International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


Downloads: 2

India | Microbiology | Volume 14 Issue 4, April 2025 | Pages: 2241 - 2248


Characterization of Plant Growth Promoting Attributes of Cyamopsis Tetragonoloba Rhizobacteria under Abiotic Stress for Sustainable Agriculture

Ritika Jain, Meenu Saraf

Abstract: Plant growth promoting rhizobacteria (PGPR) have potential to promote plant growth under extreme environmental conditions. In the present study, ten bacteria isolates having abiotic stress tolerance ability were selected from earlier study. These were isolated from rhizosphere region of cluster beans (Cyamopsis tetragonoloba), a drought - tolerant crop cultivated in arid and semi - arid regions of India. These isolates were tested for PGP attributes like phosphate solubilization, ACC deaminase activity, IAA and ammonia production under normal and stress (drought and salt) conditions. Isolates were further analyzed for exopolysaccharide (EPS) production, zinc solubilization, enzyme assay like protease, catalase and cellulase production. All 10 isolates were positive for phosphate solubilization and ammonia production, 6 isolates were showing IAA production while only 3 isolates were positive for ACC deaminase activity. All 10 isolates showed enzyme activity (protease and catalase) while only 3 isolates were positive for cellulase activity, EPS was produced by 7 isolates and only one isolate was positive for zinc solubilization. Principal Component Analysis (PCA) was also carried out to understand the variability in PGP activities shown by these 10 isolates and identify the most promising isolates. AK17 and KM6 were more prominent in PGP traits like ACC deaminase activity and IAA production while KM1 was better in phosphate solubilization.

Keywords: Cluster beans (Cyamopsis tetragonoloba), Plant growth promoting rhizobacteria (PGPR), abiotic stress, Principal Component Analysis (PCA)



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