Multivariate analysis of Rice (Oryza sativa L.) genotypes
Naznin Akter Munna 1*, Md. Mahbubur Rahman Dewan 2, Md. Masud Rana 3, A.K.M. Sajjadul Islam 4 and Md. Sakhawat Hosen Galib 5
1 Breeding Division, Bangladesh Sugarcrop Research Institute, Ishwardi, Pabna 6620, Bangladesh.
2 Rice Farming Systems Division, Bangladesh Rice Research Institute (BRRI), Gazipur 1701, Bangladesh.
3 Irrigation and Water Management Division, BRRI, Gazipur 1701, Bangladesh.
4 Rice (Hybrid & Inbred) Research & Development, Metal Agro Limited, Gazipur 1706, Bangladesh.
5 Department of Genetics and Plant Breeding, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh.
✉ *Corresponding author: [email protected].
Research article first published online: 04 October 2025.
1 Breeding Division, Bangladesh Sugarcrop Research Institute, Ishwardi, Pabna 6620, Bangladesh.
2 Rice Farming Systems Division, Bangladesh Rice Research Institute (BRRI), Gazipur 1701, Bangladesh.
3 Irrigation and Water Management Division, BRRI, Gazipur 1701, Bangladesh.
4 Rice (Hybrid & Inbred) Research & Development, Metal Agro Limited, Gazipur 1706, Bangladesh.
5 Department of Genetics and Plant Breeding, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh.
✉ *Corresponding author: [email protected].
Research article first published online: 04 October 2025.
Abstract
Rice is a vital crop worldwide, and understanding its genetic diversity is essential for effective breeding programs. This study aims to assess the genetic diversity of rice (Oryza sativa L.) and examine its genotypes using statistical approaches. The experiment was conducted using a randomized complete block design (RCBD) with three replications. Correlation analysis revealed that grain yield was positively correlated with traits such as effective tillers per square meter (r = 0.65), filled grains per panicle (r = 0.57), and thousand-grain weight (r = 0.47). Multiple linear regression analysis revealed that effective tillers per square meter (R = 0.01), filled grains per panicle (R = 0.02), and thousand-grain weight (R = 0.14) significantly contributed to grain yield. Path analysis findings revealed that effective tillers per square meter had the strongest direct effect on yield (0.35), followed by filled grains per panicle (0.31) and thousand-grain weight (0.27), accounting for 63% of the variation. Principal Component Analysis (PCA) revealed that the first three components explained more than 80% of the variance, with PC1 alone explaining 54.11%. Cluster analysis grouped the 22 genotypes into four clusters, with Cluster 1 recording a grain yield of 6.08 t/ha and a higher number of effective tillers (298.47/m²). These findings imply that effective tillers, filled grains per panicle, and thousand-grain weight are essential selection criteria for enhancing rice yields in breeding programs.
Keywords: Trait’s correlation, Regression analysis, Principal component analysis, Path analysis, Genetic variability
Rice is a vital crop worldwide, and understanding its genetic diversity is essential for effective breeding programs. This study aims to assess the genetic diversity of rice (Oryza sativa L.) and examine its genotypes using statistical approaches. The experiment was conducted using a randomized complete block design (RCBD) with three replications. Correlation analysis revealed that grain yield was positively correlated with traits such as effective tillers per square meter (r = 0.65), filled grains per panicle (r = 0.57), and thousand-grain weight (r = 0.47). Multiple linear regression analysis revealed that effective tillers per square meter (R = 0.01), filled grains per panicle (R = 0.02), and thousand-grain weight (R = 0.14) significantly contributed to grain yield. Path analysis findings revealed that effective tillers per square meter had the strongest direct effect on yield (0.35), followed by filled grains per panicle (0.31) and thousand-grain weight (0.27), accounting for 63% of the variation. Principal Component Analysis (PCA) revealed that the first three components explained more than 80% of the variance, with PC1 alone explaining 54.11%. Cluster analysis grouped the 22 genotypes into four clusters, with Cluster 1 recording a grain yield of 6.08 t/ha and a higher number of effective tillers (298.47/m²). These findings imply that effective tillers, filled grains per panicle, and thousand-grain weight are essential selection criteria for enhancing rice yields in breeding programs.
Keywords: Trait’s correlation, Regression analysis, Principal component analysis, Path analysis, Genetic variability
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Journal of Bioscience and Agriculture Research EISSN 2312-7945.