Analogy of Soil Parameters in Particle Size Analysis through Laser Diffraction Techniques

ABSTRACT A study was undertaken to optimize the parameters for particle size analysis through laser diffraction techniques. Fifty soil samples with varying soil texture, organic matter, sesquioxide content and calcareousness were collected and analyzed for soil texture by conventional (International Pipette Method-IPm) and Instrumental (Particle Size Analyser-PSA) methods. The study reveals that PSA is more accurate and preferable compared to IPm in determining the soil particle sizes. The clay content of the different samples estimated by International Pipette method and by Particle size analyzer varied from 0.9 to 48.4% and 0.35 to 41.2 %, respectively. PSA showed a good agreement (72% samples) for silt size fractions, and a slight shift in the upper limit of clay from conventional size of 2 µm could help in analysis of soil texture by PSA

Assessment of soil organic and inorganic carbon stock at different soil depths after conversion of desert into arable land in the hot arid regions of India

Soil organic carbon (SOC) and inorganic carbon (SIC) are important carbon reservoirs in terrestrial ecosystems. But, little attention was paid to carbon dynamics in hot arid regions of India. In order to assess the carbon stock after conversion of desert into irrigated arable land in arid regions, the variability of SOC and SIC concentrations in the Suratgarh block of Rajasthan, India were analyzed using geostatistics. Soil samples were collected from depths of 0–15 cm, 15–30 cm, 30–60 cm and 60–90 cm at 150 sampling sites in the study area over an area of 3000 km2. The coefficient of variation (CV) for SOC and SIC was high for all depths (> 35%). Geostatistical analysis showed that spherical, circular, Gaussian and exponential models were the best-fit models for soil carbon stocks. The average stock of SOC and SIC were 4.55 and 10.9 Mg ha−1 in the 0–15 cm soil layer, and 3.02 Mg ha−1 SOC and 12.42 Mg ha−1 SIC in the 15–30 cm soil layer, respectively. Our results showed that SOC and SIC stocks over 0–90 cm were 15.54 and 76.71 Mg ha−1, respectively. There was significantly positive correlation (r = 0.33, p < 0.01) between SOC and SIC stock in 0–90 cm depth. Our study suggested that increasing SOC might lead to an increase in SIC stocks after conversion of desert into irrigated arable land. Thus the study highlights the importance of SIC in the carbon cycle of India’s arid region

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Not AvailableAn attempt was made to identify priority zones of available micronutrients in the soils of agro-ecological subregions (AESR) of north-eastern states of India (Assam, Nagaland, Sikkim and Tripura) using geo-spatial techniques. Surface soil samples (0–25 cm) were collected from Assam (AESRs 15.2, 15.3, 15.4 and 17.1), Nagaland (AESR 17.1), Sikkim (AESR 16.2) and Tripura (AESR 17.2) and analysed for pH, organic carbon and DTPA-extractable micronutrients (Fe, Mn, Zn and Cu) by standard procedures. Regular Spline was employed as spatial interpolation techniques for obtaining spatial distribution of available micronutrients in soils. The AESR map was overlaid on spatial distribution layers to obtain spatial variability of micronutrients in the AESRs of north-eastern regions of India. Zinc deficiency was common in all the AESR. Maximum deficient area of Zn, Mn and Cu was observed in AESR 15.4, and it was regarded as the high-priority zone, whereas AESR 16.2 and AESR 17.2 were considered as low-priority zone. Rainfall, pH and organic carbon appeared to be the key factors in controlling micronutrient availability in soils of north-eastern regions of India.Not Availabl

Priority Zoning of Available Micronutrients in the Soils of Agro-ecological Sub-regions of North-East India Using Geo-spatial Techniques

Not AvailableAn attempt was made to identify priority zones of available micronutrients in the soils of agro-ecological subregions (AESR) of north-eastern states of India (Assam, Nagaland, Sikkim and Tripura) using geo-spatial techniques. Surface soil samples (0–25 cm) were collected from Assam (AESRs 15.2, 15.3, 15.4 and 17.1), Nagaland (AESR 17.1), Sikkim (AESR 16.2) and Tripura (AESR 17.2) and analysed for pH, organic carbon and DTPA-extractable micronutrients (Fe, Mn, Zn and Cu) by standard procedures. Regular Spline was employed as spatial interpolation techniques for obtaining spatial distribution of available micronutrients in soils. The AESR map was overlaid on spatial distribution layers to obtain spatial variability of micronutrients in the AESRs of north-eastern regions of India. Zinc deficiency was common in all the AESR. Maximum deficient area of Zn, Mn and Cu was observed in AESR 15.4, and it was regarded as the high-priority zone, whereas AESR 16.2 and AESR 17.2 were considered as low-priority zone. Rainfall, pH and organic carbon appeared to be the key factors in controlling micronutrient availability in soils of north-eastern regions of India.Not Availabl

Impacts of Land Use on Pools and Indices of Soil Organic Carbon and Nitrogen in the Ghaggar Flood Plains of Arid India

Changes in land use have several impacts on soil organic carbon (C) and nitrogen (N) cycling, both of which are important for soil stability and fertility. Initially, the study area was barren uncultivated desert land. During the late 1960s, the introduction of a canal in the arid region converted the barren deserts into cultivated land. The objectives of the present study were to evaluate the effects of various land use systems on temporal changes in soil organic C and N pools, and to evaluate the usefulness of different C and N management indices for suitable and sustainable land use systems under arid conditions. We quantified soil organic C and N pools in five different land uses of the Ghaggar flood plains, in hot, arid Rajasthan, India. The study focused on five land use systems: uncultivated, agroforestry, citrus orchard, rice&ndash;wheat, and forage crop. These land use systems are &ge;20 years old. Our results showed that total organic carbon (TOC) was highest (7.20 g kg&minus;1) in the forage crop and lowest in uncultivated land (3.10 g kg&minus;1), and it decreased with depth. Across different land uses, the very labile carbon (VLC) fraction varied from 36.11 to 42.74% of TOC. In comparison to the uncultivated system, forage cropping, rice&ndash;wheat, citrus orchard, and agroforestry systems increased active carbon by 103%, 68.3%, 42.5%, and 30.6%, respectively. Changes in management and land use are more likely to affect the VLC. In soil under the forage crop, there was a considerable improvement in total N, labile N, and mineral N. Lability index of C (LIC), carbon management index (CMI), and TOC/clay indices were more sensitive to distinguishing land uses. The highest value of CMI was observed in the forage crop system followed by rice&ndash;wheat and agroforestry. In the long term, adoption of the forage crop increased soil quality in the hot, arid desert environment by enhancing CMI and VLC, which are the useful parameters for assessing the capacity of land use systems to promote soil quality

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Not AvailableSoil contamination due to heavy metals has become a great concern nowadays. The main reasons for soil contamination are both natural as well as anthropogenic. Natural processes like volcanic eruption, weathering of rocks, landslides and soil erosion while anthropogenic involves several activities like smelting, mining, application of agrochemicals (pesticides, herbicides and fertilizers) and industrial wastes. Heavy metals pollution has a direct influence on the fertility of agricultural soils. The removal of heavy metals from soil is very difficult as it stored in the environment for a long time, because of its persistent nature. Several in-situ bioremediation technologies are used for removal of heavy metals from the environment. Out of that in-situ biochar application is one of the prominent technologies for remediation of heavy metals and it was found to be effective in reducing the mobility of heavy metals in soils. Biochar effectively adsorbs heavy metals and decreases bioavailability and toxin-induced stress to the biotic component of soil. In this chapter, the emphasis has been given on heavy metal pollution and types of biochar used for remediation of heavy metals from the soil and waterNot Availabl

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Not AvailableThe evaluation of soil quality is essential in monitoring the long term effects of rice cultivation. Present study investigated the effects of long term rice cultivation on soil properties and organic C pools and identified indicators for monitoring soil quality in Ghaggar-flood plains of hot arid India. Soil samples were collected from fields with 0, 10, 20, 30 and 40 years of rice cultivation. The study revealed that electrical conductivity (EC) and exchangeable sodium percentage (ESP) increased after 30–40 years of rice cultivation. Available nutrients increased with increas- ing years of rice cultivation. The organic carbon pools namely, total organic carbon (TOC), Walkley Black carbon (WBC) and particulate organic carbon (POC) were increased above 50% in 20 and above years of rice cultivation. The TOC and POC were increased by 40.6 to 132.4% and 31.7% to 104.8% in 10 to 40 years of rice cultivation. Cation exchange capacity, WBC, ESP and CaCO3 could serve as soil monitoring indicators of long term rice cultivation in arid region. The findings clearly indicated that long term rice cultivation could aggravate soil salinity and have negative impact on soil quality in arid environment.Not Availabl

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Not AvailableDefining nutrient management zones (MZs) is crucial for the implementation of sitespecific management. The determination of MZs is based on several factors, including crop, soil, climate, and terrain characteristics. This study aims to delineate MZs by means of geostatistical and fuzzy clustering algorithms considering remotely sensed and laboratory data and, subsequently, to compare the zone maps in the north-eastern Himalayan region of India. For this study, 896 grid-wise representative soil samples (0–25 cm depth) were collected from the study area (1615 km2). The soils were analysed for soil reaction (pH), soil organic carbon and available macro (N, P and K) and micronutrients (Fe, Mn, Zn and Cu). The predicted soil maps were developed using regression kriging, where 28 digital elevation model-derived terrain attributes and two vegetation derivatives were used as environmental covariates. The coefficient of determination (R2) and root mean square error were used to evaluate the model’s performance. The predicted soil parameters were accurate, and regression kriging identified the highest variability for the majority of the soil variables. Further, to define the management zones, the geographically weighted principal component analysis and possibilistic fuzzy c-means clustering method were employed, based on which the optimum clusters were identified by employing fuzzy performance index and normalized classification entropy. The management zones were constructed considering the total pixel points of 30 m spatial resolution (17, 86,985 data points). The area was divided into four distinct zones, which could be differently managed. MZ 1 covers the maximum (43.3%), followed by MZ 2 (29.4%), MZ 3 (27.0%) and MZ 4 (0.3%). The MZs map thus would not only serve as a guide for judicious location-specific nutrient management, but would also help the policymakers to bring sustainable changes in the north-eastern Himalayan region of India.Not Availabl

Geospatial Modelling for Delineation of Crop Management Zones Using Local Terrain Attributes and Soil Properties

Defining nutrient management zones (MZs) is crucial for the implementation of site-specific management. The determination of MZs is based on several factors, including crop, soil, climate, and terrain characteristics. This study aims to delineate MZs by means of geostatistical and fuzzy clustering algorithms considering remotely sensed and laboratory data and, subsequently, to compare the zone maps in the north-eastern Himalayan region of India. For this study, 896 grid-wise representative soil samples (0–25 cm depth) were collected from the study area (1615 km2). The soils were analysed for soil reaction (pH), soil organic carbon and available macro (N, P and K) and micronutrients (Fe, Mn, Zn and Cu). The predicted soil maps were developed using regression kriging, where 28 digital elevation model-derived terrain attributes and two vegetation derivatives were used as environmental covariates. The coefficient of determination (R2) and root mean square error were used to evaluate the model’s performance. The predicted soil parameters were accurate, and regression kriging identified the highest variability for the majority of the soil variables. Further, to define the management zones, the geographically weighted principal component analysis and possibilistic fuzzy c-means clustering method were employed, based on which the optimum clusters were identified by employing fuzzy performance index and normalized classification entropy. The management zones were constructed considering the total pixel points of 30 m spatial resolution (17, 86,985 data points). The area was divided into four distinct zones, which could be differently managed. MZ 1 covers the maximum (43.3%), followed by MZ 2 (29.4%), MZ 3 (27.0%) and MZ 4 (0.3%). The MZs map thus would not only serve as a guide for judicious location-specific nutrient management, but would also help the policymakers to bring sustainable changes in the north-eastern Himalayan region of India