Data/hora: 14/06/2024 20:28:19

Biblioteca(s): 	Embrapa Gado de Leite.	
Data corrente: 	02/06/2022	
Data da última atualização: 	01/12/2022	
Autoria: 	FERREIRA, F. M.; LEITE, R. V.; MALIKOUSKI, R. G.; PEIXOTO, M. A.; BERNARDELI, A.; ALVES, R. S.; MAGALHAES JUNIOR, W. C. P. de; ANDRADE, R. G.; BHERING, L. L.; MACHADO, J. C.	
Afiliação: 	FILIPE MANOEL FERREIRA, Universidade Federal de Viçosa; RODRIGO VIEIRA LEITE, Universidade Federal de Viçosa; RENAN GARCIA MALIKOUSKI, Universidade Federal de Viçosa; MARCO ANTONIO PEIXOTO, Universidade Federal de Viçosa; ARTHUR BERNARDELI, Universidade Federal de Viçosa; RODRIGO SILVA ALVES, Universidade Federal de Lavras; WALTER COELHO P DE MAGALHAES JUNIOR, CNPGL; RICARDO GUIMARAES ANDRADE, CNPGL; LEONARDO LOPES BHERING, Universidade Federal de Viçosa; JUAREZ CAMPOLINA MACHADO, CNPGL.	
Título: 	Bioenergy elephant grass genotype selection leveraged by spatial modeling of conventional and high-throughput phenotyping data.	
Ano de publicação: 	2022	
Fonte/Imprenta: 	Journal of Cleaner Production, v. 363, 132286, 2022.	
DOI: 	https://doi.org/10.1016/j.jclepro.2022.132286	
Idioma: 	Inglês	
Conteúdo: 	The burning of fossil fuels contributes to global warming. Using renewable energy sources such as elephant grass biomass mitigates anthropogenic impact on nature. The genetic selection of high-yield elephant grass genotypes is important to increase the use of this forage for energy generation. Unmanned aerial vehicles have been used for data collection and optimization of the selection of genotypes. However, statistical tests should be conducted to study the suitability of vegetation indices for predicting morphological traits. In addition, spatial sources of variation, such as soil structure heterogeneity, can disturb the selection process. This study compared the correlation between morphological traits and vegetation indices of elephant grass clones using basic linear mixed and spatial linear mixed models. In addition, we evaluated the magnitude and contribution of each index to explain the variations in traits and identify the best index for this forage. There was significant genetic variability in some morphological traits that enabled selection. Spatial models (autoregressive correlation among rows and columns) were more suitable for modeling some of the evaluated traits. There were changes in the magnitude of the correlation between traits when we considered the best-fit model instead of the non-spatial model. The increase in efficiency using the best-fitted model instead of the non-spatial model was 15.39% for heritability and 9.54% for accuracy. The total dry biomass was the only morphological trait significantly correlated with some vegetation indices, allowing for indirect selection. The coincidence index, heritability, and gains from indirect selection indicated that the normalized difference red-edge index was the best for selecting superior elephant grass high-yielding genotypes. The spatial modeling leveraged the genetic selection of high yield elephant grass genotypes for bioenergetic purposes.	
Palavras-Chave: 	Forage breeding; Genetic selection; Seleção gênica.	
Thesagro: 	Bioenergia; Capim Elefante; Seleção Fenótipa; Seleção Genética; Sensoriamento Remoto.	
Thesaurus Nal: 	Phenomics; Remote sensing.	
Categoria do assunto: 	F Plantas e Produtos de Origem Vegetal	
Registro original: 	Embrapa Gado de Leite (CNPGL)	
Exemplares: 	CNPGL - (25673-1) - UPC - AP - DD -  -  -  - DIS