Assessment and modeling using machine learning of resistance to scald (Rhynchosporium commune) in two specific barley genetic resources subsets

Genome wide association study of seedling and adult plant leaf rust resistance in two subsets of barley genetic resources

Leaf rust (LR) caused by Puccinia hordei is a serious disease of barley worldwide, causing significant yield losses and reduced grain quality. Discovery and incorporation of new sources of resistance from gene bank accessions into barley breeding programs is essential for the development of leaf rust resistant varieties. To identify Quantitative Trait Loci (QTL) conferring LR resistance in the two barley subsets, the Generation Challenge Program (GCP) reference set of 142 accessions and the leaf rust subset constructed using the Focused Identification of Germplasm Strategy (FIGS) of 76 barley accessions, were genotyped to conduct a genome-wide association study (GWAS). The results revealed a total of 59 QTL in the 218 accessions phenotyped against barley leaf rust at the seedling stage using two P. hordei isolates (ISO-SAT and ISO-MRC), and at the adult plant stage in four environments in Morocco. Out of these 59 QTL, 10 QTL were associated with the seedling resistance (SR) and 49 QTL were associated with the adult plant resistance (APR). Four QTL showed stable effects in at least two environments for APR, whereas two common QTL associated with SR and APR were detected on chromosomes 2H and 7H. Furthermore, 39 QTL identified in this study were potentially novel. Interestingly, the sequences of 27 SNP markers encoded the candidate genes (CGs) with predicted protein functions in plant disease resistance. These results will provide new perspectives on the diversity of leaf rust resistance loci for fine mapping, isolation of resistance genes, and for marker-assisted selection for the LR resistance in barley breeding programs worldwide.

Advancing the Conservation and Utilization of Barley Genetic Resources: Insights into Germplasm Management and Breeding for Sustainable Agriculture

Barley is a very important crop particularly in marginal dry areas, where it often serves as the most viable option for farmers. Additionally, barley carries great significance in the Western world, serving not only as a fundamental crop for animal feed and malting but also as a nutritious food source. The broad adaptability of barley and its ability to withstand various biotic and abiotic stresses often make this species the sole cereal that can be cultivated in arid regions. The collection and utilization of barley genetic resources are crucial for identifying valuable traits to enhance productivity and mitigate the adverse effects of climate change. This review aims to provide an overview of the management and exploitation of barley genetic resources. Furthermore, the review explores the relationship between gene banks and participatory breeding, offering insights into the diversity and utilization of barley genetic resources through some examples such as the initiatives undertaken by ICARDA. Finally, this contribution highlights the importance of these resources for boosting barley productivity, addressing climate change impacts, and meeting the growing food demands in a rapidly changing agriculture. The understanding and utilizing the rich genetic diversity of barley can contribute to sustainable agriculture and ensure the success of this vital crop for future generations globally.

Reviews of taxonomy, epidemiology, and management practices of the barley scald (Rhynchosporium graminicola) disease

Barley scald is very important in temperate and wet regions worldwide and has become one of the most important foliar diseases. Before the development of recent technologies, several scientists had argued that Rhynchosporium secalis is the causal agent of scald disease. However, the causal agent of this disease was revised and recognized as Rhynchosporium commune. Again recently, Rhynchosporium graminicola was suggested to be replaced as the causal agent of R. commune. The disease outbreak is depending on cool and frequent rainfall. Because of scald disease significance, numerous management practices have been advocated. Then, resistance materials, and mixing of resistant and susceptible cultivars have been used as the best management methods. Several studies have demonstrated that some cultivars and landraces of barley are resistant to scald disease during the seedling and adult growth stages. The first cultivar is "Atlas 46″ which was created from the cultivar "Turk". From biological method: Bacillus polymyxa, Paenibacillus polymyxa KaI245, and Bacillus subtilis are very effective in treating this disease. Finally, as a last option, different fungicides have been suggested. Pathogenicity testing, seed treatments, tillage, cultivar mixtures, and biological control are all commonly overlooked in developing countries. Cultural practices such as times of fungicide application, appropriate time of sowing to scape disease, and tillage practices which are adopted for other diseases are greatly missed for scald disease. Then, we are intended to assess the various findings available on barley scald biology, taxonomy, and management.

The Use of Pathotype Data for the Selection and Development of Barley Lines with Useful Resistance to Scald

Resistance in barley to scald caused by Rhynchosporium commune is readily overcome as virulent pathotypes in the pathogen population are selectively favoured over less virulent pathotypes. Diverse sources of resistance amongst host accessions have been found upon screening a wide array of accessions from around the world. Deciding which of these is of greatest value, and which are different from each other, takes a much greater investment of time and effort. This paper reports on the use of seedling screening techniques using 262 individual scald isolates collected from around Australia, to identify the most useful resistance sources from amongst 30 previously selected. No resistance source was effective against all isolates, but some such as Pamunkey, CI8618, CI4364 and ICARDA 4 were shown to have resistance to most isolates, whilst others were much less useful. Some of the most effective donors were shown to likely have more than one gene involved. The value of gene pyramids is discussed, as are the advantages and pitfalls of transferring the resistances from poorly adapted genetic backgrounds into better-adapted breeding lines so that they can more readily be used by breeding programs. This is a work in progress and the introgressed resistances being developed are available to all.

Machine Learning for Plant Stress Modeling: A Perspective towards Hormesis Management

Plant stress is one of the most significant factors affecting plant fitness and, consequently, food production. However, plant stress may also be profitable since it behaves hormetically; at low doses, it stimulates positive traits in crops, such as the synthesis of specialized metabolites and additional stress tolerance. The controlled exposure of crops to low doses of stressors is therefore called hormesis management, and it is a promising method to increase crop productivity and quality. Nevertheless, hormesis management has severe limitations derived from the complexity of plant physiological responses to stress. Many technological advances assist plant stress science in overcoming such limitations, which results in extensive datasets originating from the multiple layers of the plant defensive response. For that reason, artificial intelligence tools, particularly Machine Learning (ML) and Deep Learning (DL), have become crucial for processing and interpreting data to accurately model plant stress responses such as genomic variation, gene and protein expression, and metabolite biosynthesis. In this review, we discuss the most recent ML and DL applications in plant stress science, focusing on their potential for improving the development of hormesis management protocols.

Prediction of South American Leaf Blight and Disease-Induced Photosynthetic Changes in Rubber Tree, Using Machine Learning Techniques on Leaf Hyperspectral Reflectance

The efficiency of visible and near-infrared (VIS/NIR) sensors and predictive modeling for detecting and classifying South American Leaf Blight (SALB) (Pseudocercospora ulei) in rubber trees (Hevea brasiliensis) has been poorly explored. Furthermore, the performance of VIS/NIR analysis combined with machine learning (ML) algorithms for predicting photosynthetic alterations caused by SALB is unknown. Therefore, this study aimed to detect and classify the SALB levels, as well as to predict, for the first time, disease-induced photosynthetic changes in rubber trees. Leaf hyperspectral reflectance combined with five ML techniques (random forest (RF), boosted regression tree (BRT), bagged classification and regression trees (BCART), artificial neural network (ANN), and support vector machine (SVM)) were used. The RF, ANN, and BCART models achieved the best performance for classifying the SALB levels on the training dataset (accuracies of 98.0 to 99.8%), with 10-fold cross-validation repeated five times, and test dataset (accuracies of 97.1 to 100%). The ANN and RF models were better at predicting leaf gas exchange-related traits such as net CO₂ assimilation rate (A) and extrinsic water use efficiency (WUEe) in the training (R² ranged from 0.97 to 0.99) and testing (R² ranged from 0.96 to 0.99) phases. In comparison, lower performances (R² ranged from 0.24 to 0.52) were evidenced for the photochemical traits. This research provides a basis for future designs of a remote monitoring system based on early detection and accurate diagnosis of biotic stress caused by SALB, which is fundamental for more effective rubber crop protection.