Climate change has generated unpredictability in the timing and amount of rain, as well as extreme heat and cold spells that have affected grain yields worldwide and threaten food security. Sources of specific adaptation related to drought and heat, as well as associated breeding of genetic traits, will contribute to maintaining grain yields in dry and warm years. Increased crop photosynthesis and biomass have been achieved particularly through disease resistance and healthy leaves. Similarly, sources of drought and heat adaptation through extended photosynthesis and increased biomass would also greatly benefit crop improvement. Wheat landraces have been cultivated for thousands of years under the most extreme environmental conditions. They have also been cultivated in lower input farming systems for which adaptation traits, particularly those that increase the duration of photosynthesis, have been conserved. Landraces are a valuable source of genetic diversity and specific adaptation to local environmental conditions according to their place of origin. Evidence supports the hypothesis that landraces can provide sources of increased biomass and thousand kernel weight, both important traits for adaptation to tolerate drought and heat. Evaluation of wheat landraces stored in gene banks with highly beneficial untapped diversity and sources of stress adaptation, once characterized, should also be used for wheat improvement. Unified development of databases and promotion of data sharing among physiologists, pathologists, wheat quality scientists, national programmes, and breeders will greatly benefit wheat improvement for adaptation to climate change worldwide.

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The availability of information on the genetic diversity and population structure in wheat (Triticum aestivum L.) breeding lines will help wheat breeders to better use their genetic resources and manage genetic variation in their breeding program. The recent advances in sequencing technology provide the opportunity to identify tens or hundreds of thousands of single nucleotide polymorphism (SNPs) in large genome species (e.g., wheat). These SNPs can be utilized for understanding genetic diversity and performing genome wide association studies (GWAS) for complex traits. In this study, the genetic diversity and population structure were investigated in a set of 230 genotypes (F3:6) derived from various crosses as a prerequisite for GWAS and genomic selection. Genotyping-by-sequencing provided 25,566 high-quality SNPs. The polymorphism information content (PIC) across chromosomes ranged from 0.09 to 0.37 with an average of 0.23. The distribution of SNPs markers on the 21 chromosomes ranged from 319 on chromosome 3D to 2,370 on chromosome 3B. The analysis of population structure revealed three subpopulations (G1, G2, and G3). Analysis of molecular variance identified 8% variance among and 92% within subpopulations. Of the three subpopulations, G2 had the highest level of genetic diversity based on three genetic diversity indices: Shannon’s information index (I) = 0.494, diversity index (h) = 0.328 and unbiased diversity index (uh) = 0.331, while G3 had lowest level of genetic diversity (I = 0.348, h = 0.226 and uh = 0.236). This high genetic diversity identified among the subpopulations can be used to develop new wheat cultivars.

Genetic analyses of complex traits in wheat (Triticum aestivum L.) are facilitated by the availability of unique genetic tools such as chromosome substitution lines and recombinant inbred chromosome lines (RICLs) which allow the effects of genes on single chromosomes to be studied individually. Chromosome 3A of ‘Wichita’ is known to contain alleles at quantitative trait loci (QTLs) that influence variation in grain yield and agronomic performance traits relative to alleles of ‘Cheyenne’. To determine the number, location, and environmental interactions of genes related to agronomic performance on chromosome 3A, QTL and QTL × environment analyses of 98 RICLs‐3A were conducted in seven locations across Nebraska from 1999 through 2001. QTLs were detected for seven of eight agronomic traits measured and generally localized to three regions of chromosome 3A. QTL × environment interactions were detected for some QTLs and these interactions were caused by changes in magnitude and crossover interactions. Major QTLs for kernels per square meter and grain yield were associated within a 5‐centimorgan (cM) interval and appeared to represent a single QTL with pleiotropic effects. This particular QTL displayed environmental interactions caused by changes in magnitude, wherein the positive effect of the Wichita QTL allele was larger in higher yielding environments.

By physically mapping 3025 loci including 252 phenotypically characterized genes and 17 quantitative trait loci (QTLs) relative to 334 deletion breakpoints, we localized the gene-containing fraction to 29% of the wheat genome present as 18 major and 30 minor gene-rich regions (GRRs). The GRRs varied both in gene number and density. The five largest GRRs physically spanning <3% of the genome contained 26% of the wheat genes. Approximate size of the GRRs ranged from 3 to 71 Mb. Recombination mainly occurred in the GRRs. Various GRRs varied as much as 128-fold for gene density and 140-fold for recombination rates. Except for a general suppression in 25-40% of the chromosomal region around centromeres, no correlation of recombination was observed with the gene density, the size, or chromosomal location of GRRs. More than 30% of the wheat genes are in recombination-poor regions thus are inaccessible to map-based cloning.

Improvement of end‐use quality in wheat (Triticum aestivum L.) depends on thorough understanding of the influences of environment, genotype, and their interaction. Our objectives were to determine relative contributions of genotype, environment, and G × E interaction to variation in quality characteristics of hard red winter wheat. Eighteen winter wheat genotypes were grown in replicated trials at six locations in Nebraska and one site in Arizona in 1988 and 1989. Harvested grain was micromilled to produce flour samples for evaluation of protein concentration, mixing characteristics, and sodium dodecylsulfate (SDS) sedimentation. Kernel hardness was determined by microscopic evaluation of individual kernels. Genotype, environment, and interaction effects were found to significantly influence variation in all quality parameters. Variances of quality characteristics associated with environmental effects were generally larger than those for genetic factors. The magnitude of G × E effects were found to be of similar magnitude to genetic factors for mixing tolerance and kernel hardness, but were smaller for flour protein concentration, mixing time, and SDS sedimentation value. Significant differences among genotype responses (b‐values) were observed in the regressions of genotype mean on location means for each quality parameter. There were few instances of significant deviations from regression. Positive correlations between genotype grand mean and genotype b‐values for flour protein, mixing time, and mixing tolerance suggest that simultaneous improvement in both mean and stability for these traits may be difficult. Based on these results, environmental influences on enduse quality attributes should be an important consideration in cultivar improvement efforts toward enhancing marketing quality of hard red winter wheat.

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Most cultivar evaluation trials use blocked designs and are analyzed using classical analysis of variance; however, a standard analysis for blocked designs often does not adequately account for spatial variability. Recent advances in spatial statistics suggest that there are better alternatives. The primary objective of this research was to compare randomized complete block (RCB) analysis with two nearest neighbor adjustment (NNA) methods; a random field procedure was also examined, as another way to remove spatial variability. Yield data from three replicated breeding nurseries involving diverse adapted and unadapted germplasm, each grown at four locations in Nebraska during 1988–1989, were used for the comparisons. The NNA approach was superior for all nurseries at all locations, on the basis of lower coefficient of variation and greater ability to distinguish cultivar differences. For 9 of 12 trials, RCB cultivar means were highly correlated with NNA cultivar means, indicating that both procedures would identify similar elite lines. For three trials, however, the estimated cultivar yield and cultivar rank from RCB and NNA procedures were different, and so different lines would be selected. The random field analysis was applied to the trial for which the discrepancy between the RCB and NNA analysis was greatest; this yielded results similar to the nearest neighbor analysis. Our results suggest that spatial trends are common in Nebraska and compromise the accuracy and precision of standard analysis of blocked designs. Therefore, NNA or random field analysis should be used to improve the analysis of breeding trials.