Show Summary Details

Page of

date: 18 February 2018

# Effective Practices in Mitigating Soil Erosion from Fields

## Summary and Keywords

Soil erosion by water is a natural process that cannot be avoided. Soil erosion depends on many factors, and a distinction should be made between humanly unchangeable (e.g., rainfall) and modifiable (e.g., length of the field) soil erosion factors. Soil erosion has both on-site and off-site effects. Soil conservation tries to combine modifiable factors so as to maintain erosion in an area of interest to an acceptable level. Strategies to control soil erosion have to be adapted to the desired land use. Knowledge of soil loss tolerance, T, i.e., the maximum admissible erosion from a given field, allows technicians or farmers to establish whether soil conservation practices need to be applied to a certain area or not. Accurate evaluation of the tolerable soil erosion level for an area of interest is crucial for choosing effective practices to mitigate this phenomenon. Excessively stringent standards for T would imply over expenditure of natural, financial, and labor resources. Excessively high T values may lead to excessive soil erosion and hence decline of soil fertility and productivity and to soil degradation. In this last case, less money is probably spent for soil conservation, but ineffectively. Basic principles to control erosion for different land uses include maintaining vegetative and ground cover, incorporating biomass into the soil, minimizing soil disturbance, increasing infiltration, and avoiding long field lengths. Preference is generally given to agronomic measures as compared with mechanical measures since the former ones reduce raindrop impact, increase infiltration, and reduce runoff volumes and water velocities. Agronomic measures for soil erosion control include choice of crops and crop rotation, applied tillage practices, and use of fertilizers and amendments. Mechanical measures include contour, ridging, and terracing. These measures cannot prevent detachment of soil particles, but they counter sediment transport downhill and can be unavoidable in certain circumstances, at least to supplement agronomic measures. Simple methods can be applied to approximately predict the effect of a given soil conservation measure on soil loss for an area of interest. In particular, the simplest way to quantitatively predict mitigation of soil erosion due to a particular conservation method makes use of the Universal Soil Loss Equation (USLE). Despite its empirical nature, this model still appears to represent the best compromise between reliability of the predictions and simplicity in terms of input data, which are generally very difficult to obtain for other soil erosion prediction models. Soil erosion must be controlled soon after burning.

# Introduction

Soil loss due to erosion is a natural and unavoidable phenomenon, but it can become excessive, and hence intolerable, in particular situations and mostly due to anthropic factors. Soil erosion reduces land productivity since it determines a reduction of water infiltration and soil water storage capacity and the removal of organic matter and essential plant nutrients (Pimentel, 2006). Each year, about 10 million ha of cropland are lost due to soil erosion, and this is a tremendously serious problem. Approximately 3.7 billion people are currently malnourished worldwide (Pimentel, 2006), and during a period of 40 years about 30% of the world’s arable land became unproductive (Kendall & Pimentel, 1994). Technological advancements can help mitigate the adverse effects of cropland loss, but there are prices to be paid, such as higher production costs and environmental risks of a chemical nature. Overall, soil is being lost from land areas at rates 10 to 40 times faster than the rate of soil renewal. If soil erosion and population growth remain unchecked from their current rates, humanity will eventually lose the ability to feed itself (Nearing, 2013).

Approximately 50% of the earth’s land surface is devoted to agriculture, and, of the world’s agricultural land, about a third is devoted to crops and the remaining two thirds is devoted to pastures for livestock grazing (USDA, 2001). Forests occupy about 20% of the land area (WRI, 1997). World agricultural production accounts for about three quarters of the soil erosion worldwide (Pimentel, 2006). Croplands are the most susceptible to erosion because the soil is repeatedly tilled and left without covering and protecting vegetation for long or relatively long periods. Soil loss degrades arable land and eventually renders it unproductive. More than half of the world’s pasturelands are overgrazed and subject to erosive degradation. According to Myers (1993), each year 75 billion metric tons of soil are removed from the land by erosion, with most coming from agricultural and pasture land. Erosion rates on agricultural lands are estimated to be 75 times greater than those in natural forest areas. Soil erosion losses are higher in Asia, Africa, and South America—averaging 30–40 Mg ha−1yr−1—than in the United States and Europe, where they average about 10 Mg ha−1yr−1 (Taddese, 2001; USDA, 2000). Even the lowest erosion rates greatly exceed the average rate of natural soil formation from the parent material that, under agricultural conditions, is estimated to range from 0.5 to 1 Mg ha−1yr−1 (Lal, 1994; Pimentel et al., 1995). Erosion is a slow and insidious process. Indeed, a soil loss of 15 Mg ha−1 corresponds approximately to 1–1.5 mm of soil, depending on the soil bulk density, and this loss can go unnoticed by a farmer.

According to Kunkle and Harcharik (1977), the hydrological response of a basin may change dramatically if excessive soil erosion occurs. The adverse effects of this phenomenon include siltation, torrential conditions, higher peak streamflow, reduced infiltration, and hence reduced base-flow. The resulting problems are reservoir silting, degradation of water quality, farm field losses along streams, village destruction, bedload impacts, river channel cutting, flooding, noticeable bedload transport, and tendency for streams to dry up earlier or be lower during the dry season. Soil erosion phenomena can be particularly noticeable soon after the passage of a fire (Ferreira et al., 2015), and this problem is particularly relevant in Mediterranean environments, where forest fires are rather common.

Soil erosion also contributes to global warming because CO2 is added to the atmosphere when the enormous amounts of biomass carbon in the soil are oxidized. A feedback mechanism may exist wherein increased global warming intensifies rainfall, which, in turn, increases erosion and continues the cycle (Pimentel, 2006).

Therefore, mitigating soil erosion is mandatory. Taking into account that this phenomenon is unstoppable, implementing effective and economically sustainable soil conservation measures requires preliminarily establishing how much soil may be lost by a field in a given time interval. For this purpose, a distinction should be made between protection from excessive erosion of on-site resources and preservation of off-site resources from damage. In the former case, erosion must be controlled since the eroded area needs to be used for some productive purpose. In the latter case, erosion of the field might not be a problem since the area is not used for economic purposes. However, sediments originating from that field will infiltrate zones or infrastructures where sediment accumulation can cause damage (Bagarello & Ferro, 2006).

Current conservation practices are based on early soil conservation research programs (Gilley & Flanagan, 2007) established and masterfully developed by many outstanding scientists working on soil erosion, such as H. H. Bennett and W. H. Wischmeier. Bennett is considered the father of soil conservation. He began his career as a soil surveyor for the United States Department of Agriculture (USDA) Bureau of Soils in 1903. In the spring of 1935, Bennett was testifying before a congressional committee on the bill that would create the Soil Conservation Service (SCS) when dust clouds from the Dust Bowl moved over Washington, D.C. He knew that a dust storm was coming and used it to dramatically demonstrate the need for soil conservation. During his testimony, Bennett asked the legislators to look out the window and then said “This, gentlemen, is what I have been talking about” (Gilley & Flanagan, 2007). Bennett served as chief of the USDA SCS from 1935 to 1951, and he was instrumental in the development of a network of 35 soil conservation experimental stations. Research projects were initiated at these stations in the 1930s to investigate the main factors causing erosion and to identify the most effective and practical methods of controlling soil loss from agricultural areas. Wischmeier became an employee of the USDA SCS in 1940. He worked as a clerk assisting SCS research scientists until 1953, except for the period he served in World War II. Wischmeier received a BS degree in statistical theory from the University of Missouri in 1953. The National Runoff and Soil Loss Data Center (NRSLDC) was created at Purdue University in 1954, and Wischmeier served as its first director. The NRSLDC became the depository for much of the erosion data collected throughout the United States since the 1930s. The data assembled at the NRSLDC were used to develop the Universal Soil Loss Equation (USLE) (Wischmeier & Smith, 1961, 1965, 1978). This model, intended to be usable for soil conservation planning, resulted from analyses of more than 11,000 plot-years of research data from 47 locations in 24 states. The USLE is recognized as one of the most important developments in soil and water conservation in the 20th century (Gilley & Flanagan, 2007).

This article deals with mitigation of soil water erosion. The concept of soil loss tolerance is initially discussed. The principles of soil erosion control are then illustrated. Measures to stabilize burned areas soon after the passage of a fire are summarized. Vegetation-based control of sediment delivery from an eroded area is subsequently presented. Use of erosion models for soil conservation purposes is discussed. Finally, a brief illustration of some economic aspects of soil conservation is provided.

# Soil Loss Tolerance

Protection of on-site soil resources from excessive erosion implies establishment of an on-site soil loss tolerance criterion. An off-site tolerance criterion has to be applied to preserve off-site resources from soil erosion damage.

Establishing soil loss tolerance is not easy since many factors have to be taken into account, including rate of soil formation from parent material, rate of topsoil formation from subsoil, reduction of crop yield by erosion, soil depth, changes in soil properties favorable for plant growth caused by erosion, loss of plant nutrients by erosion, likelihood of rill and gully formation, sediment deposition problems within a field, sediment delivery from the erosion site, and the availability of feasible, economic, culturally and socially acceptable, as well as sustainable, soil conservation practices (Li et al., 2009; USDA, 1956). On the other hand, soil loss tolerance is a powerful conservation-planning tool because both technical and non-technical elements are combined into a single number (Toy, Foster, & Renard, 2002).

Smith (1941) was probably the first researcher dealing with the concept of permissible, or tolerable, on-site soil loss, which was defined as the soil loss rate permitting a soil fertility that increases with time or, at least, remains constant. Soil loss tolerance, T, was later defined by Wischmeier and Smith (1978, p. 2) as “the maximum level of erosion that will permit a high level of crop productivity to be sustained economically and indefinitely.” According to this definition, soil loss can be high for a particular event or in a given year but cumulative erosion has to remain low over a multi-year period. Morgan (2005) defined soil loss tolerance as the maximum permissible erosion rate at which soil fertility can be maintained over 20–25 years. Other definitions of tolerable soil erosion were summarized by Verheijen, Jones, Rickson, and Smith (2009) and Di Stefano and Ferro (2016).

Theoretically, soil erosion should be maintained at a rate that does not exceed the natural rate at which new soil forms, which can vary from 0.01 to 7.7 mm yr−1, being equal, on average, to 0.1 mm yr−1 or 1 Mg ha−1yr−1 as indicated by Zachar (1982) and Morgan (2005). Soil formation rates of 0.3–1.4 Mg ha−1yr−1 against erosion rates varying between less than 1 and 20 Mg ha−1yr−1 were reported by Posthumus, Deeks, Rickson, and Quinton (2015) for the United Kingdom.

The T values assigned to cropland and many other soils in the United States range from 2 Mg ha−1yr−1 for fragile soils to 11 Mg ha−1yr−1 for soils not readily damaged by erosion (USDA, 1956). A shallow soil underlain by bedrock is an example of the former soil category, whereas a deep loess soil is an example of the latter type of soil (Toy et al., 2002). Guidelines were developed by the USDA-NRCS (2000; i.e., the National Resources Conservation Service) for assignment of T according to the properties of root limiting layers of subsurface soil. Limiting or less favorable layers closer to the soil surface imply a reduced ability of the soil to maintain its productivity through natural and managed processes (Li, Du, Wu, & Liu, 2009). Criteria for assigning T are estimated from the physical or chemical properties of the subsurface layer, the climatically influenced soil moisture and temperature, and the economic feasibility of overcoming limiting layers or conditions by management practices. The soils are grouped into three categories (Table 1). The soils of the first group have significant limitations or permanent layers of root limitation. In the soils of the second group, the limitations are moderate root restriction or loss of productivity in a given climate is less than permanent. Finally, the soils of the third group have limitations that can be overcome in a given climate through natural or managed processes to achieve a productivity level similar to that of the non-eroded soil. Where soil is deeper than 2 m, the tolerance can be increased to 15–20 Mg ha−1yr−1 because the subsoil is capable of improvements and therefore reduction of crop yield is not expected for at least 50 years (Schertz, 1983). In Switzerland, tolerated soil erosion is either 1 or 2 Mg ha−1yr−1, depending on the vulnerability of the soil to erosion. In Norway, the threshold is set at 2 Mg ha−1yr−1 (Verheijen et al., 2009). A pragmatic approach for determining soil loss tolerance is to decide what level of environmental damage is acceptable using criteria that can be found in the literature (Table 2). According to Morgan (2005), for example, an erosion rate lower than 2 Mg ha−1yr−1 is very slight, whereas it is considered high if it ranges from 10 to 50 Mg ha−1yr−1.

Table 1. Annual Soil Loss Tolerance (Mg ha−1) Values for Different Groups of Soils According to USDA-NRCS, 2000

Depth to limiting layer (cm)

Group 1

Group 2

Group 3

0–25

2.5

2.5

7.5

25–50

2.5

5.0

7.5

50–100

5.0

7.5

10.0

100–150

7.5

10.0

10.0

>150

12.5

12.5

12.5

Group 1: the limitations are significant or the soils have permanent layers of root limitations. Group 2: the limitations are moderate root restriction or the soils have less than permanent loss of productivity in a given climate. Group 3: the limitation can be overcome in a given climate through natural or managed processes to achieve the productivity level of the non-eroded soil.

Source: Li et al. (2009; reprinted with permission).

Table 2. Classes of Soil Erosion Rates

Reference

Erosion rate (Mg ha−1yr−1)

Class

Singh et al. (1992)

<5

Slight

5–10

Moderate

10–20

High

20–40

Very high

40–80

Severe

>80

Very severe

Morgan (2005)

<2

Very slight

2–5

Slight

5–10

Moderate

10–50

High

50–100

Severe

100–500

Very severe

>500

Catastrophic

Stone and Hilborn (2012)

<6.7

Very low (tolerable)

6.7–11.2

Low

11.2–22.4

Moderate

22.4–33.6

High

>33.6

Severe

Molla and Sisheber (2017)

<5

Low

5–20

Moderate

20–50

High

50–100

Very high

100–150

Severe

>150

Extreme

Reducing soil loss to tolerable levels controls rill erosion and also reduces the likelihood of gully formation. The rule of thumb by Toy et al. (2002) is that soil loss tolerance should not exceed 15 Mg ha−1yr−1 since this is the erosion rate at which rills begin to form. According to Bagarello, Di Stefano, Ferro, and Pampalone (2015a), using soil erosion data collected in southern Italy, rilling is limited if soil loss does not exceed approximately 11.5 Mg ha−1yr−1.

Off-site tolerance criteria have to be applied to determine an allowable sediment delivery, depending on the requirements of the off-site resource to be protected against excessive erosion (Toy et al., 2002). Examples of off-site impacts of soil erosion are filling of roadside ditches or downstream reservoirs. The sediment load delivered to the ditch by a nearby area should be less than the flow transport capacity in the ditch to minimize sediment deposition occurrence. Determining the reservoir size implies preliminarily establishing the amount of sediments that can accumulate into the reservoir during its design life. The time scale for protecting off-site resources depends on the specific resource, being, for example, 5–10 years for road ditches and 100 years of more for sediment accumulation in a reservoir. A soil loss tolerance no greater than 1 Mg ha−1yr−1 may be required to reduce the effects of non-point source pollution from agricultural land to acceptable levels (Moldenhauer & Onstad, 1975; Morgan, 2005).

According to Di Stefano and Ferro (2016), quantitative estimation of soil loss tolerance should be carried out accounting for large storms rather than considering average conditions. For this reason, research on statistical distribution of the annual maximum soil loss should be carried out in different areas of the world. The concept of tolerable soil loss should always take into account the off-site effects of soil erosion, and additional investigations should be carried out to link the particle size distribution of the eroded sediment to that of the original soil in order to explain the enrichment of chemical content of the sediment with respect to the parent soil.

However, it should be taken into account that excessively strict standards for T would imply overexpenditure of natural, financial, and labor resources. Excessively high T values may lead to excessive soil erosion and hence to the decline of soil fertility and productivity and to soil degradation. In the latter case, less money may be spent for soil conservation, but ineffectively.

# Principles of Soil Erosion Control

Soil erosion control implies limiting the amount of detached and/or transported sediments. In general, reducing detachment represents the best way to control sediment delivery from an area of interest (Toy et al., 2002). If detachment is hindered, then sediment delivery is controlled and soil is also protected from degradation. Reducing transport capacity causes deposition, which is a selective process. Therefore, reduction of soil depth and degradation processes, including decreased moisture- and nutrient-holding capacities, may occur in those zones where detachment processes are active. Moreover, sediment load is enriched in fine particles, which represent the portion of the sediment load that most seriously degrades water quality. Both erosion and on-site impacts occur at the same location, whereas off-site damage caused by sediments may extend a great distance from the sediment source location (Toy et al., 2002).

When speaking of soil erosion control, a distinction should be made between humanly changeable and unchangeable soil erosion factors. For example, it could appear obvious that rainfall is an unchangeable factor whereas land use can be adapted to specific soil conservation needs. The USLE predicts the long-time average soil loss from a specific field area in a specified cropping and management system (Wischmeier & Smith, 1978). According to this model, specifically usable for soil conservation planning, slope length, cover and crop management, and support practices can be modified to control soil loss. Rainfall does not depend on the choices made by technicians or farmers. Soil erodibility is essentially unchangeable since it depends on soil texture, organic matter content, and structural and permeability characteristics that, in the context of the model, are considered immutable in the long term. Slope steepness is also unchangeable in practice since modifying the longitudinal profile of the hillslope is expected to be very costly and doubts about the effectiveness of moving large soil volumes could be raised. However, this distinction between changeable (length, land use, support practices) and unchangeable (rainfall, soil, steepness) factors is not general. For example, retarding seedbed preparation after plowing implies maintaining the soil in a condition of reduced erodibility for a longer time, which means a reduced erosive effect of rainstorms. Moreover, retiring the soil from conventional tillage or increasing its organic matter content determines a decrease of soil erodibility (e.g., Dissmeyer & Foster, 1981). Therefore, in these contexts, soil erodibility can be viewed as a changeable factor. On the other hand, land use, in the form of crop rotation, could also be a practically unchangeable factor. This is the case, for example, for an area where a given crop (e.g., wheat) is planted every year. Although it is known that this is not the best land use from an economic point of view, the farmer’s propensity to change traditional cropping systems could be unlikely. In this case, crop management strategies should be identified and suggested for practical application. Especially in semi-arid to arid environments, the possibility that these strategies are accepted by a farmer is higher if it is possible to show that the suggested strategy is also effective in terms of water conservation. Another example is the combination of row and hay crops. Row crops can result in high erosion rates, and they should be combined with dense hay crops in a crop rotation to effectively reduce the overall average annual erosion. This occurs because the low erosion rates under the hay crop offset the high erosion rates under clean-tilled row crops. However, this solution can be practically unfeasible if there is no livestock that could use the hay, since hay is seen as a non-profitable cash crop (Toy et al., 2002). In other terms, changeability of land use depends on the farmer’s willingness to accept changes and on the local situations.

As indicated by Toy et al. (2002), the fundamental erosion control principles to be applied to any land use (cropland, rangeland, construction sites, reclaimed land, landfills) can be summarized as follows. Vegetative cover should be maintained since it provides canopy, plant litter for ground cover, and root network. A high-producing and short vegetation with a dense root network near the soil surface is expected to effectively control soil erosion. Ground cover should be kept by leaving crop residues on the surface as well as by growing plants that produce high levels of litter or by applying natural or manufactured mulch material when no residues are available. According to Prosdocimi, Tarolli, and Cerdà (2016a), reduction in runoff volume, sediment concentration, and soil loss due to mulching can exceed 90%. Cover should particularly be maintained when rainfall erosivity is high. Biomass should be incorporated into the soil, and disturbance of the porous medium should be minimized. If this is not possible, the soil surface should be left rough, with large clods, for the longest possible period. Soil amendments reducing erodibility and increasing infiltration should be added. Support practices should also be used to reduce runoff. Long, steep slopes and water convergence should be avoided to prevent excessive rill erosion. If possible, the topography should be modified, taking into account that convex segments at the end of the hillslope profile must be avoided. Instead, concave segments with very flat slope steepness at the end induce deposition and hence control sediment delivery. Barriers should be placed around the source area if erosion control in this area is not sufficient. Evidently, the choice of particular soil conservation measures has to take into account the local economic and environmental constraints.

According to Toy et al. (2002), in addition to the distinction between detachment- and sediment transport-control practices, another distinction must be made among cultural-management, supporting, and structural practices.

Cultural-management practices are agronomic practices where vegetation and soil management are used to control erosion. For example, soils are very vulnerable to erosion when a fine seedbed has been prepared but a crop cover has not yet developed, particularly if rainfall erosivity is high when the soil remains bare. In these cases, it could be advisable to prepare the seedbed only a short time before seeding and also to avoid unnecessarily fine seedbeds (Giordani & Zanchi, 1995). Effects on soil erosion of vegetative canopy, ground cover from plant litter and other materials, belowground biomass from live and dead roots and incorporated organic material, as well as of mechanical soil disturbance are exploited to control erosion by agronomic practices, also using the experimental information that can be found in the literature. In Utah and Montana, for example, the erosion rate decreased approximately 200 times as the ground cover increased from less than 1% to 100% (Trimble & Mendel, 1995). Blanco-Canqui, Lal, Post, Izanrralde, and Owens (2006) suggested that plant canopy and mulch coverage are particularly effective in the protection of aggregates at the soil surface against disruption during heavy rainfall. Organic farming, due to the absence of herbicides, can support the development of weeds and increase the ground cover compared to conventional farming. On the basis of an investigation carried out in a mountainous watershed in South Korea, Arnhold et al. (2014) concluded that organic farming can potentially decrease the soil erosion risk for row crops because it supports weed development in the furrows. However, it can also increase the soil erosion risk if crop yields are reduced. The conclusion by Arnhold et al. (2014) was that organic farming alone cannot be used to effectively control erosion. Vineyards are affected by one of the highest rates of soil loss among cultivated lands (Prosdocimi et al., 2016b). In Sicilian vineyards, an alternative soil management system (vs. conventional tillage) involves planting cover crops in the inter-rows, which can reduce soil erosion by 68% as compared with conventional tillage (Novara, Gristina, Saladino, Santoro, & Cerdà, 2011). The effectiveness of soil erosion control depends on the cover crop species. In the investigation by Novara et al. (2011), for example, the lowest soil losses were found using Trifolium subterraneum, Festuca rubra, and Festuca ovina as cover crops, whereas relatively poor performance was detected with Triticum durum and Vicia sativa due to the relatively low biomass produced (Fig. 1). In an experiment carried out in eastern Spain with simulated rainfall, barley straw mulch protection was found to be very effective in reducing surface runoff (from 53% to 39% of the total rainfall when passing from unprotected to protected surface areas) and soil erosion (from 2.81 to 0.63 Mg ha−1h−1), and the benefits were achieved immediately after the application of the straw (Prosdocimi et al., 2016b). Joyce et al. (2002) suggested that rainfall infiltration should be expected to increase in cover-cropped areas as compared with fallow rotation. Moreover, cover crops seem to stabilize near saturated soil hydraulic properties over time (Bodner, Loiskandl, Buchan, & Haul, 2008).

Click to view larger

Figure 1. USLE cover and management C factor for six cover crops (VF: Vicia faba; VV: V. faba and Vicia sativa; TFL: Trifolium subterraneum, Festuca rubra, and Lolium perenne; TFF: T. subterraneum, F. rubra, and Festuca ovina; T: Triticum durum; TV: T. durum and V. sativa) (from Novara et al., 2011; reprinted with permission).

Supporting practices are applied along with cultural-management practices, and both practices are applied to the entire erosion source area. Supporting practices involve ridges, strips of vegetation, terraces, and diversions oriented in such a way to intercept runoff and reduce rill and concentrated flow erosion.

Structural practices are typically located at specific points to control erosion by channel bed scour or headcuts in permanent gullies. Barriers along the perimeter of the erosion source area are also structural practices.

Skidmore and van Donk (2003) suggested that soil erosion can be controlled by preventing runoff, controlling runoff, and decreasing soil erodibility.

Soil particles cannot be transported if runoff is eliminated or substantially reduced, which is also advantageous in terms of water conservation. Runoff initiation is prevented, for example, by mulches and cover crops, maintaining high infiltration rates. Shielding the soil surface from the direct impact of rain is expected to effectively maintain high values of saturated soil hydraulic conductivity, Ks, and hence to favor infiltration processes against excess formation during intense and prolonged rainstorms (Assouline & Mualem, 2002, 2006). An approximate assessment of these shielding effects on Ks was carried out by Bagarello, Castellini, Di Prima, and Iovino (2014) and Alagna, Bagarello, Di Prima, Giordano, and Iovino (2016) on different sandy loam, clay loam, and clay soils using the BEST (Beerkan Estimation of Soil Transfer parameters) procedure of soil hydraulic characterization (Lassabatère et al., 2006). In particular, some infiltration runs were carried out by applying water at a small distance from the soil surface, i.e., at an approximate height, hw, of 0.03 m, and dissipating its energy on the fingers of the hand, in an attempt to minimize soil disturbance due to water application (low, L, runs). Water was applied from hw = 1.5 m at other sampling points (high, H, runs). The soil surface was not shielded in this case to maximize possible damaging effects of water impact. The height of water pouring had an appreciable impact on Ks since the L runs yielded higher means than the H ones by a factor of 11.5–35.2, depending on the soil (Bagarello et al., 2014). For a sandy loam soil, sensitivity of the measured Ks to the height of water pouring decreased as the initial soil water content increased (Alagna et al., 2016). These results represent an additional support to the necessity to protect the soil surface from the direct impact of rain to prevent excessive surface runoff and hence erosion.

Runoff is retained on the cropland by contour tillage, furrow dikes, and level terraces. Strip-cropping, graded furrows and terraces, and discontinuous parallel terraces are among the practices that result in runoff occurring at non-erosive rates.

Aggregate stability can be improved and clay dispersion can be prevented by applying amendments to the soil, such as gypsum or synthetic organic polymers. Organic matter promotes a stable aggregate structure, which reduces soil erosion. In an investigation by Gilley and Risse (2000) on selected locations at which manure was added annually, runoff was reduced from 2 to 62% and soil loss decreased from 15 to 65% compared to non-manured sites.

Morgan (2005) distinguished among soil conservation measures in cultivated land, non-cultivated land, and urban areas (El-Swaify, Dangler, & Armstrong, 1982), and he suggested classifying soil conservation techniques into agronomic measures and soil management, influencing both detachment and transport of sediments, and mechanical methods, essentially effective in controlling transport alone.

The former measures utilize the role of the vegetation to protect soil against erosion. Soil management makes the porous medium resistant to erosion by improving its structure and favoring plant growth. Crop and vegetation management practices that have to be considered for soil conservation include rotation, cover crops, strip-cropping, multiple cropping, high-density planting, mulching, revegetation, and agro-forestry (Morgan, 2005). Soil management deals with organic material content, tillage practices, drainage, and soil stabilizers.

Mechanical or physical methods presuppose manipulation of soil topography and often involve engineering structures. Mechanical methods of erosion control are contour bunds, terraces, waterways, temporary measures such as silt fences and sedimentation ponds, stabilization structures, and brush matting. For example, terraces are earth embankments constructed across the slope to intercept surface runoff, convey it to a stable outlet at a non-erosive velocity, and shorten slope length (Morgan, 2005). Diversion terraces intercept runoff and channel it across the slope to a suitable outlet. They therefore run at a slight grade, usually 1:250, to the contour. Diversion terraces are not suitable for agricultural use on ground slopes greater than 7° due to the costs of construction and the close spacing that would be required. Moreover, terracing can have a negative impact on the mechanization of farming practices. Retention terraces are used where necessary to store water on the hillside. They are therefore ungraded and generally designed with the capacity to store the runoff volume expected with a 10-year return period without overtopping. These terraces are normally recommended only for permeable soils on slopes of less than 4.5°. Bench terraces consist of a series of alternating shelves and risers and are employed where steep slopes, up to 30°, need to be cultivated. The riser is vulnerable to erosion and should be protected by a vegetation cover or faced with stones or concrete. Terraces, reducing slope gradient and length, facilitate cultivation on steep slopes and are present all over the world. However, poorly designed and maintained terraces represent significant sediment sources. Tarolli, Preti, and Romano (2014) recently suggested several solutions of both a non-structural and structural nature for suitable management of such environments.

Of course, soil conservation practices are not all equally effective, and, according to Pimentel et al. (1995), implementation of appropriate practices can reduce erosion rates from 2- to 1,000-fold.

With specific reference to forest areas, a minimum of 60% forest cover is necessary to prevent serious soil erosion and landslides (Haigh, Rawat, Bartarya, & Rai, 1995; Pimentel, 2006). In stable forest ecosystems, where soil is protected by vegetation, erosion rates are low—they range from 0.004 to 0.05 Mg ha−1yr−1 (Lal, 1994; Roose, 1988). Guidelines to protect soil against erosion during site preparation for afforestation in the southern United States were reported by Kunkle and Harcharik (1977). For example, for a steepness of 20%, admissible soil exposure decreases from 30% to 10% in the passage from stable to fragile conditions. The relationships by Dissmeyer and Foster (1981) can be used to predict the effects of the percentage of the area in bare soil conditions, ABS (%), the percentage of bare soil with canopy cover, ABC (%), and the average height of drop fall from canopy, Hc (m), on erosion of forest soils. In particular, the following relationships were developed by Bagarello and Ferro (2006) to estimate the bare soil subfactor value, BS, and the canopy subfactor value, CA, from the graphs by Dissmeyer and Foster (1981) (Fig. 2):

$Display mathematics$
(1)

$Display mathematics$
(2a)

$Display mathematics$
(2b)

Erosion does not occur at 100% ground cover, and a high coverage by canopy close to the ground determines a noticeable reduction of soil erosion. Only canopy above bare soil is expected to be effective because, in the case of canopy over litter, the surface cover is the controlling factor.

Click to view larger

Figure 2. Bare soil, BS (a), and canopy, CA (b), subfactors obtained with the equations by Bagarello and Ferro (2006) interpreting the graphical relationships by Dissmeyer and Foster (1981) (Hc = height of drop fall from canopy).

A possible problem related to the practical application of soil conservation measures was recently posed by Hösl and Strauss (2016). In particular, these authors highlighted that the considerable amount of evidence supporting positive effects of conservation tillage practices toward reduction of surface runoff and soil erosion was generally obtained under ideal laboratory conditions or controlled field conditions. In other terms, the experimental approaches were not managed by farmers in the way they usually perform conservation tillage, but managed toward an optimization of the tested features. Under real-life conditions of agricultural conservation practices, especially in small farming systems, problems when applying best management techniques can occur, such as a non-sufficient soil cover for mulching treatments. An implication of the investigation by Hösl and Strauss (2016) is that more research should be carried out in real farming systems. Moreover, it is advisable to make farmers and technicians more aware of practically relevant scientific results.

Stabilization structures play an important role in gully reclamation and gully erosion control (Morgan, 2005). Small dams, usually 0.4–2.0 m in height, are made from locally available materials such as earth. Wooden planks, brushwood, or loose rock are built across gullies to trap sediments, thereby reducing channel depth and slope. The structures have a high risk of failure but provide temporary stability and are therefore used in association with agronomic treatment of the surrounding land.

# Emergency Stabilization of Burned Areas

Mitigation of erosion in burned areas requires rapid intervention after burning since degradation processes arise soon after a fire in response to the first autumn rainfall events (Ferreira et al., 2015). Development of water-repellent soil layers is favored by burning, as shown in Figure 3 (Bagarello, Giordano, Iovino, & Tinebra, 2017), and the absence of a soil surface–protective cover favors soil aggregate breakdown by the direct impact of raindrops. The enhanced connectivity due to the absence of heterogeneity favors overland flow transport downslope. The size of the burned area is generally large, and this makes any intervention covering the entire burned area very expensive and hence unsustainable (MacDonald & Larsen, 2009). Therefore, technicians need to find a solution allowing the greatest possible impact in terms of soil conservation at the lowest possible cost (Fox, Berolo, Carrega, & Darboux, 2006).

Click to view larger

Figure 3. Percentage of sites with a given water repellency level determined in an unburned and an adjacent severely burned area with the Water Drop Penetration Time test (Doerr, 1998) (from Bagarello et al., 2017).

Post-fire treatment includes emergency stabilization to stabilize hydrological and erosion processes of the burned area immediately after the fire and rehabilitation and restoration activities in the longer period (Government Accountability Office, 2006). Techniques to mitigate the degradation processes at hillslope scale soon after the passage of fire include mulching, seeding, establishment of barriers, and creation of infiltration opportunities (Ferreira et al., 2015).

Mulching covers the soil surface and hence reduces the raindrop impact and subsequent erosion. Mulching can reduce runoff volumes by 50% and soil erosion by 90% if the application coverage reaches 70% of ground cover (Giordani & Zanchi, 1995; Prats, Malvar, Vieira, & Keizer, 2016). Mulching can occur naturally if the wildfire severity is moderate or low and the leaves remaining on the trees after the fire fall down on the soil, providing natural protection against rainfall erosivity (Robichaud, Beyers, & Neary, 2000). Both straw and wood-shred mulches are highly effective (Robichaud, Lewis, Wagenbrenner, Ashmun, & Brown, 2013), but some materials, such as straw, may not be readily available. Branches and shrubs should be crushed or placed in contact with the soil surface to act as effective mulches. Hydromulch consists of a mixture of water, straw, shredded bark, recycled paper, or any other organic material together with a colorant, a bio-stimulant, seeds, tackifiers, surfactants, or other elements. The ultimate goal is to provide a layer protecting the soil against rainfall events until germination and fixation of seeds. Mulch bands composed of straw, forest residues, trunks and branches, burned or not, can be placed a few meters apart on the slopes to reduce the amount of mulch material necessary. These bands should be perpendicular to the steepest slope angle with a spacing that decreases with the slope angle.

Post-fire seeding seeks the establishment of quick-growing vegetation species in order to provide ground cover to the soil until re-establishment of native vegetation. The seeding success depends on rainfall intensity, amount, and timing (Robichaud, Lillybridge, & Wagenbrenner, 2006). The most commonly used seeds include herbaceous seeds such as ryegrass and other leguminous commercial seed mixtures. However, the seeds remaining in the ash layer can be washed downstream before they can germinate and provide an effective ground cover. Therefore, using seeds without other techniques could be ineffective because degradation processes occur soon after the fire (Beyers, 2004). Manual seeding is time-consuming and may lead to an uneven spread. Applying seeds with mulch enhances seed germination, infiltration, and soil moisture conservation (Robichaud et al., 2000). The protection provided by mulch against raindrop impact improves germination.

Log erosion barriers—nylon tubes filled in with vegetation debris or straw and other natural or engineering structures—can be used to reduce overland flow, promote water infiltration, and trap sediments. Waste resulting from clear-felling of vegetation burned during the fire or forest residues such as bark, leaves, and chopped stems may also be left in place to form micro-traps retaining ashes and storing some of the runoff water. The installation of log erosion barriers consists of felling down burned tree trunks and putting them in contact with the soil along the slope contour. The logs are then anchored by stakes or stumps. Manual application includes the digging of grooves, at a distance varying with the slope angle, where the trunks are placed. The spaces between the logs and the surface should be filled with soil to prevent passage of water and sediments through these spaces. Burned wood impossible to sell or tree trunks irreparably burned can be used as barriers. However, barriers do not seem to be highly effective in controlling soil erosion, particularly if they are not in good contact with the ground or they are off-contour. In this last case, rills can develop at the downslope end of the log. Compared to the logs, straw wattle erosion barriers (Fig. 4) have the advantage of being flexible and adapting better to the ground surface. These barriers, typically 0.25 m in diameter, are made of an external skeleton of metallic mesh or nylon wool filled with straw or wood debris. The extremities of the barrier can be folded upward to the top of the slope to increase the capacity to retain sediments. These barriers are also placed along the contour and anchored with cuttings. According to Robichaud (2005), erosion barriers seem to be effective only for low-intensity rainfall events and not for the more erosive high-intensity rainstorms.

Click to view larger

Figure 4. Straw wattle erosion barrier 10 years after installation (from Ferreira et al., 2015; reprinted with permission).

In principle, creating infiltration opportunities by breaking the soil water-repellent layer will lead to a reduction in soil erosion. However, tillage is ineffective if it does not reach the repellent layer (MacDonald & Robichaud, 2007). On the other hand, deep tillage implies high costs and hydrological alterations at the field site (Wohlgemuth, 2003), also determining an increase in soil erosion (Ferreira, Coelho, Shakesby, & Walsh, 1997; Martins et al., 2013).

In summary, forest residue mulching is estimated to reduce soil erosion by approximately 30% to 80%, depending on the type of mulching (Ferreira et al., 2015). Percentages of soil erosion reduction are of the order of 15% with the hillslope barriers and 10% with seeding. Scarifying may also increase erosion. In general, the most cost-effective hillslope treatments are mulches using available material in the area.

Rehabilitation and restoration are longer-term activities to mitigate degraded areas that cannot recover to pre-fire conditions on their own (i.e., tree planting, noxious plant reduction, fuel control) (Ferreira et al., 2015).

# Controlling Sediment Delivery Through Vegetation

Sediment delivery from a source area can be controlled by using filter strips, vegetative barriers, and grass hedges. A filter strip is a strip or area of herbaceous vegetation that removes contaminants from overland flow (NRCS-NHCP, 2010a, code 393). Minimum flow length through the filter strip should be approximately 6 m to reduce suspended solids. Vegetative barriers are permanent strips of stiff, dense vegetation established along the contour of slopes or across concentrated flow areas (NRCS-NHCP, 2010b, code 601). Barrier widths will be the larger of 1 m or 0.75 times the design vertical interval. Grass hedges, representing a special case of vegetative barriers, are narrow strips of dense perennial grass planted close to the contour (Dabney, McGregor, Wilson, & Cullum, 2009). These hedges remain erect during the year, including dormant periods.

A clear summary of the reasons why grass hedges determine a sediment yield reduction was provided by Dabney et al. (2009). The most obvious reduction is caused by decreased transport of eroded sediment due to the hydraulic retardance effect on flow that determines, during runoff events, deposition in the ponded area formed uphill from the barrier or within them. Reduction is also due to decreased runoff caused by increased infiltration within the hedge and in the uphill ponded area. Detachment is decreased within the barrier, in the uphill ponded area, and just downstream of the barrier due to its effect in spreading and retarding the runoff. Slope steepness may be reduced over time as a result of benching between grass hedges. Vegetative residues trapped against buffer vegetation result in greater hydraulic resistance, deeper flow depths, and greater opportunity for sediment deposition. Vetiver grass (Vetiveria zizanioides L.) has been promoted as a grass hedge in tropical regions. More cold-hardy hedge species include switchgrass (Panicum virgatum L.) and miscanthus (Miscanthus sinensis Andersson). One advantage of practical attractiveness of grass hedges is the small width of the barrier, meaning large areas need not be excluded from agricultural use. For example, McGregor, Dabney, and Johnson (1999) and Dabney et al. (2009) reported that the base width of a grass hedge made by transplanting miscanthus plants in 1991 was 0.6 m in 1994 and 1 m 10 years later. In an investigation carried out at the Sparacia experimental station for soil erosion measurement in Sicily, the amount of soil lost from a plot with a vetiver hedge was 38–95% less than soil lost from similar plots without any hedge, depending on the erosive event (N = 6 events; Bagarello, Di Piazza, & Ferro, 2005). The presence of the hedge also determined higher soil water contents during the dry season at relatively high depths (i.e., 40–60 cm).

Following Dillaha, Reneau, Mostaghimi, and Lee (1989), Gumiere, Le Bissonnais, Raclot, and Cheviron (2011) used the term vegetated filter (VF) to denote an area of vegetation designed to remove sediments and other pollutants from surface water runoff by different mechanisms—i.e., filtration, deposition, infiltration, adsorption, absorption, decomposition and volatilization. Vegetation at the downstream edge of a disturbed area may effectively reduce runoff volume and velocity, initially because of the increasing hydraulic roughness of the filter and subsequently by augmentation of infiltration rate over vegetation. Decreasing flow volume and velocity leads to sediment deposition as a result of a decrease in transport capacity. VFs have high sediment-trapping efficiencies as long as the flow is shallow and uniform and the filter is not submerged. Gumiere et al. (2011) summarized 147 values of sediment removal efficiency for VFs obtained from 49 field and laboratory investigations carried out with a variety of plant species. The sediment removal efficiency, ranging from 24% to 100%, was not significantly correlated with VF width (0.2–60 m) because sediment deposition occurs in the first few meters of the VF and an increase in the streamwise width of the filter has a limited impact on its efficiency. Large particles are trapped in the first few meters of the VF, and only finer particles travel farther. Neither slope (1–17%) nor unit inflow rate (0.04–1.5 L m−2) was found to affect sediment removal efficiency. On the other hand, an exponential relationship between runoff reduction (14.5–97%) and sediment reduction was detected, and sediment removal was always greater than 60% for a runoff reduction greater than 16%.

# Using Erosion Models for Soil Conservation Purposes

Soil erosion models can be used in conservation work for three primary purposes (Nearing, 2013): (1) to help a land owner or manager in the choice of suitable conservation practices among alternatives; (2) to make broad-scale erosion surveys in order to understand the scope of the problem over a region or to track changes in erosion over time; and (3) to regulate activities on the land for purposes of conservation compliance.

According to Wischmeier and Smith (1978), the greatest possible benefits from soil erosion research on field plots and small watersheds “can be realized only when the findings are converted to sound practice on the numerous farms and other erosion prone areas.” This is the reason why the USLE represents one of the simplest practical tools to attempt a quantitative design of soil conservation measures. The average rate of soil erosion, simply predicted “for each feasible alternative combination of crop system and management practices in association with a specified soil type, rainfall pattern, and topography” is compared with given soil loss tolerance values to “provide specific guidelines for effecting erosion control within specified limits” (Wischmeier & Smith, 1978). The well-known expression of the USLE is:

$Display mathematics$
(3)

where A (Mg ha−1year−1) is the mean annual soil loss, R (MJ mm ha−1h−1year−1) is the rainfall and runoff factor, K (Mg ha h ha−1MJ−1mm−1) is the soil erodibility factor, L is the slope length factor, S is the slope gradient factor, C is the cover and management factor, and P is the support practice factor. Although the USLE has been reviewed (RUSLE, i.e., Revised USLE, RUSLE2) (Renard, Foster, Weesies, McCool, & Yoder, 1997; USDA-ARS, 2008), the original model is still used in many countries since it represents an appropriate method for combining relative simplicity with the ability to use quite basic data and to yield prediction with acceptable accuracy, even in areas different from those originally considered (Bagarello, Di Piazza, Ferro, & Giordano, 2008; Bagarello et al., 2012; Hann & Morgan, 2006; Kinnell & Risse, 1998; Risse, Nearing, Nicks, & Laflen, 1993). In areas where few data are available or there is the need for simple computations, the USLE has distinct advantages over both RUSLE and process-oriented models, such as WEPP (Water Erosion Prediction Project) (Nearing, 2013).

The large diffusion of the USLE throughout the world has induced different researchers to develop USLE-based tools usable for soil conservation purposes by people not particularly expert in soil erosion science. For example, experimentally determined C values for non-U.S. environments can be found in the literature (Table 3), and maps of C and P factors of the model for a given area are also available. These maps provide a reference for the assessment of human influence on soil erosion control, and they could assist public authorities in decision-making processes related to land use, conservation strategies, and incentives. Mapping of the USLE factors has been carried out at very different spatial scales. For example, Ferro (2011) developed a map of the cover and management factor for Sicily, based on the CORINE Land Cover (Fig. 5). Maps of C (Fig. 6) and P at a much larger spatial scale (i.e., over Europe) were recently developed by Panagos et al. (2015a, 2015b).

Click to view larger

Figure 5. Map of the cover and management factor, C, for Sicily (from Ferro, 2011).

Click to view larger

Figure 6. Map of the cover and management factor, C, for the European Union (from Panagos et al., 2015a).

Table 3. Examples of Experimentally Determined Values of the Cover and Crop Management Factor, C, of the USLE

Source

Country

Vegetation cover and slope

C

El-Hassanin et al. (1993)

Burundi

Forest, 8%

0.038

Grasses, 8%

0.043

Cultivated, 8%

0.076

Cultivated, 12%

0.104

Forest, 15%

0.034

Grasses, 20%

0.018

Forest, 30%

0.013

Grasses, 30%

0.014

Cultivated, 30%

0.026

Cropping system during a three-year rotation

Preiti et al. (2017)

Italy

Natural vegetation/rye/lupin. Minimum tillage and crop residue left on the soil surface

0.128

Oat/lupin/cauliflower-potato. Conventional tillage with crop residue removed

0.513

Cauliflower-potato/oat/lupin. Strip tillage for cauliflower and potato and conventional tillage for oat and lupin

0.287

Lupin/tall fescue. Minimum tillage and crop residues left on soil surface

0.089

In general, the distinction between changeable (e.g., L, C, P) and unchangeable (R, K, S) factors allows writing the USLE in the following form:

$Display mathematics$
(4)

Equation (4) provides an estimate of the value that should be assigned to the LCP product to have a mean annual soil loss (A) equal to the tolerance (T) for given climatic (R), soil (K), and slope steepness (S) characteristics. In this case, it is obviously assumed that soil erosion control can indifferently be achieved by modifying one, two, or three factors (length of the field, cover and management practices, support practices). However, Equation (4) can be written in a different form, depending on the factors that, in a given area, can really be changed. For example, if only cover crops can be planted in a vineyard, an estimate of C = T/(RKSLP) is obtained. The calculated C value can then be used to predict the percentage of the surface area that should be protected or to choose the cover crops to be planted according to data and relationships from the literature (e.g., Bazzoffi, 2011; Novara et al., 2011). A similar approach was applied by Gabriels et al. (2003), who determined the C factor for 40 crop rotation systems on arable farms in a Belgian watershed. According to the authors, the calculated C factors can be used as a criterion to select an appropriate rotation system to reduce erosion risk on site. The applied procedure seems appropriate to make a choice among a suite of rotations that are both technically sound for an area of interest and potentially accepted or, at least, not inevitably opposed by the farmers since the rotations are all practiced in the area. Similarly, the spacing, df (L), between adjacent terrace channels can be estimated by the following relationship:

$Display mathematics$
(5)

in which m is the slope length exponent of the L factor relationship.

The RUSLE (version 1.06) (Renard et al., 1997) and the RUSLE2 (USDA-ARS, 2008) provide separate estimates for the P factor for conservation planning, as in the USLE, and the PSY factor for sediment yield, denoting off-site impacts. In other words, the PSY factor is the fractional reduction in sediment yield due to the presence of the grass hedge. Therefore, only P has to be taken into account to predict plot soil loss, and P × PSY has to be used to predict plot sediment yield. In an investigation by Dabney et al. (2009) with miscanthus hedges, no tillage yielded higher PSY factors than conventional tillage (0.50–0.65 against 0.12–0.23, depending on the cotton row spacing), perhaps because sediments were finer in the former case.

Taking into account that a large proportion of total soil erosion over a long time period is due to a few large storms (Larson, Lindstrom, & Schumacher, 1997), soil conservation practices or strategies based on mean annual soil loss values can provide adequate erosion control in most years, even if unacceptable soil loss can occur in case of the largest erosion-producing events. Therefore, conservation strategies should account for large storms rather than average weather conditions. From an engineering point of view, the design erosion storm has to be defined to establish an erosion control strategy. If a long historical sequence of soil loss values is available, then a frequency analysis can be developed and the soil erosion value having a given return period can be estimated (Baffaut, Nearing, & Govers, 1998; Bagarello, Di Stefano, Ferro, & Pampalone, 2010a; Bagarello, Di Stefano, Ferro, & Pampalone, 2011; Mannaerts & Gabriels, 2000). According to Larson et al. (1997), conservation systems should be designed for limiting soil loss to the value corresponding to a return period variable from 10 to 20 years. Using soil erosion data collected at the Sparacia experimental installation in Sicily, Bagarello et al. (2010a) suggested that the soil loss of a given return period can be estimated using a scale and a frequency factor. An estimating criterion of the annual soil loss of a given return period was developed by Bagarello et al. (2011). By this criterion, the frequency distribution of the rainfall erosivity factor can be used to design soil conservation practices.

Following Hann and Morgan (2006), the USLE can be adapted to indicatively predict ground cover effects on soil loss due to an erosive event with a given return period, TR (years), although, strictly speaking, the USLE is not recommended for the prediction of specific soil loss events (Wischmeier & Smith, 1978). To determine the event erosivity index with a return period of TR years, Re,TR (MJ mm ha−1h−1), for a site, the single-storm erosivity index, Re, is calculated (Wischmeier & Smith, 1978) or estimated (e.g., D’Asaro, D’Agostino, & Bagarello, 2007) for each erosive event occurred in the N years of rainfall records (Hollinger, Angel, & Palecki, 2002). The time series of the highest Re value for each year, Re,max, is constructed. Using the Gumbel distribution, Re,TR is then estimated as:

$Display mathematics$
(6a)

$Display mathematics$
(6b)

$Display mathematics$
(6c)

where σ‎ is the standard deviation. Alternatively, the L-moments fitting technique and the Generalized Extreme Value distribution can be used to determine Re,TR (Hollinger et al., 2002). A return period of TR = 10 years was considered by Hann and Morgan (2006) for their calculations. In the RUSLE (Renard et al., 1997), Re,10 was used for P factor calculation for contour farming. In the approach by Hann and Morgan (2006), the crop management factor, C, was assumed to only vary with the percentage of ground cover, COV (%), according to the following relationship:

$Display mathematics$
(7)

where the exponent ac, varying between 0.01 and 0.1, was set equal to 0.035. Evidently, Equation (7) can be used to predict how the event soil loss corresponding to Re,TR varies with the percentage of ground cover. Establishing the maximum acceptable erosion rate for a storm allows technicians to predict the percentage of ground cover that should be maintained so as not to exceed that erosion rate. Hann and Morgan (2006) applied this approximate procedure to evaluate the performance of erosion control measures along pipeline rights of way in different countries. The tolerable erosion rate for a storm was set equal to 10 Mg ha−1, but a higher level of erosion control, i.e., a tolerance of 2 or 5 Mg ha−1, was suggested for pipeline corridors passing through environmentally sensitive areas. Following Wischmeier and Smith (1978), the procedure should be interpreted as usable to estimate the average soil loss for a large number of storms of the assumed size, i.e., Re,TR, occurring on that field.

Other empirical soil loss prediction models have been developed in the last few years, including the USLE-M and the USLE-MM (Bagarello, Ferro, & Giordano, 2010b; Bagarello, Ferro, & Pampalone, 2015b; Bagarello et al., 2008, 2013; Kinnell & Risse, 1998). The mathematical structure of these models is similar to that of the USLE, but the erosivity term is expressed by the product between the single-storm erosion index (Wischmeier & Smith, 1978) and the runoff coefficient in both the USLE-M and the USLE-MM. These models are potentially attractive since they can specifically be applied at the event temporal scale, even if there is a current practical limitation due to the difficulty to evaluate the runoff coefficient. On the other hand, it should be noted that a model including the runoff coefficient appears usable to predict how much water should infiltrate the soil during a rainstorm to maintain event soil loss at a tolerable level. A few examples of the USLE-MM application for soil conservation planning purposes are given in Box 1. Use of pre-event soil moisture data, obtained by either satellite or modeling, appears to be a practical improvement as compared with the original USLE for predicting event soil loss at the field scale (Todisco, Brocca, Termite, & Wagner, 2015).

Box 1 Using the USLE-MM for Soil Conservation Planning

 For the Sparacia experimental site in Sicily (Italy), the event soil loss, Ae (Mg ha-1), from a bare plot can be estimated by the USLE-MM (Bagarello et al., 2015b): $Display mathematics$ where QR (−) is the event runoff coefficient, Re (MJ mm ha−1h−1) is the single-storm erosion index (Wischmeier & Smith, 1978), the exponent b1 is equal to 1.37, KMM = 0.058 Mg ha−1 per unit erosivity index is the soil erodibility factor, λ‎ (m) is the plot length, s (−) is the plot steepness, and α‎ = 0.86, a = 2.26, and b = 4.07 are empirical coefficients. In hilly agricultural areas, the soil can remain bare for a relatively long period when a fine seedbed has been prepared but a crop cover has not yet developed. Therefore, predicting soil erosion from bare plots has practical importance. Soil erosion decreases when infiltration increases. For a given plot and a given Re value, the USLE-MM can be used to estimate what is the highest runoff coefficient assuring that a given soil loss value is not exceeded. As an example, these calculations were carried out, with reference to Re = 50 and 200 MJ mm ha−1h−1, for a plot with λ‎ = 22 m and s = 0.15. Figure B1 shows the Ae vs. QR relationship for the two Re values. If a tolerable soil loss of 10 Mg ha−1 is assumed, the runoff coefficient should not exceed 0.53 for Re = 50 MJ mm ha−1h−1 and 0.13 for Re = 200 MJ mm ha−1h−1. In practical application of the method, the subsequent step is establishing if the above reported QR values are plausible. The length of the field is generally considered a modifiable soil erosion factor. Figure B2 shows the relationship between λ‎ and QR for a tolerable event soil loss of 10 Mg ha−1, s = 0.15, and Re = 50 and 200 MJ mm ha−1h−1. For Re = 50 MJ mm ha−1h−1, the field should not be longer than approximately 35 m if the runoff coefficient is 0.4, but it can be more than 100 m long if QR = 0.2 is expected. The USLE-MM can also be used to estimate, for a given QRRe value, the plot length that avoids event soil loss values greater than a given threshold on hillslopes of different steepnesses. Figure B3 provides an example of such calculations for an assumed tolerable soil loss of 10 Mg ha−1. For QRRe = 30 MJ mm ha−1h−1, λ‎ has not to exceed 12 m if the steepness of the plot is 0.2, but it can be equal to 34 m for s = 0.1.

In the control of soil erosion processes, vegetation-based strategies are increasingly preferred to engineering structures. Process-based models such as WEPP (Nearing, Foster, Lane, & Finkner, 1989) and EUROSEM (Morgan et al., 1998) can simulate the effects of vegetation on erosion, but they are too complex and data-hungry to be used as simple management tools. Nearing (2013) recently suggested that the USLE, RUSLE, and WEPP or other process-based models constitute a complementary suite of models to be chosen to meet the specific user need, but he also concluded that, at the current time, most applications of WEPP are only possible in the United States because of the availability of soil, climate, and crop information relevant for the model’s application. Simple, empirically based annual models may be appropriate and give as good, if not better, results as compared with more complex models (De Roo, 1996; Tiwari, Risse, & Nearing, 2000). Consequently, Morgan and Duzant (2008) modified the Morgan–Morgan–Finney model (Morgan, 2001; Morgan, Morgan, & Finney, 1984), usable at plot, hillslope, and small catchment scales, to allow the effects of vegetation and crop cover on erosion to be easily modeled. The model makes use of measurable properties of plant architecture, namely plant height, canopy cover, ground cover, stem diameter, and stem density, taking into account that an important effect of vegetation is to promote sediment deposition and this is a particle-size–selective process (Table 4). Evidently, the practically sustainable possibility of simulating the effects of plant cover on soil erosion using measurable parameters of soil architecture instead of global plant cover coefficients, such as in the USLE, could improve design of vegetation-based strategies to reduce soil erosion. Therefore, testing the model by Morgan and Duzant (2008) in different environments could be an advisable step toward more effective planning of vegetation-based soil conservation measures.

Table 4. Equations with Measurable Vegetation Characteristics Used in the Modified Morgan–Morgan–Finney Soil Erosion Model

Section

Equation

Estimation of rainfall energy

$LD=Rf×CC$

Estimation of rainfall energy

$KE(LD)=[(15.8×PH0.5)−5.87]LD$

Detachment of soil particles

$Hc=DRc×%c/100×Q1.5×[1−(GC+ST)]×sin0.3S×10−3$

Immediate deposition of detached particles

$vv=(2gD×NV)0.5s0.5$

LD = leaf drainage; Rf = effective rainfall; CC = canopy cover; KE = kinetic energy; PH = plant height; Hc = detachment of clay soil particles by runoff; DRc = detachability of the clay soil particles by runoff; %c = percentage of clay soil particles; Q = runoff volume; GC = proportion of the soil covered by vegetation; ST = proportion of the soil covered by stones; S = slope angle; vv = flow velocity for vegetated conditions; g = acceleration due to gravity; D = diameter of the plant stems; NV = number of stems per unit area.

Source: Morgan and Duzant (2008).

From a practical point of view, it can be necessary to predict the influence of soil conservation practices on sediment transport processes and therefore on sedimentological connectivity of the catchments. With respect to this point, Gumiere et al. (2011) classified catchment-scale erosion models as (1) those applying the P factor USLE approach, (2) those applying the watershed Sediment Delivery Ratio (SDRw) and derivations, and (3) those including new objects with specific physical parameters, such as the roughness coefficient or infiltrability, to represent land management practices. According to Gumiere et al. (2011), checking the effects of management practices on soil water erosion processes occurring in a watershed still requires development of a methodology of intermediate complexity between describing sediment transport from source to sink areas with the P factor and SDRw approaches and process-based descriptions involving changes in roughness and infiltrability along flow paths. In any case, this intermediate methodology should be simple and easy to spatialize. With reference to this point, within-basin variability of the sediment-delivery processes can be predicted by applying a sediment-delivery model at the hillslope scale, such as the Sediment Delivery Distributed (SEDD) model (Ferro, 1997; Ferro & Minacapilli, 1995; Ferro & Porto, 2000). The basin has first to be divided into elementary units (square and triangular cells, morphological units) where the variables of physically based equations or the parameters of the chosen empirical model need to be calculated. The soil loss of each elementary unit is then transformed into sediment yield reaching the basin outlet at a given temporal scale. By this approach, the zones of the watershed more affected by erosion problems in terms of both soil loss and sediment yield can be identified. Then, spatial allocation of soil conservation measures can be optimized, also depending on the intended use of each elementary area of the watershed. An important, practical advantage of the SEDD model is that all the input data can be collected by a contour line map of the basin.

# Economic Aspects of Soil Conservation

About 20 years ago, Pimentel et al. (1995) reported an estimated investment for U.S. erosion control of about $8.4 billion per year against about$44 billion in damages each year caused by erosion. Therefore, preventing erosion is economically practical. However, most measures have, or are perceived to have, negative financial and economic returns for farmers, which explains why they are so reluctant to implement erosion control measures without compensation.

In a recent assessment of the cost-effectiveness of erosion control measures in the United Kingdom, Posthumus et al. (2015) initially distinguished between on-site and off-site physical and socio-economic impacts of soil erosion on agricultural land. The former include land management difficulties associated with erosion features, such as rills and gullies, damage to crops, loss of water storage capacity, removal of nutrients and fertile topsoil, and soil and nutrient redistribution on eroding slopes. The latter include blockage of drains and water courses by sediments, water pollution, damage to infrastructures caused by muddy floods, and associated public health issues. Considering only on-site costs and benefits for the farmer, these authors concluded cost-effective erosion control measures to be contour ploughing, agro-forestry, zero tillage, and earth banks. The cost associated with contour ploughing is low as it does not involve any investment cost or loss of agricultural production. On the contrary, yields may increase possibly due to better water conservation on contoured fields (Quinton & Catt, 2004), but contour ploughing is only suitable on gently sloped fields. The high score for agro-forestry is explained by the agri-environment payments and low maintenance costs associated with this measure. Taking off-site benefits and ecosystem services into account, but excluding agri-environment payments, the most cost-effective erosion control measures are management of tramlines (i.e., marked wheelways through crops helping to ensure more even application of agrochemicals; Withers, Hodgkinson, Bates, & Withers, 2006), riparian buffer strips, mulching, in-field buffer strips, high-density planting, contour ploughing, and sediment traps. In general, erosion control measures requiring minor adaptations to conventional agriculture practice appear to be most cost-effective in reducing soil erosion. These measures are more likely to be adopted by farmers because they are less costly and require minor changes in farm management in comparison with mitigation measures involving large investments or land use change (Posthumus et al., 2015).

Critical source areas within a catchment contribute disproportionately large amounts of pollution and sediment due to soil erosion because of their active hydrological connectivity to the stream channel. Lecomte (1999) showed that spatial organization in a catchment of a soil control measure (i.e., vegetated filters) affects global runoff reduction and sediment removal efficiency, and Posthumus et al. (2015) concluded that targeting of the critical source areas within a catchment with appropriate erosion control measures will be more cost-effective in controlling soil erosion and associated nutrient losses.

The starting point of a recent investigation by Galati, Gristina, Crescimanno, Barone, and Novara (2015) was that evaluation of the economic damage caused by erosion should increase the awareness of the problem among farmers and policymakers and therefore promote the implementation of more sustainable soil management practices. These authors developed a new approach to evaluate incentives for the adoption of agri-environmental measures (AEMs) in degraded and eroded vineyards. To estimate this incentive, the replacement cost and the loss of income are calculated when the vineyard is managed with conventional tillage versus a cover crop (AEM). The authors found that the incentive could range from the loss of income due to AEM adoption to the ecosystem service benefit. Within this range, the incentive amount is determined according to efficiency criteria based on vineyard slope. Galati et al. (2015) also developed a conceptual model of public spending efficiency that should help policymakers decide how to allocate incentives so as to maximize the economic return associated with ecosystem services.

Many areas need development, especially those concerning the economic implications of soil conservation practices known to be effective from a technical point of view. For example, Prosdocimi et al. (2016a) recently concluded that the economic feasibility of mulch application is still a fundament aspect that needs to be addressed in future research activity.

## References

Alagna, V., Bagarello, V., Di Prima, S., Giordano G., & Iovino, M. (2016). Testing infiltration run effects on the estimated water transmission properties of a sandy-loam soil. Geoderma, 267, 24–33.Find this resource:

Arnhold, S., Lindner, S., Lee, B., Martin, E., Kettering, J., Nguyen, T. T., . . . Huwe, B. (2014). Conventional and organic farming: Soil erosion and conservation potential for row crop cultivation. Geoderma, 219–220, 89–105.Find this resource:

Assouline, S., & Mualem, Y. (2002). Infiltration during soil sealing: The effect of areal heterogeneity of soil hydraulic properties. Water Resources Research, 38(12), 1286.Find this resource:

Assouline, S., & Mualem, Y. (2006). Runoff from heterogeneous small bare catchments during soil surface sealing. Water Resources Research, 42, W12405.Find this resource:

Baffaut, C., Nearing, M. A., & Govers, G. (1998). Statistical distributions of soil loss from runoff plots and WEPP model simulations. Soil Science Society of America Journal, 62, 756–763.Find this resource:

Bagarello, V., Castellini, M., Di Prima, S., & Iovino, M. (2014). Soil hydraulic properties determined by infiltration experiments and different heights of water pouring. Geoderma, 213, 492–501.Find this resource:

Bagarello, V., & Ferro, V. (2006). Erosione e conservazione del solo. Milan: McGraw-Hill.Find this resource:

Bagarello, V., Di Piazza, G. V., & Ferro, V. (2005). Indagine di campo sull’efficacia del Vetiver per la conservazione del suolo e dell’acqua. Quaderni di Idronomia Montana, 24, 413–431.Find this resource:

Bagarello, V., Di Piazza, G.V., Ferro, V., & Giordano, G. (2008). Predicting unit plot soil loss in Sicily, south Italy. Hydrological Processes, 22, 586–595.Find this resource:

Bagarello, V., Di Stefano, C., Ferro, V., Giordano, G., Iovino, M., & Pampalone, V. (2012). Estimating the USLE soil erodibility factor in Sicily, south Italy. Applied Engineering in Agriculture, 28(2), 199–206.Find this resource:

Bagarello, V., Di Stefano, C., Ferro, V., & Pampalone, V. (2010a). Statistical distribution of soil loss and sediment yield at Sparacia experimental area, Sicily. Catena, 82, 45–52.Find this resource:

Bagarello, V., Di Stefano, C., Ferro, V., & Pampalone, V. (2011). Using plot loss distribution for soil conservation design. Catena, 86, 172–177.Find this resource:

Bagarello, V., Di Stefano, C., Ferro, V., & Pampalone, V. (2015a). Establishing a soil loss threshold for limiting rilling. Journal of Hydrological Engineering, 20(6), C6014001(5).Find this resource:

Bagarello, V., Ferro, V., & Giordano, G. (2010b). Testing alternative erosivity indices to predict event soil loss from bare plots in southern Italy. Hydrological Processes, 24, 789–797.Find this resource:

Bagarello, V., Ferro, V., Giordano, G., Mannocchi, F., Todisco, F., & Vergni, L. (2013). Predicting event soil loss from bare plots at two Italian sites. Catena, 109, 96–102.Find this resource:

Bagarello, V., Ferro, V., & Pampalone, V. (2015b). A new version of the USLE-MM for predicting bare plot soil loss at the Sparacia (South Italy) experimental site. Hydrological Processes, 29, 4210–4219.Find this resource:

Bagarello, V., Giordano, G., Iovino, M., & Tinebra, I. (2017). Caratterizzazione dell’idrorepellenza nei suoli del Monte Pellegrino (Palermo) percorsi dal fuoco. Proceedings of the Workshop on Attualità dell’Idraulica Agraria e delle Sistemazioni Idraulico-Forestali al cambiare dei tempi, Palermo (Italy), May 4–5, 2017.Find this resource:

Beyers, J. L. (2004). Postfire seedling for erosion control: Effectiveness and impacts on native plant communities. Conservation Biology, 18(4), 947–956.Find this resource:

Blanco-Canqui, H., Lal, R., Post, W. M., Izanrralde, R. C., & Owens, L. B. (2006). Corn stover impacts on near-surface soil properties of no-till corn in Ohio. Soil Science Society of America Journal, 70, 266–278.Find this resource:

Boardman, J., & Poesen, J. (2006). Soil erosion in Europe. Chichester, U.K.: Wiley.Find this resource:

Bodner, G., Loiskandl, W., Buchan, G., & Haul, H.-P. (2008). Natural and management-induced dynamics of hydraulic conductivity along a cover-cropped field slope. Geoderma, 146, 317–325.Find this resource:

Dabney, S. M., McGregor, K. C., Wilson, G. V., & Cullum, R. F. (2009). How management of grass hedges affects their erosion reduction potential. Soil Science Society of America Journal, 73(1), 241–254.Find this resource:

D’Asaro, F., D’Agostino, L., & Bagarello, V. (2007). Assessing changes in rainfall erosivity in Sicily during the twentieth century. Hydrological Processes, 21, 2862–2871.Find this resource:

De Roo, A. P. J. (1996). Validation problems of hydrologic and soil-erosion catchment models: Examples from a Dutch soil erosion project. In M. G. Anderson & S. M. Brooks (Eds.), Advances in hillslope processes (pp. 669–683). Chichester, U.K.: Wiley.Find this resource:

Dillaha, T. A., Reneau, R. B., Mostaghimi, S., & Lee, D. (1989). Vegetative filter strips for agricultural non-point-source pollution control. Transactions of the ASAE, 32, 513–519.Find this resource:

Dissmeyer, G. E., & Foster, G. R. (1981). Estimating the cover-management factor (C) in the universal soil loss equation for forest conditions. Journal of Soil and Water Conservation, 36(4), 235–240.Find this resource:

Di Stefano, C., & Ferro, V. (2016). Establishing soil loss tolerance. An overview. Journal of Agricultural Engineering Research, 560, 127–133.Find this resource:

Doerr, S. H. (1998). On standardizing the “Water Drop Penetration Time” and the “Molarity of an Ethanol Droplet” techniques to classify soil hydrophobicity: A case study using medium textured soils. Earth Surface Processes and Landforms, 23(7), 663–668.Find this resource:

El-Hassanin, A. S., Labib, T. M., & Gaber, E. I. (1993). Effect of vegetation cover and land slope on runoff and soil losses from the watersheds of Burundi. Agriculture, Ecosystems and Environment, 43, 301–308.Find this resource:

El-Swaify, S. A., Dangler, E. W., & Armstrong, C. L. (1982). Soil erosion by water in the tropics. University of Hawaii: College of Tropical Agriculture and Human Resources, p. 173.Find this resource:

Ferreira, A. J. D., Alegre, S. P., Coelho, C. O. A., Shakesby, R. A., Páscoa, F. M., Ferreira, C. S. S., . . . Ritsema, C. (2015). Strategies to prevent forest fires and techniques to reverse degradation processes in burned areas. Catena, 128, 224–237.Find this resource:

Ferreira, A. J. D., Coelho, C. O. A., Shakesby, R. A., & Walsh, R. P. D. (1997). Sediment and solute yield in forest ecosystems affected by fire and rip-ploughing techniques, central Portugal: A plot and catchment analysis approach. Physics and Chemistry of the Earth, 22, 309–314.Find this resource:

Ferro, V. (1997). Further remarks on a distributed approach to sediment delivery. Journal of Hydrological Sciences, 42(5), 633–647.Find this resource:

Ferro, V. (2011). MOnitoraggio e modellazione dei Fenomeni EROsivi su terreni declivi utilizzati a Seminativo (Progetto MOFEROS)—Relazione sull’attività di ricerca svolta. University of Palermo.Find this resource:

Ferro, V., & Minacapilli, M. (1995). Sediment delivery processes at basin scale. Journal of Hydrological Sciences, 40(6), 703–717.Find this resource:

Ferro, V., & Porto, P. (2000). Sediment delivery distributed (SEDD) model. Journal of Hydrologic Engineering, 5(4), 411–422.Find this resource:

Fox, D., Berolo, W., Carrega, P., & Darboux, F. (2006). Mapping erosion risk and selecting sites for simple erosion control measures after a forest fire in Mediterranean France. Earth Surface Processes and Landforms, 31, 606–621.Find this resource:

Gabriels, D., Ghekiere, G., Schiettecatte, W., & Rottiers, I. (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.Find this resource:

Galati, A., Gristina, L., Crescimanno, M., Barone, E., & Novara, A. (2015). Towards more efficient incentives for agri-environment measures in degraded and eroded vineyards. Land Degradation & Development, 26, 557–564.Find this resource:

Gilley, J. E., & Flanagan, D. C. (2007). Early investment in soil conservation research continues to provide dividends. Transactions of the ASABE, 50(5), 1595–1601.Find this resource:

Gilley, J. E., & Risse, L. M. (2000). Runoff and soil loss as affected by the application of manure. Transactions of the ASAE, 43(6), 1583–1588.Find this resource:

Giordani, C., & Zanchi, C. (1995). Elementi di conservazione del suolo. Bologna: Patron Editore.Find this resource:

Government Accountability Office (GAO). (2006). Wildland fire rehabilitation and restoration: Forest service and BLM could benefit from improved information on status of needed work. Report to the Chairman, Subcommittee on Forests and Forest Health, Committee on Resources, House of Representatives GAO-06-670, Washington, DC.Find this resource:

Gumiere, S. J., Le Bissonnais, Y., Raclot, D., & Cheviron, B. (2011). Vegetated filter effects on sedimentological connectivity of agricultural catchments in erosion modelling: A review. Earth Surface Processes and Landforms, 36, 3–19.Find this resource:

Haigh, M. J., Rawat, J. S., Bartarya, S. K., & Rai, S. P. (1995). Interactions between forest and landslide activity along new highways in the Kumaun Himalaya. Forest Ecology and Management, 78(1–3), 173–189.Find this resource:

Hann, M. J., & Morgan, R. P. C. (2006). Evaluating erosion control measures for biorestoration between the time of soil reinstatement and vegetation establishment. Earth Surface Processes and Landforms Special Issue: The Use of Vegetation for Erosion Control and Environmental Protection, 31(5), 589–597.Find this resource:

Hollinger, S. E., Angel, J. R., & Palecki, M. A. (2002). Spatial distribution, variation, and trends in storm precipitation characteristics associated with soil erosion in the United States. Illinois State Water Survey Contract Report 2002–08. Champaign, IL: Illinois State Water Survey, Atmospheric Environment Section.Find this resource:

Hösl, R., & Strauss, P. (2016). Conservation tillage practices in the alpine forelands of Austria—Are they effective? Catena, 137, 44–51.Find this resource:

International Society of Soil Science (ISSS). (1996). Terminology for soil erosion and conservation. Wageningen, The Netherlands: ITC-Enschede/ISRIC.Find this resource:

Joyce, B. A., Wallender, W. W., Mitchell, J. P., Huyck, L. M., Temple, S. R., Brostrom, P. N., & Hsiao, T.C. (2002). Infiltration and soil water storage under winter cover cropping in California’s Sacramento Valley. Transactions of the ASAE, 45, 315–326.Find this resource:

Kendall, H. W., & Pimentel, D. (1994). Constraints on the expansion of the global food supply. Ambio, 23, 198–205.Find this resource:

Kinnell, P. I. A., & Risse, L. M. (1998). USLE-M: Empirical modeling rainfall erosion through runoff and sediment concentration. Soil Science Society of America Journal, 62, 1667–1672.Find this resource:

Kok, K., Clavaux, M. B. W., Heerebout, W. M., & Bronsveld, K. (1995). Land degradation and land cover change detection using low-resolution satellite images and the CORINE database—a case study in Spain. ITC Journal, 3, 217–228.Find this resource:

Kunkle, S. H., & Harcharik, D. A. (1977). Conservation of upland wildlands for downstream agriculture. Rome: Food and Agriculture Organization of the United Nations.Find this resource:

Lal, R. (1994). Water management in various crop production systems related to soil tillage. Soil and Tillage Research, 30, 169–185.Find this resource:

Larson, W. E., Lindstrom, M. J., & Schumacher, T. E. (1997). The role of severe storms in soil erosion: A problem needing consideration. Journal of Soil and Water Conservation, 52(2), 90–95.Find this resource:

Lassabatère, L., Angulo-Jaramillo, R., Soria Ugalde, J. M., Cuenca, R., Braud, I., & Haverkamp, R. (2006). Beerkan estimation of soil transfer parameters through infiltration experiments—BEST. Soil Science Society of America Journal, 70, 521–532.Find this resource:

Lecomte, V. (1999). Transferts de produits phytosanitaires par le ruisellement et l’érosion de la parcelle au basin versant. Modélisation spatiale (PhD thesis). ENGREF et INRA, Orléans, France.Find this resource:

Li, L., Du, S., Wu, L., & Liu, G. (2009). An overview of soil loss tolerance. Catena, 78, 93–99.Find this resource:

MacDonald, L. H., & Larsen, I. (2009). Effects of forest fires and post-fire rehabilitation: A Colorado, USA case study. In A. Cerdà & P. R. Robichaud (Eds.), Fire effects on soils and restoration strategies (pp. 423–452). Enfield, NH: Science Publisher.Find this resource:

MacDonald, L. H., & Robichaud P. R. (2007). Postfire erosion and the effectiveness of emergency rehabilitation treatments over time. Final report to the Joint Fire Sciences Program, Boise, ID.Find this resource:

Mannaerts, C. M., & Gabriels, D. (2000). A probabilistic approach for predicting rainfall soil erosion losses in semiarid areas. Catena, 40, 403–420.Find this resource:

Martins, M. A. S., Machado, A. I., Serpa, D., Prats, S. A., Faria, S. R., Varela, M. E. T., . . . Keizer, J. J. (2013). Runoff and inter-rill erosion in a maritime pine and a eucalypt plantation following wildfire and terracing in north-central Portugal. Journal of Hydrology and Hydromechanics, 61, 261–268.Find this resource:

McGregor, K. C., Dabney, S. M., & Johnson, J. R. (1999). Runoff and soil loss from cotton plots with and without stiff-grass hedges. Transactions of the ASAE, 42, 361–368.Find this resource:

Moldenhauer, W. C., & Onstad, C. A. (1975). Achieving specified soil loss levels. Journal of Soil and Water Conservation, 30, 166–168.Find this resource:

Molla, T., & Sisheber, B. (2017). Estimating soil erosion risk and evaluating erosion control measures for soil conservation planning at Koga watershed in the highlands of Ethiopia. Solid Earth, 8, 13–25.Find this resource:

Morgan, R. P. C. (2001). A simple approach to soil loss prediction: A revised Morgan–Morgan–Finney model. Catena, 44, 305–322.Find this resource:

Morgan, R. P. C. (2005). Soil erosion and conservation. Oxford: Blackwell.Find this resource:

Morgan, R. P. C., & Duzant, J. H. (2008). Modified MMF (Morgan-Morgan-Finney) model for evaluating effects of crops and vegetation cover on soil erosion. Earth Surface Processes and Landforms, 32, 90–106.Find this resource:

Morgan, R. P. C., Morgan, D. D. V., & Finney, H. J. (1984). A predictive model for the assessment of erosion risk. Journal of Agricultural Engineering Research, 30, 245–253.Find this resource:

Morgan, R. P. C., Quinton, J. N., Smith, R. E., Govers, G., Poesen, J. W. A., Auerswald, K., . . . Styczen, M.E. (1998). The European soil erosion model (EUROSEM): A dynamic approach for predicting sediment transport from field and small catchments. Earth Surface Processes and Landforms, 23, 527–544.Find this resource:

Myers, N. (1993). Gaia: An atlas of planet management. Garden City, NY: Anchor/Doubleday.Find this resource:

National Resource Conservation Service (NRCS). (2010a). Conservation practice standard, filter strip. Code 393 (NRCS, NHCP).Find this resource:

National Resource Conservation Service (NRCS). (2010b). Conservation practice standard, vegetative barrier. Code 601 (NRCS, NHCP). Retrieved from https://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/nrcs143_026353.pdf.

Nearing, M.A. (2013). 22—Soil erosion and conservation. In J. Wainwright & M. Mulligan (Eds.), Environmental modelling: Finding simplicity in Complexity (2d ed., pp. 365–378). Chichester, U. K.: Wiley.Find this resource:

Nearing, M. A., Foster, G. R., Lane, L. J., & Finkner, S. C. (1989). A process-based soil erosion model for USDA-Water Erosion Prediction Project technology. Transactions of the ASAE, 32, 1587–1593.Find this resource:

Novara, A., Gristina, L., Saladino, S. S., Santoro, A., & Cerdà, A. (2011). Soil erosion assessment on tillage and alternative soil managements in a Sicilian vineyard. Soil & Tillage Research, 117, 140–147.Find this resource:

Panagos, P., Borrelli, P., Meusburger, K., Alewell, C., Lugato, E., & Montanarella, L. (2015a). Estimating the soil erosion cover-management factor at the European scale. Land Use Policy, 48, 38–50.Find this resource:

Panagos, P., Borrelli, P., Meusburger, K., van der Zanden, E. H., Poesen, J., & Aelewell, C. (2015b). Modelling the effect of support practices (P-factor) on the reduction of soil erosion by water at European scale. Environmental Science & Policy, 51, 23–24.Find this resource:

Patsukevich, Z. V., Gennadiev, A. N., & Gerasimova, M. I. (1997). Soil loss tolerance and self-rehabilitation of soils. Eurasian Soil Science, 30(5), 557–563.Find this resource:

Pimentel, D. (2006). Soil erosion: A food and environmental threat. Environment, Development and Sustainability, 8, 119–137.Find this resource:

Pimentel, D., Harvey, C., Resosudarmo, P., Sinclair, K., Kurz, D., McNair, M., . . . Blair, R. (1995). Environmental and economic costs of soil erosion and conservation benefits. Science, 267, 1117–1123.Find this resource:

Posthumus, H., Deeks, L. K., Rickson, R. J., & Quinton, J. N. (2015). Costs and benefits of erosion control measures in the UK. Soil Use and Management, 31(Suppl. 1), 16–33.Find this resource:

Prats, S. A., Malvar, M. C., Vieira, D. C. S., & Keizer, J. J. (2016). Effectiveness of hydromulching to reduce runoff and erosion in a recently burnt and logged maritime pine stand in central Portugal. Land Degradation & Development.Find this resource:

Preiti, G., Romeo, M., Bacchi, M., & Monti, M. (2017). Soil loss measure from Mediterranean arable cropping systems: Effects of rotation and tillage system on C-factor. Soil & Tillage Research, 170, 85–93.Find this resource:

Prosdocimi, M., Jordán, A., Tarolli, P., Keestra, S., Novara, A., & Cerdà, A. (2016b). The immediate effectiveness of barley straw mulch in reducing soil erodibility and surface runoff generation in Mediterranean vineyards. Science of the Total Environment, 547, 323–330.Find this resource:

Prosdocimi, M., Tarolli, P., & Cerdà, A. (2016a). Mulching practices for reducing soil water erosion: A review. Earth-Science Reviews, 161, 191–203.Find this resource:

Quinton, J. N., & Catt, J. A. (2004). The effects of minimal tillage and contour cultivation on surface runoff, soil loss and crop yield in the long-term Woburn Erosion Reference Experiment on sandy soil at Woburn, England. Soil Use and Management, 20, 343–349.Find this resource:

Renard, K. G., Foster, G. R., Weesies, G. A., McCool, D. K., & Yoder, D. C. (1997). Predicting soil erosion by water: a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE). USDA Agriculture Handbook, 703.Find this resource:

Risse, L. M., Nearing, M. A., Nicks, A. D., & Laflen, J. M. (1993). Error assessment in the Universal Soil Loss Equation. Soil Science Society of America Journal, 57, 825–833.Find this resource:

Robichaud, P. R. (2005). Measurements of post-fire hillslope erosion to evaluate and model rehabilitation treatment effectiveness and recovery. International Journal of Wildland Fire, 14(4), 475–485.Find this resource:

Robichaud, P. R., Beyers, J. L., & Neary, D. G. (2000). Evaluating the effectiveness of postfire rehabilitation treatments. GTR 63. USDA, Forest Service, Rocky Mountain Research Station.Find this resource:

Robichaud, P. R., Lewis, S. A., Wagenbrenner, J. W., Ashmun, L. E., & Brown, R. E. (2013). Post-fire mulching for runoff and erosion mitigation. Part I: Effectiveness at reducing hillslope erosion rates. Catena, 105, 75–92.Find this resource:

Robichaud, P. R., Lillybridge, T. R., & Wagenbrenner, J. W. (2006). Effects of post-fire seeding and fertilizing on hillslope erosion in north-central Washington, USA. Catena, 67, 56–67.Find this resource:

Roose, E. (1988). Soil and water conservation lessons from steep-slope farming in French speaking countries of Africa. In Conservation Farming on Steep Lands (pp. 130–131). Ankeny, IA: Soil and Water Conservation Society.Find this resource:

Roose, E. (1996). Land husbandry: Components and strategy. Rome: FAO.Find this resource:

Schertz, D. L. (1983). The base for soil loss tolerance. Journal of Soil and Water Conservation, 38(1), 10–14.Find this resource:

Singh, G., Bapu, R., Narain, P., Bhushan, L. S., & Abrol, I. P. (1992). Soil erosion rates in India. Journal of Soil and Water Conservation, 47(1), 97–99.Find this resource:

Skidmore, E. L., & van Donk, S. J. (2003). Soil erosion and conservation. In D. K. Denby & E. Nieder (Eds.), Handbook of processes and modeling in the soil-plant system (pp. 227–260). Binghamton, USA: CRC Press.Find this resource:

Smith, D. D. (1941). Interpretation of soil conservation data for field use. Agriculture Engineering, 22, 173–175.Find this resource:

Soil Science Society of America (SSSA). (2001). Glossary of soil science terms. Madison, WI: Soil Science Society of America.Find this resource:

Stone, R. P., & Hilborn D. (2012). Universal Soil Loss Equation (USLE). Factsheet ISSN 1198-712X, Agdex# 572/751, Order# 12-051, Queen’s Printer for Ontario. Retrieved from http://www.omafra.gov.on.ca/english/engineer/facts/12-051.htm.

Taddese, G. (2001). Land degradation: “A challenge to Ethiopia.” Environment Management, 27(6), 815–824.Find this resource:

Tarolli, P., Preti, F., & Romano, N. (2014). Terraced landscapes: From an old best practice to a potential hazard for soil degradation due to land abandonment. Anthropocene, 6, 10–25.Find this resource:

Tiwari, A. K., Risse, L. M., & Nearing, M. A. (2000). Evaluation of WEPP and its comparison with USLE and RUSLE. Transactions of the ASAE, 43, 1129–1135.Find this resource:

Todisco, F., Brocca, L., Termite, L. F., & Wagner, W. (2015). Use of satellite and modeled soil moisture data for predicting event soil loss at the plot scale. Hydrology and Earth System Sciences, 19, 3845–3856.Find this resource:

Toy, T. J., Foster, G. R., & Renard, K. G. (2002). Soil erosion: Processes, prediction, measurement, and control. New York: Wiley.Find this resource:

Trimble, S. W., & Mendel, A. C. (1995). The cow as a geomorphic agent – a critical review. Geomorphology 13, 233–253.Find this resource:

USDA. (1956). Joint conference on slope-practice. Washington, DC: U.S. Department of Agricultural Research Service and Soil Conservation Service.Find this resource:

USDA. (1999). National soil survey handbook: Title 430-VI. Washington, DC: U.S. Government Printing Office.Find this resource:

USDA. (2000). Changes in average annual soil erosion by water on cropland and CRP land, 1992–1997. Natural Resources Conservation Service, USDA.Find this resource:

USDA. (2001). Agricultural statistics. Washington, DC: USDA.Find this resource:

USDA-ARS. (2008). User’s reference guide. Revised Universal Soil Loss Equation, version 2 (RUSLE2). Washington, DC: USDA–Agricultural Research Service.Find this resource:

Verheijen, F. G. A., Jones, R. J. A., Rickson, R. J., & Smith, C. J. (2009). Tolerable versus actual soil erosion rates in Europe. Earth-Science Reviews, 94, 23–38.Find this resource:

Wischmeier, W. H., & Smith, D. D. (1961). A universal equation for predicting rainfall-erosion losses: An aid to conservation planning in humid regions. Special Report no. 22–66. Washington, DC: USDA-ARS.Find this resource:

Wischmeier, W. H., & Smith, D. D. (1965). Predicting rainfall-erosion losses from cropland east of the Rocky Mountains: Guide for selection of practices for soil and water conservation. Agriculture Handbook no. 282. Washington, DC: USDA.Find this resource:

Wischmeier, W. H., & Smith, D. D. (1978). Predicting rainfall-erosion losses—A guide to conservation farming. Agriculture Handbook no. 537. Washington, DC: USDA.Find this resource:

Withers, P. J. A., Hodgkinson, R. A., Bates, A., & Withers, C. M. (2006). Some effects of tramlines on surface runoff, sediment and phosphorus mobilization on an erosion prone soil. Soil Use and Management, 22(3), 245–255.Find this resource:

Wohlgemuth, P. M. (2003). Post-fire erosion control research on the San Dimas Experimental Forest: Past and present. In K. G. Renard, S. A. McElroy, & W. J. Gburek (Eds.), Proceedings, first interagency conference on research in watersheds (pp. 645–650). Washington, DC: U.S. Department of Agriculture, Agricultural Research Service.Find this resource:

WRI. (1997). World Resources Institute. New York: Oxford University Press.Find this resource:

Zachar, D. (1982). Soil erosion. Amsterdam: Elsevier.Find this resource: