Low soil moisture predisposes field-grown chickpea plants to dry root rot disease: evidence from simulation modeling and correlation analysis

Abstract

Rhizoctonia bataticola causes dry root rot (DRR), a devastating disease in chickpea (Cicer arietinum). DRR incidence increases under water defcit stress and high temperature. However, the roles of other edaphic and environmental factors remain unclear. Here, we performed an artifcial neural network (ANN)-based prediction of DRR incidence considering DRR incidence data from previous reports and weather factors. ANN-based prediction using the backpropagation algorithm showed that the combination of total rainfall from November to January of the chickpea-growing season and average maximum temperature of the months October and November is crucial in determining DRR occurrence in chickpea felds. The prediction accuracy of DRR incidence was 84.6% with the validation dataset. Field trials at seven diferent locations in India with combination of low soil moisture and pathogen stress treatments confrmed the impact of low soil moisture on DRR incidence under diferent agroclimatic zones and helped in determining the correlation of soil factors with DRR incidence. Soil phosphorus, potassium, organic carbon, and clay content were positively correlated with DRR incidence, while soil silt content was negatively correlated. Our results establish the role of edaphic and other weather factors in chickpea DRR disease incidence. Our ANN-based model will allow the location-specifc prediction of DRR incidence, enabling efcient decision-making in chickpea cultivation to minimize yield loss.