Spatial Distribution Patterns for Identifying Risk Areas Associated with False Smut Disease of Rice in Southern India

False smut disease (FSD) of rice incited by Ustilaginoidea virens is an emerging threat to paddy cultivation worldwide. We investigated the spatial distribution of FSD in different paddy ecosystems of South Indian states, viz., Andhra Pradesh, Karnataka, Tamil Nadu, and Telangana, by considering the exploratory data from 111 sampling sites. Point pattern and surface interpolation analyses were carried out to identify the spatial patterns of FSD across the studied areas. The spatial clusters of FSD were confirmed by employing spatial autocorrelation and Ripley’s K function. Further, ordinary kriging (OK), indicator kriging (IK), and inverse distance weighting (IDW) were used to create spatial maps by predicting the values at unvisited locations. The agglomerative hierarchical cluster analysis using the average linkage method identified four main clusters of FSD. From the Local Moran’s I statistic, most of the areas of Andhra Pradesh and Tamil Nadu were clustered together (at I > 0), except the coastal and interior districts of Karnataka (at I < 0). Spatial patterns of FSD severity were determined by semi-variogram experimental models, and the spherical model was the best fit. Results from the interpolation technique, the potential FSD hot spots/risk areas were majorly identified in Tamil Nadu and a few traditional rice-growing ecosystems of Northern Karnataka. This is the first intensive study that attempted to understand the spatial patterns of FSD using geostatistical approaches in India. The findings from this study would help in setting up ecosystem-specific management strategies to reduce the spread of FSD in India

Rice Blast Disease in India: Present Status and Future Challenges

Rice (Oryza sativa L.) is the staple food of the majority of Indians, and India is both the major producer and consumer of rice. Rice cultivation in India is confronted with diverse agro-climatic conditions, varying soil types, and several biotic and abiotic constraints. Among major fungal diseases of Rice in India, the blast caused by Magnaporthe oryzae is the most devastating disease, with the neck blast being the most destructive form. Most of the blast epidemic areas in India have been identified with a mixture of races blast fungus resulting in the resistance breakdown in a short period. At present, a more significant number of the rice varieties cultivated in India were bred by conventional breeding methods with blast resistance conferred by a single resistance gene. Therefore, the blast disease in India is predominantly addressed by the use of ecologically toxic fungicides. In line with the rest of the world, the Indian scientific community has proven its role by identifying several blast resistance genes and successfully pyramiding multiple blast resistance genes. Despite the wealth of information on resistance genes and the availability of biotechnology tools, not a great number of rice varieties in India harbor multiple resistance genes. In the recent past, a shift in the management of blast disease in India has been witnessed with a greater focus on basic research and modern breeding tools such as marker-assisted selection, marker-assisted backcross breeding, and gene pyramiding

LAMP-based foldable microdevice platform for the rapid detection of Magnaporthe oryzae and Sarocladium oryzae in rice seed

Abstract Rice blast (caused by Magnaporthe oryzae) and sheath rot diseases (caused by Sarocladium oryzae) are the most predominant seed-borne pathogens of rice. The detection of both pathogens in rice seed is essential to avoid production losses. In the present study, a microdevice platform was designed, which works on the principles of loop-mediated isothermal amplification (LAMP) to detect M. oryzae and S. oryzae in rice seeds. Initially, a LAMP, polymerase chain reaction (PCR), quantitative PCR (qPCR), and helicase dependent amplification (HDA) assays were developed with primers, specifically targeting M. oryzae and S. oryzae genome. The LAMP assay was highly efficient and could detect the presence of M. oryzae and S. oryzae genome at a concentration down to 100 fg within 20 min at 60 °C. Further, the sensitivity of the LAMP, HDA, PCR, and qPCR assays were compared wherein; the LAMP assay was highly sensitive up to 100 fg of template DNA. Using the optimized LAMP assay conditions, a portable foldable microdevice platform was developed to detect M. oryzae and S. oryzae in rice seeds. The foldable microdevice assay was similar to that of conventional LAMP assay with respect to its sensitivity (up to 100 fg), rapidity (30 min), and specificity. This platform could serve as a prototype for developing on-field diagnostic kits to be used at the point of care centers for the rapid diagnosis of M. oryzae and S. oryzae in rice seeds. This is the first study to report a LAMP-based foldable microdevice platform to detect any plant pathogens

Phenotypic and Genotypic screening of fifty-two rice (Oryza sativa L.) genotypes for desirable cultivars against blast disease.

Magnaporthe oryzae, the rice blast fungus, is one of the most dangerous rice pathogens, causing considerable crop losses around the world. In order to explore the rice blast-resistant sources, initially performed a large-scale screening of 277 rice accessions. In parallel with field evaluations, fifty-two rice accessions were genotyped for 25 major blast resistance genes utilizing functional/gene-based markers based on their reactivity against rice blast disease. According to the phenotypic examination, 29 (58%) and 22 (42%) entries were found to be highly resistant, 18 (36%) and 29 (57%) showed moderate resistance, and 05 (6%) and 01 (1%), respectively, were highly susceptible to leaf and neck blast. The genetic frequency of 25 major blast resistance genes ranged from 32 to 60%, with two genotypes having a maximum of 16 R-genes each. The 52 rice accessions were divided into two groups based on cluster and population structure analysis. The highly resistant and moderately resistant accessions are divided into different groups using the principal coordinate analysis. According to the analysis of molecular variance, the maximum diversity was found within the population, while the minimum diversity was found between the populations. Two markers (RM5647 and K39512), which correspond to the blast-resistant genes Pi36 and Pik, respectively, showed a significant association to the neck blast disease, whereas three markers (Pi2-i, Pita3, and k2167), which correspond to the blast-resistant genes Pi2, Pita/Pita2, and Pikm, respectively, showed a significant association to the leaf blast disease. The associated R-genes might be utilized in rice breeding programmes through marker-assisted breeding, and the identified resistant rice accessions could be used as prospective donors for the production of new resistant varieties in India and around the world

Spatial Distribution Patterns for Identifying Risk Areas Associated with False Smut Disease of Rice in Southern India

False smut disease (FSD) of rice incited by Ustilaginoidea virens is an emerging threat to paddy cultivation worldwide. We investigated the spatial distribution of FSD in different paddy ecosystems of South Indian states, viz., Andhra Pradesh, Karnataka, Tamil Nadu, and Telangana, by considering the exploratory data from 111 sampling sites. Point pattern and surface interpolation analyses were carried out to identify the spatial patterns of FSD across the studied areas. The spatial clusters of FSD were confirmed by employing spatial autocorrelation and Ripley’s K function. Further, ordinary kriging (OK), indicator kriging (IK), and inverse distance weighting (IDW) were used to create spatial maps by predicting the values at unvisited locations. The agglomerative hierarchical cluster analysis using the average linkage method identified four main clusters of FSD. From the Local Moran’s I statistic, most of the areas of Andhra Pradesh and Tamil Nadu were clustered together (at I > 0), except the coastal and interior districts of Karnataka (at I < 0). Spatial patterns of FSD severity were determined by semi-variogram experimental models, and the spherical model was the best fit. Results from the interpolation technique, the potential FSD hot spots/risk areas were majorly identified in Tamil Nadu and a few traditional rice-growing ecosystems of Northern Karnataka. This is the first intensive study that attempted to understand the spatial patterns of FSD using geostatistical approaches in India. The findings from this study would help in setting up ecosystem-specific management strategies to reduce the spread of FSD in India