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Integrating Geostatistics and GIS for Assessment ofErosion Risk on Low Density Quercus suberWoodlands of South PortugalThomas Panagopoulos a; Maria Dulce Carlos Antunes aa University of Algarve, FERN (Faculdade de Engennaria de Recursos Naturais),Faro, Portugal

Online Publication Date: 01 April 2008

To cite this Article: Panagopoulos, Thomas and Antunes, Maria Dulce Carlos(2008) 'Integrating Geostatistics and GIS for Assessment of Erosion Risk on Low

Density Quercus suber Woodlands of South Portugal', Arid Land Research and Management, 22:2, 159 — 177

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Integrating Geostatistics and GIS for Assessment ofErosion Risk on Low Density Quercus suber

Woodlands of South Portugal

Thomas Panagopoulos and Maria Dulce Carlos Antunes

University of Algarve, FERN (Faculdade de Engennaria de RecursosNaturais), Faro, Portugal

This research integrates the Revised Universal Soil Loss Equation (RUSLE) withgeostatistical techniques and a Geographic Information System (GIS) to model ero-sion potential for soil conservation planning in Quercus suber agrosilvopastoralwoodlands in the Algarve region of southern Portugal. Graphical interpretation ofthe RUSLE parameters was performed using ordinary kriging. Semi-variogramswere produced for each parameter. The maps resulting from the interpolation tech-niques were introduced in a GIS and their values reclassified. After that, spatialmodelling was used to develop the final overlay map from all the information ofthe analyzed soil properties and RUSLE parameters, simulating ‘‘a potential soilerosion map.’’ Hydraulic conductivity and the soil erodibility K factor with a nug-get-to-sill ratio of 57% and 67%, respectively, showed the weakest spatial depen-dence, whereas organic matter demonstrated the strongest (31%). The mapscreated demonstrate the existence of a heavily textured area in the southern partof the site that could affect erosion and vegetation management techniques.Hydraulic conductivity was higher than 6 cm=h in the northeastern part of theexperimental area. The correlation between the spatially interpolated and observedvalues during the semi-variogram cross-validation, using the data set for methoddevelopment, was high (r2 > 0.81). The northwestern area was the most adequatefor annual fodder cultivation. The most degraded and less suitable areas were inthe southern part, with 108 t=ha potential erosion. Site-specific management meth-ods could improve productivity and decrease the risk of erosion. The present researchshows that geostatistics and GIS are useful tools for sustainable management ofextensive agrosilvopastoral areas.

Keywords decision support system, Revised Universal Soil Loss Equation(RUSLE), soil conservation, spatial variability, sustainable management

The most typical forest landscapes of the Iberian Peninsula are savannah-type openwoodlands dominated by evergreen oak species (Quercus suber L. and Q. ilex ssp.rotundifolia). Similar landscapes can be found in other areas with theMediterranean type of climate, e.g., California, Chile, South Africa, and Australia.In the Iberian Peninsula, large areas of these woodlands depend upon human action,

Received 6 April 2006; accepted 22 December 2007.This study was partially supported by the European Community, project ARINCO

No. 95PT06002. The authors are also grateful to the INAG for access to the national rainfalldatabase.

Address correspondence to Thomas Panagopoulas, University of Algarve, FERN,Campus de Gambelas, 8000 Faro, Portugal. E-mail: [email protected]

Arid Land Research and Management, 22:159–177, 2008Copyright # Taylor & Francis Group, LLCISSN: 1532-4982 print/1532-4990 onlineDOI: 10.1080/15324980801958000

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forming a multipurpose agroforestry system called ‘‘montado’’ in Portugal and‘‘dehesa’’ in Spain. Livestock is the main product of montados, but other productssuch as cereal, cork, firewood, and game have been common since at least the MiddleAges (Gomez and Perez, 1996).

Montados have been created mainly through the clearance of natural oak forests(Moreno, Obrador, and Garcia, 2007). Trees are maintained by selective protectionfor natural regeneration, maintaining a tree density of between 20 and 60 trees perha, depending on the main use of the tract (Limon and Fernandez, 1999). TheQuercus suber L. woodlands of southern Portugal are the subject of numerousattempts to reassess their management strategy and to prevent fires. Mechanicalbrush management became prevalent during the last three decades with the develop-ment of heavy equipment such as the root plough. Following intensive vegetationmanagement techniques, soil is tilled annually for cereal intercrop, or before corkharvesting (rotation period of about 10 years), to establish fodder species, increasesoil aeration, and destroy old shrubs and other weeds (Figure 1).

According to Ruthven, Fulbright, Beasom, and Hellgren (1993), woody plantsre-establish within 2 to 20 years following mechanical brush management, dependingupon the type of treatment, woody plant species, and environmental conditions fol-lowing treatment. The capacity for natural regeneration in southern Portugal is low,and the exhaustive vegetation-management methods leave bare soil exposed to ero-sion for long periods. According to Zalidis et al. (2002) the major physical impacts ofagricultural practices on Mediterranean soils are compaction and erosion. Soil com-paction has been caused by the repetitive and cumulative effect of heavy machinery.The resulting decrease in soil porosity reduces root penetration and access to soilnutrients and alters biological activity. On the watershed scale, soil compactionincreases surface runoff because less rainwater can percolate. This increases the riskof water erosion and loss of topsoil and nutrients. Erosion is one of the major landdegradation classes that can cause irreversible degradation (Dregne, 2002). Drylanddegradation is recognized today as an important environmental problem that may

Figure 1. Montado of Algarve with different vegetation management techniques(A) ¼ undisturbed; (B) ¼ tillage vertical to the contour lines; (C) ¼ vegetation clearance withrotary-cutter mowers leaving the soil covered with leftovers; (D) ¼ ploughing parallel to thecontour lines and seeded annually with fodder species).

160 T. Panagopoulos and M. D. C. Antunes

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seriously compromise the sustainable development of the Mediterranean area(Fantechi, Peter, Balabanis, and Rubio, 1995).

Soil-erosion assessment is a capital-intensive and time-consuming exercise. Anumber of statistical and process models have been developed to predict soil erosion(Zhu, Taylor, and Sarin, 1993; Smith, Goodrich, and Quinton, 1995; Botterweg,Leek, Romstad, and Vatn, 1998; Sparovek et al., 2000). The Revised UniversalSoil Loss Equation (RUSLE) is the most widely used empirical equation for estimat-ing annual soil loss from agricultural basins (Renard et al., 1996). It is simple andeasy to use, but it lacks insights for the soil erosion process and mechanism. Accord-ing to Gray and Sotir (1996), RUSLE is used to predict mean annual soil loss and isdefined as A ¼ R K L S C P where A ¼ potential erosion (computed annual averagesoil loss in t ha�1 year�1), R ¼ rainfall and runoff factor, K ¼ soil erodibilityfactor, LS ¼ slope length and gradient factor, C ¼ vegetation cover factor, andP ¼ vegetation control practice factor.

The main purpose of RUSLE is to guide decision-making in conservation plan-ning on agricultural lands. Although RUSLE was developed to predict soil lossunder temperate conditions, it was also used in other regions with local data(Angima et al., 2003; Lufafa et al., 2003). Recent studies show that traditional mod-els of infiltration and runoff do not always work on rangeland sites (Weltz, Dunn,Reeder, and Frasier, 2003), but attempts have been made to use this equation on for-est lands (Millward and Mersey, 1999; Ozhan et al., 2005). The equation enablesplanners to predict the average rate of soil erosion for each alternative cropping sys-tem, management technique, and control practice on any particular site. A soil-losstolerance level can then be established by comparing the accepted value with thepredicted soil erosion (Wischmeier and Smith, 1978).

Geographic information systems (GIS), modelling, and geostatistics are toolsthat are becoming progressively more suitable in fields of research like forestryand agriculture (Kohl and Gertner, 1997; Bocchi, Castrignano, Fornaro, andMaggiore, 2000; Basso et al., 2001; T�ooth, Kuti, Kabos, and P�aasztor, 2001). Themajor application of geostatistics in agriculture is in soil science, mainly the esti-mation of elemental composition (White and Zasoski, 1999), soil properties (White,1999; Jesus, Panagopoulos, and Beltrao, 2002), fruit quality (Panagopoulos, Baltazar,and Antunes, 2006a), and soil microorganisms (Grundmann and Debouzie, 2000).Aside from soil properties, geostatistics has been used to examine the efficiency ofsampling methods and to create better sampling networks (Lesch, Corwin, andRobinson, 2005). However, geostatistics has not been limited to soil sciences. Someresearch has used geostatistics to model and predict crop yield (Bresler, 1989).

Aside from geostatistical works, the combination of geostatistics and GIS hasproven to be a solid base in the development of precision agriculture, which is basedon the exact knowledge of actual soil conditions and yields (Yalouris et al., 1997;Kaffka, Lesch, Bali, and Corwin, 2005; Panagopoulos, Jesus, Antunes, and Beltrao,2006b). Traditional surveys of soil fertility, together with data from soil survey maps,can be used in combination with geostatistics by decision-makers to support man-agement planning and to predict indicators related to land quality as a measure ofsustainability (Couto, Stein, and Klamt, 1997; Diodato and Ceccarelli, 2005). Out-side the agricultural field, the main use of geostatistics and GIS is either in biologyand life sciences or in geology and environmental sciences (Aspinall and Pearson,2000; Burrough, 2001; Srividya et al., 2002; Dimitrakopoulos, 2005; Goovaerts,Jacquez, and Greiling, 2005).

Geostatistics and GIS for Erosion Risk Assessment 161

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Geostatistics are statistical methodologies that use spatial coordinates to helpformulate models used in estimation and prediction. The understanding of the spa-tial distribution pattern of soil properties is important for determining soil limita-tions to plant growth and appropriate management of soil resources in forestareas. Localized problems in soil properties could be solved with simple, geographi-cally restricted amendment treatments (Horney et al., 2005).

More specifically, these technologies can enable micromanagement techniqueson a site-specific basis to account for the natural and human-induced variations thatexist in agrosilvopastoral woodlands such as variation in soil type, moisture, top-ography, chemistry, physical properties, and other factors. These technologies prom-ise the possibility of optimizing profit and reducing the adverse environmentalimpact of forest management (Larson et al., 1997).

Until recently, the variability inherent in agrosilvopastoral fields was not takeninto consideration because of the lack of tools for spatial analysis. The main goal ofthe present work is to introduce the use of geostatistical techniques and GIS to iden-tify the risk of erosion on an agrosilvopastoral Quercus suber woodland area and usethis information to give site-specific, soil-conservation solutions.

Materials and Methods

The study area lies in the Algarve region of south Portugal which has 498.279 ha andparticularly in the low-altitude mountainous formation ‘‘Serra do Caldeirao’’ with294.500 ha (Figure 2). The 65,000 people that live in the area are mainly farmersor shepherds with approximately 12,500 goats. The Serra do Caldeirao, with amaximum altitude of 589 m, has soils with low agricultural potential and moderateslopes. Most of the soils are classified as Lithosols (267.800 ha) or Orthic Luvisols(23.000 ha) derived from clay schist (Kopp, Sobral, Soares, and Woerner, 1989).

Quercus suber L., Quercus ilex L., Pinus pinea L., Pinus pinaster Ait., Ceratoniasiliquia L., Cistus spp., Olea europaea L., Prunus dulcis Miller, cereals, and fodderplants are the main vegetative species that can be found in the area. Soil erosionand forest fires are the main environmental problems of the region. The climate ofthe area is continental Mediterranean, with very hot and dry summers and mild win-ters. The Algarve region is under a strong Mediterranean climatic influence and istherefore characterized by a dry season and a very irregular distribution of rainfallduring the year, as well as over the years. The average annual precipitation isbetween 500 and 800 mm, depending on altitude.

All spatial data were processed within a GIS (ArcGIS) with a spatial resolutionof 25 m, equivalent to 1 mm on the topographic base maps (scale 1:25,000). Figure 3summarizes the methods used to derive each of the factors required by RUSLE thatwere used to estimate potential erosion, following Renard et al. (1996).

The rainfall and runoff erosivity factor R is the sum of erosive storm values EI30

occurring during a mean year. The value of EI30 for a given rainstorm equals theproduct of total storm energy (E) times the maximum 30-min intensity (I30), whereE is in MJ=ha and I30 is in mm=h. Monthly rainfall for days, where precipitationexceeds 10 mm (rain10) and the monthly number of days where precipitation exceeds10 mm (days10) were the data used from 32 meteorological stations of southernPortugal for 25 years of measurements. At each of these stations, the monthlyerosive storm empirical index EI30month was computed using the regression Eq. (1)


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according to Goovaerts (1999):

EI30month ¼ 6:56rain10� 75:09days10: ð1Þ

Monthly values were computed as the sum of EI30 for each erosive storm during thismonth. The greater the rainstorm intensity within the smallest number of days, thelarger the erosion potential. Annual erosivity was computed as the sum of monthlyerosivities. A prediction map of the R factor of the Algarve region was created fromthe annual erosivity values using ordinary kriging.

Data for soil, vegetation cover, and type of management were collected from anarea of 2 km2 near the village of Alganduro. A total of 81 soil samples were collectedin a grid scheme of 250 m distance between samples. Sampling points were localizedon a georeferenced aerial photograph of the area (Figure 4). In the field, samplinglocations were determined with a Global Positioning System (GPS Magellan 315,Magellan, Santa Clara, California) and later downloaded to the GIS.

At each location, soil samples of about 1 kg were collected for aggregate stability(0–20 cm). The mixture of soil and coarse fragments was air-dried, weighed, andcarefully sieved through a 2-mm screen and saved for analysis. This fraction wasanalyzed for physical properties (texture, coarse fragments) and organic matter usingstandard procedures described by Carter (1993). The soil erodibility K factor wasestimated from five soil parameters after reclassification of their values and follow-ing the algebraic approximation of the Wischmeier, Johnson, and Cross (1971)

Figure 2. Location of the Algarve meteorological stations and topographic map with the fieldsampling points of Alganduro experimental site.


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nomograph (Eq. 2):

K ¼ ½2:1� 10�4ð12�OMÞ M1:14 þ 3:25ðs� 2Þ þ 2:5ðp� 3Þ�=100: ð2Þ

M ¼ (%Msilt)� (%siltþ%sand), modified silt (Msilt) is a percentage of 0.002–0.1 mm, OM is organic matter, s is soil structure class, and p is permeability class.To estimate the permeability class, the field-saturated hydraulic conductivity wasmeasured in situ using a ‘‘Guelph Permeameter’’ following Reynolds and Elrick(1985).

The slope length factor (L) for this study was measured in each of the 81 sam-pling points as suggested by Renard et al. (1996). Slope length is defined as the hori-zontal distance from the origin of overland flow to the point where either the slopegradient decreases enough that deposition begins or runoff becomes concentrated ina defined channel (Wischmeier and Smith, 1978). According to Renard et al. (1996),surface runoff in rangelands will usually concentrate in less than 122 m, which is a

Figure 3. Model used in ArcGIS program to overlay the soil erosion factors according toRUSLE, following geostatistics and reclassification of their values.


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practical slope-length limit in many situations and few slope lengths as long as 305 mshould be used in RUSLE.

The slope steepness factor (S) reflects the influence of slope gradient on erosion(Renard et al., 1996). To create a digital elevation model (DEM) of the study area, acontour segment map and a spot-height point map were prepared by digitizing con-tour lines (elevation every 10 m) and spot-heights from the topographic map N� 588(1975, 1:25,000 scale). The DEM was used to calculate slope at the experimental areausing the ArcGIS Spatial Analyst (ESRI, Redlands, California). The LS algorithmwas calculated by using the USLE slope length function (Wischmeier and Smith,1978) and Nearing’s (1997) slope steepness function (Eq. 3).

S ¼ �1:5þ 17=½1þ expð2:3� 6:1 sin hÞ� ð3Þ

where S is the slope steepness factor and h is the slope angle in degrees. After deter-mining the L, the LS factor was then determined by multiplying the L and S valuesand a map of the LS factors was produced.

To estimate the vegetation cover factor (C), we recorded information on thesubfactors canopy cover, surface cover, surface roughness, soil moisture, and priorland-use for each sampling point as described by Renard et al. (1996). It wasassumed that conditions in rangelands do not change rapidly as in agricultural land;thus, the annual average of each subfactor was multiplied to yield the C-factor at thebeginning of summer.

The vegetation control practice factor (P) in RUSLE demonstrate the effect onrangeland erosion of mechanical practices, such as ripping, root plowing, contourfurrowing, and chaining, which affect erosion in several ways, such as removal ofsurface cover, runoff, amount, runoff rate, flow direction of runoff and hydraulicforces exerted by runoff on the soil (Renard et al., 1996). In the present study area,three types of vegetation management techniques on Q. suber woodlands wereconsidered. In the first case, montados are seeded annually with fodder species after

Figure 4. Aerial photograph showing field sampling points of the Alganduro experimentalarea.


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tillage vertical to the contour lines. In the second case, the shrub vegetation is leftundisturbed for at least 10 years by woodland owners (which is the rotation periodfor cork harvest), and at the harvesting year the land is ploughed parallel to the con-tour lines with the use of heavy machinery and left bare. This conservation techniquewas usually applied in fields with less than 10% inclination. The third practice is theuse of rotary-cutter mowers to clear vegetation cover at 10 cm height, leaving the soilcovered with leftovers (a technique introduced recently by the Forest Service todecrease the risk of forest fires). The areas of the above practices were mapped invector format. We examined the P values for rangelands of Wischmeier and Smith(1978) and Renard et al. (1996), taking into consideration local indices. We decidedon the use of 1, 0.6, and 0.5 P values for the three practices, respectively.

To understand the variation of the erodibility factors, graphical interpretation ofthese properties was performed by the use of geostatistics. A dataset of soil proper-ties and vegetation cover was created with their geo-referenced position in the fieldby using the ArcGIS (ESRI) program. Before creating surface diagrams, the distri-bution of data was analyzed to get a better understanding of trends, directional influ-ences, and obvious errors. Transformation and trend removal were performed whennecessary to create more accurate prediction maps. Ordinary kriging was used forthe creation of several maps. Prior to the creation of the maps, semi-variograms wereproduced for each soil factor.

Cross validation was used to compare the prediction performances of the semi-variograms. Kriging cross-validation was used to estimate which of the semivario-gram models could give the most accurate predictions of the unknown values ofthe field. The closer the mean error was to zero, and the closer the root-mean-squarestandardized error was to 1, signified that the prediction values were close to mea-sured values (Wackernagel, 1995). Where models presented similar values for meanerror and root-mean-square error, the lowest values of root-mean-square error andaverage standard error were taken into consideration. The maps that resulted fromthe interpolation techniques were introduced into a GIS, and their values reclassi-fied. After that, spatial modelling was used to develop a final overlay map for poten-tial soil erosion using RUSLE. Arithmetic overlay is the most frequently usedprocess in GIS applications. Arithmetic operators are used to add, subtract, divide,or multiply values in one data layer by a constant or by values in another data layerin a corresponding location.

Results

The first step was to generate a map of rainfall erosivity in the Algarve region ofSouth Portugal. Kriging was the geostatistical method used because this methodgives the most accurate results after validation and offers the possibility of flexibilityin assumptions required for data handling. From the prediction map produced afterordinary kriging and trend removal, it was found that the rainfall and runoff factorR for the experimental area was estimated to be 2706 MJ mm ha�1 h�1 yr�1 (Figure 5).

The descriptive statistics of soil properties and soil erodibility values (K) aregiven in Table 1. Kriging, following trend removal, was carried out on the residualdata of texture (clay, modified silt, silt, sand), organic matter, and hydraulic conduc-tivity. The prediction map for each factor was calculated, and the resulting trend wasadded back to the output surface. Many model parameters were studied to choose


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the most appropriate model of each soil erosion factor and the semi-variogram thatbest fits the experimental semi-variogram was chosen.

However, at such a scale it is necessary to use error assessment and cross-validation procedures. The cross validation of the hydraulic conductivity semi-variograms demonstrates low mean error for all models and insignificant differencesfor root-mean-square error and average standard error. A mean error equal to�0.01=0.01 showed a lack of systematic error. Since root-mean square was closeto the average standard errors, it means that the model is correctly assessing thevariability in prediction. However, root-mean-square standardized error helped toexclude the pentaspherical, Gaussian, hole effect, and k-Bessel models, whereasthe low nugget of the spherical model means smaller interpolation errors. Becauseroot-mean-square is close to the average standard errors, the mean correctly assessesthe variability in prediction. Following the same procedure, we produced maps basedon ordinary kriging of the other soil erosion factors. Table 2 presents the final semivariogram model chosen for the prediction map for each soil erosion parameteranalyzed.

The map of hydraulic conductivity, following ordinary kriging, demonstratedthat the highest hydraulic conductivity occurs at the northeastern part of the site(more than 6 cm=h), decreasing through the field and reaching the lowest values atthe southern part (0.04 cm=h). The existence of a heavily textured area in the south-ern part of the field could be seen in the texture map derived from clay percentagedata after ordinary kriging. Clay percentage at that area was between 36% and48%. The map of hydraulic conductivity partially confirms the low hydraulicconductivity of this clay area (0.04 to 0.5 cm=h, according to the prediction map).The percentage of clay decreased toward the northern side of the area, while atthe same location the percentage of sand increased.

All maps of soil properties were reclassified, weighted, and overlaid in theArcGIS program. The soil-erodibility K factor map was created following theWischmeier nomograph after arithmetic overlay (Figure 6). From this map, it could

Figure 5. Map of rainfall erosivity of the Algarve region, southern Portugal, EI30 (MJ mm=hah yr).


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be seen that the areas with heavily textured soil and low hydraulic conductivity hadthe lowest values of soil erodibility. The soil indicated a lack of structure in mostsampling areas. The organic matter content was low (0.7% to 3.9%) and did notinfluence the prediction of the soil erodibility factor, but the lowest values were inthe western part of the experimental area.

The slope length and gradient factor (LS) were computed for the study areausing information of L and S at each of the 81 sampling points. The resultingmap was overlaid in ArcGIS for arithmetic overlay. The LS surface replicated thelocal drainage network as well as the slope gradient. Lines of flow concentration(concave), where overland flow tends to accumulate, had the highest LS values.On the other hand, areas of convex topography such as ridges, where flow diverges,had low LS values. A comparison with the slope-gradient map revealed a clear effectof steepness in the LS factor, with areas of greater slopes having high LS values.

The vegetation cover factor and the vegetation control practice factor for range-lands were computed for each of the 81 sampling sites at the moment of samplingand after consulting the shepherds using the area. The kriging prediction maps pro-duced for the estimation of the vegetation cover factor show that the vegetationcover was lower toward the southern part of the experimental area, which coincideswith the frequent ploughing areas. The surface-roughness subfactor was generally

Figure 6. Map of erodibility factor K showing a low-erodibility area in the southern part ofthe Alganduro experimental area.


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higher in the southern part of the study area than in the north. Soil moisture washigher on the north and west sides of slopes and lower on south-facing slopes.

The final soil-loss prediction map was calculated multiplying the RUSLE factorlayers using map algebra in ArcGIS program (Figure 7). In this final map, two largeareas of high soil-erosion risk are identified in the southeastern and northwesternparts of the experimental area. At those localities the potential erosion was estimatedbetween 76 to 108 t=ha. A small spot with less than 45 t=ha was also taken intoconsideration. The rest of the area has a moderate risk of erosion (45 to 75 t=ha).

Discussion

Modelling soil erosion is complicated, because soil loss varies spatially and tempo-rally and depends on many factors and their interactions. Therefore, it is importantto know both the estimates and the associated uncertainties when predicting soil lossat unknown localities. In the present study, an empirical model (the RUSLE) wasused, which was developed in the United States. Thus the conclusions drawn fromthe predictive results will be uncertain without direct validation from long-term soil

Figure 7. Results of the overlay procedure of all soil-erosion factors in ArcGIS program afterreclassification and weighting of the ordinary kriging values, showing which areas are moresusceptible to soil loss in the Alganduro experimental area.


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loss experimentation and measurements under the Mediterranean conditions (Tomasand Coutinho, 1994; Abu Hammad, Lundekvam, and Børresen, 2004).

This study shows that GIS and geostatistics can be useful tools in the estimationof erosion risk for large areas. Kriging was used for prediction of soil factors in manystudies (McBratney, Santos, and Minasny, 2003). Simple random sampling and thecalculation of an average, usually used as normal procedure in soil sampling, are notalways the best techniques for identifying soil-erosion risk and analyzing spatial pro-blems (Goovaerts, Rossel, and McBratney, 2002; T�ooth et al., 2002). Meanwile, themajor criticism of grid sampling is that if the periodicity of the sampling coincideswith that of some environmental feature, ‘‘aliasing’’ will occur (Webster and Oliver,1990). Grid sampling is not well suited for estimating variograms and describing spa-tial patterns of highly variable alluvial soils, because it gives no information aboutspatial variability at distances shorter than the grid mesh (Buscaglia and Varco,2003). In contrast, grid-point sampling is most useful in rangelands when the goalis to produce a more continuous surface of soil data using interpolation techniques-rather than blocks or cells representing the central tendency of an area (Flatman andYfantis, 1984; Cole, Healy, Wood, and Foster, 2001) and according to Bogaert andRusso (1999), grid sampling is an ideal method for site-specific soil managementbecause it is unbiased, simple, and relatively quick.

Nugget was low in most RUSLE parameters nugget (Table 2), but the largeeffects of the semi-variograms from some of the soil properties studied indicated ahigh variance at short distance; thus, estimates of those semi-variograms will bepoor as mentioned by Armstrong (1998). Nugget is a parameter of a covarianceor semi-variogram model that represents independent error, measurement error,and microscale variation at spatial scales that are too fine to detect. The nugget effectis a discontinuity at the origin of either the covariance or the semi-variogram model(Chiles and Delfiner, 1999). Exponential semi-variograms of some models indicateda high variance. Ordinary kriging identified places where more intensive samplingwas required. Block-kriging could be used if the residual, spatially uncorrelatednugget is high.

The nugget-to-sill ratio presented in Table 2 indicated moderate spatial depen-dence for most parameters studied. Cambardella et al. (1994) suggested that strongspatial dependency existed if the nugget semi-variance of a variable expressed as afraction of the total semi-variance was <0.25, moderately spatially dependent if itwas 0.25 to 0.75, and weakly spatially dependent if the ratio was >0.75. Hydraulicconductivity factor with nugget-to-sill ratio of 0.57, showed moderate spatialdependence, whereas organic matter with 0.31 nugget=sill ratio indicates low vari-ation over short distances demonstrating that it was the factor with the strongestspatial correlation. The spatial dependency for the soil-erodibility K factor couldbe clearly described as moderate-to-weak, based on the nugget=sill ratio inTable 2. According to Yates, Si, Farrell, and Pennock (2006), spatial independenceis achieved when the semi-variance no longer increases and is constant (sill). Table2 shows that the distance required to reach the sill (range) was close to themaximum distance between samples for all RUSLE parameters except slope lengthand rainfall erosivity. The correlation between estimated and observed values,using the data set for the method development, was high in all maps validatedpresenting a r2 higher than 0.81.

The maps created demonstrate the existence of a heavily textured area in thesouthern part of the site that could affect erosion and vegetation management


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techniques. The type of soil at that area was mostly Luvisols, which has a higher claycontent in the subsoil than in the topsoil as a result of pedogenetic processes leadingto an argic subsoil horizon. The higher clay content in the south was due to continu-ous tillage during years, exposing bare soil for long periods, causing erosion thatremoved the sandy surface, and bringing the clay layer closer to the surface. Wheretillage was applied for long periods, soil compaction was higher and consequentlyincreased the risk of erosion from higher runoff, an occurrence that was alsoobserved by Rhoton et al. (1993) and Thierfelder, Amezquita, and Stahr, (2005).Areas with frequent root ploughing exhibited a low vegetation cover, which was alsoconfirmed by the long-term study of Ruthven et al. (1993), who reported that theforbs canopy cover increased during the first years following root ploughing but thatwoody-plant biodiversity and cover decreased.

Areas with long and steep slopes were also susceptible to erosion. Due to soillosses, soil conservation measures were needed, especially on the cultivated slopinglands. The superimposed final map of erosion risk was created to identify the areafor each of the vegetation-management techniques traditionally used in the region.With the areas of highest risk to erosion having been defined, erosion could bedecreased by suggesting simple changes in vegetation management, keeping tillageonly in areas of lowest risk.

Most of the soils of the mountainous area of Algarve are very shallow soils overhard rock or unconsolidated very stony material with A-Bt-Btg-CR horizons (Koppet al., 1989). A change in vegetation management of those areas could increaseorganic matter and accelerate soil genesis (Potter et al., 1998; Six, Conant, Paul,and Paustian, 2002; Olson, Lang, and Ebelhar, 2005). The use of the rotary-cuttermower to destroy shrub vegetation cover could be a vegetation-management tech-nique that would help to protect the soil all year round. Tillage should be avoidedin high slope areas. Enrichment with leguminous species can be used to protectthe soil during longer periods, increasing biodiversity. Minimum tillage, compostapplication, the use of the rotary-cutter mower, and enrichment with herbaceousplants could be good-practice solutions (Fischer and Wipf, 2002; Thierfelder et al.,2005). RUSLE and GIS could be used by rangeland managers to model the changeof vegetation cover and the risk of erosion in case of forest fire. Then managers coulddesign alternative scenarios by changing some of the RUSLE factors until finding anoptimal solution.

Various, easy-to-identify facts of importance were taken into consideration,such as areas of high or low organic-matter concentrations. Thus, those mapscan be important for the estimation of the optimal area for fodder cultivationand would help to predict which property is limiting production and where(Sparovek and Schnug, 2001; Reyniers, Maertens, Vrindts, and De Baerdemaeker,2006; Panagopoulos et al., 2006b). The most adequate area for annual fodder cul-tivation was the northwestern part of the experimental plot, because it had averagehydraulic conductivity, a high organic matter content and vegetation cover, andlow-to-moderate risk of erosion. The most degraded and less suitable area wasthe southwest. Geostatistics and GIS can therefore be used to identify the riskof erosion areas as well as to help apply precision management in extensiveagrosilvopastoral areas, consistent with the findings of Diodato and Ceccarelli(2004). Farmers and managers should take soil conservation measures in the north-eastern part of Algarve because otherwise soil erosion will cause severe problemswithin a few decades.


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Conclusions

Geographic information systems and geostatistics helped to predict a pattern of ero-sion and identify the location of high-risk areas for various land-use alternatives.Spatial analysis provided supporting information for allocation of resources to thoseareas and those types of practices that will provide the most effective protection. Thesubsequent kriging analysis of this data gives isopleth maps of the erosion and theother RUSLE factors. These outputs make the monitoring data understandablefor the decision-maker. Geostatistics and GIS, linked with simple erosion models,provide tools for the evaluation of vegetation-management alternatives and forthe planning of prevention of erosion practices. The spatial variability of the erosionfactors in the study area makes it an ideal practice in which to apply site-specificmanagement.

The techniques used in the present work to estimate potential erosion identifiedareas with high erosion risk that should be reclaimed and conserved. Localized pro-blems with impermeable soil in the southern part of the area could be solved withsimple geographically restricted soil-conserving treatments (terracing, establishingsoil-building vegetation, mulching with forest leftovers, land resting, etc.). Steepslopes should be reclaimed and protected. This approach could help in spending lessmoney and in stopping damage to the environment with destructive soil practicesand ineffective land management. The study was carried out for a rather small area,but its results and experiences could be widely applied in areas with similar environ-mental conditions. The present study demonstrated that this RUSLE-GIS model is auseful tool, especially for identifying high-risk areas where soil conservationpractices are needed. Various soil conservation-planning scenarios can be evaluatedeasily through database manipulations at different levels of scale by land managers.

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