Reference : Adding value to test-day data by using modified best prediction method
Scientific congresses and symposiums : Paper published in a journal
Life sciences : Animal production & animal husbandry
Life sciences : Genetics & genetic processes
http://hdl.handle.net/2268/80639
Adding value to test-day data by using modified best prediction method
English
Gillon, Alain mailto [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 mailto [Université de Liège - ULg > Sciences agronomiques > Zootechnie >]
Nov-2010
ICAR Technical Series
E. Skujina, E. Galvanoska, O. Leary & C. Mosconi
14
Proceedings of the 37th ICAR Biennial Session, Riga, Latvia
171-178
No
No
International
1563-2504
Rome
Italy
37th ICAR Session
31st May to 4th June, 2010
ICAR
Riga
Latvia
[en] lactation yields computation ; modified best prediction ; test-day model ; management tools
[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.
Researchers ; Professionals ; Students
http://hdl.handle.net/2268/80639
http://www.icar.org/Documents/technical_series/tec_series_14_Riga.pdf

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