Related Experiment Video
Updated: Aug 13, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
Modelling number of services per conception of Japanese Black cattle by random regression
Abstract:
Repeated records of number of services per conception (NSC) were collected on 607 Japanese Black cows. Data were analysed by random regression (RRM) and multiple trait (MTM) models, considering NSC in each parity as a separate trait. The chosen RRM included additive genetic and permanent environmental effects fitted with a third-order Legendre polynomials of parity. Heritabilities (h2) estimated by RRM decreased along the NSC trajectory from 0.15 in the first parity to 0.04 in the sixth parity and then increased up to 0.22 in the 10th parity. The corresponding estimates obtained by MTM ranged between 0.04 in parity 9 and 0.13 in parity 1. Permanent environmental proportions (p2) of the total phenotypic variance estimated by RRM showed similar pattern and magnitude to those of h2 estimated by the same method. On the contrary, the p2 estimated by MTM ranged between 0.04 in the first parity and 0.11 in the 10th parity. Additive genetic (r(G)), permanent environmental (r(P)) and phenotypic (r(PH)) correlations were also estimated. The values estimated by RRM between adjacent parities were higher than those of parities far apart. The corresponding values estimated by MTM were lower than those estimated by RRM with no certain trend. The results indicated that NSC in heifers is more heritable than NSC in cows with different parities. Reproductive traits are economically important traits and hence, they should be considered in breeding goals.
Related Concept Videos
Multiple Regression
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...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
On...
Mechanistic Models: Compartment Models in Individual and Population Analysis
Regression Analysis
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:
Distributions to Estimate Population Parameter
Estimating Population Standard Deviation
