Morphometric Assessment of Indigenous Cattle Breeds

Authors

  • Matteo Muller Research Scientist, Department of Machine Learning, European Institute of AI, Berlin, Germany Author

Keywords:

indigenous cattle breeds, linear body measurements, principal component analysis, discriminant function, Bos indicus, Bos taurus, breed characterisation, morphometrics

Abstract

Indigenous cattle breeds represent the product of millennia of natural selection and traditional breeding under local environmental, disease, and management conditions -- harbouring adaptive traits for heat tolerance, disease resistance, and feed efficiency under marginal conditions that are absent from high-yield commercial breeds. Yet the morphological distinctiveness of indigenous breeds, their interrelationships, and the degree to which phenotypic variation within and among breeds reflects genetic differentiation versus environmental plasticity remain incompletely characterised for the majority of the world's approximately 1,000 recognised indigenous cattle breeds. This study presents a comprehensive morphometric assessment of 24 indigenous cattle breeds from 8 countries across Sub-Saharan Africa, South Asia, and Southern Europe, measuring 18 standardised linear body measurements on 2,847 adult animals (mean 118.6 animals per breed). Principal component analysis resolved breed clusters corresponding closely to geographic origin and subspecies affiliation (Bos taurus vs. Bos indicus; zebu vs. sanga vs. taurine). Body weight was most accurately predicted by a three-variable model combining heart girth, body length, and wither height (r2 = 0.94; RMSE = 18.4 kg), providing a practical weight estimation protocol for field conditions where scales are unavailable. Discriminant function analysis correctly classified 87.4% of animals to their breed of origin from linear measurements alone -- confirming that morphometric profiles are sufficiently breed-specific to serve as a baseline for genetic diversity-morphology comparisons. The results provide the most comprehensive multi-country morphometric database for indigenous cattle breeds from these three regions, with direct applications for breed characterisation, conservation prioritisation, and field identification.

Author Biography

  • Matteo Muller, Research Scientist, Department of Machine Learning, European Institute of AI, Berlin, Germany

    Research Scientist, Department of Machine Learning, European Institute of AI, Berlin, Germany

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Published

2022-06-15

How to Cite

Morphometric Assessment of Indigenous Cattle Breeds. (2022). Zoological Archives: An International Journal, 2(1), 21-30. https://stanfordgroup.org/index.php/ZAIJ/article/view/304

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