Jose J. Marulanda

Orange‐fleshed sweetpotato (OFSP) [ Ipomoea batatas (L.) Lam] often lacks high dry matter (DM) in elite breeding material. The objectives were to examine the potential to combine yield with high root DM and high root β‐carotene (BC) content by variance‐covariance analysis and optimization of breeding scenarios for “OFSP dry and starchy,” which was exemplarily shown for the humid tropics of Peru. The trial series across seasons suggested a good chance to combine yield and quality (high DM and high BC). The unique data were used to estimate variance components and correlations among four yield traits and four quality traits. For the optimization of selections in our breeding program, we used the selection response by Cochran's approach and developed a small R script. Responses to selection were predicted for storage root yield and a modified Elston index aggregating eight traits. The differences between two‐ and three‐stage selection scenarios were small. For storage root yield, the recommended optimized two‐stage selection increased the response relative to the standard (one‐stage selection 100%) to 141.6% for 2268 plots and to 189.1% for 540 plots; the corresponding optimized three‐stage selection increased the response to 144.3% for 2268 plots and to 190.5% for 540 plots. A two‐stage selection is recommended with two to three locations at the first stage and 6–12 locations at the second stage, according to low‐ or high‐test capacity, respectively. Optimization of breeding schemes is much more important for smaller breeding programs than those with larger test capacity, and this allows testing even larger numbers of genotypes in a small breeding program.

Key message: A breeding strategy combining genomic with one-stage phenotypic selection maximizes annual selection gain for net merit. Choice of the selection index strongly affects the selection gain expected in individual traits. Selection indices using genomic information have been proposed in crop-specific scenarios. Routine use of genomic selection (GS) for simultaneous improvement of multiple traits requires information about the impact of the available economic and logistic resources and genetic properties (variances, trait correlations, and prediction accuracies) of the breeding population on the expected selection gain. We extended the R package “selectiongain” from single trait to index selection to optimize and compare breeding strategies for simultaneous improvement of two traits. We focused on the expected annual selection gain (ΔG_a)for traits differing in their genetic correlation, economic weights, variance components, and prediction accuracies of GS. For all scenarios considered, breeding strategyGSrapid(one-stage GS followed by one-stage phenotypic selection) achieved higher ΔG_athan classical two-stage phenotypic selection, regardless of the index chosen to combine the two traits and the prediction accuracy of GS. The Smith–Hazel or base index delivered higher ΔG_afor net merit and individual traits compared to selection by independent culling levels, whereas the restricted index led to lowerΔG_ain net merit and divergent results for selection gain of individual traits. The differences among the indices depended strongly on the correlation of traits, their variance components, and economic weights, underpinning the importance of choosing the selection indices according to the goal of the breeding program. We demonstrate our theoretical derivations and extensions of the R package “selectiongain” with an example from hybrid wheat by designing indices to simultaneously improve grain yield and grain protein content or sedimentation volume.

Key message A breeding strategy with moderate nursery selection followed by genomic selection and one-stage phenotypic selection maximizes annual selection gain for grain yield across a wide range of hybrid breeding scenarios. Abstract Genomic selection (GS) is a promising method for the selection of quantitatively inherited traits but its most effective implementation in routine hybrid breeding schemes requires further research. We compared five breeding strategies and varied their available budget, the costs for doubled haploid (DH) line and hybrid seed production as well as variance components for grain yield in a wide range. In contrast to previous studies, we included a nursery selection for disease resistance just before GS on grain yield. The breeding strategy GSrapid with moderate nursery selection followed by one stage GS and one final stage with phenotypic selection on grain yield had the highest annual selection gain across all strategies, budgets, costs and variance components considered and we, therefore, highly recommend its use in hybrid breeding of cereals. Although selecting on traits not correlated with grain yield in the observation nursery, this selection reduced the selection gain of grain yield, especially in the breeding schemes with GS and for selected fractions smaller than 0.3. Owing to the very high number of test candidates entering breeding strategies with GS, the costs for DH line production had a larger impact on the annual selection gain than the hybrid seed production costs. The optimum allocation of test resources maximizing annual selection gain in classical two-stage phenotypic selection on grain yield and for the recommended breeding strategy GSrapid is finally explored for maize, wheat, rye, barley, rice and triticale.