Low soil moisture predisposes field-grown chickpea plants to dry root rot disease: evidence from simulation modeling and correlation analysis
Open Access
- 22 March 2021
- journal article
- research article
- Published by Springer Science and Business Media LLC in Scientific Reports
- Vol. 11 (1), 1-12
- https://doi.org/10.1038/s41598-021-85928-6
Abstract
Rhizoctonia bataticola causes dry root rot (DRR), a devastating disease in chickpea (Cicer arietinum). DRR incidence increases under water deficit stress and high temperature. However, the roles of other edaphic and environmental factors remain unclear. Here, we performed an artificial 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 fields. The prediction accuracy of DRR incidence was 84.6% with the validation dataset. Field trials at seven different locations in India with combination of low soil moisture and pathogen stress treatments confirmed the impact of low soil moisture on DRR incidence under different 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-specific prediction of DRR incidence, enabling efficient decision-making in chickpea cultivation to minimize yield loss.Funding Information
- Council for scientific and industrial research New Delhi (SRA Pool No. 8917-A)
- Department of Biotechnology, Ministry of Science and Technology, India (DBT/2015/NIPGR/430)
- National Institute of Plant Genome Research (Core funding)
This publication has 31 references indexed in Scilit:
- Unravelling Effects of Temperature and Soil Moisture Stress Response on Development of Dry Root Rot [Rhizoctonia bataticola (Taub.)] Butler in ChickpeaAmerican Journal of Plant Sciences, 2013
- Comparing multivariate regression and artificial neural network to predict barley production from soil characteristics in northern IranArchives of Agronomy and Soil Science, 2011
- Predicting rainfed wheat quality and quantity by artificial neural network using terrain and soil characteristicsActa Agriculturae Scandinavica, Section B — Soil & Plant Science, 2010
- Influence of nutrition on disease development caused by fungal pathogens: implications for plant disease controlAnnals of Applied Biology, 2007
- Identifying important factors influencing corn yield and grain quality variability using artificial neural networksPrecision Agriculture, 2006
- Regression and Artificial Neural Network Modeling for the Prediction of Gray Leaf Spot of MaizePhytopathology®, 2005
- Weather‐based prediction of anthracnose severity using artificial neural network modelsPlant Pathology, 2004
- Soil moisture–temperature relationships: results from two field experimentsHydrological Processes, 2003
- Influence of Soil Moisture on Root Rot and Wilt of ChickpeaPlant Disease, 1992
- Influence of Water Potential of Soils and Plants on Root DiseaseAnnual Review of Phytopathology, 1972