African Journal of Agricultural Research Vol. 6(19), pp. 4522-4532, 19 September, 2011 Available online at http://www.academicjournals.org/AJAR DOI: 10.5897/AJAR11.1002 ISSN 1991-637X ©2011 Academic Journals

Full Length Research Paper

Measuring the productive efficiency of forest enterprises in Mediterranean Region of Turkey using data envelopment analysis

Mehmet KORKMAZ

Department of Forest Engineering, Faculty of Forestry, Suleyman Demirel University, Isparta 32260, Turkey. E-mail: [email protected].

Accepted 24 August, 2011

The purpose of this study is to measure proportionally the productivity of partial and total factors in state forest enterprises. As for partial productivity, land, capital and employee productivity measures are considered. The data envelopment analysis, which is a non parametric method has been used in the study. For the enveloping model, the BBC (Banker, Charnes, Cooper) models have been preferred as input-oriented. The Mediterranean Region of Turkey at thirty-seven state forest enterprises made up the study area. The productivity scores have been evaluated in accordance with 8 input and 4 output factors. According to the results of the study: land productivity had 7 enterprises, employee productivity had 9, capital productivity had 10 and the total productivity had 19 enterprises assesses to be productive. The lowest efficiency scores were evaluated to be on the employee productivity with an average value of 0.698. The inefficiency of the total productivity was thought to be due to the amount of workers and thus the amount of fees paid in big amounts. The following reason is assessed to be due to the total size of land bound to the enterprises.

Key words: Productivity, efficiency, forest enterprise, data envelopment analysis, linear programming, Turkey.

INTRODUCTION

Productivity is defined to be the output of a certain period The productivity measurements are divided into two as of production rated to the input or total of inputs charged partial productivity which evaluates each input individually to gain such output (Miraboglu, 1983). In the recent at its related level (land productivity, capital productivity years, productivity is considered to be the key related to and etc.) and the total productivity (total factor the economic and social developments of a country. Very productivity) which evaluates the productivity as a whole. obviously, productivity depends on the micro levels of The total productivity was put forward first by Solow and enterprises, the quality of politics and strategic changes Denison and is widely used in the production theory the government goes through and the results of macro based various methodologies and analysis. In some and global levels referring to the social and occupational sources, the total productivity is named as to be an environment the government management is in to indicator of technologic developments, technical changes (Prokepenko, 2002). or the development and improvement seen in the The productivity measurements which are of great concepts of knowledge (Pramongkit et al., 2001). importance in the assessment of success are focused on The productivity measurements are also of great the basis of resource (production factors) productivity. importance for forest enterprises. The expectations of The performance and the success model of an activity societies from the forest resources may show differences. are based on the variable of productivity which acts as a The assessment of such expectations in means of mediator between the dependent variable of the work product and service and in means meeting such performance and the external effects and work strategy expectations to be fulfilled, the forestry constitute an (Smith and Reece, 1999). economic activity to the society besides its biological and techniques and economic resources with the products it provides (Pearce, 1990). Thus, forestry is defined to be DEA measures the productivity of the enterprises relatively biological, technical, economic and social management (Korhonen, 1997). DEA has drawn great attention from the year it that provides the society with products and services was put forward until present and many articles and books have been written subjecting it. Tavares (2002) has stated that 3203 Korkmaz et al. 4523

(Akesen and Ekizoglu, 2010). amounts of press and published work have been realized between “Being at the center of the junction joining Asia, Europe the years 1978 and 2001 by 2152 different authors. and Africa, Turkey has several unique ecosystems DEA has defined different models according to the enveloping hosting many species of life. With its unique geographic method and the distance of enterprises to realize efficient position, rich topographic features and climatic production from the inefficient position they are located in. These models are generally as: (a) the model developed by Charnes et al. differences among regions, Turkey has an impressive (1978) (CCR) that is based on the assumption of constant returns richness in terms of fauna and flora, earning it a to scale; (b) the model developed by Banker et al. (1984) (BCC) prestigious place among the three continents. Such that, that is based on the assumption of variable returns to scale. These with nearly 30% of its total land area covered with forests, models are further classified into three as input-oriented, output- Turkey has 11000 plant species, almost totaling the oriented and the additives. The efficient production border in the number of plant species the whole continent of Europe BCC model evaluates and benefits from the decision making units in a more efficient way compared to the CCR model. Consequently, has. Besides, the Mediterranean Region forests in Turkey the efficient productivity values that are gained to be lower than the are home to very many annual and perennial plant assumed variable returns to scale are equal or bigger in values species with a worldwide importance” (OGM, 2009). according to the scale in means of the of constant returns to scale The productivity measurements of forest enterprises in assumed (Deliktas, 2002). Therefore, the input-oriented BCC model Turkey are limited only to partial productivity due to the was preferred in the study. Without changing the level of the output, difficulties faced during assessment. The reason why the models that define how more the input compositions shall be reduced to benefit of the output levels at most are defined as the only a few specified methodologies are applied to input-oriented models (Oruc, 2008). measure productivity is due to the difficulty faced The input-oriented BCC model evaluates the efficiency of DMUj determining the inputs which are mainly constituted by (j= 1,…,n) by solving the following (envelopment form) linear the actual parts of the land and the determination of the program (Cooper et al., 2007): values of the trees (Miraboglu, 1983). Furthermore, the lack of documentary of records that enable numeral facts to be revealed related to the evaluation and assessments of the relative measurements are also negative factors Subject to, which draws attention to the situation (Caglar, 1988). However, there are studies that have been applied in the recent years which aim to reach the aggression and total productivity evaluations of partial productivity values

(Senturk, 2007). Subject to, The purpose of this study is to determine the partial and total productivity values of the 37 state forest enterprises that are located in the Mediterranean Region of Turkey with the data envelopment analysis which is a non parametric method. Therefore, the comparison of enterprises will provide assessments to reveal the circumstances of the enterprises and thus the changes required for those who seem weak in various levels.

; MATERIALS AND METHODS where is a scalar; n: number of DMU , s: amount of outputs; m: Data envelopment analysis amount of inputs; :k used by DMU, i refers to the amount of Data envelopment analysis (DEA) is a non parametric technique that evaluates the multi input and output efficiency and productivity input; : j refers to the amount used by DMU, i refers to amount due to a multi-variable decision held which is a linear based of inputs; :k refers to the amount used by DMU, r refers to program (Liu et al., 2000). It is a model which defines a multi output developed from the concept that defined single outputs put forward amount of outputs; j refers to the amount used by DMU, r by Farrell (1957). The most important feature of this model which was first developed by Charnes et al. (1978) is the capability of refers to amount of outputs; : k refers to the intensity of the j defining the inefficiency amounts and resources. DEA realizes the used by the DMU. comparison between the input and outputs of similar decision The dual multiplier form of this linear program is expressed as making units (DMUs). (Cooper et al., 2007): The main assumption with DEA is that all enterprises possess similar strategic aims and the same types of outputs due to the benefit of the same types of inputs used (Golany and You, 1997).

Subject to:

4524 Afr. J. Agric. Res.

Figure 1. Study area.

The difference between the CCR model and the BCC model is

defined as due to the convexity constraint and the free signed variable from (Yun et al., 2004). If the aim function values to 1 in the primal model the DMUj is productive. If the productivity value is below 1, the DMUj is nonproductive.

. Data

The equivalent BCC fractional program is obtained from the dual The main units and institutions that realize the forestry activities in program as (Cooper et al., 2007): Turkey are the forest enterprises. Thus, the units that decide on the economic, social and technical means of the activities are the forest enterprises authorized. Moreover, all accounts and the storage of records are also realized by these enterprises (Dasdemir, 1996). Therefore, the DMUs have been decided to be the forest enterprises. The enterprises have to be comparable to gain Subject to assessment relative to each other in means of productivity (Geray, 1982). Therefore, the 37 forest enterprises that are located in the Mediterranean Region of Turkey have been included into the study as they have similarity among themselves by means of economic, technical and social means (Figure 1). These enterprises realize their activities bound to four different regional forest directorates (Table 1). For the productivity measurements of the forest enterprises, 8 Turker, 1999; Senturk, 2007; Kao and Yang, 1991; Kao et al., 1993; inputs and 4 output factor values have been considered. Such Kao, 1998, 2000). The complementary factors’ descriptive statistics factors have been determined in accordance with the studies gained for the year 2009 can be seen in Table 2. The data sources, previously applied (Safak, 2009; Dasdemir, 1996; Turker and Korkmaz et al. 4525

Table 1. The distribution of enterprises according to the regional forest directorates.

Regional forest directorates Enterprises Adana Adana, Feke, Kozan, Kadirli, Pozanti, Osmaniye, Pos, Saimbeyli, Karaisali, Yahyali Mersin Anamur, Bozyazi, Erdemli, Gulnar, Mersin, Mut, Silifke, Tarsus Akseki, Alanya, Antalya, Elmali, Finike, Gazipasa, Gundogmus, Kas, Korkuteli, Kumluca, Antalya Manavgat, Serik, Tasagil Isparta Isparta, Egirdir, Sutculer, Golhisar, Burdur, Bucak

Table 2. Descriptive statistic for the data.

Factors Mean SD Capital (TL) 2373737.93 2631818.46 Total area (ha) 269032.06 322990.55 Forest area (ha) 95731.22 35590.23 Productive forest area (ha) 46714.48 15173.45 Production costs (TL) 1675075.92 1565222.68 Employee costs (TL) 2016988.60 1247434.68 Amount of technical employee 13.81 20.61 Total amount of employee 64.27 40.58 Amount of production (m3) 40787.246 37246.629 Amount of sales (m3) 61969.396 97429.704 Sales income (TL) 4680245.04 4974020.40 Value added (TL) 3697968.82 4668457.22

the balance sheets of the enterprises, the income charts, the 7. Amount of technical employee: Refers to the total amount of the production and sales charts, the administrative and fiscal records technical employee working in the fields of the forest enterprises are supplied from the branch offices of the directorates that bind the (engineers and etc.); enterprises and the marketing processes. 8. Total amount of employee: Refers to the total amount of employee working in the field of the relative forest enterprise (workers, officers and etc.). Input factors Output factors

3 1. Capital (TL [Approx. 1TL = 0.67 US $]): The actual capital of 1. Amount of production (m ): Refers to the total amount of forest enterprises are constituted by the values of the trees and the production of logs, mine poles and etc. land. However, these values are not stated on the balance sheets 2. Sales income (TL): Refers to the total income gained from the of the enterprises (Turker, 2008). Therefore, the current assets of sales of products like logs, mine poles and etc. 3 the enterprises are considered to be the balance sheet values of 3. Amount of sales (m ): Refers to the total amount of the sales of the institutions (Dasdemir, 1996); products like logs and mine poles. 2. Total area (ha): The total area refers to the total of the land under 4. Value added (TL): The Value Added is evaluated by the net sales the management of the enterprise and those that are located as excluded from the first substances and the equipment expenses forest and non-forest lands; and depreciation (Senturk, 2007). 3. Forest area (ha): The forest area refers to the total forest land either productive (crown closure more than 10%: productive) or not In the light of the factors defined the partial productivity (crown closure less than 10%: unproductive); measurements were applied initially and then the total productivity 4. Productive forest area (ha): Refers to the total productive forest of factors in the latter. In the partial productivity measurements land under the management of the enterprise; applied, the efficiencies of the land (yield strength of land), capital 5. Production costs (TL): Refers to the total of the costs made for and employee were considered. The input and output factors used the production such as logs, mine poles and other relative to evaluate the partial and total productivity can be seen in Table 3. processes (logging, transportation and etc.); 6. Employee costs (TL): Refers to the total fees paid to the staff and personnel working in the field of the forest enterprises; RESULTS AND DISSUSSION

Partial productivity measurement increase realized in the productivity of land directly In forestry in which production is based on land, the affects the production realized on the land. Fifty percent productivity of land is of great importance. Land within of the forests in Turkey are productive (OGM, 2006) and such scope can be classified as productive, little productive and nonproductive (Miraboglu, 1983). The 4526 Afr. J. Agric. Res.

Table 3. Input and output factors according to different productivity.

Factor Asset Land Capital Employee Total Capital (TL) V V Total area (ha) V V Forest area (ha) V V Productive forest area (ha) V V Input factors Production costs (TL) V V Employee costs (TL) V V Amount of technical employee V V Total amount of employee V V

3 Amount of production (m ) V V V V 3 Amount of sales (m ) V V Output factors Sales income (TL) V V V V Value added (TL) V V V

similarly the rates are approximately the same for the The capital of an enterprise is the produced products Mediterranean Region with a rate of 49% defined as and all other services provided in production (Ertek, productive forests. Turkey area can obviously be seen 2009). The usage of capitals by greater means; the more that 27.2% of land is covered with forests; this rate is produced goods; the production of new products and much higher with a value of 35.6% considering the gaining more free time means providing three types of Mediterranean Region alone. main supplies (Geray, 1998). However, it is obvious that According to the BCC model results 7 of the 37 forest such gained good and services will only be possible if the enterprises have been defined as productive in means of capital was used wisely and efficiently. As stated earlier, the land productivity act (Table 4). These are the the balance sheets of the enterprises do not bear the enterprises located at Adana, Kadirli, Bozyazi, Gulnar, values of the land of forests and trees as they face Elmali, Manavgat and Tasagil. Thus, we can obviously difficulty in determining them. Therefore, in the state that 20% of the enterprises benefit from their land evaluations of the capital productivity applied the money productivity. The enterprise with the lowest efficiency in the cash boxes and banks together with the current score referring to the land productivity factor was the Mut stocks were considered as the current assets on the Forest Enterprise with a value of LPS = 0.360. The balance sheets of the enterprises. average value was evaluated as to be 0.714. In the evaluations applied for the productivity of the In Table 4, you can see the reference groups of the capitals, according to the BCC model 10 of the 37 forest enterprises defined as nonproductive and the weights enterprises were determined to be as productive. This means that almost 27% of the enterprises benefit from of these enterprises. For instance, Antalya forest enterprises’ reference group constituted as Elmali their capitals efficiently (Table 5). These enterprises are stated to be as Adana, Kardirli, Karaisali, Gulnar, Tarsus, ( 0,105), Bozyazi ( 0.854) and Adana ( 0.041). Elmali, Gundogmus, Korkuteli, Serik and Tasagil forest This refers to the amount of inputs required for such enterprises. The enterprise with the lowest capital enterprises determined in accordance to their weights efficiency score was stated as Antalya Forest Enterprise to change their variables in order to be defined as with a value of 0.404 whilst the average value was productive. Nonproductive enterprises are constituted determined as 0.786. The scores of the forest enterprises within at least two and at most 4 reference groups. that are especially located in cities and big districts were relatively low. In Table 5, you can see the reference group of the enterprises nonproductive in means of their capitals and the weights relative. For instance the reference enterprises of Antalya Forest Enterprise are as Tarsus ( 0.119), Serik ( 0.075), Elmali ( 0.805) and Kadirli ( 0.001). The reference enterprises of the lands in means of their land productivity and capital productivity are all different except for Elmali. The reason for such difference is the variables used for the input and output factors specific to itself. This situation also provides an Korkmaz et al. 4527

Table 4. Land productivity scores (LPS) of enterprises.

No DMUs LPS Reference group (lambda weights ) No DMUs LPS Reference group (lambda weights ) 1 Adana 1.000 1(1.000) 20 Alanya 0.452 29(0.593)-12(0.407) 2 Feke 0.634 31(0.152)-4 (0.098)-12 (0.750) 21 Antalya 0.453 22(0.105)-12(0.854)-1(0.041) 3 Kozan 0.682 31(0.323)-4(0.677) 22 Elmali 1.000 22(1.000) 4 Kadirli 1.000 4(1.000) 23 Finike 0.971 29(0.321)-1(0.005)-12(0.673) 5 Pozanti 0.401 12(0.704)-1(0.237)-31(0.059) 24 Gazipasa 0.797 1(0.034)-12(0.682)-29(0.284) 6 Osmaniye 0.622 31(0.186)-12(0.764)-1(0.050) 25 Gundogmus 0.968 1(0.114)-12(0.852)-29(0.034) 7 Pos 0.704 31(0.371)-12(0.629) 26 Kas 0.441 22(0.208)-12(0.792) 8 Saimbeyli 0.577 4(0.202)-12(0.738)-1(0.059) 27 Korkuteli 0.723 22(0.962)-12(0.038) 9 Karaisalı 0.863 1(0.037)-31(0.875)-12(0.088) 28 Kumluca 0.858 31(0.007)-12(0.942)-1(0.050) 10 Yahyalı 0.966 31(0.008)-22(0.329)-1(0.663) 29 Manavgat 1.000 29(1.000) 11 Anamur 0.722 31(0.282)-4(0.234)-12(0.484) 30 Serik 0.931 31(0.346)-12(0.579)-1(0.075) 12 Bozyazi 1.000 12(1.000) 31 Tasa₣ıl 1.000 31(1.000) 13 Erdemli 0.731 12(0.553)-22(0.405)-1(0.041) 32 Isparta 0.494 12(0.357)-31(0.003)-1(0.250)-22(0.390) 14 Gulnar 1.000 14(1.000) 33 Egirdir 0.381 12(0.434)-31(0.108)-1(0.185)-22(0.273) 15 Mersin 0.738 31(0.096)-4(0.064)-12(0.840) 34 Sutculer 0.562 31(0.103)-22(0.015)-12(0.882) 16 Mut 0.360 22(0.289)-12(0.711) 35 Golhisar 0.568 31(0.044)-12(0.844)-1(0.122) 17 Silifke 0.514 4(0.048)-1(0.114)-12(0.839) 36 Burdur 0.527 12(0.968)-1(0.009)-31(0.023) 18 Tarsus 0.600 31(0.193)-4(0.329)-12(0.478) 37 Bucak 0.798 31(0.631)-4(0,156)-12(0.214) 19 Akseki 0.375 22(0.157)-12(0.843) Mean 0.714

Table 5. Capital productivity scores (CPS) of enterprises.

No DMUs CPS Reference group (lambda weights) No DMUs CPS Reference group (lambda weights) 1 Adana 1.000 1(1.000) 20 Alanya 0.575 1(0.248)-22(0.156)-30(0.048)-25(0.548) 2 Feke 0.941 30(0.062)-18(0.938) 21 Antalya 0.404 18(0.119)-30(0.075)-22(0.805)-4(0.001) 3 Kozan 0.774 14(0.300)-18(0.539)-4(0.161) 22 Elmali 1.000 22(1.000) 4 Kadirli 1.000 4(1.000) 23 Finike 0.561 25(0.501)-1(0.024)-27(0.451)-30(0.023) 5 Pozanti 0.713 4(0.763)-30(0.149)-1(0.088) 24 Gazipasa 0.730 1(0.313)-30(0.060)-22(0.627) 6 Osmaniye 0.749 4(0.440)-18(0.528)-30(0.032) 25 Gundogmuİ 1.000 25(1.000) 7 Pos 0.745 14(0.195)-18(0.010)-30(0.137)-4(0.658) 26 Kas 0.559 18(0.108)-30(0.120)-22(0.748)-4(0.024) 8 Saimbeyli 0.704 4(0.652)-1(0.210)-30(0.138) 27 Korkuteli 1.000 27(1.000) 9 Karaisalı 1.000 9(1.000) 28 Kumluca 0.553 30(0.216)-22(0.555)-18(0.229) 10 Yahyali 0.927 27(0.719)-30(0.007)-4(0.072)-1(0.202) 29 Manavgat 0.835 4(0.013)-18(0.040)-22(0.947) 11 Anamur 0.783 30(0.223)-4(0.529)-14(0.248) 30 Serik 1.000 30(1.000) 12 Bozyazi 0.695 4(0.507)-30(0.229)-18(0.076)-22(0.188) 31 Tasagil 1.000 31(1.000) 4528 Afr. J. Agric. Res.

Table 5. Contd.

13 Erdemli 0.862 4(0.158)-30(0.021)-18(0.194)-22(0.627) 32 Isparta 0.654 30(0.075)-4(0.248)-27(0.677) 14 Gulnar 1.000 14(1.000) 33 Egirdir 0.738 4(0.434)-27(0.271)-30(0.295) 15 Mersin 0.788 30(0.170)-18(0.274)-4(0.463)-22(0.093) 34 Sutculer 0.718 4(0.401)-27(0.159)-30(0.440) 16 Mut 0.748 30(0.079)-22(0.533)-4(0.238)-1(0.150) 35 Golhisar 0.602 1(0.144)-4(0.624)-30(0.233) 17 Silifke 0.854 4(0.047)-18(0.553)-22(0.400) 36 Burdur 0.547 4(0.604)-27(0.164)-30(0.232) 18 Tarsus 1.000 18(1.000) 37 Bucak 0.727 18(0.219)-14(0.620)-30(0.161) 19 Akseki 0.566 1(0.026)-30(0.110)-25(0.456)-22(0.408) Mean 0.786

Table 6. Employee productivity scores (EPS) of enterprises.

No DMUs EPS Reference group (weights) No DMUs EPS Reference group (weights) 1 Adana 0.188 22(0.884)-31(0.116) 20 Alanya 0.571 28(0.205)-22(0.795) 2 Feke 0.756 31(0.197)-22(0.099)-4(0.179)-17(0.525) 21 Antalya 0.114 14(0.106)-22(0.684)-27(0.210) 3 Kozan 0.807 4(0.677)-31(0.323) 22 Elmali 1.000 22(1.000) 4 Kadirli 1.000 4 (1.000) 23 Finike 0.667 28(0.058)-22(0.942) 5 Pozantı 0.611 31(0.169)-4(0.122)-22(0.453)-17(0.256) 24 Gazipasa 0.686 17(0.021)-14(0.009)-31(0.013)-22(0.958) 6 Osmaniye 0.805 4(0.633)-31(0.066)-28(0.301) 25 Gundogmus 1.000 25(1.000) 7 Pos 0.775 31(0.211)-17(0.253)-4(0.536) 26 Kas 0.469 28(0.079)-22(0.847)-31(0.075) 8 Saimbeyli 0.595 4(0.537)-31(0.066)-22(0.368)-17(0.030) 27 Korkuteli 1.000 27(1.000) 9 Karaisali 0.704 14(0.111)-31(0.745)-17(0.145) 28 Kumluca 1.000 28(1.000) 10 Yahyali 0.832 14(0.052)-27(0.342)-22(0.607) 29 Manavgat 0.667 28(0.222)-22(0.778) 11 Anamur 0.490 14(0.050)-31(.508)-22(0.390)-17(0.052) 30 Serik 1.000 30(1.000) 12 Bozyazi 0.707 22(0.456)-31(0.316)-28(0.228) 31 Tasagil 1.000 31(1.000) 13 Erdemli 0.755 14(0.433)-4(0.096)-22(0.471) 32 Isparta 0.284 22(0.148)-14(0.137)-27(0.714) 14 Gulnar 1.000 14(1.000) 33 Egirdir 0.458 14(0.076)-22(0.513)-17(0.325)-31(0.086) 15 Mersin 0.180 14(0.212)-22(0.590)-31(0.198) 34 Sutculer 0.587 4(0.095)-31(0.201)-22(0.580)-28(0.124) 16 Mut 0.706 14(0.143)-27(0.857) 35 Golhisar 0.577 14(0.264)-17(0.186)-22(0.550) 17 Silifke 1.000 17(1.000) 36 Burdur 0.605 14(0.124)-22(0.188)-31(0.007)-17(0.681) 18 Tarsus 0.783 14(0.220)-17(0.699)-31(0.081) 37 Bucak 0.749 31(0.268)-14(0.390)-17(0.342) 19 Akseki 0.290 14(0.092)-27(0.908) Mean 0.687

advantageous situation to be able to realize and The most frequent measurement taken among enterprises (Turker, 2008). assess relative effects. In enterprises that are forest enterprises was the employee productivity According to the results of input-oriented BCC nonproductive in means of land productivity, there (Miraboglu, 1983). The reason for such capability model for employee productivity 9 of the 37 are similarly at least two to at most four reference was the ease to reach the data required and the enterprises were considered as efficient (Table 6). groups they are involved in. results possessing great importance for the These are Kadirli, Gulnar, Silifke, Elmali, Korkmaz et al. 4529

Table 7. Total productivity scores (TPS) of enterprises.

No DMUs TPS Reference group (lambda weights ) No DMUs TPS Reference group (lambda weights ) 1 Adana 1.000 1(1.000) 20 Alanya 0.737 25(0.458)-4(0.023)-24(0.287)-22(0.211)-30(0.021) 2 Feke 1.000 2(1.000) 21 Antalya 0.502 30(0.078)-12(0.138)-25(0.039)-23(0.015)-29(0.562)-22(0.168) 3 Kozan 0.923 14(0.244)-30(0.035)-31(0.084)-4(0.637) 22 Elmali 1.000 22(1.000) 4 Kadirli 1.000 3(1.000) 23 Finike 1.000 23(1.000) 5 Pozanti 0.768 4(0.563)-30(0.195)-22(0.242) 24 Gazipasa 1.000 24(1.000) 6 Osmaniye 0.921 4(0.680)-28(0.038)-30(0.148)-31(0.018)-12(0.115) 25 Gundogmus 1.000 25(1.000) 7 Pos 1.000 7(1.000) 26 Kas 0.640 30(0.094)-14(0.059)-22(0.258)-25(0.247)-29(0.340) 8 Saimbeyli 0.730 4(0.633)-30(0.133)-23(0.025)-12(0.003)-29(0.206) 27 Korkuteli 1.000 27(1.000) 9 Karaisali 1.000 9(1.000) 28 Kumluca 1.000 28(1.000) 10 Yahyali 1.000 10(1.000) 29 Manavgat 1.000 29(1.000) 11 Anamur 0.886 4(0.231)-12(0.409)-30(0.075)-14(0.286) 30 Serik 1.000 30(1.000) 12 Bozyazı 1.000 12(1.000) 31 Tasagil 1.000 31(1.000) 13 Erdemli 0.914 4(0.219)-30(0.049)-22(0.371)-18(0.113)-29(0.248) 32 Isparta 0.657 30(0.079)-1(0.114)-4(0.232)-27(0.575) 14 Gulnar 1.000 14(1.000) 33 Egirdir 0.738 27(0.271)-4(0.434)-30(0.295) 15 Mersin 0.870 4(0.651)-30(0.251)-14(0.002)-12(0.095) 34 Sutculer 0.858 30(0.379)-12(0.307)-4(0.219)-23(0.095) 16 Mut 0.870 4(0.133)-27(0.212)-14(0.074)-22(0.581) 35 Golhisar 0.767 25(0.021)-14(0.184)-4(0.162)-12(0.146)-22(0.435)-30(0.053) 17 Silifke 1.000 17(1.000) 36 Burdur 0.666 22(0.207)-30(0.089)-27(0.218)-4(0.325)-14(0.161) 18 Tarsus 1.000 18(1.000) 37 Bucak 0.921 31(0.143)-9(0.061)-12(0.349)-14(0.447) 19 Akseki 0.589 30(0.108)-29(0.085)-25(0.586)-22(0.220) Mean 0.891

Gundogmus, Korkuteli, Kumluca, Serik, Tasagil directorates. And moreover, this was also the Total productivity measurement forest enterprises. Therefore, 24% of the reason why Adana forest enterprise had a low enterprises are assessed to be efficient in means score though it was efficient in means of land and The total productivity is gained with the ratio of the of employee productivity. The lowest score in capital measures (EPS = 0.188). In a study total production to the total inputs used in the means of the employee productivity was the realized by Turker and Turker (1999) similar production process (Turker, 2008). All input Antalya Forest Enterprise with a value of EPS = results were found in a study realized at the Black factors used in this study to measure the 0.114 whilst the average value was determined as Sea Region in Turkey. The over employment in efficiencies of land, capital and employee 0.687. Especially the enterprises that are in the public sectors are considered to be based on productivity were also used to measure the total cities bound to the regional directorates had low political strategies. productivity. scores. The forest enterprises involved in the In Table 6 you can see the enterprises According to the results of input-oriented BCC region of the study are bound to four different reference groups with low scores in means of their model for total productivity 19 of the 37 regional directorates of which are Adana, Mersin, capitals and their lamda weights. For instance the enterprises were considered as productive (Table Antalya and Isparta. The employee productivities reference enterprises of Antalya are as Gulnar (λ 7). Therefore, it can be said that almost half of the of these forest enterprises are low due to the high = 0.106), Elmalı (λ =0.684) and Korkuteli (λ = enterprises are efficient in means of total factors. employment rates at the center of the regional 0.210). Antalya was the enterprise with the lowest scores 4530 Afr. J. Agric. Res.

Figure 2. Productive and non-productive forest enterprises according to total productivity.

in means of total productivity (TPS = 0.502). The average Comparison of different productivities according to value gained for the total productivity was 0.891. As can DEA model be seen in Figure 2 an even running distribution was seen among enterprises. Only the whole of the The summary of the study based on the DEA model can enterprises bound to Isparta Regional Forest Directorate be seen in Table 9. In terms of total productivity, 19 forest were defined as nonproductive. enterprises were productive, while only 7 enterprises The increase seen in the efficient enterprises have also were productive in terms of land productivity.The highest increased the amount of enterprises involved in the value gained in means of efficient enterprises were 19 reference groups. The reference enterprises of Antalya whilst the lowest scores were gained in means of land are as Serik (λ = 0.078), Bozyazi (λ = 0.138), productive efficiency. At the same time, the more Gundogmus (λ = 0.039), Finike (λ = 0.015), Manavgat (λ enterprises under 0.5 efficiency score were seen in = 0.562) and Elmalı (λ = 0.168). employee productivity. In conclusion, the over When the minimum inputs required for the enterprises employment reality in these enterprises needs to be to be considered as productive were examined (Table 8); reduced in some way. the greatest change was seen in to be in the amounts of The total productivity scores were higher when the employee (with a rate of 40.73%). Such decrease is compared with the scores of land, capital and employee obviously going to reduce the amount of fees paid as (Table 9). This can be explained via the different well. input/output values and other factors that are comparable Furthermore, some of the enterprises need to narrow among themselves. Thus, within such scope, the partial the land under their responsibility. This is so due to the productivity scores can be somehow improved in order to fact that the enterprises are unable to make use of the provide efficiency in total productivity scores as a whole. whole land under their responsibilities. The capital used According to Table 9, the amount of productive and the production expenses that require to be enterprises in means of land productivity was 7 and the decreased are listed among the other factors. average efficiency scores were as 0.714. This means that Korkmaz et al. 4531

Table 8. The minimum change rates in their inputs to be productive of non-productive enterprises.

Town Capital (%) Employee costs Production costs Total area The number of employee Kozan 7.73 37.19 31.35 28.91 34.08 Pozantı 23.24 26.28 39.24 87.36 23.24 Osmaniye 7.92 26.52 38.44 7.92 23.34 Saimbeyli 26.96 31.83 26.96 26.96 45.94 Anamur 15.15 48.99 11.37 11.37 26.20 Erdemli 8.62 9.46 13.11 8.62 23.50 Mersin 12.96 71.27 27.12 13.80 68.40 Mut 13.05 13.05 13.05 30.02 43.62 Akseki 41.06 53.94 41.06 41.06 81.26 Alanya 40.39 26.32 26.32 26.32 38.79 Antalya 49.79 80.46 49.79 49.79 84.72 Kaİ 35.97 45.38 37.64 35.97 35.97 Isparta 67.06 40.30 34.35 44.96 66.67 E₣irdir 42.61 37.79 26.15 63.84 73.99 Sütçüler 27.58 19.69 14.25 14.25 71.83 Gölhisar 24.48 23.28 23.28 23.28 67.38 Burdur 48.26 33.40 33.40 44.85 67.52 Bucak 7.94 14.63 18.98 7.94 67.74 Total change 12.56 26.16 12.83 24.42 40.73

Table 9. Summary results according to DEA model.

Variable Land Capital Employee Total The number of productive enterprises 7 10 9 19 The average productivity 0.714 0.786 0.687 0.891 The number of non-productive enterprises 30 27 28 18 The average productivity of non-productive enterprises 0.647 0.706 0.586 0.775 Minimum productivity score of non-productive enterprises 0.360 0.404 0.114 0.502 Maximum productivity score of non-productive enterprises 0.971 0.941 0.832 0.923

all enterprises have to reduce their amounts of input with record and a tracking system is of great importance that a rate of 28.6% to be graded as productive. Similarly, the requires to be settled. values for factors of the capital, employee and total The fact gained through this study, referring to the productivity are respectively as 21.4, 31.3 and 10.9%. inefficiency of enterprises in means of employee productivity rates as a result of over employment, obviously pointed out strategies to be followed and Conclusion revised. Moreover, it was also pointed out that the importance of any process that will provide the The DEA models featured proved to be efficient when improvement of efficiency values of the forest enterprise measuring the productivity values at forest enterprises. shall be taken with great care and that any decision This is so because the DEA model is capable of relative to the productivity of an enterprise shall be comparing the values gained in partial and total properly held in accordance with the optimum sizes measurements, which may also determine the most and considering the enterprises. least efficient enterprises. At such point, the most important issue is the fact that the land and tree values of the enterprises lacking in the documentary and balance REFERENCES sheets of the relative enterprises which prevent Akesen A, Ekizoglu E (2010). Forestry. In: Akesen A, Ekizoglu (eds) assessing the capital productive factors efficiently, are Forest Policy, TOD Pub No: 6, ISBN 978-9944-0048-3-1, Ankara, investigated. For issues to be handled and removed, a Turkey, pp. 19-36. 4532 Afr. J. Agric. Res.

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