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Variance component estimation is a statistical approach used to quantify how much of the total variation in data is due to different sources of random influence. In many studies, especially those with hierarchical or grouped data, variation arises not only from measurement error but also from random factors such as subjects, experimental units, or clusters. Estimating variance components involves dividing the overall variability into parts associated with each of these random factors so that the proportion of total variability attributable to each source can be understood.^[1] In practice, a variance component model (often a random‑effects or mixed model) is used to represent the data structure, and specialized methods like analysis of variance (ANOVA)‑based estimators or likelihood‑based methods (such as maximum likelihood or restricted maximum likelihood) are applied to estimate the unknown variance parameters. These estimates help researchers assess relative contributions of different random effects and guide interpretation in fields like experimental design, genetics, and quality assessment.^[2]

"Variance components estimation" is the estimation of error variance and other random effects by equating mean squares to their expected values in analysis of variance, later extended to balanced and unbalanced data and now replaced by maximum likelihood and restricted maximum likelihood methods.^[3]

Estimation of genetic parameters is the process of estimating genetic and phenotypic variances and covariances, such as heritability and repeatability, which describe how genetic and environmental factors contribute to traits in a population.^[4]

Methods of estimation are procedures used to calculate genetic parameters, such as variances and covariances, which describe the contribution of genetic and environmental factors to traits. Common approaches include the analysis of variance (ANOVA) method, maximum likelihood, and restricted maximum likelihood, depending on the data structure and study design.^[5]

Estimates of genetic parameters are essential in breeding programs because they help breeders understand how much of the variation in traits is due to genetics and how much is due to the environment, allowing them to predict the potential for genetic improvement and make informed selection decisions to increase desirable traits in future generations. These estimates are used to optimise breeding strategies and improve the efficiency of selection across livestock and crop populations.^[6]

Estimation of genetic parameters provides valuable information for breeding and selection, helping improve desired traits efficiently. However, the accuracy of these estimates depends on the size and structure of the data, the statistical method used, and assumptions such as normality of traits. Inaccurate estimates can lead to suboptimal selection decisions, so careful experimental design and appropriate estimation methods are important.^[7][8][9]

Genetic research in livestock is increasingly focused not only on improving production traits but also on addressing environmental and sustainability goals; for example, recent efforts highlight how researchers are analysing genetic variation related to methane emissions in grazing cattle to breed animals with lower emissions and better long‑term efficiency, showing how genetic evaluations and parameter estimates inform practical breeding strategies in the beef industry. ^[10]