Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/108935
Authors: 
Goswami, Rupak
Chatterjee, Soumitra
Prasad, Binoy
Year of Publication: 
2014
Citation: 
[Journal:] Agricultural and Food Economics [ISSN:] 2193-7532 [Publisher:] Springer [Place:] Heidelberg [Volume:] 2 [Year:] 2014 [Pages:] 1-24
Abstract: 
Identification and characterization of farming systems simplify huge diversity of farm types in complex agro-ecosystems, which is of critical importance for precise technological intervention and informed policy support. Multivariate statistical techniques like Principal Component Analysis (PCA) and Cluster Analysis (CA) may be used for a wide variety of situations associated with farm typology delineation. The present study conducted in coastal saline India demonstratively established the usefulness of such methodology in identification of predominant farm types and their characterization. Data collected from 144 farm households through questionnaire survey could identify four predominant farm types with differential income sources and resource-base. The methodological perspective employed in the study may be used as a decision support tool by extension agencies. On other hand, a differentiated, holistic and broad-based extension intervention with suitable institutional arrangement will be needed to address the need of these identified farm types. This will lead to a reduced transaction cost of the agricultural research and extension systems in diverse ecosystems in India and many similar situations in the developing countries.
Subjects: 
Cluster analysis
Economic characterization
Extension support
Farm heterogeneity
Farm typology
New Institutional Economics
Principal component analysis
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by/2.0/
Document Type: 
Article

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