Abstract

Introduction: The most desirable linear neutral prediction (BLUP) is a standard method for estimating the random effects of a hybrid model. This approach was originally developed in animal breeding to estimate breeding values and is now widely used in many fields of research. The main practical advantages of using REML/BLUP are: It allows the comparison of individuals or species over time (generation, year) and space (location, block). Possibility of simultaneous correction of environmental effects, estimation of variance components, and prediction of genetic values. The best BLUP prediction method, which estimates the averages with high accuracy, especially in mixed models, is also used to evaluate multi-environment experimental data (MET). Blup is one method is statistical. Pedigree-based blup method. Materials and methods: The BLUP method achieves this goal by combining phenotypic data and information on pedigree relationships through an index, known as family index selection. This index, which is estimated based on the coefficient of intra-class correlation, exploits the relationships of individuals within a family compared to other families in the population. Results: The results: show that BLUP has good prediction accuracy compared to other methods. Pedigree-based BLUP method can increase selection yield in production-related traits in P. zonale or shelf life of D. caryophyllus L.

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