The characterization of leaf rust (caused by ) and stripe rust (caused by f. sp. ) resistance genes is the basis for breeding resistant wheat varieties and managing epidemics of these diseases in wheat. A cross between the susceptible wheat variety 'Apav#1' and resistant variety 'Neimai 836' was used to develop a mapping population containing 148 F recombinant inbred lines (RILs). Leaf rust phenotyping was done in field trials at Ciudad Obregón, Mexico, in 2017 and 2018, and stripe rust data were generated at Toluca, Mexico, in 2017 and in Mianyang, Ezhou, and Gansu, China, in 2019. Inclusive complete interval mapping (ICIM) was used to create a genetic map and identify significant resistance quantitative trait loci (QTL) with 2,350 polymorphic markers from a 15K wheat single-nucleotide polymorphism (SNP) array and simple-sequence repeats (SSRs). The pleiotropic multipathogen resistance gene and four QTL were identified, including two new loci, and , which explained 3 to 16% of the phenotypic variation in resistance to leaf rust and 7 to 14% of that to stripe rust. The flanking SNP markers for the two loci were converted to Kompetitive Allele-Specific PCR (KASP) markers and used to genotype a collection of 153 wheat lines, indicating the Chinese origin of the loci. Our results suggest that Neimai 836, which has been used as a parent for many wheat varieties in China, could be a useful source of high-level resistance to both leaf rust and stripe rust.
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http://dx.doi.org/10.1094/PDIS-12-20-2654-RE | DOI Listing |
Theor Appl Genet
December 2024
Hungarian Research Network (HUN-REN), Centre for Agricultural Research, Agricultural Institute, Martonvásár, 2462, Hungary.
GBS read coverage analysis identified a Robertsonian chromosome from two Thinopyrum subgenomes in wheat, conferring leaf and stripe rust resistance, drought tolerance, and maintaining yield stability. Agropyron glael (GLAEL), a Thinopyrum intermedium × Th. ponticum hybrid, serves as a valuable genetic resource for wheat improvement.
View Article and Find Full Text PDFFront Plant Sci
December 2024
College of Agronomy, College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian, Shandong, China.
In order to achieve precise discrimination of leaf diseases in the Maize/Soybean intercropping system, i.e. leaf spot disease, rust disease, mixed leaf diseases, this study utilized hyperspectral imaging and deep learning algorithms for the classification of diseased leaves of maize and soybean.
View Article and Find Full Text PDFPlant Dis
December 2024
National Institute of Agricultural Sciences, Crop Protection, 166, Nongsaengmyeong-ro, Iseo-myeon, Wanju-gun, Jeollabuk-do, Korea (the Republic of), 55365;
Fig (Ficus carica L.) belonging to the Moraceae family is cultivated worldwide, with its primary production areas located in the Mediterranean region (Tous and Fergusen 1996). Yeongam-gun is a significant region for fig cultivation in Korea, accounting for 42% of the country's total fig cultivation area with approximately 1,400 fields (453ha, production yield 6000 tons).
View Article and Find Full Text PDFHeliyon
December 2024
Division of Plant Pathology, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India.
The global wheat production faces significant challenges due to major rust-causing fungi, namely f. sp. , , and f.
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November 2024
Department of Artificial Intelligence and Robotics, Sejong University, Seoul, Republic of Korea.
Accurately identifying apple diseases is essential to control their spread and support the industry. Timely and precise detection is crucial for managing the spread of diseases, thereby improving the production and quality of apples. However, the development of algorithms for analyzing complex leaf images remains a significant challenge.
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