Progress and severity of early blight of tomato in relation to weather variables in Jammu province

Gupta, Vishal; Razdan, V. K.; Sharma, Siddhartha; Fatima, Kausar

doi:10.54386/jam.v22i2.168

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…In the present studies, different models were generated using MLR out of which the model with the lower AIC value was selected and it was observed that variables like minimum temperature, morning and evening relative humidity and sunshine hours resulted in ~92%–95% and 97%–99% variation for PDI in all the varieties during 2019 and 2020, respectively. Prediction of disease in different crops using step wise regression analysis was reported by various workers (Biswas et al, 2011; Gupta et al, 2017, 2020). All the assumptions related to the developed model were acceptable in all the test varieties during both the years.…”

Section: Discussionmentioning

confidence: 92%

“…selected and it was observed that variables like minimum temperature, morning and evening relative humidity and sunshine hours resulted in ~92%-95% and 97%-99% variation for PDI in all the varieties during 2019 and 2020, respectively. Prediction of disease in different crops using step wise regression analysis was reported by various workers (Biswas et al, 2011;Gupta et al, 2017Gupta et al, , 2020. the lesser the RMSE value, the better a model is able to fit in a given dataset.…”

Section: Assumptions Of Mlr For Sheath Rot Of Ricementioning

confidence: 98%

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Effect of abiotic factors on progress and severity of sheath rot (Sarocladium oryzae) in rice

Mehta

Singh

Wani

et al. 2023

Journal of Phytopathology

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Sheath rot, caused by Sarocladium oryzae, is an emerging serious threat in rice cultivating regions especially in Union Territory of Jammu & Kashmir, causing yield losses of 20%-85%. Little and scattered information is available on the epidemiology of the disease. Hence, the field experiments were conducted during the cropping seasons of 2019 and 2020 to find out the effect of weather factors on the development of the disease as well as computation of predictive model for its initiation and spread in the field in five popular and commercially grown rice varieties viz. PB-1509, PB-1401, PR-114, PB-1121 and Arize 6444 Gold. The higher mean percent disease index (PDI) of 37.3% was recorded during 2019, whereas it was 33.8% during 2020. The effect of cropping seasons (years) on PDI, area under disease progress curve (AUDPC) and infection rate (r) was significant. Significant and negative correlation was observed between PDI and selected meteorological parameters, that is maximum and minimum temperature and relative humidity in the evening during both the cropping seasonsThe PDI had positive and significant correlation with relative humidity in the morning during the cropping season 2020. Rainfall showed significant and negative correlation with PDI in all the varieties during 2019 whereas it was non-significant and positive during 2020 in all the varieties except PR-114 where, it was positive and significantly correlated. Minimum temperature, morning and evening relative humidity and sunshine hours resulted in ~92%-95% and 97%-99% variation for PDI in all the varieties during the cropping seasons 2019 and 2020, respectively. Sheath rot was the maximum recorded at minimum temperature (11.5-20.0°C), high morning relative humidity (74.0%-94.0%) and moderate evening relative humidity (26.0%-75.0%) and sunshine hours of 2.8-9.3 h, respectively. All the assumptions related to the developed model were acceptable as the global stat, skewness, kurtosis, link function and heteroscedasticity tests had the least p-values in all the varieties during both the cropping seasons. Hence the model may be used for the prediction of sheath rot, which would be helpful in the early adoption, precise and judicious management practices.

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Section: Discussionmentioning

confidence: 92%

Section: Assumptions Of Mlr For Sheath Rot Of Ricementioning

confidence: 98%

Effect of abiotic factors on progress and severity of sheath rot (Sarocladium oryzae) in rice

Mehta

Singh

Wani

et al. 2023

Journal of Phytopathology

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“…Singh et al [8] concluded that temperature (maximum and minimum) had a significant positive correlation with disease intensity whereas relative humidity (morning and evening) was negatively correlated with tomato early blight intensity in Haryana. Gupta et al [9] reported maximum and minimum relative humidity along with rainfall had a significantly negative correlation with the disease intensity of early blight of tomato in Jammu. Early blight on lower leaves may be more severe than on upper leaves because the environment is most highly favorable within the crop canopy.…”

Section: Correlation Studiesmentioning

confidence: 97%

Effect of Weather Factors on Severity of Early Blight Incited by Alternaria solani (Ellis and Martin) Jones and Grout in Potato

Patel,

Gohel,

Prajapati

et al. 2024

IJPSS

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A study was conducted to find out the influence of weather factors, temperature, relative humidity, bright sunshine hour, rainfall, evaporation and wind speed and spore load on the incidence of Alternaria solani (Ellis and Martin) Jones and Grout induced early blight of potato (Solanum tuberosum L.) during 2019 to 2022. Eight weather factors taken from the agrometeorological observatory located at Anand Agricultural University, Anand, Gujarat, India, and were subjected to correlation analysis. The relationship between early blight and concerned weather parameters during Rabi 2020-21 revealed a highly significant positive correlation with evaporation (0.87) and maximum temperature (0.84) at the 0.01 level. PDI was significantly positively correlated with spore load (0.71) and bright sunshine hour (0.64) at the 0.05 level. Whereas Per cent Disease Intensity was negatively correlated with evening humidity (-0.78) at a 0.01 level. During the year Rabi (winter) 2021-22, a substantial negative correlation with evening humidity at the 0.05 level. PDI was positively correlated with spore load (0.79), bright sunshine (0.73) and evaporation (0.68).

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Prediction of tomato plants infected by fungal pathogens at different disease severities using E-nose and GC–MS

Sun,

Zheng

2024

J Plant Dis Prot

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Disease is a serious problem in tomato plant, causing huge economic loss. Disease detection is the premise of protection. This paper employed Electronic nose (E-nose) and Gas Chromatography-Mass Spectrometer (GC-MS), as an auxiliary technique, to identify disease type and its severity in the tomato plant. A total of twenty-ve volatile constituents were identi ed using GC-MS, their concentrations were calculated and showed the difference in different groups. Furthermore, the results of E-nose and GC-MS were compared and showed a good correlation. Moreover, the possibility of E-nose in discriminating tomato plants infected with different types and severities of disease either respectively or together was proved based on both Principal Component Analysis (PCA) and Discriminant Functions Analysis (DFA). Then, Back-propagation neural network (BPNN) was introduced and showed that the correct classi cation rates were 98.3% for training set and 97.5% for testing set for predicting disease type and severity. This study demonstrates the feasibility of E-nose in detecting tomato plants with different disease types and severities. E-nose is an excellent technique for disease identi cation, which is very meaningful for prevention of disease spread and meets actual application needs.

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Progress and severity of early blight of tomato in relation to weather variables in Jammu province

Cited by 4 publications

References 5 publications

Effect of abiotic factors on progress and severity of sheath rot (Sarocladium oryzae) in rice

Effect of abiotic factors on progress and severity of sheath rot (Sarocladium oryzae) in rice

Effect of Weather Factors on Severity of Early Blight Incited by Alternaria solani (Ellis and Martin) Jones and Grout in Potato

Prediction of tomato plants infected by fungal pathogens at different disease severities using E-nose and GC–MS

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