通过对预期料摄入量的回归来估计乳牛料效率的育种价值
M H Lidauer1, E Negussie1, E A Mäntysaari1
1Natural Resources Institute Finland (Luke), 31600 Jokioinen, Finland.
Animal : an international journal of animal bioscience
|August 13, 2023
概括
一个新的指标,预期料摄入量的回归 (ReFI),改善了奶牛料效率的选择. ReFI确定了与剩余料摄入量 (RFI) 或遗传剩余料摄入量 (gRFI) 相比,每单位能量摄入量产生更多能量校正的牛奶.
科学领域:
- 动物科学动物科学
- 遗传学 遗传学 是一个
- 营养生理学 营养生理学
背景情况:
- 奶牛的料效率对于利能力和可持续性至关重要.
- 像剩余料摄入量 (RFI) 和遗传剩余料摄入量 (gRFI) 这样的现有指标在准确评估料利用方面存在局限性.
- 准确评估料效率需要有效对比实际料摄入量与基于生理需求的预期料摄入量的指标.
研究的目的:
- 引入和评估一种新的指标,即预期料摄入量的回归 (ReFI),用于评估奶牛的料效率.
- 在基因变异,遗传性和与生产特征相关性方面,将ReFI的表现与传统指标 (RFI和gRFI) 进行比较.
- 确定ReFI在识别遗传上优越,高效率的奶牛方面的优越性.
主要方法:
- 通过回归干物摄入量 (DMI) 到预期的DMI,使用包含能量需求配方的随机回归模型开发了ReFI指标.
- 将ReFI,RFI和gRFI应用于来自654只北欧红色母乳母牛的18581个料效率记录的数据集.
- 估计了差异成分,与摄入量和生产特征的遗传相关性,并比较了根据每个指标选择的奶牛的排名和表现.
主要成果:
- 与RFI或gRFI相比,ReFI在料效率方面显示出更高的估计遗传变异 (4.7%) 和遗传性 (0.23).
- ReFI与DMI无遗传相关,与能量校正牛奶 (ECM) 有负相关,与RFI (与DMI和代谢BW正相关) 和gRFI (与DMI正相关) 不同.
- 根据ReFI育种值进行的选择,发现每单位可代谢能量摄入量ECM高12.3%,明显超过RFI (4.3%) 和gRFI (5.9%).
结论:
- ReFI指标提供了一个更准确,更有效的方法来建模乳牛的料利用效率.
- ReFI有助于识别出具有较高奶量和更高料转换效率的优质奶牛.
- 这种新型指标简化了料效率评估,并提供了直接解释为节省料的百分比的育种值.
相关概念视频
Multiple Regression
3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.0K
Regression Analysis
5.8K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
5.8K
Regression Toward the Mean
6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Determination of Expected Frequency
2.2K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.2K
Microsoft Excel: Regression Analysis
691
Regression analysis in Microsoft Excel is a powerful statistical method for examining the relationship between a dependent variable and one or more independent variables. It's used extensively in fields such as economics, biology, and business to predict outcomes, understand relationships, and make data-driven decisions. The most common type is linear regression, which attempts to fit a straight line through the data points to model the relationship between variables.
To perform regression...
To perform regression...
691
Residuals and Least-Squares Property
7.4K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.4K


