Screening of local, improved and hybrid rice genotypes against leaf blast disease (Pyricularia oryzae) at Banke district, Nepal

Acharya, Basistha; Shrestha, Suraj; Manandhar, Hira Kaji; Chaudhary, B. D.

doi:10.3126/janr.v2i1.26013

Cited by 12 publications

(9 citation statements)

References 10 publications

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“…Its incidence and severity used to be very high until a few years back especially in inner terai and foot hills where one of the most popular varieties Mansuli was cultivated and in valleys of mid hills growing Taichung-176 and Chainan-242 (Bhandari et al, 2017). In Nepal, it is locally known as "Maruwa rog" (Acharya et al, 2019).…”

Section: History and Occurrence Of Blast Diseasementioning

confidence: 99%

“…Symptoms of leaf blast typically consist of elongated diamond-shaped lesions with grey or whitish centers and brown or reddish-brown margin (Acharya et al, 2019). Later, the small spots collapse together, becomes eyeshaped in mature stage and give blistering appearance (Neupane & Bhusal, 2021).…”

Section: Symptoms On Rice Leafmentioning

confidence: 99%

“…Furthermore, rice production is affected by biotic and abiotic factors (Acharya et al, 2019). Drought, cold, acidity, salinity are abiotic factors while pests, weeds and diseases are biotic factors (Onyango, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Rice Blast, A Major Threat to the Rice Production and its Various Management Techniques

Simkhada

Thapa

2022

Turkish JAF Sci.Tech.

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Rice (Oryza sativa L.) is the most important staple cereal crop which is consumed by more than 50% of world population. It contributes 23% and 50% of total calories consumed by world and Nepalese population respectively. Among various abiotic factors affecting rice, rice blast is the most disastrous, causing 70-80% yield loss. This disease was originated in China around 7000 years ago. In Nepal, it was first reported in Thimi, Bhaktapur in 1966. It is caused by a filamentous ascomycete fungus Magnaporthe oryzae (Anaemorphic form- Pyricularia oryzae). It infects all the developmental stage of plant and produce symptoms on the leaf, collar, neck, panicle and even in the glumes. It decreases the rice production by an amount, enough to feed 60 million people every year. Cloudy weather, high relative humidity (93-99%), low night temperature (15- 20°C), longer duration of dew is the most favorable condition for the outbreak of disease. The most usual approaches for the management of rice blast diseases are management in nutrient fertilizer and irrigation, application of fungicides and plantation of resistant cultivars. Besides, the use of extracts of C. arabica are reported to have an inhibitory effect on the disease. Seed treatment with Trichoderma viridae @ 5ml/lit of water have also been found effective. The chemical means of controlling blast disease shall be reduced, instead eco-friendly measures like biocontrol agents, resistant varieties, plant extracts can be practiced for disease control. Different forecasting model can be used in order to predict the disease prevalence.

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Section: History and Occurrence Of Blast Diseasementioning

confidence: 99%

Section: Symptoms On Rice Leafmentioning

confidence: 99%

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Rice Blast, A Major Threat to the Rice Production and its Various Management Techniques

Simkhada

Thapa

2022

Turkish JAF Sci.Tech.

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“…and abiotic (such as drought, cold, salt, etc.) factors (Acharya et al, 2019). Fungal diseases are anticipated to reduce worldwide rice production by 14% annually (Singh et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Analysis of Rice Blast Resistance Dominant Genes and their Combinations in Japonica Rice

Fengshun,

Ran,

Hassan

et al. 2024

Physiologia Plantarum

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Rice blast is a disastrous fungal disease affecting rice (Oryza sativa L.) and undermines world food security. Different ecological conditions and genetic backgrounds altered the effectiveness of rice blast resistance genes. Anhui (China) is a high‐incidence area and has long been affected by rice blast. Despite chemical or other control methods of rice blast, analyzing the effects of specific rice blast resistance genes within the Anhui region, identifying favorable genes (combinations), and incorporating them into the process of new variety selection and breeding programs are effective ways to improve rice blast resistance. This research study examined the distribution of 16 rice blast resistance genes (i.e., Pi‐ta, Pi5, Pi1, Pia, etc.) in a recombinant self‐cross population of japonica rice and analyzed their correlation to panicle neck blast resistance. Five genes, Pit, Pi‐ta, Pik, Pb1, and Pi54, were identified and evaluated as dominant genes to improve panicle neck blast resistance. Further comparisons of the neck blast resistance levels between various gene combinations revealed that Pi54, when combined with Pb1/Pi‐ta (Pi54 + Pb1, Pi54 + Pi‐ta), could further increase resistance to panicle neck blast; and the highest resistance level was found with the three‐gene combination of Pi54 + Pi‐ta + Pb1. Meanwhile, the neck blast resistance capacity of Pi54 and Pb1 was significantly improved after polymerization with Pit and Pik (Pik + Pi54, Pit + Pi54, Pit + Pb1). Therefore, gene polymerization is widely regarded as a successful strategy to breed varieties with long‐lasting and broad‐spectrum resistance. It is anticipated that rice blast tolerance capacity will be considerably improved by gene polymerization of dominant genes.

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“…It affects all plant developmental stages and causes symptoms on the leaf, collar, neck, panicle, and even in the glumes. Leaf blast symptoms often consist of long, diamond-shaped lesions with a brown or reddish-brown edge and a grey or whitish center (Acharya et al, 2019). Cool and rainy conditions, water stress in the nursery, and excessive nitrogen field dressing all contribute to the disease's growth.…”

Section: Introductionmentioning

confidence: 99%

Spectral characterization and severity assessment of rice blast disease using univariate and multivariate models

et al. 2023

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Rice is the staple food of more than half of the population of the world and India as well. One of the major constraints in rice production is frequent occurrence of pests and diseases and one of them is rice blast which often causes yield loss varying from 10 to 30%. Conventional approaches for disease assessment are time-consuming, expensive, and not real-time; alternately, sensor-based approach is rapid, non-invasive and can be scaled up in large areas with minimum time and effort. In the present study, hyperspectral remote sensing for the characterization and severity assessment of rice blast disease was exploited. Field experiments were conducted with 20 genotypes of rice having sensitive and resistant cultivars grown under upland and lowland conditions at Almora, Uttarakhand, India. The severity of the rice blast was graded from 0 to 9 in accordance to International Rice Research Institute (IRRI). Spectral observations in field were taken using a hand-held portable spectroradiometer in range of 350-2500 nm followed by spectral discrimination of different disease severity levels using Jeffires–Matusita (J-M) distance. Then, evaluation of 26 existing spectral indices (r≥0.8) was done corresponding to blast severity levels and linear regression prediction models were also developed. Further, the proposed ratio blast index (RBI) and normalized difference blast index (NDBI) were developed using all possible combinations of their correlations with severity level followed by their quantification to identify the best indices. Thereafter, multivariate models like support vector machine regression (SVM), partial least squares (PLS), random forest (RF), and multivariate adaptive regression spline (MARS) were also used to estimate blast severity. Jeffires–Matusita distance was separating almost all severity levels having values >1.92 except levels 4 and 5. The 26 prediction models were effective at predicting blast severity with R2 values from 0.48 to 0.85. The best developed spectral indices for rice blast were RBI (R1148, R1301) and NDBI (R1148, R1301) with R2 of 0.85 and 0.86, respectively. Among multivariate models, SVM was the best model with calibration R2=0.99; validation R2=0.94, RMSE=0.7, and RPD=4.10. The methodology developed paves way for early detection and large-scale monitoring and mapping using satellite remote sensors at farmers’ fields for developing better disease management options.

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Screening of local, improved and hybrid rice genotypes against leaf blast disease (Pyricularia oryzae) at Banke district, Nepal

Cited by 12 publications

References 10 publications

Rice Blast, A Major Threat to the Rice Production and its Various Management Techniques

Rice Blast, A Major Threat to the Rice Production and its Various Management Techniques

Analysis of Rice Blast Resistance Dominant Genes and their Combinations in Japonica Rice

Spectral characterization and severity assessment of rice blast disease using univariate and multivariate models

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