Dr. Ramasubramanian V


Dr. Ramasubramanian V

  1. Future perspectives of Bt technology in agriculture. (Co-PI)
  2. Supply and demand analysis of professional fisheries human capital in India. (Co-PI)
  3. Data mining based spatio-temporal modeling for marine fisheries management. (Co-PI)
  4. Assessment of extent of community dependence on the coastal ecologically sensitive areas in Achra-Ratnagiri, Maharashtra. (Co-PI)
  5. Economic evaluation and livelihood assessment of leased-out-ponds under Panchayati Raj system in northern India..(Co-PI)
  6. Ecology, Livelihoods and Governance in Indian Reservoir Fisheries: An Ecosystem Based Approach. (Co-PI)
  7. Strengthening statistical computing for NARS. (Co-PI)
  8. Sub- programme on Technology Forecasting in NAIP project – Visioning, Policy Analysis and Gender (V-PAGe). (Co-PI)
  9. Risk assessment and insurance products for agriculture. (Co-PI)
  10. Neural network based forecast modeling in crops. (Co-PI)
  11. A study on editing and imputation using neural networks. (PI)
  12. Crop forecasting using state space models. (PI)
  13. Integrated National Agricultural Resources Information System (INARIS). (Co-PI)
  14. Forecasting sugarcane yield using multiple Markov chains. (PI)



Principal Scientist

  1. Best Teacher Award from Deemed University ICAR-Central Institute of Fisheries Education, Mumbai for the year 2016-17.
  2. Awarded and honoured to acquire three months international training experience in the area of Technology Forecasting at University of Houston, Houston, Texas, USA during Aug. 18 – Nov.15, 2011
  1. Vinay, A., Ramasubramanian, V., Krishnan, M. and Ananthan, P.S. (2018). Total factor productivity of Tuna fisheries in Lakshadweep, Indian Journal of Geomarine Sciences47(2), 319-32.
  2. Ramasubramanian, V., Ananthan, P.S., Krishnan, M. and Vinay, A. (2017). Technology Forecasting in Fisheries Sector: Cross Impact Analysis and Substitution Modeling, Journal of the Indian Society of Agricultural Statistics71(3), 231–239.
  3. Ray, M., Rai, A., Singh, K. N. and  Ramasubramanian V. (2017).Modeling and forecasting of hybrid rice yield using a grey model improved by the genetic algorithm, International Journal of Agricultural and Statistical Sciences13 (2), 563-566.
  4. Purushottama, G. B., Thakurdas, Ramasubramanian V., Dash, G., Akhilesh, K.V., Ramkumar, S., Kizhakudan, S.J., Singh, V.V. and Zacharia P. U. (2017). Reproductive biology and diet of grey sharpnose shark Rhizoprionodon oligolinx Springer, 1964 (Chondrichthyes: Carcharhinidae)
    from the north-eastern Arabian Sea, Indian Journal of Fisheries, 64(4): 9-20.
  5. Qureshi, N.W., Krishnan, M., Wani, S.A., Ramasubramanian, V., Sivaramane, N. and Sundaramoorthy, C. (2017) Negative Externalities in Kashmir Lake Fisheries: Transformation in Species Patronage, Use Priorities and Policy, Indian Journal of Agricultural Economics72(1), 89-101
  6. Vinay, A., Ramasubramanian, V., Azeez, P.A., Kumar, R. and Kumar, D.K. (2017). Economic Analysis of Troll Line Fisheries in Androth, Lakshadweep, India, Int.J.Curr.Microbiol.App.Sci.6(11), 3172-3179.
  7. Arun, V.V., Saharan, N., Ramasubramanian, V., Rani, A.M.B., Salin, K.R., Sontakke, R., Haridas, H. and Pazhayamadom, D.G. (2017). Multi-response optimization of Artemia hatching process using split-split-plot design based response surface methodology, Scientific Reports, doi: 10.1038/srep40394, 7: 1-16.
  8. Ray, M., Rai, A., Singh, K.N., Ramasubramanian,  V. and Kumar, A.(2017). Technology forecasting using time series intervention based trend impact analysis for wheat yield scenario in India, Technological Forecasting and Social Change118: 128–133.
  9. Kumar, D.K., Ramasubramanian, V., Krishnan, M., Ananthan, P.S., Vinay, A. Kumar, R.S. (2017). Socio-Economic Status of Fishers of Coastal India, Int. J. Curr. Microbiol. App. Sci., 6(9), 2267-2280.
  10. Mugaonkar, P., Kumar, N. R., Shelar, G., Polanco, J.F., Ramsubramanian, V. and Biradar, R. S. (2017). Case study on the non-price factors and consumer behavior for Pangasius (Pangasianodon hypophthalmus) (Valenciennes, 1840) consumption in Pune city, India, Fishery Technology54, 279 – 286.
  11. Adiga, M.S., Ananthan, P.S., Kumari, H.V.D. and  Ramasubramanian, V. (2016). Multidimensional analysis of marine fishery resources of Maharashtra, India, Ocean and Coastal Management130, 13-20.
  12. Yadav, V.K., Jahageerdar, S., Ramasubramanian, V., Bharti, V.S. and Adinarayana, J. (2016). Use of different approaches to model catch per unit effort (CPUE) abundance of fish, Indian Journal of Geo Marine Sciences,  45 (12): 1677-1687.
  13. Debroy, P., Krishnan, M., Upadhyay, A.D., Ramasubramanian, V., Criddle, K.R., Kiresur, V.R. and Datta, S.K. (2016). Resource distribution, growth and strategies for enhancing fish production in north-eastern states of India, Indian Journal of Fisheries63(2): 1-7.
  14. Adiga, M.S., Ananthan, P.S., Kumari, H.V.D. and  Ramasubramanian, V. (2016). Crisis of Sustainability or Perils of Ill-managed Open Access Fisheries? Analysis of Long-term Catch Trends in Marine Fisheries of Maharashtra and India, Agricultural Economics Research Review29 (1), 105-116.
  15. Bhowmik,A., Ramasubramanian, V., Rai, A., Kumar, A. and Kundu, M.G. (2016). Improved Estimation in Logistic Regression through Quadratic Bootstrap Approach: An Application in Agricultural  Ergonomics, Journal of the Indian Society of Agricultural Statistics70(3): 227–235.
  16. Ray, M., Rai, A., Ramasubramanian, V. and Singh K. N. (2016). ARIMA-WNN hybrid model for forecasting wheat yield time series data. Journal of the Indian Society of the Agricultural Statistics70(1), 63-70.
  17. Vinay, A., Ramasubramanian, V. and Kumar, N. B.T. (2016). Constraint Analysis of Tuna Fisheries in Lakshadweep, Indian Journal of Ecology43 (Special Issue 2): 789-792.
  18. Das, A., Kumar, N.R., Debnath, B., Krishnan, M., Kumar, A. and Ramasubramanian, V. (2016). Determinants of poverty movements among fisheries households of rural Tripura,  National Journal of Life Sciences13(2): 183-188.
  19. Chrispin, C.L, Ananthan, P.S., Sugunan, V.V., Ramasubramanian, V., Panikkar, Preetha and Landge, Asha T. (2016). Fisheries and management status of Pechiparai reservoir in Tamil nadu, Current World Environment11(1), 233-242.
  20. Ramasubramanian, V., Ananthan, P.S., Krishnan, M., Vinay, A. and Josephine, M., (2016). Teaching statistics and informatics for fisheries students: retrospect and prospects, Journal of Fisheries and Life Sciences, 1(1): 16-19.
  21. Adiga, S., Ananthan, P.S., Ramasubramanian, V. and Kumari, Divya, H.V. (2015). Validating RAPFISH sustainability indicators: Focus on multidisciplinary aspects of Indian marine fisheries, Marine Policy60, 202-207.
  22. Ramasubramanian, V., Kumar, A., Prabhu, K.V., Bhatia, V.K. and Ramasundaram, P. (2014). Forecasting technological needs and prioritizing factors in agriculture from plant breeding and genetics domain perspective: A review, Indian Journal of Agricultural Sciences84 (3), 311-316.
  23. Ramasubramanian, V. and Bhar, L. (2014). Crop yield forecasting by multiple Markov chain models and simulation, Statistics and Applications, 12(1&2), 1-13.
  24. Sadhu, S.K., Ramasubramanian, V., Rai, A. and Kumar, A. (2014). Decision tree based models for classification in agricultural ergonomics, Statistics and Applications12(1&2), 21-33.
  25. Ray, M., Ramasubramanian V., Kumar, A. and Rai, A. (2014). Application of time series intervention model for forecasting cotton yield, Statistics and Applications12(1&2), 61-70.
  26. Pandirwar, A., Kumar, A., Singh, J.K., Mani, I., Jha, S.K. and Ramasubramanian, V. (2014).  Agricultural dust protective interventions for Indian farm workers, Indian Journal of Agricultural Sciences84 (1), 28-36.
  27. Patel, R.M., Goyal, R.C., Ramasubramanian, V. and Marwaha, S. (2013).  Markov chain based crop forecast modelling software, Indian Journal of Agricultural Statistics67(3), 371-379.
  28. Jeeva, C.,  RamasubramanianV., Kumar, A., Bhatia, V.K., Geethalakshmi, V. and Premi, S.K. (2013). Forecasting technological needs and prioritizing factors for the post harvest sector of Indian Fisheries, Fishery Technology, 50 (1), 87-91.
  29. Yadav, V.K., Krishnan, M., Sharma, R., Ramasubramanian, V., Bharti, V. S. and Kumar, N. R. (2013). Soft Computing Techniques: An Application to Short Term Forecast of Inland Fish Production of India, International Journal of Advanced Trends in Computer Science and Engineering (IJATCSE), 2(5), 55-61.
  30. Bhowmik, A., Ramasubramanian, V., Chandrahas and Kumar, A. (2011).  Logistic regression for classification in agricultural ergonomics, Advances in Applied Research3(2), 163-170.
  31. Garg, K.C., Kumar, S., Bhatia, V.K,. Ramasubramanian, V., Kumar, A. and Kumari, J.  (2011). Plant genetics and breeding research: Scientometric profile of selected countries with special reference to India, Annals of Library and Information Studies58(6), 184-197.
  32. Ramasubramanian V., Agrawal, R. and.Bhar, L. (2010). Crop Forecasting using multiple Markov chains, Assam Statistical Review, 24(1),37-56.
  33. Nikam, S.S., Mishra , A. K, Sarangi, A, Paresh, B.S., Singh, D. K. and RamasubramanianV. (2010). Artificial Neural Network Models to Predict Wheat Crop Evapotranspiration, Journal of Agricultural Engineering47(2), 20-25.
  34. Jain, R., Minz, S. and Ramasubramanian V. (2009). Machine learning for forewarning crop diseases, Journal of the Indian Society of Agricultural Statistics63(1), 97-107.
  35. Ramasubramanian, V. (2008). Role of statistics and information technology in bioinformatics, Current Biotica2(1), 114-16.
  36. Ram, R., Kumar, A., Singh, A.K., Jha, S.K. and Ramasubramanian V. (2008). Ergonomic evaluation of foot operated rotary power generation, Journal of Agricultural Engineering45(3), 12-18.
  37. Ramasubramanian, V., Rai A. and Singh, R. (2007). Jackknife variance estimation under two-phase sampling, Model  Assisted Statistics and Applications, 2(1), 27-36.
  38. Ramasubramanian, V.  (2007). Utility of expert systems for the farming community, Agricultural Extension Review,18(1), 38-40.
  39. Pal, S., Ramasubramanian, V. and Mehta, S.C. (2007). Statistical models for forecasting milk production,             Journal of Indian Society of Agricultural Statistics61(2), 80-83.
  40. Misra, A.K., Om Prakash and Ramasubramanian, V.  (2004). Forewarning powdery mildew caused by Oidium mangiferae in mango (Mangiferaindica) using logistic regression models, Indian Journal of Agricultural Sciences, 74(2), 84-87.
  41. Ramasubramanian V., Singh, R. and Rai, A. (2002). Resampling based variance estimation under two-phase sampling, Journal of the Indian Society of Agricultural Statistics 55(2), 197-208.
  42. Ramasubramanian V. (2002). Impact of statistical software packages on scientific research and statistical education, Current Science,  83(6), 678.
  43. Ramasubramanian, V., Singh, R. and Rai, A. (2001). An empirical investigation on the reliability of Jackknife estimation under two-phase sampling for stratification, Indian Journal of Applied Statistics, 6, 1-11.
  44. Ramasubramanian, V. and Jain, R.C. (1999). Use of growth indices in Markov chain models for crop yield forecasting, Biometrical Journal41, 99109.
  45. Jain, R.C. and Ramasubramanian, V. (1998). Forecasting of crop yields using second order Markov Chains, Journal of the Indian Society of Agricultural Statistics51, 61-72.
  1. Ramasubramanian, V., Biradar, R.S. and Krishnan, M., 2017. Statistical Methods for Fisheries Students: A Practical Manual, Mumbai: ICAR-Central Institute of Fisheries Education, Mumbai, 129 pp.
  2. Bhatia, V.K., Ramasubramanian, V., Kumar, A., Rai, A., Satyapal, Chaturvedi, K.K. and Agrawal, R.  (2012). Subprogramme on Technology Forecasting – Visioning, Policy Analysis and Gender (VPAGe)  , IASRI, New Delhi
  3. Ramasubramanian, V. and Iquebal, M. A. (2012). Training manual on “Statistical models for forecasting in agriculture”, Vols. I & II, IASRI, New Delhi.
  4. Ramasubramanian, V. and Kumar, A. (2012). Training manual on “Technology forecasting methods with applications in agriculture”, IASRI, New Delhi.
  5. Kumar, A., Ramasubramanian, V. and Agrawal, R. (2010). Neural network based forecast modeling in crops, Project report No. P.R.07-2010, IASRI, New Delhi.
  6. Ramasubramanian, V., Bhatia, V.K., Kumar, A., Rai, A., Chaturvedi, K.K. and Satyapal (2010).  Training manual on Technology Forecasting Methodologies, Training programme organized at IASRI, New Delhi during July 13-17 2010, Technical Series IASRI/NAIP/VPAGe/03/2010, IASRI, New Delhi
  7. Rai, A., Chaturvedi, K.K., Bhatia, V.K. and Ramasubramanian, V.  (2009). Information and Communication Technology for Accelerated Growth in Agriculture, Technical Series IASRI/NAIP/VPAGe/01/09, IASRI, New Delhi
  8. Ramasubramanian V. and Chandrahas (2008). Crop forecasting using state space models, Project report, ICAR-IASRI, New Delhi
  9. Ramasubramanian V., Agrawal, R., Lal, S.B., Dubey, V.K., Jha, G.K. and Bathla, H.V.L. (2008). A study on editing and imputation using neural networks, Project report, ICAR-IASRI, New Delhi
  10. Ramasubramanian V., Agrawal, R. and Bhar, L. (2004).  Forecasting sugarcane yield using multiple Markov chains, Project report, ICAR-IASRI, New Delhi


Books edited

1.       Ramasubramanian, V., Kumar, A., Bhatia, V.K. and Satyapal (2009). Proceedings of the Workshop on Forecasting Future Technological Needs for Rice in India,  CRRI, Cuttack during July 28-29 2008, Edited Book, IASRI/NAIP/VPAGe/02/09, ICAR-IASRI, New Delhi.

Book chapters

2.       Ray, M., Rai, A.,  Ramasubramanian, V., Singh, K. N. and Rathod, S. (2017). Envisioning Crop Yield Scenario using Time Series Intervention Based Trend Impact Analysis (TIA),  ICAR-IASRI News, 22(1),  April-June.

3.       Palita, N.N., Ananthan, P. S., Panda, D. and Ramasubramanian V., 2016. Livelihood and Poverty among Fishers and Nonfishers in Hirakud Reservoir Region, Odisha, India, In: Freshwater, Fish, and the Future: Proceedings of the Global Cross-Sectoral Conference, (Ed. W. W. Taylor, D. M. Bartley, C. I. Goddard, N. J. Leonard, and R. Welcomme),Food and Agriculture Organization of the United Nations, Rome; Michigan State University, East Lansing; and American Fisheries Society, Bethesda, Maryland, pp. 159-168. [ISBN-13: 978-92-5-109263-7]

4.       Ramasubramanian, V., Kumar, A., Bishop, P., Ramasundaram, P. and Jeeva, C. J. (2015).  Applications of quantitative techniques in technology forecasting: Some case studies, In: Statistics in Forestry: Methods and Applications, Ed. Chandra, G., Nautiyal, R., Chandra, H., Roychoudhury, N. and Mohammad, N., Bonfring Publications, Coimbatore, 115-124. [ISBN: 978-93-84743-83-3]

5.       Ananthan, P.S., Ramasubramanian, V., M. Krishnan, Vinay, A. and Mary Josephine, P. (2016). A critical review of methodologies applied in supply demand analysis of human capital, In: Human Resource Management in Indian Dairy Sector Ed. Makwana, A.K., Gurjar, M.D., Kamani, K.C. and Prajapati, M.C., SMC College of Dairy Science, Anand Agricultural University, Anand, 272-279. [ISBN: 978-81-931704-3-4]

6.       Vinay A., Ramasubramanian V. and Naveen Kumar B.T. (2016).  Constraint analysis of tuna fisheries in Lakshadweep, In: Natural Resource Management: Ecological Perspectives, Vol. 2, Ed.: Peshin, R., Dhawan, A.K., Bano, B. and Risam, K.S., Proceeding of the Indian Ecological Society: International Conference, Feb. 18-20, 2016, SKUAST, Jammu, page 571. [ISBN: 978-93-5258-415-4]

7.       Qureshi, N.W., Krishnan, M., Sundaramoorthy, C., Ramasubramanian, V. and Araya, T.M. (2014). Data mining multiple stakeholders’ responses to declining Schizothorax fishery in the lakes of Kashmir, India, International Conference of the International Institute of Fisheries Economics & Trade (IIFET) 2014 Australia Conference Proceedings, 7-11 July, 2014, QUT, Brisbane, pages 63-74.

8.       Ramasubramanian, V., Agrawal, R., Lal, S.B., Rai, A., Kumar, A., Raza, S.M. and Jha, G.K. (2007).  Evaluation of  neural network application for data editing.  Proceedings of the 3rd Indian International Conference on Artificial Intelligence (IICAI-07), Pune, 1572-81 . [ISBN 978-0-9727412-2-4].

9.       Lal, S.B., Ramasubramanian,V., Agrawal, R., Raza, S.M. and Jha, G.K.  (2007). A software for imputing missing values using neural networks, Proceedings of the 3rd Indian International Conference on Artificial Intelligence (IICAI-07), Pune, 1595-1604. [ISBN 978-0-9727412-2-4].

10.   Kumar, A., Ramasubramanian, V. and Agrawal, R. (2007). Forecasting rice yield using neural networks, Proceedings of the 3rd Indian International Conference on Artificial Intelligence (IICAI-07), Pune, 1510-16. [ISBN 978-0-9727412-2-4].

Skip to toolbar