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LG biplot: a graphical method for mega-environment investigation using existing crop variety trial data.

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Crop breeding requires understanding genotype by environment (GE) interactions. This study introduces a new method to delineate mega-environments using historical trial data, enabling better crop cultivar adaptation.

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Area of Science:

  • Agricultural Science
  • Plant Breeding
  • Genetics

Background:

  • Genotype by environment (GE) interactions mean no single crop cultivar excels in all regions.
  • Effective crop adaptation requires dividing growing regions into mega-environments and breeding specific cultivars for each.
  • Delineating mega-environments relies on identifying repeatable GE patterns from multi-year, multi-location trials.

Purpose of the Study:

  • To present a novel method for mega-environment delineation using unbalanced multi-year, multi-location crop variety trial data.
  • To enable the utilization of abundant historical data for identifying repeatable GE patterns.
  • To understand the extent of unrepeatable GE within locations and mega-environments.

Main Methods:

  • Development of a new statistical method to analyze unbalanced multi-year, multi-location crop variety trial data.
  • Extraction of repeatable genotype by environment (GE) patterns from historical datasets.
  • Application of the method to delineate mega-environments and assess GE variability.

Main Results:

  • The proposed method successfully utilizes unbalanced historical trial data for mega-environment delineation.
  • Repeatable GE patterns were identified, allowing for more precise mega-environment definition.
  • The scope of unrepeatable GE was quantified at local and mega-environment levels.

Conclusions:

  • The new method provides a powerful tool for leveraging existing crop trial data to define mega-environments.
  • Accurate mega-environment delineation based on repeatable GE patterns is crucial for developing regionally adapted crop cultivars.
  • This approach enhances the efficiency and effectiveness of crop breeding programs.