Freeze stress negatively affects sucrose yield in sugarcane (Saccharum spp. hybrids), particularly during the harvest season. To understand its impact on the performance of genotypes in the Canal Point (CP) breeding program, the genotype-environment (GxE) interaction was appraised via additive main effects and multiplicative interactions (AMMI) analysis and group-based trajectory modeling (GTM). Forty-five selections of the CP01, CP02, and CP03 series and three cultivars were examined in replicated field tests at Hague and Canal Point, Florida, in the plant-cane (CP01-CP02 in 2006-07) or through the first ratoon (CP03 in 2007-09). Profile analyses of Brix, pol, and sucrose content (SC) were developed from stalks sampled at different times of year to follow their deterioration. Hague experienced more intense freeze nights (17–22 d yr⁻¹ with temperatures [T_C] from −0.4 °C to −8 °C) than CP (2 nights, rarely down to −2 °C). Temperatures ≥−2 °C increased SC in a majority of the genotypes and T_C ≤ −4 °C hastened juice deterioration. The response was nonlinear when T_C gradually declined from 0 ° to −4 °C, but linear after early freeze of ≤−4 °C. The AMMI analysis was appropriate for interpreting the GxE interaction variation, indicating a greater contribution from environments (location-year-sampling combinations) than from G or GxE interaction. The AMMI and GTM identified two to four reaction norms as differential performance under freeze: a susceptible group in the minority with SC declining constantly with T_C; the other groups typifying the common profile had an increase in SC with T_C ≥ −2 °C and a decline with T_C ≤ −4 °C. Genotypes with the highest SC at the onset of freeze tended to hold this level longer than those with the lowest content. The identification of different reaction norms suggests that a genetic component may underpin freeze adaptation in modern sugarcane cultivars relative to the old tropical hybrids. However, breeding efforts to increase the tolerance of sugarcane cultivars to temperatures <−3 °C must be given due consideration.

Switchgrass breeders need to improve the rates of genetic gain in many bioenergy-related traits in order to create improved cultivars that are higher yielding and have optimal biomass composition. One way to achieve this is through genomic selection. However, the heritability of traits needs to be determined as well as the accuracy of prediction in order to determine if efficient selection is possible. Using five distinct switchgrass populations comprised of three lowland, one upland and one hybrid accession, the accuracy of genomic predictions under different cross-validation strategies and prediction methods was investigated. Individual genotypes were collected using GBS while kin-BLUP, partial least squares, sparse partial least squares, and BayesB methods were employed to predict yield, morphological, and NIRS-based compositional data collected in 2012–2013 from a replicated Nebraska field trial. Population structure was assessed by F statistics which ranged from 0.3952 between lowland and upland accessions to 0.0131 among the lowland accessions. Prediction accuracy ranged from 0.57–0.52 for cell wall soluble glucose and fructose respectively, to insignificant for traits with low repeatability. Ratios of heritability across to within-population ranged from 15 to 0.6. Accuracy was significantly affected by both cross-validation strategy and trait. Accounting for population structure with a cross-validation strategy constrained by accession resulted in accuracies that were 69% lower than apparent accuracies using unconstrained cross-validation. Less accurate genomic selection is anticipated when most of the phenotypic variation exists between populations such as with spring regreening and yield phenotypes.