Assessment of Seasonal Variability in Soil Nutrients and Its Impact on Soil Quality under Different Land Use Systems of Lower Shiwalik Foothills of Himalaya, India

: The present study was conducted to investigate the seasonal effects of ﬁve land use systems (LUSs), i.e., wheat–rice ( Triticum aestivum—Oryza sativa ) system, sugarcane ( Saccharum ofﬁcinarum ), orange ( Citrus sinensis ) orchard, safeda ( Eucalyptus globules ) forest, and grassland, on soil quality and nutrient status in the lower Satluj basin of the Shiwalik foothills Himalaya, India. Samples were analyzed for assessment of physico-chemical properties at four soil depths, viz., 0–15, 15–30, 30–45, and 45–60 cm. A total of 120 soil samples were collected in both the seasons. Soil texture was found to be sandy loam and slightly alkaline in nature. The relative trend of soil organic carbon (SOC), macro- and micro-nutrient content for the ﬁve LUSs was forest > orchard > grassland > wheat–rice > sugarcane, in the pre- and post-monsoon seasons. SOC was highly correlated with macronutrients and micronutrients, whereas SOC was negatively correlated with soil pH (r = − 0.818). The surface soil layer (0–15 cm) had a signiﬁcantly higher content of SOC, and macro- and micro-nutrients compared to the sub-surface soil layers, due to the presence of more organic content in the soil surface layer. Tukey’s multiple comparison test was applied to assess signiﬁcant difference ( p < 0.05) among the ﬁve LUSs at four soil depths in both the seasons. Principle component analysis (PCA) identiﬁed that SOC and electrical conductivity (EC) were the most contributing soil indicators among the different land use systems, and that the post-monsoon season had better soil quality compared to the pre-monsoon season. These indicators helped in the assessment of soil health and fertility, and to monitor degraded agroecosystems for future soil conservation.


Introduction
Land integrity and soil neutrality have been drastically declining in the Himalaya region, and at an alarming rate, due to intensive cultivation [1]. The lower Satluj basin of the Shiwalik foothills of Himalaya is facing problems of deteriorating soil fertility and productivity of croplands. In the rainy seasons, heavy rainfall in this area results in soil degradation, via reducing the fertile layer of soil [2,3]. This leads to declines in soil organic carbon and the availability of micronutrients and macronutrients, which affect food security and pose environmental threats. Deforestation is the major root cause of soil degradation, because soil organic matter decreases [4,5]. Soil organic matter, of which carbon is a major part, holds a great proportion of the nutrients, cations, and trace elements that are of importance to plant growth. FAO [6] reported a 4.6% decrease in forest cover in India, which results in the decline of soil organic carbon, and causes soil degradation. Furthermore, the conversion of forest land to continuous intensive cultivation, and opting for different management practices in the fields, to meet high population demands, were

Soil Analysis
Soil samples were analyzed at the Soil Science and Agricultural Chemistry research laboratory of Khalsa College, Amritsar. Particle size distribution was recorded by the hydrometer method [15]. The soil pH was determined in 1:2 soil:water suspension, and electrical conductivity was determined in 1:2 soil:water supernatant solution after keeping the soil samples overnight [16]. Soil bulk density was measured by a clod saturation method [17], and soil particle density was analyzed using PAU moisture gauge method [18]. The soil porosity was calculated by using the formula given as: total soil porosity = (1 − Db/Dp) × 100, where, Db = bulk density (gm cm −3 ) and Dp = particle density (gm cm −3 ). Soil organic carbon (SOC) was determined according to Walkley and Black's rapid titration method, using a diphenyl amine indicator [19]. Available nitrogen was analyzed using the alkaline permanganate method [20]. Available phosphorus and available potassium were analyzed by the methods described by Olsen et al., 1954 [21] and Merwin and Peech, 1950 [22], respectively. The micronutrient cations were extracted by using the diethylenetriaminepentaacetic acid (DTPA) method [23], and estimated by using an microwave plasma-atomic emission spectrometer (Agilent 4200 MP-AES).

Statistical Analysis
Two-way analysis of variance (ANOVA) was used to study the variation among different physico-chemical properties of the soil with respect to change in land use system and soil depth of sampling, at p < 0.05 level of significance. Tukey's multiple comparison test was applied to check significant difference among the soil samples. Correlation be-

Soil Sampling, Preparation, and Analysis
For the study, five land use systems (LUSs), namely wheat-rice (Triticum aestivum-Oryza sativa) system, sugarcane (Saccharum officinarum), orange (Citrus sinensis) orchard, safeda (Eucalyptus globules) forest, and grassland from the Mukerian watershed in Punjab, were selected for collection of soil samples. There were no intercrops grown in the wheatrice (WR) and sugarcane (SU) land use systems. In the orchard, cereal, and green manure systems crops were grown. Soil sampling was done twice in the year 2019, i.e., pre-monsoon season in the month of May, and post-monsoon season in the month of November. The soil samples were collected with the help of an auger at depths of 0-15, 15-30, 30-45, and 45-60 cm, following a "Z" layout design of random sampling. Three replications were taken from each LUS. A total of 40 × 3 = 120 soil samples were collected from the five LUSs in both the seasons. Soil samples were completely air-dried, ground, and passed through a 2-mm mesh sieve for the analysis of the physical and chemical properties of the soil.

Soil Analysis
Soil samples were analyzed at the Soil Science and Agricultural Chemistry research laboratory of Khalsa College, Amritsar. Particle size distribution was recorded by the hydrometer method [15]. The soil pH was determined in 1:2 soil:water suspension, and electrical conductivity was determined in 1:2 soil:water supernatant solution after keeping the soil samples overnight [16]. Soil bulk density was measured by a clod saturation method [17], and soil particle density was analyzed using PAU moisture gauge method [18]. The soil porosity was calculated by using the formula given as: total soil porosity = (1 − Db/Dp) × 100, where, Db = bulk density (gm cm −3 ) and Dp = particle density (gm cm −3 ). Soil organic carbon (SOC) was determined according to Walkley and Black's rapid titration method, using a diphenyl amine indicator [19]. Available nitrogen was analyzed using the alkaline permanganate method [20]. Available phosphorus and available potassium were analyzed by the methods described by Olsen et al., 1954 [21] and Merwin and Peech, 1950 [22], respectively. The micronutrient cations were extracted by using the diethylenetriaminepentaacetic acid (DTPA) method [23], and estimated by using an microwave plasma-atomic emission spectrometer (Agilent 4200 MP-AES).

Statistical Analysis
Two-way analysis of variance (ANOVA) was used to study the variation among different physico-chemical properties of the soil with respect to change in land use system and soil depth of sampling, at p < 0.05 level of significance. Tukey's multiple comparison test was applied to check significant difference among the soil samples. Correlation between different soil properties was calculated by Pearson correlation coefficient and principle component analysis for each soil property under different land use systems, and was carried out by using IBM SPSS statistics.

Soil Quality Index (SQI)
Two SQ indexing methods are commonly used [7,24]. The first is unscreened transformation (the additive index), and the second uses principal component analysis (PCA). where SQI is the soil quality index, S is the linear or nonlinear scored value of individual indicators, and n is the number of indicators included in the dataset [24,25].

Principal Component Analysis Based Soil Quality Indexing (PCA-SQI)
Under each principal component (PC), only the variables having high factor loadings, and eigenvalues >1 that explained at least 5% of the data variations, were retained for indexing. Among well-correlated variables within each PC, the variable with the highest correlation coefficient (absolute value) and loading factor was chosen. If the highly weighted variables (≥±0.7 eigenvector) were not well correlated (r > 0.60), each was considered important and retained in the PC for SQ indexing. As each PC explains a certain amount of variation within the total dataset, this provides a "weight" for the variables chosen under a given PC.
The final PCA based SQI used for this study was: where PCA−SQI is the principal component analysis (PCA) based soil quality index, W i is the PCA weighing factor equal to the ratio of variance of each factor to total cumulative variance coefficients in the equation, and S i is the scored value of each SQ indicator. Soil having a higher index score indicates better performance in the soil quality indicators [2,7].

Soil Texture
The physico-chemical properties of soil play an important role in the availability of nutrients and the microbial activity of soil. The content of sand, silt, and clay significantly differed among all the LUSs at different soil depths. The vertical distribution of soil showed that sand was a dominant fraction in all the LUSs. Soil texture was generally sandy loam in nature. The sand, silt, and clay contents were observed in the range of 59.25 to 76.04, 11.23 to 26.71, and 9.02 to 16.13%, respectively, under all land use systems (Table 1). Due to litter fall under safeda forest (SF) land use, higher organic matter content was attributed to higher silt and clay content [7,8]. At lower depth, it was observed that there was no specific trend for clay content, but sand content decreased (59.25 %) and silt content increased (23.69 %) with soil depth [10,26].

Soil Bulk Density (D b ), Particle Density (D p ), and Soil Porosity
The soil D b is a crucial factor for indicating the drainage characteristics of soil under different LUSs [27]. The soil D b of the soil varied from 1.30 to 1.59 g cm −3 among all the LUSs. Significant (p < 0.05) difference was found among all LUSs at different soil depths. From Table 1, it can be seen that at depth 0-15 cm, the highest content of D b , i.e., 1.47 g cm −3 was found in SU, followed by WR (1.46), grassland (GL) (1.41), orange orchard (OR) (1.34), and SF (1.30) LUSs. The higher D b of SU and WR cropland could be due to a compaction effect, because of the continuous use of machinery and farm operations on the fields. A higher input of organic matter through leaf litter under SF (1.30 g cm −3 ) LUSs resulted in lower D b compared to the other LUSs [10]. At lower depth, an increase in D b in all the LUSs was observed, due to decreasing organic matter content with soil depth [27,28]. D p of the soil varied from 2.60 to 2.69 g cm −3 . The soil showed non-significant (p < 0.05) differences in D p , both with depth and within different LUSs. From Table 1, at depth 0-15 cm, the highest content of D p , i.e., 2.67 g cm −3 was found in WR, followed by SU (2.66), GL (2.64), OR (2.62), and SF (2.60). At lower depth, the D p generally increased under all the LUSs, due to settling of heavier particles in the lower depths [5,29]. The porosity of soil varied from 41.10 to 50.0% under different LUSs at variable depths. The soil porosity significantly differed (p < 0.05) among the five LUSs at four depths. The surface layer of SF (50.0%) had a higher soil porosity compared to OR (48.91%), GL (46.72%), WR (45.19%), and SU (44.74%) LUS, as shown in Table 1. The soil porosity decreased with depth under all the LUSs, due to compaction of soil under the soil surface, because bulk density increases with soil depth [12].

Soil pH
Soil pH is a measure of the acidity or alkalinity of the soil. It plays an important role in the availability of nutrients essential for plant growth. The soil pH under all the LUSs was slightly alkaline in nature. The data presented in Table 2 show that soils in SF land use were significantly different (p < 0.05) from all the LUSs. The soil pH values in pre-monsoon and post-monsoon seasons ranged from 7.28-7.89 and 7.31-7.90, respectively. The relative trend of pH for the five LUSs was recorded in pre-monsoon and post-monsoon season in the order of SU > WR > GL > OR > SF. Soil pH was lower, i.e., 7.28-7.57 under SF compared to the other LUSs, due to addition of organic matter to the surface soil through tree leaves and litter decomposition, resulting in release of weak organic acids which cause a decrease in pH [7,11]. The soil pH (7.28-7.65) was observed to be lower at the soil surface (0-15 cm) in both the seasons. In both the seasons, pH under all the LUSs increased gradually with increase in soil depth, due to leaching of bases and salts from the surface layer to the sub-surface layer of soil [3,10]. Soil electrical conductivity is a measure of salinity of soil. It could be an indicator of nutrient availability in the soil [30]. The soil EC (dS m −1 ) showed a range of 0.32-0.26 in WR, and of 0.14-0.23 in SF-LUS post-monsoon. The results pertaining to EC in Table 2 were not significant when WR was compared with SU, and OR was compared with SF, but GL was significantly different (p < 0.05) from the other LUSs. The EC was higher under the WR and SU, due to continuous addition of chemical fertilizers and manures in the field crops, resulting in accumulation of salts and inflow of irrigation water into the soils [12,31]. The soil EC (dS m −1 ) under different LUSs followed the trend in the order of SU (0.33) > WR (0.32) > GL (0.30) > OR (0.24) > SF (0.23) in post-monsoon seasons. A similar trend was followed in the pre-monsoon season. In both the seasons, EC gradually decreased with soil depth under all land uses [10,26].

Effect of Different LUSs on Soil Organic Carbon (SOC)
Soil organic carbon is considered an important regulating factor of soil quality, and influences directly the availability of macronutrients and micronutrients in the soil for plant uptake. The SOC (g kg −1 ) ranged from 2.21 to 9.71 in the pre-monsoon, and 3.05 to 11.70 in the post monsoon, season under all the LUSs. The SOC content was significantly different (p < 0.05) among all the LUSs at four soil depths. The trend of SOC was observed in the order SF > OR > GL > WR > SU. A similar trend was followed at all soil depths in both the seasons. The soil SOC was increased by 44.1, 42.8, 20.1, and 4.9% under SF-LUS as compared to SU, WR, GL, and OR LUSs ( Table 2). A higher SOC content was found under SF-LUS due to the regular addition of organic matter through tree leaves, and was not disturbed by tillage and other operations [7]. Eucalyptus showed a high potential for carbon storage, and was found in the SF-LUS of our study [8]. WR and SU had a minimal content of SOC compared to the other LUSs due to intensive cultivation, continuous tillage, and other farm operations that lower the SOC in soil [14,31]. Irrespective of the land-use, SOC content decreased with increase in soil depth. The SOC decreased with increasing soil depth under all LUSs because surface soil has more organic matter content compared to the deeper layers of soil [5,13]. The soil SOC content was higher in the post-monsoon season compared to the pre-monsoon season, due to more litter fall in the monsoon leading to an increase in the organic matter content in the post-monsoon season, hence SOC content increases, whereas in the pre-monsoon season higher temperature leads to rapid break down of organic matter, and thus lower organic carbon in soil [11,32,33]. In India the soil organic carbon in cultivated soils is less than 5 mg g −1 , compared to 15-20 mg g −1 in uncultivated soils. This available potential of 10-15 mg g −1 soil carbon sink could balance net emissions from fossil fuel combustion. Optimum levels of soil organic carbon can be maintained by changing from monoculture to rotation cropping, and adoption of agroforestry systems [34].

Soil Macronutrients
The availability of macronutrients was directly influenced by different LUSs, and content of available N, P, and K varied with soil depth (Table 3). Available N, P, and K were significantly (p < 0.05) higher in the surface soils (0-15 cm) of LUSs under SF and OR compared to GL, WR, and SU in pre-and post-monsoon season. The relative trend of availability of N, P, and K (kg ha −1 ) was in the order SF > OR > GL > WR > SU in both the seasons at different soil depths. The available N content was ranged lower (73.17-230.8 kg ha −1 ) in the post-monsoon season, followed by the pre-monsoon season (72.1-227.7 kg ha −1 ) under all the LUSs. The mean (kg ha −1 ) range of available P was observed to be moderate (13.36-21.39) in surface soil (0-15 cm) followed by 3.28-19.40 at soil depth D2-D4 under all the LUSs in the post-monsoon season. Exceptionally, in the SF and OR LUSs, the mean range of available P was observed to be moderate (17.01-19.4 kg ha −1 ) at 15-30 cm depth in both the seasons. The available K content was also observed to be moderate (163.9-248.3 kg ha −1 ) at soil depths D1 and D2, followed by 71.1-129 kg ha −1 at soil depths D3 and D4, respectively, under all the LUSs. The available N, P, and K content was increased by 66.0, 69.2, and 18.3% under SF in comparison with SU-LUS. Furthermore, the available N, P, and K content was observed to be increased by 63.6, 60.1, and 16.6% under SF compared to WR, and 23.0, 31.2, and 11.9% higher than GL, and 12.8, 8.2, and 6.8%, respectively in comparison with OR-LUS. In both the seasons, there was a decrease in available N, P, and K content at lower soil depth in all the LUSs. The trend followed by the different LUSs under soil depths were in order D1 > D2 > D3 > D4. Surface soil had better soil quality as the availability of nutrients is higher in surface soil compared to lower depths because the surface layer (0-15 cm) has higher organic matter [35]. Seasonally, the post-monsoon season had a higher content of available N, P, and K compared to the pre-monsoon season [5,30]. Higher content of N, P, and K was found in the SF and OR LUSs due to the addition of higher organic matter through leaf litter, which results in the mineralization of the nutrients in the soil [11,36]. Clearing of forest lands for cultivation purposes results in declining soil macronutrients in the long run. The lower content of N, P, and K in WR and SU LUSs was seen because of continuous intensive cultivation in the field crops, which intensifies the oxidation rate of organic matter, resulting in depletion of nutrients and land degradation [3,37].

Soil Micronutrients
DTPA extractable micronutrients were affected with different LUSs at variable soil depths because organic matter content can vary with different LUSs. All the LUSs had significant (p < 0.05) effects on soil micronutrients at different soil depths. The trend of DTPA-extractable micronutrients, i.e., Fe, Mn, Zn, and Cu was recorded in the order SF > OR > GL > WR > SU at different soil depths in both the seasons ( Table 4). The DTPA extractable Fe, Mn, Zn, and Cu in surface soil (0-15 cm) in the post-monsoon season was observed to be increased by 59.7, 55.9, 53.5, and 67.4%, respectively, under SF-LUS compared with SU-LUS, whereas in the case of the other LUSs, DTPA extractable Fe, Mn, Zn, and Cu were found to be increased by 54.3, 42.9, 45.7, and 56.5% under SF-LUS in comparison with WR and 24.1, 33.9, 21.1, and 14.49% higher than GL, and 14.07, 13.3, 4.8 and 4.3% higher, respectively, in comparison to OR-LUS. Soils in SF had higher levels of micronutrients due to the regular addition of organic matter in the form of leaf litter and root deposition which results in higher microbial activity in the soil, which facilitates the availability of micronutrients in the soils [13,38]. However, SU and WR showed lower contents of micronutrients compared to GL, OR, and SF due to intensive cultivation and the continuous removal of crops from the field resulting in the depletion of nutrients from the soil [11]. The soil pH and electrical conductivity under different land use systems can also affect the availability of micronutrients in the soil [38]. In both the seasons, all the micronutrients decreased gradually with increase in soil depth, under all the LUSs. The trend of DTPA extractable Zn of all LUSs under different depths was recorded in the order D1 (31%) > D2 (27%) > D3 (23%) > D4 (19%). A similar trend was followed by Fe, Mn, and Zn in both the seasons at different soil depths. The higher amount of DTPA extractable Fe, Mn, Zn, and Cu in the surface layer, was due to the higher accumulation of organic matter content in the surface soil [10]. Seasonally, it was observed that the post-monsoon season had a higher content of DTPA extractable Fe, Mn, Zn, and Cu compared to the pre-monsoon season, at different soil depths. In the pre-monsoon season micronutrient availability can be increased by applying inorganic fertilizers and organic manures. In the post-monsoon season there was less need to add fertilizers because, due to litter fall in the autumn season, the organic matter content increased the availability of micronutrients in the soil [5]. There was overall a sufficiency of all the micronutrients under all the LUSs. Fe, Mn, Zn, and Cu were present above the critical levels, indicating the optimum supply of micronutrients to soil for optimum plant growth, but the content of DTPA extractable Fe, Mn, Zn, and Cu was lower under wheat-rice and sugarcane compared to forest and orange orchard LUSs.

Correlation Matrix
A correlation analysis was conducted to determine the extent of the relationships among different soil physico-chemical properties and nutrients, as shown in Table 5. Significant correlations (p < 0.05) were observed among the macronutrients and micronutrients of the soil [10]. There was a negative correlation between SOC and pH (r = −0.818). It was also found that pH was negatively correlated with macronutrients, i.e., N, P, and K (r = −0.896, −0.798, −0.677, respectively) and also with micronutrients, i.e., Fe, Mn, Cu, and Zn (r = −0.963, −0.947, −0.990, −0.956, respectively), as shown in Table 5. SOC was highly correlated with macronutrients and micronutrients. There was a significant positive correlation (r = 0.961) between available N and SOC, because soil organic matter has a direct impact on the availability of nitrogen in the soil [11]. The dendrogram using the complete linkage method further supports the reported results of the correlation matrix ( Figure 2).

Correlation Matrix
A correlation analysis was conducted to determine the extent of the relationships among different soil physico-chemical properties and nutrients, as shown in Table 5. Significant correlations (p < 0.05) were observed among the macronutrients and micronutrients of the soil [10]. There was a negative correlation between SOC and pH (r = −0.818). It was also found that pH was negatively correlated with macronutrients, i.e., N, P, and K (r = −0.896, −0.798, −0.677, respectively) and also with micronutrients, i.e., Fe, Mn, Cu, and Zn (r = −0.963, −0.947, −0.990, −0.956, respectively), as shown in Table 5. SOC was highly correlated with macronutrients and micronutrients. There was a significant positive correlation (r = 0.961) between available N and SOC, because soil organic matter has a direct impact on the availability of nitrogen in the soil [11]. The dendrogram using the complete linkage method further supports the reported results of the correlation matrix ( Figure 2).   Under all the LUSs, the chemical properties were found to most contribute to SQI. The chemical properties of the soil generally changes on the addition of organic matter content to the soil. SF-LUS contributed more organic matter content to the soil through leaf litter. In post-monsoon season, the SQI value was higher than in the pre-monsoon season due to litter fall in the autumn season, resulting in the addition of more organic matter content to the soil in the post-monsoon season, and thus, the SQI was higher due to increased nutrient availability [24,25].

Principle Component Analysis-SQI
Principle component analysis (PCA) of various soil variables described the overall sensitivity pattern of the soil parameters, and revealed the correlation between the soil variables. Principle components (PC) with high eigenvalues were considered to represent the variations in different soil parameters. The PCA study recorded that the most imperative SQI variables for PC1 was SOC, and for PC2 it was EC. As seen in Table 6 and the bi-plot in Figure 3, the amount of variability explained by PC1 was 83.59%, with an eigenvalue of 8.36, which indicates that SOC was the highest positive factor loading value, i.e., 0.35. PC1 was assigned to the soil organic carbon factor. The second component (PC2) explained about 14.78% of variance, with an eigenvalue of 1.49, and the highest positive loading value was for EC, i.e., 079. PC2 was assigned to the soil electrical conductivity factor. The contribution of soil quality indicators, i.e., SOC (0.81) and EC (0.19), in the soil quality index is shown in Figure 4. The specific contribution of the soil quality indicators (SOC, EC) to the SQI is also presented through a radar graph ( Figure 5). The SQI obtained under each land use system was highest under SF-LUS (0.70), followed by OR (0.68), WR (0.65), SU (0.64), and GL (0.61), respectively ( Figure 6). Under different LUSs of the sub-mountain region of Punjab, the PCA performed for assessing SQI for various soil variables observed that soil organic carbon contributed most to soil quality [5,7]. SOC played a crucial role in the availability of soil macronutrients and micronutrients under the different LUSs. SOC represents the quality and quantity of soil organic matter present under forest and horticulture LUSs. The regular addition of soil organic matter the soil under forest and horticulture LUSs increases the SOC content in the soil, thus the availability of different nutrients in the soil increases and maintains the soil fertility status. Among all the LUSs, SF and OR had the highest SQI. Thus, SF and OR land uses have the potential to improve soil fertility and reduce soil degradation, by improving the physico-chemical properties of the soil.          Table 4.

Conclusions
The results of the present study revealed that forest LUSs, followed by horticulture LUSs, are considered the best and most sustainable land use, by improving soil organic carbon and having higher values for macronutrients and micronutrients than the other LUSs, due to higher organic matter content resulting from heavy litter fall at the surface soil, which improves the soil quality. This indicates that cropland has a higher risk of land degradation, because of continuous intensive cultivation and the use of manufactured inorganic fertilizers without appropriate soil management, resulting in a low fertility of the cultivable lands in the lower Shiwalik foothills of Himalaya. Therefore, in this area there is the scope to increase production and improve soil health with proper management practices, and the adoption of suitable LUSs can help in increasing the micronutrients and   Table 4.

Conclusions
The results of the present study revealed that forest LUSs, followed by horticulture LUSs, are considered the best and most sustainable land use, by improving soil organic carbon and having higher values for macronutrients and micronutrients than the other LUSs, due to higher organic matter content resulting from heavy litter fall at the surface soil, which improves the soil quality. This indicates that cropland has a higher risk of land degradation, because of continuous intensive cultivation and the use of manufactured inorganic fertilizers without appropriate soil management, resulting in a low fertility of the cultivable lands in the lower Shiwalik foothills of Himalaya. Therefore, in this area there is the scope to increase production and improve soil health with proper management practices, and the adoption of suitable LUSs can help in increasing the micronutrients and

Conclusions
The results of the present study revealed that forest LUSs, followed by horticulture LUSs, are considered the best and most sustainable land use, by improving soil organic carbon and having higher values for macronutrients and micronutrients than the other LUSs, due to higher organic matter content resulting from heavy litter fall at the surface soil, which improves the soil quality. This indicates that cropland has a higher risk of land degradation, because of continuous intensive cultivation and the use of manufactured inorganic fertilizers without appropriate soil management, resulting in a low fertility of the cultivable lands in the lower Shiwalik foothills of Himalaya. Therefore, in this area there is the scope to increase production and improve soil health with proper management practices, and the adoption of suitable LUSs can help in increasing the micronutrients and