Assessment of socio-economic vulnerability in the selected reservoirs of Maharashtra and Tamil Nadu Yadav Vinod K.1,*, Akilandeshwari A.2, Ojha S.N.3 1Senior Scientist, Fisheries Economics, Extension and Statistics DivisionICAR-Central Institute of Fisheries Education, Mumbai-400061, Maharashtra, India 2Research Scholar, Fisheries Economics, Extension and Statistics DivisionICAR-Central Institute of Fisheries Education, Mumbai-400061, Maharashtra, India 3Principal Scientist and HoD, Fisheries Economics, Extension and Statistics DivisionICAR-Central Institute of Fisheries Education, Mumbai-400061, Maharashtra, India *Corresponding author email id: vinodkumar@cife.edu.in
Abstract Inland fisheries contributed 70% of India's total fish production of 12.5 million tons in 2017–18. Inland fisheries accounted for 8.9 mt with 14% growth in 201718. The inland fisheries had grown in absolute terms, but the potential is yet to realize as the sector is hugely diverse and dynamic in terms of development. Climate change is one of the most critical global environmental challenges of the 21st century. India has to be concerned about climate change as nearly 700 million rural populations directly depend on climate-sensitive sectors (agriculture, forests, and fisheries) and natural resources (such as freshwater, mangroves, coastal zones, grasslands, and biodiversity) for their subsistence and livelihoods. Climate change has and will have an impact on inland fisheries with its influence with other anthropogenic stressors. A study was conducted in reservoirs, the sleeping giants with vast potential to contribute to total fish production on vulnerability and climatic variables. The climatic data of Maharashtra and Tamil Nadu were collected from the Indian Meteorological Department and reservoir fish production from the State Fisheries Department. The secondary data from the Census of India 2011 and 2001, various government departments formed data sources. Socio-Economic Vulnerability Index (SEVI) values of all four reservoirs ranged from low to moderate, with Bhavanisagar block being the least vulnerable and Ambegaon block being the most vulnerable. There are no significant differences in vulnerability between fishing and non-fishing villages. A graphical 2D decision matrix and identification of drivers and buffers provide a snapshot of villages’ vulnerability level based on sensitivity and adaptive capacity. Top Keywords SEVI, Dimbhe reservoir, Pechiparai reservoir, Bhavanisagar reservoir, Vaigai reservoir, Vulnerability. Top |