Modeling the spatial distribution of crop sequences at a large regional scale using land-cover survey data: A case from France
Identifieur interne : 000E38 ( Main/Exploration ); précédent : 000E37; suivant : 000E39Modeling the spatial distribution of crop sequences at a large regional scale using land-cover survey data: A case from France
Auteurs : YING XIAO [France] ; Catherine Mignolet [France] ; Jean-François Mari [France] ; Marc Benoit [France]Source :
- Computers and electronics in agriculture [ 0168-1699 ] ; 2014.
Descripteurs français
- Pascal (Inist)
- Wicri :
- geographic : France.
- topic : Terre agricole.
English descriptors
- KwdEn :
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.
Affiliations:
Links toward previous steps (curation, corpus...)
- to stream PascalFrancis, to step Corpus: 000023
- to stream PascalFrancis, to step Curation: 000981
- to stream PascalFrancis, to step Checkpoint: 000013
- to stream Main, to step Merge: 000E30
- to stream Main, to step Curation: 000E38
Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en" level="a">Modeling the spatial distribution of crop sequences at a large regional scale using land-cover survey data: A case from France</title><author><name sortKey="Ying Xiao" sort="Ying Xiao" uniqKey="Ying Xiao" last="Ying Xiao">YING XIAO</name><affiliation wicri:level="3"><inist:fA14 i1="01"><s1>INRA SAD UPR 055 ASTER, 662 Avenue Louis Buffet</s1><s2>88500 Mirecourt</s2><s3>FRA</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ><sZ>4 aut.</sZ></inist:fA14><country>France</country><placeName><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region><settlement type="city">Mirecourt</settlement></placeName></affiliation></author><author><name sortKey="Mignolet, Catherine" sort="Mignolet, Catherine" uniqKey="Mignolet C" first="Catherine" last="Mignolet">Catherine Mignolet</name><affiliation wicri:level="3"><inist:fA14 i1="01"><s1>INRA SAD UPR 055 ASTER, 662 Avenue Louis Buffet</s1><s2>88500 Mirecourt</s2><s3>FRA</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ><sZ>4 aut.</sZ></inist:fA14><country>France</country><placeName><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region><settlement type="city">Mirecourt</settlement></placeName></affiliation></author><author><name sortKey="Mari, Jean Francois" sort="Mari, Jean Francois" uniqKey="Mari J" first="Jean-François" last="Mari">Jean-François Mari</name><affiliation wicri:level="4"><inist:fA14 i1="02"><s1>Université de Lorraine, LORIA, UMR 7503</s1><s2>54506 Vandoeuvre-lès-Nancy</s2><s3>FRA</s3><sZ>3 aut.</sZ></inist:fA14><country>France</country><placeName><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region><settlement type="city">Vandœuvre-lès-Nancy</settlement></placeName><orgName type="university">Université de Lorraine</orgName></affiliation></author><author><name sortKey="Benoit, Marc" sort="Benoit, Marc" uniqKey="Benoit M" first="Marc" last="Benoit">Marc Benoit</name><affiliation wicri:level="3"><inist:fA14 i1="01"><s1>INRA SAD UPR 055 ASTER, 662 Avenue Louis Buffet</s1><s2>88500 Mirecourt</s2><s3>FRA</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ><sZ>4 aut.</sZ></inist:fA14><country>France</country><placeName><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region><settlement type="city">Mirecourt</settlement></placeName></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">INIST</idno><idno type="inist">14-0083732</idno><date when="2014">2014</date><idno type="stanalyst">PASCAL 14-0083732 INIST</idno><idno type="RBID">Pascal:14-0083732</idno><idno type="wicri:Area/PascalFrancis/Corpus">000023</idno><idno type="wicri:Area/PascalFrancis/Curation">000981</idno><idno type="wicri:Area/PascalFrancis/Checkpoint">000013</idno><idno type="wicri:explorRef" wicri:stream="PascalFrancis" wicri:step="Checkpoint">000013</idno><idno type="wicri:doubleKey">0168-1699:2014:Ying Xiao:modeling:the:spatial</idno><idno type="wicri:Area/Main/Merge">000E30</idno><idno type="wicri:Area/Main/Curation">000E38</idno><idno type="wicri:Area/Main/Exploration">000E38</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a">Modeling the spatial distribution of crop sequences at a large regional scale using land-cover survey data: A case from France</title><author><name sortKey="Ying Xiao" sort="Ying Xiao" uniqKey="Ying Xiao" last="Ying Xiao">YING XIAO</name><affiliation wicri:level="3"><inist:fA14 i1="01"><s1>INRA SAD UPR 055 ASTER, 662 Avenue Louis Buffet</s1><s2>88500 Mirecourt</s2><s3>FRA</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ><sZ>4 aut.</sZ></inist:fA14><country>France</country><placeName><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region><settlement type="city">Mirecourt</settlement></placeName></affiliation></author><author><name sortKey="Mignolet, Catherine" sort="Mignolet, Catherine" uniqKey="Mignolet C" first="Catherine" last="Mignolet">Catherine Mignolet</name><affiliation wicri:level="3"><inist:fA14 i1="01"><s1>INRA SAD UPR 055 ASTER, 662 Avenue Louis Buffet</s1><s2>88500 Mirecourt</s2><s3>FRA</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ><sZ>4 aut.</sZ></inist:fA14><country>France</country><placeName><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region><settlement type="city">Mirecourt</settlement></placeName></affiliation></author><author><name sortKey="Mari, Jean Francois" sort="Mari, Jean Francois" uniqKey="Mari J" first="Jean-François" last="Mari">Jean-François Mari</name><affiliation wicri:level="4"><inist:fA14 i1="02"><s1>Université de Lorraine, LORIA, UMR 7503</s1><s2>54506 Vandoeuvre-lès-Nancy</s2><s3>FRA</s3><sZ>3 aut.</sZ></inist:fA14><country>France</country><placeName><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region><settlement type="city">Vandœuvre-lès-Nancy</settlement></placeName><orgName type="university">Université de Lorraine</orgName></affiliation></author><author><name sortKey="Benoit, Marc" sort="Benoit, Marc" uniqKey="Benoit M" first="Marc" last="Benoit">Marc Benoit</name><affiliation wicri:level="3"><inist:fA14 i1="01"><s1>INRA SAD UPR 055 ASTER, 662 Avenue Louis Buffet</s1><s2>88500 Mirecourt</s2><s3>FRA</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ><sZ>4 aut.</sZ></inist:fA14><country>France</country><placeName><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region><settlement type="city">Mirecourt</settlement></placeName></affiliation></author></analytic><series><title level="j" type="main">Computers and electronics in agriculture</title><title level="j" type="abbreviated">Comput. electron. agric.</title><idno type="ISSN">0168-1699</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">Computers and electronics in agriculture</title><title level="j" type="abbreviated">Comput. electron. agric.</title><idno type="ISSN">0168-1699</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Cropping system</term><term>Farmland</term><term>France</term><term>Hidden Markov model</term><term>Land use</term><term>Large scale</term><term>Modeling</term><term>Regional scope</term><term>Spatial distribution</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Modélisation</term><term>Répartition spatiale</term><term>Echelle grande</term><term>Echelon régional</term><term>Occupation sol</term><term>Système culture</term><term>Modèle Markov caché</term><term>France</term><term>Terre agricole</term></keywords><keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>France</term></keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Terre agricole</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">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.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Mirecourt</li><li>Vandœuvre-lès-Nancy</li></settlement><orgName><li>Université de Lorraine</li></orgName></list><tree><country name="France"><region name="Grand Est"><name sortKey="Ying Xiao" sort="Ying Xiao" uniqKey="Ying Xiao" last="Ying Xiao">YING XIAO</name></region><name sortKey="Benoit, Marc" sort="Benoit, Marc" uniqKey="Benoit M" first="Marc" last="Benoit">Marc Benoit</name><name sortKey="Mari, Jean Francois" sort="Mari, Jean Francois" uniqKey="Mari J" first="Jean-François" last="Mari">Jean-François Mari</name><name sortKey="Mignolet, Catherine" sort="Mignolet, Catherine" uniqKey="Mignolet C" first="Catherine" last="Mignolet">Catherine Mignolet</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Wicri/Lorraine/explor/InforLorV4/Data/Main/Exploration
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000E38 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd -nk 000E38 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Wicri/Lorraine
|area= InforLorV4
|flux= Main
|étape= Exploration
|type= RBID
|clé= Pascal:14-0083732
|texte= Modeling the spatial distribution of crop sequences at a large regional scale using land-cover survey data: A case from France
}}
| This area was generated with Dilib version V0.6.33. |  |