Predictive Modelling and Epidemiological Forecasting of Sclerotinia Rot in Brassica juncea under climatic variability in Indian conditions

Pankaj Sharma^1,2,3*Pramod Kumar Rai^2,4Prabhu Dayal Meena^5,6Hariom Kumar Sharma^5,7Vijay Veer Singh^5,8Shravani Sanyal^2,7Niranjan Prasad^2,7Jitendra Kachchwaha^10,9Navin Chandra Gupta^11,7Anubhuti Sharma^5,7Nitesh Rattan Bharadwaj^5,7

• ¹Directorate of Rapeseed Mustard Research (DRMR), Bharatpur, India
• ²ICAR - National Institute of Biotic Stress Management, Raipur, India
• ³Joint Director, Raipur, India
• ⁴Director, Raipur, India
• ⁵ICAR - Directorate of Rapeseed-Mustard Research, Bharatpur, India
• ⁶Scientist, bharthpur, India
• ⁷scientist, Raipur, India
• ⁸Director, Bharthpur, India
• ⁹Watershed Organisation Trust, Pune, India
• ¹⁰scientist, pune, India
• ¹¹ICAR - National Institute for Plant Biotechnology, New Delhi, India

Sclerotinia rot (SR), caused by Sclerotinia sclerotiorum, poses a significant threat to Indian mustard (Brassica juncea L.), cultivated across major oilseed-growing regions in India. A long term field study was conducted from 2009-10 to 2021-22 to investigate the role of key agro-meteorological parameters on influencing SR incidence under three sowing windows namely, 8 October (early), 29 October(timely) and 19 November (late sown). Weekly meteorological variables, including maximum and minimum temperature(°C), relative humidity (%) during morning (07:20hr) and afternoon (14:20 hr), rainfall(mm), wind speed(km/hr), evaporation(mm), bright sunshine hours (BSSH) were collected and used to develop regression-based weather indices and random forest models to develop robust predictive models for effective forecasting. Results revealed that 29 October sowing window was consistently associated with the highest predicted SR risk (upto 39.4%), when maximum temperature hovered around 18-20°C, RH exceeded 94% in morning and BSSH fell below 3.8 hours. A strong negative correlation (R2=0.86) was observed between BSSH and SR incidence, particularly in the 29 October sowing window. Petal infestation studies confirmed early colonization pressure, with percent petal infection peaking at 20.7% during the second week of January AUPPC which provides condensed weekly petal infestation trajectories into a single measure of inoculum pressure depicts highest epidemic pressure in mid sowing window. Disease forecasting models incorporating weighted weather indices demonstrated high predictive accuracy with R2 values of 0.75, 0.76 and 0.78 for early, timely and late sowing dates respectively, when validated with 2022-23 observations. Future predictions using Random Forest model (2025-2030) indicated that the 29 October sowing remains the most vulnerable, while the 19 November sowing consistently exhibited lower disease risk due to less favorable microclimatic conditions to support apothecial formation and ascospore release. The study emphasis that sowing time, in conjugation with real-time meteorological variables significantly governs the epidemic potential of SR. The predictive models developed herein offer a reliable decision support system for major mustard growing states of the country enabling proactive disease forecasting and sustainable crop protection strategies.