Combining farmers' decision rules and landscape stochastic regularities for landscape modelling
Identifieur interne : 000122 ( PascalFrancis/Corpus ); précédent : 000121; suivant : 000123Combining farmers' decision rules and landscape stochastic regularities for landscape modelling
Auteurs : Noemie Schaller ; El Ghali Lazrak ; Philippe Martin ; Jean-François Mari ; Christine Aubry ; Marc BenoitSource :
- Landscape ecology [ 0921-2973 ] ; 2012.
Descripteurs français
- Pascal (Inist)
English descriptors
- KwdEn :
Abstract
Landscape spatial organization (LSO) strongly impacts many environmental issues. Modelling agricultural landscapes and describing meaningful landscape patterns are thus regarded as key-issues for designing sustainable landscapes. Agricultural landscapes are mostly designed by farmers. Their decisions dealing with crop choices and crop allocation to land can be generic and result in landscape regularities, which determine LSO. This paper comes within the emerging discipline called "landscape agronomy", aiming at studying the organization of farming practices at the landscape scale. We here aim at articulating the farm and the landscape scales for landscape modelling. To do so, we develop an original approach consisting in the combination of two methods used separately so far: the identification of explicit farmer decision rules through on-farm surveys methods and the identification of landscape stochastic regularities through data-mining. We applied this approach to the Niort plain landscape in France. Results show that generic farmer decision rules dealing with sunflower or maize area and location within landscapes are consistent with spatiotemporal regularities identified at the landscape scale. It results in a segmentation of the landscape, based on both its spatial and temporal organization and partly explained by generic farmer decision rules. This consistency between results points out that the two modelling methods aid one another for land-use modelling at landscape scale and for understanding the driving forces of its spatial organization. Despite some remaining challenges, our study in landscape agronomy accounts for both spatial and temporal dimensions of crop allocation: it allows the drawing of new spatial patterns coherent with land-use dynamics at the landscape scale, which improves the links to the scale of ecological processes and therefore contributes to landscape ecology.
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Format Inist (serveur)
| NO : | PASCAL 12-0134688 INIST |
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| ET : | Combining farmers' decision rules and landscape stochastic regularities for landscape modelling |
| AU : | SCHALLER (Noemie); GHALI LAZRAK (El); MARTIN (Philippe); MARI (Jean-François); AUBRY (Christine); BENOIT (Marc) |
| AF : | AgroParisTech, INRA, UMR 1048 SAD-APT, Batiment EGER, BP 01/78 850 Thiverval-Grignon/France (1 aut., 3 aut., 5 aut.); INRA, UR 055 SAD ASTER/88500 Mirecourt/France (2 aut., 6 aut.); LORIA, UMR CNRS 7503 INRIA-Grand-Est, B.P. 239/54506 Vandœuvre-lès-Nancy/France (4 aut.) |
| DT : | Publication en série; Niveau analytique |
| SO : | Landscape ecology; ISSN 0921-2973; Pays-Bas; Da. 2012; Vol. 27; No. 3; Pp. 433-446; Bibl. 2 p. |
| LA : | Anglais |
| EA : | Landscape spatial organization (LSO) strongly impacts many environmental issues. Modelling agricultural landscapes and describing meaningful landscape patterns are thus regarded as key-issues for designing sustainable landscapes. Agricultural landscapes are mostly designed by farmers. Their decisions dealing with crop choices and crop allocation to land can be generic and result in landscape regularities, which determine LSO. This paper comes within the emerging discipline called "landscape agronomy", aiming at studying the organization of farming practices at the landscape scale. We here aim at articulating the farm and the landscape scales for landscape modelling. To do so, we develop an original approach consisting in the combination of two methods used separately so far: the identification of explicit farmer decision rules through on-farm surveys methods and the identification of landscape stochastic regularities through data-mining. We applied this approach to the Niort plain landscape in France. Results show that generic farmer decision rules dealing with sunflower or maize area and location within landscapes are consistent with spatiotemporal regularities identified at the landscape scale. It results in a segmentation of the landscape, based on both its spatial and temporal organization and partly explained by generic farmer decision rules. This consistency between results points out that the two modelling methods aid one another for land-use modelling at landscape scale and for understanding the driving forces of its spatial organization. Despite some remaining challenges, our study in landscape agronomy accounts for both spatial and temporal dimensions of crop allocation: it allows the drawing of new spatial patterns coherent with land-use dynamics at the landscape scale, which improves the links to the scale of ecological processes and therefore contributes to landscape ecology. |
| CC : | 002A14D01; 002A14A02 |
| FD : | Agriculteur; Règle décision; Paysage; Modélisation; Occupation sol; Dynamique; Modèle; Fouille donnée; Ecologie paysage |
| ED : | Farmer; Decision rule; Landscape; Modeling; Land use; Dynamics; Models; Data mining |
| SD : | Agricultor; Regla decisión; Paisaje; Modelización; Ocupación terreno; Dinámica; Modelo; Busca dato |
| LO : | INIST-26279.354000502852540100 |
| ID : | 12-0134688 |
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Martin</name><affiliation><inist:fA14 i1="01"><s1>AgroParisTech, INRA, UMR 1048 SAD-APT, Batiment EGER, BP 01</s1><s2>78 850 Thiverval-Grignon</s2><s3>FRA</s3><sZ>1 aut.</sZ><sZ>3 aut.</sZ><sZ>5 aut.</sZ></inist:fA14></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><inist:fA14 i1="03"><s1>LORIA, UMR CNRS 7503 INRIA-Grand-Est, B.P. 239</s1><s2>54506 Vandœuvre-lès-Nancy</s2><s3>FRA</s3><sZ>4 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Aubry, Christine" sort="Aubry, Christine" uniqKey="Aubry C" first="Christine" last="Aubry">Christine Aubry</name><affiliation><inist:fA14 i1="01"><s1>AgroParisTech, INRA, UMR 1048 SAD-APT, Batiment EGER, BP 01</s1><s2>78 850 Thiverval-Grignon</s2><s3>FRA</s3><sZ>1 aut.</sZ><sZ>3 aut.</sZ><sZ>5 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Benoit, Marc" sort="Benoit, Marc" 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aut.</sZ><sZ>3 aut.</sZ><sZ>5 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Ghali Lazrak, El" sort="Ghali Lazrak, El" uniqKey="Ghali Lazrak E" first="El" last="Ghali Lazrak">El Ghali Lazrak</name><affiliation><inist:fA14 i1="02"><s1>INRA, UR 055 SAD ASTER</s1><s2>88500 Mirecourt</s2><s3>FRA</s3><sZ>2 aut.</sZ><sZ>6 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Martin, Philippe" sort="Martin, Philippe" uniqKey="Martin P" first="Philippe" last="Martin">Philippe Martin</name><affiliation><inist:fA14 i1="01"><s1>AgroParisTech, INRA, UMR 1048 SAD-APT, Batiment EGER, BP 01</s1><s2>78 850 Thiverval-Grignon</s2><s3>FRA</s3><sZ>1 aut.</sZ><sZ>3 aut.</sZ><sZ>5 aut.</sZ></inist:fA14></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><inist:fA14 i1="03"><s1>LORIA, UMR CNRS 7503 INRIA-Grand-Est, B.P. 239</s1><s2>54506 Vandœuvre-lès-Nancy</s2><s3>FRA</s3><sZ>4 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Aubry, Christine" sort="Aubry, Christine" uniqKey="Aubry C" first="Christine" last="Aubry">Christine Aubry</name><affiliation><inist:fA14 i1="01"><s1>AgroParisTech, INRA, UMR 1048 SAD-APT, Batiment EGER, BP 01</s1><s2>78 850 Thiverval-Grignon</s2><s3>FRA</s3><sZ>1 aut.</sZ><sZ>3 aut.</sZ><sZ>5 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Benoit, Marc" sort="Benoit, Marc" uniqKey="Benoit M" first="Marc" last="Benoit">Marc Benoit</name><affiliation><inist:fA14 i1="02"><s1>INRA, UR 055 SAD ASTER</s1><s2>88500 Mirecourt</s2><s3>FRA</s3><sZ>2 aut.</sZ><sZ>6 aut.</sZ></inist:fA14></affiliation></author></analytic><series><title level="j" type="main">Landscape ecology</title><title level="j" type="abbreviated">Landsc. ecol.</title><idno type="ISSN">0921-2973</idno><imprint><date when="2012">2012</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">Landscape ecology</title><title level="j" type="abbreviated">Landsc. ecol.</title><idno type="ISSN">0921-2973</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Data mining</term><term>Decision rule</term><term>Dynamics</term><term>Farmer</term><term>Land use</term><term>Landscape</term><term>Modeling</term><term>Models</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Agriculteur</term><term>Règle décision</term><term>Paysage</term><term>Modélisation</term><term>Occupation sol</term><term>Dynamique</term><term>Modèle</term><term>Fouille donnée</term><term>Ecologie paysage</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Landscape spatial organization (LSO) strongly impacts many environmental issues. Modelling agricultural landscapes and describing meaningful landscape patterns are thus regarded as key-issues for designing sustainable landscapes. Agricultural landscapes are mostly designed by farmers. Their decisions dealing with crop choices and crop allocation to land can be generic and result in landscape regularities, which determine LSO. This paper comes within the emerging discipline called "landscape agronomy", aiming at studying the organization of farming practices at the landscape scale. We here aim at articulating the farm and the landscape scales for landscape modelling. To do so, we develop an original approach consisting in the combination of two methods used separately so far: the identification of explicit farmer decision rules through on-farm surveys methods and the identification of landscape stochastic regularities through data-mining. We applied this approach to the Niort plain landscape in France. Results show that generic farmer decision rules dealing with sunflower or maize area and location within landscapes are consistent with spatiotemporal regularities identified at the landscape scale. It results in a segmentation of the landscape, based on both its spatial and temporal organization and partly explained by generic farmer decision rules. This consistency between results points out that the two modelling methods aid one another for land-use modelling at landscape scale and for understanding the driving forces of its spatial organization. Despite some remaining challenges, our study in landscape agronomy accounts for both spatial and temporal dimensions of crop allocation: it allows the drawing of new spatial patterns coherent with land-use dynamics at the landscape scale, which improves the links to the scale of ecological processes and therefore contributes to landscape ecology.</div></front></TEI><inist><standard h6="B"><pA><fA01 i1="01" i2="1"><s0>0921-2973</s0></fA01><fA03 i2="1"><s0>Landsc. ecol.</s0></fA03><fA05><s2>27</s2></fA05><fA06><s2>3</s2></fA06><fA08 i1="01" i2="1" l="ENG"><s1>Combining farmers' decision rules and landscape stochastic regularities for landscape modelling</s1></fA08><fA11 i1="01" i2="1"><s1>SCHALLER (Noemie)</s1></fA11><fA11 i1="02" i2="1"><s1>GHALI LAZRAK (El)</s1></fA11><fA11 i1="03" i2="1"><s1>MARTIN (Philippe)</s1></fA11><fA11 i1="04" i2="1"><s1>MARI (Jean-François)</s1></fA11><fA11 i1="05" i2="1"><s1>AUBRY (Christine)</s1></fA11><fA11 i1="06" i2="1"><s1>BENOIT (Marc)</s1></fA11><fA14 i1="01"><s1>AgroParisTech, INRA, UMR 1048 SAD-APT, Batiment EGER, BP 01</s1><s2>78 850 Thiverval-Grignon</s2><s3>FRA</s3><sZ>1 aut.</sZ><sZ>3 aut.</sZ><sZ>5 aut.</sZ></fA14><fA14 i1="02"><s1>INRA, UR 055 SAD ASTER</s1><s2>88500 Mirecourt</s2><s3>FRA</s3><sZ>2 aut.</sZ><sZ>6 aut.</sZ></fA14><fA14 i1="03"><s1>LORIA, UMR CNRS 7503 INRIA-Grand-Est, B.P. 239</s1><s2>54506 Vandœuvre-lès-Nancy</s2><s3>FRA</s3><sZ>4 aut.</sZ></fA14><fA20><s1>433-446</s1></fA20><fA21><s1>2012</s1></fA21><fA23 i1="01"><s0>ENG</s0></fA23><fA43 i1="01"><s1>INIST</s1><s2>26279</s2><s5>354000502852540100</s5></fA43><fA44><s0>0000</s0><s1>© 2012 INIST-CNRS. All rights reserved.</s1></fA44><fA45><s0>2 p.</s0></fA45><fA47 i1="01" i2="1"><s0>12-0134688</s0></fA47><fA60><s1>P</s1></fA60><fA61><s0>A</s0></fA61><fA64 i1="01" i2="1"><s0>Landscape ecology</s0></fA64><fA66 i1="01"><s0>NLD</s0></fA66><fC01 i1="01" l="ENG"><s0>Landscape spatial organization (LSO) strongly impacts many environmental issues. Modelling agricultural landscapes and describing meaningful landscape patterns are thus regarded as key-issues for designing sustainable landscapes. Agricultural landscapes are mostly designed by farmers. Their decisions dealing with crop choices and crop allocation to land can be generic and result in landscape regularities, which determine LSO. This paper comes within the emerging discipline called "landscape agronomy", aiming at studying the organization of farming practices at the landscape scale. We here aim at articulating the farm and the landscape scales for landscape modelling. To do so, we develop an original approach consisting in the combination of two methods used separately so far: the identification of explicit farmer decision rules through on-farm surveys methods and the identification of landscape stochastic regularities through data-mining. We applied this approach to the Niort plain landscape in France. Results show that generic farmer decision rules dealing with sunflower or maize area and location within landscapes are consistent with spatiotemporal regularities identified at the landscape scale. It results in a segmentation of the landscape, based on both its spatial and temporal organization and partly explained by generic farmer decision rules. This consistency between results points out that the two modelling methods aid one another for land-use modelling at landscape scale and for understanding the driving forces of its spatial organization. Despite some remaining challenges, our study in landscape agronomy accounts for both spatial and temporal dimensions of crop allocation: it allows the drawing of new spatial patterns coherent with land-use dynamics at the landscape scale, which improves the links to the scale of ecological processes and therefore contributes to landscape ecology.</s0></fC01><fC02 i1="01" i2="X"><s0>002A14D01</s0></fC02><fC02 i1="02" i2="X"><s0>002A14A02</s0></fC02><fC03 i1="01" i2="X" l="FRE"><s0>Agriculteur</s0><s5>01</s5></fC03><fC03 i1="01" i2="X" l="ENG"><s0>Farmer</s0><s5>01</s5></fC03><fC03 i1="01" i2="X" l="SPA"><s0>Agricultor</s0><s5>01</s5></fC03><fC03 i1="02" i2="X" l="FRE"><s0>Règle décision</s0><s5>02</s5></fC03><fC03 i1="02" i2="X" l="ENG"><s0>Decision rule</s0><s5>02</s5></fC03><fC03 i1="02" i2="X" l="SPA"><s0>Regla decisión</s0><s5>02</s5></fC03><fC03 i1="03" i2="X" l="FRE"><s0>Paysage</s0><s5>03</s5></fC03><fC03 i1="03" i2="X" l="ENG"><s0>Landscape</s0><s5>03</s5></fC03><fC03 i1="03" i2="X" l="SPA"><s0>Paisaje</s0><s5>03</s5></fC03><fC03 i1="04" i2="X" l="FRE"><s0>Modélisation</s0><s5>04</s5></fC03><fC03 i1="04" i2="X" l="ENG"><s0>Modeling</s0><s5>04</s5></fC03><fC03 i1="04" i2="X" l="SPA"><s0>Modelización</s0><s5>04</s5></fC03><fC03 i1="05" i2="X" l="FRE"><s0>Occupation sol</s0><s5>05</s5></fC03><fC03 i1="05" i2="X" l="ENG"><s0>Land use</s0><s5>05</s5></fC03><fC03 i1="05" i2="X" l="SPA"><s0>Ocupación terreno</s0><s5>05</s5></fC03><fC03 i1="06" i2="X" l="FRE"><s0>Dynamique</s0><s5>06</s5></fC03><fC03 i1="06" i2="X" l="ENG"><s0>Dynamics</s0><s5>06</s5></fC03><fC03 i1="06" i2="X" l="SPA"><s0>Dinámica</s0><s5>06</s5></fC03><fC03 i1="07" i2="X" l="FRE"><s0>Modèle</s0><s5>07</s5></fC03><fC03 i1="07" i2="X" l="ENG"><s0>Models</s0><s5>07</s5></fC03><fC03 i1="07" i2="X" l="SPA"><s0>Modelo</s0><s5>07</s5></fC03><fC03 i1="08" i2="X" l="FRE"><s0>Fouille donnée</s0><s5>08</s5></fC03><fC03 i1="08" i2="X" l="ENG"><s0>Data mining</s0><s5>08</s5></fC03><fC03 i1="08" i2="X" l="SPA"><s0>Busca dato</s0><s5>08</s5></fC03><fC03 i1="09" i2="X" l="FRE"><s0>Ecologie paysage</s0><s4>INC</s4><s5>87</s5></fC03><fN21><s1>100</s1></fN21><fN44 i1="01"><s1>OTO</s1></fN44><fN82><s1>OTO</s1></fN82></pA></standard><server><NO>PASCAL 12-0134688 INIST</NO><ET>Combining farmers' decision rules and landscape stochastic regularities for landscape modelling</ET><AU>SCHALLER (Noemie); GHALI LAZRAK (El); MARTIN (Philippe); MARI (Jean-François); AUBRY (Christine); BENOIT (Marc)</AU><AF>AgroParisTech, INRA, UMR 1048 SAD-APT, Batiment EGER, BP 01/78 850 Thiverval-Grignon/France (1 aut., 3 aut., 5 aut.); INRA, UR 055 SAD ASTER/88500 Mirecourt/France (2 aut., 6 aut.); LORIA, UMR CNRS 7503 INRIA-Grand-Est, B.P. 239/54506 Vandœuvre-lès-Nancy/France (4 aut.)</AF><DT>Publication en série; Niveau analytique</DT><SO>Landscape ecology; ISSN 0921-2973; Pays-Bas; Da. 2012; Vol. 27; No. 3; Pp. 433-446; Bibl. 2 p.</SO><LA>Anglais</LA><EA>Landscape spatial organization (LSO) strongly impacts many environmental issues. Modelling agricultural landscapes and describing meaningful landscape patterns are thus regarded as key-issues for designing sustainable landscapes. Agricultural landscapes are mostly designed by farmers. Their decisions dealing with crop choices and crop allocation to land can be generic and result in landscape regularities, which determine LSO. This paper comes within the emerging discipline called "landscape agronomy", aiming at studying the organization of farming practices at the landscape scale. We here aim at articulating the farm and the landscape scales for landscape modelling. To do so, we develop an original approach consisting in the combination of two methods used separately so far: the identification of explicit farmer decision rules through on-farm surveys methods and the identification of landscape stochastic regularities through data-mining. We applied this approach to the Niort plain landscape in France. Results show that generic farmer decision rules dealing with sunflower or maize area and location within landscapes are consistent with spatiotemporal regularities identified at the landscape scale. It results in a segmentation of the landscape, based on both its spatial and temporal organization and partly explained by generic farmer decision rules. This consistency between results points out that the two modelling methods aid one another for land-use modelling at landscape scale and for understanding the driving forces of its spatial organization. Despite some remaining challenges, our study in landscape agronomy accounts for both spatial and temporal dimensions of crop allocation: it allows the drawing of new spatial patterns coherent with land-use dynamics at the landscape scale, which improves the links to the scale of ecological processes and therefore contributes to landscape ecology.</EA><CC>002A14D01; 002A14A02</CC><FD>Agriculteur; Règle décision; Paysage; Modélisation; Occupation sol; Dynamique; Modèle; Fouille donnée; Ecologie paysage</FD><ED>Farmer; Decision rule; Landscape; Modeling; Land use; Dynamics; Models; Data mining</ED><SD>Agricultor; Regla decisión; Paisaje; Modelización; Ocupación terreno; Dinámica; Modelo; Busca dato</SD><LO>INIST-26279.354000502852540100</LO><ID>12-0134688</ID></server></inist></record>Pour manipuler ce document sous Unix (Dilib)
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