Assessment of partial resistance to rice blast disease

Ak, Mukherjee; Mohapatra, NK; Nayak, P.

doi:10.5958/2249-5266.2018.00046.2

Cited by 4 publications

(6 citation statements)

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“…Due to the short-lived nature of complete resistance to blast, the development of rice varieties with quantitative resistance (also called partial, rate-reducing, or slowblasting) is vital for disease resistance breeding [11,12]. Partial resistance in rice is a form of incomplete resistance characterized by reduced pathogen growth and reproduction [13,14]. This type of resistance is attributed to different components e.g., reduced infection frequency, longer latent period, lesion size, and reduced sporulation [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“…Partial resistance in rice is a form of incomplete resistance characterized by reduced pathogen growth and reproduction [13,14]. This type of resistance is attributed to different components e.g., reduced infection frequency, longer latent period, lesion size, and reduced sporulation [13][14][15]. The result is a diminished potential for inoculum production and a decreased likelihood of a blast epidemic.…”

Section: Introductionmentioning

confidence: 99%

“…Assessing a large number of rice genotypes based on their reaction to all components of partial resistance is difficult because they interact among themselves, and their effects are cumulative during the course of disease development [15]. Moreover, the severity of blast is often measured in the field by the end of the rice growth period and represents the cumulative result of all components of partial resistance [14]. While visual assessment of disease severity is a common method for selecting blastresistant rice varieties in the field, it may not provide detailed information about the underlying mechanisms of partial blast resistance [14].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the severity of blast is often measured in the field by the end of the rice growth period and represents the cumulative result of all components of partial resistance [14]. While visual assessment of disease severity is a common method for selecting blastresistant rice varieties in the field, it may not provide detailed information about the underlying mechanisms of partial blast resistance [14]. It is essential to estimate the contribution of each component of partial resistance to disease development followed by selection for a single component [15,20].…”

Section: Introductionmentioning

confidence: 99%

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Hyperspectral imaging for quantifying Magnaporthe oryzae sporulation on rice genotypes

Maina,

Oerke

2024

Plant Methods

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Background Precise evaluation of fungal conidia production may facilitate studies on resistance mechanisms and plant breeding for disease resistance. In this study, hyperspectral imaging (HSI) was used to quantify the sporulation of Magnaporthe oryzae on the leaves of rice cultivars grown under controlled conditions. Three rice genotypes (CO 39, Nipponbare, IR64) differing in susceptibility to blast were inoculated with M. oryzae isolates Guy 11 and Li1497. Spectral information (450–850 nm, 140 wavebands) of typical leaf blast symptoms was recorded before and after induction of sporulation of the pathogen. Results M. oryzae produced more conidia on the highly susceptible genotype than on the moderately susceptible genotype, whereas the resistant genotype resulted in no sporulation. Changes in reflectance spectra recorded before and after induction of sporulation were significantly higher in genotype CO 39 than in Nipponbare. The spectral angle mapper algorithm for supervised classification allowed for the classification of blast symptom subareas and the quantification of lesion areas with M. oryzae sporulation. The correlation between the area under the difference spectrum (viz. spectral difference without and with sporulation) and the number of conidia per lesion and the number of conidia per lesion area was positive and count-based differences in rice - M. oryzae interaction could be reproduced in the spectral data. Conclusions HSI provided a precise and objective method of assessing M. oryzae conidia production on infected rice plants, revealing differences that could not be detected visually.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hyperspectral imaging for quantifying Magnaporthe oryzae sporulation on rice genotypes

Maina,

Oerke

2024

Plant Methods

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“…The AMMI model is a hybrid analysis that incorporates both the additive and multiplicative components of the two way data structure. AMMI biplot analysis is considered to be an effective tool to diagnose GEI patterns graphically (Mukherjee et al, 2013). The model separates the additive variance from the multiplicative variance and then applies Principal Component Analysis (PCA) to the interaction portion to a new set of coordinate axes that explains in more detail the interaction pattern and the estimation accomplished using the least squares principle (Thillainathan and Fernandez, 2001).…”

Section: Introductionmentioning

confidence: 99%

Additive main effects and multiplicative interaction (AMMI) and genotype main effect and genotype by environment interaction (GGE) biplot analysis of large white bean (Phaseolus vulgaris L.) genotypes across environments in Ethiopia

Firew¹,

Amsalu²,

Tsegaye³

2019

Afr. J. Agric. Res.

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Undertaking a multi-environment trial prior to releasing a high yielding and stable varieties for a specific environment is a major step in plant breeding. Therefore, the objective of this work was to study the effect of Genotype × Environment Interaction (GEI) and evaluate the adaptability and stability of sixteen large white common bean genotypes. Additive main effects and multiplicative interaction (AMMI) and genotype main effect and genotype by environment interaction (GGE) biplot models were used. The experimental design was 4 × 4 triple lattice across environments. AMMI analysis of variance showed environments that explained the greater proportion (72.42%) of the total variation, followed by GEI (10.75%) and genotype (2.32%). This indicates the possibility of selecting stable genotypes. AMMI biplot analysis revealed that the first and second interaction axes captured 42.62 and 26.77% of the total variation due to GEI. GGE model showed that the nine environments used for the study belonged to two mega-environments. AMMI stability value (ASV), AMMI and GGE biplot identified one common genotype, G14 (SAA 2) that was the overall best in performance in relation to yield and stability. This suggests that for reliability and optimum result it is better to combine the result of two or more analytical tools for yield and stability in recommendation genotype for verification and release.

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Linkage of SSR markers with rice blast resistance and development of partial resistant advanced lines of rice (Oryza sativa) through marker-assisted selection

Nihad

Hasan

Kabir

et al. 2022

Physiol Mol Biol Plants

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Assessment of partial resistance to rice blast disease

Cited by 4 publications

References 0 publications

Hyperspectral imaging for quantifying Magnaporthe oryzae sporulation on rice genotypes

Hyperspectral imaging for quantifying Magnaporthe oryzae sporulation on rice genotypes

Additive main effects and multiplicative interaction (AMMI) and genotype main effect and genotype by environment interaction (GGE) biplot analysis of large white bean (Phaseolus vulgaris L.) genotypes across environments in Ethiopia

Linkage of SSR markers with rice blast resistance and development of partial resistant advanced lines of rice (Oryza sativa) through marker-assisted selection

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