Mapping soil depth in southern pampas Argentina using ancillary data and statistical learning

Autores
Peralta, Nahuel Raúl; Alesso, Carlos Agustín; Costa, Jose Luis; Martin, Nicolás Federico
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Restrictive layers such as hardpans limit the soil water and nutrients available for crops. In the southern Argentinean pampas, petrocalcic hardpans are found at variable depth within the field. Mapping the spatial distribution of depth to the petrocalcic hardpan is important for proper land evaluation, use, and management. Intensive grid sampling and spatial interpolation are labor-intensive and time-consuming mapping approaches for this soil property. In addition, the spatial distribution of this property is often difficult for interpolation techniques such as kriging. This study's objective was to evaluate the potential of soil electrical conductivity (ECa) and terrain attributes to map within-field spatial variation of soil depth using statistical learning techniques. Soil depth measurements up to 1-m depth were taken in eight fields at spatial sampling density ranging from 2 to 12 points ha–1. Spatially dense data of elevation, terrain attributes, and ECa were migrated to soil depth sampling points and also spatially aggregated to include spatial information into the featured space. Then, random forest regression models were used to predict soil depth from colocated ECa and terrain data. Models were cross-validated using a k-fold approach using entire fields as folds. The overall model (using all data) resulted in out-of-the bag R2 and RMSE of .66 and 22.8 cm respectively. Shallow (0–30 cm) and deep (0–90 cm) ECa values were the most important variables, accounting for variability at different ranges, but the importance varied between the soil types. These results suggested that field-scale ECa data and terrain attributes have potential to predict soil depth to hardpan. Further research is needed to improve the generalization of these models and improve the representation of spatial effects in order to implement site-specific management based on soil depth maps.
EEA Balcarce
Fil: Peralta, Nahuel Raúl. Bayer CropScience; Argentina.
Fil: Alesso, Carlos Agustín. Universidad Nacional del Litoral. Instituto de Ciencias Agrarias del Litoral; Argentina
Fil: Alesso, Carlos Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias Agrarias del Litoral; Argentina
Fil: Alesso, Carlos Agustín. University of Illinois at Urbana-Champaign. Department of Crop Sciences; Estados Unidos
Fil: Costa, José Luis. Instituto Nacional de Tecnología Agropecuaria (INTA), Estación Experimental Agropecuaria Balcarce; Argentina
Fil: Martin, Nicolás Federico. University of Illinois at Urbana-Champaign. Department of Crop Sciences; Estados Unidos
Fuente
Soil Science Society of America Journal 86 (1) : 65-78. (January/February 2022)
Materia
Suelo
Manejo del Suelo
Utilización de la Tierra
Soil
Soil Management
Land Use
Profundidad del Suelo
Región Pampeana, Argentina
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/26488
Acceder
id INTADig_f5c6923c87c9d85fe17bc1135380462a
oai_identifier_str oai:localhost:20.500.12123/26488
network_acronym_str INTADig
repository_id_str l
network_name_str INTA Digital (INTA)
spelling Mapping soil depth in southern pampas Argentina using ancillary data and statistical learningPeralta, Nahuel RaúlAlesso, Carlos AgustínCosta, Jose LuisMartin, Nicolás FedericoSueloManejo del SueloUtilización de la TierraSoilSoil ManagementLand UseProfundidad del SueloRegión Pampeana, ArgentinaRestrictive layers such as hardpans limit the soil water and nutrients available for crops. In the southern Argentinean pampas, petrocalcic hardpans are found at variable depth within the field. Mapping the spatial distribution of depth to the petrocalcic hardpan is important for proper land evaluation, use, and management. Intensive grid sampling and spatial interpolation are labor-intensive and time-consuming mapping approaches for this soil property. In addition, the spatial distribution of this property is often difficult for interpolation techniques such as kriging. This study's objective was to evaluate the potential of soil electrical conductivity (ECa) and terrain attributes to map within-field spatial variation of soil depth using statistical learning techniques. Soil depth measurements up to 1-m depth were taken in eight fields at spatial sampling density ranging from 2 to 12 points ha–1. Spatially dense data of elevation, terrain attributes, and ECa were migrated to soil depth sampling points and also spatially aggregated to include spatial information into the featured space. Then, random forest regression models were used to predict soil depth from colocated ECa and terrain data. Models were cross-validated using a k-fold approach using entire fields as folds. The overall model (using all data) resulted in out-of-the bag R2 and RMSE of .66 and 22.8 cm respectively. Shallow (0–30 cm) and deep (0–90 cm) ECa values were the most important variables, accounting for variability at different ranges, but the importance varied between the soil types. These results suggested that field-scale ECa data and terrain attributes have potential to predict soil depth to hardpan. Further research is needed to improve the generalization of these models and improve the representation of spatial effects in order to implement site-specific management based on soil depth maps.EEA BalcarceFil: Peralta, Nahuel Raúl. Bayer CropScience; Argentina.Fil: Alesso, Carlos Agustín. Universidad Nacional del Litoral. Instituto de Ciencias Agrarias del Litoral; ArgentinaFil: Alesso, Carlos Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias Agrarias del Litoral; ArgentinaFil: Alesso, Carlos Agustín. University of Illinois at Urbana-Champaign. Department of Crop Sciences; Estados UnidosFil: Costa, José Luis. Instituto Nacional de Tecnología Agropecuaria (INTA), Estación Experimental Agropecuaria Balcarce; ArgentinaFil: Martin, Nicolás Federico. University of Illinois at Urbana-Champaign. Department of Crop Sciences; Estados UnidosWiley2026-06-04T13:08:31Z2026-06-04T13:08:31Z2022-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/26488https://acsess.onlinelibrary.wiley.com/doi/10.1002/saj2.203500361-59951435-0661https://doi.org/10.1002/saj2.20350Soil Science Society of America Journal 86 (1) : 65-78. (January/February 2022)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2026-09-24T11:43:16Zoai:localhost:20.500.12123/26488instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2026-09-24 11:43:18.018INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Mapping soil depth in southern pampas Argentina using ancillary data and statistical learning
title Mapping soil depth in southern pampas Argentina using ancillary data and statistical learning
spellingShingle Mapping soil depth in southern pampas Argentina using ancillary data and statistical learning
Peralta, Nahuel Raúl
Suelo
Manejo del Suelo
Utilización de la Tierra
Soil
Soil Management
Land Use
Profundidad del Suelo
Región Pampeana, Argentina
title_short Mapping soil depth in southern pampas Argentina using ancillary data and statistical learning
title_full Mapping soil depth in southern pampas Argentina using ancillary data and statistical learning
title_fullStr Mapping soil depth in southern pampas Argentina using ancillary data and statistical learning
title_full_unstemmed Mapping soil depth in southern pampas Argentina using ancillary data and statistical learning
title_sort Mapping soil depth in southern pampas Argentina using ancillary data and statistical learning
dc.creator.none.fl_str_mv Peralta, Nahuel Raúl
Alesso, Carlos Agustín
Costa, Jose Luis
Martin, Nicolás Federico
author Peralta, Nahuel Raúl
author_facet Peralta, Nahuel Raúl
Alesso, Carlos Agustín
Costa, Jose Luis
Martin, Nicolás Federico
author_role author
author2 Alesso, Carlos Agustín
Costa, Jose Luis
Martin, Nicolás Federico
author2_role author
author
author
dc.subject.none.fl_str_mv Suelo
Manejo del Suelo
Utilización de la Tierra
Soil
Soil Management
Land Use
Profundidad del Suelo
Región Pampeana, Argentina
topic Suelo
Manejo del Suelo
Utilización de la Tierra
Soil
Soil Management
Land Use
Profundidad del Suelo
Región Pampeana, Argentina
dc.description.none.fl_txt_mv Restrictive layers such as hardpans limit the soil water and nutrients available for crops. In the southern Argentinean pampas, petrocalcic hardpans are found at variable depth within the field. Mapping the spatial distribution of depth to the petrocalcic hardpan is important for proper land evaluation, use, and management. Intensive grid sampling and spatial interpolation are labor-intensive and time-consuming mapping approaches for this soil property. In addition, the spatial distribution of this property is often difficult for interpolation techniques such as kriging. This study's objective was to evaluate the potential of soil electrical conductivity (ECa) and terrain attributes to map within-field spatial variation of soil depth using statistical learning techniques. Soil depth measurements up to 1-m depth were taken in eight fields at spatial sampling density ranging from 2 to 12 points ha–1. Spatially dense data of elevation, terrain attributes, and ECa were migrated to soil depth sampling points and also spatially aggregated to include spatial information into the featured space. Then, random forest regression models were used to predict soil depth from colocated ECa and terrain data. Models were cross-validated using a k-fold approach using entire fields as folds. The overall model (using all data) resulted in out-of-the bag R2 and RMSE of .66 and 22.8 cm respectively. Shallow (0–30 cm) and deep (0–90 cm) ECa values were the most important variables, accounting for variability at different ranges, but the importance varied between the soil types. These results suggested that field-scale ECa data and terrain attributes have potential to predict soil depth to hardpan. Further research is needed to improve the generalization of these models and improve the representation of spatial effects in order to implement site-specific management based on soil depth maps.
EEA Balcarce
Fil: Peralta, Nahuel Raúl. Bayer CropScience; Argentina.
Fil: Alesso, Carlos Agustín. Universidad Nacional del Litoral. Instituto de Ciencias Agrarias del Litoral; Argentina
Fil: Alesso, Carlos Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias Agrarias del Litoral; Argentina
Fil: Alesso, Carlos Agustín. University of Illinois at Urbana-Champaign. Department of Crop Sciences; Estados Unidos
Fil: Costa, José Luis. Instituto Nacional de Tecnología Agropecuaria (INTA), Estación Experimental Agropecuaria Balcarce; Argentina
Fil: Martin, Nicolás Federico. University of Illinois at Urbana-Champaign. Department of Crop Sciences; Estados Unidos
description Restrictive layers such as hardpans limit the soil water and nutrients available for crops. In the southern Argentinean pampas, petrocalcic hardpans are found at variable depth within the field. Mapping the spatial distribution of depth to the petrocalcic hardpan is important for proper land evaluation, use, and management. Intensive grid sampling and spatial interpolation are labor-intensive and time-consuming mapping approaches for this soil property. In addition, the spatial distribution of this property is often difficult for interpolation techniques such as kriging. This study's objective was to evaluate the potential of soil electrical conductivity (ECa) and terrain attributes to map within-field spatial variation of soil depth using statistical learning techniques. Soil depth measurements up to 1-m depth were taken in eight fields at spatial sampling density ranging from 2 to 12 points ha–1. Spatially dense data of elevation, terrain attributes, and ECa were migrated to soil depth sampling points and also spatially aggregated to include spatial information into the featured space. Then, random forest regression models were used to predict soil depth from colocated ECa and terrain data. Models were cross-validated using a k-fold approach using entire fields as folds. The overall model (using all data) resulted in out-of-the bag R2 and RMSE of .66 and 22.8 cm respectively. Shallow (0–30 cm) and deep (0–90 cm) ECa values were the most important variables, accounting for variability at different ranges, but the importance varied between the soil types. These results suggested that field-scale ECa data and terrain attributes have potential to predict soil depth to hardpan. Further research is needed to improve the generalization of these models and improve the representation of spatial effects in order to implement site-specific management based on soil depth maps.
publishDate 2022
dc.date.none.fl_str_mv 2022-02
2026-06-04T13:08:31Z
2026-06-04T13:08:31Z
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12123/26488
https://acsess.onlinelibrary.wiley.com/doi/10.1002/saj2.20350
0361-5995
1435-0661
https://doi.org/10.1002/saj2.20350
url http://hdl.handle.net/20.500.12123/26488
https://acsess.onlinelibrary.wiley.com/doi/10.1002/saj2.20350
https://doi.org/10.1002/saj2.20350
identifier_str_mv 0361-5995
1435-0661
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Wiley
publisher.none.fl_str_mv Wiley
dc.source.none.fl_str_mv Soil Science Society of America Journal 86 (1) : 65-78. (January/February 2022)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
_version_ 1877227343317041152
score 12.754232

Publicaciones similares