ENVIRONMENTAL IMPACT OF DIFFERENT LOGGING METHODS IN THE BANKHEAD NATIONAL FOREST, ALABAMA: A COMPARATIVE ANALYSIS. by THOMAS MBELI.

Transcription

1 ENVIRONMENTAL IMPACT OF DIFFERENT LOGGING METHODS IN THE BANKHEAD NATIONAL FOREST, ALABAMA: A COMPARATIVE ANALYSIS. by THOMAS MBELI. TENYAH A THESIS Submitted in Partial fulfillment for the requirements for the degree of Master of Science in the Department of Natural Resources and Environmental Sciences in the School of Graduate Studies Alabama A&M University Normal, AL May 2009

2 Submitted by THOMAS MBELI TENYAH in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE specializing in FORESTRY/GIS. Accepted on behalf of the Faculty of the Graduate School by the Thesis Committee: Major Advisor Dean of the Graduate School Date ii

3 Copyright by THOMAS MBELI TENYAH 2009 iii

4 This work is dedicated to my late father Jonas Tenyah Werengoh and to my family for supporting me through the program. iv

5 ENVIRONMENTAL IMPACT OF DIFFERENT LOGGING METHODS IN THE BANKHEAD NATIONAL FOREST, ALABAMA: A COMPARATIVE ANALYSIS Thomas Mbeli Tenyah, M.S. Alabama A&M University, pp. Thesis Advisor: Dr. Kozma Naka The main goal of our study was to evaluate the environmental impacts of two different harvesting methods on site characteristics. Two different methods, the cut-tolength and the tree-length logging systems were compared. The fundamental question was which of these two harvesting methods causes less soil disturbances, least residual tree damage, and was more productive and cost effective than the other. This question was answered by examining the following specific objectives: (1) compare the effects of the tree-length and cut-to-length harvesting methods on the soil surface and physical properties of the harvested areas, (2) compare the residual tree damage between these two harvesting systems, and (3) compare the productivity and cost-effectiveness of the two harvesting systems. To attain these objectives, the environmental impacts caused by these harvesting operations were measured and compared through statistical analysis. A visual inspection was conducted before and after harvesting. Pre-harvest data indicated about 98 percent of the treatment area was undisturbed while post-harvest analysis showed about 79 percent was disturbed. Statistical analysis using Generalized Linear Model (GLM) showed no significant difference in the soil disturbance between the two logging methods. However, there was significant variation in the plot to plot disturbance among the various soil disturbance classes disturbed with litter (DC2), disturbed with soil exposed (DC3), v

6 disturbed with rock and stumps (DC5), and disturbed with slash (DC6). There was also subplot variation in the amount of undisturbed area (DC1), disturbed with litter (DC2), and disturbed with slash (DC6). Soil compaction analysis did not indicate any significant difference between the two harvesting methods, but there was a significant difference between the heavily and lightly impacted areas within the treatments and there was significant difference between the disturbed and undisturbed areas. Residual stand damage was determined, by counting the number of injured trees, and assigning a damage classification before and after harvesting. Statistical analysis results obtained indicated a significant difference between the two systems. The cut-to-length (CTL) harvesting system had a higher incident of residual tree damage than the tree-length (TL) harvesting system. There was a considerable difference in the productivity and costs analysis of the two harvesting methods with the CTL system costing less to own and operate than the TL system but the TL machines had a higher productivity rate per productive machine hour (PMH) than those of the CTL system. However, the TL harvesting method yielded a higher net gain to the operator than the CTL system. KEY WORD: harvesting methods, soil disturbance, residual stand damage, machine productivity and cost. vi

7 TABLE OF CONTENTS CERTIFICATE OF APPROVAL... ii DEDICATION...iv ACKNOWLEDGEMENT...xi CHAPTER I 1.1 Statement of the problem Significance of Study Purpose and Objectives... 6 CHAPETER II 2.1 Environmental Impacts of Logging Operations Soil Impacts Residual Stand Damage Productivity and Cost CHAPTER III MATERIAL AND METHODS Study Site Background and Management History of the Research Site Experimental Design Soil Impact vii

8 3.4.1 Visual Estimation: Soil Compaction: Soil Disturbance Soil Compaction CHAPTER IV RESIDUAL STAND DAMAGE Data collection Statistical Analysis RESULTS CHAPTER V PRODUCTIVITY AND COSTS Data Collection Data Calculations and Analysis CHAPTER VI SUMMARY AND CONCLUSIONS RECOMMENDATIONS APPENDICES BIBLIOGRAPHY VITA viii

9 LIST OF TABLES Table Page 1: Experimental plot area sizes : Soil Disturbance classes : Percentage of area with surface soil disturbance : Percentage of mean soil disturbance for each logging method : ANOVA for Surface soil disturbance classes : Percent variance accounted for by sources of variance : Percentage Means by Orientation per each soil disturbance classes : Average soil compaction measured in pounds per square inch (psi) : ANOVA for Soil Compaction : Number of trees damage in each class per plot and total number of trees : Residual tree damage of each harvesting method per 100 acres : Number of residual tree damaged per treatment plot (Per 100 acres) : Contingency table for residual tree damage : Machine Productivity : Total Machine Utilization : Machine rate calculations for TL harvesting system : Operator performance ix

10 LIST OF FIGURES Figure Pages 1: Location of Bankhead National Forest : Distribution of study plots in Bankhead National Forest : Distribution of residual tree damage in to the various classes : Use ArcGIS to determine the number of points on a treatment area : Selecting Six Sampling plots of 9 points each (total = 54) : Using DME to measure soil disturbance 10m in each direction : Movement of the Harvester and Forwarder inside Somerville treatment : Skidder..85 9: Skidder : Feller Buncher : Forwarder : Shows pictures of landing areas in the TL system one year after harvesting has been completed : Shows pictures of center of main activity in the CTL system one year after harvesting has been completed.93 x

11 ACKNOWLEDGEMENT First I want to thank the Almighty for giving me the strength to accomplish this work. I would like to express my appreciation to Dr. Kozma Naka for his tireless advice and assistance throughout the course of this study. I am also thankful to members of my advisory committee, Dr. Rory Fraser, Dr. Wubishet Tadesse, and Dr. Monday Mbila for their valuable suggestions and willingness to serve on committee and not forgetting Dr. George Brown and Dr. Wang for their tremendous advice on my data analysis. I am also grateful to the entire faculty members, colleagues, friends, and under-graduate students who assisted me in one way or the other throughout the period of this study. Many thanks also go to the loggers Bobby, Kelly, and Donnie for their cooperation and the staff of the Forest Service. Funds for this work came from the School of Agriculture and Environmental Sciences, through National Science Foundation (NSF) Grant # HRD xi

12 CHAPTER I INTRODUCTION All forest operations, especially logging or timber harvesting, have environmental impacts (Putz et al., 2000). Over the past thirty years, there has been greater scrutiny of logging operations because of public concerns about the environmental and aesthetic impacts. While some of these effects might be intended, some others might be undesirable consequences. For instance, thinning of overstocked stands reduces fuel for potential forest fires, and provides wood for the timber industry. In addition to harvesting timber, many logging operations improves the health of the remaining stands by removing damaged or diseased trees, open the canopy to promote healthier trees, and promote better habitat for wildlife. (de Wasseige and Defourny, 2004). At the same time, logging operations can alter species composition and structure of the forest while causing soil disturbance, damage to residual stands, and disturbance of wildlife habitat. Logging could also potentially endanger the natural regenerative potential of the forest (de Wasseige and Defourny, 2004) 1

13 As a result, soil disturbance and residual stand damage control is required to retain the potential for natural forest regeneration, especially of the desired commercial species (Webb, 1997). The extent of damage is largely determined by the felling intensity and it is also highly dependent on the type of equipment used and the logging method employed (Hendrison, 1990). Forest managers therefore are often faced with difficult decisions about harvesting methods, type of equipment, and intensity of harvesting. Usually, their decisions are based on their estimation of extraction cost, damage to residual trees, the extent and severity of soil disturbance, and the sustainability of the forest (Landsberg et al., 2003; de Wasseige and Defourny, 2004). To many loggers, the choice of harvesting method is dictated by the type of wood desired at the receiving mill, the slope of the terrain, and potential returns. Often loggers choose the method that maximizes their short-term economic returns. Much of the logging controversy has been about whether there is compliance with the Best Management Practices (BMPs) which are believed to minimize environmental impacts and at the same time achieve high productivity at low cost (Lanford and Strokes, 1995). The logging operations can be carried out in different methods of which the following three are commonly used in the United States; 1) the tree-length logging (TL) system, 2) the cut-to-length (CLT) system, and 3) the skidder cable system. The TL and CTL are logging systems used in the Southeast of the United States. The TL, the most commonly used system involves the use of a feller-buncher and grapple-skidder while the CLT system includes a harvester and a forwarder (see Appendix 5). The difference between these two is that in the TL method, the trees are felled and then skidded on the forest floor to the landing area where they are delimbed 2

14 and loaded onto the trucks. Branches, wood residue, chips, and barks are accumulated in piles at roadsides or landing areas. The piles may be left standing or disposed of during or after the operation. On the other hand, in the CTL method, the trees are felled, delimbed and bucked to desired log sizes directly at the stump. As a result, slash is distributed all over the site. Beside the TL and CTL, another popular logging system used primarily in the West Coast is cable logging. The cable system requires more complex and expensive equipments and is more suitable on steep slopes and in swampy or rocky areas (Heinimann et al., 2001). Because of the fact that the TL and CTL are the only harvesting methods used in the northern region of Alabama, this study compares these two systems. Emphasis was placed on differences in soil impact and residual stand damage because they are the most common, visible, and easily measurable site characteristics observed after logging operations. Therefore estimating these environmental impacts caused by mechanized harvesting equipments can assist logging managers to evaluate the success of the harvesting operations in a sustainable forest management (Acar and Unver, 2004; Akay et al., 2006). Productivity and cost effectiveness was also measured and compared, so as to give an insight of which method cost less to operate and yield more returns to the logger. 1.1 Statement of the problem Different logging methods have been used to study the environmental impacts associated with forest operations. A number of these studies have compared one or more 3

15 of these methods against each other to see which of them causes more or less soil disturbance, soil compaction, and residual stand damage than the other. The results of some of these studies have not been consistent as they may vary depending on the site characteristics, the type of equipments used, and the personnel or labor force involved. For instance Lanford and Stokes, (1995) compared the cut-to-length and the tree-length thinning methods in southern Alabama and concluded that the tree-length method causes more soil disturbance and compacted the soil more than the cut-to-length method. Carter et al., (2006) evaluated the site impacts associated with three silvicultural methods in the upland hardwood of Alabama. Their results indicated that bulk density was highest in the clear-cut while soil compaction was more in the deferment cut. Shrestha et al., (2008) compared soil disturbance from horse and mule logging operations coupled with machines in southern United States. Their study suggested that even though horse and mule logging was labor intensive, it has low soil disturbance, less residual stand damage, and less environmentally disruptive in small scale logging operations. On the other hand, mechanized logging was very efficient for large tracts of timber, and it is often more disruptive to the soil. Limbeck-Lilienau (2003) measured stand damage from four different mechanized harvesting methods and found out that the least percentage of stand damage was generated from the cut-to-length system. However, no matter what harvesting method is used, there is always going to be some level of disturbance and damage to the environment. But the question is which of these harvesting methods provide a physically feasible, economically viable, and environmentally sound solution. Environmental soundness consists of several impacts, such as damage to the residual stand, to the soil, or adverse effects on human health and 4

16 safety (Limbeck-Lilienau, 2003). While many of these studies have been conducted to evaluate machine solution and to improve harvesting economics, the conservation of environment becomes even more and more essential in public discussion. Despite these conflicting results, the essential problem is whether there is a significant difference in environmental impacts of these logging methods. In the case of our study on the Bankhead National Forest, local public concern of these logging operations was taken in consideration, especially in regard to soil disturbance, residual trees, the type of equipment used, and the general well-being of the people who live and interact with the forest (Creed, personal communication, 14 th December, 2005). To address these issues, the TL method of logging was proposed in addition to the CTL. However little consideration was given to the various levels of impact associated to each of these harvesting methods in the planning process. This could have been due to the lack of precise and convincing knowledge about the various levels of environmental impacts caused by the various harvesting system. and no prior research comparing these two harvesting has been done on the Bankhead National Forest. 1.2 Significance of Study Public concerns about environmental impacts of logging operations and other forest practices and regulations have increased lately. This has compelled managers, planners, and forest-land owners to consider alternative harvesting technologies to minimize adverse effects on the soil, residual stand, and the entire watershed. However, to remain viable in the global wood market, woodland owners are seeking logging systems that are cost effective in meeting standards for timber stand improvement and 5

17 water quality. At the same time, loggers are facing tremendous economic pressure, working under conditions of intense competition and constricted profit margins. Nevertheless, the results of this study will provide definitive evidence to support whether there is a significant or no significant difference between the CTL and the TL harvesting systems. This will go a long way to improve decision making by forest managers thereby reducing the level of environmental impacts. 1.3 Purpose and Objectives The main goal of this study is to evaluate the impacts of two forest operations on site characteristics. This was achieved through the following specific objectives: 1) Compare the effects of the tree-length and cut-to-length harvesting methods on the soil surface and physical properties of the harvested areas. 2) Compare the residual tree damage between the TL and CTL harvesting systems. 3) Compare the productivity and cost-effectiveness of the TL and CTL harvesting systems. Our research question was which of these harvesting method causes less soil disturbance, least residual tree damage, and is more productive and cost effective? This study tested the following alternative hypotheses: H 1: The CTL system creates less soil surface disturbance than the TL system. H 2 : The CTL system has less incidences of residual tree damage than the TL system. H 3 : The CTL system has higher unit production cost than the TL system. 6

18 The obtained results enabled us to answer the research question above. 7

19 CHAPTER II LITERATURE REVIEW 2.1 Environmental Impacts of Logging Operations Environmental impact assessment of logging operations in a number of different countries clearly demonstrate that uncontrolled logging using heavy machines reduces the forest s ability to carry out vital environmental and ecological functions (Putz et al., 2000). The impacts of logging on the environment can be divided into two broad categories; 1) those caused by the timber harvesting itself, that is, the removal of trees from the forest thereby creating gaps in the forest, and 2) the impact caused by logging operations such as felling and dragging trees and maneuvering machinery in the forest. Removal of trees alters species composition, structure of the forest and can cause nutrient depletion (Putz et al., 2000). On the other hand, removal may provide opportunities for some species to grow while creating a loss of opportunity for others (de Wasseige and Defourny, 2004). Trees provide shade to streams and may alter stream temperature either by preventing the sun from shining directly on the water during the 8

20 day, or by preventing water from radiating the heat at night. When this phenomenon occurs on a large enough scale, precipitation is affected. Some changes in local rainfall patterns have resulted when large forest cover is removed (Webb, 1997). Meanwhile changes in transpiration may results in a greater intensity of rainfall, enhancing both runoff and erosion. Modern ground based logging operations require the use of heavy machinery in the forest. In some areas, roads must be built which often destroy the natural habitats, resulting in loss of biodiversity and sometimes leading to local and possibly global extinction of some endanger species (Putz et al., 2000). The use of heavy machinery in the forest can cause disturbance on soil surface and damage residual stand. It can also cause soil compaction and resistance to root growth which can eventually affects the productivity of the forest (Carter et al., 2000). Harvesting on steep slopes can lead to soil erosion, landslides, and water turbidity. Logging on saturated soils can cause ruts and change in drainage patterns. Forest machines use oils which, if not handled carefully, can cause pollution. The severity of these impacts depends on a number of factors which include the method of harvesting, the type of equipment used and intensity of harvesting, slope of the terrain, soil type, harvesting season, species and size of tree harvested, number of trees cut, tract size, and the experience of the operator. (Hendrison, 1990; Webb, 1997, and Botz et al., 2001; Shrestha et al., 2008). 2.2 Soil Impacts Soil disturbance has been identified as the principal impact of most forest operations (Rummer, 2002). It results from road or trail construction, equipment traffic, 9

21 and the dragging of material on the forest floor. The effects of these actions include physical dislocation of and loosening of the soil, litter and topsoil removal, soil compaction, or puddling, and disruption of activities of soil microorganisms. Soil compaction is the process of soil particles rearrangement into a denser state, thus reducing the air-filled fraction of soil pores. Extensive studies have been carried out on the impacts of forest management on soil characteristics especially soil compaction from harvesting equipments (McDonald et al., 2002). Most of these studies have tended to approach the problem of forest harvesting impact on the soil from two distinctive perspectives: (i) direct measurement of compaction at a series of sampling points (Perumpral, 1987; Carter et al., 2000, and Miller et al., 2001) and (ii) a stand-level estimation of visible disturbance (visual assessment) as in Landford and Strokes (1995), and Aust et al. (1998). The difference between the two approaches is that direct measurement of soil compaction provides an insight into the relationship between management or equipment related factors and soil characteristics meanwhile stand-level studies evaluate relative measures of harvested areas impacted in one of several qualitative categories. These categories are assigned based on visual estimates and are widely used to determine the level of impact that occurred during a harvesting operation (Aust et al., 1998 and McDonald et al., 2002). The investigator decides to term whether a portion is highly disturbed, moderately disturbed, lightly disturbed, or not disturbed at all. Although this method is widely used, it has certain limitations. It is highly subjective as the categorization is based on the investigator s discretion. Often, he or she is guided by the severity of the disturbance (Jensen and Visser, 2005). Furthermore, it is difficult to capture the entire area occupied by these disturbances classes. In an attempt to overcome 10

22 this difficulty, Jensen and Visser (2005) used line transects spaced 20 meters apart throughout the harvested area. These transect were placed perpendicular to the expected path of the skid trails and were used to collect both pre-harvest and post-harvest data. Even with this design, it is still difficult to collect the data, especially if the area is too large and more than one replication has to be measured. Moreover, using a graduated tape to measure the distances in the forest is difficult particularly in areas where the slash piles are heavy or the terrain is rough. Visual estimates are not enough to measure the impact of harvesting system on soil disturbance (Carter et al., and McDonald et al., 2002). This is because the procedure estimates the impact only on the soil surface and not the impact of machine trafficking on other soil characteristics such as soil compaction. Increases in soil compaction reduce the permeability of soil to air and water. The main causes of soil compaction are external loads from heavy machinery traffic (Carter et al., 2000). A typical measure of compaction is soil bulk density, expressed in grams per cubic centimeter (g/cm 3 ). The most common method for measuring compaction is to determine cone index values using static penetrometers (Herrick and Jones, 2002). Static penetrometers are designed to measure the force required to push a probe (usually a cone or blunt tip) through the soil at a constant (static) velocity. These probes rely on one or more discrete applications of kinetic energy to advance the probe. Cone indices from penetrometer data have been used to characterize soil compaction and resistance variability to tillage effects and root growth (Carter et al., 2000), and wheel traffic effect (McDonald et al., 20002). According to Fritton (1990), the values depend on cone properties (i.e. diameter, height, and included angle), as well as soil properties such bulk density, shear strength, water 11

23 content, and texture. Even though manually operated static penetrometers are widely used to measure soil compaction and resistance, they suffer from several limitations (Herrick and Jones, 2002). They are relatively expensive to buy and in the course of using them, they must be moved through the soil at a constant velocity. This procedure is always hard to achieve in most cases. Furthermore, each piece of equipment is designed for a relatively limited range of soil resistance and must be recalibrated on a regular basis in order to generate consistent and repeatable measurements. The dynamic penetrometer has been developed to overcome these limitations (Perumpral, 1987). With the dynamic penetrometer, there is no need to push the penetrometer through the soil at a constant velocity, and continuous force. As such, the dynamic penetrometers are not subject to the operator s variability since they do not rely on constant penetration velocity, and the kinetic energy applied by these devices is mechanically controlled (i.e., fixed hammer mass and drop heights). Presently, available dynamic penetrometers are relatively cheap ($200 $300) and new designs include some that are dropped onto the soil from a specific height and others that are driven into the soil with repeated hammer blows. A number of studies have been conducted to compare soil compaction on different harvesting methods caused by machine trafficking. Carter et al. (2006) compared soil disturbance patterns and associated changes in soil physical properties of clearcut, strip cut, and deferment cut in an upland hardwood stand in northern Alabama. Their findings showed less soil surface disturbances in the strip cut site. Bulk density was highest in clear-cut site while soil compaction was highest in the deferment cut. The differences in these results were related to different trafficking patterns on each site. 12

24 To summarize, the factors that affect soil disturbance include the method of harvesting, the type of equipments used which will include tire sizes, weight of the machines, and their movement in the forest. Heavy machines with large tire sizes will cause more soil disturbance, compaction, and more residual stand damage. High intensity of harvesting causes more soil disturbances than low intensity because of high trafficking. Harvesting on steep slope will not only reduce the productivity of the operator but also cause more residual stand damage. The experience of the operator greatly affects the ability to maneuver insider the trees thereby affecting the number of stand damage and the productivity of the system. 2.3 Residual Stand Damage Residual stand damage in the form of canopy removal and residual stem damage is another major impact of forest harvesting operations. It is mostly caused by the maneuvering of heavy harvesting machines around the residual trees. Clatterbuck (2006) indicated that more than 43% of the trees damaged or destroyed are caused by harvesting operations. The majority of residual stand damage is located along skid trails where most harvesting activities occur. Wounding can cause stem deformity and decay and significantly affect final crop volume and value (Solgi and Najafi, 2007). Therefore, controlling residual stand damage during logging is important in maintaining the health of the trees and it is also an important factor in retaining the potential for natural regeneration (Webb, 1997). Akay et al., (2006) reported that the damage on residual stand can be reduced by using the cut-to-length thinning systems. They also concluded 13

25 that well trained and motivated operators can be a significant factor in keeping the amount of residual stand damage under tolerable level. Residual stand damage is calculated by counting the number of standing trees whose stems were scratched and crowns destroyed during the harvesting process. In a study conducted on a small Appalachian mixed-hardwood woodlot in Virginia, Jensen and Visser (2005) calculated residual stand damage by counting the number of damaged trees that were within 2 m (1m in each side) of each transect during post-harvest sampling. This number was then used to estimate the number of damaged trees per acre. A damage class was assigned to each damaged tree. These classes were based on the size of the damage: minor stem damage (< 10 cm 2 ), major stem damage (>10 cm 2 ), minor crown damage (<1/3 crown destroyed), major crown damage (>1/3 crown destroyed), and undamaged. They found only14 trees per acre with minor stem damage and 4 trees per acre with minor crown damage and concluded that residual stand damage was minimal. No tree was found with major stem damage. The limitation of this method was that even though the size of the damage on the stem was calculated, categorization of the classes was subjective and there was no comparison to a standard or another system of harvesting. Johns et al. (1996) compared logging damage on residual stand in planned and unplanned harvesting operations in the Paragominas region of eastern Amazonia. Tree damage was found in five logging phases: tree felling, machine maneuvering, skidding logs to landing, constructing log landings, and constructing logging roads. Crown damage was calculated by adding the total number of open spaces and comparing them between the logging operations. The result showed more residual stand and crown damage in the 14

26 unplanned than planned logging operation. The limitations in this method were that they counted only the number of damage trees in each phase of the operation and little consideration was given to the intensity of the harvest and the severity of the damage. Furthermore, the distances calculated around a stump may not be all whole gaps (open area) and may not be easy to determine whether the gap was caused only by logging operations. In our study, the total crown and stem damage was considered by estimating the amount of branches destroyed as well as the degree of stem damage. 2.4 Productivity and Cost Timber harvesting costs have a significant influence on the application and potential use of any logging system (Bjorn et al., 2000). These costs are strongly related to the time needed for the logging operations. Most harvesting systems involve a heavy initial capital investment mainly to buy machines needed for logging operations. Hence loggers must manage their system efficiently to ensure profitability (Boltz et al., 2001). Estimating cost and predicting the performance of harvesting systems is a difficult task. The machines themselves are very complex and work in a constantly changing environment interacting with other machines and are influenced by a range of site and stand factors (Gingras, 1988; Brinker, 2002). There are two major types of costs incurred in a harvesting operation: a) operating costs and b) ownership costs. Operating costs involve expenditures on fuel, lubrication oils, repairs and maintenance, labor wages, salaries, and benefits. Ownership costs (fixed or overhead cost) include depreciation, interest, insurance, and taxes. Determining accurate logging costs for any harvesting operation can be difficult (Holtzscher and Lanford, 1997), but 15

27 two factors must be taken into account: the total cost of running a machine or system quantified in dollars ($) and the production of the machine or system (measured in tons). The unit cost of harvesting expressed as dollars per ton ($/ton) can be obtained by dividing the total cost by production. There are two main methods to determine logging cost: the cash flow and the machine rate. The cash flow method uses the loggers past data to establish a logging rate. This is done by adding all business related costs from previous years and dividing it by all tons of timber delivered for that period. This will give the average logging cost ($/ton). The merit of this method is that the cost is easy to calculate but it has the disadvantage of spreading out larger cost such as equipment or major equipment repair over the entire period. With the machine rate method, the hourly system cost is taken and dividing by the estimated hourly productivity to predict a harvest rate ($/ton). The hourly harvesting system cost is obtained by calculating the hourly machine cost of each piece of equipment in the system and adding them up. Other costs such as overhead, workshop, storage, and service vehicles are also added in the calculations. Productivity on the other hand is calculated by averaging the number of tons of wood delivered per day or week and dividing it by the number of Scheduled Machine Hour (tons/smh). The machine rate method has a limitation in that productivity not only varies to a great extent with the logging machines or harvesting system used, but also with the stand and site characteristics of the harvested area (Visser, 2007). As such, all values are estimates and the output is only good as its inputs. Nevertheless, it still provides an easier way to get an approximate idea of productivity of a logging system. 16

28 According to Visser (2007), the most accurate way of obtaining reliable productivity information for a given machine or system at a given site was to conduct a time and motion study. This could be achieved either at the entire system level or with a specific machine level. However, in this study, we used the machine rate method because of convenience and the type of data collected. A very common method of determining a specific logging machine cost is to use a machine costing spreadsheet (Brinker, 2002; Visser, 2007). The spreadsheet allows the user to enter some machine cost values which it uses to calculate the fixed operating cost, the running cost, and the labor cost for the machine. The resultant cost estimate is per scheduled machine hour (SMH) that is, the cost per every hour the machine is scheduled to work including down-time. The advantage of this method is that it is very useful for predicting approximate hourly costs of a machine and can be used to make a cost comparison between two machines. However, the result of the spreadsheet model depends on the accuracy of the inputs (Holtzscher and Lanford, 1997). Hourly cost per productive machine hour (PMH) is obtained by dividing the dollar amount spent per SMH by the utilization rate. This would reflect the cost if the machine is compensated only when it is working. In reality, the actual cost would include the final costs associated with the spreadsheet input parameters, including state, and federal taxes paid for that year. There are several studies that have tried to measure and estimate the productivity and cost of harvesting systems. Wang et al. (2004), measured productivity and cost of harvesting system in a central Appalachian hardwood forest using a handheld data logger to record time and other harvesting related factors. When the data logger was not used, 17

29 time and operational variables were measured using a stopwatch and recorded on paper. Variables measured included start and end time, distance travel from landing to tree and back, time taken to cut a tree, delimbing and loading it on a truck, tree species, and diameter of each tree at breast height (dbh). Chainsaws and cable skidders were used to perform the logging operation. All the data collected were analyzed using a general linear model (GML). In a similar study conducted by Jensen and Visser (2005) in the Shenandoah Valley, Virginia, productivity of the harvesting system was determined by conducting a time and motion study during the harvesting operation. The time study was based on the method used by Kluender and Strokes (1994). Variables recorded in each extraction cycle were time travel from deck to woods and back, bunching, choking, and unhooking logs. Distance along skid trails, length of each tree and dbh were also measured using a logger s tape. A manual chainsaw and a four wheel tractor equipped with a skidder plate and a pulling winch was used for felling and transporting the trees. The measurement of efficiency and accuracy using this method can be problematic because of possible human errors in measuring distances and time in the woods. Recent technological advancement such as the Haglof DMEs and the MultiDAT increase convenience and accuracy compare with using the logger tape and the stopwatch. A number of studies have compared effects of different harvesting methods such as the TL, cable logging, and the CLT. The results have caused forest managers to pay closer attention to the various harvesting techniques as well as their environmental impacts (Clydes et al., 1999). Gingras (1994) concluded that the CTL system causes 18

30 minimum damage to the residual stand and to the soil because logging slash is distributed throughout the harvest site rather than left piled at the landing areas as in the TL system. Lanford and Stokes (1995, 1996) compared both systems in a loblolly pine plantation in southern Alabama and found no significant differences in productivity and costs, though the CTL system had less impact on stands and sites. LeDoux and Huyler (2000) compared three harvesting methods in northern hardwood stands and found that the CTL system had higher costs than the TL skidder system and the cable logging system. Clyde et al. (1999) also compared the full-tree and CTL systems in central Louisiana. They found that the advantage of the CTL method included reduced damage on the residual stand and less soil compaction, even though the system had the disadvantage of higher equipment costs. Bjorn et al. (2000) examined productivity and cost of a shelterwood harvesting system in Norway spruce (Picea abies) stands of northern Sweden and found out that harvesting cost has a significant influence on the application of and potential use of the shelterwood system. Comparing this system to the clearcut system, they found out that the time per tree and the time per cubic meter were higher in shelterwood than in clearcutting. Most of the increase was due to longer driving times because fewer trees were harvested. Hence, they concluded that the longer the driving time, the higher the logging costs in the shelterwood system compared with the clearcutting system. Boltz et al. (2001) compared Reduced-Impact Logging (RIL) and TL techniques in selected tropical forest region. Their findings showed that RIL demonstrated clear ecological benefits by reducing harvest impacts on residual stems and more effectively maintaining forest structure relative to the TL practices. However, the financial competitiveness of RIL was less conclusive. When they conducted a comparative analysis of financial 19

31 returns to one and two cutting-cycle logging entries for representative RIL and TL operations of the eastern Amazonia, they observed that RIL harvesting operations generated higher returns relative to TL for a wide range of discount rates due to gains in harvesting efficiency and forest conservation. Despite the fact that similar studies have been carried also in Alabama, the use of latest equipment such as the DME and MultiDat will give better results. These results will provide definitive evidence as to which method of logging causes less environmental impacts or alternatively if there is no significant difference between the two harvesting methods. This will assist forest managers not only in the Bankhead National Forest (BNF), but throughout southern United States where these two methods are popular. In that way, these managers will be able to evaluate which logging system is more environmentally friendly and sustainable so as to reduce public out cry. Lastly, the results could serve as guidelines for subsequent selection for the most appropriate harvesting method on the Bankhead National. 20

32 CHAPTER III MATERIAL AND METHODS 3.1 Study Site Our study was conducted at the BNF, which is located in Lawrence, Winston, and Franklin counties of northwestern Alabama (Fig. 1). Geographically, the BNF is in the southern Cumberland Plateau region of the United States The forest type is a mixed pine-hardwood dominated by planted even-aged loblolly pines (Pinus taeda L.) and includes other species of mesophytic forest which makes it among the most biologically diverse ecosystems in the United States (USDA, 1988). This forest supports a wide variety of plant communities and species because of the variety of landforms, soil, and moisture conditions found in the area. The topography is characterized by a gently rolling plateau that is dissected by a well developed drainage pattern with canyon-like bluff and narrow flood plain. The elevation of the terrain ranges between 165 m and 325 m. The climate of the area is characterized by long, moderately hot summers and short mild winters (Smalley, 1979). The average monthly temperatures range between 5 o c in January to 25 o c in July. The average annual precipitation is 145 cm, fairly distributed 21

33 throughout the year with March being the wettest month and October being the driest month (Smalley, 1979). Most of the soils in the area are highly leached ultisols. Soils in the southern and northwestern part of the forest are primarily shaley silt loams that have developed in sandstone and shale of the Pottsville formation. Beside the type of harvesting equipment used, all logging methods are influenced by the intensity of harvesting, topography, species composition, soil type, seasonality, rainfall, and temperature of the location. The study site was selected because it was appropriate for the research question to be evaluated properly as the researcher was able to control these factors. There was no difference in the topography and species composition between the plots. The soil type had a well developed drainage pattern and the data for the study was collected in the spring of 2007 and 2008 when the temperatures with almost the same, hence no seasonality factor. However, the site location of this study could be different from the previous studied locations and this might explain why the results might be different from previous studies. 22

34 Figure 1: Location of Bankhead National Forest 23

35 3.2 Background and Management History of the Research Site The BNF was established in 1914 as a result of the Week s Act, for the primary purpose of helping to protect the nation s watersheds and streams (USDA Forest Service, 2003). Today, the 72,800 ha forest serves as a multiple use area, providing many recreational opportunities for the public, as well as forest products for the timber industry (Gaines and Creed, 2003). In the 1960s, the Forest Service initiated efforts to improve forest economic yields by replacing some upland hardwood forests with faster growing loblolly pine. This was because at the time, loblolly pine offered the best chance of high survival and success in reforestation (USDA Forest Service, 2003). These efforts coupled with favorable natural conditions led to the establishment of approximately 79,000 acres of loblolly pine on the BNF. Currently, the BNF is comprised of 51 % southern pines and 49 % hardwoods. Over the past decades, the BNF has been experiencing significant infestation of the southern pine beetle (SPB). The infestation reached its peak in the summer of 2000 and remained high through 2001, killing an estimated 18,600 acres of pine forest. Most of the mortality occurred in the Sipsey Wilderness, leaving large areas of standing dead trees that were a public safety hazard along roads and trails. These areas have also accumulated considerable forest fuel loads, which have increased the risk of damaging wildfires in the future (USDA Forest Service, 2003). In 2003, the Forest Service took action by developing and implementing the Forest Health Restoration Project (FHRP) which includes (1) thinning of 9,452 acres of overstocked loblolly pine stands, to reduce the SPB epidemic in the BNF, (2) restoration of 7,382 acres of SPB damaged stands that need to be restored through reforestation, and (3) restoring a native pine and upland 24

36 hardwood forest that will provide an ecologically richer habitat in the southern Cumberland Plateau. In an effort to attain these goals, portions of the forest were selected and placed on timber sale for commercial harvesting. The bid was offered to the highest bidder. Unfortunately in the planning process, the Forest Service gave little or no consideration to the harvesting method to be used, probably because they did not think it was necessary or they were not very much informed about the level of environmental impact caused by the various harvesting methods. This is why in the initial planning process, the CTL system or any other alternative systems were not included as a requirement in harvesting. The CTL harvesting method was later included due to public concerns but was never followed through the end. Nevertheless, a study like this will likely arouse the awareness of forest managers and subsequent harvesting decisions will take the results into consideration. 3.3 Experimental Design This study was conducted in conjunction with FHRP. Treatment plots were established in the selected stands of the BNF, which are located on upland sites and composed of 20 to 35 year old loblolly pine mixed with some hardwood. The topography is undulating with slope between 20 to 30 percent. Soil type for all the plots was uniform leached ultisols. The treatment plots were set-up in accordance with those jointly set by the BNF and Center for Forest and Ecosystems Assessment (CFEA) researchers. The research design was a completely randomized block (CRB) with three levels of nesting. The average treatment plot size was about 13 hectares (31 acres). Half of each stand 25

37 (about 6.5 ha) was planned to be using the TL system and the other half using the CTL system. According to Gaines and Creed (2003), most of these treatment plots were comprised of about 60% pine (loblolly pine or Virginia pine, Pinus virginiana), with the remainder consisting of mainly oak species (Quercus spp.). Thinning treatments were 11 m 2 ha -1 of residual basal area and 17 m 2 ha -1 residual basal area; which are about 50% to 75% retention rate. However, in this study, stands with 11 m 2 ha -1 of residual basal area were selected for consistency and be able to control the factor of intensity which might affect our results. Hardwoods were preferentially retained because that was one of the objectives of the FHRP. Figure 2: Distribution of study plots in Bankhead National Forest. 26

38 After the first two treatments were harvested (Block 1 Treatment 5 for TL and Block 1 Treatment 4 for CTL), the CTL contractor lost the bid and pulled out, causing us to make some adjustments. We located another plot in Somerville, AL (about 35 miles northeast of BNF) with similar stand and site characteristics. Treatments were replicated three times on three different plots (three treatments for CTL and three for TL). TL included Block 1 treatment 5 (B1T5), Block 4 treatment 9 (B4T9), and Block 4 treatment 4 (B4T4) while CTL included Block 1 treatment 4 (B1T4), Block 4 treatment 10 (B4T10- this treatment was numbered by us because it was not part of the CFEA study plots), and the plot located in Somerville which was not also among the study plots set by the BNF and CFEA. Since we did not have enough replications for the CTL method in BNF and Somerville is located reasonably close to BNF, we decided to include it in our study. B1T5 and B1T4 were harvested in the spring of 2007 and data collected that same spring. The rest of the plots were harvested in the spring of 2008 and data collected thereafter. Prior to harvesting in spring, the sampling plots were marked in the summer with flagging. Pre-harvesting surveys for the soil disturbance and tree damage was done and data was recorded. For the soil compaction, data was collected after harvesting in areas that were disturbed and undisturbed. After harvesting, post-harvesting data were collected by measuring the same sampling plots. The machines used in the harvesting included a JD 548G-II skidder with tire size 28Lx26, a Tigercat 845B feller-buncher with tire size 24Lx26 for the TL and Timbco TF820-D forwarder with tire size 28Lx26, and CAT 550 harvester with tire size 600/65x34 for the CTL. All tires were standard sizes from the manufacturer. 27

39 completed. Table 1 shows the entire plots and their respective area after harvesting has been Table 1: Experimental plot area sizes Method Plots Area (Acres) B1T5 26 TL B4T4 63 B4T9 44 B1T4 25 CTL Somerville 11 B4T10 19 In each of these treatment plots, six subplots of 40 m x 40 m were located and 9 points were located (20m x 20m). Each of these points was measured 10 m in the four cardinal directions (North, East, South, and West) for soil disturbance data collection (see Appendix 2). For soil compaction, three line transects were located and compaction readings were taken at points 10 m apart along these transects and data separated between disturbed and undisturbed areas. The points that were measured in the undisturbed area were used as a control to be compared with those in the disturbed areas Soil Impact Soil impact is the most prevalent to observe after timber harvesting. Impacts include changes in the bulk density, soil compaction, porosity, soil organic matter and top 28

40 soil removal (Knoepp and Swant, 1997). We used the visual estimation method proposed by Jensen and Visser, (2005) to measure the soil surface disturbance, while the dynamic cone penetrometer proposed by Miller et al., (2001) was used to measure soil compaction Visual Estimation: To estimate the impact of soil surface disturbance, a visual inspection was conducted before and after harvesting along line transects spaced 20 m apart. Pre- and post-harvest data for soil disturbance were collected using six sampling plots in each treatment area. Sub-sampling was used because the treatment areas were large (average of about 13 hectares ) and would have been time consuming to measure the entire area of the three replications. The ArcGIS software (ESRI, Redland, CA) was used to plot regular points on the treatment areas at an interval of 20 x 20 m. This provided about 1,000 points for each treatment area (see Appendix 1). ArcGIS was also used to plot a rectangular grid on the treatment area, at intervals of 100 x 100 m. The six sampling plots were placed randomly within the 100 x 100 m grids and each of them had nine points (see Appendix 2). A total of 54 points were selected on each treatment area (see formula in Appendix 3). Coordinates for each point were recorded at transect intersections using a submeter Global Positioning System (GPS) unit to allow for relocation of the points. Haglof DMEs (Distance Measuring Equipments) were used instead of a graduated tape to measure the distance of soil disturbance attributes at a 10 m distance in each direction (See appendix 4). The Haglof DME (Distance Measuring Equipment), ArGIS software, and the GPS unit are some of the innovations used in this study to improve the speed and accuracy of the measurements. The distance of each disturbance class was recorded. The 29

41 following disturbance classes were used in the order of increasing severity of the impact. These classes were adopted from Aust et al. (1998) with some modifications but were later combined for analysis purposes. Table 2: Soil Disturbance classes Class Type Disturbance Class Description 1 Undisturbed No sign of trafficking or logging 2a Disturbed with litter Trafficking with litter still in place 2b Disturbed with soil Trafficking with litter removed and little soil exposed 3 Mineral soil Large area of soils exposed 4 Rutting Narrow paths or channels caused by machine wheels 5 Rock/Stump Rock or stump exposed 6 Slash Branches and stems left in piles on the floor 7 Trail & Deck 7a Main road Constructed roads in the forest 7b Secondary roads Frequent paths used to transport timber to the roadside or landing area Soil Compaction: Soil compaction caused by machine trafficking is most often estimated through changes in soil bulk density, typically expressed in grams per cubic centimeter (g/cm 3 ). 30

42 Soil compaction is also related to soil strength, which can be measured using a dynamic or static cone penetrometer much more rapidly than collecting bulk density samples (Miller et al., 2001). In this study, we used a static cone penetrometer tester to measure the compaction (firmness) of the soil. The instrument is supplied with two tips; a ½ inch tip for use in firm soil and a ¾ inch tip for use in soft soil. The dial indicator has two scales (one for each tip) that are calibrated in pounds per square inch (psi) of the base area of the cone (tip) and are color coded for reference: green (0 200 psi), yellow ( psi), and red (300 + psi). The green range is considered as good for plant growth, fair in the yellow range and poor in the red range. For this study we used the ½ inch tip and the depth of the soil was 10 cm since the soil was hard to measure for compaction at different depths using the cone penetrometer The split-plot was used here within the completely randomized block with three levels of nesting (method, plots, range, and status). Each treatment plot was divided into three line transects; transect one (T1), transect two (T2), and transect three (T3). ArcGIS software was used to generate these three line transects running from north to south of the treatment area. These lines transects were measured 100 m apart across each treatment plot starting from the center of intensive activity away to the areas of less intensive activity. Soil compaction readings were taken every 10 m along these transects giving a total of 15 points. Each of these points was classified into disturbed and undisturbed area depending on whether it falls on a traffic lane (see Appendix 5).Those readings on the undisturbed areas were used in the analysis as a control for the disturbed areas. A total of 45 readings were taken on each treatment and GPS coordinates of each point were 31

43 recorded. In our analysis, each of these line transects were called ranges (Range 1, Range 2, and Range 3). Range 1 included areas of high activity, range 2 included areas of moderate activity, and range 3 included areas of low activity corresponding to T1, T2, and T3 respectively (see Appendix 5). This was done both on the three plots replicated for the CTL and TL harvesting methods. This information was downloaded into ArcGIS together with the positional movements of the machines to see whether there is any correlation between the movement of the machines and the level of compaction. The data for plots B1T5 and B1T4 were collected in the spring of 2007 after harvesting had been completed. Those for the other plots were collected in the spring of 2008 still after harvesting was completed. Statistical Analysis Soil Disturbance Area percentages of each disturbance class (DC) by treatment between the two harvesting methods were calculated by summing the total area covered by each DC in each treatment and dividing it by the number of points measured over all replications. The generalized linear model (GLM) was used to test whether the CTL system creates less soil disturbance than the TL system. The advantage of using the GLM is that it has a mechanism to handle unbalanced data set in the model (Heninger et al., 2002; Carter et al., 2006). This model was used to examine the variation in the different soil disturbance classes in the various treatment sites and between the two harvesting methods. The interaction between the methods, plots, subplots, points, and orientation 32

44 was also examined. The statistical design was a completely randomized block (CRB) with four levels of nesting. These levels include two methods of harvesting (CTL and TL), three plots for each method. Each plot had six sub plots, each of these subplots had nine points, and each of these points is measured in four directions. The following model was used with the disturbance classes as the dependent variables while the methods of harvesting were the independent variables: DC ijklm = f (MT + PL + SP + PT + OR). (Equation. 1) DC ijklmn = Observed disturbance classes i = Number of Replications j = Method l = Points m = Direction (Orientation) k = Subplots MT = Method PL = Plot PT = Point OR = Orientation SP = Subplot DC = Disturbance Classes (These classes include DC1, DC2, DC3, DC4, DC5, and DC6) Where: DC1 = Disturbance class 1 (Undisturbed) DC2 = Disturbance class 2 (Disturbed with litter) DC3 = Disturbance class 3 (Disturbed with soil, mineral soil, and main road) DC4 = Disturbance class 4 (Disturbed with rutting and secondary roads) DC5 = Disturbance class 5 (Disturbed with rocks/stumps) DC6 = Disturbance class 6 (Disturbed with slash) 33

45 (Some of the disturbance classifications in Table 1.1 have been merged for analysis purposes). Model: [DC1 DC2 DC3 DC4 DC5 DC6] = Method, Plot, Sub Plots, Points, and Orientation. Where: Plot = (method) Sub Plots = Sub plot (method *plot) Points = points (method * plot * sub plots) Orientation = Orientation (method * plot * sub plots * points) Test H = M E = plot (method) The F-Distribution test with α = 0.05 was used to test the hypothesis (h) to see if the CTL system creates less soil disturbance than the TL system and also if there is a significant difference in the disturbance classes between the two harvesting methods (M). In the case plot was used as error term (E) for method. The GLM and area percentages were used to analyze and compare the level of soil compaction due to trafficking between the two logging systems. These averages were calculated by adding the cone readings in each range and dividing it by the number of points to get the means. This was done on each plot in each method. The results were compared within the treatments and by each method. The t-test was used to test whether there was any significant difference in the levels of soil compaction within the treatments and between these two systems. The following GLM procedure was used to compute the results: Model: Compaction = method, plot, range, and status. 34

46 (Method = CTL, TL; plots = 1, 2, 3; range = low, moderate, high intensity of trafficking; status = disturbed and undisturbed areas) Where: Plot = (method) Range = (method*plot) Status = (method*plot*range) (The test to check for hypothesis H and error E) Test H = method E = method*plot The t-test with α = 0.05 was used to test if there is any significant difference in the level of compaction between the two harvesting methods. In this model, plot*method was used as the error term (E) for method. All of these statistical analyses were performed in SAS programming software. Results Descriptive statistics were used to summarize and understand the nature of the surface soil disturbance before and after logging. The results were tabulated and compared between the damage classes and also the two harvesting methods (see Table 3). Pre-harvest soil disturbance data indicated that about 97 % of the treatment areas were generally undisturbed before logging began. The only few disturbances that could be seen were spots of bare soil (DC3) mostly caused by horse trails or erosion and a few stretches of slash (DC6) resulting from fallen trees that have been infected by the SPB. 35

47 Both the TL and CTL pre-harvest treatments were not much different except for more areas of slash (DC6) in the TL. Table 3: Percentage of area with surface soil disturbance Pre-Harvest Post-Harvest Damage classes CTL TL CTL TL DC1- undisturbed DC2- disturbed with litter DC3- disturbance with soils exposed DC4- disturbed with rutting/sec. roads DC5- disturbed with rock/stumps DC6- disturbed with slash Total Post-harvest data indicated that the treatments plots were highly disturbed after harvesting. Only about 22 % of the treatment area in the CTL system remained undisturbed while about 78 % was disturbed. This results conforms to 89 % obtained by Jensen and Visser, (2005) and 75 % obtained by Shrestha et al., (2008). Disturbed with litter (DC2) was the most visible disturbance class in both harvesting systems accounting for about 50 % of the disturbance. Disturbance coming from road construction (DC5) was the least in both CTL and TL, 0.4 % and 0.6 % respectively. We found more exposed soils (DC3) and more slash (DC6) in the TL than CTL because of the dragging (skidding) 36

48 of trees on the forest floor to the landing area and slash was piled in heaps in the TL system. Comparing the means of soil disturbance between the two harvesting systems, we found out that means for disturbed with litter (DC2) in the both systems were high, though that for TL was higher, 4.99 and 4.16 for CTL respectively with a standard deviation of 3.56 for the TL and 3.58 for the CTL (see Table 4). This implies that disturbance with litter was the most prevalent disturbance class that characterized the two harvesting systems. Disturbed with rock/stump (DC5), disturbed with rutting/secondary roads (DC4), and disturbed with soil exposed (DC3) had the lowest means and standard deviations respectively. Table 4: Percentage of mean soil disturbance for each logging method CTL TL Damage Classes Mean Std Mean Std DC1-undisturbed DC2- disturbed with litter DC3- disturbed with soil exposed DC4- disturbed with rutting/sec. roads DC5- disturbed with rocks/stumps DC6- disturbed with slash

49 Statistical analysis for soil disturbance was done in GLM and the ANOVA results are summarized in the table 5 Table 5: ANOVA for Surface soil disturbance classes DC1 DC2 DC3 Source DF SS MS F- Value P-value SS MS F- value P-value SS MS F- Value P-value Method Plot (Method) ** ** Subplot (method*plot) ** ** Point (method*plot*subplot) Orientation (method*plot*subplot*point)

50 Table 5 (continue) DC4 DC5 DC6 Source DF SS MS F-Value P- value SS MS F-Value P- Value SS MS F-Value P-Value Method Plot (Method) ** ** Subplot (method*plot) ** ** Point (method*plot*subplot) Orientation (method*plot*subplot*point) * Significant at α = 0.05 (Table six is a continuation of table 5)

51 A total of 1296 observations were measured from a total of 324 points in the six different treatment sites. These measurements were grouped in the six different disturbance classes as mentioned earlier. Statistical evaluation of post-harvest disturbance did not detect any significant differences in the level of surface soil disturbance between the two harvesting systems at α = The interaction between the plots also indicated no significant difference between the two harvesting systems. This can be explained by the fact that the soil type in the plots was leached ultisols which are well drained soil. Thus the movement of the machines on these plots could not have caused any significant disturbance differences between the two harvesting methods. However, from the analysis, when we examined the interaction between the different disturbance classes, we found some significant variation from plot to plot in the two harvesting methods. These significant variations could be observed within all the disturbance classes except the undisturbed areas in both harvesting method. The interaction from subplots to subplots showed significant variation only in DC1 (undisturbed), DC2 (disturbed with litter), and DC6 (disturbed with slash). There were also differences in the sources of the variation of these individual classes (Table 5). ANOVA reveals that, the most significant sources of these variations between the treatment plots and subplots were orientation (direction of measurement) and the point to point differences. This can be explained by the pattern of harvesting and the minor differences in topography. Even though the slope of the treatment areas was between 20 to 30 %, the subplots had some minor differences and this may account for the variations. The loggers used directional pattern of harvesting following the direction of the slope and wind. This pattern is highly recommended by the BMPs as a means to 40

52 mitigate environmental impacts. For the undisturbed areas (DC1), these two factors contributed 42.90% and 43.50% of the variation respectively (see Table 6) while disturbed with litter (DC2) orientation accounted for 42%, and point 35%. In disturbed with soil exposed class (DC3), orientation accounted for 43.62% and point to point 43.86%; disturbed with rutting/secondary road - DC4 (orientation 55.92%, point 34.19%); disturbed with rocks/stumps - DC5 (orientation 72.62%, point 23.21%); and slash - DC6 (orientation %, point 34.39%). The main effect of method, plots, and subplots contributed very little to the surface soil disturbance between the two systems. Table 6: Percent variance accounted for by sources of variance Source DC1 DC2 DC3 DC4 DC5 DC6 Method Plot (method) Subplot (plot*method) Point (plot*method*subplot) Orientation (plot*method*subplots*point) Even though orientation was the greatest source of variation, the mean distribution of surface soil disturbance by orientation indicated there was no significant difference in orientation between North, East, South and West (Table 7). However, our method of collecting data in the cardinal points direction happens to have cut across these 41

53 disturbances in the direction of harvesting and we were fortunate to have capture it. This might explain the reason why we happened to catch soil surface disturbance by orientation being the greatest source of the disturbance. Table 7: Percentage Means by Orientation per each soil disturbance classes Orientation North East South West Damage Mean Std Mean Std Mean Std Mean Std classes Dev. Dev Dev Dev DC DC DC DC DC DC Further analysis to see if there were any differences between the means of the orientation from point to point in the two harvesting systems revealed that there was no significant difference in the directions that these soil disturbance data were collected. However, we had to keep the direction constant for consistency Soil Compaction Soil compaction was analyzed in an analysis of variance (PROC GLM, α = 0.05) with the harvesting impact range (T1 = High, T2 = Moderate, and T3 = Low) as a split 42

54 plot. Soil compaction readings were classified under the status of disturbed and undisturbed and the means calculated to show the relationship between soil compaction and the level of trafficking. The method, plots, ranges, status, and their interactions were tested for significance. In general, the results for soil compaction measured an average of 171 pound per square inch (psi) throughout the study area (see Table 8). This is generally good soil for plant growth. The various ranges (Low, Moderate, and High intensities of trafficking) in the CTL indicated higher compaction averages (156, 170, and 190 psi respectively with a total mean of 172 psi) than in the TL (147, 163, and 178 psi respectively with a total mean of 163 psi). This implies the CTL system causes more compaction than the TL system. 43

55 Table 8: Average soil compaction measured in pounds per square inch (psi) CTL TL Plots Low Moderate High Low Moderate High (T3) (T2) (T1) Total (T3) (T2) (T1) Total Rep Rep Rep Means Grand Means CLT (Rep 1 = B1T4; Rep 2 = Somerville; Rep 3 B4T10) TL (Rep 1 = B1T5; Rep 2 = B4T4; Rep 3 B4T9). The results between the plots also revealed some slight differences in the soil compaction. In the CTL, plot three (Rep 3) indicated a higher soil compaction with 193 psi, followed by plot one (Rep 1, 174 psi), and plot two (Rep 2) with the least (149 psi). For the TL, plot one (Rep 1) had the highest compaction reading (165 psi), next was plot two (Rep 2, 164 psi), and last was plot three (Rep 3) with 160 psi. 44

56 Table 9: ANOVA for Soil Compaction Source DF Sum of Squares Mean Square F-Value P Value Method Plot (method) ** Range (method*plot) Status (method*plot*range) ** Error ** Significant at α = 0.05

57 Statistical evaluation (GLM ) of soil compaction indicated that there was no significant difference in the level of soil compaction caused by the two harvesting methods (P = ). This result was contrary to that obtained by Landford and Stokes (1995) who found out that the TL system causes significantly more compaction than the TL system at all levels of disturbance. This could be explained by the fact that they used the bulk density and we used the cone penetrometer to measure the level of compaction. Furthermore, the soil type (leached ultisols) for our study area was well drained soils and could not have caused any significant difference in the soil compaction between the two harvesting method. Thus, the different soil types could be another factor that can explain for these differences in the results. Again, the tire sizes of the machine and the movement of these machines inside the plots can also explain the results we got for this study. However, the t-test indicated a significant difference (P = ) between the disturbed and the non-disturbed areas. The difference in the mean was psi for the disturbed areas and psi for the undisturbed areas. This shows a high correlation between high trafficking areas and high compaction areas in the treatment plots. The passage of these heavy equipments on the soil surface compact the soil more than in those areas of the forest where there was no machine movement. The results obtained were contrary to our expectation because with a large landing area in the TL system, we expected higher soil compaction levels than in the CTL system. However, this can be explained by the fact that in the TL unlike the CTL, the center of activities (that is the landing area) most of the slash was left in heaps and sometimes spread along the path for the machines to trample on thereby causing less impact on the soil below. 46

58 CHAPTER IV RESIDUAL STAND DAMAGE 4.1 Data collection A field survey was done to collect data of all damaged trees after the thinning operation was finished. Residual stand damage was determined by counting the number of injured trees all over the entire area harvested. For every damaged tree, the dbh, scar length and height from ground level were measured using a logger s tape. The crown damage was assessed visually and separated from the stem damage. For every single damage the location on the stem, the wound size and intensity were measured. A class number was assigned to each damage classification based on the depth of the bark removal and crown damage. This was done according to the method used by USDA Forest Service (2003). The following damage classes (DC) and description of the damage was assigned: Class 1: minor stem damage (51-100% of bark present over wound); Class 2: intermediate stem damage (less the 50% of bark present); Class 3: major stem damage ( no bark present); Class 4: minor crown damage (< 50% ); 47

59 Class 5: major crown damage (>50%). Class 6: undamaged crown but have stem damage. According to Limbeck-Lilienau (2003) and Solgi and Najafi (2007), damages in classes one and two pose no immediate threat to the trees but increases the likelihood of attack by insects or diseases. However, the probability that wounded rot fungi appear as a result of real wood injury (DC 3) is about 40 to 50% compared with the other damage categories. 4.2 Statistical Analysis To find whether the CTL system has less incidences of residual tree damage than 2 the TL system, the contingency tables ( Χ ) tests was used to compare the level of damage on the residual trees between the two harvesting systems. The following formula was used: f ^ 2 ij ij 2 Χ = ΣΣ (Equation 2) ^ f ij f Where 2 Χ = Chi Square ΣΣ = Computation of grand total f ij = Frequency of observed in row i and column j ^ f = Frequency expected in row i and column j ij This was done using the SPSS statistical analysis software. 48

60 Percentage, frequency, and means were also calculated and used to compare the level of damage between the stem and crown within the two harvesting methods. Since the harvested plots were of different sizes (Table 1.), we standardized the data by dividing the number of damaged trees in each class by the plot size and multiply it by 100 to get the number of damaged trees on a per 100 acres basis. Results A total of 474 residual trees were found damaged in the two harvesting systems. Out of these, 124 trees were found in the treatments that were harvested using the CTL method while 350 trees were found on those plots harvested using the TL method (Table 11). 49

61 Table 10: Number of trees damage in each class per plot and total number of trees Method Plot DMC1 DMC2 DMC3 Total DMC4 DMC5 DMC6 Total Undamaged Total Minor Interm. Major Stem Minor Major U-CN Crown Trees CTL Somm CTL B4T CTL B1T Total TL B1T TL B4T TL B4T Total

62 The highest number of residual tree damage in the CTL harvesting method were found on treatment Somerville (48 trees) and the lowest number on B1T4 (32 trees); while in the TL method, the highest number of damaged residual trees was found on treatment B4T9 (157 trees) and the lowest on treatment B1T5 (50 trees). Looking at these raw figures we cannot compare the tree damage because of the different sizes of the plots. Therefore, we converted them on a per 100 acres basis to standardize the data before comparing them. The results are presented in Table

63 Table 11: Residual tree damage of each harvesting method per 100 acres Damage Classes Method DMC1 DMC2 DMC3 Total DMC4 DMC5 DMC6 Total Undamaged Grand Total CTL TL Grand Total

64 The CTL had the highest number of residual damaged tree (796 tree per 100 acres) than the TL (776 tree per 100 acres). However, the TL harvesting method caused more major stem damage (DMC3-465 trees per 100 acres) than the CTL (348 trees per 100 acres). On the contrary, the CTL harvesting method caused more minor (DMC1) and intermediate (DMC2) than the TL. This because the dragging of wood in between the trees in the TL harvesting method to the landing area obviously caused more major damage while the minor and intermediate stem damages are mostly caused by machine maneuverability and machine tires. The TL had the lowest undamaged crown because the feller-buncher could cut the trees and drop them in an open area thereby preventing much crown damage. Table 12: Number of residual tree damaged per treatment plot (Per 100 acres) Site Size (acres) Method No. of residual trees damaged % Damaged B1T4 25 CTL Somerville 11 CTL B4T10 19 CTL B1T5 26 TL B4T9 44 TL B4T4 63 TL After standardization, CTL in Somerville still had the highest number of damaged trees (436 trees per 100 acres) and B4T9 for the TL method (357 trees per 100 acres) 53

65 Total No. of Damaged Trees/100 acre DMC1 (back ) Residual Tree Damage DMC2 (bark <50) DMC3 (No bark) DMC4 (Cr <50) DMC5 (Cr >50) Damage Class DMC6 (Cr No damage) CTL TL Figure 3: Distribution of residual tree damage in to the various classes DMC1, DMC2, and DMC3 = Stem damage DMC4, DMC5 = Crown damage DMC6 =Undamaged crown Generally, the results show that approximately 49% (776) of all incidents of residual tree damage were caused by the TL harvesting method while the CTL accounted for 51% (796). Major stem damage (DMC3) per 100 acres (465 trees) occurred in the TL system as compared to 348 trees in the CTL. The highest numbers of minor (DMC1) and intermediate (DMC2) stem damage occurred in the CTL system (196 versus 127 and 252 versus 184 trees respectively) for the TL harvesting method. The results also shows that the CTL system caused more crown damage that the TL system (DMC4 526 trees; 54

66 DMC5 144 trees; and undamaged crown (DMC6 116 trees compared to 612, 139, and 25 trees respectively for TL). These results are contrary to those obtained by Solgi and Najafi (2007) who reported that the TL system causes more wounding (80%) to residual trees than the forwarder in the CTL system (20%). Scarring damage can be more serious than other types of wounding because, although it may not affect tree diameter growth, it can decrease future log value. Damaged trees in the TL system were highly concentrated along skid trails. Out of the 776 residual trees damaged in TL harvesting system, 461 of them were found along the major tails. This represents about 60 % of the total stem damage in this system. The greatest number damage occurred within 2 m from the centerline of the skid trail and most of them with within 1 m above the ground. This high number can be explained by the act of dragging the felled trees by the skidder to the landing area. Han-Sup and Loren (2000) reported the greatest damage occurred within the first 10 ft from the centerline of the skid trails or skyline. Froese and Han-Sup (2002) reported highest total scarring damage (73.7%) occurred within 15 feet of the nearest skid trail centerline. Chi Square statistical analysis (P = 0.000) revealed that there is a significant different in the level of residual tree damage between the two harvesting system (Table 15). 55

67 Table 13: Contingency table for residual tree damage CTL TL DMC Observed No. Expected No. Observed No. Expected No Undamaged Total Test Statistics Chi Square DF Probability Tree Damage Separating the stem from the crown damage, also indicated a significant difference in the level of damage caused to residual trees between the two harvesting systems (P = 0.00) for stem damage and P = 0.00 for crown damage. The implication of this is that the TL harvesting method causes less residual damage than the CTL method. Limbeck-Lilienau (2003) reported that the main reasons for the differences in the level of residual tree damage between the TL and the CTL systems were the harvesting intensity and the slope. Even though with the differences in the slope, she also reported 56

68 that the correlation between terrain slope and damage level has not yet been statistically proven but a trend was reported by other authors (Stampfer et al., 2001). However, in our case, since the harvesting intensity (50% retention) and the average slope (20 to 30%) were the same for all the research plots, the disparity in the level of residual tree damage could be ascribed mainly to the different methods of harvesting. The skidding system of dragging trees to a central location (landing area) for delimbing resulted in more damage along skid trails as oppose to forwarder system. Also, different levels of operator experience cannot account for this disparity. The CTL contractor was the only operator of his machines (the forwarder and harvester) and has been in the logging business for over 36 years. He was expected to be more careful in maneuvering the machines within the trees, thus causing less damage to the standing trees. On the other hand, the TL contractor had employed two operators who had 15 and 18 years of experience respectively. However, the higher incidences of tree damage in the CTL system might have been caused by delimbing the trees in the woods unlike in the landing area as in the TL system can cause more damage to the residual trees. All of these are possible explanations why we have more residual tree damage in the CTL harvesting system than the TL system. 57

69 CHAPTER V PRODUCTIVITY AND COSTS Productivity and cost are essential components of a harvesting operation. In our study, we used two methods to evaluate cost and productivity of the CTL and TL harvesting methods. These methods include the machine rate method using the cost spreadsheet in Brinker et al., (2002) and the system productivity method (Visser, 2007). The first approach calculates the cost per PMH and SMH for each machine while the second calculates productivity based on what the system currently delivers on the average per day or week divided by the PMH (tons/pmh) or SMH (tons/smh). Machine costs are sometimes very difficult to calculate because the loggers are reluctant to release information they considered as sensitive while the second is much easier to calculate based on the actual production. 58

70 4.1 Data Collection A time study during the harvesting operation was conducted to determine the productivity of the two systems. The Machine Rate Method was used to calculate the hourly costs and estimate the productivity for each specific machine in the CTL and TL harvesting methods. These machines include a harvester and a forwarder for the CTL, and a feller-buncher and a skidder for the TL systems. Data were collected using a MultiDAT (a multi-purpose datalogger) placed on each of these machines for a duration of two weeks. These data were downloaded every three days during the length of the twoweek period and were used to calculate productive and non-productive time and to monitor interactions between equipment; operator and harvesting operations (see Table 14). Out of the two weeks the actual number of days worked were 10 days and the data was collected on a 24x7 168 hours schedule. However, the actual scheduled hours to work were 8 hours per day, 5 days per week. Other data collected included level of experience of the operators (number of years in operation both for the owners and employees), amount of fuel used per day, the price of fuel during that period, the purchase prices of the various machines, the horsepower and number of hours worked, and cost of labor per hour. 59

71 Table 14: Machine Productivity Method Machine Total Recorded Recorder Working Short Idle Total Recorded Recorder Total time Data (H) Active (H) stops Time working Working Active Production (H) Time (H) Time (%) (%) (ton) (H) (%) TL Skidder TL Feller Buncher 1,120 CTL Forwarder CTL Harvester * These numbers were obtained from the MultiData for duration of two weeks

72 Table 15: Total Machine Utilization Method Machine Total Recorded Scheduled Engine Engine Work Work in % Work in % Idling in 61 * These numbers were obtained from the MultiData for duration of two week

73 time Data (H) Time (H) running Idling Time of recorded of schedule % of (H) (H) (H) data (%) time (%) engine time (%) TL Skidder TL Feller- Buncher CTL Forwarder CTL Harvester

74 4.3 Data Calculations and Analysis After these data were collected, they were entered and calculated using a spreadsheet put together with information obtained from Brinker et al., (2002). This spreadsheet is shown in Appendix 7. The purchase prices of the machines, and the horsepower were obtained from the owners. The machine life span (expected life) was set at 5years (IRS standard for tax purposes).for the TL machines (feller-buncher and skidder) while the harvester and forwarder in the CTL system had been paid off (the owner had been using them for over 11 years). The salvage value rate, which is a percentage of the purchase price of each machine, was calculated by dividing the purchase price of the machine by the life span and dividing the quotient by the purchase price of the machine. The salvage value was obtained by multiplying the purchase price by the salvage value rate. The utilization rate was calculated based on the actual hours worked divided by the scheduled Machine hours (SMH). The actual hours worked were obtained from the MultiDAT (see Tables 14 and 15) and the SMH was set at 80 hours (calculated based on 8 hours of work per day and 5 days per week for the two weeks that the machines were working). Repair and maintenance was set at 1%, interest, insurance, tax rates, and lube and oil (percent of fuel cost) were set at 4% each (from Brinker et al., 2002). Fuel cost at the time of our study was $2.30 per gallon and the fuel consumption rate for each machine horsepower was calculated by dividing the number of gallons consumed per hour by the respective horsepower. Annual depreciation was calculated by subtracting the salvage value from the price of the machine and dividing the difference by the life span. Average yearly investment (AYI) was obtained by subtracting the salvage value (S) from the purchase price of the machine (P) and multiplying the difference by 63

75 the life span (n) plus 1 and dividing the product by 2 times the life span plus the salvage value.(ayi = (((((P-S) x (n+1))/(2 x n))+s)/50) x 2) However, since the machine only work for two weeks, the annual depreciation and average yearly investment were divided by 50 weeks in a year (50 working weeks in a year to get amount per week) times 2 (two weeks) to get the actual amount of depreciation and investment for that period of time. Productive machine hours (PMH) was calculated by multiplying SMH by utilization rate (or could be got directly from MultiDAT Table 15) Ownership cost was calculated based on interest, insurance, and tax costs based on rates obtained from Brinker et al., (2002). The total dollar amounts were calculated by multiplying the average yearly investment by their rates. The yearly ownership cost was calculated by adding the annual depreciation to the interest, insurance, and tax costs. Ownership cost per SMH was obtained by dividing the yearly ownership cost by the SMH and the ownership cost per PMH was calculated by dividing the yearly ownership cost by the PMH. Note should be taken that when we adjusted the annual depreciation and the average yearly investment to the two weeks period of operation, ownership costs are all presented only for that two weeks period. Operating costs were calculated as follows, total fuel cost for each machine was obtained by multiplying the horsepower by the fuel cost and fuel consumption rate. Lube (engine oil, hydraulic, grease, and other lubricants) cost per hour was calculated by multiplying the fuel cost per hour by the rate of fuel cost. Repair and maintenance cost was obtained by multiplying annual depreciation by rate of depreciation and dividing the product by PMH. Operator labor and benefit cost per hour was calculated by dividing operator wage and benefit rate by utilization rate. Operating cost per PMH was calculated 64

76 by adding fuel cost plus lube cost plus repair and maintenance cost and dividing the sum by operate labor and benefit cost per hour. Operating cost per SMH was obtained by multiplying the operating cost per PMH by the utilization rate. The total cost per SMH was obtained by adding the ownership cost per SMH and the operating cost per SMH. Finally the total cost per PMH was calculated by adding the ownership cost per PMH and the operating cost per PMH. The detail spreadsheet calculations for all the machines are in Appendices 7 to 10. Results The results of the machine rate calculations are shown in Table 16. For the TL harvesting method, the purchase price for the feller-buncher was $140,000 and $185,000 for the skidder. The purchase prices for the harvester and the forwarder in the CTL harvesting method were $248,000 and $200,000 respectively. Thus the total cost for equipment for the CTL machines was $448,000 as opposed to the TL machines which was $325,000. However, the CTL operator had already paid off for his equipment and incurred not equipment cost. Depreciation (decline in value of a machine due to wear, obsolescence, and weathering) for the machines in the TL harvesting method was set at 5 years even though from personal conversation with the owner, these machines could last more than 5 years depending on their usage and maintenance. Salvage value (the estimated value of an asset at the end of its useful life) of the machines was calculated to be 2 percent of purchase price and the respective dollar amounts were as follows; $34,820 for the feller-buncher, $37,000 for the skidder, $22, for the harvester, and $18, for the forwarder In Table 16 these figures for the harvester and forwarder 65

77 appear as zero for the computation of ownership cost but were used to calculate taxes, insurance, and repair and maintenance. Annual depreciation cost for the two weeks operation period was calculated to be $ for the feller-buncher, $1, for the skidder, $0.00 for the harvester, and $0.00 for the forwarder. The values are zero because equipments have been paid off but they were still calculated to know the cost of maintenance $ for the harvester and $ for the forwarder. The average yearly investment was $3, for the feller-buncher, for the skidder $5,032.00, and those for the harvester and forwarder were $0.00. The PMH (the actual hours the various machines worked) were calculated to be 16 hours for the feller-buncher, hours for the skidder, 7.58 hour for the harvester, and hours for the forwarder. Operator wage and benefit rate was set at 30 percent and cost was calculated by adding one to rate and multiplying the sum by the hourly rate, which was $12.00 per hour. Thus, the total cost of labor and fringes per person for each machine in the two harvesting method was $15.60 per hour. 66

78 Table 16: Machine rate calculations for TL harvesting system for two weeks Input Data F-Buncher Skidder Harvester Forwarder Purchase price ($) 140, , Machine horsepower rate (hp) Machine life (yrs) Salvage value, (% of purchase price) Utilization rate (%) Repair & maintenance (% of depreciation) Interest rate Insurance and tax rate (%) Fuel consumption rate (gal/hp-hr) Fuel cost ($ per gal) Lube and oil (% of fuel cost) Operator wage and benefit rate ($/hr) Scheduled machine (hrs/10 days) Calculations Salvage value ($) 34,820 37, Annual depreciation ($/10 days) , Average year investment ($/10 days) 3, , Productive machine hours (hrs/10 days) Ownership costs Interest cost ($/10 days) Insurance and tax cost ($/10 days) Yearly ownership cost ($/10 days) 1, , Ownership cost per SMH ($/hr) Ownership cost per PMH ($/hr) Operating costs Fuel cost ($/hr) Lube cost ($/hr) Repair and Maintenance cost ($/hr) Operator labor and benefit cost ($/hr) Operating cost per PMH ($) Operating cost per SMH ($/hr) Total Machine Costs Total cost per SMH ($/hr) Total cost per PMH ($/hr) Interest, insurance and taxes rates were set at 4.5 percent for the feller-buncher, 5 percent for the skidder, and 4 percent each for the harvester and the forwarder.(from Brinker et al., (2002). Therefore their respective costs for the two weeks period were as 67

79 follows; interest cost for the feller-buncher was $176.27, that for the skidder was $251.60, and those for the harvester and the forwarder were $0.00 each. Taxes and insurance costs were $ for the feller-buncher, $ for the skidder, $ for the harvester, and $ and forwarder. The yearly annual ownership for the same period was $1, for the feller-buncher, $1, for the skidder, and 0.00 for the harvester and the forwarder. Ownership cost per SMH was calculated to be $14.92 for the feller-buncher, $21.09 for the skidder, and $0.00 for the harvester and the forwarder. Ownership cost per PMH for the feller-buncher was $74.62, $83.44 for the skidder, $30.72 for the harvester and, $11.63 for the forwarder. The ownership costs for the TL harvesting method were lower than those for the CTL harvesting method because they included only taxes and insurance as all equipment had been paid off. For the operating costs, fuel and lube cost were calculated at $11.50 and $0.46 respectively for the feller buncher; $13.80 and $0.55 for the skidder; $12.65 and 0.51 for the harvester; $9.20 and $0.37 for the forwarder. Repair and maintenance cost for the feller-buncher was $0.53, $0.59 for the skidder, $1.08 for the harvester, and $0.41 for the forwarder. Operator labor and benefit cost for each machine were as follows $78.00 for the feller-buncher, $61.72 for the skidder, $ for the harvester, and $77.32 for the forwarder. Operating cost per PMH for each machine was $90.49 for the feller-buncher, $76.66 for the skidder, $ for the harvester, and $98.52 for the forwarder while operating cost per SMH for the feller-buncher was $18.10, $19.38 for the skidder, $16.85 for the harvester, and $17.46 for the forwarder. The total machine cost per SMH (ownership costs plus operating costs) was $33.02 for the feller-buncher, $40.47 for the skidder, $16.85 for the harvester, and $

80 for the forwarder. The total cost per PMH was calculated at $ for the fellerbuncher, $ for the skidder, $ for the harvester, and $98.16 for the forwarder. To assess the actual total cost for each machine in each harvesting method, we considered only PMH. In order to obtain the total dollar amount spent for the two weeks of operation when we collected the data, we computed average investment (AYI) plus operating cost per PMH multiply by the PMH and add the cost of transporting the wood to the mill. The calculations are as follows: Feller-buncher: - AYI ($3,917.12) + Operating cost ($90.49/PMH) x PMH (16 hrs) = $5, Skidder: - AYI ($5,032.00) + Operating cost ($ 76.66/PMH) x PMH (20.22 hrs) = $ Harvester: - AYI (0) + Operating cost ($177.80/PMH) x PMH (7.58 hrs) + Taxes and insurance ($232.83) + RM ($1.08) = $1, Forwarder: - AYI (0) + Operating cost ($98.16/PMH) x PMH (16.14 hrs) + taxes and insurance ($187.77) + RM (0.41) = $1, Total cost for TL (without transportation) = $11, ($5, $ 6,582.04) Total cost for CTL (without transportation) = $3, (1, $1,772.48) Transportation cost for the TL system was contracted out at $8.50 per ton. Thus, the total cost for transporting the wood to the mill cost $9,520 (1,120 tons times $8.50/ton). For the CTL system, the wood was transported to the mill by the owner using his truck. The truck cost him $75,000 and he had been using it for 11 years. It had been paid off and the salvage value was calculated at $6, The mill was located about 50 miles from the harvesting plots and the truck used to transport the wood consumed 5 69

81 gallons per every 50 mile. Therefore to and from the mill, the truck used 10 gallons per day. For the two weeks, the truck consumed 100 gallons multiplied by the price of gas at that time ($2.30 per gallon), the total cost of fuel was $ ($2.30 x 100 gallons). Lube rate for the truck was 0.04% of fuel, giving a total cost of $9.20 (0.04 x $230.00). Taxes and insurance for the two weeks was $70.41 (using the spreadsheet based on salvage value). Operator labor and benefit cost for the truck was $ ($15.60 for hourly wage x PMH). Repairs and maintenance cost was $0.15 (calculated using the spreadsheet). Total cost for operating the truck for the two weeks was $569.81` ($ $ $ $ $260.05). The final formula to calculate production cost for each machine in each harvesting method was average yearly (AYI) plus Operating cost per PMH multiplied by PMH plus transportation cost. Based on our calculations, the final total cost for the feller-buncher and the skidder in the TL harvesting system including transportation was $21, ($11, $9,520) while that for the harvester and forwarder in the CTL system was $3, ($3, $569.81). Our analysis based on this study indicated that the CTL harvesting system cost less to operate than the TL system. This result differed from that obtained by Lanford and Stokes (1995) who stated that the CTL harvesting system has a higher cost than the TL system due to initial cost of equipment. The explanation in our study was that the CTL operator had paid off for all his equipment and was only incurring operating costs plus the cost of insurance and taxes while the TL operator was incurring both ownership costs and operating costs. The productivity of each harvesting method was calculated using the system productivity method. According to Visser, (2007), this is calculated by getting the 70

82 number of tons of wood each harvesting system currently delivers per day or week and divides that number by the total machine SMH. This gives an indicative productivity (tons/smh) for each harvesting system can be compared. However, in our study we divided the total tonnage produced by the PMH to obtain the actual productivity. Actual production from the TL system averaged four trucks per day and each truck contained about 28 tons (information collected from the loggers). Thus, the average daily production was 112 tons per day. Thus for the 10 days within the two weeks of operation, 1,120 ton (112 tons/day x 10) were delivered at the mill. The PMH for the feller-buncher was 16 hours and hours for the skidder over the two weeks in TL harvesting method. Thus, the productivity for the feller-buncher was 70.tons per PMH (1,120 tons divided by 16 hours) and that for the skidder was 56 tons per PMH. For the CTL system, since the owner was working alone, the average daily production was only one truck per day (about 28 tons/day). Within the time of operation, the CTL operator worked for 10 days and produced 10 trucks load of wood (average of one truck per day) Thus, the total production was 280 tons (28 tons/day x 10 days). The PMH for the harvester was 36 tons per PMH and 17 tons per PMH for the forwarder. After calculating the total cost (costs of ownership and operation) and productivity for each harvesting method, we calculated the returns (profits) for each system to evaluate which of them yielded more profits to the operator. This was calculated by subtracting total cost from total revenue obtained during the two weeks of operation. From personal conversation with the loggers, the price paid per ton of wood at the mill was different. The mill paid the TL contractor $38/ton and the CTL, $35 per ton because the mills could get more wood from the TL system than the CTL. Total revenue 71

83 obtained from the TL harvesting method was $42, (1120 tons*$38.00/ton) and $9, (280 tons*$35.00/ton) for the CTL system. Hence, the total profit obtained from the TL harvesting was $21, ($42, $21,466.93) and $5, ($9,800 - $3,923.92) for the CTL system. These results were contrary to our expectation as the CTL harvesting method proved to be less expensive to operate and yielded less profit to the owner. Nevertheless this could be explained by way the two systems were operating under different circumstances with CTL operator working alone. If he had not paid off for all his equipment, it would have been less cost effective for him to be operating the two machines and the truck alone. The only way this method would have made profits was to have more than one operator operating the tow machines in the system as was in the case of the TL harvesting method. Furthermore, the operator performance (see Table 17) was low in the CTL harvesting method. Even thought with his longevity in the profession (about 38 years in the business), we expected to have a high performance than that of the operators in the TL system. 72

84 Table 17: Operator performance Method Machine Logged Engine Work Work in % of Idling Time (H) Running (H) Time (H) Logged Time (%) (H) TL Skidder TL Fell- Buncher CTL Forwarder CTL Harvester Looking at (table 20) the TL operators were performing much better than the CTL operator on all the two machines in each system. The performance of the operator on the skidder was 6.24 percent and feller-buncher was 4.93 percent compared to that of the forwarder 3.74 percent and harvester 1.76 percent. May be if the operator was working with another worker, he might have been motivated to work harder. Again since he was running the business as a family business and all of his equipment was paid off, he was not under any pressure to increase performance. As a consequence, the CTL harvesting method in our study did not prove to be the most effective and high profit yielding. 73

85 CHAPTER VI SUMMARY AND CONCLUSIONS This study focuses on evaluating and comparing the environmental impact of two popular harvesting systems (CTL and TL) used in North Alabama. The study site was chosen for the research because it provided the appropriate conditions for the researcher to conduct the experiments. The site had similar characteristics and some were side-byside. Care was taken by the researcher to make sure each harvesting system was conducted in similar conditions even though we later encounter some technical difficulties. We had to look for a new treatment to complete the third CTL replication after the contractor lost his bid. Furthermore, the CTL system had only one operator for both the forwarder and harvester as oppose to two in the TL system. This made comparing the productivity of the two systems more complicated. Again, we had to replace the antenna for the MultiDAT on feller-buncher about three times because it kept getting cut as the feller-buncher was maneuvering in the woods and scrubbing it against the trees thereby making it difficult to track the positional movement of the machine inside the treatments. In spite of all these problems, we were able to overcome some and carry out our research successfully and were able to answer our research questions. 74

86 Forest operations, particularly harvesting activities greatly affect the health of residual trees and causes huge soil disturbance which is not only a concern to environmentalists but also forest managers. All of these operations are not void of negative consequences ecologically but researchers are interested in researching to find out which system causes less impact and find solutions that will mitigate these adverse effects thereby promoting a more sustainable environment. The first objective of this study was to evaluate and compare the effects of the CTL and TL harvesting systems on soil surface and some soil physical properties. The results of our analysis indicated no significant differences in the soil disturbance caused by the two harvesting methods. However, we experienced some variation in the various soil disturbance classes between the two harvesting systems. The movement of heavy equipments and the dragging of harvested trees to the landing areas caused a lot of soil disturbances along trafficking trails. Based on our analysis in GLM, there was a significant variation in the level of point to point surface soil disturbance classes within the two harvesting systems except for DC5 (Road construction). Disturbance from road construction was the least in both harvesting systems because in most of the treatments, only one major road was usually constructed to serve as an outlet. Again, our analysis also revealed that the direction of logging (orientation) causes significant variation in the surface soil disturbance classes between the two systems. The interaction between methods, plots, and subplots did not show any significant variation in the level of surface soil disturbance among the various soil disturbance classes. Surface soil disturbance is important because it affects the rate of surface runoff (erosion) and consequently the 75

87 productivity of the residual trees. Slash left in piles like in the TL system also the regeneration of the forest particularly around the landing areas (see Appendix 8). Soil compaction results revealed that overall; there was no significant difference between the two systems. However, there was variation in the range of activities (center of high activities and that of low or moderate activities). In the TL system, these areas were along the major trail where harvested trees were skidded to the landing areas and around the landing areas while in the CTL systems the center of activity was along the major outlet route. The t-test indicated a significant difference between the disturbed and non-disturbed surfaces thereby showing a high correlation between areas of high trafficking and no trafficking. Soil compaction is very important to the tree root penetration into the ground and also the absorption of nutrients (Ampoorter et al., 2007). Since the movement of heavy equipment inside the forest also causes residual tree damage, our second objective was to compare the two harvesting methods and see which of them causes more residual stand damage than the other. Residual tree damage is important because it affects the health of the trees (Clatterbuck, 2006). The results of our statistical analysis revealed that residual tree damage was significantly higher for the TL system than in the CTL system. This was evident in all the damage classes but for intermediate stem damage (170 trees observed for the CTL against 154 trees for the TL). Most of the damage trees in the TL system were observed along the major skidding trail representing 72% of the total damage. A majority of the scars in the two harvesting system were found less than 1 m above the ground level. Cost and productivity are important components that determine the profitability of a logging system. Cost influences the type of harvesting system and equipments an 76

88 operator needs to buy. Thus, our third objective was to evaluate and compare which of these harvest systems will cost less to own and operate and at the same time have high productivity rate, thereby yielding higher profits to the operator. A combination of these factors will allow the operator to stay competitive in the business. The results of our analysis indicated that the CTL harvesting system has a lower total cost to own and operate than the TL harvesting system ($208.52/PMH for the harvester and $98.16/PMH for the forwarder in the CTL system, and $165.11/PMH for the feller-buncher and $160.10/PMH in the TL system). The feller-buncher and the skidder in the TL harvesting system had a higher productivity rate (70 tons/pmh and 56 tons/pmh respectively) than that of the harvester and forwarder (36 tons/pmh and 17 tons/pmh respectively) in the CTL system. This was because the CTL operator was working alone. However, with the current price offered at the mill for the full tree ($38 per ton of wood) as oppose to $35.00 per ton for the already cut logs from the CTL system, the TL operator was making a higher net gain of about $15, more than the CTL operator. If the CTL operator had not paid off for his equipment, he would have been making enormous losses. This analysis can explain why most operators around northern Alabama will prefer the TL system over the CTL. The only thing that can change this trend will be for forest manager to factor CTL harvesting method into their management objectives and give some consideration to the CTL operators in the contract bidding process so that they can compete the TL operators. 77

89 RECOMMENDATIONS In light of the results of this study, the following recommendations should be taken into consideration in order to ensure that any forest operation will mitigate its impact on the environments and also ensure sustainability. Since in our statistical analysis of soil disturbance, we found out that orientation (direction of harvesting) was significant, directional felling of trees should be taken seriously. This is a situation whereby trees are felled to fall perpendicular to the main direction of the slope as this will reduce the impact of raindrops and runoff. Directional felling is important not only because it increases production but it also reduces residual stand damage. This will entail the need for pre-harvest planning. During pre-harvest planning, the skid trails should be laid out to reduce residual tree damage, increase efficiency and reduce overall skid distance, property boundaries, and stream management zones (SMZs) or any other control points should be located so they can be avoided without slowing the operation down. More important in this pre-harvesting planning process, the researcher should be able to communicate with the operators as what kind of measurements are to be carried out, equipment to be placed on the machines, and also make sure all bid deals have been finalized. This will prevent the situation we ran into where the CTL contractor pulled out. Finally, based on the results of this study, even though the CTL harvesting system has a higher initial investment and operating cost and reduced productivity, it is still highly recommendable because: 1) it is more suitable for directional tree felling and minimizes residual stand damage than the TL harvesting system, 2) the harvester in the CTL system is highly flexible in that it can be used as a feller-buncher where whole trees are felled and bunched to an open location for 78

90 delimbing thereby avoiding much crown damage, and 3) the CTL system can be programmed to process trees into specific log lengths that meet specific factory-grade log requirements or pulpwood length. In conclusion, the decision as to which harvesting method ought to be used should be based on the management goals. This should be factored into the management plan before a bid is offered. In this way, this very important management decision is not left in the hands of the highest bidder. If this happens, then obviously the TL contractor will take precedence as seen by the results of our analysis. However, any management decision should seriously consider the environmental consequences of any forest operation as the obvious goal should be environmental sustainability. 79

91 APPENDICES Appendix 1 Figure 4: Use ArcGIS to determine the number of points on a treatment area (about 1000). 80

92 Appendix 2 Figure 5: Selecting Six Sampling plots of 9 points each (total = 54) Appendix 3 Determining the required sample size the following formula was used 1 n = (Equation 3) 2 A 1 + ( t)( cv) N n - Sample size (units/points) A - Allowable error (expressed as a % of the mean). A 25% allowable error was chosen t - Confidence level (95 % confidence level = 2) 81

93 cv - Coefficient of variation (this number is gotten from pre-sampling = 94) N - Population (total number of point estimated to be treatment area = 1000) 1 n = = 54 points (2)(94) 1000 Appendix 4 0 0(N) (W) 10 m 10 m 90 0(E) (S) Figure 6: Using DME to measure soil disturbance 10m in each direction 82

94 Appendix 5 Figure 7: Movement of the Harvester and Forwarder inside Somerville treatment. 83

95 Appendix 6: Harvesting equipment used in these operations Feller-buncher A felle- buncher is a mobile machine, either rubber tired or tracked, with a power engine, operator enclosure, and an articulating extensible arm onto which a felling head is attached. The felling head consists of grappling devices and either a disc saw or a chain saw. The operator moves the machine into position in front of a tree and maneuvers the felling head to the tree trunk. The grappling devices wrap around the tree and the saw severs the tree from the stump. The machine then takes the severed vertical tree and lowers it into a horizontal position onto a pile or bunch of trees on the ground, hence the term feller-buncher. Skidder: A grapple skidder is a rubber tired four-wheel-drive machine consisting of a power engine, operator enclosure, forward dozer blade and a maneuverable grappling device at the back of the machine. These machines are generally used where fellerbuncher machines are working. The grapple skidder backs into position adjacent to previously felled piles (bunches) of trees. The operator opens and lowers the grapple onto the trunks of the trees and then closes the grapple and raises the tree trunks slightly off the ground. The machine then moves the trees through the woods to the landing and drops them off. 84

96 Figure 8: Skidder Figure 9: Skidder Figure 10: Feller Buncher Figure 11: Forwarder Harvester A harvester is a machine that combines the features and abilities of the feller buncher and processor and that may or may not have a bunk to store and then forward the trees or cut logs to the landing. 85

97 Forwarder A forwarder is a tracked or rubber tired machine consisting of a power plant, operator enclosure, dozer blade, articulating grapple, and a bunk to the rear. This machine usually follows the processor and picks up the cut-to-length logs, places them in the bunk and then takes the logs out of the woods and piles them at the landing. It then moves back into the woods to repeat the process. A forwarder may also be used to pick up bunched trees and forward them to the landing where a machine called a delimber is used to remove the limbs, cut off the tops, and pile the logs. Delimbers: Dangle-head Delimber. The dangle head sits at the end of a boom on a wrist-like swivel. The head has wheels in which pull the tree through, delimbs, measures, and then cuts to length. These machines can process approximately 2 to 4 trees per minute depending on timber type and operator experience, with the operator fully enclosed in a protective cab. They require less landing space than the stroke delimber. Stroke Delimber The stroke delimber does not have a moveable head; it rather grabs the tree and pulls it through the main body as it removes the limbs and cuts to the desired length. A stroke delimber grasp the tree at the butt end, extend the boom and sharp knives/arms conform to the bole and cut off the limbs as the stroke arm extends. There is a bucking 86

98 saw that tops the tree at the minimum preset diameter. The bole is then drawn into the machine, a calibrated wheel/gear determines when the right log length is reached, and the bucking saw does its work. 87

99 Appendix 7: Machine rate calculation for feller-buncher for two weeks Feller-buncher 1. Input Data Purchase price (P) $ 140,000 Machine horsepower rating (hp) hp 170 Machine life (n) yrs 5 Salvage value, percent of purchase price (rv%) % Utilization rate (ut%) % 0.2 Repair and maintenance, percent of depreciation (rm%) % 0.01 Interest rate (in%) % Insurance and tax rate (it%) % Fuel consumption rate (fcr) gal/hp-hr 0.03 Fuel cost (fcg) $ per gal 2.3 Lube and oil, percent of fuel cost (lo%) % 0.04 Operator wage and benefit rate (WB) $ hr 15.6 Scheduled machine hours (SMH) hrs/10 days Calculations Salvage value (S) = (P*rv%) $ Annual depreciation (AD) = ((P-S)/n) $ /10 days Average yearly investment (AYI) = ((((P-S)*(n+1))/(2*n))+S) $ /10 days Productive machine hours (PMH) = (SMH*ut%) hrs/10 days Ownership costs Interest cost (IN) = (in%*ayi) $ 10 days Insurance and tax cost (IT) = (it%*ayi) $ 10 days Yearly ownership cost (YF$) = (AD+IN+IT) $ 10 days Ownership cost per SMH (F$SMH) = (YF$/SMH) $ hr Ownership cost per PMH (F$PMH) = (YF$/PMH) $ hr Operating costs Fuel cost (F) = (hp*fcr*fcg) $ hr 11.5 Lube cost (L) = (F*lo%) $ hr 0.46 Repair and Maintenance cost (RM) = (AD*rm%/PMH) $ hr 0.53 Operator labor and benefit cost (WB/ut%) $ hr Operating cost per PMH (V$PMH) = (F+L+RM+(WB/ut%)) $ hr Operating cost per SMH (V$SMH) = (V$PMH*ut%) $ hr Total Machine Costs Total cost per SMH (T$SMH) = (F$SMH+V$SMH) $ hr Total cost per PMH (T$PMH) = (F$PMH + V$PMH) $ hr

100 Appendix 8: Machine rate calculation for skidder for two weeks Skidder 1. Input Data Purchase price (P) $ 185,000 Machine horsepower rating (hp) hp 119 Machine life (n) yrs 5 Salvage value, percent of purchase price (rv%) % 0.2 Utilization rate (ut%) % 0.25 Repair and maintenance, percent of depreciation (rm%) % 0.01 Interest rate (in%) % 0.05 Insurance and tax rate (it%) % 0.05 Fuel consumption rate (fcr) gal/hp-hr 0.05 Fuel cost (fcg) $ per gal 2.3 Lube and oil, percent of fuel cost (lo%) % 0.04 Operator wage and benefit rate (WB) $ hr 15.6 Scheduled machine hours (SMH) hrs/10 days Calculations Salvage value (S) = (P*rv%) $ Annual depreciation (AD) = ((P-S)/n) $ 10 days Average yearly investment (AYI) = ((((P-S)*(n+1))/(2*n))+S) $ 10days Productive machine hours (PMH) = (SMH*ut%) hrs/10 days Ownership costs Interest cost (IN) = (in%*ayi) $ 10 days Insurance and tax cost (IT) = (it%*ayi) $ 10 days Yearly ownership cost (YF$) = (AD+IN+IT) $ 10 days Ownership cost per SMH (F$SMH) = (YF$/SMH) $ hr Ownership cost per PMH (F$PMH) = (YF$/PMH) $ hr Operating costs Fuel cost (F) = (hp*fcr*fcg) $ hr Lube cost (L) = (F*lo%) $ hr 0.55 Repair and Maintenance cost (RM) = (AD*rm%/PMH) $ hr 0.59 Operator labor and benefit cost (WB/ut%) $ hr Operating cost per PMH (V$PMH) = (F+L+RM+(WB/ut%)) $ hr Operating cost per SMH (V$SMH) = (V$PMH*ut%) $ hr Total Machine Costs Total cost per SMH (T$SMH) = (F$SMH+V$SMH) $ hr Total cost per PMH (T$PMH) = (F$PMH + V$PMH) $ hr

101 Appendix 9: Machine rate calculation for harvester for two weeks Harvester 1. Input Data Purchase price (P) $ 0 Machine horsepower rating (hp) hp 163 Machine life (n) yrs 0 Salvage value, percent of purchase price (rv%) % 0.09 Utilization rate (ut%) % Repair and maintenance, percent of depreciation (rm%) % 0.01 Interest rate (in%) % 0.04 Insurance and tax rate (it%) % 0.04 Fuel consumption rate (fcr) gal/hp-hr Fuel cost (fcg) $ per gal 2.3 Lube and oil, percent of fuel cost (lo%) % 0.04 Operator wage and benefit rate (WB) $ hr 15.6 Scheduled machine hours (SMH) hrs/10 days Calculations Salvage value (S) = (P*rv%) $ 0 Annual depreciation (AD) = ((P-S)/n) $ 10 days 0 Average yearly investment (AYI) = ((((P-S)*(n+1))/(2*n))+S) $ 10 days 0 Productive machine hours (PMH) = (SMH*ut%) hrs/10 days Ownership costs Interest cost (IN) = (in%*ayi) $ 10 days 0 Insurance and tax cost (IT) = (it%*ayi) $ 10 days Yearly ownership cost (YF$) = (AD+IN+IT) $ 10 days 0 Ownership cost per SMH (F$SMH) = (YF$/SMH) $ hr 0 Ownership cost per PMH (F$PMH) = (YF$/PMH) $ hr Operating costs Fuel cost (F) = (hp*fcr*fcg) $ hr Lube cost (L) = (F*lo%) $ hr Repair and Maintenance cost (RM) = (AD*rm%/PMH) $ hr 0 Operator labor and benefit cost (WB/ut%) $ hr Operating cost per PMH (V$PMH) = (F+L+RM+(WB/ut%)) $ hr Operating cost per SMH (V$SMH) = (V$PMH*ut%) $ hr Total Machine Costs Total cost per SMH (T$SMH) = (F$SMH+V$SMH) $ hr Total cost per PMH (T$PMH) = (F$PMH + V$PMH) $ hr

102 Appendix 10: Machine rate calculation for forwarder for two weeks Forwarder 1. Input Data Purchase price (P) $ 0 Machine horsepower rating (hp) hp 116 Machine life (n) yrs 0 Salvage value, percent of purchase price (rv%) % 0.09 Utilization rate (ut%) % 0.20 Repair and maintenance, percent of depreciation (rm%) % 0.01 Interest rate (in%) % 0.04 Insurance and tax rate (it%) % 0.04 Fuel consumption rate (fcr) gal/hp-hr Fuel cost (fcg) $ per gal 2.3 Lube and oil, percent of fuel cost (lo%) % 0.04 Operator wage and benefit rate (WB) $ hr 15.6 Scheduled machine hours (SMH) hrs/10 days Calculations Salvage value (S) = (P*rv%) $ 0 Annual depreciation (AD) = ((P-S)/n) $ 10 days 0 Average yearly investment (AYI) = ((((P-S)*(n+1))/(2*n))+S) $ 10 days 0 Productive machine hours (PMH) = (SMH*ut%) hrs/10 days Ownership costs Interest cost (IN) = (in%*ayi) $ 10 days 0 Insurance and tax cost (IT) = (it%*ayi) $ 10 days Yearly ownership cost (YF$) = (AD+IN+IT) $ 10 days 0 Ownership cost per SMH (F$SMH) = (YF$/SMH) $ hr 0 Ownership cost per PMH (F$PMH) = (YF$/PMH) $ hr Operating costs Fuel cost (F) = (hp*fcr*fcg) $ hr 9.2 Lube cost (L) = (F*lo%) $ hr Repair and Maintenance cost (RM) = (AD*rm%/PMH) $ hr 0 Operator labor and benefit cost (WB/ut%) $ hr Operating cost per PMH (V$PMH) = (F+L+RM+(WB/ut%)) $ hr Operating cost per SMH (V$SMH) = (V$PMH*ut%) $ hr Total Machine Costs Total cost per SMH (T$SMH) = (F$SMH+V$SMH) $ hr Total cost per PMH (T$PMH) = (F$PMH + V$PMH) $ hr

103 Appendix Figure 12: Shows pictures of landing areas in the TL system one year after harvesting has been completed.

104 92 Figure 13: Shows pictures of center of main activity in the CTL system one year after harvesting has been completed.