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Modeling the spatial distribution of crop sequences at a large regional scale using land-cover survey data: A case from France

Identifieur interne : 000023 ( PascalFrancis/Corpus ); précédent : 000022; suivant : 000024

Modeling the spatial distribution of crop sequences at a large regional scale using land-cover survey data: A case from France

Auteurs : YING XIAO ; Catherine Mignolet ; Jean-François Mari ; Marc Benoit

Source :

RBID : Pascal:14-0083732

Descripteurs français

English descriptors

Abstract

Assessing the environmental impacts of agricultural production systems requires spatially explicit information regarding cropping systems. Projecting changes in agricultural land use that are caused by changes in land management practices for analyzing the performance of land activity-related policies, such as agricultural policies, also requires this type of data for model inputs. Crop sequences, which are vital and widely adopted agricultural practices, are difficult to directly detect at a regional scale. This study presents innovative stochastic data mining that was aimed at describing the spatial distribution of crop sequences at a large regional scale. The data mining is performed by hidden Markov models and an unsupervised clustering analysis that processes sequentially observed (from 1992 to 2003) land-cover survey data on the French mainland named Teruti. The 2549 3-year crop sequences were first identified as major crop sequences across the entire territory, which included 406 (merged) agricultural districts, using hidden Markov models. The 406 (merged) agricultural districts were then grouped into 21 clusters according to the similarity of the probabilities of occurrences of major 3-year crop sequences using hierarchical clustering analysis. Four cropping systems were further identified: vineyard-based cropping systems, maize monoculture and maize/wheat-based cropping systems, temporary pasture and maize-based cropping systems and wheat and barley-based cropping systems. The modeling approach that is presented in this study provides a tool to extract large-scale cropping patterns from increasingly available time series data on land-cover and land-use. With this tool, users can (a) identify the homogeneous zones in terms of fixed-length crop sequences across a large territory, (b) understand the characteristics of cropping systems within a region in terms of typical crop sequences, and (c) identify the major crop sequences of a region according to the probabilities of occurrences.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

pA  
A01 01  1    @0 0168-1699
A02 01      @0 CEAGE6
A03   1    @0 Comput. electron. agric.
A05       @2 102
A08 01  1  ENG  @1 Modeling the spatial distribution of crop sequences at a large regional scale using land-cover survey data: A case from France
A11 01  1    @1 YING XIAO
A11 02  1    @1 MIGNOLET (Catherine)
A11 03  1    @1 MARI (Jean-François)
A11 04  1    @1 BENOIT (Marc)
A14 01      @1 INRA SAD UPR 055 ASTER, 662 Avenue Louis Buffet @2 88500 Mirecourt @3 FRA @Z 1 aut. @Z 2 aut. @Z 4 aut.
A14 02      @1 Université de Lorraine, LORIA, UMR 7503 @2 54506 Vandoeuvre-lès-Nancy @3 FRA @Z 3 aut.
A20       @1 51-63
A21       @1 2014
A23 01      @0 ENG
A43 01      @1 INIST @2 21007 @5 354000506123820060
A44       @0 0000 @1 © 2014 INIST-CNRS. All rights reserved.
A45       @0 3/4 p.
A47 01  1    @0 14-0083732
A60       @1 P
A61       @0 A
A64 01  1    @0 Computers and electronics in agriculture
A66 01      @0 NLD
C01 01    ENG  @0 Assessing the environmental impacts of agricultural production systems requires spatially explicit information regarding cropping systems. Projecting changes in agricultural land use that are caused by changes in land management practices for analyzing the performance of land activity-related policies, such as agricultural policies, also requires this type of data for model inputs. Crop sequences, which are vital and widely adopted agricultural practices, are difficult to directly detect at a regional scale. This study presents innovative stochastic data mining that was aimed at describing the spatial distribution of crop sequences at a large regional scale. The data mining is performed by hidden Markov models and an unsupervised clustering analysis that processes sequentially observed (from 1992 to 2003) land-cover survey data on the French mainland named Teruti. The 2549 3-year crop sequences were first identified as major crop sequences across the entire territory, which included 406 (merged) agricultural districts, using hidden Markov models. The 406 (merged) agricultural districts were then grouped into 21 clusters according to the similarity of the probabilities of occurrences of major 3-year crop sequences using hierarchical clustering analysis. Four cropping systems were further identified: vineyard-based cropping systems, maize monoculture and maize/wheat-based cropping systems, temporary pasture and maize-based cropping systems and wheat and barley-based cropping systems. The modeling approach that is presented in this study provides a tool to extract large-scale cropping patterns from increasingly available time series data on land-cover and land-use. With this tool, users can (a) identify the homogeneous zones in terms of fixed-length crop sequences across a large territory, (b) understand the characteristics of cropping systems within a region in terms of typical crop sequences, and (c) identify the major crop sequences of a region according to the probabilities of occurrences.
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C03 01  X  ENG  @0 Modeling @5 01
C03 01  X  SPA  @0 Modelización @5 01
C03 02  X  FRE  @0 Répartition spatiale @5 02
C03 02  X  ENG  @0 Spatial distribution @5 02
C03 02  X  SPA  @0 Distribución espacial @5 02
C03 03  X  FRE  @0 Echelle grande @5 03
C03 03  X  ENG  @0 Large scale @5 03
C03 03  X  SPA  @0 Escala grande @5 03
C03 04  X  FRE  @0 Echelon régional @5 04
C03 04  X  ENG  @0 Regional scope @5 04
C03 04  X  SPA  @0 Escala regional @5 04
C03 05  X  FRE  @0 Occupation sol @5 05
C03 05  X  ENG  @0 Land use @5 05
C03 05  X  SPA  @0 Ocupación terreno @5 05
C03 06  X  FRE  @0 Système culture @5 06
C03 06  X  ENG  @0 Cropping system @5 06
C03 06  X  SPA  @0 Sistema cultural @5 06
C03 07  X  FRE  @0 Modèle Markov caché @5 07
C03 07  X  ENG  @0 Hidden Markov model @5 07
C03 07  X  SPA  @0 Modelo Markov oculto @5 07
C03 08  X  FRE  @0 France @2 NG @5 20
C03 08  X  ENG  @0 France @2 NG @5 20
C03 08  X  SPA  @0 Francia @2 NG @5 20
C03 09  X  FRE  @0 Terre agricole @4 CD @5 96
C03 09  X  ENG  @0 Farmland @4 CD @5 96
C03 09  X  SPA  @0 Tierra agrícola @4 CD @5 96
C07 01  X  FRE  @0 Europe @2 NG
C07 01  X  ENG  @0 Europe @2 NG
C07 01  X  SPA  @0 Europa @2 NG
N21       @1 111
N44 01      @1 OTO
N82       @1 OTO

Format Inist (serveur)

NO : PASCAL 14-0083732 INIST
ET : Modeling the spatial distribution of crop sequences at a large regional scale using land-cover survey data: A case from France
AU : YING XIAO; MIGNOLET (Catherine); MARI (Jean-François); BENOIT (Marc)
AF : INRA SAD UPR 055 ASTER, 662 Avenue Louis Buffet/88500 Mirecourt/France (1 aut., 2 aut., 4 aut.); Université de Lorraine, LORIA, UMR 7503/54506 Vandoeuvre-lès-Nancy/France (3 aut.)
DT : Publication en série; Niveau analytique
SO : Computers and electronics in agriculture; ISSN 0168-1699; Coden CEAGE6; Pays-Bas; Da. 2014; Vol. 102; Pp. 51-63; Bibl. 3/4 p.
LA : Anglais
EA : Assessing the environmental impacts of agricultural production systems requires spatially explicit information regarding cropping systems. Projecting changes in agricultural land use that are caused by changes in land management practices for analyzing the performance of land activity-related policies, such as agricultural policies, also requires this type of data for model inputs. Crop sequences, which are vital and widely adopted agricultural practices, are difficult to directly detect at a regional scale. This study presents innovative stochastic data mining that was aimed at describing the spatial distribution of crop sequences at a large regional scale. The data mining is performed by hidden Markov models and an unsupervised clustering analysis that processes sequentially observed (from 1992 to 2003) land-cover survey data on the French mainland named Teruti. The 2549 3-year crop sequences were first identified as major crop sequences across the entire territory, which included 406 (merged) agricultural districts, using hidden Markov models. The 406 (merged) agricultural districts were then grouped into 21 clusters according to the similarity of the probabilities of occurrences of major 3-year crop sequences using hierarchical clustering analysis. Four cropping systems were further identified: vineyard-based cropping systems, maize monoculture and maize/wheat-based cropping systems, temporary pasture and maize-based cropping systems and wheat and barley-based cropping systems. The modeling approach that is presented in this study provides a tool to extract large-scale cropping patterns from increasingly available time series data on land-cover and land-use. With this tool, users can (a) identify the homogeneous zones in terms of fixed-length crop sequences across a large territory, (b) understand the characteristics of cropping systems within a region in terms of typical crop sequences, and (c) identify the major crop sequences of a region according to the probabilities of occurrences.
CC : 002A32C04A
FD : Modélisation; Répartition spatiale; Echelle grande; Echelon régional; Occupation sol; Système culture; Modèle Markov caché; France; Terre agricole
FG : Europe
ED : Modeling; Spatial distribution; Large scale; Regional scope; Land use; Cropping system; Hidden Markov model; France; Farmland
EG : Europe
SD : Modelización; Distribución espacial; Escala grande; Escala regional; Ocupación terreno; Sistema cultural; Modelo Markov oculto; Francia; Tierra agrícola
LO : INIST-21007.354000506123820060
ID : 14-0083732

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Pascal:14-0083732

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<NO>PASCAL 14-0083732 INIST</NO>
<ET>Modeling the spatial distribution of crop sequences at a large regional scale using land-cover survey data: A case from France</ET>
<AU>YING XIAO; MIGNOLET (Catherine); MARI (Jean-François); BENOIT (Marc)</AU>
<AF>INRA SAD UPR 055 ASTER, 662 Avenue Louis Buffet/88500 Mirecourt/France (1 aut., 2 aut., 4 aut.); Université de Lorraine, LORIA, UMR 7503/54506 Vandoeuvre-lès-Nancy/France (3 aut.)</AF>
<DT>Publication en série; Niveau analytique</DT>
<SO>Computers and electronics in agriculture; ISSN 0168-1699; Coden CEAGE6; Pays-Bas; Da. 2014; Vol. 102; Pp. 51-63; Bibl. 3/4 p.</SO>
<LA>Anglais</LA>
<EA>Assessing the environmental impacts of agricultural production systems requires spatially explicit information regarding cropping systems. Projecting changes in agricultural land use that are caused by changes in land management practices for analyzing the performance of land activity-related policies, such as agricultural policies, also requires this type of data for model inputs. Crop sequences, which are vital and widely adopted agricultural practices, are difficult to directly detect at a regional scale. This study presents innovative stochastic data mining that was aimed at describing the spatial distribution of crop sequences at a large regional scale. The data mining is performed by hidden Markov models and an unsupervised clustering analysis that processes sequentially observed (from 1992 to 2003) land-cover survey data on the French mainland named Teruti. The 2549 3-year crop sequences were first identified as major crop sequences across the entire territory, which included 406 (merged) agricultural districts, using hidden Markov models. The 406 (merged) agricultural districts were then grouped into 21 clusters according to the similarity of the probabilities of occurrences of major 3-year crop sequences using hierarchical clustering analysis. Four cropping systems were further identified: vineyard-based cropping systems, maize monoculture and maize/wheat-based cropping systems, temporary pasture and maize-based cropping systems and wheat and barley-based cropping systems. The modeling approach that is presented in this study provides a tool to extract large-scale cropping patterns from increasingly available time series data on land-cover and land-use. With this tool, users can (a) identify the homogeneous zones in terms of fixed-length crop sequences across a large territory, (b) understand the characteristics of cropping systems within a region in terms of typical crop sequences, and (c) identify the major crop sequences of a region according to the probabilities of occurrences.</EA>
<CC>002A32C04A</CC>
<FD>Modélisation; Répartition spatiale; Echelle grande; Echelon régional; Occupation sol; Système culture; Modèle Markov caché; France; Terre agricole</FD>
<FG>Europe</FG>
<ED>Modeling; Spatial distribution; Large scale; Regional scope; Land use; Cropping system; Hidden Markov model; France; Farmland</ED>
<EG>Europe</EG>
<SD>Modelización; Distribución espacial; Escala grande; Escala regional; Ocupación terreno; Sistema cultural; Modelo Markov oculto; Francia; Tierra agrícola</SD>
<LO>INIST-21007.354000506123820060</LO>
<ID>14-0083732</ID>
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