Agric. Econ. - Czech, 2014, 60(11):503-508 | DOI: 10.17221/24/2014-AGRICECON

The clustering of agricultural products and determining important countries for these clusters by the factor analysisOriginal Paper

Sabri ER1, Ahmet ÖZÇELIK2
1 Ankara Development Agency, Ankara, Turkey
2 Agricultural Economics Department, Ankara University, Ankara, Turkey

In the study, some important herbal agricultural products with respect to their production have been clustered, in addition to determining the most important or the best countries in terms of the production of certain herbal agricultural products by using the factor analysis. The FAO data set has been used in obtaining production of 30 agricultural products in 86 countries. 8 factors have been achieved by considering the Eigen values the numbers of which are greater than one. Each factor contains certain herbal agricultural products. First factor explains 40.51% of the total variation whilst the last factor explains only 3.89% of the total variability. 10 best countries for each factor have been revealed.

Keywords: Eigen value, herbal product, total variability, Varimax

Published: November 30, 2014  Show citation

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Sabri E, ÖZÇELIK A. The clustering of agricultural products and determining important countries for these clusters by the factor analysis. Agric. Econ. - Czech. 2014;60(11):503-508. doi: 10.17221/24/2014-AGRICECON.
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References

  1. Akpinar M.G., Yurdakul O. (2008): Factors effecting brand preferences for food products. Akdeniz University Journal of Agricultural Science, 21: 1-6.
  2. Çelik ª. (2012): Examination of plant production of provinces in Turkey by factor analysis. Yüzüncü Yil University, Agriculture Faculty, Journal of Agricultural Science, 22: 69-76.
  3. Dağistan E., Koç B., Gül A., Gül M. (2008): Factor analysis of sheep production: A case study of Middle-South Anatolia. Yüzüncü Yil University, Agriculture Faculty, Journal of Agricultural Science, 18: 67-77.
  4. Field A. (2000): Discovering Statistics Using SPSS for Windows. Sage publications, London-New Delhi.
  5. Ford J.K., MacCallum R.C., Tait M. (1986): The application of exploratory factor analysis in technique: A critical review and analysis. Personnel Psychology, 39: 291-314. Go to original source...
  6. Ghisay F.G., Hosseini J.F. (2010): Challenges in developing in Iran's agricultural cooperatives: A factor analysis. World Applied Science Journal, 10: 1032-1037.
  7. Lashgarara F. (2004): Factor analysis of influencing factors on adoption of sustainable agriculture among wheat farmers of Lorestan province, Iran. Islamic Azad University, Science & Research Branch,Tehran.
  8. Li J.Q. (2012): Factor analysis of agricultural innovative ability in Sichuan province. Environment and Natural Resources Research, 2: 16-20. Go to original source...
  9. Raven M.R. (1998): The Application of exploratory factor analysis in agricultural education research. Journal of Agricultural Education, 35: 9-14. Go to original source...
  10. Rietveld T., Van Hout R. (1993): Statistical Techniques for the Study of Language and Language Behavior. Mouton de Gruyter, Berlin. Go to original source...
  11. Rummel R.J. (1970): Applied Factor Analysis. Northwestern University Press, Evanston.

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