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Logging Crew Attributes by Region in the Southeast USA

Copyright © 2017 by Croatian Journal of Forest Engineering
doi: 10.5552/crojfe.2023.2250
volume: 44, issue:
pp: 9
Author(s):
  • Diniz Carlos
  • Smidt Mathew F.
  • Cooper Jason
  • Zhang Yaoqi
Article category:
Original scientific paper
Keywords:
logging business, timber harvest, productivity, labor, Piedmont, Coastal Plain

Abstract

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Urbanization, shrinking markets, and reduced forestry investment may affect harvesting efficiency in regions of the US South. To monitor these conditions, logging businesses have been tracked by surveys conducted by universities and trade associations. This project used a sampling approach coordinated with FIA utilization studies to sample logging crews based on a harvesting location. The approach was used to develop relationships among firm attributes and site attributes in six southeastern states (AL, GA, FL, NC, SC, and VA) from 2011 to 2018. The data included harvest attributes (location, harvest size and stand type) and logging firm attributes (production, crew labor, crew number, the number of machines by type, and machine age). For crew capital value, an equation was developed for this study using machine number and average machine age. The data from logging crews on 419 harvests were analyzed by region, harvest size, and stand type. Mean values for crew labor ranged from 3.1 to 7.1 workers. The average capital value per crew ranged from $220,000 to $524,000 per crew in the Coastal Plain with a narrower range in the Piedmont. In the Coastal Plain, higher productivity was detected for larger harvests and pine versus hardwood and mixed stands; however, in the Piedmont those trends were less obvious. Ratio of feller-bunchers, skidders and loaders were mostly 1:1:1 or 1:2:1 with 41% and 24% of samples, respectively. There were notable trends among Coastal Plain loggers regarding capital value and productivity with evidence supported by a production function. The differences in Piedmont (e.g., ownership size, market access, terrain, population density, etc.) may combine to limit daily production and labor productivity.

Logging Crew Attributes by Region in the Southeast USA

Carlos Diniz, Mathew Smidt, Jason Cooper, Yaoqi Zhang

Abstract

Urbanization, shrinking markets, and reduced forestry investment may affect harvesting efficiency in regions of the US South. To monitor these conditions, logging businesses have been tracked by surveys conducted by universities and trade associations. This project used a sampling approach coordinated with FIA utilization studies to sample logging crews based on a harvesting location. The approach was used to develop relationships among firm attributes and site attributes in six southeastern states (AL, GA, FL, NC, SC, and VA) from 2011 to 2018. The data included harvest attributes (location, harvest size and stand type) and logging firm attributes (production, crew labor, crew number, the number of machines by type, and machine age). For crew capital value, an equation was developed for this study using machine number and average machine age. The data from logging crews on 419 harvests were analyzed by region, harvest size, and stand type. Mean values for crew labor ranged from 3.1 to 7.1 workers. The average capital value per crew ranged from $220,000 to $524,000 per crew in the Coastal Plain with a narrower range in the Piedmont. In the Coastal Plain, higher productivity was detected for larger harvests and pine versus hardwood and mixed stands; however, in the Piedmont those trends were less obvious. Ratio of feller-bunchers, skidders and loaders were mostly 1:1:1 or 1:2:1 with 41% and 24% of samples, respectively. There were notable trends among Coastal Plain loggers regarding capital value and productivity with evidence supported by a production function. The differences in Piedmont (e.g., ownership size, market access, terrain, population density, etc.) may combine to limit daily production and labor productivity.

Keywords: logging business, timber harvest, productivity, labor, Piedmont, Coastal Plain

1. Introduction

The United States of America is among the top five producers of industrial roundwood (Lundbäck et al. 2021). According to Oswalt et al. (2014), the USA harvested approximately 368 milion cubic meters of timber per year. The Pacific Northwest was once the leading region for timber harvest volume, but now the Southeast accounts for 63 percent of the annual harvest volume (Haynes 2003, Howard and Westby 2013, Oswalt et al. 2014).

The Bureau of Labor Statistics (BLS) data indicate that across the USA there were 8300 logging firms with an average of about 6 employees per firm in 2017. Many logging businesses in the Southeast are productive and produce more than 70,000 tons per year or 350 tons per working day (Conrad et al. 2018a). The most common harvesting system in the Southeast is mechanized full-tree systems using feller-bunchers, grapple skidders and knuckleboom loaders (Lundbäck et al. 2021, Spinelli et al. 2021).

Regional surveys of logging businesses have reported attributes of logging businesses concerning equipment, transportation, labor, products marketed, business practices, and business plans (Greene et al. 1988, Smidt and Blinn 1994, Munn et al. 1998, Cubbage and Carter 1994, Knight 2016, Barrett et al. 2017, Conrad et al. 2018a). The data from surveys identify trends and estimate aspects of business performance like capital and labor productivity. Changes in business attributes and performance over time has important ramifications for forest landowners and forest industry. Therefore, the overall goal of this study was to add to the understanding of logging crew attributes and performance when those data can be related to a specific harvest. This project used a crew sampling approach based on a harvesting location with an intent to develop relationships among firm attributes and site attributes. Both the sampling and site based approaches are novel in developing insight into logging capacity.

2. Material and Methods

2.1 Logger Data

Site selection procedures followed a stratified approach by product, species, and county and a cooperating logging firm was identified with the assistance of local contacts. Procedures are outlined in harvest and utilization studies like Wall et al. (2018). The survey data from the logging crew and the site were recorded and site parameter data followed Forest Inventory and Analysis (FIA) classification schemes. Labor and machine data referred to the crew on site, while businesses may have had multiple crews. Machine age was the average age of all machines on site. For production, daily performance in loads per day was retained as the variable. Crew capital value was estimated from predicted values based on age of the most frequent machines (grapple skidders and feller-bunchers).

This study used data from logger interviews in six southeastern states (AL, GA, FL, NC, SC, and VA) from 2011 to 2018. The data included harvest attributes (location, harvest size and stand type) and logging firm attributes (production, crew labor, crew number, the number of machines by type, and machine age). FIA codes for stand types were simplified into two stand types for pine and hardwood (mixed-pine hardwood and hardwood). County maps were used to identify physiographic region where the loggers were found as Coastal Plain, Piedmont and other uplands. Other uplands and the Piedmont were grouped and labeled as Piedmont. Production in loads per day was converted to tons per day using 26.5 tons per load. Labor productivity was estimated as tons per worker per day by dividing production by crew labor. The equation developed for this study and machine number and average machine age were used for determining crew capital value. An average use of 1400 hours per year was assumed.

To compare trade-off between labor and capital, a linear production function was fit to combinations of region and stand type. Combinations with harvest size classes had too few samples. The function was fit using Proc reg (SAS 2016), using daily production in loads (Loads), crew labor (Labor), and estimated crew capital value (Capital) in 2021 dollars (Eq. 1).

 (1)

Where:

Loads production in loads per day

Labor crew labor

Capital estimated crew capital value, $.

2.2 Capital Value

Since age ranges were greater than typical depreciation functions of 5 or 6 years, data were collected to develop a value function for grapple skidders and feller-bunchers for an approach similar to Spinelli et al. (2011). Asking prices for wheeled grapple skidders and feller-bunchers were collected using a search of www.forestrytrader.com in mid July 2021. The search was limited to the two machine types and sorted by distance from Auburn, Alabama. Each machine less than 11 years old with a make, model, model year, machine hours and asking price was recorded. Search pages were set to 25 listings per page and full pages of listings were recorded until more than 50 of each machine were recorded. A nonlinear regression model (Eq. 2) was applied to the data using a ratio for machine life as machine hours divided by 10,000 hours. Values greater than 10,000 hours were assigned a value for machine life of 1. The age was estimated with 2021 as the current year. For interpretation, B0 represented the average initial purchase price in 2021 dollars, B1 represented the loss in machine value due to use, and B2 was the annual machine depreciation in dollars. Proc nlin in SAS (2016) was used to fit Eq. 2 for each machine individually and all machines.

 (2)

Where:

CV Capital value in US$

L Machine life ratio

A Machine age in years.

3. Results

3.1 Capital Value

Data for 58 grapple skidders and 64 wheeled feller-bunchers were collected. Feller-bunchers and grapple skidders had similar age distribution with means of 5.4 and 5.3, respectively and standard deviations of 2.2 and 2.1. Feller-bunchers had fewer average hours (5960) than skidders (7022) and were generally lower priced, $109,524 versus $120,188. For the nonlinear models, the p values for the three models were <0.0001, and the parameter estimates were significantly different from 0 (p<0.05) (Table 1). The wheeled feller-buncher model had the lowest MSE and parameter estimates with the smallest standard errors. The model with all machines has the largest F value and a value for MSE between the two machine models. Examination of residual plots showed no indication that regression assumptions were violated for the individual machine or combined datasets.

Table1 Model statistics and parameter estimates resulting from model fitting of Eq. 2

Statistics

 

Wheeled

feller-buncher

Grapple

skidder

All

N

 

64

58

122

Model F

 

561.03

229.79

633.5

MSE

 

5.1034 108

13.425 108

9.3559 108

Parameter estimate

B0

250,768

274,454

261,007

 

B1

–111,401

–102,051

–87,361

 

B2

1.1101

1.1146

1.1245

Standard error

B0

14,206

25,141

14,150

 

B1

21,704

49,058

25,198

 

B2

0.018

0.034

0.018

3.2 Logger Survey

Outliers were identified by examining loads per worker per day and isolating the unrealistically high (>6 loads per worker day) and low values (<0.5 loads per worker day). Most outliers appeared to be related to data entry errors in crew labor or loads per day. After the outliers were excluded, the sample included loggers on 419 harvest sites (Table 2). Most of the harvest sites (79%) were in the pine stand types. The number of crews by state and region ranged from 9 to 79 with a median of 40. For the statistics by state and region, the number of crews per firm ranged from 1.0 to 3.2 with an average of 1.5 crews. The average values for crew labor and logging machines were similar for the Coastal Plain and Piedmont. The Virginia Coastal Plain had the highest average production rate with 17.1 loads per day.

Since data were collected over several years (2011 to 2018), Pearson correlation coefficients were used to identify potential trends in production or productivity. The parameter loadsperday was not correlated with sample year (r=0.02). Productivity (loads per worker per day) was negatively corrected with sample year for all samples (r=–0.14) and the Coastal Plain (r=–0.21), but there was no correlation in the Piedmont (r=0.01). For the six-state area the total harvest increased by 30% from 2011 to 2018 and by 16% from 2013 to 2018 with data from TPO reporting tool (USDA Forest Service 2022).

Table2 Logging firm characteristics means and standard errors (in parenthesis) by region (Coastal Plain and Piedmont) and state

Region

State

N

Crew

number

Crew

labor

Logging

machines

Loads

per Day

Coastal

Plain

Alabama

47

1.7 (0.2)

5.7 (0.4)

4.6 (0.3)

12.1 (0.6)

 

Florida

36

1.5 (0.2)

5.2 (0.7)

3.4 (0.2)

10.3 (0.6)

 

Georgia

79

1.3 (0.1)

4.6 (0.2)

3.7 (0.1)

11.9 (0.5)

 

North Carolina

53

1.0 (0.0)

4.6 (0.3)

4.4 (0.2)

11.1 (0.5)

 

South Carolina

46

1.6 (0.3)

7.0 (0.5)

4.2 (0.2)

12.8 (0.7)

 

Virginia

9

1.0 (0.0)

7.6 (1.1)

5.9 (0.6)

17.1 (2.9)

Sub-Total

270

1.4 (0.1)

5.4 (0.2)

4.1 (0.1)

11.9 (0.3)

Piedmont

Alabama

11

1.2 (0.1)

5.2 (0.5)

3.9 (0.3)

10.0 (0.8)

 

Georgia

10

1.8 (0.6)

4.7 (0.6)

4.1 (0.4)

11.9 (0.6)

 

North Carolina

40

1.2 (0.1

5.4 (0.4)

4.7 (0.3)

9.8 (0.7)

 

South Carolina

35

3.2 (0.6)

7.2 (0.4)

4.0 (0.2)

13.1 (0.5)

 

Virginia

53

1.4 (0.1)

4.6 (0.2)

4.0 (0.2)

8.8 (0.8)

Sub-Total

149

1.8 (0.2)

5.5 (0.2)

4.2 (0.1)

10.4 (0.4)

Total

419

1.5 (0.1)

5.4 (0.1)

4.1 (0.1)

11.4 (0.2)

Since harvest size was a class value (1 to 10 ha; 11 to 20 ha; 21 to 40 ha; greater than 40 ha), the mean and standard error of crew labor were presented by region and stand type (Fig. 1). The mean values ranged from 3.1 to 7.1 and the only apparent trend between stand size and crew labor was in Coastal Plain hardwood stands.

Fig.1 Average crew labor by harvest size, stand type (pine and hardwood) and region (Coastal Plain and Piedmont)

For the three most frequent machines, the differences in average number per crew were consistent between hardwood and pine stands with slightly more machines per crew in hardwood stands (Fig. 2). However, there were fewer felling machines in the Piedmont hardwood than in Piedmont pine stands. The difference might be related to the presence of chainsaw felling in hardwood Piedmont samples. Forty percent of crews indicated that they had chainsaws for felling although all but 5 crews also had a felling machine. Since machine combination averages are not whole numbers, either most systems are unbalanced or the variety of sites and processes yields different machine combinations that are balanced. A system with 1 feller-buncher, 1 skidders and 1 loader (1:1:1) accounted for about 41% and 1:2:1 systems accounted for another 24%. There were too few other machines (harvesters, forwarders, chippers and processors) to estimate a mean per crew.

Fig.2 Mean machine number by type (felling, skidding, loading), species (pine and hardwood) and region (Coastal Plain and Piedmont)

The estimated average capital value per crew ranged from $220,000 to $524,000 per crew in the Coastal Plain (Fig. 3). Harvest size classes were positively associated with crew capital value for both stand types. The range in capital value was smaller in the Piedmont, $300,000 to $450,000, with a reduced influence of harvest size class. For all the samples the capital value was influenced more by equipment age, with a –0.70 correlation coefficient, than by machine number (r=0.56).

Fig.3 Estimated crew capital value f(machine age, number) by harvest size, species (pine and hardwood) and region (Coastal Plain and Piedmont)

In the Coastal Plain the estimated productivity (tons per worker per day) ranged from 36 to 73 (Fig. 4) and the trends in the Coastal Plain with harvest size class were similar to those for capital value. In the Piedmont, most combinations of harvest size and stand type had average productivity that ranged from 41 to 60 tons. The productivity for harvest size of 1–10 ha in the Piedmont was among the highest at 75 tons per worker per day. This notable departure from the trend could be influenced by the distribution of higher quality products (i.e. more saw timber) from smaller harvest areas (Kittredge et al. 1996).

Fig.4 Mean firm productivity by harvest size, species (pine, mix and hardwood) and region (Coastal Plain and Piedmont)

Since the split by harvest size resulted in too few samples per regression, production functions for combinations of region and stand type were estimated (Table 3). The models were all significant at P=0.0001 and the adjusted R2 ranged from 0.21 to 0.53. Differences in elasticity could be observed comparing both regions and stand types. The parameter estimates all demonstrate reducing returns to scale. The marginal rate of substitution (MRS) for capital is the estimated change in capital to replace 1 worker at the median values for labor and capital. MRS was much higher in the Piedmont than the Coastal Plain. The labor and capital needed to increase production by 10% from the median values were estimated. The capital changes were similar to or greater than MRS. In the Coastal Plain, the production increase would require about 20% increase in either labor or capital. Labor changes are similar for the Piedmont but changes in capital were estimated at nearly 100% of the median capital value. The capital value estimated does not include capability, but the understanding would be that newer, more valuable machines might be more reliable and have increased capability.

Table3 Model statistics and parameter estimates for production functions for region and stand type combinations

Region

Coastal Plain

Piedmont

Stand type

P

H

All

P

H

All

Intercept

–0.8851

–2.2683

–1.1607

0.3258

0.1941

0.0212

Log, labor

0.3364

0.4526

0.3583

0.3719

0.6002

0.4434

Log, capital

0.2168

0.2987

0.2339

0.1099

0.0694

0.1176

Capital, $ thousands

398

361

398

430

333

382

Labor

4

5

4

5

5

5

Loads per day

12

10

11.5

10

8

10

Marginal return of substitution, $ thousands capital

154

109

152

290

576

382

Change in labor for 10% production increase

1.3

1.2

1.2

1.5

0.9

1.2

Change in capital (thousand $) for 10% production increase

220

136

200

593

983

477

F value

56.60

24.28

78.68

14.84

10.88

25.21

P value

<0.0001

<0.0001

<0.0001

<0.0001

<0.0001

<0.0001

Adj. R2

0.3288

0.5317

0.3661

0.2151

0.3004

0.2465

MSE

0.1001

0.1224

0.1070

0.1489

0.2051

0.1824

DFError

225

39

267

99

44

146

4. Discussion

General sample statistics for crews agree with previous studies from the region. For Virginia, crew distribution by Coastal Plain and Piedmont from this sample was similar to the distribution from Barrett et al. (2017). Considering crew size and crew number per firm, the statistics were comparable to previous data from Virginia (Barrett et al. 2017), Georgia and South Carolina (Conrad et al. 2018a).

Total production per crew was recorded in different units than found in previous studies, but the comparable means from this study (10 to 12 loads per day) were similar to Conrad et al. (2018a) (42.5 and 44.6 loads per week in GA and SC). For equivalance, weekly production levels from this study would require just over 4 workdays per week and sum to about 200 working days per year. In Barrett et al. (2017), the Coastal Plain and Piedmont production was lower at 38 and 26 loads per week. The high production in the Virginia Coastal Plain of 17 loads per day may be attributed to the small sample size, but both Barrett et al. (2017) and this data indicate that the average productivity per worker was 18% greater for the Coastal Plain compared to the Piedmont or Piedmont and uplands, respectively.

The machine combinations were remarkably similar across region even though some differences were observed in crew size and productivity. Hardwood volume reduced productivity, which could be linked to the reduced productivity of processes which are sensitive to merchantable tree size (loading and delimbing).

The main types of machines in our sample were rubber-tired feller-bunchers (86%), grapple skidders (92%) and knuckleboom loaders (99%). Bolding et al. (2010), Barrett et al. (2017) and Conrad et al. (2018a) had similar results. The lack of harvesters, forwarders or processors was notable, especially when compared to other regions (e.g. Pacific North West (West et al. 2022), Lake States (Shivan et al. 2020) and North East (Leon and Benjamin 2012)). Conrad et al. (2018b) commented that the absence of CTL machines in the Southern states could be a result of product specifications by mills.

Methods of generating capital value per crew differs by source and the machines included. In the Midwest, mean investments in logging machines were under $500,000 (Blinn et al. 2015, Rickenbach et al. 2015). Barrett et al. (2017) estimated $409,000 per crew for the Piedmont and $537,000 for the Coastal Plain, which are similar to the range estimated in this sample although more than 5 years apart. In 2012, Georgia and South Carolina logging businesses had a mean capital investment of $863,000 and $783,000, respectively (Greene et al. 2013). Changes over time in investment in forestry equipment may have changed in reaction to economic conditions (Barynin et al. 2013) and changes in machine technology (Diniz and Sessions 2021).

The comparisons of productivity per worker (tons per worker per day) is sensitive to assumptions of load size and workday and work week. With a ten-hour work day, the mean productivity from this sample is roughly equivalent to SC and GA at 53 and 65 tons per worker per day (Conrad et al 2018a). Conrad and Dahlen (2019) found that two different systems (processor and conventional) had a similar range in productivity (40 to 80 tons per worker per day). Even though there were no cut-to-length (CTL) systems in the sample and they are uncommon in the region, Oliveira (2013) estimated CTL productivity in similar conditions (pine, rolling terrain) at 88 tons per worker per day.

Although these results do not reflect productivity increases over time, labor productivity has grown steadily, reflected in the time series by Conrad et al (2018a) and statewide data like He et al. (2021). From 1995 to 2015, annual growth in worker income has been similar to annual growth in productivity (He et al. 2021). The sources of increased productivity overall could explain the loss of lower productivity firms (Conrad et al. 2018a) as well as incremental increases in mechanization.

The estimated production functions found decreasing return to scale as have similar studies of southern loggers (Duc et al. 2009, Lebel and Stuart 1998, Stuart et al. 2010). The increased crew investments observed in the Coastal Plain suggest that production function estimates are appropriate. In the Coastal Plain, higher production levels are supported by larger forest ownerships and market access (Conrad et al. 2018b). The stability in Piedmont logger size across the variations observed may also reflect limited returns for increased capital or labor per crew, which is also reinforced by the production function. The Piedmont regions across these six states have experienced increased population growth with related parcelization, fragmentation, reduced forestry investment, and change in management priorities (Wear and Greis 2012). Several authors have pointed to the relationships among reduced harvest volumes, higher harvest fixed costs and reduced productivity (Cubbage et al. 1989, Greene et al. 1988, Moldenhauer and Bolding 2009).

5. Conclusion

Analysis of logger data showed that there were notable trends among Coastal Plain loggers regarding capital value and productivity with evidence supported by the production function. The numerous differences between the Coastal Plain and Piedmont (ownership size, market access, terrain, population density, etc.) have produced differences in logger productivity and capital value. It appears that system constraints may limit productivity in the Piedmont. In order for growth in productivity to be maintained, system innovations must continue to produce either increased value or increased volume per worker. Innovations in value creation may evolve as supplementary biomass harvests where there is a market (Garren at al. 2022) or increased merchandizing (Cass et al. 2009, Hamsley et al. 2009, Conrad and Dahlen 2019). For some time, loggers have relied on increasing size or capability of machines to support productivity increases. If the Piedmont harvests restrict efficiency of the conventional system, adoption of innovations that improve capital productivity and especially labor productivity are critical.

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© 2023 by the authors. Submitted for possible open access publication under the
terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Authors' addresses:

 

Carlos Diniz PhD *

e-mail: carloscezardiniz@gmail.com

Mathew Smidt PhD

e-mail: mathew.smidt@usda.gov

USDA Forest Service

Southern Research Station, USDA Forest Service

521 Devall Drive, Auburn, AL 36849

USA

 

Jason Cooper PhD

e-mail: jason.a.cooper@usda.gov

USDA Forest Service

Southern Research Station

4700 Old Kingston Pike

Knoxville, TN 37919

USA

 

Yaoqi Zhang PhD

e-mail: zhangy3@auburn.edu

Auburn University

Auburn University

3301 FWS Building, 602 Duncan Drive

Auburn University AL 36849

USA

 

* Corresponding author

 

Received: November 29, 2022

Accepted: March 24, 2023

Original scientific paper

Publishers:
Copublishers:

Web of Science Impact factor (2022): 3.200
Five-years impact factor: 3.000

Quartile: Q1 - Forestry

Subject area

Agricultural and Biological Sciences

Category/Quartile

Forestry/Q1