Selecting High-Performing and Stable Pea Genotypes in Multi-Environmental Trial (MET): Applying AMMI, GGE-Biplot, and...

Area of Science:

• Agricultural Science
• Plant Breeding
• Genetics

Background:

• Crop breeding programs generate extensive trait data valuable for optimizing improvement pipelines.
• Advanced yield trials (AYT) provide crucial data for assessing genotype performance.

Purpose of the Study:

• To analyze and test key aspects of pea breeding using ten years of advanced yield trial data.
• To evaluate the predictive success of Best Linear Unbiased Prediction (BLUP) and AMMI (Additive Main effects and Multiplicative Interaction) models.
• To identify statistical tools for understanding genotype-environment interactions (GE) and selecting adaptable pea varieties.

Main Methods:

• Leveraged ten years (2012-2021) of AYT data for green, yellow, and winter peas.
• Utilized six balanced datasets to compare BLUP and AMMI family models via cross-validation.
• Employed AMMI and GGE (Genotype + GE interaction) biplots to analyze GE interactions and genotype adaptability.

Main Results:

• BLUP models demonstrated higher predictive accuracy than AMMI models.
• AMMI and GGE biplots effectively identified genotypes with specific or broad adaptability.
• Significant yield reductions (80-87%) were observed in unfavorable environments, linked to weather variability (heat, low precipitation).

Conclusions:

• BLUP is accurate for prediction, but AMMI/GGE are vital for selecting broadly adapted genotypes.
• Understanding GE interactions is crucial for optimizing pea variety selection.
• Environmental factors, particularly weather, significantly impact pea seed yield variability.