Short communication: Estimating lactation curves for highly inhomogeneous milk yield data of an F2 population (Charolais × German Holstein)

Melzer, Nina; Trißl, Silke; Nürnberg, Gerd

doi:10.3168/jds.2017-12772

Cited by 7 publications

(9 citation statements)

References 26 publications

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“…They found that the Wilmink, Wood, and the Ali and Schaeffer models were strongly affected by the size of the sample. Melzer et al [21] suggested that with more inhomogeneous data, using the Ali and Schaeffer model may be a good solution.…”

Section: Resultsmentioning

confidence: 99%

“…Karangelil et al [3], who analysed five different models used for describing the lactation curves of Chios sheep (Wood, Wilmink, Cobby and Le Du, Cappio Borlino, Djikstra), selected the Wood model as the best adjusted to daily AMS records. In 1987, Wilmink developed his function for adjusting test-day MY data [20] and since then, a number of researchers have used that model in their analysis [17,21,22]. For instance, Otwinowska-Mindur and Ptak [8], who obtained test-day MYs for Polish Holstein-Friesian cows used the Wilmink function to adjust lactation curves.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of the fit of automatic milking system and test-day records with the use of lactation curves

Sitkowska

Kolenda

Piwczyński

2020

Asian-Australas J Anim Sci

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Objective: The aim of the paper was to compare the fit of data derived from daily automatic milking systems (AMS) and monthly test-day records with the use of lactation curves; data was analysed separately for primiparas and multiparas.Methods: The study was carried out on three Polish Holstein-Friesians (PHF) dairy herds. The farms were equipped with an automatic milking system which provided information on milking performance throughout lactation. Once a month cows were also subjected to test-day milkings (method A4). Most studies described in the literature are based on test-day data; therefore, we aimed to compare models based on both test-day and AMS data to determine which mathematical model (Wood or Wilmink) would be the better fit.Results: Results show that lactation curves constructed from data derived from the AMS were better adjusted to the actual milk yield (MY) data regardless of the lactation number and model. Also, we found that the Wilmink model may be a better fit for modelling the lactation curve of PHF cows milked by an AMS as it had the lowest values of Akaike information criterion, Bayesian information criterion, mean square error, the highest coefficient of determination values, and was more accurate in estimating MY than the Wood model. Although both models underestimated peak MY, mean, and total MY, the Wilmink model was closer to the real values.Conclusion: Models of lactation curves may have an economic impact and may be helpful in terms of herd management and decision-making as they assist in forecasting MY at any moment of lactation. Also, data obtained from modelling can help with monitoring milk performance of each cow, diet planning, as well as monitoring the health of the cow.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of the fit of automatic milking system and test-day records with the use of lactation curves

Sitkowska

Kolenda

Piwczyński

2020

Asian-Australas J Anim Sci

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“…The results emphasize the necessity of pre-analyses of the significance of the feeding group effect. To investigate the goodness of fit for the different lactation models, it has to be considered that each model depends on the underlying data structure, such as duration of lactation and shape of lactation curve (Melzer et al, 2017). Lactation curves generally synthesize the main aspects of the shape of lactation and give good results when animal groups are homogeneous and fitted to average lactation patterns (Macciotta et al, 2015;Pulina et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

“…of feeding groups = additional feeding experiments within feeding group. trait MY, no model evaluation was conducted because the AS function is well established as shown in many former analyses (Buttchereit et al, 2010;Stamer et al, 2011;Melzer et al, 2017).…”

Section: Methodsmentioning

confidence: 99%

Lactation curves and model evaluation for feed intake and energy balance in dairy cows

Harder

Stamer²,

Junge

et al. 2019

Journal of Dairy Science

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A good health status of high-performing dairy cows is essential for successful production. Feed intake affects the metabolic stability of dairy cows and can be used as a measurement for energy balance. By implementing feed intake and energy balance into the breeding goal, these traits provide great potential for an improvement in the health of dairy cows by breeders. In this study, fixed and random regression models were tested to establish appropriate models for a further analysis of this approach. A total of 1,374 Holstein-Friesian cows and 327 Simmental cows (SI) from 12 German research farms participating in a collaboration called optiKuh were phenotyped. Feed intake data recording was standardized across farms, and energy balance was calculated using phenotypic information on milk yield, milk ingredients, live weight, gestation stage, and feed intake. The phenotypic data set consisted of a total of 40,012 Holstein-Friesian and 16,996 SI with average weekly dry matter intakes of 21.8 ± 4.3 and 20.2 ± 3.6 kg/d, respectively. Observations of days in milk 1 to 350 were used to evaluate the best-fitting models and to estimate the repeatability and correlations between cow effects at different stages for feed intake and energy balance. Four parametric functions (Ali and Schaeffer and Legendre polynomials of second, third, and fourth degree) were compared to model the lactation curves. Based on the corrected Akaike information criterion and the Bayesian information criterion, the goodness of fit was evaluated to choose the best-fitting model for the finest description of lactation curves for the traits energy balance and feed intake. Legendre polynomial fourth degree was the best-fitting model for random regression models. In contrast, Ali and Schaeffer was the best choice for fixed regression models. Feed intake and energy balance acted as expected: the feed intake increased slowly at the beginning of lactation and the negative energy balance switched to a positive range around 40 to 80 d of lactation. The repeatabilities of both traits were quite similar and the repeatabilities for SI were the highest for both traits. Additionally, correlations between cow effects were closest between early days in milk. These results emphasize the possibility that the unique optiKuh data set can be used for further genetic analyses to enable genomic selection for feed intake or energy balance.

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“…Multiple statistical models have been used to describe lactation curves. Quintero et al (2007), Cankaya et al (2011), Macciotta et al (2011), Graesbøll et al (2016), Hossein-Zadeh (2016) and Melzer et al (2017) reported on different lactation curve models, which are compared for dairy cattle. These papers show some models that can generally be expressed as Y ¼ g t; b ð Þ; where Y stands for milk yield at week or day t of lactation, and time t is on a time interval T. Here b is a vector which contains the unknown model parameters to be estimated from data.…”

Section: Introductionmentioning

confidence: 99%

A study of lactation curves in dairy cattle using the optimal design of experiments methodology

López

Rodríguez

Torreblanca

2019

Italian Journal of Animal Science

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In this paper, we have worked with some of the most popular models to fit the lactation curve. Lactation curves have received considerable attention in modelling dairy cattle milk production. These curves express the evolution over time of a cattle head's milk yield. Observations of milk production over a cycle have usually been carried out periodically (i.e. daily, weekly or monthly). Optimal experiment design allows one to know the times when a herd's milk production must be recorded. An experiment design is optimal when it achieves the best parameter estimates for the curve model. This work aims to study optimal designs for certain models such as quadratic polynomial, hyperbolic, exponentials and Wilmink models (WMs). Designs are observed to depend on models and must be compared to get to know if a design for a specific model has a good behaviour with another model. Design efficiency is the tool used for this comparison. Efficiency is a value between 0 and 1a design is good if its efficiency is close to 1. The WM designs perform well and are an improvement on those designs that are routinely used in daily observations. HIGHLIGHTS Optimal experiment design allows one to know the times when a herd's milk production must be recorded. The use of optimal design methodology greatly reduces the number of instances when dairy production has to be recorded. Optimal designs reduce the costs of experimentation by allowing parameters to be estimated with fewer experimental runs.

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Short communication: Estimating lactation curves for highly inhomogeneous milk yield data of an F2 population (Charolais × German Holstein)

Cited by 7 publications

References 26 publications

Comparison of the fit of automatic milking system and test-day records with the use of lactation curves

Comparison of the fit of automatic milking system and test-day records with the use of lactation curves

Lactation curves and model evaluation for feed intake and energy balance in dairy cows

A study of lactation curves in dairy cattle using the optimal design of experiments methodology

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