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Co-Authors
- B. M. K. Raju
- A. V. M. Subba Rao
- K. V. Rao
- V. U. M. Rao
- Kausalya Ramachandran
- B. Venkateswarlu
- A. K. Sikka
- M. Srinivasa Rao
- M. Maheswari
- Ch. Srinivasa Rao
- R. Rejani
- M. Osman
- K. Srinivasa Reddy
- Manoranjan Kumar
- Prasanna Kumar
- Srinivasarao
- Josily Samuel
- 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
Rama Rao, C. A.
- A District Level Assessment of Vulnerability of Indian Agriculture to Climate Change
Abstract Views :266 |
PDF Views:93
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.Full Text
- Farm Ponds for Climate-Resilient Rainfed Agriculture
Abstract Views :270 |
PDF Views:109
Authors
Ch. Srinivasa Rao
1,
R. Rejani
1,
C. A. Rama Rao
1,
K. V. Rao
1,
M. Osman
1,
K. Srinivasa Reddy
1,
Manoranjan Kumar
1,
Prasanna Kumar
1
Affiliations
1 ICAR-Central Research Institute for Dryland Agriculture, Hyderabad-500 059, IN
1 ICAR-Central Research Institute for Dryland Agriculture, Hyderabad-500 059, IN
Source
Current Science, Vol 112, No 03 (2017), Pagination: 471-477Abstract
This article summarizes the utility of farm pond technology as an adaptation strategy to overcome water shortage due to several reasons, including climate change. This technology has the potential to increase availability of water for supplemental irrigation, increase in cropped area and productivity leading to increase in net returns from crops. Farm pond offers a solution to overcome the increased frequencies of drought, particularly mid-season and terminal drought under climate change scenario. The article advocates for policy intervention to promote one pond for each farm holding having an area of 2.0 ha at individual farm level or on community-sharing basis. Constraints for large-scale implementation of farm pond technology are also discussed.Keywords
Climate Change, Drought Management, Farm Ponds, Rainfed Agriculture, Supplemental Irrigation.Full Text
- Assessing Unrealized Yield Potential of Maize Producing Districts in India
Abstract Views :285 |
PDF Views:80
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.Full Text
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.