Evaluation of Aus rice genotypes grown under rainfed and irrigated conditions
DOI:
https://doi.org/10.3329/aba.v26i1.67021Keywords:
Agronomic traits, genotypes, rainfed, riceAbstract
Bangladesh must keep increasing the production of rice (Oryza sativa L.), which is consumed by more than half of the world's population in order to ensure a steady supply of food for everyone. A field experiment was conducted to select short durated with high yield potential Aus rice genotypes under rainfed condition in a split plot design with three replications. Two water regimes (irrigated and rainfed) were imposed into main plot and 39 Aus rice genotypes were assigned into sub plots. In order to categorize the genotypes into various groups, multivariate studies including cluster analysis, principal component analysis (PCA), and discriminantfunction analysis (DFA) were carried out using fifteen quantitative plant traits. Results based on agronomic traits, the genotypes were grouped into six clusters. Early maturing genotypes are represented in cluster II, and genotypes with outstanding yield performance under irrigation are grouped in cluster III. Additionally, clusters III and IV indicate genotypes that mature quickly and a high yield potentiality under rainfed conditions, respectively. Biological yield played a very vital role under both irrigated and rainfed conditions. PCA revealed that PC 1, 2, 3, 4, and 5 described 84.90% variation under irrigated and 84.83% variation in rainfed condition, altogether. Stepwise DFA showed function 1 and 2 accounted for a cumulative of 95.40% of total variance in irrigated and of 92.3% variance in rainfed condition. BR24, Rupsail, Kachilon, Kachiloon2, Darial, Katak-Tara2, BRRI dhan43, Bowalia, BRRI dhan55 and BR14 showed the best performance under rainfed condition. The genotypes like Laksmilota, Loroi, Dhala Saita-3 and Kala manik can be used for further study as early maturing genotypes.
Ann. Bangladesh Agric. (2022) 26 (1) : 91-111
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Copyright (c) 2022 MA Alam, AKMA Islam, MA Karim, UK Ghosh, MAR Khan
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.