Impact of Land Use Types on Soil Physicochemical Properties in the Northern and Eastern Hills of Bangladesh

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Mosharaf Hossain Sarker, Md. Abul Kashem This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8506757/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Diverse land use patterns in the Northern and Eastern Hills (AEZ 29) of Bangladesh significantly affect soil physico-chemical properties. The study aimed to delineate soil physico-chemical characteristics and their changing dynamics related to land use types in Sylhet Sadar upazila. The experiment was conducted covering four land use types: grassland, forest land, orchard, and tea garden from two soil depths, viz. surface (0–15 cm) and sub-surface (15–30 cm) with four sampling sites as replications, during September 2023-March 2024. Results showed higher silt-clay ratio in surface soil, sand dominating across all land uses and depths. Sub-surface soil had the higher organic carbon content (0.84%). Soil pH negatively correlated with organic carbon and total nitrogen but positively with organic carbon and available zinc. Bulk density was the highest in both grassland and forest soils. Furthermore, grassland soils had the higher pH (5.14 in surface; 5.22 in sub-surface, while tea garden soils showed lower (4.35 in surface; 4.42 in sub-surface). Organic carbon content was highest in tea garden soils, while total nitrogen content was highest in both orchard and tea garden soils. Grassland and orchard soils had the higher available phosphorus content, while forest and orchard soils exhibited the higher available boron and zinc content, respectively. Therefore, tea garden and orchard soil in Agro-Ecological Zone 29 had better nutrient status compared to grassland and forest soils influenced by management practices and vegetation cover. Agro-Ecological Zone Forest land Grassland Northern and Eastern Hills Orchard Tea garden Introduction Urbanization, industrialization and different agricultural practices reduce soil fertility, biodiversity and ecosystem [ 1 , 24 ]. Additionally, forest loss increases greenhouse gas emissions, reduces carbon sequestration, and raises local temperatures [ 5 ]. Intensive crop cultivation and monocultures lower soil moisture, organic matter, and microbial biomass, nutrient content and cause long-term deterioration [ 2 , 24 ]. The total land area of Bangladesh is 14.4 million ha, about 62.30% of which is arable in 2021-22 with 169.83 million total population of. The cropping intensity of the country was 171% and 198% in 1983-84 and 2020-21, respectively [ 8 ]. To provide food and other agro-products for this vast population, the cultivable land is being used intensively and causing depletion of essential plant nutrients, organic matter, soil microbes, etc. Several factors are liable for the variation in physico-chemical properties of soil, which include topography, slope gradient, parent materials, land use and climatic conditions [ 18 ]. Among different factors which are responsible for changes in soil parameters, land use pattern is an important one. Conversion of forest land to agricultural practices leads to higher soil erosion, reduction of soil fertility and ultimately soil health [ 17 , 21 ]. Agro-Ecological Zone (AEZ) 29 covers Bangladesh's Northern and Eastern Hills, including the Chittagong Hill Tracts and surrounding territories. The predominant soils are yellow-brown to strong brown loamy soils, categorized as Brown Hill soils. Acidic, porous, and friable, these soils hold low moisture. Fertility is low to medium, while nitrogen and phosphorus are very low to low. Forests, jhum (shifting) cultivation, tea plantations, and orchards thrive in the zone. However, inappropriate land use and erosion make sustainable land management in this environmentally vulnerable region difficult [ 6 ]. These hilly areas are being used for plantation and cultivation of field crops to fulfil the demand of a large population. As a consequence, different negative impacts have been noticed in these areas. Many studies have examined soil quality indicators and soil parameter changes in plain lands, but few have examined mountainous locations like the Northern and Eastern Hills. The study aimed to find out the impact of land use types on physico-chemical properties of soil in the Northern and Eastern Hills (AEZ 29) of Bangladesh. Materials and Methods Experimental location The experiment was conducted in Sylhet Sadar Upazila, encompassing the northern part of AEZ 29 (Northern and Eastern Hills) of Bangladesh. The laboratory analysis of the soil samples was performed in the Soil Chemistry and Soil Physics laboratories of the Department of Soil Science, Sylhet Agricultural University. Collection of soil Soils were collected from sixteen (16) sampling sites, including grassland, forest land, orchard and tea garden with two different depths, viz., surface soil (0–15 cm) and sub-surface soil (15–30 cm). Preparation of soil The collected soil samples were spread on brown paper in the laboratory for air drying. Debris, such as plant roots and stones, was removed during this process. The air-dried soil was passed through a standard 2 mm sieve to separate fine soil particles. The processed samples were kept in polythene bags for further physical and chemical analysis by following standard methods. Data collection : Data were collected soil physical and chemical parameters viz; soil texture [ 12 ], Silt: Clay ratio (SCR), Bulk density and Particle density [ 9 ], Soil pH [ 19 ], Organic carbon [ 20 ], Total nitrogen [ 10 ], Available phosphorus [ 21 ], Exchangeable potassium [ 15 ], Available boron [ 14 ], Available zinc [ 16 ]. Statistical analysis The mean and correlation were calculated by Microsoft Excel. The significance levels of the observed changes were tested in the computer-based statistical program R. Results and Discussion Soil physicochemical properties of AEZ 29 Table 1 indicates that sand content differed among land use types, with grassland soils exhibiting the highest sand percentages in GLS (89.44%, sample 5). To the contrary, forest soils sand in FLS (73.44%) (Sample 11) showed lower percentage of sand content. The variations modified the determination of soil texture where the majorities were classified as LS, followed by SL and S indicating a coarse textural class. The forest soils, having relatively more fine particles, were likely to provide superior moisture retention and fertility, as observed in similar ecosystems [ 4 ]. The stop criteria ratio (SCR) is another important factor because it shows the capacity of the soil to retain water and help plants grow. SCR varied from 0.11 to 3.07 among the samples. Overall, soils showing higher values of FLS (3.07; sample 13) suggest a low proportion of silt versus clay which might be more related to water retention and permeability. For instance, the 0.11 SCR value for FLSS (16th sample) shows a comparably high portion of clay content that will cause poor drainage and will store more water. Variations in the silt: clay ratio (SCR) were indicative of water-holding capacity and aeration where forest soils had a higher SCR, thus more silt content, which contributes to better water movement and aeration. The lower the SCR in alternative soils, the greater content of clay could clearly afford for water retention and drainage [ 22 ]. Bulk density is an essential indicator, which predicts the degree of porosity and compaction status of the soil. Low bulk density (1.06 g cm − 3, sample 5) in GLS may indicate loose and aerated soils; which stimulate the growth of root system. Higher bulk density in OLS on the other hand (1.83 g/cm³, sample 19) reflects a more compacted soil that impedes root intrusion and water movement through it. In the table, the particle density values ranged from 2.16 g/cm³ (GLS, sample 5) to 2.82 g/cm³ (FLS, sample 13). Soil BD of grassland was the lowest, which suggests better soil aerated and root penetration, while that of orchard was high that might be due to compaction caused by agricultural practices leading to poor water infiltration and root development [ 3 ]. Particles density Particle density is the mass of soil particles (excluding pore space) per unit volume, and is typically 2.60 g/cm³ for most mineral soils. This set of data indicates that it is within this range average, which means standard mineral content of soils. Estimated porosity in the soil (calculated from bulk and particle density) represented the proportion of void space in the soil. The highest porosity value in the GLS (50.93%, sample 5) means that it holds more water and air, which is beneficial for plants to grow well. While the sample 19 (OLS) (22.78%) shows low porosity than its saturation weighted value also representing soil more compact might not allow supportive drainage. Particle density demonstrated uniformity in mineral composition, with minor discrepancies ascribed to variances in organic matter and soil parent material [ 11 ]. Table 1 Physical properties of the soil samples collected from AEZ 29 Sample no. Land use types & soil depth* Sand (%) Silt (%) Clay (%) Textural class** Silt : Clay (SCR) Bulk density (gcm − 3 ) Particle density (gcm − 3 ) Porosity (%) 1 GLS 81.44 7.84 10.72 LS 0.73 1.69 2.47 31.66 2 GLSS 83.44 9.84 6.72 LS 1.46 - 2.40 - 3 GLS 79.44 11.28 9.28 LS 1.22 1.57 2.56 38.67 4 GLSS 78.28 13.28 8.44 LS 1.57 - 2.45 - 5 GLS 89.44 5.28 5.28 S 1.00 1.06 2.16 50.93 6 GLSS 88.44 5.28 6.28 S 0.84 - 2.22 - 7 GLS 86.80 5.92 7.28 LS 0.81 1.35 2.41 43.98 8 GLSS 82.28 9.44 8.28 LS 1.14 - 2.52 - 9 FLS 77.08 8.04 14.88 LS 0.54 1.57 2.34 32.86 10 FLSS 77.44 11.68 10.88 SL 1.07 - 2.21 - 11 FLS 73.44 8.00 18.56 SL 0.43 1.71 2.30 25.65 12 FLSS 74.56 7.00 18.44 SL 0.38 - 2.36 - 13 FLS 81.44 14.00 4.56 LS 3.07 1.40 2.82 50.35 14 FLSS 79.44 14.00 6.56 LS 2.13 - 2.76 - 15 FLS 77.44 3.00 19.56 SL 0.15 1.50 2.36 36.27 16 FLSS 79.44 2.00 18.56 SL 0.11 - 2.36 - 17 OLS 73.44 18.00 8.56 SL 2.10 1.64 2.27 27.75 18 OLSS 75.44 15.00 9.56 SL 1.57 - 2.20 - 19 OLS 77.44 16 6.56 LS 2.44 1.83 2.37 22.78 20 OLSS 76.44 12 11.56 LS 1.04 - 2.29 - 21 OLS 75.44 18.00 6.56 SL 2.74 1.38 2.10 34.29 22 OLSS 77.44 16.00 6.56 LS 2.44 - 2.21 - 23 OLS 81.44 11.56 7.00 LS 1.65 1.33 2.37 43.88 24 OLSS 79.56 12.00 8.44 LS 1.42 - 2.41 - 25 TS 81.80 9.28 8.92 LS 1.04 1.67 2.51 33.47 26 TSS 84.00 8.00 8.00 LS 1.00 - 2.42 - 27 TS 79.80 9.28 10.92 SL 0.85 1.58 2.21 28.51 28 TSS 78.00 12.00 10.00 SL 1.20 - 2.19 - 29 TS 81.80 7.28 10.92 LS 0.67 1.45 2.25 35.56 30 TSS 75.44 11.28 13.28 SL 0.85 - 2.31 - 31 TS 77.44 15.28 7.28 LS 2.10 1.47 2.17 32.26 32 TSS 73.44 17.28 9.28 SL 1.86 - 2.23 - *GLS-Grassland surface soil, GLSS-Grassland sub-surface soil, FLS-Forest land surface soil, FLSS- Forest land sub-surface soil, OLS-Orchard land surface soil, OLSS-Orchard land sub-surface soil, TS-Tea garden surface soil, TSS-Tea garden land sub-surface soil. **LS-Loamy sand, S-Sand, SL-Sandy loam Relationship among physical and chemical properties of surface soil through Pearson correlation The results showed that Sand had a negative correlation with both silt (-0.51) and clay (-0.45), with these correlations being statistically significant at the 0.05 level. Bulk density (BD) showed a significant negative correlation with OC (-0.48), as well as with N (-0.61) and Zn (-0.63), all of which were significant at the 0.01 level (Table 2 ). Organic carbon was positively correlated with nitrogen (0.51) and zinc (0.58), with both correlations being significant at the 0.05 level. Nitrogen had a significant negative correlation with pH (-0.64), and a moderate negative correlation with P (-0.26), which was not statistically significant. Zinc was positively correlated with K (0.30), but this correlation was not significant. B had a very high negative correlation with P (− 0.77) significant at 0.001, and also positive correlations with OC (0.31) and K (0.41), both significant at 0.05. In general, the correlation matrix revealed a set of intricate interrelationships among soil properties and determined that organic C was one of the key factors influencing nutrient availability. Table 2 Pearson correlation matrix among physical and chemical properties of surface soil (0–15 cm soil depth) Sand Silt Clay BD PD pH OC K N P B Zn Sand 1.00 Silt -0.51* 1.00 Clay -0.45* -0.54* 1.00 BD -0.64** 0.23 0.39* 1.00 PD 0.17 -0.05 -0.11 0.20 1.00 pH -0.18 -0.15 0.33 0.45 0.07 1.00 OC 0.00 -0.06 0.06 -0.48* -0.37 -0.56* 1.00 K 0.28 -0.01 -0.27 -0.25 -0.16 0.04 0.37 1.00 N 0.25 -0.07 -0.17 -0.61** 0.01 -0.64** 0.51* 0.07 1.00 P 0.15 0.15 -0.30 -0.13 0.28 -0.02 -0.26 -0.12 0.39 1.00 B -0.17 -0.03 0.20 0.22 -0.02 0.01 0.31 0.41 -0.31 -0.77*** 1.00 Zn 0.18 0.16 -0.35 -0.63** -0.21 -0.34 0.58* 0.30 0.29 0.01 0.12 1.00 Relationship among physical and chemical properties of sub-surface soil through Pearson correlation The results showed that sand exhibited a significant negative correlation with silt (-0.54), and also had a negative correlation with clay (-0.47), though not significant. Silt showed a negative correlation with clay (-0.49), which was significant at the 0.05 level (Table 3 ). Particle density (PD) had minimal significant correlations with other properties. The pH was negatively correlated with N (-0.71) and was significant at the 0.01 level, indicating that lower pH values were associated with higher nitrogen levels in sub-surface soils. OC showed a statistically significant, strong negative correlation with pH (-0.68), and a positive correlation with N (0.66), significant at the 0.01 level, indicating that higher organic carbon content was associated with increased nitrogen levels. Table 3 Pearson correlation matrix among physical and chemical properties of sub-surface soil (15–30 cm soildepth) Sand Silt Clay PD pH OC K N P B Zn Sand 1.00 Silt -0.54* 1.00 Clay -0.47 -0.49* 1.00 PD 0.23 -0.09 -0.15 1.00 pH 0.11 -0.02 -0.13 -0.24 1.00 OC -0.16 -0.02 0.19 -0.18 -0.68** 1.00 K 0.19 -0.11 -0.08 -0.13 0.22 0.15 1.00 N 0.02 0.07 -0.44 0.15 -0.71** 0.66** -0.36 1.00 P 0.42 0.01 -0.32 0.28 0.30 -0.42 -0.19 0.13 1.00 B -0.29 -0.01 0.27 0.15 -0.30 0.13 0.31 -0.30 -0.73*** 1.00 Zn 0.05 0.09 -0.15 0.00 -0.51* 0.62** 0.29 0.32 -0.31 0.33 1.00 Impact of different land use types on physical and chemical properties of soil Surface soil Table 4 presents that the bulk density of soil significantly differed with the different land types, where the highest bulk density (1.67 g cm − 3 ) was recorded in the forest land, followed by the grassland (1.64 g cm − 3 ), and the orchard showed the lowest bulk density (1.32 g cm − 3 ) in surface soil. Bulk density measurements suggested moderately compacted soils, with elevated values possibly associated with compaction and diminished values facilitating root penetration. Particle density of surface soil was statistically similar in the four types of land, including the slightly changed was recorded with the ranges from 2.33 to 2.40 g cm − 3 . Particle density indicated a constant mineral composition, with little fluctuations due to organic matter contributions [ 11 ]. Soil pH was significantly varied with the different land use types in AEZ 29. The grassland soil showed the highest pH (5.14) followed by the forest land (4.97), and the lowest pH (4.35) was found in the tea garden. Table 4 Land use types’ impact on bulk density of surface soil Land use types Bulk density ( g cm − 3 ) Particle density (g cm − 3 ) pH Organic carbon (%) Total nitrogen (%) Available P (ppm) Exchangeable K (meq/100g) Available B (ppm) Available Zn (ppm) Grassland 1.64 ± 0.026a 2.40 ± 0.056 5.14 ± 0.188a 0.38 ± 0.081d 0.09 ± 0.006b 7.30 ± 0.542a 0.12 ± 0.023 0.18 ± 0.025d 0.90 ± 0.069b Forest land 1.67 ± 0.061a 2.36 ± 0.074 4.97 ± 0.244a 0.69 ± 0.095c 0.06 ± 0.005c 3.63 ± 0.197c 0.16 ± 0.023 0.49 ± 0.023a 1.15 ± 0.119b Orchard 1.32 ± 0.089b 2.33 ± 0.165 4.40 ± 0.041b 0.90 ± 0.074b 0.11 ± 0.010ab 6.55 ± 0.660ab 0.15 ± 0.017 0.27 ± 0.012c 1.61 ± 0.122a Tea Garden 1.41 ± 0.043b 2.33 ± 0.054 4.35 ± 0.074b 1.13 ± 0.089a 0.13 ± 0.011a 5.18 ± 0.328bc 0.16 ± 0.012 0.35 ± 0.023b 1.20 ± 0.106b Level of Significance 0.01 NS 0.05 0.01 0.01 0.01 NS 0.01 0.01 CV (%) 7.67 8.67 7.09 16.26 18.45 18.66 26.65 13.20 17.92 Statistically different values were found in the organic carbon of surface soil, where the soil of the tea garden had the highest organic carbon (1.13%) compared to the other land types. Furthermore, the grassland had the lowest organic carbon (0.38%), followed by the forest land (0.69%). Soils were slightly acidic, likely due to decomposition of organic matter and washout of basic cations, characteristics typically associated with sandy soils. These conditions limit nutrient accessibility, particularly that of phosphate and potassium [ 13 ]. The organic carbon content was moderate, important for soil fertility but, in sandy soils, it is susceptible to rapid decomposition and leaching [ 7 ]. The total nitrogen of the surface soil significantly differed with different types of land. The tea garden had the highest total nitrogen (0.13%), followed by the orchard (0.11%); on the other hand, the lowest value (0.06%) was found in the forest land. Available phosphorus contents were statistically different in the surface soil, where the highest phosphorus (7.30) in the grassland and the lowest phosphorus was recorded in the forest land. In term of exchangeable potassium, the different types of land had no significant difference among the soil with the ranges from 0.12 to 0.16. Furthermore, the available boron and zinc were the statistically varied with the different land types. The levels of phosphorus, potassium, boron, and zinc were moderate, affected by soil texture, pH, and organic matter content, with phosphorus availability limited by acidic conditions [ 22 ]. Sub-surface soil Particle density of the sub-surface soil was statistically indifferent in the four land types (Table 5 ), including the ranges (2.31–2.38 g cm − 3 ). In terms of soil pH, significant variation was recorded, where the highest pH (5.22) was found in grassland, followed by the forest land (4.86), and the soil of the tea garden showed the lowest pH (4.42). Also, a significant difference was found in the soil organic carbon of four land use types. The tea gardens had the highest organic carbon (1.20%); on the other hand, the lowest value (0.53%) was recorded in the grassland. Sub-surface soil organic carbon levels were moderate, likely due to the leaching of organic material from top layers and diminished breakdown rates attributed to reduced microbial activity and oxygen availability at depth [ 23 ]. Table 5 Land use types’ impact on bulk density of sub-surface soil Land use types Particle density (g cm − 3 ) pH Organic carbon (%) Total nitrogen (%) Available P (ppm) Exchangeable K (meq/100g) Available B (ppm) Available Zn (ppm) Grassland 2.31 ± 0.059 5.22 ± 0.180a 0.53 ± 0.041b 0.071 ± 0.007ab 6.13 ± 0.473a 0.09 ± 0.017 0.11 ± 0.007c 0.62 ± 0.053c Forest land 2.32 ± 0.055 4.86 ± 0.196ab 0.69 ± 0.032b 0.055 ± 0.008b 3.23 ± 0.165c 0.12 ± 0.026 0.33 ± 0.023a 1.02 ± 0.092b Orchard 2.38 ± 0.130 4.54 ± 0.084bc 0.95 ± 0.103a 0.094 ± 0.005a 5.05 ± 0.556ab 0.10 ± 0.009 0.19 ± 0.015b 1.40 ± 0.099a Tea Garden 2.38 ± 0.060 4.42 ± 0.067c 1.20 ± 0.103a 0.095 ± 0.006a 3.98 ± 0.293bc 0.11 ± 0.005 0.22 ± 0.015b 1.07 ± 0.099b Level of Significance NS 0.01 0.01 0.05 0.05 NS 0.01 0.01 CV (%) 7.23 5.63 18.77 19.09 19.13 28.60 16.77 18.46 Additionally, the total nitrogen content in sub-surface soil significantly differed with the different land types, including the highest nitrogen (0.095%) in the tea garden, followed by the orchard (0.094%). The observed low total nitrogen concentration corresponds with minimal organic matter assimilation, directly affecting nitrogen availability. Therefore, the available phosphorus was statistically varied with the various land types. The grassland showed the highest phosphorus content (61.3 ppm) in soil, followed by the orchard (5.05 ppm). But, the exchangeable potassium remained statistically similar in all land types and varied from 0.09 to 0.11 meq/ 100g. Therefore, the four types of land had a significant difference in available boron, where the highest boron (0.33 ppm) was found in the forest land, followed by the orchard (0.10 ppm). Moreover, the available zinc content varied with the different land use types, ranging from 0.62–1.40 ppm. Introducing phosphorus availability was moderate, indicating impeded and dependent on organic input soil minerals, that was probably less than in surface limiting retention of phosphorus. Potassium was low to medium possibly due to leaching on the sandy soil. Boron and zinc concentrations in sub-surface soils were moderate, aligning with observations that micronutrient availability decreases with depth due to insufficient organic matter inputs and restricted replenishment via plant cycling [ 25 ]. Conclusion The study showed that the physical and chemical properties of soil (Sand, silt, clay, bulk and particle density, soil pH, organic carbon, total nitrogen, available phosphorus, exchangeable potassium, available boron, available zinc) was varied with different land use types and soil depth. The availability of different soil physico-chemical parameters and their associations are evaluated which will be an option to correlated factors influencing nutrient accessions for soil infertility and good crop yield. Declarations Consent to Publish Consent to Publish declaration: Not applicable. Consent to Participate Consent to Participate declaration: Not applicable. Ethics Approval Ethics declaration: Not applicable. The study was based on soil sampling and laboratory analysis solely and did not involve any human or animal subjects. Funding The corresponding author received funding from Sylhet Agricultural University Research System (SAURES) Author Contribution M.M.H.S. designed the study, performed data analysis, supervised the research and reviewed the manuscript. F.R. (corresponding author) conducted field sampling and laboratory analyses, assisted in data interpretation and wrote the main manuscript text. M.A.K. supervised the research and reviewed the manuscript. All authors reviewed the final manuscript. Data Availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. References Abdullah HM, Islam I, Miah MG, Ahmed Z. 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Mosharaf Hossain Sarker","email":"","orcid":"","institution":"Sylhet Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Mosharaf Hossain","lastName":"Sarker","suffix":""},{"id":578924916,"identity":"5f4882f5-d0bc-409a-bf00-38c551a63fba","order_by":2,"name":"Md. Abul Kashem","email":"","orcid":"","institution":"Sylhet Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Abul","lastName":"Kashem","suffix":""}],"badges":[],"createdAt":"2026-01-03 12:08:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8506757/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8506757/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100967347,"identity":"57b670c6-4dd0-47cf-a7f7-8cd51798cd8b","added_by":"auto","created_at":"2026-01-23 09:34:39","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":42106,"visible":true,"origin":"","legend":"","description":"","filename":"ImpactofLandUseTypesonSoilPhysicochemicalPropertiesintheNorthernandEasternHillsofBangladesh.docx","url":"https://assets-eu.researchsquare.com/files/rs-8506757/v1/93163dd6f658ac6f2d215bbd.docx"},{"id":100967346,"identity":"7b1066d2-ca8b-4895-98d7-e4afa35a73a8","added_by":"auto","created_at":"2026-01-23 09:34:39","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5279,"visible":true,"origin":"","legend":"","description":"","filename":"f42b32bb431e4bdeb1d3226cbe8f8f0f.json","url":"https://assets-eu.researchsquare.com/files/rs-8506757/v1/ef7ac5f0143666cfdca92447.json"},{"id":101397761,"identity":"a7ad22f9-f76d-4e04-b189-fc8b192ca09f","added_by":"auto","created_at":"2026-01-29 09:36:38","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":121414,"visible":true,"origin":"","legend":"","description":"","filename":"f42b32bb431e4bdeb1d3226cbe8f8f0f1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8506757/v1/1460594844805e5a7740e696.xml"},{"id":100967348,"identity":"56ae24eb-f6dc-4d95-bd35-a855ef5d4311","added_by":"auto","created_at":"2026-01-23 09:34:39","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":120677,"visible":true,"origin":"","legend":"","description":"","filename":"f42b32bb431e4bdeb1d3226cbe8f8f0f1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8506757/v1/bac0245805a8319a67e65482.xml"},{"id":101202773,"identity":"bd451322-7700-47e4-ab45-5409e19b9445","added_by":"auto","created_at":"2026-01-27 09:37:33","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":127873,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8506757/v1/f8d362832943318d47f4ee61.html"},{"id":104808103,"identity":"a3ba3301-13a2-463d-80bd-6c2570ee2f27","added_by":"auto","created_at":"2026-03-17 12:15:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1319944,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8506757/v1/9e5144b9-b382-4e0b-97ab-c6e2e77ae38d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of Land Use Types on Soil Physicochemical Properties in the Northern and Eastern Hills of Bangladesh","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUrbanization, industrialization and different agricultural practices reduce soil fertility, biodiversity and ecosystem [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Additionally, forest loss increases greenhouse gas emissions, reduces carbon sequestration, and raises local temperatures [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Intensive crop cultivation and monocultures lower soil moisture, organic matter, and microbial biomass, nutrient content and cause long-term deterioration [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The total land area of Bangladesh is 14.4\u0026nbsp;million ha, about 62.30% of which is arable in 2021-22 with 169.83\u0026nbsp;million total population of. The cropping intensity of the country was 171% and 198% in 1983-84 and 2020-21, respectively [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. To provide food and other agro-products for this vast population, the cultivable land is being used intensively and causing depletion of essential plant nutrients, organic matter, soil microbes, etc. Several factors are liable for the variation in physico-chemical properties of soil, which include topography, slope gradient, parent materials, land use and climatic conditions [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Among different factors which are responsible for changes in soil parameters, land use pattern is an important one. Conversion of forest land to agricultural practices leads to higher soil erosion, reduction of soil fertility and ultimately soil health [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAgro-Ecological Zone (AEZ) 29 covers Bangladesh's Northern and Eastern Hills, including the Chittagong Hill Tracts and surrounding territories. The predominant soils are yellow-brown to strong brown loamy soils, categorized as Brown Hill soils. Acidic, porous, and friable, these soils hold low moisture. Fertility is low to medium, while nitrogen and phosphorus are very low to low. Forests, jhum (shifting) cultivation, tea plantations, and orchards thrive in the zone. However, inappropriate land use and erosion make sustainable land management in this environmentally vulnerable region difficult [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These hilly areas are being used for plantation and cultivation of field crops to fulfil the demand of a large population. As a consequence, different negative impacts have been noticed in these areas. Many studies have examined soil quality indicators and soil parameter changes in plain lands, but few have examined mountainous locations like the Northern and Eastern Hills. The study aimed to find out the impact of land use types on physico-chemical properties of soil in the Northern and Eastern Hills (AEZ 29) of Bangladesh.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e \u003cstrong\u003eExperimental location\u003c/strong\u003e \u003cp\u003eThe experiment was conducted in Sylhet Sadar Upazila, encompassing the northern part of AEZ 29 (Northern and Eastern Hills) of Bangladesh. The laboratory analysis of the soil samples was performed in the Soil Chemistry and Soil Physics laboratories of the Department of Soil Science, Sylhet Agricultural University.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCollection of soil\u003c/strong\u003e \u003cp\u003eSoils were collected from sixteen (16) sampling sites, including grassland, forest land, orchard and tea garden with two different depths, viz., surface soil (0\u0026ndash;15 cm) and sub-surface soil (15\u0026ndash;30 cm).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePreparation of soil\u003c/strong\u003e \u003cp\u003eThe collected soil samples were spread on brown paper in the laboratory for air drying. Debris, such as plant roots and stones, was removed during this process. The air-dried soil was passed through a standard 2 mm sieve to separate fine soil particles. The processed samples were kept in polythene bags for further physical and chemical analysis by following standard methods.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eData collection\u003c/b\u003e: Data were collected soil physical and chemical parameters viz; soil texture [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], Silt: Clay ratio (SCR), Bulk density and Particle density [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], Soil pH [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], Organic carbon [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], Total nitrogen [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], Available phosphorus [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], Exchangeable potassium [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], Available boron [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], Available zinc [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStatistical analysis\u003c/strong\u003e \u003cp\u003eThe mean and correlation were calculated by Microsoft Excel. The significance levels of the observed changes were tested in the computer-based statistical program R.\u003c/p\u003e \u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSoil physicochemical properties of AEZ 29\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e indicates that sand content differed among land use types, with grassland soils exhibiting the highest sand percentages in GLS (89.44%, sample 5). To the contrary, forest soils sand in FLS (73.44%) (Sample 11) showed\u0026ensp;lower percentage of sand content. The variations modified the determination of soil\u0026ensp;texture where the majorities were classified as LS, followed by SL and S indicating a coarse textural class. The forest soils, having relatively more fine particles, were likely to provide superior\u0026ensp;moisture retention and fertility, as observed in similar ecosystems [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe stop criteria ratio (SCR) is another\u0026ensp;important factor because it shows the capacity of the soil to retain water and help plants grow. SCR varied from\u0026ensp;0.11 to 3.07 among the samples. Overall, soils showing higher values of FLS (3.07; sample 13) suggest a low proportion of silt versus clay which might be more related to water\u0026ensp;retention and permeability. For instance, the 0.11 SCR value for FLSS (16th sample) shows a comparably high portion of clay content that will cause poor drainage and\u0026ensp;will store more water. Variations in the silt: clay ratio (SCR) were indicative of\u0026ensp;water-holding capacity and aeration where forest soils had a higher SCR, thus more silt content, which contributes to better water movement and aeration. The lower the SCR\u0026ensp;in alternative soils, the greater content of clay could clearly afford for water retention and drainage [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBulk density is an essential indicator, which predicts the degree of\u0026ensp;porosity and compaction status of the soil. Low\u0026ensp;bulk density (1.06 g cm \u0026minus;\u0026thinsp;3, sample 5) in GLS may indicate loose and aerated soils; which stimulate the growth of root system. Higher bulk density in OLS on\u0026ensp;the other hand (1.83 g/cm\u0026sup3;, sample 19) reflects a more compacted soil that impedes root intrusion and water movement through it. In the table,\u0026ensp;the particle density values ranged from 2.16 g/cm\u0026sup3; (GLS, sample 5) to 2.82 g/cm\u0026sup3; (FLS, sample 13). Soil BD of\u0026ensp;grassland was the lowest, which suggests better soil aerated and root penetration, while that of orchard was high that might be due to compaction caused by agricultural practices leading to poor water infiltration and root development [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eParticles density Particle density is the mass\u0026ensp;of soil particles (excluding pore space) per unit volume, and is typically 2.60 g/cm\u0026sup3; for most mineral soils. This set of data indicates that it is within this\u0026ensp;range average, which means standard mineral content of soils.\u003c/p\u003e \u003cp\u003eEstimated porosity in the soil (calculated from bulk and particle\u0026ensp;density) represented the proportion of void space in the soil. The highest porosity value in the GLS (50.93%, sample 5) means that it holds more water and air, which is beneficial for plants to grow well. While the sample 19 (OLS) (22.78%) shows low porosity than its saturation weighted value also representing soil more compact might\u0026ensp;not allow supportive drainage. Particle density demonstrated uniformity in mineral composition, with minor discrepancies ascribed to variances in organic matter and soil parent material [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePhysical properties of the soil samples collected from AEZ 29\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample no.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLand use types \u0026amp; soil depth*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSand\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSilt\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eClay\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTextural class**\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSilt : Clay\u003c/p\u003e \u003cp\u003e(SCR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBulk density\u003c/p\u003e \u003cp\u003e(gcm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eParticle density\u003c/p\u003e \u003cp\u003e(gcm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePorosity\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e31.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGLSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" 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colname=\"c6\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLS\u003c/p\u003e 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colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e35.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e32.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e*GLS-Grassland surface soil, GLSS-Grassland sub-surface soil, FLS-Forest land surface soil, FLSS- Forest land sub-surface soil, OLS-Orchard land surface soil, OLSS-Orchard land sub-surface soil, TS-Tea garden surface soil, TSS-Tea garden land sub-surface soil.\u003c/p\u003e \u003cp\u003e**LS-Loamy sand, S-Sand, SL-Sandy loam\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRelationship among physical and chemical properties of surface soil through Pearson correlation\u003c/h3\u003e\n\u003cp\u003eThe results showed that Sand had a negative correlation with both silt (-0.51) and clay (-0.45), with these correlations being statistically significant at the 0.05 level. Bulk density (BD) showed a significant negative correlation with OC (-0.48), as well as with N (-0.61) and Zn (-0.63), all of which were significant at the 0.01 level (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Organic carbon was positively correlated with nitrogen (0.51) and zinc (0.58), with both correlations being significant at the 0.05 level. Nitrogen had a significant negative correlation with pH (-0.64), and a moderate negative correlation with P (-0.26), which was not statistically significant. Zinc was positively correlated with K (0.30), but this correlation was not significant. B had a very high negative correlation with P (\u0026minus;\u0026thinsp;0.77) significant at 0.001, and also\u0026ensp;positive correlations with OC (0.31) and K (0.41), both significant at 0.05. In general, the correlation matrix revealed a set of intricate interrelationships among soil properties and determined that organic C was one of the key factors\u0026ensp;influencing nutrient availability.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePearson correlation matrix among physical and chemical properties of surface soil (0\u0026ndash;15 cm soil depth)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e 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\u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSilt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.51*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.45*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.54*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.64**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.39*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.48*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.56*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.61**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.64**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.51*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.77***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.63**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.58*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eRelationship among physical and chemical properties of sub-surface soil through Pearson correlation\u003c/h3\u003e\n\u003cp\u003eThe results showed that sand exhibited a significant negative correlation with silt (-0.54), and also had a negative correlation with clay (-0.47), though not significant. Silt showed a negative correlation with clay (-0.49), which was significant at the 0.05 level (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Particle density (PD) had minimal significant correlations with other properties. The pH was negatively correlated with N (-0.71) and was significant at the 0.01 level, indicating that lower pH values were associated with higher nitrogen levels in sub-surface soils. OC showed a statistically significant, strong negative correlation with pH (-0.68), and a positive correlation with N (0.66), significant at the 0.01 level, indicating that higher organic carbon content was associated with increased nitrogen levels.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePearson correlation matrix among physical and chemical properties of sub-surface soil (15\u0026ndash;30 cm soildepth)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSand\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSilt\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClay\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSilt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.54*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.49*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.68**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.71**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.66**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.73***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.51*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.62**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eImpact of different land use types on physical and chemical properties of soil Surface soil\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents that the bulk density of soil significantly differed with the different land types, where the highest bulk density (1.67 g cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) was recorded in the forest land, followed by the grassland (1.64 g cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), and the orchard showed the lowest bulk density (1.32 g cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) in surface soil. Bulk density measurements suggested moderately compacted soils, with elevated values possibly associated with compaction and diminished values facilitating root penetration. Particle density of surface soil was statistically similar in the four types of land, including the slightly changed was recorded with the ranges from 2.33 to 2.40 g cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e. Particle density indicated a constant mineral composition, with little fluctuations due to organic matter contributions [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Soil pH was significantly varied with the different land use types in AEZ 29. The grassland soil showed the highest pH (5.14) followed by the forest land (4.97), and the lowest pH (4.35) was found in the tea garden.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLand use types\u0026rsquo; impact on bulk density of surface soil\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand use types\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBulk density\u003c/p\u003e \u003cp\u003e( g cm \u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParticle density\u003c/p\u003e \u003cp\u003e(g cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrganic carbon (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotal nitrogen (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAvailable P (ppm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eExchangeable K\u003c/p\u003e \u003cp\u003e(meq/100g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAvailable B (ppm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAvailable\u003c/p\u003e \u003cp\u003eZn (ppm)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGrassland\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.026a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.188a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.081d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.542a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.025d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.069b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eForest land\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.061a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.244a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.095c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.197c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.023a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.119b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOrchard\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.089b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.041b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.074b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.010ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.660ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.012c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.122a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTea Garden\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.043b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.074b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.089a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.011a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.328bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.023b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.106b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLevel of Significance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCV (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e17.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eStatistically different values were found in the organic carbon of surface soil, where the soil of the tea garden had the highest organic carbon (1.13%) compared to the other land types. Furthermore, the grassland had the lowest organic carbon (0.38%), followed\u0026ensp;by the forest land (0.69%). Soils were\u0026ensp;slightly acidic, likely due to decomposition of organic matter and washout of basic cations, characteristics typically associated with sandy soils. These conditions\u0026ensp;limit nutrient accessibility, particularly that of phosphate and potassium [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The organic carbon content was moderate, important for soil\u0026ensp;fertility but, in sandy soils, it is susceptible to rapid decomposition and leaching [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The total nitrogen of the surface soil significantly differed with different types of land. The tea garden had the highest total nitrogen (0.13%), followed by the orchard (0.11%); on the other hand, the lowest value (0.06%) was found in the forest land. Available phosphorus contents were statistically different in the surface soil, where the highest phosphorus (7.30) in the grassland and the lowest phosphorus was recorded in the forest land. In term of exchangeable potassium, the different types of land had no significant difference among the soil with the ranges from 0.12 to 0.16. Furthermore, the available boron and zinc were the statistically varied with the different land types. The levels of phosphorus, potassium, boron, and zinc were moderate, affected by soil texture, pH, and organic matter content, with phosphorus availability limited by acidic conditions [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSub-surface soil\u003c/h2\u003e \u003cp\u003eParticle density of the sub-surface soil was statistically indifferent in the four land types (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), including the ranges (2.31\u0026ndash;2.38 g cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e). In terms of soil pH, significant variation was recorded, where the highest pH (5.22) was found in grassland, followed by the forest land (4.86), and the soil of the tea garden showed the lowest pH (4.42). Also, a significant difference was found in the soil organic carbon of four land use types. The tea gardens had the highest organic carbon (1.20%); on the other hand, the lowest value (0.53%) was recorded in the grassland. Sub-surface soil organic carbon levels were moderate, likely due to the leaching of organic material from top layers and diminished breakdown rates attributed to reduced microbial activity and oxygen availability at depth [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLand use types\u0026rsquo; impact on bulk density of sub-surface soil\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand use types\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParticle density\u003c/p\u003e \u003cp\u003e(g cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrganic carbon (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal nitrogen (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAvailable P\u003c/p\u003e \u003cp\u003e(ppm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eExchangeable K\u003c/p\u003e \u003cp\u003e(meq/100g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAvailable B (ppm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAvailable\u003c/p\u003e \u003cp\u003eZn (ppm)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGrassland\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.180a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.041b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.071\u0026thinsp;\u0026plusmn;\u0026thinsp;0.007ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.473a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.007c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.053c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eForest land\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.196ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.032b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.055\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.165c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.023a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.092b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOrchard\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.084bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.103a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.094\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.556ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.015b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.099a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTea Garden\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.067c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.103a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.095\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.293bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.015b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.099b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLevel of Significance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCV (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAdditionally, the total nitrogen content in sub-surface soil significantly differed with the different land types, including the highest nitrogen (0.095%) in the tea garden, followed by the orchard (0.094%). The observed low total nitrogen concentration corresponds with minimal organic matter assimilation, directly affecting nitrogen availability. Therefore, the available phosphorus was statistically varied with the various land types. The grassland showed the highest phosphorus content (61.3 ppm) in soil, followed by the orchard (5.05 ppm). But, the exchangeable potassium remained statistically similar in all land types and varied from 0.09 to 0.11 meq/ 100g. Therefore, the four types of land had a significant difference in available boron, where the highest boron (0.33 ppm) was found in the forest land, followed by the orchard (0.10 ppm). Moreover, the available zinc content varied with the different land use types, ranging from 0.62\u0026ndash;1.40 ppm. Introducing phosphorus availability was moderate, indicating impeded and\u0026ensp;dependent on organic input soil minerals, that was probably less than in surface limiting retention of phosphorus. Potassium was low to medium possibly due to leaching\u0026ensp;on the sandy soil. Boron and zinc concentrations in sub-surface soils were moderate, aligning with observations that micronutrient availability decreases with depth due to insufficient organic matter inputs and restricted replenishment via plant cycling [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study showed that the physical and chemical properties of soil (Sand, silt, clay, bulk and particle density, soil pH, organic carbon, total nitrogen, available phosphorus, exchangeable potassium, available boron, available zinc) was varied with different land use types and soil depth. The availability of different soil physico-chemical parameters and their associations are evaluated which will be an option to correlated factors influencing nutrient accessions for soil infertility\u0026ensp;and good crop yield.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConsent to Publish\u003c/h2\u003e \u003cp\u003eConsent to Publish declaration: Not applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConsent to Participate\u003c/h2\u003e \u003cp\u003eConsent to Participate declaration: Not applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics Approval\u003c/h2\u003e \u003cp\u003eEthics declaration: Not applicable.\u003c/p\u003e\u003cp\u003eThe study was based on soil sampling and laboratory analysis solely and did not involve any human or animal subjects. \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe corresponding author received funding from Sylhet Agricultural University Research System (SAURES)\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.M.H.S. designed the study, performed data analysis, supervised the research and reviewed the manuscript. F.R. (corresponding author) conducted field sampling and laboratory analyses, assisted in data interpretation and wrote the main manuscript text. M.A.K. supervised the research and reviewed the manuscript. All authors reviewed the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdullah HM, Islam I, Miah MG, Ahmed Z. Quantifying the spatiotemporal patterns of forest degradation in a fragmented, rapidly urbanizing landscape: A case study of Gazipur, Bangladesh. 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China PubMed. 2016;27(5):1445\u0026ndash;52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.13287/j.1001-9332.201605.019\u003c/span\u003e\u003cspan address=\"10.13287/j.1001-9332.201605.019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Agro-Ecological Zone, Forest land, Grassland, Northern and Eastern Hills, Orchard, Tea garden","lastPublishedDoi":"10.21203/rs.3.rs-8506757/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8506757/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDiverse land use patterns in the Northern and Eastern Hills (AEZ 29) of Bangladesh significantly affect soil physico-chemical properties. The study aimed to delineate soil physico-chemical characteristics and their changing dynamics related to land use types in Sylhet Sadar upazila. The experiment was conducted covering four land use types: grassland, forest land, orchard, and tea garden from two soil depths, viz. surface (0\u0026ndash;15 cm) and sub-surface (15\u0026ndash;30 cm) with four sampling sites as replications, during September 2023-March 2024. Results showed higher silt-clay ratio in surface soil, sand dominating across all land uses and depths. Sub-surface soil had the higher organic carbon content (0.84%). Soil pH negatively correlated with organic carbon and total nitrogen but positively with organic carbon and available zinc. Bulk density was the highest in both grassland and forest soils. Furthermore, grassland soils had the higher pH (5.14 in surface; 5.22 in sub-surface, while tea garden soils showed lower (4.35 in surface; 4.42 in sub-surface). Organic carbon content was highest in tea garden soils, while total nitrogen content was highest in both orchard and tea garden soils. Grassland and orchard soils had the higher available phosphorus content, while forest and orchard soils exhibited the higher available boron and zinc content, respectively. Therefore, tea garden and orchard soil in Agro-Ecological Zone 29 had better nutrient status compared to grassland and forest soils influenced by management practices and vegetation cover.\u003c/p\u003e","manuscriptTitle":"Impact of Land Use Types on Soil Physicochemical Properties in the Northern and Eastern Hills of Bangladesh","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-23 09:34:28","doi":"10.21203/rs.3.rs-8506757/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"786d153e-0f95-44b2-846c-72700452b1ef","owner":[],"postedDate":"January 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T11:12:47+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-23 09:34:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8506757","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8506757","identity":"rs-8506757","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}