Figure 2
From: A joint learning approach for genomic prediction in polyploid grasses
Genomic prediction models’ performances for the populations of (Pop1) sugarcane (stalk diameter (SD) and stalk height (SH)); (Pop2) Urochloa decumbens (field green weight (FGW), leaf dry matter (LDM), leaf percentage (LP), leaf stem ratio (LSR), regrowth capacity (RC), and total dry matter (TDM)); and (Pop3) Megathyrsus maximus (green matter (GM), LDM, percentage of leaf blade (PLB), RC, stem dry matter (SDM), and TDM). The Bayesian ridge regression (BRR) and single-environment main genotypic effect model with a Gaussian kernel (SM-GK) approaches with the inclusion of different feature selection methods (CR2 and CR3) were evaluated.
