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AMMI, BLUP, and WAASB Stability Models for the Selection of Stable Chickpea (Cicer arietinum L.) Genotypes in Different Agroecological Regions of Pakistan

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The genotype×environment interaction effect is challenging in identifying stable genotypes across multiple environments. Present study was performed to check stability and adaptability of 16 chickpea genotypes in 10 different agro-ecological regions of Pakistan. Further, the importance of stability models such as additive main effects and multiplicative interaction analysis, best linear unbiased prediction, and Weighted average of absolute scores from singular value decomposition of BLUPs was assessed to identify stable genotypes. AMMI ANOVA showed that main effects of genotype, environment along with GEI were significant and first two principal components explained about 64.2% of GEI variance. LMM-BLUP found that 98.8% phenotypic variance was due to GEI variance. G1 with low PC1 score in AMMI biplot analysis was identified as the most stable and high yielding genotype. Cross-validation carried out for AMMI9 and BLUP found BLUP as the most accurate model for present data. Graphical display using BLUP based predicted grain yield ranked genotypes regarding stability and adaptability.  However, Y× WAAS graphs were more useful as they categorized genotypes and environments into clear cut groups based on their productivity and stability. We concluded that AMMI, BLUP, and WAASB methods could be used to select stable and well adapted genotypes.
Title: AMMI, BLUP, and WAASB Stability Models for the Selection of Stable Chickpea (Cicer arietinum L.) Genotypes in Different Agroecological Regions of Pakistan
Description:
The genotype×environment interaction effect is challenging in identifying stable genotypes across multiple environments.
Present study was performed to check stability and adaptability of 16 chickpea genotypes in 10 different agro-ecological regions of Pakistan.
Further, the importance of stability models such as additive main effects and multiplicative interaction analysis, best linear unbiased prediction, and Weighted average of absolute scores from singular value decomposition of BLUPs was assessed to identify stable genotypes.
AMMI ANOVA showed that main effects of genotype, environment along with GEI were significant and first two principal components explained about 64.
2% of GEI variance.
LMM-BLUP found that 98.
8% phenotypic variance was due to GEI variance.
G1 with low PC1 score in AMMI biplot analysis was identified as the most stable and high yielding genotype.
Cross-validation carried out for AMMI9 and BLUP found BLUP as the most accurate model for present data.
Graphical display using BLUP based predicted grain yield ranked genotypes regarding stability and adaptability.
  However, Y× WAAS graphs were more useful as they categorized genotypes and environments into clear cut groups based on their productivity and stability.
We concluded that AMMI, BLUP, and WAASB methods could be used to select stable and well adapted genotypes.

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