Developing crop descriptor lists - Bioversity International

34 BIOVERSITY INTERNATIONAL TECHNICAL BULLETIN SERIES NO. 13
Quantitative data on a continuous scale has a greater potential
for allowing statistical analysis than quantitative data measured
on a discrete scale, and the use of exact measurements avoids
differences in interpretation by different users. This does not mean
that characteristics described using only discrete scale are less
valuable; they are important in being diagnostic in nature, but could
complicate future statistical analysis. The unit of measurement is
also an indication of the level of resolution that is required.
If diversity of a specific trait (e.g. plant type) can be described by
two very distinct states (dwarf type, tall type), a visual scoring of
the two descriptor states could be sufficient and may be preferable
to measuring every accession and recording the plant height in cm;
this will save time and work.
It is good practice to keep observations and measurements as
simple as possible. The objective of measurements is to determine
how the trait of a specific accession compares with the diversity of
the collection. When developing descriptors, one should remember
that specialist knowledge and specialist equipment could be readily
available at a particular institution, but this might not be the case
for other institutions. In addition, methodologies that might be
executed at one institution without problems, might present
extraordinary logistical problems for institutions dealing with
different combinations of crops or climatic environments (e.g. an
institution working with one crop in comparison with a multicrop
institution).
Where a term is open to interpretation, it is best to try to make a
direct comparison with a well known standard or to use an absolute
measurement. It is also essential to evaluate the trait in a number
of randomly selected plants or a representative sample to ensure
that the full range of variation present is described.
7.2 Complexity of the measurement or observation
The complexity of measurement or observation is dependent on
extent of priming, special equipment or specific expertise required to
execute a method of measurement. The more complex a procedure,
the greater the chance of making mistakes during its execution,
requiring greater care and attention to detail.
7.3 Cost per measurement
It is recommended that the costs for each measurement be
carefully analysed, in terms of both staff time and materials. To
have comprehensive minimum characterization data, the costeffectiveness
of observing descriptors is an important consideration.
However, it must be noted that the value of data recorded is in the