Weather-based predictive assessment of Sigatoka leaf spot in banana cultivars

Abstract

Abstract

Sigatoka leaf spot, a major fungal disease of banana, causes significant yield losses. Although its development is strongly influenced by weather, the specific relationships between meteorological factors and disease severity in key Indian cultivars require quantification for effective forecasting and management. Weekly disease assessments (percent disease index; PDI) and meteorological data were collected for three banana cultivars (Grand Naine, Ney Poovan and Nendran) during the 2024–25 cropping season. The relationships between meteorological variables and PDI were evaluated through Pearson's correlation and multiple linear regression analysis. Correlation analysis identified wind speed as having the strongest negative correlation with PDI across all cultivars (r = -0.42 to -0.45), while rainfall showed a positive correlation (r = 0.27 to 0.31). Multiple regression confirmed wind speed and rainfall as the most significant predictors(p < 0.05), with relative humidity and temperature showing minimal independent effects. The findings demonstrate that managing the microclimate to reduce leaf wetness from rainfall and enhance airflow is critical. These insights form a scientific basis for prioritising management strategies such as increased plant spacing and canopy pruning to achieve sustainable control.