Linear mixed model to identify the relationship between grain yield and other yield related traits and genotype selection for sorghum

Mulugeta Tesfa; Temesgen Zewotir; Solomon Assefa Derese; Denekew Bitew Belay; Hussein Shimelis

doi:10.1016/j.heliyon.2023.e17825

Linear mixed model to identify the relationship between grain yield and other yield related traits and genotype selection for sorghum

Heliyon. 2023 Jul 1;9(7):e17825. doi: 10.1016/j.heliyon.2023.e17825. eCollection 2023 Jul.

Authors

Mulugeta Tesfa^{1

2}, Temesgen Zewotir³, Solomon Assefa Derese^{4

5}, Denekew Bitew Belay¹, Hussein Shimelis⁵

Affiliations

¹ Department of Statistics, College of Science, Bahir Dar University, P. O. Box, 79, Bahir Dar, Ethiopia.
² Department of Statistics, College of Natural and Computational Sciences, Wollo University, P. O. Box, 1145, Dessie, Ethiopia.
³ School of Mathematics, Statistics & Computer Science, University of KwaZulu-Natal, Durban, 4041, South Africa.
⁴ Department of Plant Breeding, College of Agriculture, Woldia University, P. O. Box, 53, Woldia, Ethiopia.
⁵ School of Agricultural, Earth and Environmental Sciences, Africa Centre for Crop Improvement, College of Agriculture, Engineering & Science, University of KwaZulu-Natal, Pietermaritzburg, Durban, 4041, South Africa.

Abstract

Sorghum is the most popular crop in arid and semi-arid areas, especially in Sub-Saharan African countries. Genotype effects, environmental and the interaction of genotype by environmental factors have an influence on phenotypic traits. The aim of the study is to identify the relationship between grain yield and other yield-related traits and select the genotypes which perform better in grain yield as well as to examine the association between the uncorrelated phenotypic traits and grain yield via mixed model. The data was generated using a lattice square design. Principal component analysis was used to generate uncorrelated variables for the mixed model. The study revealed that there was a difference in grain yield due to the treatment and there was a pairwise relationship among the phenotypic variables. 77.12% of the total variance of the original phenotypic variables was explained by the first three principal components and decided to use PCAs as input variables for the mixed model. All PCs had significant effects on grain yield as well as grain yield variability due to random effects associated with genotypes, genotype interaction by treatment, and replication within the treatment. The variability of grain yield due to genotype effect was explained about 45.73%, the variation of grain yield due to the interaction of genotype by the treatment was also explained about 39.06% and 1.55% of the variation of grain was explained by replication within treatment. The best performer genotypes recommended for mass production were G40 (Genotype 40), G186 (Genotype 186) and G196 (Genotype 196) without any constraint of environment. The genotypes recommended for mass production under irrigation conditions were G40 (Genotype 40), G62 (Genotype 62) and G192 (Genotype 192). G26 (Genotype 26), G55 (Genotype 55) and G49 (Genotype 49) were the genotypes recommended for mass production under stress conditions. Overall, the study recommends using a mixed model to fit the grain yield, and future work will focus on to evaluate the performance of genotypes under different environments and years of production.

Keywords: Best performer; Fixed effects; Genotype selection; Multivariate analysis; Principal component; Random effects.