Methodology of Identification and Characterization of Farming Systems in Irrigated Agriculture: Case Study in West Bengal State of India | ||
| Journal of Agricultural Science and Technology | ||
| Article 5, Volume 17, Issue 5, 2015, Pages 1127-1140 PDF (1.67 M) | ||
| Authors | ||
| S. Chatterjee1; R. Goswami* 2; P. Bandopadhyay3 | ||
| 1Department of Agricultural Economics, Bidhan Chandra Krishi Viswavidyalaya, Nadia, West Bengal, India. | ||
| 2Integrated Rural Development and Management Faculty Centre, Ramakrishna Mission Vivekananda University, Kolkata-700103, India. | ||
| 3Department of Agronomy, Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, Nadia, West Bengal, India. | ||
| Abstract | ||
| Targeted extension for heterogeneous farming systems is a challenge in developing countries. Farm type identification and characterization based on estimates of income from different farm components allows simplifying diversity in farming systems. Use of multivariate statistical techniques, such as principal component analysis (PCA) and cluster analysis (CA), help in such farm typology delineation. Using this methodological approach, the present study conducted in West Bengal, India, identified four distinct farm types, namely, farms growing food grain and jute, farms with animal husbandry and fishery based diversification with high off-farm income, farms with crop based diversification with off-farm income, and farms growing vegetables and fruits. Such typology delineation helps in differentiated, holistic, and broad-based extension intervention to address the need of different identified farm types and a reduced transaction cost in the agricultural research and extension system. inbred lines, and 9 hybrids). A total of 94 and 262 loci were amplified using 5 IRAP and 15 REMAP primers, respectively. The percentage of polymorphic loci (PPL) in populations ranged from 39% (Zivari Shahrood) to 48% (Shadegani E). The Mantel test between IRAP and REMAP cophenetic matrices evidenced no significant correlation (r= 0.29). IRAP+REMAP-based cluster analysis using UPGMA algorithm and Dice similarity coefficient depicted 6 groups among 100 melon genotypes. AMOVA revealed the higher level of genetic variation within populations (67%) compared to among populations (33%). The mean Fst values of all groups, except for group VI, were more than 0.20, demonstrating differentiation among the populations and genetic structure of the studied melon collection. | ||
| Keywords | ||
| Cluster Analysis; Economic characterization; Farm heterogeneity; Farm typology; Principal component analysis | ||
| References | ||
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