Selection of models of lactation curves to use in milk production simulation systems

Revista Brasileira de Zootecnia

Daniel De Noronha Figueiredo Vieira Da Cunha²  José Carlos Pereira²  Fabyano Fonseca E Silva¹  Oriel Fajardo De Campos¹  José Luis Braga¹  Janaina Azevedo Martuscello² 

[1] ,UFV Departamento de Zootecnia

关键词: dairy cattle;    modeling;    non-linear model;    gado de leite;    modelagem;    modelo não-linear;   

DOI  :  10.1590/S1516-35982010000400026

来源: SciELO

【 摘 要 】

The objective of this study was to select models of lactation curves with a better adjustment to the observed data in models of milk production simulation systems. A data base on 6,459 recordings of daily milk production was used. These data were obtained from monthly and fortnightly controls of milk between 2004 and 2007, from 472 lactations of animals from ten different milking cow herd farms. Based on rolling averages of milk production (MP-L/day) per cow, the ten herd farms were divided into low (L < 15), medium (15 20). Data were also divided according to the lactation numbers in first, second, third or greater. Eight lactation curve models commonly used in literature were compared. The models were individually adjusted for each lactation. The goodness of fit used for comparison of those models was the coefficient of determination, mean square error, mean square prediction error and the Bayesian information criterion. The values for the goodness of fit obtained in each model were compared by using 95% probability confidence interval. Wilmink (1987) model showed a better adjustment for cows of the first lactation numbers, whereas the Wood (1967) model showed a better adjustment for cows of the third or greater lactations numbers for the low milk production groups. Wood model showed a better adjustment for all the lactation numbers for the medium milk production group. Dijkstra (1997) model showed a better adjustment for all lactation numbers for the high milk production group. Despite of being more recent, the model by Pollott (2000), mechanist based and with a higher number of parameters, showed a good convergence for the used data.

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