Italian Journal of Animal Science (Dec 2020)

Non-parametric analysis of the effects of nongenetic factors on milk yield, fat, protein, lactose, dry matter content and somatic cell count in Murciano-Granadina goats

  • M. G. Pizarro,
  • V. Landi,
  • F. J. Navas,
  • J. M. León,
  • A. Martínez,
  • J. Fernández,
  • J. V. Delgado

DOI
https://doi.org/10.1080/1828051X.2020.1809538
Journal volume & issue
Vol. 19, no. 1
pp. 960 – 973

Abstract

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The objective of this work was to evaluate the influence of non-genetic factors on milk yield, its composition (fat, protein, dry matter and lactose) and the count of somatic cells in Murciano-Granadina goat breed, through the application of alternative nonparametric tests to routine parametric tests to explain the variability found in the population with respect the aforementioned traits. 2594 milk yield and composition records belonging to 159 goats from the selection nucleus were analysed. Predictors evaluated were farm, type of birth and parity order, live kids, parturition month, season and year, control number, control type, control month, season and year, days in lactation, days from first control, days from last control to drying, drying month, season and year. All of them presented a significant influence (p < .0001) on all the variables studied with the exception of the number of stillborn kids which did not significantly influence the percentage of each component and the year of drying which seemed not to significantly influence dry matter percentage. Conclusively, nongenetic factors affect milk yield and its compositions in the Murciano-Granadina goat breed. Additionally, the inclusion of the type of milk control and information related to the drying period in models predicting for milk yield and content may provide interesting information, which must be included in genetic evaluations to promote higher and better-quality milk production improving the profitability of autochthonous breeds.HIGHLIGHTS Non-genetic factors may affect milk components more than milk yield. Including factors related to lactation cycle can help identifying critical points. Drying-off period information promotes milk yield and quality. Studying nongenetic factors helps maximising milk predictive model potential. Factor combinations studied explain up to 41.8% of variability of milk yield.

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