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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.

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.

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).

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...