SciELO - Scientific Electronic Library Online

 
vol.85 número2Diferenciación floral y ritmo de crecimiento de las yemas de rizomas en la especie primaveral efímera Adonis amurensis Regel et RaddeGerminación de Gutierrezia solbrigii y Senecio subulatus, asteráceas endémicas de Argentina índice de autoresíndice de materiabúsqueda de artículos
Home Pagelista alfabética de revistas  

Servicios Personalizados

Revista

Articulo

Indicadores

  • No hay articulos citadosCitado por SciELO

Links relacionados

Compartir


Phyton (Buenos Aires)

versión On-line ISSN 1851-5657

Phyton (B. Aires) vol.85 no.2 Vicente López dic. 2016

 

ARTÍCULOS

The cover-management factor (C) on woodlands of the hilly areas of the Loess Plateau in North China

El factor cobertura-manejo (C) en áreas de vegetación leñosa de las zonas montañosas del Loess Plateau en el Norte de China

 

Wei TX1 & YH Liu2,1

1 Beijing Forestry University, Key Laboratory of State Forestry Administration on Soil and Water Conservation (Beijing Forestry University), Jixian Forest Ecosystem Research Station, Beijing 100083, P.R. China.
2 Beijing Mentougou Distict Environmental Protection Bureau, 102300. P.R. China;
Address correspondence to: WEI Tian-xing, e-mail: weitx@bjfu.edu.cn

Received 6.VII.2014.
Accepted 7.III.2015.

 


Abstract. Soil erosion is one of most serious environmental and production problems on the Loess Plateau in China. The objectives of this study were to quantify the influence of forest vegetation on soil erosion on slope areas in the Loess Plateau. This was made by using the subfactor method to calculate the vegetation cover management factor (C) of the Universal Soil Loss Equation (USLE). Proper local subfactor parameter values were obtained to ofer a theoretical basis and practical guidance for studying the relationship between vegetation and soil erosion on the Loess Plateau. Tree subfactors including prior land use (PLU), canopy cover (CC), surface cover (SC), surface Roughness (SR), soil moisture (SM), and covermanagement factor (C) were observed at three plant growth stages: initial, blooming and end of growing season. All observations and measurements were made on 13 runof plots in the Caijiachuan Watershed. The annual runoff sediment volume, and the subfactor and indirect methods were adopted separately to calculate the vegetation cover and management factor C of each stand, and then carry out comparative tests and comprehensive analyses. The results showed that the cover-management factor (C) calculated by the subfactor and indirect methods were in good agreement. The order refected was forest < Robinia pseudoacacia < Robinia pseudoacacia & Oriental arborvitae < Pinus tabulaeformis < orchard. Subfactors of PLU, CC, SC, SR and SM for soil loss rates of different stand types were not the same and the impact order was PLU>SC>SR>SM>CC. This indicated that plant roots, soil organisms in the surface soil layers and surface cover had a larger impact on soil loss fate than the other subfactors. Stand density was negatively correlated with vegetation cover and management factor C, suggesting that only stand density influenced soil erosion. The stand density of Robinia pseudoacacia ranged from 1200 to 2204 stems/ha. Management factor C ranged from 0.020 to 0.037. The subfactor method could be adopted to monitor the amount of soil erosion in the Loess Plateau, with the parameters being Cb=0.951, Cur=0.004513kg/(ha.cm), Cus=0.001887kg/(ha. cm), Cuf=0.5, b=0.025. The vegetation cover and management factor C of different stands in the Loess Plateau varied between 0.009 and 0.062.

Keywords: Loess Plateau; Forest vegetation; Soil erosion; Universal Soil Loss Equation; Vegetation Cover and management factor.

Resumen. La erosión del suelo es uno de los problemas ambientales y de producción en el Loess Plateau, China. Los objetivos de este estudio fueron cuantificar la influencia de la vegetación boscosa en la erosión del suelo en áreas de pendientes del Loess Plateau. Esto se hizo utilizando el método del subfactor para calcular el factor de manejo de la cobertura vegetal (C) de la ecuación de pérdida de suelo universal (USLE). Se obtuvieron valores apropiados de los parámetros locales para el subfactor para ofrecer una base teórica y guía práctica para estudiar la relación entre la vegetación y la erosión del suelo en el Loess Plateau. Se observaron tres subfactores incluyendo el uso previo de la tierra (PLU), la cobertura vegetal (CC), la cobertura de la superficie del suelo (SC), aspereza de la superficie del suelo (SR), humedad del suelo (SM), y factor cobertura-manejo (C) en tres estados del crecimiento vegetal: inicial, floración y final de la estación de crecimiento. Todas las observaciones y mediciones se hicieron en 13 parcelas de escorrentía en la Cuenca Caijiachuan. El volumen el sedimento de escorrentía anual y los métodos del subfactor e indirectos se adoptaron separadamente para calcular el factor de manejo y de cobertura vegetal C de cada sitio de muestreo, y luego efectuar pruebas comparativas y análisis comprehensivos. Los resultados mostraron que los valores obtenidos para el factor de cobertura-manejo (C) calculados por los métodos indirectos y del subfactor fueron similares. El orden obtenido fue bosque < Robinia pseudoacacia < Robinia pseudoacacia & Oriental arborvitae < Pinus tabulaeformis < huerta. Los subfactores de PLU, CC, SC, SR y SM para las tasas de pérdida de suelo de diferentes tipos de lugar de muestreo no fueron los mismos, y el orden fue PLU<SC<SR<SM<CC. Esto indicó que las raíces vegetales, los organismos del suelo en las capas superficiales del mismo y la cobertura de la superficie tuvieron un mayor impacto en la probabilidad de pérdida de suelo que los otros subfactores. La densidad del lugar de muestreo se correlacionó negativamente con la cobertura vegetal y el factor de manejo C, sugiriendo que solo la densidad del stand influenció la erosión del suelo. La densidad del lugar de muestreo de Robinia pseudoacacia varió de 1200 a 2204 tallos/ha. El factor de manejo C varió de 0.020 a 0.037. El método de subfactor se podría adoptar para registrar la cantidad de erosión del suelo en el Loess Plateau, siendo los parámetros Cb=0,951, Cur=0,004513 kg/ (ha.cm), Cus=0,001887 kg/(ha.cm), Cuf=0,5, b=0,025. La cobertura vegetal y el factor de manejo C de los diferentes sitios de muestreo en el Loess Plateau variaron entre 0,009 y 0,062.

Palabras clave: Loess Plateau; Vegetación de Bosque; Erosión del Suelo; Ecuación Universal de Pérdida del Suelo; Cobertura Vegetal y Factor de Manejo.


 

INTRODUCTION

The Loess Plateau, on the middle and upper reaches of the Yellow River, is one of the regions in China where soil erosion is most severe. This area is famous for its highly erodible fine aeolian deposits, steep slopes, heavy storms, and sparse vegetation cover. This last biological factor has been the result of intensive cultivation and improper land use (Chen et al., 1998). Although the problem has been tackled for many years, soil erosion on the Loess Plateau still covers an area of 450000 km2, 71% of the soil erosion in China. Soil erosion destroys land’s natural productivity and damages the ecosystems (Jing et al., 1997). Therefore, studies on soil erosion and the relationship between vegetation and soil erosion (Liu, 1990; Luo, 1990; Liu, 1994), especially the determination of vegetation cover and the management factor (C) are of great significance on diferent stand types of the Loess Plateau.
The role of forest vegetation on soil erosion is widely recognized (Sun & Zhu, 1995; Ghidey & Alberts, 1997; Hou et al., 1997; Huang & Liu, 2002; Zhang et al., 2003; Shi et al, 2004; Wang, 1994; Zhao et al., 2004; Niu & Wang, 2013). Much research has been conducted on the Universal Soil Loss Equation (USLE) considering the situation on different regions (Wilschmeires, 1976; Wilschmeires & Smith, 1978; Gabriels, 2003; Ozhan, 2005; Martins et al., 2010; Lee, 2012). However, because of the study condition constraints, results are generally not universal, and are dificult to apply to large scales. Taking the research progresses on the China’s soil erosion model into consideration, the current level of knowledge is similar to that in America in the 1950s (Xie, 2003). Despite lots of regional models have been built, there are no soil erosion models that could either be applied to a national scale or support basis for government-decision making (Xie, 2003).
Additionally, a large number of observation data has accumulated which needs to be analyzed and studied in a unified way for full utilization (Liu, 2001; Xie, 2003). This is especially true for calculating the vegetation cover and management factors (i.e., C). The widely used methods are (1) to calcu
late C indirectly in the USLE and (2) obtaining a multi-year average erosion (Liu, 2001; Zhang, 2002; Xie, 2003; Zhang, 2003). Some research estimates C by remote sensor data (Wu et al., 2012; Durigon, 2014). The disadvantage of using indirect methods is that they waste a lot of human and material resources, needing a relatively large project for calculation. Al-though it is still at an experimental stage, we propose using a subfactor method to calculate C, as long as every parameter is given properly. Our proposal will be more effective than the indirect methods in calculating the vegetation cover and management factor C. Besides, quantitative studies of the impact of forest vegetation on slope soil erosion, and gaining a better understanding of the erosion law and complementing USLE, both theoretically and practically, will have a greater significance.
For more than 60 years, many achievements have been attained in the role of forest vegetation for controlling soil erosion (Huang, 2002; Chen, 2003; Zhao et al., 2013; Wang et al., 2015). As early as 1936, researchers had already realized that vegetation cover plays a protective role for soil and made it an influencing factor for soil erosion (Cook, 1936). Smith also realized of the role of vegetation to slope soil erosion, and by quantifying the role of vegetation, he applied the factor into the Soil Loss Estimate Equation (Smith, 1941). Browning et al. (1947) introduced the management factor into the impact of vegetation and made it more comprehensive in quantifying the role of vegetation. Van Doron and Bartelli (1956) further took consideration of the crop rotation and management factors that affect vegetation, and made the application of vegetation impacts on soil erosion more mature. Systematic studies have been conducted in America about the estimation of C, and calculus programs have been designed to obtain the value of C in all types of land-use in America (Renard et al., 1997). The equation in the Revised Universal Soil Loss Equation (RUSLE) was adopted to calculate Factor C:

C= (SLR1EI1 + SLR2EI2 +……SLRiEIi)/EIt

Where C is the annual average; SLRi is the soil loss rate during the ith time period; EIi is the percentage of EI (Erosion Index) in the overall EI of the year; i is the number of time period; EIt is the total amount of EI (in %) during the whole time Period.
The direct application of the C-factor from the RUSLE is based on prior land use (PLU), canopy cover (CC), surface cover (SC), surface roughness (SR), and soil moisture (SM) (Renard et al., 1997). Using over 200 soil-loss ratios measured on 30 runof-erosion plots under both natural and simulated rainfall events in the TGA, Cai (1998), Yang & Shi (1994) established relationships between soil-loss ratios and canopy-cover and surface-cover subfactors. Application of the subfactor method in the calculation of vegetation cover-management factor (C) is relatively easy for calculation. In this paper, we report it on a site of the Loess Plateau of north China (Zhang, 2002).
When using the subfactor method, the vegetation growing seasons were divided into three stages: initial, blooming and final growing stages. Thereafter, we separately observed and measured the subfactors at these three stages. Subfactors included Prior Land Use (PLU), Canopy Cover (CC), Surface Cover (SC), Surface Random Roughness (SR) and Soil Moisture (SM). Finally, we calculated the vegetation cover and management factor.
The objectives of this study were to have a (1) subfactor method for calculating the vegetation cover management factor (C) of the Universal Soil Loss Equation (USLE) on the Loess Plateau; this would quantitatively give out the influence of forest vegetation to soil erosion at the scale of slope, and (2) proper local subfactor parameter value to ofer a theoretical basis and practical guidance for studies of vegetation on soil erosion in the Loess Plateau.

MATERIALS AND METHODS

Description of study area. The study area was located at the Caijaichuan watershed (36° 14’ - 36° 18’ N, 110° 40’ - 110° 48’ E), Jixian County, Shanxi Province, in the Loess Plateau, China. The site is within the scope of the Jixian Station of the Chinese National Ecosystem Observation and Research network. Elevation varies from 904 to 1520 m.a.s.l. Annual mean temperature is 10 °C and the frost-free period is 172 days. Average annual precipitation is 579.5 mm, with extreme, absolute values of 828.9 mm (in 1956) and 277.7 mm (in 1997). Mean annual evaporation is 1729 mm. The soil belongs to loess-derived Cinnamon soil type (Xu et al., 2013; Wei et al., 2014). Vegetation includes plantation and natural forest. The main plantation species are Black Locust (Robinia peseudoacacia), Chinese pine (Pinus tabulaefomis), and Oriental arborvitae (Platycladus orientalis). Native species include David poplars (Populus davidiana) and East-Liaoning oak (Quercuswutaishancia), shrubs (Rosa hugonis, Spiraea trilobata) and grasses. The forest coverage in the watershed is 72%.
The Caijiachuan watershed is in the typical hilly areas of the Loess Plateau. Natural resources, land use patterns and population density in the study watershed are typical of the surrounding region. A typical hillslope in the experimental region was selected at the study site. The elevation ranges from 1050 to 1200 m.a.s.l. The results presented in this paper were derived from one of the watersheds in the hilly areas of the Loess Plateau. However, they are representative and give an indication of possible trends occurring throughout the hilly areas of the Loess Plateau. Study subjects were 13 runof plots, each with an area of 20 m*5 m. See overview of the plots from Table 1.

Table 1. Condition of the runoff plot in Caijiachuan watershed.
Tabla 1. Condición de la parcela de escorrentía en la cuenca Caijiachuan.

BL: Black Locust, PD & CP: David poplars & Chinese pine, PD & Oak: David poplars & Quercus liaotungensis oak (Natural Secondary Forest), OA: Oriental Arborvitae.

Rainfall–runoff and soil erosion measurement. Rainfall was measured in the study watershed from 2004 to 2006. Rain gauges were also placed according to the area and geographical locations. Soil erosion was measured using long-term, onsite observations of runof on plots at hillslopes. There were 13 runof plots within a gradient of different slopes with vegetation to monitor runof and sediment yields at a plot scale, and their influencing factors. All the experimental plots covered an area of 5 meter wide x 20 meter long. From 2004-2006, runof and sediments were measured on each rainfall event. This method was conducted based on Forestry Standards “Observation Methodology for Longterm Forest Ecosystem Research” of People’s Republic of China (LY/T1952-2011).

Surface roughness measurement. Using a pin contact roughness instrument, the surface roughness of the study 13 runoff plots was measured at 3 growth stages. Growth stages were (1) initial (April-May), (2) bloom (June-September), final growing stage (October - November). The instrument consisted of 50 isometric measuring pins and the spacing among them was 1 cm. Each pin was 60 cm long.
When measuring, the instrument was first located along the slope inside the plot, making both ends close to the ground. This formed the relative height difference basic point, examined by order to see if every pin contacted the ground. Then, the relative height difference with the basic point of all the pins was read out. Various sections within the plot were chosen to have replicates.

Measuring method of root in soil. Tree sampling points in the observed runoff plot were chosen to determine roots in these points at the 0-10 cm from the soil surface. Roots were then dried and weighed, and converted to root content of the whole plot according to the surface area relationship. Then we followed the rule in RUSLE that root content at 10-20 cm soil depth was 80% of that at 0-10 cm. Thus, root content at 0-20 cm soil depth was obtained.

Measurement of biomass in the un-decomposed and semi-decomposed litter layers. Two 20cm*20cm sampling points were selected in the plot. The un-decomposed and semi-decomposed litter layers were separately collected, and their biomasses weighed after drying.

Measurement of soil moisture. Three sampling points were randomly selected in the plot. Soil was dug out from the 0-20 cm soil depth. Soil samples were dried and weighed separately. Surface soil moisture content was calculated as the difference between the soil weight before and after drying.

Calculating method of factor C according to subfactor. Cover-management factor is not only based on the combined effect of vegetation cover and management but is also related to the amount of erosive rainfall during the growing period (Wischmeier & Smith, 1985; Liu et al., 2001). Thus, the distribution of erosive rainfall within a year becomes an important determinant of the cover-management factor. This research took into consideration that the forest stands grew as the usual S growing shape. This, together with the phenological characteristics of the study area led us to divide the C factor into 3 stages. The first stage was the initial growing stage, April to May; the second stage was at blooming, June to early October; the third stage was at the end of the growing season, middle October to late November.
Using equation from RUSLE to calculate factor C:

C= (SLR1EI1 + SLR2EI2 +……SLRiEIi) / EIt

where C is the mean annual or crop value, SLRi is the soil loss rate for time period i, EIi is the percentage of Erosion Index of the annual or crop EI occurring during that time period.
i is the number of periods used in the summation, and EIt is the sum of the EI percentages for the entire time period. According to the latest research of soil loss (Lafen et al., 1985), soil loss rate follows.
SLR= PLU•CC•SC•SR•SM
where SLR is the Soil Loss Rate, PLU is the Prior Land Use subfactor, CC is the Canopy Cover subfactor, SC is the Surface Cover subfactor, SR is the Surface Roughness subfactor, and SM is the Soil Moisture subfactor.

Determining subfactor values of the cover–management factor (C). The Prior Land Use subfactor (PLU) is estimated. This was based on the soil loss prediction model (Liu et al., 2001).
The amount of residue both on the soil surface and within the soil, and the decomposition of each according to the climatic conditions and residue characteristics were taken into account as follows:

PLU= Cf·Cb ·exp{2.268[(-Cu r ·Bur)+(Cus ·Bus/CfCuf)]}

where PLU is the prior-land-use subfactor (which ranges from 0 to l), Cf is a surface-soil-consolidation factor, Cb represents the relative efectiveness of the subsurface residue in consolidation, Bur is the mass density of live and dead roots found in the upper inch of soil [kg/(ha·cm)], Bus is the mass density of incorporated surface residue in the upper inch of soil [kg/(ha·cm)], Cuf represents the impact of soil consolidation on the effectiveness of the incorporated residue, and cur and cus, are calibration coefficients indicating the impact of the subsurface residues.

Canopy Cover. CC= 1-Fc· exp(-0.328·H) where CC is the canopy-cover subfactor ranging from 0 to 1, Fc is the fraction of the land surface covered by canopy, and H (ft) is the distance that raindrops fall after striking the canopy.

Surface Cover. Increasing surface cover affects erosion by reducing the transport capacity of runoff water (Foster, 1982), by causing deposition in ponded areas (Lafen 1983), and by decreasing the surface area susceptible to raindrop impacts. It is perhaps the single most important factor in determining SLR values. Surface cover includes crop residues, rocks, cryptogams, and other nonerodible materials that are in direct contact with the soil surface (Simanton et al., 1984; Box, 1981; Meyer et al., 1972).
SC= exp[-b·Sp ·(0.06096/Ru)0.08]

where SC is the surface-cover subfactor, b is an empirical coefficient, Sp is the percentage of land area covered by surface cover, and Ru is surface roughness (in) as follows.
Surface Roughness.

where SR is the random surface roughness; ∆Z is the relative height difference of the pin; p is the number of measuring points (pins) of a certain section, and m is the number of measured sections.

Estimate method of factor C according to indirect method. As there are many factors needed for using the sub factor method to calculate vegetation cover and management factor C when using RUSLE researchers usually adopt indirect methods to calculate that factor. For example, C= A / (R•K•L•S•P), where A is the annual soil erosion, R is the rainfall erodibility factor, K is the soil erodibility factor, LS is slope length and gradient factor, and P is the soil conservation planning factor. R is the potential soil erosion ability caused by rainfall, and is a function of rainfall characteristics; K is the degree of soil (or its cross section) changes under the role of module erosivity; LS is the soil loss rate of certain actual terrestrial conditions compared to conditions which are basically the same, except for the 22.1m length and 5.14° gradient; P is the soil loss ratio of downhill plots under certain protective measures compared to that of plots which do not have any of these measures.

RESULTS

Subfactor Calculation Results. Subfactors were calculated according to the measurements made in 2006. C values are shown in Table 2.

Table 2. Calculating the annual values of each subfactor to obtain soil loss rate.
Tabla 2. Cálculo de los valores anuales de cada subfactor para obtener la tasa de pérdida de suelo.

Vegetation cover and management factor C of different stand types. According to rainfall data, rainfall erosivity and soil loss rate of different stands in 2006, values of vegetation cover and management factor C are shown in Table 3 for the different stand types.

Table 3. Vegetation cover and management factor C of different stands.
Tabla 3. Cobertura de la vegetación y factor de manejo C de diferentes sitios de muestreo.

Value of vegetation cover and management factor C ranged from 0.009 to 0.062 in the different stands in the Loess Plateau. The order was natural forest < Robinia pseudoacacia < Robinia pseudoacacai & Arborvitae < Pinus tabulaeformis < Orchard.

Vegetation cover and management factor C of the different density Black Locust. According to the relative analysis of stand density and vegetation cover and management factor C in the 7 Black Locust (Robinia pseudoacacia) stands in the study area, Table 4 was obtained.

Table 4. Linearity relation between density and C in different stands.
Table 4. Relación lineal entre la densidad y C en sitios de muestreo diferentes.

Stand density versus vegetation cover and management factor C were negative correlated, and R2 was 0.154 (i.e., there was a low correlation coefficient between stand density and factor C). The stand density of Robinia pseudoacacia was in the range of 1200-2204 plants/ha, and the value of the vegetation cover and management factor C was 0.020-0.037.

Comparison of vegetation cover and management factor C by either the indirect or the subfactor method. There are many factors needed for using the sub-factor method to calculate vegetation cover and management factor C. This is because researchers usually adopt indirect methods to calculate that factor when using RUSLE.
According to the research (Keli Zhang, 2001) on terrestrial factor LS, the relation diagram of soil erosion factor with Nomogram approximation, the soil and water conservation planning factor P, and the observations of soil erosion on runof plots in 13 different stand types in 2006, the indirect method was used to calculate the vegetation cover and management factor C as indicated in Table 5.

Table 5. Vegetation cover and management factor C of different stands by indirect calculation.
Tabla 5. Cobertura de la vegetación y factor de manejo C por cálculo indirecto.

The research demonstrated that the vegetation cover and management factor C calculated by the subfactor method was in good conformity with that calculated by using the annual soil erosion, the rainfall and soil erodibility factors, the length and gradient factors and the soil conservation support-practice factor. The ordering of vegetation cover and management factor C was refected as: Natural Forest < Robinia pseudoacacia < Robinia pseudoacacia & Arborvitae < Pinus tabulaeformis < Orchard is basically the same. So the subfactor method can be used to observe and calculate soil erosion instead of using the indirect method. Where Cb= 0.951, Cur= 0.004513kg/ (ha.cm), Cus= 0.001887kg/(ha.cm), Cuf= 0.5, b= 0.025 is reasonable.
However, we can use the sub factor method to calculate factor C, and further predict the amount of soil erosion, that is a good method for a direct calculation of the C factor.

DISCUSSION AND CONCLUSIONS

(1) The extent of the impacts of PLU, CC, SC, SR and SM to soil loss rate is different in stand types which vary in slope. The impact is PLU>SC>SR>SM>CC, from where it is concluded that plant roots, biomass of the semi-decomposition layer in the soil, and surface cover have the greatest impact on soil loss rate.
Stand density and vegetation cover and management factor C were negatively correlated, where R2 =0.154. This indicates that stand density only plays a partial role among all factors that impact soil erosion. Stand density of Robinia pseudoacacia varied in the range of 1200-2204 individuals/ha, and its vegetation cover and management factor C ranged from 0.020-0.037.
(2) Vegetation cover and management factor C are in good conformity calculated by the subfactor and indirect methods, and the discipline of Factor C reflected as: Natural Forest < Robinia pseudoacacia < Robinia pseudoacacia & Arborvitae < Pinus tabulaeformis < Orchard is basically the same. Thus, the subfactor method can be used to monitor the soil loss in the Loess Plateau. Cb= 0.951, Cur= 0.004513kg/(ha.cm), Cus= 0.001887kg/(ha.cm), Cuf= 0.5, b= 0.025. The vegetation cover and management factor C of different stands in the Loess Plateau ranged from 0.009 to 0.062. So it is feasible to calculate the factor C in the Loess Plateau using the subfactor method.
(3) Besides, using the sub factor method to calculate factor C, we can predict the amount of soil erosion. That is a good method for direct calculation of the C factor. The advantage is that accuracy and dynamic can be gained to estimate the C-value of vegetation with different species. When calculating the C-value, the input data include rainfall, soil moisture, roots in the soil, surface roughness and biomass in the litter semi-decomposition layer. Cover-management factor is not only based on the combined effects of vegetation cover and management but is also related to the amount of erosive rainfall during the growing period. Thus, the distribution of erosive rainfall within a year becomes an important determinant of the cover-management factor. Subfactors methods also add accuracy and a dynamic estimation of the annual variation.

ACKNOWLEDGEMENTS

This study was supported by the 13th five-year National Key Research and Development Project (No.2016YFC0501705, No.2015BAD07B02) Funded by the Ministry of Science and Technology (MOST), P.R. China, CFERN & GENE Award Funds on Ecological Paper, and the Chinese National Forestry Ecosystem Observation and Research Station (Jixian, Shanxi Province, CNRN) project (2005DKA10300).
We thank the other members of Jixian Station of Chinese National Ecosystem Observation and Research network for helping in the research. We especially thank Prof. Zhang Yan of the Beijing Forestry University. The authors thank the reviewers and editors for their efforts and time.

REFERENCES

1. Adrie, F.G. & E.S. Jacobs (1986). Surface roughness parameter estimates with a drag technique. American Meteorological Society 25: 1577-1582.         [ Links ]

2. Ali, S. (1993). Soil roughness measurement: chain method. Soil and Water Conservation 48: 527-529.         [ Links ]

3. Allmaras, R.R., R.E. Burwell, W.E. Larson & R.F. Holt (1966). Total porosity and random roughness of the interrow zone as influenced by tillage . USDA Conservation Research Representatives 7-28.         [ Links ]

4. Angima, S.D., M.K. ONeill, A.K. Omwega & D.E. Scott (2000). Use of tree/grass hedges for soil erosion control in Central Kenyan highlands. Journal of Soil and Water Conservation 55: 478-482.         [ Links ]

5. Bandara, J.S., A. Chisholm, A. Ekanayake & S. Jayasuriya (2001). Environmental cost of soil erosion in Sri Lanka: tax/subsidy policy options. Environmental Modelling & Software 16: 497-508.         [ Links ]

6. Bertuzzi, P., S. Garcia & L. Lchadoeuf (1995). Modeling surface roughness by Boolean approach. European Journal of Soil Science 46: 215-220.         [ Links ]

7. Bian, Y.N. & X.X. Lu (1998). Effective coverage rate of soil and water conservation forest in gully region of Loess Plateau at eastern Long and its determination method. Soil and Water Conservation in China 3: 9-10.         [ Links ]

8. Browning, G.M.., C.L. Parish & J.A. Glass (1947). A method for determining the use and limitation of rotation and conservation practices in control of soil erosion in Iowa. Soil Science Society of America Proceedings 22: 249-264.         [ Links ]

9. Burch, H., F. Forster & P. Schleppi (1996). The influence of forest cove on the hydrology of catchments in the Alptal valley. Schweizerische-Zeitschrift-fur-Forstwesen 147: 925-938.         [ Links ]

10. Cai, Q. & K.L. Tang (1996). Dynamic analysis of the influence of vegetation cover on soil erosion. Journal of Soil and Water Conservation 18: 1-5.         [ Links ]

11. Calhoun, R.S. & C.H. Fletcher (1999). Measured and predicted sediment yield from a subtropical, heavy rainfall, steep-sided river basin: Hanalei, Kauai, Hawaiian Islands. Geomorphology 30: 213-226.         [ Links ]

12. Carroll, C., M. Halpin, P. Burger, M.M. Sallaway & D.F. Yuel (1997). The effect of crop type, crop rotation, and tillage practice on runof and soil loss on a Vertisol in central Qweensland, Australia. Soil Resource 35: 925- 939.         [ Links ]

13. Carroll, C. & A. Tucker (2000). Effects of pasture cover on soil erosion and water quality on cental Queensland coal mine rehabilitation. Tropical Grasslands 34: 254-262.         [ Links ]

14. Carroll, C., L. Merton & P. Burger (2000). Impact of vegetation cover and slope on runoff, erosion, and water quality for feld plots in a range of soil and spoil materials on center Queensland coal mines Australia. Soil Resource 38: 313-327.         [ Links ]

15. Chen, Y.B., C.W. Huang, Z.W. Chen, Z.M. Guo, H.S. Su, W.M. Wang & F.S. Ruan (2003). The application and development of USLE in China. Soil and Water Conservation in China 10: 11-12.         [ Links ]

16. Chow, T.L. & Z.H. Shi (2004). Soil conservation planning at the small watershed level using RUSLE with GIS: a case study in the Tree Gorge Area of China. Catena 55: 33-48.         [ Links ]

17. Cook, H.L. (1936). The nature and controlling variable of the water erosion process. Soil Science Society of America Proceedings 1: 60-64.         [ Links ]

18. Durigon, V.L., D.F. Carvalho & M.A.H. Antunes (2014). NDVI time series for monitoring RUSLE cover management factor in a tropical watershed. International Journal of Remote Sensing 35: 441-453.         [ Links ] 

19. Gabriels, D., G. Ghekiere & W. Schiettecatte (2003). Assessment of USLE cover-management C-factors for 40 crop rotation systems on arable farms in the Kemmelbeek watershed, Belgium. Soil & Tillage Research 74: 47-53.         [ Links ]

20. Ghidey, F. & E.E. Alberts (1997). Plant root effects on soil erodibility, splash detachment, soil strength and aggregate stability. Transaction of the ASAE 40: 129-135.         [ Links ]

21. Gilly, J.E. & L.M. Risse (2002). Runoff and soil loss as afected by the application of manure. Transaction of the ASAE 43: 1583-1588.         [ Links ]

22. Hagen, L.J. (1988). New wind erosion model developments in the SDA. In: Proceedings of Wind Erosion Conference, pp. 104-116.         [ Links ]

23. Huang, M.B. & X.Z. Liu (2002). The regulating effect of Loess Plateau forest vegetation to watershed runof. Chinese Journal of Applied Ecology 13: 1057-1060.         [ Links ]

24. Isayama, H., Y. Nakai & Y. Komatsu (2004). Covered metallic stems for the management of distal malignant biliary obstruction-risk factors for the specific complications. Gastrointestinal Endoscopy 59: SAB187-AB187.         [ Links ]

25. Jing, K., J.F. Lu & J.Y. Liang (1997). Erosion environment characteristics and changing trends of the middle reach of Yellow River. Yellow River Water Conservancy Press, Zhengzhou.         [ Links ]

26. Larso, W.E. (1962). Tillage requirements for corn. Soil Water Conservation 17: 3-7.         [ Links ]

27. Lehrsch, G.A., F.D.M. Whisler & M.J. Romkens (1988). Spatial variation of parameters describing soil Surface roughness. Soil Science Society American Journal 52: 311-319.         [ Links ]

28. Lee, J.S. (2012). Suggestion of Cover-Management Factor Equation for Mountain Area in RUSLE. Journal of the Korean Society of Hazard Mitigation 12: 79-85.         [ Links ]

29. Li, Y., X.M. Zhu & J.Y. Tian (1990). Basic study of soil erosion resistance mechanics in Loess Plateau. Chinese Science Bulletin 5: 390-393.         [ Links ]

30. Licznar, P. & M.A. Nearing (2003). Artificial neural networks of soil erosion and runof prediction at the plot scale. Catena 51: 89-114.         [ Links ]

31. Lier, Q.J., G. Sparovek, D.C. Flanagan, E.M. Bloem & E. Schnug (2005). Run of mapping using WEPP erosion model and GIS tools. Computers & Geosciences 31: 1270-1276.         [ Links ]

32. Liu, B.Y., Y. Xie & K.L. Zhang (2001). Soil Erosion Prognosis Model. Science and Technology Press of China, Beijing.         [ Links ]

33. Liu, Y.B., K.L. Tang & X. Cha (1990). Experimental research of soil losses of different surface covers in slope farmland. Journal of Soil and Water Conservation 4: 25-29.         [ Links ]

34. Liu, X.D., Q.X. Wu & H.Y. Zhao (1994). The vertical interception function of forest vegetation and soil and water conservation. Research of Soil and Water Conservation 1: 8-14.         [ Links ]

35. Lopez-Bermudez, F., A. Romero-Diaz, J. Martinez-Fernandez & J. Martinez-Fernandez (1998). Vegetation and soil erosion under a semi - arid Mediterranean climate: a case study from Murcia (Spain). Geomorphology 24: 51-58.         [ Links ]

36. Luo, W.X., L.Q. Bai & X.D. Song (1990). Runoff and scouring amount in forest and grass land with diferent cover rate. Journal of Soil and Water Conservation 4: 30-34.         [ Links ]

37. Martins, S.G., S. Naves, L. Marx & J.C. Avanzi (2010). Cover-management factor and soil and water losses from eucalyptus cultivation and Atlantic Forest at the Coastal Plain in the Espirito Santo State, Brazil. Scentia Forestalis 38: 517-526        [ Links ]

38. Nilaweera, N.S. & P. Nutalaya (1999). Role of tree roots in slope stabilization. Bulletin of Engineering Geology and the Environment 57: 337-342.         [ Links ]

39. Niu, X. & B. Wang (2013). Assessment of forest ecosystem services in China: A methodology. Journal of Food, Agriculture & Environment 11: 2249-2254.         [ Links ]

40. Ozhan, S., A.N. Balci & N. Ozyuvaci (2005). Cover and management factors for the Universal Soil-Loss equation for forest ecosystems in the Marmara region, Turkey.  Forest Ecology and Management 214: 1-3, 118-123.         [ Links ]

41. Renard, K.G., G.R. Foster, G.A. Weeries et al. (1997). Predicting soil erosion by water. A guide to conservation planning with the revised universal soil loss equation (RUSEL). 1997 Agric. Handb., vol.703. US Department of Agriculture, Washington, DC, pp. 1-251.         [ Links ]

42. Shi, Z.H., C.F. Cai, S.W. Ding, T.W. Wang & T.L. Chow (2004). Soil conservation planning at the small watershed level using RUSLE with GIS: a case study in the Tree Gorge Area of China. Catena 55: 33-48.         [ Links ]

43. Shi, Q., J. Wen & M.Q. Wang (2004). Mechanical analysis and quantitatively approximation of the protection role of vegetation to slope. Research of Soil and Water Conservation 11: 126-129.         [ Links ]

44. Smith, D.D. (1941). Interpretation of soil conservation data for field use. Agric. Eng. 22: 173-175.         [ Links ]

45. Sun, L.D. & J.Z. Zhu (1995). Synthesized Effect Study and Evaluation of Soil and Water Conservation Forest System. Science and Technology Press of China, Beijing.         [ Links ]

46. Wang, X.Y, H.X. Bi, Q.F. Song & S.W. Lu (2015). Influnce of forest coverage on basin runof in China’s Loess Plateau. Polish Journal of Environmental Studies 24: 743-750.

47. Wang, Z.Y., G.Q. Wang & Q. Gao (2003). An ecological dynamics model of vegetation evolution in erosion area. Acta Ecologica Sinica 23: 99-107.         [ Links ]

48. Wang, Z.Y., G.Q. Wang, C.Z. Li & F.X. Wang (2003). Preliminary exploration and application of plant-erosion dynamics. Science in China, Ser. D, 33: 1013-1023.         [ Links ]

49. Wang, Z.Y., Y.B. Guo, C.Z. Li & F.X. Wang (2005). Application of plant-erosion status map in typical watershed. Earth Science Development 20: 148-157.         [ Links ]

50. Wang, Y.P. (1997). The mechanism of sediment yield and harness countermeasure for little valleys in the middle reaches of the Yellow River. Arid Land Resource and Environment 11: 67-77.         [ Links ]

51. Wang, Y.K. (1994). Function evaluation of forest vegetation to soil and water conservation. Research of Soil and Water Conservation 1: 24-30.         [ Links ]

52. Wang, Z.Q. (2002). Survival capability analysis of four kinds of artificial forests in Loess Plateau. Journal of Beijing Forestry University 16: 25-29.         [ Links ]

53. Wei, T.X., X.J. Zhang & J.Z. Zhu (2014). The nutrient accumulation pattern and cycling in natural secondary forests in North China. A case study from the Caijiachuan watershed, Shanxi Province. International Journal of Experimental Botany 83: 213-223.         [ Links ]

54. Wilschmeires, W.H. (1960). Cropping-management factor evaluations for a universal soil-loss equation. Soil Science Society of America, Proceedings 24: 322-326.         [ Links ]

55. Wilschmeires, W.H. (1976). Use and misuse of the universal soil loss equation. Journal of Soil and Water Conservation 31: 5-9.         [ Links ]

56. Wu, C.G., S. Li, H.D. Ren (2012). Quantitative estimation of vegetation cover and management factor in USLE and RUSLE models by using remote sensing data: a review. The Journal of Applied Ecology 23:1728-1732.         [ Links ]

57. Wu, Q.X., H.Y. Zhao, X.D. Liu & B. Han (1998). Evaluation on role of forest litter to water source conservation and soil and water conservation. Journal of Soil and Water Conservation 4: 23-28.         [ Links ]

58. Wu, Q.X., H.Y. Zhao & B. Han (1998). Effectiveness of Loess Plateau forest litter layer in soil and water conservation. Journal of Northwestern A & F University (Natural Science Edition) 29: 95-98.         [ Links ]

59. Xie, M.S. (1990). A study on determining the effective limits of the tree and bush roots strength and the best distribution of roots for stable slopes. Journal of Soil and Water Conservation 4: 17-24.         [ Links ]

60. Xie, Y., Y. Lin & Y. Zhang (2003). The development and application of USLE. Journal of Geography 22: 279-287.         [ Links ]

61. Xu, H.S., H.X. Bi, L.b. Gao, Lei Yun, Y.F. Chang., W.X, W.C. Liao & B. Bao (2013). International Journal of Agriculture & Biology 15: 998-1002.         [ Links ]

62. Xu, T.X. (2006). Coupling relationship between precipitation and vegetation and the implication in erosion on the Loess Plateau, China. Journal of Geography 61: 57-65.         [ Links ]

63. Yu, X.X., X.X. Zhang, J.L. Li, M.L. Zhang & Y.Y. Xie (2006). Effects of vegetation cover and precipitation on the process of sediment produced by erosion in a small drainage basin in loess region. Acta Ecologica Sinica 26: 1-8.         [ Links ]

64. Yuan, M.X., L. Li & F.X. Li (2004). Sediment trapping effect of litter-buffer strips. Journal of Jiangxi Agriculture University 26: 798-800.         [ Links ]

65. Zhang, G.H. & Y.M. Liang (1996). Summary of effect of vegetation cover to soil and water conservation. Research of Soil and Water Conservation 3: 104-110.         [ Links ]

66. Zhang, H.J. (2001). Effects of different stand types to the value of roughness factor n in western Shanxi. Bulletin of Soil and Water Conservation 15: 11-21.         [ Links ]

67. Zhang, Y. & B.Y. Liu (2003). Effect of Different Vegetation Types on Soil Erosion by Water. Acta Botanica Sinica 10: 1204-1209.         [ Links ]

68. Zhang, Y., J.P. Yuan & B.Y. Liu (2002). Advances of vegetation cover and management factor in soil erosion prediction model. Journal of Applied Ecology 13: 1033-1036.         [ Links ]

69. Zhang, Z.L. & J.H. Cui (2004). Analysis of the latest research and improvement of USLE. Geo-Information Science 6: 51-55.         [ Links ]

70. Zhao, W.W., B.J. Fu & Y. Qiu (2013). An Upscaling Method for Cover-Management Factor and Its Application in the Loess Plateau of China. International Journal of Environmental Research and Public Health 10: 4752-4766.         [ Links ]

71. Zheng, Z.C. & S.Q. He (2003). Research on variation characteristics of soil surface roughness. Journal of Soil and Water Conservation 5: 165-168.         [ Links ]

72. Zheng, Z.C. & S.Q. He (2004). Relationship between surface roughness and manning roughness. Journal of Mountain Research 22: 236-239.         [ Links ]

73. Zhu, X.M. & J.Y. Tian (1993). The study on strengthening antiscourability and penetrability of soil in Loess Plateau. Journal of Soil and Water Conservation 7: 1-10.         [ Links ]

Creative Commons License Todo el contenido de esta revista, excepto dónde está identificado, está bajo una Licencia Creative Commons