IJERD – International Journal of Environmental and Rural Development (2017) 8-1

Research article
erd

Logging Effects on Soil Organic Carbon and Hydraulic
Property in North Appalachian Region of Ohio USA
TORU NAKAJIMA*
Faculty of Regional Environmen Science, Tokyo University of Agriculture, Tokyo, Japan
Email: tn206473@nodai.ac.jp

RATTAN LAL
School of Environmental Natural Resources, Carbon Management and Sequestration Center,The Ohio State University, Ohio, USA
Received 7 December 2016

Accepted 5 May 2017(*Corresponding Author)

Abstract Forest soils play an important role in the global carbon cycle. Thus, documentingchanges in carbon stocks and hydraulic property by logging are essential to sustainable
management. However, information on logging in relation to soil organic carbon (SOC) stock isscarce and it can be site specific. Thus, the effects of logging on the SOC stock and hydraulicproperty were analyzed after logging at a site in the North Appalachian Experimental
Watershed (NAEW) near Coshocton, Ohio, USA. The objectives of the study were to quantify
the impacts of logging on SOC stock and hydraulic property. Results show that for the 0-30 cm
soil depth, SOC stock after logging (61.5 Mg ha-1) was 54.5% lower than that before logging(135.3 Mg ha-1). Further, soil water retention at different potential was consistently higher
before logging than that after logging. A plot of the hydraulic capacity vs. suction under naturalforest differed significantly than that after logging at 0-10 cm soil depth. Thus, logging reduced
SOC stock, and degraded hydraulic property of the surface layer.
Keywords forest logging, soil organic carbon, soil water retention

INTRODUCTION
Soils in forest ecosystems are a major sink of global carbon, in part due to the large area involved at aglobal scale. World soils contain an estimated 1550 Pg of carbon to 1 m depth, an amount that is nearly
twice that contained in the atmosphere (Lal, 2004). Within the forest biomes, forest soils constitute
31% of the total carbon stock (Kimble, 2003). Thus, forest soils play an important role in the global
carbon cycle. However, land use change causes disturbance of the forest ecosystem and can influence
the soil organic carbon (SOC) stock and soil hydraulic property (Lal, 2005). There have been
numerous studies on forest resources and their management (Lorenz and Lal, 2010). However, fieldstudies on forest management practices in relation to soil is scarce (Kimble, 2003). In addition, theeffect of SOC stock and hydraulic property can be soil and site specific. Therefore, there is a need forsignificant advances in measurement of SOC stock and hydraulic property in the forest ecosystem. In
the United States, the Renewable Resources Planning Act Assessment Report showed that in 2003,
about 33% of the total land area was under forest compared with 46% in 1630. About 120 Mha offorestlands have been logged for the forest resources and converted to other uses (e.g., agriculture).
Logging creates soil disturbance and compaction by machinery such as bulldozers, tractors etc. During
logging, the use of heavy machinery is common in the United States and many parts of the world,which alters soil hydraulic and mechanical properties and adversely affect plant growth (Soane, 1990).
Disturbed and compacted soil is highly prone to accelerated erosion and increased surface runoff (Sidleand Drlica, 1981). Displacement of surface soil can also occur during the logging operations. A soilⒸ ISERD111

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under undisturbed native forest has a high macro porosity and low bulk density, and it is prone tocompaction by heavy machinery (Huang et al., 1996, Lacey and Ryan, 2000). These factors adversely
affect plant growth due to restricted root growth, and reduced air and water availability.
OBJECTIVE
The goal of this study was to assess differences in SOC stock and hydraulic properties for before andafter logging sites in the surface layer. The hypothesis tested in this study was that logging reduces theSOC stocks, degrades hydraulic properties.
METHODOLOGY
Study Site and Soil Sampling
The experimental site was located at the North Appalachian Experimental Watershed (NAEW) near
Coshocton, Ohio, USA (40°22’N, 81°48’W). The NAEW was established in the late 1930s to study theeffect of soils, land management, geology and climate on water flow characteristics from agriculturaland forest lands (Kelley et al., 1975). The NAEW is located within the mixed oak region of theunglaciated Allegheny portion of the Appalachian Plateau in east central Ohio (Kelley et al., 1975).
The mean annual precipitation at this watershed is 950 mm and the mean annual temperature is 10.3 °C(Lorenz and Lal, 2010). Soils at NAEW are classified as Berks silt loam (loamy-skeletal, mixed, active,mesic, Typic Dystrudepts) (Dick et al., 1998; Kelley et al., 1975). The wooded areas were logged inJuly 2012. Soil sampling was carried out during September 2012 at after logging at shoulder position
with triplicate, and before logging with triplicate.
Soil Organic Carbon and Total Nitrogen
SOC and total nitrogen (TN) concentrations were determined by the dry combustion method (960 ˚C)using a Vario TOC analyzer (Elementar Inc., Hanau, Germany). The SOC stocks were calculated basedon an equivalent soil mass (ESM) basis to correct for differences in compaction among the sites (Lee etal., 2009; Ellert and Bettany, 1995). The SOC stock (Mg ha-1) was computed by multiplying the SOC
concentration by the bulk density and the equivalent soil depth using following Eq. 1:
𝑆𝑂𝐶𝐸𝑆𝑀 𝑠𝑡𝑜𝑐𝑘 = 𝜌𝑏 × 𝑑 × 𝑆𝑂𝐶𝑐𝑜𝑛 × 104 (𝑚2 ℎ𝑎−1 )(1)where, SOCESM stock is the SOC stock associated with ESM (Mg ha-1), ρb is the soil bulk density (BD)
(Mg m-3), d is the equivalent thickness of soil depth (m), and SOCcon is the SOC concentration (g kg-1).
Soil Water Retention Curve
Soil water retention curve (SWRC) was assessed by using a combination of tension table and pressure
plate extractors (Dane and Hopmans, 2002). The SWRC was determined at 0, –0.4, –1, –2.5, and –5
kPa with tension table and at –10, –20, –30, and –1500 kPa with pressure plate methods. All available
soil hydraulic models, the van Genuchten-Mualem model is the most widely used in simulation ofSWRC (van Genuchten et al., 1991). The function in Eq. 2 describes the SWRC:𝜃 = 𝜃𝑟 −𝜃𝑠 − 𝜃𝑟(2)[1 + (𝛼|𝜓𝑚 |)𝑛 ]1−1⁄𝑛Ⓒ ISERD112

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where, 𝜃 is the volumetric water content (m3 m-3), 𝜃𝑟 is the residual volumetric water content (m3 m-3),
𝜃𝑠 is the saturated volumetric water content (m3 m-3), and 𝜓𝑚 is the matric potential (kPa). Parameters𝛼 (cm-1) and 𝑛 are the empirical fitting parameters characterizing the shape of the retention curve byusing Eq. 2. Change in soil structure, land use, or plotting the differential SWRC indicates soilmanagement practices. A plot of the slope of SWRC 𝑑𝜃⁄𝑑𝜓𝑚 vs. 𝜓𝑚 can be used as an indicator ofthe change in soil structure and hydraulic properties, due to changes in land use (Radcliffe andŠimůnek, 2010; Lal and Shukla, 2004).
𝐶𝜃 = |𝑑𝜃⁄𝑑𝜓𝑚 |(3)-1

where, Cθ is hydraulic capacity (kPa )Statistical Analysis
Statistical analysis was carried out for each soil depth separately, with different management beforeand after logging sites. The data were statistically analyzed using SAS code PROC UNIVARIATE
GLMM (Generalized Linear Mixed Model) procedure(SAS 2007). Statistical significance wascomputed at p ≤ 0.05, unless otherwise stated.
RESULTS AND DISCUSSION
Soil Bulk Density, Texture, pH and EC
Results for soil BD, texture, pH, and electrical conductivity (EC) at 0-10, 10-20, and 20-30 cm soildepths at before and after logging sites are shown in Table 1. For before logging, soil BD increased
with increase in depth. However after logging, soil BD slightly decreased from 1.64 to 1.43 Mg m-3
going from 0-10 and 10-20 cm depths then increased to 1.56 Mg m-3 at 20-30 cm. For the 0-10 cm soil
depth, soil BD under after logging (1.64 Mg m-3) was 49.0% higher than that before logging (1.10 Mgm-3). For the 10-20 cm soil depth, soil BD under after logging was slightly higher than that beforelogging, but not statistically different at P ≤ 0.05. Before logging soil had 36.3 and 8.2% clay content
at 0-10 and 10-20 cm soil depth, respectively, but not statistically different those after logging soil. The
pH and EC did not differ among before and after logging at any soil depths.
Table 1 Effects of logging on soil texture, pH, electrical conductivity (EC), and bulk density(N=6 for each depth)
Depth
SandSilt
ClayTexturepHEC
Bulk Density(cm)%%%ClassµS cm-1Mg m-3
After Logging
0-10
28.5a*
55.0a16.5a
Silt Loam
4.99a
243a
1.64a
10-20
27.5a
54.3a
18.2a
Silt Loam
5.05a
152a
1.43b
20-30
28.7a
52.7a
18.6a
Silt Loam
5.23a
114a
1.56b
Before Logging
0-10
30.0a
47.5a
22.5aLoam
5.34a
244a
1.10c
10-20
27.6a
52.7a
19.7a
Silt Loam
5.50a
155a
1.32b
20-30
28.3a
55.5a
16.2a
Silt Loam
5.47a
129a
1.60a
*Means with different letters (a, b, and c) among before vs. after logging soil for each depth are not significantly different atp ≤ 0.05.
Site

Soil Organic Carbon and Total Nitrogen
Results for SOC and TN stock associated with ESM (Mg ha-1) for 0-10, 10-20, 20-30, and 0-30 cm soildepths for before and after logging are shown in Fig. 1. The logging event significantly influenced theⒸ ISERD113

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SOC stock in 0-10, 10-20, and 20-30 cm soil depths (P<0.01, P<0.01, and P<0.01, respectively). For
0-30 cm depth, SOC stock after logging soil (61.5 Mg ha-1) was 54.5% lower than that before logging
soil (135.3 Mg ha-1). For 0-10, 10-20, and 20-30 cm soil depths, SOC stock was 58.3, 48.5, and 53.8 %,respectively, higher before logging soil (62.8, 37.7, and 34.7 Mg ha-1, respectively) than after logging
soil (26.2, 19.4, and 16.0 Mg ha-1, respectively). For both before and after logging soils, SOC stocks
decreased with increase in depth. Logging effects were more predominant in the surface layer. The TNstocks were significantly affected by logging at 0-10 and 10-20 cm soil depths (P<0.01, and P<0.01).
Lower TN stocks were observed after logging soil (2.39, 1.85, and 1.62 g kg-1, at 0-10, 10-20, 20-30
soil depths, respectively) than before logging soil (4.19, 2.69, and 2.67 g kg-1, at 0-10, 10-20, 20-30 cm
soil depths, respectively). For 0-30 cm soil profile, TN stocks after logging soil (5.87 Mg ha-1) were
38.5 % lower than those before logging soil (9.55 Mg ha-1).
Soil Water Retention Curve
The data in Fig. 2 (top) show the SWRC for 0-10, 10-20, and 20-30 cm soil depths for before and afterlogging, and fitted SWRC by van Genuchten-Mualem model using RETC code (van Genuchten et al.
1991). The hydraulic capacity vs. soil moisture potential are shown in Fig. 2 (bottom).
aabababbababbaaa1Fig. 1 Effects of logging on soil organic carbon (SOC) and total nitrogen (TN) stocks associatedwith equivalent soil mass (EMS) at the 0-10, 10-20, 20-30, and 0-30 cm soil depths. Meanswith different letters among after vs. before logging for each depth are not significantly
different at p ≤ 0.05. Error bars indicate standard error (N=3)Ⓒ ISERD114

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The SWRC were well described by the van Genuchten relationship (R2 > 0.94). Overall theSWRC before logging soils were consistently higher than those after logging soils at all depths.
Between saturation and approximately 10 kPa, hydraulic capacity before logging soil was consistently
higher than that after logging soil at 0-10 cm soil depth. For 10-20 and 20-30 cm soil depths, thehydraulic capacity across the suction did not differ among before and after logging soils.
Discussion
Logging significantly affected SOC stocks at all depths (Fig. 1). For the 0-30 cm soil depth, SOC
stocks after logging soils were 54.5% lower than those before logging soils. Decline in SOC stocks twomonths after logging may be due to logging practices such as harvesting, which involve heavymachinery for cutting and transporting of trees, causing severe soil compaction, drastic soil disturbanceand a mixing of the forest floor into the mineral soils (Kimble, 2003). Furthermore, logging can cause
altered soil water content and temperature regimes, which can accelerate decomposition and decrease
net primary production (NPP) (Lal, 2005). The exposure of the soil also exacerbates losses due to soil
erosion and leaching of dissolved organic carbon (Kimble, 2003). Further, SOC sequestration could bealso decreased due to the reduction of biotic activities and decrease in soil moisture content (Lal, 2005).
Effects of logging on the reduction of SOC stocks were more predominant in surface soil layer, due tothe smaller inputs of fresh litter, decrease in decomposition with increase in depth. The SWRC is an
important indicator of soil structure, and relative distribution of micro and macro pores (Nakajima andLal, 2014). The SWRC at each potential was consistently higher before logging soil than that afterlogging soil (Fig. 2 top), primarily due to having greater hydraulic capacity between matric potential 0to 10 kPa at the 0-10 cm soil depth (Fig. 2 bottom). At 0 kPa matric potential, (or saturation point),
before logging soils had greater water holding capacity than after logging soils, indicating greater
occurrence of macro pores. This trend persisted to 10 kPa, also an indication of greater occurrence ofintermediate and micro sized pores and clay content. A lower hydraulic capacity over time or change inmanagement practice indicates progressive declines in soil structure and degradation of soil physicalproperties (Lal and Shukla, 2004).

Fig. 2 Effects of logging on soil water retention curve (SWRC) (top) and hydraulic capacity vs.
suction (bottom) for the 0-10, 10-20, and 20-30 cm soil depths. Lines (top) show the fit ofvan Genuchten equation (VG) SWRC using RECT code

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CONCLUSION
The hypotheses that logging in this study decrease SOC stocks and degrade soil hydraulic property
were supported by the results. Results also supported the following conclusions (1) the SOC stocks
after logging soil were 54.5% lower than those before logging soil for the 0-30 cm soil depth. The SOC
stocks declined sharply by logging within 2-month period (2) logging adversely affected SWRC,
especially in the 0-10 cm soil depth, probably due to decrease in macro pores and meso pores.
ACKNOWLEDGEMENTS
The authors gratefully acknowledge the support of Mr. James Buxton for sampling and valuable
discussions. The authors would also like to acknowledge the North Appalachian Experimental
Watershed, the Ohio Agricultural Research and Development Center of The Ohio State University forsupporting and providing this research opportunity.
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