Combining datasets in a dynamic residual feed intake model and comparison with linear model results in lactating Holstein cattle

A new method to estimate residual feed intake (RFI) was recently developed based on a multi-trait random regression model. This approach deals with the dynamic nature of the lactation, which is in contrast with classical linear approaches. However, an issue remains: pooling data across sites and yea...

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Autores principales:	P. Martin, V. Ducrocq, A. Fischer, N.C. Friggens
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Feed efficiency Lactation Methods Modelling Random regression Animal culture SF1-1100
Acceso en línea:	https://doaj.org/article/9dcd408e821a4d899f4debcd70160864
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spelling	oai:doaj.org-article:9dcd408e821a4d899f4debcd701608642021-11-28T04:29:30ZCombining datasets in a dynamic residual feed intake model and comparison with linear model results in lactating Holstein cattle1751-731110.1016/j.animal.2021.100412https://doaj.org/article/9dcd408e821a4d899f4debcd701608642021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S175173112100255Xhttps://doaj.org/toc/1751-7311A new method to estimate residual feed intake (RFI) was recently developed based on a multi-trait random regression model. This approach deals with the dynamic nature of the lactation, which is in contrast with classical linear approaches. However, an issue remains: pooling data across sites and years, which implies dealing with different (and sometimes unknown) diet energy contents. This will be needed for genomic evaluation. In this study, we tested whether merging two individual datasets into a larger one can lead to valuable results in comparison to analysing them on their own with the multi-trait random regression model. Three datasets were defined: the first one with 1 063 lactations, the second one with 205 lactations from a second farm and the third one combining the data of the two first datasets (1 268 lactations). The model was applied to the three datasets to estimate individual RFI as well as variance components and correlations between the four traits included in the model (fat and protein corrected milk production, BW, feed intake and body condition score), and a fixed month-year-farm effect was used to define the contemporary group. The variance components and correlations between animal effects of the four traits were very similar irrespective of the dataset used with correlations higher than 0.94 between the different datasets. The RFI estimates for animals from their single farm only were also very similar (r > 0.95) to the ones computed from the merged dataset (Dataset 3). This highlights that the contemporary group correction in the model adequately accounts for differences between the two feeding environments. The dynamic model can thus be used to produce RFI estimates from merged datasets, at least when animals are raised in similar systems. In addition, the 205 lactations from the second farm were also used to estimate the RFI with a linear approach. The RFI estimated by the two approaches were similar when the considered period was rather short (r = 0.85 for RFI for the first 84 days of lactation) but this correlation weakened as the period length grew (r = 0.77 for RFI for the first 168 days of lactation). This weakening in correlations between the two approaches when increasing the used time-period reflects that only the dynamic model permits the regression coefficients to evolve in line with the physiological changes through the lactation. The results of this study enlarge the possibilities of use for the dynamic RFI model.P. MartinV. DucrocqA. FischerN.C. FriggensElsevierarticleFeed efficiencyLactationMethodsModellingRandom regressionAnimal cultureSF1-1100ENAnimal, Vol 15, Iss 12, Pp 100412- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Feed efficiency Lactation Methods Modelling Random regression Animal culture SF1-1100
spellingShingle	Feed efficiency Lactation Methods Modelling Random regression Animal culture SF1-1100 P. Martin V. Ducrocq A. Fischer N.C. Friggens Combining datasets in a dynamic residual feed intake model and comparison with linear model results in lactating Holstein cattle
description	A new method to estimate residual feed intake (RFI) was recently developed based on a multi-trait random regression model. This approach deals with the dynamic nature of the lactation, which is in contrast with classical linear approaches. However, an issue remains: pooling data across sites and years, which implies dealing with different (and sometimes unknown) diet energy contents. This will be needed for genomic evaluation. In this study, we tested whether merging two individual datasets into a larger one can lead to valuable results in comparison to analysing them on their own with the multi-trait random regression model. Three datasets were defined: the first one with 1 063 lactations, the second one with 205 lactations from a second farm and the third one combining the data of the two first datasets (1 268 lactations). The model was applied to the three datasets to estimate individual RFI as well as variance components and correlations between the four traits included in the model (fat and protein corrected milk production, BW, feed intake and body condition score), and a fixed month-year-farm effect was used to define the contemporary group. The variance components and correlations between animal effects of the four traits were very similar irrespective of the dataset used with correlations higher than 0.94 between the different datasets. The RFI estimates for animals from their single farm only were also very similar (r > 0.95) to the ones computed from the merged dataset (Dataset 3). This highlights that the contemporary group correction in the model adequately accounts for differences between the two feeding environments. The dynamic model can thus be used to produce RFI estimates from merged datasets, at least when animals are raised in similar systems. In addition, the 205 lactations from the second farm were also used to estimate the RFI with a linear approach. The RFI estimated by the two approaches were similar when the considered period was rather short (r = 0.85 for RFI for the first 84 days of lactation) but this correlation weakened as the period length grew (r = 0.77 for RFI for the first 168 days of lactation). This weakening in correlations between the two approaches when increasing the used time-period reflects that only the dynamic model permits the regression coefficients to evolve in line with the physiological changes through the lactation. The results of this study enlarge the possibilities of use for the dynamic RFI model.
format	article
author	P. Martin V. Ducrocq A. Fischer N.C. Friggens
author_facet	P. Martin V. Ducrocq A. Fischer N.C. Friggens
author_sort	P. Martin
title	Combining datasets in a dynamic residual feed intake model and comparison with linear model results in lactating Holstein cattle
title_short	Combining datasets in a dynamic residual feed intake model and comparison with linear model results in lactating Holstein cattle
title_full	Combining datasets in a dynamic residual feed intake model and comparison with linear model results in lactating Holstein cattle
title_fullStr	Combining datasets in a dynamic residual feed intake model and comparison with linear model results in lactating Holstein cattle
title_full_unstemmed	Combining datasets in a dynamic residual feed intake model and comparison with linear model results in lactating Holstein cattle
title_sort	combining datasets in a dynamic residual feed intake model and comparison with linear model results in lactating holstein cattle
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/9dcd408e821a4d899f4debcd70160864
work_keys_str_mv	AT pmartin combiningdatasetsinadynamicresidualfeedintakemodelandcomparisonwithlinearmodelresultsinlactatingholsteincattle AT vducrocq combiningdatasetsinadynamicresidualfeedintakemodelandcomparisonwithlinearmodelresultsinlactatingholsteincattle AT afischer combiningdatasetsinadynamicresidualfeedintakemodelandcomparisonwithlinearmodelresultsinlactatingholsteincattle AT ncfriggens combiningdatasetsinadynamicresidualfeedintakemodelandcomparisonwithlinearmodelresultsinlactatingholsteincattle
_version_	1718408395635032064

Combining datasets in a dynamic residual feed intake model and comparison with linear model results in lactating Holstein cattle

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