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Chickpea is one of the major pulse crops cultivated worldwide. It is affected by many fungal diseases including rust caused by the fungus Uromyces ciceris arietini . Its severity is mainly observed during the flowering and pod development stage and this causes considerable yield losses. The present investigation was carried out at ICAR-NIASM, Baramati during 2021–2022 to assess genotypic variation in stem reserve mobilization (SRM) and its influence on loss of grain yield due to rust infection on the basis of severity of rust, seed yield and test weight. The SRM was strongly correlated (R 2  = 0.96) with yield irrespective of the severity of rust infection. Phule Vikram, a local chickpea genotype, recorded the highest grain yield (1506.6 kg/ha) and SRM (23.65 mg/g stem dry weight) while the lowest yield was recorded in Pusa Green 112 (476.7 kg/ha), a susceptible, genotype that had less SRM (3.44 mg/g stem dry weight) and partially filled grain. The results indicated that SRM has a major role in ensuring pod filling even under rust infection. Hence, it is suggested that the emphasis should be on SRM as a trait for selecting early generation breeding lines in the chickpea improvement program with precaution to reduce a penalty if any in terms of attracting pests by increased access to soluble sugars in high SRM genotypes.

Gall rust disease, caused by the pathogenic fungus Uromycladium falcatariae, has become a serious concern in sengon (Falcataria falcata (L.) Greuter & R. Rankin) [syn. Falcataria moluccana (Miq.) Barneby & J.W. Grimes, Paraserianthes falcataria (L.) Nielsen], the third most important commercial forest tree in Indonesia. The aims of the study were to determine how far the pathogen could spread from the inoculum source. We hypothesized that spores disperse over short distances, leading to a gradual infection from the inoculum source. In 2016, a research plot (100 × 500 m) was established adjacent to a four-year-old sengon plantation infected by gall rust to serve as an inoculum source. Five observation plots were established at 100 m intervals from the inoculum source. Disease prevalence monitoring was conducted 1, 2, 4, and 7 years after tree planting. The study found that disease prevalence increased over time, regardless of proximity to the inoculum source. Gall rust infection naturally and rapidly occurred and the spread could reach at least 500 m from the gall rust inoculum source. In the first year of observations, more than 53% of the trees were infected with gall rust disease. Gradual infection within that distance did not occur. In fact, plant infection occurred simultaneously. Natural plant barriers are ineffective at preventing the spread of gall rust over distances of 500 m, indicating that teliospores may play a crucial role in disease dissemination.

Groundnut (Arachis hypogaea L.) is a multi-purpose legume serving millions of farmers and their value chain actors globally. Use of old poorperforming cultivars contributes to low yields (\\1 t/ha) of groundnut in sub-Saharan Africa including Tanzania. The objectives of this study were to determine the extent of genetic variation among diverse groundnut collections using phenotypic traits and simple sequence repeat (SSR) markers to select distinct and complementary genotypes for breeding

Wheat Stripe Rust Disease (WRD) poses a significant threat to wheat crops, causing substantial yield losses and can result in total crop damage if not detected early. The localization of WRD-infected areas is a labor-intensive and time-consuming task due to the intricate and varied nature of the disease spread, especially for large plantations. Hence, segmentation of wheat crops is vital for early identification of the WRD-affected area, which allows for the implementation of targeted intervention measures. The state-of-the-art segmentation technique for WRD using the real-world semantic segmentation NWRD dataset is based on a UNet model with the Adaptive Patching with Feedback (APF) technique. However, this implementation is complex and requires significant resources and time for training due to the processing of each patch of the dataset. Our work in this paper improves the state-of-the-art by using a two-stage model: a Vision Transformer (ViT) classifier to distinguish between the rust and non-rust patches and a less complex co-salient object detection (Co-SOD) model for segmentation of the classified images. The Co-SOD model uses multiple rust patches to extract contextual features from a group of images. By analyzing multiple patches of wheat rust disease simultaneously, we can segment disease regions more accurately. Our results show that the proposed approach achieves a higher F1 score (0.638), precision (0.621), and recall (0.675) for the rust class with 5× less training time as compared to the previous works.

Unmanned aerial vehicles (UAVs) provide an intermediate scale of spatial and spectral data collection that yields increased accuracy and consistency in data collection for morphological and physiological traits than satellites and expanded flexibility and high-throughput compared to ground-based data collection. In this study, we used UAV-based remote sensing for automated phenotyping of field-grown switchgrass (Panicum virgatum), a leading bioenergy feedstock. Using vegetation indices calculated from a UAV-based multispectral camera, statistical models were developed for rust disease caused by Puccinia novopanici, leaf chlorophyll, nitrogen, and lignin contents. For the first time, UAV remote sensing technology was used to explore the potentials for multiple traits associated with sustainable production of switchgrass, and one statistical model was developed for each individual trait based on the statistical correlation between vegetation indices and the corresponding trait. Also, for the first time, lignin content was estimated in switchgrass shoots via UAV-based multispectral image analysis and statistical analysis. The UAV-based models were verified by ground-truthing via correlation analysis between the traits measured manually on the ground-based with UAV-based data. The normalized difference red edge (NDRE) vegetation index outperformed the normalized difference vegetation index (NDVI) for rust disease and nitrogen content, while NDVI performed better than NDRE for chlorophyll and lignin content. Overall, linear models were sufficient for rust disease and chlorophyll analysis, but for nitrogen and lignin contents, nonlinear models achieved better results. As the first comprehensive study to model switchgrass sustainability traits from UAV-based remote sensing, these results suggest that this methodology can be utilized for switchgrass high-throughput phenotyping in the field.

Faba bean is one of the oldest crops that originated in the Fertile Crescent and is now distributed around the world and produced under different agro-climatic conditions. It is mainly cultivated in high rainfall and irrigated areas which are favorable for the development of foliar diseases causing severe crop losses. The purpose of this study was to identify new sources of resistance to the three key foliar diseases. A total of 2000 accessions, breeding lines and populations were screened for resistance to chocolate spot, Ascochyta blight and rust diseases from 2005 to 2012, at Lattakia Station, Syria. A total of 30 lines combining Ascochyta blight, chocolate spot and rust resistance were identified from accessions collected from China, Italy, Jordan, Lebanon and Spain. These new sources of resistance will enhance the development of faba bean breeding lines with combined resistance to the three foliar diseases.

Rust diseases, including leaf rust, stripe/yellow rust, and stem rust, significantly impact wheat ( Triticum aestivum L.) yields, causing substantial economic losses every year. Breeding and deployment of cultivars with genetic resistance is the most effective and sustainable approach to control these diseases. The genetic toolkit for wheat breeders to select for rust resistance has rapidly expanded with a multitude of genetic loci identified using the latest advances in genomics, mapping and cloning strategies. The goal of this review was to establish a wheat genome atlas that provides a comprehensive summary of reported loci associated with rust resistance. Our atlas provides a summary of mapped quantitative trait loci (QTL) and characterised genes for the three rusts from 170 publications over the past two decades. A total of 920 QTL or resistance genes were positioned across the 21 chromosomes of wheat based on the latest wheat reference genome (IWGSC RefSeq v2.1). Interestingly, 26 genomic regions contained multiple rust loci suggesting they could have pleiotropic effects on two or more rust diseases. We discuss a range of strategies to exploit this wealth of genetic information to efficiently utilise sources of resistance, including genomic information to stack desirable and multiple QTL to develop wheat cultivars with enhanced resistance to rust disease.

Background Wheat rusts are important biotic stresses, development of rust resistant cultivars through molecular approaches is both economical and sustainable. Extensive phenotyping of large mapping populations under diverse production conditions and high-density genotyping would be the ideal strategy to identify major genomic regions for rust resistance in wheat. The genome-wide association study (GWAS) population of 280 genotypes was genotyped using a 35 K Axiom single nucleotide polymorphism (SNP) array and phenotyped at eight, 10, and, 10 environments, respectively for stem/black rust (SR), stripe/yellow rust (YR), and leaf/brown rust (LR). Results Forty-one Bonferroni corrected marker-trait associations (MTAs) were identified, including 17 for SR and 24 for YR. Ten stable MTAs and their best combinations were also identified. For YR, AX-94990952 on 1A +  AX-95203560 on 4A +  AX-94723806 on 3D +  AX-95172478 on 1A showed the best combination with an average co-efficient of infection (ACI) score of 1.36. Similarly, for SR, AX-94883961 on 7B +  AX-94843704 on 1B and AX-94883961 on 7B +  AX-94580041 on 3D +  AX-94843704 on 1B showed the best combination with an ACI score of around 9.0. The genotype PBW827 have the best MTA combinations for both YR and SR resistance. In silico study identifies key prospective candidate genes that are located within MTA regions. Further, the expression analysis revealed that 18 transcripts were upregulated to the tune of more than 1.5 folds including 19.36 folds (TraesCS3D02G519600) and 7.23 folds (TraesCS2D02G038900) under stress conditions compared to the control conditions. Furthermore, highly expressed genes in silico under stress conditions were analyzed to find out the potential links to the rust phenotype, and all four genes were found to be associated with the rust phenotype. Conclusion The identified novel MTAs, particularly stable and highly expressed MTAs are valuable for further validation and subsequent application in wheat rust resistance breeding. The genotypes with favorable MTA combinations can be used as prospective donors to develop elite cultivars with YR and SR resistance.

Contents Summary 1190 I. Introduction 1190 II. Rust fungi: a diverse and serious threat to agriculture 1191 III. The different facets of rust life cycles and unresolved questions about their evolution 1191 IV. The biology of rust infection 1192 V. Rusts in the genomics era: the ever-expanding list of candidate effector genes 1195 VI. Functional characterization of rust effectors 1197 VII. Putting rusts to sleep: Pucciniales research outlooks 1201 Acknowledgements 1202 References 1202 SUMMARY: Rust fungi (Pucciniales) are the largest group of plant pathogens and represent one of the most devastating threats to agricultural crops worldwide. Despite the economic importance of these highly specialized pathogens, many aspects of their biology remain obscure, largely because rust fungi are obligate biotrophs. The rise of genomics and advances in high-throughput sequencing technology have presented new options for identifying candidate effector genes involved in pathogenicity mechanisms of rust fungi. Transcriptome analysis and integrated bioinformatics tools have led to the identification of key genetic determinants of host susceptibility to infection by rusts. Thousands of genes encoding secreted proteins highly expressed during host infection have been reported for different rust species, which represents significant potential towards understanding rust effector function. Recent high-throughput in planta expression screen approaches (effectoromics) have pushed the field ahead even further towards predicting high-priority effectors and identifying avirulence genes. These new insights into rust effector biology promise to inform future research and spur the development of effective and sustainable strategies for managing rust diseases.

Key messageAdult-plant stripe rust resistance gene Yr86 in Chinese wheat cultivar Zhongmai 895 was mapped to the physical interval 710.2–713.2 Mb on the long arm of chromosome 2A.Adult-plant resistance to stripe rust is generally more durable than all-stage resistance. Chinese wheat cultivar Zhongmai 895 showed stable stripe rust resistance at the adult-plant stage. To map the genetic loci underlying its resistance, 171 doubled haploid (DH) lines from a Yangmai 16/Zhongmai 895 cross were genotyped with the wheat 660 K SNP chip. Disease severities of the DH population and parents were assessed in four environments. A major QTL designated QYryz.caas-2AL was mapped to interval 703.7–715.3 Mb on the long arm of chromosome 2A using both chip-based and KASP (kompetitive allele-specific PCR) marker-based methods, explaining 31.5 to 54.1% of the phenotypic variances. The QTL was further validated in an F2 population of cross Emai 580/Zhongmai 895 with 459 plants and a panel of 240 wheat cultivars using KASP markers. Three reliable KASP markers predicted a low frequency (7.2–10.5%) of QYryz.caas-2AL in the test panel and remapped the gene to the physical interval 710.2–713.2 Mb. Based on different physical positions or genetic effects from known genes or QTL on chromosome arm 2AL, the gene was predicted to be a new one for adult-plant stripe rust resistance and was named Yr86. Twenty KASP markers linked to Yr86 were developed in this study based on wheat 660 K SNP array and genome re-sequencing. Three of them are significantly associated with stripe rust resistance in natural population. These markers should be useful for marker-assisted selection and also provide a starting point for fine mapping and map-based cloning of the new resistance gene.