Studies on Monthly and Cumulative Monthly Milk Yield Records

Auran, Torbj⊘rn

doi:10.1080/00015127309433217

Cited by 22 publications

(1 citation statement)

References 25 publications

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“…Several alternatives were suggested to overcome these problems, such as analyzing the lactation curve parameters (coefficients) previously estimated in a first step (e.g., Shanks et al, 1981;Ferris et al, 1985;Ali and Schaeffer, 1987) or dividing the complete lactation into stages (e.g., months or trimesters of lactation) and analyzing these periods as separate traits in a multipletrait analysis (e.g., Auran, 1973;Lindgren et al, 1980;Agyemang et al, 1985). Attempts to use individual TDMY records as phenotypes in genetic evaluations were also evaluated to avoid loss of information and the approximations needed to project to 305-d lactations.…”

Section: Development Historymentioning

confidence: 99%

Invited review: Advances and applications of random regression models: From quantitative genetics to genomics

Oliveira

Brito

Lourenço

et al. 2019

Journal of Dairy Science

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An important goal in animal breeding is to improve longitudinal traits; that is, traits recorded multiple times during an individual's lifetime or physiological cycle. Longitudinal traits were first genetically evaluated based on accumulated phenotypic expression, phenotypic expression at specific time points, or repeatability models. Until now, the genetic evaluation of longitudinal traits has mainly focused on using random regression models (RRM). Random regression models enable fitting random genetic and environmental effects over time, which results in higher accuracy of estimated breeding values compared with other statistical approaches. In addition, RRM provide insights about temporal variation of biological processes and the physiological implications underlying the studied traits. Despite the fact that genomic information has substantially contributed to increase the rates of genetic progress for a variety of economically important traits in several livestock species, less attention has been given to longitudinal traits in recent years. However, including genomic information to evaluate longitudinal traits using RRM is a feasible alternative to yield more accurate selection and culling decisions, because selection of young animals may be based on the complete pattern of the production curve with higher accuracy compared with the use of traditional parent average (i.e., without genomic information). Moreover, RRM can be used to estimate SNP effects over time in genome-wide association studies. Thus, by analyzing marker associations over time, regions with higher effects at specific points in time are more likely to be identified. Despite the advances in applications of RRM in genetic evaluations, more research is needed to successfully combine RRM and genomic information. Future research should provide a better understanding of the temporal variation of biological processes and their physiological implications underlying the longitudinal traits.

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Section: Development Historymentioning

confidence: 99%

Invited review: Advances and applications of random regression models: From quantitative genetics to genomics

Oliveira

Brito

Lourenço

et al. 2019

Journal of Dairy Science

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Weekly milk prediction on dairy goats using neural networks

Fernández

Soria

Sánchez-Seiquer

et al. 2006

Neural Comput & Applic

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Artificial neural networks (NN) have been widely used for both prediction and classification tasks in many fields of knowledge; however, few studies are available on dairy science. In this work, we use NN models to predict next week's goat milk based on the current and previous milk production. A total of 35 Murciano-Granadina dairy goats were selected from a commercial farm according to number of lactation, litter size and body weight. Input variables taken into account were diet, milk production, stage of lactation and days between partum and first control. From the 35 goats, 22 goats were used to build the neural model and 13 goats were used to validate the model. It is important to emphasize that these 13 goats were not used to build the model in order to demonstrate the generalization capability of the network. Afterwards, the neural models that provided better prediction results were analysed in order to determine the relative importance of the input variables of the model. We found that the most important inputs are present and previous milk production, followed by days between parturition, and first milk control, and type of diet. Besides, we benchmark NN to other widely used prediction models, such as autoregressive system modelling or naı¨ve prediction. The results obtained with the neural models are better than with the rest of models. The best neural model in terms of accuracy provided a root mean square error equal to 0.57 kg/day and a low bias mean error equal to À 0.05 kg/day. Dairy goat farmers could make management decisions during current lactation from one week to the next (present time), based on present and/or previous milk production and dairy goat factors, without waiting until the end of lactation.

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The use of prolonged intervals between milk weighings and butterfat tests in milk recording

Ipsen¹,

Kjeldsen²

1976

Livestock Production Science

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Studies on Monthly and Cumulative Monthly Milk Yield Records

Cited by 22 publications

References 25 publications

Invited review: Advances and applications of random regression models: From quantitative genetics to genomics

Invited review: Advances and applications of random regression models: From quantitative genetics to genomics

Weekly milk prediction on dairy goats using neural networks

The use of prolonged intervals between milk weighings and butterfat tests in milk recording

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