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<p>Spot blotch of wheat, caused by <i>Bipolaris sorokiniana</i>, poses a devastating threat to wheat production globally. Although the pathogen infects the crop across all growth stages, the disease susceptibility tends to increase with an increase in crop maturity, potentially resulting in a yield loss of up to 60%. Additionally, the alterations in climatic conditions during the cropping season have increased the impact of the disease on standing crop. While management strategies are available for spot blotch disease, the indiscriminate use of chemical management strategies poses environmental as well as economic concerns. This necessitates the development of suitable risk analysis models, which would help in accurate forewarning and subsequent scheduling of management strategies. To address this gap, a study was conducted with five wheat genotypes grown during three sowing dates, i.e. early (20th November), timely (5th December), and late (20th December) for two consecutive years 2021–2022 and 2022–2023. The results revealed that early sowing exhibited the minimum disease, followed by timely sowing, with the maximum observed in late sowing. Correlation study revealed that disease severity had a significant positive correlation with maximum temperature, minimum temperature, and evaporation under early, timely and late sown conditions. The disease risk analysis, utilizing meteorological parameters (maximum temperature, minimum temperature, evaporation, and bright sunshine hours), established an equation, L.D = −&#xa0;0.4369222<i>T</i><sub>max</sub> + 0.1778705<i>T</i><sub>min</sub>&#xa0;−&#xa0;3.3430632<i>E</i> + 0.6574703BSH. The resistant reactions were predicted when the maximum temperature (<i>T</i><sub>max</sub>), minimum temperature (<i>T</i><sub>min</sub>), evaporation (<i>E</i>) and bright sunshine hour (BSH) were 24&#xa0;°C, &lt; 10&#xa0;°C, 1.0&#xa0;mm and less than 6&#xa0;h, respectively. Furthermore, the confusion matrix was able to predict the periods of risk and no risk with 88.23% accuracy using maximum temperature, minimum temperature, evaporation and bright sunshine hours as prediction parameters.</p>

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Disease risk analysis of spot blotch of wheat under different dates of sowing for Indo-Gangetic plains of India

  • 期刊论文

  • 发表日期：

    2025年2月19日

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  • Md. Asfakuddin Molla,
  • Sunita Mahapatra,
  • Sunanda Chakraborty,
  • Anubhab Hooi,
  • Poly Saha,
  • Srikanta Das,
  • B. Teja Bhushan,
  • Anirban Maji

摘要

Spot blotch of wheat, caused by Bipolaris sorokiniana, poses a devastating threat to wheat production globally. Although the pathogen infects the crop across all growth stages, the disease susceptibility tends to increase with an increase in crop maturity, potentially resulting in a yield loss of up to 60%. Additionally, the alterations in climatic conditions during the cropping season have increased the impact of the disease on standing crop. While management strategies are available for spot blotch disease, the indiscriminate use of chemical management strategies poses environmental as well as economic concerns. This necessitates the development of suitable risk analysis models, which would help in accurate forewarning and subsequent scheduling of management strategies. To address this gap, a study was conducted with five wheat genotypes grown during three sowing dates, i.e. early (20th November), timely (5th December), and late (20th December) for two consecutive years 2021–2022 and 2022–2023. The results revealed that early sowing exhibited the minimum disease, followed by timely sowing, with the maximum observed in late sowing. Correlation study revealed that disease severity had a significant positive correlation with maximum temperature, minimum temperature, and evaporation under early, timely and late sown conditions. The disease risk analysis, utilizing meteorological parameters (maximum temperature, minimum temperature, evaporation, and bright sunshine hours), established an equation, L.D = − 0.4369222Tmax + 0.1778705Tmin − 3.3430632E + 0.6574703BSH. The resistant reactions were predicted when the maximum temperature (Tmax), minimum temperature (Tmin), evaporation (E) and bright sunshine hour (BSH) were 24 °C, < 10 °C, 1.0 mm and less than 6 h, respectively. Furthermore, the confusion matrix was able to predict the periods of risk and no risk with 88.23% accuracy using maximum temperature, minimum temperature, evaporation and bright sunshine hours as prediction parameters.