REGRESSION AND PATH ANALYSIS OF SEED AND OIL YIELDS IN SOYBEAN (GLYCINE MAX L.) LINES

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1 REGRESSION AND PATH ANALYSIS OF SEED AND OIL YIELDS IN SOYBEAN (GLYCINE MAX L.) LINES Soghra Sepahvand, Ahmad Reza Golparvar * Department of Agronomy and plant Breeding, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran *corresponding author: Ahmad Reza Golparvar dragolparvar@gmail.com ABSTRACT This study was performed to identify genetic diversity of soybean lines, to determine the best parents for crossing to improve the yield and to find out more effective genetic characteristics in Khoramabad, Lorestan province, Iran. Field experiments were conducted in a four-block randomized complete block design (RCBD). Our present result implied that the lines had significant effects for all the studied traits except days taken to physiological maturity, days taken to full maturity and oil percentage. The results of correlation coefficients showed that grain yield positively correlated with number of grains per pod, harvest index and seed filling rate at the % possibility level and oil percentage at the % possibility level. The stepwise regression results illustrated that grain filling rate, grain filling period and days taken to physiological maturity had significant effects on the grain yield variations. Grain filling rate and grain filling period also justified variations of oil yield. On the base of the regression analysis, grain filling rate and period and days taken to physiological maturity were the most important trait for improving the seed and oil yields of soybean lines. Path analysis of indicated that grain filling rate and period had a significant positive effect on seed yield. Moreover, grain filling rate also had a significant positive effect on oil yield. The results of path analysis and regression analysis suggested that grain filling rate was be as effective trait to improve the seed and oil yields of soybean lines. KEYWORDS: Effective traits, genetic variation, regression and path analysis, soybean,. INTRODUCTION Soybean (Glycine max L. Merrill) is a self-pollinated species (n=4x=40) of the family Fabaceae and it is the most important oilseed in the world (Hymowitz, 004; Kim et al., 04). The people of East Asia have consumed it as soymilk and tafu. This legume is the cheapest alternative source of protein (Zarkadas et al., 00; Ma et al., 0).Grain oil and protein are major storage compounds in soybean and the seeds contain high-level oil and protein. This plant is one of the few plants that provides a complete protein as it contains amino acids essential for human health and poultry (Wang et al., 00; Torun, 99), but is low in methionine and lysine (Parsons, 99). Protein and oil contents in soybean grain are the most valuable components. Carbohydrates are used to create fatty acids and oil in grain. Seed protein and oil concentrations are approximately 4. and 9.% of seed dry mass, respectively (Fehr et al., 00; Wilson, 004). A perfect negative correlation is between grain protein and oil. Grain oil depends to a large extent on seed filling period (Hurburgh et al. 990). Obtaining information through genetic distance between populations and awareness of relationships species provide organization of germplasms and effectively sampling from genotypes in breeding programs (Sharma et al., 00). Seed yield is the most interesting trait to farmers. Yield improvement is caused by genetic and environmental controls and increasing the concentration of carbon (Orf et al., 004). For low heritability traits such as seed yield, genotypes selection can be based on several traits that have high inheritance (Asif et al., 00). Thus, these indirect selections are more efficient than direct selection for genetic improvement of seed and oil yields (falconer, 998). Many studies have examined relationships between traits, path analysis and type and reasonable amount of their effects on yield. A lot of researchers used to select genotypes with high yield. Mishra et al. (994) reported that 00-grain weights, number of grains per plant, number of pods per plant had a high direct effect on soybean yield. The results of Chatri et al., (00) showed that yield had a significant positive correlation with the number of days from germination taken to maturity and number of grains per pod. The coefficients of path analysis also showed that number of grains per pod, number of days from germination taken to maturity, plant height and number of pods per plant had a direct positive effect on seed yield. Volume- 4 Issue- (0) ISSN: 9 4 (p); 9 0 (e) 0 DAMA International. All rights reserved. 8

2 High correlation between biological yield and seed yield had also been reported by other researchers (Kumudini et al., 00; Jason et al., 009). Kumar et al. (04) evaluated 40 soybean genotypes for morphological traits and genetic parameters. They concluded that there were significant variations between all traits based on the variance analysis and mean comparisons for yield and its components. The path analysis showed that 00-grain weight had the highest effect on yield. Path analysis was used as the best standard for the selection in agronomic and biological studies (Mishra and Drolsom, 9; williams et al., 990). Because of the importance of yield enhancement and identification of effective factors in the seed and oil yields, this study aimed to investigate the relationship between yield and its components, analysis of correlation coefficients between traits and the most effective traits in soybean yield. MATERIAL AND METHODS Plant material 0 lines of F8 medium-maturing soybean provided by the? Of Iran were used in this study (Table ). Field experiments were conducted in a block randomized complete block design (RCBD) with four repetitions at Khoramabad, Lorestan province, Iran during 0-. Autumn and spring ploughing and disc were accomplished for seed bed preparation. Chemical fertilizers were added based on results of soil analysis. Before planting, seeds were treated with specific Rhizobium. Seeds were planted in four row plots. Each row was five meters in length and 0 cm apart. Sampling and notes were randomly taken from 0 plants of the internal rows. The two lateral rows were considered as margin. Furthermore, 0 cm of each end internal row was considered as margin. Table. All lines that were used in this study Row Pedigree Line Row Pedigree Line SLAND*N.M.S.B DELSSOY*WILLIAMS8 DELSSOY*WILLIAMS8 SLAND*N.M.S.B STEEL* LINHA CHELESTON*MOSTANG SPERY*VALENTA VALENTA* KARBIN VALENTA* KARBIN FORA*KARBIN HAMILTON*KARBIN VALENTA*T.M.S HAMILTON*T.M.S WILLIAMS During growth, days taken to germination, days taken to flowering, flowering period, days taken to physiological maturity, days taken to complete maturity, grain filling period and plant height were evaluated. After harvesting, biomass weight, seed yield per hectare, protein and oil yields were measured. For yield components, 0 normal plants randomly were selected per plot and their number of pods per plant, number of seeds per pod, 000-seed weight were accounted. Two square meters per plot was harvested manually to measure seed yield. Then, grain weight was obtained based on % humidity. Oil was extracted by nuclear magnetic resonance (NMR) spectroscopy. Oil yield was calculated based on grain content that multiplied by the seed yield. The amount of protein was calculated based on the nitrogen content of grain that multiplied by the 0.4 as fixed coefficient (Ntanos and Koutrobas, 00). Grain filling period was calculated based on difference between days taken to flowering and days taken to physiological maturity. Grain filling rate was calculated based on the ratio of seed yield to grain filling period. Harvest index calculated by using ratio of grain to biomass yields that multiplied by 00. RESULTS AND DISCUSSION Soybean genotypes with higher seed oil have a significant relationship with traits that related to seed yield. Thus, breeders have looked for accumulation the desired genes in soybean cultivars. Correlation coefficients among all traits in this study are shown in Table. The results of correlation coefficients showed that seed yield positively was correlated with number of grains per pod, harvest index and grain filling rate at the % level and oil percentage at the % level. Seed yield had highest correlation with harvest index. This indicated that harvest index was influenced highly significantly by seed yield. These correlations suggested that we must attention specially the traits to increase seed Volume- 4 Issue- (0) ISSN: 9 4 (p); 9 0 (e) 0 DAMA International. All rights reserved. 9

3 6 Day taken to flowering Plant length Day taken to poding days taken to physiological maturity days taken to maturity Biomass weight number of seeds per pod number of pods per plant Harvest index 0seed weight Protein percent Oil percent grain filling period grain filling rate seed yield Oil yield protein yield yield. Furthermore, oil yield positively correlated with number of grains per pod, harvest index, grain filling rate and seed yield at the % level. Oil yield had highest correlation with seed yield (r=0.9**). Table. Stepwise correlation coefficients among all traits in this study. -0/8-0/0-0/084-0/06-0/ 0/89-0/9-0/6 0/60 0/00-0/0-0/ 0/6-0/46 0/8 ** 0/ -0/098-0/6 0/40-0/ * -0/ * 0/04-0/99 0/9-0/066 0/4 0/ ** -0/6-0/96-0/060-0/006 0/0 0/9 0/44-0/4 0/ -0/0-0/06 0/ -0/ 0/0 * 0/9 ** -0/68 ** 0/64 ** 0/6-0/ 0/00 0/08-0/8 0/06 0/08-0/49 * 0/04 0/ 0/4 0/ -0/06 0/08 0/46-0/4-0/66 0/ * -0/4-0/46 * -0/ * -0/64 ** -0/48-0/069 0/06 0/4 0/46 0/44-0/04 0/0 0/44 0/49 * 0/6-0/6 ** 0/6-0/949 ** 0/8 0/0 0/4 * 0/ * -0/08 0/44 * 0/96 0/44-0/06-0/0 0/40-0/4 0/ 0/08-0/44 0/008 0/09-0/4 0/4 0/0 0/694 ** 0/44-0/86 0/98-0/0-0/ -0/ 0/09-0/00 0/90 0/9-0/0 0/6 0/4 0/8 ** 0/ -0/ 0/489 * 0/48 0/848 ** -0/ -0/6 0/096 0/0 0/49 0/40-0/46 0/ ** 0/44 0/860 ** 0/08-0/44 0/6 ** 0/ 0/89 ** 0/9 ** -0/094-0/69 0/9 0/0 0/04 0/0 0/69 0/9-0/00 0/4-0/ 0/48 * -0/0 0/90 0/69 ** 0/6 ** 0/9 * *&** significantly different at % and % probability level By using stepwise regression analysis, we can remove ineffective or less effective traits on yield. In this study, stepwise regression was used to evaluate the yield components and their relationship with yield per plant. Therefore, seed and oil yields were considered as the dependent variables versus rest traits as independent variables. Stepwise regression analysis of the oil yield was shown in Table that %.9 of its variations was belong to grain filling rate. Other traits had no significant effect on the regression model. The differences among the soybean lines in terms of oil yield could be due to variation of this trait. Table. Stepwise regression analysis of oil yield in the soybean lines. Trait Coefficients of regression components Cumulative R (%) T Test Grain filling rate ** Grain filling period ** Intercept ** ** Significantly different at % probability level. Stepwise regression analysis of the seed yield was shown in Table 4 that %99.9 of its variations was belong to grain filling rate, grain filling period and days taken to physiological maturity. Other traits had no significant effect on the regression model. The differences among the soybean lines in terms of oil yield could be due to variation of these traits. Thus, we can use them as the best traits to improve seed yield of soybeans. Investigation of the relationship between the traits and seed yield suggest that indirect selection in initial generations be performed to improve the seed yield due to its low genetic heritability. These genetic traits will have the higher efficiency than the direct selection for yield. Volume- 4 Issue- (0) ISSN: 9 4 (p); 9 0 (e) 0 DAMA International. All rights reserved. 0

4 Table 4. Stepwise regression analysis of seed yield in the soybean lines. Trait Coefficients of regression Cumulative T Test components R (%) Grain Filling Rate ** Grain Filling Period ** Days taken to physiological maturity.4.0* Intercept ** *&** significantly different at % and % probability level, respectively. In order to better understanding and interpreting the results, the variables of the regression model was used for path analysis. This method is based on calculation of the component correlation coefficients and it will help the researchers to find and identify hidden relationships among the variables. The direct or indirect effects of each trait and their relationships can determine by the coefficients. The path analysis showed that grain filling rate and grain filling period had the highest positive and direct significant effects on oil yield (Table ). These effects were and 0.49 for grain filling rate and grain filling period, respectively. As well as, grain filling rate had a negative indirect effect on oil yield through grain filling period (- 0.66). However, this value is insignificant and significant correlation was found between grain filling rate and oil yield. In contrast, grain filling period had a negative indirect effect on oil yield through grain filling rate (-0.98). Thus, we can say that selection for longer grain fill period associated with reduction of grain filling rate. Table. Path analysis of oil yield (as the dependent variable) versus the independent variables. Variable Grain Filling Rate Grain Filling Period Cumulative Effects Grain Filling Rate Grain Filling Period Residual Effects 0.4 The path analysis illustrated that grain filling rate and grain filling period had the positive and direct significant effects on seed yield with.0 and 0.4, respectively (Table 6). Grain filling rate had a negative indirect effect on seed yield through grain filling period (-0.68). In contrast, grain filling period had a negative indirect effect on seed yield through grain filling rate (-0.0). This implied that there was a hidden relationship between grain filling period and grain yield. Table 6. Path analysis of seed yield (as the dependent variable) versus the independent variables. Variable Grain Filling Rate Grain Filling Period Cumulative Effects Grain Filling Rate Grain Filling Period Residual Effects 0.0 The results showed that grain filling rate and grain filling period had a high direct correlation with seed yield. Path analysis carried out because of the inability of regression model to identify and find the effective traits, segregation necessity of correlation coefficients of the traits to the direct and indirect effects and finding traits with the greatest effect on seed yield. Path and regression analysis with standardized variables determine relationships among the traits and the relative importance of their direct and indirect effects on yield, and the correlation coefficients to be segregated to the direct and indirect effects (Bhatt, 9). Raut et al, (00), Narne et al., (00), Amarantath and vishwantaha, (990) and Rajanna et al., (000) and Kumar et al., (04) reported that number of pods per plant, number of grains per plant and grain weight had the greater direct effect on seed yield compared to the other traits. Path analysis showed that grain weight had the greatest effect on seed yield. Volume- 4 Issue- (0) ISSN: 9 4 (p); 9 0 (e) 0 DAMA International. All rights reserved.

5 CONCLUSION Based on this study, it can be concluded that grain filling rate and grain filling period are suitable indexes to improve soybeans yield. These traits are an important for selective plant breeding of the seed and oil yields. The results showed that Focusing on the seed yield is one of the main strategies to increase soybean oil yield. Correlation coefficients between traits are useful for evaluation and planning on breeding programs. Regression analysis showed that the oil filling rate and grain filling period had the greater effects on the oil yield than the other traits. It can therefore be concluded that these traits are improved to increase seed and oil yields. This issue is very important for plant breeding. In general, it can be understood from the results of the path analysis of the seed and oil yields plant that grain filling rate and grain filling period can be used to indirect selection for better improvement of soybean lines and identification of indirect and direct effects REFERENCE Amarantath K.C. and Vishwantaha S. R. (990). Path coefficient analysis for some quantitative characters in soybean. J. of Agri. Sci. 4(): -. Asif M., Mujahid M. Y., Ahmad I., Kisana N. S., Asimand M. and Mustafa S. Z. (00). Determining the direct selection Criteria for identification of high yielding lines in bread wheat (Triticum aestivum). Pakistan J. Biol. Sci. 6:48-0. Bhatt G. M. (9). Significance of path coefficient analysis in determining the nature of character association. Euphytica :8-4. Chettri M., Mondal S. and Nath R. (00). Studies on correlation and path analysis in soybean (Glycine max, L Merrill.) in the Darjeeling hills. J. Hill. Res. 6 (): 0-0. Falconer D. S. (998). Introduction to quantitative genetics, Ronald press, New York. Fehr W. R., Hoeck J. A., Johnson S. L., Murphy P. A., Nott J. D. Papilla G. I., et al. (00). Genotype and environmental influence on protein components of soybean. Crop Sci. 4: 4. Hurburgh C.R.Jr., Brumm T.J., Guinn J.M., Hartwig R.A. (990). Protein and oil patterns in U.S. and world soybean markets. J. American Oil Chemists' Soc. 6: Hymowitz T. (004). Speciation and cytogenetics. In: HR Boerma, JE Specht (eds) Soybeans: Improvement, Production, and Uses. Agron Monogr, rd edn, No 6. ASA-CSSASSSA, Madison, WI, USA, pp 9 6. Jason L. Bruin De Pedersen P. (009). Growth, yield and component changes among old and new soybean cultivars. Agron. J. 0: 4-0 Kim J. K., Kim E. H., Park I., Yu B.R., Lim J.D., Lee Y.S., Lee J.H., Kim S.H., Chung I.M. (04). Isoflavones profiling of soybean [Glycine max (L.) Merrill] germplasms and their correlations with metabolic pathways. Food Chem. : Kumar A, Pandey A, Aochen C, Pattanayak A. (04). Evaluation of genetic diversity and interrelationships of agro-morpholgical characters in soybean (Glycine max). Proc. Nat. Acad. Sci. India section B: Biological sciences. Kumudini S, Hume DJ, Chu G. 00. Genetic improvement in short season soybeans: I. Dry matter accumulation, partitioning, and leaf area duration. Crop Sci. 4:9 89. Ma L., Li B., Han F., Yan S., Wang L., Sun J. (0). Evaluation of the chemical quality traits of soybean seeds, as related to sensory attributes of soymilk. Food Chem.: Mishra A K, Ali S A, Tiwary RC, Raghuwanshi R S. (994). Correlation and path coefficient analysis in Segregating populations of soybean. Int. J. Tropical Agric. : 8-8 Mishra SN, Drolsom PN. 9. Association among certain morphological traits of diallel cross progenies in Bromus inermis LEYSS. J. Agric. Camb. 8: Narne C., Aher R.P., Dahat D.V. and Aher A.R. (00). Selection of protein rich genotypes in soybean. Crop Res. Hisar. 4 (): 06-. Ntanos D.A. and Koutroubas S.D. (00). Dry matter and N accumulation and translocation for Indica and Japonica rice Mediterranean conditions. Field Crops Res.4:9-0. Orf J.H., Diers B.W., Boerma H.R. (004). Genetic improvement: Conventional and molecular strategies In: HR Boerma, JE Specht (eds) Soybeans: Improvement, Production, and Uses. Agron Monogr, rd edn, No 6. ASA-CSSA- SSSA, Madison, WI, USA, pp Volume- 4 Issue- (0) ISSN: 9 4 (p); 9 0 (e) 0 DAMA International. All rights reserved.

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