A pedometric technique to delimitate soil-specific zones at field scale

Autores
Castro Franco, Mauricio; Córdoba, Mariano Augusto; Balzarini, Mónica Graciela; Costa, Jose Luis
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Delimitation of soil types within a farm field is key for site-specific crop management. An alternative to this, is to develop pedometric techniques that allow an efficient combination of soil survey information and high-resolution terrain attribute data. The aim of this study was to present and evaluate a pedometric technique to delimit soil-specific zones at field scale by coupled Random forest, fuzzy k-means clustering and spatial principal components algorithms (RF-KM-sPCA) and by using information from soil surveys and terrain attributes derived from a digital elevation model. The protocol involves three-steps: 1) automatic classification of small (20x20m) spatial units (SU) using the knowledge of the soil map units present in the farm landscape, 2) aggregation of SUM at farm scale and 3) validation of soil-specific zones. For the first step, we used the random forest algorithm with 10 terrain attributes. For the second step, KM-sPCA algorithms were used to cluster within field SU accounting for autocorrelation. For the third step, apparent soil electrical conductivity and yield maps was used to validate the delimitation of soil-specific zones. This technique produced more contiguous zones than other cluster methods which do not use spatiality. Six farm fields with highly differences in soils were partitioned by the proposed pedometric strategy. Apparent soil electrical conductivity and yield maps present significant differences among zones in all experimental fields. This analytic strategy, based in easy-to-obtain data, could be used to improve precision agricultural managements.
CEI  Barrow
Fil: Castro Franco, Mauricio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Barrow; Argentina
Fil: Córdoba, Mariano Augusto. Universidad Nacional de Cordoba. Facultad de Ciencias Agropecuarias. Cátedra de Estadística y Biometría; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Balzarini, Mónica Graciela. Universidad Nacional de Cordoba. Facultad de Ciencias Agropecuarias. Cátedra de Estadística y Biometría; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Costa, Jose Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina
Fuente
Geoderma 322 : 101-111. (July 2018)
Materia
Suelo
Agricultura de Precisión
Manejo del Cultivo
Reconocimiento de Suelos
Soil
Precision Agriculture
Crop Management
Soil Surveys
Nivel de accesibilidad
acceso restringido
Condiciones de uso
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/2130
Acceder
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oai_identifier_str oai:localhost:20.500.12123/2130
network_acronym_str INTADig
repository_id_str l
network_name_str INTA Digital (INTA)
spelling A pedometric technique to delimitate soil-specific zones at field scaleCastro Franco, MauricioCórdoba, Mariano AugustoBalzarini, Mónica GracielaCosta, Jose LuisSueloAgricultura de PrecisiónManejo del CultivoReconocimiento de SuelosSoilPrecision AgricultureCrop ManagementSoil SurveysDelimitation of soil types within a farm field is key for site-specific crop management. An alternative to this, is to develop pedometric techniques that allow an efficient combination of soil survey information and high-resolution terrain attribute data. The aim of this study was to present and evaluate a pedometric technique to delimit soil-specific zones at field scale by coupled Random forest, fuzzy k-means clustering and spatial principal components algorithms (RF-KM-sPCA) and by using information from soil surveys and terrain attributes derived from a digital elevation model. The protocol involves three-steps: 1) automatic classification of small (20x20m) spatial units (SU) using the knowledge of the soil map units present in the farm landscape, 2) aggregation of SUM at farm scale and 3) validation of soil-specific zones. For the first step, we used the random forest algorithm with 10 terrain attributes. For the second step, KM-sPCA algorithms were used to cluster within field SU accounting for autocorrelation. For the third step, apparent soil electrical conductivity and yield maps was used to validate the delimitation of soil-specific zones. This technique produced more contiguous zones than other cluster methods which do not use spatiality. Six farm fields with highly differences in soils were partitioned by the proposed pedometric strategy. Apparent soil electrical conductivity and yield maps present significant differences among zones in all experimental fields. This analytic strategy, based in easy-to-obtain data, could be used to improve precision agricultural managements.CEI  BarrowFil: Castro Franco, Mauricio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Barrow; ArgentinaFil: Córdoba, Mariano Augusto. Universidad Nacional de Cordoba. Facultad de Ciencias Agropecuarias. Cátedra de Estadística y Biometría; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Balzarini, Mónica Graciela. Universidad Nacional de Cordoba. Facultad de Ciencias Agropecuarias. Cátedra de Estadística y Biometría; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Costa, Jose Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina2018-03-27T12:28:27Z2018-03-27T12:28:27Z2018-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/2130https://www.sciencedirect.com/science/article/pii/S00167061173028840016-7061https://doi.org/10.1016/j.geoderma.2018.02.034Geoderma 322 : 101-111. (July 2018)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/restrictedAccess2025-09-18T10:07:07Zoai:localhost:20.500.12123/2130instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-09-18 10:07:07.419INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv A pedometric technique to delimitate soil-specific zones at field scale
title A pedometric technique to delimitate soil-specific zones at field scale
spellingShingle A pedometric technique to delimitate soil-specific zones at field scale
Castro Franco, Mauricio
Suelo
Agricultura de Precisión
Manejo del Cultivo
Reconocimiento de Suelos
Soil
Precision Agriculture
Crop Management
Soil Surveys
title_short A pedometric technique to delimitate soil-specific zones at field scale
title_full A pedometric technique to delimitate soil-specific zones at field scale
title_fullStr A pedometric technique to delimitate soil-specific zones at field scale
title_full_unstemmed A pedometric technique to delimitate soil-specific zones at field scale
title_sort A pedometric technique to delimitate soil-specific zones at field scale
dc.creator.none.fl_str_mv Castro Franco, Mauricio
Córdoba, Mariano Augusto
Balzarini, Mónica Graciela
Costa, Jose Luis
author Castro Franco, Mauricio
author_facet Castro Franco, Mauricio
Córdoba, Mariano Augusto
Balzarini, Mónica Graciela
Costa, Jose Luis
author_role author
author2 Córdoba, Mariano Augusto
Balzarini, Mónica Graciela
Costa, Jose Luis
author2_role author
author
author
dc.subject.none.fl_str_mv Suelo
Agricultura de Precisión
Manejo del Cultivo
Reconocimiento de Suelos
Soil
Precision Agriculture
Crop Management
Soil Surveys
topic Suelo
Agricultura de Precisión
Manejo del Cultivo
Reconocimiento de Suelos
Soil
Precision Agriculture
Crop Management
Soil Surveys
dc.description.none.fl_txt_mv Delimitation of soil types within a farm field is key for site-specific crop management. An alternative to this, is to develop pedometric techniques that allow an efficient combination of soil survey information and high-resolution terrain attribute data. The aim of this study was to present and evaluate a pedometric technique to delimit soil-specific zones at field scale by coupled Random forest, fuzzy k-means clustering and spatial principal components algorithms (RF-KM-sPCA) and by using information from soil surveys and terrain attributes derived from a digital elevation model. The protocol involves three-steps: 1) automatic classification of small (20x20m) spatial units (SU) using the knowledge of the soil map units present in the farm landscape, 2) aggregation of SUM at farm scale and 3) validation of soil-specific zones. For the first step, we used the random forest algorithm with 10 terrain attributes. For the second step, KM-sPCA algorithms were used to cluster within field SU accounting for autocorrelation. For the third step, apparent soil electrical conductivity and yield maps was used to validate the delimitation of soil-specific zones. This technique produced more contiguous zones than other cluster methods which do not use spatiality. Six farm fields with highly differences in soils were partitioned by the proposed pedometric strategy. Apparent soil electrical conductivity and yield maps present significant differences among zones in all experimental fields. This analytic strategy, based in easy-to-obtain data, could be used to improve precision agricultural managements.
CEI  Barrow
Fil: Castro Franco, Mauricio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Barrow; Argentina
Fil: Córdoba, Mariano Augusto. Universidad Nacional de Cordoba. Facultad de Ciencias Agropecuarias. Cátedra de Estadística y Biometría; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Balzarini, Mónica Graciela. Universidad Nacional de Cordoba. Facultad de Ciencias Agropecuarias. Cátedra de Estadística y Biometría; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Costa, Jose Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina
description Delimitation of soil types within a farm field is key for site-specific crop management. An alternative to this, is to develop pedometric techniques that allow an efficient combination of soil survey information and high-resolution terrain attribute data. The aim of this study was to present and evaluate a pedometric technique to delimit soil-specific zones at field scale by coupled Random forest, fuzzy k-means clustering and spatial principal components algorithms (RF-KM-sPCA) and by using information from soil surveys and terrain attributes derived from a digital elevation model. The protocol involves three-steps: 1) automatic classification of small (20x20m) spatial units (SU) using the knowledge of the soil map units present in the farm landscape, 2) aggregation of SUM at farm scale and 3) validation of soil-specific zones. For the first step, we used the random forest algorithm with 10 terrain attributes. For the second step, KM-sPCA algorithms were used to cluster within field SU accounting for autocorrelation. For the third step, apparent soil electrical conductivity and yield maps was used to validate the delimitation of soil-specific zones. This technique produced more contiguous zones than other cluster methods which do not use spatiality. Six farm fields with highly differences in soils were partitioned by the proposed pedometric strategy. Apparent soil electrical conductivity and yield maps present significant differences among zones in all experimental fields. This analytic strategy, based in easy-to-obtain data, could be used to improve precision agricultural managements.
publishDate 2018
dc.date.none.fl_str_mv 2018-03-27T12:28:27Z
2018-03-27T12:28:27Z
2018-07
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/20.500.12123/2130
https://www.sciencedirect.com/science/article/pii/S0016706117302884
0016-7061
https://doi.org/10.1016/j.geoderma.2018.02.034
url http://hdl.handle.net/20.500.12123/2130
https://www.sciencedirect.com/science/article/pii/S0016706117302884
https://doi.org/10.1016/j.geoderma.2018.02.034
identifier_str_mv 0016-7061
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/restrictedAccess
eu_rights_str_mv restrictedAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv Geoderma 322 : 101-111. (July 2018)
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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score 13.001348

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