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Digital Soil Texture Maps of Argentina and Their Relationship to Soil-Forming Factors and Processes

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Geopedology

Abstract

Soil texture is determined by the parent material of the soil and the resulting pedogenetic processes. Because the spatial distribution of soil texture governs several soil physical and chemical processes, our goals were to map clay, silt, and sand at different depths using the SISINTA soil profile database; generate textural soil class maps for the same soil depths; and describe the distribution of soil texture in relation to different landscape units. We used 4663 soil texture observations and 64 environmental covariates to represent the soil-forming factors (e.g., remote sensing data, climate data or geomorphology maps). We modelled clay, silt and sand at 0–15, 15–30, 30–60 and 60–100 cm, establishing an empirical relationship with environmental covariates using Random Forest to predict their spatial distribution. Finally, we performed an analysis of uncertainty through repeated cross-validation. We observed model efficiency coefficient (MEC) values between 0.452 and 0.557, with an RMSE between 8.77% and 11.21% for the clay fraction. The MEC for the silt fraction ranged from 0.561 to 0.638 with an RMSE of 10.50% to 12.01%. For the sand fraction the MEC ranged from 0.587 to 0.640 with RMSE values between 16.19% and 16.76%. The general patterns of uncertainty are consistent with areas of limited data. Our results increased the quality, quantity and accessibility of information on soil texture in Argentina by providing new insights into both the distribution of parent materials and the intensity of pedogenetic processes in each region.

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References

  • Amatulli G, McInerney D, Sethi T, Strobl P, Domisch S (2018a) Geomorpho90m, empirical evaluation and accuracy assessment of global high-resolution geomorphometric layers. https://doi.org/10.5281/zenodo.1807119

  • Amatulli G, McInerney D, Sethi T, Strobl P, Domisch S (2018b) Geomorpho90m, empirical evaluation and accuracy assessment of global high-resolution geomorphometric layers (V1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.1807125

  • Angelini ME, Rodríguez DM, Olmedo GF, Pasquier ML, Schulz GA, Aleksa AS, Angelini HP, Babelis GC, Barrios RA, Bustos MV, Carboni G, Casabella MP, Colazo JC, de Bustos ME, de la Fuente JC, Díaz RC, Di Fede BE, Escobar D, Escobar LE, Faule L, Garay JM, Godagnone RE, Hurtado P, Irigoin J, Kurtz DB, Liotta MA, Medina Herrera D, Miraglia HN, Morales Poclava MC, Navarro MF, Rigo S, Rossi JP, Sánchez JM, Valdettaro RA, Vicondo ME, Vizgarra LA (2018) Sistema de información de suelos del INTA (SISINTA): Presente y futuro. Congreso Argentino de la Ciencia del Suelo, Tucumán

    Google Scholar 

  • Bianchi AR, Cravero SAC (2010) Atlas Climático digital de la República Argentina. Ediciones INTA

    Google Scholar 

  • Bishop TFA, McBratney AB, Laslett GM (1999) Modelling soil attribute depth functions with equal-area quadratic smoothing splines. Geoderma 91(1):27–45. https://doi.org/10.1016/S0016-7061(99)00003-8

    Article  Google Scholar 

  • Castro Franco M, Domenech MB, Borda MR, Costa JL (2018) A spatial dataset of topsoil texture for the southern Argentine Pampas. Geoderma Reg 12:18–27. https://doi.org/10.1016/j.geodrs.2017.11.003. Editorial: Elsevier. ISSN: 2352-0094

    Article  Google Scholar 

  • Colazo JC, Buschiazzo DE (2010) Soil dry aggregate stability and wind erodible fraction in a semiarid environment of Argentina. Geoderma 159(1–2):228–236. https://doi.org/10.1016/j.geoderma.2010.07.016

    Article  CAS  Google Scholar 

  • Cravero SAC, Bianchi CL, Elena HJ, Bianchi AR (2017) Clima de la Argentina: Mapas digitales mensuales de precipitaciones y precipitación menos evapotranspiración potencial. Adenda del Atlas Climático digital de la República Argentina. Ediciones INTA. http://inta.gob.ar/documentos/clima-de-argentina-adenda-del-atlas-climatico-digital-de-la-republica-argentina [Acceso: 21_08_2021]

  • Frolla FD, Angelini ME, Beltrán MJ, Di Paolo LE, Peralta GE, Rodríguez DM, Schulz GA (2021) Argentina: soil organic carbon sequestration potential national map. National report. Versión 1.0. In: Global soil organic carbon sequestration potential map – GSOCseq. FAO. http://www.fao.org/fileadmin/user_upload/GSP/GSOCseq/Argentina_SOC_SequestrationPotentialNationalMap.pdf [Acceso: 22_06_2021]

  • Galantini JA, Senesi N, Brunetti G, Rosell R (2004) Influence of texture on the nitrogen and sulphur status and organic matter quality and distribution in semiarid Pampean grassland soils of Argentina. Geoderma 123(1–2):143–152. https://doi.org/10.1016/j.geoderma.2004.02.008

    Article  CAS  Google Scholar 

  • Geering HR, So HB (2006) Texture. In: Lal R (ed) Encyclopedia of Soil Science, 2nd edn. Taylor & Francis, pp 1759–1763

    Google Scholar 

  • Hengl T (2018a) Global DEM derivatives at 250 m, 1 km and 2 km based on the MERIT DEM (Version 1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.1447210

  • Hengl T (2018b) Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) at 250 m monthly for period 2014-2019 based on COPERNICUS land products (Version 1.0-1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3459830

  • Hengl T (2018c) Global landform and lithology class at 250 m based on the USGS global ecosystem map (Version 1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.1464846

  • Hevia GG, Buschiazzo DE, Hepper EN, Urioste AM, Antón EL (2003) Organic matter in size fractions of soils of the semiarid Argentina. Effects of climate, soil texture and management. Geoderma 116(3–4):265–277. https://doi.org/10.1016/S0016-7061(03)00104-6

    Article  Google Scholar 

  • IGN (2011) Límites, superficies y puntos extremos. https://www.ign.gob.ar/NuestrasActividades/Geografia/DatosArgentina/LimitesSuperficiesyPuntosExtremos [Acceso: 20_12_2021]

  • Iriondo M (1993) Geomorphology and late Quaternary of the Chaco (South America). Geomorphology 7:289–303

    Article  Google Scholar 

  • Iriondo M, Kröhling DM (1995) El sistema eólico pampeano. Museo Provincial de Ciencias Naturales “Florentino Ameghino”

    Google Scholar 

  • Janssen PHM, Heuberger PSC (1995) Calibration of process-oriented models. Ecol Model 83:55–66

    Article  Google Scholar 

  • Jenny H (1941) Factors of soil formation. McGraw-Hill, New York

    Book  Google Scholar 

  • Kuhn M, Wing J, Weston S, Williams A, Keefer C, Engelhardt A, Cooper T, Mayer Z, Kenkel B, R Core Team, Benesty M, Lescarbeau R, Ziem A, Scrucca L, Tang Y, Candan C, Hunt T (2017) caret: classification and regression training. URL https://CRAN.R-project.org/package=caret. R package version 6.0-78.

  • Lagacherie P, Arrouays D, Bourennane H, Gomez C, Nkuba-Kasanda L (2020) Analysing the impact of soil spatial sampling on the performances of Digital Soil Mapping models and their evaluation: A numerical experiment on Quantile Random Forest using clay contents obtained from Vis-NIR-SWIR hyperspectral imagery. Geoderma 375, art. 114503 [12 p.]. ISSN 0016-7061. https://doi.org/10.1016/j.geoderma.2020.114503

  • Ließ M, Glaser B, Huwe B (2012) Uncertainty in the spatial prediction of soil texture: comparison of regression tree and random forest models. Geoderma 170:70–79. https://doi.org/10.1016/j.geoderma.2011.10.010

    Article  Google Scholar 

  • McBratney AB, Santos MM, Minasny B (2003) On digital soil mapping. Geoderma 117(1-2):3–52. https://doi.org/10.1016/S0016-7061(03)00223-4

    Article  Google Scholar 

  • Meinshausen N (2006) Quantile regression forests. J Mach Learn Res 7:983–999

    Google Scholar 

  • Minasny B, McBratney AB (2015) Digital soil mapping: a brief history and some lessons. Geoderma 264(b):301–311. https://doi.org/10.1016/j.geoderma.2015.07.017

    Article  Google Scholar 

  • Moeys J (2014) The soil texture wizard: R functions for plotting, classifying, transforming and exploring soil texture data. http://cran.r-project.org/web/packages/soiltexture/vignettes/soiltexture_vignette.pdf

  • Morrás H (2017) Una interpretación geopedológica sobre los sedimentos superficiales y suelos actuales de la Cuña Boscosa, Chaco Austral, Provincia de Santa Fe. XX Congreso Geológico Argentino Actas Sesión Técnica 3:38–43. Tucumán

    Google Scholar 

  • Morrás H, Moretti LM (2016) A new geopedologic approach on the genesis and distribution of Typic and Vertic Argiudolls in the Rolling Pampa of Argentina. In: Zinck A, Metternicht G, Bocco G, del Valle H (eds) Geopedology Book. Springer, 556 p, pp 193–209

    Chapter  Google Scholar 

  • Oliva G, Escobar J, Siffredi G, Salomone J, Buono G (2006) Monitoring patagonian rangelands: the maras system. In: Aguirre-Bravo C, Pellicane PJ, Burns DP, Draggan S (eds) Monitoring science and technology symposium: unifying knowledge for sustainability in the Western Hemisphere proceedings RMRS-P-42CD, vol 42. US Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fort Collins, pp 188–193

    Google Scholar 

  • Olmedo GF, Rodríguez DM, Angelini ME (2017) Advances in digital soil mapping and soil information systems in Argentina. In: Arrouays D, Savin I, Leenaars J, McBratney AB (eds) GlobalSoilMap -digitalsoilmappingfromcountrytoglobe. CRC Press, Boca Raton, pp 13–16

    Google Scholar 

  • Olmedo GF, Angelini MA, Schulz GA, Rodríguez DM, Taboada MA, Pascale C, Escobar D, Guevara M, Colazo JC, Aleksa AS, Babelis GC, Gaitán JJ, Peralta AR, Peralta G, Rojas JM, Sainz Rosas HR, Vizgarra LA (2018) Prediction stock of soil organic carbon in Argentina (1.5) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6323695

  • Pekel JF, Cottam A, Gorelick N, Belward AS (2016) High-resolution mapping of global surface water and its long-term changes. Nature 540:418–422. https://doi.org/10.1038/nature20584

    Article  CAS  Google Scholar 

  • Pierce F, Larson W (1996) Quantifying indicators for soil quality. In: Berger A, Iams W (eds) Geoindicators. Assessing rapid environmental changes in earth systems. Balkema, Rotterdam, pp 323–335

    Google Scholar 

  • R Core Team (2022) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. https://www.R-project.org/

    Google Scholar 

  • Rodríguez DM, Schulz GA, Aleksa AS, Tenti Vuegen LM (2019) Distribution and classification of soils. In: Rubio G, Lavado R, Pereyra F (eds) The soils of Argentina, World Soils Book Series. Springer, Cham. ISBN: 978-3-319-76851-9, pp 63–79. https://doi.org/10.1007/978-3-319-76853-3_5

    Chapter  Google Scholar 

  • Rodríguez DM, Schulz GA, Tenti Vuegen LM, Angelini ME, Olmedo GF, Lavado RS (2020) Salt-affected soils in Argentina (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6323102

  • SAGyP-INTA (1989) Mapa de suelos de la provincia de Buenos Aires (Escala 1:500.000). Proyecto PNUD ARG/85/019, Buenos Aires. 544 pp

    Google Scholar 

  • SAGyP-INTA (1990) Atlas de Suelos de la República Argentina (Escala 1: 500.000 y 1: 1.000.000). Proyecto PNUD ARG/85/019, Buenos Aires. Tomo I: 731 pp, Tomo II: 677 pp

    Google Scholar 

  • Saxton KE, Rawls WJ (2006) Soil water characteristic estimates by texture and organic matter for hydrologic solutions. Soil Sci Soc Am J 70:1569–1578. https://doi.org/10.2136/sssaj2005.0117

    Article  CAS  Google Scholar 

  • Schulz GA, Rodríguez DM, Angelini ME, Moretti LM, Olmedo GF, Tenti Vuegen LM, Colazo JC, Guevara M (2022) Digital soil texture maps of Argentina (2.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6312654

  • Siragusa A (1958) República Argentina: Regiones geográficas. (mimeo)

    Google Scholar 

  • Six J, Conant RT, Paul EA, Paustian K (2002) Stabilization mechanisms of soil organic matter: implications for C-saturation of soils. Plant Soil 241:155–176. https://doi.org/10.1023/A:1016125726789

    Article  CAS  Google Scholar 

  • Soil Survey Staff (2009) Soil survey field and laboratory methods manual. Soil Survey Investigations Report No. 51, version 1.0. Burt R (ed) USDA Natural Resources Conservation Service

    Google Scholar 

  • Tenti Vuegen LM, Rodríguez DM, Moretti, LM, De la Fuente JC, Schulz GA, Angelini ME (2021) Black soils in Argentina (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6323558

  • Vaysse K, Lagacherie P (2017) Using quantile regression forest to estimate uncertainty of digital soil mapping products. Geoderma 291:55–64

    Article  Google Scholar 

  • Wadoux AMC, Heuvelink GB, Lark RM, Lagacherie P, Bouma J, Mulder VL, Libohova Z, Yang L, McBratney AB (2021) Ten challenges for the future of pedometrics. Geoderma 401:115155. https://doi.org/10.1016/j.geoderma.2021.115155

    Article  Google Scholar 

  • Yamazaki D, Ikeshima D, Tawatari R, Yamaguchi T, O’Loughlin F, Neal JC, Sampson CC, Kanae S, Bates PD (2017) A high-accuracy map of global terrain elevations. Geophys Res Lett 44:5844–5853. https://doi.org/10.1002/2017GL072874

    Article  Google Scholar 

  • Zárate MA (2003) Loess of Southern South America. Quat Sci Rev 22(18–19):1987–2006. https://doi.org/10.1016/S0277-3791(03)00165-3

    Article  Google Scholar 

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Acknowledgments

This work was carried out within the framework of INTA research projects (PNSUELO 1134032 and RIST I051) and through training in DSM by Pillar 4 for Latin America and the Caribbean of FAO’s Global Soil Partnership.

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Correspondence to G. A. Schulz .

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Schulz, G.A. et al. (2023). Digital Soil Texture Maps of Argentina and Their Relationship to Soil-Forming Factors and Processes. In: Zinck, J.A., Metternicht, G., del Valle, H.F., Angelini, M. (eds) Geopedology. Springer, Cham. https://doi.org/10.1007/978-3-031-20667-2_14

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