temperature above pre-industrial levels, and it is expected that, if current emission trends persist, global temperatures will rise by 1.5\u0026deg;C above pre-industrial levels between 2030 and 2052 (IPCC \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Carbon capture is now widely considered an important approach for limiting the rise of in the atmosphere (Mangalassery et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The soil and plant biomass that make up the large terrestrial carbon pool can absorb and store atmospheric carbon through photosynthesis (Kaul et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Soil supports essential ecosystems functios, with Carbon storage playing a crucial role in climate regulations, influencing primary productivity and nutrient cycling (Wiesmeier et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Agroforestry, the combination of trees and crops, captures and stores large amounts of atmospheric carbon dioxide (CO2) in biomass and soil, which can improve soil nutrients and combat climate change by enhancing carbon cycling (Branch and Wulfmeyer. 2019), indicating the potential to mitigate climate change through carbon sequestration (Nair et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Udawatta et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Soil organic carbon (SOC) is a major carbon pool because carbon remains stored in soil for a long period, and its level is strongly influenced by land-use type (Alemu \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). It is estimated active carbon pools are largely contributed by the plant litter, belowground decomposition and microbial biomass and because of their higher liability, they act as a major source of nutrients in topsoil (Yang et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). At the same time, fine tree roots and root exudates can add organic matter to deeper soil layers, thereby improving soil carbon storage at greater depths in agroforestry systems (Cardinael et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTrees are among the major C pools that contribute to global carbon cycling. Hence, tree-based land use systems, particularly in non-forested environments such as agricultural land, have an important impact on the process. Planting trees in agroforestry systems enhances the quantity of carbon sequestration with time as the tree\u0026rsquo;s biomass increases with the increase of tree age (Yasin et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Among different systems assessed under the Land-Use, Land-Use Change, and Forestry (LULUCF) framework, agroforestry has been reported to have a high potential for carbon sequestration (Nair and Garrity \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Global assessments indicate that land-use-based interventions can reduce emissions by nearly 30% by enhancing carbon sequestration, making them essential for meeting the emission-reduction targets outlined at the COP-25 summit (Aynekulu et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Agroforestry systems can store carbon in both vegetation and soils; however, the level of carbon storage differs among regions and system types (Chatterjee et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This storage potential depends on tree species composition, planting density, management practices, and tree age (Asigbaase et al. 2021). But the structure and functioning of agroforestry systems vary widely with local climatic conditions, including temperature, elevation, soil, and precipitation pattern (Nath et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sharma et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNumerous studies from the Himalayan region have been done on biomass production and carbon sequestration in agroforestry systems, which show that biomass production and carbon stock vary among agroforestry systems; agri-horti-silviculture and horti-silvi-pastoral which show higher biomass accumulation and carbon sequestration than agri-silviculture or boundary plantations (Panwar et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Altitude also influences carbon storage, as Singh et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) observed that the maximum total carbon density was recorded at higher elevations, However, future studies are needed to investigate the effects of particular tree-crop combinations within the agroforestry system on the levels of soil organic carbon (SOC), carbon sequestration potential and biomass accumulation in northwestern Himalayas. Therefore, more studies at regional levels are required to better understand this potential (Das et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) given the significance to this purpose, the study was conducted to explore the effect of altitude variation of different agroforestry system. This study hypothesized that biomass accumulation and carbon storage potential between different agroforestry land-use systems vary with altitudes. Keeping this in view, the present investigation was carried out with the objective of quantifying biomass, carbon stock, and soil organic carbon (SOC) of different agroforestry systems in the Garhwal Himalaya region.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSite description\u003c/h2\u003e \u003cp\u003eThe study was conducted in the hilly region of Garhwal Himalaya covering district Pauri and Rudraprayag of Uttarakhand state, India. For district Pauri, the altitude ranges from 226m to 3,068 msl, the average rainfall as 1231 mm, and temperature as 35.55℃ to -1.42℃. In Rudraprayag district, the altitude ranges from 507 to 7,061 m, with a average rainfall of about 1,913 mm. The minimum and maximum temperatures are \u0026minus;\u0026thinsp;5.95\u0026deg;C and 33.24\u0026deg;C, respectively. The land Use and Land Cover (LULC) categories of both districts are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (NMCG 2023).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLand use and land cover of Pauri and Rudraprayag district\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistrict name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuilt-up land\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCropland\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePlantation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWater bodies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eScrubland\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOpen forests\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDense forests\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSnow and glacier\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTotal geographical area\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePauri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.09 km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e328.44 km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e654.84 km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.21 km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.59 km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2512.7 km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1889.71 km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5496.58 km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRudraprayag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85 km\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.36 km\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e218.09 km\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e227.80 km\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e342.40 km\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1087.69 km\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e58.91 km\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2011.10 km\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSelection of study sites and sampling size\u003c/strong\u003e \u003cp\u003eA preliminary survey was conducted to identify the study area with a dominant agroforestry system and to document respondents' agroforestry practices. The villages are divided into three elevational zone, i.e., 800\u0026ndash;1300m (lower elevation), 1300-1800m (middle elevation) and 1800\u0026ndash;2300m (upper elevation) making a total of 12 villages (four in each altitude zone) selected for study purposes (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The sample villages were dominated by three agroforestry systems comprising of agri-silviculture (trees on bunds), agri-horticulture (scattered fruit trees with agriculture crops) and, agri-silvi-horticulture (homegarden). Initially, 17 agroforestry models were identified, of which 14 were selected to meet the study's objectives. From each model, five farms were randomly selected, resulting in a total of 70 sampled farm fields. Details of the agroforestry systems, associated components, and tree density under each system are provided in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The data represent lower, middle, and upper elevation-based agroforestry equivalents, such as LASH, MASH, MAH, HAS, etc. For data collection, 'nali' was selected as the land measuring unit, as it is a traditional unit of land measurement in the hill region of Uttarakhand. One nali is approximately 200.67 m\u0026sup2; (\u0026asymp;\u0026thinsp;0.020 ha or 0.0496 acres or 2,160 sq ft) (Bist \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeograpical description of selected village of district Pauri and Rudraprayag, Uttarakhand\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistrict\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVillage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAltitude (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eLower (800-1300m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRudraprayag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFatehpur\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u0026deg;14.080\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e078\u0026deg;56.277\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRudraprayag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKandai\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u0026deg;12.696\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e078\u0026deg;56.767\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePauri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBhandhai\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u0026deg;12.497\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e078\u0026deg;53.862\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRudraprayag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGahadkhal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u0026deg;12.448\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e078\u0026ordm;56.046\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMiddle (130\u003cb\u003e1\u003c/b\u003e-1800m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRudraprayag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKotmalla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u0026deg;16.297\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e079\u0026deg;04.535\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePauri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBughani\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u0026deg;11.658\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e078\u0026deg;51.110\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePauri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBudeshu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u0026deg;11.242\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e078\u0026deg;52.892\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePauri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarkhora\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u0026deg;10.770\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e078\u0026deg;51.198\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eHigher (180\u003cb\u003e1\u003c/b\u003e-2300m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePauri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJhinoli\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u0026deg; 01.534\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e79\u0026deg; 03.123\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRudraprayag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJundoli\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u0026deg;12.494\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e078\u0026deg;55.768\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePauri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNaini\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u0026deg;04.139\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e079\u0026deg;04.991\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePauri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMusaiti\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u0026ordm;01.794\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e079\u0026deg;01.241\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSelected agroforestry system and their component across altitudinal ranges (800-2300m)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAltitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAF System\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eForest trees spp.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHorticulture trees spp.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTrees/nali\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTrees/ha\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRabi crops\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eKharif crops\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLower (800\u0026ndash;1300 m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLAS\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eGrewia optiva\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e\u0026ndash;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eTriticum aestivum, Hordeum vulgare\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eEchinochloa frumentacea, Eleusine coracana\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLAH\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e\u0026ndash;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eCitrus sinensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ePisum sativum, Lens culinaris\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eGlycine max, Phaseolus vulgaris, Eleusine coracana\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eASH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLASH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eGrewia optiva, Ficus auriculata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eCitrus sinensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.2/4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e160/220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eAllium cepa, Allium sativum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eVigna mungo, Phaseolus vulgaris, Zea mays\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eMiddle (1300\u0026ndash;1800 m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMAS\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCeltis australis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e\u0026ndash;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eTriticum aestivum, Hordeum vulgare\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eEchinochloa frumentacea, Eleusine coracana\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMAH\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e\u0026ndash;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eCitrus sinensis, Prunus domestica, Pyrus pashia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eAllium cepa, Allium sativum, Solanum tuberosum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eCajanus cajan, Cucurbita maxima\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMAH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e\u0026ndash;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eCitrus sinensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eSolanum tuberosum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eVigna mungo, Zea mays\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eASH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMASH\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eGrewia optiva, Quercus leucotricophora\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eCitrus sinensis, Prunus domestica\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eAllium cepa, Allium sativum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eCajanus cajan, Cucurbita maxima\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eASH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMASH\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eGrewia optiva\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ePyrus pashia, Juglan regia, Citrus sinensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.8/6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e140/340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eSolanum tuberosum, Allium cepa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eVigna mungo, Lagenaria siceraria\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHigher (1800\u0026ndash;2300 m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHAS\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eQuercus leucotricophora\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e\u0026ndash;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eTriticum aestivum, Hordeum vulgare\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eEchinochloa frumentacea, Eleusine coracana\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHAH\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e\u0026ndash;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eCitrus sinensis, Malus domestica, Prunus persica\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eSolanum tuberosum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eVigna mungo, Phaseolus vulgaris\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHAH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e\u0026ndash;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eCitrus sinensis, Citrus reticulata, Citrus limon\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eSolanum tuberosum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eZea mays, Cucumis sativus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eASH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHASH\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eQuercus leucotricophora, Ficus nerifolia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eCitrus sinensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.8/3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e340/160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eTriticum aestivum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eZea mays, Curcuma longa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eASH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHASH\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eQuercus leucotricophora\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eJuglan regia, Pyrus pashia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.4/2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e220/140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eSolanum tuberosum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003ePhaseolus vulgaris, Lagenaria siceraria\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eASH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHASH\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eQuercus leucotricophora, Ficus nerifolia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eCitrus reticulata, Prunus persica\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.4/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e320/200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eSolanum tuberosum, Allium cepa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eColocasia esculenta, Cucurbita maxima\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eData collection\u003c/strong\u003e \u003cp\u003eFive sample plots of 10 \u0026times; 10 m2 were laid down in cropping systems in each site. Inside each of the larger plots, 1 \u0026times; 1 sub-plots were laid for soil sampling. Diameter at Breast Height (DBH) and tree height were measured in all plots using a diameter tape and Ravi\u0026rsquo;s multimeter. The volume of the selected tree was calculated from the recorded DBH and height using the individual-tree volume equation and the generic volume equation (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Soil samples were collected from two depths (0\u0026ndash;30 and 30\u0026ndash;60 cm) to make a composite sample. A total of 140 composite samples were analyzed to determine SOC and bulk density using a 5 cm diameter core sampler and soil auger.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVolume equation of different tree species\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTree species\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVolume equation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGrewia optiva\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eV\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.44075\u0026thinsp;+\u0026thinsp;7.49221D\u0026thinsp;\u0026minus;\u0026thinsp;36.09962D2\u0026thinsp;+\u0026thinsp;71.91238D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF.S.I.1996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eQuercus oppositifolia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.08519\u0026ndash;1.24724*D\u0026thinsp;+\u0026thinsp;7.94479*D\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF.S.I.1996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFicus palmata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026radic;V\u0026thinsp;=\u0026thinsp;0.03629\u0026thinsp;+\u0026thinsp;3.95389D\u0026thinsp;\u0026minus;\u0026thinsp;0.84421\u0026radic;D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF.S.I.1996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFicus auriculata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026radic;V\u0026thinsp;=\u0026thinsp;0.03629\u0026thinsp;+\u0026thinsp;3.95389D\u0026thinsp;\u0026minus;\u0026thinsp;0.84421\u0026radic;D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF.S.I.1996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCeltis australis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.23781\u0026ndash;2.09431*D\u0026thinsp;+\u0026thinsp;7.78268*D\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSingh et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2012\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePrunus spp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.193297\u0026ndash;2.267002 \u0026times; D\u0026thinsp;+\u0026thinsp;10.679492 \u0026times; D\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRam et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2014\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eJuglans regia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.23781\u0026ndash;2.09431*D\u0026thinsp;+\u0026thinsp;7.78268*D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSingh et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2012\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.00855\u0026thinsp;+\u0026thinsp;0.4432D2\u0026thinsp;+\u0026thinsp;0.28813D\u003csup\u003e2\u003c/sup\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF.S.I. 1996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eLaboratory analysis\u003c/strong\u003e \u003cp\u003eThe C content of the soil samples was determined using the Walkley\u0026ndash;Black method (Rosell et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) while bulk density was determined using oven dry method (Black et al. 1986).\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData estimation:\u003c/h3\u003e\n\u003cp\u003eThe Growing Stock Volume Density (GSVD) was estimated using volume equations (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The estimated GSVD (m3 ha\u0026thinsp;\u0026minus;\u0026thinsp;1) was then converted into AGBD of the tree by multiplying GSVD by BEF. Above Ground Biomass Density (AGBD) was estimated using the Biomass Expansion Factor (BEF), while Below Ground Biomass Density (BGBD) was calculated using a regression equation based on AGBD. The Total Biomass Density (TBD) was calculated as the sum of AGBD and BGBD. Tree Carbon Density (TCD) was estimated by applying a conversion factor of 0.45, assuming that 45% of total biomass is carbon. Soil Organic Carbon (SOC) and Bulk Density (BD) were calculated using standard procedures. The equations used for estimating biomass, carbon density, and soil parameters are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEquations for biomass, carbon stock and soil parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEquation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e\u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGBD\u003c/p\u003e \u003c/td\u003e \u003cd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBEF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eBrown et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1999\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.68 (for GSVD\u0026thinsp;\u0026lt;\u0026thinsp;10 m\u003csup\u003e3\u003c/sup\u003e ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95 (for GSVD is 10\u0026ndash;100 m\u003csup\u003e3\u003c/sup\u003e ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.81 (for GSVD\u0026thinsp;\u0026gt;\u0026thinsp;100 m\u003csup\u003e3\u003c/sup\u003e ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHardwood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eexp {1.91\u0026thinsp;\u0026minus;\u0026thinsp;0.34 \u0026times; ln (GSVD)} (for GSVD\u0026thinsp;\u0026le;\u0026thinsp;200 m\u003csup\u003e3\u003c/sup\u003e ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBGBD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eexp {\u0026minus;1.059\u0026thinsp;+\u0026thinsp;0.884 \u0026times; ln (AGBD)\u0026thinsp;+\u0026thinsp;0.284}\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTBD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGBD\u0026thinsp;+\u0026thinsp;BGBD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIPCC \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAGC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGBD \u0026times; Carbon fraction (conifers\u0026thinsp;=\u0026thinsp;0.46, hardwood\u0026thinsp;=\u0026thinsp;0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNegi et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2003\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBGC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBGBD \u0026times; Carbon fraction (conifers\u0026thinsp;=\u0026thinsp;0.46, hardwood\u0026thinsp;=\u0026thinsp;0.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTCD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGC\u0026thinsp;+\u0026thinsp;BGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIPCC \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSOC (t ha\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil bulk density (gcm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) \u0026times; soil depth(cm) \u0026times; SOC (%) \u0026times; 100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePearson et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2005\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBD (gcm\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;3\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eODS (g)/Volume of core sampler (cm3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003ePearson\u0026rsquo;s correlation analysis was performed to examine the relationship between biomass and carbon stock. The analysis was conducted using OriginPro 2026. ANOVA was performed to examine the variation in biomass and carbon stock among the agroforestry system. Since soil organic carbon (SOC) did not differ significantly among the agroforestry systems \u003cb\u003e(\u003c/b\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003cb\u003e)\u003c/b\u003e, no further tests were performed, whereas Tukey\u0026rsquo;s HSD test was used to compare biomass and tree carbon among agroforestry systems using OPSTAT.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eIntrazonal biomass estimation\u003c/h2\u003e \u003cp\u003eThe results showed significant differences (p\u0026thinsp;\u0026le;\u0026thinsp;0.05) in above-ground biomass density (AGBD t ha-1), below-ground biomass density (BGBD t ha-1), and total biomass density (TBD t ha-1) among agroforestry systems across different altitudinal ranges as depicted in (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). In lower elevation, the highest biomass was recorded in LASH\u003csub\u003e3\u003c/sub\u003e (86.87a, 22.51a, and 109.23a) for AGBD, BGBD, and TBD respectively, while the lowest values were observed in LAH\u003csub\u003e2\u003c/sub\u003e (15.45b, 5.13b, and 20.58b. In middle elevation, MASH\u003csub\u003e4\u003c/sub\u003e (82.02a, 22.59a, and 104.61a) and MASH\u003csub\u003e5\u003c/sub\u003e performed best in terms of AGBD, BGBD and TBD (69.08a, 20.25b, and 89.34a). MAS1 and MAH1 produced intermediate values of studied parameters while MAH\u003csub\u003e3\u003c/sub\u003e (17.07b, 5.83b, and 22.89b) showed the lowest biomass across all parameters. Similarly, in the higher elevation, HASH dominated with HASH\u003csub\u003e6\u003c/sub\u003e exhibiting the highest biomass (128.51a, 34.16a, and 162.67a), closely followed by HASH\u003csub\u003e4\u003c/sub\u003e. HAH\u003csub\u003e3\u003c/sub\u003e recorded the lowest values (36.77c, 13.09c, and 49.86c) while intermediate values were produced by HAS\u003csub\u003e1\u003c/sub\u003e and HAH\u003csub\u003e2\u003c/sub\u003e (\u0026lsquo;bc\u0026rsquo;). According to total biomass accumulation of trees was found in the order of HASH\u003csub\u003e6\u003c/sub\u003e\u0026gt; HASH\u003csub\u003e4\u003c/sub\u003e\u0026gt; HASH\u003csub\u003e5\u003c/sub\u003e\u0026gt; LASH\u003csub\u003e3\u003c/sub\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Overall, the mixed HASH system consistently accumulated carbon 2\u0026ndash;3 times more than simple agroforestry systems such as HAH at higher elevations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAboveground biomass (AGB), Belowground biomass density (BGB) and Total biomass (TBD) (t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) production in selected Agroforestry (AF) systems\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e(800-1300m)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGBD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBGBD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTBD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAS\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.82\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.47\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76.30\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAH\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.45\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.13\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.58\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLASH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86.87\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.51\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109.23\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e(1301-1800m)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAGBD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eBGBD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eTBD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAS\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.78\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.75\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.54\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAH\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.50\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.48\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.98\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.07\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.83\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.89\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMASH\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82.02\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.59\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104.61\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMASH\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.08\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.25\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89.34\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e(1801-2300m)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAGBD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eBGBD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eTBD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAS\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73.13\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.36\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.50\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAH\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.78\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.79\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87.58\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.77\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.09\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.86\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHASH\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117.37\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.74\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e149.12\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHASH\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95.73\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.75\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121.48\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHASH\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128.51\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.16\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e162.67\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eLetters used as subscript represents variance among the systems and same letters depict non-significant variance among two systems. LAS \u0026ndash; Lower altitude agri-silviculture system, LAH \u0026ndash; Lower altitude agri-horticulture system, LASH \u0026ndash; Lower altitude agri-silvi-horticulture system. MAS \u0026ndash; Middle altitude agri-silviculture system, MAH \u0026ndash; Middle altitude agri-horticulture system, MASH \u0026ndash; Middle altitude agri-silvi-horticulture system. HAS \u0026ndash; Higher altitude agri-silviculture system, HAH \u0026ndash; Higher altitude agri-horticulture system, and HASH \u0026ndash; Higher altitude agri-silvi-horticulture system. AGBD \u0026ndash; Aboveground Biomass Density, BGBD \u0026ndash; Belowground Biomass Density, TBD \u0026ndash; Total Biomass Density.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAltitude wise carbon density\u003c/h2\u003e \u003cp\u003eIn the lower elevational zone, the LASH\u003csub\u003e3\u003c/sub\u003e had considerably greater AGC t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (39.09a), BGC t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (10.12a), and TGC t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (49.22a) compared to the Citrus-based system (LAH\u003csub\u003e2\u003c/sub\u003e) with the lowest AGC (6.95b), BGC (2.30b), and TGC (9.26b). LAS\u003csub\u003e1\u003c/sub\u003e occupied an intermediate position as 34.33ab TGC. It proves a clear advantage of mixed agri-silvi-horticulture system adoption over pure systems such as agri-silviculture or agri-horticulture at lower elevation. Significant differences in carbon stocks were observed between agroforestry systems at middle elevation, with the highest AGC (36.77a) and TGC (46.93a) recorded for MASH\u003csub\u003e4\u003c/sub\u003e, demonstrating the significant cumulative contribution of woody species to carbon storage in the Himalayan region. Similarly, MASH\u003csub\u003e5\u003c/sub\u003e showed strong TGC and AGC values (40.20a and 31.08a). On the other hand, MAH\u003csub\u003e3\u003c/sub\u003e had the lowest total carbon values (10.12b). In the higher elevation, HASH\u003csub\u003e6\u003c/sub\u003e had the highest AGC (57.82a), BGC (15.37a), and TGC (73.20a), followed by HASH\u003csub\u003e4\u003c/sub\u003e (67.10ab). HAH\u003csub\u003e3\u003c/sub\u003e (Citrus-based) had the lowest TGC (22.44c) among the carbon stocks. From the results (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), carbon stock values across selected agroforestry systems followed a pattern similar to biomass production, with significantly higher carbon stocks recorded in agri-silvi-horticulture (homegarden), followed by agri-horticulture.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAboveground carbon stock (AGC), belowground carbon stock (BGC) and total carbon stock (TGC) (t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) in selected agroforestry (AF) systems\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e(800-1300m)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBGC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTGC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAS\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.92\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.41\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.34\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAH\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.95\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.31\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.26\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLASH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.10\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.13\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.22\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e(1301-1800m)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAGC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eBGC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eTGC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAS\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.35\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.54\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e234.89\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAH\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.83\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.52\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.34\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.67\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.62\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.30\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMASH\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.77\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.16\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.93\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMASH\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.08\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.11\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.20\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e(1801-2300m)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAGC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eBGC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eTGC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAS\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.91\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.16\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.07\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAH\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.05\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.35\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.41\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.55\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.89\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.44\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHASH\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.82\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.28\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67.10\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHASH\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.08\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.58\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.66\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHASH\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.82\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.37\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.20\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSoil organic carbon (SOC)\u003c/h3\u003e\n\u003cp\u003eSoil organic carbon (SOC, t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) varied across nine agroforestry systems distributed across three elevational zones. At lower elevation, SOC in the surface layer (0\u0026ndash;30 cm) ranged from 7.86 in LAH\u003csub\u003e2\u003c/sub\u003e to 85.24 in LASH\u003csub\u003e3\u003c/sub\u003e, while in the sub-surface layer (30\u0026ndash;60 cm), it ranged from 27.20 in LAS\u003csub\u003e1\u003c/sub\u003e to 90.23 in LASH\u003csub\u003e3\u003c/sub\u003e. In the middle elevation, SOC in the surface layer ranged from 50.47 in MAS\u003csub\u003e1\u003c/sub\u003e to 92.35 in MASH\u003csub\u003e5\u003c/sub\u003e, whereas in the sub-surface layer, it ranged from 30.02 in MAS\u003csub\u003e1\u003c/sub\u003e to 106.96 in MASH\u003csub\u003e4\u003c/sub\u003e. At higher elevation, comparatively higher SOC values were observed for surface SOC (78.23 in HASH\u003csub\u003e5\u003c/sub\u003e to 129.24 in HAH\u003csub\u003e2\u003c/sub\u003e) and the sub-surface layer (53.24 in HAH\u003csub\u003e2\u003c/sub\u003e; 91.84 in HASH\u003csub\u003e4\u003c/sub\u003e), indicating its significant role in C sequestration in under-reported particular ranges of agroforestry systems. Overall. The higher TGC accumulation in HASH\u003csub\u003e6\u003c/sub\u003e, while BGC values also showed a rise with increasing elevation in these systems (11.25\u0026thinsp;\u0026gt;\u0026thinsp;11.29\u0026thinsp;\u0026gt;\u0026thinsp;17.08) (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAltitude wise soil organic carbon (SOC) (t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) in selected agroforestry (AF) systems\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e(800-1300m)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0-30cm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30-60cm\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAS\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.74\u0026thinsp;\u0026plusmn;\u0026thinsp;24.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.20\u0026thinsp;\u0026plusmn;\u0026thinsp;25.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAH\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.86\u0026thinsp;\u0026plusmn;\u0026thinsp;2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.64\u0026thinsp;\u0026plusmn;\u0026thinsp;25.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLASH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.24\u0026thinsp;\u0026plusmn;\u0026thinsp;71.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.23\u0026thinsp;\u0026plusmn;\u0026thinsp;76.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.39\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.43\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.41\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e(1301-1800m)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0-30cm\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e30-60cm\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAS\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.47\u0026thinsp;\u0026plusmn;\u0026thinsp;33.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.02\u0026thinsp;\u0026plusmn;\u0026thinsp;8.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAH\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.80\u0026thinsp;\u0026plusmn;\u0026thinsp;40.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.15\u0026thinsp;\u0026plusmn;\u0026thinsp;33.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89.58\u0026thinsp;\u0026plusmn;\u0026thinsp;10.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.97\u0026thinsp;\u0026plusmn;\u0026thinsp;5.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMASH\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.58\u0026thinsp;\u0026plusmn;\u0026thinsp;28.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106.96\u0026thinsp;\u0026plusmn;\u0026thinsp;36.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMASH\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92.35\u0026thinsp;\u0026plusmn;\u0026thinsp;3.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.58\u0026thinsp;\u0026plusmn;\u0026thinsp;10.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.82\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.52\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e(1801-2300m)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0\u0026ndash;30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e30\u0026ndash;60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAS\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92.19\u0026thinsp;\u0026plusmn;\u0026thinsp;37.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.45\u0026thinsp;\u0026plusmn;\u0026thinsp;6.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAH\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129.24\u0026thinsp;\u0026plusmn;\u0026thinsp;16.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.24\u0026thinsp;\u0026plusmn;\u0026thinsp;10.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e122.53\u0026thinsp;\u0026plusmn;\u0026thinsp;9.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHASH\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114.10\u0026thinsp;\u0026plusmn;\u0026thinsp;50.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91.84\u0026thinsp;\u0026plusmn;\u0026thinsp;19.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHASH\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.23\u0026thinsp;\u0026plusmn;\u0026thinsp;2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.32\u0026thinsp;\u0026plusmn;\u0026thinsp;2.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHASH\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94.77\u0026thinsp;\u0026plusmn;\u0026thinsp;3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.42\u0026thinsp;\u0026plusmn;\u0026thinsp;2.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.46\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.52\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.79\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eF indicates ANOVA F-value and p indicates level of significance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e\n\u003ch3\u003eEcosystem carbon pool\u003c/h3\u003e\n\u003cp\u003eThe bar diagrams illustrate the relative contribution of SOC (0\u0026ndash;60 cm), AGC, and BGC to total ecosystem carbon stock across different agroforestry systems and elevational zones. Across all zones, SOC constituted the largest proportion of total carbon stock, followed by AGBC and BGBC, for which (0\u0026ndash;60 cm was the dominant carbon pool, accounting for the majority of total ecosystem carbon. The SOC exceeds the biomass C, mostly under the Q. leucotrichophora, F. nerifolia\u0026thinsp;+\u0026thinsp;C. sinensis-based agri-silvi-horticulture system (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-c), with the highest proportion of SOC to total biomass C stock at 206.95 (t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). However, the lowest value was reported in the LASH3 (G. optiva, F. auriculata\u0026thinsp;+\u0026thinsp;C. sinensis) agri-horticultural system, with a value of 41.5 (t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBiomass and carbon stocks interrelationships\u003c/p\u003e \u003cp\u003eAGBD, BGBD, and TBD showed perfect positive correlations (r\u0026thinsp;=\u0026thinsp;1.00) with each other, indicating a very strong linear relationship dominating the correlation matrix of biomass components, indicating that an increase in AGB is accompanied by proportional root biomass and that C stock is directly dependent on overall biomass accumulation. AGC exhibited strong positive correlations with AGBD (r\u0026thinsp;=\u0026thinsp;0.86), BGBD (r\u0026thinsp;=\u0026thinsp;0.84), and TBD (r\u0026thinsp;=\u0026thinsp;0.85), demonstrating that increases in both above- and below-ground biomass substantially enhance above-ground carbon storage. TGC showed correlation with all biomass density variables, suggesting that total carbon storage is primarily controlled by biomass accumulation due to the varied components in selected agroforestry systems. In contrast, BGC showed moderate correlations with biomass variables (r\u0026thinsp;=\u0026thinsp;0.64\u0026ndash;0.66), indicating a comparatively weaker response of below-ground carbon to changes in biomass, where SOC displayed weak to moderate positive relationships with both biomass and vegetation carbon variables (r\u0026thinsp;=\u0026thinsp;0.39\u0026ndash;0.48).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCross altitudinal trends in terms of Biomass, biomass carbon, SOC and carbon pool\u003c/h2\u003e \u003cp\u003eAcross all elevational zones, mixed systems such as LASH, MASH, and HASH stored a significantly larger amount of carbon than single, less diversified systems such as LAH, MAH, and HAH, elucidating the synergistic effect of mixed tree patterns on the C stocking pattern of agroforestry systems. The agri-silvi-horticulture system (homegarden) showed consistently higher AGB, BGB, and TBD. The increase in biomass with altitude may be influenced by several factors like physiography, site conditions, age, density of perennial components, management practices, etc. (Singh et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similar trends of increasing above-ground biomass with elevation have also been reported (Gupta et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kumar et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Overall, the increase in biomass with elevation across different agroforestry systems implies that precipitation plays a major role in biomass variation in the Himalayan region, with peak rainfall occurring between 2000 and 2400 m above mean sea level (Burman et al. 2025). This altitude dependent increase in C sequestration has been supported by a number of previous studies where biomass and C density have been reported to be aligned with increase in altitudinal range even up to 200\u0026ndash;2500, possible due to long lived tree species, reduced microbial activity, and decomposition rate and more possibly adaptive shift in resource allocation and utilization by the systems components.\u003c/p\u003e \u003cp\u003eIf we look into systems wise carbon allocation, the higher carbon stock observed in agri-silvi-horticulture systems may be mainly due to the inclusion of forest tree species, which allow continuous carbon storage in the woody biomass as reported in similar studies (Nad\u0026egrave;ge et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). other possible reasons could be diversified biomass inputs (woody trees-horticulture tree species-conventional cereal crops) while pure systems such as AH, or AS tend to rely more on short rotation trees and crops, leading to reduced C allocation by these systems. the findings compliment earlier work confirming higher biomass in diversified agroforestry systems such as homegarden (Maryo et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). No statistically significant difference among studied systems confirmed overlapping ecological effects and effect of management practices on S sinking rather than altitude alone as the dominant driver. This capacity of agroforestry systems depends on several factors, including species composition, tree age, structure, functional components, and planting density (Feliciano et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, farmlands with a higher tree density have been reported to sequester more carbon compared to other land-use systems (Dar et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In contrast, lower tree density observed in the study may explain the comparatively reduced carbon sequestration potential. The findings therefore, supports promotion of mixed and complex agroforestry systems in climate-vulnerable agro climatic zones, particularly at higher elevation ranges of Himalaya to enhance their climatic adaptability, mitigating effects through more C-sequestration etc. as this can contribute to achieve commendable C sink targets in the region. In the present study, SOC content was higher in the surface soil than in the subsoil. The findings align with recent studies emphasizing on complex soil stabilization rather than altitude only with a gradual decline in SOC with increasing soil depth (Eyasu and Tassew \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Devi et. 2025). A high intra-site variability in terms of large SE values indicated heterogeneous soil conditions whereas no significant difference we reported among all studied systems. Depth wise also, inconsistent trends were followed showing complex soil conditions. Among the different systems, the agri-silvi-horticulture system recorded the highest variability in SOC values at lower elevation where only slight increase was reported in surface to sub-surface level (LAH2 and LASH3), possibly due to litter accumulation, lower organic matter stabilization and higher temperature effects. This litter accumulation promotes soil carbon build-up and contributes to improved soil structure. A comparable pattern was also reported under the agr-silvi-horticulture system by Singh et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In contrast, a large proportion of biomass in agri-horticulture and agri-silviculture systems (such as boundary plantations) is removed annually through harvesting, pruning, and felling, resulting in lower carbon retention. Higher above and below ground biomass can also contribute a significant amount of C in soils that increase SOC stock (Ahirwal et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn middle altitude, moderate to high values (30\u0026ndash;106) with high sub surface value for MASH\u003csub\u003e4\u003c/sub\u003e (106.96) represented high biomass production, enhanced root biomass conditions to subsoil carbon content, most probably due to favorable climatic conditions especially for mixed systems. at higher elevations, from findings it is confirmed that SOC, and nutrient levels tend to increase with increasing altitude (Meliyo et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Sierra and Crow \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)., especially in surface soils (up to 129.24) with more stable variability compared to lower altitudes, possibly due to less decomposition of organic matter in sub soils and high SOC accumulation in surface layers. One way ANOVA revealed no significant difference in BGBD among reported systems in any single altitudinal zone in the region. Overall, LASH, MASH and HAH/HASH represented highest BGBD while LAH and MAS system indicated comparatively less accumulation particularly in surface layer. The findings proved homogeneity in below ground carbon storage within each elevation band as the accumulated amount of SOC largely depends on agroforestry system type, structure and functions influenced by environmental ad socio-economic factors (Chisanga et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The findings align with earlier studies where tree density and edaphic factors override compositional differences in root biomass allocation. For example, total biomass and SOC did not vary among different agroforestry systems despite variations in crop components, indicating dominance of micro-environmental variability with strong influence of root dynamics although, latitude alone is insufficient to explain C storage capabilities of soils especially at higher elevations. The findings support to Dar et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2026\u003c/span\u003e), demonstrating partial influence of altitude on soil characteristics thereby confirmed non linear altitudinal trends with SOC in Himalayan soils for managing C sinks under future climatic scenarios.\u003c/p\u003e \u003cp\u003eThe total carbon stock varied considerably among different practices and from region to region. The non-significant differences observed in biomass carbon stock and SOC among the studied systems suggest that higher biomass carbon does not necessarily result in higher SOC. This may be attributed to the influence of several factors, including management practices, biomass extraction, soil management, and land-use history (Nair et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In all systems, SOC stocks were found to be higher than biomass carbon stocks, indicating that soil serves as a major carbon reservoir. Another possible reason might be slower organic matter decomposition, accumulation of fine-root necromass in the soil profile leading to SOC accumulation in western Himalayan region. Negash et al. (2015) reported that high SOC levels not only improve soil quality and productivity but also represent a more stable and long-term carbon storage pool compared to biomass carbon. However, this pattern supports accumulation of more C stock with altitude despite less tree diversity and basal area under harsh climatic conditions, on SOC storage. Overall, it is emphasized to include more and more tree-based land-use systems such as agroforestry deals with problems related to changing land use patterns and global warming as earlier suggested by Li et al (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn terms of AGB, BGB, SOC relationships, correlation analysis revealed a strong structural and functional linkage between biomass and carbon sink. Moderate correlations of BGB with biomass indicated a comparatively weaker response of below-ground carbon to changes in biomass suggesting that Root C stock can be influenced by overall soil conditions and below-ground microbial conditions. The findings provide evidences to Jackson et al (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) that BGC dynamics is more complex and less directly tied to biomass than above-ground pools. A weak to moderate positive relationships of SOC with both biomass and vegetation carbon variables indicated that SOC is not solely governed by biomass inputs as earlier mentioned by Schmidt et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). but it can be influenced by the extent of soil disturbance and human interventions, particularly the extraction of biomass and agroforestry products, which may limit soil carbon accumulation (Kirsten et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This may explain the very weak correlation observed between biomass and SOC in the present study. Previous studies have reported that SOC is primarily governed by soil properties, while the influence of vegetation and topography is comparatively limited (Kinoshita et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In general, the relationship between the two variables may also be affected by other factors such as tree management, age of the agroforestry system, species composition, tree density, rotation period, elevation, climate, and inherent soil characteristics further regulate this relationship (Sharma et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The findings of the present study are consistent with earlier studies reporting a weak relationship between biomass carbon stock and SOC (Mathew et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Bania et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Collectively, the results aligned a strong linkage between biomass and vegetation carbon pools, whereas soil organic carbon exhibited a more complex and indirect relationship with vegetation attributes possibly due to faster change in biomass than SOC accumulation. The study might be helpful in providing information to decision-makers and farmers for selection of suitable agroforestry systems across different locations for efficient and long-term carbon balancing and mitigation of climate variability in the Himalayan ecosystem of Uttarakhand.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAmong the studied agroforestry systems, mixed tree-based systems such as HASH\u003csub\u003e6\u003c/sub\u003e) recorded the highest biomass density (162.67 t ha⁻\u0026sup1;), total carbon stock (81.33 t ha⁻\u0026sup1;), and higher soil organic carbon (SOC), indicating its greater carbon sequestration potential compared to other systems, especially as the elevation goes up, possible due to environmental filtering and tree composition. The results demonstrated a strong influence of system composition (forest trees\u0026thinsp;+\u0026thinsp;fruit trees\u0026thinsp;+\u0026thinsp;crops) on C stocking than altitude alone within a zone. The agri-silvi-horticulture system showed consistently better performance among all altitudes. Mixed agri-silvi-horticulture system served as an optimal option for total C storage (up to 81 t C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) at mid to higher elevations, indicating their potential to mitigate climatic challenge through more C stocking. Promoting such agroforestry systems across diverse agro-climatic zones of the Uttarakhand Himalaya can therefore contribute to long-term environmental sustainability a. It is recommended to focus futuristic studies with multi-factor approach on long-term monitoring on intrazonal and intra system performance, root-functional trait analysis, root-mediated carbon dynamics and impact of other climatic variants such as temperature, precipitation on systems components to develop robust empirical evidence on climate-smart agroforestry systems and land use planning for the region.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eFunding\u003c/strong\u003e \u003cp\u003eThe authors did not receive support from any organization for the submitted work.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eNg Manitombi devi wrote main manuscript, visualization, formal analysis and Himshikha Gusain done supervision, review and editing ans conceptualization. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe author sincerely thank the Department of Forestry and Natural resources at HNB Garhwal University for providing necessary facilities. We are also grateful to the villagers for their kind cooperation and suppport during the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhirwal J, Gogoi A, Sahoo UK (2022) Stability of soil organic carbon pools affected by land use and land cover changes in forests of the eastern Himalayan region, India. 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Forests 10(7):640. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/f10070640\u003c/span\u003e\u003cspan address=\"10.3390/f10070640\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"agroforestry-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agfo","sideBox":"Learn more about [Agroforestry Systems](http://link.springer.com/journal/10457)","snPcode":"10457","submissionUrl":"https://submission.nature.com/new-submission/10457/3","title":"Agroforestry Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Agroforestry systems, Biomass density, Carbon stock, Soil organic carbon (SOC), Altitudinal gradient, Himalayan region","lastPublishedDoi":"10.21203/rs.3.rs-9328202/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9328202/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAgroforestry systems play a significant role in climate change mitigation and enhanced carbon sequestration in biomass and soil. The present study was conducted across three altitudinal zones (800\u0026ndash;2300 m) in the Garhwal Himalaya, Uttarakhand, to assess biomass accumulation, carbon stocks, and soil organic carbon (SOC) under different agroforestry systems. A total of 14 agroforestry models representing agri-silviculture (AS), agri-horticulture (AH), and agri-silvi-horticulture (ASH) systems were evaluated across 12 villages. Results revealed significant variation in aboveground biomass density (AGBD), belowground biomass density (BGBD), and total biomass density (TBD) among systems and altitudes. Mixed systems, particularly ASH (homegarden), consistently recorded higher biomass and carbon stocks, with the highest values observed in HASH6 (162.67 t ha⁻\u0026sup1; biomass and 73.20 t C ha⁻\u0026sup1;). SOC constituted the largest carbon (C) pool across all systems, yet showed only weak correlation with biomass components, suggesting complex soil carbon dynamics. Biomass and C stocks generally increased with altitude, likely due to favorable climatic conditions and reduced decomposition rates. The study highlights that system composition, particularly the inclusion of diverse tree species, plays a critical role compared to altitude alone in determining C sequestration potential. Promoting diversified agroforestry systems can enhance long-term carbon storage and support climate-resilient land-use strategies in the Himalayan ecosystem.\u003c/p\u003e","manuscriptTitle":"Altitude-dependent Biomass accumulation and Carbon Storage Potential of Agroforestry Systems in Garhwal Region, India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-20 09:43:18","doi":"10.21203/rs.3.rs-9328202/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-23T11:41:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-21T12:30:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-21T11:51:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-21T06:15:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-20T08:56:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"328879289636302111717446757539209067986","date":"2026-04-20T04:43:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-18T10:18:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"326990544974552296966751553054518665508","date":"2026-04-17T14:18:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"277998160829837595033758406522229333993","date":"2026-04-15T13:02:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"17883864131043747613075336149579499031","date":"2026-04-14T18:10:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"160313489351254350774331750515926105296","date":"2026-04-13T16:57:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"90825374362659020891522802842740363918","date":"2026-04-13T07:01:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"209566765266243907579645537314725714018","date":"2026-04-13T04:56:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"302455232975829917251866338592012465625","date":"2026-04-12T11:11:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"33782971255897859034589905227289579757","date":"2026-04-10T13:50:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"205016710176305642153871005374278679855","date":"2026-04-10T11:26:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-10T11:09:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-10T08:45:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-09T14:26:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Agroforestry Systems","date":"2026-04-05T19:29:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"agroforestry-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agfo","sideBox":"Learn more about [Agroforestry Systems](http://link.springer.com/journal/10457)","snPcode":"10457","submissionUrl":"https://submission.nature.com/new-submission/10457/3","title":"Agroforestry Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a83b334e-e418-4a17-997f-c90f27e2562f","owner":[],"postedDate":"April 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T11:53:19+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-20 09:43:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9328202","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9328202","identity":"rs-9328202","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}