Performance evaluation of forest management plans (Case study: Iranian Caspian forests)

[Research]

Performance evaluation of forest management plans (Case study: Iranian Caspian forests)

J. Malaei Boosari¹, S. Mohammadi Limaei*¹, A. Amirteimoori²

1- Dept. of Forestry, Faculty of Natural Resources, University of Guilan, SowmehSara, Iran.

2- Dept. of Mathematics, Faculty of Basic Sciences, Islamic Azad University - Rasht branch, Rasht, Iran.

* Corresponding author’s E-mail: limaei@guilan.ac.ir

ABSTRACT

The aim of this research was to measure the relative efficiency of forest management plans in north of Iran. In order to fulfill the research, data of 12 forest management plans were collected from the financial balance sheets of Shafaroud Forest Company during a ten years period. First of all, basic Data Envelopment Analysis (DEA) models (BCC and CCR) were used to determine the efficiency. Then, due to the structure of the forest management plan, cost efficiency and revenue efficiency models based on DEA were used in order to measure the efficiency. Results indicated that 8 forest management plans were efficient based on BCC and CCR models. Furthermore, the results indicated that only one forest management plans were efficient based on cost efficiency and revenue efficiency models. These results could be due to the input oriented properties of the models, rational management and optimal use of resources.

INTRODUCTION

A forest management plan is a plan in which the amount, the location and the time of harvest along with performance duration and utilization method as well as practical and civil work is written carefully.

There are many forest management companies are involved in management of forest and wood supply in north of Iran. One of the biggest of them is Shafaroud Forest Company. This company is located in Guilan province, north of Iran. About 135000 ha of forests are managed under supervision of Shafaroud Forest Company which is located in north of Iran.  This forest is divided to 73 districts and each district is managed based on a forest management plan regulation (Shafaroud Forest Company, 2013).

One of the optimal ways of improvement and progress in forest activities is to perform the procedures and activities in these plans in order to increase efficiency. In this regard, there are different methods for measurement of efficiency of different plans in which one of the most appropriate and suitable method of measurement is data envelopment analysis (DEA). This method is a non-parametric approach that used to measure the efficiency of decision making units. In operations research and economics, DEA is used for the estimation of production frontiers. It is used to empirically measure productive efficiency of decision making units. Although DEA has a strong link to production theory in economics, the tool is also used for benchmarking in operations management, where a set of measures is selected to benchmark the performance of manufacturing and service operations. Efficiency is an important prerequisite for profitability, but maximum profits and efficiency do produce as close to the point of maximum profits as possible to ensure future productivity gains and participation. Operations working at the point of maximum profitability can offer several benefits to the wood supply system including, flexibility during periods of high and low demand, improved safety performance, improved environmental performance, and increased professionalism (Shannon, 1998).

One way to promote forestry activities in the implementation of projects and increase productivity and efficiency, is doing things right (Jason et al, 1991).

Building on the ideas of Farrell (1957), the seminal work "Measuring the efficiency of decision making units" by Charnes et al., (1978) applies linear programming to estimate an empirical production technology frontier for the first time. Therefore, DEA was introduced by Charnes et al., (1978) as an efficient model to evaluate the performance of organizations in the field of operations research. 

Many papers have been presented about DEA and various models are introduced for evaluating the performance of homogenous decision making units. DEA is used to measure the efficiency of forest industries in the various countries. For example, the use of DEA in the analysis of forestry area, forest management, operation, sawmills factories, paper mills and etc. Non-parametric models can easily combine several inputs and outputs regardless of their market values. (Kao & Yang, 1991, 1992) were in fact among the first people who used DEA to measure the efficiency of forest industries. Their research began to establish new branches in performance studies in forestry development to future, for example Joro and Viitala (1999), and Bogetoft et al (2003) on forest management, Lebel & Stuart (1998), and Hailu & Veeman (2003) on forest exploitation, Fotiou (2000), Nyrud & Baardsen (2008), Salehirad & Sowlati (2005) in sawmill factories.

Mohammadi Limaei (2013) investigated the efficiency of 14 Iranian forest companies and forest management units in north of Iran. Efficiency of the companies was estimated using a traditional DEA model and a two-stage DEA model.

Zadmirzaei & Mohammadi Limaei (2013) investigated the efficiency of Mazandaran Wood and Paper Company using DEA in Iran. Network DEA models with parallel structure were used to evaluate and measure its performance. Results showed that the company in all studied years had good performances according to the parallel models in constant and variable returns to scale (CCR, BCC models) and the scale efficiency (SE). There is not any research related using DEA in a different forest management plans at a company so far in Iran. Therefore, the aim of this research is to investigate the performance evaluation and determine the relative efficiency of Shafaroud Forest Company using DEA models to disseminate the necessary information for manager of forest plans until they will be able to adjust the units’ operating scale and become more efficient through growth.

MATERIAL AND METHODS

Study area

Data collection

 In this study the required data were collected from financial balance sheets and the profit-loss sheets of Shafaroud Forest Company. All forest management plans managed for 10 years periods (2001 to 2011). Nominal data of 12 forest management plan including 4 inputs (fixed costs, variable costs, area of forest management plan and stock) and 3 outputs (total revenue, profit and amount of wood production) were collected (Table 1). The nominal data were deflated using Consumer Price Index (CPI) of Iran on the base year of 2004 (Table 2).

Methods

CCR Model

Performance evaluation is an important task for a decision making unit (DMU) to find its weaknesses so that subsequent improvements can be made. DEA model introduced by Charnes, Cooper and Rhodes in 1978 is called CCR model. Since the pioneering work of Charnes et al. (1978), DEA has demonstrated to

be an effective technique for measuring the relative efficiency of a set of DMUs which utilize the same inputs to produce the same outputs. Suppose there are n DMUs. The kth DMU utilizes m inputs , m to produce s outputs , r = 1,. . . , s. Its efficiency  is calculated via the following CCR model (Charnes et al., 1978):

= amount of input i used by unit j,

= amount of output r produced by unit j, = the weight given to input i.Where  and  are the most favorable multipliers to be applied to the rth output and ith input for DMUk in

calculating its efficiency  and e is a small non- Archimedean quantity (Charnes et al., 1978; Charnes and Cooper, 1984) which prohibits any input/output factor to be ignored. CCR model is a constant return to scale model.

                                                                      (1)

Table 1. Forest management plans in Shafaroud Forest Company, north of Iran.

Table 2. The real (deflated) data.

BCC model

BCC introduced by Banker, Chames and Cooper (1984). The input-oriented BCC model evaluates the efficiency of  {o=1…n) by solving the following linear program form (Mehrgan, 2004):

                                                                              (2)

BCC model is a variable return to scale model. The difference between this model and CCR model is a free variable (W). In BCC model for each unit we can determine the W variable returns to scale.

If w<0, we have decreasing return to scale.

If w=0, we have constant return to scale.

If w>0, we have increasing return to scale.

Data weighting

In order to determine the appropriate weight of the data in DEA model, the questionnaires were used.

Questionnaires were prepared in order to weight the input and output data  ​​due to differences in their importance based on the interviewers views at different forest manage-ment plans. Therefore, 35 questionnaires were distributed and completed by the profess-ionals. The score of weight was from 0 to 100% in questionnaires.

The average weight gained from questionnaires is given in Table 3.

Table 3. Input and output weight of different plans.

Cost efficiency model

The cost efficiency model is presented such as:

(3)

Revenue efficiency model

The revenue efficiency model is given below:

(4)

Where  and  are the unit price of the input  and unit price of the output  of , respectively. These price data may vary from one  to another. The cost efficiency and revenue efficiency of  is defined as:

And

The cost and revenue efficiency scores are within the range of 0 and 1 (Zhu, 2008).

Data analysis

In order to do the analysis, first of all the basic DEA models (BCC and CCR) were analyzed using DEA Solver software (Cooper et al, 2007). Then, for advanced models (cost efficiency and revenue efficiency) GAMS software was used (Rosenthal, 1988.).

RESULTS

Results of BCC and CCR models are shown in Tables 4 and 5.

Results indicated that the forest management plans numbers (2, 4, 7, 9, 10 and 12) are efficient and their score efficiency is 1 and the rest are deficient.

As it shown in Table 5, plans numbers (2, 3, 4, 6, 7, 9, 10 and 12) are effective and the rest are deficient. Table 6, shows the input-oriented CCR model. Input and output must be modified for each forest management plan in order to reach the efficiency frontier. It means that each plan should decrease its input and increase its output. 

For example plan number 1 is not efficient and in order to reach the efficiency we should change all of inputs. For instance, the first entry is the fixed costs and the amount is 1705 Iranian million Rials (Table 2) and the company should decrease 82.05% of this input in order to be efficient.

The second input which includes the variable cost and its value is 1247 Iranian million Rials (Table 2) and the company should decrease 83.58%of this input in order to be efficient and so on. As it shown in Table 5, plans (2, 4, 7, 9, 10 and 12) are efficient.

Table 7 shows how much ineffective plans in term of percentage have to change to achieve the efficiency frontier model (BCC).

Table 4. Results of CCR model.

Table 5. Results of BCC model.

Table 6. The percentage changes of deficient plans in order to achieve the efficiency frontier model (CCR).

Table7. Percentage change ineffective Plans to achieve the efficiency frontier model (BCC).

The results of cost efficiency and revenue efficiency models are shown in Table 8. Plans number 6, 7 and 12 have efficiency score of 1 or efficient in cost efficiency model. Furthermore, Plans number 3, 4, 6 and 9 are efficient in revenue efficiency model. Results also show that just plan number 6 is efficient in both cost efficiency and revenue efficiency model.

Table 8. Results of cost efficiency and revenue efficiency models.