Stem rust caused by Puccinia graminis f. sp. tritici (Pgt) remains a constraint to wheat production in East Africa. In this study, we characterized the genetics of stem rust resistance, identified QTLs, and described markers associated with stem rust resistance in the spring wheat line CI 14275. The 113 recombinant inbred lines, together with their parents, were evaluated at the seedling stage against Pgt races TTKSK, TRTTF, TPMKC, TTTTF, and RTQQC. Screening for resistance to Pgt races in the field was undertaken in Kenya, Ethiopia, and the United States in 2016, 2017, and 2018. One gene conferred seedling resistance to race TTTTF, likely Sr7a. Three QTL were identified that conferred field resistance. QTL QSr.cdl-2BS.2, that conferred resistance in Kenya and Ethiopia, was validated, and the marker Excalibur_c7963_1722 was shown to have potential to select for this QTL in marker-assisted selection. The QTL QSr.cdl-3B.2 is likely Sr12, and QSr.cdl-6A appears to be a new QTL. This is the first study to both detect and validate an adult plant stem rust resistance QTL on chromosome arm 2BS. The combination of field QTL QSr.cdl-2BS.2, QSr.cdl-3B.2, and QSr.cdl-6A has the potential to be used in wheat breeding to improve stem rust resistance of wheat varieties.
Three isolates of sugarcane mosaic virus (SCMV) from sugarcane and maize in Thailand, were selected and used as viral sources for coat protein (CP) genes cloning by immunocapture reverse transcription polymerase chain reaction (IC-RT-PCR). The CP gene sequences and their deduced amino acids were determined. The sequences of three cloned CP genes, UT6TH-sgc and UD7TH-sgc from sugarcane, and SBC2TH-mz from maize were compared with other previously reported sequences of SCMV-CP gene and their phylogeny were studied. The analysis revealed that Thai SCMV CP gene contained 942 nucleotides encoded for coat protein MW of 33.65 kDa. The nucleotide sequence identity among cloned CPs from three isolates were 98-99% to each other. The N-terminus of the Thai SCMV CP contained a distinctive sequence, especially at nucleotide positions 28-46 of the N-terminal variable 70 amino acid residues which discriminated Thai SCMV from all previously reported SCMV isolates. Thai SCMV were placed in a separate branch of SCMV-MDB cluster which is closely related to most of SCMV isolates from maize, and distinct from the other cluster containing sugarcane-SCMV strains.
Diseases have adverse effects on crop production and yield loss. Various diseases such as leaf rust, stem rust, and strip rust can affect yield quality and quantity for a studied area. In addition, manual wheat disease identification and interpretation is time-consuming and cumbersome. Currently, decisions related to plants mainly rely on the level of expertise in the domain. To resolve these challenges and to identify wheat disease as early as possible, we implemented different deep learning models such as Inceptionv3, Resnet50, and VGG16/19. This research was conducted in collaboration with Bishoftu Agricultural Research Institute, Ethiopia. Our main objective was to automate plant-disease identification using advanced deep learning approaches and image data. For the experiment, RGB image data were collected from the Bishoftu area. From the experimental results, the VGG19 model classified wheat disease with 99.38% accuracy.
Wheat rusts are the key biological constraint to wheat production in Ethiopia—one of Africa’s largest wheat producing countries. The fungal diseases cause economic losses and threaten livelihoods of smallholder farmers. While it is known that wheat rust epidemics have occurred in Ethiopia, to date no systematic long-term analysis of past outbreaks has been available. We present results from one of the most comprehensive surveillance campaigns of wheat rusts in Africa. More than 13,000 fields have been surveyed during the last 13 years. Using a combination of spatial data-analysis and visualization, statistical tools, and empirical modelling, we identify trends in the distribution of wheat stem rust (Sr), stripe rust (Yr) and leaf rust (Lr). Results show very high infection levels (mean incidence for Yr: 44%; Sr: 34%; Lr: 18%). These recurrent rust outbreaks lead to substantial economic losses, which we estimate to be of the order of 10s of millions of US-D annually. On the widely adopted wheat variety, Digalu, there is a marked increase in disease prevalence following the incursion of new rust races into Ethiopia, which indicates a pronounced boom-and-bust cycle of major gene resistance. Using spatial analyses, we identify hotspots of disease risk for all three rusts, show a linear correlation between altitude and disease prevalence, and find a pronounced north-south trend in stem rust prevalence. Temporal analyses show a sigmoidal increase in disease levels during the wheat season and strong inter-annual variations. While a simple logistic curve performs satisfactorily in predicting stem rust in some years, it cannot account for the complex outbreak patterns in other years and fails to predict the occurrence of stripe and leaf rust. The empirical insights into wheat rust epidemiology in Ethiopia presented here provide a basis for improving future surveillance and to inform the development of mechanistic models to predict disease spread.
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