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The consumers interest in organic food and farmers’ search for cultivars with increased usefulness for organic farming have contributed to the revival of ancient cereal species and their launch onto the food market. In view of the above, the aim of this study was to determine the physicochemical properties, antioxidant potential and baking quality of grain and flour of primitive rye (Secale cereale var. Multicaule Polish: Krzyca), and to compare these parameters with open-pollinated and hybrid cultivars of common rye. The following determinations were made: the morphological and mechanical properties of grain, milling energy and the protein, starch, ash and free phenolic content of the analyzed flours, their amylograph characteristics and antioxidant potential. It was found that primitive rye has shorter kernels, lower thousand-kernel weight and a higher contribution of redness in color compared with common rye. In primitive rye grain rupture force was determined at 68.9 N and rupture energy at 35.6 mJ. Flours made from primitive rye grain have a higher content of ash and free phenolic compounds, lower starch content and similar antioxidant potential relative to common rye flours. The results of the amylograph test revealed that primitive rye flours were characterized by high baking quality. The primitive rye flours can be alternative ingredients for bread making and provide health advantage such as higher content of phenolic compounds. However, further research is needed to analyze variations in the properties of primitive rye grain and flour resulting from changes in environmental and climatic conditions.

Key MessageMulti-trait genomic prediction models are useful to allocate available resources in breeding programs by targeted phenotyping of correlated traits when predicting expensive and labor-intensive quality parameters.Multi-trait genomic prediction models can be used to predict labor-intensive or expensive correlated traits where phenotyping depth of correlated traits could be larger than phenotyping depth of targeted traits, reducing resources and improving prediction accuracy. This is particularly important in the context of allocating phenotyping resource in plant breeding programs. The objective of this work was to evaluate multi-trait models predictive ability with different depth of phenotypic information from correlated traits. We evaluated 495 wheat advanced breeding lines for eight baking quality traits which were genotyped with genotyping-by-sequencing. Through different approaches for cross-validation, we evaluated the predictive ability of a single-trait model and a multi-trait model. Moreover, we evaluated different sizes of the training population (from 50 to 396 individuals) for the trait of interest, different depth of phenotypic information for correlated traits (50 and 100%) and the number of correlated traits to be used (one to three). There was no loss in the predictive ability by reducing the training population up to a 30% (149 individuals) when using correlated traits. A multi-trait model with one highly correlated trait phenotyped for both the training and testing sets was the best model considering phenotyping resources and the gain in predictive ability. The inclusion of correlated traits in the training and testing lines is a strategic approach to replace phenotyping of labor-intensive and high cost traits in a breeding program.

Crops yield and quality are difficult to predict using soil physico-chemical parameters. Because of their key roles in nutrient cycles, we hypothesized that there is an untapped predictive potential in the soil microbial communities. To test our hypothesis, we sampled soils across 80 wheat fields of the province of Quebec at the beginning of the growing season in May-June. We used a wide array of methods to characterize the microbial communities, their functions and activities, including: (1) amplicon sequencing, (2) real-time PCR quantification and (3) community-level substrate utilization. We also measured grain yield and quality at the end of the growing season, and key soil parameters at sampling. The diversity of fungi, the abundance of nitrification genes and the use of specific organic carbon sources were often the best predictors for wheat yield and grain quality. Using 11 or less parameters, we were able to explain 64-90% of the variation in wheat yield and grain and flour quality across the province of Quebec. Microbial-based regression models outperformed basic soil-based models for predicting wheat quality indicators. Our results suggest that the measurement of microbial parameters early in the season could help predict accurately grain quality and quantity.

Key message Over the last 32   years, a large gain in grain yield (24 %) was achieved in official German variety trials, and despite considerable loss in protein concentration (−7.9 %), winter wheat baking quality was partially improved over the last 32   years. On-farm gain in grain yield (32 %) exceeded gain in trials, but at yield level about 25   dt   ha −1 lower. Breeding progress was very successfully transferred into both progress in grain yield and on-farm baking quality. Long-term gains in grain yield and baking quality of 316 winter wheat varieties from German official trials were evaluated. We dissected progress into a genetic and a non-genetic part to quantify the contribution of genetic improvement. We further investigated the influence of genotype and environment on total variation by estimating variance components. We also estimated genetic and phenotypic correlation between quality traits. For trial data, we found a large gain in grain yield (24%), but a strong decline in protein concentration (−8.0%) and loaf volume (−8.5%) relative to 1983. Improvement of baking quality could be achieved for falling number (5.8%), sedimentation value (7.9%), hardness (13.4%), water absorption (1.2%) and milling yield (2.4%). Grain yield, falling number and protein concentration were highly influenced by environment, whereas for sedimentation value, hardness, water absorption and loaf volume genotypes accounted for more than 60% of total variation. Strong to very strong relations exist among protein concentration, sedimentation value, and loaf volume. On-farm yields were obtained from national statistics, and grain quality data from samples collected by national harvest survey. These on-farm data were compared with trial results. On-farm gain in grain yield was 31.6%, but at a mean level about 25 dt ha −1  lower. Improvement of on-farm quality exceeded trial results considerably. A shift to varieties with improved baking quality can be considered as the main reason for this remarkable improvement of on-farm baking quality.

Allohexaploid bread wheat (Triticum aestivum L.) provides approximately 20% of calories consumed by humans. Lack of genome sequence for the three homeologous and highly similar bread wheat genomes (A, B, and D) has impeded expression analysis of the grain transcriptome. We used previously unknown genome information to analyze the cell type–specific expression of homeologous genes in the developing wheat grain and identified distinct co-expression clusters reflecting the spatiotemporal progression during endosperm development. We observed no global but cell type– and stage-dependent genome dominance, organization of the wheat genome into transcriptionally active chromosomal regions, and asymmetric expression in gene families related to baking quality. Our findings give insight into the transcriptional dynamics and genome interplay among individual grain cell types in a polyploid cereal genome.

Key messageGenomic selection shows great promise for pre-selecting lines with superior bread baking quality in early generations, 3 years ahead of labour-intensive, time-consuming, and costly quality analysis.The genetic improvement of baking quality is one of the grand challenges in wheat breeding as the assessment of the associated traits often involves time-consuming, labour-intensive, and costly testing forcing breeders to postpone sophisticated quality tests to the very last phases of variety development. The prospect of genomic selection for complex traits like grain yield has been shown in numerous studies, and might thus be also an interesting method to select for baking quality traits. Hence, we focused in this study on the accuracy of genomic selection for laborious and expensive to phenotype quality traits as well as its selection response in comparison with phenotypic selection. More than 400 genotyped wheat lines were, therefore, phenotyped for protein content, dough viscoelastic and mixing properties related to baking quality in multi-environment trials 2009–2016. The average prediction accuracy across three independent validation populations was r = 0.39 and could be increased to r = 0.47 by modelling major QTL as fixed effects as well as employing multi-trait prediction models, which resulted in an acceptable prediction accuracy for all dough rheological traits (r = 0.38–0.63). Genomic selection can furthermore be applied 2–3 years earlier than direct phenotypic selection, and the estimated selection response was nearly twice as high in comparison with indirect selection by protein content for baking quality related traits. This considerable advantage of genomic selection could accordingly support breeders in their selection decisions and aid in efficiently combining superior baking quality with grain yield in newly developed wheat varieties.

The spectrophotometric Bradford assay was adapted for the analysis of gluten protein contents (gliadins and glutenins) of spelt, durum wheat, emmer and einkorn. The assay was applied to a set of 300 samples, including 15 cultivars each of common wheat, spelt, durum wheat, emmer and einkorn cultivated at four locations in Germany in the same year. The total protein content was equally influenced by location and wheat species, however, gliadin, glutenin and gluten contents were influenced more strongly by wheat species than location. Einkorn, emmer and spelt had higher protein and gluten contents than common wheat at all four locations. However, common wheat had higher glutenin contents than einkorn, emmer and spelt resulting in increasing ratios of gliadins to glutenins from common wheat (< 3.8) to spelt, emmer and einkorn (up to 12.1). With the knowledge that glutenin contents are suitable predictors for high baking volume, cultivars of einkorn, emmer and spelt with good predicted baking performance were identified. Finally, spelt, emmer and einkorn were found to have a higher nitrogen partial factor productivity than common and durum wheat making them promising crops for a more sustainable agriculture.

The increasing prevalence of coeliac disease (CD) and gluten-related disorders has led to increasing consumer demand for gluten-free products with quality characteristics similar to wheat bread. The replacement of gluten in cereal-based products remains a challenge for scientists, due to its unique role in network formation, which entraps air bubbles. When gluten is removed from a flour, starch is the main component left. Starch is used as gelling, thickening, adhesion, moisture-retention, stabilizing, film forming, texturizing and anti-staling ingredient. The extent of these properties varies depending on the starch source. The starches can additionally be modified increasing or decreasing certain properties of the starch, depending on the application. Starch plays an important role in the formulation of bakery products and has an even more important role in gluten-free products. In gluten-free products, starch is incorporated into the food formulation to improve baking characteristics such as the specific volume, colour and crumb structure and texture. This review covers a number of topics relating to starch; including; an overview of common and lesser researched starches; chemical composition; morphology; digestibility; functionality and methods of modification. The emphasis of this review is on starch and its properties with respect to the quality of gluten-free products.

In view of their key roles in many soil- and plant-related processes, we hypothesized that soil microorganisms could play a larger role in determining wheat baking quality than nitrogen fertilization. A field experiment was conducted under bread wheat production conditions, where different fertilization treatments, ranging from 0–120 kg/ha NH₄NO₃, were applied. Soil samples were taken in May, June and July. Functional genes in the nitrogen cycle were quantified and amplicons of the 16S rRNA gene and the ITS region were sequenced. Wheat yields were measured, and the grain baking quality was analysed for each plot. Fertilisation did not significantly influence the yields and the grain quality. Many bacterial and fungal Amplicon Sequence Variants showed significant positive or negative correlations with yield and grain baking quality parameters. Among the functional gene quantified, the archaeal amoA showed strong negative correlations with the wheat yields and many grain and flour quality parameters. Regression models were able to explain up to 81% of the variability in grain quality based on the microbial data from the May sampling. A better understanding of the microbiology of wheat fields could lead to an optimized management of the N fertilization to maximize yields and grain quality.

This research was supported by: - IPK Gatersleben core funding. - The Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, grant 491250510). - Alexander von Humboldt Foundation (Georg Forster Fellowship). - The Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R355).