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Anthocyanins are plant secondary metabolites belonging to the polyphenol class of natural water-soluble phytopigments. The accumulation of anthocyanins in different plant tissues can improve plant survival under adverse conditions. In addition, plants with the resulting colorful morphology can be utilized as landscape plants. Triticum boeoticum (syn. Triticum monococcum ssp. aegilopoides, 2n=2x=14, AbAb) serves as a valuable genetic resource for the improvement of its close relative common wheat in terms of enhancing resilience to various biotic and abiotic stresses. In our previous study, the EMSmutagenized mutant Z2921 with a red glume, stem, and rachis was generated from T. boeoticum G52, which has a green glume, stem, and rachis. In this study, the F1, F2, and F2:3 generations of a cross between mutant-type Z2921 and wildtype G52 were developed. A single recessive gene, tentatively designated RgM4G52, was identified in Z2921 via genetic analysis. Using bulked segregant exome capture sequencing (BSE-Seq) analysis, RgM4G52 was mapped to chromosome 6AL and was flanked by the markers KASP-58 and KASP-26 within a 3.40-cM genetic interval corresponding to 1.71-Mb and 1.61-Mb physical regions in the Chinese Spring (IWGSC RefSeq v1.1) and Triticum boeoticum (TA299) reference genomes, respectively, in which seven and four genes related to anthocyanin synthesis development were annotated. Unlike previously reported color morphology-related genes, RgM4G52 is a recessive gene that can simultaneously control the color of glumes, stems, and rachis in wild einkorn. In addition, a synthetic Triticum dicoccum-T. boeoticum amphiploid Syn-ABAb-34, derived from the colchicine treatment of F1 hybrids between tetraploid wheat PI 352367 (T. dicoccum, AABB) and Z2921, expressed the red stems of Z2921. The flanking markers of RgM4G52 developed in this study could be useful for developing additional common wheat lines with red stems, laying the foundation for marker-assisted breeding and the fine mapping of RgM4G52. [ABSTRACT FROM AUTHOR]

Introduction: Wheat awns are crucial determinants of wheat yield due to their capacity to photosynthesize and exchange gas. Understanding the genetic basis of awn length (AL) is essential for improving wheat yield in molecular breeding programs. Methods: In this study, quantitative trait loci (QTLs) of AL were analyzed using recombinant inbred line (RIL) mapping population referred to as YY-RILs, which was derived from a cross between Yannong 15 (YN15) and Yannong 1212 (YN1212). Results and discussion: Seven putative additive QTLs and 30 pairwise epistatic QTLs for AL were identified. Among them, five novel additive QTLs (except qAl- 2A and qAl-5A.2) and 30 novel pairwise epistatic QTLs were identified. qAl-5A.1 was repeatedly identified in all five environment datasets, which was considered to be one novel stable QTL for AL with minor additive effects. eqAl-2B.2-2 significantly interacted with eight loci and could be of great importance in regulating awn development. The genes associated with the major stable QTL of qAl-5A.2 and the minor stable QTL of qAl-2A were B1 and WFZP-A, respectively. Awn lengths exhibited significant genetic correlations with kernel weight and kernels per spike, which could affect grain protein content to a lesser extent. This study enhances our understanding of the genetic basis of awn development and identifies novel genes as well as markers for future genetic improvement of wheat yield. [ABSTRACT FROM AUTHOR]

Bread wheat (T. aestivum) is one of the world's most widely consumed cereals. Since micronutrient deficiencies are becoming more common among people who primarily depend upon cereal-based diets, a need for better-quality wheat varieties has been felt. An association panel of 154 T. aestivum lines was evaluated for the following quality traits: grain appearance (GA) score, grain hardness (GH), phenol reaction (PR) score, protein percent, sodium dodecyl sulfate (SDS) sedimentation value, and test weight (TWt). In addition, the panel was also phenotyped for grain yield and related traits such as days to heading, days to maturity, plant height, and thousand kernel weight for the year 2017-18 at the Borlaug Institute for South Asia (BISA) Ludhiana and Jabalpur sites. We performed a genome-wide association analysis on this panel using 18,351 genotyping-bysequencing (GBS) markers to find marker-trait associations for quality and grain yield-related traits. We detected 55 single nucleotide polymorphism (SNP) marker trait associations (MTAs) for quality-related traits on chromosomes 7B (10), 1A (9), 2A (8), 3B (6), 2B (5), 7A (4), and 1B (3), with 3A, 4A, and 6D, having two and the rest, 4B, 5A, 5B, and 1D, having one each. Additionally, 20 SNP MTAs were detected for yield-related traits based on a field experiment conducted in Ludhiana on 7D (4) and 4D (3) chromosomes, while 44 SNP MTAs were reported for Jabalpur on chromosomes 2D (6), 7A (5), 2A (4), and 4A (4). Utilizing these loci in marker-assisted selection will benefit from further validation studies for these loci to improve hexaploid wheat for better yield and grain quality [ABSTRACT FROM AUTHOR]

Multi-parent populations (MPPs) are attractive for genetic and breeding studies because they combine genetic diversity with an easy-to-control population structure. Most methods for mapping QTLs in MPPs focus on the detection of QTLs in single environments. Little attention has been given to mapping QTLs in multienvironment trials (METs) and to detecting and modeling QTL-byenvironment interactions (QEIs). We present mixed model approaches for the detection and modeling of consistent versus environment-dependent QTLs, i.e., QTL-by-environment interaction (QEI). QTL effects are assumed to be normally distributed with variances expressing consistency or dependence on environments and families. The entries of the corresponding design matrices are functions of identity-by-descent (IBD) probabilities between parents and offspring and follow from the parental origin of offspring DNA. A polygenic effect is added to the models to account for background genetic variation. We illustrate the wide applicability of our method by analyzing several public MPP datasets with observations from METs. The examples include diallel, nested association mapping (NAM), and multi-parent advanced inter-cross (MAGIC) populations. The results of our approach compare favorably with those of previous studies that used tailored methods. [ABSTRACT FROM AUTHOR]

Bread wheat germplasm is accessed from the International Maize and Wheat Improvement Centre (CIMMYT) and the International Centre for Agricultural Research in the Dry Areas (ICARDA) by Australian wheat breeders and researchers through the CIMMYT Australia ICARDA Germplasm Evaluation (CAIGE) program. The CAIGE program coordinates the selection, importation, quarantine, dissemination, and evaluation of the imported bread wheat germplasm and the management of associated data and information. This paper describes the CAIGE model and assesses both the genetic and economic impacts of these materials on the Australian wheat industry after commercialisation of wheat breeding in the early 21st century and the establishment of CAIGE. The CAIGE concept was validated using data collected and analysed from multi-environment trials between 2017 and 2020. The impact of cultivars with and without CAIGE contribution to pedigree on yield was estimated using production-by-variety statistics. Net gain in yield, estimated as the yield difference between CAIGE and Non-CAIGE varieties, was multiplied by the percentage contribution to pedigree to estimate the additional yield. The CAIGE bread wheat program identified diverse, high-yielding, and disease-resistant germplasm and significantly improved the capture and dissemination of information. The benefit-cost ratio, calculated as the sum of benefits divided by investments, indicated that, for every dollar invested in CAIGE, a further $20 was generated in benefits. The internal rate of return was estimated at 163% and the modified rate at 18%. The benefits of these international materials to Australian wheat breeding remained significant. [ABSTRACT FROM AUTHOR]

Rice-wheat and maize-wheat rotations are major cropping systems in the middle and lower reaches of Yangtze River in China, where high nitrogen (N) inputs and low N efficiency often exacerbate resource waste and environmental pollution. Due to the changes in factors such as soil properties and moisture content, the N fate and the N utilization characteristics of wheat in different rotations are significantly different. Efficient N management strategies are thus urgently required for promoting maximum wheat yield in different rotation systems while reducing N loss. A 2-year field experiment using isotopic (15N) tracer technique was conducted to evaluate the fate of 15N-labeled urea in wheat fields and the distribution characteristics of N derived from different sources. The wheat yield and N use efficiency under various N rates (180 and 240 kg ha-1, abbreviated as N180 and N240) and preceding crops (rice and maize, abbreviated as R-wheat and M-wheat) were also investigated. The results showed that N240 increased N uptake and grain yield by only 8.77-14.97% and 2.51-4.49% compared with N 180, but decreased N agronomic efficiency (NAE) and N physiological efficiency (NPE) by 14.78-18.79% and 14.06-31.35%. N240 also decreased N recovery in plants by 2.8% on average compared with N180, and increased N residue in soil and N loss to the environment. Compared with that of basal N, the higher proportion of topdressing N was absorbed by wheat rather than lost to the environment. In addition, the accumulation of topdressing N in grain was much higher than that of basal N. Compared with that in R-wheat treatment, plants in M-wheat treatment trended to absorb more 15N and reduce unaccounted N loss, resulting in higher yield potential. Moreover, the M-wheat treatment increased N recovery in 0-20 cm soil but decreased 80-100 cm soil compared with R-wheat treatment, indicating a lower risk of N loss in deeper soil. Collectively, reducing N application rate and increasing the topdressing ratio is an effectiveway to balance sustainable crop yield for a secure food supply and environmental benefit, which ismore urgent in rice-wheat rotation. [ABSTRACT FROM AUTHOR]

Soil salinity poses a significant threat to agricultural productivity, impacting the growth and yield of wheat (Triticum aestivum L.) plants. This study investigates the potential of melatonin (MT; 100 µM) and hydrogen sulfide (H2S; 200 µM sodium hydrosulfide, NaHS) to confer the tolerance of wheat plants to 100 mM NaCl. Salinity stress induced the outburst of reactive oxygen species (ROS) resulting in damage to the chloroplast structure, growth, photosynthesis, and yield. Application of either MT or NaHS augmented the activity of antioxidant enzymes, superoxide dismutase, ascorbate peroxidase, glutathione reductase, and reduced glutathione (GSH) levels, upregulated the expression of Na+ transport genes (SOS1, SOS2, SOS3, NHX1), resulting in mitigation of salinity stress. Thus, improved stomatal behavior, gas-exchange parameters, and maintenance of chloroplast structure resulted in enhanced activity of the Calvin cycle enzymes and overall enhancement of growth, photosynthetic, and yield performance of plants under salinity stress. The use of DL-propargylglycine (PAG, an inhibitor of hydrogen sulfide biosynthesis) and p-chlorophenyl alanine (p-CPA, an inhibitor of melatonin biosynthesis) to plants under salt stress showed the comparative necessity of MT and H2S in mitigation of salinity stress. In the presence of PAG, more pronounced detrimental effects were observed than in the presence of p-CPA, emphasizing that MT was involved in mitigating salinity through various potential pathways, one of which was through H2S. [ABSTRACT FROM AUTHOR]

Introduction: Wheat grain filling processes under post-anthesis stress scenarios depend mainly on stem traits and remobilization of stem water-soluble carbohydrates (WSC). Methods: A diverse panel of advanced semi-dwarf spring wheat lines, representing a natural variation in stem traits (WSC content, stem diameter, peduncle length, and stem wall width), was used to identify specific traits that reliably reflect the relationship between WSC and grain yield. The panel was phenotyped under various environmental conditions: well-watered, waterlimited, and heat stress in Mexico, and terminal-drought in Israel. Results: Environmental stresses reduced grain yield (from 626 g m-2 under wellwatered to 213 g m-2 under heat), lower internode diameter, and peduncle length. However, stem-WSC generally peaked 3-4 weeks after heading under all environmental conditions except heat (where it peaked earlier) and expressed the highest values under water-limited and terminal-drought environments. Increased investment in internode diameter and peduncle length was associated with a higher accumulation of stem WSC, which showed a positive association with yield and kernel weight. Across all environments, there were no apparent trade-offs between increased crop investment in internode diameter, peduncle length, and grain yield. Discussion: Our results showed that selecting for genotypes with higher resource investment in stem structural biomass, WSC accumulation, and remobilization could be a valuable strategy to ameliorate grain size reduction under stress without compromising grain yield potential. Furthermore, easy-tomeasure proxies for WSC (stem diameter at specific internodes and length of the last internode, i.e., the peduncle) could significantly increase throughput, potentially at the breeding scale. [ABSTRACT FROM AUTHOR]

Grain albumin is highly nutritious and closely related to the processing quality of wheat. However, few studies have explored the grain albumin content (GAC) in wheat. This study aims to uncover quantitative trait loci (QTLs) linked to wheat GAC by analyzing a doubled haploid (DH) population derived from common wheat cultivars ShanNong23 and ZhouMai17. We detected six QTLs controlling GAC on chromosomes 1B, 5A, and 6D, with individual QTL explaining 5.78% to 22.29% of the GAC variation. The effect of QGac.cau-1B.1 on GAC is attributed to the presence of the 1BL/1RS translocation, indicating that the 1BL/1RS translocation increase of GAC compared with the non-1BL/1RS translocation lines. The higher GAC observed in 1BL/1RS lines could be primarily attributed to the increased accumulation of omega-secalin, omega-gliadin, low molecular weight glutenin subunit and ribosomal protein content. Additionally, we also found that the SDS-sedimentation value of whole wheat flour was decreased by adding albumin solution. These results advance our understanding of the genetic basis of GAC and offer novel perspectives for enhancing wheat quality through genetic enhancements. [ABSTRACT FROM AUTHOR]

The gelatinization and retrogradation characteristics of wheat starch affect the eating quality of Chinese-style food. Rapid Visco Analyzer (RVA) parameters have been widely used as important indicators to evaluate and improve the quality of wheat starch. However, the genetic basis of RVA parameters remains to be further explored. In the present study, a natural population was genotyped using 90K single nucleotide polymorphism (SNP) arrays, and the RVA parameters of this population grown in five environments were evaluated. The results showed that 22,068 high-quality SNP markers were identified and distributed unequally on the chromosomes. According to the genetic distance, 214 wheat materials were divided into four groups. Except for the pasting temperature (PTT), six parameters followed a normal distribution. Based on the general linear model, 969 significant association SNPs were detected by genome-wide association studies (GWAS), and chromosomes 7A and 2B had the most associated SNPs. Breakdown viscosity (BV) was associated with the most SNPs (n = 238), followed by PTT (n = 186), peak viscosity (PV; n = 156), trough viscosity (TV; n = 127), and final viscosity (FV; n = 126). According to the average linkage disequilibrium (LD), 33 stable quantitative trait loci (QTLs) were identified for single parameters in multiple environments, of which 12 were associated with BV, followed by peak time (PT; n = 8) and PTT (n = 7). On the other hand, 67 pleiotropic QTLs were identified for multiple parameters. Three candidate genes--TasbeIIa, TasbeI, and TassIIa--were screened for phenotyping analysis. The grain width and the weight of the TasbeIIa and TaSSIIa knockout (KO) lines were significantly lower than those of the TasbeI KO lines and the control (CK). The KO lines had smaller endosperm cells, smaller A-type starch granules, and higher amylose content. The TasbeI KO lines showed normal RVA curves, while the TasbeIIa KO lines showed flat curves. However, the TaSSIIa lines failed to paste under the RVA temperatures. Conclusively, the SNPs/QTLs significantly associated with the RVA parameters and genetic resources with novel haplotypes could be used to improve the quality of wheat starch. [ABSTRACT FROM AUTHOR]

The Poaceae family, commonly known as the grass family, encompasses a diverse group of crops that play an essential role in providing food, fodder, biofuels, environmental conservation, and cultural value for both human and environmental well-being. Crops in Poaceae family are deeply intertwined with human societies, economies, and ecosystems, making it one of the most significant plant families in the world. As the major reservoirs of essential nutrients, seed grain of these crops has garnered substantial attention from researchers. Understanding the molecular and genetic processes that controls seed formation, development and maturation can provide insights for improving crop yield, nutritional quality, and stress tolerance. The diversity in photosynthetic pathways between C3 and C4 plants introduces intriguing variations in their physiological and biochemical processes, potentially affecting seed development. In this review, we explore recent studies performed with omics technologies, such as genomics, transcriptomics, proteomics and metabolomics that shed light on the mechanisms underlying seed development in wheat and maize, as representatives of C3 and C4 plants respectively, providing insights into their unique adaptations and strategies for reproductive success. [ABSTRACT FROM AUTHOR]

Methods: Cultivar characterization indicated that the increase in yield was negatively affected by spike number and positively affected by the number of kernels per spike before the 2000s and kernel weight after the 2000s. Field trials were conducted across two ecological zones over two consecutive wheatgrowing seasons. The results showed that genetic gains in grain yield, spike number, and kernel weight during 1955 to 2021 were 0.629%, 0.574%, and 0.332% year-1 on a relative basis or 39.12 kg ha-1, 24,350 hm-2, and 0.15 g year-1 on an absolute basis, respectively. However, the increase in the kernel number per spike was not significant. Moreover, cultivar replacement explained 25.6%, 12.8%, and 37.5% of the total variance in grain yield, spike number, and kernel weight, respectively. In summary, during the initial grain-filling stage, wheat cultivar replacement led to the shortening of grain-filling duration and rapid grain-filling rate. However, a longer active grain-filling duration was produced by prolonged durations of rapid and late grain-filling. Additionally, the experimental year had a greater effect on the kernel number, which explained 53.2% of the total variance. Ultimately, modern wheat cultivars had a greater kernel weight. Results: Although the increase in kernel weight has affected grain yield during cultivar replacements in the Haihe Plain, the potential for further yield increase through kernel weight enhancement alone is limited. Consequently, future breeding efforts and cultivation practices should focus on improving spike traits and canopy architecture to enhance productivity. [ABSTRACT FROM AUTHOR]

Powdery mildew (PM), caused by Blumeria graminis f. sp. tritici, poses a significant threat to wheat production, necessitating the development of genetically resistant varieties for long-term control. Therefore, exploring genetic architecture of PM in wheat to uncover important genomic regions is an important area of wheat research. In recent years, the utilization of meta-QTL (MQTL) analysis has gained prominence as an essential tool for unraveling the complex genetic architecture underlying complex quantitative traits. The aim of this research was to conduct a QTL meta-analysis to pinpoint the specific genomic regions in wheat responsible for governing PM resistance. This study integrated 222 QTLs from 33 linkage-based studies using a consensus map with 54,672 markers. The analysis revealed 39 MQTLs, refined to 9 high-confidence MQTLs (hcMQTLs) with confidence intervals of 0.49 to 12.94 cM. The MQTLs had an average physical interval of 41.00 Mb, ranging from 0.000048 Mb to 380.71 Mb per MQTL. Importantly, 18 MQTLs co-localized with known resistance genes like Pm2, Pm3, Pm8, Pm21, Pm38, and Pm41. The study identified 256 gene models within hcMQTLs, providing potential targets for marker-assisted breeding and genomic prediction programs to enhance PM resistance. These MQTLs would serve as a foundation for fine mapping, gene isolation, and functional genomics studies, facilitating a deeper understanding of molecular mechanisms. The identification of candidate genes opens up exciting possibilities for the development of PM-resistant wheat varieties after validation. [ABSTRACT FROM AUTHOR]

Accurate wheat ear counting is one of the key indicators for wheat phenotyping. Convolutional neural network (CNN) algorithms for counting wheat have evolved into sophisticated tools, however because of the limitations of sensory fields, CNN is unable to simulate global context information, which has an impact on counting performance. In this study, we present a hybrid attention network (CTHNet) for wheat ear counting from RGB images that combines local features and global context information. On the one hand, to extract multi-scale local features, a convolutional neural network is built using the Cross Stage Partial framework. On the other hand, to acquire better global context information, tokenized image patches from convolutional neural network feature maps are encoded as input sequences using Pyramid Pooling Transformer. Then, the feature fusion module merges the local features with the global context information to significantly enhance the feature representation. The Global Wheat Head Detection Dataset and Wheat Ear Detection Dataset are used to assess the proposed model. There were 3.40 and 5.21 average absolute errors, respectively. The performance of the proposed model was significantly better than previous studies. [ABSTRACT FROM AUTHOR]

Hybrid breeding can increase the competitiveness of wheat (Triticum aestivum L.) in Sub-Saharan Africa by fostering more public-private partnerships and promoting investment by the private sector. The benefit of hybrid wheat cultivars in South Africa has previously been demonstrated but due to the high cost of hybrid seed production, hybrid breeding has not received significant attention in the past decade. Considering the renewed commitment of the private sector to establish wheat as a hybrid crop globally, coupled with significant research investment into enhancement of outcrossing of wheat, hybrid wheat breeding in Southern and Eastern Africa should be revisited. Our study aimed to identify genetically distinct germplasm groups in spring wheat that would be useful in the establishment of heterotic pools targeting this region. Multi-environment yield testing of a large panel of F1 test hybrids, generated using global elite germplasm, was carried out between 2019 and 2020 in Argentina, Africa, Europe, and Australia. We observed significant genotype by environment interactions within our testing network, confirming the distinctiveness of African trial sites. Relatively high additive genetic variance was observed highlighting the contribution of parental genotypes to the grain yield of test hybrids. We explored the genetic architecture of these parents and the genetic factors underlying the value of parents appear to be associated with their genetic subgroup, with positive marker effects distributed throughout the genome. In testcrosses, elite germplasm from the International Maize and Wheat Improvement Center (CIMMYT) appear to be complementary to the genetically distinct germplasm bred in South Africa. The feasibility of achieving genetic gain via heterotic pool establishment and divergence, and by extension the viability of hybrid cultivars in Sub-Saharan Africa, is supported by the results of our study. [ABSTRACT FROM AUTHOR]

Better crop stand establishment, a function of rapid and uniform seedling emergence, depends on the activities of germination-related enzymes, which is problematic when there is insufficient soil moisture. Different ways are in practice for counteracting this problem, including seed priming with different chemicals, which are considered helpful in obtaining better crop stand establishment to some extent through improved seed germination and seedling emergence. In this growth room experiment, caffeine was used as a seed priming agent to improve germination under moisture scarcity. Polyethylene glycol-8000 (18%) was added to Hoagland’s nutrient solution to create drought stress (−0.65 MPa). The experiment was arranged in a completely randomized design (CRD), having four replications of each treatment. A newly developed wheat genotype SB-1 was used for the experimentation. Different doses of caffeine, i.e., 4 ppm, 8 ppm, 12 ppm, and 16 ppm, including no soaking and water soaking, were used as seed priming treatments. Water deficit caused oxidative stress and adversely affected the seed germination, seedling vigor, activities of germination enzymes, photosynthetic pigments, and antioxidative defense mechanism in roots and shoots of seedlings. Caffeine seed priming ameliorated the negative effects of water deficit on seed germination and seedling vigor, which was attributed to the reduction in lipid peroxidation and improvement in the activities of germination-related enzymes like glucosidase, amylase, and protease. Conclusively, seed priming with 12 ppm caffeine outperformed the other treatments and hence is recommended for better crop stand establishment under conditions of soil moisture deficit. [ABSTRACT FROM AUTHOR]

This study was conducted in controlled environmental conditions to systematically evaluate multi-traits responses of winter wheat (Triticum aestivumL.) genotypes to different salinity levels. Responses were assessed at the germination to early seedling stage (Experiment 1). Seeds of different genotypes (n=292) were subjected to three salinity levels (0 [control], 60, and 120 mM NaCl). Principal Component Analysis (PCA) revealed that among studied traits seedling vigor index (SVI) contributed more towards the diverse response of genotypes to salinity stress. Based on SVI, eight contrasting genotypes assumed to be tolerant (Gage, Guymon, MTS0531, and Tascosa) and susceptible (CO04W320, Carson, TX04M410211) were selected for further physio-biochemical evaluation at the booting stage (Experiment 2) and to monitor grain yield. Higher level of salinity (120 mM NaCl) exposure at the booting stage increased thylakoid membrane damage, lipid peroxidation, sugars, proline, and protein while decreasing photosynthesis, chlorophyll index, starch, and grain yield. Based on grain yield, the assumed magnitude of the genotypic response shown in Experiment 1 was not analogous in Experiment 2. This indicates the necessity of individual screening of genotypes at different sensitive growth stages for identifying true salinity-tolerant and susceptible genotypes at a particular growth stage. However, based on higher grain yield and its least percentage reduction under higher salinity, Guymon and TX04M410211were identified as tolerant, andGage and CO04W320 as susceptible at the booting stage, and their biparental population can be used to identify genomic regions for booting stage-specific salinity response. [ABSTRACT FROM AUTHOR]

Introduction: The precise detection of weeds in the field is the premise of implementing weed management. However, the similar color, morphology, and occlusion between wheat and weeds pose a challenge to the detection of weeds. In this study, a CSCW-YOLOv7 based on an improved YOLOv7 architecture was proposed to identify five types of weeds in complex wheat fields. Methods: First, a dataset was constructed for five weeds that are commonly found, namely, Descurainia sophia, thistle, golden saxifrage, shepherd's purse herb, and Artemisia argyi. Second, a wheat weed detection model called CSCWYOLOv7 was proposed to achieve the accurate identification and classification of wheat weeds. In the CSCW-YOLOv7, the CARAFE operator was introduced as an up-sampling algorithm to improve the recognition of small targets. Then, the Squeeze-and-Excitation (SE) network was added to the Extended Latent Attention Networks (ELAN) module in the backbone network and the concatenation layer in the feature fusion module to enhance important weed features and suppress irrelevant features. In addition, the contextual transformer (CoT) module, a transformer-based architectural design, was used to capture global information and enhance self-attention by mining contextual information between neighboring keys. Finally, the Wise Intersection over Union (WIoU) loss function introducing a dynamic nonmonotonic focusing mechanism was employed to better predict the bounding boxes of the occluded weed. Results and discussion: The ablation experiment results showed that the CSCWYOLOv7 achieved the best performance among the other models. The accuracy, recall, and mean average precision (mAP) values of the CSCW-YOLOv7 were 97.7%, 98%, and 94.4%, respectively. Compared with the baseline YOLOv7, the improved CSCW-YOLOv7 obtained precision, recall, and mAP increases of 1.8%, 1%, and 2.1%, respectively. Meanwhile, the parameters were compressed by 10.7% with a 3.8-MB reduction, resulting in a 10% decrease in floating-point operations per second (FLOPs). The Gradient-weighted Class Activation Mapping (Grad-CAM) visualization method suggested that the CSCW-YOLOv7 can learn a more representative set of features that can help better locate the weeds of different scales in complex field environments. In addition, the performance of the CSCW-YOLOv7 was compared to the widely used deep learning models, and results indicated that the CSCW-YOLOv7 exhibits a better ability to distinguish the overlapped weeds and small-scale weeds. The overall results suggest that the CSCW-YOLOv7 is a promising tool for the detection of weeds and has great potential for field applications. [ABSTRACT FROM AUTHOR]

Introduction: In plant breeding, we often aim to improve multiple traits at once. However, without knowing the economic value of each trait, it is hard to decide which traits to focus on. This is where "desired gain selection indices" come in handy, which can yield optimal gains in each trait based on the breeder's prioritisation of desired improvements when economic weights are not available. However, they lack the ability to maximise the selection response and determine the correlation between the index and net genetic merit. Methods: Here, we report the development of an iterative desired gain selection index method that optimises the sampling of the desired gain values to achieve a targeted or a user-specified selection response for multiple traits. This targeted selection response can be constrained or unconstrained for either a subset or all the studied traits. Results: We tested the method using genomic estimated breeding values (GEBVs) for seven traits in a bread wheat (Triticum aestivum) reference breeding population comprising 3,331 lines and achieved prediction accuracies ranging between 0.29 and 0.47 across the seven traits. The indices were validated using 3,005 double haploid lines that were derived from crosses between parents selected from the reference population. We tested three user-specified response scenarios: a constrained equal weight (INDEX1), a constrained yield dominant weight (INDEX2), and an unconstrained weight (INDEX3). Our method achieved an equivalent response to the user-specified selection response when constraining a set of traits, and this response was much better than the response of the traditional desired gain selection indices method without iteration. Interestingly, when using unconstrained weight, our iterative method maximised the selection response and shifted the average GEBVs of the selection candidates towards the desired direction. Discussion: Our results show that the method is an optimal choice not only when economic weights are unavailable, but also when constraining the selection response is an unfavourable option. [ABSTRACT FROM AUTHOR]

Spring wheat (Triticum aestivum L.) remains an important alternative to winter wheat cultivation at Northern latitudes due to high risk of overwintering or delayed sowing of winter wheat. We studied nine major agronomic traits in a set of 299 spring wheat genotypes in trials across 12-year-site combinations in Lithuania, Latvia, Estonia, and Norway for three consecutive years. The dataset analyzed here consisted of previously published phenotypic data collected in 2021 and 2022, supplemented with additional phenotypic data from the 2023 field season collected in this study. We combined these phenotypic datasets with previously published genotypic data generated using a 25K single nucleotide polymorphism (SNP) array that yielded 18,467 markers with a minor allele frequency above 0.05. Analysis of these datasets via genome-wide association study revealed 18 consistent quantitative trait loci (QTL) replicated in two or more trials that explained more than 5% of phenotypic variance for plant height, grain protein content, thousand kernel weight, or heading date. The most consistent markers across the tested environments were detected for plant height, thousand kernel weight, and days to heading in eight, five, and six trials, respectively. No beneficial effect of the semi-dwarfing alleles Rht-B1b and Rht-D1b on grain yield performance was observed across the 12 tested trials. Moreover, the cultivars carrying these alleles were low yielding in general. Based on principal component analysis, wheat genotypes developed in the Northern European region clustered separately from those developed at the southern latitudes, and markers associated with the clustering were identified. Important phenotypic traits, such as grain yield, days to heading, grain protein content, and thousand kernel weight were associated with this clustering of the genotype sets. Interestingly, despite being adapted to the Nordic environment, genotypes in the Northern set demonstrated lower grain yield performance across all tested environments. The results indicate that spring wheat germplasm harbors valuable QTL/alleles, and the identified traitmarker associations might be useful in improving Nordic-Baltic spring wheat germplasm under global warming conditions. [ABSTRACT FROM AUTHOR]

Rapidly obtaining the chlorophyll content of crop leaves is of great significance for timely diagnosis of crop health and effective field management. Multispectral imagery obtained from unmanned aerial vehicles (UAV) is being used to remotely sense the SPAD (Soil and Plant Analyzer Development) values of wheat crops. However, existing research has not yet fully considered the impact of different growth stages and crop populations on the accuracy of SPAD estimation. In this study, 300 materials from winter wheat natural populations in Xinjiang, collected between 2020 to 2022, were analyzed. UAV multispectral images were obtained in the experimental area, and vegetation indices were extracted to analyze the correlation between the selected vegetation indices and SPAD values. The input variables for the model were screened, and a support vector machine (SVM) model was constructed to estimate SPAD values during the heading, flowering, and filling stages under different water stresses. The aim was to provide a method for the rapid acquisition of winter wheat SPAD values. The results showed that the SPAD values under normal irrigation were higher than those under water restriction. Multiple vegetation indices were significantly correlated with SPAD values. In the prediction model construction of SPAD, the different models had high estimation accuracy under both normal irrigation and water limitation treatments, with correlation coefficients of predicted and measured values under normal irrigation in different environments the value of r from 0.59 to 0.81 and RMSE from 2.15 to 11.64, compared to RE from 0.10% to 1.00%; and under drought stress in different environments, correlation coefficients of predicted and measured values of r was 0.69--0.79, RMSE was 2.30--12.94, and RE was 0.10%--1.30%. This study demonstrated that the optimal combination of feature selection methods and machine learning algorithms can lead to a more accurate estimation of winter wheat SPAD values. In summary, the SVM model based on UAV multispectral images can rapidly and accurately estimate winter wheat SPAD value. [ABSTRACT FROM AUTHOR]

Powdery mildew caused by Blumeria graminis f. sp. tritici (Bgt) seriously threatens wheat production worldwide. It is imperative to identify novel resistance genes from wheat and its wild relatives to control this disease by host resistance. Dasypyrum villosum (2n = 2x = 14, VV) is a relative of wheat and harbors novel genes for resistance against multi-fungal diseases. In the present study, we developed a complete set of new wheat-D. villosum disomic introgression lines through genomic in situ hybridization (GISH), fluorescence in situ hybridization (FISH) and molecular markers analysis, including four disomic substitution lines (2n=42) containing respectively chromosomes 1V#6, 2V#6, 3V#6, and 6V#6, and four disomic addition lines (2n=44) containing respectively chromosomes 4V#6, 5V#6, 6V#6 and 7V#6. These lines were subsequently evaluated for their responses to a mixture Bgt isolates at both seedling and adultplant stages. Results showed that introgression lines containing chromosomes 3V#6, 5V#6, and 6V#6 exhibited resistance at both seedling and adult-plant stages, whereas the chromosome 4V#6 disomic addition line NAU4V#6-1 exhibited a high level of adult plant resistance to powdery mildew. Moreover, two translocation lines were further developed from the progenies of NAU4V#6- 1 and the Ph1b mutation line NAU0686-ph1b. They were T4DL·4V#6S wholearm translocation line NAU4V#6-2 and T7DL·7DS-4V#6L small-fragment translocation line NAU4V#6-3. Powdery mildew tests of the two lines confirmed the presence of an adult-plant powdery mildew resistance gene, Pm4VL, located on the terminal segment of chromosome arm 4V#6L (FL 0.6- 1.00). In comparison with the recurrent parent NAU0686 plants, the T7DL·7DS- 4V#6L translocation line NAU4V#6-3 showed no obvious negative effect on yield-related traits, providing a new germplasm in breeding for resistance. [ABSTRACT FROM AUTHOR]

The number of wheat spikes has an important influence on wheat yield, and the rapid and accurate detection of wheat spike numbers is of great significance for wheat yield estimation and food security. Computer vision and machine learning have been widely studied as potential alternatives to human detection. However, models with high accuracy are computationally intensive and time consuming, and lightweight models tend to have lower precision. To address these concerns, YOLO-FastestV2 was selected as the base model for the comprehensive study and analysis of wheat sheaf detection. In this study, we constructed a wheat target detection dataset comprising 11,451 images and 496,974 bounding boxes. The dataset for this study was constructed based on the Global Wheat Detection Dataset and the Wheat Sheaf Detection Dataset, which was published by PP Flying Paddle. We selected three attention mechanisms, Large Separable Kernel Attention (LSKA), Efficient Channel Attention (ECA), and Efficient Multi-Scale Attention (EMA), to enhance the feature extraction capability of the backbone network and improve the accuracy of the underlying model. First, the attention mechanism was added after the base and output phases of the backbone network. Second, the attention mechanism that further improved the model accuracy after the base and output phases was selected to construct the model with a two-phase added attention mechanism. On the other hand, we constructed SimLightFPN to improve the model accuracy by introducing SimConv to improve the LightFPN module. The results of the study showed that the YOLO-FastestV2-SimLightFPN-ECA-EMA hybrid model, which incorporates the ECA attention mechanism in the base stage and introduces the EMA attention mechanism and the combination of SimLightFPN modules in the output stage, has the best overall performance. The accuracy of the model was P=83.91%, R=78.35%, AP= 81.52%, and F1 = 81.03%, and it ranked first in the GPI (0.84) in the overall evaluation. The research examines the deployment of wheat ear detection and counting models on devices with constrained resources, delivering novel solutions for the evolution of agricultural automation and precision agriculture. [ABSTRACT FROM AUTHOR]

Introduction: Targeted herbicide application refers to precise application of herbicides in weed-infested areas according to the location and density of farmland weeds. At present, targeted herbicide application in wheat fields generally faces problems including the low herbicide adhesion rate, leading to omission and excessive loss of herbicides. Methods: To solve these problems, changes in the impact force of herbicide and the weed leaves in the operation process of a spraying system were studied from the interaction between weeds and herbicides applied. A dynamic model of weed leaves was established. On this basis, the research indicated that the herbicide adhesion rate is highest under spraying pressure of 0.4 MPa and flow rate of 0.011 kg/s when the spray height is 300 mm. To study the dynamic deformation of weed leaves and the distribution of liquid herbicides in the external flow field under weed-herbicide interaction, a dynamic simulation model of herbicide application was built using the finite element method. Results and Discussion: The results show that when the spray height is 300 mm, the maximum weed leaf deformation index (LDI) is 0.43 and the velocity in the external flow field is 0 m/s under spraying pressure of 0.4 MPa and flow rate of 0.011 kg/s. This finding indicates that the herbicide is not splashed elsewhere and the turbulence intensity in the weed area is 2%, implying steady flow of the herbicide, most of which can be retained on weed leaves. Field test results of application quality of the herbicide show that the maximum LDI is 0.41 and the coverage of the herbicide in the sheltered area below the leaves is 19.02% when the spraying pressure is 0.4 MPa, flow rate is 0.011 kg/s, and spray height is 300 mm. This solves the problem of a low rate of utilization of herbicides because the herbicide passes through weed plants, and achieves the precision herbicide application in wheat fields. [ABSTRACT FROM AUTHOR]

This article explores the potential benefits of using zinc lysine and biochar to enhance the growth and physiological performance of wheat plants under saline stress. The study found that the combined application of biochar and zinc lysine significantly improved various physiological attributes and reduced oxidative stress indicators. The authors suggest that this combined treatment could be a practical solution to mitigate salt stress in plants, but further field studies are needed to validate these findings. The document also includes a list of references that library patrons can use to further explore topics related to plant stress and agricultural practices. [Extracted from the article]

Enhancing grain yield is a primary goal in the cultivation of major staple crops, including wheat. Recent research has focused on identifying the physiological and molecular factors that influence grain weight, a critical determinant of crop yield. However, a bottleneck has arisen due to the trade-off between grain weight and grain number, whose underlying causes remain elusive. In a novel approach, a wheat expansin gene, TaExpA6, known for its expression in root tissues, was engineered to express in the grains of the spring wheat cultivar Fielder. This modification led to increases in both grain weight and yield without adversely affecting grain number. Conversely, a triple mutant line targeting the gene TaGW2, a known negative regulator of grain weight, resulted in increased grain weight but decreased grain number, potentially offsetting yield gains. This study aimed to evaluate the two aforementioned modified wheat genotypes (TaExpA6 and TaGW2) alongside their respective wild-type counterparts. Conducted in southern Chile, the study employed a Complete Randomized Block Design with four replications, under well-managed field conditions. The primary metrics assessed were grain yield, grain number, and average grain weight per spike, along with detailed measurements of grain weight and dimensions across the spike, ovary weight at pollination (Waddington’s scale 10), and post-anthesis expression levels of TaExpA6 and TaGW2. Results indicated that both the TaExpA6 and the triple mutant lines achieved significantly higher average grain weights compared to their respective wild types. Notably, the TaExpA6 line did not exhibit a reduction in grain number, thereby enhancing grain yield per spike. By contrast, the triple mutant line showed a reduced grain number per spike, with no significant change in overall yield. TaExpA6 expression peaked at 10 days after anthesis (DAA), and its effect on grain weight over the WT became apparent after 15 DAA. In contrast, TaGW2 gene disruption in the triple mutant line increased ovary size at anthesis, leading to improved grain weight above the WT from the onset of grain filling. These findings suggest that the trade-off between grain weight and number could be attributed to the overlapping of the critical periods for the determination of these traits. [ABSTRACT FROM AUTHOR]

The tolerance to salinity stress is an intricate phenomenon at cellular and whole plant level that requires the knowledge of contributing physiological and biochemical processes and the genetic control of participating traits. In this context, present study was conducted with objective to evaluate the physiological, biochemical, and genetic responses of different wheat genotypes including bread wheat (BW) and synthetic hexaploids (SHs) under saline and control environment. The experiment was conducted in two factorial arrangement in randomized complete block design (RCBD), with genotypes as one factor and treatments as another factor. A significant decline in physiological traits (chlorophyll, photosynthesis, stomatal conductance, transpiration, and cell membrane stability) was observed in all genotypes due to salt stress; however, this decline was higher in BW genotypes as compared to four SH genotypes. In addition, the biochemical traits including enzymes [superoxide dismutase, catalase, and peroxidase (POD)] activity, proline, and glycine betaine (GB) illustrated significant increase along with increase in the expression of corresponding genes (TaCAT1, TaSOD, TaPRX2A, TaP5CS, and TaBADH-A1) due to salt stress in SHs as compared to BW. Correspondingly, highly overexpressed genes, TaHKT1;4, TaNHX1, and TaAKT1 caused a significant decline in Na+/K+ in SH as compared to BW genotypes under salt stress. Moreover, correlation analysis, principal component analysis (PCA), and heatmap analysis have further confirmed that the association and expression of physiological and biochemical traits varied significantly with salinity stress and type of genotype. Overall, the physiological, biochemical, and genetic evaluation proved SHs as the most useful stock for transferring salinity tolerance to other superior BW cultivars via the right breeding program. [ABSTRACT FROM AUTHOR]

In crops like wheat, terminal drought is one of the principal stress factors limiting productivity in rain-fed systems. However, little is known about root development after heading, when water uptake can be critical to wheat crops. The impact of water-stress on root growth was investigated in two wheat cultivars, Scout and Mace, under well-watered and post-anthesis water stress in three experiments. Plants were grown outside in 1.5-m long pots at a density similar to local recommended farming practice. Differences in root development were observed between genotypes, especially for water stress conditions under which Scout developed and maintained a larger root system than Mace. While under well-watered conditions both genotypes had shallow roots that appeared to senesce after heading, a moderate water stress stimulated shallow-root growth in Scout but accelerated senescence in Mace. For deep roots, post-heading biomass growth was observed for both genotypes in well-watered conditions, while under moderate water stress, only Scout maintained net growth as Mace deep roots senesced. Water stress of severe intensity affected both genotypes similarly, with root senescence at all depths. Senescence was also observed above ground. Under well-watered conditions, Scout retained leaf greenness (i.e. stay-green phenotype) for slightly longer than Mace. The difference between genotypes accentuated under moderate water stress, with rapid post-anthesis leaf senescence in Mace while Scout leaf greenness was affected little if at all by the stress. As an overall result, grain biomass per plant (‘yield’) was similar in the two genotypes under well-watered conditions, but more affected by a moderate stress in Mace than Scout. The findings from this study will assist improvement in modelling root systems of crop models, development of relevant phenotyping methods and selection of cultivars with better adaptation to drought. [ABSTRACT FROM AUTHOR]

Both subsoiling tillage (ST) and ridge and furrow rainfall harvesting (RF) are widely implemented and play an important role in boosting wheat productivity. However, information about the effects of ST coupled with RF during the summer fallow season on wheat productivity and environmental issues remains limited. This study aims to explore the effects of ST coupled with RF on water harvesting, wheat productivity-yield traits, water and nutrient use efficiency and quality, and soil nitrate-N residue in dryland winter wheat-summer fallow rotation at the intersection of southern Loess Plateau and western Huang-Huai-Hai Plain in China in 2018-2022. Three tillage practices--deep plowing with straw turnover (PTST), subsoiling with straw mulching (STSM), and STSMcoupled with RF (SRFSM)-- are conducted during the summer fallow season. The results indicated that tillage practices during the summer fallow season significantly impacted wheat productivity and soil nitrate-N residue. Compared to PTST, STSM significantly enhanced rainfall fallow efficiency and water use efficiency by 7.0% and 14.2%, respectively, as well as N, P, and K uptake efficiency by 16.9%, 16.2%, and 15.3%, and thus increased grain yield by 14.3% and improved most parameters of protein components and processing quality, albeit with an increase in nitrate-N residue in the 0- to 300-cm soil depth by 12.5%. SRFSM, in turn, led to a further increase in water storage at sowing, resulting in an increase of water use efficiency by 6.8%, as well as N, P, and K uptake efficiency and K internal efficiency by 11.8%, 10.4%, 8.8%, and 4.7%, thereby significantly promoting grain yield by 10.2%, and improving the contents of all the protein components and enhancing the processing quality in grain, and simultaneously reducing the nitrate-N residue in the 0- to 300-cm soil layer by 16.1%, compared to STSM. In essence, this study posits that employing subsoiling coupled with ridge-furrow rainfall harvesting (SRFSM) during the summer fallow season is a promising strategy for enhancing wheat yield, efficiency, and quality, and simultaneously reducing soil nitrate-N residue within the dryland summer fallow-winter wheat rotation system. [ABSTRACT FROM AUTHOR]

Canopy height serves as an important dynamic indicator of crop growth in the decision-making process of field management. Compared with other commonly used canopy height measurement techniques, ultrasonic sensors are inexpensive and can be exposed in fields for long periods of time to obtain easy-to-process data. However, the acoustic wave characteristics and crop canopy structure affect the measurement accuracy. To improve the ultrasonic sensor measurement accuracy, a four-year (2018-2021) field experiment was conducted on maize and wheat, and a measurement platform was developed. A series of single-factor experiments were conducted toinvestigate the significant factors affectingmeasurements, including the observation angle (0-60°), observation height (0.5-2.5 m), observation period (8:00 -18:00), platform moving speed with respect to the crop (0-2.0 m min-1), planting density (0.2-1 time of standard planting density), and growth stage (maize from three -leaf to harvest period and wheat from regreening to maturity period). The results indicated that both the observation angle and planting density significantly affected the results of ultrasonic measurements (p-value< 0.05), whereas the effects of other factors on measurement accuracy were negligible (p-value > 0.05). Moreover, a double-input factor calibrationmodelwasconstructed to assess canopy heightunder different years by utilizing the normalized difference vegetation index and ultrasonic measurements. The model was developed by employing the least-squares method, and ultrasonic measurement accuracy was significantly improved when integrating themeasured value of canopy heights and the normalized difference vegetation index (NDVI). The maize measurement accuracy had a root mean squared error (RMSE) ranging from 81.4 mm to 93.6 mm, while the wheat measurement accuracy had an RMSE from 37.1mmto 47.2mm. The research results effectively combine stable and low-cost commercial sensors with ground-based agricultural machinery platforms, enabling efficient and non-destructive acquisition of crop height information. [ABSTRACT FROM AUTHOR]

Enlarging the lateral space of drip tubes saves irrigation equipment costs (drip tubes and bypass), but it will lead to an increased risk of grain yield heterogeneity between wheat rows. Adjusting wheat row spacing is an effective cultivation measure to regulate a row's yield heterogeneity. During a 2-year field experiment, we investigated the variations in yield traits and photosynthetic physiology by utilizing two different water- and fertilizer-demanding spring wheat cultivars (NS22 and NS44) under four kinds of drip irrigation patterns with different drip tube lateral spacing and wheat row spacing [① TR4, drip tube spacing (DTS) was 60 cm, wheat row horizontal spacing (WRHS) was 15 cm; ② TR6, DTS was 90 cm, WRHS was 15 cm; ③ TR6L, DTS was 90 cm, WRHS was 10 cm, inter-block spacing (IBS) was 35 cm; and ④ TR6S, DTS was 80 cm, WRHS was 10 cm, IBS was 25 cm]. The results showed that under 15-cm equal row spacing condition, after the number of wheat rows served by a single tube increased from four (TR4, control) to six (TR6), NS22 and NS44 exhibited a marked decline in yield. The decline of NS22 (9.93%) was higher than that of NS44 (9.04%), and both cultivars also showed a greater decrease in grain weight and average grain-filling rate (AGFR) of inferior grains (NS22: 23.19%, 13.97%; NS44: 7.78%, 5.86%) than the superior grains (NS22: 10.60%, 8.33%; NS44: 4.89%, 4.62%). After the TR6 was processed to narrow WRHS (from 15 to 10 cm) and add IBS (TR6L: 35 cm; TR6S: 25 cm), the grain weight per panicle (GWP) and AGFR of superior and inferior grains in the third wheat row (RW3) of NS22 and NS44 under TR6L increased significantly by 26.05%, 8.22%, 14.05%, 10.50%, 5.09%, and 5.01%, respectively, and under TR6S, they significantly increased by 20.78%, 9.91%, 16.19%, 9.28%, 5.01%, and 4.14%, respectively. The increase in GWP and AGFR was related to the increase in flag leaf area, net photosynthetic rate, chlorophyll content, relative water content, actual photochemical efficiency of PSII, and photochemical quenching coefficient. Among TR4, TR6, TR6L, and TR6S, for both NS22 and NS44, the yield of TR6S was significantly higher than that of TR6 and TR6L. Furthermore, TR6S showed the highest economic benefit. [ABSTRACT FROM AUTHOR]

Aims: Root system architecture (RSA) plays an important role in the plant’s ability to sustain yield under abiotic stresses such as drought. Preceding crops (precrops) can affect the yield of the proceeding crop, partially by affecting the RSA. This experiment aims to explore the interactions between precrop identity, crop genotype and drought at early growth stages. Methods: Rhizotrons, sized 60 × 80 × 3.5 cm, were used to assess the early root growth of two winter wheat (Triticum aestivum L.) genotypes, using precroptreated soil around the seedlings and differing water regimes. The rhizotrons were automatically imaged 3 times a week to track root development. Results: Precrop-treated soil affected the RSA and changes caused by the reduced water treatment (RWT) were different depending on the precrop. Largest of these was the 36% reduction in root depth after wheat, but 44% after OSR. This indicates that effects caused by the precrop can be simulated, at least partially, by transferring precrop-treated soils to controlled environments. The genotypes had differential RSA and reacted differently to the RWT, with Julius maintaining an 8.8-13.1% deeper root system compared to Brons in the RWT. In addition, the combined environmental treatment affected the genotypes differently. Conclusion: Our results could help explain discrepancies found from using precrops to enhance yield as they indicate differences in the preceding crop effect when experiencing drought stress. Further, these differences are affected by genotypic interactions, which can be used to select and adapt crop genotypes for specific crop rotations, depending on the year. Additionally, we have shown a viable method of stimulating a partial precrop effect at the seedling stage in a controlled greenhouse setting using field soil around the germinated seed. [ABSTRACT FROM AUTHOR]

Integrating high-throughput phenotyping (HTP) based traits into phenomic and genomic selection (GS) can accelerate the breeding of high-yielding and climate-resilient wheat cultivars. In this study, we explored the applicability of Unmanned Aerial Vehicles (UAV)-assisted HTP combined with deep learning (DL) for the phenomic or multi-trait (MT) genomic prediction of grain yield (GY), test weight (TW), and grain protein content (GPC) in winter wheat. Significant correlations were observed between agronomic traits and HTP-based traits across different growth stages of winter wheat. Using a deep neural network (DNN) model, HTP-based phenomic predictions showed robust prediction accuracies for GY, TW, and GPC for a single location with R2 of 0.71, 0.62, and 0.49, respectively. Further prediction accuracies increased (R2 of 0.76, 0.64, and 0.75) for GY, TW, and GPC, respectively when advanced breeding lines from multi-locations were used in the DNN model. Prediction accuracies for GY varied across growth stages, with the highest accuracy at the Feekes 11 (Milky ripe) stage. Furthermore, forward prediction of GY in preliminary breeding lines using DNN trained on multi-location data from advanced breeding lines improved the prediction accuracy by 32% compared to single-location data. Next, we evaluated the potential of incorporating HTP-based traits in multi-trait genomic selection (MT-GS) models in the prediction of GY, TW, and GPC. MTGS, models including UAV data-based anthocyanin reflectance index (ARI), green chlorophyll index (GCI), and ratio vegetation index 2 (RVI_2) as covariates demonstrated higher predictive ability (0.40, 0.40, and 0.37, respectively) as compared to single-trait model (0.23) for GY. Overall, this study demonstrates the potential of integrating HTP traits into DL-based phenomic or MT-GS models for enhancing breeding efficiency. [ABSTRACT FROM AUTHOR]

Wheat is a major food crop that plays a crucial role in the human diet. Various breeding technologies have been developed and refined to meet the increasing global wheat demand. Several studies have suggested breeding strategies that combine generation acceleration systems and molecular breeding methods to maximize breeding efficiency. However, real-world examples demonstrating the effective utilization of these strategies in breeding programs are lacking. In this study, we designed and demonstrated a synergized breeding strategy (SBS) that combines rapid and efficient breeding techniques, including speed breeding, speed vernalization, phenotypic selection, backcrossing, and marker-assisted selection. These breeding techniques were tailored to the specific characteristics of the breeding materials and objectives. Using the SBS approach, from artificial crossing to the initial observed yield trial under field conditions only took 3.5 years, resulting in a 53% reduction in the time required to develop a BC2 nearisogenic line (NIL) and achieving a higher recurrent genome recovery of 91.5% compared to traditional field conditions. We developed a new wheat NIL derived from cv. Jokyoung, a leading cultivar in Korea. Milyang56 exhibited improved protein content, sodium dodecyl sulfate-sedimentation value, and loaf volume compared to Jokyoung, which were attributed to introgression of the Glu-B1i allele from the donor parent, cv. Garnet. SBS represents a flexible breeding model that can be applied by breeders for developing breeding materials and mapping populations, as well as analyzing the environmental effects of specific genes or loci and for trait stacking. [ABSTRACT FROM AUTHOR]

The judicious management of water and nitrogen (N) is pivotal for augmenting crop productivity and N use efficiency, while also mitigating environmental concerns. With the advent of the High-Farmland Construction Program in China, one-off irrigation has become feasible for most dryland fields, presenting a novel opportunity to explore the synergistic strategies of water and N management. This study delves into the impact of one-off alternate furrow irrigation (AFI) and topdressing N fertilizer (TN) on soil nitrate-N distribution, and N productivity--including plant N accumulation, translocation, and allocation, and grain yield, protein content, N use efficiency of winter wheat (Triticum aestivum L.) in 2018-2019 and 2019-2020. Experimental treatments administered at the jointing stage comprised of two irrigation methods--every (EFI) and alternative (AFI) furrow irrigation at 75 mm, and two topdressing N rates--0 (NTN) and60 (TN) kgNha-1. Additionally, a conventional local farmer practice featuring no irrigation and no topdressing N (NINTN) was served as control. Compared to NINTN, EFINTN substantially increased aboveground N accumulation, grain yield, and protein yield, albeit with a reduction in grain protein content by 8.1%-10.6%. AFI, in turn, led to higher nitrate-N accumulation in the 60-160 cm soil depth at booting and anthesis, but diminished levels at maturity, resulting in a significant surge in N accumulation from anthesis to maturity and its contribution to grain, N fertilizer partial factor productivity (PFPN), and N uptake efficiency (NUPE), thereby promoting grain yield by 9.9% and preserving grain protein content. Likewise, TN enhanced soil nitrate-Nat keygrowth stages, reflected in marked improvements in N accumulation both from booting to anthesis and from anthesis to maturity, as well as in grain yield, protein content, and protein yield. The combination of AFI and TN (AFITN) yielded the highest grain yield, protein content, with PFPN, NUPE, and N internal efficiency outstripping those of EFINTN, but not AFINTN. In essence, one-off AFI coupled with TN at the jointing stage is a promising strategy for optimizing soil nitrate-N and enhancing wheat N productivity in dryland where one-off irrigation is assured. [ABSTRACT FROM AUTHOR]

Hessian fly (HF), Mayetiola destructor, is a major insect pest that causes severe losses in grain yield and quality of wheat (Triticum aestivum). Growing resistant cultivars is the most cost-effective approach to minimize wheat yield losses caused by HF. In this study, 2,496 wheat accessions were screened for resistance to the HF biotype 'Great Plains' (GP) in the greenhouse experiments. To purify seeds from heterogeneous resistant accessions, we recovered single resistant plants from 331 accessions that had at least one resistant plant after HF infestation of a global collection of 1,595 accessions and confirmed 27 accessions with high resistance (HR), and 91 accessions with moderate resistance (MR) to the GP biotype using purified seeds. Screening of 203 U.S. winter wheat accessions in three experiments identified 63 HR and 28 MR accessions; and screening of three additional Asian panels identified 4 HR and 25 MR accessions. Together, this study identified 96 HR accessions and 144 MR accessions. Analysis of the geographic distribution of these HR and MR accessions revealed that these countries with HF as a major wheat pest usually showed higher frequencies of resistant accessions, with the highest frequency of HR (81.3%) and MR (30.6%) accessions identified from the U.S. In addition, phenotyping of 39 wheat accessions that carry known HF resistance genes showed that all the accessions except H1H2 remain effective against GP biotype. Some of these newly identified resistant accessions may contain new HF resistance genes and can be valuable sources for developing HF resistant wheat cultivars. [ABSTRACT FROM AUTHOR]

Currently, the control of rhizosphere selection on farms has been applied to achieve enhancements in phenotype, extending from improvements in single root characteristics to the dynamic nature of entire crop systems. Several specific signals, regulatory elements, and mechanisms that regulate the initiation, morphogenesis, and growth of new lateral or adventitious root species have been identified, but much more work remains. Today, phenotyping technology drives the development of root traits. Available models for simulation can support all phenotyping decisions (root trait improvement). The detection and use of markers for quantitative trait loci (QTLs) are effective for enhancing selection efficiency and increasing reproductive genetic gains. Furthermore, QTLs may help wheat breeders select the appropriate roots for efficient nutrient acquisition. Single-nucleotide polymorphisms (SNPs) or alignment of sequences can only be helpful when they are associated with phenotypic variation for root development and elongation. Here, we focus on major root development processes and detail important new insights recently generated regarding the wheat genome. The first part of this review paper discusses the root morphology, apical meristem, transcriptional control, auxin distribution, phenotyping of the root system, and simulation models. In the second part, the molecular genetics of the wheat root system, SNPs, TFs, and QTLs related to root development as well as genome editing (GE) techniques for the improvement of root traits in wheat are discussed. Finally, we address the effect of omics strategies on root biomass production and summarize existing knowledge of the main molecular mechanisms involved in wheat root development and elongation. [ABSTRACT FROM AUTHOR]

Introduction: In agriculture, especially wheat cultivation, farmers often use multi-variety planting strategies to reduce monoculture-related harvest risks. However, the subtle morphological differences among wheat varieties make accurate discrimination technically challenging. Traditional variety classification methods, reliant on expert knowledge, are inefficient for modern intelligent agricultural management. Numerous existing classification models are computationally complex, memory-intensive, and difficult to deploy on mobile devices effectively. This study introduces G-PPW-VGG11, an innovative lightweight convolutional neural network model, to address these issues. Methods: G-PPW-VGG11 ingeniously combines partial convolution (PConv) and partially mixed depthwise separable convolution (PMConv), reducing computational complexity and feature redundancy. Simultaneously, incorporating ECANet, an efficient channel attention mechanism, enables precise leaf information capture and effective background noise suppression. Additionally, G-PPW-VGG11 replaces traditional VGG11's fully connected layers with two pointwise convolutional layers and a global average pooling layer, significantly reducing memory footprint and enhancing nonlinear expressiveness and training efficiency. Results: Rigorous testing showed G-PPW-VGG11's superior performance, with an impressive 93.52% classification accuracy and only 1.79MB memory usage. Compared to VGG11, G-PPW-VGG11 showed a 5.89% increase in accuracy, 35.44% faster inference, and a 99.64% reduction in memory usage. G-PPWVGG11 also surpasses traditional lightweight networks in classification accuracy and inference speed. Notably, G-PPW-VGG11 was successfully deployed on Android and its performance evaluated in real-world settings. The results showed an 84.67% classification accuracy with an average time of 291.04ms per image. Discussion: This validates the model's feasibility for practical agricultural wheat variety classification, establishing a foundation for intelligent management. For future research, the trained model and complete dataset are made publicly available. [ABSTRACT FROM AUTHOR]

Reducing plant height (PH) is one of the core contents of the "Green Revolution", which began in the 1960s in wheat. A number of 27 reduced-height (Rht) genes have been identified and a great number of quantitative trait loci (QTLs) for PH have been mapped on all 21 chromosomes. Nonetheless, only several genes regulated PH have been cloned. In this study, we found the interval of QTL QPh-1B included an EST-SSR marker swes1079. According to the sequence of swes1079, we cloned the TaOSCA1.4 gene. We developed a CAPS marker to analyze the variation across a natural population. The result showed that the PH was significantly different between the two haplotypes of TaOSCA1.4-1B under most of the 12 environments and the average values of irrigation and rainfed conditions. This result further demonstrated that TaOSCA1.4 was associated with PH. Then, we validated the TaOSCA1.4 via RNAi technology. The average PHs of the wild-type (WT), RNAi lines 1 (Ri-1) and 2 (Ri-2) were 94.6, 83.6 and 79.2 cm, respectively, with significant differences between the WT and Ri-1 and Ri-2. This result indicated that the TaOSCA1.4 gene controls PH. TaOSCA1.4 is a constitutively expressed gene and its protein localizes to the cell membrane. TaOSCA1.4 gene is a member of the OSCA gene family, which regulates intracellular Ca2+ concentration. We hypothesized that knock down mutants of TaOSCA1.4 gene reduced regulatory ability of Ca2+, thus reducing the PH. Furthermore, the cell lengths of the knock down mutants are not significantly different than that of WT. We speculate that TaOSCA1.4 gene is not directly associated with gibberellin (GA), which should be a novel mechanism for a wheat Rht gene. [ABSTRACT FROM AUTHOR]

The Soil Plant Analysis Development (SPAD) is a vital index for evaluating crop nutritional status and serves as an essential parameter characterizing the reproductive growth status of winter wheat. Non-destructive and accurate monitorin3g of winter wheat SPAD plays a crucial role in guiding precise management of crop nutrition. In recent years, the spectral saturation problem occurring in the later stage of crop growth has become a major factor restricting the accuracy of SPAD estimation. Therefore, the purpose of this study is to use features selection strategy to optimize sensitive remote sensing information, combined with features fusion strategy to integrate multiple characteristic features, in order to improve the accuracy of estimating wheat SPAD. This study conducted field experiments of winter wheat with different varieties and nitrogen treatments, utilized UAV multispectral sensors to obtain canopy images of winter wheat during the heading, flowering, and late filling stages, extracted spectral features and texture features from multispectral images, and employed features selection strategy (Boruta and Recursive Feature Elimination) to prioritize sensitive remote sensing features. The features fusion strategy and the Support Vector Machine Regression algorithm are applied to construct the SPAD estimation model for winter wheat. The results showed that the spectral features of NIR band combined with other bands can fully capture the spectral differences of winter wheat SPAD during the reproductive growth stage, and texture features of the red and NIR band are more sensitive to SPAD. During the heading, flowering, and late filling stages, the stability and estimation accuracy of the SPAD model constructed using both features selection strategy and features fusion strategy are superior to models using only a single feature strategy or no strategy. The enhancement of model accuracy by this method becomes more significant, with the greatest improvement observed during the late filling stage, with R² increasing by 0.092-0.202, root mean squared error (RMSE) decreasing by 0.076-4.916, and ratio of performance to deviation (RPD) increasing by 0.237-0.960. In conclusion, this method has excellent application potential in estimating SPAD during the later stages of crop growth, providing theoretical basis and technical support for precision nutrient management of field crops. [ABSTRACT FROM AUTHOR]

Introduction: Given their remarkable capacity to convert atmospheric nitrogen into plant-accessible ammonia, nitrogen-fixing microbial species hold promise as a sustainable alternative to chemical nitrogen fertilizers, particularly in economically significant crops like wheat. This study aimed to identify strains with optimal attributes for promoting wheat growth sustainably, with a primary emphasis on reducing reliance on chemical nitrogen fertilizers. Methods: We isolated free nitrogen-fixing strains from diverse rhizospheric soils across Morocco. Subsequently, we conducted a rigorous screening process to evaluate their plant growth-promoting traits, including nitrogen fixation, phosphate solubilization, phytohormone production and their ability to enhance wheat plant growth under controlled conditions. Two specific strains, Rhodotorula mucilaginosa NF 516 and Arthrobacter sp. NF 528, were selected for in-depth evaluation, with the focus on their ability to reduce the need for chemical nitrogen supply, particularly when used in conjunction with TSP fertilizer and natural rock phosphate. These two sources of phosphate were chosen to assess their agricultural effectiveness on wheat plants. Results and discussion: Twenty-two nitrogen-fixing strains (nif-H+) were isolated from various Moroccan rhizospheric soils, representing Bacillus sp., Pseudomonas sp., Arthrobacter sp., Burkholderia sp. and a yeast-like microorganism. These strains were carefully selected based on their potential to promote plant growth. The findings revealed that the application of Rhodotorula mucilaginosa NF 516 and Arthrobacter sp. NF 528 individually or in combination, significantly improved wheat plant growth and enhanced nutrients (N and P) uptake under reduced nitrogen regimes. Notably, their effectiveness was evident in response to both natural rock phosphate and TSP, demonstrating their important role in wheat production under conditions of low nitrogen and complex phosphorus inputs. This research underscores the significant role of nitrogen-fixing microorganisms, particularly Rhodotorula mucilaginosa NF 516 and Arthrobacter sp. NF 528, in wheat production under conditions of low nitrogen and complex phosphorus inputs. It showcases their potential to reduce chemical nitrogen fertilization requirements by up to 50% without compromising wheat plant yields. Our study emphasizes the importance of bacterial biological nitrogen fixation in meeting the remaining nitrogen requirements beyond this reduction. This underscores the vital role of microbial contributions in providing essential nitrogen for optimal plant growth and highlights the significance of biological nitrogen fixation in sustainable agriculture practices. [ABSTRACT FROM AUTHOR]

Background: Stripe rust, caused by the fungus Puccinia striiformis f.sp. tritici (Pst), poses a significant threat to global wheat production. Objectives: This study aims to analyze the distribution of stripe rust resistance genes, characterize resistance phenotypes at the seedling stage of 137 spring and 149 winter wheat varieties in Xinjiang, China, and discern differences in resistance between spring and winter wheat varieties. Design: We used various Pst races (CYR23, CYR29, CYR31, CYR32, CYR33, CYR34) to characterize seedling resistance of spring and winter wheat varieties and to correlate resistance to the presence of wheat resistance genes (Yr5, Yr9, Yr10, Yr15, Yr17, Yr18, Yr26, Yr41, Yr80, Yr81) using molecular markers. Results: Among spring wheat varieties, 62, 60, 42, 26, 51, and 24 varieties exhibited resistance to CYR23, CYR29, CYR31, CYR32, CYR33, and CYR34, respectively, with four varieties resistant to all varieties. Among winter wheat varieties, 66, 32, 69, 26, 83, 40 varieties demonstrated resistance to CYR23, CYR29, CYR31, CYR32, CYR33, and CYR34, respectively, with four varieties resistant to all varieties. Molecular testing revealed that, in spring wheat, 2, 17, 21, 61, 10, 0, 10, 79, and 32 varieties carried Yr9, Yr10, Yr15, Yr17, Yr18, Yr26, Yr41, Yr80, and Yr81 genes, respectively. In winter wheat, 40, 20, 7, 143, 15, 1, 6, 38, and 54 varieties carried Yr9, Yr10, Yr15, Yr17, Yr18, Yr26, Yr41, Yr80, and Yr81 genes, respectively. Notably, winter wheat exhibited a significantly higher resistance frequency than spring wheat, particularly in the incidence of Yr9, Yr10, Yr17, Yr18, and multi-gene combinations. Conclusion: In summary, this study provides information on seedling stage resistance to stripe rust 286 Xinjiang wheat varieties, elucidates the distribution of resistance genes in this population, and offers a mechanistic basis for breeding durable resistance in wheat. varieties from Xinjiang. [ABSTRACT FROM AUTHOR]

Introduction: Field wheat ear counting is an important step in wheat yield estimation, and how to solve the problem of rapid and effective wheat ear counting in a field environment to ensure the stability of food supply and provide more reliable data support for agricultural management and policy making is a key concern in the current agricultural field. Methods: There are still some bottlenecks and challenges in solving the dense wheat counting problem with the currently available methods. To address these issues, we propose a new method based on the YOLACT framework that aims to improve the accuracy and efficiency of dense wheat counting. Replacing the pooling layer in the CBAM module with a GeM pooling layer, and then introducing the density map into the FPN, these improvements together make our method better able to cope with the challenges in dense scenarios. Results: Experiments show our model improves wheat ear counting performance in complex backgrounds. The improved attention mechanism reduces the RMSE from 1.75 to 1.57. Based on the improved CBAM, the R2 increases from 0.9615 to 0.9798 through pixel-level density estimation, the density map mechanism accurately discerns overlapping count targets, which can provide more granular information. Discussion: The findings demonstrate the practical potential of our framework for intelligent agriculture applications. [ABSTRACT FROM AUTHOR]

Lodging is a crucial factor that limits wheat yield and quality in wheat breeding. Therefore, accurate and timely determination of winter wheat lodging grading is of great practical importance for agricultural insurance companies to assess agricultural losses and good seed selection. However, using artificial fields to investigate the inclination angle and lodging area of winter wheat lodging in actual production is time-consuming, laborious, subjective, and unreliable in measuring results. This study addresses these issues by designing a classification-semantic segmentation multitasking neural network model MLP_U-Net, which can accurately estimate the inclination angle and lodging area of winter wheat lodging. This model can also comprehensively, qualitatively, and quantitatively evaluate the grading of winter wheat lodging. The model is based on U-Net architecture and improves the shift MLP module structure to achieve network refinement and segmentation for complex tasks. The model utilizes a common encoder to enhance its robustness, improve classification accuracy, and strengthen the segmentation network, considering the correlation between lodging degree and lodging area parameters. This study used 82 winter wheat varieties sourced from the regional experiment of national winter wheat in the Huang-Huai-Hai southern area of the water land group at the Henan Modern Agriculture Research and Development Base. The base is located in Xinxiang City, Henan Province. Winter wheat lodging images were collected using the unmanned aerial vehicle (UAV) remote sensing platform. Based on these images, winter wheat lodging datasets were created using different time sequences and different UAV flight heights. These datasets aid in segmenting and classifying winter wheat lodging degrees and areas. The results show that MLP_U-Net has demonstrated superior detection performance in a small sample dataset. The accuracies of winter wheat lodging degree and lodging area grading were 96.1% and 92.2%, respectively, when the UAV flight height was 30 m. For a UAV flight height of 50 m, the accuracies of winter wheat lodging degree and lodging area grading were 84.1% and 84.7%, respectively. These findings indicate that MLP_U-Net is highly robust and efficient in accurately completing the winter wheat lodging-grading task. This valuable insight provides technical references for UAV remote sensing of winter wheat disaster severity and the assessment of losses. [ABSTRACT FROM AUTHOR]

Organic fertilizer substitution is an effective measure for increasing both the quantity and quality of wheat grain while reducing chemical fertilizer input. However, the effects of reducing nitrogen (N) fertilizer combined with organic fertilizer substitution on grain yield, grain protein content and protein yield, plant N accumulation and translocation, N use efficiency, soil fertility, N apparent surplus and nitrate-N residue in rain-fed drought-prone areas remains limited. In this study, field experiments were conducted over four consecutive seasons (2019-2023) at two sites with four treatments: zero N application (ZN), farmer N application (FN), reduced 20% N of FN (RN), and organic fertilizer substituting 20% N of RN (OSN). The results showed that compared with the ZN treatment, the FN, RN and OSN treatments increased grain yield and its components, grain protein content and protein yield, aboveground N accumulation at the anthesis and maturity stages, pre-anthesis N translocation, post-anthesis N accumulation, N use efficiency, soil fertility. Compared with RN and FN, OSN increased grain yield by 17.12% and 15.03%, grain protein yield by 3.31% and 17.15%, grain N accumulation by 17.78% and 15.58%, and N harvest index by 2.63% and 4.45% averaged across years and sites, respectively. Moreover, OSN increased the contents of organic matter, total N, available P and available K in both 0-20 and 20-40 cm soil layers, decreased N apparent surplus and nitrate-N residue in 0-100 cm, and pH in both 0-20 and 20-40 cm soil layer. Fundamentally, this study suggests that integrating a 20% reduction N from conventional farmer practices with the utilization of organic fertilizer to replace 20% of the chemical N fertilizer (OSN) represents an effective strategy. This approach shows promise in enhancing wheat grain yield, grain protein yield, and N use efficiency. Additionally, it supports the improvement of soil fertility while simultaneously reducing soil nitrate-N residues and the apparent surplus of N in rain-fed drought-prone regions. [ABSTRACT FROM AUTHOR]

Some species of the genus Aegilops, a wild relative of wheat, carry chromosomes that after introducing to wheat exhibit preferential transmission to progeny. Their selective retention is a result of the abortion of gametes lacking them due to induced chromosomal aberrations. These chromosomes are termed Gametocidal (Gc) and, based on their effects, they are categorized into three types: mild, intense or severe, and very strong. Gc elements within the same homoeologous chromosome groups of Aegilops (II, III, or IV) demonstrate similar Gc action. This review explores the intriguing dynamics of Gc chromosomes and encompasses comprehensive insights into their source species, behavioral aspects, mode of action, interactions, suppressions, and practical applications of the Gc system in wheat breeding. By delving into these areas, this work aims to contribute to the development of novel plant genetic resources for wheat breeding. The insights provided herein shed light on the utilization of Gc chromosomes to produce chromosomal rearrangements in wheat and its wild relatives, thereby facilitating the generation of chromosome deletions, translocations, and telosomic lines. The Gc approach has significantly advanced various aspects of wheat genetics, including the introgression of novel genes and alleles, molecular markers and gene mapping, and the exploration of homoeologous relationships within Triticeae species. The mystery lies in why gametes possessing Gc genes maintain their normality while those lacking Gc genes suffer abnormalities, highlighting an unresolved research gap necessitating deeper investigation. [ABSTRACT FROM AUTHOR]

The Flavin Monooxygenase (FMO) gene superfamily in plants is involved in various processes most widely documented for its involvement in auxin biosynthesis, specialized metabolite biosynthesis, and plant microbial defense signaling. The roles of FMOs in defense signaling and disease resistance have recently come into focus as they may present opportunities to increase immune responses in plants including leading to systemic acquired resistance, but are not well characterized.We present a comprehensive catalogue of FMOs found in genomes across vascular plants and explore, in depth, 170 wheat TaFMO genes for sequence architecture, cis-acting regulatory elements, and changes due to Transposable Element insertions. A molecular phylogeny separates TaFMOs into three clades (A, B, and C) for which we further report gene duplication patterns, and differential rates of homoeologue expansion and retention among TaFMO subclades.We discuss Clade B TaFMOs where gene expansion is similarly seen in other cereal genomes. Transcriptome data from various studies point towards involvement of subclade B2 TaFMOs in disease responses against both biotrophic and necrotrophic pathogens, substantiated by promoter element analysis. We hypothesize that certain TaFMOs are responsive to both abiotic and biotic stresses, providing potential targets for enhancing disease resistance, plant yield and other important agronomic traits. Altogether, FMOs in wheat and other crop plants present an untapped resource to be exploited for improving the quality of crops. [ABSTRACT FROM AUTHOR]

Introduction: Heat stress at terminal stage of wheat is critical and leads to huge yield losses worldwide. microRNAs (miRNAs) play significant regulatory roles in gene expression associated with abiotic and biotic stress at the post-transcriptional level. Methods: In the present study, we carried out a comparative analysis of miRNAs and their targets in flag leaves as well as developing seeds of heat tolerant (RAJ3765) and heat susceptible (HUW510) wheat genotypes under heat stress and normal conditions using small RNA and degradome sequencing. Results and discussion: A total of 84 conserved miRNAs belonging to 35 miRNA families and 93 novel miRNAs were identified in the 8 libraries. Tae-miR9672a-3p, taemiR9774, tae-miR9669-5p, and tae-miR5048-5p showed the highest expression under heat stress. Tae-miR9775, tae-miR9662b-3p, tae-miR1120a, tae-miR5084, tae-miR1122a, tae-miR5085, tae-miR1118, tae-miR1130a, tae-miR9678-3p, taemiR7757-5p, tae-miR9668-5p, tae-miR5050, tae-miR9652-5p, and tae-miR9679-5p were expressed only in the tolerant genotype, indicating their role in heat tolerance. Comparison between heat-treated and control groups revealed that 146 known and 57 novel miRNAs were differentially expressed in the various tissues. Eight degradome libraries sequence identified 457 targets of the differentially expressed miRNAs. Functional analysis of the targets indicated their involvement in photosynthesis, spliceosome, biosynthesis of nucleotide sugars and protein processing in the endoplasmic reticulum, arginine and proline metabolism and endocytosis. Conclusion: This study increases the number of identified and novel miRNAs along with their roles involved in heat stress response in contrasting genotypes at two developing stages of wheat. [ABSTRACT FROM AUTHOR]

159 wheat varieties obtained from ICARDA, CYR32, CYR33 and CYR34 were used to evaluate the stripe rust resistance in this study. Seedling resistance was carried out in the green house at the two-leaf stage. Adult-plant resistance was carried out between 2022 and 2023 in Xining and Guide, respectively. A total of 24,151 high-quality SNP loci were obtained from a 55K SNP chip data. Genome-wide association study was carried out between SNP loci and stripe rust resistance. Seedling resistance screening revealed that 91.8% (146) of wheat varieties were resistant to CYR32 and CYR33, while only 49.7% (79) of wheat varieties were resistant to CYR34. Adult-plant resistance showed 153 (96.2%) germplasms represented resistance in 2022, while only 85 (53.4%) showed resistance in 2023. An association study using the 55K SNP chip data results combined with disease ratings of 159 materials at both the seedling and adult stages discovered 593 loci related to stripe rust resistance (P = 0.0001). These loci exhibited contribution rates ranging from 11.1% to 18.7%. Among them, 71 were significantly related to resistance against CYR32 at the seedling stage, with a contribution rate of 12.7%-17.2%. Constituting the vast majority, 518 loci distributed across 21 chromosomes were significantly related to CYR33 at the seedling stage, with a contribution rate of 12.6%-18.7%. Fewer loci were found to be associated with disease resistance in adult plants. In E1 environment, a sole locus was detected on chromosome 2B with a contribution rate of 14.4%. In E2 environment, however, exhibited three loci across chromosomes 2B, 4A, and 7B with contribution rates ranging from 11.1% to 16.9%. A total of 68 multi-effect loci were significantly related to resistance against both CYR32 and CYR33 at the seedling stage, and one stable locus was significantly associated with stripe rust resistance at the adult plant stage. [ABSTRACT FROM AUTHOR]

Introduction: Agronomic traits are key components of wheat yield. Exploitation of the major underlying quantitative trait loci (QTLs) can improve the yield potential in wheat breeding. Methods: In this study, we constructed a recombinant inbred line (RIL) population from Mingxian 169 (MX169) and Pindong 34 (PD34) to determine the QTLs for grain length (GL), grain width (GW), grain length-to-width ratio (LWR), plant height (PH), spike length (SL), grain number per spike (GNS), and the thousand grain weight (TGW) across four environments using wheat 90K SNP array. Results: A QTL associated with TGW, i.e., QTGWpd.swust-6BS, was identified on chromosome 6B, which explained approximately 14.1%--16.2% of the phenotypic variation. In addition, eight QTLs associated with GL were detected across six chromosomes in four different test environments. These were QGLpd.swust- 1BL, QGLpd.swust-2BL, QGLpd.swust-3BL.1, QGLpd.swust-3BL.2, QGLpd.swust-5DL, QGLpd.swust-6AL, QGLpd.swust-6DL.1, and QGLpd.swust- 6DL.2. They accounted for 9.0%--21.3% of the phenotypic variation. Two QTLs, namely, QGWpd.swust-3BS and QGWpd.swust-6DL, were detected for GW on chromosomes 3B and 6D, respectively. These QTLs explained 12.8%--14.6% and 10.8%--15.2% of the phenotypic variation, respectively. In addition, two QTLs, i.e., QLWRpd.swust-7AS.1 and QLWRpd.swust-7AS.2, were detected on chromosome 7A for the grain LWR, which explained 10.9%--11.6% and 11.6%-- 11.2% of the phenotypic variation, respectively. Another QTL, named QGNSpdswust- 6DS, was discovered on chromosome 6D, which determines the GNS and which accounted for 11.4%--13.8% of the phenotypic variation. Furthermore, five QTLs associated with PH were mapped on chromosomes 2D, 3A, 5A, 6B, and 7B. These QTLs were QPHpd.swust-2DL, QPHpd.swust-3AL, QPHpd.swust-5AL, QPHpd.swust-6BL, and QPHpd.swust-7BS, which accounted for 11.3%--19.3% of the phenotypic variation. Lastly, a QTL named QSLpd.swust-3AL, conferring SL, was detected on chromosome 3A and explained 16.1%--17.6% of the phenotypic variation. All of these QTLs were defined within the physical interval of the Chinese spring reference genome. Discussion: The findings of this study have significant implications for the development of fine genetic maps, for genomic breeding, and for markerassisted selection to enhance wheat grain yield. [ABSTRACT FROM AUTHOR]