ORBi: Gillon Alain - Adding value to test-day data by using modified best prediction method

Reference : Adding value to test-day data by using modified best prediction method


	Document type :	Scientific congresses and symposiums : Unpublished conference
	Discipline(s) :	Life sciences : Animal production & animal husbandry Life sciences : Genetics & genetic processes
	To cite this reference:	http://hdl.handle.net/2268/20987

Title :	Adding value to test-day data by using modified best prediction method
Language :	English
Author, co-author :	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 >]
Publication date :	27-Aug-2009
On invitation :	No
Audience :	International
Event name :	60th EAAP Annual Meeting
Event date :	August 24-27, 2009
Event organizer :	EAAP
Event place (city) :	Barcelona
Event country :	Spain
Abstract :	[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.
Target :	Researchers ; Students
Permalink :	http://hdl.handle.net/2268/20987

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