Articles | Volume 14, issue 10
https://doi.org/10.5194/essd-14-4719-2022
https://doi.org/10.5194/essd-14-4719-2022
Data description paper
 | 
28 Oct 2022
Data description paper |  | 28 Oct 2022

Colombian soil texture: building a spatial ensemble model

Viviana Marcela Varón-Ramírez, Gustavo Alfonso Araujo-Carrillo, and Mario Antonio Guevara Santamaría

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Cited articles

Aitchison, J.: The statistical analysis of compositional data, Chapman and Hall, Blackburn Press, 460 pp., ISBN-10 1930665784, 1986. a
Amirian-Chakan, A., Minasny, B., Taghizadeh-Mehrjardi, R., Akbarifazli, R., Darvishpasand, Z., and Khordehbin, S.: Some practical aspects of predicting texture data in digital soil mapping, Soil Till. Res., 194, 104289, https://doi.org/10.1016/j.still.2019.06.006, 2019. a, b, c
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Angelini, M. E., Heuvelink, G. B., Kempen, B., and Morrás, H. J.: Mapping the soils of an Argentine Pampas region using structural equation modelling, Geoderma, 281, 102–118, https://doi.org/10.1016/j.geoderma.2016.06.031, 2016. a
Araujo, M. A., Zinn, Y. L., and Lal, R.: Soil parent material, texture and oxide contents have little effect on soil organic carbon retention in tropical highlands, Geoderma, 300, 1–10, https://doi.org/10.1016/j.geoderma.2017.04.006, 2017. a
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These are the first national soil texture maps obtained via digital soil mapping. We built clay, sand, and silt maps using spatial assembling with the best possible predictions at different depths. Also, we identified the better model for each pixel. This work was done to address the lack of soil texture maps in Colombia, and it can provide soil information for water-related applications, ecosystem services, and agricultural and crop modeling.
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