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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]

Biplots have been widely used in recent years for the analysis of multi-environment trials through the genotype plus genotype × environment (GGE) biplot analysis or through the additive main effects and multiplicative interaction analysis. Sometimes the environments are structured in blocks of environments, for example, regions or years. In this paper, we propose a new biplot technique for describing genotype × environment interactions that takes into account this additional information. We call it a GGB biplot for genotype plus genotype × block of environments biplot. This biplot has interesting geometrical properties that will be covered in this paper. Similarly to what was done for the GGE biplot (Laffont et al., 2007), we also establish a link between the partitioning of the total sum of squares provided by the GGB biplot and the partitioning provided by the analysis of variance. Simulated and real data are used to illustrate this new biplot. [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]

Uniquely naming wheat (Triticum aestivum L.) plant parts is useful for communicating plant development research and the effects of environmental stresses on normal wheat development. Over the past 30+ years, several naming systems have been proposed for wheat shoot, leaf, spike, spikelet, floret, and caryopsis structures. While these systems work very well in most field situations, several problems have emerged that can lead to confusion in uniquely naming all possible tillers and leaves. This confusion is unnecessary and leads to errors in communication that can be avoided. This paper describes a naming system that extends the previous systems so that all canopy structures are uniquely identified and additionally incorporates a phenological component if desired. [ABSTRACT FROM AUTHOR]

This paper reviews recent progress in wheat (Triticum aestivum L.), rice (Oryza sativa L.), and maize (Zea mays L.) yields resulting from substantial breeding efforts in mostly favorable environments and examines its physiological basis. Breeding and improved agronomy lift potential yield (PY), namely yield with the best variety and management in the absence of manageable abiotic and biotic stresses, and PY increase is a key component of progress in farm yield (FY), the other component being closure of the PY to FY gap. Changes in PY and FY are reviewed for several key production regions, namely the United Kingdom and the Yaqui Valley of Mexico for wheat, Japan and Central Luzon in the Philippines for rice, and Iowa and briefly sub Saharan Africa for maize The PY growth rates have fallen and are currently generally no more than 1% per annum and usually much less. The trajectory of FY with time often closely parallels PY, but, especially in developing countries, there remain large yield gaps. In at least one instance (maize in Iowa) the gap between PY and FY appears to be closing rapidly. Current genetic progress is linked to increased bio- mass accumulation, and this will remain the way for- ward in the future given the limits to increased harvest index (HI). There is evidence that recent progress is related to increased photosynthesis (e.g., greater radiation use efficiency (RUE) at the canopy level and/or maximum photosynthetic rate Pmax at saturating irradiance at the leaf level) before and around anthesis. There is no theoretical reason why this trend cannot continue, especially given the vast genetic resources already found within each crop species. However, it will not be easily or cheaply accomplished, so prospects for higher rates of potential yield growth appear to be limited, notwithstanding new molecular tools and claims to the contrary. Closing the yield gap, therefore, becomes more important. Many factors are involved, but breeding can also help farmers achieve this through, for example, improved host plant resistance. [ABSTRACT FROM AUTHOR]

Most wheat (Triticum aestivum L.) breeding programs rely on gene transfer from the wild and cultivated secondary gene pool (GP-2) for trait enhancement, but none has been successful in improving end-use grain quality using gene transfer from the wild GP-2. In this paper, the efficacy of prolamin subunits encoded by genes of Dasypyrum villosum (L.) Candargy (Dv) for the improvement of wheat end-use grain quality has been assessed by small- and large-scale analyses on wheat introgression lines (ILs) derived from Triticum aestivum cv. Chinese Spring x Dv hybridization. Prolamin genes from Dv and wheat are coexpressed in the ILs and differentially affect bread-making quality. The ascertained effects correlate weakly with the size of the introgressed Dv chromatin and are not confounded by the cryptic variation of the genetic background. Dasypyrum villosum chromatin, including genes at the GIu-V1 locus, significantly improves wheat bread-making quality, and chromatin from the short arm of chromosome 6V also improves protein and micro-nutrient content. These positive effects allow breeders to consider Dv as an important source of genes for grain quality improvement in wheat, also in conjunction with the introgression of Dv chromatin into wheat for enhancing expression of other traits such as disease resistance. [ABSTRACT FROM AUTHOR]

The objectives of this study were to assess the potential of using spectral reflectance indices (SRI) as an indirect selection tool to differentiate spring wheat (Triticum aestivum L.) genotypes for grain yield under irrigated conditions. This paper demonstrates only the first step in using the SRI as indirect selection criteria by reporting genetic variation for SRI among genotypes, the effect of phenology and year on SRI and their interaction with genotypes, and the correlations between SRI and grain yield and yield components of wheat. Three field experiments—15 CIMMYT globally adapted genotypes (GHIST), 25 random F3-derived lines (RLs1), and 36 random F3-derived lines (RLs2)—were conducted under irrigated conditions at the CIMMYT research station in northwest Mexico in three different years. Five previously developed SRI (photochemical reflectance index [PRI], water index [WI], red normalized difference vegetation index [RNDVI], green normalized difference vegetation index [GNDVI], simple ratio [SR]) and two newly calculated SRI (normalized water index-1 [NWI-1] and normalized water index-2 [NWI-2]) were evaluated in the experiments. In general, genotypic variation for all the indices was significant. Near infrared radiation (NIR)-based indices (WI, NWI-1, NWI-2) gave the highest levels of association with grain yield during the 3 yr of the study. A clear trend for higher association between grain yield and the NIR-based indices was observed at heading and grainfilling than at booting. Overall, NIR-based indices were more consistent and differentiated grain yield more effectively compared to the other indices. The results demonstrated the potential of using SRI as a tool in breeding programs for selecting for increased genetic gains for yield. [ABSTRACT FROM AUTHOR]

Crop cultivar registration requires multienvironment trials for assessing the value for cultivation and use (VCU). The series of trials usually extends across 3 yr, with some cultivars being discarded each year. Selection gives rise to a missing-data or drop-out pattern that is not completely random. The present paper studies the effect of drop-out on the validity of mixed model procedures such as REML and BLUP. It is shown on the basis of the pertinent statistical theory and simulations that selection is ignorable providing that all data used in the selection process are included in the analysis. Simulations show that REML is preferable to ML and BLUP is preferable to BLUP. It is suggested that cultivar registration authorities can benefit from multivariate mixed model analyses comprising all traits on which selection is based. [ABSTRACT FROM AUTHOR]

Six empirical functions were compared for describing the product yields of agricultural crops grown while subject to increasing levels of root-zone salinity. The four nonlinear functions fit the test data from a spring wheat (Triticum aestivum L., cv. Biggar) experiment conducted in Canada's Salt Tolerance Testing Facility closer than the two linear functions. Although each of the four nonlinear declining functions could reasonably describe the data, the modified compound-discount equation recorded the lowest root mean square error and the highest R² value. Additional response data using the nonlinear discount function obtained from 33 separate trials averaged 11% closer in statistical fit and 45% lower in statistical error than the best linear function. The discount function (Yr = 1/(1 + [(C/C50)exp(sC50)]) follows a sigmoidal form and relates relative yield (Yr) to a measure of root-zone salinity (C) such as the solute concentration with an electrical conductivity of an equivalent saturated soil paste extract (ECe). This function features two parameters, the salinity level producing 50% of the nonsalinc crop yield (C50) and the absolute value of the general decline in relative yield with salinity at and near C50, the steepness constant (a). These parameters combine to form a single- value, salinity-tolerance index (ST-Index) consisting of the 50% reduction in crop yield (C50) plus the tendency to maintain some product yield as the crop is subjected to increasing salinity levels approaching C50, i.e., ST-Index = C50 + s(C50). The ST-Index for the Biggar wheat registered 6.44. Approximations for C50 and s can be derived from the threshold salinity (Ct) and declining slope (b) parameters of the threshold-slope linear response function [Yt = 1 - b(C - Ct]. Procedures for converting Ct to C50 and b to a offer linkages between these linear and nonlinear response function parameters, and are further explored in this paper's companion. The resulting ST-Index-values equal 6.56, 9.43, and 5.67 for sample field (corn, Zea mays L.), forage (alfalfa, Medicago sativa L. and falcata L.), and vegetable (radish, Raphanus sativus L.) crops, respectively. [ABSTRACT FROM AUTHOR]