Transcript Using administrative data to compile agricultural statistics

Using administrative data to compile
agricultural statistics
Experiences from the Census of Agriculture 2010
Fiona O’Callaghan, CSO
29th September 2011
Outline
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Background
Available data
Analysis/aggregation
Merging of data
Lessons learnt
Future developments
Summary
Background
▫ CSO Statement of Strategy includes as a high level
goals
 Minimise response burden, and extend the statistical
use of administrative records
 Improve the scope, quality & timeliness of our
statistics
 Achieve greater efficiencies using best practices
▫ SPAR Report - Statistical Potential of
Administrative Records
Background
▫ Up until 2010, CSO conducted 2 annual farm
surveys (June & Dec) with sample sizes ranging
from 15,000 to 20,000 farms. Farm Structure
Survey approx 50,000 farms.
 June survey: average response burden approx. 30 mins.
 December survey: average response burden approx. 18
mins.
 COA 2010: average response burden 26 mins.
Available Data
• CSO identified six major data holdings within
DAFF which contained relevant information
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Animal Identification and Movement System (AIM)
Single Payment System (SPS)
Organic Database
REPS database
Animal Health Computer System (AHCS)
Corporate Client System (CCS)
Available Data
▫ Three of these databases were used for COA 2010
 CCS was used in developing the register
 AIM was used for cattle
 SPS was used for crops/cereals
Analysis/aggregation
▫ Corporate Client System
 Contains name, address, DOB, herd number etc.
 This was merged with the existing CSO Agriculture
Register to form a new Register for COA 2010
 Issues involving the “unique” identifier, Herd Number,
resulting in duplicates
 Result – 153,904 Census forms issued
Analysis/aggregation
▫ AIM – involves the use of electronic means to capture
data on animal movements through computer links
established at livestock markets, meat plants, and
export points
 Data available since 2002
 At an aggregate level, the AIM figures for the bovine
population have been consistently higher than the
CSO estimates for the corresponding date (on
average approx. 5% higher)
 Preliminary comparative analysis performed by CSO
in 2008 and 2009
 Eliminated 11 cattle questions from Census form
Analysis/aggregation
▫ AIM data consists of the following variables
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Tag number
Herd number
DOB
Gender
Breed
Breed Type (Beef/Dairy)
Animal Class (Cow, bull, etc.)
Date of calving event
Analysis/aggregation
▫ Need to convert this information into totals for the
following categories
Breeding Cattle
Other Cattle
Dairy Cows
Other Cows
Male: 2 years and over
Female: 2 years and over
Dairy Heifers*
Other Heifers
Male: 1-2 years
Female: 1-2 years
Bulls
Male: under 1 year
Female: under 1 year
* Heifers in calf intended for the dairy herd
Analysis/aggregation
• Information on heifers-in-calf not available in
AIM database, but a proxy can be estimated.
• Other categories can be derived directly using
gender, DOB, animal class.
• Eurostat requires further breakdown of
categories into animals for slaughter – currently
developing a methodology to model this.
Analysis/aggregation
▫ SPS
 Information on every eligible parcel of land
 XY coordinates, Herd Number, Area, Use
 Preliminary analysis performed in 2009
 Eliminated 14 crops/cereals questions from Census
form
Analysis/aggregation
▫ SPS
 XY coordinates used to assign NUTS Region codes at
farm level
 Approx. 45% farms are spread over >1 DED - in these
instances the DED containing the largest area owned
was assigned
Merging of data
▫ Three separate data files
 Census returns
 AIM data
 SPS data
▫ “Unique” identifier – Herd Number – but many
instances where one farmer could be associated
with more than one Herd Number
Merging of data
▫ Labour intensive task of matching by name &
address
 Issues with Father & Son with same name & address
 Different versions of names on different databases –
Seamus/James, Sean/John etc.
 Non-unique addresses
▫ Farms that returned to CSO as Retired/Dead/Not
a Farm etc. but active on admin. data
Lessons Learnt
• More collaboration between DAFF & CSO
• Confidentiality – one-way transfer of
information
• Parallel pilot run in 2009
Future Developments
• Beyond the SPAR Initiative – cross sector
efficiencies
Piggy-backing on SFP online applications to
collect remaining survey items on June Survey
• Exploiting geo-coordinates
▫ To produce interactive maps
▫ To link with other databases
▫ Create area frame designs
Summary
• Positive development for Farmers & CSO
▫ Reduction in response burden – 25 questions
dropped
▫ Reduction in editing & processing of data
• Result - a high quality register of agricultural
holdings, and high quality data
Questions?