Correlation-regression Study of Phenology, Growth, Yield Attributes, Grain Quality and Agrometeorological Indices of Gobindabhog Rice

Abstract

Gobindabhog is a native cultivar of the lower Gangetic plains and rahr (red and laterite) region of Bengal, which has been traditionally cultivated for about 400–500 years. Cereal crops are vulnerable to climate change due to their dependence on predictable temperatures, rainfall, and growing seasons. The aim of the paper is to explore a correlation-regression study of phenology, growth, yield attributes, grain quality and agrometeorological indices [growing degree days (GDD), heliothermal units (HTU), Photothermal units (PTU)] of Gobindabhog Rice. Considering this fact, a field experiment was conducted using split-plot design with 3 replications during the kharif season of 2010 and 2011 in the New Alluvial Zone of West Bengal. The results of the present study revealed strong positive correlations with GDD4L-AT (0.756**) and GDDAT-PI (0.610**) suggest these stages are critical for vegetative growth. Head rice recovery was positively influenced by GDDPI-F (0.400**) and GDDD-M (0.319**). HTU had strong positive correlations with plant height (0.589**), grain yield (0.546**), and number of panicles/m² (0.318**), but negative effects on milling %, head rice recovery, and kernel length. HTUD-M was favourable for straw yield (0.320**), milling recovery (0.567**), and filled grains (0.370**), indicating strong post-harvest potential. The equation includes six predictors, such as HTUPI-F, HTUS-E, HTUE-4L, PTUD-M, PTUMi-D, and GDDD-M, with most showing high significance. This implies that grain yield was strongly influenced by thermal conditions spanning from panicle initiation to the dough stage. Meanwhile, kernel morphology and sensory traits showed weaker environmental predictability, suggesting a need for genetic or non-linear modelling approaches. These insights can guide stage-specific agronomic interventions and trait prioritisation for breeding programmes.