Soil Losses in Rural Watersheds With Environmental Land Use Conflicts 2014 Science of the Total Environment 1

8/10/2019 Soil Losses in Rural Watersheds With Environmental Land Use Conflicts 2014 Science of the Total Environment 1

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Soil losses in rural watersheds with environmental land use conicts

F.A.L. Pacheco a,, S.G.P. Varandas b, L.F. Sanches Fernandes b, R.F. Valle Junior b

a Chemistry Research Centre, University of Trs-os-Montes and Alto Douro, Vila Real, Portugalb Centre for Research and Technology of Agro-Environment and Biological Sciences, University of Trs-os-Montes and Alto Douro, Vila Real, Portugal

H I G H L I G H T S

Conceive environmental land use conicts (LUC) in rural watersheds. Investigate soil erosion in watersheds with LUC. Predict soil erosion in the absence of LUC.

a b s t r a c ta r t i c l e i n f o

Article history:

Received 1 January 2014Received in revised form 10 March 2014Accepted 16 March 2014Available online 3 April 2014

Editor: Simon Pollard

Keywords:

Soil lossHydric erosion

Environmental land use conictUSLEGIS

Soil losses were calculated in a rural watershed where environmental land use conicts developed in the courseof a progressive invasion of forest andpasture/forest lands by agriculture,especiallyvineyards.The hydrographicbasin is located in the Douro region where the famous Port wine is produced (northern Portugal) and the soillosses were estimated by the Universal Soil Loss Equation (USLE) in combination with a Geographic InformationSystem (GIS). Environmental land use conicts were set up on the basis of land use and land capability maps,coded as follows: 1agriculture, 2pasture, 3pasture/forest, and 4forest. The difference between the codesof capability and use denes a conict class, where a negative or nil value means no conict and a positive ivalue means classiconict. The reliability of soil loss estimates was tested by a check of these values againstthe frequency of stone wall instabilities in vineyard terraces, with good results. Using the USLE, the averagesoil loss (A) was estimated inA= 12.2 t ha1 yr1 and potential erosion risk areas were found to occupy

28.3% of the basin, dened where soil losses are larger than soil loss tolerances. Soil losses in no con ict regions(11.2 t ha1 yr1) were signicantly different from those in class 2 (6.8 t ha1 yr1) and class 3 regions(21.3tha1 yr1)thatintotaloccupy2.62km2 (14.3%ofthebasin).Whensimulatingascenarioofnocon ictacross theentire basin, whereby land usein class 2 conict regions is setup to permanent pasturesand in class 3conict regions to pine forests,it was concluded thatA =0.95tha1 yr1 (class 2) orA =9.8tha1 yr1

(class 3), which correspond to drops of 86% and 54% in soil loss relative to the actual values. 2014 Elsevier B.V. All rights reserved.

1. Introduction

Among the factors explaining the intensity of soil erosion, plantcover and land uses are considered the most important, exceeding theinuence of rainfall intensity and slope gradient (Garca-Ruiz, 2010;Kosmas et al., 1997; Thornes, 1990; Wainwright and Thornes, 2004).Estimates of soil losses under various plant cover and land use settingsare reported in quite a number of studies (Cerdan et al., 2010;Durn-Zuazo et al., 2013; Lpez-Vicente et al., 2013; Nunes et al.,2011; Tefera and Sterk, 2010; Vacca et al., 2000). Given the disparityof erosion rates among the different settings, it becomes evident that achange in the plant cover or land use of a region will inevitably lead toa soil loss increment or decrement in that region.

Cases of soil loss increment are frequently related to deforestationand substitution of forests by crops, meadows or permanent culturessuch as orchards or vineyards. Usually, these land use changes resultin gully development and shallow landslides that can increase thesediment load in rivers and ultimately contribute to the formation ofnew sedimentary structures includinguvial terraces, alluvial fans anddeltas (Beguera et al., 2006; Garca-Ruiz and Valero-Garcs, 1998;Martnez-Casasnovas and Snchez-Bosch, 2000). Cases of soil lossdecrement are often related to farmland abandonment (Bellin et al.,2011; Ruiz-Flao, 1993; Ruiz-Flao et al., 1992), although abandonedfarms have often been associated with important erosion processesshortly after desertion (Bellin et al., 2009; Lesschen et al., 2007).The decrement occurs in the long term and is associated with therecovery of the vegetation cover (dense forest or shrub) and theimprovement of the chemical, physical and hydrological properties ofsoils (Bonet, 2004; Garca-Ruiz and Lana-Renault, 2011). Other cases

Science of the Total Environment 485486 (2014) 110120

Corresponding author. Fax: +351 259 350480.E-mail address:[email protected](F.A.L. Pacheco).

http://dx.doi.org/10.1016/j.scitotenv.2014.03.069

0048-9697/ 2014 Elsevier B.V. All rights reserved.

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of soil loss decrement are related to reforestation of croplands (Wanget al., 2012).

In rural watersheds, land uses are usually characterized byfarmlands in the lowland valleys, where soils are thicker and morefertile; pastures for livestock production, vineyards and orchards inthe mid-altitude valleys, with small forest spots in the adjacenthillsides;and continuous forests in the highlands. In traditionalagrariansystems, these land uses are conforming to the land capability

determined by an evaluation of soil characteristics such as depth orfertility and local environmental conditions such as topographic slopeor water availability (Agroconsultores, Ltd., Coba, Ltd., 1991). But thereare rural watersheds where the actual land use deviates from themost capable use, in which case an environmental land use conict isgenerated with consequences on soil erosion intensity (Mello Filho,1992; Valle Junior, 2008; Valle Junior et al., 2013, 2014).

Notwithstandingthe literature about the impact of landuse changesonsoil erosion isvast (Alkharabsheh et al., 2013; Ciampalini et al., 2012;Cotler and Ortega-Larrocea,2006; Heckmann, 2014; Szilassi et al., 2006;Wijitkosum, 2012; Zokaib and Naser, 2011), only a few papers werespecically dedicated to the analysis of soil losses in places whereenvironmental land use conicts were developed (Haygarth and Ritz,2009; Olarieta et al., 2008; Zucca et al., 2010). The purpose of thispaper is to contribute with some new insights about this topic. Firstly,land use conicts are investigated in a rural watershed, by comparingthe maps of land use and land capability using the approach ofValle

Junior (2008). Secondly, soil losses determined by the Universal SoilLoss Equation (Wischmeier and Smith, 1978) are compared amongplaces that are inside or outside the conict areas.

2. Study area

The Meia Lgua streamis a right margin tributary of the Douro Riverlocated in the southern limit of the Vila Real district, Trs-os-Montesand Alto Douro province, north of Portugal (Fig. 1). The stream is a6.5 km long 9.2% inclined water course following NWSE and NNESSW directions determined by the local fracture network. The

hydrographic basin covers an area of approximately 18.3 km2, being

asymmetrical to a NNESSW longitudinal axis with the wider hillsideslocated at the right margin.

Altitudes in the basin of Meia Lgua stream range from 50 to 650 mandannual precipitation from 1240 to 1540 mm.As is typical forthe SWEuropean countries, precipitation regime in the area is characterized bylong dry periods followed by heavy rain bursts. According toBrandoet al. (2001), the maximum precipitation (Pmax, in mm) in a periodcomprehended between 10 min and 24 h (D, in minutes) is described

by the relationshipPmax= a ln(D)

b, witha= 21.484 andb=6.997 around the study area. This relationship provides an estimateof 163.2 mm for the local maximum precipitation in 24 h.

The watershed is entirely shaped on Cambrian schists andgraywackes, the alteration of which produced leptosols along thewestern boundary of the catchment and the NWSE branch of themain valley, uvisols along the NNESSW branch of the main valley,and anthrosols elsewhere (Fig. 2). Using a method by Smith andStamey (1964), Catalo (2009) estimated the following erosiontolerances for these soil types (values in t ha1 yr1): 3.83 forleptosols, 12.93 for theuvisols and 14.81 for the anthrosols. Based onsoil characteristics such as depth or fertility as well as on environmentalconditions such as topographic slope or water availability, landcapability outside the main urban areas was assessed and dened bythe codes AiPjFk, where A, P and F mean agriculture, pasturing andforestry, respectively, and the subscripts mean not adapted (0), welladapted (1), moderately adapted (2), poorly adapted (3) or con-ditionally adapted (4). According to this nomenclature, a parcel codedas A0P3F1is not adapted to agriculture, is poorly adapted to pasturingandis well adaptedto forestry.The most capable usehas thecode closerto 1. In the basin of Meia Lgua stream, because specic soil typesdeveloped under specic environmental conditionsleptosols inthe hilly regions, uvisols along the valleys and anthrosols in thevineyards, the most capable uses (Fig. 2) reect the soil characteristics,as follows: agriculture in the anthrosols, forestry in the leptosols andpasturing for livestock production mixed with forestry in theuvisols(Agroconsultores, Ltd., Coba, Ltd., 1991).

Land is mostly (75%) occupied by vineyards, the reminder beingused for orchards, oliveyards, cropland, small spots of pine, eucalyptus

and various deciduous forests; or taken by urban areas, roads and

Fig. 1.Geographic location, digital elevation model and precipitation contours in the hydrographic basin of Meia Lgua stream.

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other infrastructures, small ponds, and bare land (Fig. 3). Given therugged topography, most vineyard plantations were accompanied bythe construction of terraces supported by stone walls.

3. Materials and methods

3.1. Software and digital database

The modeling of soil losses and environmental land use conicts atthe scale of a stream watershed is facilitated through the use of ageographic information system (GIS), the reason why the ArcGISversion 10 software (ESRI, 2010) was used in this study. Thecartographic and alphanumeric data required for running the soil lossand environmental land use conict models were obtained from theinformation sources listed inAppendix A. The ArcGIS tools used tocalculate the soil losses and delineate the conict areas are enumeratedinAppendix B.

3.2. Soil losses

Soil losses were calculated by the Universal Soil Loss Equation(USLE) introduced by Wischmeier and Smith (1978), revised byRenard et al. (1997)and adopted in numerous studies (Wijitkosum,

2012; Martn-Fernndez and Martnez-Nez, 2011). The USLE

evaluates the long term average annual soil loss (A) by sheet and rillerosion and is dened by:

A R K L S C P 1

where

A Soil loss per unit of area per unit of time (t ha1 yr1);R Rainfallrunoff factor (MJ mm h1 ha1 yr1);K Soil erodibility factor (t h MJ1 mm1);

L Slope length factor (dimensionless);S Slope steepness factor (dimensionless);C Cover-management factor (dimensionless);P Support practice factor (dimensionless).

The rainfallrunoff factor is related to the kinetic energy of a stormand its maximum 30-minute intensity. Using probabilistic modelsof rainfall distribution,Ferro et al. (1991) showed that R can beapproached by a power function:

R a P6;2

b

2

whereP6,2 is the averagerainfall of 2-year return period 6-hour duration

precipitation events and a and b are spatially dependent tting

Fig. 2.Soil and land suitability maps of the Meia Lgua stream watershed.

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parameters. For continental Portugal,Brando et al. (2001)estimateda= 0.1437 and b = 2.2.

The soil erodibility factor quanties the inherent erodibility of aparticular soil. It is a measure of the soil particles' susceptibility todetachment and transport by rainfall and runoff. Determination oferodibility is based on a diversity of data including the percentages insand, silt, clay (texture) and organic matter, as well as the permeabilityand structure of the top soil layer. Based on these types of data,Pimenta(1998) estimated Kfor a number of soil types includingthose formed inthestudyarea(valuesinthMJ1 mm1): 0.035 for the anthrosols,0.027 for theuvisols, and 0.040 for the leptosols and urban soils.

The slope length factor is the ratio of expected soil loss to that

observed for a eld of 22.1 m in length, while the slope steepness factoris the ratio of expected soil loss to that observed for aeld of 9% slope(inclination of 0.09 rad). In a formulation byMoore and Burch (1986)factors L andSina point of aeld are combined into a single parameter(LS) and calculated by:

LS

22:1

0:4

sin 0:0899

1:33

whereis the distance from the onset of overlandow to the locationwhere deposition occurs or when runoff enters a channel that is biggerthan a rill, andis the eld inclination in radians; the numbers 0.4 and0.3 aretting parameters whereas 0.0899 is the sine value of 0.09 rad.

The land cover/management factor is an index for the protective

coverage of canopy and organic material in direct contact with the

ground. It is measured as the ratio of soil loss from land croppedunder specic conditions to the corresponding loss from tilled landunder clean-tilled continuous fallow conditions. High values ofCfactoroccur on bare land while low values are found in the areas of denseforest or grain cover (Park et al., 2011). Based on the cartography ofland occupation of continental PortugalPimenta (1998)estimatedvalues for theCfactor as depicted inTable 1.

The support practice factor is the ratio of expected soil loss to thatobserved for a eld where soil conservation practices have beenimplemented.Wischmeier and Smith (1978)assigned values ofPtovarious conservation practices, such as terraces or cultures alongtopographic contours, making them vary depending on the slope of

Fig. 3.Simplied land use map of the Meia Lgua stream watershed.

Table 1

Cfactors attributed to different land uses and occupations.Adapted fromPimenta (1998).

Land use/occupation FactorC

Small ponds 0.005Roads and other infrastructures 0.01Urban areas 0.01Orchards 0.05Pine forests 0.05Oliveyards 0.1Other deciduous forests 0.1Eucalyptus forests 0.2Vineyards 0.2Cropland 0.3Bare land 0.3



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the eld. The conservation practices implemented in the study areaare mostly the vineyard terraces (Fig. 3), the reason why the followingPvalues were used in keeping with the hillside slope: class 27%,P = 0.1; class 812%, P = 0.12; class 1318%, P= 0.16; class1924%, P= 0.18.

All factors required for the USLE were prepared as raster layers witha cell size of 25 m. Then these layers were overlaid in ArcGIS andprocessed for soil loss calculation according to Eq.(1).

3.3. Environmental land use conicts and soil losses

In a wide range of environments, many authors conrmed that soillosses decrease exponentially as the percentage of vegetation coverincreases (Nunes et al., 2011; and references therein). The percentageof vegetation cover is inherently related to land use, as clearlydemonstrated by Cerdan et al. (2010)who quantied averagesoil lossesfor a number of land use types (values in t ha1 year1): bare land(15.1), vineyards (12.2), orchards (11.8), arable land (4.4), shrubland(0.5), grassland (0.3), and forests (0.1).

As soil loss is specically associated to land use, any change to theuse feeds back to the loss. In general, a change caused by deforestationfor agricultural needs or grazing will intensify erosion (Szilassi et al.,2006; Wijitkosum, 2012; Zucca et al., 2010), while a change related toafforestation or conversion of crop lands to grass or shrubland willattenuate erosion (Alkharabsheh et al., 2013; Wijitkosum, 2012).However, the extent of soil degradation may depend on whetherdeforestation occurs in areas that are considered proper for agricultureor grazing, or away from them. In case the land capability evaluationof a forest spot is in favor of its use for agriculture or grazing,deforestation is considered the natural expansion of farming, orlivestock production, with presumed modest consequences on soilerosion. Otherwise, deforestation will be viewed as the invasion offorests by farms with assumed signicant impacts on soil loss. In thiscase, the land use change is also said to develop an environmentalland use conict. Because conict areas may be critical as regards soildegradation, experts and policy makers should give preference tothese areas as study sites in projects that aim to reduce efciently soil

loss by adequate land use planning.The concept of environmental land use conict was introduced by

Mello Filho (1992)and developed byValle Junior (2008)andValleJunior et al. (2013, 2014). According to these authors, a conict mayexist if the actual use deviates from a use standing on a capabilityevaluation (called natural use). To codify the natural (Code N) as wellas the actual (Code A) land uses,Valle Junior et al. (2013)dened fourgeneral classes(Table2):CodeN=CodeA=1forcroppingagriculture,Code N = Code A = 2 for pasturing livestock, Code N = Code A = 3 formosaic of natural pastures and forest spots, and Code N = Code A = 4for forestry. Then, the environmental land use conict was estimatedby the equation:

Conflict Classi Code NiCode Aj

with 1in and 1jn 4

wherenis the number of classes (4). According to Eq.(4), a) the noconict areas are represented by regions where Code Ni Code

Aj 0, with the negative values representing the areas with potentialfor a sustainable expansion of agriculture or grazing; b) areas suitedfor pasturing livestock (Code N = 2) but actually used for croppingagriculture (Code A = 1) are classied as Class 1 (minor) conictareas; c) areas with potential for forestry (Code N = 4) or a mixedoccupation by forests and pastures (Code N = 3) but occupied with afarm (Code A = 1) are referred to as Class 3 (major) or Class 2(moderate) conict areas, respectively.

The approach ofValle Junior et al. (2013, 2014)was used in thepresent study, with the natural and actual land uses being representedbyFigs. 2 and 3, respectively. As with the USLE, factors required forthe conict analysis were prepared as raster layers with a cell size of25 m. Then these layers were overlaid in ArcGIS and used in Eq.(4)tocalculate the environmental land use conict.

4. Results and discussion

4.1. Soil losses and erosion risk

The spatial distribution of USLE factors is illustrated in Fig. 4and the map of soil losses is shown in Fig. 5. The average soilloss is12.2tha1 yr1, which is a relatively high value but acceptableconsidering the dominance of the land use by vineyards. Besides, thelosses are equivalent to regional-scale multi-decennial erosion rates(10.5 t ha1 yr1) estimated by stock unearthingburyingmeasurements in a vineyard of Languedoc, France (Paroissien et al.,2010). They arealsowithinthe range of decennial erosion rates estimatedin vineyardhillslopes of Burgundy(2.612.3tha1 yr1), Monthlie,France, also based on vine-stock bio-markers (Brenot et al., 2008). In aregion of craggy topography also dominated by vineyards but located inSpain, average erosion rates determined byUsn (1998)were largerapproaching 22 t ha1 yr1.

In approximately one third of the basin (34.6%) soil losses by hydricerosion are b2 t ha1 yr1. These losses may be considered verylow as they are smaller than the erosion tolerances calculated forthe most sensible soil type (leptosols, with 3.83 t ha1 yr1).However, there are sectors of the basin (23.6%) where the soil losses

are N15 t ha1 yr1. In these cases the losses may be consideredexcessive as they are larger than the erosion tolerances calculated forthe least sensible soil type (anthrosols, with 14.81 t ha1 yr1). Bycross tabulating the spatial distribution of soil losses (Fig. 5) andsoil types (Fig. 2), one nds that losses are larger than tolerancesin 28.3% of the basin, and hence that soil is at risk of erosion in thatarea. Cases of erosion risk in vineyard elds were also reportedin Navarre,Spain, where long-term erosion rates estimated by botanicalbenchmarks (30 t ha1 yr1) greatly exceeded even themost conservative soil loss tolerance thresholds, locally assumed to be511 t ha1 yr1 (Casal et al., 2009).

4.2. Soil erosion and hillside instability

A common consequence of soil erosion is hillside instability. Thisis especially evident in vineyard regions, where the necessity ofincreasing incomes, reducing manpower and increasing mechanizationusually results in the substitution of traditional vineyards located onsmall terraces with stone walls by land leveling and the constructionof new terraces. The new terraces are wide enough to accommodatemachinery, although this requires an increase in the height betweenterraces, with consequent instability (Garcia-Ruz, 2010). In thePeneds-Anoia vineyard region, where these new types of terraceswere built, a rainstorm of about 90 mm in 24 h was enough tocause landslides, particularly on the lower third of the hill slopes. Insome cases the upper soil levels were not preserved because theyhad to be dug out, mixing the fertile topsoil with the subsoil(Ramos and Martnez-Casasnovas, 2006a). The damage caused by

landslides has an important economic cost.Martnez Casasnovas

Table 2

Classication codes of natural (N) or actual (A) land uses selected for the analysis ofconicts.

Land use Classication code (A, N)

Agriculture 1Pastures for livestock production 2Pastures for livestock production/forestry 3Forestry 4

The conict is calculated by Eq.(4).



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and Ramos (2006)calculated that the costs of nutrient losses anddamage to infrastructure caused by landslides and soil erosion innew terraced areas represent about 14% of annual incomes from the

vineyards.In areas occupied by vineyards where terraces were built and

supported by stone walls, signs of damage in the walls (e.g. deformation,repairs)can be usedas an indication of hillside instability. In the attemptto check the estimates of soil loss presented in this study against anindependent variable,a cross tabulation wasmade betweentheA values(Fig. 5) and the frequency of stone wall instabilities reported inSeixaset al. (2006)and also illustrated inFig. 5. Firstly, an area (At) wascomputed that gathers all sectors of the basin where stone wallinstabilities were observed (At= 5.6 km

2). Secondly, the full range ofsoil loss values within At was assembled to form 9 categories, asillustrated in theX-axis ofFig. 6. Thirdly, the spatial coverage of eachcategory (Ac) was calculated and the results were converted intopercentages (Ac / At 100). Fourthly, the number of stone wall

instabilities (Ic) were computed within each soil loss category and the

results were converted into frequencies (Fi=Ic/It 100, whereIt=696 is the total number of instabilities reported inSeixas et al.,2006). Under the hypothesis of independence between soil erosion

and hillside instability, the frequency of stone wall instabilitiesassigned to a class iof soil loss (Fi, in %) is expected to match thespatial coverage of that class (Ai, in %), i.e. = Fi/ Ai 1; otherwise, N 1. The scenario of independence is ought to occur where soillosses are small and that of the scenario of explicit dependence iswhere the losses are large. The relationship between and A in thestudy area is shown inFig. 6and visibly conrms the expectations:a) for soil losses 10 t ha1 yr1, 1 and hence hillsideinstability is not explicitly linked to erosion at these levels of soilloss; b) however, for A N10 t ha1 yr1, 1.5 5 meaningthat stone wall instabilities are 1.5 to 5 times larger than expectedand that erosion might be the specic cause of hillside instability atthese levels of soil loss. The consistency between and A values issupportive of a reliable estimation of soil losses in the hydrographic

basin of Meia Lgua stream using the USLE equation.

Fig. 4.Spatial distribution of USLE factors (Eq.(1)) within the Meia Lgua stream watershed.



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4.3. Impact of environmental land use conicts on soil losses

The impact of land use changes on the erosion of the Peneds-Anoia(Catalonia, Spain) vineyard soils has been studied extensively byMartnez-Casasnovas and Snchez-Bosch (2000). The analysis was

based on the comparison of the estimated soil loss rates in the periodjust before the mechanization (1950s) and in the most recent past(1990s). Multi temporal data such as aerial photographs and digitalterrain models, the revised universal soil loss equation and GIS analysiswere used for that purpose. The results show a clear negative soil lossbalance, with 12.6% of the agricultural land having experienced majornegative changes. This negative balance was associated with theincrease of the area dedicated to vineyards, the transformation ofold traditional vineyard plantations to modern trained plantations andto the removing of conservation practices to adapt plots to cropmechanization. In this study, the purpose is to assess the impact of

environmental land use conicts on soil erosion.The areas of environmental land use conict are represented by the

hatched regions inFig. 5, which occupy 2.62 km2 (14.3% of thebasin).Inthese regions land use deviates from land capability and the question topose is if this circumstance modies soil erosion intensity. Likewise theanalysis of soil loss in relation to stone wall instability (Section 4.2),under the hypothesis of independence between soil loss and land useconict the average soil loss as well as the spatial coverage of soil lossclasses should be similar regardless the region (no conict, class 2 orclass 3), otherwise each region should be characterized by a specicaverage soil loss and spatial coverage.

In the hydrographic basin of Meia Lgua stream, the average soilloss differs signicantly among no conict (11.2 t ha1 yr1), class2(6.8tha1 yr1)andclass3(21.3tha1 yr1)conict regions.

The spatial coverage of soil loss classes is illustrated inFig. 7.Noconict

Fig. 5.Map of soil losses and of environmental land use conicts.

Fig. 6.Relationship between soil loss and stone wall instability. Additional information in

the text.

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and class 2 conict regions show a similar coverage with the small-loss

classes occupying larger areas of the basin and the large-loss classesthose of smaller areas. These distributions are similar to the spatialcoverage of total area (dashdot line), which represents the averagepattern of spatial coverage of each soil loss class. At odds with thesedistributions, the class 3 conict regions show a relatively uniformspatial coverage of soil loss classes (0.5 km2 per class). In view ofthese results, the hypothesis of independence may be conrmed forthe no conict and class 2 conict regions but certainly cannot extendto the class 3 conict regions. Overall,Figs. 5 and 7demonstrate thatthe class 3 conict regions are homogeneous-coverage intensivelyeroded environments whereas the no conict and class 2 conictregions are heterogeneous-coverage moderately to gently erodedenvironments, respectively. The next step is to investigate the factorscontrolling erosion in the different regions.

Theaverage USLE factorsin the no conict,class 2 and class 3 conictregions are depicted inTable 3. FactorR shows verylittle variation acrossthethree regions (570 R573MJmmh1 ha1 yr1) meaningthat soil loss estimates are not inuenced by the climatic factor.

In the case of factor K, it is clear that values are smaller in the class 2conict regions (0.02 t h MJ1 mm1) than in the no conict orclass 3 conict regions (0.030.04 t h MJ1 mm1). The reasonfor this discrepancy is simple:the class 2 conict regions are distributedalong the southern branch of the main valley where the soil cover iscomposed ofuvisols, the soil type with the lowestKvalue (compareFigs. 2, 4 and 5).

LikewiseK, theLSvalues are also smaller in class 2 than in the otherconict regions because theclass 2 regions arerestricted tothe southernbranch of the main valley, where the average hillside gradient is gentle(7.9%). On the other hand, no conict regions are characterized by

smallerLSvalues (7.6) than the class 3 conict regions (10.4), mostlybecause the latter regions tended to implement agricultural activities

along the NWSE trending branch of Meia Lgua stream where hillsideslopes may exceed 40%.

Likewise R , theCvalues also shows limited variation across theregions (0.16 C 20). This is because the entire basin was convertedinto a large agriculturaleld, essentially planted with vineyards andorchards, even in the areas where land capability set up for forestry orpasturing mixed with forestry is the most adequate land use.

In case land capability had been respected soil losses could have

been much smaller. For example, in the class 3 con

ict regions theactualCvalue is on average 0.16 but could have been 0.05 if land hadbeen occupied by pine forests (Table 1). In this case the average soilloss would drop from 21.3 t ha1 yr1 (the actual value) to0.05 / 0.16 21.3 = 6.7 t ha1 yr1. Even in the class 2 conictregions, where erosion is weak because terrain slopes are gentleand the bedrock is covered by uvisols, soil losses could havebeen even smaller if the region had been occupied by permanentpastures, as determined by land capability. In this caseC= 0.02(Table 1), instead of the actual 0.2, and A = 0.02 / 0.2 6.8 =0.68 t ha1 yr1. The suppor t practices in the no conict regionsare characterized by an averagePvalue of 0.5 which is smaller thanthe values in the class 2 (P= 0.7) or class 3 (P= 0.7) conictregions. Apparently, the invasion of areas suited for forests orpermanent pastures by agriculture was not accompanied by theproper soil conservation measures. If protection practices in theconict regions were implemented as in the no conict regions thesoil losses would drop to A = 0.5 / 0.7 6.8 = 4.9 t ha1 yr1

in class 2 and to A= 0.5 / 0.7 21.3 = 15.2 t ha1 yr1 inclass 3 conict regions.

It is hard to predict the overall impact of environmental land useconicts on the intensity of soil erosion, given the lack of some data.For example, there is a limited possibility of verifying the impact offactorKbecause this would require specic information on texture,organic matter content, permeability and structure of the top soil layersacross the hydrographic basin of Meia Lgua stream, before andafter the creation of land use conicts, which is not available. It is alsonearly impossible to check the impact of factorLSunless the values of and in Eq. (3) could be estimated from a digital elevation

model older than the period of vineyard and orchard plantations inthe conict areas. The consequences for soil loss of changingCandPin the course of a conict creation were forecasted in the previousparagraph. Now, the attempt is to predict the return of conict regionsto an undisturbed condition, where: a) class 2 conict regions areused for permanent pastures (C= 0.02) and class 3 conict regionsfor pine forests (C= 0.05), respecting land capability; b) class 2 andclass 3 conict regions are set pristine and hence not inuencedby support practices (P= 1). In this case, A = 0.95 t ha1 yr1

(class 2) orA = 9.8 t ha1 yr1 (class 3), which correspond todrops of 86% and 54% in soil loss relative to present day values,respectively. In the rst case soil losses would become ratherlow, which is comprehensible as class 2 conict regions occupywatershed sectors where terrain slopes are minimal and soil types

are relatively insensible to erosion. In the second case soil losseswould become close to the average value of no conict regions (A=11.2 t ha1 yr1), as expected.

4.4. Causes and controls of soil losses in vineyards of SW Europe

Soil loss is a signicant environmental problem in semi-aridagricultural environments of SW Europe, especially in vineyards. Themain reason for the high erosion rates in these vineyards is simple:the soil is almost bare for a large part of the year (Lasanta and Sobrn,1988). Between November and April the plants lack leaves, and inMay the foliage is still moderate. Even in summer, when the plantshave reached maximum development, part of the soil is unprotected,unless straw has been added between the vine rows or herbicide has

been applied without plowing. For this reason, vineyards provide little

Fig. 7.Spatial coverage of soil loss classes within the Meia Lgua stream watershed.Additional information in the text.

Table 3

Average USLE factors in no conict, class 2 conict and class 3 conict regions.

Factor Unit Region

No conict Class 2 Class 3

R MJ mm h1 ha1 yr1 570 573 571K ton h MJ1 mm1 0.03 0.02 0.04LS 7.60 4.70 10.38C 0.18 0.20 0.16P 0.51 0.73 0.68

USLE factors were determined by Eq.(1), conict regions by Eq.(4).



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protection for the soil under the SW Europe precipitation regime, sincethe autumn and spring rainfall occurs when the soil is almost bare.

Although the average soil loss can be explained essentially by landuse, thefullrange of valuesestimated forthe basin of Meia Lgua streamcan only be interpreted if other factors are accounted for. According toGarca-Ruiz (2010)the other factors are rainfall intensity and slopegradient. Meyer and Martnez-Casasnovas (1999) estimated theprobability of existence of gully erosion in vineyards of the Peneds-

Anoia region, using the value of slope degree in a logistic regressionmodel that yielded an overall accuracy of 84.6%. In La Rioja region,Spain, Arnez et al.(2007) developed a linear equation involving rainfallintensity, soil resistance to drop detachment, slope gradient and gravelcover, which explained 74% of the measured soil loss. In the sameregion, Lasanta and Sobrn (1988) concluded that under similargradient conditions, soil erosion in vineyards is controlled by landmanagement practices and the grain size distribution of the soil. Theformer regulates runoff rates, and the latter explains the inltrationcapacity and soil particle cohesion. In the basin of Meia Lgua stream(Fig. 4a) it seems that terrain slope can help justify the noteworthinessof the variability of soil losses shown inFig. 5. Since theAvalues aregenerally larger across the western margin of the basin where relief ismore pronounced and terrain slopes are larger (compareFig. 1withFig. 5), it can be postulated that soil losses in the hydrographic basinof Meia Lgua stream are essentially determined by the local land use(mostlyvineyards) being intensied or attenuated where hillsideslopesare steep or gentle, respectively.

Soil losses can be reduced through implementation of managementpractices. The following measures are proposed for the basin of MeiaLgua stream. Therst option is the supercial tillage using a rotaryhoe. This can reducesignicantly (4.5 times) total soil loss, as comparedto no-tillage associated with herbicide application and leading to baresoil (Raclot et al., 2009). In vineyards subject to land preparation formechanization, major soilmovements are required. Thisrearrangementhas enormous environmental implications not only due to changes inthe landscape morphology but also due to soil degradation. Theresulting cultivated soils are very poor in organic matter and highlysusceptible to erosion, which reduces the possibilities of water intake

as most of the rain is lost as runoff. In these cases, reduction of soilloss may be accomplished by a massive addition of organic wastes topromote aggregate formation increasing porosity and inltration. Thismanagement practice has been implemented in vineyards of thePeneds-Anoia region, with a 26% raise in inltration and a reductionof 2043% in the concentration of sediment in runoff (Ramos andMartnez-Casasnovas, 2006b). However, the addition of large quantitiesof cattle manure to the Peneds-Anoia vineyards led to an increase inthe nitrate concentrations of downstream surface waters.

Eventually, the most effective management practice is thereplacement of conventional tillage by soil treatments with covercrops. In a vineyard located in the Henares River basin southeast ofMadrid, Spain, Ruiz-Colmenero et al. (2013) demonstrated thaterosion plots under traditional tillage yielded substantially more

erosion (5.88 t ha

1 yr

1) than when subject to treatmentswith cover crops ofBrachypodium(0.78 t ha1 yr1) or Secale(1.27 t ha1 yr1). Equivalent results were obtained byNovaraet al. (2011)in a typical blanc wine grape irrigated vineyard located insouthwestern Sicily, with different cover crops.

5. Conclusions

Soil losses and environmental land use conicts were assessed in asmall watershed located in northern Portugal, called hydrographicbasin of Meia Lguastream. Soil losses were calculated by the UniversalSoil Loss Equation (USLE). Land use conicts were set up on the basisof discrepancies between land use and land capability. The land useis dominated by vineyards. The average soil loss in the watershed is

12.2 t ha1

yr1

. This is comparable to regional-scale multi-

decennial erosion rates estimated in other SW European vineyardregions. In 28.3% of the basin soil losses exceed the tolerance thresholdsof soil types, meaning that the soil is at risk of erosion in those areas.Water erosion is also the cause of hillside instability in the areaswhere soil loss N 10 t ha1 yr1. Environmental land use conictsproved to be an important source of soil loss increments, especiallywhen actual values were compared with counterparts predicted for ascenario of no conict. Under a scenario of no conict, soil losses were

found to be 54% to 86% smaller than when land is used for purposesother than those determined by capability. Soil losses could be reducedthrough implementation of management practices, including thereplacement of conventional tillage by soil treatments with cover crops.

Conict of interest

The authors have no conicts of interest.

Acknowledgments

The authors would like to thank the Coordination of Improvementof Higher Education Personnel (CAPES) for the scholarship Proc.no. 10297/12-0, the University of Trs-os-Montes and Alto Douro(UTAD) and the Center for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB) for technical support,and the Federal Institute of Tringulo Mineiro (IFTM), Brazil. As regardsthe rst author, the research was funded by the strategic project of theVila Real Chemistry Research Center (PEst-OE/QUI/UI0616/2014).As regards the other authors, the research was supported by EuropeanUnion Funds (FEDER/COMPETE - Operational CompetitivenessProgramme) and by national funds (FCT - Portuguese Foundation forScience and Technology) under the project FCOMP-01-0124-FEDER-022692.

Appendix A. Information sources

The characterization of topographic relief was based on altimetrydata produced at the scale of 1:50,000 and acquired from thePortuguese Geographic Institute (http://www.igeo.pt), moreprecisely topographic contours with a vertical equidistance of25E m, randomly distributed altimetric points and geodetic vertices.These data are referenced in the Datum 73 coordinate system,Altimetric Datum of Cascais, Hayford ellipsoid and rectangularcoordinates with Gauss projection. The mapping of soils wasbased on the 1:100,000 scale cartography produced in 1990 by theTrs-os-Montes and Alto Douro University in a partnership with theAgroconsultores and Coba Company. This cartography is available indigital format at the website of the Information Network of EmergencySystems (http://scrif.igeo.pt), under the headingProject for the digital

conversion of the soils, land use and land capability maps of northeastTrs-os-Montes. The dataset is referenced in the 1924 InternationalDatum coordinate system, Transverse Mercator projection, andrectangular coordinates with Gauss projection. The mapping ofuse and occupation of land was based on the 1:25,000 scalecartography produced in 2000 by the National Center forGeographic Information, resulting from the interpretation of aerialphotographs, available at http://www.cnig.pt. The dataset isreferenced in the same system as the altimetry data. The climatedata (precipitation) was obtained in the form of listings from theWater Resources National Information System, being available athttp://snirh.pt/. These listings include information on the locationof the climatic stations, referenced in the Lisbon Datum coordinatesystem, ellipsoid of Hayford, Transverse Mercator projection, and

rectangular coordinates with Gauss projection.

http://www.igeo.pt/http://scrif.igeo.pt/http://www.cnig.pt/http://snirh.pt/http://snirh.pt/http://www.cnig.pt/http://scrif.igeo.pt/http://www.igeo.pt/


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Appendix B. ArcGIS tools

The map of soil losses (Fig. 5) was based on the production of rastermaps for the USLE factors (Eq. (1)) succeeded by their spatialmultiplication using the ArcGIS toolSpatial Analyst Tools N Map AlgebraNRaster Calculator. The raster map of factorR(Fig. 4a) was based ondiscrete values of P6,2(Brando et al., 2001) measured at severaludometric stations located around the watershed, being predominantly

in

uenced by two of them: the stations10H/01and 3 M/01 of theWaterResources NationalInformation System (http://snirh.pt/), located to theSW andNE of thebasin, respectively.TheP6,2valueswereusedinEq.(2)to calculate theRvalues, which were then interpolated over the entirebasin using the toolSpatial Analyst Tools NInterpolation NIDW. Giventhe geographic position of the 10H/01 and 3 M/01 udometric stationsrelative to the basin, the spatial distribution ofRresulted in a NWSEtrend. The mapping of factorKwas based on the cartography of soilsillustrated inFig. 2. This cartographic data were contained in a polygonshapele of ArcGIS, which was linked to an attribute table where factorKvalues (Section 3.2) were indicated. To produce the raster map offactor K(Fig.4a) this shapelewasusedasfeatureinthetoolConversionTools NTo RasterNFeature to Rasterwhile theKvalues were used asconversion attribute. The raster map of factorLS(Fig. 4b) was producedby application of Eq.(3)to every pixel in the rasterles of parameters and. Parameter was equated to thenumber of pixels accumulatingow into pixelj(j) multiplied by the pixel size. The rasterle ofjresulted from a sequential application of two hydrologic tools toa Digital Elevation Model (DEM) of the basin (Fig. 1), namely thetool Spatial Analyst Tools NHydrology N Flow Directionand the toolSpatial Analyst Tools NHydrology N Flow Accumulation. The raster le= sin(tan(d/ 100) was computed by the raster calculator, wheresinandtanare trigonometric functions anddis a terrain slope rastermap derived from the DEM and computed by the toolSpatial AnalystTools N Surface N Slope. To produce the raster map of factorC(Fig. 4c)one followed the approach used for factorK. In this case, the shapelecontained the cartography of land use and occupation (Fig. 3) and theattribute table enumerated the factorCvalues (Table 1). Finally, themap of factor P(Fig. 4d) was produced in four consecutive steps:

a) the attributes calledagandP1were added to the polygon shapelecontaining the vineyard terraces (Fig. 4). The attributeagdistinguishesthe areas covered by conservation practices (ag= 1) from the otherareas (ag= 0). The attributeP1is the support practice factor in areasnot covered by conservation practices, i.e.P1= 1 where ag= 0;b) two raster les were produced, one foragand the other forP1,using the conversion to raster tool; c) the slope raster map (d) wasreclassied into a P2raster le considering the relationship set upbetween slope class and support practice factor (Section 3.2), usingthe toolSpatial Analyst Tools NReclass NReclassify; and d) the map offactor Pwas calculated as P=P1+agP2using the raster calculator.

The map of environmental land use conicts (Fig. 5) was based onthe production of raster maps for natural (Fig. 2) and actual (Fig. 3)land uses, with the uses reclassied according to the codes listed in

Table 2, succeeded by their subtraction (Eq.(4)).

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