Quantifying Rainfall-Driven Splash Dispersal of Ascochyta rabiei in Chickpea: Experimental Evidence for Intensity Thresholds and Epidemiological Model Improvement

Ascochyta blight of chickpea is a major constraint in semi-arid rainfed systems, and epidemics are strongly driven by rainfall-mediated splash dispersal, yet most weather-based models still rely on an empirical 2 mm daily rainfall threshold and lack experimental quantification of the rainfall–lesion relationship and associated thresholds. To address this gap, a calibrated open-channel rainfall simulator was used in a greenhouse to impose six event rainfall levels from 0 to 8 mm per 10 minutes on potted chickpea plants supplied with residue inoculated by Ascochyta rabiei, generating 96 pot-level lesion counts. A negative binomial GLMM combined with segmented regression was then fitted to derive the rainfall–lesion response function and to estimate a breakpoint at an event rainfall of about 1.1 mm. Lesion numbers increased slowly with rainfall at low amounts but rose much more steeply once the threshold was exceeded. After normalisation, this function was incorporated into the ascotraceR model, and simulations for 4 regions and 28 site–years showed an increase in mean AUDPC R2R^2R2 from 0.65 to 0.81 and a reduction in root mean square error from 210 to 145 disease·day, with clear improvement in capturing the cumulative effects of repeated light rain events. The study provides quantitative threshold evidence for rain-splash dispersal of A. rabiei and illustrates a practical pathway in which physical experiment parameterisation is used to update computational epidemiological models and refine rainfall triggers and fungicide timing rules.