|MANZANILLA-PECH, CORALIA I.V. - Wageningen University|
|VEERKAMP, ROEL - Wageningen University|
|TEMPELMAN, ROB - Michigan State University|
|VAN PELT, M - Wageningen University|
|WEIGEL, KENT - University Of Wisconsin|
|VANDEHAAR, MIK - Michigan State University|
|LAWLOR, TON - Holstein Association Usa, Inc|
|SPURLOCK, DIANE - Iowa State University|
|ARMENTANO, LOU - University Of Wisconsin|
|STAPLES, CHARLIE - University Of Florida|
|HANIGAN, MARK - Virginia Tech|
|DE HAAS, YVETTE - Wageningen University|
Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/15/2015
Publication Date: 12/17/2015
Citation: Manzanilla-Pech, C., Veerkamp, R., Tempelman, R., Van Pelt, M., Weigel, K., Vandehaar, M., Lawlor, T., Spurlock, D., Armentano, L., Connor, E.E., Staples, C., Hanigan, M., De Haas, Y. 2016. Genetic parameters between feed-intake-related traits and conformation in 2 separate dairy populations—the Netherlands and United States. Journal of Dairy Science. 99(1):443-457.
Interpretive Summary: Reducing feed intake while increasing production in the breeding objective should improve feed efficiency and, therefore, dairy producer profit if other economically important traits such as fertility and health are maintained. To be able to include feed intake in the selection index, specialized equipment is required to obtain individual cow feed intake records, which can be expensive and labor intensive. Thus, it is unrealistic that large numbers of records will be available to enable accurate estimation of genetic parameters to predict breeding values in a progeny testing scheme. An alternative approach is to use other traits as predictors of feed intake, such as production traits (e.g., milk, fat, and protein content) or body conformation traits, which have been shown to have strong genetic correlations with feed intake. The objective of this study was to estimate genetic correlations between 6 feed-intake-related traits and 7 conformation traits for dairy cattle from the Netherlands (NL) and the U.S. Results indicated that the heritabilities and genetic correlations between feed-intake-related traits and conformation traits showed the same pattern in both countries. Dry matter intake can be predicted with accuracies up to 0.43 in NL and 0.64 in the U.S. by a combination of conformation traits (stature, chest width, and body depth), and up to 0.74 in NL and 0.95 in US when milk energy output is added to the index. However, these accuracies should be taken with caution because they may be overestimated given the high standard error of the estimated genetic correlations between the target and the predictor traits. Therefore, recording individual cow feed intake continues to have a high priority.
Technical Abstract: To include feed-intake-related traits in the breeding goal, accurate estimates of genetic parameters of feed intake, and its correlations with other related traits (i.e., production, conformation) are required to compare different options. However, the correlations between feed intake and conformation traits can vary depending on the population. Therefore, the objective was to estimate genetic correlations between 6 feed-intake-related traits and 7 conformation traits within dairy cattle from 2 countries, the Netherlands (NL) and the United States (US). The feed-intake-related traits were dry matter intake (DMI), residual feed intake (RFI), milk energy output (MilkE), milk yield (MY), body weight (BW), and metabolic body weight (MBW). The conformation traits were stature (ST), chest width (CW), body depth (BD), angularity (ANG), rump angle (RA), rump width (RW), and body condition score (BCS). Feed intake data were available for 1,665 cows in NL and for 1,920 cows in US, from 83 nutritional experiments (48 in NL and 35 in US) conducted between 1991 and 2011 in NL and between 2007 and 2013 in US. Additional conformation records from relatives of the animals with DMI records were added to the database, giving a total of 37,241 cows in NL and 28,809 in US with conformation trait information. Genetic parameters were estimated using bivariate animal model analyses. The model included the following fixed effects for feed-intake-related traits: location by experiment-ration, age of cow at calving modeled with a second order polynomial by parity class, location by year-season, and days in milk, and these fixed effects for the conformation traits: herd by classification date, age of cow at classification, and lactation stage at classification. Both models included additive genetic and residual random effects. The highest estimated genetic correlations involving DMI were with CW in both countries (NL = 0.45 and US = 0.61), followed by ST (NL = 0.33 and US = 0.57), BD (NL = 0.26 and US = 0.49), and BCS (NL = 0.24 and US = 0.46). The MilkE and MY were moderately correlated with ANG in both countries (0.33 and 0.47 in NL, and 0.36 and 0.48 in US). Finally, BW was highly correlated with CW (0.77 in NL and 0.84 in US) and with BCS (0.83 in NL and 0.85 in US). Feed-intake-related traits were moderately to highly genetically correlated with conformation traits (ST, CW, BD, and BCS) in both countries, making them potentially useful as predictors of DMI.