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

Abstract

-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 <M < 20) and high (H > 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.

Clustering and Characterization of the Lactation Curves of Dairy Cows Using K-Medoids Clustering Algorithm

“…The best fitting models are not the same in each clusters as shown in Table 5 . The results are consistent with the those of previous studies [ 3 , 19 ]. Previous studies performed goodness of fit tests of the LC model for lactation data by grouping the data according to the period in which the milking data was obtained, the level of milk production, and parity, with the aim of finding a model that optimally describes lactation.…”

“…To facilitate precision dairy farming, however, it is useful to identify and analyze LC shapes that can be influenced by individual variation, feed management, or disease. In previous studies, LCs have been analyzed after grouping dairy cows based on production levels, parity differences, or presence of disease [ 3 , 19 , 20 , 21 ]. But the limitations remain because those studies tried to derive LCs for predefined groups instead of extracting physiological characteristics from various LCs.…”

The use of dijkstra mechanistic model for genetic analysis of the lactation curve characteristics and their relationships with age at first calving and somatic cell score of Iranian dairy cows

“…In Dijkstra model the highest milk yield (36.91 kg) was observed after the second months of lactation (71 days). The PY and TP estimated was higher than reported in literature for Holstein dairy cattle (Nasri et al, 2008) while Cunha et al (2010) reported PY and TP using Dijkstra model for low, medium and high milk production farms. These amounts for the first lactation number were 15.10 kg and 25 days, 17.69 kg and 77 days and 29.90 kg and 77 days respectively.…”

“…These amounts for the first lactation number were 15.10 kg and 25 days, 17.69 kg and 77 days and 29.90 kg and 77 days respectively. Val-Arreolaet al (2004) and Cunha et al (2010) reported that Dijkstra model overestimate TP compare to other lactation model and this model is prefer for the lactations whose time to peak occurs on a day farther from the beginning of lactation. Based on Val-Arreola et al (2004) and Nasri et al (2008) estimation of maximum milk yield using Dijkstra equation is accurate.…”

Trends of milk production in Saanen and crossbred Boer–Saanen goats

“…Além disso, a comparação da curva de lactação permitiria agrupar animais que tivessem a produção mais semelhante entre si, facilitando o manejo alimentar, reprodutivo e também de descarte e seleção, reduzindo os custos de produção da atividade (VAL-ARREOLA et al, 2004;VANRADEN et al, 2006;CUNHA et al, 2010;GLÓRIA et al, 2010).…”