Sugarcane cultivars are interspecific hybrids with an aneuploid, highly heterozygous polyploid genome. The complexity of the sugarcane genome is the main obstacle to the use of marker-assisted selection in sugarcane breeding. Given the promising results of recent studies of plant genomic selection, we explored the feasibility of genomic selection in this complex polyploid crop. Genetic values were predicted in two independent panels, each composed of 167 accessions representing sugarcane genetic diversity worldwide. Accessions were genotyped with 1,499 DArT markers. One panel was phenotyped in Reunion Island and the other in Guadeloupe. Ten traits concerning sugar and bagasse contents, digestibility and composition of the bagasse, plant morphology, and disease resistance were used. We used four statistical predictive models: bayesian LASSO, ridge regression, reproducing kernel Hilbert space, and partial least square regression. The accuracy of the predictions was assessed through the correlation between observed and predicted genetic values by cross validation within each panel and between the two panels. We observed equivalent accuracy among the four predictive models for a given trait, and marked differences were observed among traits. Depending on the trait concerned, within-panel cross validation yielded median correlations ranging from 0.29 to 0.62 in the Reunion Island panel and from 0.11 to 0.5 in the Guadeloupe panel. Cross validation between panels yielded correlations ranging from 0.13 for smut resistance to 0.55 for brix. This level of correlations is promising for future implementations. Our results provide the first validation of genomic selection in sugarcane.
Digestibility is a key parameter in the evaluation of feeds; however, the measurements on animals require heavy experimental trials, which are hardly feasible when large numbers of determinations are required -for example, in genetic studies. This experiment aimed at investigating the possibility to predict digestibility from NIRS spectra measured on faeces. A total of 196 samples were available from a digestibility experiment investigating the effects of age and genetic background of Large White pigs fed the same diet, rich in fibre (NDF = 21.4% DM). Digestibility of dry matter (dDM), organic matter (dOM), nitrogen content (dN), energy (dE) and apparent digestible energy content (ADE) were calculated, as well as total N content of faeces (N). The faeces samples were submitted to reflectance NIRS analysis after freeze-drying and grinding. Calibration errors and validation errors were, respectively, 0.08 and 0.13% DM for total N in faeces, 0.97% and 1.08% for dDM, 0.79% and 1.04% for dOM, 1.04% and 1.47% for dN, 0.87% and 1.12% for dE and 167 and 213 kJ/kg DM for ADE. These results indicate that NIRS can account for digestibility differences due to animal factors, with an acceptable accuracy. NIRS appears to be a promising tool for large-scale evaluations of digestibility. It could also be used for the study of digestibility of different feeds, after appropriate calibration based on a wide range of feed types.
We investigated the sources of variation in forage quality in plants from species-rich Mediterranean rangelands in southern France. Digestibility was affected by species growth form, harvest date, developmental stage and management regime, and differed between leaves, stems and reproductive parts. The dry matter content of the different plant parts, an estimate of the density of their tissues closely related to fibre content, emerged as a good predictor and an easily measured trait to estimate digestibility in the wide range of species spanned in our study.
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