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 deficit stress and high temperature. However, the roles of other edaphic and environmental factors remain unclear. Here, we performed an artificial neur...

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Autores principales:	Ranjita Sinha, Vadivelmurugan Irulappan, Basavanagouda S. Patil, Puli Chandra Obul Reddy, Venkategowda Ramegowda, Basavaiah Mohan-Raju, Krishnappa Rangappa, Harvinder Kumar Singh, Sharad Bhartiya, Muthappa Senthil-Kumar
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/199889d48a1c45f5a264bc731870137a
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spelling	oai:doaj.org-article:199889d48a1c45f5a264bc731870137a2021-12-02T13:24:14ZLow soil moisture predisposes field-grown chickpea plants to dry root rot disease: evidence from simulation modeling and correlation analysis10.1038/s41598-021-85928-62045-2322https://doaj.org/article/199889d48a1c45f5a264bc731870137a2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85928-6https://doaj.org/toc/2045-2322Abstract 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.Ranjita SinhaVadivelmurugan IrulappanBasavanagouda S. PatilPuli Chandra Obul ReddyVenkategowda RamegowdaBasavaiah Mohan-RajuKrishnappa RangappaHarvinder Kumar SinghSharad BhartiyaMuthappa Senthil-KumarNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ranjita Sinha Vadivelmurugan Irulappan Basavanagouda S. Patil Puli Chandra Obul Reddy Venkategowda Ramegowda Basavaiah Mohan-Raju Krishnappa Rangappa Harvinder Kumar Singh Sharad Bhartiya Muthappa Senthil-Kumar Low soil moisture predisposes field-grown chickpea plants to dry root rot disease: evidence from simulation modeling and correlation analysis
description	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.
format	article
author	Ranjita Sinha Vadivelmurugan Irulappan Basavanagouda S. Patil Puli Chandra Obul Reddy Venkategowda Ramegowda Basavaiah Mohan-Raju Krishnappa Rangappa Harvinder Kumar Singh Sharad Bhartiya Muthappa Senthil-Kumar
author_facet	Ranjita Sinha Vadivelmurugan Irulappan Basavanagouda S. Patil Puli Chandra Obul Reddy Venkategowda Ramegowda Basavaiah Mohan-Raju Krishnappa Rangappa Harvinder Kumar Singh Sharad Bhartiya Muthappa Senthil-Kumar
author_sort	Ranjita Sinha
title	Low soil moisture predisposes field-grown chickpea plants to dry root rot disease: evidence from simulation modeling and correlation analysis
title_short	Low soil moisture predisposes field-grown chickpea plants to dry root rot disease: evidence from simulation modeling and correlation analysis
title_full	Low soil moisture predisposes field-grown chickpea plants to dry root rot disease: evidence from simulation modeling and correlation analysis
title_fullStr	Low soil moisture predisposes field-grown chickpea plants to dry root rot disease: evidence from simulation modeling and correlation analysis
title_full_unstemmed	Low soil moisture predisposes field-grown chickpea plants to dry root rot disease: evidence from simulation modeling and correlation analysis
title_sort	low soil moisture predisposes field-grown chickpea plants to dry root rot disease: evidence from simulation modeling and correlation analysis
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/199889d48a1c45f5a264bc731870137a
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Low soil moisture predisposes field-grown chickpea plants to dry root rot disease: evidence from simulation modeling and correlation analysis

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