STABILITY ANALYSIS FOR YIELD AND QUALITY TRAITS IN RICE (Oryza sativa L.)
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Date
2022-08-17
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guntur
Abstract
The present investigation was carried out with 25 rice genotypes at Agricultural College Farm, Bapatla of Acharya N. G. Ranga Agricultural University during kharif 2019 to identify stable genotypes across early, normal and late kharif seasons, in addition to assessment of variability parameters, character association and path coefficients for yield, yield components and quality traits.
The analysis of variance (ANOVA) for early kharif, normal kharif and late kharif revealed highly significant differences among the genotypes for all the characters in all the three environments indicating the existence of sufficient variation among the genotypes. Pooled analysis revealed significant mean squares due to genotypes and environments for yield, yield components and quality traits indicating the existence of significant variation among the genotypes in addition to considerable environmental variance. Highly significant genotype x environment interaction was also observed for most of the traits indicating a variable response of the genotypes to the different seasons. Further, environmental indices of grain yield, yield components and quality traits revealed variable response of the environments to the different traits. Normal kharif was observed to be suitable for traits like plant height, ear bearing tillers per plant, panicle length, hulling %, milling %, head rice recovery, iron content and calcium content while early kharif was noticed to be conducive for days 50 % flowering, panicle length, test weight, grain yield per plot, hulling %, head rice recovery, protein content and iron content. In contrast, late kharif was observed to be suitable for number of grains per panicle, grain yield per plot, milling %, amylose content and zinc content.
The ANOVA of Eberhart and Russell’s stability model revealed the significance of environment + (genotype x environment) interactions for all the traits. The significance of environment (linear) component of environment + (genotype x environment) for all the traits, except for days to 50 % flowering, hulling % and zinc content indicated significant differences between environments and their influence on
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the genotypes for expression of these traits. Genotype x environment (linear) component was also observed to be significant for most of the characters indicating that the genotypes were diverse for their regression response to change with the environment. The mean squares for pooled deviation (non-linear) were also significant for most of the characters indicating that both linear and non-linear components contributed to the genotype x environment interaction observed for various characters of the present study.
The stability parameters are mean (X), regression coefficient (bi) and deviation from regression coefficient (S2di) revealed greater number of genotypes were stable with wider adaptability across environments for various traits compared to specific environments (poor / favourable). Eight genotypes (BPT 5204, Bahadur, IR 50, DRR Dhan 46, BPT 2766, Jarava, Gontra Bidhan and BPT 2782) were noticed to possess high grain yield per plot in addition to non-significant regression coefficient (bi=1) and minimrm deviation from regression (S2di=0).
The studies on genetic parameters of variability revealed high range combined with high GCV, PCV, heritability and genetic advance as per cent of mean for number of grains per panicle indicating the pre-ponderance of additive gene action and scope for improvement of this trait through simple selection.
A detailed study of the results on character associations revealed grain yield per plot had positive significant association with days to 50% flowering, hulling%, milling%, head rice recovery, amylose content and iron content. Path analysis also indicated positive direct effect of iron content followed by amylose content on grain yield per plot. High direct effects of these traits therefore appear to be the main factor for their strong association with grain yield per plot. Hence, these traits should be considered as important selection criteria in rice improvement programmes and direct selection for these traits is recommended for yield improvement. The results also revealed low residual effects for path co-efficient indicating that variables studied in the present investigation explained about 72.7 per cent of the variability in grain yield per plot and therefore, few other attributes, besides the characters studied are contributing for grain yield per plot.
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STABILITY ANALYSIS FOR YIELD AND QUALITY TRAITS IN RICE (Oryza sativa L.)