Search Results (84)

Kazakhstan possesses considerable potential for the development of organic agriculture. In organic production systems, the use of chemical plant protection products is restricted or completely excluded, making the cultivation of genetically resistant wheat lines to major fungal diseases one of the most effective approaches for maintaining stable grain production. The current study aimed to evaluate disease resistance in wheat genotypes by integrating phenotypic screening and marker-assisted selection and their validation under organic farming conditions. A total of 50 facultative and introgressive wheat lines were evaluated under an artificial infection background for resistance to yellow rust, leaf rust, stem rust, and common bunt. Molecular marker analysis was performed to identify resistance-associated alleles. Integrated phenotypic and molecular analyses enabled the identification of three promising genotypes, namely 1675-52, 1723-32, and 1716-24. They combined a high level of resistance to yellow rust and common bunt with the presence of resistance-associated alleles. These selected genotypes were subsequently validated under organic field conditions. The results demonstrated that these lines maintained stable resistance to yellow rust and common bunt and produced seed yield ranging from 5.45 to 5.94 t/ha, exceeding that of the standard cv. Almaly (4.88 t/ha). The obtained results confirm the effectiveness of integrating phenotypic screening with marker-assisted selection for identifying wheat genotypes with complex disease resistance. These genotypes represent promising prebreeding resources for organic agriculture, subject to validation across a wider range of environments. Full article

Yellow rust (Puccinia striiformis f. sp. tritici) is one of the most destructive diseases in wheat (Triticum aestivum L.) in Kazakhstan, causing significant yield losses. Owing to the high susceptibility of widely cultivated varieties, the development of resistant genotypes remains a key objective for sustainable crop protection. The aim of this study was to evaluate the resistance of wheat lines to yellow rust and to identify effective resistance genes. The research was conducted under artificial infection conditions using hybrid populations of the F2–F5 generations. The genotypes were assessed and ranked according to their resistance levels, and molecular markers were applied to detect resistance genes. Significant variability in disease response was observed. Analysis of variance revealed a strong effect of genotype on the infection coefficient (p < 0.001). Lines from later generations (F5) presented lower infection levels. Most genotypes carried the Yr5 gene, highlighting its major role in resistance, whereas Yr10 was less common. Yr15 and Yr18 were detected in some lines and were associated with partial (adult plant) resistance. Moderately susceptible forms predominated, indicating widespread quantitative resistance. However, highly resistant lines (CI = 0–1) and immune forms were identified, representing valuable material for breeding programs. Full article

Wheat yellow (stripe) rust (Puccinia striiformis f. sp. tritici) remains a major constraint to wheat production, yet relationships between infection level, yield, and quality-related traits are often inconsistent. This study evaluated how contrasting yellow rust races and infection intensities influence yield, test weight (TW), thousand kernel weight (TKW), and crude protein content in commercial winter wheat varieties. Field trials were conducted in 2016, 2021, and 2023, representing seasons that differed in yellow rust incidence and severity. The yellow rust race was changed in 2023 compared to the yellow rust race that was the same in 2016 and 2021. Associations between qualitative variables (variety and year) and quantitative variables (yield, TKW, TW, disease index (DI), and protein content) were analyzed using principal component analysis for mixed data and regression modeling. At low to moderate infection levels, TW showed a stronger negative linear association with yellow rust DI than TKW, suggesting that TW acts as an early indicator of source limitation. In contrast, TKW declined only when genotypes could no longer compensate for reduced assimilate supply, after which both traits responded similarly under severe physiological stress. Protein concentration increased under high infection levels, but its association with yield loss and DI was weak. Under high disease pressure, yield and quality-related responses were highly variable and genotype-specific at comparable DI levels, demonstrating that equivalent symptom expression does not necessarily translate into equivalent physiological disruption. These results show that yield and quality responses to yellow rust cannot be inferred from DI alone, highlighting the importance of physiological tolerance and source–sink efficiency in breeding and disease management strategies. Full article

In major wheat-growing regions, rust diseases and common bunt significantly reduce wheat productivity, especially in years with favorable conditions for phytopathogen development and limited resistant cultivar use. Thus, the development of genetically resistant wheat cultivars carrying combinations of valuable resistance genes is an effective strategy to mitigate these losses. In this study, 156 advanced winter wheat breeding lines were evaluated for resistance to yellow (stripe) rust, leaf (brown) rust, and common bunt under an artificial infection background. Concurrently, molecular screening was performed using DNA markers to detect rust (Yr5, Yr10, Yr15, Lr9, Lr34/Yr18, and Lr37/Yr17) and common bunt resistance genes (Bt8, Bt9, Bt10, Bt11, and Bt12). Based on the integrated analysis of phenotypic and DNA marker-based molecular data, fourteen and five lines resistant to common bunt and yellow rust, respectively, were identified, and alleles associated with resistance were also detected. Notably, one line (9909) exhibited high resistance to both rust diseases and common bunt. These selected advanced breeding lines represent promising candidates for the development of wheat cultivars with enhanced disease resistance, thereby supporting sustainable productivity in wheat-growing regions. Full article

Leaf and spike diseases can significantly reduce wheat yield and grain quality. To mitigate these impacts, an integrated disease management approach can be adopted, incorporating measures such as the use of resistant cultivars, fungicides and nitrogen fertilization. This study aimed to evaluate the impact of these practices on chlorophyll a fluorescence, yield components, and the technological quality of wheat grains. The area under the disease progress curve (AUDPC) was correlated with the maximum efficiency of photosystem II (PSII) photochemistry (F_v/F_m), as measured at the dough development stage (ZGS80) under field conditions, which also affected quality parameters. Additionally, an increase in AUDPC values reduced the thousand kernel weight (TKW) and test weight (TW). Conversely, AUDPC values for tan spot, powdery mildew and leaf rust were positively related to ash content (affecting flour color), protein content (PC) and grain falling number. Both the recommended nitrogen rate (130 kg ha⁻¹) and the high rate (200 kg ha⁻¹) increased grain protein content (PC) and gluten index (GI), while maintaining dough stability and water absorption. Fungicide application increased flour lightness and yellowness. Overall, integrated disease management combining moderately resistant cultivars, fungicide applications and nitrogen fertilization reduced AUDPC values, increased F_v/F_m (indicating optimal physiological performance) and ensured yield components and maintenance of wheat technological quality. Full article

Wheat stripe rust is an important fungal disease that threatens global wheat production, and precise monitoring in field environments is crucial for disease prevention and control. This study proposes a cross-scale monitoring method based on optimized hyperspectral vegetation index to address the issues of low efficiency of traditional monitoring methods and susceptibility of spectral signals to interference in field environments. Through comparative studies between experimental fields (n = 68) and large fields (n = 155), the performance of six vegetation indices was systematically evaluated, and optimized versions were designed. The study mainly found that the Yellow Rust Severity Index optimized (YRSI_O) index exhibited the best monitoring performance, with a field determination coefficient R² of 0.5713 (experimental field R² = 0.6118). The unmanned aerial vehicle (UAV) hyperspectral system combined with optimized vegetation index can effectively control spectral reflectance fluctuations, with a recognition accuracy of up to 85.2% in severely infected areas. This study also elucidated the three-stage physiological response mechanism of optimizing indicators on disease progression. This study provides key technical support for the practical application of hyperspectral technology in field monitoring of wheat stripe rust, and the proposed research method can be extended to other fields of crop disease monitoring. Full article

Yellow rust (YR), caused by Puccinia striiformis f. sp. tritici (Pst), is a global disease infecting wheat that seriously affects the yield and the quality of grains. Wheat breeding line C855 is immune to the mixed Pst isolates CYR32 + CYR33 + CYR34 under field conditions. To identify the Yr-loci carried by C855, in this study, an F₂ population derived from the crossing of C855 with Yannong 999, a YR-sensitive cultivar, was established, and the infection type (IT) of each F₂ individual was estimated. The correlation analysis results show that YR resistance was significantly positively correlated with grain weight and grain size. Using a 120K single-nucleotide polymorphism (SNP) array, the F₂ population was genotyped, and a high-density genetic map covering 21 wheat chromosomes and consisting of 5362 SNP markers was built. Then, five Yr-QTLs on chromosomes 1B, 2A, 2B, and 2D were identified. Of these, the QTL on chromosome 2A, temporarily named QYr.sxau-2A.1, is a major-effect QTL explaining 15.62% of the phenotypic variance. One PCR-based marker SSR2A-14 for QYr.sxau-2A.1 was developed, and the C855 allele of SSR2A-14 corresponded to the stronger Yr resistance. QYr.sxau-2A.1, located in the 228.02~241.58 Mbp physical interval, is different from all the known Yr loci on chromosomes 2A. Within the interval, there are 30 annotated genes, including a nucleotide-binding site and a leucine-rich repeat (NBS-LRR)-encoding gene with the linkage marker NRM2A-16 of QYr.sxau-2A.1. Our results reveal a novel major-effect QYr.sxau-2A.1, which provided resistance to YR and is a molecular marker for wheat breeding. Full article

The fungal pathogen Puccinia striiformis f. sp. tritici, which causes yellow rust disease, poses a significant economic threat to wheat production not only in Uzbekistan but also globally, leading to substantial reductions in grain yield. This study aimed to develop yellow rust-resistance wheat lines by introgressing Yr10 and Yr15 genes into high-yielding cultivar Grom using the marker-assisted backcrossing (MABC) method. Grom was crossed with donor genotypes Yr10/6*Avocet S and Yr15/6*Avocet S, resulting in the development of F₁ generations. In the following years, the F₁ hybrids were advanced to the BC₂F₁ and BC₂F₂ generations using the MABC approach. Foreground and background selection using microsatellite markers (Xpsp3000 and Barc008) were employed to identify homozygous Yr10- and Yr15-containing genotypes. The resulting BC₂F₂ lines, designated as Grom-Yr10 and Grom-Yr15, retained key agronomic traits of the recurrent parent cv. Grom, such as spike length (13.0–11.9 cm) and spike weight (3.23–2.92 g). Under artificial infection conditions, the selected lines showed complete resistance to yellow rust (infection type 0). The most promising BC₂F₂ plants were subsequently advanced to homozygous BC₂F₃ lines harboring the introgressed resistance genes through marker-assisted selection. This study demonstrates the effectiveness of integrating molecular marker-assisted selection with conventional breeding methods to enhance disease resistance while preserving high-yielding traits. The newly developed lines offer valuable material for future wheat improvement and contribute to sustainable agriculture and food security. Full article

Yellow rust (Puccinia striiformis) is a common wheat disease that significantly reduces yields, particularly in seasons with cooler temperatures and frequent rainfall. Early detection is essential for effective control, especially in key wheat-producing regions such as Southern Dobrudja, Bulgaria. This study presents a UAV-based approach for detecting yellow rust using only RGB imagery and deep learning for pixel-based classification. The methodology involves data acquisition, preprocessing through histogram equalization, model training, and evaluation. Among the tested models, a UnetClassifier with ResNet34 backbone achieved the highest accuracy and reliability, enabling clear differentiation between healthy and infected wheat zones. Field experiments confirmed the approach’s potential for identifying infection patterns suitable for precision fungicide application. The model also showed signs of detecting early-stage infections, although further validation is needed due to limited ground-truth data. The proposed solution offers a low-cost, accessible tool for small and medium-sized farms, reducing pesticide use while improving disease monitoring. Future work will aim to refine detection accuracy in low-infection areas and extend the model’s application to other cereal diseases. Full article

Wheat stripe (yellow) rust, caused by the fungus Puccinia striiformis f. sp. tritici (Pst), is one of the most threatening wheat diseases worldwide. Monitoring the virulence of Pst population is essential for managing wheat stripe rust. In this study, 18 wheat Yr single-gene lines were used to identify the virulence patterns of 67 isolates collected from 13 provinces in China in 2020, from which 33 Pst races were identified. The frequency of virulence to different Yr genes varied from 1.49% to 97.01%, with 4.48% to Yr1, 26.87% to Yr6, 11.94% to Yr7, 95.52% to Yr8, 19.40% to Yr9, 11.94% to Yr17, 2.99% to Yr24, 35.82% to Yr27, 38.81% to Yr43, 97.01% to Yr44, 8.96% to YrSP, 1.49% to Yr85, 95.52% to YrExp2, and 7.46% to Yr76. None of the isolates were virulent to Yr5, Yr10, Yr15, and Yr32. Among the 33 races, PstCN-062 (with virulence to Yr8, Yr44, and YrExp2) and PstCN-001 (with virulence to Yr8, Yr43, Yr44, and YrExp2) were the prevalent races, with frequencies of 28.36% and 11.94%, respectively. These results provide valuable information for breeding resistant wheat cultivars for controlling stripe rust. Full article

Yellow rust is one of the most destructive fungal diseases affecting wheat, significantly reducing yield and grain quality. Early detection is crucial for effective plant protection and disease management. This study aims to develop and validate a methodology for early diagnosis of yellow rust using the Normalized Difference Vegetation Index (NDVI) derived from UAV-acquired spectral data. This research was conducted in an experimental wheat field near General Toshevo, Bulgaria, which is owned by the Dobrudja Agricultural Institute (DAI). A widely cultivated winter wheat variety, Enola, was monitored using UAV-based imaging, and the NDVI values were analyzed to assess the correlation between spectral reflectance and infection severity. The NDVI showed a moderate correlation as an indicator of pathogen-induced stress, with moderate predictive capability (R² = 51.4%) for assessing yellow rust infection severity. The results demonstrated that UAV-based NDVI analysis could effectively detect early-stage infections and monitor the spatial spread of the disease. The proposed methodology enables large-scale, non-invasive monitoring of wheat health, facilitating early disease detection. This approach can help optimize disease management strategies, although ground-based validation remains essential to distinguish between different stress factors affecting vegetation. Full article

Lesion mimic phenotypes, characterized by leaf spots formed in the absence of pathogens or pests, are often associated with reactive oxygen species (ROS) accumulation and cell necrosis. This study identified a novel and stable homozygous spotted phenotype (HSP) from the F₈ population of common wheat (XN509 × N07216). The yellow spots that appeared at the booting stage were light-sensitive, and accompanied by cell necrosis and H₂O₂ accumulation. Compared with homozygous normal plants (HNPs), HSPs exhibited enhanced resistance to stripe rust and powdery mildew without compromising yield. RNA-Seq analysis at three stages revealed that differentially expressed genes (DEGs) between HSPs and HNPs were significantly enriched in KEGG pathways related to photosynthesis and photosynthesis-antenna proteins. GO analysis highlighted chloroplast and light stimulus-related down-regulated DEGs. Fine mapping identified TaSpl1 within a 0.91 Mb interval on chromosome 3DS, flanked by the markers KASP188 and KASP229, using two segregating populations comprising 1117 individuals. The candidate region contained 42 annotated genes, including 14 DEGs based on previous BSR-Seq data. PCR amplification and qRT-PCR verification identified the expression of TraesCS3D02G022100 was consistent with RNA-Seq data. Gene homology analysis and silencing experiments confirmed that TraesCS3D02G022100 was associated with stay-green traits. These findings provide new insights into the genetic regulation of lesion mimics, photosynthesis, and disease resistance in wheat. Full article

In an effort to enhance wheat’s resilience against rust diseases, our research explores the genetic underpinnings of resistance in a diverse collection of winter bread wheat accessions. Leaf rust (Puccinia triticina), yellow rust (Puccinia striiformis f. sp. tritici), and stem rust (Puccinia graminis f. sp. tritici) are significant threats to global wheat production. By leveraging host genetic resistance, we can improve disease management strategies. Our study evaluated 55 wheat accessions, including germplasm from Kazakhstan, from Uzbekistan, from Russia, from Kyrgyzstan, France, and CIMMYT under field conditions in southern Kazakhstan from 2022 to 2024. The results showed a robust resistance profile: 49.1% of accessions exhibited high to moderate resistance to leaf rust, 12.7% to yellow rust, and 30.9% to stem rust. Notably, ten accessions demonstrated resistance to multiple rust species, while seven showed resistance to two rusts. Twenty accessions were selected for further seedling resistance and molecular analysis. Three accessions proved resistant to six isolates of P. triticina, two to four isolates of P. striiformis, and four to five isolates of P. graminis. Although no genotypes were found to be universally resistant to all rust species at the seedling stage, two accessions—Bezostaya 100 (Russia) and KIZ 90 (Kazakhstan)—displayed consistent resistance to leaf and stem rust in both seedling and field evaluations. Molecular analysis revealed the presence of key resistance genes, including Lr1, Lr3, Lr26, Lr34, Yr9, Yr18, Sr31, Sr57, and the 1AL.1RS translocation. This work provides valuable insights into the genetic landscape of wheat rust resistance and contributes to the development of new wheat cultivars that can withstand these diseases, enhancing global food security. Full article

Climate change is driving the spread of transboundary wheat diseases, necessitating the development of resilient wheat varieties for sustainable agriculture. Wheat rusts, including leaf rust (LR), yellow rust (YR), and stem rust (SR), remain among the most economically significant diseases, causing substantial yield losses worldwide. Enhancing genetic diversity by identifying and deploying rust resistance genes is crucial for durable resistance in wheat breeding programs. This study aimed to identify quantitative trait loci (QTL) associated with rust resistance in the CIMMYT wheat line Kasuku, released in Kenya in 2018. A recombinant inbred line (RIL) population (181 lines) derived from Kasuku (triple rust-resistant) and Apav#1 (triple rust-susceptible) was evaluated under artificial LR and YR epidemics in Mexico and YR and SR in Kenya. QTL mapping using genotyping-by-sequencing (DArTSeq) and phenotypic data identified four major loci: QLrYrSr.cim-1BL (Lr46/Yr29/Sr58) on 1BL, conferring resistance to LR, YR, and SR; QLrYr.cim-2AS (Yr17/Lr37) on 2AS, providing LR and YR resistance; QLrYr.cim-3AL on 3AL; and QLrYrSr.cim-6AL on 6AL, representing novel loci associated with multiple rust resistances. Additionally, minor QTL were also identified: for LR (QLr.cim-2DS on 2DS, QLr.cim-6DS on 6DS), for YR (QYrKen.cim-3DS on 3DS, QYrKen.cim-6BS on 6BS), and for SR (QSr.cim-2BS on 2BS, QSr.cim-5AL on 5AL, QSr.cim-6AS on 6AS). RILs carrying these QTL combinations exhibited significant reductions in rust severity. Flanking markers for these loci are being used to develop Kompetitive Allele-Specific PCR (KASP) markers for fine mapping and marker-assisted selection (MAS). These findings contribute to the strategic deployment of rust resistance genes in wheat breeding programs, facilitating durable resistance to multiple rust pathogens. Full article

Leaf rust and yellow rust are globally significant fungal diseases that severely impact wheat production, causing yield losses of up to 60% in highly susceptible cultivars. Early and accurate detection is crucial for integrating precision crop protection strategies to mitigate these losses. This study investigates the potential of 3D LiDAR technology for monitoring rust-induced physiological changes in wheat by analyzing variations in plant height, biomass, and light reflectance intensity. Results showed that grain yield decreased by 10–50% depending on cultivar susceptibility, with the durum wheat cultivar ‘Kiko Nick’ and bread wheat ‘Califa’ exhibiting the most severe reductions (~50–60%). While plant height and biomass remained relatively unaffected, LiDAR-derived intensity values strongly correlated with disease severity (R² = 0.62–0.81, depending on the cultivar and infection stage). These findings demonstrate that LiDAR can serve as a non-destructive, high-throughput tool for early rust detection and biomass estimation, highlighting its potential for integration into precision agriculture workflows to enhance disease monitoring and improve wheat yield forecasting. To promote transparency and reproducibility, the dataset used in this study is openly available on Zenodo, and all processing code is accessible via GitHub, cited at the end of this manuscript. Full article