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Combining ability effects for yield related traits can serve as selection criteria to pursue breeding for optimal biomass allocation in wheat (Triticum aestivum). The objective of this paper is to provide information based on a retrospective quantitative genetic analysis on combining ability studies of wheat for yield and yield‐related traits to predict potential genetic gains achievable in improving biomass allocation for drought tolerance and soil carbon storage. The study compares data on the general combining ability and specific combining ability effects of wheat for yield and related traits under optimum and drought stressed conditions from 40 studies around the world. Days to heading (DTH), plant height (PH), number of tillers per plant (TN), kernels per spike (KPS), 1,000‐kernel weight (TKW), shoot biomass (SB), and grain yield (GY) exhibited wide variation for general combining ability (GCA) and specific combining ability effects. Progeny performance increased by 14.30 and 4.04% for SB and GY, respectively, compared with parental values under optimum water conditions. Number of tillers and SB exhibited positive associations with GY (.45 ≤ r ≤.85, p <.05) under both water conditions. Meta effect sizes for drought stress were negative. The highest meta‐effect sizes were calculated for DTH (−4.5) followed by SB (−2.0), whereas KPS (−1.25) had the lowest. The genetic gains for PH, SB, and other yield components showed that divergent crosses involving complementary parents could enhance biomass allocation patterns in wheat. This could be used as a basis for improving biomass allocation to roots. Core Ideas: There is sufficient genetic variation in wheat genotypes for manipulating biomass allocation.The number of kernels per spike is stable under varying moisture conditions.Shoot biomass and grain yield expression are severely confounded by drought.Aboveground traits are important for indirect selection of root traits. [ABSTRACT FROM AUTHOR]

Genetic improvement of crop plants is dependent on the availability of genetic variation, which can be present naturally or can be created using mutations. Ethyl methanesulfonate (EMS) mutagenesis provides an attractive tool to create variant alleles of genes of interest in any plant. In the present paper, we report development of an EMS‐mutagenized population in a hard red winter wheat (Triticum aestivum L.) variety Jagger. Jagger has been a leading variety across the United States and has been grown internationally as well, for over two decades now. It possesses many useful traits related to disease resistance, cold tolerance, and milling and baking qualities. Because it is a landmark variety, the genome of Jagger is being sequenced by an international collaborative team, which will speed up gene mapping and cloning efforts in it. The Jagger Targeting Induced Local Lesions In Genome (TILLING) resource developed in this work will be useful for functional validation of these genes. The mutagenized population was developed using EMS and was characterized phenotypically and genotypically. Gene‐wise mutation screening was performed for three genes located on different chromosomes across all the three genomes. The population has an overall mutation frequency of 1/40.7 kb, and we were able to find multiple knockout, missense, and missplicing mutations for each targeted gene. The population will be useful as a forward and reverse genetic screen for a number of valuable traits. [ABSTRACT FROM AUTHOR]

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]