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Identifying Mega‐Environments and Targeting Genotypes
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1997
Year
GeneticsAgricultural EconomicsTotal VariationGenomicsLandscape GeneticsYield PredictionTargeting GenotypesGenetic DiversitySustainable AgriculturePublic HealthQuantitative GeneticsCrop YieldStatistical GeneticsCrop Growth ModelingGenetic VariationLouisiana CornGenotype‐environment InteractionsPopulation GeneticsAgricultural ModelingGenetic AdmixturePopulation GenomicsMedicine
To maximize yield across heterogeneous crop regions, it is often necessary to subdivide the area into relatively homogeneous mega‑environments and breed genotypes adapted to each. The study aims to identify criteria for evaluating mega‑environment analyses and to apply the AMMI model to this purpose. The authors illustrate the AMMI‑based analysis with a Louisiana corn trial, using flexible statistical strategies that focus on relevant variation, integrate genotype and environment information, and are enhanced by novel graphs to identify mega‑environments. Preliminary results show that a small, workable number of mega‑environments can exploit interactions and increase yields.
To maximize yield throughout a crop's heterogeneous growing region, despite differences in cultivar rankings from place to place due to genotype‐environment interactions, frequently it is necessary to subdivide a growing region into several relatively homogeneous mega‐environments and to breed and target adapted genotypes for each mega‐environment. The objectives of this study are to identify relevant criteria for evaluating mega‐environment analyses and to apply the A dditive M ain Effects and M ultiplicative I nteraction (AMMI) model to mega‐environment analysis. The proposed analysis is illustrated using a Louisiana corn ( Zea mays L.) trial. Statistical strategies for identifying mega‐environments should meet four criteria: flexibility in handling yield trials with various designs, focus on that fraction of the total variation that is relevant for identifying mega‐environments, duality in giving integrated information on both genotypes and environments, and relevance for the primary objective of showing which genotypes win where. The AMMI model meets these criteria effectively when the usual biplots are supplemented with several new types of graphs designed to address questions about mega‐environments. Preliminary results indicate that a small and workable number of mega‐environments often suffices to exploit interactions and increase yields.