|Reference : Adding value to test-day data by using modified best prediction method|
|Scientific congresses and symposiums : Unpublished conference|
|Life sciences : Animal production & animal husbandry|
Life sciences : Genetics & genetic processes
|Adding value to test-day data by using modified best prediction method|
|Gillon, Alain [Université de Liège - ULg > Sciences agronomiques > Zootechnie >]|
|Abras, Sven [Association Wallonne de l'Elevage > > > >]|
|Mayeres, Patrick [Association Wallonne de l'Elevage > > > >]|
|Bertozzi, Carlo [Association Wallonne de l'Elevage > > > >]|
|Gengler, Nicolas [Université de Liège - ULg > Sciences agronomiques > Zootechnie >]|
|60th EAAP Annual Meeting|
|August 24-27, 2009|
|[en] Computation of lactation yields from test-day yield has lost much of its importance for genetic evaluations as
the use of test-day models is rather widespread. At the same time its importance for intra-farm management
increases at farms as a base for advanced management tools. The most common official method to compute
lactation yield is the Test Interval Method (TIM). Alternative methods for computing cumulated productions
were developed. These methods can be considered as improvements of TIM as the interpolation method,
or completely different methods as multiple-trait prediction (MTP) and best prediction (BP). Research
in this field has shown the potential to compute lactation parameters (e.g., cumulated production) with
test-day models. The aim of this study was to develop a new method which takes into account advantages
and disadvantages of existing methods, and to test its potential to provide useful tools to help farmers to
make management decisions. The second objective was to compare the accuracy and the robustness of this
method with those of BP and TIM. Because of its similarities with BP, the method developed here was
called mBP, for modified-BP. The main difference from BP is the definition of the standard lactation curve.
To minimize bias, components of standard lactation curves proper to each herd are computed jointly with
random individual effects. Recently a new version of mBP was tested that puts expectations of constant
animal effects to observed average values using Bayesian prediction, a feature also used by MTP.
|Researchers ; Students|
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