Laurent Bonnal

Experimental assessment of the accuracy of genomic selection in sugarcane

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.

Prediction by near infrared spectroscopy of the composition of plant raw materials from the organic fertiliser industry and of crop residues from tropical agrosystems

The dynamics of carbon (C) and nitrogen (N) of plant residues and organic fertilisers are of great interest for agricultural and global warming studies. The proportion of the fractions obtained from biochemical analyses (fibres by sequential Van Soest analysis) can be used for predicting both C and N transformation of organic materials in soils. Considering the expensive and time-consuming Van Soest method, the principal aim of this study was to elaborate near infrared (NIR) calibrations for fibres, in order to use them for consecutive studies (for example, our works on transformation of added organics or TAO model). A wide set of organic fertilisers and their raw materials was sampled, including plant materials originating from temperate (especially Mediterranean) and tropical regions. The particular objective of this work was to build NIR calibrations for fibre fractions, along with C and N content, in plant materials used in the organic fertiliser industry and green house gases mitigating strategies. The second particular objective was to test for two levels of validation of the equations previously elaborated: (1) validation with a set of randomly chosen samples that was not considered during the calibration step, (2) extrapolation of the predictive capacity of the equations when applying them to outliers that were previously discarded. The fibres were the best predicted parameters, as R² = 0.95, 0.91, 0.97, 0.97 for neutral detergent soluble, hemicelluloses, cellulose and lignin, respectively, whereas the characteristics of total organic matter had R² varying from 0.87 (N Kjeldahl) to 0.94 (C Dumas). The accuracy of the calibrations developed for fibres was confirmed by the first level of validation, since the standard errors of prediction were close to the corresponding standard errors of cross-validation and the standard errors of calibration. Nevertheless, the calibrations developed for ash and C Dumas were not so good. Surprisingly, at the second level of validation, some outliers were not so badly predicted. This can illustrate the robustness of the calibrations for cellulose, lignin and, to a lesser extent, N Dumas which are key parameters for our modelling works on C and N transformation of added organics in soils.

Prediction of the chemical composition of poultry excreta by near infrared spectroscopy.

The potential of near infrared (NIR) spectroscopy for the determination of the chemical composition of poultry excreta was investigated, within the framework of studies on heritability of digestive efficiency in broilers. Samples in the calibration and validation databases (DB1 and DB2) corresponded to animals fed with a similar wheat-based diet. A second validation study was performed on excreta samples from animals fed more variable diets, including peas and maize (DB3). Excreta samples were freeze-dried and ground. Near infrared reflectance spectra were taken on a monochromator spectrometer between 400nm and 2500nm. Samples were analysed for mineral matter (MM), gross energy (GE), starch, crude fat (CFAT), total nitrogen (NTOT), uric acid nitrogen (NUA) and protein nitrogen estimated directly (PNTERP) or by difference between NTOT and NUA (PNUA). Depending on the parameters studied, 250 to 700 samples were analysed by reference methods. The standard error of cross-validation (SECV) and R2 of calibrations were: 0.60% and 0.96 for MM, 166 kJ kg−1 and 0.99 for GE, 0.59% and 1.00 for starch, 0.44% and 0.99 for CFAT, 0.25% and 0.89 for NTOT and 0.22% and 0.97 for NUA, respectively. Calibration for PNTERP (SECV=0.07%; R2=0.98) was much more precise than PNUA (SECV=0.21%, R2=0.85). Validation carried out on databases DB2 and DB3 resulted in standard errors of prediction (on DB2) and extrapolation (on DB3) generally higher than SECV, while remaining relatively precise with prediction r2 values from 0.83 to 0.99 and extrapolation r2 from 0.86 to 0.98, with the exception of PNUA for which r2 was 0.22 and 0.64, respectively. For some parameters, the lower validation performance was due to biases, particularly in the case CFAT and NUA for prediction and MM, GE and NUA for extrapolation. Global calibrations made with DB1+DB2+DB3 were more precise (GE, NTOT) or equally precise (all other parameters) than with DB1 alone. These results confirmed the potential precision of calibrations for the major organic compounds in poultry excreta and suggested that their use could be extended to excreta issued from a wider range of diets without losing precision.

Influence of management regime and harvest date on the forage quality of rangelands plants: the importance of dry matter content

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.

Selecting the quality of mule duck fatty liver based on near-infrared spectroscopy

Background“Foie gras” is produced predominantly in France and about 90% of the commercialized product is obtained from male mule ducks. The melting rate (percentage of fat released during cooking) is the main criterion used to determine the quality of “foie gras”. However, up to now the melting rate could not be predicted without causing liver damage, which means that selection programs could not use this criterion.MethodsFatty liver phenotypes were obtained for a population of over 1400 overfed male mule ducks. The phenotypes were based on two types of near-infrared spectra (on the liver surface and on ground liver) in order to predict the melting rate and liver composition (ash, dry matter, lipid and protein contents). Genetic parameters were computed in multiple traits with a “sire-dam” model and using a Gibbs sampling approach.ResultsThe estimates for the genetic parameters show that the measured melting rate and the predicted melting rate obtained with two near-infrared spectrometer devices are genetically the same trait: genetic correlations are very high (ranging from +0.89 to +0.97 depending on the mule duck parental line and the spectrometer) and heritabilities are comparable. The predictions based on the spectra of ground liver samples using a laboratory spectrometer correlate with those based on the surface spectra using a portable spectrometer (from +0.83 to +0.95 for dry matter, lipid and protein content) and are particularly high for the melting rate (higher than +0.95). Although less accurate than the predictions obtained using the spectra of ground liver samples, the phenotypic prediction of the melting rate based on surface spectra is sufficiently accurate to be used by “foie gras” processors.ConclusionsNear-infrared spectrometry is an efficient tool to select liver quality in breeding programs because animals can be ranked according to their liver melting rate without damaging their livers. Thus, these original results will help breeders to select ducks based on the liver melting rate, a crucial criterion that defines the quality of the liver and for which there was previously no accurate predictor.

Genotypic covariations of traits underlying sorghum stem biomass production and quality and their regulations by water availability: Insight from studies at organ and tissue levels

Sweet and biomass sorghum are expected to contribute increasingly to bioenergy production. Better understanding the impacts of the genotypic and environmental variabilities on biomass component traits and their properties is essential to optimize energy yields. This study aimed to evaluate whether traits contributing to stem biomass growth and biochemical composition at different biological scales (co)vary with the genotype and the water status in sorghum. Height genotypes were studied over two years in field conditions in southern France under two water treatments (well watered vs. 25 days’ dry down during stem elongation). Main stem internode number, size, (non)structural carbohydrate, and lignin contents were measured at the end of the stress period and/or at final harvest, together with biochemical and histological analyses of the youngest expanded internode. The tallest genotypes showed the highest stem dry weights and lignin contents. Stem (structural) biomass density was positively correlated with lignin content, particularly in internode parenchyma. Stem soluble sugar and lignin contents were inversely proportional across genotypes and water conditions. Genotypes contrasted for drought sensitivity and recovery capacity of stem growth and biochemical composition. The length and cell wall deposition of internodes expanding under water deficit were reduced and did not recover, these responses being weakly correlated. Genotypic variability was pointed out in the growth recovery of internodes expanding under re‐watered conditions. According to the observed genotypic variability and the absence of antagonistic correlations between the responses of the different traits to water availability, it is suggested that biomass sorghum varieties optimizing their responses to water availability in terms of growth and cell wall deposition can be developed for different bioenergy targets.