Socio-economic modifications of the Universal Soil Loss Equation

While social scientists have 1 long focused on socio-economic and 2 demographic factors, physical 3 modelers typically study soil loss 4 using physical factors. In the current 5 environment, it is becoming 6 increasingly important to consider 7 both approaches simultaneously for 8 the conservation of soil and water, and 9 the improvement of land use 10 conditions. This study uses physical 11 and socio-economic factors to find a 12 coefficient that evaluates the 13 combination of these factors. It aims to 14 determine the effect of socio15 economic factors on soil loss and, in 16 turn, to modify the Universal Soil Loss 17 Equation (USLE). The methodology 18 employed in this study specifies that 19 soil loss can be calculated and 20 predicted by comparing the degree of 21 soil loss in watersheds, with and 22 without human influence, given the 23 same overall conditions. A coefficient 24 for socio-economic factors, therefore, 25 has been determined based on 26 adjoining watersheds (WS I and II), 27 employing simulation methods. 28 Combinations of C and P factors were 29 used in the USLE to find the impact of 30 their contributions on soil loss. The 31 results revealed that these 32 combinations provided good 33 estimation of soil loss amounts for the 34 second watershed, i.e. WS II, from the 35 adjoining watersheds studied in this 36 work. This study shows that a 37 coefficient of 0.008 modified the 38 USLE to reflect the socio-economic 39 factors as settlement influencing the 40 amount of soil loss in the studied 41 watersheds. 42


Introduction
Soil erosion is a natural process for landscape development if accelerated denudation 20 processes by human impact. Moreover, it determines the landscape and the landforms, the soil and water quality, the vegetation recovery and the fate of the societies (Zhao et al., 2013). This phenomenon is a globally environmental threat that reduces the productivity of all natural ecosystems (Kertész, 2009;Pimentel and Burgess, 2013;Leh et al., 2013) including soil where the adaptation capacity is weak (Cerdà, 2000; Printer-friendly Version

Interactive Discussion
Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | the world's arable land is significantly degraded by soil erosion. Additionally, erosioninduced soil quality deterioration is prevalent all over the world (Harden, 2001;Zhao et al., 2013) obstructing the global food source and socio-economic security. Young (1993) indicated that the challenges of soil erosion are more severe in the heavily populated, under-developed, and ecologically fragile areas of the world. Lal (1981) and 5 Eswaran et al. (2001) asserted that misuse of soils, resulting from a desperate attempt by farmers to increase production for the growing population aggravates soil quality degradation. Tesfahunegn (2013) further claims that severity of such degradation is higher in developing countries where the economy mainly depends on agriculture.
Soil erosion, which is one of the primary issues that forestry and agriculture agen-10 cies have to deal with, is a critical problem in Turkey. The current population of Turkey is 76.7 million (TUİK, 2014), and the land surface area is 78 million ha; this comprises 36 % of agricultural land, 27.6 % of rangeland, and 29.8 % of forest and shrub cover, with the remaining 6.5 % of land accounting for settlements and water bodies (OSİB, 2005). To put it bluntly, it is anticipated that there will be a dramatic increase in set-15 tlements due to rapid population growth which results in intensive construction in the mountainous areas of which especially used agriculture and forest. Indeed, soil erosion is a key issue in mountainous regions worldwide (Leh et al., 2013;Mandal and Sharda, 2013;Haregeweyn et al., 2013;Wang and Shao, 2013). Mountain soils develop in very sensitive environments subject to natural and anthropic disturbances (e.g. Cerdà and 20 Lasanta, 2005;Vanwalleghem et al., 2011;Van der Waal et al., 2012;García Orenes et al., 2012), and they are often located at the interface with densely settled areas, which may be considerably affected by sediment release from upstream erosion (Ziadat and Taimeh, 2013;Cao et al., 2014;Lieskovský and Kenderessy, 2014). Similarly, watersheds of Turkey are located at mountainous areas and these areas mainly under 25 the effect of soil erosion impact water quality and quantity. Thus, their soils are very sensitive to soil erosion. Furthermore, land use management practices are becoming increasingly important due to growth in improper land use in the country and existing Introduction considerable spatial heterogeneity in terms of land use and management, topography, and socio-economic conditions all over Turkey. Foley et al. (2011) made a global emphasis on the soil erosion problem that the global population is predicted to reach 9 billion by 2050; in combination with changes in dietary behavior, a large net increase in productivity and/or agricultural area is needed. 5 Additionally, Brevik et al. (2015) argued that soils are thus under increasing environmental pressure, and this will have consequences for the capacity of the soil to continue to perform its variety of functions. Environmental degradation from human pressures and land use has become a major worldwide problem (Wilson, 1992), however, the effects are felt more in developing countries due to the high population growth rate and 10 the associated rapid depletion of natural resources (Feoli et al., 2002). According to Udo et al. (1990) soils are impoverished and may have also been destroyed by erosion in very densely populated areas. Similarly, on the national level, soil erosion is expected to increase (Nearing et al., 2004;IPCC, 2007). Thus, amelioration measures should be taken in all countries especially at the regional and national level. Introduction context, the most efficient approach for minimizing erosion problem is thought to be the use of resources in a timely and organized manner. Haregeweyn et al. (2013) stated that critical erosion hotspots are defined as parts of watersheds with high erosion rates. These hydrological units are also under the influence of human activity including socioeconomic factors causes changing the character of the watershed. On the other hand, 5 determining the influential socio-economic "causes" of erosion is just as complex. Furthermore, data to be determined causes of erosion is very scarcely limited. According to MacGillivray (2007), many of the political and socio-economic factors, however, are regionally effective and intangible. On the other hand, it is important to assess the degree of soil erosion under different environmental and socio-economic situations in order to identify and apply suitable land management interventions (Castro et al., 2001) understand the causes and effects of soil erosion. Therefore, there is a need for more research on the relationship between cause and effect of erosion. Haregeweyn et al. (2013), however, signified that spatial data to determine soil erosion in the developing countries is often scarce and possibilities to identify source areas for erosion and sed-15 iment are very limited. As a matter of fact, Turkey also should be considered to be one of them. Land degradation and in particular, soil erosion, has long been studied as a physical process by scientists using USLE from backgrounds as a diverse as geography, geology, agronomy, and engineering (Boardman et al., 2013). USLE proceeds to be 20 the most widely used model for soil loss estimations. Several studies have been performed in India (Ali and Sharda, 2005;Sharda and Ali, 2008;Narain et al., 1994) and other countries (Van Rompaey et al., 2002;Larsonm et al.,1997) to estimate the performance of the USLE in predicting soil loss under different situations (Mandal and Sharda, 2013). Besides, in eastern Himalayan regionpotential soil erosion rates for dif-25 ferent states of the region were estimated by collecting data on various parameters of USLE by Mandal and Sharda, 2013. However, the USLE is often criticized for its limited applications (Castro et al., 2001), and inability to recognize the cause-effect factors on erosion or the amount of soil Introduction  Jayarathne et al. (2010) established that there is a strong positive relationship between land degradation and soil erosion, as well as land degradation and population density. Strong negative relationships were also observed between land degradation and land/man ratio. Boardman et al. (2003) stated the physical and socio-economic factors drive soil erosion; therefore, these factors need to be addressed in tandem.

5
However, it is often the case that the studies on this subject are not given in an interdisciplinary fashion (Boardman et al., 2003). Given this view, evaluating physical factors with socio-economic factors is the best starting point for determining the degree of soil loss using two different disciplines. Additionally, Evans (1996) made an attempt with his assessment of the socio-economic and physical drivers, impacts and costs of erosion In the present study, socio-economic factors were spatially considered as settlements including humans and animal shelters. Thus, cropping management (C fac-20 tor) and erosion control practice (P factor) were used to estimate the contribution of socio-economic factors in the USLE (Wischmeier and Smith, 1962, 1965, 1978Lal, 1994). In addition, a calculation method was suggested to determine a coefficient that would consider the interactions of physical and socio-economic factors using a simulation method. The amount of soil loss resulting from human and animal influence in Introduction In this study, we hypothesized the presence of settlements in the study area, where the impact on erosion in the USLE depended on the number of people and animals. The objective is to determine if any of these factors contribute to erosion, and how much the factors would influence the outcome of the USLE. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | efficient were obtained from the past references (Doğan and Güçer, 1976;Arnoldus, 1977;Balcı, 1996;Cebel et al., 2013).

Data from GIS and past references
The topographic features and land use data of the two adjoining watersheds were obtained using GIS methods and the topographic data of the watersheds (Tables 1-3).

Data obtained for the USLE
The USLE is used in Turkey as the most common mathematical model for predicting the amounts of soil loss in forests and rangelands. Previously, Turkey has been studied 20 primarily with reference to the R, C, and P factors in the model (Doğan and Güçer, 1976;Çanga, 2006). The C and P factors for the watersheds were adapted from past references (Balcı, 1996), and many other previous studies were investigated in terms of the various USLE factors. The values for the C and P factors reported by Balcı (1996) were determined for a study area with properties identical to those of the existing study SED 7, 1731-1759, 2015

Socio-economic modifications of the Universal Soil Loss Equation
A. Erol et al.  (Tables 4 and 5). The USLE can be presented as follows: where (A) is the annual soil loss (t ha −1 year −1 ). In Eq. (1), the impacts of slope length and steepness were usually combined into one single factor (Randle et al., 2003), known as the topographic factor (LS) (Balcı, 1996), which can be computed as follows: s and l calculated to the LS factor for the studied watersheds were 1.32 for WS I and 0.714 for WS II (Tables 6 and 7). As can be seen in these tables, the K, R, C, and P factors established for the USLE for dense forests, open forests, orchards, and agricultural lands in both watersheds were obtained from the past references (Doğan and Güçer, 10 1976; Arnoldus, 1977;Balcı, 1996;Doğan et al., 2000;Cebel et al., 2013). Finally, all the factors of the USLE were used to determine the total annual soil loss (Tables 6 and  7). It has been established that the K, R, L, and S factors were represented in a distinct layer in the USLE (LIFE+ Programme, 2011), which explains why the potential and actual erosion amounts were not calculated for comparison (Table 8). It is well known 15 that actual erosion values cannot be calculated for settlements and greenhouses. This is because these areas do not have enough vegetation cover to influence the calculations. The USLE can only be used to calculate actual erosion values; however, potential erosion calculations do not take into account land use and vegetation. As the two values cannot be compared, potential erosion values used for settlement and greenhouse 20 areas.

Data analysis
The available soil loss amounts and the degree of socio-economic factors for each of the watersheds were calculated with considering the past references. All the physical data of the study area were obtained using GIS, and used to evaluate the contributions of the socio-economic factors to the total annual erosion (A) and find a coefficient in USLE. C and P values for the socioeconomic factors in the USLE were obtained from the average of C and P values taking their total of all existing values. In other words, to the coefficient for socioeconomic factor as settlement were found using 5 all C and P values to obtain a average value. Subsequently, C and P factors were analyzed to find their averages. The contributions of socio-economic factors to the total annual soil loss amounts were established. In the process, simple mathematical equations were used to find the coefficient. These steps are detailed in Table 8.
The calculation of the factors affecting soil loss amounts for WS I was completed 10 using the traditional USLE, because this watershed was assumed not to be under the influence of any human impact. However, the annual amount of soil loss in WS II was determined using both physical factors used in the USLE and the modified coefficient in the USLE. The sequence of calculation steps aimed to generate the required coefficient. Ac-15 cordingly, each progression was defined separately as follows. The total number of people and animals in the settlement were the socio-economic factor (Se); it was used to find the amount of soil loss in the settlement (Se_E). The equation used the ratio of settlement numbers to total watershed area (ha) multiplied by the amount of soil loss (A) from the USLE (Step 1).

20
The second process was stated as (Soc-e-F_E), which was the amount of soil loss due to socio-economic factors. This result was calculated using the amount of soil loss per person and per animal (Step 2 and 3).
Step (1) and (3) was taken to find the contribution of socio-economic factors in A (t ha −1 year −1 ) (Step 4).

25
The ratio of (Soc-e-F_E) to A gave the coefficient. This coefficient also represented the total C and P values contributing to the averages of the available C and P used in the study. After establishing human and livestock impacts per unit of soil loss amount, the contribution of settlement land on the total soil loss amount could be identified (measured in 15 kg). Consequently, the soil loss amounts were calculated with the total soil loss amount of the USLE. This coefficient was also simulated with different C and P factor combinations and the mean of the coefficients for each of the C and P factors combinations with total soil loss was determined. The means of these coefficients were identified as the correction coefficient of socio-economic factors, which contribute to the total soil 20 loss in the USLE.

SED
The coefficient, which can be added as a correction coefficient, was calculated as 0.008. The modified USLE can be represented as follows: The correction coefficient is determined as follows: where (A) is the USLE output (t ha −1 year −1 ) and SE is the settlement land area (ha).
The range of the determined coefficient, through simulation, is developed mathematical equation with the coefficient is shown in Table 10.
There are very few studies on this issue. Halim et al. (2007) studied the integration of biophysical and socio-economic factors in assessing erosion hazard; they found seven 5 key hazard factors; of which five were biophysical factors: soil texture (silt content), maximum rainfall erosivity (I30), slope (LS-factor), land cover (C factor), soil conservation practices (P-factor); and two were socio economic factors: farmer's perception on erosion and income).
In this study, it was considered that all factors in the USLE affect erosion; however, , were used to calculate their effects or contribution to the total soil erosion as socio-economic factors in the area. Boardman et al. (2003) stated that the socio-economic and physical factors drive soil erosion. It was considered that socio-economic factors, such as human population and livestock, contributed to soil loss.

5
Changes in soil loss, determined with the new equation, were considered to be the result of human and animal settlements. The values of the soil loss amounts with the modification coefficient in the USLE are represented in Table 9.

Conclusions
In this study, variations in soil loss due to settlements including humans and livestock 10 have been determined for watershed named WS II. The settlement area in this watershed is very small, such that the contribution of socio-economic factors appears limited. It is highly possible that soil loss would increase in large settlement areas. The findings of this study demonstrate that investigation of many watersheds are required to ensure a wider applicability of these findings and determine more reliable coefficients that can 15 be incorporated into the USLE. In this context, many different watersheds in order to compare with each other should be studied with this approach and more data should be gathered regarding the socio-economic factors of the watersheds in Turkey.
Admittedly, the resulting correction factor relative conventional USLE amounts to just 0.8 % is not enough to evaluate impacts of the settlement on the soil loss for the water-20 shed used in this study. We estimate that the most important reason of this is to ratio of settlements in the entire watershed is too small. However, since Antalya is a resort area and increasingly prone to settlements in the mountainous areas, it is highly likely that risk of soil loss will increase in the future.
It is well known that there is a need to improve existing methods for the estimation of    , 48, 1965. Wischmeier, W. H. andSmith, D. D.: Predicting Rainfall Erosion Losses-Guide to Conservation Planning, Agriculture Handbook No. 537, US Department of Agriculture, 1978. 5 Young, A.: Land degradation in South Asia: its severity, causes, and effects upon the people, Final report, Economic and Social Council, FAO, UN, Rome, 1993. Zhao, G., Mu, X., Wen, Z., Wang, F., and Gao, P.: Soil erosion, conservation, and ecoenvironment Table 7. Factors affecting the USLE and the soil loss amounts for Watershed II. Rainfall factor (R); soil erodibility factor (K); topographic factor (LS); cropping management factor (C); and erosion control practice factor (P). Soc-e-F_E = Pp_E + Apn_E The amount of soil loss of socio-economic factors 4 Se_E_c = Soc-e-F/Se_E The contribution in the amount of soil loss of the settlements 5 Soc-e-F_E/A= Coefficient Coefficient which corresponding to the average = P + C values/ P value of all P and C factors impacted on socio-+ C numbers economic factors Introduction