eFarmer

1
eFarmer Content Management and IACS Experiences

• Dr. László PITLIK István PETO
• Department of Business Informatics
• Szent István University, Gödöllo, 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,
  ACDIVOCA 1997-1998)
• MAINFOKA URL-catalogue of online sources
  (FVM, ACDIVOCA)
• 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)
• SPELGRPITIDARACAPRI EU-level concept of
  CM(ACDIVOCA, PHARE, EU 1997-2004)

5
Definitions

• ERP system (accountancy, MIS) of the
  enterpriseHandles 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 systemDescribes 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 sectorBased 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-OForecast-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 FADN database,
Gazdálkodás 8. számú különkiadás XLVIII.
évfolyam, 2004