863 resultados para multi-attribute analysis
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Pós-graduação em Engenharia Elétrica - FEIS
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Pós-graduação em Geociências e Meio Ambiente - IGCE
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Objective. Assessment of genetic parameters for accumulative productivity trait (ACP) and genetic correlations with age at first calving (AFC), between calving interval of first and second parity (BCI1) and longevity (LONG). Materials and methods. 8584 Brahman female records were used with an animal model in multi-trait analysis with restricted maximum likelihood method, implemented using the WOMBAT software. The models considered the fixed effects of contemporary group, parity and weaning weight of first calf covariate, the only random effect was the genetic additive direct. Weaning weight (P240) was included to reduce the effect of selection on the estimation of variance components. Results. The heritability estimates were 0.3 +/- 0.04, 0.11 +/- 0.03, 0.07 +/- 0.03 and 0.24 +/- 0.04 for AFC, BCI1, LONG and ACP respectively. Correlations between ACP and the other features were moderate to high and favorable. Conclusions. ACP can be included in breeding programs for Brahman, and used as selection criteria for its moderate heritability and genetic correlation with reproductive traits.
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Pós-graduação em Genética e Melhoramento Animal - FCAV
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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Given the importance of Guzera breeding programs for milk production in the tropics, the objective of this study was to compare alternative random regression models for estimation of genetic parameters and prediction of breeding values. Test-day milk yields records (TDR) were collected monthly, in a maximum of 10 measurements. The database included 20,524 records of first lactation from 2816 Guzera cows. TDR data were analyzed by random regression models (RRM) considering additive genetic, permanent environmental and residual effects as random and the effects of contemporary group (CG), calving age as a covariate (linear and quadratic effects) and mean lactation curve as fixed. The genetic additive and permanent environmental effects were modeled by RRM using Wilmink, All and Schaeffer and cubic B-spline functions as well as Legendre polynomials. Residual variances were considered as heterogeneous classes, grouped differently according to the model used. Multi-trait analysis using finite-dimensional models (FDM) for testday milk records (TDR) and a single-trait model for 305-days milk yields (default) using the restricted maximum likelihood method were also carried out as further comparisons. Through the statistical criteria adopted, the best RRM was the one that used the cubic B-spline function with five random regression coefficients for the genetic additive and permanent environmental effects. However, the models using the Ali and Schaeffer function or Legendre polynomials with second and fifth order for, respectively, the additive genetic and permanent environmental effects can be adopted, as little variation was observed in the genetic parameter estimates compared to those estimated by models using the B-spline function. Therefore, due to the lower complexity in the (co)variance estimations, the model using Legendre polynomials represented the best option for the genetic evaluation of the Guzera lactation records. An increase of 3.6% in the accuracy of the estimated breeding values was verified when using RRM. The ranks of animals were very close whatever the RRM for the data set used to predict breeding values. Considering P305, results indicated only small to medium difference in the animals' ranking based on breeding values predicted by the conventional model or by RRM. Therefore, the sum of all the RRM-predicted breeding values along the lactation period (RRM305) can be used as a selection criterion for 305-day milk production. (c) 2014 Elsevier B.V. All rights reserved.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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The mining of sand, currently, is essential for urban growth, by providing input for the building industry. The consequences of this mining activity to environmental triggers may be severe and irreversible. Among the major impacts caused by sand mining the riparian vegetation removal is detached. The riparian vegetation is essential for balance and maintaining the local ecosystem. For all that had been shown, is possible to verify the importance of environmental studies in areas wich there are mining. This study aimed specially to assess environmental impacts triggered by a mining, located near the headwaters of the stream Mandu, situated in Ajapi, District of Rio Claro-SP. For this purpose, we used remote sensing techniques and GIS to produce thematic maps of slope, pedology, geology, land use and occupation of the soil, and riparian vegetation, using the capabilities of GIS / ArcGIS. The slope map was based on data from the Cartographic IGC 1979, scale 1:10,000. For the production of pedological and geological maps were used Semi-Detailed soil survey of the state of São Paulo, 1981 (1:100,000) and the Geological Map of Zaine (1994), scale 1:50,000, respectively. Since the maps of Use and Land Occupation and Riparian Forest were obtained by visual interpretation of the image of CBERS 2010 following the merger between the HRC and CCD images. From these mappings, and through multi-criteria analysis, map of susceptibility to erosion was made, which supported the environmental assessment of the studied area, indicating susceptible and unsuitable areas for the deployment of economic activities and urban sprawl. This study serves as a model can be replicated in other watersheds, assisting in the proper use planning and land use, aiming at the rational use of natural resources
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Pós-graduação em Zootecnia - FCAV
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)