Author Details

Scroll

Refine your search

Collections

Basic & Applied Science Collection

Co-Authors

Journals

Current Science

Year

Authors

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All

Singh, K. P.

Soft X-Ray Focusing Telescope Aboard Astrosat:Early Results

Abstract Views :218 | PDF Views:79

Authors

K. P. Singh ¹, G. C. Dewangan ², S. Chandra ¹, S. Bhattacharayya ¹, V. Chitnis ¹, G. C. Stewart ³, N. J. Westergaard ⁴

Affiliations
1 Tata Institute of Fundamental Research, Homi Bhabha Road, Mumbai 400 005, IN
2 Inter-University Centre for Astronomy and Astrophysics, Pune 411 007, IN
3 Department of Physics and Astronomy, University of Leicester, Leicester, GB
4 Technical University of Denmark, National Space Institute, DK

Source

Current Science, Vol 113, No 04 (2017), Pagination: 587-590

Abstract

The Soft X-ray focusing Telescope (SXT) is a moderate-resolution X-ray imaging spectrometer supplementing the ultraviolet and hard X-ray payloads for broadband studies of cosmic sources with AstroSat. Well suited for observing bright X-ray sources, SXT observations of nearby active galactic nuclei (AGN), binary star systems with compact companions, active stars, etc. are producing long soft X-ray light curves and high-quality spectra. The strong X-ray variability and multiple spectral components exhibited by SXT observations of nearby Seyfert 1 galaxies show excellent promise to probe accretion disks and central engines in AGN through multi-band variability and spectroscopy.

Keywords

Active Galactic Nuclei, Stars, Supernova Remnants, X-Ray Astronomy.

Full Text

References

Singh, K. P. et al., AstroSat Mission. Proc. SPIE, 9144, Space Telescopes and Instrumentation 2014: UV to Gamma Ray, at Montreal; doi:10.1117/12.2062667 (2014).

Wolter, H., Grazing incidence mirror systems as imaging optics for X-rays. Ann. Phys., 1952, 10, 94; 286.

Singh, K. P. et al., In-orbit performance of SXT aboard AstroSat. Proc. SPIE, 2016, 9905, 99051E.

Singh, K. P. et al., Soft X-ray focusing telescope aboard AstroSat: design, characteristics and performance. J. Astrophys. Astron., 2017, 38, 29.

Decourchelle, A. et al., XMM-Newton observation of the Tycho supernova remnant. A&A, 2001, 365, L218.

Lalitha, S. and Schmitt, J. H. M. M., X-ray activity cycle on the active ultra-fast rotator AB Doradus A? Implication of corrected coronal and photometric variability. A&A, 2013, 559, A119.

Multi-Colour Hues of the Universe Observed with AstroSat

Abstract Views :214 | PDF Views:78

Authors

K. P. Singh ¹, D. Bhattacharya ²

Affiliations
1 Department of Astronomy and Astrophysics, Tata Institute of Fundamental Research, Homi Bhabha Road, Mumbai 400 005, IN
2 Inter-University Centre for Astronomy and Astrophysics, Post Bag 4, Ganeshkhind, Pune 411 007, IN

Source

Current Science, Vol 113, No 04 (2017), Pagination: 602-609

Abstract

India's space astronomy observatory AstroSat was launched on 28 September 2015, carrying instruments to observe cosmic sources over a large spectral band, from optical/UV to hard X-rays. The mission, with all its payloads, has been operating successfully since its launch. After an initial period of performance verification and calibration, the satellite is now in full science operation. This article gives a brief introduction about the capabilities of the mission and presents some of the early science results.

Keywords

Active Galaxies, Cosmic Sources, Multi-Wavelength Astronomy, Multi-Colour Hues, Supernova Remnants.

Full Text

References

Singh, K. P. et al., ASTROSAT mission. In Proceedings of SPIE, Space Telescopes and Instrumentation 2014: UV to Gamma Ray (eds Takahashi, T., den Herder, J.-W. A. and Bautz, M.), 2014, vol. 9144, pp. 91441S-1 to S-15 Montreal; doi: 10.1117/12.2062667.

Bhalerao, V. et al., The cadmium zinc telluride imager on AstroSat. JApA, 2017, 38, 31–40.

Ramadevi, M. C. et al., Early in-orbit performance of scanning sky monitor onboard AstroSat. J. Astrophys. Astr, 2017, 38, 32–34.

Singh, K. P. et al., In-orbit performance of SXT aboard AstroSat. In Proceedings of SPIE, Space Telescopes and Instrumentation 2016: Ultraviolet to Gamma Ray (eds den Herder, J.-W. A., Takahashi, T. and Bautz, M.), 2016, vol. 9905, pp. 99051E-1 to E-10; doi:10.1117/12.2235309.

Singh, K. P. et al., Soft X-ray focusing telescope aboard AstroSat: design, characteristics and performance. J. Astrophys. Astr., 2017, 38, 29.

Subramaniam, A. et al., In-orbit performance of UVIT on ASTROSAT. In Proceedings of SPIE, Space Telescopes and Instrumentation 2016: Ultraviolet to Gamma Ray (eds den Herder, J.-W. A., Takahashi, T. and Bautz, M.), 2016, vol. 9905, pp. 99051F-1 to F-10; doi:10.1117/12.2235271 (arxiv: 1608.01073).

Tandon, S. N. et al., In-orbit performance of UVIT and first results. J. Astophys. Astr., 2017, 38, 28.

Vadawale, S. V. et al., In-orbit performance AstroSat CZTI. In Proceedings of SPIE, Space Telescopes and Instrumentation 2016: Ultraviolet to Gamma Ray (eds den Herder, J.-W. A., Takahashi, T. and Bautz, M.), 2016, vol. 9905, pp. 99051G-1 to G-11.

Yadav, J. S. et al., In Proceedings of SPIE Space Telescopes and Instrumentation 2016a: Ultraviolet to Gamma Ray (eds den Herder, J.-W. A., Takahashi, T. and Bautz, M.), 2016, vol. 9905, pp. 99051D; doi:10.1117/12.2231857.

Subramaniam, A. et al., A hot companion to a blue straggler in NGC 188 as revealed by the ultra-violet imaging telescope (UVIT) on AstroSat. ApJL, 2016, 833, L27–L31.

Tandon, S. N., Ghosh, S. K., Hutchings, J., Stalin, S. and Subramaniam, A., Ultraviolet Imaging Telescope (UVIT) on AstroSat. Curr. Sci., 2017, 113(4), 583–586.

Chattopadhyay, T., Vadawale, S. V., Rao, A. R., Sreekumar, S. and Bhattacharya, D., Prospects of hard X-ray polarimetry with Astrosat-CZTI. Experiment. Astr., 2014, 37, 555–577.

Rao, A. R. et al., AstroSat CZT imager observations of GRB 151006A: timing, spectroscopy, and polarization study. ApJ, 2016, 833, 86–95.

Yadav, J. S. et al., AstroSat/LAXPC reveals the high-energy variability of GRS 1915+105 in the X class. ApJ, 2016, 833, 27–35.

Misra, R. et al., AstroSat/LAXPC observation of Cygnus X-1 in the hard state. ApJ, 2017, 835, 195–200.

Singh, K. P., Dewangan, G. C., Chandra, S., Bhattacharayya, S., Chitnis, V., Stewart, G. C. and Westergaard, N. J., Soft X-ray focusing telescope aboard AstroSat: early results. Curr. Sci., 2017, 113(4), 587–590.

Buhler, R. and Blandford, R., The surprising Crab pulsar and its nebula: a review. Rep. Prog. Phys., 2014, 77, 066901 (pp. 15).

Fabian, A. C., Willingale, R., Pye, J. P., Murray, S. S. and Fabianno, G., The X-ray structure and mass of the Cassiopeia A supernova remnant. MNRAS, 1980, 193, 175–188.

Favata, F. et al., The broad-band X-ray spectrum of the CAS A supernova remnant as seen by the BeppoSAX observatory. A&A, 1997, 324, L49–L52.

Fesen, R. A., Kwitter, K. B. and Downes, R. A., H-alpha images of the Cygnus Loop – a new look at shock-wave dynamics in an old supernova remnant. AJ, 1992, 104, 719–724.

Levenson, N. A., Graham, J. R. and Snowden, S. L., The Cygnus loop: a soft-shelled supernova remnant. ApJ, 1999, 526, 874–880.

Capturing the Cosmic Light:A Handbook of Astrophotography

Yield Prediction in Wheat (Triticum aestivum L.) using Spectral Reflectance Indices

Abstract Views :184 | PDF Views:75

Authors

N. S. Chandel ¹, P. S. Tiwari ¹, K. P. Singh ¹, D. Jat ¹, B. B. Gaikwad ¹, H. Tripathi ¹, K. Golhani ¹

Affiliations
1 ICAR-Central Institute of Agricultural Engineering, Bhopal - 462 038, IN

Source

Current Science, Vol 116, No 2 (2019), Pagination: 272-278

Abstract

Influence of nitrogen on vegetative growth of wheat is significant, and can be monitored and assessed using vegetation indices derived from canopy reflectance at different phenological growth stages. The aim of the present work was to establish a regression model for yield prediction of wheat using spectral reflectance indices (SRIs), normalized difference nitrogen index (NDNI), normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and soil adjusted vegetation index (SAVI) for selected phenological growth stages of wheat. The canopy spectral reflectance was recorded during three winter seasons (2014–2017) for irrigated wheat. A hyperspectral library of canopy reflectance was developed, which enables the study of spectra independent of different nitrogen management practices. It indicated that the precise level of nitrogen for irrigated wheat may be 90 kg ha^-1 in vertisols under agro-climatic of central India. Coefficient of variation (CV) was determined based on significance test between eight levels of nitrogen and SRI values. On the basis of CV, NDVI and NDWI were selected among the four spectral indices for the study of correlation between grain and biomass yields and nitrogen levels for four growth stages, viz. tillering, booting, heading and milking. A regression model was developed to find the best representative stage for yield prediction among the four stages. The regression model indicated that the relations of NDVI with grain and biomass yields were stronger in the heading stage, and it resulted in 96% accurate estimation of grain and biomass yields in irrigated wheat.

Keywords

Nitrogen Management, Spectral Reflectance, Vegetation Indices, Wheat, Yield Estimation.

Full Text

References

Li, F. et al., Estimating N status of winter wheat using a handheld spectrometer in the North China Plain. Field Crops Res., 2008, 106(1), 77-85.

Hansen, P. M. and Schjoerring, J. K., Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least square regression. Remote Sensing Environ., 2002, 86, 542-553.

Clay, D. E., Kim, K. I., Chang, J., Clay, S. A. and Dalsted, K., Characterizing water and nitrogen stress in corn using remote sensing. Agron. J., 2006, 98, 579-587.

Zhang, J. H., Wang, K., Bailey, J. S. and Wang, R. C., Predicting nitrogen status of rice using multispectral data at canopy scale. Pedosphere, 2006, 16, 108-117.

Scharf, P. C., Schmidt, J. P., Kitchen, N. R., Sudduth, K. A., Hong, S. Y., Lory, J. A. and Davis, J. G., Remote sensing for nitrogen management. J. Soil Water Conserv., 2002, 57(6), 518- 524.

Jia, L. L., Buerkert, A., Chen, X. P., Romheld, V. and Zhang, F. S., Low altitude aerial photography for optimum N fertilization of winter wheat on the North China Plain. Field Crops Res., 2004, 89, 389-395.

Osborne, S. L., Schcpcr, J. S., Frdncis, D. D. and Schlcmmcr, M. R., Detection of phosphorous and nitrogen deficiencies in corn using spectral radiance measurement. Agronomy J., 2002, 94, 1215- 1221.

Goel, P. K., Prasher, S. O., Landry, J. A., Patel, R. M., Bonnell, R. B., Viau, A. A. and Millerm J. R., Potential of airborne hyperspectral remote sensing to detect nitrogen deficiency and weed infestation in corn. Comput. Electron. Agric., 2003, 38(2), 99-114.

Lawrence, R. L. and Ripple, W. J., Comparisons among vegetation indices and bandwise regression in a highly disturbed, heterogeneous landscape: Mount St. Helens, Washington. Remote Sensing Environ., 1998, 64(1), 91-102.

Shibayama, M. and Akiyama, T., Estimating grain yield of maturing rice canopies using high spectral resolution reflectance measurements. Remote Sensing Environ., 1991, 36, 45-53.

Laudien, R., Burky, K., Doluschitz, R. and Bareth, G., Establishment of a web-based spectral database for the analysis of hyperspectral data from Rhizoctonia solani-inoculated sugarbeets. Sugar Ind., 2006, 131(3), 164-170.

Pradhan, S., Bandyopadhyaya, K. K. and Joshi, D. K., Canopy reflectance spectra of wheat as related to crop yield, grain protein under different management practices. J. Agrometeorol., 2012, 14(1), 21-25.

Zuzna, M., Frantisek, Z. and Kvet, J., Normalized difference vegetation index (NDVI) in the management of mountain meadows. Boreal Environ. Res., 2008, 13, 147-432.

Huete, A. R., A soil adjusted vegetation index (SAVI). Remote Sensing Environ., 1988, 17, 37-53.

Gontia, N. and Tiwari, K., Yield estimation model and water productivity of wheat crop (Triricum aestivum) in an irrigated command using remote sensing and GIS. J. Indian Soc. Remote Sensing, 2011, 39(1), 27-37.

Gao, B., NDWI a normalized difference water index for remote sensing of vegetation liquid water from Space. Remote Sensing Environ., 1996, 58, 257-266.

Serrano, L., Penuelasa, J. and Ustin, S., Remote sensing of nitrogen and lignin in Mediterranean vegetation from AV1RIS data: decomposing biochemical from structural signals. Remote Sensing Environ., 2002, 81, 355-364.

Baethgen, W. E. and Alley, M. M., Optimizing soil and fertilizer nitrogen use by intensively managed winter wheat. II. Critical levels and optimum rates of nitrogen fertilizer. Agron. J., 1989, 81, 120-125.

Gnyp, M. L. et al., Hyperspectral data analysis of nitrogenfertilization effect on winter wheat using spectrometer in North China Plain. In Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, First Workshop of IEEE, 2009, pp. 1-4.

Prasad, B., Carver, B., Stone, M. L., Babar, M. A., Raun, W. R. and Klatt and A. R., Potential use of spectral reflectance indices as a selection tool for grain yield in winter wheat under great plains conditions. Crop Sci., 2007, 47, 1426-1440.

Lukina, E. V. et al., Effect of row spacing, growth stage, and nitrogen rate on spectral irradiance in winter wheat. J. Plant Nutr., 2000, 23, 103-122.

Weisz, R., Crozier, C. R. and Heiniger, R. W., Optimizing nitrogen application timing in no-till soft red winter wheat. Agron. J., 2001, 93, 435-442.

Arnall, D. B. et al., Relationship between coefficient of variation measured by spectral reflectance and plant density at early growth stages in winter wheat. J. Plant Nutr., 2006, 29, 1983-1997.

Raun, W. R., Johnson, G. V., Stone, M. L., Solie, J. B., Lukina, E. V., Thomason, W. E. and Schepers, J. S., In-season prediction of potential grain yield in winter wheat using canopy reflectance. Agron. J., 2001, 93, 131-138.

Rouse, J. W., Haas, R. H., Schell, J. A., Deering, D. W. and Harlan, J. C., Monitoring the vernal advancements and retrogradation of natural vegetation. In NASA/GSFC Final Report, Greenbelt, MD, USA, 1974, pp. 1-37.

Mechanized urea spraying system for balers to enhance the nutritional quality of straw: a step to prevent straw burning

Abstract Views :124 | PDF Views:63

Authors

Satya Prakash Kumar ¹, Dilip Jat ¹, S. B. N. Rao ², M. Chandrasekharaiah ², K. P. Singh ¹, P. C. Jena ¹

Affiliations
1 ICAR-Central Institute of Agricultural Engineering, Bhopal 462 038, India, IN
2 ICAR-National Institute of Animal Nutrition and Physiology, Bengaluru 560 030, India, IN

Source

Current Science, Vol 123, No 11 (2022), Pagination: 1381-1386

Abstract

A large portion of unused crop residues is burnt in the fields primarily to clear the left-over straw and stubbles after the combine harvest. Studies have reported several ill effects of crop-residue burning on soil organic carbon and fertility, including reduction in productivity in the long term, environmental pollution and human health. It also produces greenhouse gases causing global warming. Rice and wheat produce large amounts of residue in India. Non-availability of labour, the high cost of residue removal from the field and the increasing use of combines in harvesting the crops are the main reasons for burning crop residues in the fields. Rice straw is unsuitable animal feed due to its high silica content and wheat straw for due to its hard stem and difficulty chewing in unchopped form. Using supplements like urea and is feeding straw sprayed with urea improves its nutritive value and intake. We have developed a mechanized baler equipped with a urea spraying system for spraying urea during baling operations on crop residues (paddy or wheat straw) to enhance their nutritional value. The developed system was evaluated in combine a harvested wheat residue field and nutritional analysis was performed. The crude protein in untreated wheat straw increased from 3.68% to 10.10% after urea treatment. The metabolizable energy was also found to improve by 3% compared to untreated straw. Thus, urea-treated bales have potential use in dairy farming

Keywords

Combine harvest, crop residues, mechanized straw baler, nutritional value, urea treatment.

Full Text

References

Anon., Pocket Book of Agricultural Statistics, Directorate of Economics and Statistics, Ministry of Agriculture and Farmers’ Welfare, Government of India, 2019; https://eands.dacnet.nic.in/ PDF/Pocket%20Book%202019.pdf (accessed on 19 May 2020).

Kumar, N. et al., Challenges and opportunities in productivity and sustainability of rice cultivation system: a critical review in Indian perspective. Cereal Res. Commun., 2021, 1–29.

Anon., Fourth advance estimates of production of foodgrains for 2020–21, Press Information Bureau; https://static.pib.gov.in/ WriteReadData/specificdocs/documents/2021/aug/doc202181121.pdf (accessed on 20 September 2021).

Jain, N., Sehgal, V. K., Singh, S. and Kaushik, N., Estimation of surplus crop residue in India for biofuel production. Technology Information, Forecasting and Assessment Council, New Delhi, 2018.

Anon., Development of spatially resolved air pollution emission inventory of India. The Energy and Resources Institute (TERI), New Delhi, 2019, p. 13.

Datta, A., Emmanuel, M. A., Ram, N. K. and Dhingra, S., Crop residue management: solution to achieve better air quality. Dis-cussion Report, TERI, 2020.

Sarkar, S. et al., Management of crop residues for improving input use efficiency and agricultural sustainability. Sustainability, 2020, 12(23), 9808.

Jain, N., Bhatia, A. and Pathak, H., Emission of air pollutants from crop residue burning in India. Aerosol Air Qual. Res., 2014, 14, 422–430.

Hesammi, E., Talebi, A. B. and Hesammi, A., A review on the burning of crop residue on the soil properties. WALIA J., 2014, 30, 192–194.

Bimbraw, A. S., Generation and impact of crop residue and its management. Curr. Agric. Res. J., 2019, 7(3), 304.

Zhang, H. et al., Particle size distribution and polycyclic aromatic hydrocarbons emissions from agricultural crop residue burning. Environ. Sci. Technol., 2011, 45, 5477–5482.

Gupta, H. S. and Dadlani, M., Crop residues management with conservation agriculture: potential, constraints and policy needs. Indian Agricultural Research Institute, New Delhi, 2012.

Prasad, C. S., Joshi, A. L., Singh, M., Singhal, K. K. and Singh, R.B., Methods to increase intake of straws. In Handbook for Straw Feeding System (eds Kiran Singh and Schiere, J. B.), ICAR, New Delhi, 1995, p. 185; https://edepot.wur.nl/333326

Singh, M. et al., Wheat straw, production and utilization as animal feed. In Handbook for Straw Feeding Systems in Livestock Pro-duction, Indian Council of Agricultural Research (ICAR), New Delhi, 1995, pp. 339–352.

Jat, D., Chandel, N. S., Gurjar, B. and Jha, A., Performance of pneumatic loader for loose straw handling on a farmyard. Range Manage. Agrofor., 2020, 41(1), 116–125.

Kumar, S. P., Jat, D., Rao, S. B. N., Chandrasekharaiah, M., Mehta, C. R., Singh, K. P. and Nandeha, N., On-the-go urea spraying sys-tem of baler for enhancing the nutritional quality of paddy straw. Range Manage. Agrofor., 2021, 42(2), 328–333.

Sharma, D. D., Joshi, A. L., Schiere, J. B. and Ibrahim, M. N. M., Treatment of crop residues: a review. In Handbook for Straw Feeding Systems in Livestock Production, ICAR, New Delhi, 1995, pp. 263–270.

Walli, T. K. et al., Urea treatment of straw. In Handbook for Straw Feeding Systems in Livestock Production, ICAR, New Delhi, 1995, pp. 271–290.

Bamaga, O. A., Thakur, T. C. and Verma, M. L., Effect of ammo-nia (urea) treatment of baled wheat straw on its nutritive value. Indian J. Anim. Nutr., 2000, 17, 8–12.

Kumar, U., Thakur, T. C. and Jaiswal, R. S., Ammoniation of baled paddy straw on ammonia (urea) treatment plant. Indian J. Anim.Nutr., 2010, 27(1), 1–9.

Sahay, C. S., Pathak, P. K. and Singh, S. K., Mechanization in fodder production. ICAR-Indian Grassland and Fodder Research Institute, Jhansi, 2016, pp. 1–42.

Das, M. M., Singh, K. K., Misra, A. K., Pathak, P. K., Kumar, A., Pandey, S. and Das, N., Development of on-farm mechanized urea treatment process during threshing and utilization of urea treated wheat straw in lactating Murrah buffaloes (Bubalus bubalis). Range Manage. Agrofor., 2017, 38, 115–120.

Schiere, J. B., Ibrahim, M. N. M., Sewalt, V. J. H. and Zemmelink, G., Response of growing cattle given rice straw to lick blocks con-taining urea and molasses. Anim. Feed Sci. Technol., 1989, 26(3–4), 179–189.

Chandrasekharaiah, M., Reddy, M. R. and Reddy, G. V. N., Effect of feeding urea treated maize stover on growth and nutrient utili-zation by sheep and goats. Small Ruminant Res., 1996, 22, 141–147.

Chandrasekharaiah, M., Reddy, M. R. and Reddy, G. V. N., Opti-mum conditions for urea–ammonia treatment of maize stover. IndianJ. Anim. Sci., 1997, 67(1), 73–75.

AOAC, Method 978.02. In Official Methods of Analysis, Associa-tion of Analytical Chemist, Arlington, VA, USA, 2000.

Van Soest, P. V., Robertson, J. B. and Lewis, B. A., Methods for dietary fiber, neutral detergent fiber, and non-starch polysaccha-rides in relation to animal nutrition. J. Dairy Sci., 1991, 74(10), 3583–3597.

Anon., Rice cattle feed. Indiamart, 2022; https://www.indiamart.com/proddetail/rice-cattle-feed-22175971730.html (accessed on 5 July 2022).

Username
Password
Remember me