Content area
Across the globe, soil quality is significantly impacted by changes in land use that convert natural ecosystems into managed ones, profoundly affecting the physical, chemical, and biological characteristics of soils. The present study evaluated soil quality across four land-use systems (forest, cultivated, pasture, and scrub) and at two altitudinal ranges, viz., 651–1800 m (mid-hill region) and 1801–2200 m (high hill region) in the Mandi district of Himachal Pradesh, located in the northwestern Himalayas. Representative soil samples (0–15 cm depth) from each land-use system were collected and analysed to assess a range of physical, chemical, and biological characteristics. To calculate the soil quality index (SQI), a total of 23 soil characteristics were initially measured and reduced into a minimum data set via principal component analysis. The SQI across different land-use types ranged from medium (0.58) to high category (0.98) with the higher values observed in Forests and Pastures. Soil attributes like bulk density (BD), particle density (PD), pH, EC, Cd, Cr, Pb, and viable actinomycetes count decline with altitude, while porosity, maximum water-holding capacity, SOC, N, P, K, Ca, Mg, S, Cu, Fe, Mn, Zn, and viable fungal and actinomycetes counts increase with altitude. Overall, the study emphasizes that land use and altitude significantly influence soil quality in the northwestern Himalayas, underscoring the significance of sustainable land management techniques to maintain and improve soil quality in a variety of landscapes.
INTRODUCTION
The global advancement of human civilization has resulted in the excessive utilization of natural resources, leading to significant shifts in land-use patterns and land cover [51, 61]. Sub-mountainous regions such as the Himalayas are rich in natural resources and play a crucial role in supporting diverse ecosystems. However, the Himalayas, owing to their recent geological formation and complex folding, represent a fragile ecosystem. The region’s harsh climate further limits the availability of land for agricultural and related activities [12, 19, 60]. Increasing human activity exacerbates these challenges, with forest areas being transformed into other land uses driven by population growth, agricultural development, technological advancements, and land governance policies. Nearly 90% of global deforestation is attributed to the expansion of agricultural land [28]. Since 1990, more than 80 million hectares of global primary forests have been lost, with deforestation rates reaching 10 million hectares per year between 2015 and 2020 [27].
Agriculture is typically the main driver of the economy in developing countries, along with the need to expand urban centers; therefore, vast tracts of natural land have been turned into arable and urban centers [58]. The transformation of forestlands, combined with the impact of climate change and development activities, has intensified soil erosion, surface runoff, and soil health deterioration [25, 46, 75]. Agricultural practices significantly influence soil quality and impact the physical, chemical, and biological properties of soil. For instance, cultivation can alter soil organic matter levels [3], thereby affecting the structure and function of ecosystems. Land use practices, such as tillage, can degrade soil health by compacting soil, reducing water infiltration, and increasing runoff and erosion [1, 52]. In contrast, land use with abundant vegetation supports water infiltration, preventing erosion and nutrient loss [20, 38].
Soil plays an essential role in sustaining life on Earth by acting as the foundation of the food chain, supporting agricultural productivity, and maintaining environmental quality [9, 76]. It serves as a natural filter for water [57] and functions as a carbon sink, helping to mitigate climate change by storing carbon dioxide [64]. Additionally, soil hosts diverse microorganisms that are crucial for nutrient cycling, plant growth, and the maintenance of soil fertility. Therefore, understanding the relationship between land use and soil health is critical for implementing sustainable practices that promote the integrity of soil ecosystems.
The conversion of natural habitats into managed ecosystems poses risks to soil quality, and there is an urgent need to evaluate the impacts of these land-use changes. This study assesses the effects of various land-use practices-forest, cultivated land, grassland/pasture, and scrubland-on key physical, chemical, and biological soil parameters in the mid- and high-hill zones of the Mandi district in Himachal Pradesh. The hypothesis is that variations in land uses such as forests, agriculture, pastures, and scrublands have influenced soil characteristics. The study also aimed to develop a soil quality index to reflect the current state of soil quality under these different land-use systems.
OBJECTS AND METHODS
Study area. This study was conducted in the Mandi district of Himachal Pradesh, located in the western Himalayas (Fig. 1). The district spans between 31°13′50″ N and 32°04′30″ N latitudes and 76°37′20″ E and 77°23′15″ E longitudes, covering a total area of 3950 km2. The altitude ranges from 550 m above mean sea level (amsl) near Sandhol to approximately 4400 m at Nagru Peak. Mandi district is characterized by diverse topography, comprising mountain ranges, hills, and valleys, with altitudes rising from west to east and south to north.
[See PDF for image]
Fig. 1.
Study area.
Climatic conditions. The climate of the Mandi District varies with altitude. At lower altitudes (651–1800 m amsl), the climate ranges from subtropical to sub-temperate, whereas at higher altitudes (1801–2200 m amsl), it is predominantly temperate. The mean maximum temperature fluctuated between 3 and 35°C. Higher altitudes remain cold throughout the year, with frequent snowfall in winter, whereas lower altitudes experience hot summers.
Experimental details. To meet the study’s objectives, a survey was conducted, and secondary data was gathered from the Department of Economics and Statistics, Himachal Pradesh, to identify the dominant land-use patterns in the district. Four major land-use types, forest, cultivated land, pasture, and scrubland, were selected for the study at two altitudinal ranges: 651–1800 and 1801–2200 m. This created a total of eight treatment combinations (land use × altitude), with each combination replicated thrice at both altitudinal ranges. The experimental sites located in the mid-hill region (651–1800 m) included Sunder Nagar, Balh Valley, and Dharampur, while those in the high-hill region (1801–2200 m) comprised Janjehli, Barot Valley, and Churag. The soils of the mid-hill region belong to the Planosol group, whereas those of the high-hill region are classified under the Podzol group as per the WRB.
Sample collection. Soil samples were collected in triplicate from the surface layer (0–15 cm depth) of each selected land-use system at all sites in October to assess the effects of different land-use systems on soil quality. The samples were taken by making a V-shaped cut in the soil with a khurpa down to a depth of 15 cm and then collecting a 4 cm-thick uniform slice of soil running parallel to the V-shaped cut. After collecting, all soil samples were dried in the shade, processed, and stored in polythene bags for later analysis.
Soil physical and chemical analysis. In the laboratory, the bulk density (BD) and particle density (PD) of the soil were analyzed as per Blake and Hartage [15] and [16], respectively and results were reported in g cm–3. The porosity of the soil was calculated based on BD and PD measurements. The maximum water-holding capacity (MWHC) of the soil was assessed using the Keen-Raczkowski (K.R.) box method [56] and was expressed as a percentage. To determine the hydrogen ion concentration, the potentiometric method was employed using a microprocessor-based pH meter (model 510, EIA make) in accordance with the procedure described by Jackson [31]. The electrical conductivity (EC) of the soil was measured using a microprocessor-based conductivity meter (model 1601, EIA Make). The organic carbon content of the soil was determined using the rapid titration method described by Walkley and Black [79]. The alkaline potassium permanganate method developed by [71] was used to determine the available nitrogen content of the soil, which was reported in units of kg ha–1. The available phosphorus in the soil was analyzed using 0.5 N NaHCO3 as an extractant [54] and determined colorimetrically by the stannous chloride-reduced ammonium molybdate method in the HCl system [31]. The available potassium in the soil was evaluated by utilizing a neutral normal ammonium acetate solution as the extracting agent [50], and results were reported in units of kg ha–1. The available sulfur in the sample was assessed using the turbidimetric method, with 0.15% CaCl2 serving as the extractant [84]. The results of the analysis are reported in kg ha–1. The available calcium (Ca) and magnesium (Mg) in the soil were determined using a neutral normal ammonium acetate as an extractant [50], and the results were reported in cmol (p+) kg–1. To assess heavy metals, 0.5 g of soil sample was digested in nitric acid and hydrogen peroxide according to the US EPA 3050 B method [2]. An ICP spectrometer (model ICAP 6300 duo) from Thermo Scientific was used to estimate the concentration of heavy metals, measured in mg kg–1.
Soil microbiological analysis. The estimation of the total viable microbial count was carried out as per the plating method described by Rao [59], with results reported as CFU g–1. Nutrient agar (NA), potato dextrose agar (PDA), and Kenknight agar media were used to determine bacterial, fungal, and actinomycetes populations, respectively.
Soil Quality Index computation. The SQI indexing process consists of three steps: (1) selecting indicators or variables, (2) scoring, and (3) integration. The cornerstone of the SQI is the selection of important soil quality parameters. The selected parameters were measured and compressed into a minimum data set (MDS) using principal component analysis (PCA). Of all the variables in the initial collection, the non-correlated variables contain the maximum information. The varimax criterion, which minimizes variance between indicators or variables, was used to transform or rotate the data. This provides a minimum dataset of variables while reducing redundancy and bias. Only principal components (PC) with eigenvalues ≥1 [32] and highly weighted variables were chosen. In cases where multiple variables from a single PC were selected, the multivariate correlation coefficient was used to eliminate redundancy, retaining the variable with the strongest correlation. The selected indicators were assigned unitless standard scores (0–1 scale) based on their relative importance. The scores for each indicator (S) were generated by arranging them in either ascending or descending order according to their favorable or detrimental values. For indicators where “higher is better,” each recorded value was divided by the highest recorded value, which was assigned a score of 1. Conversely, indicators stating “lower is better” were calculated by dividing the lowest recorded value by each observation, with each awarded a score of 1. The observations were evaluated using a “higher is better” approach until a specific threshold value was reached. Beyond that threshold, indicators were scored using a “lower is better” approach for those that do not fall into either category [42]. The following formula, presented below, was used to calculate the SQI:the score of an indicator is denoted as “S”, and the weighted factor produced by PCA is represented by “W”.
Statistical analysis. The data generated from the present investigation were statistically examined using a randomized block design (RBD) factorial design method. The selection for the soil quality index (SQI) calculation was carried out using SPSS version 23, employing a minimum dataset (MDS) and principal component analysis (PCA). PAST software version 4.03 was utilized to generate PCA biplots and correlation matrices.
RESULTS
Physical soil quality parameters. Soil physical properties, including bulk density, particle density, porosity, and maximum water holding capacity across different land uses (forest, cultivated, pasture, and scrub) at two altitudinal ranges showed significant differences (Table 1). The BD of land uses at two altitudinal ranges varied between 1.22 and 1.55 g cm–3, with soil under forests having the lowest (1.25 g cm–3) and soil in the high hill zone having the lowest BD. The PD of soil varied between 2.33 and 2.64 g cm–3 under various land uses. Land use-wise forest cover has the lowest PD (2.35 g cm–3) and altitude-wise soil PD decreased with an increase in altitude. In terms of land uses the BD and PD of soil were lowest in forests followed by pasture, cultivated land, and scrubland. The soil porosity ranged from 41.16 to 47.71% among selected land uses with forest soils having the highest value at 46.88%. As the altitude increased, the soil’s porosity went up as well. The MWHC of selected land uses varied from 37.77 to 46.34%. Forest land use recorded the highest value (45.86%) for soil bulk density and the MWHC increases with altitude. The highest porosity and MWHC were recorded in forests followed by pasture, cultivated, and scrub land uses. These findings suggest that maintaining forested areas and minimizing soil disturbance in agricultural lands could help improve soil physical characteristics in this region.
Table 1. . Influence of land use and altitude on soil physical characteristics
Soil Parameters | Altitude | Land Uses | p-value | ||||
|---|---|---|---|---|---|---|---|
forest | cultivated | pasture | scrub | mean | |||
BD, g cm–3 | A1 | 1.27 ± 0.04 | 1.46 ± 0.03 | 1.33 ± 0.03 | 1.55 ± 0.02 | 1.40 ± 0.12 | L: 0.00 A: 0.00 L × A: 0.87 |
A2 | 1.22 ± 0.02 | 1.38 ± 0.02 | 1.27 ± 0.02 | 1.50 ± 0.03 | 1.34 ± 0.12 | ||
Mean | 1.25 ± 0.04 | 1.42 ± 0.04 | 1.30 ± 0.03 | 1.53 ± 0.03 | |||
PD, g cm–3 | A1 | 2.36 ± 0.01 | 2.58 ± 0.04 | 2.41 ± 0.03 | 2.64 ± 0.01 | 2.50 ± 0.12 | L: 0.00 A: 0.00 L × A: 0.90 |
A2 | 2.33 ± 0.03 | 2.53 ± 0.02 | 2.37 ± 0.04 | 2.60 ± 0.01 | 2.46 ± 0.12 | ||
Mean | 2.35 ± 0.03 | 2.55 ± 0.04 | 2.39 ± 0.04 | 2.62 ± 0.02 | |||
Porosity, % | A1 | 46.04 ± 1.80 | 43.47 ± 0.74 | 44.95 ± 0.80 | 41.16 ± 0.36 | 43.91 ± 2.12 | L: 0.00 A: 0.00 L × A: 0.91 |
A2 | 47.71 ± 0.22 | 45.25 ± 0.16 | 46.41 ± 0.39 | 42.25 ± 1.16 | 45.41 ± 2.17 | ||
Mean | 46.88 ± 1.47 | 44.36 ± 1.09 | 45.68 ± 0.98 | 41.71 ± 0.97 | |||
MWHC, % | A1 | 45.38 ± 0.45 | 42.14 ± 0.08 | 43.49 ± 0.61 | 37.77 ± 0.56 | 42.20 ± 2.96 | L: 0.00 A: 0.00 L × A: 0.04 |
A2 | 46.34 ± 0.40 | 43.78 ± 0.50 | 44.63 ± 0.62 | 40.13 ± 0.12 | 43.72 ± 2.39 | ||
Mean | 45.86 ± 0.65 | 42.96 ± 0.95 | 44.06 ± 0.73 | 38.95 ± 1.34 | |||
A1 = 651–1800 m; A2 = 1801–2200 m; L = Land use; A = Altitude.
Chemical soil quality parameters. Chemical characteristics of soil quality are important markers of soil fertility, health, and ability to support ecosystem functioning and plant growth. Different chemical characteristics of soil, including pH, EC, SOC, available N, P, K and S, exchangeable Ca and Mg, and heavy metals (Tables 2, 3), showed varied trends across different land use practices and altitudinal ranges. The results of the chemical analysis are shown and explained in the sections below.
Table 2. . Influence of land use and altitude on soil chemical characteristics
Soil Parameters | Altitude | Land Uses | p-value | ||||
|---|---|---|---|---|---|---|---|
forest | cultivated | pasture | scrub | mean | |||
pH | A1 | 6.17 ± 0.07 | 6.74 ± 0.11 | 6.37 ± 0.04 | 6.94 ± 0.10 | 6.56 ± 0.32 | L: 0.00 A: 0.00 L × A: 0.02 |
A2 | 5.98 ± 0.09 | 6.32 ± 0.66 | 6.13 ± 0.09 | 6.47 ± 0.60 | 6.22 ± 0.21 | ||
Mean | 6.07 ± 0.13 | 6.53 ± 0.25 | 6.25 ± 0.15 | 6.70 ± 0.27 | |||
EC, dS m–1 | A1 | 0.16 ± 0.02 | 0.18 ± 0.02 | 0.16 ± 0.01 | 0.20 ± 0.02 | 0.18 ± 0.02 | L: 0.00 A: 0.00 L × A: 0.07 |
A2 | 0.11 ± 0.01 | 0.17 ± 0.01 | 0.13 ± 0.01 | 0.18 ± 0.02 | 0.15 ± 0.03 | ||
Mean | 0.14 ± 0.03 | 0.17 ± 0.01 | 0.15 ± 0.02 | 0.19 ± 0.02 | |||
SOC, g kg–1 | A1 | 12.14 ± 0.31 | 9.42 ± 0.50 | 11.35 ± 0.31 | 7.98 ± 1.00 | 10.22 ± 1.78 | L: 0.00 A: 0.00 L × A: 0.01 |
A2 | 18.05 ± 2.51 | 10.69 ± 0.43 | 12.98 ± 1.67 | 9.31 ± 0.42 | 12.76 ± 3.71 | ||
Mean | 15.10 ± 3.61 | 10.06 ± 0.81 | 12.17 ± 1.40 | 8.65 ± 1.00 | |||
Available N, kg ha–1 | A1 | 336.67 ± 3.23 | 290.74 ± 6.94 | 329.52 ± 8.86 | 195.08 ± 7.51 | 288 ± 59.23 | L: 0.00 A: 0.01 L × A: 0.01 |
A2 | 353.57 ± 3.27 | 284.92 ± 8.62 | 350.36 ± 1.03 | 195.54 ± 6.41 | 296.10 ± 67.22 | ||
Mean | 345.12 ± 9.70 | 287.83 ± 7.69 | 339.94 ± 12.73 | 195.31 ± 6.25 | |||
Available P, kg ha–1 | A1 | 45.11 ± 1.68 | 60.51 ± 0.90 | 41.86 ± 1.65 | 40.37 ± 1.01 | 46.96 ± 8.44 | L: 0.00 A: 0.00 L × A: 0.10 |
A2 | 49.69 ± 0.29 | 61.79 ± 1.65 | 47.48 ± 2.20 | 43.95 ± 1.29 | 50.73 ± 7.13 | ||
Mean | 47.40 ± 2.73 | 61.15 ±1.38 | 44.67 ± 3.54 | 42.16 ± 2.21 | |||
Available K, kg ha–1 | A1 | 263.02 ± 4.28 | 243.70 ± 14.43 | 257.69 ± 6.96 | 197.93 ± 2.42 | 240.58 ± 27.69 | L: 0.00 A: 0.00 L × A: 0.06 |
A2 | 289.37 ± 2.63 | 250.21 ± 8.13 | 262.57 ± 6.21 | 204.69 ± 5.32 | 251.71 ± 32.37 | ||
Mean | 276.20 ± 14.78 | 246.90 ± 11.06 | 260.13 ± 6.48 | 201.31 ± 5.23 | |||
Exchangeable Ca, cmol, p+ kg–1 | A1 | 4.51 ± 0.09 | 3.61 ± 0.08 | 3.74 ± 0.16 | 3.06 ± 0.07 | 3.73 ± 0.55 | L: 0.00 A: 0.00 L × A: 0.12 |
A2 | 4.84 ± 0.07 | 3.66 ± 0.04 | 3.87 ± 0.15 | 3.17 ± 0.05 | 3.88 ± 0.64 | ||
Mean | 4.67 ± 0.19 | 3.63 ± 0.06 | 3.80 ± 0.16 | 3.12 ± 0.64 | |||
Exchangeable Mg, cmol, p+ kg–1 | A1 | 1.73 ± 0.04 | 1.37 ± 0.03 | 1.45 ± 0.06 | 1.09 ± 0.01 | 1.41 ± 0.24 | L: 0.00 A: 0.00 L × A: 0.76 |
A2 | 1.79 ± 0.03 | 1.42 ± 0.02 | 1.49 ± 0.02 | 1.11 ± 0.02 | 1.45 ± 0.25 | ||
Mean | 1.76 ± 0.05 | 1.40 ± 0.03 | 1.47 ± 0.04 | 1.10 ± 0.02 | |||
Available S, kg ha–1 | A1 | 50.98 ± 1.26 | 43.36 ± 1.24 | 47.68 ± 0.36 | 38.54 ± 0.96 | 45.14 ± 4.95 | L: 0.00 A: 0.00 L × A: 0.36 |
A2 | 52.77 ± 1.46 | 47.48 ± 2.25 | 49.54 ± 0.69 | 40.08 ± 1.88 | 47.47 ± 5.08 | ||
Mean | 51.88 ± 1.57 | 45.42 ± 2.78 | 48.61 ± 1.13 | 39.31 ± 1.58 | |||
A1 = 651–1800 m; A2 = 1801–2200 m; L = Land use; A = Altitude.
Table 3. . Influence of Land Use and Altitude on Soil Physical Characteristics
Heavy Metals | Altitude | Land Uses | p-value | ||||
|---|---|---|---|---|---|---|---|
forest | cultivated | pasture | scrub | mean | |||
Essential Heavy Metals | |||||||
Cu, mg kg–1 | A1 | 0.77 ± 0.06 | 0.62 ± 0.07 | 0.70 ± 0.02 | 0.32 ± 0.02 | 0.60 ± 0.18 | L: 0.00 A: 0.00 L × A: 0.60 |
A2 | 0.86 ± 0.05 | 0.70 ± 0.04 | 0.80 ± 0.05 | 0.35 ± 0.05 | 0.68 ± 0.21 | ||
Mean | 0.82 ± 0.07 | 0.66 ± 0.07 | 0.75 ± 0.06 | 0.33 ± 0.04 | |||
Fe, mg kg–1 | A1 | 16.40 ± 0.11 | 10.56 ± 0.31 | 13.56 ± 0.24 | 4.79 ± 0.56 | 11.33 ± 4.50 | L: 0.00 A: 0.00 L × A: 0.04 |
A2 | 16.97 ± 0.26 | 11.55 ± 0.21 | 14.90 ± 0.34 | 6.42 ± 0.16 | 12.46 ± 4.17 | ||
Mean | 16.69 ± 0.36 | 11.06 ± 0.59 | 14.23 ± 0.78 | 5.61 ± 0.96 | |||
Mn, mg kg–1 | A1 | 4.63 ± 0.56 | 3.21 ± 0.18 | 3.90 ± 0.16 | 1.14 ± 0.11 | 3.22 ± 1.39 | L: 0.000 A: 0.01 L × A: 0.72 |
A2 | 5.08 ± 0.30 | 3.52 ± 0.08 | 4.25 ± 0.17 | 1.25 ± 0.11 | 3.53 ± 1.49 | ||
Mean | 4.85 ± 0.47 | 3.37 ± 0.21 | 4.07 ± 0.24 | 1.20 ± 0.12 | |||
Zn, mg kg–1 | A1 | 4.18 ± 0.05 | 2.71 ± 0.19 | 3.69 ± 0.11 | 2.09 ± 0.06 | 3.17 ± 0.86 | L: 0.00 A: 0.00 L × A: 0.93 |
A2 | 4.38 ± 0.13 | 2.98 ± 0.23 | 3.90 ± 0.21 | 2.39 ± 0.13 | 3.41 ± 0.83 | ||
Mean | 4.28 ± 0.14 | 2.85 ± 0.24 | 3.79 ± 0.19 | 2.24 ± 0.19 | |||
Non-Essential Heavy Metals, mg kg–1 | |||||||
Cd, mg kg–1 | A1 | 0.003 ± 0.001 | 0.011 ± 0.002 | 0.003 ± 0.001 | 0.003 ± 0.001 | 0.005 ± 0.004 | L: 0.00 A: 0.05 L × A: 0.80 |
A2 | 0.002 ± 0.001 | 0.009 ± 0.002 | 0.002 ± 0.001 | 0.002 ± 0.001 | 0.004 ± 0.003 | ||
Mean | 0.003 ± 0.001 | 0.010 ± 0.002 | 0.003 ± 0.001 | 0.002 ± 0.001 | |||
Cr, mg kg–1 | A1 | 0.008 ± 0.002 | 0.033 ± 0.011 | 0.008 ± 0.001 | 0.006 ± 0.001 | 0.014 ± 0.012 | L: 0.00 A: 0.09 L × A: 0.16 |
A2 | 0.007 ± 0.002 | 0.023 ± 0.003 | 0.007 ± 0.001 | 0.005 ± 0.002 | 0.011 ± 0.008 | ||
Mean | 0.007 ± 0.001 | 0.028 ± 0.009 | 0.008 ± 0.001 | 0.006 ± 0.001 | |||
Pb, mg kg –1 | A1 | 0.03 ± 0.01 | 0.09 ± 0.01 | 0.04 ± 0.01 | 0.03 ± 0.01 | 0.05 ± 0.03 | L: 0.00 A: 0.17 L × A: 0.85 |
A2 | 0.02 ± 0.01 | 0.08 ± 0.01 | 0.03 ± 0.02 | 0.03 ± 0.01 | 0.04 ± 0.03 | ||
Mean | 0.03 ± 0.01 | 0.09 ± 0.01 | 0.03 ± 0.01 | 0.03 ± 0.01 | |||
A1 = 651–1800 m; A2 = 1801–2200 m; L = Land use; A = Altitude.
pH, EC, and SOC. Soil pH ranged from 5.98 to 6.94 under various land uses at two altitudinal ranges, with forest land use having the lowest value at 6.07. The EC of soil ranges from 0.11 to 0.20 dS m–1. Significant variations among land uses were noticed with minimum soil EC of 0.14 dS m–1 under forest soils. There is a drop in both pH and EC values as the altitude increases. In terms of land use the minimum pH and EC was recorded under forests followed by pasture, cultivated and scrubland. SOC ranged from 7.98 to 18.05 g kg–1 across land uses and altitudes, with the forest having the highest value at 15.10 g kg–1 followed by pasture, cultivated, and scrubland uses. The rise in altitude was associated with an increase in SOC.
Available N, P, and K. Soil-available nitrogen ranged from 195.08 to 353.57 kg ha–1, with forests having the highest amount (345.12 kg ha–1). The soil available phosphorus ranges from 40.37 to 61.79 kg ha–1 depending on land use and altitude. Cultivated land had the highest available phosphorus concentration of 61.15 kg ha–1. The soil available potassium ranged from 197.93 to 289.37 kg ha–1, with the highest content being found in soils under forest at 276.20 kg ha–1. The highest amount of readily available N and K was recorded in the forest, followed by pasture, cultivated land, and scrubland, while available P was highest in cultivated land followed by forest, pasture, and scrubland. N, P, and K availability in the soil increased as altitude increased.
Exchangeable Ca, Mg, and available S. Soil exchangeable calcium ranged from 3.06 to 4.84 cmol (p+) kg–1, with higher values in soils under forest (4.67 cmol (p+) kg–1). The soil exchangeable magnesium values ranged from 1.09 to 1.79 cmol (p+) kg–1. Among land uses significantly the highest soil exchangeable magnesium of cmol (p+) kg–1 was recorded under forest. Soil exchangeable calcium and magnesium among land uses was highest under forest followed by pasture, cultivated, and scrub. The available sulphur under different land uses and altitudes ranged from 38.54 to 52.77 kg ha–1, with forest soils having the highest content of 51.88 kg ha–1 followed by pasture, cultivated, and scrub. Irrespective of land uses maximum exchangeable calcium, magnesium, and available sulphur content were recorded in soils at higher altitudes.
Essential heavy metals (Cu, Fe, Mn, Zn). Soil copper content varied between 0.32 and 0.86 mg kg–1, with the highest level under forest (i.e., 0.82 mg kg–1). Soil iron content varied between 4.79 and 16.97 mg kg–1 in mid and high hill regions, with the highest being 16.69 mg kg–1 in soils under forest. Soil manganese content ranged from 1.14 to 5.08 mg kg–1, with the highest level in soil under forest (4.85 mg kg–1). Soil zinc content varied between 2.09 and 4.38 mg kg–1 in both mid and high hill regions. The highest soil zinc content was found in the forest (4.28 mg kg–1). The highest values for all essential heavy metals (Cu, Fe, Mn, Zn) have been recorded in the forest followed by pasture, cultivated, and scrubland. Irrespective of land use higher values for essential heavy metals (Cu, Fe, Mn, Zn) were recorded in soils at higher altitudes.
Non-essential heavy metals (Cd, Cr, Pb). The soil cadmium content among different land uses and altitudes ranged from 0.002 to 0.011 mg kg–1. Among land uses significantly higher soil cadmium content of 0.010 mg kg–1 was recorded under cultivated land. The soil chromium content ranged from 0.005 to 0.033 mg kg–1 and higher soil chromium content of 0.028 mg kg–1. The soil lead content under various land uses of mid and high hill regions ranged from 0.02 to 0.09 mg kg–1 and land use wise significantly highest soil lead content of 0.09 mg kg–1 was recorded in soils under cultivated land. Further, the effect of altitude and the interaction of land uses and altitudes was found to be non-significant in the case of all three non-essential heavy metals.
Biological parameter (viable bacterial, fungal, and actinomycetes count). The viable microbial count in the soils of different land uses and altitudes of the Northwestern Himalayas demonstrated significant variations, indicating the impact of land use practices and altitude on soil microbial diversity (Table 4).
Table 4. . Variations in the viable microbial count based on land use and altitude
Viable microbial count | Altitude | Land Uses | p-value | ||||
|---|---|---|---|---|---|---|---|
forest | cultivated | pasture | scrub | mean | |||
Bacterial, ×106 cfu g–1 | A1 | 310.30 ± 8.84 | 259.28 ± 6.47 | 298.96 ± 1.49 | 139.32 ± 5.25 | 251.97 ± 70.94 | L: 0.00 A: 0.00 L × A: 0.80 |
A2 | 295.24 ± 4.45 | 245.50 ± 6.18 | 284.08 ± 3.82 | 118.82 ± 10.25 | 235.91 ± 73.41 | ||
Mean | 302.77 ± 10.36 | 252.39 ± 9.44 | 291.52 ± 8.55 | 129.07 ± 13.37 | |||
Fungal, ×103cfu g–1 | A1 | 5.55 ± 0.41 | 3.88 ± 0.07 | 4.42 ± 0.04 | 2.98 ± 0.04 | 4.21 ± 0.98 | L: 0.00 A: 0.00 L × A: 0.06 |
A2 | 6.07 ± 0.31 | 4.48 ± 0.05 | 5.12 ± 0.15 | 3.15 ± 0.09 | 4.71 ± 1.11 | ||
Mean | 5.81 ± 0.39 | 4.18 ± 0.33 | 4.77 ± 0.40 | 3.07 ± 0.11 | |||
Actinomycetes, ×103 cfu g–1 | A1 | 11.81 ± 0.40 | 8.16 ± 0.80 | 9.94 ± 0.36 | 4.29 ± 0.20 | 8.55 ± 2.93 | L: 0.00 A: 0.00 L × A: 0.03 |
A2 | 13.80 ± 0.21 | 8.85 ± 0.34 | 11.43 ± 0.40 | 4.98 ± 0.42 | 9.76 ± 3.42 | ||
Mean | 12.80 ± 1.13 | 8.50 ± 0.67 | 10.69 ± 0.88 | 4.64 ± 0.40 | |||
A1 = 651–1800 m; A2 = 1801–2200 m; L = Land use; A = Altitude
The soil bacterial counts among different land uses and altitudes ranged from 139.32 to 310.30 × 106 CFU g–1. Land use wise significantly highest bacterial counts of 302.77 × 106 CFU g–1 were recorded in soils under forest. The soil bacterial counts decreased with an increase in altitude. The soil fungal counts among different land uses and altitudes ranged from 2.98 to 6.07 × 103 CFU g–1. Among land uses, significantly the highest fungal counts of 5.81 × 103 CFU g–1 were recorded in soils under forest. The soil actinomycetes count among different land uses and altitudes ranged from 4.29 to 13.80 × 103 CFU g–1. Among land uses, the highest actinomycetes counts of 12.80 × 103 CFU g–1 were recorded in soils under forest. Soil fungal and actinomycetes counts increased with the rise in altitude. This study indicated that microbial activity is higher in forest and pasture soils than in cultivated and scrubland soils, regardless of altitude.
Soil quality indices of selected land uses. The PCA results (Figs. 2, 3) revealed significant differences in soil quality indices across the mid-hill and high-hill regions. In both regions, two principal components explained most of the variance, with the first accounting for 79.56 and 79.40% of the variance in the mid- and high-hill regions, respectively. The second component accounted for an additional 18.79 and 18.76%, leading to a cumulative variance of over 98.34 and 98.16% in the mid- and high-hill regions, respectively. This indicates that the selected soil quality indicators strongly influence soil quality, and that their combined effects are critical for evaluating the impact of land use on soil health in these regions.
[See PDF for image]
Fig. 2.
Principal component analysis of 23 soil parameters across the first two principal components for 4 land uses using a scatterplot of given scores in the mid-hill region.
[See PDF for image]
Fig. 3.
Principal component analysis of 23 soil parameters across the first two principal components for 4 land uses using a scatterplot of given scores in the high-hill region.
In both the mid-hill and high hill region, BD, PD, pH, and EC displayed strong negative loadings on the first component, whereas viable microbial counts, porosity, SOC, available N, K, S, exchangeable Ca, Mg, S, and essential heavy metals like Cu, Fe, Mn, Zn contributed positively. The second component showed high loadings of Cd, Cr, Pb and available P. The MDS for the mid-hill region as identified using the procedure outlined in Section 2.6.
For the mid-hill region, the MDS for SQI analysis consisted of 3 soil parameters- viable actinomycetes counts, PD, and exchangeable Ca from the first principal component, along with 3 soil parameters, including Cd, Cr, and Pb from the second principal component. However, for the high hill region, the MDS consisted of 2 soil parameters i.e., viable actinomycetes counts and exchangeable Ca from the first principal component, and 3 soil parameters i.e., Cd, Cr, and Pb from the second principal component. These indicators were chosen based on the method described in Section 2.6.
The Soil Quality Index (SQI) varied across different land uses in both the mid-hill and high-hill zones (Fig. 4). Forest areas recorded better soil quality as indicated by the highest SQI value of 0.98 in both mid-hill and high-hill regions, which falls under the high category. Similarly, the SQI values of pasture fields were also high, with the high hill region measuring 0.81 and the mid-hill zone measuring 0.87. Conversely, the SQI values of cultivated and scrub land uses were comparatively lower. Cultivated lands fell into the medium soil quality category with SQIs of 0.70 and 0.61 in the mid-hill and high hill zones, respectively. In a similar vein, scrub areas had SQIs that were in the middle range, measuring 0.71 for mid-hill and 0.58 for high hill. Overall, forest and pasture lands had healthier soil compared to cultivated and scrub areas, with the mid-hill zone consistently showing better soil quality across all types of land use.
[See PDF for image]
Fig. 4.
SQI of selected land uses for the mid and high hill region. Soil quality classification: SQI < 0.5 = low; 0.50–0.75 = Medium; >0.75 = High ([85]).
DISCUSSIONS
Land use and soil physical characteristics. The findings of this study indicate that specific land uses exert a substantial influence on the soil’s physical characteristics within the region under investigation. Forests had the lowest soil BD and PD due to good vegetation cover and less anthropogenic disturbances, while pasture land displayed intermediate values due to reduced organic matter because of grazing pressure and poor management. Cultivated soils had a significant increase in soil BD and PD due to decreased SOC because of regular cultivation, while the highest BD and PD in scrublands is because of scanty vegetation cover, which led to the built-up of very less organic matter. As reported in prior studies, an inverse relationship between BD and SOC is evident [30, 74, 86], highlighting the importance of organic matter in reducing BD. The increased PD in cultivated and scrubland areas is linked to intensified land use and reduced organic matter, respectively. McBride et al. [48] and Schjonning et al. [63] found that particle density decreases with increasing soil organic matter content.
The maximum soil porosity and MWHC under forest are attributed to lower BD and high organic matter content, which could have resulted in increased micropores in the soil. The high organic matter content and earthworm activities lead to increased soil porosity. While significant decreases in other selected land use are attributed to increased bulk density and decreased organic matter. The increased porosity is linked to earthworm activity and greater organic content in forest soils, as described by Bizuhoraho et al. [14]. The amount of water held in the soil at a certain pressure level decreases as the intensity of soil usage increases [17].
Overall, these findings indicate that soil physical health generally improves with increasing altitude. Higher-altitude soils (1801–2200 m) had lower BD and PD, along with higher porosity and MWHC. This improvement is likely due to the increased SOC at higher altitudes, as cooler temperatures slow down decomposition rates, allowing more organic matter to accumulate. A decreasing trend of BD with an increase in altitude was also noted by [34, 39, 66] in the Western Himalayas. Greater porosity at higher altitudes might be because of the fact that soils in temperate climates are more likely to contain more micropores [22], which eventually results in increased porosity. Similarly, higher MWHC in the temperate zone may be because of increased SOC in soils of the temperate zone [80]. The soil MWHC increased with altitude from the sub-tropical to the trans-Himalayan zone [22].
The results clearly show that land use types significantly influence soil physical properties in the mid-hill and high-hill regions of the Northwestern Himalayas. Forested areas consistently exhibit superior soil quality with lower bulk density, higher porosity, and enhanced water-holding capacity, attributed to high organic matter content and reduced land use intensity.
LAND USE AND SOIL CHEMICAL CHARACTERISTICS
pH and EC. Soil pH and EC varied significantly across different land use types and altitudes. A lower soil pH in the forest might be because of high organic matter, the release of organic acids during the decomposition process and plant root exudates [82]. A significant negative correlation was observed between soil pH and altitude [35]. Soil pH tended to decrease with increasing aboveground biomass [10], suggesting that areas with denser vegetation may contribute to more acidic soil conditions. Therefore, the lower pH in higher altitudes might be because of increased SOC. The findings of the study are consistent with those of [8, 55], who likewise noted relatively lower pH in soils from pastures and forests.
Soil EC was within an acceptable range for all land use systems and none of the systems impacted it adversely. Soils at higher altitudes have less soluble salts as higher altitudes tend to experience more soil leaching because of the heavy precipitation and slower transpiration rate. Balkrishna et al. [6] and Kumar et al. [39] also reported a decrease in soil EC with a rise in altitude in the western Himalayas.
SOC. Soil organic carbon (SOC) plays a crucial role in regulating soil processes and in supporting ecosystem functions. The highest levels of SOC are typically found in forests, which can be attributed to the high input of organic matter from dense vegetation and lower rate of organic matter decomposition [33, 35, 39]. Among Indian forest ecosystems, the mountainous forests of the Himalayas have the highest soil carbon concentrations [47, 68]. The increase in SOC at higher altitudes is likely due to the slower decomposition rates caused by colder temperatures [11] as the SOC and mean annual temperature exhibited a statistically significant negative correlation [23, 34]. Singh et al. [68] also highlighted that land use and climatic conditions strongly influence SOC levels in the Himalayas, with temperate regions showing the highest SOC while subtropical regions show the lowest.
Available N, P, and K. The abundant presence of organic matter and thriving populations of bacteria, fungi, and actinomycetes in forested areas contributes to efficient nutrient recycling, which in turn leads to higher availability of nitrogen (N) and potassium (K) in the soil. Soils rich in organic matter tend to have higher nitrogen concentrations [36, 65]. Soil potassium is predominantly produced by the microbial breakdown and mineralization of soil organic matter and an increase in soil organic matter raises the potassium content of the soil [69].
As altitude rises, both soil N and K availability exhibit an increasing trend. Elevated nitrogen content at higher altitudes owing to greater organic matter accumulation [39, 77] due to temperate type of climate. may be related to the improved soil conditions in these zones. Similar trends of an increase in K at higher altitudes have been reported in the Western Himalayan region by [35].
The highest levels of available phosphorus (P) were found under cultivated land use, which may be attributed to the frequent application of phosphatic fertilizers [37]. This trend indicates that human intervention through fertilization plays a significant role in phosphorus availability across land use types.
The availability of N, P, and K varied significantly with land use and altitude. Forests tend to have increased N and K due to organic matter content and higher viable microbial counts, which enhances their availability. Whereas, cultivated lands show higher P levels, likely influenced by fertilization practices. Altitude-wise, the availability of N, P, and K also showed an increasing trend due to higher accumulation of organic matter in a temperate climate.
Exchangeable Ca, Mg, and available S. The highest values of exchangeable calcium (Ca), magnesium (Mg), and available sulphur (S) observed in forest soils can be attributed to efficient nutrient recycling, primarily driven by high organic matter content and the presence of abundant viable microbes, including bacteria, fungi, and actinomycetes. Banday et al. [7] documented a positive relationship between exchangeable Ca and SOC, supporting the notion that organic matter contributes significantly to calcium availability in forest ecosystems. Similarly, Singh et al. [67] also reported higher Ca concentrations in soils under forest cover.
The greater exchangeable Mg content in forest soils aligns with the findings of Tufa et al. [78], who reported higher Mg levels in forested areas. Therefore, an increase in exchangeable Ca and Mg at higher altitudes could be linked to better vegetation cover, increased organic matter and a more favorable microclimate.
There is a significant correlation between SOC with S [73] as a large fraction of available S is derived from organic sources [70], which explains the higher S content at higher altitudes. Elevated SOC at these altitudes enhances the availability of S, contributing to improved nutrient dynamics in forest soils.
Therefore, the higher concentrations of exchangeable Ca, Mg, and available S in forest soils, particularly at higher altitudes, are closely related to organic matter accumulation, favorable microclimatic conditions, and efficient nutrient cycling within these ecosystems.
Essential heavy metals (Cu, Fe, Mn, Zn). The highest value for all the essential heavy metals (Cu, Fe, Mn, Zn) under forest land use may be ascribed to relatively higher organic matter content due to the enormous amount of leaf litter and root biomass. The massive litterfall and root biomass, improves aeration, impedes oxidation, and precipitation of essential heavy metals, and supplements chelating substances, which improves dissolution and availability of the essential heavy metals in forest soils [24, 62].
SOC has a positive relationship with Cu and Zn content [20] which might be the reason for their higher concentration in forest soils. The greater Zn level in the forest also aligns with the findings of Chandel et al. [18], who reported a positive correlation between Zn and available N. Hence, the higher concentration of SOC and available N might be the reason for higher Zn concentration at higher altitudes. Similarly, Cu is strongly linked to soil organic matter (SOM) and forms more stable organic complexes compared to other divalent transition metals [83]. Therefore, the greater Cu levels at higher altitudes are most likely owing to the higher soil organic matter content due to the slower decomposition rate at lower temperatures.
The observed trends in Fe content align with those of Colombo et al. [21], who identified soil pH as the primary factor influencing total Fe concentrations. The inverse relationship between soil pH and Mn & Fe content reported by Choudhury et al. [20], helps explain the increased Fe and Mn concentrations in forest soils and at altitude-wise at higher altitudes due to lower soil pH.
Non-essential heavy metals (Cd, Cr, Pb). The concentrations of non-essential heavy metals, including Cd, Cr, and Pb, showed clear variation based on land use, with cultivated lands exhibiting significantly higher levels; however, altitude-wise, there were no significant differences.
Phosphate fertilizers, compounds used for liming, and bio-fertilizers are examples of inorganic fertilizers that trigger release of heavy metals in cultivated soils [26]. Cadmium levels are notably higher in cultivated soils, which can be linked to the extensive use of phosphate fertilizers. Cd is one of the numerous other minerals found in phosphate and apatite rocks, which are used to make phosphate fertilizers [72]. Similarly, anthropogenic sources such as chemical fertilizers and pesticides are also responsible for the presence of Cr and Pb in cultivated lands [53]. High chromium concentration may be linked to herbicides, pesticides and fertilizers applications [4]. Whereas an increase in lead content due to the use of insecticides, fertilizers, and air depositions [43].
In non-cultivated lands such as forests, pastures, and scrublands, the concentration of non-essential heavy metals remained likely because of the absence of agricultural inputs. The higher levels of Cd, Cr, and Pb in cultivated lands highlight the need for sustainable soil management practices to mitigate heavy metal contamination and to safeguard soil health in agricultural ecosystems.
LAND USE AND SOIL VIABLE BACTERIAL, FUNGAL, AND ACTINOMYCETES COUNT
Microbes support several important ecological functions in the soil, including soil structure improvement, soil aggregation, soil nutrient recycling, and so on. As a result, measuring Viable Microbial Count is critical for understanding soil’s physical, chemical, and biological characteristics. The highest viable microbial counts (bacteria, fungi and actinomycetes) in forest soils may be ascribed to its higher SOC, which might have acted as a carbon source for microorganisms for its enhanced proliferation. The results are in line with Kumar et al. [40], Mehta et al. [49] and Wani et al. [81] who have also reported greater microbial populations in forest soils, likely attributed to the abundance of organic matter in the forest, which serves as a significant source of carbon.
There is a significant fall in the viable bacterial populations at higher altitudes in the Himalayas [44, 45], presumably due to the lower temperature at higher altitudes.
In the case of both fungi and actinomycetes, the viable counts increased with an increase in altitude. The acidic pH and plenty of substrate in the forest soils promote the activity of fungi [5], which might be the reason for the higher fungal population at higher altitudes. Similarly, Bhutia et al. [13] recorded that actinomycete counts increased with an increase in altitude in the western Himalayas.
CONCLUSIONS
In this study, we assessed the impact of land use and altitude on soil quality in mid-hill and high-hill regions of the Northwestern Himalayas. Our findings revealed significant variations in key physical, chemical and biological soil parameters across different land use types (forest, cultivated, pasture, and scrub) and altitudes (651–1800 and 1801–2200 m) in northwestern Himalayan conditions. Forested areas consistently demonstrated superior soil quality, exhibiting lower bulk density, higher porosity, enhanced water-holding capacity, and elevated levels of organic carbon, nutrients (N, P, K, Ca, Mg, and S), and essential micronutrients (Cu, Fe, Mn, and Zn) compared to cultivated and scrubland areas. Cultivated lands, while benefiting from phosphorus inputs, demonstrated moderate soil compaction and lower nutrient content due to agricultural activities. Overall, the results underscore the critical role of forest ecosystems in maintaining soil health, particularly at higher altitudes, and emphasize the necessity for sustainable land use practices to mitigate soil degradation in cultivated and scrubland areas.
ACKNOWLEDGMENTS
The authors extend their gratitude to the Department of Environmental Science, College of Forestry, Yashwant Singh Parmar University of Horticulture and Forestry, Nauni, Solan, Himachal Pradesh for providing the facilities required to carry out this investigation.
AUTHOR CONTRIBUTION
Conceptualization: Shubham Sharma, Satish Kumar Bhardwaj, Daulat Ram Bhardwaj. Data curation: Shubham Sharma, Satish Kumar Bhardwaj, Gaurav Rana, Sumit Nangla. Investigation: Shubham Sharma, Satish Kumar Bhardwaj, Daulat Ram Bhardwaj. Methodology: Shubham Sharma, Satish Kumar Bhardwaj, Gaurav Rana; Supervision: Satish Kumar Bhardwaj, Daulat Ram Bhardwaj. Writing-original draft: Shubham Sharma. Writing-review and editing: Satish Kumar Bhardwaj, Vinay Kumar Rachappanavar, Sakshi Visvamitera, Ishani Sharma
FUNDING
This work was supported by ongoing institutional funding. No additional grants to carry out or direct this particular research were obtained.
DATA AVAILABILITY
The data used in the writing of this paper have been tabulated in the supplementary materials.
ETHICS APPROVAL AND CONSENT TO PARTICIPATE
This work does not contain any studies involving human and animal subjects.
CONFLICT OF INTEREST
The authors of this work declare that they have no conflicts of interest.
Publisher’s Note.
Pleiades Publishing remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
AI tools may have been used in the translation or editing of this article.
REFERENCES
1 Angon, P. B.; Anjum, N.; Akter, M. M.; Kc, Sh.; Suma, R. P.; Jannat, S. An overview of the impact of tillage and cropping systems on soil health in agricultural practices. Advances in Agriculture; 2023; 2023, 8861216. [DOI: https://dx.doi.org/10.1155/2023/8861216]
2 APHA. Standards Methods for the Examination of Water and Waste Water; 2005; Washington DC, American Public Health Association:
3 Arai, M.; Ikazaki, K.; Anzai, T.; Celestial, V. P.; Tumbay, J. V.; Santillana, I. S.; Wagai, R. Protective role of reactive aluminum phases to stabilize soil organic matter against long-term cultivation in the humid tropics under volcanic influence. Soil Science and Plant Nutrition; 2024; 71, pp. 27-37.1:CAS:528:DC%2BB2cXisV2gtbjF [DOI: https://dx.doi.org/10.1080/00380768.2024.2415455]
4 Atafar, Z.; Mesdaghinia, A.; Nouri, J.; Homaee, M.; Yunesian, M.; Ahmadimoghaddam, M.; Mahvi, A. H. Effect of fertilizer application on soil heavy metal concentration. Environ. Monit. Assess.; 2010; 160, pp. 83-89.1:CAS:528:DC%2BD1MXhsFyiu7rP [DOI: https://dx.doi.org/10.1007/s10661-008-0659-x]
5 Babu, S.; Mohapatra, K. P.; Yadav, G. S.; Lal, R.; Singh, R.; Avasthe, R. K.; Das, A.; Chandra, P.; Gudade, B. A.; Kumar, A. Soil carbon dynamics in diverse organic land use systems in North Eastern Himalayan ecosystem of India. Catena; 2020; 194, 104785.1:CAS:528:DC%2BB3cXhsVegtLnL [DOI: https://dx.doi.org/10.1016/j.catena.2020.104785]
6 Balkrishna, A.; Sharma, I. P.; Kushwaha, A. K.; Arya, V. Effect of altitudinal variation on soil nutrient properties at various sites in Garhwal Himalaya, Uttarakhand. Ecological Frontiers; 2024; 44, pp. 589-594. [DOI: https://dx.doi.org/10.1016/j.chnaes.2023.11.003]
7 Banday, M.; Bhardwaj, D. R.; Pala, N. A. Influence of forest type, altitude and NDVI on soil properties in forests of North Western Himalaya, India. Acta Ecol. Sin.; 2019; 39, pp. 50-55. [DOI: https://dx.doi.org/10.1016/j.chnaes.2018.06.001]
8 Bardgett, R. D.; Jones, A. C.; Jones, D. L.; Kemmitt, S. J.; Cook, R.; Hobbs, P. J. Soil microbial community patterns related to the history and intensity of grazing in sub-montane ecosystems. Soil Biol. Biochem.; 2001; 33, pp. 1653-1664.1:CAS:528:DC%2BD3MXotFOltbc%3D [DOI: https://dx.doi.org/10.1016/s0038-0717(01)00086-4]
9 Basset, C. Soil security: The cornerstone of national security in an era of global disruptions. Soil Security; 2024; 16, 100154. [DOI: https://dx.doi.org/10.1016/j.soisec.2024.100154]
10 Bhandari, J.; Zhang, Ya. Effect of altitude and soil properties on biomass and plant richness in the grasslands of Tibet, China, and Manang District, Nepal. Ecosphere; 2019; 10, e02915. [DOI: https://dx.doi.org/10.1002/ecs2.2915]
11 Bhardwaj, D. R.; Salve, A.; Kumar, J.; Kumar, A.; Sharma, P.; Kumar, D. Biomass production and carbon storage potential of agroforestry land use systems in high hills of north-western Himalaya: an approach towards natural based climatic solution. Biomass Convers. Biorefin.; 2023; 14, pp. 18079-18092.1:CAS:528:DC%2BB3sXktFOmtb4%3D [DOI: https://dx.doi.org/10.1007/s13399-023-03952-0]
12 Bhatta, K. P.; Robson, B. A.; Suwal, M. K.; Vetaas, O. R. A pan-Himalayan test of predictions on plant species richness based on primary production and water-energy dynamics. Frontiers of Biogeography; 2021; 13, e49459. [DOI: https://dx.doi.org/10.21425/f5fbg49459]
13 Bhutia, P. L.; Gupta, B.; Yadav, R. P.; Gupta, A. K.; Bhutia, K. G.; Rai, P. Soil physico-chemical and biological properties as affected by vegetation systems and elevation in western Himalayas. Range Management and Agroforestry; 2021; 42, pp. 86-94.
14 Bizuhoraho, T.; Kayiranga, A.; Manirakiza, N.; Mourad, Kh. A. The effect of land use systems on soil properties; A case study from Rwanda. Sustainable Agriculture Research; 2018; 7, 30. [DOI: https://dx.doi.org/10.5539/sar.v7n2p30]
15 G. R. Blake and K. H. Hartge, “Bulk density,” in Methods of Soil Analysis, Part 1: Physical and mineralogical methods, 2nd ed., Ed. by A. Klute (Soil Science Society of America, American Society of Agronomy, Madison, 1986), pp. 363–375. https://doi.org/10.2136/sssabookser5.1.2ed.c13
16 G. R. Blake and K. H. Hartage, “Particle density” in Methods of Soil Analysis, Part 1: Physical and mineralogical methods, 2nd ed., Ed. by A. Klute (Soil Science Society of America, Madison, 1986), pp. 377–382. https://doi.org/10.2136/sssabookser5.1.2ed.c14
17 Bormann, H.; Klaassen, K. Seasonal and land use dependent variability of soil hydraulic and soil hydrological properties of two Northern German soils. Geoderma; 2008; 145, pp. 295-302. [DOI: https://dx.doi.org/10.1016/j.geoderma.2008.03.017]
18 Chandel, S.; Hadda, M. S.; Mahal, A. K. Soil quality assessment through minimum data set under different land uses of submontane Punjab. Commun. Soil Sci. Plant Anal.; 2018; 49, pp. 658-674.1:CAS:528:DC%2BC1cXivVynt74%3D [DOI: https://dx.doi.org/10.1080/00103624.2018.1425424]
19 Chettri, D.; Datta, P.; Behera, B. Climate change and household food security in the Himalayas: A systematic review of the challenges and household adaptative measures. Environmental Development; 2024; 51, 101019. [DOI: https://dx.doi.org/10.1016/j.envdev.2024.101019]
20 Choudhury, B. U.; Ansari, M. A.; Chakraborty, M.; Meetei, T. T. Effect of land-use change along altitudinal gradients on soil micronutrients in the mountain ecosystem of Indian (Eastern) Himalaya. Sci. Rep.; 2021; 11, 14279.1:CAS:528:DC%2BB3MXhs1Cmur%2FO [DOI: https://dx.doi.org/10.1038/s41598-021-93788-3]
21 Colombo, C.; Palumbo, G.; He, J.-Z.; Pinton, R.; Cesco, S. Review on iron availability in soil: Interaction of Fe minerals, plants, and microbes. J. Soils Sediments; 2014; 14, pp. 538-548.1:CAS:528:DC%2BC3sXhvVKitL%2FE [DOI: https://dx.doi.org/10.1007/s11368-013-0814-z]
22 Deb, P.; Debnath, P.; Denis, A. F.; Lepcha, O. T. Variability of soil physicochemical properties at different agroecological zones of Himalayan region: Sikkim, India. Environ., Dev. Sustainability; 2018; 21, pp. 2321-2339. [DOI: https://dx.doi.org/10.1007/s10668-018-0137-8]
23 Devi, S. Influence of trees and associated variables on soil organic carbon: A review. Journal of Ecology and Environment; 2021; 45, 5. [DOI: https://dx.doi.org/10.1186/s41610-021-00180-3]
24 Dhaliwal, M. K.; Dhaliwal, S. S. Impact of manure and fertilizers on chemical fractions of Zn and Cu in soil under rice-wheat cropping system. Journal of the Indian Society of Soil Science; 2019; 67, pp. 85-91. [DOI: https://dx.doi.org/10.5958/0974-0228.2019.00009.4]
25 P. Ekka, S. Patra, M. Upreti, G. Kumar, A. Kumar, and P. Saikia, “Land Degradation and its impacts on biodiversity and ecosystem services,” in Land and Environmental Management through Forestry, Ed. by A. Raj, M. J. Jhariya, A. Banerjee, S. Nema, and K. Bargali (Scrivener Publishing, Beverly, 2023), pp. 77–101. https://doi.org/10.1002/9781119910527
26 Fan, Yu.; Li, Yo.; Li, H.; Cheng, F. Evaluating heavy metal accumulation and potential risks in soil-plant systems applied with magnesium slag-based fertilizer. Chemosphere; 2018; 197, pp. 382-388.1:CAS:528:DC%2BC1cXhsFKlsb0%3D [DOI: https://dx.doi.org/10.1016/j.chemosphere.2018.01.055]
27 FAO and UNEP, The State of the World’s Forests 2020. Forests, Biodiversity and People (FAO publication, Rome, 2020).
28 FAO, COP26: Agricultural Expansion Drives Almost 90 Percent of Global Deforestation (FAO, 2025). https://www.fao.org/newsroom/detail/cop26-agricultural-expansion-drives-almost-90-percent-of-global-deforestation/en, Accessed April 13, 2025.
29 Gitari, J. M.; Muraya, M. M.; Onyango, B. O.; Maingi, J. M. The influence of soil’s physicochemical properties and land use systems on the abundance of actinomycetes populations. South Asian Journal of Research in Microbiology; 2023; 16, pp. 20-37. [DOI: https://dx.doi.org/10.9734/sajrm/2023/v16i2304]
30 Hu, P.-L.; Liu, S.-J.; Ye, Y.-Y.; Zhang, W.; Wang, K.-L.; Su, Y.-R. Effects of environmental factors on soil organic carbon under natural or managed vegetation restoration. Land Degradation and Development; 2018; 29, pp. 387-397. [DOI: https://dx.doi.org/10.1002/ldr.2876]
31 Jackson, M. L. Soil Chemical Analysis; 1973; New Delhi, Prentice Hall of India Pvt Ltd.:
32 Kaiser, H. F. The application of electronic computers to factor analysis. Educ. Psychol. Meas.; 1960; 20, pp. 141-151. [DOI: https://dx.doi.org/10.1177/001316446002000116]
33 M. Kaith, P. Tirkey, D. R. Bhardwaj, J. Kumar, and J. Kumar, “Carbon sequestration potential of forest plantation soils in Eastern Plateau and Hill Region of India: A promising approach toward climate change mitigation,” Water, Air, Soil Pollut.234 (2023). https://doi.org/10.1007/s11270-023-06364-y
34 Kerr, D. D.; Ochsner, T. E. Soil organic carbon more strongly related to soil moisture than soil temperature in temperate grasslands. Soil Sci. Soc. Am. J.; 2019; 84, pp. 587-596.1:CAS:528:DC%2BB3cXjsVegsr0%3D [DOI: https://dx.doi.org/10.1002/saj2.20018]
35 Kewlani, P.; Negi, V. S.; Bhatt, I. D.; Rawal, R. S.; Nandi, S. K. Soil nutrients concentration along altitudinal gradients in Indian Western Himalaya. Scand. J. For. Res.; 2021; 36, pp. 98-104. [DOI: https://dx.doi.org/10.1080/02827581.2020.1871065]
36 Kiflu, A.; Beyene, Sh. Effects of different land use systems on selected soil properties in South Ethiopia. Journal of Soil Science and Environmental Management; 2013; 4, pp. 100-107. [DOI: https://dx.doi.org/10.5897/jssem2013.0380]
37 Kumar, J.; Thakur, C. L.; Bhardwaj, D. R.; Kumar, S.; Dutt, B. Effects of integrated nutrient management on performance of bhringraj (Eclipta prostrata L.) and soil fertility under the Grewia optiva Drummond. canopy in a mid-hill agroecosystem of north western Himalayas. Agrofor. Syst.; 2023; 97, pp. 711-726. [DOI: https://dx.doi.org/10.1007/s10457-023-00822-6]
38 Kumar, M.; Paramaputra, Kh.; Mousa, A.; Yi, S. Kong, A. Garg, and V. Anggraini, “Field based analysis of vegetation and climate impacts on the hydrological properties of urban vegetated slope. Sci. Rep.; 2025; 15, 7702.1:CAS:528:DC%2BB2MXlvVWlurg%3D [DOI: https://dx.doi.org/10.1038/s41598-025-92031-7]
39 Kumar, S.; Prabhakar, M.; Bhardwaj, D. R.; Thakur, C. L.; Kumar, J.; Sharma, P. Altitudinal and aspect-driven variations in soil carbon storage potential in sub-tropical Himalayan forest ecosystem: Assisting nature to combat climate change. Environ. Monit. Assess.; 2024; 196, 126.1:CAS:528:DC%2BB2cXpt1Oruw%3D%3D [DOI: https://dx.doi.org/10.1007/s10661-024-12297-8]
40 Kumar, Sh. Sh.; Mir, Sh. A.; Wani, O. A.; Babu, S.; Yeasin, M.; Bhat, M. A.; Hussain, N. A. I. Ali Wani, R. Kumar, D. Yadav, and S. R. Dar, “Land-use systems regulate carbon geochemistry in the temperate Himalayas, India. J. Environ. Manage.; 2022; 320, 115811.1:CAS:528:DC%2BB38XitlahsbzL [DOI: https://dx.doi.org/10.1016/j.jenvman.2022.115811]
41 Lavelle, P.; Spain, A. V. Soil Ecology; 2005; Dordrecht, Springer:
42 Liebig, M. A.; Varvel, G.; Doran, J. A simple performance-based index for assessing multiple agroecosystem functions. Agron. J.; 2001; 93, pp. 313-318. [DOI: https://dx.doi.org/10.2134/agronj2001.932313x]
43 Luo, W.; Lu, Yo.; Giesy, J. P.; Wang, T.; Shi, Ya.; Wang, G.; Xing, Yi. Effects of land use on concentrations of metals in surface soils and ecological risk around Guanting Reservoir, China. Environ. Geochem. Health; 2007; 29, pp. 459-471.1:CAS:528:DC%2BD2sXhtFyqs77M [DOI: https://dx.doi.org/10.1007/s10653-007-9115-z]
44 Lyngwi, N. A.; Koijam, Kh.; Sharma, D.; Joshi, S. R. Cultivable bacterial diversity along the altitudinal zonation and vegetation range of tropical Eastern Himalaya. Rev. Biol. Trop.; 2013; 61, pp. 467-490. [DOI: https://dx.doi.org/10.15517/rbt.v61i1.11141]
45 Ma, L.; Liu, L.; Lu, Ya.; Chen, L.; Zhang, Zh.; Zhang, H.; Wang, X.; Shu, L.; Yang, Q.; Song, Q.; Peng, Q.; Yu, Z.; Zhang, J. When microclimates meet soil microbes: Temperature controls soil microbial diversity along an elevational gradient in subtropical forests. Soil Biol. Biochem.; 2022; 166, 108566.1:CAS:528:DC%2BB38XhvVOrtLg%3D [DOI: https://dx.doi.org/10.1016/j.soilbio.2022.108566]
46 D. Mandal and T. Roy, “Climate change impact on soil erosion and land degradation,” in Climate Change Impacts on Soil-Plant-Atmosphere Continuum, Ed. by H. Pathak, D. Chatterjee, S. Saha, and B. Das (Springer, Singapore, 2024), pp. 139–161. https://doi.org/10.1007/978-981-99-7935-6_5
47 Martin, D.; Lal, T.; Sachdev, C. B.; Sharma, J. P. Soil organic carbon storage changes with climate change, landform and land use conditions in Garhwal hills of the Indian Himalayan mountains. Agric., Ecosyst. Environ.; 2010; 138, pp. 64-73.1:CAS:528:DC%2BC3cXmslektbs%3D [DOI: https://dx.doi.org/10.1016/j.agee.2010.04.001]
48 McBride, R. A.; Slessor, R. L.; Joosse, P. J. Estimating the particle density of clay-rich soils with diverse mineralogy. Soil Sci. Soc. Am. J.; 2012; 76, pp. 569-574.1:CAS:528:DC%2BC38XktFOhsLc%3D [DOI: https://dx.doi.org/10.2136/sssaj2011.0177n]
49 Mehta, P.; Baweja, P. K.; Bhardwaj, S. K.; Aggarwal, R. K. Impact of canopy and seasonal dynamics on different forest soil microbial community composition in mid hills of Himachal Pradesh. Int. J. Curr. Microbiol. Appl. Sci.; 2018; 7, pp. 494-502.1:CAS:528:DC%2BC1MXhslKgtrfN [DOI: https://dx.doi.org/10.20546/ijcmas.2018.709.059]
50 Merwin, H. D.; Peech, M. Exchangeability of soil potassium in the sand, silt, and clay fractions as influenced by the nature of the complementary exchangeable cation. Proceedings Soil Science Society of America Journal; 1951; 15, pp. 125-128.1:CAS:528:DyaG38XhtVGk [DOI: https://dx.doi.org/10.2136/sssaj1951.036159950015000c0026x]
51 Ya. H. Mir, Sh. Mir, M. A. Ganie, J. A. Bhat, A. M. Shah, M. Mushtaq, and I. Irshad, “Overview of land use and land cover change and its impacts on natural resources,” in Ecologically Mediated Development Promoting Biodiversity Conservation and Food Security, Ed. by H. S. Jatav, V. D. Raiput, and T. Minkina (Springer Nature Singapore, Singapore, 2025), pp. 101–130. https://doi.org/10.1007/978-981-96-2413-3_5
52 Negese, A. Impacts of land use and land cover change on soil erosion and hydrological responses in Ethiopia. Appl. Environ. Soil Sci.; 2021; 2021, 6669438. [DOI: https://dx.doi.org/10.1155/2021/6669438]
53 Ogunwole, J. O.; Ogunleye, P. O. Surface soil aggregation, trace, and heavy metal enrichment under long-term application of farm yard manure and mineral fertilizers. Commun. Soil Sci. Plant Anal.; 2004; 35, pp. 1505-1516.1:CAS:528:DC%2BD2cXksFSgsLw%3D [DOI: https://dx.doi.org/10.1081/css-120038551]
54 S. R. Olsen, C. V. Cole, and F. S. Watanabe, “Estimation of available phosphorus in soil by extraction with sodium bicarbonate,” USDA Circular No. 939 (US Government Printing Office, Washington DC, 1954).
55 Perie, C.; Ouimet, R. Organic carbon, organic matter and bulk density relationships in boreal forest soils. Can. J. Soil Sci.; 2008; 88, pp. 315-325.1:CAS:528:DC%2BD1cXptFynt7c%3D [DOI: https://dx.doi.org/10.4141/CJSS06008]
56 Piper, C. S. Soil and Plant Analysis; 1966; Bombay, Hans Publishers:
57 Sh. Prasad, L. Ch. Malav, J. Choudhary, S. Kannojiya, M. Kundu, S. Kumar, and A. N. Yadav, “Soil microbiomes for healthy nutrient recycling,” in Current Trends in Microbial Biotechnology for Sustainable Agriculture, Ed. by A. N. Yadav, J. Singh, C. Singh, and N. Yadav (Springer, Singapore, 2021), pp. 1–21. https://doi.org/10.1007/978-981-15-6949-4_1
58 Rajbanshi, J.; Das, Sh.; Paul, R. Quantification of the effects of conservation practices on surface runoff and soil erosion in croplands and their trade-off: A meta-analysis. Sci. Total Environ.; 2023; 864, 161015.1:CAS:528:DC%2BB3sXhvFWlsQ%3D%3D [DOI: https://dx.doi.org/10.1016/j.scitotenv.2022.161015]
59 Rao, S. N. S. Soil Microorganism and Plant Growth; 1995; New Delhi, Oxford and IBH Publishing Co.:
60 N. Rawat, N. Pandey, A. Joshi, and D. Pandey, “Influence of climate change on Himalayan ecosystem: A review,” in Climate Change Impact on Himalayan Biodiversity, Ed. by D. Arya, N. Chandra, R. Kumar, M. L. Upadhayay, and A. P. Mishra, Environmental Science and Engineering (Springer, Cham, 2025), pp. 415–424. https://doi.org/10.1007/978-3-031-77149-1_19
61 Roy, P. S.; Ramachandran, R. M.; Paul, O.; Thakur, P. K.; Ravan, Sh.; Behera, M. D.; Sarangi, Ch.; Kanawade, V. P. Anthropogenic land use and land cover changes — A review on its environmental consequences and climate change. Journal of the Indian Society of Remote Sensing; 2022; 50, pp. 1615-1640. [DOI: https://dx.doi.org/10.1007/s12524-022-01569-w]
62 Saha, S.; Saha, B.; Seth, T.; Dasgupta, Sh.; Ray, M.; Pal, B.; Pati, S.; Mukhopadhyay, S. K.; Hazra, G. Micronutrients availability in soil–plant system in response to long-term integrated nutrient management under rice–wheat cropping system. J. Soil Sci. Plant Nutr.; 2019; 19, pp. 712-724.1:CAS:528:DC%2BC1MXitFKisrzM [DOI: https://dx.doi.org/10.1007/s42729-019-00071-6]
63 Schjønning, P.; McBride, R. A.; Keller, T.; Obour, P. B. Predicting soil particle density from clay and soil organic matter contents. Geoderma; 2017; 286, pp. 83-87.1:CAS:528:DC%2BC28XhvVGis7vM [DOI: https://dx.doi.org/10.1016/j.geoderma.2016.10.020]
64 Shaaban, M.; Nunez-Delgado, A. Soil adsorption potential: Harnessing Earth’s living skin for mitigating climate change and greenhouse gas dynamics. Environ. Res.; 2024; 251, 118738.1:CAS:528:DC%2BB2cXmsVyks7Y%3D [DOI: https://dx.doi.org/10.1016/j.envres.2024.118738]
65 Sheikh, M. A.; Anjum, J.; Tiwari, A. Assessment and current dynamics of nutrients with reference to nitrogen attributes in subalpine forests of Western Himalaya, India. Environ. Monit. Assess.; 2022; 194, 477.1:CAS:528:DC%2BB38XhsFClsLfK [DOI: https://dx.doi.org/10.1007/s10661-022-10145-1]
66 Singh, N.; Riyal, M. K.; Singh, B.; Khanduri, V. P.; Rawat, D.; Singh, Ch.; Pinto, M. M. S. C.; Kumar, M. Carbon sequestration potential of agroforestry versus adjoining forests at different altitudes in the Garhwal Himalayas. Atmosphere; 2024; 15, 313.1:CAS:528:DC%2BB2cXosVykurw%3D [DOI: https://dx.doi.org/10.3390/atmos15030313]
67 Singh, R.; Bhardwaj, D. R.; Pala, N. A.; Rajput, B. S. Variation in soil properties under different land uses and attitudinal gradients in soils of the Indian Himalayas. Acta Ecol. Sin.; 2018; 38, pp. 302-308. [DOI: https://dx.doi.org/10.1016/j.chnaes.2017.12.003]
68 Singh, S. K.; Pandey, C. B.; Sidhu, G. S.; Sarkar, D.; Sagar, R. Concentration and stock of carbon in the soils affected by land uses and climates in the western Himalaya, India. Catena; 2011; 87, pp. 78-89.1:CAS:528:DC%2BC3MXoslCksbY%3D [DOI: https://dx.doi.org/10.1016/j.catena.2011.05.008]
69 Six, J.; Conant, R. T.; Paul, E. A.; Paustian, K. Stabilization mechanisms of soil organic matter: Implications for C-saturation of soils. Plant Soil; 2002; 241, pp. 155-176.1:CAS:528:DC%2BD38XltV2jsbo%3D [DOI: https://dx.doi.org/10.1023/A:1016125726789]
70 Srinivasarao, C. H.; Ganeshamurthy, A. N.; Ali, M.; Singh, R. N.; Singh, K. K. Sulphur availability and response of mungbean and urdbean to sulphur on different soil types of pulse growing regions of India. Commun. Soil Sci. Plant Anal.; 2004; 35, pp. 1713-1723.1:CAS:528:DC%2BD2cXksFSgsbs%3D [DOI: https://dx.doi.org/10.1081/css-120038564]
71 Subbiah, B. V.; Asija, G. L. A rapid procedure for estimation of available nitrogen in soils. Curr. Sci.; 1956; 25, pp. 259-260.1:CAS:528:DyaG1cXpvFOgtQ%3D%3D
72 Suciu, N. A.; Vivo, R. D.; Rizzati, N.; Capri, E. Cd content in phosphate fertilizer: Which potential risk for the environment and human health?. Curr. Opin. Environ. Sci. Health; 2022; 30, 100392. [DOI: https://dx.doi.org/10.1016/j.coesh.2022.100392]
73 Suri, D.; Sharma, V. K.; Kumar, P.; Upadhayay, R. G.; Nazir, G.; Anjali, K. Sulphur dynamics under different land uses of Outer Himalayan region of Himachal Pradesh. Environment Conservation Journal; 2021; 22, pp. 265-270.1:CAS:528:DC%2BB38XhtVSqs7zJ [DOI: https://dx.doi.org/10.36953/ecj.2021.22331]
74 Talat, A. E.; Chen, Yu.; He, Yu.; Cai, Z.; Wang, J. Quantification of the Loess Plateau’s soil hydrodynamics in relation to bulk density. Soil Sci. Soc. Am. J.; 2025; 89, e70036.1:CAS:528:DC%2BB2MXnslektL8%3D [DOI: https://dx.doi.org/10.1002/saj2.70036]
75 Talukder, B.; Ganguli, N.; Matthew, R.; VanLoon, G. W.; Hipel, K. W.; Orbinski, J. Climate change-triggered land degradation and planetary health: A review. Land Degradation and Development; 2021; 32, pp. 4509-4522. [DOI: https://dx.doi.org/10.1002/ldr.4056]
76 J. C. Tarafdar, “Role of soil biology on soil health for sustainable agricultural production,” in Structure and Functions of Pedosphere, Ed. by B. Giri, R. Kapoor, Q. S. Wu, and A. Varma (Springer Nature Singapore, Singapore, 2022), pp. 67–81. https://doi.org/10.1007/978-981-16-8770-9_3
77 Tellen, V. A.; Yerima, B. P. K. Effects of land use change on soil physicochemical properties in selected areas in the North West region of Cameroon. Environmental Systems Research; 2018; 7, 3. [DOI: https://dx.doi.org/10.1186/s40068-018-0106-0]
78 Tufa, M.; Melese, A.; Tena, W. Effects of land use types on selected soil physical and chemical properties: The case of Kuyu District, Ethiopia. Eurasian Journal of Soil Science; 2019; 8, pp. 94-109.1:CAS:528:DC%2BC1MXisVGks7rM [DOI: https://dx.doi.org/10.18393/ejss.510744]
79 Walkley, J.; Black, I. A. Estimation of soil organic carbon by chromic acid titration method. Soil Sci.; 1934; 37, pp. 29-38.1:CAS:528:DyaA2cXitlGmug%3D%3D [DOI: https://dx.doi.org/10.1097/00010694-193401000-00003]
80 Wang, Ch.; Zhao, Ch.; Xu, Zh.; Wang, Ya.; Peng, H. Effect of vegetation on soil water retention and storage in a semi-arid alpine forest catchment. Journal of Arid Land; 2013; 5, pp. 207-219. [DOI: https://dx.doi.org/10.1007/s40333-013-0151-5]
81 Wani, F. Sh.; Akhter, F.; Mir, Sh.; Baba, Z. A.; Maqbool, Sh.; Yo, M. Zargar, and S. U. Nabi, “Assessment of soil microbial status under different land use systems in North Western Zone of Kashmir. Int. J. Curr. Microbiol. Appl. Sci.; 2018; 7, pp. 266-279.1:CAS:528:DC%2BC1MXhslaksbbL [DOI: https://dx.doi.org/10.20546/ijcmas.2018.708.032]
82 Wani, O. A.; Sharma, V.; Kumar, Sh. S.; Babu, S.; Sharma, K. R.; Rathore, S. S.; Marwaha, S.; Ganai, N. A.; Dar, S. R.; Yeasin, M.; Singh, R.; Tomar, J. Climate plays a dominant role over land management in governing soil carbon dynamics in North Western Himalayas. J. Environ. Manage.; 2023; 338, 117740.1:CAS:528:DC%2BB3sXntVeksr4%3D [DOI: https://dx.doi.org/10.1016/j.jenvman.2023.117740]
83 Wei, X.; Hao, M.; Shao, M.; Gale, W. J. Changes in soil properties and the availability of soil micronutrients after 18 years of cropping and fertilization. Soil Tillage Res.; 2006; 91, pp. 120-130. [DOI: https://dx.doi.org/10.1016/j.still.2005.11.009]
84 Williams, C. H.; Steinbergs, A. Soil sulphur fractions as chemical indices of available sulphur in some Australian soils. Aust. J. Agric. Res.; 1959; 10, 340.1:CAS:528:DyaG1MXnsVersw%3D%3D [DOI: https://dx.doi.org/10.1071/ar9590340]
85 Xu, M.; Zhao, Yu.; Liu, G.; Argent, R. M. Soil quality indices and their application in the hilly loess plateau region of China. Soil Research; 2006; 44, pp. 245-254. [DOI: https://dx.doi.org/10.1071/sr05083]
86 Yang, L.; Song, X.; Lyu, S.; Shen, W.; Gao, Ya. Dynamics and fractions of soil organic carbon in response to 35 years of afforestation in subtropical China. Plant Soil; 2024; 500, pp. 481-494.1:CAS:528:DC%2BB2cXjs1aksrk%3D [DOI: https://dx.doi.org/10.1007/s11104-024-06493-1]
© Pleiades Publishing, Ltd. 2025.