The of physico-chemical characteristics are detailed and discussed

The physico-chemical characteristics of the soil from different land use patterns are summarized in Table 2 and the relationship between among parameters in different land uses are presented in table 3. The results of correlation studies of physico-chemical characteristics are detailed and discussed below;

 The data indicated that moisture content ranged from 10.52% (industrial area soil samples) to 21.20% (forest area soil samples). From table 2, it was seen that forest soil had higher moisture content as compared to industrial, agricultural, residential and agro residential area. Moisture content showed significant and positive correlation with W.H.C. (0.854), Organic carbon (0.854), organic matter (0.864), Ca (0.809), Mg (0.839), N (0.861) and P (0.820) at 0.01 level, but showed non-significant and negative correlation with pH (-0.224) and potassium (-0.168). Moisture content showed positive and non-significant correlation with electric conductivity (0.415) and sodium (0.453). The W.H.C. increase with increasing level of organic carbon and with increasing % of silt and clay particle in the soil because clay and silt particle have a much higher surface area to hold a greater quantity of water. W.H.C. showed highly significant and positive correlation with organic matter (0.829) at 0.01 level. The greater water holding capacity were found in forest area (51.11%) soil sample as compared to industrial area (40.70%), residential area (41.97%), agro-residential area (42.11%) and agricultural area (46.22%). There was a similar studied by Awotoye et al. (2009) who reported the values of WHC, pH, OM and Ca as 56.86%, 6, 3.13% and 5.13meq/100gm respectively.

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   The data indicated that pH ranged from 6.39 (forest area soil samples) to 7.56 (Agro-residential soil samples) which is similar to the findings of Singh and Ramakrishnan (1981) i.e. 6.1 to 6.5.. From table 2, it was seen that agricultural soil had higher pH as compared to industrial, residential and forest area. Variations in pH among land use systems reflect differences in uptake of exchangeable bases, N- fixation. Production. Joshi and Negi (2015) had studied the physico-chemical properties along the soil profile of two dominant forest types in western Himalaya (Chamoli and Champawat district of Uttarakhand) viz, oak and pine soils. They had recorded the pH of the oak soil (range 4.2-6.2) was comparable to that of pine soil (range 4.3-6.3). Both the soil was found to be slightly acidic. The result of the study indicated that the forest soil had lower pH as compared to the other land use areas. A highly significant and negative correlation (-0.905 at 0.01 level) was observed between pH and electrical conductivity and non-significant and negative correlation with organic carbon (-0.417), organic matter (-0.501), calcium (-0.075), magnesium (-0.250), pH (-0.224) and water holding capacity (-0.433). in other hand, soil pH of surface layers exhibited non-significant and positive correlation with Nitrogen (0.079), phosphorus (0.182) and potassium (0.021). Marcotullio, Braimoh and Onishi (2008) have documented the impact of urbanization on soil. According to them urbanization alters the biological, chemical and physical properties of soil and hence degrading its quality resulting in loss of vegetation, poor water infiltration, accumulation of heavy metal, excess water runoff and  soil erosion. Soil EC measures the dissolved material in an aqueous solution which relates to the ability of the material to conduct electric current through it. The values of EC were observed to be in 0.60-3.41 ds/cm, where EC was not found very high. Hence, suitable fertilizer should be added to the soil for ensuring the maximum crop production. Relatively higher conductivity observed in forest area because of nature environment. Soil surface electrical conductivity showed significant and positive correlation with organic matter (0.725).

The physico-chemical characteristics of the soils as was shown (table 2) under all land use patterns, the soil physico-chemical properties like organic carbon and organic matter and available nutrient concentration were in decreasing order with more accumulation on the top surface soils. The maximum percentage of organic carbon was found in forest areas (2.39%), the organic carbon in other land use types was 0.52% in industrial area, 1.05% in residential areas, 1.00% in agro-residential area and 0.78% in agriculture area of Bhagtanpur village. Organic matter and organic carbon were found to be low in industrial area due to industrial waste contamination. Higher soil organic carbon in the land use system resulted in to high CEC, which further affected the availability of Na, Ca and Mg. organic matter was also found higher in forest area due to litter decomposition i.e. 4.85%. The lowest concentration of organic matter was found in industrial area i.e. 0.87%. The amount of organic matter was almost same in a residential area (1.59%) and agricultural area (1.55%). 1.47% organic matter was found in agro-residential area which low down than the residential and agricultural area. Joshi and Negi had recorded that in pine soil, organic carbon (0.46- 1.64%). Similar results were observed in our study of forest soil. Organic matter showed highly significant and positive correlation with organic carbon (0.957), moisture content (0.864) and water holding capacity (0.829), but showed non-significant and positive correlated with potassium content. The positive correlation between organic carbon and nitrogen could be because of the release of mineralizable nitrogen from soil organic matter in proportionate amounts (Vanilarasu and Balakkrishnamurthy, 2014) and adsorption of NH4-N by humus complexes in soil. The role of organic matter in the buildup of soil nutrients appears crucial in all ecosystems and depends on the high foliage cover and vegetation biomass and higher rate of litter production and subsequent decomposition. Hence, it can be deduced that the nutrient return of vegetation to the soil would depend on the nutrient uptake by the biomass of each plant community (Awotoye et al. 2009).


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