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      Best linear unbiased prediction in combination with path analysis in processing grapes


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          ABSTRACT The knowledge of correlations between multiple characteristics in plant breeding leads to more effective selection strategies. The path analysis allows refining these correlations and partitioning them into direct and indirect effects on the main variable. The path analysis becomes more effective when based on predicted genotypic values rather than phenotypic values. The objective was to evaluate correlations between the main agronomic characteristics of grapevine cultivation and their direct and indirect effects on yield per plant to improve selection strategies to reach superior progenies. A randomized complete block design was installed using four cultivars and two rootstocks, five repetitions, and plots of four plants. Data from three crop seasons were analyzed from a mixed model and genetic correlations were subject to the path analysis. A high and positive significant correlation was found between average fruit production and the number of clusters per plant. On the other hand, the average production per plant showed a low correlation to cluster width and height per grapevine. Wider and higher berries tend to increase berry fresh mass and therefore increase the contents of soluble solids and reducing sugars. Among the features, the number of clusters per plant has the strongest direct effect on fruit production in grape cultivars. Berry fresh mass, berry length, and berry width were indirectly influenced by the number of clusters and showed high heritability compared to yield and number of clusters. These characteristics could be used in indirect selection.

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          Most cited references38

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          Present and future Köppen-Geiger climate classification maps at 1-km resolution

          We present new global maps of the Köppen-Geiger climate classification at an unprecedented 1-km resolution for the present-day (1980–2016) and for projected future conditions (2071–2100) under climate change. The present-day map is derived from an ensemble of four high-resolution, topographically-corrected climatic maps. The future map is derived from an ensemble of 32 climate model projections (scenario RCP8.5), by superimposing the projected climate change anomaly on the baseline high-resolution climatic maps. For both time periods we calculate confidence levels from the ensemble spread, providing valuable indications of the reliability of the classifications. The new maps exhibit a higher classification accuracy and substantially more detail than previous maps, particularly in regions with sharp spatial or elevation gradients. We anticipate the new maps will be useful for numerous applications, including species and vegetation distribution modeling. The new maps including the associated confidence maps are freely available via www.gloh2o.org/koppen.
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            Best linear unbiased estimation and prediction under a selection model.

            Mixed linear models are assumed in most animal breeding applications. Convenient methods for computing BLUE of the estimable linear functions of the fixed elements of the model and for computing best linear unbiased predictions of the random elements of the model have been available. Most data available to animal breeders, however, do not meet the usual requirements of random sampling, the problem being that the data arise either from selection experiments or from breeders' herds which are undergoing selection. Consequently, the usual methods are likely to yield biased estimates and predictions. Methods for dealing with such data are presented in this paper.
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              GENES: a software package for analysis in experimental statistics and quantitative genetics

              GENES is a software package used for data analysis and processing with different biometric models and is essential in genetic studies applied to plant and animal breeding. It allows parameter estimation to analyze biological phenomena and is fundamental for the decision-making process and predictions of success and viability of selection strategies. The program can be downloaded from the Internet (http://www.ufv.br/dbg/genes/genes.htm or http://www.ufv.br/dbg/biodata.htm) and is available in Portuguese, English and Spanish. Specific literature (http://www.livraria.ufv.br/) and a set of sample files are also provided, making GENES easy to use. The software is integrated into the programs MS Word, MS Excel and Paint, ensuring simplicity and effectiveness in data import and export of results, figures and data. It is also compatible with the free software R and Matlab, through the supply of useful scripts available for complementary analyses in different areas, including genome wide selection, prediction of breeding values and use of neural networks in genetic improvement.

                Author and article information

                Scientia Agricola
                Sci. agric. (Piracicaba, Braz.)
                Escola Superior de Agricultura "Luiz de Queiroz" (Piracicaba, SP, Brazil )
                : 81
                : e20220218
                [3] Campinas São Paulo orgnameUniversidade Estadual de Campinas orgdiv1Centro de Biologia Molecular e Engenharia Genética Brazil
                [2] Campinas São Paulo orgnameInstituto Agronômico orgdiv1Centro de Fibras e Grãos Brazil
                [1] Jundiaí São Paulo orgnameInstituto Agronômico orgdiv1Centro Avançado de Pesquisa e Desenvolvimento de Frutas Brazil
                [4] Botucatu São Paulo orgnameUniversidade Estadual Paulista orgdiv1Faculdade de Ciências Agronômicas Brazil
                S0103-90162024000100601 S0103-9016(24)08100000601

                This work is licensed under a Creative Commons Attribution 4.0 International License.

                : 11 October 2022
                : 22 March 2023
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 38, Pages: 0

                SciELO Brazil

                Crop Science

                tropical vitiviniculture,Vitis spp,correlation,genotypic values,mixed models


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