Abstracts

INTRODUCTION

Soil is one of the most important environmental resources on Earth. It holds a peculiar position in the hydrological and nutritional cycles and has influence on several forms of terrestrial life, besides storing and releasing greenhouse gases, and filtering and remediating pollutants. However, many of the ecosystem functions of the soil are lost due to its degradation. Thus, soil conservation and recovery are some of the great challenges for humanity in the XXI century. In this scenery, water erosion accelerated by anthropic activities is one of the main soil degrading processes, with impacts on farming and cattle raising productivity and on natural resources, since recovery rates is not fast enough to compensate for soil loss associated to erosion (Needelman, 2013NEEDELMAN, B. A. What are soils? Nature Education Knowledge. 4(3), 2013. Available in http://www.nature.com/scitable/knowledge/library/what-are-soils-67647639. Acesso em: junho de 2013.
http://www.nature.com/scitable/knowledge... ).

In Brazil, water erosion causes an annual loss of approximately US$ 5 billion considering soil loss, nutrient loss and costs of extrinsic impacts (Pruski, 2006PRUSKI, F.F. Prejuízos decorrentes da erosão hídrica e tolerância de perdas de solo. In: PRUSKI, F.F.Conservação do Solo e da Água. 2.ed. Viçosa: Editora UFV, 2006. cap. 2, p. 24-39.).

This way, studies on water erosion in different types of land use and management are essential for land use planning, soil and water conservation, and recovery of degraded areas (Avanzi et al., 2013AVANZI, J.C. et al. Spatial distribution of water erosion risk in a watershed with eucalyptus and Atlantic Forest.. Ciência e Agrotecnologia 37(5):427-434, 2013.).

Thus, water erosion modeling is an important and consistent tool to defining areas more susceptible to soil loss, which considers spatial and temporal variability of characteristics that interfere in the eroding process (Bhattarai; Dutta, 2007BHATTARAI, R.; DUTTA, D. Estimation of soil erosion and sediment yield using GIS at catchment scale. Water Resources Management. 21(10):1635-1647, 2007.).

Empirical predictive models have been applied to estimate water erosion in basins using the Revised Universal Soil Loss Equation (RUSLE) (Renard et al., 1997RENARD K.G. et al. Predicting soil erosion by water: a guide to conservation planning with the revised universal soil loss equation (RUSLE). Washington, DC: USDA, Agriculture Handbook. (703):384, 1997. ). The RUSLE presents the same formulation as the Universal Soil Loss Equation (USLE) (Wischmeier; Smith, 1978). Both models are the most applied to estimate water erosion due to simplicity and availability of information (Oliveira et al., 2013OLIVEIRA, P.T.S. et al. Erosion risk mapping applied to environmental zoning.. Water Resources Management 25(3):1021-1036, 2011.) and include such factors as rainfall erosivity (R); soil erodibility (K); topographic (LS); cover and management (C), and, support practice (P). In addition, by RUSLE model these factors are calculated with adjustments to seasonal variations and geographic information system (GIS) techniques (Renard et al., 1997RENARD K.G. et al. Predicting soil erosion by water: a guide to conservation planning with the revised universal soil loss equation (RUSLE). Washington, DC: USDA, Agriculture Handbook. (703):384, 1997. ; Beskow et al., 2009BESKOW, S. et al. Soil erosion prediction in the Grande River Basin, Brazil using distributed modeling. Catena. 79(1):49-59, 2009.; Morgan; Nearing, 2011MORGAN, R.P.C.; NEARING, M.A. Handbook of erosion modeling. West Sussex: Wiley - Blackwell, 2011. 352p.).

The increased demand for farm products is one of the main reasons for changes in land cover which may intensify water erosion (Martinelli; Filoso, 2008MARTINELLI, L.A.; FILOSO, S. Expansion of sugarcane ethanol production in Brazil: environmental and social challenges. Ecological Applications.18(4):885-898, 2008. ). Therefore, in water erosion modeling studies, multi temporal analyses of land use have served as a tool to evaluate such changes and their consequences in the yield and transportation of sediments, in the long term (Jordan et al., 2005JORDAN, G. et al. Historical land use changes and their impact on sediment fluxes in the Balatom basin (Hungary). Agriculture, Ecosystems & Environment. 108(2):119-133, 2005. ). Therefore, the aim of this study was to evaluate the effect of changes in land use from 1986 to 2011 to estimate water erosion by RUSLE and compare the results to soil loss tolerance (SLT) in dystrophic Red Latosol at a sub-basin in the Southeastern region in Brazil.

MATERIAL AND METHODS

Study Area And Soil Description

The Ribeirão Caçús sub-basin is located at Alfenas municipality, South of Minas Gerais state, Southeastern Brazil (Figure 1). It has 2,080 ha, within the coordinates 21° 26' to 21° 29' S and 45° 56' to 46° 00' W with altimetric variation of 780 to 960 m. It belongs to the Rio Grande basin and is a direct tributary of the Furnas Hydroelectric Power Plant Reservoir. Climate, in accordance with Köppen, is the tropical mesothermal (CwB) (Sparovek et al., 2007SPAROVEK, G. et al. Computer assisted Köppen climate classification: case study for Brazil. International Journal of Climatology. 27(2):257-266, 2007.) with average annual rainfall of 1,500 mm.

Digital soil classification was carried out with ArcGIS 10.0 software using the State of Minas Gerais Soil Map (UFV et al., 2010) and the classes of relief obtained from the declivity map starting with digital elevation models (DEM) derived from the Shuttle Radar Topography Mission (SRTM) according to procedures by EMBRAPA (2006)EMBRAPA - EMPRESA BRASILEIRA DE PESQUISAS AGROPECUÁRIA. Sistema Brasileiro De Classificação De Solos. 2.ed. Rio de Janeiro: EMBRAPA Solos, 2006. 306p. The mapping units defined were: dystrophic Red Latosol in the following classes of relief: plain (LVd1), slight undulating (LVd2) and undulating (LVd3) and Undifferentiated Floodplain Soils (SIV) (Figure 1).

Revised Universal Soil Loss Equation (RUSLE)

The RUSLE model with GIS technique and with remote sensing, in association with field work, soil physical analyses and data found in literature were applied to estimate water erosion. However, it is worth highlighting that floodplain areas were not taken in consideration since they are subject to sediment accumulation (Mitasova et al., 1996MITASOVA, H. et al. Terrain modeling and soil erosion simulation: applications for Ft. Hood Report for USA CERL. Urbana-Champaign: University of Illinois. 2001. Disponível em: http://www4.ncsu.edu/~hmitaso/gmslab/reports/cerl01/finalreport/report01/default.htm#1. General aspects of Modeling and GIS. Acesso em 16 de outubro de 2013.
http://www4.ncsu.edu/~hmitaso/gmslab/rep... ).

The RUSLE is composed of 5 factors (Equation 1) (Renard et al., 1997RENARD K.G. et al. Predicting soil erosion by water: a guide to conservation planning with the revised universal soil loss equation (RUSLE). Washington, DC: USDA, Agriculture Handbook. (703):384, 1997. ).

Where: A = average annual soil loss (Mg ha^-1 year^-1); R = rainfall erosivity factor (MJ mm ha^-1 h^-1 year^-1); K = soil erodibility factor (Mg h MJ^-1 mm^-1); LS = topographic factor, which includes length and declivity of slope (dimensionless); C = cover and management factor (dimensionless) and P = support practices factor (dimensionless).

Rainfall erosivity factor (R) was obtained from the rainfall erosivity map in the South of Minas Gerais by Aquino et al. (2012AQUINO, R.F. et al. Variabilidade espacial da erosividade da chuva na região Sul de Minas Gerais, Brasil. Ciência e Agrotecnologia. 36(5):533-542, 2012.), who determined the values of R considering fifty-four pluviometric stations by using Fournier Index adjusted for Lavras - MG (Val et al., 1986VAL, L.A. et al. Erosividade das chuvas em Lavras, MG. Ciência e prática. 10(2):199-209, 1986.), the kriging method being used as interpolator.

For the K factor, the number 1 indirect model by Silva et al. (1999SILVA, M.L.N. et al. Proposição de modelos para estimativa da erodibilidade de latossolos brasileiros. Pesquisa Agropecuária Brasileira. 34(12):2287-2298, 1999.), proposed to estimate erodibility of Brazilian Latosols, was applied. According to the authors, number 1 model showed the highest determination coefficient (r²⁾. The number 1 model is expressed in equation 2.

Where: Y = erodibility (Mg h MJ-1 mm-1) and other variables described in table 1.

Variable X₁₄ was based on recognition of soil in the region under influence of Furnas Reservoir (Brasil, 1962BRASIL. Ministério da Agricultura. Levantamento de reconhecimento dos solos da região sob Influência do Reservatório de Furnas. Rio de Janeiro: Boletim Técnico n° 13 do Serviço Nacional de Conservação dos Solos, 1962. 462p.). Variables X₁₆ to X₁₉ were obtained locally from each mapped Latosol unit under native vegetation. In these variables, the classification and value attribution were in accordance with Silva et al. (1999SILVA, M.L.N. et al. Proposição de modelos para estimativa da erodibilidade de latossolos brasileiros. Pesquisa Agropecuária Brasileira. 34(12):2287-2298, 1999.). Variables X₂₂ to X₂₇ and X₃₁ and X₃₂ were obtained from texture analyses by the hydrometer method (Gee; Bauder, 1986GEE, G.W; BAUDER, J.W. Particle size analysis. In: KLUTE, A. (ed.). Methods of soil analysis: part 1. Madison: American Society of Agronomy, 1986. p. 383-411.). For that purpose, a sample was collected from the surface layer (0 - 20 cm) under native vegetation of each Latosol unit (Figure 2) according to procedures by Lemos et al. (2005)LEMOS, R.C. et al. Manual de descrição e coleta de solos no campo. 5.ed. Viçosa: SBCS, 2005. 52p.. Variable X₃₄ was obtained in accordance with EMBRAPA (2011)EMBRAPA - EMPRESA BRASILEIRA DE PESQUISA AGROPECUÁRIA. Manual de métodos de análise de solos. 2. ed. Rio de Janeiro: EMBRAPA Solos, 2011. 230p..

LS factor was estimated by applying the RUSLE 3D model (Mitasova et al., 1996MITASOVA, H. et al. Modelling topographic potential for erosion and deposition using GIS. International Journal of Geographical Information System.10(5):629- 641, 1996.) represented in equation 3.

Were: LS = topographic factor; A = contribution area upstream by unit of cell length (m²⁾ for a DEM; B = DEM cell size; θ = slope gradient, and, m and n = constants that depend on type of surface runoff flow and of soil properties.

DEM Topodata, obtained from Instituto Nacional de Pesquisas Espaciais (INPE), with pixel resolution of approximately 30 m, corresponding to orbit/point 21s465ZN, was used. The adoption of this DEM was based on Bhattarai e Dutta (2007) BHATTARAI, R.; DUTTA, D. Estimation of soil erosion and sediment yield using GIS at catchment scale. Water Resources Management. 21(10):1635-1647, 2007., who obtained more accurate water erosion estimate with DEM of 30 m resolution and pointed that accuracy would be related to similarity between the resolution and the 22.13 m slope length originally used by the USLE.

Starting from the DEM, the contribution area and declivity parameters were obtained with the ArcGIS 10.1 software and in the extension TauDEM 5.1.1 (Tarboton; Mohammed, 2013TARBOTON, D.G.; MOHAMMED, I. N. TauDem 5.1.1: terrain analysis using digital elevation models. Available in: http://hydrology.usu.edu/taudem/taudem5/downloads.html. Access on: 23 ago. de 2013.
http://hydrology.usu.edu/taudem/taudem5/... ). First, the DEM was corrected by filtering anomalous altitude values pixels. Next, the direction flow was obtained by D ∞ (deterministic infinity) method (Tarboton, 1997TARBOTON, D.G. A new method for the determination of flow directions and upslope areas in the grid digital elevation models. Water Resources Research. 33(2):309-319, 1997.) which, automatically, gave the slope degree (Figure 2A) and the contribution area (Figure 2B). Oliveira et al. (2012) OLIVEIRA, A.H. et al. Consistência hidrológica de modelos digitais de elevação (MDE) para definição da rede de drenagem na sub-bacia do horto florestal terra dura, Eldorado do Sul , RS.. Revista Brasileira de Ciência do Solo 36(4):1259-1267, 2012. pointed out that the D ∞ method is the most efficient in simulating surface runoff.

In equation 3, m and n values were 0.4 and 1.0, respectively, designated to represent laminar and concentrated flows in areas with high spatial variability of land use and soil properties, and, therefore, considered the most fit for complex reliefs such as hydrographic basins (Mitasova et al., 2001MITASOVA, H. et al. Terrain modeling and soil erosion simulation: applications for Ft. Hood Report for USA CERL. Urbana-Champaign: University of Illinois. 2001. Disponível em: http://www4.ncsu.edu/~hmitaso/gmslab/reports/cerl01/finalreport/report01/default.htm#1. General aspects of Modeling and GIS. Acesso em 16 de outubro de 2013.
http://www4.ncsu.edu/~hmitaso/gmslab/rep... ; Oliveira et al., 2013_____________. et al. Development of topographic factor modeling for application in soil erosion models. In: SORIANO, M. C. H. Soil processes and current trends in quality assessment. Rijeka: InTech, 2013. p. 111-138.).

In this study, the P factor was not considered since it is the least found in literature and collecting the data empirically is time-consuming and expensive. This decision is supported by Bahadur (2009BAHADUR, K.C.K. Mapping soil erosion susceptibility using remote sensing and GIS: A case of the Upper Name Wa Watershed, Nan Province, Thailand. Environmental Geology. 57(3):695-705, 2009.) who points out that several scientific publications disregard this factor in the water erosion estimate by the USLE/RUSLE.

The C factor was obtained from multi temporal mapping of land use attributing values in accordance with literature. For mapping satellite images Landsat-5 Thematic Mapper (TM), in TM2, TM4 and TM5 bands, corresponding to orbit 219 point 74, were obtained from INPE. Dates selected were 05/02/1986, 08/17/1996, 09/14/2006 and 08/27/2011, due to favorable weather conditions and to the dry season, which favors discrimination between pasture and cultivated land in the images.

Images treatment was carried out with ENVI 4.5 and ArcGIS 10.1 softwares. First, the images were geometrically corrected by control points with reference to WGS-84 (World Geodetic System). At this stage, images can be overlapped concerning the same site at the piece of land in study and to integrate information extracted from images with other plans of cartographic information: drainage, roads, contour lines, soil maps, etc. In order to classify land use, the visual interpretation of images technique was adopted based on TM4, TM5 and TM2 color composition, in RGB (Red, Green and Blue). The land use class map was based on color, texture, pattern and context analyses, besides works of field verification.

Discriminated land use classes and respective values of C factor adopted were: native forest 0.002 and eucalyptus: 0.30 (Martins et al., 2010MARTINS, S.G. et al. Fator cobertura e manejo do solo e perdas de solo e água em cultivo de eucalipto e em Mata Atlântica nos Tabuleiros Costeiros do Estado do Espírito Santo. Scientia Forestalis. 38(87):517-526, 2010.); pasture: 0.1 (Roose, 1977ROOSE, E.J. Application of the universal soil loss equation of Wischmeier and Smith in West Africa. In: GREENLAND, D.J.; LAL, R. Soil conservation and management in the humid tropics. New Jersey: Jophn Wiley & Sons, 1977. p.177-187.); coffee: 0.088 (Prochnow et al., 2005PROCHNOW, D. et al. Razão de perdas de terra e fator C da cultura do cafeeiro em cinco espaçamentos, em Pindorama (SP).. Revista Brasileira de Ciência do Solo 29(1):91-98, 2005. ); corn: 0.122 (De Maria; Lombardi Neto, 1997DE MARIA, I.C.; LOMBARDI NETO, F. Razão de perdas de terra e fator C para sistemas de manejo da cultura do milho.. Revista Brasileira de Ciência do Solo 21:263-270, 1997. ); sugarcane: 0.1 (Nilo et al., 2011NILO, S.F. A. de. et al. Impacto técnico e econômico das perdas de solo e nutrientes por erosão no cultivo da cana-de-açúcar. Engenharia Agrícola. 31(3):539-550, 2011. ) and bare soil: 1.0.

To assess whether land use changes degraded the soil of the area, the soil loss averages were compared by t test at 5% of significance for each year mapped. The soil loss average and standard deviation values were obtained from ArcGIS 10.1 software and the t test was accomplished on SISVAR statistic software (Ferreira, 2014FERREIRA, D.F. SISVAR: A guide for its bootstrap procedures in multiple comparisons. Ciência e Agrotecnolgia. 38(2):109-112, 2014.).

Soil Loss Tolerance

The limit values for SLT were determined by the method proposed by Bertol and Almeida (2000) BERTOL, I.; ALMEIDA, J.A. Tolerância de perda de solo por erosão para os principais solos do Estado de Santa Catarina. Revista Brasileira de Ciência do Solo. 24(3):657-668, 2000. (Equation 4). In accordance with Oliveira et al. (2011)OLIVEIRA, P.T.S. et al. Erosion risk mapping applied to environmental zoning.. Water Resources Management 25(3):1021-1036, 2011., this method is the most used for SLT calculation in Brazil.

Where: SLT = soil loss tolerance (Mg ha^-1 year^-1); h = effective depth of soil (mm), limited to 1,000 mm; r_a = relation that expresses at the same time the effect of texture relation between horizons B and A and of the clay content in horizon A; m = factor that expresses the effect of organic matter in the 0 - 20 cm soil layer; p = factor that expresses the effect of soil permeability and 1,000 = constant that expresses the period of time necessary to erode a 1,000 mm thick soil layer.

Variables h, r e r_a were obtained from Brasil (1962)BRASIL. Ministério da Agricultura. Levantamento de reconhecimento dos solos da região sob Influência do Reservatório de Furnas. Rio de Janeiro: Boletim Técnico n° 13 do Serviço Nacional de Conservação dos Solos, 1962. 462p., the clay content in horizon A was obtained by the method of variable X₂₅ (Table 1), variable m was obtained from EMBRAPA (2011)EMBRAPA - EMPRESA BRASILEIRA DE PESQUISA AGROPECUÁRIA. Manual de métodos de análise de solos. 2. ed. Rio de Janeiro: EMBRAPA Solos, 2011. 230p. and variable p was determined at field by a constant permeameter method (Lima et al., 1990LIMA, J.M. et al. Dispersão do material de solo em água para avaliação indireta da erodibilidade de latossolos.. Revista Brasileira de Ciência do Solo14:85-90, 1990.) with three repetitions in each Latosol unit under native forest. SLT values were correlated to soil loss map, where SLT was discriminated for each Latosol unit.

RESULTS AND DISCUSSION

From the RUSLE parameters, the erosivity value for the study area is of 6,500 MJ mm ha^-1 h^-1 year^-1 (Aquino et al., 2012AQUINO, R.F. et al. Variabilidade espacial da erosividade da chuva na região Sul de Minas Gerais, Brasil. Ciência e Agrotecnologia. 36(5):533-542, 2012.). For those authors, such erosivity with inadequate use and management may lead to relevant soil losses caused by water erosion linked to the intensity, frequency and quantity of rainfall in the region.

From variables in equation 2, listed in table 1, the erodibility of each Latosol unit mapped was estimated and the values are presented in figure 3A.

The values of X₂₆ and X₃₄ variables in table 1 and of the erodibility in unit LVd2 (Figure 3A) were discrepant in relation to Brazilian Latossols (Silva et al., 1999SILVA, M.L.N. et al. Proposição de modelos para estimativa da erodibilidade de latossolos brasileiros. Pesquisa Agropecuária Brasileira. 34(12):2287-2298, 1999.). In fact, the erodibility is complex and from combination of various soils attributes. But, these differences are related to heterogeneous behavior of tropical soils attributes, especially in water-dispersible analysis. The presence of iron oxide, aluminum and silt contributed for the elevation of granulometric fractions due to water-dispersible clay.

LS values obtained from equation 3 varied spatially from 0 to 5.4 (Figure 3B), which show the RUSLE-3D model suitability (Mitasova et al., 1996MITASOVA, H. et al. Modelling topographic potential for erosion and deposition using GIS. International Journal of Geographical Information System.10(5):629- 641, 1996.) concerning estimates of water erosion at basins, due the highest LS values were concentrated where accumulation of the surface runoff flow was more intense. Oliveira et al. (2013) _____________. et al. Development of topographic factor modeling for application in soil erosion models. In: SORIANO, M. C. H. Soil processes and current trends in quality assessment. Rijeka: InTech, 2013. p. 111-138.pointed out that soil loss estimate by applying the RUSLE 3D model does not differ statistically from empirical determination.

Multitemporal map of land use is shown in figure 4, and variation in the land use, in the period is described in table 2.

Spatial distribution of soil loss by water erosion (Figure 5) was determined in equation 1, by multiplying its factors using ArcGIS 10.1. Spatial distribution in soil loss qualitative classes, adapted from Beskow et al. (2009)BESKOW, S. et al. Soil erosion prediction in the Grande River Basin, Brazil using distributed modeling. Catena. 79(1):49-59, 2009., is presented in table 3.

Considering table 3, the percentage of areas with slight, slight/moderate and moderate classes decreased and the areas with moderate/high, high, very high classes increased in small extent. The soil loss average value was of 2.41, 2.47, 2.65 and 2.65 Mg ha^-1 year^-1, respectively. Thus according to t test there was no significant difference among the average values (p = 0.18). Therefore, the land use changes did not degrade the water and soil resources.

Variable values in equation 4 and SLT values for Latosol units are presented in table 4.

Areas at Ribeirão Caçús sub-basin with soil loss above SLT are shown in figure 6.

Data shown in figure 6 point out that the percentage of area that exceeded SLT in 1986, 1996, 2006 and 2011, were 7.9, 8.1, 8.3 e 8.4%, respectively. This increase was not considerable and turned evident that there was not water and soil degradation by land use changes in Ribeirão Caçús sub-basin. However, such values reveal that the degraded area is consistently high, around 8.0 %, throughout the period.

Changes in land cover influence soil loss by water erosion and may cause environmental and socio-economical impacts. Therefore, monitoring this dynamics is essential to implementing measures for management and planning of natural and economic resources, aiming the mitigating of degradation processes such as those observed at Ribeirão Caçús sub-basin, which is reference in the reservoir system of Furnas Hydroelectric Power Plant and presents a history of consistent use along several years.

CONCLUSIONS

The obtained data demonstrate that the land use changes did not degrade the soils of sub-basin in the study period.

Application of the RUSLE model in geographic information systems is a useful tool to estimate spatial and temporal distribution of water erosion, compared to SLT, and to identify areas more vulnerable to degradation, as exemplified at Ribeirão Caçús sub-basin, in the South of Minas Gerais, Southeast of Brazil.

ACKNOWLEDGEMENTS

The authors acknowledge Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG - PPM 00422-13), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq - 471522/2012-0 and 305010/2013-1) and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), for financial support and scholarships.

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