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The principal goals of a plant breeding program are to provide breeders with cultivar information for selection purposes and to provide farmers with high‐yielding and stable cultivars. For that reason, multi‐environment trials need to be done to predict future cultivar yield, and a robust statistical procedure is needed to provide reliable information on the tested cultivars. In Sweden, the statistical procedure follows the tradition of modeling cultivar effects as fixed. Moreover, the analysis is performed separately by zone and level of fungicide treatment, and so the factorial information regarding cultivar × zone × fungicide combinations is not explored. Thus, the question arose whether the statistical method could be improved to increase accuracy in zone‐based cultivar prediction, since the cultivar recommendation is zone based. In this paper, the performance of empirical best linear unbiased estimation (E‐BLUE) and empirical best linear unbiased prediction (E‐BLUP) are compared using cross‐validation for winter wheat (Triticum aestivum L.) and spring barley (Hordeum vulgare L.), in single‐year and multiyear series of trials. Data were obtained from three agricultural zones of Sweden. Several linear mixed models were compared, and model performance was evaluated using the mean squared error of prediction criterion. The E‐BLUP method outperformed the E‐BLUE method in both crops and series. The prediction accuracy for zone‐based yield was improved by using E‐BLUP because the random‐effects assumption for cultivar × zone interaction allows information to be borrowed across zones. We conclude that E‐BLUP should replace the currently used E‐BLUE approach to predict zone‐based cultivar yield. [ABSTRACT FROM AUTHOR]

The majority of studies evaluating genomic selection (GS) for plant breeding have used single‐trait, single‐site models that ignore genotype × environment interaction (GEI) effects. However, such studies do not accurately reflect the complexities of many applied breeding programs, and previous papers have found that models that incorporate GEI effects and multiple traits can increase the accuracy of genomic estimated breeding values (GEBVs). This study's goal was to test GS methods for prediction in scenarios that simulate early‐generation yield testing by correcting for field spatial variation, and fitting multienvironment and multitrait models on data for 14 traits of varying heritability evaluated in unbalanced designs across four environments. Corrections for spatial variation increased across‐environment trait heritability by 25%, on average, but had little effect on model predictive ability. Results between all models were generally equivalent when predicting the performance of newly introduced genotypes. However, models incorporating GEI information and multiple traits increased prediction accuracy by up to 9.6% for low‐heritability traits when phenotypic data were sparsely collected across environments. The results suggest that GS models using multiple traits and incorporating GEI effects may best be suited to predicting line performance in new environments when phenotypic data have already been collected across a subset of the total testing environments. [ABSTRACT FROM AUTHOR]

Variability of production environments challenges wheat (Triticum aestivum L.) breeders to develop genotypes with the potential to perform well under different levels of resource availability. An important trait that may provide plasticity to wheat is productive tiller number (PTN). This paper reports two studies. The first study tested the impact of a quantitative trait locus (QTL) for PTN on yield and other traits in a set of near-isogenic lines (NILs) grown over nine environments in the Pacific Northwest. Results showed that an allele for high tiller number at QTn.mst-6B enhanced early tiller (ETN) formation regardless of environments. Under favorable conditions, the large number of early tillers translated into a capacity for high PTN. Seed weight and seed diameter were negatively affected by the QTn.mst-6B high tiller allele. The second study assessed the impact of QTn. mst-6B under different competition and soil resource levels in three trials in Bozeman, MT. Three treatments were imposed on NIL pairs in replicated experiments: (i) bordered rows that limited soil resources to each NIL, (ii) nonbordered rows with less competition that provided more soil resources to each NIL, and (iii) space-planted plants with limited competition for soil resources. Results complemented those found in the multienvironment trials, as the high tiller allele caused high PTN at low competition levels when soil resources were more abundant. Increased yield as a result of the high tiller allele at QTn.mst-6B was only detectable in the highest resource treatment and environment. [ABSTRACT FROM AUTHOR]