Combined effects of ground pressure, traffic intensity, and travel speed levels on selected soil physical properties.

doi:10.21203/rs.3.rs-2103114/v1

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Combined effects of ground pressure, traffic intensity, and travel speed levels on selected soil physical properties.

https://doi.org/10.21203/rs.3.rs-2103114/v1

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Among the various factors affecting soil compression during wood extraction, travel speed hasn’t received much attention. The objectives of this study were to (a) evaluate the impacts of two tractor types, one light (Massey Ferguson 285) and one heavy model (Timberjack 450C) equipped with different tire sizes and uneven axle loads with regard to forest soil physical properties and rut formation under various traffic frequencies and travel speeds, and (b) examine the relationship between travel speed and soil bulk density. Treatments included six different traffic frequencies (0, 1, 3, 6, 10 and 15 passes) and three travel speed levels (1, 3 and 5 m s^− 1) of the two tractors examined on six machine-operating trails with soil moisture contents of 20% and 23%, respectively. The examined light and heavy tractor types had single tires on both axles, dimensioned 750–18 and 24.5–32 at the front and 18.4–30 and 24.5–32 on the rear axle, respectively. Results suggest that bulk density positively correlated with traffic intensity in all examined levels of travel speed. Porosity in the topsoil (0–10 cm) decreased with an increasing number of passes and this trend was also evidenced as the travel speed decreased. The heavy tractor created, in all cases, deeper ruts than those generated by the light tractor. The results indicate that equipment type, traffic intensity, and travel speed exerted significant impacts on the examined soil physical properties and their combinations should be taken into consideration to mitigate the adverse effects of skidding operations.

Soil compaction

Porosity

Axle load

Rut depth

Tractor

Skidder

Forest soil compaction during ground-based wood harvesting operations consists an important deterioration agent by altering the physical and chemical properties of the soil, soil fauna, and plant diversity in the north mountainous forests of Iran (Solgi et al. 2018; Naghdi et al. 2016a; Naghdi et al. 2016b; Solgi et al. 2020). As the magnitude and duration of damages can be extensive, carefully designed managerial actions are demanded (Ezzati et al. 2012; Naghdi et al. 2018a). Compaction is the process of rearranging and compacting solid soil particles, resulting in undesirable soil changes such as increased bulk density (Froehlich et al. 1981; Naghdi et al. 2015) and reduced total porosity (Rab 1994; Najafi et al. 2009; Solgi et al. 2016). Water regime is also affected by reductions in soil permeability, infiltration capacity, and water movement (Froehlich et al. 1981; Murray and Buttle 2004; Ezzati et al. 2012) that may lead to increased surface runoff and erosion (Greacen and Sands 1980; Solgi et al. 2014; Safari et al. 2016).

Reduced air permeability and soil porosity limit root penetration of the soil and root expansion, elongation, branching, density, and penetration of primary roots, as well as root access to the uptake of soil moisture and nutrients (Botta et al. 2007, Greacen and Sands 1980). The afore mentioned, often combined, root growth effects can have a substantial impact on seedling performance (Kobe 2006). Therefore, seedling establishment and survival, as well as, at a later stage, seedling growth and production, can be harmed by soil compaction (Greacen and Sands 1980; Arocena 2000; Grigal 2000; Yoshida et al. 2005; Zenner et al. 2007; Kim et al. 2010; Soto et al. 2015). The severity of seedling growth and production, on the other hand, is multifactorial, and depends on a variety of parameters, such as soil type and tree species (Kozlowski 1999; Ampoorter et al. 2011).

Excessive deformation or rutting of the trafficked region is one of the first evidence that vehicular movement is connected to forest soil disturbance (Najafi et al. 2009). Ruts in moist or wet soils are caused by vertical and horizontal soil displacement to the middle or sides of the machine-operating trail and are linked to shearing pressures and soil compression (Horn et al. 2004). After machine trafficking, Davies et al. (1973) considered rut depth to be the most important indication of soil compaction. The initial soil attributes (e.g. coarse content, soil texture, soil moisture content, etc.), vehicle specifications (e.g. number of tires, tire width, axle load, and ground pressure), and traffic intensity consist important factors (Botta et al. 2006; Naghdi and Solgi 2014).

Due to the complicated nature of the interaction between machine features and site conditions at harvest, the degree and severity of soil compaction can vary in terms of intensity and distribution. The amount of disturbance caused by logging operations is dependent on a variety of parameters, including the work capacity and environmental efficiency of machine operators (Greacen and Sands 1980; Picchio et al. 2012; Solgi et al. 2016; Solgi et al. 2017; Naghdi et al. 2020). Understanding vehicle-soil interactions (Cambi et al. 2015) allows for the design of optimized machine operating trail networks that will reduce the negative consequences of ground-based skidding operations (cf. Grigal 2000).

Traffic intensity has been found to have a substantial impact on the degree of soil deterioration among machine parameters (Ampoorter et al. 2007). According to similar studies, the majority of compaction happens during the first few passes of a vehicle, with subsequent passes having less influence. However, additional passes further increase bulk density and lower non-capillary porosity to, potentially, crucial levels for tree growth (McNabb et al. 1997). The type of machinery used in skidding operations may also determine the severity and spatial extent of compaction (Solgi et al. 2016). Naghdi et al. (2015) reported smaller increases in bulk density caused by a crawler skidder compared to those caused by two different rubber-tired skidder models over a range of slope gradients. Sheridan (2003), on the other hand, observed no significant differences in soil impact between steel-tracked and rubber-tired skidders, highlighting the hazards of presuming that lower machine static ground pressures will inevitably result in soil impacts of lesser magnitude. Furthermore, the tire inflation pressure has been reported as the most influential tire parameter (Botta et al. 2012). Botta et al. (2002) and Solgi et al. (2016) have associated ground pressure with topsoil compaction, whose increases also lead to more severe soil disturbance (Naghdi and Solgi 2015). The use of wide (flotation) tires exerts results in lower soil density increases (Botta et al. 2012). Finally, the skidder travel speed has also been reported as a factor determining the impact of vehicle traffic on the soil (Jamshidi et al. 2013). However, compared to other components of machine traffic, the impacts of skidder travel speed on soil disturbance have received less attention.

A primary concern of forest managers is the minimization of adverse and often long-lasting impacts of vehicular passes, especially when ground-based skidding is applied (Rab 2004). Determining the extent of disturbance caused by different travel speeds of operating skidders is critical in this direction, especially in developing countries, where the purchase of more modern and sophisticated equipment is not possible due to financial constraints (Koutsianitis and Tsioras 2017).

The aims of this study were: (i) to quantify the effect of skidding operations on forest topsoil compaction (0–10 cm), as exerted by two tractor types, one light and one heavy model; and (ii) to examine the relationship between the travel speed of these tractor types and soil bulk density and at different levels of traffic intensity.

Site description

The study was carried out in August 2019 in the Tanian forest, located in northern Iran (37°26’N, 49°13’E). The study area was located in low altitude (120 m above sea level) and on sloped terrain that has been afforested, mostly, with poplar and alder. The soil class of the study area belonged to Cambisols according to the World Reference Base (WRB). The soil texture along the examined machine operating trails was clay-loam and it was determined using the Bouyoucos hydrometer method (Kalra and Maynard 1991). The soil size distribution of the machine operating trails is presented in Table 1.

Table 1. Soil particle size distributions at a depth of 0–10 cm for the machine operating trails. The range of particle size was < 0.002, 0.0021–0.05 and 0.051–2 mm for clay, silt, and sand, respectively. Soil texture in all trails was clay loam.

Sample trails	Soil particle size distributions (g 100 g^-1)
Sample trails	Clay	Silt	Sand
Machine operating trail 1	29	39	32
Machine operating trail 2	35	37	22
Machine operating trail 3	31	40	29
Machine operating trail 4	27	39	34
Machine operating trail 5	30	38	32
Machine operating trail 6	29	37	34

The average depth of soil to the bedrock was 70 cm. These areas are at high risk of erosion due the combination of steep mountainous conditions, heavy precipitation, and marl and limey sediments. Marl’s constitution of 35% lime and 65% clay results in low infiltration capacities and high susceptibility to intense run-off and erosion. The average annual rainfall was 990 mm according to the closest national weather station, located 20 km away. The maximum and minimum mean monthly rainfalls of 149 mm and 44 mm have been measured in October and August, respectively. The mean annual temperature of the study area is 16 °C, with February being the coldest month of the year. The weather conditions were dry and warm during the data collection period and the trail used for the skidder measurements had an average soil moisture content of 20% compared to 23% for the trail used for the agricultural tractor measurements. In both cases, the soil had not been driven on before.

Forest operations and machine specifications

Tree-felling and processing at the study site were carried out motor-manually by chainsaws, whereas axes were used in thinning operations. The machines used in the skidding operations were a rubber-tired cable skidder (Timberjack 450C) and an agricultural tractor (Massey Ferguson 285) (Figure 1), hereafter referred to as heavy and light tractors, respectively, whose main technical specifications are presented in Table 2. No further pieces of equipment (e.g. extra tracks or chains) were attached on the pneumatic tires of the two machines.

Table 2. Technical specifications of the two tractors.

	Tractors
	2WD Massey Ferguson 285 (Light)	4WD Timberjack 450C (Heavy)
Engine power (kW)	62	132
Front tires	750–18	24.5–32
Rear tires	18.4–30	24.5–32
Total weight (kg)	3114	11450
Front weight (kg)	1420	6540
Rear weight (kg)	1694	4910
Soil contact area (m²) for a single tire (front)	0.0484	0.1277
Soil contact area (m²) for a single tire (rear)	0.1771	0.1151
Ground pressure front tire (kPa)	143.9	251.1
Ground pressure rear tire (kPa)	46.9	209.2

Experimental design and data collection

We tried to examine the impacts of different ground pressures, as exerted by the two tractor types on the soil surface layer (0–10 cm depth). Various combinations of machine passes numbers and travel speeds on six machine-operating trails were used to measure soil dry bulk density, total porosity and rut depth, and compare these results from trafficked areas to those of undisturbed soil.

All trails were had a low longitudinal gradient (<5%) and no lateral gradient. Treatment plots were designated to examine the combination of i) two, commonly used in skidding operations in Iran, rubber-tired tractors, ii) six levels of machine passes (0, 1, 3, 6, 10, and 15 passes) and iii) three travel speed levels (1, 3 and 5 m s^-1) Each machine was driven unloaded on an individual trail.

Public truck scales were used to determine the individual and total axle loads of each machine prior to data collection. The tire/soil contact area was measured using the methodology described in Solgi et al. (2016). The average static ground pressure for each machine axle was calculated as the total axle load divided by the tire-soil contact area for both tires (Botta et al. 2009).

The number of combinations examined was 36 (2 machine types × 6 levels of machine passes × 3 levels of travel speed), which were randomly replicated three times resulting in 108 designated plots, with an area of 40 m² (10 m long and 4 m wide) each. Between all adjacent plots, 5 m long buffer zones interfered to avoid interactions. In each plot, five line transects, perpendicular to the skidder direction of travel and at a distance of 2 m between them were drawn. Along each transect two soil samples from the depth of 0–10 cm were collected, one from the left wheel track (LWT), and one from the right wheel track (RWT) (Figure 2).

Soil sampling was carried out by a soil hammer and cores. The cores had a net (inside) diameter of 5 cm and a length of 10 cm, which resulted in a weight of 325 g. Samples were then placed in polyethylene bags, sealed, labeled, and transported to the laboratory, where they were promptly weighed to obtain their wet mass. The samples were oven dried at 103± 2°C and their moisture content was determined gravimetrically after drying (Kalra and Maynard 1991).

Soil bulk density was determined according to Equation (1):

D_b = W_d / VC (1)

Where D_b is the dry bulk density (g cm^-3),

W_d is the weight of the dry soil (g), and

VC is the volume of the soil cores (196.25 cm³).

Total soil porosity was determined according to Equation (2):

AP = (1–D_b / 2.65) (2)

Where AP is the total porosity (%),

D_b is the dry bulk density (g cm^-3), and

2.65 (g cm^-3) is the particle density.

Ruts with a depth exceeding i) 2 cm from the top of the mineral soil surface and ii) 2 m in length were sampled, following the method described by Najafi et al. 2009. According to this method, a specially formed 1 m long iron bar with 40 rods spaced at 25 mm intervals on it is used. Rut depth was calculated as the average depth of 40 rod readings on the iron bar in ruts that fulfilled the sampling criteria (Figure 3).

One-way and three-way Analysis of Variance were used to identify significant differences with regard to average bulk density, total porosity and rut depth among the various factor combinations and to assess the significance of interaction effects. Tukey’s HSD post-hoc test was used to determine the significance of differences between average bulk densities, total porosities and rut depths for the different traffic level and travel speed treatments (Zar 1999). Paired t-tests were used to analyze soil properties data for the two ground pressure levels. Statistical calculations were performed using SPSS version 11.5 and for all tests, the significance level was set to α=0.05.

Soil type and moisture content

The soil particle size distributions of the six adjacent machine-operating trails were found to be similar and belonged to the clay loam soil type (Table 1). Due to the dry and warm conditions during the data collection and the average soil moisture content within the range of 20 – 23%, it was decided to omit soil moisture from further analysis in the present study.

Soil dry bulk density

Dry bulk density in undisturbed areas ranged from 0.66 to 0.71 g cm^-3(Table 3), but machine traffic increased it to the range of 0.83–1.56 g cm^-3. Dry bulk density was significantly affected by machine ground pressure, traffic frequency and travel speed, as well as by the interactions of traffic frequency × ground pressure, and traffic frequency × travel speed (Table 4).

tractor trails by 8%.

Table 3. Effect of the number of machine passes and travel speed on soil bulk density (g cm^-³). Different letters indicate statistically significant differences (α ≤ 0.05) among group means based on Tukey’s HSD test.

Travel speed (m s^-1)

Machine type

4WD Timberjack 450C

2WD Massey Ferguson 285

0.70^Ae±0.05

0.94^Ad±0.08

1.17^Ac±0.09

1.46^Ab±0.12

1.52^Aa±0.13

1.56^Aa±0.14

0.69^Ae±0.05

0.85^Ad±0.07

1.05^Ac±0.08

1.26^Ab±0.12

1.42^Aa±0.12

1.49^Aa±0.12

0.71^Ae±0.05

0.90^Ad±0.07

1.13^ABc±0.10

1.32^Bb±0.11

1.45^Ba±0.11

1.51^ABa±0.12

0.66^Af±0.07

0.83^Ae±0.09

0.98^ABd±0.09

1.17^ABc±0.08

1.34^ABb±0.13

1.45^Aa±0.10

0.69^Af±0.04

0.89^Ae±0.07

1.05^Bd±0.09

1.21^Cc±0.09

1.33^Cb±0.11

1.47^Ba±0.13

0.69^Af±0.06

0.83^Ae±0.07

0.93^Bd±0.09

1.12^Bc±0.10

1.28^Bb±0.09

1.45^Aa±0.13

Different letters indicate statistically signiﬁcant differences (p < 0.05) among group means based on Tukey’s HSD test.

Capital letters highlight statistically significant differences among travel speed classes (column).

Lower case letters refer to the comparison made among six classes of traffic intensity on each machine type and travel speed class (row).

Table 4. Analysis of variance (p values) of the effects of machine passes, ground pressure and travel speed on soil physical properties.

	p-values
Source of variable	Bulk density	Total porosity	Rut depth
Passes	≤ 0.05	≤ 0.05	≤ 0.05
Ground pressure	≤ 0.05	≤ 0.05	≤ 0.05
Travel Speed	≤ 0.05	≤ 0.05	≤ 0.05
Passes × Ground pressure	≤ 0.05	0.613	0.127
Passes × Travel speed	≤ 0.05	0.446	≤ 0.05
Ground pressure × Travel speed	0.725	≤ 0.05	0.118
Passes × Ground pressure × Travel speed	0.329	≤ 0.05	≤ 0.05

p-values less than 0.05 are given in bold.

The average soil bulk density on the cable skidder trail increased with machine passes, ranging from 0.89 g cm^-3 (after one pass) to 1.56 g cm^-3 (after 10 passes). This was also valid for the agricultural tractor trail, however, the bulk density values were lower, ranging from 0.83 g cm^-3 (after one pass) to 1.49 g cm^-3 (after 10 passes) (Table 3).

For both forest machines, dry bulk density increased with increasing traffic intensity at all travel speed levels (Table 3) and with decreasing travel speed at all traffic intensities (Table 3). The results showed that a travel speed increase from 1 to 5 m s^-1, decreased the soil bulk density on the skidder trails by about 11% and on the agricultural

The initial first pass resulted in the highest increase of dry bulk density (by at least 23 and 30% compared to the undisturbed soil, for the light and heavy tractor, respectively), subsequent passes resulted in further, more limited relative increases (8% and 6% after 15 passes for the light and heavy model, respectively) (Figure 4).

Data analysis showed that the heavier machine increased soil bulk density more than the lighter one (Figure 5), this might indicate that the decisive role of ground pressure as a soil compaction factor during forest operations. Furthermore, there is evidence of a strong positive relationship between the travel speed of the tractor and soil bulk density that is supported by the linear regression models with very high predictive powers (R²>0.90), for both tractor types.

Total porosity

Total porosity in undisturbed areas ranged between 71.9% and 72.6% and did not differ significantly among the trails (Table 5), nevertheless, machine traffic reduced it to the range of 42.3–67.5%. Total porosity was significantly affected by ground pressure, travel speed, traffic frequency, the interaction of ground pressure × travel speed, and the interaction of all three factors (Table 4). Traffic of the light agricultural tractor caused mean reductions in total soil porosity of 6.7%, 15.4%, 23.6%, 31.2% and 36.2% following 1, 3, 6, 10 and 15 passes, respectively. The reductions in total soil porosity following heavy tractor traffic were higher, namely, 12.0%, 20.3%, 29.8%, 34.8% and 38.9% after 1, 3, 6, 10 and 15 passes, respectively.

In both tractor types, total porosity decreased consistently with decreasing travel speed at all traffic intensities (Table 5). Low travel speed resulted in the lowest total porosities in both cases (Figure 6).

Table 5. Effect of the number of machine passes on total porosity (%). Different letters indicate statistically significant differences (α ≤ 0.05) among group means based on Tukey’s HSD test.

Travel speed (m s^-1)

Machine type

4WD Timberjack 450C

2WD Massey Ferguson 285

72.1^Aa ±4.3

62.7^Ab ±4.9

54.7^Ac ±3.8

46.2^Ad ±3.7

43.8^Ae ±3.9

42.3^Ae ±2.8

72.1^Aa ±5.1

66.9^Ab ±4.5

58.7^Ac ±3.9

52.3^Ad ±3.4

46.5^Ae ±3.4

45.2^Ae ±2.7

72.6^Aa ±4.5

63.9^Ab ±5.1

57.1^ABc ±3.5

50.6^Bd ±3.8

45.8^Be ±3.6

43.9^Ae±3.1

71.9^Aa ±4.7

67.6^Ab ±4.8

60.8^ABc ±4.1

55.4^ABd ±3.9

49.8^ABe ±3.7

45.7^Af ±2.8

72.5^Aa ±4.6

64.4^Ab ±4.8

61.2^Bc ±3.9

55.6^Cd ±4.2

52.1^Ce ±3.8

46.5^Bf ±2.9

72.3^Aa ±5.2

67.5^Ab ±4.6

63.6^Bc ±3.7

57.3^Bd ±3.9

52.4^Be ±4.0

47.1^Af ±3.1

Different letters indicate statistically signiﬁcant differences (p < 0.05) among group means based on Tukey’s HSD test.

Capital letters highlight statistically significant differences among travel speed classes (column).

Lower case letters refer to the comparison made among six classes of traffic intensity on each machine type and travel speed class (row).

Rutting

Following machine traffic, rut depth ranged from 2.2 to 43.2 cm (Table 6) and it was significantly affected by machine ground pressure, traffic frequency, travel speed, the interaction of traffic frequency × travel speed, and the interaction of all three factors (Table 4). Ground pressure didn’t exert a significant effect on rut depth after one machine pass but this changed when traffic increased to three passes. Three passes could be perceived as a threshold value, above which rut depth values almost doubled in the heavy tractor treatments compared to those found in the lighter tractor treatments.

Table 6. Effect of the number of machine passes on rut depth (cm). Different letters indicate statistically significant differences (α ≤ 0.05) among group means based on Tukey’s HSD test.

Travel speed (m s^-1)

Machine type

4WD Timberjack 450C

2WD Massey Ferguson 285

0^Ae

3.6^Ad ±0.28

21.8^Ac±1.35

32.5^Ab ±2.47

43.2^Aa ±3.45

0^Ae

2.2^Ad ±0.17

10.1^Ac ±0.87

15.9^Ab ±1.19

23.7^Aa ±1.83

0^Ae

3.1^Ad ±0.24

15.4^Bc ±1.08

23.7^Bb ±1.85

32.6^Ba ±2.88

0^Ad

0^Bd

7.3^Bc ±0.59

12.4^Bb ±0.93

18.1^Ba ±1.42

0^Ad

0^Bd

9.7^Cc ±0.76

16.2^Cb ±1.29

25.3^Ca ±2.16

0^Ad

0^Bd

6.5^Cc ±0.43

10.7^Cb ±0.74

14.6^Ca ±1.17

Different letters indicate statistically signiﬁcant differences (p < 0.05) among group means based on Tukey’s HSD test.

Capital letters highlight statistically significant differences among travel speed classes (column).

Lower case letters refer to the comparison made among six classes of traffic intensity on each machine type and travel speed class (row).

Regardless of the tractor type, rut depth increased invariably with i) increasing traffic intensity at all examined travel speed levels (Table 4) and ii) decreasing travel speed at all traffic intensities (Table 4). Finally, low travel speed resulted in the highest rut depths for both tractor types (Figure 7).

An unavoidable consequence of skidding operations is the pressure exerted by the weights of the moving machinery and the attached logs to the soil causing disturbances, such as increased soil compaction (Naghdi et al. 2015). Compaction of the soil increases bulk density, reduces soil porosity, decreases infiltration rates, and lowers soil permeability (Froehlich et al. 1981). By modifying these important soil properties, surface run-off and erosion are facilitated, and plant growth can be threatened (Solgi et al. 2014; Naghdi et al. 2016a). Therefore, managerial actions should focus on the optimal location of upper landings and careful designation of machine trails to minimize soil disturbance and the damages to the residual trees (Solgi et al. 2017; Naghdi et al. 2018b).

A precise prediction of soil disturbances is not possible due to the multifactorial nature of the topic. The effects of soil type, terrain, skidding equipment, machine specifications, machine operation (e.g. travel speed), weather conditions during the skidding, and the capacity of the machine operators on soil disturbances (e.g. Najafi et al. 2009; Naghdi and Solgi 2014; Solgi et al. 2016; Naghdi et al. 2020) have been the topic of previous research, compared to the effects of ground pressure and travel speed of operating machinery, which have received considerably less attention.

The results showed a positive association between dry bulk density and traffic frequency (Table 3), a finding shared with Najafi et al. (2009) and Solgi et al. (2020) but the pattern of increase differentiated between the two tractor types; the heavy tractor exerted by far the greatest impact on the soil during its first few passes. Tolon-Becerra et al. (2011) reported that after a number of single wheel passes, soil deformation was higher after the first pass rather than the subsequent ones. According to Picchio et al. (2012), the largest amount of soil compaction occurs after the first, two or three, passes, after which it increases asymptotically.

The heavier Timberjack 450C exerted more than four times higher ground pressure underneath the rear axle than the agricultural tractor (209.2 kPa vs 46.9 kPa), which also resulted in greater soil bulk densities. Our finding is in line with those of Solgi et al. (2016) who reported of a strong positive relationship between ground pressure and compaction effects during machine traffic. Smith and Dickson (1990) also positively correlated reported ground pressure with soil bulk density changes from the soil surface down to the studied depth of 24 cm.

Soil compaction was also affected by the travel speed of the examined vehicles. An increase of the skidder speed from 1 to 5 m s^− 1 decreased the soil bulk density by about 12%, whereas the respective decrease in the case of the agricultural tractor was about 8%. Similar results were reported by Carman (2002), who also reported of reduced magnitude of soil disturbance (e.g. bulk density, rolling resistance, and soil layer deformation) as result of increased travel speed. According to Jamshidi et al. (2013), this finding might be due to the decreased contact duration between the tractor wheels and the soil surface at higher travelling speeds.

Total porosity on the machine operating trails was found considerably lower than the total porosity of the undisturbed soil. As in the case of bulk density, a strong positive association between ground pressure and soil porosity is indicated. The traffic frequency levels exerted a considerable impact, as one machine pass decreased total porosity by 6.7% and 12.1% in the cases of the light and heavy tractor, respectively (Table 5).

In both machine types, total porosity decreased with decreasing travel speed. Lower total porosity values as those found on machine operating trails may further worsen soil aeriation and the resultant respiration processes of microorganisms (Horn et al. 2004; Solgi et al. 2016).

Mean rut depth increased with increasing ground pressure, increasing traffic frequency and decreasing travel speed of machine. The positive relationship between rut depth and traffic frequency observed in this study has been documented (Botta et al. 2006; Eliasson and Wästerlund 2007; Najafi et al. 2009; Solgi et al. 2018). The fact that the first pass of the heavy and light tractors did not induce rutting in this study may be attributed to the relatively low soil moisture content range (20% − 23%) during the data collection period. Generally, soil moisture content during forest operations has been positively associated with rut depth (McCurdy et al. 2004; Naghdi and Solgi 2014).

The higher values of rut depth after 3, 6, 10 and 15 passes of the heavy tractor resulted in the higher ground pressure exerted on the soil and a considerable increase in rut depth. Botta et al. (2002, 2006) has reached a similar conclusion, stressing the direct relationship between topsoil disturbance and the exerted ground pressure. In a later study, Botta et al. (2009) reported of a light tractor causing deeper ruts than a heavier one. This result was attributed to it’s the smaller tire and, evidently, the smaller soil contact area that resulted in higher ground pressure.

In our study, machine passes at slower travel speeds were associated with more severe ruts compared to that with faster travel speeds due to higher contact duration between soil surface and tractor wheel, a result also reported by Jamshidi et al. (2013).

This study was carried out with the objective of examining the combined effects of ground pressure, number of machine passes and travel speed of two tractor types on selected soil properties. The results demonstrate that all the examined factors affect soil disturbance. Attention should be given to heavier machines exerting higher ground pressure, especially in sensitive areas where the use of lighter equipment should be prioritized but these must not be equipped with small-dimensioned tires. The adverse effects of low travel speed and high traffic frequency can be mitigated by careful planning of forest operations and, when necessary, redesigning the forest road network. As an intermediate step, a map of the trail network where problematic areas will be defined would be very helpful to machine operators, facilitating a more eco-efficient wood extraction. Furthermore, limiting the movement of harvesting and extraction machines only to the predefined machine-operating trails can also help to reduce ground disturbance. This is crucial since research confirms that the highest level of disturbance occurs within the first machine passes.

Disclosure statement

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Ethical approval

Not applicable.

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All authors consent to participate in this manuscript.

Consent to Publish

All authors consent to publish this manuscript.

Authors Contributions

All authors contributed to the study conception and design. All authors read and approved the final manuscript.

Funding

The authors did not receive support from any organization for the submitted work.

Conflicts of interest/Competing Interests

There is no conflict of interest among authors.

Availability of data and materials

All data and materials as well as software application or custom code support our published claims and comply with field standards.

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Combined effects of ground pressure, traffic intensity, and travel speed levels on selected soil physical properties.

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