1. 1st Joint Workshop Pesticides Statistics
2005 Transition Facility – Phare Programmes
Vilnius 3 April 2007
Sampling and stratification: a main issue

2. Structure of the presentation
Sampling design and data collection
Target/sampled population
Identification of target population
Anecdotal vs Statistical
Stratification issues
Lessons from Phare 2002
Alternative solutions?

3. Sampling design - Data collection
A crucial issue in statistics is the representativeness of the data with respect to the objective of the survey: it is addressed through the sampling design
Sampling design is a fundamental part of data collection for scientifically-based decision making
Adequate sampling design is a key issue in collecting appropriate and defensible data
Sampling design consists in indicating the number of samples to be surveyed and their identification
It is intended that resulting data are adequately representative of target population and defensible for their use

4. Target population / Sampled population
Target population = a set of all units that comprise the items of interest = the population about which decision makers want to be able to draw conclusions
if objective is measuring quantity of PPPs on wheat, the target population is the area sown in wheat...and by extension the holdings or households with wheat production
Sampled population: part of the target population that is accessible and available for sampling (ideally the same...)
but never the same (incomplete household lists, not updated registers, farms under the thresholds, also critical considerations on accessibility, time, money)
Sampling frame: list of all possible sampling units for which the sample can be selected

5. Identification of target population
Target population should be identified through any registration or list system
Several potential sources of information
Lists of units: Registers (but updating far from a simple exercise), Census lists (but what to do when Census is out of date)
Area frame (points, segment, link spatial information and holding)
Several existing information systems whose target population is of interest for PPPs:
FSS, FADN, IACS, Crop Surveys (Lists or area frames)

6. Anecdotal or statistical
“Probability based”means applying sampling theory and involving random selection of sampling units
Essential feature is that each member of population has a known probability of selection
when selecting for example a specific region (even if its profile is considered as similar to national profile) statistical inference cannot be made at national level
Selecting sampling units on the basis of expert knowledge is therefore not a statistical approach but a judgmental (anecdotal) approach in sampling design
example of discrimination between good and poor farms: may be a prerequisite which should rather be a result of the survey?

7. Stratification issues
Main reason of stratification is to ensure a more representative sample by distributing the sample through various dimensions of the population
Auxiliary variables are providing the information used to establish the strata with two objectives:
strata are allocated so that the population included in each stratum is as homogeneous as possible
stratified sampling should produce better estimates with greater precision than simple random sampling
In the case of PPPs, which auxiliary variable could be highly correlated and utilized?
Several candidate variables
Allocation of sample size in each stratum also to be debated

8. Lessons from Phare 2002 (Wheat)
Extreme diversity of approaches from pure statistical approaches to “anecdotal” approaches
Various sources for identifying target population: census, statistical registers (updated or not maintained)
Frequently selection of regions according to judgment expert = difficult to do inference of national figures
When stratification: according to sown area, size groups, structures, UAA classes
Main issue: quality of registers or farm/households lists: impact on the organisation of surveys, necessity to plan reserve lists, impact on the results

9. Improvements? Multi-stage sampling?
Complete enumeration of randomly selected areas, even if sampling errors are higher due to clustering effects
Exploring better the opportunities given by existing surveys for anchoring the PPPs items?
(and implicitly accepting to utilize available surveys at time t for providing more regular data?)
Models, use of GIS?