Development of a regional soil productivity index using an artificial neural network approach

Autores
de Paepe, Josefina; Alvarez, Roberto
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Soil productivity indices represent ratings of the potential plant biomass production of soils. Inductive approaches determine productivity based on inferred effects of soil properties on yield. Conversely, deductive approaches use yield information to estimate productivity. Our objective was to compare the performance of both types of productivity indices for assessing regional soil productivity for wheat (Triticum aestivum L.) yield in the Pampas. Soil data from soil surveys and interpolated climate information were utilized. Wheat yield data from a 40-yr period and representing ?45 Mha were used. Inductive productivity indices showed a low correlation with observed yield (R2 < 0.45, P = 0.05). The best performance of deductive empirical methods was attained using a blind guess option, but soils could only be rated when yield data were available. Yield models based on the neural network approach had good performance (R2 = 0.614, root mean square error [RMSE] = 548 kg ha–1) and was used for regional productivity index development. This index could be extrapolated to soils for which yield data are not available, and its validation with yield averages was optimal (R2 = 0.728, P = 0.05). Regional high productivity was achieved for combinations of medium to high levels of soil organic C and soil available water storage capacity variables, which showed a positive interaction. This methodology for assessing soil productivity based on an empirical yield-based model may be applied in other regions of the world and for different crops.
Fil: de Paepe, Josefina. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Alvarez, Roberto. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
Suelos
Productividad
Materia Orgánica
Redes Neuronales
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/26265
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Development of a regional soil productivity index using an artificial neural network approachde Paepe, JosefinaAlvarez, RobertoSuelosProductividadMateria OrgánicaRedes Neuronaleshttps://purl.org/becyt/ford/4.1https://purl.org/becyt/ford/4Soil productivity indices represent ratings of the potential plant biomass production of soils. Inductive approaches determine productivity based on inferred effects of soil properties on yield. Conversely, deductive approaches use yield information to estimate productivity. Our objective was to compare the performance of both types of productivity indices for assessing regional soil productivity for wheat (Triticum aestivum L.) yield in the Pampas. Soil data from soil surveys and interpolated climate information were utilized. Wheat yield data from a 40-yr period and representing ?45 Mha were used. Inductive productivity indices showed a low correlation with observed yield (R2 < 0.45, P = 0.05). The best performance of deductive empirical methods was attained using a blind guess option, but soils could only be rated when yield data were available. Yield models based on the neural network approach had good performance (R2 = 0.614, root mean square error [RMSE] = 548 kg ha–1) and was used for regional productivity index development. This index could be extrapolated to soils for which yield data are not available, and its validation with yield averages was optimal (R2 = 0.728, P = 0.05). Regional high productivity was achieved for combinations of medium to high levels of soil organic C and soil available water storage capacity variables, which showed a positive interaction. This methodology for assessing soil productivity based on an empirical yield-based model may be applied in other regions of the world and for different crops.Fil: de Paepe, Josefina. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Alvarez, Roberto. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaAmer Soc Agronomy2013-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/26265de Paepe, Josefina; Alvarez, Roberto; Development of a regional soil productivity index using an artificial neural network approach; Amer Soc Agronomy; Agronomy Journal; 105; 6; 10-2013; 1803-18130002-1962CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.2134/agronj2013.0070info:eu-repo/semantics/altIdentifier/url/https://dl.sciencesocieties.org/publications/aj/abstracts/105/6/1803info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:54:11Zoai:ri.conicet.gov.ar:11336/26265instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:54:11.587CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Development of a regional soil productivity index using an artificial neural network approach
title Development of a regional soil productivity index using an artificial neural network approach
spellingShingle Development of a regional soil productivity index using an artificial neural network approach
de Paepe, Josefina
Suelos
Productividad
Materia Orgánica
Redes Neuronales
title_short Development of a regional soil productivity index using an artificial neural network approach
title_full Development of a regional soil productivity index using an artificial neural network approach
title_fullStr Development of a regional soil productivity index using an artificial neural network approach
title_full_unstemmed Development of a regional soil productivity index using an artificial neural network approach
title_sort Development of a regional soil productivity index using an artificial neural network approach
dc.creator.none.fl_str_mv de Paepe, Josefina
Alvarez, Roberto
author de Paepe, Josefina
author_facet de Paepe, Josefina
Alvarez, Roberto
author_role author
author2 Alvarez, Roberto
author2_role author
dc.subject.none.fl_str_mv Suelos
Productividad
Materia Orgánica
Redes Neuronales
topic Suelos
Productividad
Materia Orgánica
Redes Neuronales
purl_subject.fl_str_mv https://purl.org/becyt/ford/4.1
https://purl.org/becyt/ford/4
dc.description.none.fl_txt_mv Soil productivity indices represent ratings of the potential plant biomass production of soils. Inductive approaches determine productivity based on inferred effects of soil properties on yield. Conversely, deductive approaches use yield information to estimate productivity. Our objective was to compare the performance of both types of productivity indices for assessing regional soil productivity for wheat (Triticum aestivum L.) yield in the Pampas. Soil data from soil surveys and interpolated climate information were utilized. Wheat yield data from a 40-yr period and representing ?45 Mha were used. Inductive productivity indices showed a low correlation with observed yield (R2 < 0.45, P = 0.05). The best performance of deductive empirical methods was attained using a blind guess option, but soils could only be rated when yield data were available. Yield models based on the neural network approach had good performance (R2 = 0.614, root mean square error [RMSE] = 548 kg ha–1) and was used for regional productivity index development. This index could be extrapolated to soils for which yield data are not available, and its validation with yield averages was optimal (R2 = 0.728, P = 0.05). Regional high productivity was achieved for combinations of medium to high levels of soil organic C and soil available water storage capacity variables, which showed a positive interaction. This methodology for assessing soil productivity based on an empirical yield-based model may be applied in other regions of the world and for different crops.
Fil: de Paepe, Josefina. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Alvarez, Roberto. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description Soil productivity indices represent ratings of the potential plant biomass production of soils. Inductive approaches determine productivity based on inferred effects of soil properties on yield. Conversely, deductive approaches use yield information to estimate productivity. Our objective was to compare the performance of both types of productivity indices for assessing regional soil productivity for wheat (Triticum aestivum L.) yield in the Pampas. Soil data from soil surveys and interpolated climate information were utilized. Wheat yield data from a 40-yr period and representing ?45 Mha were used. Inductive productivity indices showed a low correlation with observed yield (R2 < 0.45, P = 0.05). The best performance of deductive empirical methods was attained using a blind guess option, but soils could only be rated when yield data were available. Yield models based on the neural network approach had good performance (R2 = 0.614, root mean square error [RMSE] = 548 kg ha–1) and was used for regional productivity index development. This index could be extrapolated to soils for which yield data are not available, and its validation with yield averages was optimal (R2 = 0.728, P = 0.05). Regional high productivity was achieved for combinations of medium to high levels of soil organic C and soil available water storage capacity variables, which showed a positive interaction. This methodology for assessing soil productivity based on an empirical yield-based model may be applied in other regions of the world and for different crops.
publishDate 2013
dc.date.none.fl_str_mv 2013-10
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/11336/26265
de Paepe, Josefina; Alvarez, Roberto; Development of a regional soil productivity index using an artificial neural network approach; Amer Soc Agronomy; Agronomy Journal; 105; 6; 10-2013; 1803-1813
0002-1962
CONICET Digital
CONICET
url http://hdl.handle.net/11336/26265
identifier_str_mv de Paepe, Josefina; Alvarez, Roberto; Development of a regional soil productivity index using an artificial neural network approach; Amer Soc Agronomy; Agronomy Journal; 105; 6; 10-2013; 1803-1813
0002-1962
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.2134/agronj2013.0070
info:eu-repo/semantics/altIdentifier/url/https://dl.sciencesocieties.org/publications/aj/abstracts/105/6/1803
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Amer Soc Agronomy
publisher.none.fl_str_mv Amer Soc Agronomy
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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