Assessment of timber faller working conditions in mixed hand and tethered-machine cut harvest units on steep slopes- A case study in western Oregon

ABSTRACT

Adoption of tethered-assist harvester technology on steep terrain by the forest industry has decreased workplace accidents. However, there are portions of harvest units that remain inaccessible to mechanized falling, therefore requiring manual falling as well. This study characterized the differences in terrain and forest conditions between manual and machine felled areas within the same harvest units. The hypothesis is that manual fallers will work more time in challenging terrain on harvest units using both mechanized and manual falling when compared to harvest units using manual falling only. This was tested using field data from three new harvest units, six previously harvested units, timber faller surveys, and harvest managers’ interviews. For both field datasets, only slope was a statistically significant predictor of falling method. Further, both managers and fallers confirmed steep slope as one of the main reasons for requiring manual falling in addition to rocky bluffs, unstable terrain, and lack of access. This study indicates that when harvest units on steep terrain are felled with mixed falling methods, timber fallers likely work on steeper slopes than the machine, and spend a larger proportion of work hours on steeper ground than the average slope of a harvest unit.

KEYWORDS:

• Manual falling
• tethered logging
• mixed harvesting system
• steep terrain

Introduction

Technology advancement has shifted timber harvesting and processing from labor-intensive manual operations to mechanization around the world over the past several decades (Silversides 1997, as cited in Lindroos et al. 2017). Although the integration of computerized control systems into ground-based machines is able to improve productivity, reduce energy consumption, and increase wood utilization, the application of mechanized harvesting was generally limited to moderate slopes. However, the recent development and introduction of tethered logging technology has made it possible to extend the use of mechanized timber harvesting to steep slope applications. Like many other regions in the world, tethered logging systems, also known as winch-assisted harvesting systems, are being increasingly adopted in the timber industry in the Pacific Northwest (PNW) of the United States.

In tethered harvesting systems, harvesters, feller-bunchers, forwarders, loaders, and skidders are connected to a cable winch. The cable assists the machine with traction, enhancing its gradeability and stability, thus allowing it to operate effectively on steep slopes. There are two major tethered systems in use in the PNW, classified based on the type of anchor: static and dynamic systems.

In a static system, the winch is mounted on the working machine for harvesting or extraction, and the cable line is stationary in relation to the ground. The cable is usually secured to a tree, stump, or another stationary piece of equipment (Green 2019). This system is commonly used for thinning applications in the PNW, typically for short log production up to 71 cm (28 inches) in diameter. In the dynamic system, the winch is mounted to another specialized heavy equipment, such as a modified excavator or bulldozer, which serves as an anchor and provides power to the winch. The cable attached to the working machine constantly moves on the ground with the uphill or downhill movement of the machine (Green 2019; Holzfeind et al. 2020). The anchor machine is usually placed at the top of the harvest unit and can be paired with different harvesting, forwarding, or skidding machines. A dynamic system is commonly used in clear-cutting applications in the PNW.

As logging has become increasingly mechanized, machine and computer-integrated technologies have altered the health and injury issues associated with logging (Qin 2015). For example, the risk of injury is greatly reduced with mechanization as machine operators work inside protected machine cabs with falling object protection systems (FOPS) compared to manual forest workers who are directly exposed to hazardous work conditions (Qin 2015; Newman et al. 2018; Maslow 2019). Also, with the use of machines, work in harsh weather conditions and the removal of potentially dangerous trees such as snags and blowdowns can be carried out with a significantly reduced risk of injury to the workers (Naillon 2017).

For improved safety, Garland (2018) reported that replacing manual work with equipment for accident-prone activities such as harvesting and other silvicultural operations has significantly reduced the number of forestry-related accidents. In Sweden, Axelsson (1998) found that mechanization reduced injury risk by 73% in 1990 compared to chainsaw-based methods. Similarly, research by Naillon (2017) showed a sevenfold decrease in the injury rate when comparing accepted accident compensation claims of mechanized operations to those of non-mechanized operations in Washington State.

While the tethered-assist technology allows mechanized harvesting into steep slopes, some areas may remain inaccessible to ground-based machines. This is due to factors such as terrain conditions, soil stability, and equipment limitations (Naillon and Rappin 2019). Tethered machines may not be able to access the harvest unit when the terrain is steep, rugged or has rocky outcrops, therefore they must be felled manually. If manual timber fallers are only assigned to areas inaccessible to machines, timber fallers may have to spend more hours on challenging grounds continuously, travel longer distances between scattered patches, and may have a greater chance of getting exposed to hazardous work conditions (Naillon 2017; Naillon and Rappin 2019).

Most research related to tethered-assist technology has been in areas such as productivity, environmental impact, mechanics, and system safety. The extent and characteristics of areas requiring manual timber falling in mixed systems has not been included in previous studies. With the ongoing and foreseeable shortage of labor workforce in forestry (He et al. 2021), it becomes critical to understand the potential impact of tethered-assist technology on timber fallers’ health and safety, its implication on recruitment and training, and wage and insurance structures.

This study aimed to characterize the differences in terrain and forest conditions between manual (or hand) and mechanically felled areas within harvest units through both quantitative and qualitative approaches using field data collection, surveys, and interviews. We hypothesized that in contrast to the traditional manual falling only operations on steep slopes, the use of mechanized harvesting on steep slopes in combination with manual falling would result in manual fallers having to work in more difficult and challenging terrain for more of their work hours. The rationale for this study is based on the concerns voiced by loggers and industry professionals reported in Naillon (2017), where the author documented interviews of timber falling company owners and reported their perspectives on the safety aspects of tether-assist mechanized operations.

Materials and methods

Study area

In collaboration with a private landowner, nine harvest units located on industrial forest lands of Northwest Oregon coastal mountains were studied (Figure 1). Among them, six units were harvested two to three years prior to the study period (hereafter referred to as “previous harvest units”), and three units were harvested during the study period (hereafter referred to as “new harvest units”). As with much of the timber production land in the Pacific Northwest, the landscapes of the study harvest units are characterized by hills with steep faces, slippery and unstable surfaces resulting from heavy precipitation, and rough terrain (Green 2019). The major tree species in the units were Douglas-fir (Pseudotsuga menziesii), Western Hemlock (Tsuga heterophylla), and Western Red cedar (Thuja plicata). The units are being managed with a 35- to 40-year rotation period. The climate of the mountain range is moderate because of marine influences, and annual precipitation is heavy, varying from 152 to 457 cm. These harvest units were selected mainly because dynamic tethered-assist harvesting operations were used or planned to be used during clearcuts. The area of the study harvest units ranged from 30 to 47 hectares.

Figure 1. Study area map showing new and previous harvest units.

Hypothesis

We hypothesized that the areas inaccessible to tethered-assist harvest machines assigned to hand fallers would have the following characteristics:

• Large diameter trees: Due to cutting diameter limit of tethered-assist harvest machines, hand-falling areas may have more larger diameter trees compared to machine-falling areas.

• Steeper ground slope: Tethered-assist harvesters may not be able to access extremely steep or broken slopes. Slope could be an important aspect affecting the safety of timber fallers. Steep slope can increase the possibility of hazardous conditions for fallers such as slips, falling, and rolling of trees, branches, etc. (Garland et al. 2019). It also makes it more difficult for the faller to safely position themselves for cutting, as well as making their way out of the falling zone.

• Shallower soil depth: Shallow soils might not provide enough traction or grouser engagement for heavy equipment to operate. Safetree: Steep Slope Risk Assessment Form. 2021. New Zealand: Safetree; [accessed (2021) has identified thin soil with less than 15 cm (5.9 inches) as a high-risk factor for mechanized logging in their Steep Slope Risk Assessment form for New Zealand. Thus, shallow, or rocky soils could be a factor that limits machine working areas, leaving these to be felled by hand fallers. Slipping and falling can be more likely in rocky areas, particularly when they are wet.

• More hazard trees: Hazard trees may be more common on steep terrain and exposed hillsides. Hazard trees including snags, leaning trees, hang-ups, blowdowns, and widow makers are one of the most common causes of fatalities among hand fallers. In particular, snags are identified as one of the primary hazards for timber fallers in the Logging Safety Manual by Oregon Fatality Assessment and Control Evaluation (OR-FACE, 2007). Snags are weak and unstable, often with hollow trunks, thus making it difficult to predict and control the falling time and direction (Foley 2017).

More unusual ground features: The areas left to hand falling may have greater presence of unusual ground features, including rocky outcrops, gullies, and headwalls.

Data collection

Data collection was carried out on the study harvest units in order to obtain the information needed to characterize machine and hand-falling areas in terms of the above-mentioned attributes.

New harvest units

Pre-harvesting data collection: Fifty plots were placed on each of the three harvest units using ArcMap 10.8.1 (150 plots in total). The plots were systematically located at a fixed distance over the harvest units covering the variations of the site including terrain features and the two-harvest systems (i.e., mechanized and manual). All of the plots were circular with a radius of 5.09 m (16.7 ft.). After locating the plot in the field and establishing the plot area, the following predictor variables were measured on each plot:

• Tree size: DBH of all trees in the plot were measured using a diameter tape.

• Terrain slope: The ground slope perpendicular to the contours was measured in percentage using a laser range finder. Two measurements were taken from each end of the plot along the perpendicular line and an average value was calculated.

• Soil depth: Soil depth was measured by a probe in five random locations in each plot and an average value was calculated.

• Hazard trees: The number of hazard trees were counted in each plot. Hazard trees are defined as a tree with heavy lean, hung up with other trees, broken or multiple tops, and snags of any condition.

Post-harvest data collection: After being harvested, the new harvest units were visited again in order to determine and record the boundaries of the areas that were felled by hand using GPS. To identify which falling method was used for trees in an area, cut pattern on the stump was inspected. The stumps of hand-felled trees usually have two cuts with splintered wood between the cuts, while machine felled stumps usually have a straight surface. The boundaries were then superimposed to the plot locations in GIS to determine which method was used in each plot location.

Previous harvest units

Fifty plots were placed systematically over each of the six previous harvest units using ArcGIS (300 plots in total) with the same procedure as for the new harvest units. Slope was measured using the topographical harvest unit layout maps provided by the landowner. The maps already contained the boundary information of hand and machine-felled areas. Plot level stand and soil conditions of these units were not assessed because they were harvested a few years ago and had potentially experienced soil disturbance and alteration (e.g., compaction, displacement, erosion, etc.). These alterations are especially critical in the first soil layer as it will determine the degree of grouser engagement and therefore drives the decision to use the machine in specific areas.

Survey and interview

For the qualitative data collection, pre-tested sets of open-ended questions were developed to allow participants to answer in an open text format based on their experience and understanding (Smith et al. 2009). After Institutional Review Board (IRB) approval, an anonymous survey was sent to timber fallers and an interview was solicited from the study units harvest managers.

For timber fallers, a total of 15 surveys were sent via mail to five different logging companies in Oregon known to be using tether-assist harvest systems. Of them, two agreed to participate. The questions for timber fallers were designed with the aim of assessing their perception about working as fallers in a mixed falling method unit and determine reasons for requiring hand falling. For the harvest managers, questions were asked regarding their decision-making and planning processes for each of the study units.

Data analysis

New harvest units

Average tree DBH, average slope, average soil depth, and the total number of hazard trees per plot were calculated. To model the two falling methods on the variables measured in the field, a multivariate logistic regression was developed using the measured predictor variables (i.e., harvest unit characteristics variables). This method can be used in situations in which we want to model a binary response variable (falling method) on a mixture of continuous and categorical predictor variables (Scheiner and Gurevitch 2001).

After checking for assumptions specific to logistic regression, the model was fitted to determine which predictor variables were statistically significant, and the variables that were not relevant were left out of the final model. The modeling and tests were performed using R (R Core Team 2021).

A simple logistic model can be expressed using equation 1(1) $logit\left(Y\right)=naturallog\left(odds\right)=ln\left(\frac{\pi }{1-\pi }\right)=\alpha +{\beta }_1\ast X_1+\dots .+{\beta }_k\ast X_k$(1) (Peng et al. 2002):

(1) $logit\left(Y\right)=naturallog\left(odds\right)=ln\left(\frac{\pi }{1-\pi }\right)=\alpha +{\beta }_1\ast X_1+\dots .+{\beta }_k\ast X_k$(1)

Where,

π = probability of the event,

α = Y intercept,

β_s = regression coefficients,

X_s = predictors, and

k = number of predictors

For calculating the log-odds of hand falling, equation 2 was used:

(2) $M={\beta }_0+{\beta }_1\ast DBH+{\beta }_2\ast Slope+{\beta }_3\ast Soil\mathrm{\_}depth+{\beta }_4\ast Hazard$(2)

Where,

M: log odds of hand falling, plot was hand felled (1) or machine felled (0)

DBH: plot average DBH (cm)

Slope: plot average slope (%)

Soil depth: plot average soil depth (cm)

Hazard: sum of the number of hazard trees per plot (units)

β_x: Model parameters

Previous harvest units

An average slope per plot was calculated from the maps. Similar to the New Harvest Units, a logistic regression was used to model the binary response variable “falling method” on the predictor slope. After checking for assumptions specific to logistic regression, the model was fitted using R (R Core Team 2021).

The model for the previous harvest units is expressed using equation 3:

(3) $M={\beta }_0+{\beta }_1\ast Slope$(3)

Where,

M: log odds of hand falling, plot was hand felled (1) or machine felled (0)

Slope: plot average slope (%)

β_x: Model parameters

Survey and interview

An analysis of the survey responses was done using the deductive approach. It is an approach in which researchers have a predetermined structure or theories, and the analysis is made based on that structure using a preplanned framework (Williams et al. 2004; Burnard et al. 2008). The survey responses among anonymous timber fallers were screened to find the evidence supporting (or not supporting) our research hypotheses. The response from the harvest manager’s interviews was summarized to observe if they were in agreement with the results of the field data analysis.

Results

New harvest units

Out of this group of units, one (NH2) was almost completely felled by machine (only 2% of the area was manually felled), mainly due to the operator’s preference. For that reason, it was excluded from further analyses. The other two units, i.e., NH1 and NH3, experienced relatively equal distribution of the two falling methods with 40% and 52% of the unit being felled by machine, respectively. Among 100 plots across the two harvest units, 54 were hand felled and the remaining 46 were felled by machine. All of the 100 plots were analyzed as a single dataset.

Descriptive statistics showed that the differences in DBH and average soil depth between the two groups was minimal, and the number of hazard trees between the two groups had a difference of 29 trees/ha on average (Table 1).

Table 1. Summary statistics of measured characteristic variables (new and previous harvest units).

Falling Methods		Hand Felled	Machine Felled	Total/Mean
New Harvest Units
Number of Plots		54	46	100
DBH (cm)	Mean	43	42.5	43
	SD	14.2	14.5	14.3
Terrain slope (Percent)	Mean	46.2	31.6	39.5
	SD	17.2	19.2	19.5
Soil depth (cm)	Mean	60.8	59.1	60
	SD	12.2	12.8	12.4
Hazard trees	Mean (trees per plot)	1.5	1.3	1.4
	SD (trees per plot)	1.7	1.4	1.6
	Mean (trees per ha)	189	160.3	174.6
Previous Harvest Units
Number of Plots		167	133	300
Slope (Percent)	Mean	61.7	32.6	48.8
	SD	22.4	20	25.8

Hand-felled areas were found to have steeper slopes compared to machine felled areas across the aggregated data from the two units as well as in each separate unit (Figure 2). The average slope for hand felled and machine felled areas from the combined units was 46.2% (25°) and 31.6% (18°), respectively (Table 1).

Figure 2. Mean (red dot) and median slope (black line inside box) for different falling methods for new harvest units. The dark blue dots represent outliers.

The logistic model output indicates that the variables average DBH, average soil depth, and number of hazard trees per plot were not statistically significant (p > .50) and were dropped from the model (Table 2). However, the slope was a significant predictor of falling method (p < .001). The reduced logistic regression model is shown on equation 4:

(4) $M_{NH}=-2.49+0.042\ast Slope$(4)

Table 2. Output statistics from logistic regression analysis (new and previous harvest units).

Predictor	Estimate	Standard Error (β)	Wald’s Z Stat	p-value	e^β (odds ratio)
New Harvest Units
Intercept	−2.4902	1.5717	−1.5840	0.1131	0.0829
DBH	0.0152	0.0408	0.3740	0.7083	1.0154
Slope	0.0425	0.0123	3.4610	0.0005	1.0434
Hazard trees	0.0777	0.1492	0.5210	0.6024	1.0808
Soil depth	0.3180	0.5667	0.5610	0.5747	1.3744
Previous Harvest Units
Intercept	−2.4891	0.3333	−7.4670	0.0000	0.0830
Slope	0.0592	0.0070	8.4110	< 2e-16	1.0610

Where,

M_NH = log odds of hand falling, plot was hand felled (1) or machine felled (0)

Slope = terrain slope (%)

According to the model, the log of the odds of an area being manually felled within a mixed harvest method unit is positively related to the slope (p < .01). The odds increase by exp^(0.042) or 1.04 for each unit increase in slope (%). For example, the odds of an area being felled manually are 1.57 (exp^{(−2.49 + 0.042*70)}) for 70% slope, and 0.68 (exp^{(−2.49 + 0.042*50)}) for 50% slope.

The most common ground feature found in hand felled areas was the presence of large rocks (7.4% of the total hand felled plots), followed by areas such as a headwall of creeks and gullies (3.7% of the total hand felled plots), indicating that approximately 10% of the hand felled areas contained unusual features. On the contrary, no unusual features were found in the machine felled areas.

Previous harvest units

The proportion of machine-felled areas within the previous harvest units was between 25 and 45%, with an average of 37%, leaving the majority of the areas to hand fallers.

The mean slope of the manually felled areas was greater than that of the machine-felled areas, which is shown in both the aggregated data across the units and individual units (Figure 3). The average terrain slopes of manual and machine felled areas were 61.7 and 32.6% (32° and 18°), respectively (Table 1).

Figure 3. Boxplot showing mean (red dot) and median slope (black line inside box) for different falling methods for (top) aggregated data and (bottom) individual units [h: hand-felled & m: machine-felled] for previous harvest units. The dark blue dots represent outliers.

Similar to the new harvest units, average slope had a significant effect on falling method (p < .0001). The logistic regression for the previous harvest units is expressed using equation 5:

(5) $M_{PH}=-2.49+0.059\ast Slope$(5)

Where,

M_PH = log odds of hand falling, plot was hand felled (1) or machine felled (0)

Slope = terrain slope (%)

The odds that an area of a harvest unit employing mixed falling methods is felled by hand increase by exp^(0.059) or 1.06 for each unit increase in slope (%). For example, the odds of an area being felled by hand are 5.15 (exp^{(−2.49 + 0.059*70)}) for 70% slope, and 1.58 (exp^{(−2.49 + 0.059*50)}) for 50% slope (Table 2).

Survey and interview

Timber fallers survey

A total of 7 surveys were collected after 15 surveys were mailed to hand fallers and submitted anonymously. When asked about the perception of hand falling in mixed hand and machine operations, 86% of the respondents strongly agreed that the work of hand fallers is concentrated in the most difficult areas, which supports our research hypothesis, while the remaining 14% neither agreed nor disagreed. The factors that were identified by the respondents as hazards were rocky terrain (86%) and steep slope (71%), followed by other factors, such as windfall patches and areas with hazard trees.

Moreover, survey results showed that steep slopes were the primary reason why the machine could not operate in certain areas, resulting in the need for hand falling. Steep slope was followed by rocky terrain, highly compacted or loose ground, lack of access, and oversize trees (Figure 4).

Figure 4. Timber-fallers perception about reasons for the need of hand falling in hand and machine mixed falling operations.

Harvest manager interview

The interview responses from the harvest managers of the study units showed that steep slope is a common reason for requiring hand falling among all nine harvest units (Table 3). Other reasons include tree size, short reach of the machine, and poor stocking.

Table 3. Reasons for requiring hand falling for the study units.

Unit ID	Reasons for requiring hand falling
NH1	Areas too steep for machineTimber larger than machine capacity
NH2	Areas too steep for machine
NH3	Areas too steep for operator comfortIncised draws, headwalls, and wet areas
PH1	Areas too steep for the machine
PH2	Areas too steep for the machine
PH3	Areas too steep for the machinePoorly stocked (unproductive to set the machine up)
PH4	Areas too steep for the machineReaches were too short to justify the setup
PH5	Areas too steep for the machine
PH6	Areas too steep for the machine

Hand falling was performed prior to mechanical falling for two of the new harvest units. In contrast, for NH2, the machine did its work first, and hand fallers later harvested a small area that was inaccessible to the machine. Similarly, four harvest units were felled by machine first in the case of the previous harvest units; only two units were felled by hand fallers first.

Discussion

In both new and previous harvest units, slope was the only significant predictor of the falling methods. Slope also consistently appeared as the major reason for hand falling in the timber faller survey and harvest manager interview, as well as in anecdotal evidence from the logging and forestry community.

Our results show that the average slope of the hand felled areas in the new harvest units was 46% (25°), which is a 14% (8°) greater than the average slope of the machine-felled areas. If the unit had been felled only by hand, the average slope for hand falling would have been only 39% (21°). This finding can be translated into the proportion of timber faller work hours spent on steep slopes. For example, assuming the timber faller has the same productivity regardless of ground slope, our data show that 54% of the total timber faller hours had to be spent on areas steeper than a 50% slope in the new harvest units. In contrast, if the entire units were to be cut by hand, only 38% of the total timber faller hours required to cut the harvest units would have been spent on areas steeper than a 50% slope. The total amount of timber faller hours being spent on steep slopes may not be reduced by hand falling only, but the percentage of work hours spent on steep slopes relative to the total work hours is lower when the unit is cut by hand only.

Similarly, the data from the previous harvest units show that hand fallers worked in areas that are 30% (17°) steeper on average than the machine-felled areas. Timber fallers had to spend 63% of their work hours on slopes greater than 50%, compared to only 40% of their time if the units were hand felled only.

Having to spend a larger proportion of work hours on steeper slopes may mean more consistent and difficult work to perform, which can potentially increase fatigue-related operational risks for timber fallers (Garland et al. 2019). This is in line with the statement made by Naillon and Rappin (2019) about a potential increase of hazards due to the introduction of steep slope mechanized logging technology.

The odds of hand falling increases at a much higher rate on the previous harvest units (1.06 per unit increase in slope versus 1.04), compared with the new harvest units. This difference is given by the fact that the previous harvest units have steeper slopes in general, which are also assigned to hand fallers.

The Occupational Safety and Health Administration (OSHA, 2016) has stated that the operation of tethered equipment beyond a 40 or 50% slope is only permitted if the manufacturer specifies it. However, a slope greater than 50% was observed in approximately 20% and 16% of the new and previous harvest machine-felled plots, respectively. This variation could be attributed to the machine operator’s experience and comfort level in their ability to operate on steep slopes (Harvest manager, personal comm. 4 May 2021).

In our logistic models, there was no evidence of tree size being a good predictor for falling methods. We noticed during field work that machines occasionally felled trees larger than their cutting capacity through multiple cuts. Nevertheless, “timber size larger than machine capability” was identified by the harvest manager (NH1) as a reason for hand falling (Table 3). Similarly, soil depth was no different between hand and machine felled areas. Shallow soil was not identified as the primary reason for hand falling in any of the harvest units, but machine instability was identified as a reason to avoid machine falling in our survey.

Although there was no evidence of the number of hazard trees being a good predictor of falling method, 11% of the hand-felled plots across the study harvest units contained more than three hazard trees. If the entire harvest units were manually felled, only 8% of the total area would have more than three hazard trees. This result shows that hand fallers could be exposed to more hazard trees during work hours in the harvest units where both manual and mechanized falling takes place. More hazard trees indicate a more dangerous work environment for hand fallers (Sullman and Kirk 2001; Angwin et al. 2012).

Seven out of the eight study harvest units had more than 50% of the unit cut manually. This is not in agreement with what was noted in Naillon and Rappin (2019) and Naillon (2017) that hand falling areas are scattered in small patches when mixed with machine falling. Terrain condition might dominate the determination of manual and machine falling areas, but we learned throughout the study, that it may also depend on the machine operator, machine accessibility, unstable terrain, and the preference of harvest managers in the planning process. For example, some harvest managers leave timber falling decisions to individual logging contractors, while others plan the areas to be hand felled in advance, which can also depend on the availability of falling crews.

Even though hand falling preceded machines in two new harvest units, as mentioned in the harvest managers interview, the reverse was the case for most previous harvest units. For the new harvest units, the lead hand faller was fully engaged in the planning process, allowing them to assess and explore solutions for any potential hazardous situations ahead of time. This approach is consistent with the recommendations of Naillon and Rappin (2019) and Naillon (2017) to have the hand fallers cut first and involve them in the planning process to improve their safety. The authors affirmed that when the machine goes first, new hazards could be created and added for the hand fallers. For instance, the remaining trees to be cut by hand fallers will be “brushed in” if the machine operator does not place the cut trees properly. This may block the escape route for fallers and make walking in the area more difficult. Also, fallers may be exposed to increased numbers of windblown trees and broken limbs and treetops that may result from earlier strikes by trees felled with machines or from the increased exposure of the remaining trees to winter weather (Naillon 2017; Naillon and Rappin 2019). Moreover, there could be an increased risk of injury for hand fallers and rigging crews from the increased number of rolling logs, rocks, and other chunks on steep slopes (Wempe and Keefe 2017; Newman et al. 2018; Naillon and Rappin 2019). We believe an early engagement of timber fallers in the operational planning process could be a simple step that could reduce safety hazards for hand-fallers.

Our findings from the field data are generally confirmed by the hand faller survey results. Eighty-six percent of the respondents stated that steep slopes are the main reason for hand falling. Although seventy-one percent indicated that rocky terrain (71%) is a reason for hand falling, there was not clear evidence of this in our field data, which may be attributed to the geographic location of the study units. Lack of machine accessibility (29%) was another reason that we could not validate using our field data.

Conclusions

This study characterized the impact of terrain conditions in choosing the falling methods in order to understand the changes in work environment of timber fallers when machine and hand falling are mixed in steep slope harvest units. Our field data collection, interview and survey conducted in western Oregon indicate that:

When harvest units in steep terrain are felled by both hand and machine, timber fallers work on steeper slopes than the machine, potentially spending a larger proportion of work hours in steeper ground than the harvest unit average.

Slope was the only variable that was a significant predictor of falling methods. Tree size, soil depth, and the number of hazard trees were not significant in the logistic regression model.

Both the harvest manager and timber fallers consider steep slopes as a major reason for hand falling. Other reasons include rocky bluffs, unstable terrain, machine accessibility, and poor stocking.

This study provides the initial assessment of the working conditions and potential safety issues for timber fallers working on mixed manual and machine timber falling operations on steep terrain. Although our study is limited due to sample size and its inference is limited to western Oregon, it provides an insight about potential changes in the work environment for timber fallers that may be introduced by tethered steep slope logging technology. We believe early engagement of timber fallers into operational planning process and decision-making would be a positive step toward avoiding and mitigating potential hazards.

Future studies may consider the influence of the machine operator’s experience and cover a wider geographical area across a variety of forest ownerships for better understanding of decision-making on hand falling. Future studies may also conduct a worker safety assessment and analyze accident data to understand long-term consequences of the new technology introduction. These future studies will provide a base to work toward forest worker’s health and safety, workforce training, worker’s compensation, and insurance rate adjustment for the new work environments.

Acknowledgements

We would like to thank the landowner who provided us with the harvest unit maps and permission to visit the units for the purpose of collecting field data, and the harvest manager of the units for being our point of contact throughout the whole study period. Our sincere gratitude also goes out to the logging companies who accepted our invitation to participate in the survey, as well as timber fallers who shared their knowledge and perceptions with us.

Disclosure statement

No potential conflict of interest was reported by the author(s).