Refine your search
Co-Authors
- M. Srinivasa Rao
- C. A. Rama Rao
- B. M. K. Raju
- A. V. M. Subba Rao
- K. V. Rao
- V. U. M. Rao
- Kausalya Ramachandran
- B. Venkateswarlu
- A. K. Sikka
- M. Maheswari
- Ch. Srinivasa Rao
- B. Ramya Sri
- Srinivasa R. Nandam
- D. R. K. Sastry
- Y. K. V. Rao
- T . Ganesh
- N. Sateesh
- A. Bharati
- C. Annapurna
- A. V. Raman
- Srinivasarao
- Josily Samuel
- M. Osman
- N. Ravi Kumar
- R. Nagarjuna Kumar
- V. V. Sumanth Kumar
- K. A. Gopinath
- N. Swapna
Journals
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
Srinivasa Rao, M.
- Some Random Thoughts on Wild Life - Game Preservation
Abstract Views :180 |
PDF Views:0
Authors
Source
Indian Forester, Vol 82, No 6 (1956), Pagination: 320-321Abstract
No abstract- A District Level Assessment of Vulnerability of Indian Agriculture to Climate Change
Abstract Views :278 |
PDF Views:97
Authors
C. A. Rama Rao
1,
B. M. K. Raju
1,
A. V. M. Subba Rao
1,
K. V. Rao
1,
V. U. M. Rao
1,
Kausalya Ramachandran
1,
B. Venkateswarlu
2,
A. K. Sikka
3,
M. Srinivasa Rao
1,
M. Maheswari
1,
Ch. Srinivasa Rao
1
Affiliations
1 ICAR-Central Research Institute for Dryland Agriculture, Hyderabad 500 059, IN
2 Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani 431 462, IN
3 Natural Resource Management Division, ICAR, New Delhi 110 012, IN
1 ICAR-Central Research Institute for Dryland Agriculture, Hyderabad 500 059, IN
2 Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani 431 462, IN
3 Natural Resource Management Division, ICAR, New Delhi 110 012, IN
Source
Current Science, Vol 110, No 10 (2016), Pagination: 1939-1946Abstract
Assessing vulnerability to climate change and variability is an important first step in evolving appropriate adaptation strategies to changing climate. Such an analysis also helps in targeting adaptation investments, specific to more vulnerable regions. Adopting the definition of vulnerability given by IPCC, vulnerability was assessed for 572 rural districts of India. Thirty eight indicators reflecting sensitivity, adaptive capacity and exposure were chosen to construct the composite vulnerability index. Climate projections of the PRECIS model for A1B scenario for the period 2021-2050 were considered to capture the future climate. The data on these indicators were normalized based on the nature of relationship. They were then combined into three indices for sensitivity, exposure and adaptive capacity, which were then averaged with weights given by experts, to obtain the relative vulnerability index. Based on the index, all the districts were divided into five categories with equal number of districts. One more district was added to 'very high' and 'high' categories. The analysis showed that districts with higher levels of vulnerability are located in the western and peninsular India. It is also observed that the highly fertile Indo-Gangetic Plains are relatively more sensitive, but less vulnerable because of higher adaptive capacity and lower exposure.Keywords
Agriculture, Adaptive Capacity and Exposure, Climate Change, Sensitivity, Vulnerability.- Evaluation of Performance of CNC Turning Centres Alternative Method
Abstract Views :158 |
PDF Views:0
Authors
Affiliations
1 Department of Mechanical Engineering, Sri Indu College of Engineering and Technology, Ibrahimpatnam, Hyderabad, IN
2 Mechanical Engineering Group, DMRL, DRDO, Kanchanbagh, Hyderabad, IN
1 Department of Mechanical Engineering, Sri Indu College of Engineering and Technology, Ibrahimpatnam, Hyderabad, IN
2 Mechanical Engineering Group, DMRL, DRDO, Kanchanbagh, Hyderabad, IN
Source
International Journal of Engineering Research, Vol 5, No SP 2 (2016), Pagination: 395-397Abstract
CNC Turning Centers are extensively used in aerospace, defence and automobile industries for manufacture of critical and complicated components accurately. The final quality of machined part depended on performance of machine tool in addition to cutting parameters, cutting tool and work material. Therefore periodic evaluation of performance of machine tool is essential for manufacture of precision components. But the standard methods involve highly sophisticated and costly instrumentation. Therefore, A simplified alternative method has been identified based on quality of the machined component. In this study, three similar CNC Turning centers of identical model but varied year of makes from same machine tool manufacturer were selected. These machines are being used at DMRL for various individual applications since from their installations. Two types of work piece materials namely soft material (Aluminium) and hard material (Stainless Steel) were selected for machining operation by the above CNC machine tools for performance evaluation. Subsequently, Machining experiments were conducted on the both cylindrical components of the above materialswith appropriate cutting parameters and cutting tools under similar working conditions. The parameters roundness and surface roughness of the machined components were inspected by high precision measuring instruments. Theinspection data is analysed for evaluation of performance of the machine tool. The result gave significant help for selection of appropriate machine tool for achieving accuracy of the component.Keywords
CNC Turning Centers, Surface Roughness and Roundness.- On Some Collections of Echinodermata from Andhra Pradesh and Orissa Coasts of India
Abstract Views :234 |
PDF Views:152
Authors
D. R. K. Sastry
1,
Y. K. V. Rao
1,
T . Ganesh
1,
M. Srinivasa Rao
1,
N. Sateesh
1,
A. Bharati
1,
C. Annapurna
1,
A. V. Raman
1
Affiliations
1 Marine Biology Laboratory, Andhra University, Visakhapatnam-530 003, IN
1 Marine Biology Laboratory, Andhra University, Visakhapatnam-530 003, IN
Source
Records of the Zoological Survey of India - A Journal of Indian Zoology, Vol 112, No 3 (2012), Pagination: 61-87Abstract
Accounts of faunal resources of different coastal segments are important to trace the availability and to assess the extent of distribution as well as the similarity among the fauna and the habitats. Knowing the echinoderm resources is additionally important because of their connection with the health of the environment.- Assessing Unrealized Yield Potential of Maize Producing Districts in India
Abstract Views :290 |
PDF Views:83
Authors
B. M. K. Raju
1,
C. A. Rama Rao
1,
K. V. Rao
1,
Srinivasarao
1,
Josily Samuel
1,
A. V. M. Subba Rao
1,
M. Osman
1,
M. Srinivasa Rao
1,
N. Ravi Kumar
1,
R. Nagarjuna Kumar
1,
V. V. Sumanth Kumar
2,
K. A. Gopinath
1,
N. Swapna
1
Affiliations
1 ICAR-Central Research Institute for Dryland Agriculture, Santoshnagar, Hyderabad 500 059, IN
2 International Crops Research Institute for the Semi-Arid Tropics, Patancheru, Hyderabad 502 324, IN
1 ICAR-Central Research Institute for Dryland Agriculture, Santoshnagar, Hyderabad 500 059, IN
2 International Crops Research Institute for the Semi-Arid Tropics, Patancheru, Hyderabad 502 324, IN
Source
Current Science, Vol 114, No 09 (2018), Pagination: 1885-1893Abstract
The projected demand of maize production in India in 2050 is 4–5 times of current production. With the scope for area expansion being limited, there is need for enhancement of yield. This calls for identifying areas where huge unrealized yield potential exists. With a view to address the issue, the present study delineates homogeneous agro-climatic zones for maize production system in India taking district as a unit and using the factors production, viz. climate, soil, season and irrigated area under the crop. There are 146 districts in India that grow maize as a major crop. They were divided into 26 zones using multivariate cluster analysis. Study of variation in yield between districts within a zone vis-a-vis crop management practices adopted in those districts was found useful in targeting the yield gaps. These findings can have direct relevance to the maize farmers and district level administrators.Keywords
Agro-Climatic Zone, Climate, Cluster, Irrigation, Potential Yield, Yield Gap.References
- Amarasinghe, U. A. and Om Prakash Singh, Changing consumption patterns of India: implications on future food demand. In India’s Water Future: Scenarios and Issues (eds Amarasinghe, U. A., Shah, T. and Malik, R. P. S.). International Water Management Institute, Colombo, Sri Lanka, 2008, pp. 131–146.
- Raju, B. M. K., Rama Rao, C. A. and Venkateswarlu, B., Growth performance of major rainfed crops in India. Indian J. Dryland Agric. Res. Dev., 2010, 25(1), 17–22.
- CRIDA, Annual Report 2013–2014. Central Research Institute for Dryland Agriculture, Hyderabad, India, 2014, p. 197.
- Sehgal, J., Mandal, D. K., Mandal, C. and Vadivelu, S., Agro-Ecological Regions of India. Second Edition, Tech. Bull. No. 24, NBSS and LUP, Nagpur, India, 1992, p. 130.
- Sehgal, J., Mandal, D. K. and Mandal, C., Agro-Ecological Subregions of India (Map). NBSS and LUP, Nagpur, India, 1995.
- ICRISAT, ICAR, Typology construction and economic policy analysis for sustainable rainfed agriculture. A report on sustainable rainfed agriculture research and development: Database development, typology construction and economic policy analysis (Module I), International Crops Research Institute for Semi-Arid Tropics, Hyderabad, Andhra Pradesh, India, 1999, pp. 142.
- Williams, C. L., Hargrove, W. W., Liebman, M. and James, D. E., Agro-ecoregionalization of Iowa using multivariate geographical clustering. Agric., Ecosyst. Environ., 2008, 123, 161–174.
- Kumar, S., Raju, B. M. K., Rama Rao, C. A., Kareemulla, K. and Venkateswarlu, B., Sensitivity of yields of major rainfed crops to climate in India. Indian J. Agric. Econ., 2011, 66(3), 340–352.
- Davatgar, N., Neishabouri, M. R. and Sepaskhah, A. R., Delineation of site specific nutrient management zones for a paddy cultivated area based on soil fertility using fuzzy clustering. Geoderma, 2012, 173–174, 111–118.
- Fu, Q., Wang, Z. and Jiang, Q., Delineating soil nutrient management zones based on fuzzy clustering optimized by PSO. Math. Comput. Model., 2010, 51, 1299–1305.
- Ortega, R. A. and Santibanez, O. A., Determination of management zones in corn (Zea mays L.) based on soil fertility. Comput. Electron. Agric., 2007, 58, 49–59.
- Ruß, G. and Kruse, R., Data Mining in Agriculture: Exploratory Hierarchical Clustering for Management Zone Delineation in Precision Agriculture. In Advances in Data Mining Applications and Theoretical Aspects, Proceedings of 11th Industrial ConferenceICDM-2011, LNAI 6870 (ed. Perner, P.), Springer-Verlag Berlin Heidelberg, 2011, pp. 161–173.
- DES, 2014; http://eands.dacnet.nic.in/
- Agricultural Census, 2014; http://agcensus.nic.in/
- Raju, B. M. K. et al., Revisiting climatic classification in India: a district level analysis. Curr. Sci., 2013, 104(4), 492–495.
- Dunne, K. A. and Willmott, C. J., Global distribution of plant-extractable water capacity of soil (Dunne). Oak Ridge, Tennessee, USA (Oak Ridge National Laboratory Distributed Active Archive Center), 2000; doi:10.3334/ORNLDAAC/545.
- Joshi, P. K., Singh, N. P., Singh, N. N., Gerpacio, R. V. and Pingali, P. L., Maize in India: Production Systems, Constraints, and Research Priorities. Mexico, DF: CIMMYT, 2005, p. 42.
- Srinivasarao, Ch., Ravindra Chary, G., Mishra, P. K., Subba Reddy, G., Sankar, G. R. M., Venkateswarlu, B. and Sikka, A. K., Rainfed Farming A Compendium of Doable Technologies, All India Coordinated Research Project for Dryland Agriculture, ICAR – Central Research Institute for Dryland Agriculture, Hyderabad, India, 2014, p. 194.