Definition and Analysis of New                 Agricultural Farm Energetic Indicators                 using Spatial OLAP  ...
2PlanSpatial Data Warehouse and Spatial OLAPAgricultural farm energy consumption diagnosis and relatedindicatorsNew energy...
Spatial Data Warehouse                 and Spatial OLAPPour mieuxaffirmerses missions,le Cemagrefdevient Irsteawww.irstea.fr
4   Data warehouse and OLAPData warehouse is: "A collection of integrated, subject oriented,  time-variant, and non-volati...
5          Spatial OLAP (1/4)Spatial OLAP : "A visual platform built especially to support rapid and  easy spatiotemporal ...
6   Spatial OLAP (2/4)Spatial data warehouse: "A collection of integrated, subject  oriented, time-variant, and non-volati...
7  Spatial OLAP (3/4) What is the total sold by month and city at 50 Km far from  Paris
8        Spatial OLAP (4/4)              Talend   Oracle Spatial,   GeoMondrian,Tools                                     ...
Agricultural farm energy                 consumption diagnosis and                 related indicatorsPour mieuxaffirmerses...
10   Agricultural farm energy consumption   diagnosis (1/4)Main goals:- Quantify energy consumption per processes to ident...
11  Agricultural farm energy consumption  diagnosis (2/4)Diagnosis are grouped:Global agro-environmental diagnoses: these ...
12   Agricultural farm energy consumption   diagnosis (3/4)Energetic diagnoses: these allow an energy balance at the farm ...
13   Agricultural farm energy consumption   diagnosis (4/4)Main limitations:they define indicators only at a global scale,...
New energy indicators to                 assess farm performance at                 a detailed scalePour mieuxaffirmerses ...
15   New energy indicators (1/4)Analysis scale:Spatial scale: the hectare is the spatial reference unit used to express th...
16   New energy indicators (2/4)These indicators were calculated for the three main farm activities:Crop management activi...
17  New energy indicators (3/4)Example simple indicator: the fuel consumption per plot, technical   operation, year and pr...
18New energy indicators (4/4)
Spatial OLAP analysis of                 new indicatorsPour mieuxaffirmerses missions,le Cemagrefdevient Irsteawww.irstea.fr
20   Spatial OLAP analysis of new indicators (1/9)A SOLAP system to represent, store and analyze the new indicators→ inter...
21   Spatial OLAP analysis of new indicators (2/9)Usage of a UML profile implemented in the CASE tool Magic DrawAutomatic ...
22Spatial OLAP analysis of new indicators (3/9) “Fuel to weed a plot of wheat" “Total consumption of fuel to weed the enti...
23   Spatial OLAP analysis of new indicators (4/9)Dimensions:Campaigns (Campagne): production cycles expressed in years (e...
24   Spatial OLAP analysis of new indicators (5/9)Measures:the area worked (surface_w)the number of animals (animaux_nb)th...
25  Spatial OLAP analysis of new indicators (6/9)Implementation:SDW tier: PostGIS is a Spatial DBMS natively supporting sp...
26  Spatial OLAP analysis of new indicators (7/9)Aggregated indicator "the amount of fuel used for cultivating one hectare...
27  Spatial OLAP analysis of new indicators (8/9)Spatial drill-down operation: “energy consumption per plot” indicator
28  Spatial OLAP analysis of new indicators (9/9)Drill-down on the technical operations dimension: “more details on energy...
29  Spatial OLAP analysis of new indicators“fuel consumption per plot, technical operation, year and production”
30  Conclusions and perspectivesWe propose some new indicators to assess agricultural farm  performance at a most detailed...
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Definition and Analysis of New Agricultural Farm Energetic Indicators using Spatial OLAP, Sandro Bimonte, Jean-Pierre Chanet, Marylis Pradel, Kamal Boulil - - Research Centre on Technologies, information systems and processes for agriculture (Cemagref,),

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Definition and Analysis of New Agricultural Farm Energetic Indicators using Spatial OLAP, Sandro Bimonte, Jean-Pierre Chanet, Marylis Pradel, Kamal Boulil - - Research Centre on Technologies, information systems and processes for agriculture (Cemagref,),

  1. 1. Definition and Analysis of New Agricultural Farm Energetic Indicators using Spatial OLAP Sandro Bimonte, Kamal Boulil, Jean-Pour mieuxaffirmerses missions, Pierre Chanet, Marilys Pradelle Cemagrefdevient Irstea Irstea, Clermont-Ferrand, France Sandro.bimonte@irstea.frwww.irstea.fr
  2. 2. 2PlanSpatial Data Warehouse and Spatial OLAPAgricultural farm energy consumption diagnosis and relatedindicatorsNew energy indicators to assess farm performance at a detailedscaleSpatial OLAP analysis of new indicatorsConclusions
  3. 3. Spatial Data Warehouse and Spatial OLAPPour mieuxaffirmerses missions,le Cemagrefdevient Irsteawww.irstea.fr
  4. 4. 4 Data warehouse and OLAPData warehouse is: "A collection of integrated, subject oriented, time-variant, and non-volatile data from various sources into a single and consistent data repository to support decision- making” (Inmon, 1996)Hypercube Temps Time 4000 1000Facts, measures, dimensions, and hierarchies 8000 Client Costumer 7000Aggregations (SUM, MIN, MAX, AVG, COUNT) 3000 8000 12000 1000OLAP analysis 2000 6000Drilling, Slicing, pivot, etc. 8000 Produit ProductInteractive analysis and aggregation of huge volume of data •What is the total of sales per month, client and product ?
  5. 5. 5 Spatial OLAP (1/4)Spatial OLAP : "A visual platform built especially to support rapid and easy spatiotemporal analysis and exploration of data following a multidimensional approach comprised of aggregation levels available in cartographic displays as well as in tabular and diagram displays” (Bédard, 1997)Cartographic representation:Visualization of distribution of factsVisualization spatial relationships between facts and dimensionsVisualization of facts at different granularities
  6. 6. 6 Spatial OLAP (2/4)Spatial data warehouse: "A collection of integrated, subject oriented, time-variant, and non-volatile spatial data from various sources into a single and consistent data repository to support decision-making” (Stefanovic, et al.2000)Spatio-multidimensional model:Spatial dimesion: Dimension with spatial attributesSpatial measure: Geometry and/or results of spatial operatorsSpatial Aggregation (e.g. UNION, INTRESECTION)SOLAP operators: Drilling in spatial dimension, slicing the hypercube uing spatial predicats
  7. 7. 7 Spatial OLAP (3/4) What is the total sold by month and city at 50 Km far from Paris
  8. 8. 8 Spatial OLAP (4/4) Talend Oracle Spatial, GeoMondrian,Tools Jrubik, GeWOlap, … SDI PosteGIS, … Map4Decision, …
  9. 9. Agricultural farm energy consumption diagnosis and related indicatorsPour mieuxaffirmerses missions,le Cemagrefdevient Irsteawww.irstea.fr
  10. 10. 10 Agricultural farm energy consumption diagnosis (1/4)Main goals:- Quantify energy consumption per processes to identify possible improvements by acting either on the production system, practices or equipment- Compare energy performance of livestock and crop farming- Establish reference values for the productionsEnergy indicators : Variables that provide information on avaiable data, used to take decisions simple (e.g. fuel) or aggregated indicators (e.g. energy balance)Inputs are (i) direct and indirect energy flows and (ii) energy consuming operations Direct energies (e.g. electricity, fuels and gases) Indirect energies are energies used to produce outputs (e.g. fertilizers, crop protection products)
  11. 11. 11 Agricultural farm energy consumption diagnosis (2/4)Diagnosis are grouped:Global agro-environmental diagnoses: these consider the farm as a whole and assess the global environmental impact of the farm (Sum of the quantities of direct and indirect energy used by the farm, expressed in MJ per year)Field agro-environmental diagnoses: these assess the environmental performance of the farm at the field scale (Sum of the quantities of direct and indirect energy used by the farm, expressed in MJ per hectare per year)Sustainability diagnoses: these assess farm sustainability performance through environmental, social and economic sustainability issues
  12. 12. 12 Agricultural farm energy consumption diagnosis (3/4)Energetic diagnoses: these allow an energy balance at the farm scale by quantifying energy inputs and outputsPrediagnoses or autodiagnoses: these assess the energy consumption of farm equipment and give an idea of the main possible improvementsLife cycle assessment: these methods assess the environmental impact on a system by inventory pollutant emissions, raw materials and energy during its whole life cycle (Energy consumption based on the functional unit used, either the hectare or milk liter)
  13. 13. 13 Agricultural farm energy consumption diagnosis (4/4)Main limitations:they define indicators only at a global scale, avoiding the finest analysis of energy consumption related to technical operations and plotsthey are based on classical data storage systems that do not allow interactive cartographic analysis of huge volumes of dataAn analysis at a most detailed scale (field, production activity or operation) will require: 1. More precise data acquisition systems 2. A set of new indicators 3. Systems allowing visual/cartographic interactive analysis at different granularities/scales of huge data volumes
  14. 14. New energy indicators to assess farm performance at a detailed scalePour mieuxaffirmerses missions,le Cemagrefdevient Irsteawww.irstea.fr
  15. 15. 15 New energy indicators (1/4)Analysis scale:Spatial scale: the hectare is the spatial reference unit used to express the technical-economic results on a farmTemporal scale: the hour is the temporal reference unit for farmers This unit is frequently used to express fuel consumption for mobile equipment and electric/gas consumption for electric equipmentProduction scale: all of the energy flows involved in farm activities (crop or cattle management) can also be expressed at the production scale The results will be expressed with the common production unit used by the farmer depending on the type of farm production (e.g., tons of dry matter, liters of milk, etc.)
  16. 16. 16 New energy indicators (2/4)These indicators were calculated for the three main farm activities:Crop management activities: technical operations on crops (sowing, plowing, fertilization, harvest, etc.)Cattle management activities: technical operations on cattle, such as care, feeding and milk/meat production (milking, slaughtering, etc.)General activities: technical operations that cannot be allocated to cattle or crop management (cleaning, logistics, transport, etc.)These indicators can be aggregated at the global scale using the sum
  17. 17. 17 New energy indicators (3/4)Example simple indicator: the fuel consumption per plot, technical operation, year and production “140 liters of fuel used for the parcel ‘13 pal’ of the farm of Montoldre in 2010 during the plowing operation for the production of wheat”Example aggregated indicator: fuel consumption per farm, technical operation, year and production “The aggregates on spatial scale are calculated by summing all the plots values belonging to the same farm”
  18. 18. 18New energy indicators (4/4)
  19. 19. Spatial OLAP analysis of new indicatorsPour mieuxaffirmerses missions,le Cemagrefdevient Irsteawww.irstea.fr
  20. 20. 20 Spatial OLAP analysis of new indicators (1/9)A SOLAP system to represent, store and analyze the new indicators→ interactive analysis of huge volumes of (aggregated) indicators values and it provides a cartographic representationNew indicators and spatio-multidimensional model:Indicators = measuresInputs (energy, time, spatial scale, technical operation and production) = dimensionsAggregated indicators = aggregated measures
  21. 21. 21 Spatial OLAP analysis of new indicators (2/9)Usage of a UML profile implemented in the CASE tool Magic DrawAutomatic implementation on the SOLAP architecture→ fast and flexible schema developmentStereotypes for each spatio-multidimensional elemente.g. "Fact" for the facts, "SpatialAggLevel" for spatial dimension levelsA stereotype is an extension of a UML element (class, attribute, etc.=
  22. 22. 22Spatial OLAP analysis of new indicators (3/9) “Fuel to weed a plot of wheat" “Total consumption of fuel to weed the entire farm per year"
  23. 23. 23 Spatial OLAP analysis of new indicators (4/9)Dimensions:Campaigns (Campagne): production cycles expressed in years (e.g., wheat produced in 2009)Time (Temps): classical temporal dimension, in which days are grouped by month and yearProducts (Produits): the input and output products (intrant and extrant). Products are grouped recursively into larger classes of productsOperators (Opérateurs): people who perform the operationEquipment (Attelage): machines and tools usedLocation: the spatial dimension that groups plots (parcelle) by farm (exploitation), department (département) and region (région)Productions: type of production (e.g. wheat)Technical Operations (Opérations Técniques): the technical operations performed, which are grouped by functions
  24. 24. 24 Spatial OLAP analysis of new indicators (5/9)Measures:the area worked (surface_w)the number of animals (animaux_nb)the amount of product (input represented with “intrant” and output denoted with “extrant”) used during work or no work (denoted with “w” and “nw”, respectively)the duration in hours (duree_w and dure_nw)the distance traveled (distance_parcourue_w and distance_parcourue_nw)The measures are aggregated using the sum along all dimensions
  25. 25. 25 Spatial OLAP analysis of new indicators (6/9)Implementation:SDW tier: PostGIS is a Spatial DBMS natively supporting spatial dataSOLAP Server and Client:JMap-SOLAP only existing SOLAP tool
  26. 26. 26 Spatial OLAP analysis of new indicators (7/9)Aggregated indicator "the amount of fuel used for cultivating one hectare of wheat on the Montoldre farm”
  27. 27. 27 Spatial OLAP analysis of new indicators (8/9)Spatial drill-down operation: “energy consumption per plot” indicator
  28. 28. 28 Spatial OLAP analysis of new indicators (9/9)Drill-down on the technical operations dimension: “more details on energy consumption by plot”
  29. 29. 29 Spatial OLAP analysis of new indicators“fuel consumption per plot, technical operation, year and production”
  30. 30. 30 Conclusions and perspectivesWe propose some new indicators to assess agricultural farm performance at a most detailed scaleWe also show how it is possible to represent these indicators using a spatial data warehouse and analyze them by means of a classical SOLAP systemWe are working on the estimation/interpolation of missing values at most detailed dimensions levels

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