Assessing genotype adaptability and stability in perennial forage breeding trials using random regression models for...

Area of Science:

• Plant breeding
• Quantitative genetics
• Forage agronomy

Background:

• Genotype selection for perennial forage dry matter yield (DMY) relies on longitudinal data, capturing temporal trends crucial for identifying high-performing varieties across seasons.
• Understanding genotype-specific performance over time is essential for successful breeding programs in forage species.

Purpose of the Study:

• To present and evaluate a random regression model (RRM) approach for selecting genotypes based on longitudinal DMY data.
• To propose methods for estimating adaptability and stability using RRM-derived reaction norms.
• To assess the utility of RRM in analyzing genotype performance across multiple breeding trials and perennial species.

Main Methods:

• Applied random regression models (RRM) to longitudinal DMY data from 10 breeding trials across three perennial species: alfalfa, guineagrass, and brachiaria.
• Estimated genotype adaptability using the area under the curve (AUC) and stability using the coefficient of variation (CV) of reaction norms.
• Analyzed the covariance structure approximated by RRM, identifying an autoregressive pattern.

Main Results:

• The random regression model (RRM) consistently approximated the (co)variance structure of longitudinal DMY data with an autoregressive pattern.
• RRM provided valuable insights into genotype seasonality and performance trends in perennial forage breeding.
• Reaction norm interpretation enabled breeders to select genotypes based on their specific seasonal responses.

Conclusions:

• Random regression models (RRM) are recommended for analyzing longitudinal traits in perennial forage breeding trials.
• RRM facilitates genotype selection by providing a framework to interpret reaction norms and understand seasonal performance.
• The proposed methods for adaptability and stability estimation enhance the RRM approach for forage genetic improvement.