Puccinia Triticina Research Articles

Evaluation of Bread Wheat (Triticum aestivum L.) Germplasm for Wheat Rust Diseases Resistance and Yield Performance

The most important biotic constraints for wheat production in Ethiopia are Wheat rust diseases, insects, and weeds. Among wheat rust diseases, stem rust and yellow rust are the more limiting factors for yield. A study was undertaken at Kulumsa and Melkasa in alpha lattice design with two replications from July to December 2023 to evaluate the performance of introduced bread wheat materials from CIMMYT for wheat rust diseases and yield. Data were collected for stem rust, yellow rust, and agronomic traits, including grain yield. The ANOVA showed high and significant genotypic variation (p<0.05) on Days to heading (DTH), Hectoliter weight (HLW), Thousand kernels weight (TKW), and Yield (YLD). Then, the result from LSD mean comparision revealed genotypes: EBW222102 YLD=7.7 t ha-1, EBW222106 YLD= 7.61 t ha-1, EBW222108 YLD=7.66 tha-1, EBW222136 YLD= 7.76 t ha-1 to be significantly higher than the check variety Daka YLD= 6.22 t ha-1 (p<0.05) at Kulumsa. Genotypes: EBW222108, EBW222109, EBW222111, EBW222112, EBW222114, EBW222125, EBW222142, and EBW222143 showed resistance to moderate resistance for yellow rust and stem rust across both locations. About eighteen genotypes were susceptible to stem rust diseases with the coefficient of infection YRCI≥30 at Melkasa. Genotypes EBW222111, EBW222126, EBW222129, EBW222134, and EBW222142 delivered high yield, greater than or equal to 2 t ha-1 at Melkasa. Generally, selection for disease resistance and high-yielding genotypes enable the development and release of noble varieties for wheat-producing farmers.

Marker trait association and candidate gene identification for brown rust disease in sugarcane

AbstractBrown rust (caused by Puccinia melanocephala H. & P. Sydow) is one of the most devastating diseases in commercial sugarcane production. It could reduce sugarcane yield by up to 50% depending on the susceptibility levels of cultivars. Breeding disease‐resistant cultivars is the most effective, economical, and environmentally friendly option to control brown rust. A genome‐wide association study was conducted on a field trial using 432 sugarcane clones following an augmented design with two replications. Brown rust was screened using the whorl inoculation method over two crop cycles. The genotype data were obtained through target enrichment sequencing technologies. The gene actions considering six different models and marker dosage effects were included during the marker‐trait analysis. A total of seven, nine, and seven nonredundant marker‐trait associations were identified for plant cane, first ratoon, and across two crop cycles, respectively. The most significant (p‐value 6.17E−20) marker (chr01p59833543) has the additive effect of −0.63 for the diplo‐additive model and reduced disease severity the most (41.35%) due to heterozygote (AG) over homozygote allele (AA) combination in the tested clones. Gene annotation of the monoploid sugarcane genome R570 suggested that six putative candidate genes were co‐located with significant markers associated with brown rust resistance in sugarcane. The putative candidate genes regulated the formation of a cell wall barrier that plays a crucial role in controlling brown rust pathogen infection. The results of this study will open the path to exploiting new resistance sources for brown rust resistance in commercial sugarcane.

Characterization of Physio-Biochemical Properties for Leaf Rust in Wheat Associated with Yield Losses and Disease Resistance Patterns

Characterization of Physio-Biochemical Properties for Leaf Rust in Wheat Associated with Yield Losses and Disease Resistance Patterns

Wheat Rust Disease Detection Using Convolutional Neural Network

Wheat is a vital crop globally, playing a crucial role in ensuring food security. Any damage to wheat crops, particularly from diseases like wheat rust, can exacerbate the global food crisis. Wheat rust, along with other crop diseases, poses a serious threat to agricultural sustainability and food supply. Technological approaches to detect crop health can significantly enhance disease prevention compared to traditional manual methods. This study focuses on detecting wheat rust diseases, specifically wheat leaf rust, stem rust, and yellow rust, all of which vary in their impact on crops. By employing image analysis of wheat plants, the research aims to accurately distinguish between healthy crops and those affected by rust diseases. The experiments consider multiple variables, such as learning rate, dropout, and train-test split ratio, reflecting a comprehensive research approach. The model achieved an impressive 99.64% accuracy in detecting wheat rust, highlighting its potential for early disease detection and prevention.

Development of genetically modified rust resistant wheat: A breakthrough by dinted introgression of novel DREB2C and HSFA2 genes under stress induced expression

Wheat is a major staple food worldwide yet numerous yield limiting agents affect its productivity. Stripe rust is a major culprit in this context and efforts have been made to culminate this pathogen using conventional as well as advanced innovative techniques. Transgenic technology is of significant importance in this context and numerous success stories are evident to prove its worth. In the current study, two novel genes HSFA2 and DREB2C were expressed in an elite wheat genotype Akbar, Fakhre-e-Bhakhar under constitutive CAMV35S promoter and stress inducible rd29 Promoters. The shoot cut method was used for the Agrobacterium-mediated transformation and putative transformants were selected on kanamycin 50 mg/L. The resultant transformants were tested through PCR for transgene integration whereas expression analysis was carried out through realtime qPCR. Expression of both of the aforementioned genes was found to be higher under rd29 promoter as compared with transgene(s) expression under CAMV35S promoter. In the bioassay, transgenic wheat plants demonstrated significant tolerance to stress, exhibiting only minor spotting under constitutive expression conditions. Upon exposure to stress, these plants showed exceptional resistance to stripe rust, producing large, bold grains compared to individual trait expressions and negative controls. Subsequently, the DREB2C gene was knocked out to determine if stripe rust control was specifically attributed to this gene. Following the knockout, the onset of stripe rust was comparable to that of the negative control. This led to the conclusion that pyramiding the DREB2C gene with HSFA2 through dual expression represents a novel and highly effective strategy for controlling the widespread stripe rust in wheat. This approach also offers resistance to high temperatures (above 32 °C) from the pollination stage through to maturity.

YOLOv5s-Based Image Identification of Stripe Rust and Leaf Rust on Wheat at Different Growth Stages.

Stripe rust caused by Puccinia striiformis f. sp. tritici and leaf rust caused by Puccinia triticina, are two devastating diseases on wheat, which seriously affect the production safety of wheat. Timely detection and identification of the two diseases are essential for taking effective disease management measures to reduce wheat yield losses. To realize the accurate identification of wheat stripe rust and wheat leaf rust during the different growth stages, in this study, the image-based identification of wheat stripe rust and wheat leaf rust during different growth stages was investigated based on deep learning using image processing technology. Based on the YOLOv5s model, we built identification models of wheat stripe rust and wheat leaf rust during the seedling stage, stem elongation stage, booting stage, inflorescence emergence stage, anthesis stage, milk development stage, and all the growth stages. The models were tested on the different testing sets in the different individual growth stages and in all the growth stages. The results showed that the models performed differently in disease image identification. The model based on the disease images acquired during an individual growth stage was not suitable for the identification of the disease images acquired during the other individual growth stages, except for the model based on the disease images acquired during the milk development stage, which had acceptable identification performance on the testing sets in the anthesis stage and the milk development stage. In addition, the results demonstrated that wheat growth stages had a great influence on the image identification of the two diseases. The model built based on the disease images acquired in all the growth stages produced acceptable identification results. Mean F1 Score values between 64.06% and 79.98% and mean average precision (mAP) values between 66.55% and 82.80% were achieved on each testing set composed of the disease images acquired during an individual growth stage and on the testing set composed of the disease images acquired during all the growth stages. This study provides a basis for the image-based identification of wheat stripe rust and wheat leaf rust during the different growth stages, and it provides a reference for the accurate identification of other plant diseases.

Biocontrol of Hemileia vastatrix, the Causal Agent of Coffee Leaf Rust, by Paenibacillus sp. NMA1017.

Coffee leaf rust (CLR), caused by Hemileia vastatrix, is considered a highly important phytosanitary problem in Mexico. Currently, there are few microorganisms used as biocontrol alternatives to chemical control of CLR in organic coffee fields in Mexico. This study evaluates the use of Paenibacillus sp. NMA1017 as a biocontrol agent to inhibit the development of H. vastatrix in in vitro and in vivo (greenhouse) experiments. Hemileia vastatrix urediniospores were placed on water agar plates, and then Paenibacillus sp. NMA1017 was inoculated simultaneously or 8 h later. Urediniospores germination rate was reduced by 94% when the NMA1017 strain was inoculated simultaneously with the urediniospores and reduced by 38% when NMA1017 was inoculated 8 h later. Experiments with 8-month-old Bourbon coffee plants that were infected with H. vastatrix showed that disease incidence was reduced by 38, 90, and 50% when NMA1017 was applied 8 days before, simultaneously, or 8 days after the application of H. vastatrix, respectively. Paenibacillus sp. NMA1017 also reduced the severity of CLR on the leaves by up to 62%. The germination urediniospores of other rust pathogens such as Puccinia sorghi (maize leaf rust), Puccinia triticina (wheat leaf rust), Puccinia graminis f. sp. tritici (black stem rust of wheat), Uromyces striatus (alfalfa leaf rust), and Phragmidium sp. (rosebush leaf rust) were also inhibited. Use of the potential biocontrol agent Paenibacillus sp. NMA1017 might help reduce the application of chemical fungicides for the control of CLR, making coffee a more sustainable crop and providing management options for organic coffee growers.

Phenotypic Selection of Bread Wheat (Triticum aestivum L.) Elite Lines for Diseases Resistance and Yield Potential

Wheat diseases and pests globally threaten wheat production. Wheat rust diseases are the most distractive biotic constraint of wheat in Ethiopia. Therefore, this experiment aimed to select and develop wheat rust resistance bread wheat varieties for the farmers. The result of the study revealed highly significant variation existed among tested genotypes for Plant Height (PHT), Days to Heading (DTH), Days to Maturity (DTM), Thousand Kernel Weight (TKW), Hectoliter Weight (HLW), and Yield (YLD) at (P< 0.001). Out the forty-nine introduced genotypes, five genotypes: EBW222153, EBW222159, EBW222160, EBW222162, and EBW222164, showed resistance to moderately resistance for both yellow rust and stem rust diseases across the two test locations. Also, they delivered high average yield: 5.9 tha-1, 4.46 tha-1, 5.17tha-1,4.15 tha-1, and 4.79tha-1 respectively. Furthermore, all the above-mentioned genotypes, except EBW222162, showed significantly higher yield at (P<0.05) than the check variety, Shaki. Furthermore, EBW222153, EBW222159, EBW222160, EBW222162, EBW222164, and EBW222178 would resistance to moderately resistance for stem rust at Melkasa. Thus, the selection of these genotypes and advancing to the next stage of breeding pipelines enables to release noble bread wheat varieties for the farmers.

Gamma-Aminobutyric Acid (GABA) as a Defense Booster for Wheat against Leaf Rust Pathogen (Puccinia triticina).

Wheat leaf rust, caused by Puccinia triticina, poses a growing threat to global wheat production, necessitating alternative strategies for effective disease management. This study investigated the potential of gamma-aminobutyric acid (GABA) to enhance resistance to leaf rust in two wheat cultivars: the susceptible Morocco and moderately resistant Sakha 94 cultivar. Our findings revealed that GABA significantly improved resistance in both cultivars to P. triticina, particularly in Morocco, by mitigating disease severity and reducing pustule density and size while extending both incubation and latent periods. This study assessed the effectiveness of two GABA application methods: plants received 1 mM GABA treatment, as a foliar spray, twenty-four hours prior to infection (pre-GABA), and plants received 1 mM GABA treatment both 24 h before and after infection (pre-/post-GABA), with the latter yielding significantly better results in reducing infection severity and improving plant resilience. Additionally, GABA application influenced stomatal behavior, promoting closure that may enhance resilience against leaf rust. GABA application on plants also modulated the production of reactive oxygen species (ROS). This led to a stronger oxidative burst in both susceptible and moderately resistant cultivars. GABA increased O2●- levels in guard cells and surrounding stomata, enhancing stomatal closure and the hypersensitive response. GABA enhanced the accumulation of soluble phenols and increased the activity of key antioxidant enzymes, catalase (CAT) and peroxidase (POX), which are vital for managing oxidative stress. To the best of our knowledge, this investigation represents the first report into the impact of GABA on wheat leaf rust disease.

Modeling spatiotemporal distribution of yellow rust wheat pathogen using machine learning algorithms: Insights from environmental assessment

The yellow rust pathogen (Puccinia striiformis Westend) poses a significant threat to wheat production in the world, necessitating a comprehensive understanding of its spatiotemporal distribution and the influence of climatic factors. In this study, we employed an ensemble of four prominent machine learning algorithms to assess the impact of various environmental and remote sensing variables on the spread of yellow rust at a national scale. Our analysis incorporated 55 climatic parameters, including monthly temperature, precipitation, solar radiation, and wind speed. The results demonstrated that the RF algorithm yielded robust predictions, with a Receiver Operator Characteristic (ROC) of 0.916 and True Skill Statistic (TSS) of 0.748. Furthermore, the study identified key influencing variables for wheat disease modeling, such as annual precipitation, temperature seasonality, and isothermality. Projections based on the model indicate a potential decrease in disease spread by 2050 in specific regions. The findings underscore the efficacy of ensemble modeling in predicting the spatiotemporal distribution of yellow rust on a large scale, offering valuable insights for the development of robust agricultural management strategies in the face of evolving climate conditions.

Registration of ‘MN‐Rothsay’ spring wheat with high grain yield and lodging resistance

Abstract‘MN‐Rothsay’ (Reg. No. CV‐1208, PI 702731) hard red spring wheat (Triticum aestivum) was released by the University of Minnesota Agricultural Experiment Station in 2022 because of its high grain yield and lodging resistance. MN‐Rothsay is a medium‐late maturity cultivar with below average plant height, containing the semi‐dwarfing alleles Rht‐B1b and Rht24b. The lodging resistance of MN‐Rothsay is similar to ‘Linkert’, a 2013 release that was the most widely grown cultivar in Minnesota from 2016 to 2020, largely due to its superior lodging resistance. MN‐Rothsay has moderate resistance to Fusarium head blight (caused primarily by Fusarium graminearum Schwabe), good resistance to prevalent races of pathogens causing leaf rust (Puccinia triticina Eriks.) and stem rust (P. graminis Pers.: Pers. f. sp. tritici Eriks. & E. Henn.), and is well‐adapted to the north central United States.

Bacterial N-Acyl Homoserine Lactone Priming Enhances Leaf-Rust Resistance in Winter Wheat and Some Genomic Regions Are Associated with Priming Efficiency.

Leaf rust (Puccinia triticina) is a common disease that causes significant yield losses in wheat. The most frequently used methods to control leaf rust are the application of fungicides and the cultivation of resistant genotypes. However, high genetic diversity and associated adaptability of pathogen populations hamper achieving durable resistance in wheat. Emerging alternatives, such as microbial priming, may represent an effective measure to stimulate plant defense mechanisms and could serve as a means of controlling a broad range of pathogens. In this study, 175 wheat genotypes were inoculated with two bacterial strains: Ensifer meliloti strain expR+ch (producing N-acyl homoserine lactone (AHL)) or transformed E. meliloti carrying the lactonase gene attM (control). In total, 21 genotypes indicated higher resistance upon bacterial AHL priming. Subsequently, the phenotypic data of 175 genotypes combined with 9917 single-nucleotide polymorphisms (SNPs) in a genome-wide association study to identify quantitative trait loci (QTLs) and associated markers for relative infection under attM and expR+ch conditions and priming efficiency using the Genome Association and Prediction Integrated Tool (GAPIT). In total, 15 QTLs for relative infection under both conditions and priming efficiency were identified on chromosomes 1A, 1B, 2A, 3A, 3B, 3D, 6A, and 6B, which may represent targets for wheat breeding for priming and leaf-rust resistance.

Identification and Screening of Leaf Rust Resistance Genes in Wheat Cultivars Through Microsatellites

Wheat is one of the important staple food being grown worldwide and in Pakistan. The wheat rust-causing fungi are very eco-adaptive and evolve rapidly to overcome genetic resistance of wheat varieties. Among all rust-causing fungi, Puccinia triticina causes leaf rust in wheat, and inflicts heavy yield losses. Deploying resistant varieties against leaf rust is the most effective, environmentally-friendly and economic way to control the disease. Therefore, new sources of genetic resistance are continually sought to develop rust resistance in wheat varieties, and nowadays rust resistant varieties have been developed through the accumulation of slow rusting or minor genes that would perform better in fields. Hence, the purpose of this study is to screen a few of the available leaf rust-resistant germplasm in field and evaluate their field response on genetic basis. For this purpose, minor genes were detected through the amplification of simple sequence repeat (SSR) in the selected wheat genotypes through screened primers. Field experiment was conducted. Molecular studies to identify the minor genes were performed. Meteorological data was recorded at the observatory laboratory of the same area. All the epidemiological factors (Temperature (maximum, minimum), Relative Humidity, and Pan Evaporation) showed significant correlation in tested 5 varieties ZARDANA89, ZARLASHTA99, RASKOH 05, BHAKKAR-2000, GA-2002 which were conducive for disease development except rainfall. PCR amplification of Xgwm118, and Xgwm165, showed the range of alleles in 14 genotypes. It was observed that 22 verities showed resistance, 3 were moderately resistant, 3 varieties were moderately susceptible, 8 varieties were moderately susceptible, and 5 varieties were moderately susceptible against leaf rust pathogen.

Puccinia triticina avirulence protein AvrLr21 directly interacts with wheat resistance protein Lr21 to activate wheat immune response

Wheat leaf rust, caused by Puccinia triticina (Pt), remains a constant threat to wheat production worldwide. Deployment of race-specific leaf rust (Lr) resistance genes in wheat provides effective protection against leaf rust, but often leads to selective pressures that drive the rapid emergence of new virulent Pt isolates in nature. However, the molecular mechanisms underlying the evasion of Lr-delivered resistance by leaf rust remain largely unknown. Here, we identify an avirulence gene AvrLr21 in Pt that triggers Lr21-dependent immune responses. BSMV (Barley stripe mosaic virus)-mediated host-induced gene silencing assay shows that silencing AvrLr21 compromises Lr21-mediated immunity. AvrLr21 interacts directly with Lr21 protein to induce a hypersensitive response in tobacco leaves. The evolved Lr21-breaking Pt isolates can suppress Lr21-mediated immunity. Our data provide a basis for studying the molecular determinants in Pt-wheat incompatible interaction and monitoring natural Pt populations to prioritize the deployment of Lr resistance genes in the field.

A thaumatin-like effector protein suppresses the rust resistance of wheat and promotes the pathogenicity of Puccinia triticina by targeting TaRCA.

Thaumatin-like proteins (TLPs) in plants play a crucial role in combating stress, and they have been proven to possess antifungal properties. However, the role of TLPs in pathogens has not been reported. We identified a effector protein, Pt9029, which contained a Thaumatin domain in Puccinia triticina (Pt), possessing a chloroplast transit peptide and localized in the chloroplasts. Silencing Pt9029 in the Pt physiological race THTT resulted in a notable reduction in virulence and stunted growth and development of Pt hypha in near-isogenic wheat line TcLr2b. Overexpression of Pt9029 in wheat exerted a suppressive effect on H2O2 production, consequently impeding the wheat's disease resistance mechanisms. The TLP domain of Pt9029 targets the Rubisco activase (TaRCA) in chloroplasts. This interaction effectively inhibited the function of TaRCA, subsequently leading to a decrease in Rubisco enzyme activity. Therefore, this indicates that TLPs in Pt can inhibit host defense mechanisms during the pathogenic process of Pt. Moreover, TaRCA silencing resulted in reduced resistance of TcLr2b against Pt race THTT. This clearly demonstrated that TaRCA positively regulates wheat resistance to leaf rust. These findings reveal a novel strategy exploited by Pt to manipulate wheat rust resistance and promote pathogenicity.

Montmorillonite nanoclay triggers immunity responses in wheat against Puccinia striiformis f. sp. tritici, and suppresses uredospore germination

Puccinia striiformis f. sp. tritici causes the important disease, yellow rust of wheat (Triticum aestivum). Montmorillonite nanoclay (MNC) is naturally occurring and biodegradable. This study assessed in vitro anti-germination effects of MNC on P. striiformis uredospores. Application of MNC at 150 mg L-1 completely inhibited uredospore germination, and MNC at 100 mg L-1 reduced yellow rust severity in wheat plants by 89%. Expression of defense-related genes was increased after MNC treatment at 100 mg L-1, by 5.23-fold for jasmonate and ethylene-responsive factor 3 (JERF3), 4.89-fold for chitinase class II (CHI II), and 2.37-fold for pathogenesis-related protein 1 (PR1). Applying MNC at 100 mg L-1 also activated the antioxidant enzymes POD to 62.1 unit min-1 g-1 fresh wt, PPO to 21.6 units min-1 g-1 fresh wt, and CAT to 36.6 units min-1 g-1 fresh wt. MNC also enhanced phenolic content in wheat leaves (to 1489.53 mg 100 g-1 f. wt), and reduced lipid oxidation levels (to 5.6 μmol MDA g-1 fresh wt). MNC at 100 mg L-1 also mitigated damaging effects of P. striiformis infections on host leaf cell ultrastructure, increased leaf photosynthetic pigments, and increased wheat plant growth. These results show that MNC has potential as a natural control agent for yellow rust of wheat, although field testing of MNC is necessary before this material can be recommended for wheat production.

EF yolov8s: A Human–Computer Collaborative Sugarcane Disease Detection Model in Complex Environment

The precise identification of disease traits in the complex sugarcane planting environment not only effectively prevents the spread and outbreak of common diseases but also allows for the real-time monitoring of nutrient deficiency syndrome at the top of sugarcane, facilitating the supplementation of relevant nutrients to ensure sugarcane quality and yield. This paper proposes a human–machine collaborative sugarcane disease detection method in complex environments. Initially, data on five common sugarcane diseases—brown stripe, rust, ring spot, brown spot, and red rot—as well as two nutrient deficiency conditions—sulfur deficiency and phosphorus deficiency—were collected, totaling 11,364 images and 10 high-definition videos captured by a 4K drone. The data sets were augmented threefold using techniques such as flipping and gamma adjustment to construct a disease data set. Building upon the YOLOv8 framework, the EMA attention mechanism and Focal loss function were added to optimize the model, addressing the complex backgrounds and imbalanced positive and negative samples present in the sugarcane data set. Disease detection models EF-yolov8s, EF-yolov8m, EF-yolov8n, EF-yolov7, and EF-yolov5n were constructed and compared. Subsequently, five basic instance segmentation models of YOLOv8 were used for comparative analysis, validated using nutrient deficiency condition videos, and a human–machine integrated detection model for nutrient deficiency symptoms at the top of sugarcane was constructed. The experimental results demonstrate that our improved EF-yolov8s model outperforms other models, achieving mAP_0.5, precision, recall, and F1 scores of 89.70%, 88.70%, 86.00%, and 88.00%, respectively, highlighting the effectiveness of EF-yolov8s for sugarcane disease detection. Additionally, yolov8s-seg achieves an average precision of 80.30% with a smaller number of parameters, outperforming other models by 5.2%, 1.9%, 2.02%, and 0.92% in terms of mAP_0.5, respectively, effectively detecting nutrient deficiency symptoms and addressing the challenges of sugarcane growth monitoring and disease detection in complex environments using computer vision technology.

Developing Predictive Models and Early Warning Systems for Invading Pathogens: Wheat Rusts.

Innovations in aerobiological and epidemiological modeling are enabling the development of powerful techniques to infer connectivity networks for transboundary pathogens in ways that were not previously possible. The innovations are supported by improved access to historical and near real-time highly resolved weather data, multi-country disease surveillance data, and enhanced computing power. Using wheat rusts as an exemplar, we introduce a flexible modeling framework to identify characteristic pathways for long-distance spore dispersal within countries and beyond national borders. We show how the models are used for near real-time early warning systems to support smallholder farmers in East Africa and South Asia. Wheat rust pathogens are ideal exemplars because they continue to pose threats to food security, especially in regions of the world where resources for control are limited. The risks are exacerbated by the rapid appearance and spread of new pathogenic strains, prodigious spore production, and long-distance dispersal for transboundary and pandemic spread.

Exploring the Frontier of Wheat Rust Resistance: Latest Approaches, Mechanisms, and Novel Insights.

Wheat rusts, including leaf, stripe, and stem rust, have been a threat to global food security due to their devastating impact on wheat yields. In recent years, significant strides have been made in understanding wheat rusts, focusing on disease spread mechanisms, the discovery of new host resistance genes, and the molecular basis of rust pathogenesis. This review summarizes the latest approaches and studies in wheat rust research that provide a comprehensive understanding of disease mechanisms and new insights into control strategies. Recent advances in genetic resistance using modern genomics techniques, as well as molecular mechanisms of rust pathogenesis and host resistance, are discussed. In addition, innovative management strategies, including the use of fungicides and biological control agents, are reviewed, highlighting their role in combating wheat rust. This review also emphasizes the impact of climate change on rust epidemiology and underscores the importance of developing resistant wheat varieties along with adaptive management practices. Finally, gaps in knowledge are identified and suggestions for future research are made. This review aims to inform researchers, agronomists, and policy makers, and to contribute to the development of more effective and sustainable wheat rust control strategies.

Genetic Diversity of Promising Spring Wheat Accessions from Russia and Kazakhstan for Rust Resistance.

Spring bread wheat (Triticum aestivum) is a major crop in Russia and in Kazakhstan. The rust pathogens, leaf rust caused by the fungus Puccinia triticina, stem rust incited by P. graminis and yellow rust caused by P. striiformis, are the significant biotic factors affecting wheat production. In this study, 40 new promising spring wheat genotypes from the Kazakhstan-Siberia Network for Spring Wheat Improvement (KASIB) were tested for resistance to leaf, stem and yellow rust at the seedling stage, and for identification of rust resistance genes using molecular markers. In addition, the collection was tested for leaf rust resistance and grain yields in the South Urals agroclimatic zone of Russia in 2023. As a result, 16 accessions with seedling resistance to leaf rust, 21 to stem rust and 4 to yellow rust were identified. Three breeding accessions were resistant to all rust species, and nine to P. triticina and P. graminis. Wheat accessions resistant to leaf rust at the seedling stage were also resistant in the field. Molecular analysis showed the presence of cataloged resistance genes, Lr1, Lr3a, Lr9, Lr10, Lr19, Lr20, Lr24, Lr26, Sr15, Sr24, Sr25, Sr31, Sr38, Yr9 and Yr18; uncatalogued genes Lr6Agi1 and Lr6Agi2 from Thinopyrum intermedium and LrAsp from Aegilops speltoides; and 1AL.1RS translocation. The current analysis showed an increase in leaf and stem rust resistance of new KASIB genotypes and their genetic diversity due to the inclusion of alien genetic material in breeding.