Stability analysis for various quantitative traits in soybean [Glycine max (L.) Merrill]

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1 Legume Research, 39 (4) 2016 : Print ISSN: / Online ISSN: AGRICULTURAL RESEARCH COMMUNICATION CENTRE Stability analysis for various quantitative traits in soybean [Glycine max (L.) Merrill] Gunjan Tiwari*, Kamendra Singh, Pushpendra and N.K. Singh G.B. Pant University of Agriculture and Technology, Pantnagar , Uttrakhand, India. Received: Accepted: DOI: /lr.v0iOF ABSTRACT The present investigation was carried out to study stability performance over twelve environments for yield and yield contributing characters in twenty two genetically diverse genotypes of soybean using a randomized complete block design. The partitioning of (environment + genotype x environment) mean squares showed that environments (linear) differed significantly and were quite diverse with regards to their effects on the performance of genotypes for yield and its components. Stable genotypes were identified for wider and specific environments with high per se performance (over general mean) for majority of yield component traits. The investigation revealed that the genotypes ABL 55, ABL 20, ABL 62 and ABL 45 were desirable and stable across the environments for different yield contributing traits. Other genotypes ABL 43 and ABL 17 were found to be suitable for favourable situations, while genotypes ABL19 were adapted to poor environments for yield and majority of yield contributing traits. Key words: Plant population, Quantitative traits, Soybean, Stability analysis, Sowing dates. INTRODUCTION Soybean [Glycine max (L.) Merrill] has emerged as an important oilseed crop (20% oil) with 40% high quality protein and has established its potential to reduce the oil and protein gap in the diet of the masses. The availability of a large number of superior varieties, vacant niches in the form of kharif fallows, scientific production technology, creation of market facilities and export avenues as well as the development of processing facilities played an important role in making soybean one of the leading commercial oilseed crop of India. Increased awareness of the health benefits of this wonder crop led to its greater domestic consumption both as human food and animal feed. The main goal when growing soybean crops anywhere is to maximize net profit mainly through increasing seed yields. However, unpredictable weather conditions and variations across the locations, years and seasons severely affect production and productivity of soybean. Deployment of high yielding stable varieties in production system is seems to be an important objective for sustainable agriculture (Carpenter and Board, 1997). Further, integration of high yield potential with early maturity and adaptability to wide range of different environments is a viable option to increase soybean area and production. Average world productivity i.e. 2.5 tonnes/ha level has not yet been achieved in tropical countries like India where soybean productivity is only 1.2t/ha (Anonymous, *Corresponding author s gnjntiwari1@gmail.com. 2013). Low productivity in India is mainly due to the short growing periods available in subtropical conditions, limited varietal stability and narrow genetic base of soybean cultivars (Singh and Hymowitz, 2001). Crop yield fluctuates due to lack of suitable varieties to different growing seasons or conditions. A specific genotype does not always exhibit the same phenotypic characteristics under all environments and different genotypes respond differently to a specific environment. Gene expression is subject to modification by the environment; therefore, phenotypic expression of the genotype is environmentally dependent (Kang, 1998). The development of new cultivars involves breeding of cultivars with desired characteristics that add value to the product and the stability of these traits in target environments. Inconsistent genotypic responses to environmental factors from location to location and year to year are a function of genotype x environment (GxE) interactions. Genotype x environment interactions has been defined as the failure of genotypes to achieve the same relative performance in different environments (Baker, 1988). Identification of yieldcontributing trait, knowledge of Genotype x Environment interactions and yield stability are important for breeding new cultivars with improved adaptation to the environmental constraints prevailing in the target environments. Currently, there is a need for increasing soybean genetic diversity in India so that new cultivars suitable for manufacturing soyfoods can be developed. To avoid genetic

2 518 LEGUME RESEARCH - An International Journal vulnerability associated with the narrowing of the genetic base of any crop, the GE interactions of the germplasm are important (Kang, 1998). Therefore, in the present investigation an attempt has been made to evaluate soybean genotypes for yield and its component characters under different environments to identify genotypes with suitable performance in variable environments. MATERIALS AND METHODS The experiments were conducted with twenty two diverse genotypes comprised of 12 advance breeding lines and 10 released varieties of soybean during kharif season of 2010 and 2011 at Norman E. Borlaug Crop Research Centre, GBPUA&T, Pantnagar, UK, India. The experimental material was planted and evaluated, in randomized complete block design with three replications, for two consecutive years in six different environments each year created by different sowing dates namely, June 15 (early sown), June 30 (timely sown) and July 15 (late sown) with plant densities namely, 0.4 million plants/ha (optimum) and 0.6 million plants/ha (high). These densities were maintained by varying plant to plant distance (by thinning after days after sowing). Each replication was divided into 22 plots, each plot had 3 rows, each 3m long and spaced 45cm apart. All the recommended cultural practices were adopted to raise a good crop. Observations were recorded on five randomly selected plants from each genotype in all the three replications for days to 50% flowering, days to maturity, plant height (cm), number of nodes per plant, number of primary branches per plant, number of pods per plant, number of seeds per pod, dry matter weight per plant (g), 100-seed weight (g), harvest index (%) and seed yield per plant (g). The data were statistically analyzed and the genotypes were assessed for their stability of performance across environments following the method described by Eberhart and Russell (1966). RESULTS AND DISCUSSION The analysis of variance revealed significant mean squares for all the characters studied. The pooled analysis of variance indicated the presence of significant G x E for days to maturity, plant height, dry matter weight per plant, 100-seed weight and seed yield per plant, indicating differential response of genotypes in different environments for these traits, however, other traits were unaffected by the changing environments (Table 1). The mean squares due to environments were highly significant for all the traits, suggesting the existence of considerable variation among environments. i.e., the environments created by plant densities, sowing dates over year was justified Table 1: Pooled stability analysis of variance for important economic characters in soybean over twelve environments Source of Degree Mean Sum of Square variation of freedom Days to Days to Plant Number of Number of Number of Number of Dry matter 100- Harvest Seed 50% maturity height nodes primary pods per seeds per weight seed Index yield flowering per plant branches plant pod per plant weight per plant per plant Genotype (G) ** 61.53** ** 7.99** 3.48** ** 0.028** ** 12.29** ** ** Environment (E) ** ** ** ** ** ** 0.07** ** 3.24** ** ** G E * 27.20** ** 0.09* ** E + (G E) ** 91.37** ** 28.18** 7.42** ** 0.01** ** 0.23** ** ** E (Linear) ** ** ** ** ** ** 0.80** ** 35.64** ** ** G E (Linear) ** 31.40** ** ** ** 0.28** ** ** Pooled deviation ** ** 0.43** ** 0.01** ** 0.06** 46.05** 28.54** Pooled error Total (SS) *, ** Significant at 5% and 1 % probability levels, respectively

3 Volume 39 Issue 4 (2016) 519 and had linear effects. Similar results were reported by Gurmu et al. (2009); Tyagi and Khan (2010) and Verma et al. (2011). The partitioning of mean squares (environments + genotype x environments) showed that environments (linear) differed significantly and were quite diverse with respect to their effects on the performance of genotypes for yield and its components. Further, the higher magnitude of mean squares due to environments (linear) as compared to genotype x environment (linear) exhibited that linear response of environments accounted for the major part of total variation for all the characters studied. The significant mean squares due to genotypes x environment (linear) component against pooled deviation for days to 50 % flowering, days to maturity, plant height, number of primary branches per plant, dry matter weight per plant, 100-seed weight, harvest index and seed yield per plant suggested that the genotypes were diverse for their regression response to change with the environmental fluctuations. Similarly, the significant mean squares due to pooled deviation observed for all the characters except days to maturity and plant height suggested that the deviation from linear regression also contributed substantially towards the differences in stability of genotypes. Thus, both linear (predictable) and non-linear (un-predictable) components significantly contributed to genotype x environment interaction observed for seed yield and majority of yield component characters. This suggested that predictable as well as un predictable components were involved in the differential response of genotypes. Similar results stating the variability in trait expression over environments and calculation of various assignable factors from stability analysis were reported by Hossain et al. (2003); Dhillion et al. (2009) and Tyagi and Khan (2010). The mean values, regression coefficient (bi), and deviation from regression (S 2 di) for twenty two genotypes pooled over twelve environments are presented in Table 2. The characters like days to maturity, plant height, number of nodes per plant, number of seeds per pod, hundred seed weight and harvest index showed higher number of predictable genotypes, while days to 50% flowering, number of primary branches per plant, number of pods per plant, dry matter weight per plant and seed yield per plant had less numbers of predictable genotypes. Further, the stable genotypes identified for wider environments and specific (either favourable or poor) environments with high per se performance (over general mean) for different characters are presented in Table 2. It is evident from the Table that the genotype ABL 55 was found to be most desirable and stable for days to 50% flowering, number of nodes per plant, number of primary branches per plant and harvest index, however, it showed adaptability to rich environment for plant height, dry matter weight per plant and seed yield per plant. On the other hand, genotype ABL 43 identified to be adapted to rich environment for days to 50% flowering, plant height, number of primary branches per plant, dry matter weight per plant, hundred seed weight and seed yield per plant. The genotype ABL 20 exhibited general adaptation to number of nodes per plant and number of seeds per pod whereas specific adaptation to poor environment for days to maturity, number of primary branches per plant, number of pods per plant, dry matter weight per plant and seed yield per plant. The genotype, ABL 17 also showed specific adaptability to rich environment for plant height, number of pods per plant, number of seeds per pod, dry matter weight per plant, harvest index and seed yield per plant. The genotype ABL 62 was found to be most stable for plant height and number of nodes per plant, however, it showed sensitivity to rich environment for number of pods per plant, number of seeds per pod, dry matter weight per plant and seed yield per plant. ABL 45 exhibited general adaptation for plant height, number of nodes per plant and number of pods per plant whereas, it showed specific adaptability to rich environment for days to 50% flowering, days to maturity, dry matter weight per plant and seed yield per plant. Among all the twenty two genotypes, ABL 19 exhibited suitability to poor environment for days to 50% flowering, number of pods per plant, number of seeds per pod, dry matter weight per plant and seed yield per plant. In general, the genotypes developed at Pantnagar showed stability and suitability to maximum number of traits than any other genotypes included in the study. This may be due to the inherent adaptability of these genotypes for tarai region, which gave them edge over other genotypes for uniform/ better trait expression. The overall results of the stability based on Eberhart and Russell model (1966) revealed that none of the soybean genotype included in the study showed stable performance for all the characters. Similar findings with variable explanations were advocated by Sudaric et al. (2001); Kumar et al. (2002); Hossain et al. (2003); Dhillion et al. (2009); Tyagi and Khan (2010) and Verma et al. (2011). In summary, this study showed the presence of substantial amount of GE interactions among the twenty two soybean genotypes for different characters. Promising genotypes with general and specific adaptation for different quantitative characters were identified.

4 520 LEGUME RESEARCH - An International Journal Table 2: Stability parameters for various quantitative characters in twenty two genotypes of soybean pooled over environments * & ** significance at 5% and 1% level of probability

5 * & ** significance at 5% and 1% level of probability Table 2: Contd Volume 39 Issue 4 (2016) 521

6 522 LEGUME RESEARCH - An International Journal REFERENCES Anonymous (2013). Annual Report. Directors Report and Summary Table of Experiments. National Research Centre for Soybean, Indore, ICAR, p Baker, R. J. (1988). Tests for crossover genotype x environment interactions.can. J. Plant Sci., 68: Carpenter, A.C. and Board, J.E. (1997). Growth dynamic factors controlling soybean yield stability across plant populations. Crop Sci., 37: Dhillon, S. K., Singh, G., Gill, B. S. and Singh, P. (2009). Stability analysis for grain yield and its components in soybean [Glycine max (L.) Merrill.]. Crop Improv., 36: Eberhart, A. and Russell, W. (1966). Stabilty parameters for comparing varieties. Crop Sci., 6: Gurmu, F., Mohmmed, H., and Alemaw, G. (2009). Genotype x environment interactions and stability of soybean forgrain yield and nutrition quality. African Crop Science Journal, 17: Hossain, M. A., Rahman, L. and Shamsuddin, A. K. M. (2003). Genotype-Environment interaction and stability analysis in soybean. Journal of Biological Science, 3: Kang, M.S. (1998). Using genotype-by-environment interaction for crop cultivar development. Adv. Agro., 35: Kumar, D., Singh, K. and Singh, P. (2002). Stability analysis for seed yield and its components over different plant densities in soybean [Glycine max (L.) Merrill]. Legume Res., 25: Singh, R. J. and Hymowitz, T. (2001). Exploitation of wild potential Glycine species for improving the soybean. In: Bhatnagar P. S. (ed.), Proceedings of India Soy Forum: Sudaric, A., Vrataric, M. and Sudar, R. (2001). Stability analysis of grain yield and grain quality in soybean breeding. Sjemenarstvo, 18: Tyagi, S. D. and Khan, M. H. (2010). Genotype x environment interaction and stability analysis for yield and its components in soybean [Glycine max (L.) Merrill]. Soybean Genetics Newsletter, 37: 1-9. Verma, N. Sah, R.P., Kumar, R. and Ghosh, J. (2011). Stability analysis in soybean [Glycine max (L.) Merrill]. Soybean Research, 9: