Opportunities for scaling up research on agricultural dynamics
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>> PAPER http://bit.ly/FSD5_Rizzo_abstract << Use of crop sequences for data-mining of remotely sensed time series across multiple scales. Several interdisciplinary perspectives (e.g., landscape agronomy, land system science, ecoagriculture) urge agronomy to contextualize the characterization of agricultural activities within the land management system. This challenges the discipline to scale up the analysis of agricultural dynamics from farm to landscape levels, where conflicting choices may emerge about the management of natural resources. Shortcoming of data covering large areas, especially about farming practices and decision-making processes, is a major constraint. Nevertheless, we consider that segmenting the land according to the observed land cover sequences can eventually incorporate a relevant part of the farmers' medium-term decision-making processes, influenced for instance by climate changes or territorial conflicts related to local resources. Furthermore, focusing on land cover sequences may provide a consistent target of analysis across multiple scales. Our aim is to discuss the relevance of a data-mining method to handle remotely sensed data and analyze temporal and spatial agricultural dynamics in a landscape perspective. The method, originally developed to handle large and labor-demanding survey datasets, is based on the stochastic segmentation with Hidden Markov Models. It firstly identifies temporal regularities of the crop/grassland sequences, then use them to segment into homogeneous patches the study area (potentially ranging from farmland to region). Starting from a case study carried out on a 12-year time series of satellite images for the Yar watershed (Brittany, France) we address the potential contribution of this kind of approach to improve the dynamic analysis of farming systems. [...] >> More info at http://bit.ly/FSD5_Rizzo_abstract
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.
CONCLUSIONS
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landbcoverb
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databsourceszb
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variabilitybofbcultivatedbcropszb
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Policy–BRÉR.–’,
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Rounsevellfetfal]f2012
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Schallerfetfal]f2012
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Rizzofetfal]f2013
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Dan–J–Geogr–SS,ÉJS–G’
MarifsfLefBerf2006f
Temporal–and–spatial–data–mining…–
Soft–Comput–SDÉBD’–SB
Marifetfal]f2013
Time–space–stochastic–modelling…–Environ–Model–Softw–
B’É.SM–.J
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basin…–Sci–Total–Environ–,JRÉS,–,.
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sequences…–Comput–Electron–Agric–SD.ÉRS–’,
Duryfetfal]f2013
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crop–farms…–Eur–J–Agron–RDÉS–SD
T7p.bScalingbupbfrombfarmbtoblandscapeb,b
multiscalebscenariobanalysisbofbagriculturalbsystems
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Opportunities for scaling up research on agricultural dynamics
- 1. They interpretationy ofyresultsyleadsytoy reviseqtheqmodely parametersyinyanyiterativey andyinteractiveyprocess yyy e.g., different sequence lengths and numbers of landscape segments F S D Agricultural systems by design SEPTEMBER 7-10, 2015 MONTPELLIER France sampleb points .landbcoverbinbsampledbyears5 tV4tœ44444444444444444444444444…44444444444444444444444444444tn .geographicb coordinates5 5thdInternationaldSymposiumd fordFarmingdSystemdDesign wantqtoqknowqmore? ridavidekfsd@gmail7com DavideyRizzo[yPhD fullqreferences http(]]bit7ly] FSD3_Rizzo_ref Opportunities for scaling up research on agricultural dynamics pppp Use–of–crop–sequences–for–databmining of–remotely–sensed–time–series across–multiple–scales Davide4RizzoS4 El4Ghali4Lazrak4¹4 Jean‐François4Mari4¹4²4 Laurence4Hubert‐Moy4³4 Marc4Benoît4² :sUMR4œè6q4HGIR74INRH‐INPT74Castanet‐Tolosan74FRH ¹ssINRA74SHD‐HSTER74Mirecourt74FRH ²ssLORIA74UMR4féVy4N4INRIH74Vandœuvre‐lès‐Nancy74FRH ³sLETG‐Rennes‐COSTEL74UMR4CNRS4péé64Université4de4Rennes4è74Rennes74FRH DbRizzobbEGbLazrakbbJFbMaribbLbHubert-MoybbMbBenoîtp dddddddDATA-MININGdMETHOD temporalb segmentation linearbmodel bbbbspatialb segmentation ergodicbmodel .allbstatesbareb interconnected5 model topology & hierarchy yyylandycoveryclassificationy qysequenceylengthy context4wisey approximationyofyGDy spaceyasysimpleychain space filling (fractal) curve visualisationb,binterpretation webbdynamicbinterfaceb[-7] agronomicd adaptation DATAdPREPARATION KNOWLEDGEd EXTRACTION landscapeysegmentation temporo4spatialyclasses Scalingyupy theyresearchyonyfarmingy systemsyfromyplot]farmytoy landscapeylevelywillymakeypossibleytoy buildyfarmingysystemydesignyfromyay landscapeqagronomyyperspectivey[C4–] 7 Thisyimpliesytoyimproveytheyunderstandingyofy theyrelationybetweenylandypatterns[yfarmingy practicesyandynaturalyresourcesy[H411] 7 Globaly statisticsyindicateyay stagnationyinytotalyyieldsy forymajorycrops7yFurthery improvementsyrequireyagronomistsy toyenhanceytheywaysytheyyaddressythey farmingyuseyofylandyandywatery[1] 7y Consideringytheylimitednessyofythesey resources[yimprovementsyofyfarmingy activitiesywouldydefinitelyybenefityfromy smarterqspatialqdesignyofycroppingyandy mixedycrop4livestockysystemsy[G[j] 7 hypothesisq observationyqy modellingyofyagriculturaly landycoverysequencesycany incorporateyfarmers’y medium4termy decision4makingy processes Landscapessegmentationsinto4patches4of4 land4cover4sequences4is4an4innovative4 approach4to4identify4land4management4units4 relating4farmers’4choices4and4natural4 resource4management34 Perspectives:sinforming4local4planners’4 decision‐making4with4spatially4explicit4 insights4about4farming4system4dynamics34 Hence74supporting4a4shared4landscape4design4 based4on4the4diversity4in4farming4systems34 YarpcasepstudyBbabwatershedbofbq-z8b km²b.BrittanyBbFrance5bwherebthebmainb cropsb–bmaizeBbwheatbandbgrasslandsb–b arebrelatedbtobindustrialbbreedingzb Ab-9–yearbtimebseriesb.-••C–922x5bofb satellitebimagesbwasbclassifiedbintobqblandb coverbclassesbforbindividualbfieldb polygonsBbthenbsampledbwithbab92mb regularbpointbgridzb This,approach,proved,its,applicability,at,different,scales, ranging,from,local,farming,systems,to,large,watershed,and, regional,areas,[16‐17] ., Further,implementations,are,being,developed,based,on,the, similar,principles:,first,,identification,of,sequences,,then, mapping,[3,18] . CONCLUSIONS inputqdatay–ysampledyony ayregularlyyspacedypointygridy –yareyscannedywithyayfractaly curveywhereyeachypointyholdsyay timeysequenceyofylandycovers7y stochasticqsegmentationy withyhierarchicalyHiddenyMarkovy Modelsy-HMMwywhoseyspatialystatesy areytemporalyHMMycapableyofy assigningyayprobabilityytoyaytimey sequencey[1G41j] 7 keypfeaturesp •pD15pAnalysisbofb timebregularitiesbofb landbcoverb sequencesbinsteadbofb individualblandbcoverb changeszb •pD25bContextbwiseb spatialbsegmentationz •bScalingbupbresearchbonbagriculturalbdynamicsb requiresbhetherogeneousbandbdetailedb databsourceszb bbbb •bAvailableblandbcoverbmapsbrarelybensureb continuitybinbtimeBbseamlessbcoveringbofblargeb areasbandbdetailedbcropbclassificationzb bbbb bb•bDatababoutbfarmingpmanagementbareb difficultbtobretrievebbecauseb ofbthebcontinuousb adaptationsbtobcomplexb drivingbfactorsbb[-0–-8]z •bExistingbdatasetsbprovideb promisingbbasesbtobreconstructb agriculturalblandbcoverb sequences4b French)TerUti,)Land)Parcel) Identification)System)(LPIS,) differently)available)throughout) Europe)) )))) •bAdvancesbinbremotepsensingb canbhelpbfacingbtheblackbofbdataBb althoughbtheblistbofbidentifiedb cropbtypesbshouldbbebimprovedz Reversingbthebclassicalb spacepdominantbapproachb.focusedb onblocalblandbcoverbchanges5btob overcomebthebinter–annualblocalb variabilitybofbcultivatedbcropszb bbbbbb Timepdominantpmodelingb frameworkbfostersbinsteadbab landscapebsegmentationbbasedbonb similarblandbcoverbsequencesb thusbmappingbspatialbunitsbofb .potentially5bsimilarbfarmingb practiceszb Webdistinguishedbpermanentb frombtemporarybgrasslandb andbidentifiedbandblocalizedb thebtypebofbrotationsbinbwhichb temporarypgrasslandsb werebinvolvedzb bbb •,Possible,hotspot,zones,at,the,origin, of,the,high,nitrogen,rates,observed,for, several,years,in,the,local,rivers., Althoughbinputbmapsb derivedbfrombremoteb sensingbwerebthematicallyblessb diversebthanbthosebderivedbfromb fieldbsurveysBbtime–spaceb segmentationbrevealedbsomeb interestingbspatialbdynamicsbofb landbcoverbsequenceszb Acevedof2011– Interdisciplinary– progress–in–food– production…–Environ– Conserv–,GÉSRS–JS fMorainefetfal]f2014 Farming–system–design–for– innovative…–Animal–GÉS.DB–SJ Murguefetfal]f2105 Toward–integrated–water–and– agricultural–land–management…–Land–Use– Policy–BRÉR.–’, Thenailfetfal]f2009 The–contribution–of–cropbrotation–organization…–Agric– Ecosyst–Environ–S,SÉ.DJ–SM Leenhardtfetfal]f2010 Describing–and–locating–cropping–systems…–Agron–Sustain–Dev–,DÉS,S–G BenoîtXfRizzofetfal]f2012 Landscape–agronomyÉ–a–new–field–for–addressing–agricultural–landscape–dynamicsz– Landsc–Ecol–SDÉS,GR–MB Boiffinfetfal]Xf2014 Agronomieh–espaceh–territoire…–Cah–Agric–.,ÉJ.–G, ScherrfsfMcNeelyf2008 Biodiversity–conservation–and–agricultural–sustainability…– Philos–Trans–R–Soc–B–Biol–Sci–,’,ÉBJJ–MB Rounsevellfetfal]f2012 Challenges–for–land–system–sciencez–Land–Use–Policy–.MÉGMM–MSD Schallerfetfal]f2012 Combining–farmers’–decision–rules–and–landscape…– Landsc–Ecol–.JÉB,,–B’ Rizzofetfal]f2013 Farming–systems–designing–landscapes…– Dan–J–Geogr–SS,ÉJS–G’ MarifsfLefBerf2006f Temporal–and–spatial–data–mining…– Soft–Comput–SDÉBD’–SB Marifetfal]f2013 Time–space–stochastic–modelling…–Environ–Model–Softw– B’É.SM–.J fLandaisfsfDeffontainesf1988 Les–pratiques–des–agriculteurs…– Études–Rural–SDMÉS.R–RG fDusseuxfetfal]f2011f Identification–of–grazed–and–mown–grasslands…–’th– Intz–Workshop–Analz–MultibTemporal–Remote–Sensz– Imagesh–ppz–SBR–G Mignoletfetfal]f2007 Spatial–dynamics–of–farming–practices–in–the–Seine– basin…–Sci–Total–Environ–,JRÉS,–,. 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