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Erosion presents a significant challenge to the efficient use and sustainability of soil resources in the context of land use and management. The aim of the present study is to assess soil losses and determine erosion risk categories in sub-basins located within the borders of basins areas of Samsun province over a period of approximately 30 years (between 1989-2020) using the Revised Universal Soil Loss Equation (RUSLE) methodology. Based on the collected data, we plan to propose conservation measures to mitigate soil erosion in the sub-basins using the Sustainable Land Use Planning (SLUP) model. We first analysed the land use and land cover of the basin between 1989 and 2020, identifying temporal changes during this period. The study found that the use of pasture areas in the basin decreased from 12079 ha in 1989 to 10094 ha in 2020, marking a significant proportional decrease of 16%. In contrast, artificial areas doubled over a period of approximately 31 years, indicating the highest increase with 86%. The calculated average soil losses for 1989 and 2020 were 7.53 t/ha/year and 7.86 t/ha/year, respectively. After analysing the changes in land use and erosion levels between 1989 and 2020, it is clear that the increase in agricultural area is mainly due to changes in pasture areas. Therefore, it is essential to implement soil conservation measures and modify tillage techniques in areas classified as having erosion degrees of 4.0 and 5.0, while considering SLUP in the basin.
Abstract
Erosion presents a significant challenge to the efficient use and sustainability of soil resources in the context of land use and management. The aim of the present study is to assess soil losses and determine erosion risk categories in sub-basins located within the borders of basins areas of Samsun province over a period of approximately 30 years (between 1989-2020) using the Revised Universal Soil Loss Equation (RUSLE) methodology. Based on the collected data, we plan to propose conservation measures to mitigate soil erosion in the sub-basins using the Sustainable Land Use Planning (SLUP) model. We first analysed the land use and land cover of the basin between 1989 and 2020, identifying temporal changes during this period. The study found that the use of pasture areas in the basin decreased from 12079 ha in 1989 to 10094 ha in 2020, marking a significant proportional decrease of 16%. In contrast, artificial areas doubled over a period of approximately 31 years, indicating the highest increase with 86%. The calculated average soil losses for 1989 and 2020 were 7.53 t/ha/year and 7.86 t/ha/year, respectively. After analysing the changes in land use and erosion levels between 1989 and 2020, it is clear that the increase in agricultural area is mainly due to changes in pasture areas. Therefore, it is essential to implement soil conservation measures and modify tillage techniques in areas classified as having erosion degrees of 4.0 and 5.0, while considering SLUP in the basin.
Keywords: Erosion, Sustainable land use planning, RUSLE.
(ProQuest: ... denotes formulae omited.)
Introduction
Soil is a fundamental component of our planet and plays a crucial role in promoting sustainable development. It is essential for addressing global challenges such as food and water security and climate change, as highlighted by Koch et al. (2013) and McBratney et al. (2014). Furthermore, the impact of soil on ecosystem services is increasingly recognized (Dominati et al., 2010). Soil erosion is a significant global issue that affects soil function. According to a report that involved around 200 soil scientists and analysed over 2000 publications, soil erosion is one of the most important problems facing soils (Montanarella et al., 2016). It occurs when the topmost layer of nutrient-rich soil is moved, and it is a crucial land issue. Improper land use and vegetation cover interventions caused by anthropogenic factors lead to irreversible soil loss over time (Le Roux et al., 2008; Aiello et al., 2015). The root cause of soil loss is primarily due to insufficient policy implementation rather than a lack of knowledge. It is important to note that this is an objective evaluation and not a subjective one. Addressing erosion requires consideration of multiple factors, including geomorphological and climatic risks, as well as human interventions (Panagos et al., 2016). According to the FAO (2016) report, agricultural areas lose approximately 75 billion tonnes of soil every year due to erosion, costing the global economy around 400 billion.
Land-use planning involves evaluating the productivity, climate, and topography of land to determine suitable economic use options. It also considers the sustainable transfer of natural resources to future generations and socio-economic conditions (Palom et al., 2017). Correctly analyzing and managing resource use in line with needs and problems is crucial (Erpul et al., 2014). Land use planning aims to prevent soil depletion caused by misuse and overuse, as soils are limited natural resources. This approach serves the dual purpose of limiting soil loss and suggesting optimal ways to use land for the benefit of individuals while preserving nature and its resources (FAO, 1976, 1985).
Soil erosion detection is essential in planning soil conservation strategies for catchments. An integrated approach, using modelling studies and remote sensing techniques, provides a consistent methodology for determining soil loss. Several experimental models analysed factors such as climate, topography, and soil characteristics in the study areas to assess the effect of erosion. The Universal Soil Loss Equation (USLE) (Wischmeier and Smith, 1978) and the Revised USLE (RUSLE) (Renard et al., 1997) are globally recognized models for studying soil loss. These models were utilized in several research studies conducted in recent years in Turkey (Imamoglu and Dengiz, 2017; Karaş, 2020; Baskan, 2021; Gürtekin and Gökçe, 2021).
Soil loss is a major concern for catchment management in Türkiye and worldwide. Studies on this issue aim to promote sustainable catchment planning and management. However, current studies follow decision processes to assess the situation and address this problem. The methodologies employed, however, are underdeveloped. For this reason, the sustainable land use planning model which takes a comprehensive approach to the issue and can generate resolutions for implementing sustainable land management to prevent soil erosion is implemented in the sub-basins situated within the confines of Tekkeköy district in Samsun province, using objectives. These objectives are i) to determine the land use and land cover between 1989 and 2020, ii) to analyse the temporal change in land use over many years, iii) to employ the RUSLE equation supported by remote sensing and geographic information systems to detect soil losses and erosion risk classes occurring in between 1989 and 2020, and iv-) based on the data obtained from the study, the final objective is to propose conservation measures to reduce and prevent soil erosion through the use of the Sustainable Land Use Planning (SLUP) Model for soils distributed under diverse land use and coverage in the examined region.
Material and Methods
Description of the study area
This study was carried out in Tekkeköy district of Samsun province, which contains approximately 13 micro-basins. The study area coordinates between 41°, 12', 45.0072" North and 36°, 27', 24.9984" East. In addition, it is located at the 13th km of Samsun-Ordu highway and the total area of the study area is 225.6 km2 (Figure 1).
The study area's elevation ranges from 0 to 951 metres above sea level. The elevation map shows that the northern and north-eastern regions have mostly flat or nearly flat slopes, while the south-western area has a higher number of steep slopes, consisting mostly of mountains. These regions make up 42.7% of the study area. (Figure 2). The study area experiences a predominantly Central Black Sea climate. Summers are characterised by high temperatures and humidity, with mild winters and rainy falls and springs. August is generally the warmest month, with an average temperature of 25°C, while January typically has the coldest temperatures, averaging at 7°C (MGM, 2022). The geological pattern of the study area indicates that the north and northeast, which have sloping terrain, are mainly composed of alluvial deposits, while the rest is mainly composed of sandstone-mudstone and limestone. In the southeast of the area, there are volcanic rocks consisting of basalt and andesite. The predominant soil groups in the region are brown forest soil and grey-brown podzolic soil. Alluvial soils can also be found in the northern and north-eastern areas of the region (Figure 2).
Soil sampling
For this study, a total of 328 soil samples were used for this study, with a soil depth of 0-30 cm. The samples were coordinated using GPS from the sub-basins of Tekkeköy district, taking into account land use-land cover types (Figure 3).
Soil samples, obtained from the field, underwent pre-treatment procedures such as drying and 2 mm sieving, before being made ready for analysis. In addition, changes in land use and cover in the region over time were assessed and mapped. The RUSLE equation was employed to create a dataset and map for each parameter of the area to determine soil losses. The evaluations informed the development of strategies using the sustainable land use planning model (SLUP) model.
Land use and land cover
Landsat 5 and Landsat 8 satellite imagery captured on 26th August 1989 and 31st August 2020 were analysed to investigate changes in land use and land cover within the study area over the years. The study findings suggest changes in the land use and cover of the study area over the years. To identify these changes, a classification of CORINE land use land cover level 1 was conducted. The satellite imagery obtained was analysed using a supervised classification method and ENVI 5.5v and ArcMap 10.5v software.
Soil loss and risk map
The USLE is a globally-applied empirical model used to estimate soil erosion loss via five input parameters. In the basin, the Revised USLE (RUSLE) model was performed to determine soil losses. This model allows for the integration of climatic and morphological features with land use data (Renard et al., 1997). The following equation (Eq. 1) was then utilized.
... (Eq.1)
In the equation: A: Average soil loss (t ha-1 year-1), R: Rainfall erosion factor, K: Soil erodibility factor, L: Slope length, S: Slope steepness, C: Land cover and management and P: Soil conservation practices
Rainfall erosivity factor (R)
While calculating the rainfall erosivity factor (MJ ha-1 year-1 × mm h-1), the Modified Fournier Index (MFI) values developed by Arnoldus (1977, 1980) for each of the meteorological stations in the basin (such as; Samsun Region, Samsun Airport, Ladik, Çarşamba, Ayvacık, Unye, Tokat, Ordu) were first calculated (Eq. 2). Then, the following equation (Eq. 3) proposed by Irvem et al. (2007) was used to calculate the RUSLE-R factor.
... (Eq.2)
Here; Pj corresponds to the monthly mean precipitation (mm) in month j from j = 1 to 12 and Pi, i corresponds to the annual mean precipitation (mm) in the year.
... (Eq. 3)
Where; R: Erosivity factor of rainfall (MJ ha-1 y-1 ×mm h-1), MFI: It is the calculated MFI of the meteorological station.
Soil erodibility factor (K)
The soil erodibility is an indicator of how easily soil particles can separate and be carried away by runoff and precipitation. It was calculated by using 328 soil samples taken from 0-30 cm soil depth. Walkley and Black (1934) method was used to determine organic matter and Bouyoucous (1952) hydrometer method was used for texture analysis. Wet sieving method was used to determine fine sand under laboratory conditions. In addition, the methodology of Soil Survey Manual (USDA, 2017) was used to determine the structure and permeability classes for each soil. A large K value suggests more soil erosion risk, whereas a small K value suggests decreased soil erosion sensitivity. The following equation (Eq. 4) developed by Wischmeier and Smith (1978) was used to determine the susceptibility of soils to erosion.
... (Eq. 4)
In the equation; K: Soil erosion susceptibility factor, M: Particle size parameter, a: Organic matter content %, b: Structure type code, c: Water permeability code, d: Conversion coefficient to metric system (d=7.59).
The following equation was used to determine the particle size (M) parameter in the equation (Eq. 5).
M = (% very fine sand + % Silt) · (100 - % Clay) (Eq. 5)
Slope length and steepness factor (LS)
The LS factor refers to the degree of slope (S) and the length of slope (L). The study area was calculated and mapped in ArcMap 10.5 programme using the Digital Elevation Model with 30 m resolution. In the programme, filling the missing pixels, flow accumulation, flow direction and slope values were calculated respectively. LS factor was calculated with the help of the following equation (Eq. 6) (Wischmeier and Smith, 1978; Desmet and Govers, 1996; Mitasova et al., 1996).
LS=1.6·Pow(("Flow accumulation"·cell size)/22.1,0.6)·Pow(Sin("Slope"·0.01745)/0.09,1.3) (Eq. 6)
Land cover and management factor (C)
The cover management factor represents the ratio of soil lost under a particular land cover to that of bare soil. This component indicates the efficacy of a certain vegetation cover in protecting the soil. NDVI is a widely used index for vegetation monitoring and many researchers have adopted this method to determine the plant management factor (Durigon et al., 2014; Dutta, 2016, van der Kniff et al., 2000a). Landsat 5 and Landsat 8 satellite images of 26.08.1989 and 31.08.2020 were used to calculate this index. The following formula (Eq. 7) was used to convert the NDVI values obtained from satellite images into C factor.
... (Eq. 7)
Where; α=1 and β=2 (van der Kniff et al., 2000b).
Soil conservation practices (P)
The ratio of soil loss for a specific farming practice compared to uphill and slope farming is referred to as the conservation practice factor. Typically, the soil conservation practices factor is set at 1 in complicated situations where it is challenging to ascertain conservation measures, or when there is no evidence that conservation measures have been utilised within an area (Renard et al., 1991). In our analysis, we set the soil conservation practices (P) factor at 1 since no soil conservation measures were implemented in the catchment area.
Soil loss tolerance (T)
Soil loss tolerance (T) can be defined as the maximum soil loss that can be accepted assuming that soils remain productive and fertile in a sustainable manner (Stamey and Smith, 1964; Wischmeier and Smith, 1978; Johnson, 1987). Pretorius and Cooks (1989) point that soil loss tolerance are used to identify critical areas in catchments by comparing soil erosion and T values. The soil loss tolerance values determined within the scope of the study were used according to the following table (Table 1) calculated on the basis of effective root depth (McCOrmack et al., 1982).
Sustainable land use planning model (SLUP) and data analysis
The model makes a comparison by considering the soil loss tolerance (T) value (Mc Cormack et al., 1982) according to the plant root depth in the basins where soil loss (A, t ha-1 year-1) is determined by RUSLE and digital soil map. Graphical chart was presented in Figure 4.
The model ratios the potential soil losses (A) estimated for the study area to the determined soil loss tolerance value (T). According to the results of (A/T) ratio, the current situation is revealed and the erosion situation in the basin is graded and defined according to five classes as given in Table 2 (Karas, 2020).
Finally, in the SLUP model, the A/T value can be applied to classify land use. For example, if the A/T value is estimated at 5.0, fourth-degree erosion is happening on agricultural land. A/T values between 4.0 and 6.0 indicate high erosion values, as shown in Table 2. In such circumstances, cultural techniques such as contour farming and strip cropping cannot reduce soil loss in the designated area below the tolerance limit. According to Table 2, This type of land should be converted to natural use (such as forests or pastures) to achieve sustainable management. This approach resembles how physical infrastructure, such as drains, terraces, and fences, can prevent excessive erosion in forest or pasture areas.
Results and Discussions
Land cover and land use change
In the current study, Landsat 5 and 8 satellite images were used to determine temporal variations in land use and cover from 1989 to 2020. In 1989, the initial year of the study region, farming areas and grazing pastures comprised a substantial part of the basin. The basin is composed of various land uses, with agricultural areas covering 36.25% of the total area, followed by pasture, forest, and artificial areas such as settlements, roads, airports, and industrial zones. Mountainous and high-altitude regions also occupy a significant portion of the basin. Forest areas include 25.15% of the basin, while pasture areas account for
Based on the 2020 data, the basin is considerably covered by agricultural areas comprising 39.31%. Pasture and forest areas make up 54.85% of the study area. Artificial areas total 5.42% of the region, and settlement areas are predominantly situated in the lowland parts of the district, especially as the industry area is located in this area (Figure 5). Considering the topographic structure of the land, there is a pressure on pasture areas, especially with the increase in hazelnut production in the region. When the change between 1989 and 2020 is analysed, it is seen that there is an increase of 8.43% in agricultural areas and the majority of these areas have been transformed from pasture areas. In the study area, there is no major change in forest areas. When we look at the proportional change between 1989 and 2020, it is determined that the artificial areas have increased by 86%. This increase was caused by the proximity of the basin to the organised industrial zone and population increase. In another study conducted in Tekkeköy district, the change in artificial areas over the years was examined and it was observed that these areas increased from 3.5% to 10.6% (İç et al., 2021).
Findings of RUSLE parameters
An individual map was generated for each of the factors in the RUSLE equation and presented at Figure 6. The rainfall erosion factor was calculated separately for both periods and the data cover the years 1980-1989 and 1990-2020. According to the MFI values for the first period, the values ranged between 47.10 and 107.1. The highest MFI values were determined at Ünye and Ordu meteorological stations whereas the lowest value was determined for Tokat meteorological station. The MFI values of the meteorological stations utilized in the second period demonstrate that values range between 63.28 and 86.79. High MFI values are noticeable at the meteorological stations in Çarşamba district and the airport. The rainfall erosivity factor values of the meteorological stations in the neighbouring districts were calculated using the Inverse Distance Weighted (IDW) interpolation method (Table 4) and shown on the map (Figure 6).
In terms of the distribution of the rainfall erosivity factor in the second period, it was found that the station in Çarşamba district had the highest value of 2696.48, whereas the Samsun Regional meteorological station had the lowest with a factor of 1328.10. Previous studies suggest that areas with high levels of rainfall and predominantly rain-form precipitation are the most significant factors contributing to high levels of precipitation erosion. In this study, Doğan (2002) analysed data from 96 stations and found that stations located in coastal areas experience high levels of erosive precipitation, whereas high altitude areas primarily receive snowfall which has low erosive properties. Inverse Distance Weighted (IDW) interpolation method was used to determine the spatial distribution of the obtained soil erodobility factor (K) values and the K factor values of the station are given in Table 5.
K factor values, determined by analysing 328 soil samples from the basin using a global positioning system varied between 0.0132 and 0.0527, as shown in Table 5 and it was determined that strongly erodible regions cover 64.02% of the basin. This sensitivity of soils can be attributed to the geographical characteristics of the area, including an excess of high-altitude locations and varying soil properties such as low organic matter content due to using intensive agricultural practices and soil texture or the sand content of the soils. In this case, soils with a higher sand and silt content formed on siliceous parent material such as granite and sandstone are more susceptible to erosion than soils with a heavier (clay) texture formed on the rocks with a lower quartz content (clay, limestone, marl, etc.). Moderately erodibility is found in 34.76% of the remaining areas of the basin. Furthermore, upon scrutinizing the soil erodibility factor map, it becomes apparent that the vulnerable zones are situated in the central and coastal regions of the basin. The lower values were determined only in a very small part of the study area (Figure 6).
The values of the slope length and steepness factor obtained in the study vary between 0 and 147.025. It is well known that the LS factor reaches very high values in areas where the slope is long and steep. The greatest variability in elevation and the longest and steepest slopes are found in the southern and southwestern parts of the study area. Very high values of LS factor are obtained in these areas and the highest value of 147.025 is found in these regions. The northern and north-western parts of the study area are the areas where the LS factor has low values. As can be seen in Figure 6, these are the areas where most of the agricultural production takes place and the slope is flat or almost flat. When the LS factor distribution map is analysed, we see low LS factor values around the mountainous areas. When these areas are examined, it is understood that there are mostly the upper parts of the forested areas and the peaks where the slope is close to flat. The LS factor distribution map is similar to the slope of the study area. Beskow et al. (2009) stated that in areas where the LS factor is greater than 10, the sensitivity of soils to erosion due to topography increases very much and soil protection measures should be prioritised in these areas.
It should be noted, however, that most of the terrain is covered with poorly forest vegetation and intensive cultivated. The erosion potential of the areas where the land cover-land use and management factor value approaches 1 increases. C factor values, which we examined in both study years within the scope of the study, were determined to vary between 0.13 and 0.61 for 1989 and between 0.18 and 0.63 for 2020 (Figure 6). It is seen that the regions with low land cover and management factor cover the forested areas of the basin. In addition, the regions with the highest management factor constitute artificial areas. When we look at the distribution of the C factor in the central and inner parts of the basin, it is seen that it has values slightly above the average and when we look at the land use status of these areas, it is seen that they consist of pasture and mostly agricultural areas. In soil conservation and management studies, it should be planned to increase the vegetation on the soil surface in order to reduce the effect of erosion (Kirkby et al., 2008). Finally, since it is assumed that no soil conservation measures were taken in the basin between 1989 and 2020, the soil conservation practices (P) factor was used as 1 for the present study.
Determination of soil loss and risk assessment
Following the utilisation of the R, K, LS, C, and P factors in the RUSLE potential soil loss formula, a soil erosion risk map was created and analysed using ArcMap software. Consistent pixel resolution (30 x 30 m) was applied to each factor for accurate analysis. Consequently, soil loss distribution maps for the basin in 1989 and 2020 were produced, as displayed in Figure 7. These maps were then categorised according to erosion risk classes.
Gołąb and Urban (2017) reported that there are related studies applying the RUSLE method and GIS to soil erosion analysis; however, variations exist in methodology. This study partially contrasts with prior research that examined terrain features and their implications for land use and climate. These studies substantiate that uncontrolled and unsustainable deforestation notably augments soil erosion. In this study, while the average soil loss was 7.53 t/ha/year for 1989, this value was found to be 7.86 t/ha/year for 2020. Compared to 1989, this corresponds to an increase of approximately 4.38%. Considering that the accepted average soil loss value for Turkey is 6.14 t/ha/year (Çakal et al., 1995), it is determined that soil erosion in the research area is a problem in both periods and land use planning is required. Several studies were conducted in basins found in humid and sub-humid areas, and their findings exhibit similarities. For instance, in the Mustafakemalpaşa stream basin situated in the lower parts of the Marmara region and traversing the borders of Bursa, Kütahya, and Balıkesir districts, Özsoy (2007) employed the RUSLE equation to determine the amount of soil loss. In the present study, the annual potential soil loss was found to be 11,296,061.71 tonnes per hectare per year, with 11.18 tonnes per hectare per year being the mean value. Analysing the Mustafakemalpaşa Stream drainage basin shows that the basin's elevation ranges from 4 to 2116 meters. Consistent with this study's findings, the regions with medium and steep slopes exhibited significant soil loss. In another study, İnaç et al. (2021) determined that the average amount of soil loss was 3.55 tonnes per hectare per year and 5.68 tonnes per hectare per year in the years 2001 and 2012, respectively, in a basin located in the Artova district of Tokat province. The study notes that the basin is primarily composed of agricultural plots.
Soil loss toleran
The soil loss tolerance values determined in the study were calculated on the basis of the effective plant root depth. It is assumed that the tolerable limits of soil loss values vary between 2.2 and 11.2 t ha-1 year-1. When analysing the effective soil depth of our study area, 4 different depth classes are formed. Figure 7 shows the spatial distribution of soil loss tolerance values according to effective root depth.
Sustainable land use planning model (SLUP)
The soil loss (A) map of the study area, calculated by the RUSLE equation for the years 1989 to 2020, was related to the soil loss tolerance (T) values determined by the effective plant root depth of the catchment. The A/T ratio obtained in the study area determines the degree of erosion risk in the catchment, and the SLUP model provides recommendations for soil conservation measures to be taken in this regard. The maps and spatial distributions of erosion severity for two periods are shown in Table 6, Figures 8 and 9.
When analysing the first period of the catchment, the erosion degree is 1.0 in the areas with low slope and more agricultural production, and proportionally covers an area of 62.71%. The areas with high (4) and very high to severe (5) erosion levels represent 4.55% and 6.88% of the total area respectively, and these areas have very critic A/T ratio.
When analysing the 2020 data, it is seen that the high coded as 4 and very high to severe shown with 5 classes, where the degree of erosion is the highest, covers 4.85% and 7.2% of the basin. It is seen that there is an increase compared to the first period of the basin with 14.12% of the areas where the degree of erosion is 2 in the low to moderate class.
Table 7 also shows that the areas with moderate to high (3), high (4) and very high to severe (5) erosion levels have increased proportionally compared to the first period. In this context, in the areas where the degree of erosion is 4, improvement measures should focus on changing the qualities that are not suitable for land use. In particular, it is necessary to increase the percentage of pasture cover, to increase the diversity of products and, where the type of land use has been changed, to apply management practices appropriate to the soil characteristics.
The first degree erosion measures of the SLUP model recommend that cropping systems can be changed, tillage systems should be preferred more for soil conservation and appropriate agricultural equipment should be used. In the first degree erosion data of the research area between 1989 and 2020, it was determined that there was a decrease in pasture, forest and agricultural areas compared to the first period and the artificial areas doubled. Although erosion in the first degree seems to be low, the expansion of industrial zones and residential areas in the study area has been identified as a major risk for agricultural areas. Within the scope of the study, the characteristics of agricultural areas should be preserved and artificial areas should be selected in a planned manner by paying attention to soil conservation measures.
Considering the second and third degree erosion classes in the research area, it is seen that there is a significant increase in agricultural areas between the two periods. On the other hand, in terms of land use, it was determined that there was a decrease in the distribution of pasture areas between the two periods. In the studies conducted, it was determined that the land management factor in pasture areas was 2.5 times lower in terms of preventing erosion compared to agricultural areas. This situation was explained by Lugato et al. (2014) that the protection of pastures increases soil organic carbon accumulation and as a result, aggregation increases and prevents erosion. When the second and third degree erosion classes of pasture areas are evaluated, while measures should be taken to protect and enrich their vegetation, the change in their distribution class in line with agricultural areas triggers erosion more. In areas where agricultural production is carried out, soil compaction increases as a result of tillage and this leads to increased erosion. Within the scope of the research, cultural measures and physical structure improvements should be carried out in addition to first degree soil protection measures. The use of systems such as contour farming, intercropping, mixed cropping or agroforestry and the protection of these areas should be prioritized. In addition, within the scope of this study, it was determined that the regions where the degree of erosion increases towards the fourth and fifth classes are the high and sloping areas with poor vegetation cover. Compared to the first year of the research area, the decrease in pasture areas continues in areas where the degree of erosion increases. During the field study, it was determined that there was no soil conservation planning applied to the area, and it is thought that these areas have entered the category of sensitive areas with a change in quality in the direction of agricultural production.
Forest areas show distributions in sensitive areas due to their nature and in high areas where agricultural production cannot be carried out. In addition, these areas consist of shallow and poor development soils. According to the current land use plan, the areas with the fourth and fifth degrees of erosion account for about 13% of the total area. Taking into account the SLUP model, land planning of these areas should be changed so that agricultural areas are replaced by forests or pastures where no tillage is practiced. In addition to the land qualification change, the bio-physical measures of the lands (improvement of stream beds, gradual stabilization structures, construction of grassy waterways, etc.) and appropriate forest management (afforestation, deforestation, controlled cutting, etc.) must be provided
Conclusion
Changes in land use and land cover have both advantages and disadvantages in terms of accelerating and reducing soil loss. In our study area, the increase in agricultural land in recent years has put pressure on pastures and forests. Especially in the high altitude pasture and forest areas, low vegetation cover and bare soil surface pose a high risk of erosion. In addition, the main problem causing erosion in pastureland is overgrazing. In these areas, enrichment of the vegetation cover and pasture improvement works should be carried out. Soil loss also affects land productivity. As erosion increases, soil fertility decreases. Biophysical measures such as terracing, fertilisation or reducing the frequency of tillage can be used to prevent erosion from degrading the physical and chemical properties of soils. When the topographic characteristics of our study area are examined, it is seen that the majority of it consists of sloping lands. In sloping areas, soil losses due to erosion and the structural structures of the soils deteriorate and their productivity decreases. In the next stage, these areas are taken out of agriculture with changes in quality. Especially these areas should be protected and their potential should be increased by soil conservation practices.
As a result, while the average soil loss was 7.53 t/ha/year for 1989, this value was found to be 7.86 t/ha/year for 2020. Compared to 1989, it corresponds to an increase of approximately 4.38%. These values are higher than the average soil loss values in Turkey and this situation of the basin should be taken into consideration in planning. When the topographic structure of the study area is examined, the fact that the southern and south-western parts of the basin consist of high slopes and mountainous areas is a factor limiting agricultural production. These areas consist mainly of pasture and woodland and are under pressure to change their quality for various reasons. Especially in recent times, these areas have been converted into agricultural land, resulting in a loss of the physical and chemical properties of the soils. Sustainable land use and planning of these areas should protect soil quality and prevent soil loss by taking into account the productivity dynamics of the land.
Future research on soil erosion is needed because of the vast potential for practical applications of this information and the need to provide new scientific and technical understanding of the subject. Particular emphasis should be placed on the use of modern tools and techniques in research to facilitate better and easier data collection, processing, modelling and monitoring of erosive processes. The ability to identify areas where erosion could potentially become a significant problem due to irrational land use is another way of demonstrating adequacy, in addition to clearly defining areas that are currently at risk of erosion. The ability to manage land and natural resources more effectively is why this information is so important for the study region.
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