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 : Paper published in a journal
	Discipline(s) :	Life sciences : Genetics & genetic processes Life sciences : Animal production & animal husbandry
	To cite this reference:	http://hdl.handle.net/2268/80639

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 :	Nov-2010
Journal title :	ICAR Technical Series
Publisher :	E. Skujina, E. Galvanoska, O. Leary & C. Mosconi
Volume :	14
Special issue title :	Proceedings of the 37th ICAR Biennial Session, Riga, Latvia
Pages :	171-178
On invitation :	No
Audience :	International
ISSN :	1563-2504
City :	Rome
Country :	Italy
Event name :	37th ICAR Session
Event date :	31st May to 4th June, 2010
Event organizer :	ICAR
Event place (city) :	Riga
Event country :	Latvia
Keywords :	[en] lactation yields computation ; modified best prediction ; test-day model ; management tools
Abstract :	[en] Computation of lactation yields from test-day data has lost much of its importance for genetic evaluations as the use of test-day models is currently quite widespread. In the other hand its interest for intra-farm management is increasing as a base for advanced management tools. The first and principal aim of this study was to develop a method which takes into account advantages and disadvantages of existing methods, and to test its potential to provide useful management tools to dairy farmers. A test-day model with modifications to able daily run and management tools was developed. Because of its similarities with best prediction, the method developed here was called modified best prediction. The second objective was to compare the accuracy of this new method with best prediction and test interval methods. Modified best prediction showed good results for predicting daily yields and was slightly better than best prediction for lactation yields prediction. Management tools obtained with modified best prediction are explained.
Target :	Researchers ; Professionals ; Students
Permalink :	http://hdl.handle.net/2268/80639
Other URL :	http://www.icar.org/Documents/technical_series/tec_series_14_Riga.pdf

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