Abstract
Agricultural energy consumption is an important environmental and social issue. Several diagnoses have been proposed to define indicators for analyzing energy consumption at large scale of agricultural farm activities (year, farm, family of production, etc.). However, to define ad-hoc environmental energetic policies to better monitor and control energy consumption, new indicators at a most detailed scale are needed. Moreover, by defining detailed scale indicators, large quantities of geo-referenced data need to be collected to feed these energetic diagnoses. This huge volume of data represents another important limitation of systems that implement these diagnoses because they are usually based on classical data storage systems (such as spreadsheet tools and Database Management Systems). These systems do not allow for interactive analysis at different granularities/scales of huge volumes of data and do not provide any cartographic representation. By contrast, Spatial OLAP (SOLAP) and spatial data warehouse (SDW) systems allow for the analysis of huge volumes of geo-referenced data by providing aggregated numerical values visualized by means of interactive tabular, graphical and cartographic displays. Thus, in this paper, we (i) propose new appropriate indicators to analyze agricultural farm energy performance at a detailed scale and (ii) show how SDW and SOLAP technologies can be used to represent, store and analyze these indicators by simultaneously producing expressive reports.
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References
Abdullah, A., Brobst, S., Umer, M., Khan, M.: The Case for an Agri Data Warehouse: Enabling Analytical Exploration of Integrated Agricultural Data. In: International Conference on Databases and Applications, pp. 139–144. IASTED/ACTA Press (2004)
Bailey, A.P., Basford, W., Penlington, N., Park, J., Keatinge, J., Rehmana, T., Tranter, R.B., Yates, C.: A comparison of energy use in conventional and integrated arable farming systems in the UK Agriculture. Ecosystems and Environment 97, 241–253 (2003)
Bédard, Y., Han, J.: Fundamentals of Spatial Data Warehousing for Geographic Knowledge Discovery. In: Geographic Data Mining and Knowledge Discovery. Taylor & Francis (2001)
Bimonte, S., Tchounikine, A., Miquel, M., Pinet, F.: When Spatial Analysis Meets OLAP: Multidimensional Model and Operators. International Journal of Data Warehousing and Mining 6(4), 33–60 (2010)
Boulil, K., Bimonte, S., Pinet, F.: Un modèle UML et des contraintes OCL pour les entrepôts de données spatiales. De la représentation conceptuelle à l’implémentation. Ingénierie des Systèmes d’Information 16(6), 11–39 (2011)
Chaudhary, S., Sorathia, V., Laliwala, Z.: Architecture of sensor based agricultural information system for effective planning of farm activities. In: IEEE International Conference on Services Computing, pp. 93–100. IEEE (2004)
Clements, A., Pfeiffera, D., Otteb, M., Morteoc, K., Chenb, L.: A global livestock production and health atlas (GLiPHA) for interactive presentation, integration and analysis of livestock data. Preventive Veterinary Medicine 56(1), 19–32 (2004)
Gras, R., Benoît, M., Deffontaines, J.P., Duru, M., Lafarge, M., Langlet, A., Osty, P.L.: Le Fait Technique en Agronomic: Activité Agricole, Concepts et Méthodes d’Étude. L’Harmattan, Paris (1989)
Maceachren, A.M., Gahegan, M., Pike, W., Brewer, I., Cai, G., Lengerich, E., Hardistyn, F.: Geovisualization for Knowledge Construction and Decision Support. Computer Graphics and Application 24(1), 13–17 (2004)
Mitchell, G., May, A., McDonald, A.: PICABUE: A methodological framework for the development of indicators of sustainable development. International Journal of Sustainable Development and World Ecology 2(2), 104–123 (1995)
Nilakanta, S., Scheibe, K., Rai, A.: Dimensional issues in agricultural data warehouse designs. Journal Computers and Electronics in Agriculture Archive 60(2), 263–278 (2008)
Pervanchon, F., Bockstaller, C., Girardin, P.: Assessment of energy use in arable farming systems by means of an agro-ecological indicator: The energy indicator. Agricultural Systems 72(2), 149–172 (2002)
Pradel, M., Boffety, D.: Quels indicateurs et solutions technologiques adaptés pour évaluer finement les performances énergétiques des exploitations agricoles? Sciences, Eaux et Territoires 7, 16–29 (2012)
Rai, A., Dubeya, V., Chaturvedia, V., Malhotraa, K.: Design and development of data mart for animal resources. Computers and Electronics in Agriculture 64(2), 111–119 (2008)
Kimball, R.: The Data Warehouse Toolkit: Practical Techniques for Building Dimensional Data Warehouses. John Wiley & Sons, New York (1996)
Schulze, C., Spilke, J., Lehner, W.: Data modeling for Precision Dairy Farming within the competitive field of operational and analytical tasks. Computers and Electronics in Agriculture 59(1), 39–55 (2007)
SOLAGRO: Energie dans les exploitations agricoles: état des lieux en Europe et éléments de réflexion pour la France. Etude ADEME/ MAP 0671C0036 (2007)
Thornsbury, S., Davis, K., Minton, T.: Adding Value to Agricultural Data: A Golden Opportunity. Review of Agricultural Economics 25(2), 550–568 (2003)
Vilain, L.: La méthode IDEA, Indicateurs de durabilité des exploitations, Guide d’utilisation, Educagri Edition (2003)
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Bimonte, S., Boulil, K., Chanet, JP., Pradel, M. (2012). Definition and Analysis of New Agricultural Farm Energetic Indicators Using Spatial OLAP. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2012. ICCSA 2012. Lecture Notes in Computer Science, vol 7334. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31075-1_28
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DOI: https://doi.org/10.1007/978-3-642-31075-1_28
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