Genetic parameters for calf feeding traits derived from automated milk feeding machines and number of bovine respiratory disease treatments in North American Holstein calves
ABSTRACT: Precision livestock farming technologies, such as automatic milk feeding machines, have increased the availability of on-farm data collected from dairy operations. We analyzed feeding records from automatic milk feeding machines to evaluate the genetic background of milk feeding traits and...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-04-01
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| Series: | Journal of Dairy Science |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0022030223007683 |
| _version_ | 1797249194569760768 |
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| author | Jason R. Graham Maria E. Montes Victor B. Pedrosa Jarrod Doucette Masoomeh Taghipoor André C. Araujo Leonardo S. Gloria Jacquelyn P. Boerman Luiz F. Brito |
| author_facet | Jason R. Graham Maria E. Montes Victor B. Pedrosa Jarrod Doucette Masoomeh Taghipoor André C. Araujo Leonardo S. Gloria Jacquelyn P. Boerman Luiz F. Brito |
| author_sort | Jason R. Graham |
| collection | DOAJ |
| description | ABSTRACT: Precision livestock farming technologies, such as automatic milk feeding machines, have increased the availability of on-farm data collected from dairy operations. We analyzed feeding records from automatic milk feeding machines to evaluate the genetic background of milk feeding traits and bovine respiratory disease (BRD) in North American Holstein calves. Data from 10,076 preweaning female Holstein calves were collected daily over a period of 6 yr (3 yr included per-visit data), and daily milk consumption (DMC), per-visit milk consumption (PVMC), daily sum of drinking duration (DSDD), drinking duration per-visit, daily number of rewarded visits (DNRV), and total number of visits per day were recorded over a 60-d preweaning period. Additional traits were derived from these variables, including total consumption and duration variance (TCV and TDV), feeding interval, drinking speed (DS), and preweaning stayability. A single BRD-related trait was evaluated, which was the number of times a calf was treated for BRD (NTT). The NTT was determined by counting the number of BRD incidences before 60 d of age. All traits were analyzed using single-step genomic BLUP mixed-model equations and fitting either repeatability or random regression models in the BLUPF90+ suite of programs. A total of 10,076 calves with phenotypic records and genotypic information for 57,019 SNP after the quality control were included in the analyses. Feeding traits had low heritability estimates based on repeatability models (0.006 ± 0.0009 to 0.08 ± 0.004). However, total variance traits using an animal model had greater heritabilities of 0.21 ± 0.023 and 0.23 ± 0.024, for TCV and TDV, respectively. The heritability estimates increased with the repeatability model when using only the first 32 d preweaning (e.g., PVMC = 0.040 ± 0.003, DMC = 0.090 ± 0.009, DSDD = 0.100 ± 0.005, DS = 0.150 ± 0.007, DNRV = 0.020 ± 0.002). When fitting random regression models (RRM) using the full dataset (60-d period), greater heritability estimates were obtained (e.g., PVMC = 0.070 [range: 0.020, 0.110], DMC = 0.460 [range: 0.050, 0.680], DSDD = 0.180 [range: 0.010, 0.340], DS = 0.19 [range: 0.070, 0.430], DNRV = 0.120 [range: 0.030, 0.450]) for the majority of the traits, suggesting that RRM capture more genetic variability than the repeatability model with better fit being found for RRM. Moderate negative genetic correlations of −0.59 between DMC and NTT were observed, suggesting that automatic milk feeding machines records have the potential to be used for genetically improving disease resilience in Holstein calves. The results from this study provide key insights of the genetic background of early in-life traits in dairy cattle, which can be used for selecting animals with improved health outcomes and performance. |
| first_indexed | 2024-04-24T20:26:37Z |
| format | Article |
| id | doaj.art-1afb298991f64aa4a937c847d9237a5e |
| institution | Directory Open Access Journal |
| issn | 0022-0302 |
| language | English |
| last_indexed | 2024-04-24T20:26:37Z |
| publishDate | 2024-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Dairy Science |
| spelling | doaj.art-1afb298991f64aa4a937c847d9237a5e2024-03-22T05:38:17ZengElsevierJournal of Dairy Science0022-03022024-04-01107421752193Genetic parameters for calf feeding traits derived from automated milk feeding machines and number of bovine respiratory disease treatments in North American Holstein calvesJason R. Graham0Maria E. Montes1Victor B. Pedrosa2Jarrod Doucette3Masoomeh Taghipoor4André C. Araujo5Leonardo S. Gloria6Jacquelyn P. Boerman7Luiz F. Brito8Department of Animal Sciences, Purdue University, West Lafayette, IN 47907Department of Animal Sciences, Purdue University, West Lafayette, IN 47907Department of Animal Sciences, Purdue University, West Lafayette, IN 47907Agriculture Information Technology (AgIT), Purdue University, West Lafayette, IN 47907Université Paris-Saclay, INRAE, AgroParisTech, UMR Modélisation Systémique Appliquée aux Ruminants, 91120, Palaiseau, FranceDepartment of Animal Sciences, Purdue University, West Lafayette, IN 47907Department of Animal Sciences, Purdue University, West Lafayette, IN 47907Department of Animal Sciences, Purdue University, West Lafayette, IN 47907Department of Animal Sciences, Purdue University, West Lafayette, IN 47907; Corresponding authorABSTRACT: Precision livestock farming technologies, such as automatic milk feeding machines, have increased the availability of on-farm data collected from dairy operations. We analyzed feeding records from automatic milk feeding machines to evaluate the genetic background of milk feeding traits and bovine respiratory disease (BRD) in North American Holstein calves. Data from 10,076 preweaning female Holstein calves were collected daily over a period of 6 yr (3 yr included per-visit data), and daily milk consumption (DMC), per-visit milk consumption (PVMC), daily sum of drinking duration (DSDD), drinking duration per-visit, daily number of rewarded visits (DNRV), and total number of visits per day were recorded over a 60-d preweaning period. Additional traits were derived from these variables, including total consumption and duration variance (TCV and TDV), feeding interval, drinking speed (DS), and preweaning stayability. A single BRD-related trait was evaluated, which was the number of times a calf was treated for BRD (NTT). The NTT was determined by counting the number of BRD incidences before 60 d of age. All traits were analyzed using single-step genomic BLUP mixed-model equations and fitting either repeatability or random regression models in the BLUPF90+ suite of programs. A total of 10,076 calves with phenotypic records and genotypic information for 57,019 SNP after the quality control were included in the analyses. Feeding traits had low heritability estimates based on repeatability models (0.006 ± 0.0009 to 0.08 ± 0.004). However, total variance traits using an animal model had greater heritabilities of 0.21 ± 0.023 and 0.23 ± 0.024, for TCV and TDV, respectively. The heritability estimates increased with the repeatability model when using only the first 32 d preweaning (e.g., PVMC = 0.040 ± 0.003, DMC = 0.090 ± 0.009, DSDD = 0.100 ± 0.005, DS = 0.150 ± 0.007, DNRV = 0.020 ± 0.002). When fitting random regression models (RRM) using the full dataset (60-d period), greater heritability estimates were obtained (e.g., PVMC = 0.070 [range: 0.020, 0.110], DMC = 0.460 [range: 0.050, 0.680], DSDD = 0.180 [range: 0.010, 0.340], DS = 0.19 [range: 0.070, 0.430], DNRV = 0.120 [range: 0.030, 0.450]) for the majority of the traits, suggesting that RRM capture more genetic variability than the repeatability model with better fit being found for RRM. Moderate negative genetic correlations of −0.59 between DMC and NTT were observed, suggesting that automatic milk feeding machines records have the potential to be used for genetically improving disease resilience in Holstein calves. The results from this study provide key insights of the genetic background of early in-life traits in dairy cattle, which can be used for selecting animals with improved health outcomes and performance.http://www.sciencedirect.com/science/article/pii/S0022030223007683precision dairy farmingcalf pneumoniaautomatic milk feeding machinesvariance componentsheritability |
| spellingShingle | Jason R. Graham Maria E. Montes Victor B. Pedrosa Jarrod Doucette Masoomeh Taghipoor André C. Araujo Leonardo S. Gloria Jacquelyn P. Boerman Luiz F. Brito Genetic parameters for calf feeding traits derived from automated milk feeding machines and number of bovine respiratory disease treatments in North American Holstein calves Journal of Dairy Science precision dairy farming calf pneumonia automatic milk feeding machines variance components heritability |
| title | Genetic parameters for calf feeding traits derived from automated milk feeding machines and number of bovine respiratory disease treatments in North American Holstein calves |
| title_full | Genetic parameters for calf feeding traits derived from automated milk feeding machines and number of bovine respiratory disease treatments in North American Holstein calves |
| title_fullStr | Genetic parameters for calf feeding traits derived from automated milk feeding machines and number of bovine respiratory disease treatments in North American Holstein calves |
| title_full_unstemmed | Genetic parameters for calf feeding traits derived from automated milk feeding machines and number of bovine respiratory disease treatments in North American Holstein calves |
| title_short | Genetic parameters for calf feeding traits derived from automated milk feeding machines and number of bovine respiratory disease treatments in North American Holstein calves |
| title_sort | genetic parameters for calf feeding traits derived from automated milk feeding machines and number of bovine respiratory disease treatments in north american holstein calves |
| topic | precision dairy farming calf pneumonia automatic milk feeding machines variance components heritability |
| url | http://www.sciencedirect.com/science/article/pii/S0022030223007683 |
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