Berry D P, Coffey M P, Pryce J E, de Haas Y, Løvendahl P, Krattenmacher N, Crowley J J, Wang Z, Spurlock D, Weigel K, Macdonald K, Veerkamp R F
Animal & Grassland Research and Innovation Centre, Teagasc, Moorepark, Co. Cork, Ireland.
Animal and Veterinary Sciences, Scotland's Rural College (SRUC), Easter Bush Campus, Midlothian EH25 9RG, United Kingdom.
J Dairy Sci. 2014;97(6):3894-905. doi: 10.3168/jds.2013-7548. Epub 2014 Apr 14.
Feed represents a large proportion of the variable costs in dairy production systems. The omission of feed intake measures explicitly from national dairy cow breeding objectives is predominantly due to a lack of information from which to make selection decisions. However, individual cow feed intake data are available in different countries, mostly from research or nucleus herds. None of these data sets are sufficiently large enough on their own to generate accurate genetic evaluations. In the current study, we collate data from 10 populations in 9 countries and estimate genetic parameters for dry matter intake (DMI). A total of 224,174 test-day records from 10,068 parity 1 to 5 records of 6,957 cows were available, as well as records from 1,784 growing heifers. Random regression models were fit to the lactating cow test-day records and predicted feed intake at 70 d postcalving was extracted from these fitted profiles. The random regression model included a fixed polynomial regression for each lactation separately, as well as herd-year-season of calving and experimental treatment as fixed effects; random effects fit in the model included individual animal deviation from the fixed regression for each parity as well as mean herd-specific deviations from the fixed regression. Predicted DMI at 70 d postcalving was used as the phenotype for the subsequent genetic analyses undertaken using an animal repeatability model. Heritability estimates of predicted cow feed intake 70 d postcalving was 0.34 across the entire data set and varied, within population, from 0.08 to 0.52. Repeatability of feed intake across lactations was 0.66. Heritability of feed intake in the growing heifers was 0.20 to 0.34 in the 2 populations with heifer data. The genetic correlation between feed intake in lactating cows and growing heifers was 0.67. A combined pedigree and genomic relationship matrix was used to improve linkages between populations for the estimation of genetic correlations of DMI in lactating cows; genotype information was available on 5,429 of the animals. Populations were categorized as North America, grazing, other low input, and high input European Union. Albeit associated with large standard errors, genetic correlation estimates for DMI between populations varied from 0.14 to 0.84 but were stronger (0.76 to 0.84) between the populations representative of high-input production systems. Genetic correlations with the grazing populations were weak to moderate, varying from 0.14 to 0.57. Genetic evaluations for DMI can be undertaken using data collated from international populations; however, genotype-by-environment interactions with grazing production systems need to be considered.
饲料成本在奶牛生产系统的可变成本中占很大比例。国家奶牛育种目标中明确未纳入采食量指标,主要是因为缺乏用于选择决策的信息。然而,不同国家都有个体奶牛的采食量数据,大多来自研究或核心牛群。但这些数据集单独来看都不够大,无法得出准确的遗传评估结果。在本研究中,我们整理了来自9个国家10个群体的数据,并估计了干物质采食量(DMI)的遗传参数。共有来自6957头奶牛第1至5胎次的10068条记录以及1784头生长中的小母牛的224174条测定日记录。对泌乳奶牛的测定日记录拟合随机回归模型,并从这些拟合曲线中提取产犊后70天的预测采食量。随机回归模型分别为每个泌乳期包含一个固定的多项式回归,以及产犊的牛群 - 年份 - 季节和实验处理作为固定效应;模型中的随机效应包括每个胎次个体动物相对于固定回归的偏差以及每个牛群相对于固定回归的平均特定偏差。产犊后70天的预测DMI用作后续使用动物重复性模型进行遗传分析的表型。整个数据集中产犊后70天预测奶牛采食量的遗传力估计值为0.34,在不同群体中从0.08到0.52不等。各泌乳期采食量的重复性为0.66。在有小母牛数据的2个群体中,生长中小母牛采食量的遗传力为0.20至0.34。泌乳奶牛和生长中小母牛采食量之间的遗传相关性为0.67。使用合并的系谱和基因组关系矩阵来改善群体间的联系,以估计泌乳奶牛DMI的遗传相关性;5429头动物有基因型信息。群体分为北美、放牧、其他低投入和高投入欧盟群体。尽管遗传相关性估计值的标准误较大,但不同群体间DMI的遗传相关性估计值从0.14到0.84不等,但在代表高投入生产系统的群体之间更强(0.76至0.84)。与放牧群体的遗传相关性较弱至中等,从0.14到0.57不等。可以使用从国际群体整理的数据进行DMI的遗传评估;然而,需要考虑与放牧生产系统的基因型 - 环境互作。