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Pearl millet [Pennisetum glaucum (L.) R. Br.] is a subtropical grain and forage crop. It is privileged with several desirable forage attributes. Nevertheless, research on pearl millet is limited, especially as a forage crop, in developing countries. Therefore, the objectives of this study were to investigate the field performance and stability of pearl millet genotypes for forage yield across seven environments. The study was conducted in seven environments (combination of locations and seasons) during the 2016/2017–2018/2019 seasons. Twenty-five pearl millet genotypes, selected based on forage yield from a core collection of 200 accessions, were arranged in an alpha lattice design with three replications. The parameters measured were fresh forage yield, days to flowering, plant height, number of culms m−2, leaf-to-stem ratio, and stem girth. The combined analysis revealed that environments, genotypes, and their interaction had significant effects on all traits studied except the genotypic effect on stem girth. Across the seven environments, four genotypes (G14, G01, G12, and G22) outyielded the check genotype in fresh matter yield by 20.7, 16.5, 11.0 and 9.8%, respectively. The additive main effects and multiplicative interaction (AMMI) analysis showed that the genotype, environment, and their interaction were highly significant (p ≤ 0.001) for fresh matter yield. The results of AMMI stability values (ASVs) and the genotype selection index (GSI) combined with the AMMI estimate-based selection showed that genotypes G14, G22 and G01 were the most stable and adapted genotypes and were superior to the check genotype. These results indicate that forage pearl millet varieties could be developed directly through evaluating the wealth of available collections or indirectly through hybridization in crop breeding programs.

Abstract Background Increasing grain nutritional value in sorghum (Sorghum bicolor) is a paramount breeding objective, as is increasing drought resistance (DR), because sorghum is grown mainly in drought-prone areas. The genetic basis of grain nutritional traits remains largely unknown. Marker-assisted selection using significant loci identified through genome-wide association study (GWAS) shows potential for selecting desirable traits in crops. This study assessed natural variation available in sorghum accessions from around the globe to identify novel genes or genomic regions with potential for improving grain nutritional value, and to study associations between DR traits and grain weight and nutritional composition. Results We dissected the genetic architecture of grain nutritional composition, protein content, thousand-kernel weight (TKW), and plant height (PH) in sorghum through GWAS of 163 unique African and Asian accessions under irrigated and post-flowering drought conditions. Several QTLs were detected. Some were significantly associated with DR, TKW, PH, protein, and Zn, Mn, and Ca contents. Genomic regions on chromosomes 1, 2, 4, 8, 9, and 10 were associated with TKW, nutritional, and DR traits; colocalization patterns of these markers indicate potential for simultaneous improvement of these traits. In African accessions, markers associated with TKW were mapped to six regions also associated with protein, Zn, Ca, Mn, Na, and DR, suggesting the potential for simultaneous selection for higher grain nutrition and TKW. Our results indicate that it may be possible to select for increased DR on the basis of grain nutrition and weight potential. Conclusions This study provides a valuable resource for selecting landraces for use in plant breeding programs and for identifying loci that may contribute to grain nutrition and weight with the hope of producing cultivars that combine improved yield traits, nutrition, and DR.

High-throughput phenotyping serves as a framework to reduce chronological costs and accelerate breeding cycles. In this study, we developed models to estimate the phenotypes of biomass-related traits in soybean (Glycine max) using unmanned aerial vehicle (UAV) remote sensing and deep learning models. In 2018, a field experiment was conducted using 198 soybean germplasm accessions with known whole-genome sequences under 2 irrigation conditions: drought and control. We used a convolutional neural network (CNN) as a model to estimate the phenotypic values of 5 conventional biomass-related traits: dry weight, main stem length, numbers of nodes and branches, and plant height. We utilized manually measured phenotypes of conventional traits along with RGB images and digital surface models from UAV remote sensing to train our CNN models. The accuracy of the developed models was assessed through 10-fold cross-validation, which demonstrated their ability to accurately estimate the phenotypes of all conventional traits simultaneously. Deep learning enabled us to extract features that exhibited strong correlations with the output (i.e., phenotypes of the target traits) and accurately estimate the values of the features from the input data. We considered the extracted low-dimensional features as phenotypes in the latent space and attempted to annotate them based on the phenotypes of conventional traits. Furthermore, we validated whether these low-dimensional latent features were genetically controlled by assessing the accuracy of genomic predictions. The results revealed the potential utility of these low-dimensional latent features in actual breeding scenarios.

Global warming has led to the expansion of arid lands and more frequent droughts, which are the largest cause of global food production losses. In our previous study, we developed TaPYLox wheat overexpressing the plant hormone abscisic acid (ABA) receptor, which is important for the drought stress response in plants. TaPYLox showed resistance to drought stress and acquired water-saving traits that enable efficient grain production with less water use. In this study, we used TaPYLox to identify ABA-dependent and -independent metabolites in response to drought stress. We compared the variation of metabolites in wheat under well-watered, ABA treatment, and drought stress conditions using the ABA-sensitive TaPYLox line and control lines. The results showed that tagatose and L-serine were ABA-dependently regulated metabolites, because their stress-induced accumulation was increased by ABA treatment in TaPYLox. In contrast, L-valine, L-leucine, and DL-isoleucine, which are classified as branched chain amino acids, were not increased by ABA treatment in TaPYLox, suggesting that they are metabolites regulated in an ABA-independent manner. Interestingly, the accumulation of L-valine, L-leucine, and DL-isoleucine was suppressed in drought-tolerant TaPYLox under drought stress, suggesting that drought-tolerant wheat might be low in these amino acids. 3-dehydroshikimic acid and α-ketoglutaric acid were decreased by drought stress in an ABA-independent manner. In this study, we have succeeded in identifying metabolites that are regulated by drought stress in an ABA-dependent and -independent manner. The findings of this study should be useful for future breeding of drought-tolerant wheat.

The inoculation of Epichloë endophytes into modern cereals, resulting in systemic infection, depends on the genetics of both the host and the endophyte strain deployed. Until very recently, the only modern cereal to have been infected with Epichloë, in which normal phenotype seed-transmitted associations were achieved, is rye (Secale cereale). Whilst minor in-roads have been achieved in infecting hexaploid wheat (Triticum aestivum), the phenotypes of these associations have all been extremely poor, including host death and stunting. To identify host genetic factors that may impact the compatibility of Epichloë infection in wheat, wheat–alien chromosome addition/substitution lines were inoculated with Epichloë, and the phenotypes of infected plants were assessed. Symbioses were identified whereby infected wheat plants were phenotypically like uninfected controls. These plants completed their full lifecycle, including the vertical transmission of Epichloë into the next generation of grain, and represent the first ever compatible wheat–Epichloë associations to be created.

Due to the low genetic diversity in the current wheat germplasm, gene mining from wild relatives is essential to develop new wheat cultivars that are more resilient to the changing climate. Aegilops tauschii, the D-genome donor of bread wheat, is a great gene source for wheat breeding; however, identifying suitable genes from Ae. tauschii is challenging due to the different morphology and the wide intra-specific variation within the species. In this study, we developed a platform for the systematic evaluation of Ae. tauschii traits in the background of the hexaploid wheat cultivar ‘Norin 61’ and thus for the identification of QTLs and genes. To validate our platform, we analyzed the seed dormancy trait that confers resistance to preharvest sprouting. We used a multiple synthetic derivative (MSD) population containing a genetic diversity of 43 Ae. tauschii accessions representing the full range of the species. Our results showed that only nine accessions in the population provided seed dormancy, and KU-2039 from Afghanistan had the highest level of seed dormancy. Therefore, 166 backcross inbred lines (BILs) were developed by crossing the synthetic wheat derived from KU-2039 with ‘Norin 61’ as the recurrent parent. The QTL mapping revealed one novel QTL, Qsd.alrc.5D, associated with dormancy explaining 41.7% of the phenotypic variation and other five unstable QTLs, two of which have already been reported. The Qsd.alrc.5D, identified for the first time within the natural variation of wheat, would be a valuable contribution to breeding after appropriate validation. The proposed platform that used the MSD population derived from the diverse Ae. tauschii gene pool and recombinant inbred lines proved to be a valuable platform for mining new and important QTLs or alleles, such as the novel seed dormancy QTL identified here. Likewise, such a platform harboring genetic diversity from wheat wild relatives could be a useful source for mining agronomically important traits, especially in the era of climate change and the narrow genetic diversity within the current wheat germplasm.

Abstract Background The rapid and accurate identification of a minimal-size core set of representative microbial species plays an important role in the clustering of microbial community data and interpretation of clustering results. However, the huge dimensionality of microbial metagenomics datasets is a major challenge for the existing methods such as Dirichlet multinomial mixture (DMM) models. In the approach of the existing methods, the computational burden of identifying a small number of representative species from a large number of observed species remains a challenge. Results We propose a novel approach to improve the performance of the widely used DMM approach by combining three ideas: (i) we propose an indicator variable to identify representative operational taxonomic units that substantially contribute to the differentiation among clusters; (ii) to address the computational burden of high-dimensional microbiome data, we propose a stochastic variational inference, which approximates the posterior distribution using a controllable distribution called variational distribution, and stochastic optimization algorithms for fast computation; and (iii) we extend the finite DMM model to an infinite case by considering Dirichlet process mixtures and estimating the number of clusters as a variational parameter. Using the proposed method, stochastic variational variable selection (SVVS), we analyzed the root microbiome data collected in our soybean field experiment, the human gut microbiome data from three published datasets of large-scale case-control studies and the healthy human microbiome data from the Human Microbiome Project. Conclusions SVVS demonstrates a better performance and significantly faster computation than those of the existing methods in all cases of testing datasets. In particular, SVVS is the only method that can analyze massive high-dimensional microbial data with more than 50,000 microbial species and 1000 samples. Furthermore, a core set of representative microbial species is identified using SVVS that can improve the interpretability of Bayesian mixture models for a wide range of microbiome studies. Video Abstract

Abstract Microbiota are a major component of agroecosystems. Root microbiota, which inhabit the inside and surface of plant roots, play a significant role in plant growth and health. As next-generation sequencing technology allows the capture of microbial profiles without culturing the microbes, profiling of plant microbiota has become a staple tool in plant science and agriculture. Here, we have increased sample handling efficiency in a two-step PCR amplification protocol for 16S rRNA gene sequencing of plant root microbiota, improving DNA extraction using AMPure XP magnetic beads and PCR purification using exonuclease. These modifications reduce sample handling and capture microbial diversity comparable to that obtained by the manual method. We found a buffer with AMPure XP magnetic beads enabled efficient extraction of microbial DNA directly from plant roots. We also demonstrated that purification using exonuclease before the second PCR step enabled the capture of higher degrees of microbial diversity, thus allowing for the detection of minor bacteria compared with the purification using magnetic beads in this step. In addition, our method generated comparable microbiome profile data in plant roots and soils to that of using common commercially available DNA extraction kits, such as DNeasy PowerSoil Pro Kit and FastDNA SPIN Kit for Soil. Our method offers a simple and high-throughput option for maintaining the quality of plant root microbial community profiling.

Abstract Wild relatives of modern crops represent a promising source of genetic variation that can be mined for adaptations to climate change. Aegilops tauschii, the D-sub-genome progenitor of bread wheat (Triticum aestivum), constitutes a reservoir of genetic diversity for improving bread wheat performance and environmental resilience. Leaf hairiness plays an essential biological role in plant defense against biotic and abiotic stress. We investigated the natural variation in leaf hair density (LHD) among 293 Ae. tauschii accessions. Genome-wide association studies were performed for LHD with 2430 and 3880 DArTseq derived single nucleotide polymorphism (SNP) markers in two lineages of this species, TauL1 and TauL2, respectively. In TauL1, three marker-trait associations (MTAs) were located on chromosome 2D, whereas in TauL2, eight MTAs were identified, two associations were localized on each of the chromosomes 2D, 3D, 5D, and 7D. The markers explained phenotypic variation (R2) from 9 to 13% in TauL1 and 11 to 36% in TauL2. The QTLs identified in chromosomes 2D and 5D might be novel. Our results revealed more rapid and independent evolution of LHD in TauL2 compared to TauL1. The majority of LHD candidate genes identified are associated with biotic and abiotic stress responses. This study highlights the significance of intraspecific diversity of Ae. tauschii to enhance cultivated wheat germplasm.

Plant response to drought is an important yield-related trait under abiotic stress, but the method for measuring and modeling plant responses in a time series has not been fully established. The objective of this study was to develop a method to measure and model plant response to irrigation changes using time-series multispectral (MS) data. We evaluated 178 soybean (Glycine max (L.) Merr.) accessions under three irrigation treatments at the Arid Land Research Center, Tottori University, Japan in 2019, 2020 and 2021. The irrigation treatments included W5: watering for 5 d followed by no watering 5 d, W10: watering for 10 d followed by no watering 10 d, D10: no watering for 10 d followed by watering 10 d, and D: no watering. To capture the plant responses to irrigation changes, time-series MS data were collected by unmanned aerial vehicle during the irrigation/non-irrigation switch of each irrigation treatment. We built a random regression model (RRM) for each of combination of treatment by year using the time-series MS data. To test the accuracy of the information captured by RRM, we evaluated the coefficient of variation (CV) of fresh shoot weight of all accessions under a total of nine different drought conditions as an indicator of plant’s stability under drought stresses. We built a genomic prediction model (MTRRM model) using the genetic random regression coefficients of RRM as secondary traits and evaluated the accuracy of each model for predicting CV. In 2020 and 2021,the mean prediction accuracies of MTRRM models built in the changing irrigation treatments (r = 0.44 and 0.49, respectively) were higher than that in the continuous drought treatment (r = 0.34 and 0.44, respectively) in the same year. When the CV was predicted using the MTRRM model across 2020 and 2021 in the changing irrigation treatment, the mean prediction accuracy (r = 0.46) was 42% higher than that of the simple genomic prediction model (r =0.32). The results suggest that this RRM method using the time-series MS data can effectively capture the genetic variation of plant response to drought.

In the face of climate change, bringing more useful alleles and genes from wild relatives of wheat is crucial to develop climate-resilient varieties. We used two populations of backcrossed recombinant inbred lines (BIL1 and BIL2), developed by crossing and backcrossing two intra-specifically diverse Aegilops tauschii accessions from lineage 1 and lineage 2, respectively, with the common wheat cultivar ‘Norin 61′. This study aimed to identify quantitative trait loci (QTLs) associated with heat stress (HS) tolerance. The two BILs were evaluated under heat stress environments in Sudan for phenology, plant height (PH), grain yield (GY), biomass (BIO), harvest index (HI), and thousand-kernel weight (TKW). Grain yield was significantly correlated with BIO and TKW under HS; therefore, the stress tolerance index (STI) was calculated for these traits as well as for GY. A total of 16 heat-tolerant lines were identified based on GY and STI-GY. The QTL analysis performed using inclusive composite interval mapping identified a total of 40 QTLs in BIL1 and 153 QTLs in BIL2 across all environments. We detected 39 QTLs associated with GY-STI, BIO-STI, and TKW-STI in both populations (14 in BIL1 and 25 in BIL2). The QTLs associated with STI were detected on chromosomes 1A, 3A, 5A, 2B, 4B, and all the D-subgenomes. We found that QTLs were detected only under HS for GY on chromosome 5A, TKW on 3B and 5B, PH on 3B and 4B, and grain filling duration on 2B. The higher number of QTLs identified in BIL2 for heat stress tolerance suggests the importance of assessing the effects of intraspecific variation of Ae. tauschii in wheat breeding as it could modulate the heat stress responses/adaptation. Our study provides useful genetic resources for uncovering heat-tolerant QTLs for wheat improvement for heat stress environments.

A thorough exploration of the micrometeorological aspects influencing canopy temperature in contrasting wheat cultivars can unveil the specific mechanisms of adaptation to heat stress. However, information on wheat microclimates in hot environments for crop improvement is lacking. Here, we used a micrometeorological method to investigate wheat’s response to high temperatures. Field experiments were conducted in the Gezira Scheme, Sudan, to compare two high-yielding heat-tolerant cultivars, Imam and Bohaine, in terms of canopy temperature depression (CTD), air temperature gradient (ATG), and vapor pressure gradient (VPG) from a 2 m height to canopy level. The maximum air temperature at 2 m during the main growing season was 37 °C. Air temperature at canopy level was mostly lower in the Imam field than in the Bohaine field, and it was positively correlated with and higher than radiometric canopy surface temperature. The maximum CTD during the reproductive stage was 4.7–6.5 °C in the Bohaine field and 5.0–7.2 °C in the Imam field. ATG was also larger in the Imam field, attributed to the greater leaf area of the Imam canopy, as presumed from the NDVI difference between fields. ATG was negatively correlated with VPG in both fields, and the relationship was stronger at lower nighttime wind speeds and weaker at higher daytime wind speeds. These results indicate that the micrometeorological approach can be used to compare cultivars in high-temperature environments.

Good appearance throughout the year is important for perennial ornamental plants used for rooftop greenery. However, the methods for evaluating appearance throughout the year, such as plant color and growth activity, are not well understood. In this study, evergreen and winter-dormant parents of Phedimus takesimensis and 94 F1 plants were used for multispectral imaging. We took 16 multispectral image measurements from March 2019 to April 2020 and used them to calculate 15 vegetation indices and the area of plant cover. QTL analysis was also performed. Traits such as the area of plant cover and vegetation indices related to biomass were high during spring and summer (growth period), whereas vegetation indices related to anthocyanins were high in winter (dormancy period). According to the PCA, changes in the intensity of light reflected from the plants at different wavelengths over the course of a year were consistent with the changes in plant color and growth activity. Seven QTLs were found to be associated with major seasonal growth changes. This approach, which monitors not only at a single point in time but also over time, can reveal morphological changes during growth, senescence, and dormancy throughout the year.

The change in appearance during the seasonal transitions in ornamental greening plants is an important characteristic. In particular, the early onset of green leaf color is a desirable trait for a cultivar. In this study, we established a method for phenotyping leaf color change by multispectral imaging and performed genetic analysis based on the phenotypes to clarify the potential of the approach in breeding greening plants. We performed multispectral phenotyping and quantitative trait locus (QTL) analysis of an F1 population derived from 2 parental lines of Phedimus takesimensis, known to be a drought and heat-tolerant rooftop plant species. The imaging was conducted in April of 2019 and 2020 when dormancy breakage occurs and growth extension begins. Principal component analysis of 9 different wavelength values showed a high contribution from the first principal component (PC1), which captured variation in the visible light range. The high interannual correlation in PC1 and in the intensity of visible light indicated that the multispectral phenotyping captured genetic variation in the color of leaves. We also performed restriction site-associated DNA sequencing and obtained the first genetic linkage map of Phedimus spp. QTL analysis revealed 2 QTLs related to early dormancy breakage. Based on the genotypes of the markers underlying these 2 QTLs, the F1 phenotypes with early (late) dormancy break, green (red or brown) leaves, and a high (low) degree of vegetative growth were classified. The results suggest the potential of multispectral phenotyping in the genetic dissection of seasonal leaf color changes in greening plants.

Increasing the water use efficiency of crops is an important agricultural goal closely related to the root system —the primary plant organ for water and nutrient acquisition. In an attempt to evaluate the response of root growth and development of soybean to water supply levels, 200 genotypes were grown in a sandy field for 3 years under irrigated and non-irrigated conditions, and 14 root traits together with shoot fresh weight and plant height were investigated. Three-way ANOVA revealed a significant effect of treatments and years on growth of plants, accounting for more than 80% of the total variability. The response of roots to irrigation was consistent over the years as most root traits were improved by irrigation. However, the actual values varied between years because the growth of plants was largely affected by the field microclimatic conditions (i.e., temperature, sunshine duration, and precipitation). Therefore, the best linear unbiased prediction values for each trait were calculated using the original data. Principal component analysis showed that most traits contributed to principal component (PC) 1, whereas average diameter, the ratio of thin and medium thickness root length to total root length contributed to PC2. Subsequently, we focused on selecting genotypes that exhibited significant improvements in root traits under irrigation than under non-irrigated conditions using the increment (I-index) and relative increment (RI-index) indices calculated for all traits. Finally, we screened for genotypes with high stability and root growth over the 3 years using the multi-trait selection index (MTSI).Six genotypes namely, GmJMC130, GmWMC178, GmJMC092, GmJMC068, GmWMC075, and GmJMC081 from the top 10% of genotypes scoring MTSI less than the selection threshold of 7.04 and 4.11 under irrigated and non-irrigated conditions, respectively, were selected. The selected genotypes have great potential for breeding cultivars with improved water usage abilities, meeting the goal of water-saving agriculture.

Abstract Multispectral (MS) imaging enables the measurement of characteristics important for increasing the prediction accuracy of genotypic and phenotypic values for yield‐related traits. In this study, we evaluated the potential application of temporal MS imaging for the prediction of aboveground biomass (AGB) in soybean [Glycine max (L.) Merr.]. Field experiments with 198 accessions of soybean were conducted with four different irrigation levels. Five vegetation indices (VIs) were calculated using MS images from soybean canopies from early vegetative to early reproductive stage. To predict the genotypic values of AGB, VIs at the different growth stages were used as secondary traits in a multitrait genomic prediction. The prediction accuracy of the genotypic values of AGB from MS and genomic data largely outperformed that of the genomic data alone before the flowering stage (90% of accessions did not flower), suggesting that it would be possible to determine cross‐combinations based on the predicted genotypic values of AGB. We compared the prediction accuracy of a model using the five VIs and a model using only one VI to predict the phenotypic values of AGB and found that the difference in prediction accuracy decreased over time at all irrigation levels except for the most severe drought. The difference in the most severe drought was not as small as that in the other treatments. Only the prediction accuracy of a model using the five VIs in the most severe droughts gradually increased over time. Therefore, the optimal timing for MS imaging may depend on the irrigation levels.

Transposable elements (TEs) constitute ~80% of the complex bread wheat genome and contribute significantly to wheat evolution and environmental adaptation. We studied 52 TE insertion polymorphism markers to ascertain their efficiency as a robust DNA marker system for genetic studies in wheat and related species. Significant variation was found in miniature inverted-repeat transposable element (MITE) insertions in relation to ploidy with the highest number of “full site” insertions occurring in the hexaploids (32.6 ± 3.8), while the tetraploid and diploid progenitors had 22.3 ± 0.6 and 15.0 ± 3.5 “full sites,” respectively, which suggested a recent rapid activation of these transposons after the formation of wheat. Constructed phylogenetic trees were consistent with the evolutionary history of these species which clustered mainly according to ploidy and genome types (SS, AA, DD, AABB, and AABBDD). The synthetic hexaploids sub-clustered near the tetraploid species from which they were re-synthesized. Preliminary genotyping in 104 recombinant inbred lines (RILs) showed predominantly 1:1 segregation for simplex markers, with four of these markers already integrated into our current DArT-and SNP-based linkage map. The MITE insertions also showed stability with no single excision observed. The MITE insertion site polymorphisms uncovered in this study are very promising as high-potential evolutionary markers for genomic studies in wheat.

Wheat is highly sensitive to temperature beyond the optimum. To improve wheat adaptation to heat stress, the best option is to exploit the diversity of wild wheat progenitors. This study aimed to identify germplasm and quantitative trait loci associated with heat stress tolerance from wild emmer wheat diversity. We evaluated a diverse set of multiple derivative lines harboring chromosome segments from nine wild emmer wheat parents under four environments: two optimum environments at Tottori, Japan and Dongola, Sudan, one moderate heat stress environment, and one severe heat stress environment at Wad Medani, Sudan. Genome-wide association analysis was conducted with 13,312 SNP markers. Strong marker-trait associations (MTAs) were identified for chlorophyll content at maturity on chromosomes 1A and 5B: these MTAs explained 28.8 and 26.8% of the variation, respectively. A region on chromosome 3A (473.7–638.4 Mbp) contained MTAs controlling grain yield, under optimum and severe heat stress. Under severe heat stress, regions on chromosomes 3A (590.4–713.3 Mbp) controlled grain yield, biomass, days to maturity and thousand kernel weight, and on 3B (744.0–795.2 Mbp) grain yield and biomass. Heat tolerance efficiency (HTE) was controlled by three MTAs, one each on chromosomes 2A, 2B, and 5A under moderate heat stress and one MTA on chromosome 3A under severe heat stress. Some of the MTAs found here were previously reported, but the new ones originated from the wild emmer wheat genomes. The favorable alleles identified from wild emmer wheat were absent or rare in the elite durum wheat germplasm being bred for heat stress tolerance. This study provides potential genetic materials, alleles, MTAs, and quantitative trait loci for enhancing wheat adaptation to heat stress. The derivative lines studied here could be investigated to enhance other stress tolerance such as drought and salinity.

Narrow genetic diversity in the wheat gene pool restricts the improvement of wheat quality traits. Aegilops tauschii possesses valuable genetic diversity that can be used to improve not only biotic and abiotic stresses in arid regions but also wheat yield and quality. Our study, which used 392 multiple synthetic derivatives (MSD) panel developed with Ae. tauschii Coss. introgressions, had three main aims: to explore the genetic diversity of high-molecular-weight glutenin subunits (HMW-GS), to investigate the dough strength and the relationship between protein content and grain yield, and to identify lines with a good flour quality. A wide range of allelic diversity was observed at the Glu-D1 locus, reflecting the impact of the different introgressed portions of Ae. tauschii, and a wide variation was found in dough strength even between lines having the same composition of HMW-GS. We report a negative impact on dough strength of subunit 5t+10t from Ae. tauschii and a relatively positive impact of subunit 2t+12.1t. We identified four MSD lines with significantly enhanced flour quality. Regressing the grain yield of the MSD lines against protein content showed no correlation between the two traits and identified lines with comparable grain yield to the recurrent parent and higher protein content. The identified MSD lines could provide a valuable genetic resource for enhancing the end-use quality of flour without any loss in productivity.

With the widespread use of high-throughput phenotyping systems, growth process data are expected to become more easily available. By applying genomic prediction to growth data, it will be possible to predict the growth of untested genotypes. Predicting the growth process will be useful for crop breeding, as variability in the growth process has a significant impact on the management of plant cultivation. However, the integration of growth modeling and genomic prediction has yet to be studied in depth. In this study, we implemented new prediction models to propose a novel growth prediction scheme. Phenotype data of 198 soybean germplasm genotypes were acquired for 3 years in experimental fields in Tottori, Japan. The longitudinal changes in the green fractions were measured using UAV remote sensing. Then, a dynamic model was fitted to the green fraction to extract the dynamic characteristics of the green fraction as five parameters. Using the estimated growth parameters, we developed models for genomic prediction of the growth process and tested whether the inclusion of the dynamic model contributed to better prediction of growth. Our proposed models consist of two steps: first, predicting the parameters of the dynamics model with genomic prediction, and then substituting the predicted values for the parameters of the dynamics model. By evaluating the heritability of the growth parameters, the dynamic model was able to effectively extract genetic diversity in the growth characteristics of the green fraction. In addition, the proposed prediction model showed higher prediction accuracy than conventional genomic prediction models, especially when the future growth of the test population is a prediction target given the observed values in the first half of growth as training data. This indicates that our model was able to successfully combine information from the early growth period with phenotypic data from the training population for prediction. This prediction method could be applied to selection at an early growth stage in crop breeding, and could reduce the cost and time of field trials.

Drought is a major threat to food security worldwide, and drought-tolerant plants are a convenient model to study the mechanisms underlying drought tolerance. Some of the studies on drought tolerance mechanisms have been performed under controlled environments, while others have been undertaken under natural field conditions; nevertheless, it is important to evaluate the similarities and differences between the results obtained in each case. In this study, the physiological responses of a drought-tolerant wildtype and a drought-susceptible watermelon cultivar were evaluated under both natural and artificial environments. Although different in intensity, the trend of physiological responses was similar in both environments. After five days of exposure to drought, the wildtype showed a sharper decline (80.91%) in CO2 assimilation in the field than in a growth chamber (65.81%). The non-photochemical quenching (NPQ) parameters showed lower values in the cultivar than in the wildtype, regardless of growth environment; additionally, final NPQ values recorded for the wildtype were significantly higher in field plants than in those grown in a growth chamber. Regression analysis showed a highly significant correlation between NPQ and photosynthesis in the wildtype in both environments, while the relationships were non-significant in field-cultivated watermelon. These findings demonstrate that artificial conditions can be used to study the trends of plant responses to environmental stress, but results must be interpreted with caution.

Abstract The application of remote sensing in plant breeding can provide rich information about the growth processes of plants, which leads to better understanding concerning crop yield. It has been shown that traits measured by remote sensing were also beneficial for genomic prediction (GP) because the inclusion of remote sensing data in multitrait models improved prediction accuracies of target traits. However, the present multitrait GP model cannot incorporate high‐dimensional remote sensing data due to the difficulty in the estimation of a covariance matrix among the traits, which leads to failure in improving its prediction accuracy. In this study, we focused on growth models to express growth patterns using remote sensing data with a few parameters and investigated whether a multitrait GP model using these parameters could derive better prediction accuracy of soybean [Glycine max (L.) Merr.] biomass. A total of 198 genotypes of soybean germplasm were cultivated in experimental fields, and longitudinal changes of their canopy height and area were measured continuously via remote sensing with an unmanned aerial vehicle. Growth parameters were estimated by applying simple growth models and incorporated into the GP of biomass. By evaluating heritability and correlation, we showed that the estimated growth parameters appropriately represented the observed growth curves. Also, the use of these growth parameters in the multitrait GP model contributed to successful biomass prediction. We conclude that the growth models could describe the genetic variation of soybean growth curves based on several growth parameters. These dimension‐reduction growth models will be indispensable for extracting useful information from remote sensing data and using this data in GP and plant breeding.

Wheat occupies a special role in global food security since, in addition to providing 20% of our carbohydrates and protein, almost 25% of the global production is traded internationally. The importance of wheat for food security was recognised by the Chief Agricultural Scientists of the G20 group of countries when they endorsed the establishment of the Wheat Initiative in 2011. The Wheat Initiative was tasked with supporting the wheat research community by facilitating collaboration, information and resource sharing and helping to build the capacity to address challenges facing production in an increasingly variable environment. Many countries invest in wheat research. Innovations in wheat breeding and agronomy have delivered enormous gains over the past few decades, with the average global yield increasing from just over 1 tonne per hectare in the early 1960s to around 3.5 tonnes in the past decade. These gains are threatened by climate change, the rapidly rising financial and environmental costs of fertilizer, and pesticides, combined with declines in water availability for irrigation in many regions. The international wheat research community has worked to identify major opportunities to help ensure that global wheat production can meet demand. The outcomes of these discussions are presented in this paper.

The Global Wheat Head Detection (GWHD) dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/institutions. With an associated competition hosted in Kaggle, GWHD_2020 has successfully attracted attention from both the computer vision and agricultural science communities. From this first experience, a few avenues for improvements have been identified regarding data size, head diversity, and label reliability. To address these issues, the 2020 dataset has been reexamined, relabeled, and complemented by adding 1722 images from 5 additional countries, allowing for 81,553 additional wheat heads. We now release in 2021 a new version of the Global Wheat Head Detection dataset, which is bigger, more diverse, and less noisy than the GWHD_2020 version.

Heat stress is one of the major environmental constraints for wheat production; thus, a comprehensive understanding of the metabolomic responses of wheat is required for breeding heat-tolerant varieties. In this study, the metabolome responses of heat-tolerant genotypes Imam and Norin 61, and susceptible genotype Chinese Spring were comparatively analyzed using Fourier transform infrared (FTIR) spectroscopy in combination with chemometric data mining techniques. Principal component analysis of the FTIR data suggested a spectral feature partially overlapping between the three genotypes. FTIR spectral biomarker assay showed similar heat responses between the genotypes for markers Fm482 and Fm1502, whereas genotype-dependent variations were observed for other markers. The markers Fm1251 and Fm1729 showed contrasting behaviors between heat-tolerant and susceptible genotypes, suggesting that these markers may potentially serve as a tool for distinguishing heat-tolerant genotypes. Linear discriminant analysis (LDA) of the spectra demonstrated a clear separation between the three genotypes in terms of the heat stress effect. Analysis of LDA coefficients identified several spectral regions that were potentially responsible for the discrimination of FTIR spectra between different genotypes and environments. These results suggest that a combination of FTIR and chemometrics can be a useful technique for characterizing the metabolic behavior of diverse wheat genotypes under heat stress.

Abstract Background The tertiary gene pool of bread wheat, to which Leymus racemosus belongs, has remained underutilized due to the current limited genomic resources of the species that constitute it. Continuous enrichment of public databases with useful information regarding these species is, therefore, needed to provide insights on their genome structures and aid successful utilization of their genes to develop improved wheat cultivars for effective management of environmental stresses. Results We generated de novo DNA and mRNA sequence information of L. racemosus and developed 110 polymorphic PCR-based markers from the data, and to complement the PCR markers, DArT-seq genotyping was applied to develop additional 9990 SNP markers. Approximately 52% of all the markers enabled us to clearly genotype 22 wheat-L. racemosus chromosome introgression lines, and L. racemosus chromosome-specific markers were highly efficient in detailed characterization of the translocation and recombination lines analyzed. A further analysis revealed remarkable transferability of the PCR markers to three other important Triticeae perennial species: L. mollis, Psathyrostachys huashanica and Elymus ciliaris, indicating their suitability for characterizing wheat-alien chromosome introgressions carrying chromosomes of these genomes. Conclusion The efficiency of the markers in characterizing wheat-L. racemosus chromosome introgression lines proves their reliability, and their high transferability further broadens their scope of application. This is the first report on sequencing and development of markers from L. racemosus genome and the application of DArT-seq to develop markers from a perennial wild relative of wheat, marking a paradigm shift from the seeming concentration of the technology on cultivated species. Integration of these markers with appropriate cytogenetic methods would accelerate development and characterization of wheat-alien chromosome introgression lines.

Jatropha (Jatropha curcas L.) has been identified as a potential bioenergy feedstock in arid regions, but knowledge of the diversity of its chemical characteristics is limited. In this study, 61 Jatropha accessions growing in Botswana, where both severe drought and winter frosts frequently occur, were analyzed for their seed chemical properties. Histogram analyses and meta-analysis comparisons with seeds from other countries/continents showed that the median/mean dry seed weight, toxic compound phorbol esters, and C18:0 fatty acid levels in the Botswanan accessions were lower than those from other countries/continents. A clustered heat map analysis indicated five clades for the Botswanan accessions, and their physicochemical traits were also categorized into five groups. Many positive and negative correlations were observed among the chemical traits, including negative correlations between the C18:3 (linolenic acid) content and yield-related traits (lipid content and dry seed weight). Principal component analysis highlighted the existence of accessions with highly deviated seed chemical compositions, such as those enriched in C18:0/C18:1 and C16:0/C16:1/C18:3 fatty acids. Overall, the present study suggests considerable diversity in the seed chemical compositions of Botswanan Jatropha accessions. Various accessions could be useful as feedstock for specific industrial products, as well as for breeding materials for the fortification of specific chemical ingredients.

Several marker-assisted selection (MAS) or backcrossing (MAB) approaches exist for polygenic trait improvement. However, the implementation of MAB remains a challenge in many breeding programs, especially in the public sector. In MAB introgression programs, which usually do not include phenotypic selection, undesired donor traits may unexpectedly turn up regardless of how expensive and theoretically powerful a backcross scheme may be. Therefore, combining genotyping and phenotyping during selection will improve understanding of QTL interactions with the environment, especially for minor alleles that maximize the phenotypic expression of the traits. Here, we describe the introgression of stay-green QTL (Stg1–Stg4) from B35 into two sorghum backgrounds through an MAB that combines genotypic and phenotypic (C-MAB) selection during early backcross cycles. The background selection step is excluded. Since it is necessary to decrease further the cost associated with molecular marker assays, the costs of C-MAB were estimated. Lines with stay-green trait and good performance were identified at an early backcross generation, backcross two (BC2). Developed BC2F4 lines were evaluated under irrigated and drought as well as three rainfed environments varied in drought timing and severity. Under drought conditions, the mean grain yield of the most C-MAB-introgression lines was consistently higher than that of the recurrent parents. This study is one of the real applications of the successful use of C-MAB for the development of drought-tolerant sorghum lines for drought-prone areas.

Kernel hardness influences the milling and baking quality of wheat. Stress environments such as heat and combined heat-drought can produce harder kernels, thereby affecting the overall wheat quality. Beside puroindoline genes that are known to determine hardness, other QTLs contribute to the hardness. These QTLs, especially under stress conditions, need extensive research. Moreover, understanding the modification or stabilization of hardness under stress condition and the relationship with stress tolerance will facilitate the selection of superior lines that maintain both high yield and quality even under the stress environment. Therefore, in the current work, we aimed to identify the genetic loci and marker trait associations (MTAs) that contributes for hardness under optimum conditions in Japan, and heat and combined heat-drought (HD) conditions in Sudan. We used a panel of multiple synthetic derivatives (MSD) having diverse Aegilops tauschii genome segments and investigated the association between hardness stabilization and stress tolerance. Under stress conditions, we observed that less reduction of kernel weight is associated with either low change or stable kernel hardness. We identified 47 markers associated with hardness under all conditions; the D genome was the main contributor. For the first time, we found a significant association with hardness under stress conditions on chromosome 4D. We dissected several candidate genes associated with the change of hardness under stress conditions. Our results will improve the understanding of the genetic factors that affect wheat hardness stability.

Aegilops tauschii Coss., the D genome donor of hexaploid wheat (Triticum aestivum L.), is the most promising resource used to broaden the genetic diversity of wheat. Taxonomical studies have classified Ae. tauschii into two subspecies, ssp. tauschii and ssp. strangulata. However, molecular analysis revealed three distantly related lineages, TauL1, TauL2 and TauL3. TauL1 and TauL3 includes the only ssp. tauschii, whereas TauL2 includes both subspecies. This study aimed to clarify the phylogeny of Ae. tauschii and to find the traits that can differentiate between TauL1, TauL2 and TauL3, or between ssp. tauschii and ssp. strangulata. We studied the genetic and morpho-physiological diversity in 293 accessions of Ae. tauschii, covering the entire range of the species. A total of 5880 high-quality SNPs derived from DArTseq were used for phylogenetic cluster analyses. As a result, we observed wide morpho-physiological variation in each lineage and subspecies. Despite this variation, no key traits can discriminate lineages or subspecies though some traits were significantly different. Of 124 accessions previously lacking the passport data, 66 were allocated to TauL1, 57 to TauL2, and one to TauL3.

Aegilops tauschii, the D-genome donor of bread wheat, is a storehouse of genetic diversity that can be used for wheat improvement. This species consists of two main lineages (TauL1 and TauL2) and one minor lineage (TauL3). Its morpho-physiological diversity is large, with adaptations to a wide ecological range. Identification of allelic diversity in Ae. tauschii is of utmost importance for efficient breeding and widening of the genetic base of wheat. This study aimed at identifying markers or genes associated with morpho-physiological traits in Ae. tauschii, and at understanding the difference in genetic diversity between the two main lineages. We performed genome-wide association studies of 11 morpho-physiological traits of 343 Ae. tauschii accessions representing the entire range of habitats using 34,829 DArTseq markers. We observed a wide range of morpho-physiological variation among all accessions. We identified 23 marker–trait associations (MTAs) in all accessions, 15 specific to TauL1 and eight specific to TauL2, suggesting independent evolution in each lineage. Some of the MTAs could be novel and have not been reported in bread wheat. The markers or genes identified in this study will help reveal the genes controlling the morpho-physiological traits in Ae. tauschii, and thus in bread wheat even if the plant morphology is different.

With the aim of providing genetic materials for breeding drought-resistant wheat varieties, the physiological and metabolic plasticity of three drought-resistant wheat multiple synthetic derivative lines (MSDLs) and their backcross parent “Norin 61” (N61) were evaluated in response to drought stress. The results indicated that the D-genome introgressions from Aegilops tauschii into the MDSLs improved their drought-adaptive traits. Specifically, MNH5 and MSD345 showed higher photosynthesis rates and triose phosphate utilization than N61 under control conditions, resulting in greater accumulation of glucose and sucrose in the shoots. However, under drought stress, MNH5 and MSD345 had higher intrinsic water use efficiency than MSD53 and N61. The total antioxidant capacity and superoxide dismutase activity increased in all three MSDLs, whereas no significant changes were found in N61 in response to drought stress. Metabolome analysis identified six common drought-induced metabolites in all of the investigated genotypes. However, four metabolites (adenine, gamma aminobutyric acid, histidine, and putrescine) each specifically accumulated in an MSDL in response to drought stress, suggesting that these metabolites are important for MSDL drought resistance. In conclusion, MNH5 and MSD345 showed better adaptive responses to drought stress than MSD53 and N61, suggesting that these two MSDLs could be explored for breeding drought-resistant wheat lines.

The protein content and its type are principal factors affecting wheat (Triticum aestivum) end product quality. Among the wheat proteins, glutenin proteins, especially, high molecular weight glutenin subunits (HMW-GS) are major determinants of processing quality. Wheat and its primary gene pool have limited variation in terms of HMW-GS alleles. Wild relatives of wheat are an important source of genetic variation. For improvement of wheat processing quality its wild relative Thinopyrum elongatum with significant potential was utilized. An attempt was made to replace Th. elongatum chromosome long arm (1EL) carrying HMW-GS genes related to high dough strength with chromosome 1AL of wheat with least or negative effect on dough strength while retaining the chromosomes 1DL and 1BL with a positive effect on bread making quality. To create chromosome specific translocation line [1EL(1AS)], double monosomic of chromosomes 1E and 1A were created and further crossed with different cultivars and homoeologous pairing suppressor mutant line PhI. The primary selection was based upon glutenin and gliadin protein profiles, followed by sequential genomic in situ hybridization (GISH) and fluorescent in situ hybridization (FISH). These steps significantly reduced time, efforts, and economic cost in the generation of translocation line. In order to assess the effect of translocation on wheat quality, background recovery was carried out by backcrossing with recurrent parent for several generations and then selfing while selecting in each generation. Good recovery of parent background indicated the development of almost near isogenic line (NIL). Morphologically also translocation line was similar to recipient cultivar N61 that was further confirmed by seed storage protein profiles, RP-HPLC and scanning electron microscopy. The processing quality characteristics of translocation line (BC4F6) indicated significant improvement in the gluten performance index (GPI), dough mixing properties, dough strength, and extensibility. Our work aims to address the challenge of limited genetic diversity especially at chromosome 1A HMW-GS locus. We report successful development of chromosome 1A specific translocation line of Th. elongatum in wheat with improved dough strength.

Jatropha curcas L. (Jatropha), a shrub species of the family Euphorbiaceae, has been recognized as a promising biofuel plant for reducing greenhouse gas emissions. However, recent attempts at commercial cultivation in Africa and Asia have failed because of low productivity. It is important to elucidate genetic diversity and relationship in worldwide Jatropha genetic resources for breeding of better commercial cultivars. Here, genetic diversity was analyzed by using 246 accessions from Mesoamerica, Africa and Asia, based on 59 simple sequence repeat markers and eight retrotransposon-based insertion polymorphism markers. We found that central Chiapas of Mexico possesses the most diverse genetic resources, and the Chiapas Central Depression could be the center of origin. We identified three genetic groups in Mesoamerica, whose distribution revealed a distinct geographic cline. One of them consists mainly of accessions from central Chiapas. This suggests that it represents the original genetic group. We found two Veracruz accessions in another group, whose ancestors might be shipped from Port of Veracruz to the Old World, to be the source of all African and Asian Jatropha. Our results suggest the human selection that caused low productivity in Africa and Asia, and also breeding strategies to improve African and Asian Jatropha. Cultivars improved in the productivity will contribute to expand mass commercial cultivation of Jatropha in Africa and Asia to increase biofuel production, and finally will support in the battle against the climate change.

Jatropha (Jatropha curcas L.) represents a renewable bioenergy source in arid regions, where it is used to produce not only biodiesel from the seed oil, but also various non-oil biomass products, such as fertilizer, from the seed cake following oil extraction from the seeds. Jatropha plants also generate large amounts of fallen leaves during the cold or drought season, but few studies have examined the utilization of this litter biomass. Therefore, in this study, we produced biochar from the fallen leaves of jatropha using a simple and economical carbonizer that was constructed from a standard 200 L oil drum, which would be suitable for use in rural communities, and evaluated the use of the generated biochar as a soil conditioner for the cultivation of Swiss chard (Beta vulgaris subsp. cicla “Fordhook Giant”) as a model vegetable in an acidic and undernourished soil in Botswana. Biochar application improved several growth parameters of Swiss chard, such as the total leaf area. In addition, the dry weights of the harvested shoots were 1.57, 1.88, and 2.32 fold higher in plants grown in soils containing 3%, 5%, and 10% biochar, respectively, compared with non-applied soil, suggesting that the amount of biochar applied to the soil was positively correlated with yield. Together, these observations suggest that jatropha fallen leaf biochar could function as a soil conditioner to enhance crop productivity.

Wheat cultivars with wide introgression have strongly impacted global wheat production. Aegilops geniculata (MgUg) is an important wild relative with several useful traits that can be exploited for wheat improvement. Screening of Ae. geniculata addition lines indicated a negative effect of 1Ug and the positive effect of 1Mg chromosome on wheat dough strength. Negative effect of 1Ug is probably associated with variation in number and position of the tripeptide repeat motif in the high molecular weight glutenin (HMW-G) gene. To utilize the positive potential of 1Mg chromosome, three disomic substitution lines (DSLs) 1Mg(1A), 1Mg(1B) and 1Mg(1D) were created. These lines were characterized for morphological, cytogenetic properties and biochemical signatures using FISH, 1D-, 2D-PAGE and RP-HPLC. Contribution of wheat 1A, 1B and 1D chromosomes towards dough mixing and baking parameters, chapatti quality, Fe/Zn content and glume color were identified. Observed order of variation in the dough mixing and baking parameters {1Mg(1D) ≤wheat ≤1Mg(1B) ≤1Mg(1A)} indicated that chromosome specific introgression is desirable for best utilization of wild species' potential.

Post-anthesis drought stress is one of the main constraints on the production of wheat (Triticum aestivum L.). Because field screening for post-anthesis drought tolerance is difficult, effective and validated methods to simulate drought in order to identify sources of tolerance can facilitate screening of breeding materials. Chemical desiccants are widely used to simulate post-anthesis drought stress. We aimed to identify physiological traits that respond to desiccants as they do to drought. We examined the responses of ‘Norin 61’ to six treatments in a greenhouse: irrigated control, drought after anthesis, and 2% or 4% potassium chlorate (KClO3) at anthesis (A) or grain filling (GF). We measured δ13C in leaves, aboveground fresh biomass, stomatal conductance, chlorophyll content, harvest index, and grain yield. Both 2% and 4% KClO3 at both A and GF simulated the effect of drought stress. Selection of drought-tolerant genotypes can be aided by chlorophyll content and δ13C measurement of leaves when 2% or 4% KClO3 is used to simulate drought.

The complex process of allopolyploid speciation includes various mechanisms ranging from species crosses and hybrid genome doubling to genome alterations and the establishment of new allopolyploids as persisting natural entities. Currently, little is known about the genetic mechanisms that underlie hybrid genome doubling, despite the fact that natural allopolyploid formation is highly dependent on this phenomenon. We examined the genetic basis for the spontaneous genome doubling of triploid F1 hybrids between the direct ancestors of allohexaploid common wheat (Triticum aestivum L., AABBDD genome), namely Triticumturgidum L. (AABB genome) and Aegilopstauschii Coss. (DD genome). An Ae. tauschii intraspecific lineage that is closely related to the D genome of common wheat was identified by population-based analysis. Two representative accessions, one that produces a high-genome-doubling-frequency hybrid when crossed with a T. turgidum cultivar and the other that produces a low-genome-doubling-frequency hybrid with the same cultivar, were chosen from that lineage for further analyses. A series of investigations including fertility analysis, immunostaining, and quantitative trait locus (QTL) analysis showed that (1) production of functional unreduced gametes through nonreductional meiosis is an early step key to successful hybrid genome doubling, (2) first division restitution is one of the cytological mechanisms that cause meiotic nonreduction during the production of functional male unreduced gametes, and (3) six QTLs in the Ae. tauschii genome, most of which likely regulate nonreductional meiosis and its subsequent gamete production processes, are involved in hybrid genome doubling. Interlineage comparisons of Ae. tauschii's ability to cause hybrid genome doubling suggested an evolutionary model for the natural variation pattern of the trait in which non-deleterious mutations in six QTLs may have important roles. The findings of this study demonstrated that the genetic mechanisms for hybrid genome doubling could be studied based on the intrinsic natural variation that exists in the parental species.

Due to the low genetic diversity in the current wheat germplasm, gene mining from wild relatives is essential to develop new wheat cultivars that are more resilient to the changing climate. Aegilops tauschii, the D-genome donor of bread wheat, is a great gene source for wheat breeding; however, identifying suitable genes from Ae. tauschii is challenging due to the different morphology and the wide intra-specific variation within the species. In this study, we developed a platform for the systematic evaluation of Ae. tauschii traits in the background of the hexaploid wheat cultivar 'Norin 61' and thus for the identification of QTLs and genes. To validate our platform, we analyzed the seed dormancy trait that confers resistance to preharvest sprouting. We used a multiple synthetic derivative (MSD) population containing a genetic diversity of 43 Ae. tauschii accessions representing the full range of the species. Our results showed that only nine accessions in the population provided seed dormancy, and KU-2039 from Afghanistan had the highest level of seed dormancy. Therefore, 166 backcross inbred lines (BILs) were developed by crossing the synthetic wheat derived from KU-2039 with 'Norin 61' as the recurrent parent. The QTL mapping revealed one novel QTL, Qsd.alrc.5D, associated with dormancy explaining 41.7% of the phenotypic variation and other five unstable QTLs, two of which have already been reported. The Qsd.alrc.5D, identified for the first time within the natural variation of wheat, would be a valuable contribution to breeding after appropriate validation. The proposed platform that used the MSD population derived from the diverse Ae. tauschii gene pool and recombinant inbred lines proved to be a valuable platform for mining new and important QTLs or alleles, such as the novel seed dormancy QTL identified here. Likewise, such a platform harboring genetic diversity from wheat wild relatives could be a useful source for mining agronomically important traits, especially in the era of climate change and the narrow genetic diversity within the current wheat germplasm. [ABSTRACT FROM AUTHOR]