Weather Disease Interactions and Forewarning Models for Leaf Spot of Groundnut (Arachis hypogaea L.) under different Varieties and Sowing Windows

Abstract

Groundnut (Arachis hypogaea L.) is a significant crop of oilseeds suffering from many diseases, including early and late leaf spot and leaf rust, the most commonly distributed and economically significant foliar diseases of groundnut that cause severe crop damage A field experiment was conducted during kharif seasons of 2017 and 2018 at the Department of Agricultural Meteorology Farm, CAFT in Agricultural Meteorology, College of Agriculture, Pune, Maharashtra, to study the weather disease interactions and forewarning models for Cercospora leaf spot of groundnut (Arachis hypogaea L.) under different varieties and sowing environments. The experiment was laid out in a split-plot design with three replications. Treatments comprised four groundnut varieties: JL-501 (V1), RHRG-6083 ‘Phule Unnati’ (V2), TAG-24 (V3), and JL-776 ‘Phule Bharati’ (V4) in main plots, and four sowing windows: 25th MW (18–24 June, S1), 26th MW (25 June–1 July, S2), 27th MW (2–8 July, S3), and 28th MW (9–15 July, S4) in sub-plots. Leaf spot incidence appeared around 30 days after sowing and continued until harvest, with peak intensity during September–October. Across sowing windows, the highest average disease intensity was recorded in S2 (26th MW) at 63.69% and 58.75% in 2017 and 2018, respectively, followed by S4 (28th MW) with 49.43% and 45.43%. Multiple linear regression models based on weather parameters (maximum and minimum temperature, morning and afternoon relative humidity, and bright sunshine hours) were used to predict per cent disease intensity (PDI). Leaf spot diseases severity was showed significant positive correlation with maximum temperature, evaporation and bright sunshine hours whereas minimum temperature, afternoon humidity and wind speed were showed significant negative correlation. The coefficient of determination (r²) for S1 sowing ranged from 86–96% across varieties; for S2, 84–96%; for S3, 87–90%; and for S4, 90–96%, indicating high predictive accuracy. The study demonstrates that sowing time significantly influences leaf spot intensity in groundnut, and weather-based regression models can effectively predict disease progression, supporting timely forewarning and management strategies.