Salinity prediction in loamy soils from 1:2.5 electrical conductivity: Particle size and laboratory conditions

Autores
Tarditti, Andrés J.; Heredia, Olga S.; Perez, Mónica Gabriela; Casas, Cecilia
Año de publicación
2026
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Soil salinity represents a threat to global food security due to the extent of the affected area and its negative impact on plants. Although the electrical conductivity in the saturation extract (ECe) is the reference parameter for soil salinity diagnosis, its measurement is laborious and subjective. Measuring electrical conductivity at a soil-to-water ratio of 1:2.5 (EC1:2.5) is a fast reproducible method; however, its equivalence with ECe is not unequivocal and may depend on multiple edaphic and methodological factors. We developed predictive models to estimate ECe from EC1:2.5 measured under four laboratory measurement conditions that arise from combining two preparation matrices (suspension and extraction) and two resting times (2 and 23 h). A total of 127 loamy soil samples were collected at different depths from agricultural and temperate grasslands. For each sample, ECe, four EC1:2.5 (following tested methods, all in dS.m−1), and particle size were determined. Of these samples, 103 were used to develop predictive models, and the remaining 24 were used for validation, complemented with 16 additional samples from the literature (n = 40). Decision tree analyses identified four data subsets defined by combined thresholds of EC1:2.5 and clay content. The EC1:2.5 threshold varied according to the methodology applied (from 4.9 to 6.1 dS.m−1). All the models predicted higher ECe-EC1:2.5 slope when clay content was above 10%. In suspension 23-h and extraction 2-h methods, soils with > 10% clay and high sand percentage predicted higher ECe values than those with < 10% clay and low sand percentage. Validation showed acceptable predictive accuracy with the highest values for the extraction 23-h method (R2 ≥ 0.99; MSE = 0.38 dS.m−1; RMSE = 0.56). Rapid EC1:2.5 measurements combined with clay and sand percentage provide a simple and accurate alternative for salinity diagnoses of loamy soils.
Instituto de Suelos
Fil: Tarditti, Andrés. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Recursos Naturales; Argentina
Fil: Heredia, Olga. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Recursos Naturales; Argentina
Fil: Pérez, Mónica Gabriela. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; Argentina
Fil: Casas, Cecilia. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Recursos Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA); Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA); Argentina
Fil: Casas, Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA); Argentina
Fil: Casas, Cecilia. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA); Argentina
Fuente
European Journal of Soil Science 77 (3) : e70339 (May–June 2026)
Materia
Suelo
Tipos de Suelos
Salinidad del Suelo
Soil
Soil Types
Soil Salinity
Granulometric Fractions
Predictive Models
Salinity Diagnoses
Soil Texture
Fracciones Granulométricas
Modelos Predictivos
Diagnóstico de Salinidad
Textura del Suelo
Nivel de accesibilidad
acceso restringido
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:20.500.12123/26489
Acceder
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oai_identifier_str oai:localhost:20.500.12123/26489
network_acronym_str INTADig
repository_id_str l
network_name_str INTA Digital (INTA)
spelling Salinity prediction in loamy soils from 1:2.5 electrical conductivity: Particle size and laboratory conditionsTarditti, Andrés J.Heredia, Olga S.Perez, Mónica GabrielaCasas, CeciliaSueloTipos de SuelosSalinidad del SueloSoilSoil TypesSoil SalinityGranulometric FractionsPredictive ModelsSalinity DiagnosesSoil TextureFracciones GranulométricasModelos PredictivosDiagnóstico de SalinidadTextura del SueloSoil salinity represents a threat to global food security due to the extent of the affected area and its negative impact on plants. Although the electrical conductivity in the saturation extract (ECe) is the reference parameter for soil salinity diagnosis, its measurement is laborious and subjective. Measuring electrical conductivity at a soil-to-water ratio of 1:2.5 (EC1:2.5) is a fast reproducible method; however, its equivalence with ECe is not unequivocal and may depend on multiple edaphic and methodological factors. We developed predictive models to estimate ECe from EC1:2.5 measured under four laboratory measurement conditions that arise from combining two preparation matrices (suspension and extraction) and two resting times (2 and 23 h). A total of 127 loamy soil samples were collected at different depths from agricultural and temperate grasslands. For each sample, ECe, four EC1:2.5 (following tested methods, all in dS.m−1), and particle size were determined. Of these samples, 103 were used to develop predictive models, and the remaining 24 were used for validation, complemented with 16 additional samples from the literature (n = 40). Decision tree analyses identified four data subsets defined by combined thresholds of EC1:2.5 and clay content. The EC1:2.5 threshold varied according to the methodology applied (from 4.9 to 6.1 dS.m−1). All the models predicted higher ECe-EC1:2.5 slope when clay content was above 10%. In suspension 23-h and extraction 2-h methods, soils with > 10% clay and high sand percentage predicted higher ECe values than those with < 10% clay and low sand percentage. Validation showed acceptable predictive accuracy with the highest values for the extraction 23-h method (R2 ≥ 0.99; MSE = 0.38 dS.m−1; RMSE = 0.56). Rapid EC1:2.5 measurements combined with clay and sand percentage provide a simple and accurate alternative for salinity diagnoses of loamy soils.Instituto de SuelosFil: Tarditti, Andrés. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Recursos Naturales; ArgentinaFil: Heredia, Olga. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Recursos Naturales; ArgentinaFil: Pérez, Mónica Gabriela. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Casas, Cecilia. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Recursos Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA); Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA); ArgentinaFil: Casas, Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA); ArgentinaFil: Casas, Cecilia. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA); ArgentinaWiley2026-06-04T13:12:15Z2026-06-04T13:12:15Z2026-04-18info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/26489https://bsssjournals.onlinelibrary.wiley.com/doi/10.1111/ejss.703391365-23891351-0754https://doi.org/10.1111/ejss.70339European Journal of Soil Science 77 (3) : e70339 (May–June 2026)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/restrictedAccesshttp://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:20.500.12123/26489instacron: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 11:43:17.816INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Salinity prediction in loamy soils from 1:2.5 electrical conductivity: Particle size and laboratory conditions
title Salinity prediction in loamy soils from 1:2.5 electrical conductivity: Particle size and laboratory conditions
spellingShingle Salinity prediction in loamy soils from 1:2.5 electrical conductivity: Particle size and laboratory conditions
Tarditti, Andrés J.
Suelo
Tipos de Suelos
Salinidad del Suelo
Soil
Soil Types
Soil Salinity
Granulometric Fractions
Predictive Models
Salinity Diagnoses
Soil Texture
Fracciones Granulométricas
Modelos Predictivos
Diagnóstico de Salinidad
Textura del Suelo
title_short Salinity prediction in loamy soils from 1:2.5 electrical conductivity: Particle size and laboratory conditions
title_full Salinity prediction in loamy soils from 1:2.5 electrical conductivity: Particle size and laboratory conditions
title_fullStr Salinity prediction in loamy soils from 1:2.5 electrical conductivity: Particle size and laboratory conditions
title_full_unstemmed Salinity prediction in loamy soils from 1:2.5 electrical conductivity: Particle size and laboratory conditions
title_sort Salinity prediction in loamy soils from 1:2.5 electrical conductivity: Particle size and laboratory conditions
dc.creator.none.fl_str_mv Tarditti, Andrés J.
Heredia, Olga S.
Perez, Mónica Gabriela
Casas, Cecilia
author Tarditti, Andrés J.
author_facet Tarditti, Andrés J.
Heredia, Olga S.
Perez, Mónica Gabriela
Casas, Cecilia
author_role author
author2 Heredia, Olga S.
Perez, Mónica Gabriela
Casas, Cecilia
author2_role author
author
author
dc.subject.none.fl_str_mv Suelo
Tipos de Suelos
Salinidad del Suelo
Soil
Soil Types
Soil Salinity
Granulometric Fractions
Predictive Models
Salinity Diagnoses
Soil Texture
Fracciones Granulométricas
Modelos Predictivos
Diagnóstico de Salinidad
Textura del Suelo
topic Suelo
Tipos de Suelos
Salinidad del Suelo
Soil
Soil Types
Soil Salinity
Granulometric Fractions
Predictive Models
Salinity Diagnoses
Soil Texture
Fracciones Granulométricas
Modelos Predictivos
Diagnóstico de Salinidad
Textura del Suelo
dc.description.none.fl_txt_mv Soil salinity represents a threat to global food security due to the extent of the affected area and its negative impact on plants. Although the electrical conductivity in the saturation extract (ECe) is the reference parameter for soil salinity diagnosis, its measurement is laborious and subjective. Measuring electrical conductivity at a soil-to-water ratio of 1:2.5 (EC1:2.5) is a fast reproducible method; however, its equivalence with ECe is not unequivocal and may depend on multiple edaphic and methodological factors. We developed predictive models to estimate ECe from EC1:2.5 measured under four laboratory measurement conditions that arise from combining two preparation matrices (suspension and extraction) and two resting times (2 and 23 h). A total of 127 loamy soil samples were collected at different depths from agricultural and temperate grasslands. For each sample, ECe, four EC1:2.5 (following tested methods, all in dS.m−1), and particle size were determined. Of these samples, 103 were used to develop predictive models, and the remaining 24 were used for validation, complemented with 16 additional samples from the literature (n = 40). Decision tree analyses identified four data subsets defined by combined thresholds of EC1:2.5 and clay content. The EC1:2.5 threshold varied according to the methodology applied (from 4.9 to 6.1 dS.m−1). All the models predicted higher ECe-EC1:2.5 slope when clay content was above 10%. In suspension 23-h and extraction 2-h methods, soils with > 10% clay and high sand percentage predicted higher ECe values than those with < 10% clay and low sand percentage. Validation showed acceptable predictive accuracy with the highest values for the extraction 23-h method (R2 ≥ 0.99; MSE = 0.38 dS.m−1; RMSE = 0.56). Rapid EC1:2.5 measurements combined with clay and sand percentage provide a simple and accurate alternative for salinity diagnoses of loamy soils.
Instituto de Suelos
Fil: Tarditti, Andrés. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Recursos Naturales; Argentina
Fil: Heredia, Olga. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Recursos Naturales; Argentina
Fil: Pérez, Mónica Gabriela. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; Argentina
Fil: Casas, Cecilia. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Recursos Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA); Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA); Argentina
Fil: Casas, Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA); Argentina
Fil: Casas, Cecilia. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA); Argentina
description Soil salinity represents a threat to global food security due to the extent of the affected area and its negative impact on plants. Although the electrical conductivity in the saturation extract (ECe) is the reference parameter for soil salinity diagnosis, its measurement is laborious and subjective. Measuring electrical conductivity at a soil-to-water ratio of 1:2.5 (EC1:2.5) is a fast reproducible method; however, its equivalence with ECe is not unequivocal and may depend on multiple edaphic and methodological factors. We developed predictive models to estimate ECe from EC1:2.5 measured under four laboratory measurement conditions that arise from combining two preparation matrices (suspension and extraction) and two resting times (2 and 23 h). A total of 127 loamy soil samples were collected at different depths from agricultural and temperate grasslands. For each sample, ECe, four EC1:2.5 (following tested methods, all in dS.m−1), and particle size were determined. Of these samples, 103 were used to develop predictive models, and the remaining 24 were used for validation, complemented with 16 additional samples from the literature (n = 40). Decision tree analyses identified four data subsets defined by combined thresholds of EC1:2.5 and clay content. The EC1:2.5 threshold varied according to the methodology applied (from 4.9 to 6.1 dS.m−1). All the models predicted higher ECe-EC1:2.5 slope when clay content was above 10%. In suspension 23-h and extraction 2-h methods, soils with > 10% clay and high sand percentage predicted higher ECe values than those with < 10% clay and low sand percentage. Validation showed acceptable predictive accuracy with the highest values for the extraction 23-h method (R2 ≥ 0.99; MSE = 0.38 dS.m−1; RMSE = 0.56). Rapid EC1:2.5 measurements combined with clay and sand percentage provide a simple and accurate alternative for salinity diagnoses of loamy soils.
publishDate 2026
dc.date.none.fl_str_mv 2026-06-04T13:12:15Z
2026-06-04T13:12:15Z
2026-04-18
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/20.500.12123/26489
https://bsssjournals.onlinelibrary.wiley.com/doi/10.1111/ejss.70339
1365-2389
1351-0754
https://doi.org/10.1111/ejss.70339
url http://hdl.handle.net/20.500.12123/26489
https://bsssjournals.onlinelibrary.wiley.com/doi/10.1111/ejss.70339
https://doi.org/10.1111/ejss.70339
identifier_str_mv 1365-2389
1351-0754
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/restrictedAccess
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 restrictedAccess
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 European Journal of Soil Science 77 (3) : e70339 (May–June 2026)
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
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