Cubic-spline interpolation to estimate effects of inbreeding on milk yield in first lactation Holstein cows

Abstract

Milk yield records (305d, 2X, actual milk yield) of 123,639 registered first lactation Holstein cows were used to compare linear regression (y = β0 + β1X + e), quadratic regression, (y = β0 + β1X + β2X2 + e) cubic regression (y = β0 + β1X + β2X2 + β3X3 +e) and fixed factor models, with cubic-spline interpolation models, for estimating the effects of inbreeding on milk yield. Ten animal models, all with herd-year-season of calving as fixed effect, were compared using the Akaike corrected-Information Criterion (AICc). The cubic-spline interpolation model with seven knots had the lowest AICc, whereas for all those labeled as “traditional”, AICc was higher than the best model. Results from fitting inbreeding using a cubic-spline with seven knots were compared to results from fitting inbreeding as a linear covariate or as a fixed factor with seven levels. Estimates of inbreeding effects were not significantly different between the cubic-spline model and the fixed factor model, but were significantly different from the linear regression model. Milk yield decreased significantly at inbreeding levels greater than 9%. Variance component estimates were similar for the three models. Ranking of the top 100 sires with daughter records remained unaffected by the model used.

Milk yield prediction between Bunaji Holstein Friesian cross bred and Holstein Frisian cows using linear, cubic and quadratic models

“…The result also agreed with the report of Druet et al, (2003) and Silvestre et al, (2006) who used the Wood models and cubic spline regression models for modeling lactation curves. Also, White et al, ,(1999), Sahin andEfe, (2010); Nicolo et al, (2010); Koncagul and Yazgan, (2011); Geha et al, (2011) used cubic spline regression for modeling of lactation curves of dairy cattle and all reported that cubic model produce a better fit when compared to other models.…”