03055naa a2200361 a 450000100080000000500110000800800410001902400550006010000200011524501550013526000090029052019480029965000140224765000190226165000150228065000190229565000240231465000240233865000250236265300200238765300220240765300220242970000170245170000220246870000190249070000190250970000170252870000340254570000190257970000190259870000190261777300570263621436692022-12-01       2022    bl uuuu     u00u1 u    #d7 ahttps://doi.org/10.1016/j.jclepro.2022.1322862DOI1 aFERREIRA, F. M.  aBioenergy elephant grass genotype selection leveraged by spatial modeling of conventional and high-throughput phenotyping data.h[electronic resource]  c2022  aThe 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.  aPhenomics  aRemote sensing  aBioenergia  aCapim Elefante  aSeleção Fenótipa  aSeleção Genética  aSensoriamento Remoto  aForage breeding  aGenetic selection  aSeleção gênica1 aLEITE, R. V.1 aMALIKOUSKI, R. G.1 aPEIXOTO, M. A.1 aBERNARDELI, A.1 aALVES, R. S.1 aMAGALHAES JUNIOR, W. C. P. de1 aANDRADE, R. G.1 aBHERING, L. L.1 aMACHADO, J. C.  tJournal of Cleaner Productiongv. 363, 132286, 2022.