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eFarmer – Content Management and IACS Experiences

This presentation discusses a holistic approach to content management in agriculture, focusing on maximizing subsidies, operational efficiency, and decision-making processes. Topics include definitions, available content structure, data processing methodologies, and more. Explore examples of connected projects and country-level concepts of content management. Gain insights on utilizing ERP systems, external information systems, and data structure for informed decision-making.

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eFarmer – Content Management and IACS Experiences

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  1. eFarmer – Content Management and IACS Experiences Dr. László PITLIK –István PETŐ Department of Business Informatics Szent István University, Gödöllő, Hungary eFarmer Workshop, Bratislava;June 15 2004

  2. Contents • Main objective of the presentation of CM • SZIE – Connected Projects • Definitions • Structure of Available Content • Dimensions • Examples for Application • Methodology of Data Processing • Main Categories of Methods

  3. Main objectives • This conception is the idealised and maximised approach of Content Management in agriculture. • Therefore every modules mentioned here can be included or excluded from the final concept – every potential combination of the included modules can be interpreted as a homogeneous system. • The expression „content” in the proposal might give quite wide score for the above-mentioned decision. • Decision criterion: e.g. maximising of income along the Project Business Plan.

  4. SZIE – Connected Projects • REMETE – county-level concept of CM(USAID, ACDI&VOCA; 1997-1998) • MAINFOKA – URL-catalogue of online sources(FVM, ACDI&VOCA) • ikTAbu – data-assets management & online algorithms(OMFB IKTA, 1999-2001) • MIMIR – IIER (IACS) – country-level concept of CM(1998-2004) • INFO-PERISCOPE – concept of external info-system(NKFP; 2001-2003) • eGovernment – anomalies in data-assets management(SZT; 2002) • SPELGR–PIT–IDARA–CAPRI – EU-level concept of CM(ACDI&VOCA, PHARE, EU; 1997-2004)

  5. Definitions • „ERP system” (accountancy, MIS) of the enterprise:Handles data created during the operation of enterprise.Role in eFarmer:Creates the basis for any control of subsidies – the official annexes of claims • External information system:Describes the (natural, legal, economic, social etc.) environment of organisations and has important role in planning, decision-arrangement, benchmarking.Role in eFarmer: community and country level requirements • Planning and monitoring system for the agricultural sector:Based on the transparent, consistent system of Economic Accounts of Agriculture.Role in eFarmer: maximising in country-level the efficiency of subsidy call-in from the EU.

  6. Structure of Available Content I. The available data-assets can be sorted according to the following dimensions: • Actual data [A]Planned / Calculated data [P] • Data about Internal conditions of the enterprise [I] External data (about the environment of enterprise) [E] • Numerical (incl. GIS) data [N]Textual data [T] • Data for public use (e.g. online sources) [O]Restricted Data [R] 16 different combinations of options should be handled

  7. Structure of Available Content II/a • Actual data [A]: • [I]-[N]-[O]: Data from PR-studies of farms • [I]-[N]-[R]: Supplying of data from farms to authorities • [I]-[T]-[O]: Brochures about enterprises • [I]-[T]-[R]: Internal regulations and reports of enterprises • [E]-[N]-[O]: Thresholds for tender evaluation • [E]-[N]-[R]: Parameters of project-monitoring • [E]-[T]-[O]: Tender guides, professional studies • [E]-[T]-[R]: Documents for limited access (e.g. for members of professional bodies)

  8. Structure of Available Content II/b • Planned / Calculated data [P]: • [I]-[N]-[O]: Enterprise-analyses for public use (e.g. shareholders) • [I]-[N]-[R]: Enterprise-analyses for credit-claims • [I]-[T]-[O]: Comments to enterprise-analyses for public use • [I]-[T]-[R]: Comments to enterprise-analyses for target groups • [E]-[N]-[O]: Data about economic trends for public use • [E]-[N]-[R]: Data about economic trends for target groups • [E]-[T]-[O]:Forecast-studies for public use • [E]-[T]-[R]: Forecast-studies for special target groups

  9. Methodology of Data Processing • Numerical analyses (classic methods of statistics, data mining, object-comparison) • Visualisation of numeric values (pivot, OLAP) • Numeric control-mechanisms(EAA) • Hybrid solutions (expert systems) • Text-based solutions (automatic translation & recognition) • Document management

  10. Thank you for the attention! http://miau.gau.hu

  11. Contents • Main objectives • Information sources • Payment agency (participation later) • Agricultural Chamber (participation later) • Media (IACS-articles) • Non-representative interviews (farmers, advisors) • FADN as a basis of the farm selection (basic information) • Theory (potential problems in IACS) • Required structure of information

  12. Main objective • Defining and structuring the raw data that might result in information value-added within the framework of eFarmer.This information value-added consists of:+ higher rate of making use of subsidy on farm-level,+ higher efficiency of subsidy call-in on country-level,– lower operational cost on enterprise- and country-level.

  13. Experiences I. - Media • http://www.nol.hu/(27 May 2004) • Number of registered farmers: 305.000 • Registered area after submission: approx. 5.400.000 ha • Registered area after primary checks: 4.400.000 ha(Overclaiming for about 1.000.000 ha)

  14. Experiences II. - Interviews Outlines: • Non-representative, personal impressions • Target-groups: farmers, advisors, experts Conclusion: • There are no accomplished manuals for certain modules (like MEPAR) • There is no codified internal controlling method yet (e.g. in the PA) • Completing these application forms is no more complicated than any forms for previous subsidies.

  15. Experiences III. - FADN • Authenticity of eFarmer project would be supported by referring to a representative sample of enterprises  FADN-system. • Brief overview of FADN-system in Hungary: • Number of farms: approx. 1900 • Attributes: Geographic area (county/region), economic size (ESU), legal status (individuals, companies), type of farming Source: Pesti-Keszthelyi-Tóth (2004)

  16. Experiences IV. - Theory • Comprehensiveness (object-oriented) • Identification of every related documents (incl. documents of on-farm inspections, annexes of claims) • Identification of every possible actions (incl. additional completion of documentation, appeal processes) • Accuracy – supporting: • Identification of parcels (analyses on the grounds of game-theory to handle overclaiming) • Planning on country-level (maximising of subsidy call-in) • Planning on farm-level  MAX(subsidies – related costs)

  17. Required structure of information about claims to detect the information value-added effects • Multi-dimensional structure for drill-down (pivot, OLAP) • Dimensions: • Detailed Regional aspects • Detailed Farm attributes (economic size, legal form, activity) • Description of the schemes • Typical errors in submitted claims • Advisors contributing in filling in the claims (and the related costs) • „Problematic” claims(rejection, additional completion, appeal) • …

  18. Thank you for the attention! http://miau.gau.hu/magisz Bibliography: Pesti Csaba-Keszthelyi Krisztián-Tóth Tamás (2004): Regional comparison of farms on the basis of the FADNdatabase, Gazdálkodás 8. számúkülönkiadás XLVIII. évfolyam, 2004

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