Page 190 - Invited Paper Session (IPS) - Volume 2

IPS 208 Johan S. et al.
additional administrative sources. The IFS explicitly mentions more
administrative sources, thereby reducing the number of cases in which prior
information on their use and quality is required (R7). The administrative
sources which are regulated to support the CAP are the organic farming
registers, the vineyard register, the integrated administration and control
system, the systems for the identification and registration of bovine and ovine
animals and the administrative sources associated with specific rural
development measures. For example, in the 2020 agricultural census, the data
for the ‘Rural development’ module on farms benefiting from rural
development measures are expected to be collected entirely from
administrative registers, while the variables of livestock in the core are
expected to be collected from the animal registers.
Other methods and innovative approaches (S3) refer to modelling, expert
estimates, remote sensing, and so on. Some of the variables in the ‘Animal
housing and manure management’ module can be estimated using models.
For example, the annual average number of animals in each category can be
estimated using models based either on the number of animals raised, divided
by the number of livestock raising cycles per year, or on a combination of the
number of places and the number of empty days. To give another example,
the quantity of manure produced by a given category of livestock can be
estimated on the basis of the number of animals under certain types of
management. Such methods reduce the administrative burden on national
authorities and the costs they incur, but also the effort that farmers have to
make to recall and estimate this information (R8).
Under the IFS, in order to allow member states to flexibly choose the
source and reduce the burden of data collection further, information on the
‘Machinery and equipment’ (in 2023), ‘Orchard’ (in 2023) and ‘Vineyard’ (in
2026) modules may be based on the year directly preceding or following the
reference year, as long as it reflects the situation in the reference year (R9).
This information is assumed to be slow-changing. Previously, all data had to
be collected for the reference year.
While the increased use of S2 and S3 reduces the overall burden, it poses
challenges with respect to assessment of data quality. Data quality is affected
by both sampling and non-sampling errors. Only data collections based on
samples are affected by sampling errors. In this case, the theory for probability
samples allows for the sampling design to control for this type of error, and
the IFS regulation sets precision targets for certain variables collected on a
sampling basis. However, it is impossible to control for non-sampling errors
or to target them in advance, because they happen in a non-controlled
manner for every statistical process. They cancel each other out or add to or
multiply each other, depending on the specific data collection and the specific
context. No sound theory is available to predict them. This is more problematic

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