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Early Access

Evaluation and screening of rice (Oryza sativa L.) genotypes for drought tolerance under reproductive-stage stress

DOI
https://doi.org/10.14719/pst.15675
Submitted
23 May 2026
Published
23-09-2026
Versions

Abstract

Drought stress at the reproductive-stage is a major constraint limiting rice productivity in rainfed ecosystems. The present study evaluated 450 rice (Oryza sativa L.) genotypes along with 8 checks under normal and reproductive-stage drought stress conditions during kharif 2020 at Bihar Agricultural University, Sabour, India, using an augmented randomised complete block design. Drought stress was imposed by withholding irrigation 25–30 days before flowering. Significant variation was observed among genotypes for all studied traits under both environments, indicating substantial genetic diversity for drought response. Stress conditions caused marked reductions in grain yield and related traits, particularly panicle fertility and filled grains per panicle, due to increased spikelet sterility. Genotypes were initially screened using drought susceptibility index (DSI) and those with a DSI of less than 1 were shortlisted for further analysis. To ensure stringent selection, genotypes were ranked based on DSI values and the lowest 20th percentile was considered as drought-tolerant. Further evaluation based on yield under stress and key reproductive traits identified 26 promising drought-tolerant genotypes. Among these, six genotypes, namely ARC 11245 (IRGC 42651-2), SAITA (IRGC 31618-1), KWANG LU AI 4 (IRGC 28480-2), DM 49 (IRGC 8775-1), CRILLO LA FRIA (IRGC 10793-1) and 77 UPLA (GERVEX 159-C1), exhibited DSI values of zero and showed superior yield stability under stress. Principal component analysis revealed that the first three components explained 79.68 % of the total variation, with grain yield, filled grains, total grains and panicle fertility contributing most to variability under stress. The identified genotypes provide promising genetic resources for future drought-tolerance breeding following multi-environment validation.

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