Developing crop descriptor lists - Bioversity International

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24 BIOVERSITY INTERNATIONAL TECHNICAL BULLETIN SERIES NO. 13 Including reference standards: Example #23 Leaf colour (Reference standard) 1 Green yellow Deglet nour 2 Yellow green Mejhoul 3 Green Besser Helou 4 Bluish green Ammari, Menakher 5 Dark green Ghars Including drawings: Example #24 Seed coat texture (See Figure 3) 1 Smooth 2 Partially rough 3 Radially rough 4 Partially radially rough 5 Reticulately rough 6 Partially reticulately rough 99 Other (specify in descriptor Remarks) 1 2 3 4 5 6 Figure 3. Seed coat texture.
Developing crop descriptor lists 25 Decisions on whether descriptor states need to be recorded separately or can be merged into a group will depend on the importance of the state in describing (within and between accessions), the diversity of the crop or gene pool. In general, the descriptor states of qualitative characteristics are given consecutive numbers starting with ‘1’ and often have no upper limit. With emerging imaging techniques, it is possible to convert the qualitative data (where the range of expression is continuous) to quantitative data, such as in colour and texture descriptors. In this case, the data can also be analysed quantitatively. 5.2 Quantitative data Quantitative data consist of measures or counts that use numerical values, allowing statistical analyses, for which descriptions such as means and standard deviations are meaningful. Quantitative descriptors are those in which the expression covers the full range of variation from one extreme to the other. Different states of expression of quantitative data can be recorded using discrete (countable data, such as “number of plants”), or continuous (measurable data, such as plant height, weight, length) scales. Many quantitative characters that are continuously variable are recorded on a 1 to 9 scale, in which ‘1’ stands for ‘very short’ or ‘very low’, and ‘9’ corresponds to the highest expression such as ‘very high’ or ‘very long’ (see Section 10). Nevertheless, the use of exact measurements is highly recommended, especially for easily measurable characteristics such as length or width, because there is no room for subjective interpretation. The use of ‘classes’ can prevent or complicate considerably statistical analysis of data. Such analysis is becoming increasingly important as many genebanks are now focusing on the use of conserved germplasm and on measuring withinaccession (within-population) variation. It is easy to re-classify after measurements have been made, but it is not possible to translate those classes back to the exact data. Sometimes only a selection of the states is described (e.g. 1, 3, 5, 7 and 9) for such descriptors. Where this occurs, the full range of codes is available for use by extension of the given codes or by interpolation between them.
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