Introduction

Soil provides products such as food, feed, fiber, and fuel as well as cultural and spiritual services. It regulates carbon storage, water quality, and floods; additionally, biomass production, nutrient cycling, soil formation, and water cycling are services that are supported by soil. In the literature, all these benefits are collectively referred to as soil ecosystem services (e.g., Pereira et al. 2018; Helming et al. 2018; Dominati et al. 2010; Morel et al. 2015; Comerford et al. 2013; Blanco and Lal 2008).

However, throughout history, humans have exploited soil resources without any soil conservation practices in different regions of the world (Karlen and Rice 2015; Brevik et al. 2015). Therefore, soil degradation has become a serious global problem since the inherent properties of soils have been ignored (Hammad et al. 2006; Pimentel 2006). Thirty-three percent of soils in the world are classified as having a moderate or high degradation status, according to the Status of the World’s Soil Resources (SWSR) report (FAO and ITPS 2015). Erosion, which is the displacement of upper soil by rainfall and runoff (Barthès and Roose 2002), is a common threat causing soil degradation (Panagos et al. 2018; Panagos et al. 2014; Pimentel 2006; Renschler et al. 1999; Ostovari et al. 2016; Lal 1995), and this erosion can cause other problems, such as a loss of soil organic matter, salinization, compaction, contamination, and waterlogging.

The annual soil loss amounting from arable lands is 75 billion tons and costs approximately $400 billion each year in agricultural production worldwide (ELD 2015). Erosion also causes an annual loss of 7.6 million tons in cereal production (FAO 2017). In the European Union, 7.2% of the total area is subject to severe erosion, and the loss in gross domestic product due to soil erosion is estimated at approximately €155 million (Panagos et al. 2018).

In addition to current degradation across the world, concerns over food security resulting from the increasing world population and climate change may lead to further pressure on soil resources, especially in forestlands (Gomiero 2016), and could result in increased degradation. Converting forestlands into cropland causes an expansion of soil degradation due to a rapid decrease in soil fertility (Emadi et al. 2009; Gomiero 2016; Pereira and Martinez-Murillo 2018). When all these problems and the present state of an area that is suitable for agriculture (Gomiero 2016) are considered, sustainable soil management is a sensible option to solve or alleviate soil-related problems globally. Sustainable soil management (SSM) is described as “Soil management is sustainable if the supporting, provisioning, regulating, and cultural services provided by soil are maintained or enhanced without significantly impairing either the soil functions that enable those services or biodiversity. The balance between the supporting and provisioning services for plant production and the regulating services the soil provides for water quality and availability and for atmospheric greenhouse gas composition is a particular concern” in the revised world soil chapter (FAO 2015a). In the SWSR report, ten basic threats hindering the accomplishment of the SSM are stated. These threats consist of soil erosion by water and wind, soil organic carbon loss, soil nutrient imbalance, soil salinization, soil contamination, acidification, loss of soil biodiversity, soil sealing, soil compaction, and waterlogging (FAO and ITPS 2015). Among these, soil erosion has been identified as the most significant threat to SSM (FAO 2017; Panagos et al. 2014; Bridges and Oldeman 1999).

Soil erodibility is the susceptibility of soil to detachment and transport by water (Renard et al. 1991), and it is generally associated with the inherent properties of the soil (Renschler et al. 1999), making it the main factor for predicting soil erosion (Kulikov et al. 2017). Therefore, information about soil erodibility is the main necessity for an assessment of the soil degradation process and conservation techniques in a watershed (Vaezi et al. 2016). Soil erodibility is properly determined by performing direct measurements of field plots (Kinnell 2010). However, only a few relations between soil properties and soil erodibility have been investigated by researchers because field measurements are expensive and time-consuming (Panagos et al. 2014).

In Turkey, soil degradation caused by soil erosion is one of the most important environmental problems (Ozalp et al. 2017; Yuksel et al. 2016; Yuksel et al. 2008). Both topographic factors, such as slope and elevation, and anthropogenic factors, such as land-use change, poor plant coverage, improper land-use practices, and inadequate erosion control measures, are primary reasons for soil erosion, especially in the Mediterranean region, where an increase in temperature and a decrease in precipitation are expected due to climate change (Reis et al. 2017; Turkes et al. 2000; Kadioglu et al. 2017). Twenty percent of the country is exposed to moderate soil erosion, while 36% and 22% are exposed to severe and very severe erosion, respectively (Yuksel et al. 2008).

Revealing the actual state of soil resources is essential to achieve sustainable soil management (Tóth et al. 2018; FAO 2015a). In this context, the aim of this study was to compare different land-use types with respect to erodibility and some soil properties and evaluate the existing conditions within the framework of sustainable soil management. This study will also provide a soil database policymakers can use to sustain soil resources in the area.

Material and methods

Study area

The Korsulu stream watershed is located in Andirin county of Kahramanmaras city in the Mediterranean region of Turkey (Fig. 1). The study area located in the Mediterranean region that is expected to be affected by climate change is approximately 16,928 ha. The average annual precipitation is approximately 1136 mm. The average annual temperature is 11.23 °C, while the maximum and minimum temperatures are 35.2 °C (July) and − 14.8 °C (January), respectively (GDMS, 2018).

Fig. 1
figure 1

The location of the study area

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In the study area, the elevation ranges from 1229 to 2357 m. The average slope gradient is approximately 33%. In this highland region, agricultural areas are generally intensified at the center of the watershed, and conventional tillage practices are used in agricultural production. Pasture areas are located both between agriculture and forest as well as above forest areas. Local people generally use these areas for grazing. Forest areas are generally located in mountainous areas surrounding the watershed. Productive forest stands are centered on the west side of the watershed, while unproductive forest stands are generally located on the east side of the watershed.

Methods

The main focus of this study was to reveal the effects of land use on erodibility and some soil properties within the framework of sustainable soil management. Therefore, land use, which can be influenced by human practices, was selected as a factor affecting soil properties and, finally, soil erodibility. In this context, all soil samples were taken from the 1229 to 1629 m elevation band and 12–20% slope class in the watershed. A total of 90 (30 for each land-use type, namely, forest, pasture, and agriculture) sampling points were determined (Table 1). A total of 180, 90 undisturbed and 90 disturbed, soil samples were collected from a depth of 0 to 20 cm. The disturbed soil samples were air-dried and then prepared for analysis by sieving with a 2-mm sieve. The undisturbed soil samples were saturated with water for analysis. Texture analysis was performed according to the Bouyoucos (1936) hydrometer method. The particle density value was determined with a volumetric flask (Ozyuvacı 1975). The bulk density values of soils were determined by using undisturbed soil samples taken with steel cylinders (Gulcur 1974; Ozyuvacı 1976). Porosity was calculated using an equation based on the relationship between bulk density and particle density (Oztan 1980). Permeability was measured using a method based on Darcy law (Ozyuvacı 1978). Water holding capacity was calculated with the help of a cylindrical sample using a permeability calculation (Ozyuvacı 1975). Soil pH and electrical conductivity (EC) were measured with a digital pH meter and EC meter, respectively, in a 1:2.5 soil/water suspension. Soil organic matter (SOM) was calculated according to the Walkley–Black (1934) wet oxidation method. Field capacity and permanent wilting point were determined by the pressure-membrane extraction apparatus at 1/3 and 15 bar, respectively (Richards 1941). One-way ANOVA and correlation analyses were performed using SPSS 16.0 package software. The differences between groups in the one-way ANOVA test were determined with Duncan’s test.

Table 1 The elevation and slope properties of the sampling locations
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In this study, USLE-K, the erodibility, was calculated using the following equation proposed by Wischmeier and Smith (1978).

$$ \mathrm{K}=\left[2.1\times {10}^{-4}\times \left(12-\mathrm{OM}\right)\times {\mathrm{M}}^{1.14}+3.25\times \left({\mathrm{c}}_{\mathrm{soilstr}}-2\right)+2.5\times \left({\mathrm{c}}_{\mathrm{perm}}-3\right)\right]/100 $$

where

K = erodibility

OM = organic matter content (%)

M = particle size parameter, which is calculated as

M = (msilt + mvfs) × (100 – mc)

where msilt is the percent silt content, mvfs is the percent very fine sand, and mc is the percent clay content.

Csoilstr = soil structure code

Cperm = soil permeability code

The erodibility map was produced using the simple Kriging tool in ArcGIS 10.3 software.

Results and discussion

The results obtained for soil and statistical analysis and related evaluations are given as follows.

Texture

In the study area, the average sand ratio of soils in forest was 51.64%, while the average sand ratios of soils in pasture and agriculture were 47.12% and 35.98%, respectively. The silt ratio was 20.44% in the forest, while it was 20.70% and 29.86% in the pasture and agriculture, respectively. In addition, soils in forest had the lowest clay ratio of 27.92%, followed by pasture with 32.18% and agriculture with 34.17%. There was a statistically significant difference among the sand, silt, and clay ratios in terms of the land-use types (Table 2). In this study, the reason why soils had the highest clay ratio in agriculture was attributed to excessive physical and chemical decomposition caused by agricultural practices. The lowest clay ratio in forest resulted from the displacement of clay, most likely as a consequence of leaching. The higher slope, porosity, and permeability in the forest also supported this opinion. Similar results were found in the studies conducted by Yilmaz et al. (2008) and Bayramin et al. (2008). No significant difference between forest and pasture in terms of texture was found in this study, which was similar to the study by Karagul (1994).

Table 2 Results of one-way ANOVA
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Particle density, bulk density, and porosity

The average particle density of soils was 2.36 g cm−3 in forest, 2.39 g cm−3 in pasture, and 2.40 g cm−3 in agriculture; the average bulk density was 1.24 g cm−3 in forest, 1.40 g cm−3 in pasture, and 1.53 g cm−3 in agriculture; the average porosity was 47.78% in forest, 40.89% in pasture, and 36.34% in agriculture. The difference in particle density values was not statistically significant, while the differences in both bulk density and porosity values were statistically significant under the different land-use types (Table 2).

The highest particle density in agriculture can be attributed to lower organic matter content. Ozyuvacı (1978) revealed that differences in particle density depend on soil organic matter as well as lithological properties. When the correlation analysis results were examined, a statistically significant negative correlation existed between particle density and organic matter (Table 3).

Table 3 Pairwise correlations between the soil properties of the study area
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The bulk density of soils is affected by tillage practices (Celik 2005) and organic matter content (Gol 2002). In this study, forest had the lowest bulk density due to higher soil organic matter, while the bulk density in agriculture was the highest due to tillage. It is also well known that grazing causes an increase in bulk density in pasture lands.

In forests, porosity is generally higher because of factors such as higher organic matter, litter, roots, and activity of soil animals (Blanco and Lal 2008). In one study, a strong positive correlation was determined between soil porosity and bulk density (Balci 1973). Gajić (2013) also reported a strong positive correlation between porosity and organic matter. When correlation analysis was considered, both bulk density and organic matter had a strong positive correlation with porosity (Table 3).

Permeability and water holding capacity

Permeability had the highest level in the forest with a value of 553.87 mm h−1, and pasture and agriculture followed it with values of 242.56 mm h−1 and 125.60 mm h−1, respectively. The water holding capacity was highest in forest, with a value of 39.25%, and pasture and agriculture followed it with percentages of 33.72% and 29.60%, respectively. There was a statistically significant difference among the land-use types in terms of both the permeability and the water holding capacity of the soils (Table 2).

A higher permeability in forests results from a lower clay ratio, higher organic matter and enhanced growth of the root system (Haghighi et al. 2010; Ozalp et al. 2016). The lower permeability in pasture and agriculture can be attributed to soil compaction due to grazing and adverse effects of tillage practices on the distribution of soil pore size, respectively (Gue’rif et al. 2001; Celik 2005).

The lower water holding capacity in pasture and agriculture can be related to the lower organic matter. When the correlation analysis results were investigated, it was determined that the positive correlation between the water holding capacity and organic matter was statistically significant, while no statistically significant negative correlation existed between the water holding capacity and clay ratio (Table 3). The unexpected negative correlation between the clay and water holding capacity resulted from the lower clay and the higher organic matter in the forest. This situation revealed that organic matter was more effective than clay in terms of the water holding capacity in the study area. Reicosky (2005) reported that organic matter can hold up 20 times more water than its weight.

pH and organic matter

The average pH of soils was 6.60, 7.23, and 7.25 in forest, agriculture, and pasture, respectively, while the average SOM was 6.27%, 4.56%, and 2.05% in forest, pasture, and agriculture, respectively. It was determined that the differences among pH values and the differences among SOM values were statistically significant among the land-use types (Table 2).

Organic acids arise from the transformation of litter material to humus due to decomposition (Tan 1994; Karagul 1999), and this process leads to a decrease in the pH value of forest. In addition, removing bivalent basic cations through leaching can reduce pH. The higher organic matter and permeability in forests supported this pattern in the study area. When the correlation analysis results were examined, it was determined that a statistically significant negative correlation existed between organic matter and pH, while a negative correlation existed between permeability and pH (Table 2). In another study, a higher pH value in agriculture was attributed to decreased organic matter and plowing processes (Biro et al. 2011).

Similar to our results, Korkanc et al. (2008) found that forestlands had higher organic matter than pasture and agriculture in their study. This situation can be attributed to more litter material, humus, and shading effects on organic matter decomposition in forestlands (Worku et al. 2014; Kassa et al. 2017; Yilmaz et al. 2008). The lower organic matter in agriculture is generally due to tillage practices, planting techniques, and residue management (Hajabbasi et al. 1997; Magdoff and Weil 2004).

Electrical conductivity

The mean electrical conductivity values of soils were determined to be 163.67 uS cm−1 for forest, 171.30 uS cm−1 for pasture, and 229.38 uS cm−1 for agriculture, and the difference among land-use types was statistically significant (Table 2).

The higher EC in agriculture can result from irrigation and fertilizer applications in the study area. Zhaoyong et al. (2014) reported that land-use types affected salinization and that agricultural areas had the highest salinization. The authors indicated that agricultural activities such as irrigation, fertilization, and farming were the reasons for salinization.

Field capacity and wilting point

The field capacity ratios of soils in forest, pasture, and agriculture were determined to be 30.52%, 31.13%, and 31.13%, respectively, while the wilting point ratios of soils in forest, pasture, and agriculture were determined to be 19.93%, 20.86%, and 21.38%, respectively. Differences among land-use types were not statistically significant in terms of field capacity, while they were statistically significant in terms of wilting point (Table 2).

The reason why there was no statistically significant difference among forest, pasture, and agriculture can be attributed to the fact that forest had a higher organic matter value, while agriculture and pasture had a higher clay value in the study area. This situation can equalize the field capacity percentage among the land-use types because organic matter is as effective as clay in relation to field capacity (Kar et al. 2013).

The higher wilting point percentage in agriculture can be attributed to the higher clay value in agriculture than forest because clay is more effective than organic matter in terms of its effect on wilting points (Kar et al. 2013).

USLE-K

In the study area, the average USLE-K value was determined to be 0.09 for forest, while it was determined to be 0.12 and 0.22 for pasture and agriculture, respectively. The difference between agriculture and forest and pasture was statistically significant, while no statistically significant difference was found between forest and pasture (Table 2). Similar results were found by Celik (2005), namely, the authors reported erodibility values of 0.11, 0.14, and 0.26 for the 0–10 cm depth and 0.11, 0.13, and 0.27 for the 0–20 cm depth in forest, pasture, and agriculture, respectively, in the Mediterranean highland of Turkey. In this study, forest was included in the slightly erodible class, while pasture and agriculture were included in the moderately and highly erodible classes, respectively (Table 4).

Table 4 USLE-K value classes of soil (Wischmeier and Smith 1978)
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The lower organic matter and permeability in agriculture can be the primary reasons why agriculture had the highest erodibility value. In agricultural areas, soil tillage leads to a physical disturbance that disintegrates macroaggregates and exposes carbon to microbial decomposition. The decomposition of soil organic matter causes higher erodibility (Ebabu et al. 2019; Jeloudar et al. 2018). Tillage practices also damage the distribution of pore size and decrease macro-porosity by soil compaction (Allen 1985; Celik 2005). Therefore, erodibility increases due to lower permeability. Another reason for increasing erodibility can be the removal of crop residue causing a lower organic matter value in agricultural areas (Jeloudar et al. 2018). Prichett and Fisher (1987) reported that SOM and permeability can be easily altered by human activities compared with soil texture. The lower erodibility in the forest can be attributed to the higher SOM. When the erodibility map was examined, it was observed that most of the study area was prone to erosion (Fig. 2).

Fig. 2
figure 2

Erodibility map of the study area

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An examination of the land-use map in Fig. 3 shows that erosion, the most important threat to sustainable soil management, has been ignored when using soil resources in agriculture and pasture.

Fig. 3
figure 3

Land-use map of the study area

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In addition to the effects of land use, slope and elevation are two of the most important factors affecting erodibility. The soils on steep slopes can be eroded more quickly than new soil can form. This process leads to a decrease in the soil colloids and increases the erodibility on the steep parts of a watershed (FAO 2015b). Lawrence (1992) stated that the tendency of soils to be shallow, coarse in texture, and low in organic matter in steep areas results in higher erodibility, while erodibility is lower in flat areas with deep and organic-rich soils. Altitude has indirect effects on the soil formation process by influencing the climatic conditions that govern the distribution of vegetation and the chemical and biological processes in soils (Bangroo et al. 2017). Organic matter in soils can increase with elevation in vegetation, such as forests, since the decomposition of litter is slow due to lower temperatures (Zhang et al. 2011). However, SOM generally decreases with elevation. As elevation increases, the growing season gets shorter and climate conditions become more severe, making it difficult for vegetation to survive. As a consequence, less organic matter accumulates in the soil (Saeed et al. 2014; Bromley 1995; Panthi 2010). When the actual conditions of the slope (Fig. 4) and the elevation (Fig. 5) in the study area were evaluated with the results of the present study, it became evident that sustainable soil management is a necessity in the study area.

Fig. 4
figure 4

Slope map of the study area

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Fig. 5
figure 5

Elevation map of the study area

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Conclusion

This study showed that human activities, especially those associated with agriculture, have negatively impacted the soil properties and consequently the soil erodibility. Consequently, sustainable soil management was necessary to ensure the preservation of soil resources in the study area for future generations. In this context, awareness campaigns related to the importance of sustainable soil management for the sake of food security and climate change should be launched. In addition, farmers should be trained in agricultural practices that prevent soil loss and provide a yield increase. Grazing capacity in pasture areas should be increased by an effective rangeland management plan. When an optimum pasture condition is reached, the government should subsidize stock farming. This action not only decreases erodibility in pastures but also provides farmyard manure, which has a positive effect on erodibility (Kadlec et al. 2012) in agricultural areas.

This study also showed that SOM was the most effective factor affecting erodibility in the study area. Although the forest had the highest sand ratio, erodibility was lowest in the forest due to the higher SOM. In addition to the effects of land use, steep terrain and elevation conditions played an important role in increasing the extent of erodibility in the study area. This situation revealed the importance of forest ecosystems on sustainable soil management and the potentially destructive effects of deforestation on the soil resources in the Mediterranean highland of Turkey.