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.

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.

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).

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.

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).

Results of Cluster analysis (hierarchical grouping)

Accordance of grouping analysis, n people can form g

groups (g