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.
Highlights
Adoption of new technologies in agriculture is of central interest to both academicians and policy makers since this is directly related to the efficiency of an agricultural research and extension system (Bozeman 2000)
While the practical focus is on institutional arrangement and policy interventions, the academic focus has been on methodological insights gained and its possible upscaling
The methodological implication and scope for modification and upscaling The study departs from conventional methodology of economic characterization of predominant farming systems in terms of at least two dimensions
Summary
Adoption of new technologies in agriculture is of central interest to both academicians and policy makers since this is directly related to the efficiency of an agricultural research and extension system (Bozeman 2000). These technologies do not fit well into heterogeneous smallholder systems, which need specific technological solutions Such inherent variability often influences farmers’ response to various technologies that aim at improving farm productivity and natural resource management (Lal et al 2001; Emtage and Suh 2005). Both in agricultural and social sciences, complexity and diversity has been under-perceived and undervalued resulting in their neglect, under-estimation and exclusion from government statistics and policy framework (Chambers et al 1989). The heterogeneity of the farming systems for which different technologies are needed, has been ruefully ignored
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