| Literature DB >> 36051229 |
Ashutosh Kushwah1, Dharminder Bhatia1, Gurpreet Singh2, Inderjit Singh1, Suruchi Vij1, Shayla Bindra1, Kadambot H M Siddique3, Harsh Nayyar4, Sarvjeet Singh1.
Abstract
Drought is a major abiotic stress that drastically reduces chickpea yields. The present study was aimed to identify drought-responsive traits in chickpea by screening a recombinant inbred line population derived from an inter-specific cross between drought cultivar of GPF2 (C. arietinum L.) and drought sensitive accession of ILWC292 (C. reticulatum), at two locations in India. Twenty-one traits, including twelve morphological and physiological traits and nine root-related traits were measured under rainfed (drought-stress) and irrigated conditions (no-stress). High genotypic variation was observed among RILs for yield and root traits indicated that selection in these germplasms would be useful in achieving genetic progress. Both correlation and principal component analysis revealed that plant height, number of pods per plant, biomass, 100-seed weight, harvest index, membrane permeability index, and relative leaf water content were significantly correlated with yield under both irrigated and drought stress environments. Root length had significant positive correlations with all root-related traits except root length density in drought-stressed plants. Path analysis and multiple and stepwise regression analyses showed that number of pods per plant, biomass, and harvest index were major contributors to yield under drought stress conditions. Thus, a holistic approach across these analyses identified number of pods per plant, biomass, harvest index, and root length as key traits for improving chickpea yield through indirect selection for developing drought-tolerant cultivars. Overall, on the basis of yield components morphological and root traits, a total of 15 promising RILs were identified for their use in chickpea breeding programs for developing drought tolerant cultivars. Supplementary Information: The online version contains supplementary material available at 10.1007/s12298-022-01218-z. © Prof. H.S. Srivastava Foundation for Science and Society 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.Entities:
Keywords: Association analysis; Genetic variability; Path coefficient analysis; Principal component analysis; Regression analysis; Root related traits
Year: 2022 PMID: 36051229 PMCID: PMC9424481 DOI: 10.1007/s12298-022-01218-z
Source DB: PubMed Journal: Physiol Mol Biol Plants ISSN: 0974-0430