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Determinants of economic efficiency:A case study of hazelnut (Corylusavellana) farms in Samsun Province,TurkeyOsman Kilic a , Vedat Ceyhan b & Isil Alkan ca Department of Agricultural Economics, Faculty of Agriculture ,Ondokuz Mayis University , Samsun, Turkey E-mail:b Department of Agricultural Economics, Faculty of Agriculture ,Ondokuz Mayis University , Samsun, Turkeyc Department of Economics, Faculty of Economics andAdministrative Sciences , Ondokuz Mayis University , Samsun,TurkeyPublished online: 19 Feb 2010.

To cite this article: Osman Kilic , Vedat Ceyhan & Isil Alkan (2009) Determinants of economicefficiency: A case study of hazelnut (Corylus avellana) farms in Samsun Province, Turkey, NewZealand Journal of Crop and Horticultural Science, 37:3, 263-270, DOI: 10.1080/01140670909510272

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New Zealand Journal of Crop and Horticultural Science, 2009, Vol. 37: 263-270 2631175-8783 (Online); 0014-0671 (Print)/09/3703-0263 © The Royal Society of New Zealand 2009

Determinants of economic efficiency: a case study of hazelnut(Corylus avellana) farms in Samsun Province, Turkey

OSMAN KILICVEDAT CEYHAN

Department of Agricultural EconomicsFaculty of AgricultureOndokuz Mayis UniversitySamsun, Turkeyemail: okilic@omu.edu.tr

ISIL ALKANDepartment of EconomicsFaculty of Economics and Administrative

SciencesOndokuz Mayis UniversitySamsun, Turkey

Abstract The purposes of this research were tomeasure the cost efficiency of sample hazelnut(Corylus avellana) farms and to explore determinantsof economic efficiency in the eastern Black Searegion of Turkey. Data envelopment analysis (DEA)was used to estimate efficiency measures of samplefarms. Farm managers from 151 randomly selectedfarms were interviewed for farm-level data betweenproduction periods in 2005 and 2006. Researchresults revealed that inefficient hazelnut farmswould have needed to lower production costs by44% to perform as well as other similar best practicefarms. The analysis of the measures of technicalefficiency showed that pure technical inefficiencywas the primary cause of technical inefficiency. Theresults also showed that one of the most importantpositive factors in economic efficiency was suckercontrol. The age and education level of operatorsand credit use were the other influencing factors ineconomic efficiency. Strategies for a better farm-level education, farm extension programmes focusedon sucker control, and providing farmers with greateraccess to credit may help to increase economicefficiency of hazelnut farms.

H08058; Online publication date 31 August 2009Received 19 May 2008; accepted 9 June 2009

Keywords cost efficiency; data envelopmentanalysis; hazelnut farms; determinants ofefficiency

INTRODUCTION

Although hazelnut (Corylus avellana L.) is pro-duced in many countries around the world, fewcountries have production levels that affect worldhazelnut markets. Turkey, Italy, the United States,and Spain produce the majority of hazelnuts forexport. These four primary hazelnut producersshare c. 90% of world hazelnut production.Turkey accounts for 70% of world hazelnutproduction/export and is the authority in hazelnutmarkets (FAO 2007).

Hazelnut has always taken first place as anexport crop among agricultural products in Turkey.It provides substantial foreign exchange earnings,and has social and economic importance forproducers in hazelnut cultivation areas. Particularlyin provinces on the east Black Sea coast of Turkey,the basic income source for most farmers is hazelnutproduction. The increase in production by otherhazelnut producing countries will continue to reduceTurkey's export advantage. As a consequence,Turkey must decrease the costs of production andraise product quality to the level desired by exportmarkets. To reduce the costs of hazelnut productionand increase yields, input use must become moreefficient. Therefore, research on the efficiency ofhazelnut farms and its determinants is vital forsound policy recommendations and redirection ofmanagerial actions.

Many earlier studies of technical efficiency inagriculture have focused on crop and livestock farmsby using the production function, the parametric ornon-parametric frontier function, and mathematicalprogramming with cross-sectional, panel, or aggre-gate data (Sidhu 1974; Kalirajan & Flinn 1983;Banker et al. 1984; Squires & Tabor 1991 ; Pinherio1992; Battese et al. 1996; Laura Gow & Langemeier1999; Tzouvelekas et al. 2001; Latruffe et al. 2002;

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264 New Zealand Journal of Crop and Horticultural Science, 2009, Vol. 37

Helfand 2003). Similarly, efficiency analysis has beenapplied relatively little in Turkey. There have beensome studies on efficiency in Turkish agriculture(Gunden et al. 1998; Zaim & Cakmak 1998; Akturk2000; Demirci 2001; Cinemre et al. 2006; Cinemre& Ceyhan 2006; Ozcelik et al. 2006), but there hasthus far been no study carried out by applying dataenvelopment analysis (DEA) to hazelnut farms.Therefore, the objectives of this paper were to calculatefarm level efficiency measures and to evaluate thedeterminants of cost efficiency in hazelnut farms inthe Black Sea Region, Turkey.

MATERIALS AND METHODS

DEA model for hazelnut farmsData envelopment analysis (DEA) was used tocalculate efficiency measures. DEAis one of severaltechniques that can be used to calculate the bestpractice production frontier (Coelli et al. 1998;Kumbhakar & Lovell 2000). Thanassoulis (2001)pointed out that further detailed analysis and possiblyinspection of the best and the worst performers arethen necessary to understand the production processand derive useful information that may help boththe worst and the best performers to make furtherimprovements in efficiency. The DEA approachprovides an analytical tool for determining effectiveand ineffective performances.

Efficiency is defined in this research in a relativesense as the distance between observed input-outputcombinations and the best practice frontier. TheFarrell input-oriented measure of efficiencies wasused as a measure of efficiency since farms tendto have a greater control over their inputs thanover their outputs. Farrell (1957) proposed that theefficiency of a firm consists of two components:(1) technical efficiency, which reflects the abilityof a firm to obtain maximal output from a givenset of inputs; and (2) allocative efficiency, whichreflects the ability of a firm to use the inputs inoptimal proportions, given their respective pricesand the production technology. These two measuresare then combined to provide a measure of costefficiency (also called economic efficiency). TheFarrell measure equals 1 for farms on the efficiencyfrontier, and then decreases with inefficiency.

We constructed a DEA model assuming that eachhazelnut farm produces a quantity of hazelnut (y,)using multiple inputs (x,) and that each farm (i)is allowed to set its own set of weights for bothinputs and output. The data for all farms are denoted

by the K x TV input matrix (X) and M x TV outputmatrix (Y). Using piece wise technology, an input-oriented measure of technical efficiency (TE) can becalculated for the i-th farm as the solution to linearprogramming (LP):

Minimise,^ 8

Subject to -_y, + YA>0 (1)

8x,. - Xk > 0

X>0

where 8 is the technical efficiency score having avalue 0<8<l. If the value equals 1, the farm is on thefrontier; the vector X is an TV x 1 vector of weightsthat defines the linear combination of the peers ofthe i-th farm.

The input-based minimum cost for the i-thfarm can be obtained by solving the following LPproblem:

Minimise^,.,wTj x*

Subject to -_y, + YÀ>0

x* -Xk>0(2)

where w, is a vector of input prices for the i-th hazelnutfarm; superscript T is the transpose function; x* isthe cost-minimising vector of input quantities for thei-th hazelnut farm calculated by the LP, given theinput prices wj and output level y:, and A is a TV x 1constant vector. Equation 1 and Equation 2 representthe cost minimisation under constant returns-to-scale (CRS) technology. CRS means that outputincreases in proportion to changes in all inputs. Theeconomic efficiency (EEt CRS) of the i-th hazelnutfarm is calculated as:

x,. (3)

That is, EEiCRS is the ratio of the minimum costto the observed cost, given input prices and CRStechnology (Coelli et al. 1998).

Coelli et al. (1998) pointed out that the CRS modelis only appropriate when the farm is operating at anoptimal scale. Factors such as imperfect competitionand financial constraints may prevent a firm fromoperating at optimal scale. Since hazelnut farms inthe research area conducted their activities underimperfect competition, and because the size of manyhazelnut farms made them ineligible for institutionalloans, we transformed Equation 1 to the variablereturns-to-scale (VRS) technology model by addingthe convexity constraint NYK = 1, where ATI is anTV x 1 vector of ones and X is an TV x 1 vector of

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Kilic et al.—Determinants of economic efficiency 265

constant to Equation 1. In this example, the TEscores under VRS was calculated using Equation 1,with the convexity constraint added to decomposethe technical efficiency scores into two components:"pure technical efficiency" (PTE), which reflectsthe ability of a firm to obtain maximal outputs at anoptimal scale; and "scale efficiency" (SE), whichreflects the distance of an observed firm from themost productive scale size. Scale efficient farmsare of appropriate size and thus do not need to bereorganised to improve output or earnings. Scaleefficiency was calculated as the ratio of the technicalefficiency score of the farm under CRS technologyto the technical efficiency score of the farm VRStechnology. Farms were classified as scale efficientif the SE = 1 or if the TEVRS = TECRS. Farm-level scaleinefficiency was determined by comparing technicalefficiency score under non-increasing returns toscale (MRS) with the technical efficiency scoreunder CRS. If SE < 1 and TENIRS = TECRS, farms wereclassified as scale inefficient owing to increasingreturns to scale (1RS). If SE < 1 and TENIRS > TECRS,farms were classified scale inefficient owing todecreasing return to scale (DRS). The allocativeefficiency was calculated residually by:

AE^EE^ITE, (4)

In this study, the two-stage approach was preferredin assessing the influence of various factors uponinefficiency because of several advantages (e.g., priorassumptions are not required regarding directionof influence, accommodation of more than onevariable with continuous or categorical variables).A Tobit regression of inefficiencies on potentialdeterminants was used because the inefficiencyscores are truncated at 0 and 1. The Tobit model isspecified as follows:

yt =0 if y* < 0

(5)

y* = ßx, JV(O, a2)

where yt is the measure of economic efficiencyfor farm i, y* is the unobservable variable, xt areexplanatory variables that influence the economicefficiencies of the farms, and ß and u are parametersof the model and the random error term, respectively(Greene 2000).

For statistical analysis, SPSS statistical packagewas used. Efficiency scores were measured usingDEAP 2.1. The LIMDEP program was used toestimate the effects of explanatory variables oneconomic efficiency scores.

DataThis study, after identification of survey objectives,used a well-designed state-of-the-art instrumentto capture information that is of great interest andrelevance to the questions under study. During thesampling process, following identification of thestudy population, the sample frame was defined andsample size was determined by the simple randomsampling method (Yamane 1967). We used a 90%confidence level and 10% precision level whendetermining optimum sample size. Random numbersgenerated from a random number table were used toselect farms from the population. Using a structuredsurvey of 151 hazelnut farms selected by randomsampling, we collected the input-output data used inthis study during the production year of 2005-06.

The cost efficiency of hazelnut farms wasmodelled in a six-input, single output framework.Quantity of hazelnut produced in 2006 was used tomeasure output (kg/year). Six inputs were used forcost efficiency analysis: working capital (us$/year;1 us$ =1.6 Turkish liras), labour (h), harvest costs(us$/year), nitrogen (N) use (kg/year), phosphorus(P) use (kg/year), and orchard size (ha). Otherdata collected were demographic characteristicsof farmers, farmer characteristics, and hazelnutproduction characteristics.

The sample hazelnut farms produced 2.7 t/year,on average. The minimum hazelnut production was0.5 t and the maximum was 12 t. To reach theirpresent level of production, hazelnut farms usedc. US$8500 of working capital, 3.21 h labour, and350 kg fertiliser, mostly in N. In addition, samplefarms paid c. US$1800 for harvesting hazelnuts. Inefficiency analysis, we assumed that all farms facedthe same relative price for labour, N and P, land, andcapital. Costs of labour, N and P, land, and capitalwere us$1.54/h, us$0.25/kg, us$0.35/kg, US$1369/ha, and us$0.10 per us$, respectively.

We included in Table 4 the descriptive statisticsof variables such as hazelnut production, workingcapital, hazelnut yield, labour use, N and P useassociated with returns to scale.

The variables included in the tobit analysis canbe divided into three groups: personal characteristicsof farmers (the age and education level of farmoperators, and family size); farm characteristics(farm size, the number of plots, hazelnut orchards/total farmland, credit use), and hazelnut productioncharacteristics (the age of hazelnut orchards, locationof orchards, varieties, sucker control, the share offamily labour, and marketplace). The variables oflocation of orchards, variety of hazelnut, and sucker

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266 New Zealand Journal of Crop and Horticultural Science, 2009, Vol. 37

control were represented in the model by a dummyvariable, whereas the variable of marketplacerepresented by a proxy variables in the model aremerchant, factory and cooperatives.

In the research area the education level of farmoperators is generally low and their average age is52. On average, they produce hazelnuts in 3.17 haof land that constitutes 91% of the total farmlandwith a four-person family. The share of family labourhad a mean of 0.64 with a standard deviation of0.28. Farmers used US$1093.85 of credit on averagein a year for hazelnut production. Concerning theproduction characteristics, the mean age of thehazelnut orchards is 52. Most of the hazelnutorchards are located in highlands and most farmerspreferred to grow mixed varieties in the researcharea. Sixty-three percent of the farmers practicesucker control. After the harvesting period, manyfarmers had a preferred merchant to market theirhazelnuts (Table 1).

RESULTS AND DISCUSSION

The sample hazelnut farms have similar characteristicsto average Turkish hazelnut farms. The mean farmsize is c. 3.5 ha in the sample hazelnut farms, whereasthat of the average Turkish hazelnut farms is 3 ha.The number of plots in the sample hazelnut farmsis somewhat lower than that of the Turkish hazelnutfarms. The share of hazelnut orchards in the totalfarmland for the sample and average Turkish hazelnutfarms is 49% and 94%, respectively. The number oftractors is higher in the sample farms. However,the family size is lower than the average Turkishhazelnut farm. For yield, the sample hazelnut farmsare more productive compared with the averageTurkish hazelnut farms (Table 2).

Based on the results of efficiency analysis, overalleconomic efficiency of hazelnut farms ranged from0.13 to 1. The average was 0.56 with a standarddeviation of 0.20 (Table 3). On average, inefficientfarms would have needed to lower fertiliser,

Table 1farms.

Descriptive statistics of input and output measures and factors influencing efficiency for a sample of hazelnut

Variable

Efficiency analysisHazelnut production (kg/year)Working capital (us$/year)Labour (h)Harvest cost (us$/year)Nitrogen (kg)Phosphorus (kg)Hazelnut orchards size (ha)Tobit modelPersonal characteristics

Age of farm operators (years)Education level of farm operators (in years)Family size (person)

Farm characteristicsFarm size (ha)No. of plots (unit)Hazelnut orchards/total farmlandCredit use (us$/year)

Hazelnut production characteristicsAge of hazelnut orchards (years)Location of orchards*VarietiesTSucker controlShare of family labourMarketing place§

Mean

2754.708451.52

3.211756.38251.0099.233.17

51.786.624.37

3.482.490.91

1093.73

51.781.461.701.150.641.34

SD

1953.4611643.34

1.241339.60201.87227.81

2.06

10.972.781.59

2.381.700.16

2664.85

10.970.500.460.350.280.76

Min.

500.00107.69

1.25261.5421.000.000.72

27.001.002.00

0.721.000.090.00

27.001.001.001.000.000.00

Max.

12000.0090615.38

6.506153.851300.001325.00

12.00

75.0015.008.00

13.310.001.00

18461.54

75.002.002.002.001.003.00

'Dummy variables: (1) if the orchard is located on the plain; (0) if the orchard is located in the highlands.îDummy variables: (1) if farmers grow one variety in orchards; (0) if farmers grow mixed varieties in orchards.tDummy variables: (1) if the sucker control is done; (0) if the sucker control is not done.§Proxy variable and 1, 2, and 3 reflects merchant, factory, and cooperatives, respectively.

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Kilic et al.—Determinants of economic efficiency 267

harvesting and labour costs by 44% to performas well as other similar, best practice farms in thesample.

Efficiency measures for sample hazelnut farms arepresented in Table 3. In the research area, the primarysource of economic inefficiency was allocative.Almost 93% of the sample hazelnut farms wereallocatively inefficient. These farms used the wronginput mix given input prices, so that their costswere 34% higher than the cost-minimising level.The estimated technical efficiency measures for thesample of hazelnut farms varied from 0.52 to 1.0,with a sample average of 0.85. This result indicated

that the hazelnut farms could reduce their input useby 16% without output reduction. Fifty-eight percentof the sample farms had a higher technical efficiencycoefficient than the mean technical efficiency.

The analysis of the measures of technicalefficiency showed that pure technical inefficiencywas the primary cause of technical inefficiency. Themean pure technical efficiency was 0.67. The meanscale efficiency was 0.79, with a standard deviationof 0.169 (Table 3). Individual analysis of the farmsindicated that 10% of the sample hazelnut farmshad constant returns to scale (CRS), whereas thepercentages of hazelnut farms exhibiting increasing

Table 2 Mean value of some comparative characteristics for the average Turkishhazelnut farm and sample hazelnut farm. (Average Turkish hazelnut farm wasbased on the results of Turkstat (2006), Fiskobirlik (2006), and Anon. (1992).)

Characteristics

Farm size (ha)Hazelnut orchard size (ha)No. of plotsHazelnut yield (kg/ha)% of farms with tractorFamily size (person)

Average Turkishhazelnut farm

2.951.453.038580.185.86

Average samplehazelnut farm

3.483.172.408670.314.37

Table 3 Efficiency measures for sample hazelnut farms.

Efficiency measures

OverallAllocativeTechnicalPure technicalScale

Mean

0.5570.6570.8500.6700.790

SD

0.2030.1910.1500.1990.169

Min.

0.1250.1680.5230.2450.266

Efficient farms (no.)

65

471415

Table 4 Summary of returns to scale results. Different letters reflect the significance at the 5% level for differencebetween returns to scale. (1RS, increasing return to scale; CRS, constant returns-to-scale; DRS, decreasing return toscale.)

Hazelnut Working Hazelnut Nitrogen PhosphorusNo. of production capital Labour use yield use usefarms (kg/year) (us$/ha) (AWU) (kg/ha) (kg/ha) (kg/ha)

1RSCRS:DRS

127159

2220.51»4946.67"6555.56C

3584.25»4365.45"1855.29»"

3.12s

3.10a

4.75"

849.50s

1260.60"966.80s

79.65s

76.94s

78.84"

32.73s

12.08s

43.27"

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268 New Zealand Journal of Crop and Horticultural Science, 2009, Vol. 37

returns to scale (1RS) and decreasing returns to scale(DRS) were 84% and 6%, respectively (Table 4).

The 15 scale-efficient hazelnut farms had largerhazelnut production compared with the hazelnutfarms having 1RS. In addition, scale-efficient farmsused less labour per year, less N and P per ha, and

more working capital than inefficient ones (P <0.05). Therefore, scale-efficient farms obtained morehazelnut yield compared with inefficient farms (P <0.05) (Table 4).

Table 5 represents results of the tobit model onthe relationship between economic efficiency and

Table 5 Results of the Tobit analysis: efficiency determinants.

Variable

Personal characteristicsAge of farm operators (years)Education level of farm operators (in years)Family size (person)Farm characteristicsFarm size (ha)No. of plots (unit)Hazelnut orchards/total farmlandCredit use (us$/year)Hazelnut production characteristicsAge of hazelnut orchards (years)Location of orchardsVarietiesSucker controlShare of family labourMarketing placeLog likelihood

Estimatedcoefficient

0.430.560.14

0.26-0.34

0.510.49

0.320.320.610.780.820.55

37.08

SE

0.170.160.12

0.160.150.980.12

0.210.500.310.450.630.22

P [1 Z 1 > z]

0.01300.00550.2523

0.98430.01300.60650.0100

0.88090.51980.84480.08050.19350.80170.0000

Table 6 Differences between economically efficient and inefficient hazelnut farms. (Figures in parentheses areSEs.)

CharacteristicsEconomically efficient Economically i

farms (n = 6) farms (n =nefficient145) P [1 Z 1 > z]

Personal characteristicsAge of farm operators (years)Education level of farm operators (in years)Family size (person)Farm characteristicsFarm size (ha)No. of plots (unit)Credit use (us$/year)Total asset (1000 us$/year)Working capital (1000 us$/year)Labour (AWU)Harvest cost (1000 us$/year)Nitrogen (kg)Phosphorus (kg)Share of family labourHazelnut production characteristicsHazelnut orchards size (ha)Age of hazelnut orchards (years)Hazelnut production (kg/year)Hazelnut yield (kg/ha)

59.67 (12.37)7.50 (2.95)4.83 (1.72)

2.75(3.17)1.50 (0.84)

1666.67 (4082.52)79.92 (95.39)16.75 (36.22)3.79 (1.29)1.75 (13.22)

69.40 (63.99)--

2.75 (2.82)50.83 (9.70)

4725.00 (4472.56)1369.60 (436.72)

51.46(10.83)6.59 (2.77)4.35 (1.59)

3.51(2.12)2.71 (1.74)

993.10(2608.52)71.42 (71.90)

8.11(9.62)3.19(1.24)1.98(1.85)

79.56 (63.86)32.60 (73.07)

0.83 (0.39)

3.19(2.02)40.14(10.15)

2667.97 (1757.43)877.82 (291.63)

0.7530.4910.470

0.9720.0920.2360.7800.0750.2510.6670.7220.000

-

0.3840.4980.0100.000

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Kilic et al.—Determinants of economic efficiency 269

its determinants. Most signs related to efficiencydeterminants were as expected. All variablesevaluated under farm characteristics were positive,with the exception of number of plots. The credituse coefficient was positive, which indicated that thefarms that used more credit were more efficient thanthe lower ones (P < 0.01). The coefficient of plotnumber indicated that hazelnut farms having smallerplots were more efficient than others (P < 0.05). Thevariables of share of family labour, farm size, andshare of hazelnut orchards in farmland were also apositive sign. However, they were not statisticallysignificant (P > 0.10).

The variables of the education level and ageof the operator indicated that more educated andolder operators were more efficient (P < 0.05). Thevariable of family size had negative effects ; however,it was statistically insignificant (P > 0.10).

All the coefficients of the hazelnut productioncharacteristics group were not statistically significant(P > 0.10), with the exception of sucker control.All insignificant variables positively influencedthe economic efficiency. The estimated coefficientfor sucker control indicated that increasing suckercontrol led to more economically efficient farms (P<0.10).

Based on the results of the comparative efficiencyanalysis, the economically efficient hazelnut farmscarried out their activities on a smaller number ofplots with a relatively high level of working capital.However, their P use was lower than on inefficientfarms. In addition, farmers on economically efficientfarms were better educated and more experienced.Efficient farms also had higher hazelnut productionand hazelnut yield/ha (Table 6).

CONCLUSION

DEA was used to calculate efficiency measures and todetermine the factors affecting economic efficiencyin Samsun hazelnut farms. The mean technical,allocative, and economic efficiencies for hazelnutfarms were 0.85,0.66, and 0.56, respectively. Of 151farms included in the analysis, 47 were technicallyefficient, 5 were allocatively efficient, and 6 wereeconomically efficient. Analysis of technicalefficiency showed that most of the hazelnut farmswere both operationally and scale inefficient. Mosthazelnut farms could become more efficient byadjusting input use and operation scale.

The most important determinant of economicefficiency was sucker control. The age and education

level of operators and credit use were otherdeterminants of economic efficiency. Strategies fora better farm-level education and farm extensionprogrammes focused on sucker control may help toincrease efficiency. Farmer training and extensionactivities are relatively low-cost methods of achievingincreases in productive efficiency. However, suchincreases strongly depend on the effectivenessof the presentations by research and extensionorganisations. Turkey's Ministry of Agricultureand Rural Affairs (MARA) has tried to prepareand implement extension and training programmesthroughout the country, including the research area.Branches of some input sales companies have alsocontributed by transferring information to hazelnutfarmers. However, the efficiency of such efforts isstill unsatisfactory owing to bureaucracy, limitedinvestment, insufficient numbers of skilled extensionpersons, and information gaps on technical andeconomic aspects of hazelnut farming. Trainingfocusing on sucker control and cultural practicesmay help increase efficiency in this research area.

In addition, encouragement of young rural peopleto stay in agriculture might have beneficial effectson economic efficiency. Providing farmers withgreater access to credit would require governmentsupport through legal and regulatory frameworks.Although the Turkish government has given aninterest rate subsidy to all farmers, Kilic et al.(2005) reported that hazelnut farmers did not usethis credit because of high transaction costs. Thus, agovernment-supported pilot programme that reducesthe transaction costs of providing credit to farmerswould have the potential to increase efficiency. Sucha credit programme could help farmers expand theirfacilities from small-scale to modern facilities andbenefit from economies of scale, which may alsoincrease economic efficiency.

ACKNOWLEDGMENTS

We thank anonymous referees for their constructivecomments and suggestions. We acknowledge OndokuzMayis University, Turkey for financial assistance.

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