AbstractTo investigate the relationships between some principalattributions of morphology with seed yield per soybean, the18 soybean genotypeswere examined by random complete block design (RCBD) study. These study was also carried outthree replicates to gain reliable results. The results of variance analysisindicated that, there were a significance differences among all soybeangenotypes. Moreover, the results of correlated analysis revealed thatbiological yield (0.96), harvest index (0.92), and number of branches (0.92)had the uttermost correlation with seed yield.
To data factor analysis, fourindependent variables justified 99.92 percent of all data. The first variable,seed yield, justified 96.71 percent of entire variance. To examine soybean seedyield, Multiple-Regression Model with method Analytical Regression Model(step-by-step) was utilized. This model proved that biological yield, thousandseed weight, and harvest index entered into model respectively andjustified 98.
85 percent of variation of seed yield. Correlated coefficients ofconsidered attributions were 0.96, 0.78, and 0.92 respectively. All of theseindexes had significant at 1% in statistical process.
Therefore, these traitscan be notability used in soybean breeding programs. Also, accordance of clusteranalysis. the sample was divided into three groups.
Keywords: Soybean, Morphological Traits, FactorAnalysis, Step-by-step RegressionIntroductionSoybean as strategic plant can cope with nutria demandsby the production of 40% protein and 20% oil (Monthly oilindustry, 2004). In Iran, approximately 100000hectare of farmland under proper weather conditions has been planting insoybean. Moreover, in several provinces e.g., Golestan, Gilan, Mazandaran, andArdebil around 2.2 tones soybean a hectare has been cultivated (Hymowitzand Kaizuma, 2008). Therewith, Soybeanas well as five oily plants (oil palm, rapeseed, cotton seed, peanut, andsunshine seed) can produce 84% oil of the world (Top Fer et al.,1995).
Hence, soybean and its attributions have substantialroles in economics. Hence, the cognition of attributions’ relations and theirinteractions are crucial for all repairing plans (Acquahet al., 1992).
It’s also worthwhile toutter that soybean has a considerable interaction with daylight; therefor,exploring convenient genotypes, determining appropriate period of cultivation,and varieties of soybean areessential factors to plant of this seed. Two principals has been heeded toproduce high qualified soybean, namely, variety of soybean with high potential genotype and variety of soybean withhigh adaptability.Kumudini et al., (2002) compered the new and old variety of soybean, theresults indicated that new cultivar of soybean had high quality due to the longdurability of leaf at filling crustacean level and the escalation of drymaterials at this level. The results of several studies also demonstrated that soybeanwith high-level of yield is reachable through high harvest index and moredevotee of Photosynthesis into natal parts, whereas increasing surface of leafuntil graining has contradictory relation with seed yield (Kumudini et al.
, 2002). In this vein, JianJin et al., (2010) studied 41 varieties of soybean and they found out that the duration (fromsheathing to graining) was overriding to produce outstanding qualified soybean.Khan and Hatam (Khan and Hatam, 2000) illustrated that most of morphological attributions hadmeaningful and positive correlation with seed yield.Masudi et al (2009), also reported that bush weight, numbers ofseed and in bush had higher correlation with soybean yield.
On the contrary, instudy of Bangaret al (2003) it was found that soybean yield had significance relation with weightof 100 grains, numbers of days from germination to 50% flowering, and time ofcultivation. Henrico et al., (2004)as well as Akhtarand Sneller (1996) studies indicated that numbers of seed per planthad meaningful correlation with seed yield, whereas, this attribution had thehighest direct impact on yield. Rezaizad (1999) investigated the existence relations between seedyield 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 seedyield.In this vein, due to the complicated relations amongattributions, the exact results cannot be reported through simple correlated coefficients.
For this purpose, multi-variables statistical model isutilized to recognize the relations among attributions. Thus, data factoranalysis as statistical method which revealed high correlations amongvariables, is required to decrease data and get the fruits of data (Moqadam et al., 2004). The study on 14 attributions of 20 cultivarof soybean demonstrated four results through variables analysis method: first variable justified 38.
83 percent of data and wascalled as natal variable; second variable justified 21.4 percent of data and was called as seed specifications variable; thirdvariable justified 17.35 percent of data and was called as yield variable andthe final variable justified 7.5 percent of data and was called as number ofseed per pod variable (Sabokdast nodemi et al.
, 2010). Zhao et al., (1991)employed data factor analysis method in 12 important agricultural attributionsby 16 soybean genotype in China. These attributions were classified into fourgroups. The first variable was contained number of seed per plant and numbers ofpod 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 thirdvariable was included number of pod per plant, hundred seed weight, and weightof seed per plant. The four variable was comprised number of branches.
Motivated by previous research, it was resulted that cultivar of soybean hadthe significance impact on soybean seed yield. It is also notify to utter that,various sorts released different yields accordance of environment conditionsand their adaptabilities to those conditions. Thus recent studies tried to gaindesired sort through modifying agricultural variables including history ofplanting, model of planting, etc. Thecurrent study also attempted to assess yield and yield component of prevalent soybeancultivars and employ these cultivars in future repairing plans.Materials and method Thisstudy was cried out in experimental field of martyr Beheshit Company in Dezfulcity (capital of Khuzestan Province in south west of Iran).
To battle againstweeds, Treflan spray was used (2.5 liters for one hectare). 200 kg/ha of potassium sulfate, 150 kg/haTriple superphosphate and 50 kg/ha nitrogen fertilizer were used. Thedemanding nitrogen was amounted 150 kg/ha at fourth and fifth leaf levels andwas amounted 100 kg/ha at graining level to the plant, due to the lack ofactivated bacteria fixed soybean nitrogen, This RCBD study employed 18 genotypesof soybean and carried out three replicates.
Each Crete contained 4 rows- 4m inlength and 60cm in width- and the given gap between bushes was 5cm. Aftercomplete growth, 10 bushes were chosen randomly from each Crete. The consideredattributions 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-timeassessment of attributions of selected bushes.
This data was grouping throughSAS 9.1 software (variance analysis) as well as Duncan Model (to compereaverage of data). Toanalysis variables Step-by-step Regression and SAS 9.1 software and to analysiscorrelation and cluster, SPSS 18 software were utilized.Results and discussionThe results of variance analysis (table 1) proved thatthe impact of block and traits was significant for all attributions at 1percent probable level. The most coefficient of genotype variation was belongedto number of node and the least coefficient was owned to biological yield. Theresults of compering average of attribution in table 2 revealed that the most numberof 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 (44.04kg/ha) were existence in salend. The saman cultivar showed the most plantheight (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) alsobelonged to Gorgan 3. olser andcartter (2004) stated that some components of yield consisting seedsize, number of seed per pod and numbers of pod per plant, etc. are crucialfactors in progression of soybean yield; therefore, genotypes empowered with thesehigh qualified constituents have much more potations genetically. Farahani Padet al., (2012) demonstrated that the impact of cultivar on thousand seed weightand seed yield in four cultivar were meaningful.
In most of products, yield isdefined as the mixture of huge numbers of biological processes occurred duringgrowing. Accordance of Ghorban zade neghab et al., (2013) study, Zane cultivarwith 14.8 gr had the most weight ofhundred seeds and sahar cultivar with 9.
2 had the least weightof hundred seeds among studied cultivars. The results illustrated that the Zaneseed had the most weight of hundred seeds due to few numbers of seed per plantand lack of competition among seeds.Correlation analysisDetermination of correlation analysis was one of the indexesto assess the existence relations among attributions. The result revealed thatseed yield had the most correlation with biological yield (0.
96) (table 3).Correspondently, Masudiet al. (2009) Yunesi hamze khanluet al., (2010), Namdari andMahmudi (2013), as well as Iqbal et al.
, (2003) reported the meaningful correlationbetween seed yield and these four attributions: numbers of pod per plant,number of seed per pod, harvest index and number of branches. Similar findingswere reached by Pedersenand 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 wereeffective factors on improvement of soybean yield; therefore, genotypes withthese high qualified attributions had more potential.
This lends evidence toprevious studies which suggested that cultivar of Selend, SG5, and Gorgan 3 aresuperior proceed than others (Amaranthath et al., 1990; Das et al., 1989; Pendyet al., 1973; Rajput et al.
, 1986).Factor analysisThe considerable studies were conducted to assess theimpact of relations on attribution proceed via analyzing coefficients to factoranalysis. The recent research concentrated on causal analysis and determinationof crucial criteria for repairing soybean yield. In the current study, theresults of analyzing 10 morphological attribution through cardinal factors,highlighted four principal variables (table 4). These four variables explained96.71%, 0.
0235%, 0.0065%, and 0.0021% of data diversities respectively and aswhole, they clarified 99.
92% of data diversities. There is also a directrelationship between variables variance and variables value in datainterpretations. In this vein, subscription rate was a part of variancevariable related to common variables. In addition, there was directrelationship between subscription rate and accurate rate (Henricoet al., 2004). By observing of revolved variablecoefficients, it was found that the first variable coefficients,proceed variable, covered most of data and contained the big and positive coefficientsof seed proceed, biological proceed, removal index, and tie numbers (table 4). SimilarlyYunesi 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, harvestindex and were crucial attributions to improve soybean yield. Moreover, Narjesi et al., (2008) tested 17 attributions of 30 soybeangenotypes. The result proved that two variables of phenology and yieldjustified 28.
21% and 16.56% of data diversities respectively. They alsodeclared that harvest index and seed numbers had the biggest effect on soybeanseed yield.The second variable, yield component, contained the bigand positive coefficients of biological yield, harvest index, thousand seed weightas well as pod length and also covered 2.35% of data diversities. The third variable, contained the big and positivecoefficients of number of node as well as plant height and covered 0.
65% ofdata diversities. It also contradictswith Kohkan and et al., (2010) study in which 12 traits of 141 soybeanline were examined. Based on their results, the first variable, phono-geneticvariable, 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 perplant and covered 29.
18% of data diversities. The fourth variable, crustacean variable, was containedthe big and positive coefficients of attributions including numbers of pod perplant, numbers of branches as well as pod length and also covered 0.21% of datadiversities.
In the same vein, Yahueian et al., (2010), found four main variables via factoranalysis in stress conditions. These variables justified 78.38% of datadiversities. The first variable, phonologic-morphological variable covered mostof obtained data.
The second variable or yield and yield component, the thirdvariable or quality of seed, and fourth variable in stress conditions seed sizewere identified.In this study, step- by- step regression model wasutilized. In this model after entering the new variable into the model, the oldone was assessed by the model too. Hence, in this model, the most meaningfulvariable remained in functions. Furthermore, in this model few variables but important ones wereexamined (Henrico et al., 2004).
The results indicated that some attributions consisting biological yield,thousand seed weight, and harvest index entered into the model and covered98.85% of seed yield diversities (table 5). The of inclination regression linealso revealed that attributions of biological yield, thousand seed weight, and harvestindex were 1 percent meaningful instatistical process. Some research declared that removal index is the bestvariable 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 ggroups (g < n). In other words, grouping analysis of genotypes mustclassified based on similarity rate of separated groups (Zareh, 2011).
To select the cutting location and to determineoptimal group numbers (g), the minimize variance method with below formula wasused:Group numbers= g= = =3Respecting to the results of Cluster analysis, cultivarsof soybean were classified into three group including Salend, SG5 and Gorgan 3had the best yield (figure 1). Alipour Yamchi et al., (2011) also examined genetic varieties andgrouping of pea genotypes (Cicer arietinum). They stated that clasteranalysis accordance of morphological attributions formed four independent groupsof genotypes. Safari and et al., (2008) formed three independent groups ofpeanut cultivars (Arachis hypogaea) via cluster analysis.ConclusionAs result, analysis variable revealed that throughcombined selection of attributions, there are several possibilities to repair soybeanseed yield in future plans.Authorcontribution statement SGHConceived and designed research, wrote manuscript and acted as correspondingauthor.
BAF and AN Supervised development of work, analyzed thedata, helped in data interpretation and manuscript evaluation. NMNConducted experiments, contributed new reagents and drafted the manuscript. Allauthors read and approved the final manuscript.
Compliance withethical standardsConflictof interest The authors declare that they have no conflict ofinterest.