SA Graan Junie 2015

FOCUS

Special

Value adding

Junie 2015

Using advanced data min-

ing tools

Further analyses of the data can be done us-

ing principal component analyses (for data

clustering) and CART trees. The power of

using both techniques is that if applied cor-

rectly, a similar outcome can be expected.

PCA plots can be very abstract and difficult

to interpret, but clusters can be shown as

groups of data on a 3D plot. For the crop

quality survey data, different seasons ap-

pear as distinct groups on a 3D factor plot. It

supports the result of a CART tree analysis

done where the effects of season, region

and continuous parameters, such as per-

centage protein, were used to predict an

outcome such as Milling Index.

For the eleven year test period, and using

Milling Index data illustrated in Figure 6

as an example, the most important factor

influencing milling quality was season.

Hectolitre mass and percentage protein

were second and third in terms of impor-

tance with the effect of region in fourth

place of importance. In combination, these

factors then produced highly significant

models for future use.

Please contact the SAGL (

co.za

) or Dr Corinda Erasmus (

corinda@

iburst.co.za

) for further details. Expla-

nation of the production regions linked

to their numbers can be obtained from

Figure 5: Hectolitre mass; eleven seasons combined (white maize).

Figure 6: Roff milling index; eleven seasons combined (white maize).

Data mining opens up new possibilities

for identifying maize

crop quality trends