The objective of this work was to select superior sweet orange (Citrus sinensis) genotypes with higher yield potential based on data from eight harvests, using the residual or restricted maximum likelihood/best linear unbiased prediction (REML/BLUP) methodology. The experiment was carried out from 2002 to 2008 and in 2010 in the municipality of Rio Branco, in the state of Acre, Brazil. Analyzes of deviance were performed to test the significance of the components of variance according to the random effects of the used model, and parameters were estimated from individual genotypic and phenotypic variances. A selection intensity of 20% was adopted regarding genotypic selection, i.e., only the best 11 of the 55 genotypes tested were selected. The estimates of the genetic parameters show the existence of genetic variability and the selection potential of the studied sweet orange genotypes. The genotypic correlation between harvests is of low magnitude, except for the variable average fruit mass, and, as a reflex, there is a change in the ordering of the genotypes. Genotypes 5, 48, 19, 14, and 47 stand out as being the most productive, and, therefore, are the most suitable for selection purposes. Genotypes 14 and 47 show superior performance for the character set evaluated.
When associated an adequate shading and nutrition the seedlings to reach characteristics that allow them tosurvive in the field and increase production. Thus, this work aimed to evaluate the production of Oenocarpusbataua seedlings in response to different shade environments and controlled-release fertilizer doses. Thestudy was carried out from at the Embrapa nursery located in the municipality of Rio Branco, Acre state. Theexperimental design was complete randomized blocks with three replications and six plants per plot. Thetreatments were distributed according to a 4 x 5 factorial scheme, i.e., four shade environments (20%, 30%, 50%and 75%) and five CRF doses (0.0, 1.5, 3.0, 4.5 and 6.0 kg m–3) mixed with the substrate. Biometric, biomass andquality variables were evaluated at 305 days after transplanting. The ANOVA was applied for the qualitativefactor and regression analysis was performed for the quantitative factor, both at 5% probability. The treatmentswere grouped by analyzing canonical variables, a multivariate statistical and realized the Pearson’s correlationbetween variables was determined through correlation networks. The shading environment and the controlledreleasefertilizer positively influenced the growth and quality of Oenocarpus bataua seedlings. The seedlings ofOenocarpus bataua have better biometric characteristics produced in a nursery with 50% shading. Oenocarpusbataua seedlings show better quality when 3.88 kg m-3 of controlled release fertilizer are used.
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