Proceedings of the XLVII Italian Society of Agricultural Genetics - SIGA Annual Congress

Verona, Italy - 24/27 September, 2003

ISBN 88-900622-4-X

Poster Abstract - 2.12

NEW METHODS FOR THE CLASSIFICATION OF POPLAR CLONES BY MEANS OF MORPHOLOGICAL DESCRIPTORS

F.M.STEFANINI**, A. GIORCELLI***, F.PICCO***, A.CAMUSSI*

*) Dipartimento di Biotecnologie agrarie and

**) Dipartimento di Statistica Università di Firenze

***) Istituto di Sperimentazione per la Pioppicoltura, Casale Monferrato (AL)

poplar clones, classification methods, descriptors, Random Forest

Tests for distinctness, homogeneity and stability of Poplar clones are still based on the use of proper descriptors of the main morphological and phenetic characteristics of the plant. Although the importance of a precise identification of clones is widely acknowledged, no sound technique has yet come into wide use. Many descriptors related to economic and productivity traits show a reduced repeatability or within clone variability. The poor discriminant power of single traits can be counterbalanced by a multivariate approach.

In the frame of a research project to develop models for the joint analysis of descriptors with different statistical properties, we have analysed experimental data related to 30 Poplar clones, representative of the germplasm collection. The experimental design consists in ten replications in two localities (Casale Monferrato and Mantova). In each replication 18 descriptors (both qualitatitive and quantitative) were recorded on two plants. The individual discriminant power of descriptors were estimated by usual statistical techniques (linear model and logistic model).

A multivariate approach by means of parametric procedure is ineffective due to the joint presence of variables with different sampling properties.

We have applied some new numerical techniques based on computer simulation approaches to overcome difficulties due to the probability distribution of different traits. Among others, the procedure known as Random Forest was particularly suitable for clones discrimination. Random Forest, proposed by Leo Breiman (University of California, Berkeley,USA), is based on the building of a large set (Forest) of classification trees, generated at random, which are allowed to evolve generation by generation on the basis of computer simulations. Some internal estimates are produced and they are useful to describe the classification process and the relative importance of single traits. The procedure can include data of different distributive properties (quantitative traits, categorical variables and so on).

With our data, we received a good classification performance with a mean misclassification rate of 0,13 and with 22 clones with a rate under this value.

The procedure allowed to individuate the best variables according to classification ability. Final aim of the work is to individuate simple rules that can be easily applied in the typical condition of nursery practice.