Literature DB >> 32883388

Predicting milk yield in Pelibuey ewes from the udder volume measurement with a simple method.

Roger Iván Espinosa-Mendoza1,2, Darwin Nicolas Arcos-Álvarez1, Ricardo Alfonso Garcia-Herrera1, Gamaliel Antonio-Molina2, Ricardo Vicente-Pérez3, Ulises Macias-Cruz4, Manuel González Ronquillo5, Augusto Cesar Lizarazo Chaparro6, Alfonso Juventino Chay-Canul1.   

Abstract

In this research communication we describe the creation of an equation for the prediction of milk yield (MY) from udder volume (UV). A total of 280 measurements were collected between 5 and 15 d postpartum (pp) from 36 multiparous Pelibuey ewes. Study variables were measured between 2 and 9 weeks pp and MY was measured by manual milking, UV prior to and following milking was measured using the technique of making moulds from aluminium foil. The MY ranged from 0.09 to 0.83 kg/d, meanwhile UV prior and following milking ranged from 155 to 1940 and 90 to 1520 cm3, respectively. Measurements of UV had a moderate to high (P < 0.01; 0.58 ≤ r ≤ 0.78) correlation with MY. The UV prior to milking was the best prediction model for MY, which explained 62% of the variation in MY. This equation presented moderate precision (r2 = 0.61) and high accuracy (bias correction factor = 0.94), confirming a good reproducibility index (concordance correlation coefficient = 0.73). Modelling efficiency (MEF = 0.59) showed moderate concordance between observed and predicted values. In conclusion, MY in lactating Pelibuey ewes could be predicted in a moderate way using the predictor variable UV measured with the technique of moulds made with aluminium foil.

Entities:  

Keywords:  Milk production; Pelibuey ewes; prediction equations

Mesh:

Year:  2020        PMID: 32883388     DOI: 10.1017/S002202992000076X

Source DB:  PubMed          Journal:  J Dairy Res        ISSN: 0022-0299            Impact factor:   1.904


  1 in total

1.  Estimation of milk yield based on udder measures of Pelibuey sheep using artificial neural networks.

Authors:  J C Angeles-Hernandez; F A Castro-Espinoza; A Peláez-Acero; J A Salinas-Martinez; A J Chay-Canul; E Vargas-Bello-Pérez
Journal:  Sci Rep       Date:  2022-05-30       Impact factor: 4.996

  1 in total

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