Abstract analysis, four independent variables justified 99.92 percent

Abstract

To investigate the relationships between some principal
attributions of morphology with seed yield per soybean, the18 soybean genotypes
were examined by random complete block design (RCBD) study. These study was also carried out
three replicates to gain reliable results. The results of variance analysis
indicated that, there were a significance differences among all soybean
genotypes. Moreover, the results of correlated analysis revealed that
biological yield (0.96), harvest index (0.92), and number of branches (0.92)
had the uttermost correlation with seed yield. To data factor analysis, four
independent variables justified 99.92 percent of all data. The first variable,
seed yield, justified 96.71 percent of entire variance. To examine soybean seed
yield, Multiple-Regression Model with method Analytical Regression Model
(step-by-step) was utilized. This model proved that biological yield, thousand
seed weight, and harvest index entered into model respectively and
justified 98.85 percent of variation of seed yield. Correlated coefficients of
considered attributions were 0.96, 0.78, and 0.92 respectively. All of these
indexes had significant at 1% in statistical process. Therefore, these traits
can be notability used in soybean breeding programs. Also, accordance of cluster
analysis. the sample was divided into three groups.

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Keywords: Soybean, Morphological Traits, Factor
Analysis, Step-by-step Regression

Introduction

Soybean as strategic plant can cope with nutria demands
by the production of 40% protein and 20% oil (Monthly oil
industry, 2004). In Iran, approximately 100000
hectare of farmland under proper weather conditions has been planting in
soybean. Moreover, in several provinces e.g., Golestan, Gilan, Mazandaran, and
Ardebil around 2.2 tones soybean a hectare has been cultivated (Hymowitz
and Kaizuma, 2008). Therewith, Soybean
as well as five oily plants (oil palm, rapeseed, cotton seed, peanut, and
sunshine seed) can produce 84% oil of the world (Top Fer et al.,
1995). Hence, soybean and its attributions have substantial
roles in economics. Hence, the cognition of attributions’ relations and their
interactions are crucial for all repairing plans (Acquah
et al., 1992). It’s also worthwhile to
utter that soybean has a considerable interaction with daylight; therefor,
exploring convenient genotypes, determining appropriate period of cultivation,
and varieties of soybean are
essential factors to plant of this seed. Two principals has been heeded to
produce high qualified soybean, namely, variety of soybean with high potential genotype and variety of soybean with
high adaptability.

Kumudini et al., (2002) compered the new and old variety of soybean, the
results indicated that new cultivar of soybean had high quality due to the long
durability of leaf at filling crustacean level and the escalation of dry
materials at this level. The results of several studies also demonstrated that soybean
with high-level of yield is reachable through high harvest index and more
devotee of Photosynthesis into natal parts, whereas increasing surface of leaf
until graining has contradictory relation with seed yield (Kumudini et al., 2002). In this vein, Jian
Jin et al., (2010) studied 41 varieties of soybean and they found out that the duration (from
sheathing to graining) was overriding to produce outstanding qualified soybean.
Khan and Hatam (Khan and Hatam, 2000) illustrated that most of morphological attributions had
meaningful and positive correlation with seed yield.
Masudi et al (2009), also reported that bush weight, numbers of
seed and in bush had higher correlation with soybean yield. On the contrary, in
study of Bangar
et al (2003) it was found that soybean yield had significance relation with
weight of 100 grains, numbers of days from germination to 50% flowering, and
time of cultivation. Henrico et al., (2004) as well as Akhtar and Sneller (1996) studies indicated that numbers of seed per plant
had meaningful correlation with seed yield, whereas, this attribution had the
highest direct impact on yield. Rezaizad (1999) investigated the existence relations between seed
yield and its components and he explored that number of seed per plant,
biological yield, and numbers of pod per plant had the most correlation with seed
yield.

In this vein, due to the complicated relations among
attributions, the exact results cannot be reported through simple correlated coefficients. For this purpose, multi-variables statistical model is
utilized to recognize the relations among attributions. Thus, data factor
analysis as statistical method which revealed high correlations among
variables, is required to decrease data and get the fruits of data (Moqadam et al., 2004). The study on 14 attributions of 20 cultivar
of soybean demonstrated four results through variables analysis method: first variable justified 38.83 percent of data and was
called as natal variable; second variable justified 21.4 percent of data and was called as seed specifications variable; third
variable justified 17.35 percent of data and was called as yield variable and
the final variable justified 7.5 percent of data and was called as number of
seed per pod variable (Sabokdast nodemi et al., 2010). Zhao et al., (1991)
employed data factor analysis method in 12 important agricultural attributions
by 16 soybean genotype in China. These attributions were classified into four
groups. The first variable was contained number of seed per plant and numbers of
pod per plant. The second variable was consisted plant height, number of node,
height of the first pod from land and day numbers needed to flower. The third
variable was included number of pod per plant, hundred seed weight, and weight
of seed per plant. The four variable was comprised number of branches.
Motivated by previous research, it was resulted that cultivar of soybean had
the significance impact on soybean seed yield. It is also notify to utter that,
various sorts released different yields accordance of environment conditions
and their adaptabilities to those conditions. Thus recent studies tried to gain
desired sort through modifying agricultural variables including history of
planting, model of planting, etc.  The
current study also attempted to assess yield and yield component of prevalent soybean
cultivars and employ these cultivars in future repairing plans.

Materials and method

This
study was cried out in experimental field of martyr Beheshit Company in Dezful
city (capital of Khuzestan Province in south west of Iran). To battle against
weeds, Treflan spray was used (2.5 liters for one hectare). 200 kg/ha of potassium sulfate, 150 kg/ha
Triple superphosphate and 50 kg/ha nitrogen fertilizer were used. The
demanding nitrogen was amounted 150 kg/ha at fourth and fifth leaf levels and
was amounted 100 kg/ha at graining level to the plant, due to the lack of
activated bacteria fixed soybean nitrogen, This RCBD study employed 18 genotypes
of soybean and carried out three replicates. Each Crete contained 4 rows- 4m in
length and 60cm in width- and the given gap between bushes was 5cm. After complete
growth, 10 bushes were chosen randomly from each Crete. The considered
attributions consisting number of branches, number of pod per plant, plant height,
pod length, number of node, thousand seed weight, biological yield, seed yield,
and harvest index were studied. All data was obtained through three-time
assessment of attributions of selected bushes. This data was grouping through
SAS 9.1 software (variance analysis) as well as Duncan Model (to compere
average of data). To
analysis variables Step-by-step Regression and SAS 9.1 software and to analysis
correlation and cluster, SPSS 18 software were utilized.

Results and discussion

The results of variance analysis (table 1) proved that
the impact of block and traits was significant for all attributions at 1
percent probable level. The most coefficient of genotype variation was belonged
to number of node and the least coefficient was owned to biological yield. The
results of compering average of attribution in table 2 revealed that the most number
of branches (13.33), number of pod per plant (101), number of node (11.33),
biological yield (765 kg/ha), seed yield (337 kg/ha), harvest index (44.04
kg/ha) were existence in salend. The saman cultivar showed the most plant
height (101.33) from farmland. On the other hand, the most pod length (6.73Cm)
was observed in SG5 cultivar. The most thousand seed weight (240.66 gr) also
belonged to Gorgan 3. olser and
cartter (2004) stated that some components of yield consisting seed
size, number of seed per pod and numbers of pod per plant, etc. are crucial
factors in progression of soybean yield; therefore, genotypes empowered with these
high qualified constituents have much more potations genetically. Farahani Pad
et al., (2012) demonstrated that the impact of cultivar on thousand seed weight
and seed yield in four cultivar were meaningful. In most of products, yield is
defined as the mixture of huge numbers of biological processes occurred during
growing. Accordance of Ghorban zade neghab et al., (2013) study, Zane cultivar
with 14.8 gr had the most weight of
hundred seeds and sahar cultivar with 9.2 had the least weight
of hundred seeds among studied cultivars. The results illustrated that the Zane
seed had the most weight of hundred seeds due to few numbers of seed per plant
and lack of competition among seeds.

Table1)
Analysis of variance of measured traits in soybean cultivars

Table
2) Comparison of the studied traits in soybean cultivars

Correlation analysis

Determination of correlation analysis was one of the indexes
to assess the existence relations among attributions. The result revealed that
seed yield had the most correlation with biological yield (0.96) (table 3).
Correspondently, Masudi
et al. (2009) Yunesi hamze khanlu
et al., (2010), Namdari and
Mahmudi (2013), as well as Iqbal et al., (2003) reported the meaningful correlation
between seed yield and these four attributions: numbers of pod per plant,
number of seed per pod, harvest index and number of branches. Similar findings
were reached by Pedersen
and Lauer (2004), Shibels et al., (1996) and Kumudini et al.,
(2001). Respecting the plant height, number of node onto cardinal branch,
numbers of branches, number of pod per plant and weight of thousand seeds were
effective factors on improvement of soybean yield; therefore, genotypes with
these high qualified attributions had more potential. This lends evidence to
previous studies which suggested that cultivar of Selend, SG5, and Gorgan 3 are
superior proceed than others (Amaranthath et al., 1990; Das et al., 1989; Pendy
et al., 1973; Rajput et al., 1986).

Table
3) Simple phenotypic correlation coefficients among studied
traits in soybean cultivars

Factor analysis

The considerable studies were conducted to assess the
impact of relations on attribution proceed via analyzing coefficients to factor
analysis. The recent research concentrated on causal analysis and determination
of crucial criteria for repairing soybean yield. In the current study, the
results of analyzing 10 morphological attribution through cardinal factors,
highlighted four principal variables (table 4). These four variables explained
96.71%, 0.0235%, 0.0065%, and 0.0021% of data diversities respectively and as
whole, they clarified 99.92% of data diversities. There is also a direct
relationship between variables variance and variables value in data
interpretations. In this vein, subscription rate was a part of variance
variable related to common variables. In addition, there was direct
relationship between subscription rate and accurate rate (Henrico
et al., 2004). By observing of revolved variable
coefficients, it was found that the first variable coefficients,
proceed variable, covered most of data and contained the big and positive coefficients
of seed proceed, biological proceed, removal index, and tie  numbers (table 4). Similarly
Yunesi hamze khanlu et al., (2010)
examined variable analysis of 9 attributions within 33 mutated soybean lines.
They illustrated that numbers pod per plant, numbers of seed per plant, harvest
index and were crucial attributions to improve soybean yield. Moreover, Narjesi et al., (2008) tested 17 attributions of 30 soybean
genotypes. The result proved that two variables of phenology and yield
justified 28.21% and 16.56% of data diversities respectively. They also
declared that harvest index and seed numbers had the biggest effect on soybean
seed yield.

Table
4) Factor analysis by principal component method with varimax for the studied
traits

The second variable, yield component, contained the big
and positive coefficients of biological yield, harvest index, thousand seed weight
as well as pod length and also covered 2.35% of data diversities.

The third variable, contained the big and positive
coefficients of number of node as well as plant height and covered 0.65% of
data diversities.  It also contradicts
with Kohkan and et al., (2010) study in which 12 traits of 141 soybean
line were examined. Based on their results, the first variable, phono-genetic
variable, was consisted traits including yield, numbers of branches per plant,
numbers of pod per plant, numbers of seed per plant, as well as seed weight per
plant and covered 29.18% of data diversities.

The fourth variable, crustacean variable, was contained
the big and positive coefficients of attributions including numbers of pod per
plant, numbers of branches as well as pod length and also covered 0.21% of data
diversities. In the same vein, Yahueian et al., (2010), found four main variables via factor
analysis in stress conditions. These variables justified 78.38% of data
diversities. The first variable, phonologic-morphological variable covered most
of obtained data. The second variable or yield and yield component, the third
variable or quality of seed, and fourth variable in stress conditions seed size
were identified.

In this study, step- by- step regression model was
utilized. In this model after entering the new variable into the model, the old
one was assessed by the model too. Hence, in this model, the most meaningful
variable remained in functions. 
Furthermore, in this model few variables but important ones were
examined (Henrico et al., 2004). The results indicated that some attributions consisting biological yield,
thousand seed weight, and harvest index entered into the model and covered
98.85% of seed yield diversities (table 5). The of inclination regression line
also revealed that attributions of biological yield, thousand seed weight, and harvest
index were 1 percent meaningful  in
statistical process. Some research declared that removal index is the best
variable to justify soybean seed yield (Shukla et al., 1980; Weilenmanm detau and Luguez, 2000; Narjesi et al., 2008).

Table
5) step by step regression analysis for
seed yield as a function variable and other traits as independent variable

 

Results of Cluster analysis (hierarchical grouping)

Accordance of grouping analysis, n people can form g
groups (g

x

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