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A Methodological Approach to Mapping Acid Sulfate Soils, the Spatial Variability of Acidity and Salinity, and Hazards at the Field Scale in a Sector of the Sinú River Floodplain, Colombia

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Abstract 

There is currently no comprehensive acid sulfate soil (ASS) hazard mapping in Colombia. This study aims to create reliable prediction surfaces to estimate the types and subtypes of inland ASS, spatial variability of acidity and salinity, and acidification and salinization hazards. We used a combination of factor analysis, geostatistical tools with ordinary and indicator kriging, and Geographic Information System utilities to design a spatial prediction model. The studied variables were soil reaction, redox potential, and electrical conductivity at the A and B horizons obtained from a detailed systematic sampling in a sector of the Sinú River floodplain, Colombia. Two factors were identified; the first allowed us to delimit the homogeneous behavior of acidity as well as the types and subtypes of inland ASS precisely while the second facilitated the identification of ASS subtypes. Our findings indicate that 82% of the area reports very high and high to moderate acidification hazards in active ASS. A high salinization hazard exists in 26% of active ASS and 74% of both active and post-active ASS. These findings suggest serious acidification and salinization hazards and the need for urgent appropriate economic and environmental management. The approach applied here can be implemented at a field scale to improve understanding of the activity and behavior of ASS based on the acidity and salinity, which can facilitate a more reliable mapping of acidification and salinization hazards.

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Data Availability

The datasets generated during and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request.

References 

  • Abdennour, M. A., Douaoui, A., Piccini, C., Pulido, M., Bennacer, A., Bradaï, A., Barrena, J., & Yahiaoui, I. (2020). Predictive mapping of soil electrical conductivity as a Proxy of soil salinity in south-east of Algeria. Environmental and Sustainability Indicators, 8, 100087. https://doi.org/10.1016/j.indic.2020.100087

    Article  Google Scholar 

  • Acevedo,S D. C., Álvarez Sánchez, M., Hernández Acosta, E., Maldonado Torres, R., Pérez Grajales, M., & Castro Brindis, R. (2008). Variabilidad espacial de propiedades químicas del suelo y su uso en el diseño de experimentos. Terra Latinoamericana. https://doi.org/10.1016/j.indic.2020.100087

  • Aguirre, N. (1995). Morphodynamics analysis of the northern Sinú River basin (Master dissertation). International Institute for Aerospace Survey and Earth Science. IGAC private document.

  • Ahern, C., McElnea, A., & Sullivan, L. (2004). Acid sulphate soils. Laboratory methods guidelines. Retrieved June 2, 2021, from https://www.environment.nsw.gov.au/resources/soils/acid-sulfate-soils-laboratory methods-guidelines.

  • Alesso, C. A. (2014). Variabilidad espacial y temporal de rendimientos de maíz (Zea mays L.) y soja [Glycine max (L.) Merr.] y de las propiedades del suelo en las condiciones edafoclimáticas de la pampa llana Santafesina. (Doctor dissertation). Universidad Nacional de Córdoba, Argentina. Retrieved November 7, 2021, from https://1library.co/document/q76kdkoy-variabilidad-espacial-rendimientos-glycine-propiedades-condiciones-edafoclimaticas-santafesina.html

  • Anza, M., Garbisu, C., Salazar, O., Epelde, L., Alkorta, I., & Martínez-Santos, M. (2021). Acidification alters the functionality of metal polluted. Applied Soil Ecology, 163, 103920.

    Article  Google Scholar 

  • Arétouyap, Z., Nouck, P. N., Nouayou, R., Kemgang, F. E. G., Toko, A. D. P., & Asfahani, J. (2016). Lessening the adverse effect of the semivariogram model selection on an interpolative survey using kriging technique. Springerplus, 5(1), 1–11.

    Article  Google Scholar 

  • Beucher, A., Adhikari, K., Breuning-Madsen, H., Greve, M., Österholm, P., Fröjdö, S., Jensen, N. H., & Greve, M. (2017). Mapping potential acid sulfate soils in Denmark using legacy data and LiDAR-based derivatives. Geoderma, 308, 363–372.

    Article  CAS  Google Scholar 

  • Bui, E. N. (2018). High-resolution mapping of acid sulfate soils in Northern Australia through predictive models. Environmental Chemistry Letters, 16(4), 1449–1455.

    Article  CAS  Google Scholar 

  • Burrough, P., Van Mensvoort, M., & Bos, J. (1988). Spatial analysis as a reconnaissance survey technique: An example from acid sulphate soil regions of the Mekong Delta, Viet Nam. Retrieved October 1, 2021, from https://library.wur.nl/WebQuery/wurpubs/fulltext/74708

  • Camacho-Tamayo, J. H. (2013). Relación espacial entre la conductividad eléctrica y algunas propiedades químicas del suelo. Revista UDCA Actualidad & Divulgación Científica, 16(2), 401–408.

    Google Scholar 

  • Cambardella, C. A., Moorman, T. B., Parkin, T., Karlen, D., Novak, J., Turco, R., & Konopka, A. (1994). Field-scale variability of soil properties in central Iowa soils. Soil Science Society of American Journal. https://doi.org/10.2136/sssaj1994.03615995005800050033x

    Article  Google Scholar 

  • Castro, H., and M. Gómez. 2015. Suelos sulfatados ácidos. El caso del valle alto del río Chicamocha. Retrieved October 11, 2021, from https://repositorio.uptc.edu.co/handle/001/3889

  • Cohen, J. (1992). Statistical power analysis. Current Directions in Psychological Science, 1(3), 98–101.

    Article  Google Scholar 

  • Combatt Caballero, E., Jarma Orozco, A., & Palencia Luna, M. (2019). Modeling the requirements of agricultural limestone in acid sulfate soils of Brazil and Colombia. Communications in Soil Science and Plant Analysis, 50(8), 935–947.

    Article  CAS  Google Scholar 

  • Combatt Caballero, E., Mercado Fernández, T., & Palencia Severiche, G. (2009). Alteración Química de la solución de un suelo sulfatado acido, con encalamiento y lavado en columna disturbadas. Revista UDCA Actualidad & Divulgación Científica, 12(1), 101–111.

    Google Scholar 

  • Corwin, D., & Lesch, S. (2005). Characterizing soil spatial variability with apparent soil electrical conductivity: I. Survey protocols. Computers and Electronics in Agriculture, 46(1–3), 103–133.

  • Creeper, N. L., Hicks, W. S., Shand, P., & Fitzpatrick, R. W. (2015). Geochemical processes following freshwater reflooding of acidified inland acid sulfate soils: An in situ mesocosm experiment. Chemical Geology, 411, 200–214.

    Article  CAS  Google Scholar 

  • Cutillas, P. P., Barberá, G. G., & García, C. C. (2017). Efectos de las variables ambientales en la estimación de materia orgánica del suelo a escala regional en un ambiente semiarido (Región de Murcia, España). Boletín de La Asociación de Geógrafos Españoles. Retrieved June 2, 2021, from https://bage.age-geografia.es/ojs/index.php/bage/article/view/2497/2353

  • Delbari, M., Amiri, M., & Motlagh, M. B. (2016). Assessing groundwater quality for irrigation using indicator kriging method. Applied Water Science, 6(4), 371–381.

    Article  CAS  Google Scholar 

  • ESRI. (2016). What is the ArcGIS Geostatistical Analyst extension? Retrieved June 5, 2021, from https://desktop.arcgis.com/es/arcmap/104/extensions/geostatistical-analyst/what-is-arcgis-geostatistical-analyst-.htm

  • Estévez Nuño, V. (2020). Machine Learning methods for classification of Acid Sulfate soils in Virolahti. (Master dissertation). Arcada University of Applied Science. Finland. Retrieved June 2, 2021, from https://www.theseus.fi/bitstream/handle/10024/341151/Estevez_VirginiaN.pdf?sequence=5&isAllowed=y

  • Estévez, V., Beucher, A., Mattbäck, S., Boman, A., Auri, J., Björk, K.-M., & Österholm, P. (2022). Machine learning techniques for acid sulfate soil mapping in southeastern Finland. Geoderma, 406, 115446

  • Fanning, D. S. (2013). Acid sulfate soils. In S. E. Jorgensen (Ed.), Encyclopedia of environmental management (pp. 26–30). CRC Press.

    Google Scholar 

  • Ferchichi, H., Farhat, B., Ben-Hamouda, M. F., & Ben-Mammou, A. (2017). Understanding groundwater chemistry in Mediterranean semi-arid system using multivariate statistics techniques and GIS methods: Case of Manouba aquifer (Northeastern Tunisia). Arabian Journal of Geosciences, 10(23), 1–15.

    Article  CAS  Google Scholar 

  • Fernández-Herrera, C., Combatt-Caballero, E., & Rivera-Jiménez, H. (2011). Algunas características de la entomofauna de suelos sulfatados ácidos en Córdoba. Colombia. Revista Mexicana De Ciencias Agrícolas, 2(3), 461–470.

    Google Scholar 

  • Fitzpatrick, R., Grealish, G., Chappell, A., Marvanek, S., & Shand, P. (2010). Spatial variability of subaqueous and terrestrial acid sulfate soils and their properties, for the Lower Lakes South Australia. Retrieved June 4, 2021, from https://publications.csiro.au/rpr/download?pid=csiro:EP104644&dsid=DS6

  • Fitzpatrick, R., Marvanek, S., Shand, P., Merry, R., Thomas, M., & Raven, M. (2008a). Acid sulfate soil maps of the River Murray below Blanchetown (Lock 1) and Lakes Alexandrina and Albert when water levels were at pre-drought and current drought conditions. Retrieved June 4, 2021, from https://publications.csiro.au/rpr/download?pid=procite:dab940bd-8ada-41bf-b4cf-f1d4e4f1ecd2&dsid=DS1

  • Fitzpatrick, R., Powell, B., & Marvanek, S. (2008b). Atlas of Australian acid sulfate soils. Retrieved June 4, 2021, from https://www.researchgate.net/publication/284686754_Atlas_of_Australian_acid_sulfate_soils/link/5832a0c708ae004f74c2b2cf/download

  • Fitzpatrick, R., Shand, P., & Mosley, L. (2017). Acid sulfate soil evolution models and pedogenic pathways during drought and reflooding cycles in irrigated areas and adjacent natural wetlands. Geoderma, 308, 270–290.

    Article  CAS  Google Scholar 

  • Gotway, C. A., & Hartford, A. H. (1996). Geostatistical methods for incorporating auxiliary information in the prediction of spatial variables. Journal of Agricultural, Biological, and Environmental Statistics. https://doi.org/10.2307/1400558

    Article  Google Scholar 

  • Grealish, G., Fitzpatrick, R., & Shand, P. (2014). Regional distribution trends and properties of acid sulfate soils during severe drought in wetlands along the lower River Murray, South Australia: Supporting hazard assessment. Geoderma Regional, 2, 60–71.

    Article  Google Scholar 

  • Greve, M., Greve, M., Kheir, R. B., Bøcher, P., Larsen, R., & McCloy, K. (2010). Comparing decision tree modeling and indicator Kriging for mapping the extent of organic soils in Denmark. In Digital Soil Mapping (pp. 267–280). Springer.

  • Huang, Y., Li, Z., Ye, H., Zhang, S., Zhuo, Z., Xing, A., & Huang, Y. (2019). Mapping soil electrical conductivity using Ordinary Kriging combined with Back-propagation network. Chinese Geographical Science, 29(2), 270–282.

    Article  Google Scholar 

  • Husson, O. (1998). Spatio-temporal variability of acid sulphate soils in the plain of reeds, Vietnam: Impact of soil properties, water management and crop husbandry on the growth and yield of rice in relation to microtopography. (Doctor dissertation). Wageningen University and Research. Retrieved June 4, 2021, from https://edepot.wur.nl/206281

  • Husson, O. (2013). Redox potential (Eh) and pH as drivers of soil/plant/microorganism systems: A transdisciplinary overview pointing to integrative opportunities for agronomy. Plant and Soil, 362(1), 389–417.

    Article  CAS  Google Scholar 

  • Husson, O., Verburg, P., Phung, M. T., & Van Mensvoort, M. (2000). Spatial variability of acid sulphate soils in the Plain of Reeds, Mekong delta. Vietnam. Geoderma, 97(1–2), 1–19.

    CAS  Google Scholar 

  • Ihl, T., Bautista, F., Cejudo Ruíz, F. R., Delgado, M., Quintana Owen, P., Aguilar, D., & Goguitchaichvili, A. (2015). Concentration of toxic elements in topsoils of the metropolitan area of Mexico City: A spatial analysis using Ordinary kriging and Indicator kriging. Revista Internacional De Contaminación Ambiental, 31(1), 47–62.

    CAS  Google Scholar 

  • Instituto Geográfico Agustín Codazzi (2017, March 20). Producción agropecuaria en los grandes distritos de riego de Colombia ha sido Improvisada. Retrieved July 12,2021, from https://www.igac.gov.co/es/noticias/produccion-agropecuaria-en-los-grandes-distritos-de-riego-de-colombia-ha-sido-improvisada

  • Iñigo, V., Andrades, M., Alonso-Martirena, J., Marín, A., & Jiménez-Ballesta, R. (2011). Multivariate statistical and GIS-based approach for the identification of Mn and Ni concentrations and spatial variability in soils of a humid Mediterranean environment: La Rioja, Spain. Water, Air, & Soil Pollution, 222(1), 271–284.

    Article  Google Scholar 

  • Jamieson, P., Porter, J., & Wilson, D. (1991). A test of the computer simulation model ARCWHEAT1 on wheat crops grown in New Zealand. Field Crops Research, 27(4), 337–350.

    Article  Google Scholar 

  • Jaramillo, D. F. (2016). Definition of homogeneous fertility areas through factorial and geostatistical analysis. Boletín De Ciencias De La Tierra, 39, 5–11.

    Article  Google Scholar 

  • Jayalath, N., Mosley, L., Fitzpatrick, R., & Marschner, P. (2016). Addition of organic matter influences pH changes in reduced and oxidised acid sulfate soils. Geoderma, 262, 125–132.

    Article  CAS  Google Scholar 

  • John, K., Afu, S., Isong, I., Aki, E., Kebonye, N., Ayito, E., Chapman, P., Eyong, M., & Penížek, V. (2021). Mapping soil properties with soil-environmental covariates using geostatistics and multivariate statistics. International Journal of Environmental Science and Technology. https://doi.org/10.1007/s13762-020-03089-x

    Article  Google Scholar 

  • Johnston, K., Ver Hoef, J. M., Krivoruchko, K., & Lucas, N. (2001). Using ArcGIS geostatistical analyst. Retrieved June 3, 2021, from http://downloads2.esri.com/support/documentation/ao_/Using_ArcGIS_Geostatistical_Analyst.pdf

  • López, L. E. G. (2019). Protocolo para realizar análisis factorial en variables que afectan las condiciones laborales. Ingeniare, 26, 13–33.

    Article  Google Scholar 

  • Machado, R. M. A., & Serralheiro, R. P. (2017). Soil salinity: Effect on vegetable crop growth. Management practices to prevent and mitigate soil salinization. Horticulturae, 3(2), 30.

  • Martínez, Z. (2008). Influencia de los niveles freáticos en la manifestación de la acidez por sulfuros en un área piloto del Bajo Sinú, Córdoba (Master dissertation). Universidad Nacional de Colombia.

  • Martínez, Z., & Zapata, R. (2009). Características hidrodinámicas en suelos sulfatados ácidos de un sector de la parte baja de la cuenca del río Sinú. Suelos Ecuatoriales, 39(2), 126–131.

    Google Scholar 

  • Mendonça, S. K. G., de Moraes, E. M. V., Otero, X. L., Ferreira, T. O., Correa, M. M., de Sousa, J. E. S., & do Nascimento, C. W. A., Neves, L. V. de M. W., & de Souza Junior, V. S. (2021). Occurrence and pedogenesis of acid sulfate soils in northeastern Brazil. CATENA, 196, 104937.

    Article  Google Scholar 

  • Murillo, D., Ortega, I., Carrillo, J. D., Pardo, A., & Rendón, J. (2012). Comparación de métodos de interpolación para la generación de mapas de ruido en entornos urbanos. Ingenierías USBMed, 3(1), 62–68.

    Article  Google Scholar 

  • Montoya, O. (2007). Aplicación del análisis factorial a la investigación de mercados. Scientia Et Technica, 1(35), 286.

    Google Scholar 

  • Piccini, C., Marchetti, A., Farina, R., & Francaviglia, R. (2012). Application of indicator kriging to evaluate the probability of exceeding nitrate contamination thresholds. International Journal of Environmental Research, 6(4), 853–862.

    CAS  Google Scholar 

  • Reza, S., Baruah, U., Singh, S., & Das, T. (2015). Geostatistical and multivariate analysis of soil heavy metal contamination near coal mining area. Northeastern India. Environmental Earth Sciences, 73(9), 5425–5433.

    Article  CAS  Google Scholar 

  • Roberts, R. C., & McConchie, J. (2017). Preliminary assessment of the acid sulphate soils hazard in the Auckland region. Proceedings of the 20th NZGS Geotechnical Symposium, Napier.

  • Shan, Y., Tysklind, M., Hao, F., Ouyang, W., Chen, S., & Lin, C. (2013). Identification of sources of heavy metals in agricultural soils using multivariate analysis and GIS. Journal of Soils and Sediments, 13(4), 720–729.

    Article  CAS  Google Scholar 

  • Soares, M. F., Centeno, L. N., Timm, L. C., Mello, C. R., Kaiser, D. R., & Beskow, S. (2020). Identifying covariates to assess the spatial variability of saturated soil hydraulic conductivity using robust cokriging at the watershed scale. Journal of Soil Science and Plant Nutrition, 20(3), 1491–1502.

    Article  CAS  Google Scholar 

  • Sukitprapanon, T., Suddhiprakarn, A., Kheoruenromne, I., Anusontpornperm, S., & Gilkes, R. J. (2016). A comparison of potential, active and post-active acid sulfate soils in Thailand. Geoderma Regional, 7(3), 346–356.

    Article  Google Scholar 

  • Sun, X. L., Wu, Y. J., Zhang, C., & Wang, H. L. (2019). Performance of median kriging with robust estimators of the variogram in outlier identification and spatial prediction for soil pollution at a field scale. Science of the Total Environment, 666, 902–914.

    Article  CAS  Google Scholar 

  • Uusi-Kämppä, J., Keskinen, R., Heikkinen, J., Guagliardi, I., & Nuutinen, V. (2019). A map-based comparison of chemical characteristics in the surface horizon of arable acid and non-acid sulfate soils in coastal areas of Finland. Journal of Geochemical Exploration, 200, 193–200.

    Article  Google Scholar 

  • Viloria, J. (2004). La economía del departamento de Córdoba: Ganadería y minería como sectores claves. Retrieved August 12, 2021, from https://www.banrep.gov.co/sites/default/files/publicaciones/archivos/DTSER-51.pdf

  • Vithana, C. L., Ulapane, P. A., Chandrajith, R., Sullivan, L. A., Bundschuh, J., Toppler, N., Ward, N. J., & Senaratne, A. (2021). Acid sulfate soils on the west coast of Sri Lanka: A review. Geoderma Regional, e00382. https://doi.org/10.1016/j.geodrs.2021.e00382

  • Wani, M. A., Wani, J., Bhat, M., Kirmani, N., Wani, Z. M., & Bhat, S. N. (2013). Mapping of soil micronutrients in Kashmir agricultural landscape using ordinary kriging and indicator approach. Journal of the Indian Society of Remote Sensing, 41(2), 319–329.

    Article  Google Scholar 

  • Webster, R., & Oliver, M. A. (2007). Geostatistics for environmental scientists. John Wiley & Sons.

    Book  Google Scholar 

  • World Wide Fund for Nature. (2018, February 2). Cinco datos clave sobre los humedales. Retrieved July 13, 2021, from https://www.wwf.org.co/ ?322310/Cinco-datos-clave-sobre-los-humedales

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Acknowledgements

The authors wish to thank the University of Córdoba for the logistical support and the time assigned to produce this paper and additionally, to the National University of Colombia, Medellín, for providing the necessary training to develop this research.

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  1. Environmental Engineering Department, University of Córdoba, Córdoba, Colombia

    Zoraya Martínez L, Doris Mejía A & Viviana Soto B

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  1. Zoraya Martínez L
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  2. Doris Mejía A
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Correspondence to Zoraya Martínez L.

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Martínez L, Z., Mejía A, D. & Soto B, V. A Methodological Approach to Mapping Acid Sulfate Soils, the Spatial Variability of Acidity and Salinity, and Hazards at the Field Scale in a Sector of the Sinú River Floodplain, Colombia. Water Air Soil Pollut 233, 217 (2022). https://doi.org/10.1007/s11270-022-05658-x

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