SWEET11b transports both sugar and cytokinin in developing barley grains.

Even though Sugars Will Eventually be Exported Transporters (SWEETs) have been found in every sequenced plant genome, a comprehensive understanding of their functionality is lacking. In this study, we focused on the SWEET family of barley (Hordeum vulgare). A radiotracer assay revealed that expressing HvSWEET11b in African clawed frog (Xenopus laevis) oocytes facilitated the bidirectional transfer of not only just sucrose and glucose, but also cytokinin. Barley plants harboring a loss-of-function mutation of HvSWEET11b could not set viable grains, while the distribution of sucrose and cytokinin was altered in developing grains of plants in which the gene was knocked down. Sucrose allocation within transgenic grains was disrupted, which is consistent with the changes to the cytokinin gradient across grains, as visualized by magnetic resonance imaging and Fourier transform infrared spectroscopy microimaging. Decreasing HvSWEET11b expression in developing grains reduced overall grain size, sink strength, the number of endopolyploid endosperm cells, and the contents of starch and protein. The control exerted by HvSWEET11b over sugars and cytokinins likely predetermines their synergy, resulting in adjustments to the grain's biochemistry and transcriptome.

The INCREASE project: Intelligent Collections of food-legume genetic resources for European agrofood systems.

Food legumes are crucial for all agriculture-related societal challenges, including climate change mitigation, agrobiodiversity conservation, sustainable agriculture, food security and human health. The transition to plant-based diets, largely based on food legumes, could present major opportunities for adaptation and mitigation, generating significant co-benefits for human health. The characterization, maintenance and exploitation of food-legume genetic resources, to date largely unexploited, form the core development of both sustainable agriculture and a healthy food system. INCREASE will implement, on chickpea (Cicer arietinum), common bean (Phaseolus vulgaris), lentil (Lens culinaris) and lupin (Lupinus albus and L. mutabilis), a new approach to conserve, manage and characterize genetic resources. Intelligent Collections, consisting of nested core collections composed of single-seed descent-purified accessions (i.e., inbred lines), will be developed, exploiting germplasm available both from genebanks and on-farm and subjected to different levels of genotypic and phenotypic characterization. Phenotyping and gene discovery activities will meet, via a participatory approach, the needs of various actors, including breeders, scientists, farmers and agri-food and non-food industries, exploiting also the power of massive metabolomics and transcriptomics and of artificial intelligence and smart tools. Moreover, INCREASE will test, with a citizen science experiment, an innovative system of conservation and use of genetic resources based on a decentralized approach for data management and dynamic conservation. By promoting the use of food legumes, improving their quality, adaptation and yield and boosting the competitiveness of the agriculture and food sector, the INCREASE strategy will have a major impact on economy and society and represents a case study of integrative and participatory approaches towards conservation and exploitation of crop genetic resources.

Genetic diversity, distribution and domestication history of the neglected GGAtAt genepool of wheat.

KEY MESSAGE: We present a comprehensive survey of cytogenetic and genomic diversity of the GGAtAt genepool of wheat, thereby unlocking these plant genetic resources for wheat improvement. Wheat yields are stagnating around the world and new sources of genes for resistance or tolerances to abiotic traits are required. In this context, the tetraploid wheat wild relatives are among the key candidates for wheat improvement. Despite its potential huge value for wheat breeding, the tetraploid GGAtAt genepool is largely neglected. Understanding the population structure, native distribution range, intraspecific variation of the entire tetraploid GGAtAt genepool and its domestication history would further its use for wheat improvement. The paper provides the first comprehensive survey of genomic and cytogenetic diversity sampling the full breadth and depth of the tetraploid GGAtAt genepool. According to the results obtained, the extant GGAtAt genepool consists of three distinct lineages. We provide detailed insights into the cytogenetic composition of GGAtAt wheats, revealed group- and population-specific markers and show that chromosomal rearrangements play an important role in intraspecific diversity of T. araraticum. The origin and domestication history of the GGAtAt lineages is discussed in the context of state-of-the-art archaeobotanical finds. We shed new light on the complex evolutionary history of the GGAtAt wheat genepool and provide the basis for an increased use of the GGAtAt wheat genepool for wheat improvement. The findings have implications for our understanding of the origins of agriculture in southwest Asia.

Comparative Molecular and Metabolic Profiling of Two Contrasting Wheat Cultivars under Drought Stress.

Drought is one of the most important threats to plants and agriculture; therefore, understanding of the mechanisms of drought tolerance is crucial for breeding of new tolerant varieties. Here, we assessed the effects of a long-term water deficit stress simulated on a precision phenotyping system on some morphological criteria and metabolite traits, as well as the expression of drought associated transcriptional factors of two contrasting drought-responsive African wheat cultivars, Condor and Wadielniel. The current study showed that under drought stress Wadielniel exhibits significant higher tillering and height compared to Condor. Further, we used gas chromatography and ultra-high performance liquid chromatography mass-spectrometry to identify compounds that change between the two cultivars upon drought. Partial least square discriminant analysis (PLS-DA) revealed that 50 metabolites with a possible role in drought stress regulation were significantly changed in both cultivars under water deficit stress. These metabolites included several amino acids, most notably proline, some organic acids, and lipid classes PC 36:3 and TAG 56:9, which were significantly altered under drought stress. Here, the results discussed in the context of understanding the mechanisms involved in the drought response of wheat cultivars, as the phenotype parameters, metabolite content and expression of drought associated transcriptional factors could also be used for potential crop improvement under drought stress.

Adjustment of photosynthetic activity to drought and fluctuating light in wheat.

Drought is a major cause of losses in crop yield. Under field conditions, plants exposed to drought are usually also experiencing rapid changes in light intensity. Accordingly, plants need to acclimate to both, drought and light stress. Two crucial mechanisms in plant acclimation to changes in light conditions comprise thylakoid protein phosphorylation and dissipation of light energy as heat by non-photochemical quenching (NPQ). Here, we analyzed the acclimation efficacy of two different wheat varieties, by applying fluctuating light for analysis of plants, which had been subjected to a slowly developing drought stress as it usually occurs in the field. This novel approach allowed us to distinguish four drought phases, which are critical for grain yield, and to discover acclimatory responses which are independent of photodamage. In short-term, under fluctuating light, the slowdown of NPQ relaxation adjusts the photosynthetic activity to the reduced metabolic capacity. In long-term, the photosynthetic machinery acquires a drought-specific configuration by changing the PSII-LHCII phosphorylation pattern together with protein stoichiometry. Therefore, the fine-tuning of NPQ relaxation and PSII-LHCII phosphorylation pattern represent promising traits for future crop breeding strategies.

A tiered approach to genome-wide association analysis for the adherence of hulls to the caryopsis of barley seeds reveals footprints of selection.

BACKGROUND: Seeds of domesticated barley are grouped into two distinct types, which differ in morphology. Caryopses covered by adaxial (palea) and abaxial (lemma) hulls that tightly adhere to the pericarp at maturity give rise to hulled seeds whereas caryopses without adhering hulls give rise to naked seeds. The naked caryopsis character is an essential trait regarding the end use of barley. RESULTS: To uncover the genetic basis of the trait, a genome-wide association study (GWAS) has been performed in a panel comprising 222 2-rowed and 303 6-rowed spring barley landrace accessions. In addition to the well-described Nud locus on chromosome 7H, three novel loci showed strong associations with the trait: the first locus on 2H was specifically detected in 6-rowed accessions, the second locus on 3H was found in 2-rowed accessions from Eurasia and the third locus on 6H was revealed in 6-rowed accessions from Ethiopia. PCR analysis of naked accessions also confirmed the absence of a 17 kb region harboring the Nud gene on chromosome 7H for all but one naked accession. The latter was characterized by a slightly variant phenotype of the caryopsis. CONCLUSION: Our findings provide evidence of the pervasiveness of the 17 kb deletion in spring barley from different geographic regions and at the same time reveal genomic footprints of selection in naked barley, which follow both geographic and morphological patterns.

Genome-wide association mapping in a diverse spring barley collection reveals the presence of QTL hotspots and candidate genes for root and shoot architecture traits at seedling stage.

BACKGROUND: Adaptation to drought-prone environments requires robust root architecture. Genotypes with a more vigorous root system have the potential to better adapt to soils with limited moisture content. However, root architecture is complex at both, phenotypic and genetic level. Customized mapping panels in combination with efficient screenings methods can resolve the underlying genetic factors of root traits. RESULTS: A mapping panel of 233 spring barley genotypes was evaluated for root and shoot architecture traits under non-stress and osmotic stress. A genome-wide association study elucidated 65 involved genomic regions. Among them were 34 root-specific loci, eleven hotspots with associations to up to eight traits and twelve stress-specific loci. A list of candidate genes was established based on educated guess. Selected genes were tested for associated polymorphisms. By this, 14 genes were identified as promising candidates, ten remained suggestive and 15 were rejected. The data support the important role of flowering time genes, including HvPpd-H1, HvCry2, HvCO4 and HvPRR73. Moreover, seven root-related genes, HERK2, HvARF04, HvEXPB1, PIN5, PIN7, PME5 and WOX5 are confirmed as promising candidates. For the QTL with the highest allelic effect for root thickness and plant biomass a homologue of the Arabidopsis Trx-m3 was revealed as the most promising candidate. CONCLUSIONS: This study provides a catalogue of hotspots for seedling growth, root and stress-specific genomic regions along with candidate genes for future potential incorporation in breeding attempts for enhanced yield potential, particularly in drought-prone environments. Root architecture is under polygenic control. The co-localization of well-known major genes for barley development and flowering time with QTL hotspots highlights their importance for seedling growth. Association analysis revealed the involvement of HvPpd-H1 in the development of the root system. The co-localization of root QTL with HERK2, HvARF04, HvEXPB1, PIN5, PIN7, PME5 and WOX5 represents a starting point to explore the roles of these genes in barley. Accordingly, the genes HvHOX2, HsfA2b, HvHAK2, and Dhn9, known to be involved in abiotic stress response, were located within stress-specific QTL regions and await future validation.

Genetic architecture and temporal patterns of biomass accumulation in spring barley revealed by image analysis.

BACKGROUND: Genetic mapping of phenotypic traits generally focuses on a single time point, but biomass accumulates continuously during plant development. Resolution of the temporal dynamics that affect biomass recently became feasible using non-destructive imaging. RESULTS: With the aim to identify key genetic factors for vegetative biomass formation from the seedling stage to flowering, we explored growth over time in a diverse collection of two-rowed spring barley accessions. High heritabilities facilitated the temporal analysis of trait relationships and identification of quantitative trait loci (QTL). Biomass QTL tended to persist only a short period during early growth. More persistent QTL were detected around the booting stage. We identified seven major biomass QTL, which together explain 55% of the genetic variance at the seedling stage, and 43% at the booting stage. Three biomass QTL co-located with genes or QTL involved in phenology. The most important locus for biomass was independent from phenology and is located on chromosome 7HL at 141 cM. This locus explained ~20% of the genetic variance, was significant over a long period of time and co-located with HvDIM, a gene involved in brassinosteroid synthesis. CONCLUSIONS: Biomass is a dynamic trait and is therefore orchestrated by different QTL during early and late growth stages. Marker-assisted selection for high biomass at booting stage is most effective by also including favorable alleles from seedling biomass QTL. Selection for dynamic QTL may enhance genetic gain for complex traits such as biomass or, in the future, even grain yield.

Dissecting the phenotypic components of crop plant growth and drought responses based on high-throughput image analysis.

Significantly improved crop varieties are urgently needed to feed the rapidly growing human population under changing climates. While genome sequence information and excellent genomic tools are in place for major crop species, the systematic quantification of phenotypic traits or components thereof in a high-throughput fashion remains an enormous challenge. In order to help bridge the genotype to phenotype gap, we developed a comprehensive framework for high-throughput phenotype data analysis in plants, which enables the extraction of an extensive list of phenotypic traits from nondestructive plant imaging over time. As a proof of concept, we investigated the phenotypic components of the drought responses of 18 different barley (Hordeum vulgare) cultivars during vegetative growth. We analyzed dynamic properties of trait expression over growth time based on 54 representative phenotypic features. The data are highly valuable to understand plant development and to further quantify growth and crop performance features. We tested various growth models to predict plant biomass accumulation and identified several relevant parameters that support biological interpretation of plant growth and stress tolerance. These image-based traits and model-derived parameters are promising for subsequent genetic mapping to uncover the genetic basis of complex agronomic traits. Taken together, we anticipate that the analytical framework and analysis results presented here will be useful to advance our views of phenotypic trait components underlying plant development and their responses to environmental cues.

Genome-wide association mapping and biochemical markers reveal that seed ageing and longevity are intricately affected by genetic background and developmental and environmental conditions in barley.

Globally, over 7.4 million accessions of crop seeds are stored in gene banks, and conservation of genotypic variation is pivotal for breeding. We combined genetic and biochemical approaches to obtain a broad overview of factors that influence seed storability and ageing in barley (Hordeum vulgare). Seeds from a germplasm collection of 175 genotypes from four continents grown in field plots with different nutrient supply were subjected to two artificial ageing regimes. Genome-wide association mapping revealed 107 marker trait associations, and hence, genotypic effects on seed ageing. Abiotic and biotic stresses were found to affect seed longevity. To address aspects of abiotic, including oxidative, stress, two major antioxidant groups were analysed. No correlation was found between seed deterioration and the lipid-soluble tocochromanols, nor with oil, starch and protein contents. Conversely, the water-soluble glutathione and related thiols were converted to disulphides, indicating a strong shift towards more oxidizing intracellular conditions, in seeds subjected to long-term dry storage at two temperatures or to two artificial ageing treatments. The data suggest that intracellular pH and (bio)chemical processes leading to seed deterioration were influenced by the type of ageing or storage. Moreover, seed response to ageing or storage treatment appears to be significantly influenced by both maternal environment and genetic background.

Dissecting spatiotemporal biomass accumulation in barley under different water regimes using high-throughput image analysis.

Phenotyping large numbers of genotypes still represents the rate-limiting step in many plant genetic experiments and in breeding. To address this issue, novel automated phenotyping technologies have been developed. We investigated for a core set of barley cultivars if high-throughput image analysis can help to dissect vegetative biomass accumulation in response to two different watering regimes under semi-controlled greenhouse conditions. We found that experiments, treatments, genotypes and genotype by environment interaction (G × E) can be characterized at any time point by certain digital traits. Biomass accumulation under control and stress conditions was highly heritable. Growth model-derived maximum vegetative biomass (K max), inflection point (I) and regrowth rate (k) were identified as promising candidate traits for genome-wide association studies. Drought stress symptoms can be visualized, dissected and modelled. Especially the highly heritable regrowth rate, which had the biggest influence on biomass accumulation in stress treatment, seems promising for future studies to improve drought tolerance in different crop species. A proof of concept study revealed potential correlations between digital traits obtained from pot experiments under greenhouse conditions and agronomic traits from field experiments. Overall, non-invasive, imaging-based phenotyping platforms under greenhouse conditions offer excellent possibilities for trait discovery, trait development and industrial applications.

Implementation of theoretical non-photochemical quenching (NPQ(T)) to investigate NPQ of chickpea under drought stress with High-throughput Phenotyping.

Non-photochemical quenching (NPQ) is a protective mechanism for dissipating excess energy generated during photosynthesis in the form of heat. The accelerated relaxation of the NPQ in fluctuating light can lead to an increase in the yield and dry matter productivity of crops. Since the measurement of NPQ is time-consuming and requires specific light conditions, theoretical NPQ (NPQ(T)) was introduced for rapid estimation, which could be suitable for High-throughput Phenotyping. We investigated the potential of NPQ(T) to be used for testing plant genetic resources of chickpea under drought stress with non-invasive High-throughput Phenotyping complemented with yield traits. Besides a high correlation between the hundred-seed-weight and the Estimated Biovolume, significant differences were observed between the two types of chickpea desi and kabuli for Estimated Biovolume and NPQ(T). Desi was able to maintain the Estimated Biovolume significantly better under drought stress. One reason could be the effective dissipation of excess excitation energy in photosystem II, which can be efficiently measured as NPQ(T). Screening of plant genetic resources for photosynthetic performance could take pre-breeding to a higher level and can be implemented in a variety of studies, such as here with drought stress or under fluctuating light in a High-throughput Phenotyping manner using NPQ(T).

High-Throughput Spike Detection in Greenhouse Cultivated Grain Crops with Attention Mechanisms-Based Deep Learning Models.

Detection of spikes is the first important step toward image-based quantitative assessment of crop yield. However, spikes of grain plants occupy only a tiny fraction of the image area and often emerge in the middle of the mass of plant leaves that exhibit similar colors to spike regions. Consequently, accurate detection of grain spikes renders, in general, a non-trivial task even for advanced, state-of-the-art deep neural networks (DNNs). To improve pattern detection in spikes, we propose architectural changes to Faster-RCNN (FRCNN) by reducing feature extraction layers and introducing a global attention module. The performance of our extended FRCNN-A vs. conventional FRCNN was compared on images of different European wheat cultivars, including "difficult" bushy phenotypes from 2 different phenotyping facilities and optical setups. Our experimental results show that introduced architectural adaptations in FRCNN-A helped to improve spike detection accuracy in inner regions. The mean average precision (mAP) of FRCNN and FRCNN-A on inner spikes is 76.0% and 81.0%, respectively, while on the state-of-the-art detection DNNs, Swin Transformer mAP is 83.0%. As a lightweight network, FRCNN-A is faster than FRCNN and Swin Transformer on both baseline and augmented training datasets. On the FastGAN augmented dataset, FRCNN achieved a mAP of 84.24%, FRCNN-A attained a mAP of 85.0%, and the Swin Transformer achieved a mAP of 89.45%. The increase in mAP of DNNs on the augmented datasets is proportional to the amount of the IPK original and augmented images. Overall, this study indicates a superior performance of attention mechanisms-based deep learning models in detecting small and subtle features of grain spikes.

Engaging Precision Phenotyping to Scrutinize Vegetative Drought Tolerance and Recovery in Chickpea Plant Genetic Resources.

Precise and high-throughput phenotyping (HTP) of vegetative drought tolerance in chickpea plant genetic resources (PGR) would enable improved screening for genotypes with low relative loss of biomass formation and reliable physiological performance. It could also provide a basis to further decipher the quantitative trait drought tolerance and recovery and gain a better understanding of the underlying mechanisms. In the context of climate change and novel nutritional trends, legumes and chickpea in particular are becoming increasingly important because of their high protein content and adaptation to low-input conditions. The PGR of legumes represent a valuable source of genetic diversity that can be used for breeding. However, the limited use of germplasm is partly due to a lack of available characterization data. The development of HTP systems offers a perspective for the analysis of dynamic plant traits such as abiotic stress tolerance and can support the identification of suitable genetic resources with a potential breeding value. Sixty chickpea accessions were evaluated on an HTP system under contrasting water regimes to precisely evaluate growth, physiological traits, and recovery under optimal conditions in comparison to drought stress at the vegetative stage. In addition to traits such as Estimated Biovolume (EB), Plant Height (PH), and several color-related traits over more than forty days, photosynthesis was examined by chlorophyll fluorescence measurements on relevant days prior to, during, and after drought stress. With high data quality, a wide phenotypic diversity for adaptation, tolerance, and recovery to drought was recorded in the chickpea PGR panel. In addition to a loss of EB between 72% and 82% after 21 days of drought, photosynthetic capacity decreased by 16-28%. Color-related traits can be used as indicators of different drought stress stages, as they show the progression of stress.

Genome-wide association study in two-row spring barley landraces identifies QTL associated with plantlets root system architecture traits in well-watered and osmotic stress conditions.

Water availability is undoubtedly one of the most important environmental factors affecting crop production. Drought causes a gradual deprivation of water in the soil from top to deep layers and can occur at diverse stages of plant development. Roots are the first organs that perceive water deficit in soil and their adaptive development contributes to drought adaptation. Domestication has contributed to a bottleneck in genetic diversity. Wild species or landraces represent a pool of genetic diversity that has not been exploited yet in breeding program. In this study, we used a collection of 230 two-row spring barley landraces to detect phenotypic variation in root system plasticity in response to drought and to identify new quantitative trait loci (QTL) involved in root system architecture under diverse growth conditions. For this purpose, young seedlings grown for 21 days in pouches under control and osmotic-stress conditions were phenotyped and genotyped using the barley 50k iSelect SNP array, and genome-wide association studies (GWAS) were conducted using three different GWAS methods (MLM GAPIT, FarmCPU, and BLINK) to detect genotype/phenotype associations. In total, 276 significant marker-trait associations (MTAs; p-value (FDR)< 0.05) were identified for root (14 and 12 traits under osmotic-stress and control conditions, respectively) and for three shoot traits under both conditions. In total, 52 QTL (multi-trait or identified by at least two different GWAS approaches) were investigated to identify genes representing promising candidates with a role in root development and adaptation to drought stress.

Challenges and Opportunities behind the Use of Herbaria in Paleogenomics Studies.

Paleogenomics focuses on the recovery, manipulation, and analysis of ancient DNA (aDNA) from historical or long-dead organisms to reconstruct and analyze their genomes. The aDNA is commonly obtained from remains found in paleontological and archaeological sites, conserved in museums, and in other archival collections. Herbarium collections represent a great source of phenotypic and genotypic information, and their exploitation has allowed for inference and clarification of previously unsolved taxonomic and systematic relationships. Moreover, herbarium specimens offered a new source for studying phenological traits in plants and for disentangling biogeography and evolutionary scenarios of species. More recently, advances in molecular technologies went in parallel with the decreasing costs of next-generation sequencing (NGS) approaches, which paved the way to the utilization of aDNA for whole-genome studies. Although many studies have been carried out combining modern analytic techniques and ancient samples, such as herbarium specimens, this research field is still relatively unexplored due to the need for improving strategies for aDNA manipulation and exploitation from ancient samples. The higher susceptibility of aDNA to degradation and contamination during herbarium conservation and manipulation and the occurrence of biochemical postmortem damage can result in a more challenging reconstruction of the original DNA sequence. Here, we review the methodological approaches that have been developed for the exploitation of historical herbarium plant materials, such as best practices for aDNA extraction, amplification, and genotyping. We also focus on some strategies to overcome the main problems related to the utilization of herbarium specimens for their exploitation in plant evolutionary studies.

Selection and adaptive introgression guided the complex evolutionary history of the European common bean.

Domesticated crops have been disseminated by humans over vast geographic areas. Common bean (Phaseolus vulgaris L.) was introduced in Europe after 1492. Here, by combining whole-genome profiling, metabolic fingerprinting and phenotypic characterisation, we show that the first common bean cultigens successfully introduced into Europe were of Andean origin, after Francisco Pizarro's expedition to northern Peru in 1529. We reveal that hybridisation, selection and recombination have shaped the genomic diversity of the European common bean in parallel with political constraints. There is clear evidence of adaptive introgression into the Mesoamerican-derived European genotypes, with 44 Andean introgressed genomic segments shared by more than 90% of European accessions and distributed across all chromosomes except PvChr11. Genomic scans for signatures of selection highlight the role of genes relevant to flowering and environmental adaptation, suggesting that introgression has been crucial for the dissemination of this tropical crop to the temperate regions of Europe.

Deep Learning Based Greenhouse Image Segmentation and Shoot Phenotyping (DeepShoot).

Background: Automated analysis of large image data is highly demanded in high-throughput plant phenotyping. Due to large variability in optical plant appearance and experimental setups, advanced machine and deep learning techniques are required for automated detection and segmentation of plant structures in complex optical scenes. Methods: Here, we present a GUI-based software tool (DeepShoot) for efficient, fully automated segmentation and quantitative analysis of greenhouse-grown shoots which is based on pre-trained U-net deep learning models of arabidopsis, maize, and wheat plant appearance in different rotational side- and top-views. Results: Our experimental results show that the developed algorithmic framework performs automated segmentation of side- and top-view images of different shoots acquired at different developmental stages using different phenotyping facilities with an average accuracy of more than 90% and outperforms shallow as well as conventional and encoder backbone networks in cross-validation tests with respect to both precision and performance time. Conclusion: The DeepShoot tool presented in this study provides an efficient solution for automated segmentation and phenotypic characterization of greenhouse-grown plant shoots suitable also for end-users without advanced IT skills. Primarily trained on images of three selected plants, this tool can be applied to images of other plant species exhibiting similar optical properties.

Dissection of a grain yield QTL from wild emmer wheat reveals sub-intervals associated with culm length and kernel number.

Genetic diversity in wheat has been depleted due to domestication and modern breeding. Wild relatives are a valuable source for improving drought tolerance in domesticated wheat. A QTL region on chromosome 2BS of wild emmer wheat (Triticum turgidum ssp. dicoccoides), conferring high grain yield under well-watered and water-limited conditions, was transferred to the elite durum wheat cultivar Uzan (T. turgidum ssp. durum) by a marker-assisted backcross breeding approach. The 2B introgression line turned out to be higher yielding but also exhibited negative traits that likely result from trans-, cis-, or linkage drag effects from the wild emmer parent. In this study, the respective 2BS QTL was subjected to fine-mapping, and a set of 17 homozygote recombinants were phenotyped at BC4F5 generation under water-limited and well-watered conditions at an experimental farm in Israel and at a high-throughput phenotyping platform (LemnaTec-129) in Germany. In general, both experimental setups allowed the identification of sub-QTL intervals related to culm length, kernel number, thousand kernel weight, and harvest index. Sub-QTLs for kernel number and harvest index were detected specifically under either drought stress or well-watered conditions, while QTLs for culm length and thousand-kernel weight were detected in both conditions. Although no direct QTL for grain yield was identified, plants with the sub-QTL for kernel number showed a higher grain yield than the recurrent durum cultivar Uzan under well-watered and mild drought stress conditions. We, therefore, suggest that this sub-QTL might be of interest for future breeding purposes.