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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, e-mail: [email protected]
ABSTRACT
In early stage of diagnosis, infertility is a clinical condition; however, it can cause so many
consequences in infertile couples. In Iran, fertility is culturally and socially important for
couples (especially women) and their families. Among psychological factors of infertility,
fear, anxiety, hopelessness and despair can prone every human being to various mental
illnesses. Since level of awareness plays a significant role in shaping individual’s attitudes,
behavior and actions in confronting with difficulties, if people be familiar with infertility
symptoms, consequences and solutions, they will feel more confident and have more mental
peace. Investigating this issue in various aspects of infertility seems crucial. This descriptive
study aimed to examine the level of awareness of 102 students of Biology at University of
Sistan and Baluchestan about the psychological consequences of infertility. This level of
awareness in 33 students (33%) was low, in 50 students (48.5%), it was moderate and in 19
students (18.4%), it was high. Their level of awareness had no significant correlation with
their semester (p<0.05); however, it had a significant relationship with their gender
(p=0.049).Therefore, through analyzing people’s beliefs, interests and awareness, one can
change their attitude and performance in confronting with psychological consequences of
infertility applying educational programs, workshops, books and media in addition to
medical interventions.
KEYWORDS: Awareness,
Infertility, Psychological consequences,
INTRODUCTION
Soybean (Glycine max L. Merrill) is a self-pollinated species (2n=4x=40) of the family Fabaceae
and it is the most important oilseed in the world (Hymowitz, 2004; Kim et al., 2014). The people of
East Asia have consumed it as soymilk and tafu. This legume is the cheapest alternative source of
protein (Zarkadas et al., 2007; Ma et al., 2015).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., 2003; Torun, 1992), but is low in methionine and lysine (Parsons, 1991). 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 42.1 and 19.5% of
seed dry mass, respectively (Fehr et al., 2003; Wilson, 2004). A perfect negative correlation is
between grain protein and oil. Grain oil depends to a large extent on seed filling period (Hurburgh
et al. 1990). 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., 2002).
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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., 2004). For low
heritability traits such as seed yield, genotypes selection can be based on several traits that have
high inheritance (Asif et al., 2003). Thus, these indirect selections are more efficient than direct
selection for genetic improvement of seed and oil yields (falconer, 1998). 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 ,1994) reported
that 100-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, 2003) 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.
High correlation between biological yield and seed yield had also been reported by other
researchers (Kumudini et al., 2001; Jason et al., 2009).( Kumar et al,2014) 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 100-grain weight had the highest effect on
yield. Path analysis were used as the best standard for the selection in agronomic and biological
studies (Mishra and Drolsom, 1973; williams et al., 1990). 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.
MATERIALS AND METHODS
Plant material
20 lines of F8 medium-maturing soybean provided by the ? of Iran were used in this study (Table
1). Field experiments were conducted in a block randomized complete block design (RCBD) with
four repetitions at Khoramabad, Lorestan province, Iran during 2012-13. 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 50 cm apart.
Sampling and notes were randomly taken from 10 plants of the internal rows. The two lateral rows
were considered as margin. Furthermore, 50 cm of each end internal row was considered as margin.
Row
Pedigree
Line
Row Pedigree
Line
1
2
3
4
5
6
7
8
SPRY*NEMAHA
SLAND*N.M.S.B
SPRY*NEMAHA
SPRY*NEMAHA
DELSSOY*WILLIAMS82
DELSSOY*WILLIAMS83
SLAND*N.M.S.B
SPRY*NEMAHA
5
8
1
4
5
6
4
3
11
12
13
14
15
16
17
18
2
7
2
3
5
7
3
7
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SPRY*NEMAHA
SPERY*VALENTA
VALENTA* KARBIN
VALENTA* KARBIN
FORA*KARBIN
HAMILTON*KARBIN
VALENTA*T.M.S
HAMILTON*T.M.S
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STEEL* LINHA
1
CHELESTON*MOSTAN
8
G
Table 1. All lines that were used in this study
9
10
19
20
SPRY*NEMAHA
WILLIAMS
1
2
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, 10 normal plants randomly were selected per plot and their
number of pods per plant, number of seeds per pod, 1000-seed weight were accounted. Two square
meters per plot was harvested manually to measure seed yield. Then, grain weight was obtained
based on 12% 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 0.54 as fixed coefficient
(Ntanos and Koutrobas, 2002). 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 100.
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 2. 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 1% level and oil percentage at the 5% 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 yield. Furthermore, oil yield positively correlated with number of grains per
pod, harvest index, grain filling rate and seed yield at the 1% level. Oil yield had highest correlation
with seed yield (r=0.975**).
Volume- 4 Issue- 2 (2015)
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protein yield
Oil yield
seed yield
grain filling rate
grain filling period
Oil percent
Protein percent
0111seed weight
Harvest index
number of pods per plant
number of seeds per pod
Biomass weight
days taken to maturity
days taken to physiological maturity
Day taken to poding
Plant length
Day taken to flowering
Germination percent
Table 2. Stepwise correlation coefficients among all traits in this study.
1
-0/318
1
-0/320
-0/084
1
-0/061
-0/325
0/189
1
-0/239
-0/236
0/360
0/007
1
-0/075
-0/215
0/136
-0/246
0/835
1
0/131
-0/098
-0/263
0/401
-0/527 *
-0/523 *
**
**
1
0/041
-0/299
0/129
-0/066
0/374
0/571
-0/363
1
-0/396
-0/060
-0/006
0/072
0/295
0/442
-0/134
0/372
1
-0/305
-0/061
0/211
-0/237
0/520 *
0/591**
-0/685**
0/624**
0/362
1
*
-0/227
0/070
0/028
-0/218
0/106
0/078
-0/491
0/034
0/251
0/354
1
0/253
-0/056
0/108
0/146
-0/234
-0/266
0/533 *
-0/433
-0/462*
-0/551*
-0/564**
1
-0/418
-0/069
0/026
0/241
0/346
0/244
-0/204
0/305
0/442
0/497
0/361
-0/617
-0/036
-0/037
0/140
1
0/256
-0/949
-0/437
**
*
*
*
*
**
1
0/118
0/207
0/477
0/512
-0/083
0/447
0/196
0/244
0/377
0/018
-0/144
0/008
0/092
-0/143
0/314
0/250
0/694
**
0/144
-0/286
0/398
-0/302
1
-0/311
-0/137
0/097
-0/030
0/290
0/393
-0/207
0/576
0/374
0/851**
0/133
-0/327
0/489*
0/248
0/848**
1
-0/371
-0/136
0/096
0/027
0/349
0/410
-0/246
0/573**
0/434
0/860**
0/208
-0/441
0/671**
0/252
0/819**
0/975**
1
-0/094
-0/169
0/159
0/101
0/054
0/130
0/269
0/191
-0/005
0/334
-0/321
0/478*
-0/025
0/190
0/569**
0/672**
0/559*
1
*&** significantly different at %5 and %1 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 3 that %71.9 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.
Volume- 4 Issue- 2 (2015)
ISSN: 2319–4731 (p); 2319–5037 (e)
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Table 3. Stepwise regression analysis of oil yield in the soybean lines.
Trait
Coefficients of regression components Cumulative R2 (%)
12.83
71.9
Grain filling rate
99.3
Grain filling period 10.76
596.72
Intercept
** significantly different at %1 probability level.
T Test
16.42**
9.15**
-8.105**
Stepwise regression analysis of the seed yield was shown in Table 4 that %99.91 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.
Table 4. Stepwise regression analysis of seed yield in the soybean lines.
Trait
Coefficients
of
regression Cumulative
components
(%)
49.26
71.9
Grain Filling Rate
39.72
99.9
Grain Filling Period
1
Days taken to physiological 5.4
maturity
-2440.04
Intercept
*&** significantly different at %5 and %1 probability level, respectively.
R2 T Test
128.97**
60.77**
2.70*
-14.94**
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 5). These effects were 0.984 and 0.549 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 (-0.166). 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 (-0.298). Thus, we can
say that selection for longer grain ﬁll period associated with reduction of grain filling rate.
Table 5. Path analysis of oil yield (as the dependent variable) versus the independent
variables.
Variable
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Grain Filling Rate
Grain Filling Period
ISSN: 2319–4731 (p); 2319–5037 (e)
Cumulative Effects
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Grain Filling Rate 0.984
Grain
Filling -0.298
Period
0.234
Residual Effects
-0.166
0.549
0.819
0.252
The path analysis illustrated that grain filling rate and grain filling period had the positive and
direct significant effects on seed yield with 1.015 and 0.554, respectively (Table 6). Grain filling
rate had a negative indirect effect on seed yield through grain filling period (-0.168). In contrast,
grain filling period had a negative indirect effect on seed yield through grain filling rate (-0.307).
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
Residual Effects
Grain Filling Rate
1.015
-0.307
Grain Filling Period
-0.168
0.554
Cumulative Effects
0.847
0.247
0.035
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, 1973).
(Raut et al, 2001), (Narne et al,2002),( Amarantath and vishwantaha, 1990; Rajanna et al,2000 ;
Kumar et al,2014) 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.
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 is 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
Volume- 4 Issue- 2 (2015)
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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
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