Journal of Environmental Biology

Temporal dynamics and prediction model of sheath blight in rice under prevailing weather conditions

P.K. Kasniya¹*, G.S. Makkar², M. Kumar³, R. Singh⁴, A. Singh⁵, J. Singh², V.S. Adarsh⁶, P.P. Gopinath⁶ and A. Kumar⁷      

¹Department of Plant Pathology, Chaudhary Charan Singh Haryana Agricultural University, Hisar – 125 004, India

²Krishi Vigyan Kendra, Punjab Agricultural University, Ropar–140 001, India

³Department of Mathematics & Statistics, Chaudhary Charan Singh Haryana Agricultural University, Hisar – 125 004, India

⁴Division of Natural Resource Management, ICAR, New Delhi – 110 012, India

⁵Department of Plant Pathology, Punjab Agricultural University, Ludhiana – 141 004, India

⁶Department of Agricultural Statistics, Kerala Agricultural University, Thiruvananthapuram – 695 522, India

⁷Department of Agricultural Meteorology, Chaudhary Charan Singh Haryana Agricultural University, Hisar – 125 004, India

Received: 17 July 2024                   Revised: 18 October 2024                   Accepted: 06 January 2025

*Corresponding Author Email : pawankasniya@gmail.com                  *ORCiD: https://orcid.org/0009-0004-5371-2033

Abstract

Aim: To study the effect of weather variables in the development of sheath blight in rice.

Methodology: The nursery of rice cv. PR 121 was raised in last week of May in four consecutive kharif seasons 2019 to 2022 and one-month-old seedlings were transplanted in the field. The sheath blight disease was assessed using a 0-9 rating scale at the appearance of first symptoms and correlated with weather variables viz., maximum temperature (T_max), minimum temperature (T_min), relative humidity morning (RH_m), relative humidity evening (RH_e), rainfall (RF) and sunshine hours (SS). The multiple linear regression was applied to establish the relationship between weather variables and disease severity.

Results: Disease severity was highest when the maximum temperature was 30-33ºC, minimum temperature was 21-24ºC, morning relative humidity exceeding 95%, and evening relative humidity exceeding 65%. The disease severity showed a significant negative correlation with T_min (-0.93), T_max (-0.52), RH_e (-0.45) and RF (-0.41), while a positive correlation with RH_m (0.30). A predictive model with 0.88 coefficient of determination indicated that the weather variables can explain 88.0% of the variability in disease severity.

Interpretation: Sheath blight is a major destructive disease affecting rice in Punjab, with severity levels ranging 56.44% to 78.22% during the year 2019 to 2022. The predictive model developed to analyze the disease progression, yielded a R² value (coefficient of determination) of 0.88 and indicated that minimum temperature and rainfall were the key factors in the development of the disease.

Key words: Multiple regression, Predictive model, Sheath blight, Variance inflation factors, Weather variables

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