Description and typology of intensive Chios dairy sheep farms in Greece
Authors:
- A.I. GelasakisA.I. Gelasakis
- G.E. ValergakisGVG.E. Valergakis
- G. ArsenosG. Arsenos
- G. BanosGBG. Banos
- 1 June 2012
- journal article
- Published by American Dairy Science Association in Journal of Dairy Science
- Vol. 95 (6) , 3070-3079
Abstract
The aim was to assess the intensified dairy sheep farming systems of the Chios breed in Greece, establishing a typology that may properly describe and characterize them. The study included the total of the 66 farms of the Chios sheep breeders' cooperative Macedonia. Data were collected using a structured direct questionnaire for in-depth interviews, including questions properly selected to obtain a general description of farm characteristics and overall management practices. A multivariate statistical analysis was used on the data to obtain the most appropriate typology. Initially, principal component analysis was used to produce uncorrelated variables (principal components), which would be used for the consecutive cluster analysis. The number of clusters was decided using hierarchical cluster analysis, whereas, the farms were allocated in 4 clusters using k-means cluster analysis. The identified clusters were described and afterward compared using one-way ANOVA or a chi-squared test. The main differences were evident on land availability and use, facility and equipment availability and type, expansion rates, and application of preventive flock health programs. In general, cluster 1 included newly established, intensive, well-equipped, specialized farms and cluster 2 included well-established farms with balanced sheep and feed/crop production. In cluster 3 were assigned small flock farms focusing more on arable crops than on sheep farming with a tendency to evolve toward cluster 2, whereas cluster 4 included farms representing a rather conservative form of Chios sheep breeding with low/intermediate inputs and choosing not to focus on feed/crop production. In the studied set of farms, 4 different farmer attitudes were evident: 1) farming disrupts sheep breeding; feed should be purchased and economies of scale will decrease costs (mainly cluster 1), 2) only exercise/pasture land is necessary; at least part of the feed (pasture) must be home-grown to decrease costs (clusters 1 and 4), 3) providing pasture to sheep is essential; on-farm feed production decreases costs (mainly cluster 3), and 4) large-scale farming (feed production and cash crops) does not disrupt sheep breeding; all feed must be produced on-farm to decrease costs (mainly cluster 3). Conducting a profitability analysis among different clusters, exploring and discovering the most beneficial levels of intensified management and capital investment should now be considered.Keywords
Funding Information
- Greek State Scholarships Foundation
This publication has 14 references indexed in Scilit:
- Structure and performance of Awassi and Assaf dairy sheep farms in northwestern SpainJournal of Dairy Science, 2011
- Segmenting farms for analysing agricultural trajectories: A case study of the Navarra region in SpainAgricultural Systems, 2007
- Overview of the economic and social importance of the livestock sector in Cyprus with particular reference to sheep and goatsSmall Ruminant Research, 2006
- Typification of farming systems for constructing representative farm models: two illustrations of the application of multi-variate analyses in Chile and PakistanAgricultural Systems, 2003
- Past, Present, and Future Perspectives of Small Ruminant Dairy ResearchJournal of Dairy Science, 2001
- Present state and future outlook of the small ruminant sectorSmall Ruminant Research, 1999
- Use of cluster analysis for identification and classification of farming systems in Qingyang County, Central North ChinaAgricultural Systems, 1990
- Socio-economic classification of farm households —conceptual, methodical and empirical considerationsEuropean Review of Agricultural Economics, 1987
- Agricultural typology concept and methodAgricultural Systems, 1977
- The Application of Electronic Computers to Factor AnalysisEducational and Psychological Measurement, 1960