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

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

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.

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

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.

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

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)

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

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

Thank you for the attention!

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

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.

Experiences I. - Media  (27 May 2004)  Number of registered farmers:  Registered area after submission: approx ha  Registered area after primary checks: ha (Overclaiming for about ha)

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.

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  Attributes: Geographic area (county/region), economic size (ESU), legal status (individuals, companies), type of farming Source: Pesti-Keszthelyi-Tóth (2004)

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)

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)  …

Thank you for the attention! Bibliography: Pesti Csaba-Keszthelyi Krisztián-Tóth Tamás (2004): Regional comparison of farms on the basis of the FADN database, Gazdálkodás 8. számú különkiadás XLVIII. évfolyam, 2004