Correlation and Regression between Growth and Yield of Different Rice Cultivars in Coastal Cauvery Delta Region of Karaikal
N. Gobikashri
*
Department of Agronomy, Pandit Jawaharlal Nehru College of Agriculture and Research Institute (Pajancoa & RI), Karaikal, Puducherry – 609 603, India.
R. Mohan
Department of Agronomy, Pandit Jawaharlal Nehru College of Agriculture and Research Institute (Pajancoa & RI), Karaikal, Puducherry – 609 603, India.
R. Poonguzhalan
Department of Agronomy, Pandit Jawaharlal Nehru College of Agriculture and Research Institute (Pajancoa & RI), Karaikal, Puducherry – 609 603, India.
L. Aruna
Department of Soil Science & Agricultural Chemistry, Pajancoa & RI, Karaikal, Puducherry – 609 603, India.
S. Nadaradjan
Department of Plant Breeding & Genetics, Pajancoa & RI, Karaikal, Puducherry – 609 603, India.
S. Mala
Department of Agronomy, Pandit Jawaharlal Nehru College of Agriculture and Research Institute (Pajancoa & RI), Karaikal, Puducherry – 609 603, India.
S. Thirumeninathan
Department of Agronomy, Pandit Jawaharlal Nehru College of Agriculture and Research Institute (Pajancoa & RI), Karaikal, Puducherry – 609 603, India.
C. Nivetha
Department of Agronomy, Pandit Jawaharlal Nehru College of Agriculture and Research Institute (Pajancoa & RI), Karaikal, Puducherry – 609 603, India.
*Author to whom correspondence should be addressed.
Abstract
Aims: The study aims to test the correlations and simple linear regression relationships among growth parameters, yield attributes, and grain yield of various rice varieties, and to determine the most important physiological traits that affect grain yield in the coastal delta.
Study Design: Randomised Block Design (RBD) with seven rice cultivars and three replications.
Place and Duration of Study: The study was conducted at the Instructional Farm (A19 field), Pandit Jawaharlal Nehru College of Agriculture and Research Institute (PAJANCOA & RI), Karaikal, Puducherry, India, during the Kharif 2025–2026 season.
Methodology: Seven rice cultivars differing in crop duration were evaluated under recommended agronomic practices. Growth parameters, yield attributes, grain yield, and straw yield were recorded at appropriate crop stages. Analysis of variance (ANOVA) was performed to determine cultivar differences. Pearson's correlation analysis was performed to determine the relationships among measured variables, and simple linear regression models were developed to predict grain yield from significant growth parameters.
Results: Cultivars showed significant variations in plant height, test weight, panicle length, panicle weight, leaf area index at flowering, and grain yield. Dry matter production at harvest exhibited the strongest positive correlation with grain yield (r = 0.95, p < 0.001), followed by leaf area index at flowering (r = 0.86, p < 0.05) and straw yield (r = 0.82, p < 0.05). Regression analysis showed that dry matter production was the best predictor of grain yield (R² = 0.9076, p = 0.00091), followed by leaf area index (R² = 0.7469, p = 0.01211) and straw yield (R² = 0.6777, p = 0.02289).
Conclusion: Dry matter accumulation and canopy maintenance at flowering are the primary physiological determinants associated with rice productivity under coastal delta conditions. The developed empirical regression models offer valuable insights for in-season physiological characterisation and cultivar selection, though multi-season validation across varied coastal stress conditions is required for wider application.
Keywords: Rice cultivars, grain yield, dry matter production, leaf area index, straw yield, pearson correlation, simple linear regression, yield attributes