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Wheat (Triticum spp.) is one of the most important cereal crops in the world. Several diseases affect wheat production and can cause 20-80% yield loss annually. Out of these diseases, stripe rust, also known as yellow rust (Puccinia striiformis f. sp. tritici), stem rust (Puccinia graminis f. sp. tritici), leaf rust (Puccinia recondita), and powdery mildew (Blumeria graminis f. sp. tritici) are the most important fungal diseases that infect the foliar part of the plant. Many efforts were made to improve wheat resistance to these diseases. Due to the continuous advancement in sequencing methods and genomic tools, genome-wide association study has become available worldwide. This analysis enabled wheat breeders to detect genomic regions controlling the resistance in specific countries. In this review, molecular markers significantly associated with the resistance of the mentioned foliar diseases in the last five years were reviewed. Common markers that control broad-spectrum resistance in different countries were identified. Furthermore, common genes controlling the resistance of more than one of these foliar diseases were identified. The importance of these genes, their functional annotation, and the potential for gene enrichment are discussed. This review will be valuable to wheat breeders in producing genotypes with broad-spectrum resistance by applying genomic selection for the target common markers and associated genes.

Wheat (Triticum spp and, particularly, T. aestivum L.) is an essential cereal with increased human and animal nutritional demand. Therefore, there is a need to enhance wheat yield and genetic gain using modern breeding technologies alongside proven methods to achieve the necessary increases in productivity. These modern technologies will allow breeders to develop improved wheat cultivars more quickly and efficiently. This review aims to highlight the emerging technological trends used worldwide in wheat breeding, with a focus on enhancing wheat yield. The key technologies for introducing variation (hybridization among the species, synthetic wheat, and hybridization; genetically modified wheat; transgenic and gene-edited), inbreeding (double haploid (DH) and speed breeding (SB)), selection and evaluation (marker-assisted selection (MAS), genomic selection (GS), and machine learning (ML)) and hybrid wheat are discussed to highlight the current opportunities in wheat breeding and for the development of future wheat cultivars.

Abstract Background Improving grain yield in cereals especially in wheat is a main objective for plant breeders. One of the main constrains for improving this trait is the G × E interaction (GEI) which affects the performance of wheat genotypes in different environments. Selecting high yielding genotypes that can be used for a target set of environments is needed. Phenotypic selection can be misleading due to the environmental conditions. Incorporating information from phenotypic and genomic analyses can be useful in selecting the higher yielding genotypes for a group of environments. Results A set of 270 F3:6 wheat genotypes in the Nebraska winter wheat breeding program was tested for grain yield in nine environments. High genetic variation for grain yield was found among the genotypes. G × E interaction was also highly significant. The highest yielding genotype differed in each environment. The correlation for grain yield among the nine environments was low (0 to 0.43). Genome-wide association study revealed 70 marker traits association (MTAs) associated with increased grain yield. The analysis of linkage disequilibrium revealed 16 genomic regions with a highly significant linkage disequilibrium (LD). The candidate parents’ genotypes for improving grain yield in a group of environments were selected based on three criteria; number of alleles associated with increased grain yield in each selected genotype, genetic distance among the selected genotypes, and number of different alleles between each two selected parents. Conclusion Although G × E interaction was present, the advances in DNA technology provided very useful tools and analyzes. Such features helped to genetically select the highest yielding genotypes that can be used to cross grain production in a group of environments.

Abstract Background Wheat (Triticum aestivium L.) is an important crop globally which has a complex genome. To identify the parents with useful agronomic characteristics that could be used in the various breeding programs, it is very important to understand the genetic diversity among global wheat genotypes. Also, understanding the genetic diversity is useful in breeding studies such as marker-assisted selection (MAS), genome-wide association studies (GWAS), and genomic selection. Results To understand the genetic diversity in wheat, a set of 103 spring wheat genotypes which represented five different continents were used. These genotypes were genotyped using 36,720 genotyping-by-sequencing derived SNPs (GBS-SNPs) which were well distributed across wheat chromosomes. The tested 103-wheat genotypes contained three different subpopulations based on population structure, principle coordinate, and kinship analyses. A significant variation was found within and among the subpopulations based on the AMOVA. Subpopulation 1 was found to be the more diverse subpopulation based on the different allelic patterns (Na, Ne, I, h, and uh). No high linkage disequilibrium was found between the 36,720 SNPs. However, based on the genomic level, D genome was found to have the highest LD compared with the two other genomes A and B. The ratio between the number of significant LD/number of non-significant LD suggested that chromosomes 2D, 5A, and 7B are the highest LD chromosomes in their genomes with a value of 0.08, 0.07, and 0.05, respectively. Based on the LD decay, the D genome was found to be the lowest genome with the highest number of haplotype blocks on chromosome 2D. Conclusion The recent study concluded that the 103-spring wheat genotypes and their GBS-SNP markers are very appropriate for GWAS studies and QTL-mapping. The core collection comprises three different subpopulations. Genotypes in subpopulation 1 are the most diverse genotypes and could be used in future breeding programs if they have desired traits. The distribution of LD hotspots across the genome was investigated which provides useful information on the genomic regions that includes interesting genes.

Understanding the genetic complexity of traits is an important objective of small grain temperate cereals yield and adaptation improvements. Bi-parental quantitative trait loci (QTL) linkage mapping is a powerful method to identify genetic regions that co-segregate in the trait of interest within the research population. However, recently, association or linkage disequilibrium (LD) mapping using a genome-wide association study (GWAS) became an approach for unraveling the molecular genetic basis underlying the natural phenotypic variation. Many causative allele(s)/loci have been identified using the power of this approach which had not been detected in QTL mapping populations. In barley (Hordeum vulgare L.), GWAS has been successfully applied to define the causative allele(s)/loci which can be used in the breeding crop for adaptation and yield improvement. This promising approach represents a tremendous step forward in genetic analysis and undoubtedly proved it is a valuable tool in the identification of candidate genes. In this review, we describe the recently used approach for genetic analyses (linkage mapping or association mapping), and then provide the basic genetic and statistical concepts of GWAS, and subsequently highlight the genetic discoveries using GWAS. The review explained how the candidate gene(s) can be detected using state-of-art bioinformatic tools. Keywords: Association mapping, Hordeum vulgare L, Gene identification, QTL mapping, Barley breeding, GWAS

Stem rust caused by Puccinia graminis f. sp. tritici Eriks. is an important disease of common wheat globally. The production and cultivation of genetically resistant cultivars are one of the most successful and environmentally friendly ways to protect wheat against fungal pathogens. Seedling screening and genome-wide association study (GWAS) were used to determine the genetic diversity of wheat genotypes obtained on stem rust resistance loci. At the seedling stage, the reaction of the common stem rust race QFCSC in Nebraska was measured in a set of 212 genotypes from F3:6 lines. The results indicated that 184 genotypes (86.8%) had different degrees of resistance to this common race. While 28 genotypes (13.2%) were susceptible to stem rust. A set of 11,911 single-nucleotide polymorphism (SNP) markers was used to perform GWAS which detected 84 significant marker-trait associations (MTAs) with SNPs located on chromosomes 1B, 2A, 2B, 7B and an unknown chromosome. Promising high linkage disequilibrium (LD) genomic regions were found in all chromosomes except 2B which suggested they include candidate genes controlling stem rust resistance. Highly significant LD was found among these 59 significant SNPs on chromosome 2A and 12 significant SNPs with an unknown chromosomal position. The LD analysis between SNPs located on 2A and Sr38 gene reveal high significant LD genomic regions which was previously reported. To select the most promising stem rust resistant genotypes, a new approach was suggested based on four criteria including, phenotypic selection, number of resistant allele(s), the genetic distance among the selected parents, and number of the different resistant allele(s) in the candidate crosses. As a result, 23 genotypes were considered as the most suitable parents for crossing to produce highly resistant stem rust genotypes against the QFCSC.

Common bunt (caused by Tilletia caries and T. Foetida) is a major wheat disease. It occurs frequently in the USA and Turkey and damages grain yield and quality. Seed treatment with fungicides is an effective method to control this disease. However, using fungicides in organic and low-income fields is forbidden, and planting resistant cultivars are preferred. Due to the highly effective use of fungicides, little effort has been put into breeding resistant genotypes. In addition, the genetic diversity for this trait is low in modern wheat germplasm. Synthetic wheat genotypes were reported as an effective source to increase the diversity in wheat germplasm. Therefore, a set of 25 synthetics that are resistant to the Turkish common bunt race were evaluated against the Nebraska common bunt race. Four genotypes were found to be very resistant to Nebraska’s common bunt race. Using differential lines, four isolines carrying genes, Bt10, Bt11, Bt12, and Btp, were found to provide resistance against both Turkish and Nebraska common bunt races. Genotypes carrying any or all of these four genes could be used as a source of resistance in both countries. No correlation was found between common bunt resistance and some agronomic traits, which suggests that common bunt resistance is an independent trait.

Barley (Hordeum vulgare L.) thrives in the arid and semi-arid regions of the world; nevertheless, it suffers large grain yield losses due to drought stress. A panel of 426 lines of barley was evaluated in Egypt under deficit (DI) and full irrigation (FI) during the 2019 and 2020 growing seasons. Observations were recorded on the number of days to flowering (NDF), total chlorophyll content (CH), canopy temperature (CAN), grain filling duration (GFD), plant height (PH), and grain yield (Yield) under DI and FI. The lines were genotyped using the 9K Infinium iSelect single nucleotide polymorphisms (SNP) genotyping platform, which resulted in 6913 high-quality SNPs. In conjunction with the SNP markers, the phenotypic data were subjected to a genome-wide association scan (GWAS) using Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK). The GWAS results indicated that 36 SNPs were significantly associated with the studied traits under DI and FI. Furthermore, eight markers were significant and common across DI and FI water regimes, while 14 markers were uniquely associated with the studied traits under DI. Under DI and FI, three (11_10326, 11_20042, and 11_20170) and five (11_20099, 11_10326, 11_20840, 12_30298, and 11_20605) markers, respectively, had pleiotropic effect on at least two traits. Among the significant markers, 24 were annotated to known barley genes. Most of these genes were involved in plant responses to environmental stimuli such as drought. Overall, nine of the significant markers were previously reported, and 27 markers might be considered novel. Several markers identified in this study could enable the prediction of barley accessions with optimal agronomic performance under DI and FI.

Exploiting genetically diverse lines to identify genes for improving crop performance is needed to ensure global food security. A genome-wide association study (GWAS) was conducted using 46,268 SNP markers on a diverse panel of 143 hexaploid bread and synthetic wheat to identify potential genes/genomic regions controlling agronomic performance (yield and 26 yield-related traits), disease resistance, and grain quality traits. From phenotypic evaluation, we found large genetic variation among the 35 traits and recommended five lines having a high yield, better quality, and multiple disease resistance for direct use in a breeding program. From a GWAS, we identified a total of 243 significant marker-trait associations (MTAs) for 35 traits that explained up to 25% of the phenotypic variance. Of these, 120 MTAs have not been reported in the literature and are potentially novel MTAs. In silico gene annotation analysis identified 116 MTAs within genes and of which, 21 MTAs were annotated as a missense variant. Furthermore, we were able to identify 23 co-located multi-trait MTAs that were also phenotypically correlated to each other, showing the possibility of simultaneous improvement of these traits. Additionally, most of the co-located MTAs were within genes. We have provided genomic fingerprinting for significant markers with favorable and unfavorable alleles in the diverse set of lines for developing elite breeding lines from useful trait-integration. The results from this study provided a further understanding of genetically complex traits and would facilitate the use of diverse wheat accessions for improving multiple traits in an elite wheat breeding program.

Abstract Hybrid wheat (Triticum spp.) has the potential to boost yields and enhance production under changing climates to feed the growing global population. Production of hybrid wheat seed relies on male sterility, the blocking of pollen production, to prevent self-pollination. One method of preventing self-pollination in the female plants is to apply a chemical hybridizing agent (CHA). However, some combinations of CHA and genotypes have lower levels of sterility, resulting in decreased hybrid purity. Differences in CHA efficacy are a challenge in producing hybrid wheat lines for commercial and experimental use. Our primary research questions were to estimate the levels of sterility for wheat genotypes treated with a CHA and determine the best way to analyze differences. We applied the CHA sintofen (1-(4-chlorphyl)-1,4-dihydro-5-(2-methoxyethoxy)-4-oxocinnoline-3-carboxylic acid; Croisor 100) to 27 genotypes in replicate. After spraying, we counted seed in bagged female heads to evaluate CHA efficacy and CHA-by-genotype interaction. Using logit and probit models with a threshold of 7 seeds, we found differences among genotypes in 2015. Sterility was higher in 2016 and fewer genotypic differences were found. When CHA-induced sterilization is less uniform as in 2015, zero-inflated and hurdle count models were superior to standard mixed models. These models calculate mean seed number and fit data with limit-bounded scales collected by agronomists and plant breeders to compare genotypic differences. These analyses can assist in selecting parents and identifying where additional optimization of CHA application needs to occur. There is little work in the literature examining the relationship between CHAs and genotypes, making this work fundamental to the future of hybrid wheat breeding.

Abstract Background Automated phenotyping technologies are continually advancing the breeding process. However, collecting various secondary traits throughout the growing season and processing massive amounts of data still take great efforts and time. Selecting a minimum number of secondary traits that have the maximum predictive power has the potential to reduce phenotyping efforts. The objective of this study was to select principal features extracted from UAV imagery and critical growth stages that contributed the most in explaining winter wheat grain yield. Five dates of multispectral images and seven dates of RGB images were collected by a UAV system during the spring growing season in 2018. Two classes of features (variables), totaling to 172 variables, were extracted for each plot from the vegetation index and plant height maps, including pixel statistics and dynamic growth rates. A parametric algorithm, LASSO regression (the least angle and shrinkage selection operator), and a non-parametric algorithm, random forest, were applied for variable selection. The regression coefficients estimated by LASSO and the permutation importance scores provided by random forest were used to determine the ten most important variables influencing grain yield from each algorithm. Results Both selection algorithms assigned the highest importance score to the variables related with plant height around the grain filling stage. Some vegetation indices related variables were also selected by the algorithms mainly at earlier to mid growth stages and during the senescence. Compared with the yield prediction using all 172 variables derived from measured phenotypes, using the selected variables performed comparable or even better. We also noticed that the prediction accuracy on the adapted NE lines (r = 0.58–0.81) was higher than the other lines (r = 0.21–0.59) included in this study with different genetic backgrounds. Conclusions With the ultra-high resolution plot imagery obtained by the UAS-based phenotyping we are now able to derive more features, such as the variation of plant height or vegetation indices within a plot other than just an averaged number, that are potentially very useful for the breeding purpose. However, too many features or variables can be derived in this way. The promising results from this study suggests that the selected set from those variables can have comparable prediction accuracies on the grain yield prediction than the full set of them but possibly resulting in a better allocation of efforts and resources on phenotypic data collection and processing.

Abstract Stem rust (caused by Puccinia graminis f. sp. tritici) is a major disease of wheat. To understand the genetic basis of stem rust resistance in Nebraska winter wheat, a set of 330 genotypes representing two nurseries (DUP2015 and TRP2015) were evaluated for resistance to a Nebraska stem rust race (QFCSC) in two replications. The TRP2015 nursery was also evaluated for its resistance to an additional 13 stem rust races. The analysis of variance revealed significant variation among genotypes in both populations for stem rust resistance. Nine stem rust genes, Sr6, Sr31, Sr1RS Amigo , Sr24, Sr36, SrTmp, Sr7b, Sr9b, and Sr38, were expected and genotyped using gene-specific markers. The results of genetic analysis confirmed the presence of seven stem rust resistance genes. One genotype (NE15680) contained target alleles for five stem rust resistance genes and had a high level of stem rust resistance against different races. Single marker analysis indicated that Sr24 and Sr38 were highly significantly associated with stem rust resistance in the DUP2015 and TRP2015 nurseries, respectively. Linkage disequilibrium analysis identified the presence of 17 SNPs in high linkage with the Sr38-specific marker. These SNPs potentially tagging the Sr38 gene could be used in marker-assisted selection after validating them in additional genetic backgrounds.

Wheat (Triticum aestivum L.) is one of the most important crops in the world. Four spike-related traits, namely, spike weight (SW), spike length (SL), the total number of spikelets per spike (TSNS), total kernels per spike (TKNS), and thousand-kernel weight (TKW), were evaluated in 270 F3:6 Nebraska winter wheat lines in two environments (Lincoln and North Platte, NE, USA). All genotypes in both locations exhibited high genetic variation for all yield traits. High positive correlations were observed among all yield-related traits in each location separately. No or low correlation in yield-related traits was observed between the two environments. The broad-sense heritability estimates were 72.6, 72.3, 71.2, 72.3, and 56.1% for SW, SL, TSNS, TKNS, and TKW, respectively. A genome-wide association study (GWAS) was used to identify SNPs associated with yield traits. In the Lincoln environment, 44 markers were found to be significantly associated with spike-related traits (SW, SL, TSNS, TKNS, and TKW), while 41 were detected in North Platte. Due to the strong significant genotype x environment, no common SNP markers were found between the two locations. Gene annotation of the significant markers revealed candidate genes encoded for important proteins that are associated directly or indirectly with yield traits. Such high genetic variation among genotypes is very useful for selection to improve yield traits in each location separately.

One option to achieving greater resiliency for barley production in the face of climate change is to explore the potential of winter and facultative growth habits: for both types, low temperature tolerance (LTT) and vernalization sensitivity are key traits. Sensitivity to short-day photoperiod is a desirable attribute for facultative types. In order to broaden our understanding of the genetics of these phenotypes, we mapped quantitative trait loci (QTLs) and identified candidate genes using a genome-wide association studies (GWAS) panel composed of 882 barley accessions that was genotyped with the Illumina 9K single-nucleotide polymorphism (SNP) chip. Fifteen loci including 5 known and 10 novel QTL/genes were identified for LTT—assessed as winter survival in 10 field tests and mapped using a GWAS meta-analysis. FR-H1, FR-H2, and FR-H3 were major drivers of LTT, and candidate genes were identified for FR-H3. The principal determinants of vernalization sensitivity were VRN-H1, VRN-H2, and PPD-H1. VRN-H2 deletions conferred insensitive or intermediate sensitivity to vernalization. A subset of accessions with maximum LTT were identified as a resource for allele mining and further characterization. Facultative types comprised a small portion of the GWAS panel but may be useful for developing germplasm with this growth habit.

Abstract Classical plant breeding has been instrumental in changing the genetic makeup of crop plants for better ecological adaptation and improved quality. This paper provides insights of the genomic changes effected in hard winter wheat (Triticum aestivum L.) through decades of breeding and selection in the Great Plains of the United States. Population structure and differentiation analyses were conducted on 185 wheat cultivars released from 1943 to 2013. Cultivars were grouped into four distinct clusters using discriminant analysis of principal components (DAPC). One of the clusters was unique in that 15 out of the 18 individuals were recent releases (2000–2010), while 12 of the 18 shared the cultivar ‘Jagger’ in their genetic background. Jagger carries a 2NS/2AS translocation segment from Aegilops ventricosa, an important segment for resistance to several foliar diseases. Using the outlier approach, Wright's population fixation index (Fst) identified 450 loci that were directionally selected. The largest signature of selection was found on chromosome 2A. Genetic diversity was high while the inbreeding coefficient was low, indicating extensive hybridization and germplasm exchange among breeding programs within the region. Foliar disease pressure and selection for resistance helped shape the microevolution of wheat in the southern Great Plains. The results showed that high genetic diversity remains in hard winter wheat cultivars adapted to the Great Plains of the USA, and modern plant breeding did not cause any sizable reduction in genetic diversity of the crop in this region.

Abstract Background Common bunt (caused by Tilletia caries and T. foetida) has been considered as a major disease in wheat (Triticum aestivum) following rust (Puccinia spp.) in the Near East and is economically important in the Great Plains, USA. Despite the fact that it can be easily controlled using seed treatment with fungicides, fungicides often cannot or may not be used in organic and low-input fields. Planting common bunt resistant genotypes is an alternative. Results To identify resistance genes for Nebraska common bunt race, the global set of differential lines were inoculated. Nine differential lines carrying nine different genes had 0% infected heads and seemed to be resistant to Nebraska race. To understand the genetic basis of the resistance in Nebraska winter wheat, a set of 330 genotypes were inoculated and evaluated under field conditions in two locations. Out of the 330 genotypes, 62 genotypes had different degrees of resistance. Moreover, plant height, chlorophyll content and days to heading were scored in both locations. Using genome-wide association study, 123 SNPs located on fourteen chromosomes were identified to be associated with the resistance. Different degrees of linkage disequilibrium was found among the significant SNPs and they explained 1.00 to 9.00% of the phenotypic variance, indicating the presence of many minor QTLs controlling the resistance. Conclusion Based on the chromosomal location of some of the known genes, some SNPs may be associated with Bt1, Bt6, Bt11 and Bt12 resistance loci. The remaining significant SNPs may be novel alleles that were not reported previously. Common bunt resistance seems to be an independent trait as no correlation was found between a number of infected heads and chlorophyll content, days to heading or plant height.

Abstract Background Synthetic hexaploid wheat (SHW) is a reconstitution of hexaploid wheat from its progenitors (Triticum turgidum ssp. durum L.; AABB x Aegilops tauschii Coss.; DD) and has novel sources of genetic diversity for broadening the genetic base of elite bread wheat (BW) germplasm (T. aestivum L). Understanding the diversity and population structure of SHWs will facilitate their use in wheat breeding programs. Our objectives were to understand the genetic diversity and population structure of SHWs and compare the genetic diversity of SHWs with elite BW cultivars and demonstrate the potential of SHWs to broaden the genetic base of modern wheat germplasm. Results The genotyping-by-sequencing of SHW provided 35,939 high-quality single nucleotide polymorphisms (SNPs) that were distributed across the A (33%), B (36%), and D (31%) genomes. The percentage of SNPs on the D genome was nearly same as the other two genomes, unlike in BW cultivars where the D genome polymorphism is generally much lower than the A and B genomes. This indicates the presence of high variation in the D genome in the SHWs. The D genome gene diversity of SHWs was 88.2% higher than that found in a sample of elite BW cultivars. Population structure analysis revealed that SHWs could be separated into two subgroups, mainly differentiated by geographical location of durum parents and growth habit of the crop (spring and winter type). Further population structure analysis of durum and Ae. parents separately identified two subgroups, mainly based on type of parents used. Although Ae. tauschii parents were divided into two sub-species: Ae. tauschii ssp. tauschii and ssp. strangulate, they were not clearly distinguished in the diversity analysis outcome. Population differentiation between SHWs (Spring_SHW and Winter_SHW) samples using analysis of molecular variance indicated 17.43% of genetic variance between populations and the remainder within populations. Conclusions SHWs were diverse and had a clearly distinguished population structure identified through GBS-derived SNPs. The results of this study will provide valuable information for wheat genetic improvement through inclusion of novel genetic variation and is a prerequisite for association mapping and genomic selection to unravel economically important marker-trait associations and for cultivar development.

Wheat stripe rust (caused by Puccinia striiformis f. sp. tritici) is a major disease that damages wheat plants and affects wheat yield all over the world. In recent years, stripe rust became a major problem that affects wheat yield in Egypt. New races appeared and caused breakdowns in the resistant genotypes. To improve resistance in the Egyptian genotypes, new sources of resistance are urgently needed. In the recent research, a set of 95 wheat genotypes collected from 19 countries, including Egypt, were evaluated for their resistance against the Egyptian race(s) of stripe rust under field conditions in the two growing seasons 2018/2019 and 2019/2020. A high genetic variation was found among the tested genotypes. Single marker analysis was conducted using a subset of 71 genotypes and 424 diversity array technology (DArT) markers, well distributed across the genome. Out of the tested markers, 13 stable markers were identified that were significantly associated with resistance in both years (p-value ≤ 0.05). By using the sequence of the DArT markers, the chromosomal position of the significant DArT markers was detected, and nearby gene models were identified. Two markers on chromosomes 5A and 5B were found to be located within gene models functionally annotated with disease resistance in plants. These two markers could be used in marker-assisted selection for stripe rust resistance under Egyptian conditions. Two German genotypes were carrying the targeted allele of all the significant DArT markers associated with stripe rust resistance and could be used to improve resistance under Egyptian conditions.

Abstract Background ‘Apogee’ has a very short life cycle among wheat cultivars (flowering 25 days after planting under a long day and without vernalization), and it is a unique genetic material that can be used to accelerate cycling breeding lines. However, little is known about the genetic basis of the super-short life of Apogee wheat. Results In this study, Apogee was crossed with a strong winter wheat cultivar ‘Overland’, and 858 F2 plants were generated and tested in a greenhouse under constant warm temperature and long days. Apogee wheat was found to have the early alleles for four flowering time genes, which were ranked in the order of vrn-A1 > VRN-B1 > vrn-D3 > PPD-D1 according to their effect intensity. All these Apogee alleles for early flowering showed complete or partial dominance effects in the F2 population. Surprisingly, Apogee was found to have the same alleles at vrn-A1a and vrn-D3a for early flowering as observed in winter wheat cultivar ‘Jagger.’ It was also found that the vrn-A1a gene was epistatic to VRN-B1 and vrn-D3. The dominant vrn-D3a alone was not sufficient to cause the transition from vegetative to reproductive development in winter plants without vernalization but was able to accelerate flowering in those plants that carry the vrn-A1a or Vrn-B1 alleles. The genetic effects of the vernalization and photoperiod genes were validated in Apogee x Overland F3 populations. Conclusion VRN-A1, VRN-B1, VRN-D3, and PPD-D1 are the major genes that enabled Apogee to produce the very short life cycle. This study greatly advanced the molecular understanding of the multiple flowering genes under different genetic backgrounds and provided useful molecular tools that can be used to accelerate winter wheat breeding schemes.

Heat stress is one of the abiotic stresses that cause a significant reduction in barley yield. Climate change will increase the number of heatwaves, which will result in more deterioration in the agricultural sector. Therefore, understanding the physiological changes that occur in the plant to tolerate heat stress is very important. A collection of 60 Egyptian spring barley genotypes has been tested for heat stress under field conditions. To quantify the changes in yield-related traits and the grain-reserve parameters as indicators for heat tolerance, several traits were scored. The causative genes that regulate the variation of all traits of interest were identified via single-marker analysis using 16,966 single nucleotide polymorphisms (SNP). Heat stress reduced yield-related traits, while some physiological traits (chlorophyll index, soluble carbohydrates, amino acids, and proline contents) increased. The genotypes were classified into four classes, A, B, C, and D, based on a reduction in grain yield per spike (GYPS) of 10%, 20%, 30%, and 40%, respectively. The physiological aspects were extensively studied in each group. The tolerant genotypes (class A) retained high yield-related traits as well as high reserved metabolites relative to the sensitive class D. The single-marker analysis and gene annotations revealed that the most effective markers and genes resided on chromosomes 1H and 4H. One of these markers, S4_250499621, was found to be associated with increased proline content, increased chlorophyll content, and decreased reduction in grain yield per spike and thousand kernel weight. This study is a part of our extended evaluation of this collection under various abiotic stresses at different developmental stages to develop climate-resilient crops.

Latest advances in unmanned aerial vehicle (UAV) technology and convolutional neural networks (CNNs) allow us to detect crop lodging in a more precise and accurate way. However, the performance and generalization of a model capable of detecting lodging when the plants may show different spectral and morphological signatures have not been investigated much. This study investigated and compared the performance of models trained using aerial imagery collected at two growth stages of winter wheat with different canopy phenotypes. Specifically, three CNN-based models were trained with aerial imagery collected at early grain filling stage only, at physiological maturity only, and at both stages. Results show that the multi-stage model trained by images from both growth stages outperformed the models trained by images from individual growth stages on all testing data. The mean accuracy of the multi-stage model was 89.23% for both growth stages, while the mean of the other two models were 52.32% and 84.9%, respectively. This study demonstrates the importance of diversity of training data in big data analytics, and the feasibility of developing a universal decision support system for wheat lodging detection and mapping multi-growth stages with high-resolution remote sensing imagery.

As one of the key crop traits, plant height is traditionally evaluated manually, which can be slow, laborious and prone to error. Rapid development of remote and proximal sensing technologies in recent years allows plant height to be estimated in more objective and efficient fashions, while research regarding direct comparisons between different height measurement methods seems to be lagging. In this study, a ground-based multi-sensor phenotyping system equipped with ultrasonic sensors and light detection and ranging (LiDAR) was developed. Canopy heights of 100 wheat plots were estimated five times during a season by the ground phenotyping system and an unmanned aircraft system (UAS), and the results were compared to manual measurements. Overall, LiDAR provided the best results, with a root-mean-square error (RMSE) of 0.05 m and an R2 of 0.97. UAS obtained reasonable results with an RMSE of 0.09 m and an R2 of 0.91. Ultrasonic sensors did not perform well due to our static measurement style. In conclusion, we suggest LiDAR and UAS are reliable alternative methods for wheat height evaluation.

Drought at pre-anthesis stages can influence barley growth and results in yield losses. Therefore, it is important to understand how drought at pre-anthesis can affect different traits associated with yield reduction in barley. The objective of this study was to understand the relevance of the genetic background of major flowering time genes in barley plants subjected to pre-anthesis drought and its impact on yield and yield components. A glasshouse experiment using a Randomized Complete Block Design was conducted to investigate the effect of drought and its timing on yield and yield components on eleven barley genotypes, which were selected to represent genetic diversity of major flowering time genes (PPDH1, PPDH2, HvVrn1, HvVrn2 and HvVrn3). Barley plants were exposed to three water regimes, non-stressed and stressed, which was applied at two pre-anthesis growth stages, tillering (SS) and stem elongation (SE). Results identified differences among genotypes in all measured traits. Grain yield, grain number and "thousand kernel weight" were reduced in all genotypes due to drought, irrespective of the growth stage. Early flowering genotypes had better performance as reflected in higher yield compared with late flowering genotypes. Results verified the fundamental importance of early flowering to improve productivity in response to pre-anthesis drought. The results of this study can help in selecting barley lines for future breeding purposes with improved resilience to drought conditions in Mediterranean environments.