CHARACTERISATION OF VEGETATIVE GROWTH AND PRODUCTIVITY PATTERNS BETWEEN NEW ZEALAND PERSIMMON ORCHARDS

Vegetative growth and fruit productivity datasets obtained over two seasons from 24 New Zealand persimmon orchards were processed by an unsupervised neural network, a self organising map (SOM), that clustered the orchard samples.
For each of the two years, the SOM found that the orchard samples could be grouped into three clusters.
The salient characteristics that distinguished each cluster were determined using analysis of variance (ANOVA) of the clusters in relation to 15 vegetative and fruit productivity attributes.
In addition, ANOVA of fruit maturity and market quality data, derived from fresh and dry weight, peel hue, soluble solids and soluble tannin measurements of fruit harvested 25 weeks from full-bloom, was used to assess differences in maturity between clusters.
For both years, similar patterns were found in attributes that distinguished each cluster.
For example, cluster one was characterised by high productivity, specific leaf weight and maturity; whereas cluster two had low productivity, high specific leaf weight and moderate or high maturity; and cluster three had low specific leaf weight, productivity and maturity.
From these observations, binomial 2×2 cross-classification tables were constructed to test the association between specific leaf weight, productivity and market quality.
Trees with a high specific leaf weight were about three times more likely to have higher productivity and about one and a half times as likely to have higher relative market quality than trees with a low specific leaf weight.

II International Persimmon Symposium

A.D. Mowat

https://doi.org/10.17660/ActaHortic.2003.601.16