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Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) is the world’s most extensive social safety programme widely studied for its contribution to achieving poverty alleviation and strengthening the natural base in the country. However, there is a gap in research linking the programme implementation with the national and sub-national development goals, which are crucial to achieving the global goals of sustainable development. This study thus aims to derive district-level insights into MGNREGA implementation, taking Uttarakhand State as an example using spatial analysis tools. Getis-Ord statistic was used to obtain spatial clustering of the works implemented under the programme until 2017. Overlay analysis was synergistically applied on satellite images-based thematic envelopes, viz. land use land cover (LULC) and the terrain, which exerts a latent control on the overall programme implementation besides shaping the local development and planning efforts. The LULC and topography demonstrated a profound impact on the distribution of works in the State. Results obtained show a marked variation in work preference and their spatial clustering in various districts, thus, on the one hand, indicating the demand-driven nature of the programme and, on the other, its alignment with the local developmental needs of the State. The findings might help design fine -tuned plans for the State towards attaining far-reaching social-economic and environmental dividends.

Nations across the world share common responsibility towards achieving Sustainable Development Goals (SDGs). To monitor the progress of individual goals and their global-level comparisons, a set of targets and indicators are developed by the experts. However, systematic methods for assessing spatio-temporal progress towards achieving the SDGs are lacking. This study demonstrates the use of geographically referenced information (GIS) analysis in mapping the SDGs as achieved under the Mahatma Gandhi National Rural Employment Generation Act (MGNREGA) programme in India, taking Uttarakhand state as a case study. Geotagged data of assets representing various work categories permissible under MGNREGA are linked to the targets and indicators of various SDGs. Kernel Density Estimation (KDE) function is used to derive spatially explicit maps. Sub-national-level composite analysis of overall contribution of the MGNREGA to SDGs is carried out district wise for better understanding. Results obtained show significant spatial variation in the distribution of works across the districts, reflecting their varying priorities as MGNREGA is a demand-driven scheme. The future implication of the study is a vastly improved ability to derive latent information based on geographical indicators for targeting interventions and developing informed strategies towards SDGs.

The Banking System Plays a Vital Role in the Modern Banking Sector. In every state, progress depends mainly on the banking system. It is a similar payment system that allows bank clients or alternate money establishments to attempt to make a variety of monetary transactions during the entire period on the website of the commercial banks. The officials, scholars and banks have attracted interest. And Card transfer has assumed importance in the rear of modernization of the world, the effects from market forces and regulatory innovations. There have been created to make electronic payments accessible and incorporated. This is recent developments that have contributed immensely to economic growth of the banking sector. The information technology and e-commerce product is e-Banking. This research sheds light on the effects that affect clients' engagement with e-banking services.

Reflectance spectroscopy can provide an alternate approach to traditional method for estimation of a large scale of major soil parameter. In the present study, an airborne high-resolution hyperspectral data Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) was used to spatially predict topsoil calcium carbonate (CaCO3) and clay content in parts of Karnataka, India. A total of 24 locations were selected over agricultural and waste land, and soil samples were collected, and surface reflectance was measured using an ASD Field Spec Pro Spectroradiometer in the laboratory. Continuum Removal (CR) method was used to normalize the reflectance spectra. Continuum Removal Absorption Depth (CRAD) of bands 2205 and 2340 nm was used to predict clay and CaCO3 content in soils through linear regression. The mean clay and CaCO3 content of 35.6% (95%CI 28.07–43.16%) and 4.16% (95%CI 3.61–4.71%) for surface soils were predicted by continuum removal linear regression (CRLR) method with estimation errors of RMSE = 11.0 and 2.19, R2 = 0.51 and 0.58, respectively. The 95% confidence intervals were used to calculate the uncertainty of the prediction which showed 42.3% and 26.4% for clay and CaCO3 prediction, respectively. The uncertainty assessment shows that CRLR approach is not a very promising tool for quantitative spatial prediction of soil clay; however, it can be used fairly for CaCO3 prediction from airborne hyperspectral data. The higher uncertainties in the clay estimates may be due to the nature and types of various clay minerals present in the studied soils. The regression models developed may or may not be utilized for other regions with similar variability.

Shrimp is the most valued fish traded internationally, the USA, EU, and Japan being the major countries importing shrimp from Asian countries and Ecuador. Import rejections due to quality issues lead to substantial economic loss. Year-on-year change fails to capture the nature of rejection. Unit rejection rate (URR), relative rejection rate (RRR), the trend in shrimp exports, and causes of rejection in the USA, EU, and Japan during 2002–2017 were analysed. India (151,000 t), Ecuador (95,457 t), and Vietnam (35,225 t) are the major exporter to the USA, EU, and Japan (2017). Transitional probability revealed India, China, and Thailand retained major part of their share in the USA and Japan markets. In EU market, India gained entire share of Indonesia and 93% of Bangladesh share and Vietnam retained major portion (97%) of its share. Number of consignments rejected was variable but declined of late. Indian shrimp exports were stable at US and EU markets with index of 6.90% and 7.48% for exports and 11.89% and 12.14% for rejections, respectively. URR of Indian shrimp exports declined and were 0.015, 0.03, and 0.02 for USA, EU, and Japan, with higher RRR for imports from Vietnam at EU and Japan. Box-Jenkins analysis revealed Indian shrimp rejections at the USA was higher than EU and Japan. Microbiological causes dominated the rejections by USA. Chemical was the major cause for rejections at EU and Japan. Results suggest significant improvement in the quality compliance of Indian shrimp exports. The study also used panel data analysis to assess the determinants of shrimp exports to the major importers.

Regional- and national-level studies by researchers across the globe have used exploratory spatial data analysis (ESDA) to reveal interesting facts about the various environmental socioeconomic issues. However, its utility in rural development and planning has not been demonstrated at large. We used hotspot and emerging hotspot analysis tools of ESDA to analyse the spatial and temporal clustering of rural assets created under the world’s largest social safety programme, i.e. MGNREGA, in Prakasam district of Andhra Pradesh, India. Results of hotspot and emerging hotspot analysis show that most of the assets tend to cluster at village level at selected locations and particular periods during the course of the project implementation. It also highlights the complementary nature of the two approaches in intuitively differentiating the geotagged rural assets. We also deployed a three-dimensional approach for visualizing the spatio-temporal associations among the event regions that enable a better understanding of the underlying spatial processes and the reasons behind their creation at a specific location by further corroborating with other spatial inputs and information. We conclude that ESDA tools are highly useful for simultaneous visualization of spatial and temporal clusters. The empirical results presented in the study could be helpful and valuable in enhanced planning and implementation of MGNREGA as well as other similar rural development programmes.

The accurate and complete spatial distribution of soil texture is required for proper land use planning, soil management practices and other interventions linked to environmental protection. In the present study, we spatially predicted sand, silt and clay content in top soil (0–15 cm) in parts of Gadag district in Karnataka, India using satellite-based indices and geostatistical modeling. The study was conducted to evaluate the Landsat 8 Operational Land Imager (OLI) remote sensing data as auxiliary variable along with CartoDEM for spatial prediction of sand, silt and clay content with a total of 57 number of field observations. The bands 2, 3, 4, 5, 6 and 7, Normalized Difference Vegetation Index (NDVI), The Grain Size Index (GSI) and the relationship between band 4 and 3, 4 and 7 and 6 and 7 of Landsat 8 OLI and slope, elevation of CartoDEM were used as covariates for modeling. Among these covariates NDVI and relationship between band 4 and band 7 were not significantly correlated with the sand, silt and clay content. However digital number (DN) of band 2, 3, 4, 5, 6, 7, GSI and relationship between band 4 to band 3 were significantly correlated with the surface soil sand, silt and clay content. The DN of band 2, 3, 4, 5, 6, 7, GSI and relationship between band 4 to band 3 were selected as auxiliary data for the estimation of sand and silt content, whereas the DN of band 5 and 7 explained most of the variability of soil clay. The Regression Kriging (RK) and Multiple Linear Regression (MLR) were employed to predict soil texture and compared for better accuracy of estimation. The greater spatial variability (58.8, 48.2 and 33.2%) of sand, silt and clay content respectively were predicted with lower estimation errors (RMSE = 7.41, 4.68 and 7.64, R2 = 0.95, 0.55 and 0.91 respectively) in RK as compared to MLR method (RMSE = 9.18, 5.65 and 11.13, R2 = 0.90, 0.38 and 0.80 respectively). The average decrease of 31.3, 19.2 and 17.5% in the prediction error was observed in RK over MLR approach in clay, sand and silt prediction respectively. The uncertainty of the prediction was calculated by 95% confidence intervals, which showed 7.51, 43.5 and 49.6% for sand, silt and clay prediction respectively. The current study showed that RK approach can be useful to predict sand, silt and clay content over MLR approach.

Enterocytozoon hepatopenaei (EHP) is endemic in major shrimp farming countries affecting the economic sustainability, production and supply of shrimp in the global market. The study reports development of a stochastic model to estimate the economic loss due to EHP to Indian Penaeus vannamei shrimp farms and identify the associated key risk factors at the farm-level. Information on the cost of production and revenue were collected through a survey of shrimp farms (n = 281) from September to November 2020. The quantitative risk analysis for specific effects of the disease was estimated by Monte Carlo simulation using excel@Risk. The occurrence of the EHP was found to be positively correlated with the stocking density. The observed harvest of lower size shrimp and higher FCR could be attributed to EHP. Biological loss due to EHP was 75.21% and was highly significant. The cost of prevention of EHP was 15.22% and the treatment cost was 4.30%, along with an extraordinary cost of 5.26% with an average loss of ₹ 61,778 (US$ 813) ton−1 at farm-level. Regression sensitivity analysis revealed farm gate price was the strongest stochastic variable and one unit increase in farm gate price of shrimp positively influenced the net return by 0.76 units. The significant factors negatively influencing the net returns were expenditure on feed (0.51), seed (0.19) and labour (0.18). The net return estimated using Monte Carlo simulation in EHP affected farms was ₹ 14,390 (US$ 189.37) and the distribution revealed that nearly half of the farmers could lose their investment due to EHP. The study quantified the economic impact of EHP at the farm-level and identified the regional variation in the risk factors impacting the cost-benefit analysis of Indian shrimp farming.

Coastal brackishwater resources are highly fertile, rich in biodiversity and provide livelihood to traditional fisher families. However, due to natural disturbances and manmade interruptions, its fishery resources are depleted, hence, the livelihoods of dependent fishers are threatened. Aquaculture of fishes in customized cages and pens is a proficient approach for the efficient utilization of this productive natural resource. In this context, a three-tier cage farming system of Asian Seabass fish (Lates calcarifer) comprised of a nursery, pre-grow out and grow-out phases was developed and validated for its techno-economic viability with the participation of fisher folk. The technical indicators viz., fish survival, feed conversion, growth rate, productivity, economic parameters viz., Benefit Cost Ratio, Pay-back period and Internal Rate of Return and the livelihood security pointers have substantially shown that the system is technically feasible, economically viable, socially acceptable and hence, sustainable. Planned interventions in the form of appropriate policy guidelines to undertake community involved cage aquaculture in the coastal brackishwaters, a scheme for periodical flushing of bar mouths to ensure connectivity with the sea, institutional credit and insurance support are outlined to up-scale the adoption of cage farming technology in the brackishwaters across the coastal states of India. It is a win-win approach for the efficient utilization of coastal brackishwaters for augmenting fish production and enhancing the livelihood security of coastal fishers.

Technical efficiency (TE) measures the ability of the farmer to obtain maximum outputs from a given combination of inputs. Stochastic frontier production function analysis (SFA) was employed to assess the technical efficiencies of Pacific white shrimp ( Penaeus ( Litopenaeus ) vannamei ) farming in India. The findings of this comprehensive study shown that the mean technical efficiency of P. vannamei farms in the country was 0.9013, indicating that the farms achieved 90% of the maximum possible output from a given set of inputs. Maximum likelihood estimates of production function indicated that shrimp stocking density, feed quantity and its management, access to technical consultancy, adoption of zero water exchange, cropping intensity and duration of the culture were the major determinants of TE. The ANOVA and student ‘t’ analyses have shown significant differences in the TE of P. vannamei farming across salinity of culture systems (p P. vannamei farming in tune with the scale and systems of operation being adopted. Ensuring disease free seed supply, cost effective feed and provision of electricity to aquaculture farms in the rates applicable to agriculture farms and promoting energy efficient automatic feed dispensers, aerators, generators, and popularising HDPE (high-density polyethylene) lining of ponds bottom would aid in sustaining the efficiencies of production systems and sustainability of P. vannamei farming in India. Statement of relevance Pacific white shrimp ( Penaeus vannamei ) has been farmed extensively in the coastal states of India. It is imperative to optimize usage of critical inputs and technology in P. vannamei shrimp farming to enhance the efficiency and profitability of production systems. Assessing the technical efficiency (TE) of the production systems points out the aspects to be paid attention by the farmers to optimize the inputs usage and enhance the profitability and sustainability of shrimp farming. Therefore, this study is timely and would aid in providing technical and policy inputs for the aquaculture researchers and planners for sustaining the shrimp farming in India.

Reliable mapping of soil organic carbon (SOC) and Soil Inorganic Carbon (SIC) densities and estimates of their pool size are important from global warming perspective to understand the sequestration potential and losses. In this study, first spatially explicit mapping of SOC and SIC at 250 m resolution and an estimate of their pool size in India was undertaken using a large number of remote sensing derived data layers and data mining approach. The SOC and SIC densities up to 100 cm depth or paralithic contact (whichever is shallower) were estimated for 1198 soil samples located across India using a stratified random sampling that integrated land use, soil, topography and agro-ecological regions. Using Random forests (RF) based spatial prediction procedure with climatic, land cover, rock type, soil type, multi-year NDVI, irrigation status as independent input variables, models for predicting carbon density at 250 m spatial resolution were developed. For modelling with RF algorithm, about 898 soil profile observations (75% observations) were used, while the rest of 300 (25% of total observations) were used for validation. It was observed that the data distribution of sample points don't have significant influence on RF model predictions. The relationship between observed and predicted values was characterized by Mean Squared Deviation (MSD) and Root Mean Squared Error (RMSE) parameters. The SOC, SIC and total soil carbon pool size of India has been estimated at 22.72 ± 0.93 Pg,12.83 ± 1.35 Pg and 35.55 ± 1.87 Pg, respectively, which are comparable to previous studies while providing first spatially explicit 250 m map of their distribution. The spatial distribution indicates that majority of the carbon stock resides in the northern part of India. The soil carbon stock of eastern India has contribution from organic carbon, while the western portion has contribution mainly from inorganic carbon.

Economic loss due to Enterocytozoon hepatopenaei (EHP), white spot syndrome virus (WSSV) infections and other diseases to Penaeus vannamei farming in India was estimated through a questionnaire-based survey of shrimp farms in major shrimp farming states of India during 2018–2019. The survey covered an area of 7259 ha of shrimp farming in 23 coastal districts based on multistage stratified sampling (n = 909). Probability of disease occurrence (PDO) was calculated to estimate the economic loss due to diseases. Double log regression was employed to examine the relationship between economic loss with production, stocking density, culture period, average body weight, survival, FCR and incidence of disease. Loss of production (t ha−1crop−1) was highest in infections due to WSSV (2.58 ± 0.32), followed by EHP (1.80 ± 0.24), mixed infections of EHP and WSSV (1.89 ± 0.53), vibriosis (0.97 ± 0.42), running mortality syndrome (1.1 ± 0.39) and other diseases (1.72 ± 0.36). Although WSSV caused the highest loss of production (t ha−1 crop−1), EHP with a 17% probability of occurrence, accounted for a production loss of 0.77 M tons, with a corresponding revenue loss of Rs. 3977 crores (US$ 567.62 M). Although the probability of occurrence of WSSV was estimated (25%) to be higher than that of EHP, the estimated production loss due to WSSV was lower, 0.33 M tons, with a corresponding revenue loss of Rs. 1670 crores (US$ 238.33 M). National loss of revenue due to EHP was higher primarily because, in Andhra Pradesh, primary shrimp farming state had the PDO of 22% for EHP against 8% for WSSV. The total employment loss due to diseases was estimated to be 1.65 M man-days worth US$ 7.07 M. The study revealed that the overall probability of infectious disease occurrence in the country was at 49% leading to an annual loss of 0.21 M ton shrimp worth US$ 1.02 B. Economic loss due to shrimp diseases in Indian shrimp farming warrants prioritized implementation of better management practices (BMP) and biosecurity protocols along with policy interventions to reduce the direct and indirect losses.

The lockdown on account of the Coronavirus disease 2019 (COVID-19) adversely impacted the food production sector including aquaculture, globally. Unfortunately, it coincided with the major shrimp farming season in India which contributes 60% of the national annual shrimp production hence the impact was substantial. An on-line survey was carried out among the stakeholders of the shrimp farming sector to evaluate the prospective impact of COVID-19 related lockdown across the shrimp supply chain. The study estimated an economic loss of 1.50 billion USD to the shrimp aquaculture sector during the current year. It is expected that shrimp production and its export performance may be declining by 40% in the current season. The Garret ranking and Rank Based Quotient analyses projected severe constraints in shrimp seed production and supply, disruptions in the supply chain, logistics, farming, processing, marketing and loss of employment and income for the workers due to the pandemic. To mitigate the impact, the Government of India declared fisheries and aquaculture as an essential activity, facilitated the movement of inputs and services. Further, a major Fisheries Development Scheme(PMMSY) with a financial outlay of 267 million USD has been announced to usher in a blue revolution by strengthening the value chain, doubling the fisher/farmer income, employment generation, economic and social security for fishers/fish farmers adhering to the sustainability principles. Short and medium-term technical and policy measures are suggested to tide over the impact of COVID-19 related lockdown and related restrictions., Highlights • Coronavirus disease 2019 (COVID-19) related lockdown has adversely impacted the food production sector including aquaculture across the nations. • An assessment conducted to evaluate the prospective impact of COVID-19 lockdown in the value chain show that Indian shrimp aquaculture sector may incur an economic loss of 1.5 billion USD during the current year (2020-2021). • It was estimated that shrimp production may decline by 40% in the current season. Further, negative impacts in shrimp seed production and supply, loss of employment, disruptions in the supply chain, restrictions in transport for movement of materials, problems in processing and marketing affected the sector badly. • The Government of Andhra Pradesh the state produces 70% Indian shrimp production has announced a minimum procurement price and maximum sale price for the farmed shrimp and shrimp seed respectively which are to be implemented strongly. • Technology advisories including customised strategies in farming, promotion of indigenous Indian white shrimp species, cargo shipment of SPF shrimp brood stocks and promotion of domestic marketing were proposed to tide over the impacts of COVID-19 related lockdown and other restrictions.

The present study aims at analysis of spatial and temporal variability in agricultural land cover during 2005–6 and 2011–12 from an ongoing program of annual land use mapping using multidate Advanced Wide Field Sensor (AWiFS) data aboard Resourcesat-1 and 2. About 640–690 multi-temporal AWiFS quadrant data products per year (depending on cloud cover) were co-registered and radiometrically normalized to prepare state (administrative unit) mosaics. An 18-fold classification was adopted in this project. Rule-based techniques along with maximum-likelihood algorithm were employed to deriving land cover information as well as changes within agricultural land cover classes. The agricultural land cover classes include – kharif (June–October), rabi (November–April), zaid (April–June), area sown more than once, fallow lands and plantation crops. Mean kappa accuracy of these estimates varied from 0.87 to 0.96 for various classes. Standard error of estimate has been computed for each class annually and the area estimates were corrected using standard error of estimate. The corrected estimates range between 99 and 116 Mha for kharif and 77–91 Mha for rabi. The kharif, rabi and net sown area were aggregated at 10 km × 10 km grid on annual basis for entire India and CV was computed at each grid cell using temporal spatially-aggregated area as input. This spatial variability of agricultural land cover classes was analyzed across meteorological zones, irrigated command areas and administrative boundaries. The results indicate that out of various states/meteorological zones, Punjab was consistently cropped during kharif as well as rabi seasons. Out of all irrigated commands, Tawa irrigated command was consistently cropped during rabi season.

The relationship between soil salinity parameters and their influence on soil spectral characteristics were analyzed using both satellite data (Hyperion) and reflectance data of soil samples collected from parts of Ahmedabad district of Gujarat, India. The soil spectral reflectance curves were assessed using absorption feature parameters by DISPEC software to identify suitable spectral band for salinity characterization. The Hyperion data of the study area were processed and classified into different classes by spectral angle mapper algorithm using spectral library generated from soil spectra. The results showed that among all the observed soil parameters Electrical Conductivity, Exchangeable Sodium Percentage, Cation Exchange Capacity and Mg++ predictions can be made accurately based on partial least square regression models developed from selected wavelengths. Out of the total study area moderately saline-sodic, severely saline-sodic, severely saline and slightly saline soils occupy 23.5, 12.6, 10.9 and...

Vibriosis is one of the important diseases causing economic loss to the shrimp industry worldwide. The present study reports field observations on the immune stimulatory effect of vibrio bacterin in commercial tiger shrimp (Penaeus monodon) grow-out culture ponds (n = 62) which were grouped under three stocking densities; low (6-8 nos per m2 ), medium (9-11 nos per m2 ) and high (12-14 nos per m2 ). The bacterin was administered in feed as a top dressing at final concentration equivalent to 2 × 108 CFU per kilogram feed twice a week throughout the culture period. In 20 representative ponds, total haemocyte count and prophenoloxidase activity in shrimp were significantly (P

Regional estimates of soil carbon pool have been made using various approaches that combine soil maps with sample databases. The point soil organic carbon (SOC) densities are spatialized employing approaches like regression, spatial interpolation, polygon based summation, etc. The present work investigates a data mining based spatial imputation for spatial assessment of soil organic carbon density. The study area covers Andhra Pradesh and Karnataka states of India. Field sampling was done using stratified random sampling method with land cover/use, soil type, agro-ecological regions for defining strata. The spatial data at 1 km resolution on climate, NDVI, land cover, soil type, topography was used as input for modeling the top 30 cm Soil Organic Carbon (SOC) density. To model the SOC density, a Random Forest (RF) based model with optimal parameters and input variables has been adopted. Experiment results indicate that 500 number of trees with 5 variables at each split could explain the maximum variability of soil organic carbon density of the study area. Out of various input variables used to model SOC density, land use / cover was found to be the most significant factor that influences SOC density with a distinct importance score of 34.7 followed by NDVI with a score of 12.9. The predicted mean SOC densities range between 2.22 and 13.2 Kg m−2 and the estimated pool size of SOC in top 30 cm depth is 923 Tg for Andhra Pradesh and 1,029 Tg for Karnataka. The predicted SOC densities using this model were in good agreement with the measured observations (R = 0.86).

The key objective of this study was to assess the suitability of soil and source water quality of abandoned shrimp farms in selected coastal states of India, for resuming aquaculture activities. Soil samples along with associated source water were collected from 60 disused shrimp ponds in different districts of Andhra Pradesh, Tamil Nadu, Odisha and Kerala and analysed for basic physico-chemical parameters. Results of the analysis indicated that the reason for disuse might be reduction in price/repeated occurrences of diseases and that the sampled farms are suitable for aquaculture use. Most of the soil in Andhra Pradesh and Tamil Nadu are suitable for aquaculture without any remediation. Few ponds in Jagatsingpur and Puri districts of Odisha and Kerala needs simple remediation measures like application of lime to increase the soil pH. Source water in Tamil Nadu, Andhra Pradesh and Odisha (Harishpur Creek, Budhabalanga Creek and the coastal canal) fall under high saline areas and are suitable for culture of tiger shrimp Penaeus monodon , white shrimp Litopenaeus vannamei and finfishes like Asian seabass Lates calcarifer and mullets whereas, Kerala and northern parts of Chilka Lake of Odisha fall under low saline areas and are suitable for culture of white shrimp L. vannamei and pearlspot Etroplus suratensis .

A study was carried out in village Mohammadabad, Nalgonda district, Andhra Pradesh to demonstrate the potential of high-resolution remote sensing data in generating soil resource database at village level that would facilitate natural resource developmental planning. For this study, detail soil resource database was generated by interpreting IRS Resourcesat-1 LISS-IV + Cartosat-1 merged data and by undertaking soil survey and soil analysis. This database was integrated with cadastral map and was analysed for preparation of an action plan for the village that included recommendation on suitable crops, soil and water conservation measures and distribution of soil health card to farmers. The study indicates that the LISS-IV + Cartosat-1 merged satellite data helps in mapping soils at phase level and in delineation of more number of pure soil mapping units i.e. consociations at 1:10.000 scale. The plot-wise resource potential and constraints reveals that significant inter and intra-field soil variability exists. Soil fertility assessment indicates that the soils of the village are low to medium in fertility (available NPK Content) and micronutrients like copper, manganese and iron content is above the critical level for optimum growth of crops while zinc content is less than the critical level.

Lateritic soils of Mathamangalam, Kannur District, located in midlands of Kerala, were morphologically studied, characterized, classified and mapped at 1:50,000 scale using remote sensing techniques. The terrain of the study area being hilly and covered with perennial vegetation, soil-landscape model was applied. For this purpose physiographic information was inferred from SRTM DEM, Resourcesat-1 LISS-III satellite image and topographical maps. The interpreted units were validated in the field and characterized through soil-site examination, soil profile study and soil analysis. The study indicated that the lateritic soils of midlands of Kerala vary in physical, chemical and morphological properties in relation to micro-relief. Soils developed on moderately steeply sloping side slopes (15–30% slope) are deep, moderately well drained with gravelly clay textured, where as the soils developed on moderately slopping side slope (10–15% slope) are very deep and well drained. The soils of valleys are very deep, moderately well drained with fine texture. Very gently sloping (1–3%) laterite plateau tops have extremely shallow soils associated with rock outcrops. These soils mainly belong to Order Ultisols followed by Inceptisols and Entisols. These were further grouped up to Family and Series level by tentatively establishing seven soil series. This study helps in understanding the behaviour of lateritic soils of midlands of Kerala, which can be useful in generation of interpretative maps and in optimizing the land use.

Land use information is one of the most sought inputs for various resource and environmental management studies as well as climate models. In this study, an attempt was made to obtain land cover information from temporal data set of Advance Wide Field Sensor aboard Indian Remote Sensing satellite IRS-P6 using data mining classification technique. This study mainly focuses on the utility of visually interpreted thematic maps as an additional input for improving classification accuracies. The temporal data sets were co-registered to sub-pixel accuracy and were atmospherically corrected using modified dark pixel subtraction method. The visual thematic maps (wastelands and forest cover maps) were also co-registered to satellite data to a near pixel accuracy. Digital values were extracted for various classes and rule sets were generated using See-5 data mining software. These rule sets were ported into ERDAS Imagine Knowledge Engineer and the temporal data sets were classified. The results indicate that tempor...

We predicted total carbon (TC) stocks in the 0–30 cm soil depth of two southern states in India using field observations, environmental covariates and geospatial approaches. We compared the Geographically Weighted Regression Kriging (GWRK) with Linear Regression Kriging (LRK) approach to predict the TC stocks. Greater spatial heterogeneity in TC stocks (2–15 kg m−2) were predicted with lower estimation errors (RMSE = 2 kg m−2 R2 = 0.63) in GWRK in comparison to the LRK approach (RMSE = 3 kg m−2, R2 = 0.55). The average decrease in the prediction error was 39% in GWRK in comparison to the LRK approach. The total TC stock in the 0–30 cm depth of the study area was estimated at 1.5 Pg C with upper and lower prediction intervals of 1 and 2 Pg C, respectively. The cropland stored largest (65%, 1 Pg C) amount of TC stocks followed by forest (21%, 0.31 Pg C) and plantation (8%, 0.12 Pg C) land cover types. Among soil types the Alfisols stored largest (33%, 0.49 Pg C) amount of TC stocks followed by Inceptisols (23%; 0.35 Pg C) and Entisols (18%, 0.27 Pg C). The uncertainty in TC stock predictions ranged from 41 to 75% and 69 to 91% under various land covers and soil types, respectively. Highest uncertainties in predicted TC stocks were associated with the forest land cover type and Mollisols soil order. Similarly, lowest uncertainties were found in the built up areas and Aridisols soil order. Our results suggest that GWRK is a useful approach to spatially predict the TC stocks at regional scales.