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Grain weight (GW) and number per unit area of land (GN) are the primary components of grain yield in wheat. In segregating populations both yield components often show a negative correlation among themselves. Here we use a recombinant doubled haploid population of 105 individuals developed from the CIMMYT varieties Weebill and Bacanora to understand the relative contribution of these components to grain yield and their interaction with each other. Weebill was chosen for its high GW and Bacanora for high GN. The population was phenotyped in Mexico, Argentina, Chile and the UK. Two loci influencing grain yield were indicated on 1B and 7B after QTL analysis. Weebill contributed the increasing alleles. The 1B effect, which is probably caused by to the 1BL.1RS rye introgression in Bacanora, was a result of increased GN, whereas, the 7B QTL controls GW. We concluded that increased in GW from Weebill 7B allele is not accompanied by a significant reduction in grain number. The extent of the GW and GN trade-off is reduced. This makes this locus an attractive target for marker assisted selection to develop high yielding bold grain varieties like Weebill. AMMI analysis was used to show that the 7B Weebill allele appears to contribute to yield stability.

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The understanding of ecophysiological basis of wheat (Triticum aestivum L.) grain yield potential provides a useful framework to complement conventional breeding aimed at achieving genetic gains. This study analyzed the ecophysiological performance of an elite wheat mapping population (105 double-haploid lines derived from two modern cultivars, Bacanora and Weebil, with similar phenology but different and stable combinations of grain number per area unit (GN) and grain weight (GW) resulting in high grain yield) grown in four contrasting high-yielding environments, to determine the most successful strategies to increase grain yield potential. Main effect of environment on grain yield was significant (p < 0.0001) but the genotypic component was larger than genotype × environment interaction (30%). A robust and positive relationship between grain yield and biomass production was observed across all environments (r 2 > 0.82, p < 0.0001), and relatively high harvest indexes were expressed (0.39-0.51). While GN was clearly the dominant numerical component in terms of association with grain yield (r 2 > 0.51, p < 0.0001), a wide range in both components (i.e., GN and GW) was observed across all environments. This population represents a valuable resource for prebreeding studies, as the transgressive segregation in physiological and numerical yield components in combination with favorable expression of all agronomic traits could allow a fine phenotyping and mapping to identify key traits and quantitative trait loci linked with grain yield.