Early prediction of biomass in hybrid rye based on hyperspectral data surpasses genomic predictability in less-related breeding material.

KEY MESSAGE: Hyperspectral data is a promising complement to genomic data to predict biomass under scenarios of low genetic relatedness. Sufficient environmental connectivity between data used for model training and validation is required. The demand for sustainable sources of biomass is increasing worldwide. The early prediction of biomass via indirect selection of dry matter yield (DMY) based on hyperspectral and/or genomic prediction is crucial to affordably untap the potential of winter rye (Secale cereale L.) as a dual-purpose crop. However, this estimation involves multiple genetic backgrounds and genetic relatedness is a crucial factor in genomic selection (GS). To assess the prospect of prediction using reflectance data as a suitable complement to GS for biomass breeding, the influence of trait heritability ([Formula: see text]) and genetic relatedness were compared. Models were based on genomic (GBLUP) and hyperspectral reflectance-derived (HBLUP) relationship matrices to predict DMY and other biomass-related traits such as dry matter content (DMC) and fresh matter yield (FMY). For this, 270 elite rye lines from nine interconnected bi-parental families were genotyped using a 10 k-SNP array and phenotyped as testcrosses at four locations in two years (eight environments). From 400 discrete narrow bands (410 nm-993 nm) collected by an uncrewed aerial vehicle (UAV) on two dates in each environment, 32 hyperspectral bands previously selected by Lasso were incorporated into a prediction model. HBLUP showed higher prediction abilities (0.41 - 0.61) than GBLUP (0.14 - 0.28) under a decreased genetic relationship, especially for mid-heritable traits (FMY and DMY), suggesting that HBLUP is much less affected by relatedness and [Formula: see text]. However, the predictive power of both models was largely affected by environmental variances. Prediction abilities for DMY were further enhanced (up to 20%) by integrating both matrices and plant height into a bivariate model. Thus, data derived from high-throughput phenotyping emerges as a suitable strategy to efficiently leverage selection gains in biomass rye breeding; however, sufficient environmental connectivity is needed.

Integration of genotypic, hyperspectral, and phenotypic data to improve biomass yield prediction in hybrid rye.

KEY MESSAGE: Hyperspectral and genomic data are effective predictors of biomass yield in winter rye. Variable selection procedures can improve the informativeness of reflectance data. Integrating cutting-edge technologies is imperative to sustainably breed crops for a growing global population. To predict dry matter yield (DMY) in winter rye (Secale cereale L.), we tested single-kernel models based on genomic (GBLUP) and hyperspectral reflectance-derived (HBLUP) relationship matrices, a multi-kernel model combining both matrices and a bivariate model fitted with plant height as a secondary trait. In total, 274 elite rye lines were genotyped using a 10 k-SNP array and phenotyped as testcrosses for DMY and plant height at four locations in Germany in two years (eight environments). Spectral data consisted of 400 discrete narrow bands ranging between 410 and 993 nm collected by an unmanned aerial vehicle (UAV) on two dates on each environment. To reduce data dimensionality, variable selection of bands was performed, resulting in the least absolute shrinkage and selection operator (Lasso) as the best method in terms of predictive abilities. The mean heritability of reflectance data was moderate ([Formula: see text] = 0.72) and highly variable across the spectrum. Correlations between DMY and single bands were generally significant (p < 0.05) but low (≤ 0.29). Across environments and training set (TRN) sizes, the bivariate model showed the highest prediction abilities (0.56-0.75), followed by the multi-kernel (0.45-0.71) and single-kernel (0.33-0.61) models. With reduced TRN, HBLUP performed better than GBLUP. The HBLUP model fitted with a set of selected bands was preferred. Within and across environments, prediction abilities increased with larger TRN. Our results suggest that in the era of digital breeding, the integration of high-throughput phenotyping and genomic selection is a promising strategy to achieve superior selection gains in hybrid rye.

Monitoring cellular C:N ratio in phytoplankton by means of FTIR-spectroscopy.

Statistical growth rate modelling can be applied in a variety of ecological and biotechnological applications. Such models are frequently based on Monod or Droop equations and, especially for the latter, require reliable determination of model input parameters such as C:N quotas. Besides growth rate modelling, a C:N quota quantification can be useful for monitoring and interpretation of physiological acclimation to abiotic and biotic disturbances (e.g., nutrient limitations). However, as high throughput C:N quota determination is difficult to perform, alternatives need to be established. Fourier-transformed infrared (FTIR) spectroscopy is used to analyze a variety of biochemical, chemical, and physiological parameters in phytoplankton. Hence, a quantification of the C:N quota should also be feasible. Therefore, using FTIR spectroscopy, six phytoplankton species from among different phylogenetic groups have been analyzed to determine the effect of nutrient limitation on C:N quota patterns. The typical species-specific response to increasing nitrogen limitation was an increase in the C:N quota. Irrespective of this species specificity, we were able to develop a reliable multi-species C:N quota prediction model based on FTIR spectroscopy using the partial least square regression (PLSR) algorithm. Our data demonstrate that the PLSR approach is more robust in C:N quota quantification (R2  = 0.93) than linear correlation of C:N quota versus growth rate (R2 ranges from 0.74 to 0.86) or biochemical information based on FTIR spectra (R2 ranges from 0.82 to 0.89). This accurate prediction of C:N values may support high throughput measurements in a broad range of future approaches.

FTIR spectra of algal species can be used as physiological fingerprints to assess their actual growth potential.

Fourier transform infrared (FTIR) spectra were measured from cells of Microcystis aeruginosa and Protoceratium reticulatum, whose growth rates were manipulated by the availability of nutrients or light. As expected, the macromolecular composition changed in response to the treatments. These changes were species-specific and depended on the type of perturbation applied to the growth regime. Microcystis aeruginosa showed an increase in the carbohydrate-to-protein ratio with decreased growth rates, under nutrient limitation, whereas light limitation induced a decrease of the carbohydrate-to-protein ratio with decreasing proliferation rates. The macromolecular pools of P. reticulatum showed a higher degree of compositional homeostasis. Only when the lowest light irradiance and nutrient availability were supplied, an increase of the carbohydrate-to-protein FTIR absorbance ratio was observed. A species-specific partial least squares (PLS) model was developed using the whole FTIR spectra. This model afforded a very high correlation between the predicted and the measured growth rates, regardless of the growth conditions. On the contrary, the prediction based on absorption band ratios generally used in FTIR studies would strongly depend on growth conditions. This new computational method could constitute a substantial improvement in the early warning systems of algal blooms and, in general, for the study of algal growth, e.g. in biotechnology. Furthermore, these results confirm the suitability of FTIR spectroscopy as a tool to map complex biological processes like growth under different environmental conditions.