Influence of weather parameters on powdery mildew of mango inflorescence in humid tropics of South Gujarat

Bana, JK; Choudhary, Jaipal Singh; Ghoghari, PD; Sharma, H. M.; Kumar, Sushil; Patil, S. J.

doi:10.54386/jam.v22i4.473

Cited by 7 publications

(5 citation statements)

References 4 publications

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“…The present results can be used for better decision making well in advance to check the incidence and disease development in accordance with the weather. The present findings are corroborated by the works done by Gupta and Sharma (2009), Gupta and Sharma (2005) and Bana et al (2020).…”

Section: Effect Of Epidemiological Parameters On Powdery Mildew Of Ap...supporting

confidence: 91%

Prevalence and Epidemiology of Apple Powdery Mildew in Northwest Himalayan Region of Himachal Pradesh

Neg,

Tomar,

Bhandari

et al. 2024

IJEP

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A survey was conducted to assess the incidence and severity of powdery mildew of apples during April to July of 2020 and 2021 at the different locations of Kinnaur district, Himachal Pradesh, India. Assessment scale of Hofer et al. (2019) with slight modification was used for cultivar screening. Apple seedlings were grown in the pots in the polyhouse of the Department of Plant Pathology in order to test the pathogenicity of Podosphaera leucotricha. Pathogenicity was confirmed through inoculation tests by gently pressing diseased leaves on young leaves of five asymptomatic, potted one-year-old Gale Gala seedlings of apple. Five non-inoculated plants were used for a control treatment. Overall, twenty different apple cultivars were screened against powdery mildew planted under field conditions at Krishi Vigyan Kendra Sharbo, Kinnaur, Himachal Pradesh, India. Out of these cultivars, five cultivars viz. Red Delicious, Royal Delicious, Red Velox, Auvil Early Fuji and Oregon Spur II, were found to be less susceptible to Podosphaera leucotricha, causing powdery mildew of apples, while Granny Smith, Golden Delicious, and Gale Gala were found extremely susceptible. Gala series of cultivars were found to be extremely susceptible to the pathogen. Incidence of the disease ranged from 25.42% to 53.80% and severity from 39.75% to 66.25%, respectively. According to the present study the disease initiated during the first week of May and peaked in the month of July in district Kinnaur. Temperatures between 18–24°C and RH between 50–65% were found to be favourable for the disease development. The conditions in the region were found to favourable for disease development and spread.

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Section: Effect Of Epidemiological Parameters On Powdery Mildew Of Ap...supporting

confidence: 91%

Prevalence and Epidemiology of Apple Powdery Mildew in Northwest Himalayan Region of Himachal Pradesh

Neg,

Tomar,

Bhandari

et al. 2024

IJEP

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“…Similarly, relative humidity, rainfall and wind speed in the range of 85 -95%, 15.5 -20.75 mm and 1.0 -5.5 Km/h, respectively also found conducive for potato late blight disease and its development. Bana et al, (2020) showed that the disease incidence and disease severity of powdery mildew have significant negative relationship with morning relative humidity (r = -0.631; p<0.05 and r = -0.721; p<0.01) and average relative humidity (r = -0.766 and r = -0.787; p<0.01). The temperature (maximum and average) and evaporation showed positive relationship with disease incidence and severity of the disease.…”

Section: Multiple Regression Modelmentioning

confidence: 99%

Forecasting models for forewarning Anthracnose and web blight of mung bean (Vigna radiata) under Tarai zone of Uttarakhand

et al. 2023

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Mung bean (Vigna radiata L.) is attacked by numerous diseases of which anthracnose and web blight are predominant in Tarai Zone of Uttarakhand. Anthracnose and web blight of mung bean are caused by Colletotrichum lindemuthianum and Rhizoctonia solani, respectively. Their occurrence and development are highly influenced by weather conditions during the cropping season. Based on epidemiological data recorded at Pantnagar for two consecutive years (2019 and 2020), disease predictive models were developed using stepwise multiple regression. The result from the recorded data revealed that rainfall, T(min), rainy days, and morning relative humidity were statistically significant. Whereas, the T(max), evening relative humidity, and bright sunshine hours were statistically non-significant. Significant weather parameters were employed to develop suitable web blight and anthracnose prediction models for commonly grown varieties of mung bean. The prediction models were further validated using the web blight and anthracnose incidence data collected in mung bean varieties in 2021. The root mean square error values varied between 0.0002 – 0.0011, which shows that the models are accurate and acceptable.

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“…Traditional statistical approaches (e.g., stepwise linear regression, multivariable linear regression, logistic regression, etc.) have shown limitations for outbreak predictions [15,[29][30][31]. Recent machine-learning approaches such as neural networks, decision trees, random forests, and support vector machines demonstrate an improved predictive accuracy for disease prediction [30,[32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Meteorological Impacts on Rubber Tree Powdery Mildew and Projections of Its Future Spatiotemporal Pattern

Kong,

Wu,

Cao

et al. 2024

Agriculture

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Meteorological conditions play a crucial role in driving outbreaks of rubber tree powdery mildew (RTPM). As the climate warms and techniques improve, rubber cultivation is expanding to higher latitudes, and the changing climate increases the RTPM risk. Rubber plantations on Hainan Island, situated on the northern margin of the tropics, have been selected as a case study to explore the meteorological mechanisms behind RTPM outbreaks quantitatively using a structural equation model, and project current and future RTPM outbreak patterns under different climate change scenarios by building predictive models based on data-driven algorithms. The following results were obtained: (1) days with an average temperature above 20 °C and days with light rain were identified as key meteorological drivers of RTPM using structural equation modeling (R2 = 0.63); (2) the Bayesian-optimized least-squares boosted trees ensemble model accurately predicted the interannual variability in the historical RTPM disease index (R2 = 0.79); (3) currently, due to the increased area of rubber plantations in the central region of Hainan, there is a higher risk of RTPM; and (4) under future climate scenarios, RTPM shows a decreasing trend (at a moderate level), with oscillating and sporadic outbreaks primarily observed in the central and northwest regions. We attribute this to the projected warming and drying trends that are unfavorable for RTPM. Our study is expected to enhance the understanding of the impact of climate change on RTPM, provide a prediction tool, and underscore the significance of the climate-aware production and management of rubber.

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Influence of weather parameters on powdery mildew of mango inflorescence in humid tropics of South Gujarat

Cited by 7 publications

References 4 publications

Prevalence and Epidemiology of Apple Powdery Mildew in Northwest Himalayan Region of Himachal Pradesh

Prevalence and Epidemiology of Apple Powdery Mildew in Northwest Himalayan Region of Himachal Pradesh

Forecasting models for forewarning Anthracnose and web blight of mung bean (Vigna radiata) under Tarai zone of Uttarakhand

Meteorological Impacts on Rubber Tree Powdery Mildew and Projections of Its Future Spatiotemporal Pattern

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