Fitting milk production curves through nonlinear mixed models

PICCARDI, M.; MACCHIAVELLI, R.; CAPITAINE FUNES, A.; BÓ, G.; BALZARINI, M.

Journal of Dairy Research

CAMBRIDGE UNIV PRESS

Lugar: Cambridge; Año: 2017

0022-0299

he aim of this work was to fit and compare three non-linear models (Wood, Milkbot and diphasic) to model lactation curves from two approaches: (with and without cow random effect). Knowing the behavior of lactation curves is critical for decision-making in a dairy farm. Knowledge of the model of milk production progress along each lactation is necessary not only at the mean population level (dairy farm), but also at individual level (cow-lactation). The fits were made in a group of high production and reproduction dairy farms; in first and third lactations in cool seasons. A total of 2,167 complete lactations were involved, of which 984 were first-lactations and the remaining ones, third lactations (19,382 milk yield tests). PROC NLMIXED in SAS was used to make the fits and estimate the model parameters. The diphasic model resulted to be computationally complex and barely practical. Regarding the classical Wood and MilkBot models, although the information criteria suggest the selectio