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Additive Main Effects and Multiplicative Interaction Analysis of Two International Maize Cultivar Trials
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1990
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Field TrialPrecision AgricultureEngineeringGeneticsAgricultural EconomicsAdditive Main EffectsCrop ImprovementMultiplicative Interaction AnalysisSustainable AgricultureGenotype‐environment InteractionBiostatisticsPublic HealthStatisticsQuantitative GeneticsCrop YieldStatistical GeneticsGenotype‐environmental InteractionAgricultural BiotechnologyGenetic VariationInternational MaizePlant BreedingAgricultural ScienceAgricultural ModelingCrop ProtectionCrop Science
CIMMYT evaluates maize germplasm in extensive international trials, but genotype‑environment interaction limits yield estimate accuracy and complicates genotype selection for specific environments. The study aimed to apply the AMMI method to analyze data from two international maize cultivar trials. The AMMI approach uses additive genotype and environment effects with multiplicative genotype‑environment interaction terms. In trial one, AMMI with one principal component axis increased yield estimate precision by a factor of 4.30 and identified a different top‑yielding genotype in 72% of environments, while in trial two, AMMI without principal components increased precision by a factor of 2.59.
The methodology used by the International Maize and Wheat Improvement Center (CIMMYT) to develop and improve its maize ( Zea mays L.) germplasm involves evaluation of families or experimental varieties in extensive international testing trials. The genotype‐environmental interaction is produced by differential genotypic responses to varied environmental conditions. Its effect is to limit the accuracy of yield estimates and complicate the identification of specific genotypes for specific environments. The objective of this study was to use the Additive Main effects and Multiplicative Interaction (AMMI) method, with additive effects for genotypes and environments and multiplicative terms for genotype‐environment interaction, for analyzing data from two international maize cultivar trials. Results from the first trial were: (i) predictive assessment selected AMMI with one principal component axis, (ii) AMMI increased the precision of yield estimates equivalent to increasing the number of replications by a factor of 4.30, (iii) AMMI provided much insight into genotype‐environment interactions, and (iv) AMMI selected a different highest‐yielding genotype than did treatment means in 72% of the environments. Results for the second trial were that predictive assessment selects the AMMI with none of the principal component axes, which increased precision equivalent to increase the number of replications by a factor of 2.59.