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Use of a partial least-squares regression model to predict test day of milk, fat and protein yields in dairy goats

  • N.P.P. Macciotta (a1), C. Dimauro (a1), N. Bacciu (a1), P. Fresi (a2) and A. Cappio-Borlino (a1)
  • DOI:
  • Published online: 01 March 2007

A model able to predict missing test day data for milk, fat and protein yields on the basis of few recorded tests was proposed, based on the partial least squares (PLS) regression technique, a multivariate method that is able to solve problems related to high collinearity among predictors. A data set of 1731 lactations of Sarda breed dairy Goats was split into two data sets, one for model estimation and the other for the evaluation of PLS prediction capability. Eight scenarios of simplified recording schemes for fat and protein yields were simulated. Correlations among predicted and observed test day yields were quite high (from 0·50 to 0·88 and from 0·53 to 0·96 for fat and protein yields, respectively, in the different scenarios). Results highlight great flexibility and accuracy of this multivariate technique.

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N. Bouloc , F. Barillet , D. Boichard , J. P. Sigwald and J. Bridoux 1991. Etudes des possibilities d'allegement des controle laitier official chez les caprins. Annales de Zootechnie 40: 125139.

S. De Jong 1993. SIMPLS: an alternative approach to Partial Least Squares regression. Chemometrics and Intelligent Laboratory Systems 18: 251263.

A. P. Kominakis , Z. Abas , I. Maltaris and E. Rogdakis 2002. A preliminary study of the application of artificial neural networks to prediction of milk yield in dairy sheep. Computers and Eletronics in Agriculture 35: 3548.

N. P. P. Macciotta , D. Vicario , G. Pulina and A. Cappio–Borlino 2002. Test day and lactation yield predictions in Italian Simmental cows by ARMA methods. Journal of Dairy Science 85: 31073114.

P. Mayeres , J. Stoll , J. Bormann , R. Reents and N. Gengler 2004. Prediction of daily milk, fat and protein production by a random regression test day model. Journal of Dairy Science 87: 19251933.

T. Naes and H. Martens 1985. Comparison of prediction methods for multicollinear data. Communications in Statistics, Simulation and Computation 14: 545576.

M. H. Pool and T. H. E. Meuwissen 1999. Prediction of daily milk yields from a limited number of test days using test day model. Journal of Dairy Science 82: 15551564.

L. R. Schaeffer and J. Jamrozik 1996. Multiple–trait prediction of lactation yields for dairy cows. Journal of Dairy Science 79: 20442055.

L. O. Tedeschi 2006. Assessment of adequacy of mathematical models. Agricultural Systems 89: 225247.

J. Vasconcelos , A. Martins , M. F. Petim–Batista , J. Colaco , R. W. Blake and J. Carvalheira 2004. Prediction of daily and lactation yields of milk, fat, and protein using an autoregressive repeatability test day model. Journal of Dairy Science 87: 25912598.

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Animal Science
  • ISSN: 1357-7298
  • EISSN: 1748-748X
  • URL: /core/journals/animal-science
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