Rice Blast Disease in Climate Change Times

Bevitori, R.; Ghini, R.

doi:10.4172/2375-4338.1000e111

Cited by 12 publications

(7 citation statements)

References 13 publications

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“…Among various factors responsible for low yield of rice, blast caused by P. oryzae plays an important role which causes yield loss of 11-15% worldwide annually [3]. Under climatic changes such as lacking in water, many rice diseases particularly, blast disease of rice may increase [4,5].…”

Section: Introductionmentioning

confidence: 99%

Detection of Blast Resistant Gene in Rice by Host-pathogen Interaction and DNA-Marker

Hossain¹,

Ali²,

Hossain³

et al. 2018

ujar

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Rice blast disease caused by Pyricularia oryzae is a major disease in Bangladesh. In the search of resistant source against P. oryzae, resistant gene (R-gene) was assessed by (i) host-pathogen interaction and (ii) PCR-based screening at the laboratory of Bangladesh Rice Research Institute. One hundred and forty four plant materials were classified into 10 cultivar groups (CG) based on their reaction patterns to four distinct blast isolates (SDBI; standard differential blast isolate) H-11-64, H-1-8, H-1-1 and H-11-67. The reaction patterns of plant materials to these four standard blast isolates indicated presence of Pish, Pi9, Pita2 and Pita genes and their combination in the genetic background of tested plant materials. Gene specific primer 195R-1/195F-1, Pita440 (YL153)/Pita440 (YL154), OSM89 and AOL45/AOL48 were used to identify and confirm the presence of Pi9, Pita, Pita2 and Pish genes in plant materials, respectively through PCR-based assay. Plant materials, H13, H23, H25, H35, H47, H49, H136, H138 harbored all three genes, Pita, Pita2 and Pish in their genetic background. The Pi9 gene together with Pita2 was detected in local rice cultivar H100 and H129, which were confirmed by DNA analysis in PCR assay. These materials could be used in gene pyramiding in promising high yielding cultivar for durable blast resistance.

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Section: Introductionmentioning

confidence: 99%

Detection of Blast Resistant Gene in Rice by Host-pathogen Interaction and DNA-Marker

Hossain¹,

Ali²,

Hossain³

et al. 2018

ujar

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“…The shaded region highlights the 95% confidence interval region. disease spread and variation in the intensity is dependent on the spatial distribution of weather, already, it has been shown that changes in climate are expected to affect the spread of diseases such as rice blast [8] [17]. Therefore, to be able to appropriately implement disease forecasting models for effective controlling of the disease [18], there is need to provide such weather variables at high spatial and temporal resolutions [10].…”

Section: Discussionmentioning

confidence: 99%

“…These weather variables are well established correlates of severity and abundance of common plant diseases [7]. Modelling the spatial variation, especially in the future would therefore be useful in mitigating against the occurrence of plant diseases in the process providing empirical evidence that can be used for optimally targeted control measures [8]. More so, providing such information at high resolutions can help in improving the accuracy of disease occurrence prediction models.…”

Section: Introductionmentioning

confidence: 99%

Spatial Modelling of Weather Variables for Plant Disease Applications in Mwea Region

Ouma¹,

Odera²,

Mukundi³

2016

GEP

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Climate change is expected to affect the agricultural systems, such as crop yield and plant disease occurrence and spread. To be able to mitigate against the negative impacts of climate change, there is a need to use early warning systems that account for expected changes in weather variables such as temperature and rainfall. Moreover, providing such information at high spatial and temporal resolutions can be useful in improving the accuracy of an early warning system. This paper describes a methodology that can be used to produce high spatial and temporal resolutions of minimum temperature, maximum temperature and rainfall in an agricultural area. We utilize MarkSim GCM, a weather file generator that incorporates IPCC based climate change models to downscale the weather variables at monthly intervals. An ensemble of 17 GCM models is used within the RCP 8.0 emission scenario within the latest model based CMIP5. We first assess the usability of the model, by comparing results produced to what has been recorded at weather station level over a vast region. Then, we estimate the correction factors for model results by implementing a linear regression that is used to assess the relationship between the variables and the deviation of model outputs to the weather station data. Finally, we use kriging geostatistical technique to interpolate the weather data, for the year 2010. Results indicated that the model overestimated the results of maximum temperature, while underestimating the result of minimum temperature. Variability in the recorded weather variables was also evident, indicating that the response variables such as plant disease severity dependent on such weather information could vary in the area. These datasets can be useful especially in predicting the occurrence of plant diseases, which are affected by either rainfall or temperature.

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“…Pyricularia oryzae fungus forms spot on rice plants. Morphologically, the typical shape of leaf blast spot is rhombus with two tapered edges (Asyuma 1965;Pasha et al 2013;Bevitori and Ghini 2015). The fully-developed spot has brown edges and is dark green to bluish gray in color.…”

Section: Spot Sizementioning

confidence: 99%

Short Communication: Biocontrol activity of Phyllosphere fungi on mungbean leaves against Cercospora canescens

Sopialena

Palupi

2017

Biodiversitas

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Sopialena, Palupi PJ. 2017. Study of climatic factors on the population dynamics of Pyricularia oryzae on some varieties of paddy rice (Oryza sativa). Biodiversitas 18: 701-708. The occurrence of plant diseases is influenced by three factors: pathogen, environment, and host. Environment playing important role in the development of the disease is the climatic factor, whereas the resistance of plants also influences the development of disease. The research was conducted during five months from April to August 2016 and the location of the research was in paddy fields in Tanah Merah, North Samarinda, East Kalimantan, Indonesia. The study aims to determine the climatic factors (temperature and humidity) which are most dominant to the rate of spot width, the infection rate of pathogen attack intensity on Pyricularia oryzae and the development of P. oryzae spores in several varieties of paddy rice (Inpari 7, Ciherang and Cibogo). The parameters in the study are climate factors namely humidity and temperature, the width rate of spot blast disease, the attack intensity of pathogenic P. oryzae and the spores number of P. oryzae. Research shows that the population of P. oryzae is strongly influenced by temperature. Low temperature is the most dominant factor affecting the rate of infection of blast disease. The number of pathogen population also affects the attack intensity of the disease. A higher population of P. oryzae led to higher intensity of blast disease in rice plants. Moreover, varieties of rice plants also affect P. oryzae attack. The more vulnerable the plants, the higher level of P. oryzae attack on the plant. The lowest intensity attack of the disease is found in Ciherang, then Cibogo and the most vulnerable is Inpari 7.

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Rice Blast Disease in Climate Change Times

Cited by 12 publications

References 13 publications

Detection of Blast Resistant Gene in Rice by Host-pathogen Interaction and DNA-Marker

Detection of Blast Resistant Gene in Rice by Host-pathogen Interaction and DNA-Marker

Spatial Modelling of Weather Variables for Plant Disease Applications in Mwea Region

Short Communication: Biocontrol activity of Phyllosphere fungi on mungbean leaves against Cercospora canescens

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