State-space analysis of soil data: an approach based on space-varying regresion models.

The assessment of the relationship among soil properties (such as total nitrogen and organic carbon) taken along lines called transects is a subject of great interest in agricultural experimentation. This question has been usually approached through standard state-space methods by some authors in the soil science literature. Important limitations of the mentioned procedures used in practice are pointed out and discussed in this paper, specially those related to the model parameters, meaning and practical interpretation. In the standard state-space approach, based on an autoregressive structure, it does not present any parameters that express the variables relationship at the same point in space, but only at lagged points. Also, its model parameters (in the transition matrix) have a global meaning and not a local one, not expressing more directly the soil heterogeneity. Therefore, the objective here is to propose an alternative state-space approach, based on dynamic (space-varying parameters) regression models in order to avoid the mentioned drawbacks. Soil total nitrogen and soil organic carbon samples were collected on a Typic Haplustox. Samples were taken along a line (transect) located in the middle of two adjacent contour lines. The transect samples, totaling 97, were collected in the plow layer (0-0.20 m) at points spaced 2 meters appart. Results show the comparative advantages of the proposed method (based on an alternative state-space approach) in relation to the standard state-space analysis. Such advantages are related to a more adequate incorporation of soil heterogeneity along the spatial transect resulting in a better model fitting, and greater flexibility of the model's building process with an easier interpretability of the local model coefficients.

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Bibliographic Details
Main Authors: TIMM, L. C., BARBOSA, E. P., SOUZA, M. D. de, DYNIA, J. F., REICHARD, K.
Other Authors: L. C. TIMM, CENA/USP; E. P. BARBOSA, IMECC/Unicamp; MANOEL DORNELAS DE SOUZA, CNPMA; J. F. DYNIA, CENA/USP; K. REICHARD, CENA/USP.
Format: Artigo de periódico biblioteca
Language:English
eng
Published: 2006-10-02
Subjects:Análise do solo, Modelo matemático., Química do solo,
Online Access:http://www.alice.cnptia.embrapa.br/alice/handle/doc/15026
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spelling dig-alice-doc-150262017-09-19T00:11:21Z State-space analysis of soil data: an approach based on space-varying regresion models. TIMM, L. C. BARBOSA, E. P. SOUZA, M. D. de DYNIA, J. F. REICHARD, K. L. C. TIMM, CENA/USP; E. P. BARBOSA, IMECC/Unicamp; MANOEL DORNELAS DE SOUZA, CNPMA; J. F. DYNIA, CENA/USP; K. REICHARD, CENA/USP. Análise do solo Modelo matemático. Química do solo The assessment of the relationship among soil properties (such as total nitrogen and organic carbon) taken along lines called transects is a subject of great interest in agricultural experimentation. This question has been usually approached through standard state-space methods by some authors in the soil science literature. Important limitations of the mentioned procedures used in practice are pointed out and discussed in this paper, specially those related to the model parameters, meaning and practical interpretation. In the standard state-space approach, based on an autoregressive structure, it does not present any parameters that express the variables relationship at the same point in space, but only at lagged points. Also, its model parameters (in the transition matrix) have a global meaning and not a local one, not expressing more directly the soil heterogeneity. Therefore, the objective here is to propose an alternative state-space approach, based on dynamic (space-varying parameters) regression models in order to avoid the mentioned drawbacks. Soil total nitrogen and soil organic carbon samples were collected on a Typic Haplustox. Samples were taken along a line (transect) located in the middle of two adjacent contour lines. The transect samples, totaling 97, were collected in the plow layer (0-0.20 m) at points spaced 2 meters appart. Results show the comparative advantages of the proposed method (based on an alternative state-space approach) in relation to the standard state-space analysis. Such advantages are related to a more adequate incorporation of soil heterogeneity along the spatial transect resulting in a better model fitting, and greater flexibility of the model's building process with an easier interpretability of the local model coefficients. 2017-09-19T00:11:21Z 2017-09-19T00:11:21Z 2006-10-02 2003 2017-09-20T11:11:11Z Artigo de periódico Scientia Agricola, Piracicaba, v. 60, n. 2, p. 371-376, 2003 http://www.alice.cnptia.embrapa.br/alice/handle/doc/15026 en eng openAccess
institution EMBRAPA
collection DSpace
country Brasil
countrycode BR
component Bibliográfico
access En linea
databasecode dig-alice
tag biblioteca
region America del Sur
libraryname Sistema de bibliotecas de EMBRAPA
language English
eng
topic Análise do solo
Modelo matemático.
Química do solo
Análise do solo
Modelo matemático.
Química do solo
spellingShingle Análise do solo
Modelo matemático.
Química do solo
Análise do solo
Modelo matemático.
Química do solo
TIMM, L. C.
BARBOSA, E. P.
SOUZA, M. D. de
DYNIA, J. F.
REICHARD, K.
State-space analysis of soil data: an approach based on space-varying regresion models.
description The assessment of the relationship among soil properties (such as total nitrogen and organic carbon) taken along lines called transects is a subject of great interest in agricultural experimentation. This question has been usually approached through standard state-space methods by some authors in the soil science literature. Important limitations of the mentioned procedures used in practice are pointed out and discussed in this paper, specially those related to the model parameters, meaning and practical interpretation. In the standard state-space approach, based on an autoregressive structure, it does not present any parameters that express the variables relationship at the same point in space, but only at lagged points. Also, its model parameters (in the transition matrix) have a global meaning and not a local one, not expressing more directly the soil heterogeneity. Therefore, the objective here is to propose an alternative state-space approach, based on dynamic (space-varying parameters) regression models in order to avoid the mentioned drawbacks. Soil total nitrogen and soil organic carbon samples were collected on a Typic Haplustox. Samples were taken along a line (transect) located in the middle of two adjacent contour lines. The transect samples, totaling 97, were collected in the plow layer (0-0.20 m) at points spaced 2 meters appart. Results show the comparative advantages of the proposed method (based on an alternative state-space approach) in relation to the standard state-space analysis. Such advantages are related to a more adequate incorporation of soil heterogeneity along the spatial transect resulting in a better model fitting, and greater flexibility of the model's building process with an easier interpretability of the local model coefficients.
author2 L. C. TIMM, CENA/USP; E. P. BARBOSA, IMECC/Unicamp; MANOEL DORNELAS DE SOUZA, CNPMA; J. F. DYNIA, CENA/USP; K. REICHARD, CENA/USP.
author_facet L. C. TIMM, CENA/USP; E. P. BARBOSA, IMECC/Unicamp; MANOEL DORNELAS DE SOUZA, CNPMA; J. F. DYNIA, CENA/USP; K. REICHARD, CENA/USP.
TIMM, L. C.
BARBOSA, E. P.
SOUZA, M. D. de
DYNIA, J. F.
REICHARD, K.
format Artigo de periódico
topic_facet Análise do solo
Modelo matemático.
Química do solo
author TIMM, L. C.
BARBOSA, E. P.
SOUZA, M. D. de
DYNIA, J. F.
REICHARD, K.
author_sort TIMM, L. C.
title State-space analysis of soil data: an approach based on space-varying regresion models.
title_short State-space analysis of soil data: an approach based on space-varying regresion models.
title_full State-space analysis of soil data: an approach based on space-varying regresion models.
title_fullStr State-space analysis of soil data: an approach based on space-varying regresion models.
title_full_unstemmed State-space analysis of soil data: an approach based on space-varying regresion models.
title_sort state-space analysis of soil data: an approach based on space-varying regresion models.
publishDate 2006-10-02
url http://www.alice.cnptia.embrapa.br/alice/handle/doc/15026
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