Evaluation of Palm Groves Technical Efficiency Using

1 1 2 * Corresponding author’s email: [email protected] email: author’s * Corresponding Gholamreza Zamanian Roodkhanehbar Area, Iran Bootstrap Data EnvelopmentDataBootstrap Analysis: o Study A Case Usin EfficiencyTechnical Groves Palm of Evaluation efficiency date, technical Roodkhanehbar, Keriteh Province, ment Hormozgan Analysis, Envelop- Data bootstrap, Keywords: Accepted: 08 Octber 2015 Received: 26 August 2015, Rudan County is a county in Hormozgan Province in Iranin Province County is a Hormozgan in county Rudan PhD Candidate, PhD Candidate, ofEconomics, University Bal Sistan Agricultural and Assistant Professor, ofEconomics, University ZaBaluchestan, Sistan and ISSN:2159-5860 (Online) ISSN: 2159-5852(Print) Available online on: www.ijamad.iaurasht.ac.ir International Journal of Agricultural Management and Development (IJAMAD) 1 and Mostafa Khajeh Hassani

Abstract with others in an attempt to optimize allocation of cac fr ucsfl rhrit t sae their share orchardists to successful for chance a plication that it is recommended to train the orcha 14%of them operate efficiently. Thestudy, then, c of date producers operate with less than 50% effici ofRoodkhanehbar area in 2013. Theresults suggeste analysis and sampling 50 palm groves of Keriteh dat date production in the Rudan County groves in this area using input-oriented bootstrap income in this area, directly or indirectly. As a r region.This study aims toevaluate technical effic R fiiny a a rtcl motne o h orchard the to importance critical a hasefficiency Keriteh palm trees, is one of the most importantar most the of one is trees,palmKeriteh oodkhnehbararea,havingapproximately 111thousand 2* uchestan, Zahedan, IranZahedan, uchestan, hedan, Iranhedan, 1 and the source of peoples’ esult, its production data envelopment rdists, providing inputs. iencyofpalm ency and only arriesthe im- experiences e producers ss n this in ists dthat 64% eas of eas s of s g f 475 International Journal of Agricultural Management and Development, 6(4): 475-487, December, 2016. 476 International Journal of Agricultural Management and Development, 6(4): 475-487, December, 2016. regions oftheregions country. rdciiy nee o goe o different of groves of indexes productivity hi i nto groves nation in chain and traditional production methods in producing Colombia, and America, because of inappropriate othercompetitors Egypt,suchasChina, Sudan, to compared area unit per performance great 37 producing countries in 2012, and has had not of dates, this country was ranked twelfth among country after Iraq and Pakistan,. third the as ranked 2011is in Iran dates of tons meeting domestic to demands addition andexporting In Egypt. 112030 after production date in dates in 2012, Iran had the world’s second rank sector of Iran includes a wide range of activities consider these facts in theirshould planning.economists the and makers Agricultura sector, policy country agricultural in potentials high of egregious. Taking this into account and because further becomes policy this of importance the which single-product export has on the country, effects harmful and years recent in depression market oil Considering revenues. products oil non- increase and revenues oil on dependency decrease to is Iran in implemented policies and should be improved by studying efficiencyand studying by improved be should crease the competition power in foreign markets, in- subsequently, and advantages, comparative increase and position the maintain to reason, Fgr 1, rdcn 101 ilo tn of tons million 1.061 producing 1), (Figure the development of the country. support thereby As shown and below provide opportunities work can more substitute, potential a as which and the most important of them is date production, t s las eesr ta te performance the that necessary always is it Despite Iran's rank in production and export and production in rank Iran's Despite n o te ot motn eooi goals economic important most the of One Evaluationof Palm Groves Technical Efficiency / Zamanian INTRODUCTION FO 2012) (FAO, Figure 1. The first five date producers of the world Fr this For . l extensive studies have already been conducted been already have studies extensive data envelopment analysis. Despite the fact that oriented input bootstrap using 2013 in groves efficiency of technical Roodkhanehbar the regionKeriteh evaluate to palm aimed study present the necessities, perceived the Given location. units; theselead to improvement in resource al- resource in improvement to theselead units; palm grovesas well as identification of inefficient region the of efficiency technical of evaluation resources of allocation the in improvement this region are laborers at palm groves, and any in people the of and majority The Afghanistan. India, China, of markets foreign in supply food f h rgo. ht s f ra iprac is importance great of is What region. the of development the accelerate considerably will which profit, increaselabor consequently will which leads to the reduction of production costs, industrial and livestock human, as produced is and Mazafati and Piaram as such varieties to harvest dates in this and some other provinces. cultivation and production. It takes five months variety is not considered among other expensive eet rns rsetvl i trs f palm of terms in respectively ranks, seventh red Kolk. This province is located in fourth and Zarak,and Mordasang, Keriteh, Khasoei, aram, Pi- Shahani, Khanizi, Mazafati, include rieties va- favorable and important most The gardens. palm Hormozgan in planted are which of 80 country, the in dates of varieties 100 over are portant producers of dates in the country. There palm trees. Despite being very nutritious, Keriteh 111and hectare one over and groves thousands 100 about with cultivation date Keriteh under area biggest the has area This lives. residents’ of quality the on influence much so has tivity is one of the palm growing areas where this ac- Roodkhanehbar area, located in Rudan County, Hormozgan Province is one of the most im- most the of one is Province Hormozgan and KhajehHassani firms actual actual situation the maximum amount of output include them of important most the analysis, opment envel- data bootstrap used have them of few a tivities efficiency inside and outside the country, ac- sector agricultural evaluating of field the in esrd eaie o h efcec o other of efficiency the to relative measured is unit) making (decision firm a of efficiency and function, production the estimate to as so the data used and the results of model estimation,of description study;brief of methodology and 2009) Odeck, 2006; Dong & Featherstone, 2006; Gocht & Balcombe, inefficient otherwise otherwise inefficient (DMU), Unit on frontier, this is and placed is which efficient Making Decision Every tion. its as frontier an of func- estimation production of part a considered and possibility production called collection a constructed he principles, Citing five methods. ories using nonparametric ican agricultural sector based on Economic the- not required to follow a certain default order default certain a follow to required is not it method, this In outputs. and factors eral sev- with the process production of includes it characteristic that such method universalizing Farrell by the (DEA) Analysis opment re- To remedy 1978. and until years for silent mained application did practical it much method, find not Farrell the in raised itations lim- and measuring in problems scientific of Theoretical foundations are presented here. (CCR) introduced the method of Data Envel- Data of method the introduced Rhodes (CCR) and Cooper, Charnes, problem, this In 1957 Farrell calculated efficiency of Amer- (Cooper et al., 2006) al., et (Cooper MATERIALS AND METHODS (Balcombe et al., 2008; Brümmer, 2001; Evaluationof Palm Groves Technical Efficiency / Zamanian Figure 2 . A theoretical foundations theoretical A. (Farrell, 1957) (Farrell, : The first sample . Unfortunately, in Unfortunately, . . Because . f nt i Fgr 2 infcnl changes significantly 2 compared to Figure Figure 3 in A unit of As shown in the following figures, the efficiency and efficient frontier of the population, populationunknown an of sample a is sample is unknown culated by using a sample, because the studied the because sample, a using by culated be cannot input from a cal- produced particular distribution characteristics of an estimatorand Tibshirani (in ca and is applied to estimate sampling to the variation of sample combination. Bootstrap ciency scores obtained by DEA method relative main population. s rsmln tcnqe rpsd y Efron by proposed technique resampling a is effi- of sensitivity and ranking demonstrate to pends on the sample size to its nonparametric nature; however, it also de- due are model this of shortcomings the all not the previous frontier crumbles down. Of course, to the samplesuch that by changing the sample, sensitive is and on depends DEAfrontier seen, nonparametric nature of DEA model. As can be evaluate the variability to approach Bootstrap DEA entitledof efficiency method a for every sa siae f h smln dsrbto o the of distribution sampling the of estimate an as used is which estimator for distribution empirical an form estimates pseudo thousands These obtained. is score efficiency pseudo of estimation pseudo a sample pseudo each using sample);by (original samples actual of series of set a replace sampling,which random simple using by samples pseudo of thousands among simplest format, Bootstrap is a random selecting (Ebadi, 2011; Efron & Tibshirani, 1993) used is technique Bootstrap the method, this In To overcome this problem, when it is difficult to obtain it with other method pling Figure3 This efficiency change is a consequence of consequence a is change efficiency This (Löthgren & Tambour, 1996; Simar, 1996) : : Thesecond sample and KhajehHassani (Bahadori et al., 2013) (Bahadori et al., 2013) Simar (1996) designed . In the . ses m- s) . . . 477 International Journal of Agricultural Management and Development, 6(4): 475-487, December, 2016. 478 International Journal of Agricultural Management and Development, 6(4): 475-487, December, 2016. 2 andoutput- oriented efficiencies for the input oriented model is described is model oriented input the n h uptstfr is shown by equation (2) equation as shown is (3) as below: and the output set for for set input the ponents, includes input set and output setso that ∂Y(x) assumption can be explained by a set of standard assumptions y X(y) expressed by Farrell can be shown as a subset of output shown with production set of set production with shown and so on outputs and inputs of assumptiondisposability equation: feasible and physical set (x, y), as the following Bootstrap and (DGP) process generation data Overall output y y output provided by Shephard including the convexity the including Shephard by provided Grosskopf, 2006; Simar & Wilson, 1998) to definetocalculationas indexesobtain toinput- The statements presented for input oriented model can be can be easi model The oriented for input statements presented k ) as equations (6) and (7). In the following just X(y)={X This set which may be shown as its two com- ∂X(y)={x The alreadyThementioned equationsusedbe can The relation between two sets of (2) and (3) and (2) of sets two between relation The β ψ= θ ∂Y(x)={y The activity of a production unit produces q produces unit production a of activity The Y(x)={Y If k k =Min{β =Min{ θ {(x, y) , respectively, and in equations (4) and (5): k or =1 (y Y(x) Evaluationof Palm Groves Technical Efficiency / Zamanian ∈ (Shephard, 1970) , the firm the , ∈ ∈ θ θ R | | ∈ x R X(y) R y | | | + ∈ θ R ∈ βy + q + x hc ae hw as shown are which ) p X(y), p + Y(x), βy | k | (x,y) k p+q (x,y) using p. inputs p. using ∈ ∈ for all y (Y(x) for all x) and the X(y | X(x x → θ (x ∈ψ ∈ψ k x k )} k )} y} Input x can produces ∉ , y , ∉ } } () 0< X(y), () β>1} Y(x), k ) . The efficient frontier operates efficiently. operates ψ (x ∈ consisting of consisting θ R <1} k ∂X(y) + th . q 2 ) firm( (1) (Färe& can be can and (3) (2) (7) (4) (5) (6) x k , level fiin frontier efficient of production unit production of desired . Therefore, sets in theefficiency form of ratio of radial distance of firm is to be measured ee o ipt retd t fr a production at firm kth oriented input of level using the equation method equation the using i=1,2,…,n} this aim. Assume data generation process of process generation data Assume aim. this for practical and appropriate most the be can frontier of even if ape netmto frsetdst f , of sets respected of estimation an sample, the following equation (x efficiency,oriented the input calculate to order efficientfrontier this production unit 2, ..., n} ..., 2, set sample random a construct can process eration gen- data Supposedly,using well. as unknown used to generate data set data ( generate to sample used main the by like estimation acceptable an to reached be can and known is description, further it will For be beneficial input. less to definefunctio using produce can and ifcl o ipsil, h Bosrp method Bootstrap the impossible, or difficult set ( is very difficult, especially when especially difficult, very is ciency level of level ciency process of process all depend on data generationand subsequently apig rpris f siaos r very are estimators of properties sampling like of analysis and circumstances specification partial thiswhere In method. parametric Yet, if efficiency level is lower than unit Note that X oeta apigpoete f , , Note that sampling properties of k y , ly ly rewritten foras model well output oriented ∂ (x ψ) k n, orsodd o h pseudo the to corresponded and, ) k y ρ ρ k | and thus input set o t euvln pit n h efficient the on point equivalent its to , bythe following equation: y ; by using by ; is known, achieving them by k )= ∂X(y) ρ ; and can be obtained by obtained be can and ; X , which is unknown. Furthermore, unknown. is which , θ ∂ (x x k k (DGP) , k | X (∂X(y)) th ∂X(y) y and KhajehHassani ∂ production unit ( unit production (x k (x (x M ) k k is intersection point of the k , y , , y | method and this pseudo this and method (Simar & Wilson, 2000) y as represented by represented as k n radius and k k ) are unknown, the effi-the unknown, are ) ) (X(y)) χ . Since the production can be estimated via estimated be can operates inefficiently * M = and estimation of estimation and {(xi*, yi*) {(xi*, and production M θ θ M x χ k is a non- a is ) ) k ={(x ad in and , χ method ( will be will θ , is ), ρ | k <1) i=1, , we , i ,y (8) (9) i ρ ) n . , | estimator population ofthe estimators of qaini ai n rei is a consistent es- equation is valid and true if 2,...B) estimators , , and consequently and , method production on depended totally are , estimators would be too difficult from the original sample original the difficultfrom too be would sample Yet, by using Yet,by analytically. calculate to difficult be may they rbto wl smlt sml dsrbto of distribution sample simulate will tribution tained as relation (10). (11), bias of can be obtained from the main the from obtained be can of bias (11), civd sn , B pseudo samples achieved. Using simply be can distributions sample of mation χ χ ρ which is an unknown method. Additionally even iae of timate n utmtl efcec lvl o each for , (b=1, studied unit is calculated. The empirical density level , and efficiency ultimately and obtained, are sample pseudo each for B) …, 2, pseudo estimates of measure the efficiency level efficiency the measure to order Accordingly,in unknown. but interest y is based on the idea that if is an acceptable an is of approximation if that idea the on based is of approximation Carlo Mont is . The Bootstrap subjectmethod to distribution function ee of level 6, 9, n (0, epciey Te above The respectively. (10), and (9), (6), k It should be noted that sample properties of properties sample that noted be should It Where, Where, ) the equation (11) must be satisfied. was known, obtaining them using them obtaining known, was ny f a b fly nw; n hs case, this In known; fully be can if only r gnrtd ad hn by then and generated, are χ * Evaluationof Palm Groves Technical Efficiency / Zamanian ; and thus an estimation of the efficiency θ ρ k k , and are defined by equations by defined are and , th s ugse b t te equation the to by suggested As . Ψ firm under the study ( Mont Carlo method Carlo Mont , X(y), ρ , the known Bootstrap dis- Bootstrap known the , ∂ X(y) θ and k θ as follows: as k of the firm ( firm the of θ k , an approxi- an , which are of x M k M , y χ b method, k method ) is ob- * (b=1, (10) (12) (11) x ρ k , to fetmtr is shown as equation (18). ation of estimator but just modified. In addition, the standardeliminated devi- not is estimator of bias the Hence, is not its exact value but an approximate one. approximate an but value exact its not is estimator which is applied as the obtained bias, obtain an estimate of(12)using (15). Thus: equation (17)bellow: as obtained is estimator bias-corrected the ), ( equation. following the as stated be may space Bootstrap by its t h ed f hs eto, h confidence the section, this of end the At s ugse b te qain 1) oe can one (11), equation the by suggested As Therefore: By correcting the bias of the main estimator main the of bias the correcting By For this estimator, the term of bias-corrected of term the estimator, this For The expected value for may be substituted be may for value expected The The equivalent of the above equation in the in equation above the of equivalent The Mont Carlo approximation and KhajehHassani as follows: (13) (18) (14) (15) (16) (17) 479 International Journal of Agricultural Management and Development, 6(4): 475-487, December, 2016. 480 International Journal of Agricultural Management and Development, 6(4): 475-487, December, 2016. rlzda ratherthan tralizedat ) from ( estimator bias-corrected of centrality the with corrected empirical density distribution a function need We bias. the correcting after provided hw i fgr 4 i i mvs o h lf by left the 1 to moves it if 4, figure in shown est fnto t te et by left the to function density be shown as and the confidence interval confidence the and as shown be itiuin ucin o , of functions distribution equations (19)and (20), respectively. with coverage level (1-2α) can be shown by the which has not been answered in this section is how interval of interval unbalanced, it would be preferred to select the select to preferred be would it unbalanced, n s eprcl est fnto , function density empirical so and 2,..., B) eina h itiuincnrlt f .What centralitydistribution the as median of In which which In Therefore, the empirical density function can function density empirical the Therefore, should be selected. Since the answer depends on is applied to determine confidence interval , the empirical density function will be cen- . If the empirical density function is Evaluationof Palm Groves Technical Efficiency / Zamanian θ k . Thus, we should move the empirical α θ th k is determined and the empirical the and determined is percent (critical value) in phrase in value) (critical percent Figure4. (Bahadoriet al., 2013) b1 2.. B) 2,..., (b=1, Bias-correction ofdistribution function 2 (b=1, (19) . As . (20) θ is k . method ( envelopment analysis data describes briefly the the method of estimating loihso selection algorithms of s hw b ti fote i a ust of subset a is frontier this ; by shown is unit for the above input set at the output level of level output the at set input above the for ee () of ) ( level production level of for each for definitions, above-mentioned the Considering Data envelopment analysis the following equation: i=1,...,2,n) scale assumption, may be computed by solving by computed be may assumption, scale collection resultingcollection fromsamplethe Simar & Wilson, 1998, 2000). The estimated efficient input oriented frontier Given the above equation, the input set for the (x measures the efficiency of decision making i , y n otie fo dfnto of definition from obtained and The M i ) DMU(x method), and third one addresses various based on determination of production data envelopment analysis approach i and its general form is shown as shown is form general its and th DMU i , y , and KhajehHassani y i ) is estimated as follows: , the estimated efficiency estimated the , ih aibe eun to returns variable with M (Bahadorietal., 2013; , the second subsection χ= ( χ= (x i (21) , y , (22) DEA i y ), , . is shown as (25): as shown is in o ioun cre f nu st Each set. input of curve isoquant of tions input in the observed sample of input- output, input- of sample observed the in input sample is obtained from random radial devia- radial random from obtained is sample input amount required for generating pseudo generating for required amount input rt dt a a pcfc ee o otu, the output, of level specific a at data erate process sampling. Bootstrap data Bootstrap sampling. analysis envelopment nu-retdfote is calculated via the above equation. In this equation, input-oriented frontier level of level ( with a specific the production that input level consumed of level a is to function efficiently. (By moving from moving (By efficiently. function to between the points in which the which in points the between the equation (23),using the linear planning: determination of method method of determination most important step The in the bootstrap method issues. is proper complex on inference tical statis- to perform caliber of its in terms proven been has effectiveness it which method pling estimator distribution of estimations obtained from re- from obtained estimations of distribution sample empirical using distribution of estimator lying the use of the Bootstrap The assumption in method population. of under-sample is to estimat (SW) h Bosrp aa neomn analysis envelopment data Bootstrap The 2006; Schmidt, 2008) are based on the same model of data generation algorithms proposed by Simar, Simar, by proposed algorithms x ned i odr o siae h efficiency the estimate to order in Indeed, The Bootstrap method is a statistical resam- statistical a is method Bootstrap The i , y , i , and and , ) and its equivalent point on the efficient the on point equivalent its and ) i along with radius ( radius with along (DGP) Evaluationof Palm Groves Technical Efficiency / Zamanian th Lothgren and Tambour and (1998) Lothgren firm ( ), the ratio of radial distance radial of ratio the ), ( firm , and it is assumed that to gen- to that assumed is it and , . ρ θ or or x i y ) DGP i (Cooper et al., et (Cooper DMU Wilson Wilson (1998) should achieve from data from operates (DMU) (LT) x (23) (24) i to e , radial equivalent of firm frontier) ( efficient constructed (on mates of distribution and esti- some from taken efficiencyvalues pseudo frontier input using then sample; served like rithms are described together with their difference input frontier curve is estimated using the ob- the using estimated is curve frontier input generated within two steps. In the first step, the are data resembling outputs, and inputs of ratio sampling are as follows: Subject to the observed process. Bootstrap simulation is to imitate data generation put-oriented efficiency index. The basic idea of in- random the with shown completely be can processes production of elements random that and ratios input and output to subject idea an is that the Data Generation Process Generation Data the that SW of compatibility. In the second step, the re- estimatiosembling process, and is based smoothed on the argument uses step first the in algorithm pseudo inputs are generated by generated are inputs pseudo ciency scores are taken from a similar distribution the firm efficiency. It is assumed the actual effi- which is compared with firm location to calculate (in input pseudo from sample pseudo produced by efficient frontier of distance radial theculating ae efcec soe ue i generating in the efficiencies.Unlike pseudo used scores efficiency mated esti- of distribution empirical from resembling provided by algorithm the in step This (25). in introduced are follow: as LT ofBootstrap efficiency is performed through cal- aus te nu-upt vectors input-output the , values hr is an unobserved point Where otta agrtm of algorithms Bootstrap Steps of bootstrap algorithm provided by provided algorithm bootstrap of Steps . sn te rgnl siae efficiency estimated original the Using 1. LT algorithm algorithm). In what follows, θ i algorithm) or the original input amounts (in F θ , i=1, 2, …, n …, 2, i=1, , LT x i , yi is established based on a simple ) on the efficient frontier and KhajehHassani F θ , the amounts of Bootstrap , therefore it can be said be can it therefore , LT and LT LT DGP (DGP) and method, SW SW iteration n each in model ∂ algo- (25) X(y) SW LT s. n 481 International Journal of Agricultural Management and Development, 6(4): 475-487, December, 2016. 482 International Journal of Agricultural Management and Development, 6(4): 475-487, December, 2016. algorithm with respect to the second to fourth to second the to respect with algorithm steps. In the second step of smoothing process, smoothing of step second the In steps. ( cording ac- to the efficiencies empirical estimated distribution of values core smooth- main the SW algorithm (Lothgren, 1998; Lothgren & Tambour,& 1999) Lothgren 1998; (Lothgren, are changed to the form ofequation (26). sampling are represented as re- independent and re- from obtained efficiencies The placement. efficiencies technical equation (27). f suo fiiny crs Apiain of Application modi- reflective on based is process smoothing scores. efficiency pseudo of fiiny for the specific firm. efficiency Bootstrap of times B make to order in repeated (28) with linear programming. model the solving by obtained is data pseudo sembled based on the resamplingre- are scores efficiency Bootstrap and frontier technical efficienc of efficiency based on the resampledoriginal datasample.addition,In Bootstrap are iterations based the resulted from are turn in that scoresciency scores obtained from empirical distribution of effi onthe original data, asthe main estimations does n, r ue t poue smoothed-resamples produce to used are ing, SW The algorithm introduced by Simar and Wilson 2. resampling is performed from performed is resampling 2. . suo otta dt i gnrtd from generated is data Bootstrap Pseudo 3. 4. Estimating Bootstrap efficiency scores using n hs loih, h etmtd Bootstrap estimated the algorithm, this In 5. Second to fourth steps of this algorithm are ) is different from Lothgren and Tambour’s Evaluationof Palm Groves Technical Efficiency / Zamanian δ i ∗ , i=1, 2,…, n n estimated (28) (26) (27) . y - . n poes s omd n w ses Frt a First, to added steps: is disturbance two small in formed is process ing auso ehia fiiny . The smooth- values of technical efficiency selecting bandwidth parameter ( is process smoothing of use in issues important symmetric image of image symmetric first, a small disturbance equal to indicates Bandwidth and and Bandwidth indicates vral pooe by proposed variable a for law selection bandwidth automated strong, placement from empirical distribution of main of distribution empirical from placement re- with independently taken resample smooth rnirpit . One of the most frontier point (1986) correction in resampling sequence is applied. Wilson(1998) and Simar by discussed been has process This Lothgern (1998) Lothgern follows: distribution) is added to added is distribution) standard independent identical normal a from internal standard deviation of efficiencyvalues of deviation standard internal shows that value of value that shows approaches to select bandwidth ( sulting from re- scores efficiency input-oriented (the tance, dis- of unit within bounded are scores ficiency h qain(9 ognrt as the equation (29)to generate pseudo efficiency. Considering the fact that ef- that fact efficiency.the pseudo Considering fication of function, already mentioned by If it is determined In order to produce smoothed pseudo efficiency hr, represents anestimation of estimated where, and hwd n i suy tee r several are there study, his in showed R13 Gaussian kernel density DEA is interquartile range of empirical of range interquartile is in detail. Suppose that is a non- a is that Suppose detail. in , using Mont Carlo simulation, Carlo Mont using , ), reflective process is used in and KhajehHassani h δ δ i * can be calculated using a using calculated be can i +h * +h ε ivra (1986) Silverman i ε * ε δ i >1 * i i * ∗ reflection, on the on reflection, Silverman (1986) in order to make to order in changes to a , h a be taken been has δ h ). As i ∗ ). In his study, h and then the then and of estimation ε i * Silverman (where, (29) (30) as h . ciency scores ( scores ciency the followingthe equation: itiuino . ( values efficiency Afterresampling above steps, smoothed distribution of by fundamental difference between the two algorithms second provided The value. mean scores efficiency scores of i oiyn as follows: via modifying output level) of consumed input of input consumed of level) output ih, h etmtd otta input-oriented Bootstrap estimated the rithm, with Bootstrap pseudo frontier. associated data pseudo the from produced are scores efficiency pseudo the which in gorithm is quant curve, which is in contrast with contrast in is which curve, quant is production pseudo Bootstrap on located point corresponding its to data original from sulting rgnlsml) , are replaced into original sample) the from (calculated scores efficiency main of according to the ratio of radial distance (at fixed scores are applied to determine bandwidth ( bandwidth determine to applied are scores Summaryof Statistics Table1 2. In this stage, the smoothed resampling effi- 1. Input-output vectors used in the calculation hr, is resampling main efficiency where, SW 2.1 Using the equation (30), estimated efficiency Standarddeviation Variance Minimum Mean Maximum LW algorithm can be briefly described as below: and Evaluationof Palm Groves Technical Efficiency / Zamanian SW γ is in the fourth step. In * ) are as follows: as ) are i th production firm is obtained 1960.179 Date(kg) 3842300 2241.96 10000 300 γ * ) is determined is ) i th Date palm(N) SW firm; re- firm; 143.1673 19885.94 170.34 LT 500 algo- 10 (32) (31) h al- ). cordingly, in ac- this study, palm,and the efficiency dominant of Keriteh the the is variety Province, Keriteh Hormozgan in planted rieties ducers of Hormozgan Province. Among the va- Saifullah, and Baghshah) is one of the date pro- Narges, Regab, Hizbandegan, Delbanan, Chah- 1986;Simar & Wilson, 1998) palm production has been evaluated. Random evaluated. been has production palm Bagh- MiyanChilan, of villages the (including one-hectare cultivation, RoodkhanehbarRegion Data and Specification of Variables . estimatedefficiencydistributionofscores sampling along with replacement from empirical the following equation: duced. the model (34)with linear planning: f otta efcece o the of firm specified efficiencies Bootstrap of set B-member a make to times B repeated are s band sn ped dt ad y solving by and data pseudo using obtained is With about 100 palm groves and more than more and groves palm 100 about With ispro- } 2.3Using equation (29), a string of { . Bosrp suo aa r gnrtd as generated are data pseudo Bootstrap 2.4 4. The estimator of Bootstrap efficiency scores 5. The second to fourth steps of this algorithm 2.2 The values of { of values The 2.2 627.9034 380502.5 Water(h) 540.46 3085 25 Ltge, 98 Silverman, 1998; (Lothgren, Labor(p/d) and KhajehHassani 22.12528 472.1751 28.22 103 δ 2 i * } are produced by re- by produced are } . 903295263 30611.141 21152.14 Land(h) 197100 1230 (34) (33) 483 International Journal of Agricultural Management and Development, 6(4): 475-487, December, 2016. 484 International Journal of Agricultural Management and Development, 6(4): 475-487, December, 2016. Bias-Corrected TechnicalEfficiency Scores and the Formationof Conf Table2 Average sampling and questionnaire were used to deter- The Table was applied to determine minimum determine to applied was Table data. The collect to used was table Bartlett’s and mine the sample size of the studied population, studied the of size sample the mine P- G 50 49 48 47 46 45 44 43 42 41 40 39 38 37 36 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 10 11 9 8 7 6 5 4 3 2 1 0.00065 -0.0002 -0.0006 -0.0002 -0.0003 -0.0014 -0.0001 -0.0002 -0.0005 -0.0016 -0.0008 -0.0017 -0.0023 -0.0012 -0.0002 -0.0019 -0.0006 -0.0008 -0.0001 -0.0012 -0.0006 -0.0006 -0.0006 -0.0046 -0.0004 -0.0009 -0.0007 -0.0002 -0.0004 -0.0023 -0.0005 -0.0002 -0.0001 -0.0011 -0.0011 0.0001 0.0005 0.0002 0.0024 0.0002 0.0002 0.0001 -0.001 -0.003 -0.002 Evaluationof Palm Groves Technical Efficiency / Zamanian Es-Bi 0 0 0 0 0 0 - Bi-Corr 0.0703 0.0669 0.0133 0.0268 0.0662 0.1537 0.1868 0.1336 0.1348 0.1359 0.1297 0.1834 0.2707 0.2712 0.2165 0.4087 0.3147 0.1206 0.2553 0.2642 0.2586 0.0932 0.2971 0.2967 0.0853 0.2122 0.3403 0.1324 0.1673 0.4347 0.5443 0.3237 0.0449 0.0943 0.3041 0.2061 0.0821 0.1769 0.2288 0.2511 0.1138 0.1188 0.1152 CRS 0.073 0.035 0.181 0.018 0.018 28 1 1 1 0.0032 0.0058 0.0016 0.0028 0.0075 0.0061 0.0055 0.0222 0.0231 0.0213 0.0214 0.0109 0.0308 0.0249 0.0016 0.0099 0.0137 0.0172 0.0164 0.0162 0.0173 0.0253 0.0253 0.0207 0.0331 0.0347 0.0515 0.0307 0.0234 0.0134 0.0125 0.0021 0.0346 0.0142 0.0191 0.0047 0.0109 0.0121 0.0275 0.0105 0.0077 0.0156 0.0161 S-Dev 0.015 0.012 0.042 0.017 0.01 0 0 0 0.0296 0.0567 0.0109 0.0224 0.0551 0.0628 0.0607 0.2755 0.2109 0.1544 0.2297 0.2298 0.1827 0.3543 0.1028 0.2169 0.2226 0.2207 0.1491 0.0763 0.2477 0.2566 0.0153 0.0696 0.0953 0.1806 0.1278 0.1594 0.0147 0.2857 0.1083 0.1366 0.3635 0.0372 0.0776 0.0992 0.2567 0.1744 0.0974 0.0695 0.1517 0.2023 0.1108 0.1125 0.1146 0.1102 0.457 0.262 16 L 1 1 1 Conf-int 0.0786 0.0827 0.0786 0.1564 0.0396 0.0756 0.0157 0.3422 0.3383 0.0205 0.1004 0.1376 0.2379 0.1786 0.4646 0.2888 0.2978 0.2923 0.2151 0.0519 0.0212 0.1528 0.1965 0.4972 0.6163 0.3725 0.2842 0.1546 0.1542 0.2086 0.3084 0.2533 0.2022 0.0937 0.3434 0.1371 0.0311 0.2115 0.1104 0.1311 0.2311 0.1116 0.371 0.136 0.392 0.148 0.249 0.311 98 U 1 1 1 -0.11064 -0.7914 -0.0957 -0.2659 -0.0825 -0.0534 -0.0618 -0.0282 -0.0045 -0.0291 -0.1095 -0.2429 -0.0461 -0.1462 -0.1589 -0.1567 -0.1297 -0.0775 -0.0402 -0.0566 -0.0323 -0.1584 -0.0287 -0.0674 -0.0093 -0.0883 -0.1079 -0.0682 -0.0672 -0.0431 -0.0835 -0.0629 -0.1049 -0.0694 -0.0539 -0.2714 -0.1961 -0.1138 -0.1162 -0.0411 0.0004 0.0002 over a hectare, at probability level of 10% error, groves palm 100 of population a for table, this on Based data. categorical and continuous for size population given a of size sample returned -0.034 -0.017 -0.106 -0.705 -0.209 -0.101 Es-Bi 0 0 0 Bi-Corr 0.5406 0.4164 0.3664 0.6769 0.5218 0.3825 0.3563 0.4494 0.2849 0.6544 0.2158 0.5541 0.6097 0.5936 0.1814 0.5543 0.1727 0.5031 0.1504 0.1929 0.0808 0.1274 0.2215 0.3794 0.6656 0.1995 0.4412 0.3985 0.2098 0.4398 0.4999 0.1367 0.2603 0.1544 0.4956 0.3012 0.3335 0.5682 0.1161 0.1141 0.2711 0.306 0.286 VRS 0.12 1 1 1 1 1 1 1 6 idenceIntervals 0.0058 0.0031 0.0062 0.0015 0.0028 0.0166 0.0154 0.0137 0.0176 0.0244 0.0016 0.0108 0.0208 0.0197 0.0196 0.0212 0.0108 0.0225 0.0255 0.0182 0.0345 0.0078 0.0125 0.0172 0.0279 0.0121 0.0106 0.0046 0.0021 0.0266 0.0176 0.0371 0.0221 0.0303 0.0239 0.0136 0.0125 0.0121 0.0147 0.0115 0.0114 S-Dev 0.006 0.015 0.031 0.017 0.016 0.019 0.01 and KhajehHassani 96 0 0 0 0.7396 0.1059 0.2246 0.2581 0.2474 0.1487 0.0769 0.2467 0.2548 0.0153 0.0697 0.0949 0.1802 0.1273 0.0297 0.0223 0.0622 0.0601 0.2329 0.1776 0.0697 0.1337 0.1825 0.0993 0.0775 0.0371 0.0147 0.3173 0.1886 0.1369 0.3816 0.8546 0.3549 0.2724 0.1539 0.2372 0.2961 0.1909 0.1611 0.1104 0.1199 0.1126 0.1141 0.1101 0.378 0.319 0.011 0.07 42 L 1 1 1 Conf-int 0.0793 0.1564 0.0397 0.0903 0.0158 0.1575 0.1791 0.2145 0.3423 0.3372 0.0204 0.1005 0.1376 0.2396 0.5017 0.9241 0.4537 0.3519 0.1546 0.1535 0.1487 0.2077 0.3799 0.2504 0.4928 0.7753 0.1408 0.2919 0.3247 0.2807 0.2305 0.0943 0.1741 0.2385 0.4122 0.1369 0.0513 0.0213 0.4047 0.2455 0.0311 0.1106 0.3117 0.3118 0.082 0.212 0.196 0.111 48 U 1 1 1 0.693905 0.492356 0.200721 0.175439 0.586328 0.503608 0.618531 0.206023 0.549094 0.468622 0.608322 0.869056 0.347842 0.172493 0.686642 0.439575 0.501649 0.765048 0.598779 0.603026 0.367904 0.200768 0.248563 0.479477 0.759753 0.603471 0.759916 0.624542 0.558851 0.460747 0.433328 0.435647 0.997795 0.304575 0.104227 0.169549 0.980189 0.178191 0.343183 0.903465 0.551805 0.110833 0.53527 0.11658 0.5443 0.3147 0.2971 0.2122 S-E 1 1 8 1 scores fordate producers 5. Figure 4 3 22% these SE effects and refers to pure technical ef-effectstechnical SE pure these to refers and groves should be collected palm 46 least at of population a with sample a in Table 1. listed is statistics region. the the Aof summary in production palm of efficiency the evaluate to used were (N) palms date of number the and (h), water (hectares), land Day), (person/Labor labor including inputs basic four and grams) (Kilo- product palm of amount the regarding Information taken. was groves palm 50 of size the with sample a 2013, in region khanehbar with over a hectare cultivation of palm in Rood- As such, in this paper, from producer population ciencies (SE). ciencies Additionally, using specifi- VRS ne dt evlpet nlss provided efficiency technical the analysis Wilson, and Simar by envelopment data ented VRS assuming survey, this in calculated were level confidence 95% at (B=1000) iterations, with 1000 intervals confidence related and scores cation permits the calculation of TE devoid of devoid TE of calculation the permits cation effi- scale by TE of measures gross in results when specification CRS Using (TE). ficiency all firms are not operating at an optimal scale optimal an at operating not are firms all propriate when all firms are operating at an optimal scale, and it implies the total technical ef- Constant Return to Scale (here, restrictions [ ] in CRS model ] is CRS model in [ restrictions toConstant (here, Return Scale Variable Returns to Scale sn sote Bosrp te input-ori- the Bootstrap, smoothed Using 3 14% and CRS and RESULTS AND DISCUSSION itiuin f ue ehia efficiency technical pure of Distribution Evaluationof Palm Groves Technical Efficiency / Zamanian 26% 4 . CRS assumption is only ap- only is assumption CRS . 38% (Barlett et al., 2001) . for dateproducers Figure 6. Keriteh palm cultivation were about 44%, which for VRS under DEA input-oriented Bootstrap from resulting scores efficiency technical pure bound-upper bound) efficiency can be calculated by the ratio of tech- scale efficiency;therefore, scale efficiency and pure to decomposed is TE ficiency.Therefore, listed: and was equal to 0.0005. Silverman by proposed (30) equation the via The bandwidth used in this study was computed scores. an average of 66% saving in resource consump- produce the same amount of product with about to able are region this in growers that indicates ia efcec ad ue ehia efficiency technical pure and efficiency nical ficiently. 30% and that only than 14% of growers operated ef- less efficiency had groves palm studied of 38% production that demonstrates also 5 Figure their costs. reduce therefore, and tion, As can be seen in the table above, the average S-E: Scale Efficiency ofIt LU: ofdne nevl (lower- interval Confidence (L-U): Conf-Int S-Dev: Standard Deviation Bi-Corr: Bias-corrected technical efficiency Es-Bi: Estimated bias P-G: Palm groves Abbreviations used in the above table are as are table above the in used Abbreviations The results are listed in Tablelisted are results The 2. Figure and 2 30% 20% not applied.) Distribution of pure scale efficiency scores and KhajehHassani 24% 26% 485 International Journal of Agricultural Management and Development, 6(4): 475-487, December, 2016. 486 International Journal of Agricultural Management and Development, 6(4): 475-487, December, 2016. input access. it is In access. recommended regard, this input production as well. as production to establish a center for training and experience proper facilitate andto consumption resource to improve the general knowledge of optimum of knowledge general the improve to prpit pooig n euaig actions educating and promoting appropriate tempt to optimize efficient use of inputs and inputs of use efficient optimize to tempt could orchardists successful where share, share their experiences with others in an at- an in others with experiences their share region can be remarkably increased by taking by increased remarkably be can region to 100%), the efficiency of producers in this in producers of efficiency the 100%), to 20 to 50% is easier than increasing it from 70 from it increasing than easier is 50% to 20 ovnet n ese ta icesn i for from efficiency it increasing (e.g., levels, higher increasing than easier and convenient more is efficiency of levels lower alsobecau- seincreasing and efficiencies), scale optimum and management both of terms (in producers inefficient of number high and scores ciency effi- low the Given 0.50. than less efficiency scale had producers of 50% and 0.50 than less efficiency showed management had producers of 64% efficiencies scale and management in Figures 2 and 3, the distribution of scores of of scores 2 3, distribution and the in Figures listed as Furthermore, scale. optimum in ating oper- and management efficient by outputs of icant savings will be possible for given quantity weakly, signif- and operated respectively, 0.50 and 0.44 to equal scores efficiencies scale and this this region, having average (pure) management this in production date of efficiency scale and (pure) management the evaluate to aimed profit and welfare ofthe people ofthe region. level finally,and inputs, existing with both increases output increases and costs, production reduces consumption, inputs product saving to in technical efficiency (TE) of the activity leads for people in Roodkhanebar Region, any increase income of source and activity main the directly region. As shown in Table 2, date producers of in Table producers shown region. As date 2, ciiy f ot f hm a be fr distant from efficient production capacity. far been has them of most of activity the that shows This 0.7. than more was ducers scale efficiency score for only 20% of date pro- CONCLUSION AND RECOMMENDATIONS Due to the importance of the issue, this study this of the issue, Due to the importance Since producing dates is either directly or in- or directly either is dates producing Since Similarly, as shown in the Figure 6 below, the Evaluationof Palm Groves Technical Efficiency / Zamanian Cooper, W., Seiford, L., & Tone, K. (2006). Dong, Fengxia, & Featherstone, Allen M. (2006). Brümmer, B. (2001). Estimating confidence intervals Ebadi, S. (2011). A method for ranking efficiency Balcombe, K., Fraser, I., Latruffe, L., Rahman,L., Latruffe,Fraser, I., K., Balcombe, Färe,R., Grosskopf,& (2006).. FAO, STAT. (2012). 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Organizational research: 4 Studies, Business and Economic Chinese of 37 Research in Operation And Its Applications, paper. Ebadi’s Saeed the in in provided process existing problems “Investigating for estimating non parametric efficient frontier 2012. Applied Economics, 40 Economics, Applied farming. rice Bangladesh in efficiency of of application An (2008). L. Smith, & M., G. (2013). Use of Bootstrap simulation process and its Applications, 29 scores using Bootstrap. and London: Chapman and Hall/CRC Vol. Probability, and Applied 57. Statistics on Monographs Bootstrap, the to (1), 57-75. (2), 113-135. ACKNOWLEDGEMENT Information Technology, Learning, REFERENCES (3), 285-306. and KhajehHassani European Review of Agricultural Research in Operation (2), 29-44. (15), 1919-1925. (15), Newdirections: (1), 43-50. Introduction New YorkNew , 321-335. Journal : :

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