Wheat breeding to better feed a growing world: historic insights and future potential elaborated using a diverse cultivars collection and extended phenotyping.

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Khaoula El Hassouni, Muhammad Afzal, Philipp Boeven, Jost Dörnte, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4523213/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Wheat is one of the most important staple crops playing a pivotal role to sustainably feed the growing world population. Wheat breeding mainly focused on improving agronomy and techno-functionality for bread or pasta production, but nutrient content is becoming increasingly more important to fight malnutrition. We therefore investigated 282 bread wheat cultivars from seven decades of wheat breeding in Central Europe on 63 different traits related to agronomy, quality and nutrients under multiple field trials. Wheat breeding has tremendously increased grain yield, resistance against diseases and lodging as well as baking quality across last decades. Whereas, mineral content slightly decreased without selection on it, probably due to its negative correlation with grain yield. The significant genetic variances determined for almost all traits show the potential for further improvement but significant negative correlations among grain yield and baking quality as well as grain yield and mineral content complicate their combined improvement. Thus, compromises in improvement of these traits are necessary to feed a growing global population. Biological sciences/Plant sciences Biological sciences/Genetics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Wheat ( Triticum aestivum subsp. aestivum ) ranks among the most important staple crops with a global growing area of about 219 million ha and a total grain production reaching 808 million tons in 2022 ( https://www.fao.org/faostat/en/#data/QCL , accessed on 27.02.2024). This globally important food crop for human consumption provides a high nutritional value with starch, proteins, minerals, fibers and vitamins 1,2 . Given its prevalence in human diets, wheat cultivars must meet particular quality criteria for the production of different types of end-products. To meet the industrial requirements, wheat cultivars are classified in many countries based on baking quality as part of the registration process in the official seed catalogue. In Germany, this classification system includes four classes from highest to lowest quality corresponding to E, A, B and C class, respectively 3 . The classification into quality classes depends on minimum standards in seven quality traits. Baking quality of wheat is related to the rheological properties of the dough, the dough strength and extensibility, which are largely determined by the protein content and its quality 4 . Around 80% of the wheat protein belongs to the gluten, which forms a unique dough network under input of water and kneading energy 5,6 . Therefore, wheat cultivars were often investigated with different tests regarding protein or gluten content and quality, dough characteristics as well as bread loaf volume 7 . Positive correlations between protein content and protein quality measured as sedimentation volume and loaf volume of bread were reported 8,9,10 . A successful wheat cultivar must combine acceptable end-use quality with high grain yield and low susceptibility against diseases and lodging in the field with a reduced need for chemical plant protection. Unfortunately, protein content appears to have a strong negative correlation with grain yield 11,12 . Thus, breeders had to find a balanced way of selection on these traits using innovative approaches, for instance the grain-protein deviation, which became popular in Central Europe during the last decade 13 . Comparing results from official registration trials of wheat cultivars in Germany from 1984–2014, Laidig et al. 10 and Voss-Fels et al. 14 have shown large improvements mainly in grain yield and disease resistance and to a limited extent for end-use quality. With the high demand for healthy food nowadays and the worldwide health problems of micronutrients deficiencies known as hidden hunger 15 , it is also worthwhile to implement nutritional traits in the wheat supply chain 16,17 . For instance, the international research organization CIMMYT in partnership with HarvestPlus have launched a large wheat breeding program which shall combine good agronomy and end- use quality with increased content of the minerals Fe and Zn in developing countries 16 . However, these efforts need to be extended to the global wheat supply chain. Until now, studies investigating the potential of combining nutrient content with better agronomical performance and end use quality are completely lacking for the important wheat production region of Central Europe. Therefore, we investigated 282 bread wheat cultivars from seven decades of wheat breeding in Central Europe on 63 traits related to agronomy, baking and nutritional quality across up to eight field environments. Our objectives were to (i) quantify the variance due to cultivar, environment and cultivar-by-environment interaction for the investigated traits, (ii) explore the phenotypic correlations between the 63 traits, (iii) investigate the temporal trends in traits realized across seven decades of wheat breeding, and (iv) elaborate the potential to combine agronomical attributes and baking quality with nutritional quality to feed the global population. Results and Discussion In this study, we investigated 63 different traits across 282 wheat cultivars (Supplementary table 1 ) originating from different European countries and different decades of wheat breeding tested under multiple field environments. Besides classical agronomic traits like grain yield, plant height and susceptibility of wheat cultivars against diseases, commonly measured quality traits like protein content, sedimentation volume and falling number, we dived deeply into techno-functional traits for dough and bread making quality as well as nutrient content measuring dozens of minerals and sugars. To our knowledge, this data set is one of the largest experimental data sets ever reported in wheat breeding representing a perfect basis to elaborate the historic and future potential of wheat breeding to better feed the growing world. Prerequisites of successful breeding fulfilled for the most agronomic, quality and nutritional traits Very important prerequisites for successful breeding of a new trait are: ( 1 ) a significant difference in the trait between the existing wheat cultivars, i.e. significant genetic variance, ( 2 ) a high heritability, i.e. the amount of genetic variance compared to the overall phenotypic (visible) variance, ( 3 ) speed of measuring the trait across several samples, and ( 4 ) feasibility to combine a new trait with the other traits for selection for, i.e. the new valuable trait expression should not be negatively correlated with the other traits. Table 1 Description of all the measured traits, measurement method and test speed. Trait abbreviation Trait name and measurement unit Measurement method Traits categories by test speed GY Grain yield (dt/ha) Combine harvester at 14% moisture Fast PLH Plant height (cm) Height in cm from the ground to tip of the ears DTH Days to heading (number of days) Number of days when 60% of the plants per plot showing the first spikelets TW Test weight (kg/hl) NIRS TKW Thousand kernel weight (g) The weight of 1000 grains Awns Awns (0/1) Visual scoring in field (0) for absent and ( 1 ) for present Ldg Lodging ( 1 – 9 ) Visual scoring in field from 1–9 (absence – severe lodging) YR Yellow rust ( 1 – 9 ) Visual scoring of disease in field from 1–9 (healthy – highly susceptible) LR Leaf rust ( 1 – 9 ) Mildew Powdery Mildew ( 1 – 9 ) SEP Septoria ( 1 – 9 ) FHB Fusarium head blight ( 1 – 9 ) Prot Protein content (%) Dumas method - ICC 167 SDS Sedimentation test (ml) Sediment in test tube - ICC 151 FN Falling number (s) Perten falling number system - ICC 107/1 Hard Hardness NIRS WI White index Konica Minolta chromameter CR-410 for flour color measurement YI Yellow index FE Flour extraction rate (%) Flour with type 550/ used kernels *100 with Brabender Quadrumat Junior mill Gluto Glutograph Brabender Glutograph Intermediate WG Wet gluten Perten Glutomatic PV Peak1 (cP) Rapid visco-analyzer-4 Newport scientific - ICC 162 TV Trough viscosity (cP) Bd Breakdown (cP) FV Final Viscosity (cP) Sb Setback (cP) PT Peak maximum time (min) P.Temp Pasting Temperature (°C) TBM Torque before maximum (BU) Brabender Glutopeak 803400 TM Torque maximum (BU) PMT Peak maximum time (s) TAM Torque after maximum (BU) A-amylase Alpha-amylase activity SD unit/g Enzymatic test kit - Megazyme α-Amylase SD Assay Kit (K-AMYLSD) WA Water absorption (ml/100 g flour) Farinograph - ICC115/1 Time-consuming YV yield of volume (ml/100 g flour) Rapid-Mix Test - Rapeseed displacement according to Neumann/Doose E90 Energy (cm²) at 90 min Brabender Extensograph E - ICC114/1 RES90 Resistance to Extension (BE) at 90 min EXT90 Extensibility (mm) at 90 min R90 Ratio DT Development time (min) Perten micro-doughLAB 2800 - ICC 184 ST Stability (min) SOF Softening (mNm) MTI Mixing Tolerance Index (mNm) PE PeakEnergy (Wh/kg) Ba Barium (mg/kg) Inductively coupled plasma combined with optical emission spectroscopy (ICP-OES) and mass spectrometry (ICP‐MS) for mineral content quantification Ca Calcium (mg/kg) Cu Copper (mg/kg) Fe Iron (mg/kg) K Potassium (mg/kg) Mg Magnesium (mg/kg) Mn Manganese (mg/kg) P Phosphorus (mg/kg) S Sulphur (mg/kg) Zn Zinc (mg/kg) Al Aluminum (mg/kg) Glycerol Glycerol (mg/g) High-performance anion-exchange chromatography with pulsed amperometric detection (HPAEC) for sugars quantification Glucose Glucose (mg/g) Fructose Fructose (mg/g) Sucrose Sucrose (mg/g) Maltose Maltose (mg/g) DP < 8 Oligosaccharide with degree of polymerization 8 Oligosaccharide with degree of polymerization > 8 (mg/g) Total DP Total oligosaccharide (mg/g) For all investigated traits, we found a significant genetic variance (Fig. 1 a, Supplementary Table 2), but its magnitude relative to the other sources of variation varied considerably across traits. It ranged from 0.8% of total variance for susceptibility against the fungal disease powdery mildew to 90.8% for yellow index measuring the colour of the extracted flour. By contrast, the variance of cultivar-by-environment interaction was quite low for most traits indicating that the ranking of cultivars was quite stable across the different test locations. This confirms previous findings in the literature for those of the 63 traits measured in this study, which have been already investigated 10,18,19 . Due to unreplicated samples for measuring the nutritional traits, the cultivar-by-environment variance could not be quantified. Regarding full decomposition of phenotypic variance, large magnitude of the variance due to environment stands out for almost all traits (Fig. 1 a). For instance, average grain yield across all cultivars tested at single locations ranged from 73 dt/ha to 107 dt/ha over the six test environments although the agricultural practices and fertilizers’ supply were comparable (data not shown). This is most probably due to the micro-environmental differences between the different field environments, which is a well-known factor for its impact on numerous traits and multiple crops observed in breeders’ trials 10,18,20,21,22 . The significant cultivar-by-environment variance underlines the large importance of multi-location field trials in research and product development to estimate robust adjusted means and breeding values for traits of interest. As many crops exist with hundreds of cultivars, which can be grown across diverse environments. Such experimental studies become expensive and large in terms of the number of test plots, but remain crucial for generalizing results of the trials reliably. For majority of the investigated traits, heritability was higher than 0.6 (Fig. 1 b). For some traits such as the element Al, most of the sugars and oligosaccharides, the heritability was below 0.5, which is to our knowledge for sugars yet not reported in literature for wheat but confirms findings from maize 23 . Compared to the estimates of heritability for frequently reported traits in literature such as grain yield 24 , plant height, disease susceptibility 25,26 as well as quality traits like protein content and sedimentation volume 27,28 , our heritability estimates were high to very high. This can be explained by the large number of tested wheat cultivars expanding the genetic variance to the existing maximum and by the robust experimental set up using several field locations across two years allowing for meaningful conclusion for future wheat breeding. Summarizing, for many of the 63 traits, the prerequisite 1 and 2 of a significant genetic variance and a high heritability are fulfilled. For further discussion and illustrations, we eliminated traits with heritability < 0.4. For an efficient selection, breeders must evaluate hundreds of different candidate lines to find the best compromise regarding all traits of interest, which requires methods and techniques to rapidly measure the traits of interest on several samples. Similarly, trading wheat samples and products along the supply chain at all stakeholders on certain traits requires also rapid technologies to measure these traits as recently indicated in an opinion paper for future supply chains 17 . We tried to summarize the 63 traits into three groups regarding speed of the test methods i.e., fast, intermediate and time-consuming. Although agronomic evaluations require testing across at least one field season, breeders are well equipped for doing the final measurement for yield, agronomy and disease susceptibility of hundreds of candidate lines within a day. In near future, that will get even faster because of advancement in unmanned aerial vehicles technologies 29,30,31 these traits to the group with fast determination methods. Similarly, more than 100 samples per day can be processed using the following kernel and quality tests: protein content via NIRs, sedimentation volume, falling number, kernel hardness and size. Intermediate speed with a few dozen samples a day could be realized with glutograph glutopeak, rapid-viscoanalyser, wet gluten and flour colour measurements. Baking tests, detailed rheological tests as well as measuring minerals and sugars with the reference methods are slow. Thus, there is urgent need to develop fast methods for these traits, which accurately predict the respective traits, for healthy nutrition with high-quality products in future 17 . For many minerals, the X ray fluorescence detection method predicts their content very precisely with the capability to measure between 50–100 samples per day making its use in wheat breeding or trading highly interesting 32,33 . Correlation network defines limits for the combination of different traits Coefficients of correlation among 58 traits were determined (Supplementary Table 3) and we tried to summarize their relationship in a network analyses (Fig. 2 ). At first glance, three distinct clusters of trait associations become visible: ( 1 ) few sugars and oligosaccharides, ( 2 ) falling number, α-amylase activity and the traits of the rapid-visco-analyser, and ( 3 ) a high number of traits comprising agronomic and quality traits as well as few minerals. Cluster 2 represents all direct or indirect measurements of starch quality performed in our study. Rapid-visco analyser measures pasting properties of a flour-water suspension, which are largely influenced by the starch properties of the examined flour 34 . With increased amount and activity of α-amylase, starch is degraded to smaller sugar components impacting pasting properties, which is indirectly measured by falling number test 35,36 . Consequently, the clustering of these traits in our network analyses is logical and expected. Interestingly, starch properties are neither correlated with agronomic traits nor with dough quality traits to a higher extent. Thus, they could be combined with good agronomy or good dough and baking quality measured as loaf volume. This is of particular interest, as they determine important breadcrumb characters, which are not measured in baking tests routinely used in wheat variety testing. Cluster 3 contains many traits and therefore we tried to reduce its complexity in Fig. 2 b by focusing only on traits with significant correlations, which made few subgroups visible. For instance, loaf volume is highly positively correlated with protein content (r = 0.74, Supplementary Table 3) but also to protein quality as determined by SDS (r = 0.51, Supplementary Table 3). Furthermore, several parameters of dough quality tests were found to be positively correlated with protein content and protein quality, e.g. glutograph, extensograph (E90, EXT90), glutopeak (TBM, TM, TAM) and micro-dough-lab (PE). This is in line with findings in the literature for these traits 10,27 . Protein content is also positively correlated with most measured minerals with largest coefficient of correlation of r = 0.73 to S content (Supplementary Table 3), which confirms previous studies 37,38 . S content is correlated (r > 0.45) with loaf volume, SDS, EXT90 and glutograph. This reasonably strong association of S with dough and baking quality might be either due to the correlation with protein content, or by the fact that disulfide bonds are crucial for establishment of the gluten network in a dough 39,40 or both of it. The single trait kernel hardness was correlated (r > 0.5) with water absorption, Ext90, SDS, TM, TBM, TAM (all glutopeak parameters), flour extraction rate and yellow index of the extracted flour. Whereas, coefficient of correlation of the kernel hardness with white index of the extracted flour and pasting temperature from RVA was < 0.5 (Supplementary Table 3). Kernel hardness in wheat is largely influenced by the puroinduline genes 41 Pin-a (Pina-D1b) and Pin-b (Pinb-D1b). While Pina-D1b is fixed in EU wheat germplasm to the soft wild type allele as confirmed in our study (data not shown), we could observe variation in the tested wheat cultivars for Pinb-D1b segregating for soft wild type allele (a) and three different hard alleles (b, c, d; Supplementary Fig. 1). The large impact of the soft allele at Pinb-D1b was especially pronounced for kernel hardness, SDS, water absorption, pasting temperature and colour of the extracted flour (Supplementary Fig. 1), while the different hardness alleles had only a minor effect on these traits. These findings fit well with a previous study on 94 German wheat cultivars 28 and highlight the high potential of molecular (marker-based) selection if loci with large effect on individual traits are known. Owing to the demand to feed a growing world limited in the agricultural area, quality and nutrient content must be combined with high grain yield. Unfortunately, grain yield and protein content are strongly negatively correlated as often shown in literature 10,11,42 and confirmed once again in our data set (r = − 0.78). Even more, grain yield and loaf volume as well as grain yield and the concentration of several minerals are also negatively correlated (Supplementary Table 3). Consequently, improving these traits in parallel is complicated requiring priority settings and strategies to find best compromises for the future world nutrition. While numerous approaches were reported for combined selection of high grain yield and high protein content, e.g. protein yield or grain protein-deviation; cf. Thorwarth et al. 13 , strategies for the combination of high grain yield with high baking quality and high nutritional value are rare. In conclusion, the high genetic variance coupled with a high heritability for most of the investigated traits would enable efficient wheat breeding for better agronomy, baking quality as well as mineral content of future wheat cultivars. However, the slow speed of test methods especially for baking quality and some minerals as well as negative correlations between grain yield, baking quality and mineral content makes their efficient selection and combination difficult. Large historic breeding success on agronomy and partly on baking quality The tested 282 wheat cultivars were registered between 1961 and 2020. The historic wheat cultivars were chosen based on their large market relevance during the respective decades, but most of the investigated wheat cultivars were registered after 2000 (Supplementary Table 1). Owing to that unbalanced data structure, we focus on important and large trends. For grain yield, an average increase from 81 dt/ha to almost 100 dt/ha was observed for wheat cultivars from 1961 and 2020, respectively (Fig. 3 ). Moreover, important agronomic traits like reduced susceptibility against fungal diseases, e.g. Septoria tritici blotch or powdery mildew, as well as reduced risk of lodging due to reduced plant height were achieved by breeding, which are of utmost importance if future agriculture will be based on less agrochemical inputs. These findings are in line with previous studies for German 10,14 and international wheat germplasm 43 underlining significant success and importance of plant breeding for feeding the growing world population under climate change adopting more sustainable agricultural practices. Protein and wet gluten content were lower in modern than in historic wheat cultivars, but the reduction from 1961 to 2020 was much less pronounced than expected by their large and negative correlation with grain yield and the tremendous increase of grain yield in the same period. This highlights the breeders’ strategy to improve grain yield and in parallel efforts to avoid large reductions of protein content. Additionally, although negatively correlated to grain yield, trends of selection for traits related to protein quality, e.g. SDS, EXT90, or loaf volume increased over time until ~ 1990 and appeared to stagnate or slightly decreased afterwards. These results confirm previous studies 44,45 and underline that even negatively correlated traits can be jointly improved. Interestingly, the different wheat cultivars clustered for grain yield, protein content and baking quality across the decades also according to their quality classes (E > A > B > C baking quality class referring to the German reference system) as visible by the different coloured dots in Fig. 3 . Therefore, we divided the wheat cultivars into two groups (Fig. 4 ): high baking quality (E + A quality cultivars) and limited baking quality (B + C quality cultivars) making several important findings observable. First, breeding for high quality was based on higher protein content at the expense of reduced grain yield. Second, while quality remained constant on average or even declined in wheat cultivars belonging to B + C quality group, it was increased up to 1990 and then kept constant in the E + A quality group. Third, a big variation in quality traits became visible in all quality groups opening the avenue for combination of quality with good agronomy. Since the pioneering work of Payne et al. 46 , the monitoring and selection on certain high-molecular weight protein subunits became feasible and was intensively used by breeders. In particular, the alleles at glutenin locus Glu-1 at the homologous chromosomes 1A, 1B and 1D were intensively monitored and selected. Therefore, we determined them across all 282 wheat cultivars (Supplementary table 4 and Supplementary Fig. 2). Following trends across registration years appeared: at Glu-A1 locus, breeders reduced the frequency of allele “0” by increasing the frequencies of alleles “1” and “2*”. Similarly, at Glu-B1, the frequency of allele “6 + 8” was reduced and the frequency of allele “7 + 9” was increased. Furthermore, the frequencies of these alleles varied considerably across the quality classes. Compared to cultivars from low baking quality class C, cultivars from highest baking quality class E had higher (lower) frequencies of alleles “1” and “2*” (“0”) at Glu-A1, higher (lower) frequencies of alleles “7 + 8” and “7 + 9” (“6 + 8”) at Glu-B1 and higher (lower) frequencies of alleles “5 + 10” (“2 + 12”) at Glu-D1 (Supplementary Fig. 2). This is a further proof of the effectiveness of selection on single genes or gene families, as long as they have a large effect on a trait like also discussed above for the Puroinduline gene and kernel hardness. Regarding mineral content, the trend across registration years was decreasing for almost all of them. Although European wheat breeders have not yet selected for or against minerals, this trend was expected due to the negative correlation between grain yield and mineral content (Fig. 2 , Supplementary Table 3). Nevertheless, researchers from CIMMYT in partnership with HarvestPlus program have shown that good agronomy and baking quality could also be combined with increased mineral content. Similarly, we see a clear tendency in our data set that wheat cultivars of better-quality groups, e.g. E class, appear to have higher mineral content than cultivars of poor baking quality class C (Fig. 3 , Supplementary Fig. 3). Outlook: Future breeding combines high mineral content, high baking quality and good agronomic preformance We therefore chose the wheat cultivars belonging to the 20% with the highest loaf volume and showed their variability in grain yield, disease susceptibility and mineral content in Fig. 5 . The five cultivars with highest yield in that group were marked with light blue across all boxplots and important market cultivars were shown with their names. Overall, a large variability for all regarded traits was present in that group of cultivars with high loaf volume showing the potential to combine high grain yield, good diseases susceptibility, good baking quality and elevated mineral content. For instance, the cultivar “Patras” with significant market share since several years in Germany had a grain yield of 97.6 dt/ha, disease susceptibility from 1.5 for Mildew to 3.5 for LR and concentration of Fe and Zn in the upper half of tested wheat cultivars. As visible by the best wheat cultivars for individual traits (yellow dots in Fig. 5 ), however, the combination of different negatively correlated traits requires a compromise at the expense of reduced individual traits values. Thus, the weight given to the individual traits in the final combination must be elaborated carefully and might differ across world regions and time warranting further research. Nevertheless, the large expenses and complications with negative correlations to grain yield in wheat breeding for higher mineral content are only justifiable, if consumers eat in future much more whole grain products with adopted bread making processes. This is due to the fact that the majority of minerals is in the outer layers and embryo of wheat grains, which are removed during milling of the widely used extracted flour. Additionally, minerals like Fe and Zn are bound in wheat flour to phytic acid, making them non bio-available for humans. During the bread making process, however, phytic acid can be largely degraded making minerals bioavailable by longer dough fermentation and (natural) addition of the enzyme phytase 47 . With our recent global challenges to feed a growing world population sustainably under a changing climate, malnutrition, and the large impact of our diet on chronic diseases 48 , it appears worthwhile and urgent to make impactful investments across supply chains for improving yield, sustainable agricultural production and nutrient content for as many crops as possible 17 . Therefore, our study investigating 63 traits related to agronomic performance, baking quality and nutrients in 282 wheat cultivars grown across up to eight environments is an important first step for future European wheat breeding. However, further research is warranted to ( 1 ) establish rapid methods to measure nutrients and complicated dough and baking qualities, ( 2 ) deepen our understanding of the genomics behind the different traits in order to establish marker-based selections and/or being able to break up negative correlations of traits, and ( 3 ) attract more interest by the stakeholders and consumers towards healthy diets and their realization across global supply chains. Methods Plant material and field experiments This study evaluated a diverse panel of 282 old and modern winter wheat cultivars originating from different European countries. The cultivars were released between 1961 and 2021. Most of cultivars were released in Germany (for more details see Supplementary Table 1). The entire panel underwent field-testing in two consecutive cropping seasons in yield and observation trials across Germany. Yield trials were carried out at locations: Hovedissen (51°59'12.8"N, 8°44'54.4"E), Leutewitz (51°8'35.3"N, 13°25'9.0"E) and Seligenstadt (49°51'3.30'' N, 10°05'21.60''O) in 2020, and at Hovedissen, Asendorf (52°6'4.722"N, 9°1'35.79"E) and Seligenstadt in 2021. Observation trials were conducted in years 2020 and 2021 at locations Hohenheim (48°43′07.3"N, 9°11′08.7"E), Hovedissen, Leutewitz and Wetze (51°44'24.0"N, 9°54'36.0"E). In total, there were six environments (three locations and two years) for yield trials and eight environments (four locations and two years) for observation trials. Both trials were conducted using a partially replicated (P-rep) design with 300 plots at each location. For yield trials plots size ranged from 7.2 to 13.4 m² depending on the location and standard field practices according to the local agricultural practice were implemented for fertilizer, fungicide, herbicide and growth regulator applications. Observation trials were conducted in small plots of size between 0.5 to 1 m² without any applications of fungicides or growth regulators. Phenotyping and reference analytics The traits assessed in this study are described in detail in Table 1 . Grain yield (GY) reported as dt/ha was recorded in yield trials by harvesting all plots with a combine harvester and adjusted to 14% moisture content. Days to heading (DTH) was recorded as days elapsing between the first day of the year until 60% of plants had emerged heads. Plant height (PLH) was measured in cm from the ground to tip of the spikes, excluding awns. From the harvest of each plot, thousand kernel weight (TKW) and test weight (TW) were determined according to the variety registration regulations. Awns were recorded as either absent (0) or present ( 1 ). Lodging (Ldg) was measured shortly before harvest on a 1–9 scale, where 1 denotes absence of lodging and 9 severe lodging with plot completely flattened. In the observation trials, disease susceptibility of the plants was recorded for yellow rust (YR), leaf rust (LR), fusarium head blight (FHB), powdery mildew (Mildew) and Septoria tritici leaf blotch (SEP) at adult plant stage. All diseases were visually rated using a scale ranging from 1 to 9, where 1 refers to healthy and 9 to completely infected plants. For detailed quality analyses, the harvested samples of the yield trials were used. Due to the extensive time required for analyzing the quality traits, the samples from only four of six yield environments were used. These four environments were chosen based on quality of the field trials and protein content, where too low protein contents were avoided which would be deemed unacceptable in wheat trading. The following quality parameters were analyzed according to the reference methods or standard methods of the International Association for Cereal Science and Technology (ICC): Grain protein content (Prot; ICC 167), SDS sedimentation volume (SDS; ICC 151), falling number (FN; ICC 107/1), Farinograph water absorption (WA; ICC 115/1), Extensograph (E90, EXT90, RES90, R90; ICC 114/1), Rapid Visco Analyser (PV, TV, Bd, FV, Sb, PT, P.Temp; ICC 162) and Micro-dough LAB (DT, ST, SOF, MTI, PE; ICC 184). Kernel hardness was measured by near infrared (NIR) spectroscopy. White and yellow indices (WI & YI) were determined as L and b* values reading on chromameter Konica Minolta using extracted flour. Dough and gluten properties were measured using the standard routines of the Perten Glutomatic for wet gluten, Brabender Glutograph and the Brabender GlutoPeak according to the manufacturer’s instructions. The baking test was performed according to the Rapid Mix Test (RMT) 49 . A subset of 800 samples, consisting of 200 samples from the four environments, were analyzed for mineral elements and sugars. Mineral content of whole grain flours was determined in mg/kg by inductively coupled plasma combined with optical emission spectroscopy (ICP-OES) and mass spectrometry (ICP‐MS) without technical replication. Values below the detection limit of 0.025 mg/kg were excluded. Using whole grain flour of each sample, sugars, sugar alcohols and oligosaccharides were quantified by High-performance anion-exchange chromatography with pulsed amperometric detection (HPAEC) technique (sugar alcohols, mono- and disaccharides adapted from Thermo Fisher Scientific Technical Note 72225; oligosaccharides according to Thermo Fisher Scientific Application Note 1149) after aqueous extraction without technical replication in the lab 50 . Alpha-amylase analysis was performed using the Megazyme α-Amylase SD Assay Kit 51 . Electrophoretic analysis was performed to identify high molecular weight glutenin subunits (HMW-GS) composition of the wheat cultivars used in this study. Protein extraction was carried out according to Osborne 52 followed by disulphide bond reduction with Dithiothreitol. HMW-GS were separated by sodium-dodecyl-sulphate polyacrylamide gel electrophoresis (SDS-PAGE). The HMW-GS were identified using the previously proposed nomenclature by Payne and Lawrence 53 . Allele specific PCR markers were used to screen different mutations in the Puroindoline genes PinaD1 and PinbD1. Data analysis All statistical computations were performed with the R software 54 (R Development Core Team 2018) and software package ASReml-R 3.0 55 . In the statistical analyses, each environment represents the location-by-year combination. Phenotypic data analysis was performed according to the following statistical model, given in Eq. ( 1 ): $${y}_{ikno}=u+{g}_{i}+{env}_{k}+{g}_{i}:{env}_{k}+{rep}_{kn}+{b}_{kno}+{e}_{ikno}$$ 1 where y ikno was the phenotypic observation for the ith cultivar tested in the kth environment in the nth replication in the oth incomplete block, u was the general mean, g i the genotypic effect of the ith cultivar, env k the effect of the kth environment, g i : env k was the cultivar-by-environment interaction, rep kn was the effect of the nth replication in the kth environment, b kno was the effect of the oth incomplete block of the nth replication in the kth environment and e ikno was the residual. Variance components were estimated using the restricted maximum likelihood (REML) method assuming a random model in a classical one-stage analysis. The significance of random terms was tested by model comparison using a likelihood ratio test 56 . Average values across environments were estimated as Best Linear Unbiased Estimates (BLUEs) assuming fixed genotypic effects. Broad sense heritability (H²) was calculated according to the following Eq. (2) 57,58 : $${H}^{2}=1-\frac{\vartheta }{{2\sigma }_{G}^{2}}$$ 2 Where ϑ is the mean variance of a difference of two best linear unbiased predictors and \({\sigma }_{G}^{2}\) the genetic variance. Pearson correlation coefficients were estimated for all traits using the BLUEs across environments and the R package ‘corrplot’ 59 . Correlation network analysis was performed using the R package ‘qgraph’ 60 . To test whether the measured traits have a temporal trend across breeding periods (registration years of cultivars), we used the LOESS regression by fitting a smooth curve through a scatterplot. Box-and-whisker plots were constructed by ggplot2 package 61 using the BLUEs across environments. The values in Fig. 5 c were normalized as follows: $$x=\frac{{x}_{i}}{Max\_x}$$ Where x i is the trait observation value of a cultivar and Max_x is the trait maximum value. Declarations Competing interests The authors declare no competing interests. Author contributions Conceptualization, CFHL, JS, KE-H, ST; Data analysis and figures preparation, KE-H; Field trials and phenotyping, MR, MSp, NP, JD, MK, PHGB, JS, KE-H; Quality assessment; FP, MR, MSp, NP, JD, MK, PHGB, JS, KE-H, CFHL; Writing – Original Draft Preparation, KE-H, CFHL; Review and editing, PT, MA, MSi, ST, PHGB, JS, JD, MK, NP, MSp, MR, MSi, ST, CFHL. All authors read and approved the final manuscript. Acknowledgements The project is supported by funds of the Federal Ministry of Food and Agriculture (BMEL) based on a decision of the Parliament of the Federal Republic of Germany via the Federal Office for Agriculture and Food (BLE) under the innovation support program. Project Betterwheat – FKZ: 2818405A18. Data availability The datasets used and analysed during the current study available from the corresponding author on reasonable request. References Shewry, P. R. & Hey, S. J. 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D. & Borsboom, D. qgraph: Network Visualizations of Relationships in Psychometric Data. J. Stat. Soft. 48, 1–18; 10.18637/jss.v048.i04 (2012). Wickham, H. Ggplot2. Elegrant graphics for data analysis (Springer, Switzerland, 2016). Additional Declarations (Not answered) Supplementary Files Supplementarytable1.xlsx Supplementary Table 1. List of the 282 wheat cultivars used in this study, their country of origin, year of registration and quality class. Supplementarytable2.xlsx Supplementary Table 2. Summary statistics of all measured traits among 282 bread wheat cultivars. Var G genotypic variance, Var E variance of environment, Var GxE genotype-by-environme Supplementarytable3.xlsx Supplementary Table 3. Pearson correlation matrix of all measured traits. Supplementarytable4.xlsx Supplementary Table 4. Allelic diversity of glutenin composition and frequency of high molecular weight glutenins in bread. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4523213","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":312525477,"identity":"68bb78a9-57de-48e4-be3f-ad44ef886d45","order_by":0,"name":"Khaoula El Hassouni","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-9379-828X","institution":"State Plant Breeding Institute, University of Hohenheim","correspondingAuthor":true,"prefix":"","firstName":"Khaoula","middleName":"El","lastName":"Hassouni","suffix":""},{"id":312525478,"identity":"63d0f577-311c-49a7-a30b-90adb0d8617f","order_by":1,"name":"Muhammad Afzal","email":"","orcid":"https://orcid.org/0000-0002-1020-4133","institution":"University of Hohenheim","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Afzal","suffix":""},{"id":312525479,"identity":"66698ed0-bcbe-4e40-8a4e-c7d7a37a1529","order_by":2,"name":"Philipp Boeven","email":"","orcid":"","institution":"Limagrain GmbH","correspondingAuthor":false,"prefix":"","firstName":"Philipp","middleName":"","lastName":"Boeven","suffix":""},{"id":312525480,"identity":"41dc637b-a3a8-42be-805a-13b80e304127","order_by":3,"name":"Jost Dörnte","email":"","orcid":"","institution":"Deutsche Saatveredelung AG (DSV)","correspondingAuthor":false,"prefix":"","firstName":"Jost","middleName":"","lastName":"Dörnte","suffix":""},{"id":312525481,"identity":"5eabe30d-036d-48ac-ad3d-7ab2ae023c18","order_by":4,"name":"Michael Koch","email":"","orcid":"","institution":"Deutsche Saatveredelung AG (DSV)","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Koch","suffix":""},{"id":312525482,"identity":"cde8a4fd-ecd2-4e96-96f0-2c4dc2060186","order_by":5,"name":"Nina Pfeiffer","email":"","orcid":"","institution":"KWS Lochow GmbH","correspondingAuthor":false,"prefix":"","firstName":"Nina","middleName":"","lastName":"Pfeiffer","suffix":""},{"id":312525483,"identity":"3f94d054-50f5-4462-984b-9858bc0500cd","order_by":6,"name":"Franz Pfleger","email":"","orcid":"","institution":"DIGeFa GmbH","correspondingAuthor":false,"prefix":"","firstName":"Franz","middleName":"","lastName":"Pfleger","suffix":""},{"id":312525484,"identity":"3402a469-79a8-49d6-8487-66e848ac0be2","order_by":7,"name":"Matthias Rapp","email":"","orcid":"","institution":"W. von Borries-Eckendorf GmbH \u0026 Co. 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(\u003cstrong\u003ea\u003c/strong\u003e) Percentage of the phenotypic variation explained by each variance component for all traits. (\u003cstrong\u003eb\u003c/strong\u003e) Heritability for all measured traits. Horizontal lines indicate traits categories depending on the speed of the test. For a detailed description of the traits, see Table 1.\u003c/p\u003e","description":"","filename":"Fig.141.png","url":"https://assets-eu.researchsquare.com/files/rs-4523213/v1/aa621397b342feaf30bb2002.png"},{"id":59015886,"identity":"259f68b2-18a9-44d4-bbaa-59a406460c38","added_by":"auto","created_at":"2024-06-25 10:30:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1165815,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation network of mean values across environments of all 63 traits for 282 wheat cultivars. Green and red lines connecting the nodes indicate positive and negative correlations, respectively. Thickness of the lines indicates the correlation strength. Correlations ranked from -0.86 to 0.98. \u003cstrong\u003e(a)\u003c/strong\u003eCorrelation network including significant and non-significant associations among the traits. \u003cstrong\u003e(b)\u003c/strong\u003e Zoomed-in view of the central subgroup of significant traits associations from a.\u003c/p\u003e","description":"","filename":"Fig.142.png","url":"https://assets-eu.researchsquare.com/files/rs-4523213/v1/d4c8ad2b3e7ba07018e25855.png"},{"id":59015546,"identity":"76caf178-017c-4fb3-96db-21c072c5e1a6","added_by":"auto","created_at":"2024-06-25 10:22:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":570584,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplot of different traits showing a temporal trend according to the registration year of the wheat cultivars. LOESS regression represented in black.\u003c/p\u003e","description":"","filename":"Fig.143.png","url":"https://assets-eu.researchsquare.com/files/rs-4523213/v1/d98e9cfe79d15a85c78c8e1e.png"},{"id":59015057,"identity":"a8ad6ae8-a0d4-40d7-ab8c-edb1432481a6","added_by":"auto","created_at":"2024-06-25 10:14:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":233489,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplot of different traits with a dual temporal trend according to the registration year of the wheat cultivars. LOESS regression represented in red for cultivars with E and A quality class and in blue for cultivars with B and C quality class.\u003c/p\u003e","description":"","filename":"Fig.144.png","url":"https://assets-eu.researchsquare.com/files/rs-4523213/v1/97c20c3ae62239e0147272a8.png"},{"id":59015059,"identity":"f6ec5c15-14d1-4f0a-a815-d0417dde5eb4","added_by":"auto","created_at":"2024-06-25 10:14:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":115153,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots showing the grain yield, disease response and content of nine minerals for the 20% best cultivars for loaf volume. The dots colored in blue are the top 5 cultivars within the 20% selection combining very good baking quality, high yield (a) with low disease susceptibility (b) and good nutritional value in minerals (c). The dots colored in yellow are the best 3 cultivars in the whole panel for each individual trait. “Patras”, “Asory” and “Ponticus” are widely cultivated cultivars in Germany. In (c), the values of mineral content were normalized between 0 and 1.\u003c/p\u003e","description":"","filename":"Fig.145.png","url":"https://assets-eu.researchsquare.com/files/rs-4523213/v1/9690b10ed9a69a84640cbe31.png"},{"id":59836334,"identity":"3ebf5031-67a9-4e8a-a9c9-a7075bf1a331","added_by":"auto","created_at":"2024-07-08 08:39:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3192414,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4523213/v1/452396ef-faaf-4350-abc3-8f06aa9ca1ae.pdf"},{"id":59015056,"identity":"66ee296c-d828-470e-b8b2-5c9a5f0e2e9e","added_by":"auto","created_at":"2024-06-25 10:14:13","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17511,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table 1. List of the 282 wheat cultivars used in this study, their country of origin, year of registration and quality class.\u003c/p\u003e","description":"","filename":"Supplementarytable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4523213/v1/bde959761b18343ef197a893.xlsx"},{"id":59015545,"identity":"ebf0d676-e6cc-4f10-b6a5-5f8f8ad161ee","added_by":"auto","created_at":"2024-06-25 10:22:13","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":17605,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table 2. Summary statistics of all measured traits among 282 bread wheat cultivars. Var G genotypic variance, Var E variance of environment, Var GxE genotype-by-environme\u003c/p\u003e","description":"","filename":"Supplementarytable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4523213/v1/709b2579820f79c82cff652e.xlsx"},{"id":59015062,"identity":"9a54b4c0-5d6d-4205-98ce-f0f001280cbb","added_by":"auto","created_at":"2024-06-25 10:14:13","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":46827,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table 3. Pearson correlation matrix of all measured traits.\u003c/p\u003e","description":"","filename":"Supplementarytable3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4523213/v1/c86caadcc3f19eb0d2a5cd88.xlsx"},{"id":59015063,"identity":"ca6bd9ad-d148-40f9-acab-438c7b7c5fa4","added_by":"auto","created_at":"2024-06-25 10:14:13","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":9960,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table 4. Allelic diversity of glutenin composition and frequency of high molecular weight glutenins in bread.\u003c/p\u003e","description":"","filename":"Supplementarytable4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4523213/v1/f74f3826a5add0a6ba97f9ab.xlsx"},{"id":59015064,"identity":"83edee00-622d-4b55-8a68-aabefb5f463c","added_by":"auto","created_at":"2024-06-25 10:14:13","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1647897,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SupplementarymaterialElHassounietal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4523213/v1/364b136826a22df7ac67a521.pdf"}],"financialInterests":"(Not answered)","formattedTitle":"Wheat breeding to better feed a growing world: historic insights and future potential elaborated using a diverse cultivars collection and extended phenotyping.","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e subsp. \u003cem\u003eaestivum\u003c/em\u003e) ranks among the most important staple crops with a global growing area of about 219\u0026nbsp;million ha and a total grain production reaching 808\u0026nbsp;million tons in 2022 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fao.org/faostat/en/#data/QCL\u003c/span\u003e\u003cspan address=\"https://www.fao.org/faostat/en/#data/QCL\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on 27.02.2024). This globally important food crop for human consumption provides a high nutritional value with starch, proteins, minerals, fibers and vitamins\u003csup\u003e1,2\u003c/sup\u003e. Given its prevalence in human diets, wheat cultivars must meet particular quality criteria for the production of different types of end-products. To meet the industrial requirements, wheat cultivars are classified in many countries based on baking quality as part of the registration process in the official seed catalogue. In Germany, this classification system includes four classes from highest to lowest quality corresponding to E, A, B and C class, respectively\u003csup\u003e3\u003c/sup\u003e. The classification into quality classes depends on minimum standards in seven quality traits.\u003c/p\u003e \u003cp\u003eBaking quality of wheat is related to the rheological properties of the dough, the dough strength and extensibility, which are largely determined by the protein content and its quality\u003csup\u003e4\u003c/sup\u003e. Around 80% of the wheat protein belongs to the gluten, which forms a unique dough network under input of water and kneading energy\u003csup\u003e5,6\u003c/sup\u003e. Therefore, wheat cultivars were often investigated with different tests regarding protein or gluten content and quality, dough characteristics as well as bread loaf volume\u003csup\u003e7\u003c/sup\u003e. Positive correlations between protein content and protein quality measured as sedimentation volume and loaf volume of bread were reported\u003csup\u003e8,9,10\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA successful wheat cultivar must combine acceptable end-use quality with high grain yield and low susceptibility against diseases and lodging in the field with a reduced need for chemical plant protection. Unfortunately, protein content appears to have a strong negative correlation with grain yield\u003csup\u003e11,12\u003c/sup\u003e. Thus, breeders had to find a balanced way of selection on these traits using innovative approaches, for instance the grain-protein deviation, which became popular in Central Europe during the last decade\u003csup\u003e13\u003c/sup\u003e. Comparing results from official registration trials of wheat cultivars in Germany from 1984–2014, Laidig et al.\u003csup\u003e10\u003c/sup\u003e and Voss-Fels et al.\u003csup\u003e14\u003c/sup\u003e have shown large improvements mainly in grain yield and disease resistance and to a limited extent for end-use quality. With the high demand for healthy food nowadays and the worldwide health problems of micronutrients deficiencies known as hidden hunger\u003csup\u003e15\u003c/sup\u003e, it is also worthwhile to implement nutritional traits in the wheat supply chain\u003csup\u003e16,17\u003c/sup\u003e. For instance, the international research organization CIMMYT in partnership with HarvestPlus have launched a large wheat breeding program which shall combine good agronomy and end- use quality with increased content of the minerals Fe and Zn in developing countries\u003csup\u003e16\u003c/sup\u003e. However, these efforts need to be extended to the global wheat supply chain. Until now, studies investigating the potential of combining nutrient content with better agronomical performance and end use quality are completely lacking for the important wheat production region of Central Europe.\u003c/p\u003e \u003cp\u003eTherefore, we investigated 282 bread wheat cultivars from seven decades of wheat breeding in Central Europe on 63 traits related to agronomy, baking and nutritional quality across up to eight field environments. Our objectives were to (i) quantify the variance due to cultivar, environment and cultivar-by-environment interaction for the investigated traits, (ii) explore the phenotypic correlations between the 63 traits, (iii) investigate the temporal trends in traits realized across seven decades of wheat breeding, and (iv) elaborate the potential to combine agronomical attributes and baking quality with nutritional quality to feed the global population.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003eIn this study, we investigated 63 different traits across 282 wheat cultivars (Supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) originating from different European countries and different decades of wheat breeding tested under multiple field environments. Besides classical agronomic traits like grain yield, plant height and susceptibility of wheat cultivars against diseases, commonly measured quality traits like protein content, sedimentation volume and falling number, we dived deeply into techno-functional traits for dough and bread making quality as well as nutrient content measuring dozens of minerals and sugars. To our knowledge, this data set is one of the largest experimental data sets ever reported in wheat breeding representing a perfect basis to elaborate the historic and future potential of wheat breeding to better feed the growing world.\u003c/p\u003e\n\u003ch3\u003ePrerequisites of successful breeding fulfilled for the most agronomic, quality and nutritional traits\u003c/h3\u003e\n\u003cp\u003eVery important prerequisites for successful breeding of a new trait are: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) a significant difference in the trait between the existing wheat cultivars, i.e. significant genetic variance, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) a high heritability, i.e. the amount of genetic variance compared to the overall phenotypic (visible) variance, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) speed of measuring the trait across several samples, and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) feasibility to combine a new trait with the other traits for selection for, i.e. the new valuable trait expression should not be negatively correlated with the other traits.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of all the measured traits, measurement method and test speed.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrait abbreviation\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrait name and measurement unit\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMeasurement method\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTraits categories by test speed\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGY\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrain yield (dt/ha)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCombine harvester at 14% moisture\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"18\" rowspan=\"19\"\u003e \u003cp\u003eFast\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePLH\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlant height (cm)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHeight in cm from the ground to tip of the ears\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDTH\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDays to heading (number of days)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of days when 60% of the plants per plot showing the first spikelets\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTest weight (kg/hl)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNIRS\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTKW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThousand kernel weight (g)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe weight of 1000 grains\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAwns\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAwns (0/1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVisual scoring in field (0) for absent and (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) for present\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLdg\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLodging (\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7 CR8\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e–\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVisual scoring in field from 1–9 (absence – severe lodging)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYellow rust (\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7 CR8\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e–\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eVisual scoring of disease in field from 1–9 (healthy – highly susceptible)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeaf rust (\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7 CR8\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e–\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMildew\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePowdery Mildew (\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7 CR8\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e–\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSEP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeptoria (\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7 CR8\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e–\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFHB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFusarium head blight (\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7 CR8\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e–\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProt\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProtein content (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDumas method - ICC 167\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSDS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSedimentation test (ml)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSediment in test tube - ICC 151\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFalling number (s)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePerten falling number system - ICC 107/1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHard\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHardness\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNIRS\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite index\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eKonica Minolta chromameter CR-410 for flour color measurement\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYellow index\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlour extraction rate (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFlour with type 550/ used kernels *100 with Brabender Quadrumat Junior mill\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGluto\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlutograph\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBrabender Glutograph\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"13\" rowspan=\"14\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWet gluten\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePerten Glutomatic\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeak1 (cP)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eRapid visco-analyzer-4 Newport scientific - ICC 162\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrough viscosity (cP)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBd\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBreakdown (cP)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinal Viscosity (cP)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSb\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSetback (cP)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeak maximum time (min)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP.Temp\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePasting Temperature (°C)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTBM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTorque before maximum (BU)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eBrabender Glutopeak 803400\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTorque maximum (BU)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePMT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeak maximum time (s)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTAM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTorque after maximum (BU)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eA-amylase\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlpha-amylase activity SD unit/g\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnzymatic test kit - Megazyme α-Amylase SD Assay Kit (K-AMYLSD)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater absorption (ml/100 g flour)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFarinograph - ICC115/1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"29\" rowspan=\"30\"\u003e \u003cp\u003eTime-consuming\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyield of volume (ml/100 g flour)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRapid-Mix Test - Rapeseed displacement according to Neumann/Doose\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eE90\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnergy (cm²) at 90 min\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eBrabender Extensograph E - ICC114/1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRES90\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResistance to Extension (BE) at 90 min\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEXT90\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtensibility (mm) at 90 min\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eR90\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRatio\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDevelopment time (min)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003ePerten micro-doughLAB 2800 - ICC 184\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eST\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStability (min)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSOF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoftening (mNm)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMTI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixing Tolerance Index (mNm)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeakEnergy (Wh/kg)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBa\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBarium (mg/kg)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"10\" rowspan=\"11\"\u003e \u003cp\u003eInductively coupled plasma combined with optical emission spectroscopy (ICP-OES) and mass spectrometry (ICP‐MS) for mineral content quantification\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCa\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCalcium (mg/kg)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCu\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCopper (mg/kg)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFe\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIron (mg/kg)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eK\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePotassium (mg/kg)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMg\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMagnesium (mg/kg)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMn\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eManganese (mg/kg)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhosphorus (mg/kg)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSulphur (mg/kg)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eZn\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZinc (mg/kg)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAl\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAluminum (mg/kg)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlycerol\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlycerol (mg/g)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eHigh-performance anion-exchange chromatography with pulsed amperometric detection (HPAEC) for sugars quantification\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlucose\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlucose (mg/g)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFructose\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFructose (mg/g)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSucrose\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSucrose (mg/g)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaltose\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaltose (mg/g)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDP \u0026lt; 8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOligosaccharide with degree of polymerization \u0026lt; 8 (mg/g)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDP \u0026gt; 8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOligosaccharide with degree of polymerization \u0026gt; 8 (mg/g)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal DP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal oligosaccharide (mg/g)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor all investigated traits, we found a significant genetic variance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, Supplementary Table\u0026nbsp;2), but its magnitude relative to the other sources of variation varied considerably across traits. It ranged from 0.8% of total variance for susceptibility against the fungal disease powdery mildew to 90.8% for yellow index measuring the colour of the extracted flour. By contrast, the variance of cultivar-by-environment interaction was quite low for most traits indicating that the ranking of cultivars was quite stable across the different test locations. This confirms previous findings in the literature for those of the 63 traits measured in this study, which have been already investigated\u003csup\u003e10,18,19\u003c/sup\u003e. Due to unreplicated samples for measuring the nutritional traits, the cultivar-by-environment variance could not be quantified.\u003c/p\u003e \u003cp\u003eRegarding full decomposition of phenotypic variance, large magnitude of the variance due to environment stands out for almost all traits (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). For instance, average grain yield across all cultivars tested at single locations ranged from 73 dt/ha to 107 dt/ha over the six test environments although the agricultural practices and fertilizers’ supply were comparable (data not shown). This is most probably due to the micro-environmental differences between the different field environments, which is a well-known factor for its impact on numerous traits and multiple crops observed in breeders’ trials\u003csup\u003e10,18,20,21,22\u003c/sup\u003e. The significant cultivar-by-environment variance underlines the large importance of multi-location field trials in research and product development to estimate robust adjusted means and breeding values for traits of interest. As many crops exist with hundreds of cultivars, which can be grown across diverse environments. Such experimental studies become expensive and large in terms of the number of test plots, but remain crucial for generalizing results of the trials reliably.\u003c/p\u003e \u003cp\u003eFor majority of the investigated traits, heritability was higher than 0.6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). For some traits such as the element Al, most of the sugars and oligosaccharides, the heritability was below 0.5, which is to our knowledge for sugars yet not reported in literature for wheat but confirms findings from maize\u003csup\u003e23\u003c/sup\u003e. Compared to the estimates of heritability for frequently reported traits in literature such as grain yield\u003csup\u003e24\u003c/sup\u003e, plant height, disease susceptibility\u003csup\u003e25,26\u003c/sup\u003e as well as quality traits like protein content and sedimentation volume\u003csup\u003e27,28\u003c/sup\u003e, our heritability estimates were high to very high. This can be explained by the large number of tested wheat cultivars expanding the genetic variance to the existing maximum and by the robust experimental set up using several field locations across two years allowing for meaningful conclusion for future wheat breeding. Summarizing, for many of the 63 traits, the prerequisite 1 and 2 of a significant genetic variance and a high heritability are fulfilled. For further discussion and illustrations, we eliminated traits with heritability \u0026lt; 0.4.\u003c/p\u003e \u003cp\u003eFor an efficient selection, breeders must evaluate hundreds of different candidate lines to find the best compromise regarding all traits of interest, which requires methods and techniques to rapidly measure the traits of interest on several samples. Similarly, trading wheat samples and products along the supply chain at all stakeholders on certain traits requires also rapid technologies to measure these traits as recently indicated in an opinion paper for future supply chains\u003csup\u003e17\u003c/sup\u003e. We tried to summarize the 63 traits into three groups regarding speed of the test methods i.e., fast, intermediate and time-consuming. Although agronomic evaluations require testing across at least one field season, breeders are well equipped for doing the final measurement for yield, agronomy and disease susceptibility of hundreds of candidate lines within a day. In near future, that will get even faster because of advancement in unmanned aerial vehicles technologies\u003csup\u003e29,30,31\u003c/sup\u003e these traits to the group with fast determination methods. Similarly, more than 100 samples per day can be processed using the following kernel and quality tests: protein content via NIRs, sedimentation volume, falling number, kernel hardness and size. Intermediate speed with a few dozen samples a day could be realized with glutograph glutopeak, rapid-viscoanalyser, wet gluten and flour colour measurements. Baking tests, detailed rheological tests as well as measuring minerals and sugars with the reference methods are slow. Thus, there is urgent need to develop fast methods for these traits, which accurately predict the respective traits, for healthy nutrition with high-quality products in future\u003csup\u003e17\u003c/sup\u003e. For many minerals, the X ray fluorescence detection method predicts their content very precisely with the capability to measure between 50–100 samples per day making its use in wheat breeding or trading highly interesting\u003csup\u003e32,33\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation network defines limits for the combination of different traits\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCoefficients of correlation among 58 traits were determined (Supplementary Table\u0026nbsp;3) and we tried to summarize their relationship in a network analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). At first glance, three distinct clusters of trait associations become visible: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) few sugars and oligosaccharides, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) falling number, α-amylase activity and the traits of the rapid-visco-analyser, and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) a high number of traits comprising agronomic and quality traits as well as few minerals. Cluster 2 represents all direct or indirect measurements of starch quality performed in our study. Rapid-visco analyser measures pasting properties of a flour-water suspension, which are largely influenced by the starch properties of the examined flour\u003csup\u003e34\u003c/sup\u003e. With increased amount and activity of α-amylase, starch is degraded to smaller sugar components impacting pasting properties, which is indirectly measured by falling number test\u003csup\u003e35,36\u003c/sup\u003e. Consequently, the clustering of these traits in our network analyses is logical and expected. Interestingly, starch properties are neither correlated with agronomic traits nor with dough quality traits to a higher extent. Thus, they could be combined with good agronomy or good dough and baking quality measured as loaf volume. This is of particular interest, as they determine important breadcrumb characters, which are not measured in baking tests routinely used in wheat variety testing.\u003c/p\u003e \u003cp\u003eCluster 3 contains many traits and therefore we tried to reduce its complexity in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb by focusing only on traits with significant correlations, which made few subgroups visible. For instance, loaf volume is highly positively correlated with protein content (r = 0.74, Supplementary Table\u0026nbsp;3) but also to protein quality as determined by SDS (r = 0.51, Supplementary Table\u0026nbsp;3). Furthermore, several parameters of dough quality tests were found to be positively correlated with protein content and protein quality, e.g. glutograph, extensograph (E90, EXT90), glutopeak (TBM, TM, TAM) and micro-dough-lab (PE). This is in line with findings in the literature for these traits\u003csup\u003e10,27\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eProtein content is also positively correlated with most measured minerals with largest coefficient of correlation of r = 0.73 to S content (Supplementary Table\u0026nbsp;3), which confirms previous studies\u003csup\u003e37,38\u003c/sup\u003e. S content is correlated (r \u0026gt; 0.45) with loaf volume, SDS, EXT90 and glutograph. This reasonably strong association of S with dough and baking quality might be either due to the correlation with protein content, or by the fact that disulfide bonds are crucial for establishment of the gluten network in a dough\u003csup\u003e39,40\u003c/sup\u003e or both of it.\u003c/p\u003e \u003cp\u003eThe single trait kernel hardness was correlated (r \u0026gt; 0.5) with water absorption, Ext90, SDS, TM, TBM, TAM (all glutopeak parameters), flour extraction rate and yellow index of the extracted flour. Whereas, coefficient of correlation of the kernel hardness with white index of the extracted flour and pasting temperature from RVA was \u0026lt; 0.5 (Supplementary Table\u0026nbsp;3). Kernel hardness in wheat is largely influenced by the puroinduline genes\u003csup\u003e41\u003c/sup\u003e Pin-a (Pina-D1b) and Pin-b (Pinb-D1b). While Pina-D1b is fixed in EU wheat germplasm to the soft wild type allele as confirmed in our study (data not shown), we could observe variation in the tested wheat cultivars for Pinb-D1b segregating for soft wild type allele (a) and three different hard alleles (b, c, d; Supplementary Fig.\u0026nbsp;1). The large impact of the soft allele at Pinb-D1b was especially pronounced for kernel hardness, SDS, water absorption, pasting temperature and colour of the extracted flour (Supplementary Fig.\u0026nbsp;1), while the different hardness alleles had only a minor effect on these traits. These findings fit well with a previous study on 94 German wheat cultivars\u003csup\u003e28\u003c/sup\u003e and highlight the high potential of molecular (marker-based) selection if loci with large effect on individual traits are known.\u003c/p\u003e \u003cp\u003eOwing to the demand to feed a growing world limited in the agricultural area, quality and nutrient content must be combined with high grain yield. Unfortunately, grain yield and protein content are strongly negatively correlated as often shown in literature\u003csup\u003e10,11,42\u003c/sup\u003e and confirmed once again in our data set (r = − 0.78). Even more, grain yield and loaf volume as well as grain yield and the concentration of several minerals are also negatively correlated (Supplementary Table\u0026nbsp;3). Consequently, improving these traits in parallel is complicated requiring priority settings and strategies to find best compromises for the future world nutrition. While numerous approaches were reported for combined selection of high grain yield and high protein content, e.g. protein yield or grain protein-deviation; cf. Thorwarth et al.\u003csup\u003e13\u003c/sup\u003e, strategies for the combination of high grain yield with high baking quality and high nutritional value are rare.\u003c/p\u003e \u003cp\u003eIn conclusion, the high genetic variance coupled with a high heritability for most of the investigated traits would enable efficient wheat breeding for better agronomy, baking quality as well as mineral content of future wheat cultivars. However, the slow speed of test methods especially for baking quality and some minerals as well as negative correlations between grain yield, baking quality and mineral content makes their efficient selection and combination difficult.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eLarge historic breeding success on agronomy and partly on baking quality\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe tested 282 wheat cultivars were registered between 1961 and 2020. The historic wheat cultivars were chosen based on their large market relevance during the respective decades, but most of the investigated wheat cultivars were registered after 2000 (Supplementary Table\u0026nbsp;1). Owing to that unbalanced data structure, we focus on important and large trends. For grain yield, an average increase from 81 dt/ha to almost 100 dt/ha was observed for wheat cultivars from 1961 and 2020, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Moreover, important agronomic traits like reduced susceptibility against fungal diseases, e.g. Septoria tritici blotch or powdery mildew, as well as reduced risk of lodging due to reduced plant height were achieved by breeding, which are of utmost importance if future agriculture will be based on less agrochemical inputs. These findings are in line with previous studies for German\u003csup\u003e10,14\u003c/sup\u003e and international wheat germplasm\u003csup\u003e43\u003c/sup\u003e underlining significant success and importance of plant breeding for feeding the growing world population under climate change adopting more sustainable agricultural practices.\u003c/p\u003e \u003cp\u003eProtein and wet gluten content were lower in modern than in historic wheat cultivars, but the reduction from 1961 to 2020 was much less pronounced than expected by their large and negative correlation with grain yield and the tremendous increase of grain yield in the same period. This highlights the breeders’ strategy to improve grain yield and in parallel efforts to avoid large reductions of protein content. Additionally, although negatively correlated to grain yield, trends of selection for traits related to protein quality, e.g. SDS, EXT90, or loaf volume increased over time until ~ 1990 and appeared to stagnate or slightly decreased afterwards. These results confirm previous studies\u003csup\u003e44,45\u003c/sup\u003e and underline that even negatively correlated traits can be jointly improved.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInterestingly, the different wheat cultivars clustered for grain yield, protein content and baking quality across the decades also according to their quality classes (E \u0026gt; A \u0026gt; B \u0026gt; C baking quality class referring to the German reference system) as visible by the different coloured dots in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Therefore, we divided the wheat cultivars into two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e): high baking quality (E + A quality cultivars) and limited baking quality (B + C quality cultivars) making several important findings observable. First, breeding for high quality was based on higher protein content at the expense of reduced grain yield. Second, while quality remained constant on average or even declined in wheat cultivars belonging to B + C quality group, it was increased up to 1990 and then kept constant in the E + A quality group. Third, a big variation in quality traits became visible in all quality groups opening the avenue for combination of quality with good agronomy.\u003c/p\u003e \u003cp\u003eSince the pioneering work of Payne et al.\u003csup\u003e46\u003c/sup\u003e, the monitoring and selection on certain high-molecular weight protein subunits became feasible and was intensively used by breeders. In particular, the alleles at glutenin locus Glu-1 at the homologous chromosomes 1A, 1B and 1D were intensively monitored and selected. Therefore, we determined them across all 282 wheat cultivars (Supplementary table 4 and Supplementary Fig.\u0026nbsp;2). Following trends across registration years appeared: at Glu-A1 locus, breeders reduced the frequency of allele “0” by increasing the frequencies of alleles “1” and “2*”. Similarly, at Glu-B1, the frequency of allele “6 + 8” was reduced and the frequency of allele “7 + 9” was increased. Furthermore, the frequencies of these alleles varied considerably across the quality classes. Compared to cultivars from low baking quality class C, cultivars from highest baking quality class E had higher (lower) frequencies of alleles “1” and “2*” (“0”) at Glu-A1, higher (lower) frequencies of alleles “7 + 8” and “7 + 9” (“6 + 8”) at Glu-B1 and higher (lower) frequencies of alleles “5 + 10” (“2 + 12”) at Glu-D1 (Supplementary Fig.\u0026nbsp;2). This is a further proof of the effectiveness of selection on single genes or gene families, as long as they have a large effect on a trait like also discussed above for the Puroinduline gene and kernel hardness.\u003c/p\u003e \u003cp\u003eRegarding mineral content, the trend across registration years was decreasing for almost all of them. Although European wheat breeders have not yet selected for or against minerals, this trend was expected due to the negative correlation between grain yield and mineral content (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Supplementary Table\u0026nbsp;3). Nevertheless, researchers from CIMMYT in partnership with HarvestPlus program have shown that good agronomy and baking quality could also be combined with increased mineral content. Similarly, we see a clear tendency in our data set that wheat cultivars of better-quality groups, e.g. E class, appear to have higher mineral content than cultivars of poor baking quality class C (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Supplementary Fig.\u0026nbsp;3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eOutlook: Future breeding combines high mineral content, high baking quality and good agronomic preformance\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe therefore chose the wheat cultivars belonging to the 20% with the highest loaf volume and showed their variability in grain yield, disease susceptibility and mineral content in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The five cultivars with highest yield in that group were marked with light blue across all boxplots and important market cultivars were shown with their names. Overall, a large variability for all regarded traits was present in that group of cultivars with high loaf volume showing the potential to combine high grain yield, good diseases susceptibility, good baking quality and elevated mineral content. For instance, the cultivar “Patras” with significant market share since several years in Germany had a grain yield of 97.6 dt/ha, disease susceptibility from 1.5 for Mildew to 3.5 for LR and concentration of Fe and Zn in the upper half of tested wheat cultivars. As visible by the best wheat cultivars for individual traits (yellow dots in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), however, the combination of different negatively correlated traits requires a compromise at the expense of reduced individual traits values. Thus, the weight given to the individual traits in the final combination must be elaborated carefully and might differ across world regions and time warranting further research.\u003c/p\u003e \u003cp\u003eNevertheless, the large expenses and complications with negative correlations to grain yield in wheat breeding for higher mineral content are only justifiable, if consumers eat in future much more whole grain products with adopted bread making processes. This is due to the fact that the majority of minerals is in the outer layers and embryo of wheat grains, which are removed during milling of the widely used extracted flour. Additionally, minerals like Fe and Zn are bound in wheat flour to phytic acid, making them non bio-available for humans. During the bread making process, however, phytic acid can be largely degraded making minerals bioavailable by longer dough fermentation and (natural) addition of the enzyme phytase\u003csup\u003e47\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWith our recent global challenges to feed a growing world population sustainably under a changing climate, malnutrition, and the large impact of our diet on chronic diseases\u003csup\u003e48\u003c/sup\u003e, it appears worthwhile and urgent to make impactful investments across supply chains for improving yield, sustainable agricultural production and nutrient content for as many crops as possible\u003csup\u003e17\u003c/sup\u003e. Therefore, our study investigating 63 traits related to agronomic performance, baking quality and nutrients in 282 wheat cultivars grown across up to eight environments is an important first step for future European wheat breeding. However, further research is warranted to (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) establish rapid methods to measure nutrients and complicated dough and baking qualities, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) deepen our understanding of the genomics behind the different traits in order to establish marker-based selections and/or being able to break up negative correlations of traits, and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) attract more interest by the stakeholders and consumers towards healthy diets and their realization across global supply chains.\u003c/p\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003ch2\u003ePlant material and field experiments\u003c/h2\u003e\u003cp\u003eThis study evaluated a diverse panel of 282 old and modern winter wheat cultivars originating from different European countries. The cultivars were released between 1961 and 2021. Most of cultivars were released in Germany (for more details see Supplementary Table\u0026nbsp;1). The entire panel underwent field-testing in two consecutive cropping seasons in yield and observation trials across Germany. Yield trials were carried out at locations: Hovedissen (51°59'12.8\"N, 8°44'54.4\"E), Leutewitz (51°8'35.3\"N, 13°25'9.0\"E) and Seligenstadt (49°51'3.30'' N, 10°05'21.60''O) in 2020, and at Hovedissen, Asendorf (52°6'4.722\"N, 9°1'35.79\"E) and Seligenstadt in 2021. Observation trials were conducted in years 2020 and 2021 at locations Hohenheim (48°43′07.3\"N, 9°11′08.7\"E), Hovedissen, Leutewitz and Wetze (51°44'24.0\"N, 9°54'36.0\"E). In total, there were six environments (three locations and two years) for yield trials and eight environments (four locations and two years) for observation trials. Both trials were conducted using a partially replicated (P-rep) design with 300 plots at each location. For yield trials plots size ranged from 7.2 to 13.4 m² depending on the location and standard field practices according to the local agricultural practice were implemented for fertilizer, fungicide, herbicide and growth regulator applications. Observation trials were conducted in small plots of size between 0.5 to 1 m² without any applications of fungicides or growth regulators.\u003c/p\u003e\u003ch3\u003ePhenotyping and reference analytics\u003c/h3\u003e\u003cp\u003eThe traits assessed in this study are described in detail in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Grain yield (GY) reported as dt/ha was recorded in yield trials by harvesting all plots with a combine harvester and adjusted to 14% moisture content. Days to heading (DTH) was recorded as days elapsing between the first day of the year until 60% of plants had emerged heads. Plant height (PLH) was measured in cm from the ground to tip of the spikes, excluding awns.\u003c/p\u003e\u003cp\u003eFrom the harvest of each plot, thousand kernel weight (TKW) and test weight (TW) were determined according to the variety registration regulations. Awns were recorded as either absent (0) or present (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Lodging (Ldg) was measured shortly before harvest on a 1–9 scale, where 1 denotes absence of lodging and 9 severe lodging with plot completely flattened. In the observation trials, disease susceptibility of the plants was recorded for yellow rust (YR), leaf rust (LR), fusarium head blight (FHB), powdery mildew (Mildew) and Septoria tritici leaf blotch (SEP) at adult plant stage. All diseases were visually rated using a scale ranging from 1 to 9, where 1 refers to healthy and 9 to completely infected plants.\u003c/p\u003e\u003cp\u003eFor detailed quality analyses, the harvested samples of the yield trials were used. Due to the extensive time required for analyzing the quality traits, the samples from only four of six yield environments were used. These four environments were chosen based on quality of the field trials and protein content, where too low protein contents were avoided which would be deemed unacceptable in wheat trading. The following quality parameters were analyzed according to the reference methods or standard methods of the International Association for Cereal Science and Technology (ICC): Grain protein content (Prot; ICC 167), SDS sedimentation volume (SDS; ICC 151), falling number (FN; ICC 107/1), Farinograph water absorption (WA; ICC 115/1), Extensograph (E90, EXT90, RES90, R90; ICC 114/1), Rapid Visco Analyser (PV, TV, Bd, FV, Sb, PT, P.Temp; ICC 162) and Micro-dough LAB (DT, ST, SOF, MTI, PE; ICC 184). Kernel hardness was measured by near infrared (NIR) spectroscopy. White and yellow indices (WI \u0026amp; YI) were determined as L and b* values reading on chromameter Konica Minolta using extracted flour. Dough and gluten properties were measured using the standard routines of the Perten Glutomatic for wet gluten, Brabender Glutograph and the Brabender GlutoPeak according to the manufacturer’s instructions. The baking test was performed according to the Rapid Mix Test (RMT)\u003csup\u003e49\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eA subset of 800 samples, consisting of 200 samples from the four environments, were analyzed for mineral elements and sugars. Mineral content of whole grain flours was determined in mg/kg by inductively coupled plasma combined with optical emission spectroscopy (ICP-OES) and mass spectrometry (ICP‐MS) without technical replication. Values below the detection limit of 0.025 mg/kg were excluded. Using whole grain flour of each sample, sugars, sugar alcohols and oligosaccharides were quantified by High-performance anion-exchange chromatography with pulsed amperometric detection (HPAEC) technique (sugar alcohols, mono- and disaccharides adapted from Thermo Fisher Scientific Technical Note 72225; oligosaccharides according to Thermo Fisher Scientific Application Note 1149) after aqueous extraction without technical replication in the lab\u003csup\u003e50\u003c/sup\u003e. Alpha-amylase analysis was performed using the Megazyme α-Amylase SD Assay Kit\u003csup\u003e51\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eElectrophoretic analysis was performed to identify high molecular weight glutenin subunits (HMW-GS) composition of the wheat cultivars used in this study. Protein extraction was carried out according to Osborne\u003csup\u003e52\u003c/sup\u003e followed by disulphide bond reduction with Dithiothreitol. HMW-GS were separated by sodium-dodecyl-sulphate polyacrylamide gel electrophoresis (SDS-PAGE). The HMW-GS were identified using the previously proposed nomenclature by Payne and Lawrence\u003csup\u003e53\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAllele specific PCR markers were used to screen different mutations in the Puroindoline genes PinaD1 and PinbD1.\u003c/p\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eAll statistical computations were performed with the R software\u003csup\u003e54\u003c/sup\u003e (R Development Core Team 2018) and software package ASReml-R 3.0\u003csup\u003e55\u003c/sup\u003e. In the statistical analyses, each environment represents the location-by-year combination. Phenotypic data analysis was performed according to the following statistical model, given in Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e):\u003c/p\u003e\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${y}_{ikno}=u+{g}_{i}+{env}_{k}+{g}_{i}:{env}_{k}+{rep}_{kn}+{b}_{kno}+{e}_{ikno}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cp\u003ewhere y\u003csub\u003eikno\u003c/sub\u003e was the phenotypic observation for the ith cultivar tested in the kth environment in the nth replication in the oth incomplete block, u was the general mean, g\u003csub\u003ei\u003c/sub\u003e the genotypic effect of the ith cultivar, env\u003csub\u003ek\u003c/sub\u003e the effect of the kth environment, g\u003csub\u003ei\u003c/sub\u003e : env\u003csub\u003ek\u003c/sub\u003e was the cultivar-by-environment interaction, rep\u003csub\u003ekn\u003c/sub\u003e was the effect of the nth replication in the kth environment, b\u003csub\u003ekno\u003c/sub\u003e was the effect of the oth incomplete block of the nth replication in the kth environment and e\u003csub\u003eikno\u003c/sub\u003e was the residual.\u003c/p\u003e\u003cp\u003eVariance components were estimated using the restricted maximum likelihood (REML) method assuming a random model in a classical one-stage analysis. The significance of random terms was tested by model comparison using a likelihood ratio test\u003csup\u003e56\u003c/sup\u003e. Average values across environments were estimated as Best Linear Unbiased Estimates (BLUEs) assuming fixed genotypic effects. Broad sense heritability (H²) was calculated according to the following Eq.\u0026nbsp;(2)\u003csup\u003e57,58\u003c/sup\u003e:\u003c/p\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${H}^{2}=1-\\frac{\\vartheta }{{2\\sigma }_{G}^{2}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003cp\u003eWhere ϑ is the mean variance of a difference of two best linear unbiased predictors and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\sigma }_{G}^{2}\\)\u003c/span\u003e\u003c/span\u003ethe genetic variance.\u003c/p\u003e\u003cp\u003ePearson correlation coefficients were estimated for all traits using the BLUEs across environments and the R package ‘corrplot’\u003csup\u003e59\u003c/sup\u003e. Correlation network analysis was performed using the R package ‘qgraph’\u003csup\u003e60\u003c/sup\u003e. To test whether the measured traits have a temporal trend across breeding periods (registration years of cultivars), we used the LOESS regression by fitting a smooth curve through a scatterplot. Box-and-whisker plots were constructed by ggplot2 package\u003csup\u003e61\u003c/sup\u003e using the BLUEs across environments. The values in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec were normalized as follows:\u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$x=\\frac{{x}_{i}}{Max\\_x}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003eWhere x\u003csub\u003ei\u003c/sub\u003e is the trait observation value of a cultivar and Max_x is the trait maximum value.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eConceptualization, CFHL, JS, KE-H, ST; Data analysis and figures preparation, KE-H; Field trials and phenotyping, MR, MSp, NP, JD, MK, PHGB, JS, KE-H; Quality assessment; FP, MR, MSp, NP, JD, MK, PHGB, JS, KE-H, CFHL; Writing \u0026ndash; Original Draft Preparation, KE-H, CFHL; Review and editing, PT, MA, MSi, ST, PHGB, JS, JD, MK, NP, MSp, MR, MSi, ST, CFHL. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe project is supported by funds of the Federal Ministry of Food and Agriculture (BMEL) based on a decision of the Parliament of the Federal Republic of Germany via the Federal Office for Agriculture and Food (BLE) under the innovation support program. Project Betterwheat \u0026ndash; FKZ: 2818405A18.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003eThe datasets used and analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eShewry, P. R. \u0026amp; Hey, S. J. The contribution of wheat to human diet and health. Food and Energy Security 4, 178\u0026ndash;202 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeegels, P. L. 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Soft. 48, 1\u0026ndash;18; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.18637/jss.v048.i04\u003c/span\u003e\u003cspan address=\"10.18637/jss.v048.i04\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWickham, H. \u003cem\u003eGgplot2. Elegrant graphics for data analysis\u003c/em\u003e (Springer, Switzerland, 2016).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4523213/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4523213/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWheat is one of the most important staple crops playing a pivotal role to sustainably feed the growing world population. Wheat breeding mainly focused on improving agronomy and techno-functionality for bread or pasta production, but nutrient content is becoming increasingly more important to fight malnutrition. We therefore investigated 282 bread wheat cultivars from seven decades of wheat breeding in Central Europe on 63 different traits related to agronomy, quality and nutrients under multiple field trials. Wheat breeding has tremendously increased grain yield, resistance against diseases and lodging as well as baking quality across last decades. Whereas, mineral content slightly decreased without selection on it, probably due to its negative correlation with grain yield. The significant genetic variances determined for almost all traits show the potential for further improvement but significant negative correlations among grain yield and baking quality as well as grain yield and mineral content complicate their combined improvement. Thus, compromises in improvement of these traits are necessary to feed a growing global population.\u003c/p\u003e","manuscriptTitle":"Wheat breeding to better feed a growing world: historic insights and future potential elaborated using a diverse cultivars collection and extended phenotyping.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-25 10:14:08","doi":"10.21203/rs.3.rs-4523213/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f8094384-8230-4566-999a-05294c164f93","owner":[],"postedDate":"June 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":33031869,"name":"Biological sciences/Plant sciences"},{"id":33031870,"name":"Biological sciences/Genetics"}],"tags":[],"updatedAt":"2024-07-10T08:51:55+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-25 10:14:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4523213","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4523213","identity":"rs-4523213","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}