Progress depends on both the size and efficiency of a research project. Increasing the number of genotypes includes more of the markedly superior entries, while increasing the number of replications improves accuracy and hence promotes correct identification of the truly superior entries. It also quantifies the deleterious impact of suboptimal choices. It is applicable to trials with at least three genotypes and three environments. That is, the design and the analysis of an experiment interact, so the best design depends on the efficiency of the analysis.
Efficient experiments require good designs and good analyses, appropriately integrated.
Also, the advantages are a mixture of quantifiable factors, like an AMMI gain factor of 2.15, and qualitative factors, like a deeper understanding of complex GE interactions. Particularly because most breeding programs have several concurrent stages of selection and advantages compound, the actual figure in specific cases is frequently larger. In other words, routinely some of the potential benefit from yield-trial data is not captured by conventional analyses and is wasted. I suggest that this counter thesis, rather than the present thesis, is the one that would prove exceedingly difficult to defend. It is not plausible that statistics has made no meaningful advance over the past one-half century.