The main objectives of this study were to evaluate the prediction performance of genomic and near-infrared spectroscopy (NIR) data and whether the integration of genomic and NIR predictor variables can increase the prediction accuracy of two feedstock quality traits (fiber and sucrose content) in a sugarcane population (Saccharum spp.). The following three modeling strategies were compared: M1 (genome-based prediction), M2 (NIR-based prediction), and M3 (integration of genomics and NIR wavenumbers). Data were collected from a commercial population comprised of three hundred and eighty-five individuals, genotyped for single nucleotide polymorphisms and screened using NIR spectroscopy. We compared partial least squares (PLS) and BayesB regression methods to estimate marker and wavenumber effects. In order to assess model performance, we employed random sub-sampling cross-validation to calculate the mean Pearson correlation coefficient between observed and predicted values. Our results showed that models fitted using BayesB were more predictive than PLS models. We found that NIR (M2) provided the highest prediction accuracy, whereas genomics (M1) presented the lowest predictive ability, regardless of the measured traits and regression methods used. The integration of predictors derived from NIR spectroscopy and genomics into a single model (M3) did not significantly improve the prediction accuracy for the two traits evaluated. These findings suggest that NIR-based prediction can be an effective strategy for predicting the genetic merit of sugarcane clones.

The main objectives of this study were to evaluate the prediction performance of genomic and near-infrared spectroscopy (NIR) data and whether the integration of genomic and NIR predictor variables can increase the prediction accuracy of two feedstock quality traits (fiber and sucrose content) in a sugarcane population (Saccharum spp.). The following three modeling strategies were compared: M1 (genome-based prediction), M2 (NIR-based prediction), and M3 (integration of genomics s and NIR wavenumbers). Data were collected from a commercial population comprised of three hundred and eighty-five individuals, genotyped for single nucleotide polymorphisms (SNPs) and screened using NIR spectroscopy. We compared partial least squares (PLS) and BayesB regression methods to estimate marker and wavenumber effects. In order to assess model performance, we employed random sub-sampling cross-validation to calculate the mean Pearson correlation coefficient between observed and predicted genotypic values. Our results showed that models fitted using BayesB were most predictive than PLS models. We found that NIR (M2) provided the highest prediction accuracy, whereas genomics (M1) presented the lowest predictive ability, regardless of the measured traits and regression methods used. The integration of predictors derived from NIR spectroscopy and genomics into a single model (M3) did not significantly improve the prediction accuracy for the two traits evaluated. These findings suggest that NIR-based prediction can be an effective strategy for predicting the genotypic value of sugarcane clones.

No abstract

This study aimed to investigate genetic parameters and selection strategies for sugarcane resistance to borer and determine the effects of selection for borer resistance on yield traits. The infestation index by borer (IIB) and yield traits were assessed over 3 years in 35 sugarcane clones planted in four locations in southeastern Brazil. There was no genotype × environment (year or location) interaction for borer resistance. Consequently, selection may be performed at only one location and year or by the mean of clones in several locations and/or years. The borer population affected the genotypic variance for borer resistance but when genotypic variance was significant, heritability was elevated and suitable genetic gain was achieved. Selection for borer resistance reduced the genetic gains for tons of cane per hectare, theoretical recoverable sugar, and tons of sugar per hectare; however, satisfactory selection gains for both borer resistance and yield were achieved using selection index.

Sugarcane production plays a fundamental role in the Brazilian economy, both for sugar production and renewable energy generation. The development of new cultivars to meet the current needs of the sugarcane industry sector requires efficient phenotyping methods, which should incorporate simplification, speed, accuracy, and consistency. In order to contribute to the development of new phenotyping strategies, this work aimed to develop multivariate regression models using Partial Least Squares (PLS) to classify sugarcane clones based on sugarcane biomass quality parameters, namely fiber (FIB) and apparent sucrose (SC) content. A NIR instrument was used to acquire the reflectance spectra of 196 sugarcane bagasse - collected in two different harvest seasons - and fresh stalk samples. The values predicted by these models allowed the construction of a vector using a confusion matrix that informs whether the clone should be selected or not. PLS models selected to predict each trait under study presented high accuracy and precision, besides small values of false-positive rate and good concordance indication by the Kappa statistic test. The results obtained indicate that the use of fresh stalk samples rather than bagasse samples for the prediction of SC and FIB is recommended as it delivered higher predictive power and is of a more straightforward usage. The utilization of NIR combined with multivariate techniques may help breeding programs in the classification of sugarcane clones based on biomass quality parameters.

The simplified system (SS) is a new method of selecting families in the first experimentation phase used in some sugarcane (Saccharum officinarum L.) breeding programs in Brazil. In the SS, clones are obtained from families whose genotypes are not individualized in the field. Such selected clones are used to compose the cloned family trial. In the conventional method (CM) of breeding, each genotype is individualized to form part of the family evaluation experiment. This work aimed to compare and verify if the SS provides genetic information at least similar to the CM. Two experiments were conducted, one for the CM and the other for the SS. Sixty full‐sib families were evaluated in each experiment. The characteristics evaluated were average stalk height, average stalk diameter, the content of total soluble solids (BRIX), the number of stalks per meter, average stalk mass, tonnes of stalks per hectare, and tonnes of BRIX per hectare. The mixed model methodology was used to estimate the genetic parameters and predict the families' genotypic values. Both methods enabled high precision in the evaluation of families and similar average genetic gains (10.1% for CM and 9.6% for SS). The SS makes it possible to select families with high genotypic value, high accuracy, and genetic gains comparable to the CM. Therefore, although the SS presents similar results as the CM, its simplicity and ease of being installed in the field in a smaller experimental area make it a promising methodology for selecting cloned families in a sugarcane breeding program.