Modeling temporal genetic variability using mixed models improves yield stability and selection efficiency in Coffea...

Area of Science:

• Agricultural Science
• Plant Breeding
• Genetics

Background:

• Climatic variability poses challenges for Coffea canephora breeding programs.
• Identifying genotypes with both high productivity and temporal stability across seasons is crucial.

Purpose of the Study:

• To evaluate 44 Coffea canephora genotypes over four crop seasons.
• To identify the most suitable statistical model for genetic evaluation under variable conditions.

Main Methods:

• Utilized mixed linear models (REML/BLUP) with seven variance-covariance structures.
• Employed the flexible model M7, featuring an unstructured genetic covariance matrix and year-specific residual variances.
• Assessed plot-level heritability, genotype-mean heritability, repeatability, and selective accuracy.

Main Results:

• Model M7 demonstrated the best fit, indicated by the lowest AIC and BIC.
• Heritability estimates were high (plot-level: 0.64-0.68; genotype-mean: 0.84-0.86), with repeatability at 0.89 and selective accuracy at 0.92-0.94.
• Identified genotypes Bicudo, A1, and AD1 as having high predicted genotypic values and persistence indices, despite atypical genetic correlations in 2023 due to heat stress.

Conclusions:

• Flexible mixed models enhance the reliability of genetic evaluations for Coffea canephora.
• These approaches support resilience-oriented breeding strategies, accelerating genetic gain and identifying superior genotypes for variable environments.