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Current IssueEditorial BoardOnline First ArticlesArchive

volume 45 issue 1 (march 2011) : 1 - 9

CRAFTING LIVESTOCK PRODUCTION ZONES : A PRINCIPAL COMPONENTS APPROACH

G
G. Kathiravan*
S
S. Selvam1
1Veterinary College and Research Institute, T.A.N.U.V.A.S., Namakkal – 637 002, India.

  • Submitted|

  • First Online |

  • doi

Cite article:- Kathiravan* G., Selvam1 S. (2025). CRAFTING LIVESTOCK PRODUCTION ZONES : A PRINCIPAL COMPONENTS APPROACH. Indian Journal of Animal Research. 45(1): 1 - 9. doi: .

ABSTRACT

A study was carried out to determine the versatility of different districts of Tamil Nadu state of India for milk and meat production, using secondary data collected from various sources. Factor analysis with principal component extraction was carried out to detect the interrelationship among attributes of livestock production. The component of cow milk was the major factor in the state’s milk production, compared to buffalo milk. In mutton and chevon production, high Eigen value indicated the possibility of improvement of mutton producton to larger extent. The district-wise potentials for cow and buffalo milk production, mutton and chevon production were worked out based on resources availability in each district and presented.

REFERENCES

  1. Anjani Kumar and Singh, D.K. (2008). Livestock production systems in India: An appraisal across agro-ecological regions Indian Journal of Agricultural Economics, 63 (4): 577-597.
  2. Birthal, P. S. and Rao, P. P (2002). Introduction and overview of the conclusions. In: Technology Options for Sustainable Livestock Production in India, Proceedings of the Workshop on Documentation, Adoption and Impact of Livestock Technologies in India ICRISAT-Patancheru, India 18–19 January 2001.
  3. Browne, M.W. (1968). A comparison of factor analytic techniques. Psychometrika, 33:267-334.
  4. Dave, M.B, et al. (2007). Consumer’s perception about Maybelline’s product: A case study of Surat city using Factor Analysis. The SPSS Analyst, Oct-Dec., 2007.13-21.
  5. Delgado, C. et al. (1999). Livestock to 2020 – The Next Food Revolution. Food, Agriculture and the Environment Discussion Paper 28, IFPRI/ FAO/ ILRI.
  6. Dziuben, C.D. and Shirkey, E.C. (1974). When is a correlation matrix appropriate for factor analysis? Psychological Bulletin, 81(6): 358-361.
  7. Guttman, L. (1953). Image theory for the structure of Quantitative Variance. Psychometrika, 18 : 227-296.
  8. Haris, C.W. (1962). Some Rao – Guttman relationships. Psychometrika, 27: 247-263.
  9. Harman, H.H. (1976). Modern factor analysis Chicago: University of Chicago Press.
  10. Hegde, N.G. (2006). Livestock Development for Sustainable Livelihood of Small Farmers. Souvenir of the 39th Annual General Meeting and 48th National Symposium on “Energising Rural India: A Challenge to Livestock Industry”. Compound Livestock Feed Manufactures Association of India (CLFMA), Manesar, Haryana. August 26, 2006: 50-63.
  11. Hotelling, H. (1993). Analysis of a complex of statistical variables into principal components. Journal of Education Psychology, 24:417-441 & 498-520.
  12. Joreskog, K.G. and Lawley, D.N. (1968). New methods in maximum likelihood factor analysis. British Journal of Mathematical and Statistical Psychology, 21:85-96.
  13. Kaiser, H.F. and Gaffrey, J. (1965). Alpha factor analysis. Psychometrika, 30: 1-14.
  14. Kumar, P. (1998) Food demand-supply projections. Indian Agricultural Research Institute, New Delhi.
  15. Lawley, M.S. and Maxwell, A.E. (1971). Factor Analysis as a Statistical Method.: Butterworth and Co. London.
  16. Thompson, G.H. (1934). Hotelling’s method modified to give Spearman’s g. Journal of Education Psychology, 25: 366-374.
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    volume 45 issue 1 (march 2011) : 1 - 9

    CRAFTING LIVESTOCK PRODUCTION ZONES : A PRINCIPAL COMPONENTS APPROACH

    G
    G. Kathiravan*
    S
    S. Selvam1
    1Veterinary College and Research Institute, T.A.N.U.V.A.S., Namakkal – 637 002, India.

    • Submitted|

    • First Online |

    • doi

    Cite article:- Kathiravan* G., Selvam1 S. (2025). CRAFTING LIVESTOCK PRODUCTION ZONES : A PRINCIPAL COMPONENTS APPROACH. Indian Journal of Animal Research. 45(1): 1 - 9. doi: .

    ABSTRACT

    A study was carried out to determine the versatility of different districts of Tamil Nadu state of India for milk and meat production, using secondary data collected from various sources. Factor analysis with principal component extraction was carried out to detect the interrelationship among attributes of livestock production. The component of cow milk was the major factor in the state’s milk production, compared to buffalo milk. In mutton and chevon production, high Eigen value indicated the possibility of improvement of mutton producton to larger extent. The district-wise potentials for cow and buffalo milk production, mutton and chevon production were worked out based on resources availability in each district and presented.

    REFERENCES

    1. Anjani Kumar and Singh, D.K. (2008). Livestock production systems in India: An appraisal across agro-ecological regions Indian Journal of Agricultural Economics, 63 (4): 577-597.
    2. Birthal, P. S. and Rao, P. P (2002). Introduction and overview of the conclusions. In: Technology Options for Sustainable Livestock Production in India, Proceedings of the Workshop on Documentation, Adoption and Impact of Livestock Technologies in India ICRISAT-Patancheru, India 18–19 January 2001.
    3. Browne, M.W. (1968). A comparison of factor analytic techniques. Psychometrika, 33:267-334.
    4. Dave, M.B, et al. (2007). Consumer’s perception about Maybelline’s product: A case study of Surat city using Factor Analysis. The SPSS Analyst, Oct-Dec., 2007.13-21.
    5. Delgado, C. et al. (1999). Livestock to 2020 – The Next Food Revolution. Food, Agriculture and the Environment Discussion Paper 28, IFPRI/ FAO/ ILRI.
    6. Dziuben, C.D. and Shirkey, E.C. (1974). When is a correlation matrix appropriate for factor analysis? Psychological Bulletin, 81(6): 358-361.
    7. Guttman, L. (1953). Image theory for the structure of Quantitative Variance. Psychometrika, 18 : 227-296.
    8. Haris, C.W. (1962). Some Rao – Guttman relationships. Psychometrika, 27: 247-263.
    9. Harman, H.H. (1976). Modern factor analysis Chicago: University of Chicago Press.
    10. Hegde, N.G. (2006). Livestock Development for Sustainable Livelihood of Small Farmers. Souvenir of the 39th Annual General Meeting and 48th National Symposium on “Energising Rural India: A Challenge to Livestock Industry”. Compound Livestock Feed Manufactures Association of India (CLFMA), Manesar, Haryana. August 26, 2006: 50-63.
    11. Hotelling, H. (1993). Analysis of a complex of statistical variables into principal components. Journal of Education Psychology, 24:417-441 & 498-520.
    12. Joreskog, K.G. and Lawley, D.N. (1968). New methods in maximum likelihood factor analysis. British Journal of Mathematical and Statistical Psychology, 21:85-96.
    13. Kaiser, H.F. and Gaffrey, J. (1965). Alpha factor analysis. Psychometrika, 30: 1-14.
    14. Kumar, P. (1998) Food demand-supply projections. Indian Agricultural Research Institute, New Delhi.
    15. Lawley, M.S. and Maxwell, A.E. (1971). Factor Analysis as a Statistical Method.: Butterworth and Co. London.
    16. Thompson, G.H. (1934). Hotelling’s method modified to give Spearman’s g. Journal of Education Psychology, 25: 366-374.
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