TITLE:
Decision Trees as a Tool to Select Sugarcane Families
AUTHORS:
Luiz A. Peternelli, Diego P. Bernardes, Bruno P. Brasileiro, Marcio H.P. Barbosa, Raphael H. T. Silva
KEYWORDS:
Statistical Learning, Plant Breeding, Saccharum Spp., Synthetic Data, Supervised Learning
JOURNAL NAME:
American Journal of Plant Sciences,
Vol.9 No.2,
January
25,
2018
ABSTRACT: New strategies are required in the sugarcane selection
process to optimize the genetic gains in breeding programs. Conventional
selection strategies have the disadvantage of requiring the weighing of all the
plants in a plot or a sample of stalks and the counting of the number of stalks
in all the experimental plots, which cannot always be performed because more
than 200,000 genotypes routinely comprise the first test phase (T1) of most
sugarcane breeding programs. One way to circumvent this problem is to use
decision trees to rank the yield components (the stalk height, the stalk
diameter and the number of stalks) and to subsequently use this categorization
to select the best families for a specific trait. The objective of this study
was to evaluate the categorization of yield components using the classification
and regression tree (CART) algorithm as a family selection strategy by
comparing the performance of CART with those of conventional methods that
require the weighing of stalks, such as the best linear unbiased prediction
(BLUP) with sequential (BLUPS) or individual simulated (BLUPIS) procedures.
Data from five experiments performed in May 2007 in a randomized block design
were analyzed. Each experiment consisted of five blocks, 22 families and two
controls (commercial varieties). CART effectively defined the classes of the
yield components and selected the best families with an accuracy of 74%
compared to BLUPS and BLUPIS. Families with at least 11 stalks per linear meter
of furrow resulted in productivities that were above the average productivity
of the commercial varieties used in this study and are, therefore, recommended
for selection.