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Selection of umbu-cajazeira clones using the REML/BLUP
- Source :
- Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA-Alice), Empresa Brasileira de Pesquisa Agropecuária (Embrapa), instacron:EMBRAPA
- Publication Year :
- 2017
- Publisher :
- Revista Brasileira de Ciencias Agrarias, 2017.
-
Abstract
- Genetic parameters and genotypic values of fruits from umbu-cajazeira clones were determined using the restricted maximum likelihood/best linear unbiased prediction (REML/BLUP) method, from the morphological, physicochemical and chemical characterization of fruits. The experiment was composed of six experimental clones, in three randomized complete block designs and three replicates per plot in an unbalanced arrangement in Ipanguaçu/RN, due to non-fructification of some clones of the experiment. Morphological, physicochemical and chemical characterization of 20 fruits of each clone was performed. The variance components for REML and medium components (BLUP), were estimated using the mixed model?s method REML/BLUP. The three best clones selected were from Serra do Mel, Açu, and Carnaubais. The method used allowed selection of clones with high soluble solids content and pulp yield based on the genotypic value of clones. Fruit clones from Serra do Mel can be used for pulp processing or fresh consumption. The Açu clone showed a high pulp yield and is recommended pulp processing. Carnaubais, Alto do Rodrigues, and Ipanguaçu generated fruits for fresh consumption. Made available in DSpace on 2018-05-22T00:55:42Z (GMT). No. of bitstreams: 1 Rafaela2018.pdf: 362307 bytes, checksum: 163d2243ec4ed1d85d93b1b9f976343d (MD5) Previous issue date: 2018-05-21
- Subjects :
- clone (Java method)
Mixed model
Restricted maximum likelihood
Pulp (paper)
Umbu-cajazeira
Variabilidade genética
Melhoramento Genético Vegetal
engineering.material
Best linear unbiased prediction
Biology
REML/BLUP
Spondias
Horticulture
Soluble solids
engineering
Variance components
General Agricultural and Biological Sciences
Selection (genetic algorithm)
Consumo ao natural
Subjects
Details
- ISSN :
- 19810997
- Volume :
- 12
- Database :
- OpenAIRE
- Journal :
- Revista Brasileira de Ciências Agrárias - Brazilian Journal of Agricultural Sciences
- Accession number :
- edsair.doi.dedup.....cddd8793c70f2c96564ed57ec3baf0a1
- Full Text :
- https://doi.org/10.5039/agraria.v12i4a5485