1 introduction o introduction o innovation agriculture statistics in three phases: until end of...

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Introduction

Introduction

Innovation Agriculture Statistics in three phases:

Until end of 2007: Preliminary research Key issue: problems and possibilities

Until mid 2008: Business Analysis (BA) Key issue: the “what question” (SWOT) and priorities

2008-2009: Implementation Key issue: integration in SN-wide developments: standardisation and general tools

Innovation of the Dutch Agriculture Statistics

ICAS4, Beijing 2007

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Current situation

Sketch of desired situation

Results of pilots

Content


ICAS4, Beijing 2007

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Current situation

Yearly Farm Structure Survey (FSS) ± 90.000 units: Farm register maintained by Min. of Agriculture physical production full survey co-production between SN and Ministry Main customer: European Union / Eurostat

Supplemented by I0-15 smaller surveys (satellite surveys) Zoom in on a specific subject of the FSS Sample surveys conducted by SN itself


ICAS4, Beijing 2007

Agriculture Accounts / Prices and volumes (National Accounts) Agriculture services Labour

Stand alone system characterised as a stovepipe modelSmall customer base

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Reducing the survey burden on enterprises optimal use of all register information coordination of definition and concepts (e.g. XBRL)

Efficiency (budget cuts), processes / IT-systems

Other: new EU-BR-regulation, new NACE, authentic registers

Developments

Changing information needs of customers accessibility (new media) / quicker more integrated, e.g. agriculture related to other areas integrated micro-data e.g. CAP leads to the need for new information (production methods)

Growing competition (data electronically available) other organisations can do better and quicker in their domain SN has to add new value (integrating datasets)


ICAS4, Beijing 2007

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Elements of a new way of making statistics

Balance against customer needs (information development). Add new value.

Integration of datasets

Full use of registers

Efficient processes: redesign, standardization

Chains and networks of information flows


ICAS4, Beijing 2007

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FR BR

NACE Agriculture, excl. Forestry and Fishing

NACE groups outside Agriculture

Economically too small, will not lead to a unit in the BR

- Financial data- Employment and wages- Other sources- NA/A-accounts

Connected to:

Economic demographyof agriculture enterprises(Mutatieonderzoek)

Related to environment, rural development, nature, agro-chains etc.

FSS +

satellite

surveys

- Parcels/crops (BRP)- Livestock (I&R)- Nature conservation- Land use register- Organic production- Subsidies (IACS)- Etc.

:Connected to

Sketch of desired situation

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FR BR





Connected to:


Related to environment, rural development, nature agro-chains etc.

FSS +

satellite

surveys


:Connected to

Pilots

Pilot 1

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Pilot 1: FR and population FR as a derivate of the register of the Ministry of Agriculture

Set up a FR as a system Current source is the best option (e.g. coverage and timeliness) Ministry continues to maintain population of units More attention for its statistical function (administrative source)

(e.g. more influence from SN)

Better definition of “agriculture activities” and units, e.g.: Head office and their locations/parcels (Agriculture Holding) Use of thresholds (> 3 SGM) / smaller activities Activities from foreign enterprises


ICAS4, Beijing 2007

Events (e.g. deaths, births etc. of enterprises), changes in FR

Improve the way non-response is handled

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FR BR





Connected to:



FSS +

satellite

surveys


:Connected toPilot 1

Pilots 2 en 3

Pilots

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Pilots 2 and 3: Connection FR - BR

A connection between FR - BR Key: Name and Address 75.000 units of FR (out of 90.000) connected to one or more units BR Too difficult, too many mismatches, need too many resources Conclusion: wait until general identifiers are added to all main registers (e.g. Social security number and enterprise identifier)

Comparison between Farm type (FR, 2005) – new NACE Theoretical: it is impossible to relate all categories of the Farm type

to a group in the NACE and vice versa. Extra decision rules are needed. Practically: if units of FR and BR are coupled and Farm type and NACE are compared:

Categories of livestock (4 and 5): 89% Categories of crops (1, 2 and 3): < 77% Specialist fruit (32): 31% Mixed farming (6,7 and 8): < 10%


ICAS4, Beijing 2007

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FR BR





Connected to:



FSS +

satellite

surveys



Pilots 2 en 3

Pilot 4

Pilots

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Pilot 4: use of tax data

Financial/production statistics for agriculture based on tax data feasible E.g. PS Agr. = 18,5 bil. <> Agr. Accounts = 18,4 bil. (turnover)

Problems with weighing, outliers and coupling

Cannot separate agriculture and non-agriculture turnover and costs in tax data

Other big advantage: micro database with financial and structural data (for scientific research)


ICAS4, Beijing 2007

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FR BR





Connected to:



FSS +

satellite

surveys



Pilots 2 en 3

Pilot 4Pilots

5 en 6

Pilots

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Pilots 5 and 6: use of agriculture registers

Livestock (e.g. Cows) National level: good match (3,7 million FSS = 3,7 million I&R) Micro-level: still many differences (25% I&R = FSS; 90% I&R and FSS differ =< 10 cows) Can be used to replace satellite survey(s) on livestock More research is needed to replace questions in FSS (e.g. exact time of observation)

Crops (based on parcel information) Can be used on national and micro level Can replace (part of the) questions in FSS Problems with detail, classifications and groups missing


ICAS4, Beijing 2007

Geographic information Good tool for publications on regional level (parcel and stable level, better than FSS) Good tool for analysis (e.g. changes over time, problems FSS)

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FR BR





Connected to:



FSS +

satellite

surveys



Pilots 2 en 3

Pilot 4Pilots

5 en 6

Pilot 7

Pilots

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谢谢你

再见

Thank you for your attention

Any questions?


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1 introduction o introduction o innovation agriculture statistics in three phases: until end of...

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