Selection indices and support vector machines in the selection of sugarcane families

Abstract

The present study aimed to compare selection indices, namely: Smith and Hazel multiplicative, Mulamba and Mock's, and the support vector machines algorithm for sugarcane families selection. We used two datasets, from Moreira et al. (2021) and from Ferreira et al. (2022), both related to the sugarcane breeding program conducted at the Center for Sugar cane Research and Breeding at the Federal University of Viçosa, Oratórios, Minas Gerais. Both experiments were conducted in a randomized complete block design. We constructed the selection indices via mixed models approach. We adopted a selection percentage of 18% of the top families for the selection process. In both studies, we considered as explanatory traits: the number of stalks, stalks diameter and stalk height, and as the response trait the tons of stalks per hectare per family. In the dataset from Ferreira et al. (2022), the support vector machine was a better approach to select sugarcane families by learning from the data after multivariate simulation. Whereas in the dataset from Moreira et al. (2021), using similar methodology, lower performance for support vector machines was obtained. Keywords: Synthetic data. Indirect selection. Yield prediction. Machine learning. BLUP

The release of ‘LCP 85‐384’ and its contribution to the Louisiana sugar industry

“…Consequently, sucrose content on a family mean basis has been a highly prioritized selection criteria in the seedling and first line trial stages of our program. Family selection, in the seedling stage, is widely practiced to different extents in several countries including Australia (Cox & Stringer, 1998; Hogarth et al., 1990; Jackson et al., 1995a, 1995b; Kimbeng et al., 2000; Wei et al., 2022), the United States (Chang & Milligan, 1992a, 1992b; Milligan et al., 1990b; Tai et al., 2003), India (Shanthi et al., 2011; Shanthi et al., 2008), Brazil (Ferreira et al., 2022; Muetanene et al., 2023), and South Africa (Mbuma et al., 2018; Mbuma, 2016; Mbuma et al., 2020; Zhou, 2014; Zhou et al., 2013). Sucrose content is customarily used to assess family performance whereas individual seedling or clone selection within families relies on Brix, %, as measured using a hand‐held refractometer (Kimbeng & Cox, 2003).…”