Introduction

In the northern forests of Iran, the management costs in various phases of forest utilization are considerably complicated, specifically in terms of the felling or harvesting phase, and this has highlighted the importance of managing costs. The factor which increases the difficulty of cost management is time-consuming manual cutting which will lead to higher environmental damages. Nowadays, one of the key purposes of the forest production units is to decrease costs and increase the efficiency of the utilization systems. An examination of methods applied in the past are needed so as to access the principal approaches. Moreover, according to recent mechanization developments and various utilization applications, the measurement of the efficiency of forest harvesting machines and decline of utilization costs are vital to determine (Naghdi and Mohammadi 2009). Among all of the utilization phases, harvesting as the initial phase has a critically important effect on the following phases. In this initial stage, in order to harvest the trees, skilled and trained workers must be employed to ensure harvesting results in the least damage to the residual stand and the regeneration. Nearly 40% of tree volume would be wasted (Murphy and Twaddle 1986) maybe because of untrained workers. Harvesting by chainsaw is one of the specific work components and has a special importance in that it must be carried out in a certain period of time (Nikooy et al. 2007). It has been shown that manual harvesting compared to mechanized methods leads to more damage to harvested logs (Greene and McNeel 1987). The main goal of the harvesting worker is to decrease costs and reduce damage from utilization operations. Therefore, the entire components of the utilization system must be evaluated and clarified. Harvesting is one of the components done with the help of chainsaws in Iran (Sobhani et al. 2007). Through previous studies, a mathematic model has been developed to predict the working time of machines and to compare the functionality of the utilization system and machines. Recent research on the analysis of the various costs of utilization has shown that consideration of harvesting costs is important. In recent research, Nikooy et al. (2007) examined the production costs of a cutting crew (using chainsaws) in the Asalam forests of Guilan province. The mathematic model of the cutting time prediction was developed under the influence of certain variables, including tree diameter, distance between trees for harvesting, and the longitudinal slope per cutting cycle. In another study, Ettehadi and Majnounian (2010), in order to define qualitative and quantitative wood loss from harvesting with chainsaws in Kheirood forest, Nowshahr County, concluded that close supervision as well as thorough training of the workers could reduce wood loss significantly. Due to the fact that utilization operations, particularly the harvesting phase is potentially hazardous, training the utilization working team must be emphasized. Fathi et al. (2011) in the Kheirood Forest suggested that the production rate of harvesting by chainsaw with delay time and without, was 15.6 m3/h (9.6 trees per hour) and 86.2 m3/h (16 trees per hour), respectively. This study also showed that by increasing diameter, the production rate without delay time had exponentially increased and accordingly, the cost of production decreased at the same rate. Wang et al. (2004), in order to examine the efficiency and cost of harvesting with chainsaws compared with logging with a cable skidder in broad-leaved forests of Appalachia in the USA, indicated that the cutting time of each tree was mostly influenced by diameter and the distance of trees to each other. Zeljko and Jurij (2005), in order to access the most effective model to improve the efficiency of utilization teams working in Croatian forests, concluded that in the cutting stage, the average standard time for each person in the team (consisting of five members), of which two were chainsaw operators, was 25.29 m3/min. The average chainsaw efficiency for each member was 4.28 m3/day.

Li et al. (2006) examined the production and cost of three utilization systems in central Appalachia and noted that there had been several effective variables associated with chainsaw cutting, including tree diameter and distance between trees. Furthermore, the production cost per unit of chainsaw was notably higher compared to the Feller-Buncher and Harvester machines.

Our study aimed to examine the production rate and costs of various steps of harvesting a tree in a forest operation as well as to identify various approaches to prevent extra costs in forestry plans.

Materials and methods

Study area

Vaston District Forest is approximately 1611 ha and located between 53°06′52″ and 53°10′55″ eastern longitude and 36°02′18″ and 36°18′13″ northern latitude (Fig. 1). The research area covers 242 ha (Anonymous 2005).

Fig. 1
figure 1

Study area (Vaston district in Mazandaran province)

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Study method

Continuous time study was applied in this research, i.e., the starting time of work and beginning time of each phase were recorded. After the end of the work period, the total time and consumed times for each phase were measured. The chainsaw was a STIHL 070 and the work crew consisted of a chainsaw operator and co-worker. The chainsaw operator moved along the contour lines to the marked trees. In the first phase, before cutting down the marked trees, the felling direction of the tree was determined with regards to: (1) direction of leaning; (2) the protection of the remaining stand; (3) the maintenance of individual tree health; (4) the conservation of existing regeneration; and (5) the consideration of skid trails and felling methods. After any tree leaning and felling direction was identified, the chainsaw operator cut the tree. In the second stage, during the cutting phase done by the cutting crew without guidance to the chainsaw operator, the harvesting components and any delays were recorded. In the third phase, after the cutting was finished, the azimuth of the harvested tree was recorded. After the field work, a costing model suggested by the National Forest and Rangeland Organization was used to calculate the cutting cost of each tree taking into account with and without delay time (Fig. 2).

Fig. 2
figure 2

Frequency distribution of sample trees based on species

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Statistical calculations used software SPSS, as well as regression and correlation models to investigate the difference between the real leaning degree and the defined one, the difference between the defined leaning degree and the felling degree, and the difference between the real leaning degree and felling degree, direction, volume and diameter.

Results

Among all sample trees, Fagus orientalis Lipsky, Carpinus betulus L. and other species were 27.4, 54.7 and 17.9%, respectively.

Considering that the Pearson correlation coefficient (0.9) was higher than the default value (0.05), the null hypothesis (lack of relationship between cutting time and the difference of real leaning and felling degrees) was accepted.

The Pearson correlation coefficient (0.12) was higher than the default value (0.05), the null hypothesis (lack of relationship between cutting time and the difference of real and defined leaning degrees) was accepted (Table 1).

Table 1 Significant relationships between the research components
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A cutting or felling error reflects the difference of the angle between the defined direction and the fallen tree direction, considering the significance level of Pearson correlation coefficient test (0.00) which is lower than the default value (0.05). In other words, there is a relationship between the difference of defined leaning and felling degrees and the difference of real and defined leaning degrees. This indicates that the harvesting team under-performed in felling the tree along the defined direction since with the increase of the difference between real and defined tree leaning, the value of felling error rose as well and this is meaningful (Table 1).

Regarding the significance level of the Pearson correlation coefficient test (0.01) which was lower than the default rate (0.05), it illustrates the relationship between the difference of real leaning and felling degrees and real leaning and defined leaning degrees. With respect to the Pearson correlation coefficient test (0.19) which was higher than the default value (0.05) and representing the null hypothesis (lack of correlation between the difference of real leaning and felling degrees and difference of defined and felling degrees) it was accepted. With regards to the significance level of Pearson correlation coefficient test (0.02) which is lower than default value (0.05), there is a significant relationship between type of species and cutting time (Table 1).

About 65% of tree cutting time variance could be determined by a linear relationship (R2 = 0.65) with prediction variables (Tables 2, 3).

Table 2 Analysis of variance of the effect of independent variables
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Table 3 Coefficients of independent variables of cutting time
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Tree cutting time depends on the volume increase and reduction of tree leaning and felling degrees (Table 3). In the regression equation below, X1 and X2 are the difference of tree leaning and felling and tree volume, respectively.

$$ {\text{Y}} = 168.9 - 0.14{\text{X}}_{1} + 0.7{\text{X}}_{2} $$
(1)

The results indicate that costs of the planned cutting method were $0.36 per m3 and of the common cutting were $0.64 per m3. The total planned and normal hourly costs of cutting and bucking were $4.94 and the total available hourly costs of cutting and bucking was $9.61.

Discussion

Felling trees in an accurate direction is extremely important. If a tree was felled in an unpredictable direction, it may damage remaining trees and regeneration. Slight damage may be negligible, although any level of damage is to be avoided. Since damage caused by felling and incorrect skidding to the residual stand could lead to a decrease in a tree’s resistance to decay and to various diseases (Rice et al. 2001). In conditions which an average error of leading each tree to fall would be ±35°, the risk of damage to the remaining stand will intensify (Krueger 2004). In Table 1, there is no meaningful difference between cutting time and the difference of defined tree leaning and felling degrees. It is notable that the chainsaw operator either did not spend sufficient time on harvesting or lacked the expertise to fell trees along their defined directions in a certain time. Here the defined direction to fell the tree did not affect cutting time. This finding is in agreement to ones reported by Ershadifar et al. (2011). None of the cutting crews accomplished direction of cutting and there was no correlation between cutting time and difference of real leaning and felling degrees (Table 1). Trees leaning and the felling direction illustrate that the chainsaw group lacked essential expertise and did not apply proper tools to fall trees in the correct direction. This factor did not also have any effects on cutting time. Moreover, there were no observed correlations between cutting time and difference of real and defined tree leaning degrees (Table 1). In other words, the chainsaw operator and other crew members did not possess sufficient skills or practical experience to fell trees along the defined direction. According to Table 1, there is a correlation between the difference of defined leaning and felling degrees and the difference of real and defined leaning degrees. Thus, based on tree leaning and the defined direction to fall it, it is obvious that trees were fallen along defined directions and also shows that in this case, the chainsaw crew accomplished more precisely and used more leading tools and equipment. So, there was a relationship between difference of real tree leaning and felling degrees and difference of real and defined tree leaning degrees (Table 1). According to the real and defined tree leanings, it is notable that the chainsaw crew could fell trees more precisely along the defined side, revealing that there was a significant relationship between these factors. However, there was no relationship between the difference of real leaning and felling degrees and the difference of defined leaning and felling degrees (Table 1), indicating that the chainsaw team lacked crucial skills. Therefore, there was no marked relationship between defined direction and tree felling and real tree leaning. In mechanized cutting systems, trees could be harvested along any directions by a Feller-Buncher machine. Damage to the stand is minimal. In addition, if the cutter part of the Feller-Buncher is equipped with a chainsaw instead of a blade, there will be less damage to the trunk (Han and Kellogg 2000). In practical terms, in the Caspian Hyrcanian forests of Iran, virtually 50% of the trees could be fallen along their leaning direction without any impediments. Further, nearly 40% of other trees could be fallen with the help of wedges and cutting techniques along lateral directions. It is necessary to fall 8–9% of trees using wedges and 1–2% using hand winches against the tree leaning direction (Lotfalian 2012). The cutting time depends on the increase in volume and decrease in the difference of real leaning and felling degrees (Table 3). Rizvandi and Jorgholami (2012) suggested that multivariable regression equations to conventionally calculate cutting time were a function of tree diameters and the distance between fallen tree variables. However, in the planned method (by forest engineers on the contrary of common method which was acted by chainsaw operator without any former plan), time was a function of diameter and felling direction variables. Additionally, in two common and planned methods, when diameters increased, the costs of production per volume unit declined exponentially. Kato (1993) showed that tree diameter was a significant factor in harvesting and if the volume of a tree were high, costs increased and conversely, when volume was less costs decrease.

Nikooy et al. (2007), in the Asalam forests in Gilan province, developed a mathematical model to predict cutting time which was dependent on certain variables, including diameter, distance between trees to be felled and the longitudinal slope per cycle. Fathi et al. (2011) examined the production level produced using chainsaws and showed that in the cutting step, production levels increased exponentially if diameters rose. Rizvandi and Jorgholami (2012) showed that tree cutting with the planned method is more costly compared to the common method. While the present findings show that costs of the planned cuttings are $0.36 per m3, they are $ 0.64 per m3 with the common method. The total planned and common hourly costs related to cutting and bucking were $4.94 and the total available hourly costs were $9.61. Sobhani et al. (2007) illustrated that in the Kheirood-Kenar forests at Nowshahr city, daily costs for each chainsaw crew, including one chainsaw operator and two participants, was estimated at $14.40. Nikooy et al. (2007) suggested that in the Asalam Forests in Gilan province, the harvesting costs per tree with delay (representing lack of appropriate planning) and without delay were $1.08 and $0.85 respectively.

Rizvandi and Jorgholami (2012) showed that with the planned harvesting method, the cutting time was higher compared to the common method and subsequently production costs were significantly higher. However, although planned cutting may increase utilization costs, these costs will be compensated if the efficiency of the skidding operation increases and damage to the remaining stand is diminished. The costs of production per unit in the planned method with and without delay time were $2.85 and $2.14 per m3 compared to the common method which were $1.95 and $1.38 per m3, respectively. There are an array of factors that affect production and efficiency of harvesting. Many of these are hidden and some cannot be quantified. In this study, the rate of production with the planned method was higher than the production level in the common method. Rizvandi and Jorgholami (2012) suggested that the hourly production of harvesting with the common method was higher than the one estimated in the present research. It was similar in the case of the planned method. Generally, production levels in the planned method were higher than with the common method. Comparisons indicated that cutting crews did not achieve tree cutting along the defined direction. This was partly due to the fact that workers were not properly equipped with modern techniques of tree cutting and to the use of traditional methods which led to the failure to drop trees in defined directions. In most industrialized countries, work science has been the foundation for dramatic developments for nearly 50 years. Unfortunately in Iran, this area of research has not received significant attention. Without providing a worktable based on precise and accurate principles, operational planning would face basic problems. In this study, because of the direct relationship between cutting times, tree diameters, and slopes, it is recommended that the harvest of large diameter trees on steep slopes be carried out by experienced chainsaw operators, and to employ less experienced ones on lesser sloped sites. Certain solutions could be implemented so as to decrease wood waste resulting from harvesting. For example, training programs could be providing for harvesting personnel for improving their skill levels, restraining from cutting in form of tree contract per tree, for training in appropriate methods to apply hand winches as well as continuous supervision of the cutting crew (Akay et al. 2006). Furthermore, trees should be fallen along forest gaps as far as possible to minimize damage to adjacent standing trees (Lamson et al. 1985). For many of utilization management units, it is important to understand variables such as diameter at breast height, volume, cutting errors, and other physical characteristics that influence costs and revenues. In this study, the aim was to provide a prediction time model for each cutting cycle and then analyze it. It focused on recognizing the importance of key factors on the harvesting phase for the management unit, providing a comprehensive schedule in order to increase productivity and control costs.