Decision Trees as a Tool to Select Sugarcane Families

• 作者: Bruno P. Brasileiro Diego P. Bernardes Luiz A. Peternelli Marcio H.P. Barbosa Raphael H. T. Silva
• 来源期刊: 美国植物学期刊(英文)
• 学科: 医学
• 关键词: Statistical LEARNING Plant BREEDING SACCHARUM Spp. Synthetic Data Supervised LEARNING
• 年，卷（期）: 2018,（2）
• 页码范围: 216-230
• 页数: 15页
• 分类号: R73

摘要

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.