C. M. B. Damasceno

Mining Genetic Diversity of Sorghum as a Bioenergy Feedstock

Sorghum is a drought-tolerant rainfed crop that requires about 30 % less nitrogen fertilizer than corn to produce equal amount of ethanol per acre under non- irrigated conditions. Excellent genetic and genomic resources exist for improvement of sorghum as a bioenergy source. We expect a huge impact on biomass yield, qual- ity, and conversion efficiency with appropriate plant breeding and biotechnology tools in order to develop energy sorghum germplasm that allows highly efficient pro- duction of biofuel. The outlined improvement should produce benefits that include: (1) genetic improvement of a biomass crop with significantly reduced overall cost of biomass-to-ethanol conversion; (2) selection of a reliable bioenergy feedstock that is drought tolerant, inexpensive to grow, environmentally friendly and cultivated in nearly all temperate and tropical climate regions; (3) expansion of the production area for bioenergy crops by developing cold tolerance germplasm and hybrids and by offering both annual and perennial sweet sorghum types; and (4) reduction in cell wall lignin for improved efficiency in production of biofuels.

Genomic prediction applied to high-biomass sorghum for bioenergy production

The increasing cost of energy and finite oil and gas reserves have created a need to develop alternative fuels from renewable sources. Due to its abiotic stress tolerance and annual cultivation, high-biomass sorghum (Sorghum bicolor L. Moench) shows potential as a bioenergy crop. Genomic selection is a useful tool for accelerating genetic gains and could restructure plant breeding programs by enabling early selection and reducing breeding cycle duration. This work aimed at predicting breeding values via genomic selection models for 200 sorghum genotypes comprising landrace accessions and breeding lines from biomass and saccharine groups. These genotypes were divided into two sub-panels, according to breeding purpose. We evaluated the following phenotypic biomass traits: days to flowering, plant height, fresh and dry matter yield, and fiber, cellulose, hemicellulose, and lignin proportions. Genotyping by sequencing yielded more than 258,000 single-nucleotide polymorphism markers, which revealed population structure between subpanels. We then fitted and compared genomic selection models BayesA, BayesB, BayesCπ, BayesLasso, Bayes Ridge Regression and random regression best linear unbiased predictor. The resulting predictive abilities varied little between the different models, but substantially between traits. Different scenarios of prediction showed the potential of using genomic selection results between sub-panels and years, although the genotype by environment interaction negatively affected accuracies. Functional enrichment analyses performed with the marker-predicted effects suggested several interesting associations, with potential for revealing biological processes relevant to the studied quantitative traits. This work shows that genomic selection can be successfully applied in biomass sorghum breeding programs.

Uso da espectroscopia no infravermelho próximo para qualificação do sorgo biomassa para energia.

O sorgo do tipo biomassa e uma cultura que apresenta grande potencial como fonte de biomassa para geracao de energia, em funcao de sua alta produtividade, tolerância a seca e por ser mecanizavel. Assim, a cultura e uma alternativa de biomassa vegetal para ser utilizada em processos de cogeracao de energia eletrica. O objetivo do trabalho foi desenvolver modelos de calibracao multivariada, utilizando a espectroscopia no infravermelho proximo, para analise do teor de carbono, nitrogenio, cinzas, poder calorifico superior, glicose, xilose e arabinose em sorgo biomassa. As amostras foram analisadas por metodos de referencia e os resultados associados ao espectro de infravermelho proximo (NIR) de cada amostra. Em seguida, foram desenvolvidos, para cada constituinte, os modelos de calibracao multivariada utilizando o algoritmo PLS (Partial Least Square). Os indicadores estatisticos utilizados para avaliar o desempenho e validacao dos modelos foram: raiz quadrada do erro medio de calibracao ? RMSEC, raiz quadrada do erro medio de validacao cruzada ? RMSECV, raiz quadrada do erro medio de predicao ? RMSEP e R2 (coeficiente de determinacao para o conjunto de calibracao e validacao). Tambem foram avaliadas a relacao de desempenho do desvio - RPD e a razao de intervalo de erro ? RER. Obtevese uma boa correlacao entre os valores previstos pelo modelo e os valores obtidos pelo metodo de referencia para todas as propriedades avaliadas tanto para as amostras do conjunto de calibracao (RMSEC) como para as do conjunto de validacao externa (RMSEP). Tambem foram obtidos valores de RPD e RER, respectivamente, acima de 3 e de 10, para todos os constituintes analisados, sendo assim, considerados adequados para a realizacao das analises quantitativas de composicao quimica na qualificacao do sorgo biomassa como fonte de materia-prima para cogeracao de energia.

Phenotypic and molecular characterization of sweet sorghum accessions for bioenergy production.

Sweet sorghum [Sorghum bicolor (L.) Moench] is a type of cultivated sorghum characterized by the accumulation of high levels of sugar in the stems and high biomass accumulation, making this crop an important feedstock for bioenergy production. Sweet sorghum breeding programs that focus on bioenergy have two main goals: to improve quantity and quality of sugars in the juicy stem and to increase fresh biomass productivity. Genetic diversity studies are very important for the success of a breeding program, especially in the early stages, where understanding the genetic relationship between accessions is essential to identify superior parents for the development of improved breeding lines. The objectives of this study were: to perform phenotypic and molecular characterization of 100 sweet sorghum accessions from the germplasm bank of the Embrapa Maize and Sorghum breeding program; to examine the relationship between the phenotypic and the molecular diversity matrices; and to infer about the population structure in the sweet sorghum accessions. Morphological and agro-industrial traits related to sugar and biomass production were used for phenotypic characterization, and single nucleotide polymorphisms (SNPs) were used for molecular diversity analysis. Both phenotypic and molecular characterizations revealed the existence of considerable genetic diversity among the 100 sweet sorghum accessions. The correlation between the phenotypic and the molecular diversity matrices was low (0.35), which is in agreement with the inconsistencies observed between the clusters formed by the phenotypic and the molecular diversity analyses. Furthermore, the clusters obtained by the molecular diversity analysis were more consistent with the genealogy and the historic background of the sweet sorghum accessions than the clusters obtained through the phenotypic diversity analysis. The low correlation observed between the molecular and the phenotypic diversity matrices highlights the complementarity between the molecular and the phenotypic characterization to assist a breeding program.