Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1171223
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dc.contributor.authorADUNOLA, P.
dc.contributor.authorFLORES, E. T.
dc.contributor.authorRIVA-SOUZA, E. M.
dc.contributor.authorFERRAO, M. A. G.
dc.contributor.authorSENRA, J. F. B.
dc.contributor.authorCOMÉRIO, M.
dc.contributor.authorESPINDULA, M. C.
dc.contributor.authorVERDIN FILHO, A. C.
dc.contributor.authorVOLPI, P. S.
dc.contributor.authorFONSECA, A. F. A. da
dc.contributor.authorFERRÃO, R. G.
dc.contributor.authorMUNOZ, P. R.
dc.contributor.authorFERRÃO, L. F. V.
dc.date.accessioned2025-01-06T19:47:02Z-
dc.date.available2025-01-06T19:47:02Z-
dc.date.created2025-01-06
dc.date.issued2024
dc.identifier.citationThe Plant Phenome Journal, v. 7, n. 1, e20109, 2024.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1171223-
dc.descriptionGenomic prediction has been proposed as the standard method to predict the genetic merit of unphenotyped individuals. Despite the promising results reported in the plant breeding literature, its routine implementation remains difficult for some crops. This is the case with Coffea canephora, in which costs and availability of molecular tools are major challenges for most breeding programs. To circumvent this, the use of near-infrared spectroscopy (NIR) has been recently proposed as an alternative to complement marker-assisted selection. The so-called phenomic selection relies on the reflectance spectrum to capture similarities between individuals and emerges as a valid approach for prediction. With promising results reported in multiple annual crops, we hypothesize that phenomic prediction could be a cost-efficient approach to incorporate into a practical coffee breeding program. To test it, we relied on a diverse population of C. canephora, evaluated for yield production, in two geographical locations over four harvest seasons. Our contributions in this paper are twofold: (i) We compared phenomic and genomic selection results, and showed large predictive abilities when NIR is used as a predictor for within and across-location predictions, and (ii) we presented a critical view of how both information sets could be combined into a contemporaneous coffee breeding program. Altogether, our results show how multi-omic information could be integrated in the same framework to leverage genetic gains in the long term.
dc.language.isoeng
dc.rightsopenAccess
dc.titleA comparison of genomic and phenomic selection methods foryield prediction in Coffea canephora.
dc.typeArtigo de periódico
dc.subject.thesagroCoffea Canephora
dc.subject.nalthesaurusGenomics
dc.subject.nalthesaurusPhenomics
dc.subject.nalthesaurusYields
dc.format.extent214 p.
riaa.ainfo.id1171223
riaa.ainfo.lastupdate2025-01-06
dc.identifier.doihttps://doi.org/10.1002/ppj2.20109
dc.contributor.institutionPAUL ADUNOLA, UNIVERSITY OF FLORIDA; ESTEFANIA TAVARES FLORES, UNIVERSITY OF FLORIDA; ELAINE M. RIVA-SOUZA, INSTITUTO CAPIXABA DE PESQUISA, ASSISTÊNCIATÉCNICA E EXTENSÃO RURAL; MARIA AMELIA GAVA FERRAO, CNPCA; JOÃO FELIPE B. SENRA, INSTITUTO CAPIXABA DE PESQUISA, ASSISTÊNCIATÉCNICA E EXTENSÃO RURAL; MARCONE COMÉRIO, INSTITUTO CAPIXABA DE PESQUISA, ASSISTÊNCIATÉCNICA E EXTENSÃO RURAL; MARCELO CURITIBA ESPINDULA, CNPCA; ABRAÃO C. VERDIN FILHO, INSTITUTO CAPIXABA DE PESQUISA, ASSISTÊNCIATÉCNICA E EXTENSÃO RURAL; PAULO S. VOLPI, INSTITUTO CAPIXABA DE PESQUISA, ASSISTÊNCIATÉCNICA E EXTENSÃO RURAL; AYMBIRE FRANCISCO A DA FONSECA, CNPCA; ROMARIO G. FERRÃO, MULTIVIX GROUP; PATRICIO R. MUNOZ, UNIVERSITY OF FLORIDA; LUIS FELIPE V. FERRÃO, UNIVERSITY OF FLORIDA.
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