ICAR National Bureau of Soil Survey and Land Use Planning

Nāgpur, India

ICAR National Bureau of Soil Survey and Land Use Planning

Nāgpur, India

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Vasu D.,ICAR National Bureau of Soil Survey and land Use Planning | Singh S.K.,ICAR National Bureau of Soil Survey and land Use Planning | Sahu N.,ICAR National Bureau of Soil Survey and land Use Planning | Tiwary P.,ICAR National Bureau of Soil Survey and land Use Planning | And 5 more authors.
Soil and Tillage Research | Year: 2017

Crop productivity under rainfed farming systems in India is low due to poor water and nutrient management. The available small scale information of soil nutrients is inadequate to effectively manage individual farms held by small and marginal farmers. Large scale spatial variability assessment using grid sampling method is a feasible option to identify critical nutrient deficiency zones. The present study was conducted in a part of semi-arid tropical Deccan plateau region, India, to assess the spatial variability of soil pH, organic carbon (OC), soil available nitrogen (N), phosphorus (P), potassium (K) and sulphur (S). A total of 1508 composite samples (0–15 cm) were collected by adopting 325 × 325 m grid interval (one sample for 10 ha area) and they were analysed for soil fertility parameters. Coefficient of variation (CV) indicated that OC, N, P, K and S were high in heterogeneity (CV > 35%). Moreover, pH, P, K and S were non-normally distributed and log transformation produced normalised dataset. The semivariogram parameters (nugget to sill ratio, range and slope) indicated that the spatial distribution of soil properties were inconsistent. The spatial variability of parameters were mapped by ordinary kriging using exponential (pH and OC) and spherical (N, P, K and S) models selected based on root mean square error (RMSE) and r2 values. Multi-nutrient deficiencies were observed in most parts of the study area and N was acutely deficient. Farm level nutrient availability status was derived from spatial variability maps and critical nutrient deficiency zones were identified. Nutrient management recommendations based on soil test results were delivered to farmers for adopting need based variable rate of fertilizer application. The generated maps can serve as an effective tool for farm managers and policy makers in site specific nutrient management. © 2017 Elsevier B.V.


Moharana P.C.,Regional Center | Naitam R.K.,Regional Center | Verma T.P.,Regional Center | Meena R.L.,Regional Center | And 5 more authors.
Archives of Agronomy and Soil Science | Year: 2017

Maintaining soil organic carbon (SOC) in arid ecosystem is important for soil productivity and restoration of deserted sandy soil in western plain of India. There is a need to understand how the cropping systems changes may alter SOC pools including total organic carbon (TOC), particulate organic C (POC), water soluble carbon (WSC), very labile C (VLC), labile C (LC), less labile C (LLC) and non-labile C (NLC) in arid climate. We selected seven major agricultural systems for this study viz., barren, fallow, barley–fallow, mustard–moth bean, chickpea–groundnut, wheat–green gram and wheat–pearl millet. Result revealed that conversion of sandy barren lands to agricultural systems significantly increased available nutrients and SOC pools. Among all studied cropping systems, the highest values of TOC (6.12 g kg−1), POC (1.53 g kg−1) and WSC (0.19 g kg−1) were maintained in pearl millet–wheat system, while the lowest values of carbon pools observed in fallow and barren land. Strong relationships (P < 0.05) were exhibited between VLC and LC with available nutrients. The highest carbon management index (299) indicates that wheat–pearl millet system has greater soil quality for enhancing crop productivity, nutrient availability and carbon sequestration of arid soil. © 2017 Informa UK Limited, trading as Taylor & Francis Group


Reza S.K.,ICAR National Bureau of Soil Survey and Land Use Planning | Nayak D.C.,ICAR National Bureau of Soil Survey and Land Use Planning | Mukhopadhyay S.,ICAR National Bureau of Soil Survey and Land Use Planning | Chattopadhyay T.,ICAR National Bureau of Soil Survey and Land Use Planning | Singh S.K.,ICAR National Bureau of Soil Survey and Land Use Planning
Archives of Agronomy and Soil Science | Year: 2017

Alluvial soils constitute significant portion of cultivated land in India and it contributes towards food grain production predominantly. The objectives of this study were to assess the spatial variability of soil pH, organic carbon (OC), available (mineralizable) nitrogen (N), available phosphorus (P), available potassium (K) and available zinc (Zn) of alluvial floodplain soils of Kadwa block, Katihar district, Bihar, India. A total of 85 soil samples, representative of the plough layer (0–25 cm depth from surface) were randomly collected from the study area. The values of soil pH, OC, N, P, K and Zn varied from4.4 to 8.4, 0.20% to 1.20%, 141 to 474, 2.2 to 68.2, 107 to 903 kg ha–1 and 0.22 to 1.10 mg kg–1, respectively. The coefficient of variation value was highest for available P (94.3%) and lowest for soil pH (11.3%). Spherical model was found to be the best fit for N, P and Zn contents, while exponential model was the best fit for OC, and Gaussian model was the best-fit model for pH and K. The nugget/sill ratio indicates that except pH and available K all other soil properties were moderately spatially dependent (25–57%). Soil properties exhibited different distribution pattern. It was observed that the use of geostatistical method could accurately generate the spatial variability maps of soil nutrients in alluvial soils. © 2017 Informa UK Limited, trading as Taylor & Francis Group


Vasu D.,ICAR National Bureau of Soil Survey and Land Use Planning | Singh S.K.,ICAR National Bureau of Soil Survey and Land Use Planning | Ray S.K.,Regional Center | Duraisami V.P.,Tamil Nadu Agricultural University | And 4 more authors.
Geoderma | Year: 2016

Assessment of soil quality index (SQI) using only the surface soil properties provides an incomplete information as the crop productivity is influenced by both surface and subsurface properties, with the latter being inherently linked to pedogenic processes. Two different SQIs were estimated for soil surface (0–15 cm) and control section (0–100 cm) using soil profile data of six identified soil series in part of semi-arid tropical (SAT) Deccan plateau and correlated with crop yield. Principal component analysis (PCA) and expert opinion (EO) methods were used for selecting minimum soil data set (MDS). Additive and weighted index methods were compared for SQI estimation. SQI obtained showed variation as PCA and EO methods produced different results. In general, weighted index SQIs were better correlated with crop yield than the additive index SQIs for both PCA and EO methods. EO derived weighted index SQI were comparable for both surface and control section except for few cases and consistent in their correlation with the crop yield, indicating its better performance as compared to PCA. Reason is that the PCA is a data dimension reduction technique whereas EO method is primarily conceived by the experts on cause-effect relationship of soil properties (such as hydraulic conductivity, CaCO3 and exchangeable sodium percentage) that are influenced by regressive pedogenic processes in SAT environments. Results showed that consideration of both surface and control section soil properties helps in establishing a good relationship between soil functions and management goal. In addition, it also satisfies the need to integrate both surface and subsurface soil information for soil quality assessment. © 2016 Elsevier B.V.


Reza S.K.,ICAR National Bureau of Soil Survey and Land Use Planning | Nayak D.C.,ICAR National Bureau of Soil Survey and Land Use Planning | Chattopadhyay T.,ICAR National Bureau of Soil Survey and Land Use Planning | Mukhopadhyay S.,ICAR National Bureau of Soil Survey and Land Use Planning | And 2 more authors.
Archives of Agronomy and Soil Science | Year: 2015

Knowledge of spatial variation of soil is important in site-specific farming and environmental modeling. Soil particles size and water distribution are most important soil physical properties that governing nearly all of the other attributes of soils. The objectives of this study were to determine the degree of spatial variability of sand, silt and clay contents, and water content at field capacity (FC), permanent wilting point (PWP), and available water content (AWC) of alluvial floodplain soils. Data were analyzed both statistically and geostatistically to describe the spatial distribution of soil physical properties. Soil physical properties showed large variability with greatest variation was observed in sand content (68%). Exponential and spherical models were fit well for the soil physical properties. The nugget/sill ratio indicates except clay all other soil physical properties were moderate spatially dependent (37–70%). Cross-validation of the kriged map shows that prediction of the soil physical properties using semivariogram parameters is better than assuming mean of observed value for any unsampled location. The spatial distribution of water retention properties closely followed the distribution pattern of sand and clay contents. These maps will help to planner to develop the variable rate of irrigation (VRI) for the study area. © 2015 Taylor & Francis


Reza S.K.,ICAR National Bureau of Soil Survey and Land Use Planning | Baruah U.,ICAR National Bureau of Soil Survey and Land Use Planning | Sarkar D.,ICAR National Bureau of Soil Survey and Land Use Planning | Singh S.K.,ICAR National Bureau of Soil Survey and Land Use Planning
Arabian Journal of Geosciences | Year: 2016

Soil properties like pH, organic carbon (OC), available nitrogen (AN), available phosphorus (AP), and available potassium (AK) vary spatially from a field to a larger region scale and determine the soil fertility. This study addressed the spatial variability of soil properties in Brahmaputra plains, northeastern India using geostatistical method. For this, a total of 767 soil samples from a depth of 0–25 cm at an approximate interval of 1 km were collected over the entire Bongaigaon district of Assam. Data were analyzed both statistically and geostatistically on the basis of semivariogram. Soil properties showed large variability with greatest variation was observed in AP (86 %) where as the smallest variation was in pH (19 %). The semivariogram for all soil properties were best fitted by exponential models and showed a highest (2.7 km) range for OC and lowest (1.2 km) for AP. The nugget/sill ratio indicates a strong dependence for pH (12 %), moderate spatial dependence for available nutrients (53–72 %) and a weak spatial dependence for OC (77 %). Evaluation of spatial maps indicated that except for AN due to high root mean square error (61.8), kriging could successfully interpolate other soil properties. Soil pH highly negatively correlated with OC (−0.330**) and AN (−0.228**) and highly positive correlated with AP (0.334**) and AK (0.164**). A highly significant correlation was also found between OC and AN (0.490**). © 2016, Saudi Society for Geosciences.


Gajare A.S.,ICAR National Bureau of Soil Survey and Land Use Planning | Mandal D.K.,ICAR National Bureau of Soil Survey and Land Use Planning | Prasad J.,ICAR National Bureau of Soil Survey and Land Use Planning
Indian Journal of Agricultural Sciences | Year: 2016

Information on dynamics of soil organic carbon (SOC) in agricultural soils is important for sustained crop productivity, maintenance of soil health and alleviating the climate related stress. Researchers have found that oxidizable soil organic carbon (SOC) fractions are more important in maintaining the soil quality than total organic carbon (TOC). The SOC measured by Walkley and Black method is not sensitive to assess soil quality, but labile fractions of TOC is directly related to the soil productivity and quality. It is therefore, imperative to find out its quantum of SOC and fractions of TOC for better C management and carbon sequestration. In such an endeavour, a case study on carbon dynamics was undertaken for cotton-growing shrink-swell soils of Jalgaon district, Maharashtra, to quantify the SOC and its fractions in TOC and their interrelation with the crop yield. The surface (0-30 cm) soil samples (75) were collected from dominant cotton-growing shrink- swell soils in 2011-12 and analyzed for SOC, very labile carbon (VLC), labile carbon (LC), less labile C (LLC) and non-labile C (NLC). Factorial relationship between SOC with TOC and their relationship with crop yield was worked out. The result indicated that VLC, LC, LLC and NLC contributed to the tune of 15.33%, 11.85%, 51.15% and 21.07% of the TOC, respectively. The dichromate oxidizable SOC (y) was found linearly related to the TOC (x) by the equation, y = 0.782x + 0.025 (R2 = 0.932), indicating that oxidizable SOC comprised 78.2 % of the TOC, in other words, a correction factor of 1.278 (inverse of the slope of linear regression line) may be used to convert SOC values in shrink-swell soils of Jalgaon. The crop yield was closely related to the SOC (r = 0.642) compared to TOC (r = 0.610). Considering the maximum and minimum cotton yield, the threshold value and maximum value of SOC were worked out to be 5.688 and 8.312 g C/kg, respectively, reflecting the carbon sequestrations potential of soils. Among the different fractions, VLC was found to be well correlated (r = 0.512) with the crop productivity. The computed threshold and maximum value for VLC were 0.547 and 2.147 g C/kg, respectively. The study thus establishes that only 27.18 % of active carbon (VLC+LC) are important for the crop production in cotton- growing shrink-swell soils. © 2016, Indian Council of Agricultural Research. All rights reserved.


Sahu N.,ICAR National Bureau of Soil Survey and Land Use Planning | Singh S.K.,ICAR National Bureau of Soil Survey and Land Use Planning | Reddy G.P.O.,ICAR National Bureau of Soil Survey and Land Use Planning | Kumar N.,ICAR National Bureau of Soil Survey and Land Use Planning | And 2 more authors.
Journal of the Indian Society of Remote Sensing | Year: 2016

In the present study, an attempt has been made to describe the technique for large-scale soil mapping using remote sensing data. Based on erosional and depositional processes, seven major landforms namely plateau top, scarp slopes, plateau spurs, pediment, undulating plain, valley and floodplain have been delineated using Cartosat-1 DEM (10 m), contour (10 m) and hillshade. Using two seasons high-resolution IRS-P6 LISS-IV data, six land use/land cover classes namely double crop, single crop, orchard, wasteland with and without scrub and degraded forest have been identified using visual interpretation. A detailed slope map has been generated from Cartosat-1 DEM and reclassified into seven classes. On the basis of landform, slope, land use/land cover and ground truth, 37 Physiography-Landuse Units (PLU) were identified and described. PLU-soil relationship was developed by correlating soil-site characteristics and physical and chemical properties of soils. Six soil series were identified in major landforms and soil map depicting phases of soil series was developed. The study revealed that the combined use of Cartosat-1 DEM (10 m) and high-resolution IRS-P6 LISS-IV data will be of immense help in identifying soil patterns for large-scale soil resource inventory useful for village-level agricultural planning. © 2016 Indian Society of Remote Sensing

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