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dc.contributor.author
Domenech, Marisa Beatriz
dc.contributor.author
Castro Franco, Mauricio
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dc.contributor.author
Costa, Jose Luis
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dc.contributor.author
Amiotti, Nilda Mabel
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dc.date.available
2019-03-29T17:53:11Z
dc.date.issued
2017-03
dc.identifier.citation
Domenech, Marisa Beatriz; Castro Franco, Mauricio; Costa, Jose Luis; Amiotti, Nilda Mabel; Sampling scheme optimization to map soil depth to petrocalcic horizon at field scale; Elsevier Science; Geoderma; 290; 3-2017; 75-82
dc.identifier.issn
0016-7061
dc.identifier.uri
http://hdl.handle.net/11336/72845
dc.description.abstract
Soil depth has played a key role in the development of soil survey, implementation of soil-specific management and validation of hydrological models. Generally, soil depth at field scale is difficult to map due to complex interactions of factors of soil formation at field scale. As a result, the conventional sampling schemes to map soil depth are generally laborious, time consuming and expensive. In this study, we presented, tested and evaluated a method to optimize the sampling scheme to map soil depth to petrocalcic horizon at field scale. The method was tested with real data at four agricultural fields localized in the southeast Pampas plain of Argentina. The purpose of the method was to minimize the sample dataset size to map soil depth to petrocalcic horizon based on ordinary cokriging, five calibration sample sizes (returned by Conditioned Latin hypercube –cLHS-), and apparent electrical conductivity (ECa) or elevation as variables of auxiliary information. The results suggest that (i) only 30% of samples collected on a 30-m grid are required to provide high prediction accuracy (R2 > 0.95) to map soil depth to petrocalcic horizon; (ii) an independent validation dataset based on 50% of the samples on a 30-m grid is adequate to validate the most realistic accuracy estimate; and (iii) ECa and elevation, as variables of auxiliary information, are sufficient to map soil depth to petrocalcic horizon. The method proposed provides a significant improvement over conventional to map soil depth and allows reducing cost, time and field labour. Extrapolation of the results to other areas needs to be tested.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier Science
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.subject
Argentina
dc.subject
Conditioned Latin Hypercube
dc.subject
Digital Soil Mapping
dc.subject
Ordinary Cokriging
dc.subject
Precision Agriculture
dc.subject.classification
Agricultura
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dc.subject.classification
Agricultura, Silvicultura y Pesca
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dc.subject.classification
CIENCIAS AGRÍCOLAS
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dc.title
Sampling scheme optimization to map soil depth to petrocalcic horizon at field scale
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2019-03-29T12:08:03Z
dc.journal.volume
290
dc.journal.pagination
75-82
dc.journal.pais
Países Bajos
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dc.journal.ciudad
Amsterdam
dc.description.fil
Fil: Domenech, Marisa Beatriz. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Barrow; Argentina
dc.description.fil
Fil: Castro Franco, Mauricio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: Costa, Jose Luis. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Balcarce; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: Amiotti, Nilda Mabel. Universidad Nacional del Sur. Departamento de Agronomía; Argentina
dc.journal.title
Geoderma
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.geoderma.2016.12.012
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0016706116310096
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