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Aquaculture plays an important role to increase farm income and hence alleviate widespread poverty in the country. Fish is considered as the principal source of animal protein. The research was conducted to study the knowledge level of improved fish production technology among farmers in Nidamarru block of West Godavari district of Andhra Pradesh. The research was designed to ascertain the socio-demographic characteristics of the farmers, determine available technologies, and identify the major problems faced by farmers in using improved fish production technologies. Purposive sampling procedure was used to select 120 respondents for the study. Data were analyzed using frequency count, percentage, standard deviation, and indexing. The result shows that average age of the respondents was 45.5 years. Sixty four percent respondents are illiterate. Major sources of information of fish farming are progressive farmers, friends. Average land holding of respondents was 2-3 acres. Most of the respondents have agriculture as a main occupation. Most of the respondents having 70% of risk bearing capacity. The results shows that most of the respondents have high level of progressiveness. Overall knowledge of the respondents is low level i.e. 38.33% . Most of the respondents were aware of the technologies and adopted them. Indexing showed that Low market price of fish was ranked as the major problem followed by Unavailability of fish seed and their proper supply in using improved fish production technology.
Keywords:
Fish production technology ,knowledge ,Improved
Cite Article:
"Knowledge level of Farmers on Improved Fish Production Technology in West Godavari district of Andhra Pradesh ,India", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 6, page no.368 - 373, June-2023, Available :http://www.ijrti.org/papers/IJRTI2306059.pdf
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000205107
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator