Volume 15, Issue 4 (12-2024)
Res Anim Prod 2024, 15(4): 70-82 |
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Ala Noshahr F, Seyedsharifi R, Seifdavati J, Hedayat-Evrigh N. (2024). Estimation of Genetic Parameters of the Feed Efficiency Trait using the Random Regression Model in Dairy Cows. Res Anim Prod. 15(4), 70-82. doi:10.61186/rap.15.4.70
URL: http://rap.sanru.ac.ir/article-1-1428-en.html
URL: http://rap.sanru.ac.ir/article-1-1428-en.html
1- Department of Animal Sciences, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabi, Iran
2- Department of Animal Sciences, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran
2- Department of Animal Sciences, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran
Abstract: (874 Views)
Extended Abstract
Background: With the increase in feed costs and the environmental impacts becoming more apparent, the global population growing, and a greater focus on sustainability, methods for improving the efficiency of converting feed into milk in dairy cows have become increasingly important. An efficient cow is one that consumes less feed for the same amount of milk and milk solids it produces, and remains healthy and fertile. This makes it possible to reduce costs without reducing production. Through technological advances, accurate measurements of feed efficiency traits, such as dry matter intake, body weight, residual feed intake, and milk composition, have become readily available. Therefore, this study aimed to estimate the genetic parameters of the feed efficiency trait and potential selection strategies for incorporating this economic trait into breeding programs.
Methods: In the present study, 35,478 records of dry matter intake (DMI), 36,353 records of energy-modified milk (ECM), 27,896 records of metabolic body weight (MBW), and 24,508 records of residual feed intake (RFI) from 5,123 first lactation Holstein cows from the National Breeding Center during 2008 to 2018 were used. In addition, cows with a first calving age greater than 40 months were excluded from the analysis. The pedigree file included information for up to 10 generations for the phenotyped cows, resulting in a pedigree file with 9,471 animals, of which 978 and 3,577 were sires and dams, respectively. Phenotypic data recorded from 5 to 305 DIM were used to create biweekly lactation (Bi-WL) groups by dividing DMI into 14 (total 21 Bi-WL classes). The main data were collected in the form of daily (DMI and MBW), weekly (ECM and MBW) and monthly (MBW) records. Therefore, animals should have at least two records for DMI, ECM and MBW in a given Bi-WL. Heritability, variance components and genetic correlations between lactation weeks were calculated during the lactation period using a random regression model. In order to estimate the covariance between traits, a two-trait random regression animal model was used. Homogeneous residual variances were considered for the two-trait analysis, to enable convergence of the results. Both single and two-trait models were analyzed using the AIREML algorithm by WOMBAT. Also, correction values for the random regression coefficients were implemented using the BLUP method by BLUPF90.
Results: Heritability estimates ranged from 0.17 to 0.41 for dry matter intake, 0.28 to 0.45 for energy-corrected milk, 0.48 to 0.78 for metabolic body weight, and 0.1 to 0.2 for residual feed intake. Heritability estimates for RFI were moderate, ranging from 0.2 in the first Bi-WL class to 0.13 in the last Bi-WL class with minimal heritability in Bi-WL classes 14 and 15 (0.1). Variation between lactation stages for RFI was also observed. Genetic correlations for RFI ranged from 0.26 to 0.99. The lowest correlations were observed between mid-lactation (Bi-WL classes 5 to 11; approximately 70 to 154 DIM) and late lactation (Bi-WL classes 12 to 21; approximately 168 to 305 DIM). Intratrait genetic correlations were strongest between closely spaced weeks of lactation for all traits studied. Genetic correlations between RFI and MBW ranged from 0.64 in early lactation to -0.45 in late lactation. Analysis of the proportion of bulls sharing the top 10% throughout lactation indicated that bulls with the highest ranking for RFI in mid-lactation were likely to remain among the top bulls throughout the rest of lactation. Also, the post-peak lactation period, i.e. days 140 to 226 of lactation (Bi-WL class 10 to 19), could be a good framework for selecting for feed efficiency traits.
Conclusion: To assess the effects of DMI, ECM, and MBW when selecting for RFI, the mean EBV for all traits was used, based on the top 10% of bulls for RFI. The EBV values from the top 10% of bulls for RFI were 2.79 standard deviations below the population mean, indicating that a reduction in RFI (increase in efficiency) would be expected in the population if these animals were selected. Selection based on RFI favors animals with lower RFI values. Negative RFI values indicate that the animal is consuming less feed than expected based on production and other moderating factors. The estimated genetic correlations between traits over time suggest that potentially different metabolic mechanisms are active between lactation stages. Given the changes in these correlations over the lactation period, it is important to consider different lactation stages separately and to model all traits simultaneously in a selection program. Understanding the relationship between DMI, MBW, ECM, RFI and traits such as energy balance and body condition score is important for an integrated and successful approach to breeding for FE traits. Also, animals with high genetic potential for production tend to consume more feed to meet their high production needs. The aim of addressing feed use inefficiency is a potential way to improve farm efficiency while reducing producer costs.
Background: With the increase in feed costs and the environmental impacts becoming more apparent, the global population growing, and a greater focus on sustainability, methods for improving the efficiency of converting feed into milk in dairy cows have become increasingly important. An efficient cow is one that consumes less feed for the same amount of milk and milk solids it produces, and remains healthy and fertile. This makes it possible to reduce costs without reducing production. Through technological advances, accurate measurements of feed efficiency traits, such as dry matter intake, body weight, residual feed intake, and milk composition, have become readily available. Therefore, this study aimed to estimate the genetic parameters of the feed efficiency trait and potential selection strategies for incorporating this economic trait into breeding programs.
Methods: In the present study, 35,478 records of dry matter intake (DMI), 36,353 records of energy-modified milk (ECM), 27,896 records of metabolic body weight (MBW), and 24,508 records of residual feed intake (RFI) from 5,123 first lactation Holstein cows from the National Breeding Center during 2008 to 2018 were used. In addition, cows with a first calving age greater than 40 months were excluded from the analysis. The pedigree file included information for up to 10 generations for the phenotyped cows, resulting in a pedigree file with 9,471 animals, of which 978 and 3,577 were sires and dams, respectively. Phenotypic data recorded from 5 to 305 DIM were used to create biweekly lactation (Bi-WL) groups by dividing DMI into 14 (total 21 Bi-WL classes). The main data were collected in the form of daily (DMI and MBW), weekly (ECM and MBW) and monthly (MBW) records. Therefore, animals should have at least two records for DMI, ECM and MBW in a given Bi-WL. Heritability, variance components and genetic correlations between lactation weeks were calculated during the lactation period using a random regression model. In order to estimate the covariance between traits, a two-trait random regression animal model was used. Homogeneous residual variances were considered for the two-trait analysis, to enable convergence of the results. Both single and two-trait models were analyzed using the AIREML algorithm by WOMBAT. Also, correction values for the random regression coefficients were implemented using the BLUP method by BLUPF90.
Results: Heritability estimates ranged from 0.17 to 0.41 for dry matter intake, 0.28 to 0.45 for energy-corrected milk, 0.48 to 0.78 for metabolic body weight, and 0.1 to 0.2 for residual feed intake. Heritability estimates for RFI were moderate, ranging from 0.2 in the first Bi-WL class to 0.13 in the last Bi-WL class with minimal heritability in Bi-WL classes 14 and 15 (0.1). Variation between lactation stages for RFI was also observed. Genetic correlations for RFI ranged from 0.26 to 0.99. The lowest correlations were observed between mid-lactation (Bi-WL classes 5 to 11; approximately 70 to 154 DIM) and late lactation (Bi-WL classes 12 to 21; approximately 168 to 305 DIM). Intratrait genetic correlations were strongest between closely spaced weeks of lactation for all traits studied. Genetic correlations between RFI and MBW ranged from 0.64 in early lactation to -0.45 in late lactation. Analysis of the proportion of bulls sharing the top 10% throughout lactation indicated that bulls with the highest ranking for RFI in mid-lactation were likely to remain among the top bulls throughout the rest of lactation. Also, the post-peak lactation period, i.e. days 140 to 226 of lactation (Bi-WL class 10 to 19), could be a good framework for selecting for feed efficiency traits.
Keywords: Dairy Cows, Dry Matter Intake, Energy Corrected Milk, Feed Efficiency, Metabolic Body Weight, Residual Feed Intake
Type of Study: Research |
Subject:
ژنتیک و اصلاح نژاد دام
Received: 2024/04/21 | Accepted: 2024/08/12
Received: 2024/04/21 | Accepted: 2024/08/12
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