Technical
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This gives rise to the possibility of describing the performance of the classes of cultivars, not so much in numbers but in words that users may easily understand
Figure 5: Separation of classes B and D along 3rd axis
the combined data from cultivars represented in both tables S1 and L1, I have chosen to identify 6 classes. Each of those 6 classes represents a cluster of cultivars that are similar to one another and therefore occupy a particular space in the scatter distribution of cultivars as shown in Figure 3. The space occupied by 4 of these classes are shown in Figure 4. On the axis 1-2 scattergram the separation
of Classes B and D is not so clear as the others. Another axis, axis 3, accounted for a further 11 % of the variation in the data. This may be thought of as extending vertically through the page so we are, in effect, looking at our cultivar constellation from the side. In that way, the separation of the cultivars not shown as belonging to a particular class in Figure 4 becomes much more distinct and the separation of classes B and D along this third axis is shown in Figure 5 above. Now we have divided the perennial
ryegrass cultivars into 6 fairly distinct classes and the cultivars in each class should all share similar characteristics. We can now look at what those characteristics actually are and we can assign named cultivars to each class. The constituent cultivars in each of the 6 classes are indicated in Table 1. Another problem with the presentation of
the data in the booklet is that the actual performance scores, 7.1, 6.3, 8.5 etc., and which have all been standardised, actually vary very little one with another. The measures for ‘visual merit’ in sports, for example, vary only between 5.3 and 8.1, a range of just 2.8. This gives little indication of how significant any variations that are presented actually are. If we assume that all of the measurements
included are equally important, there is no reason why each one could not be presented, for example, as a percentage of the actual range of the results obtained. Thus, a visual merit result of 5.3, the lowest score recorded, could be given a value of 0% while one of 8.1, the highest, could be given 100%. A score of 6.7 would have a value of 51 %. This might be a clearer way of expressing the differences
100 I PC FEBRUARY/MARCH 2017
without giving the actual results in whatever units they were originally measured, something that might, though not necessarily, be equally confusing. So, each of our 6 classes has an average
score for each of the measured variables and those scores, expressed as a percentage of the range actually occurring, are shown in Table 2 . This gives rise to the possibility of
describing the performance of the classes of cultivars, not so much in numbers but in words that users may easily understand. Such descriptions have, I think, the potential to be much more widely understood and utilised. From the data in Table 2, they might read something like this:
• Class A: Relatively low shoot density, fineness of leaf and visual merit and very poor winter and summer greenness. Good red thread resistance, especially at lawn height.
• Class B: Good shoot density, leaf fineness and visual merit. Resistant to red thread at close mowing, but not at sports height. Cuts cleanly, but very poor winter and summer greenness.
• Class C: Excellent winter and summer greenness and red thread resistance. Very poor shoot density, leaf fineness and visual merit, cutting only poorly.
• Class D: Very good density, leaf fineness and visual merit. Very poor red thread
Table 1: Constituent cultivars in each of the 6 classes
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