Genetic Correlation for Pre-Weaning and Post-Weaning Traits of Mediterranean Buffaloes from Brazil, Estimated By Bayesian Inference

The aim of this study was to estimate genetic, environmental and phenotypic correlation between birth weight (BW) and weight at 205 days age (W205), BW and weight at 365 days age (W365) and W205-W365, using Bayesian inference. The Brazilian Program for Genetic Improvement of Buffaloes provided the data that included 3,883 observations from Mediterranean breed buffaloes. With the purpose to estimate variance and covariance, bivariate analyses were performed using Gibbs sampler that is included in the MTGSAM software. The model for BW, W205 and W365 included additive direct and maternal genetic random effects, maternal environmental random effect and contemporary group as fixed effect. The convergence diagnosis was achieved using Geweke, a method that uses an algorithm implemented in R software through the package Bayesian Output Analysis. The calculated direct genetic correlations were 0.34 (BW-W205), 0.25 (BW-W365) and 0.74 (W205-W365). The environmental correlations were 0.12, 0.11 and 0.72 between BW-W205, BW-W365 and W205-W365, respectively. The phenotypic correlations were low for BW-W205 (0.01) and BW-W365 (0.04), differently than the obtained for W205-W365 with a value of 0.67. The results indicate that BW trait have low genetic, environmental and phenotypic association with the two others traits. The genetic correlation between W205 and W365 was high and suggests that the selection for weight at around 205 days could be beneficial to accelerate the genetic gain.

A strategy to fill gaps in soil survey over large spatial extents: an example from the Murray-Darling basin of Australia

We re-mapped the soils of the Murray-Darling Basin (MDB) in 1995-1998 with a minimum of new fieldwork, making the most out of existing data. We collated existing digital soil maps and used inductive spatial modelling to predict soil types from those maps combined with environmental predictor variables. Lithology, Landsat Multi Spectral Scanner (Landsat MSS), the 9-s digital elevation model (DEM) of Australia and derived terrain attributes, all gridded to 250-m pixels, were the predictor variables. Because the basin-wide datasets were very large data mining software was used for modelling. Rule induction by data mining was also used to define the spatial domain of extrapolation for the extension of soil-landscape models from existing soil maps. Procedures to estimate the uncertainty associated with the predictions and quality of information for the new soil-landforms map of the MDB are described. (C) 2002 Elsevier Science B.V. All rights reserved.

Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping

Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm) in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI) and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI), derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The two classifiers were trained and validated for each soil class using 300 and 150 samples respectively, representing the characteristics of these classes in terms of the discriminating variables. According to the statistical tests, the accuracy of the classifier based on artificial neural networks (ANNs) was greater than of the classic Maximum Likelihood Classifier (MLC). Comparing the results with 126 points of reference showed that the resulting ANN map (73.81 %) was superior to the MLC map (57.94 %). The main errors when using the two classifiers were caused by: a) the geological heterogeneity of the area coupled with problems related to the geological map; b) the depth of lithic contact and/or rock exposure, and c) problems with the environmental correlation model used due to the polygenetic nature of the soils. This study confirms that the use of terrain attributes together with remote sensing data by an ANN approach can be a tool to facilitate soil mapping in Brazil, primarily due to the availability of low-cost remote sensing data and the ease by which terrain attributes can be obtained.

Correlações genotípicas, fenotípicas e ambientais entre caracteres de mamoneira

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Genetic parameters for buffalo milk yield and milk quality traits using Bayesian inference

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Estimativas de parâmetros genéticos e proposição de critérios de seleção para pesos na raça Canchim

The objectives of this study were to obtain heritability and genetic, phenotypic and environmental correlation estimates for birth (B), weaning (WW), yearling (YW) and 18 - (EW) and 24 - month (TW) weights, and to propose selection criteria for body weight in Canchim cattle. The data were analyzed by the least-squares method with models that included the fixed effects of herd, year and month of birth, sex of calf and age of cow at calving, and the random effects of sire within herd. The heritability estimates obtained were equal to 0.36 0.06 (BW), 0.47 0.06 (WW), 0.53 0.07 (YW), 0.54 0.08 (EW) and 0.27 0.06 (TW). The genetic correlations were equal to 0.51 (BW and WW), 0.36 (BW and YW), 0.14 (BW and EW), 0.00 (BW and TW), 0.92 (WW and YW), 0.77 (WW and EW), 0.75 (WW and TW), 0.94 (YW and EW), 0.86 (YW and TW) and 0.85 (EW and TW). The phenotypic correlations ranged from 0.19 to 0.72, and the environmental correlations from 0.11 to 0.61. The results showed that, in general, mass selection for weight will result in genetic progress, selection for weight at any age will result in correlated changes at other ages, and EW and TW are good selection criteria for increasing weight in the Canchim breed.

Estudo da habilidade de permanência de fêmeas da raça Canchim aos 76 meses de idade

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Heterose e capacidade combinatória em melão rendilhado

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Modelling spillover effects in spatial stochastic frontier analysis

In the last two decades, authors have begun to expand classical stochastic frontier (SF) models in order to include also some spatial components. Indeed, firms tend to concentrate in clusters, taking advantage of positive agglomeration externalities due to cooperation, shared ideas and emulation, resulting in increased productivity levels. Until now scholars have introduced spatial dependence into SF models following two different paths: evaluating global and local spatial spillover effects related to the frontier or considering spatial cross-sectional correlation in the inefficiency and/or in the error term. In this thesis, we extend the current literature on spatial SF models introducing two novel specifications for panel data. First, besides considering productivity and input spillovers, we introduce the possibility to evaluate the specific spatial effects arising from each inefficiency determinant through their spatial lags aiming to capture also knowledge spillovers. Second, we develop a very comprehensive spatial SF model that includes both frontier and error-based spillovers in order to consider four different sources of spatial dependence (i.e. productivity and input spillovers related to the frontier function and behavioural and environmental correlation associated with the two error terms). Finally, we test the finite sample properties of the two proposed spatial SF models through simulations, and we provide two empirical applications to the Italian accommodation and agricultural sectors. From a practical perspective, policymakers, based on results from these models, can rely on precise, detailed and distinct insights on the spillover effects affecting the productive performance of neighbouring spatial units obtaining interesting and relevant suggestions for policy decisions.

CORRELATION BETWEEN FLOWERING, FRUCTIFICATION AND ENVIRONMENTAL VARIABLES IN Solanum lycocarpum A. St.-Hil, SOLANACEAE

Solanum lycocarpum (lobeira) is a typical and abundant species of brazilian Cerrado, which occupies mainly surrounding disturbed areas. It has interesting characteristics from the point of view of reproductive biology, that probably are favoring the large occupation of habitats by the species. Based on the fact that the species produces flowers and fruit during all the year, the present study had the purpose to verify the association between flower and fruit production with environmental variables (temperature, relative humidity and precipitation), aiming to support future studies referring to reproductive biology and ecology of plant species from Cerrado biome. A population of S. lycocarpum composed of 34 plants in reproductive phase, situated in Morrinhos, south of the State of Goias, Brazil,, was evaluated. All the plants were geographically referenced with a GPS receptor. Observations were made monthly during 13 months (June, 2005 to July, 2006) quantifying open flowers and fruits produced in the intervals between the observations. It was possible suggest high conversion of flowers in fruits. The Spearman rank correlation showed positive correlation of flower number with precipitation and relative humidity. Fruit number was not correlated with the environmental variables.

Parametric correlation functions to model the structure of permanent environmental (co)variances in milk yield random regression models

The objective of the present study was to estimate milk yield genetic parameters applying random regression models and parametric correlation functions combined with a variance function to model animal permanent environmental effects. A total of 152,145 test-day milk yields from 7,317 first lactations of Holstein cows belonging to herds located in the southeastern region of Brazil were analyzed. Test-day milk yields were divided into 44 weekly classes of days in milk. Contemporary groups were defined by herd-test-day comprising a total of 2,539 classes. The model included direct additive genetic, permanent environmental, and residual random effects. The following fixed effects were considered: contemporary group, age of cow at calving (linear and quadratic regressions), and the population average lactation curve modeled by fourth-order orthogonal Legendre polynomial. Additive genetic effects were modeled by random regression on orthogonal Legendre polynomials of days in milk, whereas permanent environmental effects were estimated using a stationary or nonstationary parametric correlation function combined with a variance function of different orders. The structure of residual variances was modeled using a step function containing 6 variance classes. The genetic parameter estimates obtained with the model using a stationary correlation function associated with a variance function to model permanent environmental effects were similar to those obtained with models employing orthogonal Legendre polynomials for the same effect. A model using a sixth-order polynomial for additive effects and a stationary parametric correlation function associated with a seventh-order variance function to model permanent environmental effects would be sufficient for data fitting.

Correlation between Environmental Factors and Prevalence of Vibrio parahaemolyticus in Oysters Harvested in the Southern Coastal Area of Sao Paulo State, Brazil

The presence of Vibrio parahaemolyticus in 123 oyster samples collected from an estuary on the southern coast of Sao Paulo state, Brazil, was investigated. Of the 123 samples, 99.2% were positive with densities ranging from <3 to 10(5) most probable number (MPN)/g. Densities correlated significantly with water temperature (r = 0.48; P < 0.001) but not with salinity (r = -0.09; P = 0.34). The effect of harvest site on counts was not significant (P > 0.05). These data provide information for the assessment of exposure of V. parahaemolyticus in oysters at harvest.

Environmental modulation of ethanol-induced locomotor activity: Correlation with neuronal activity in distinct brain regions of adolescent and adult Swiss mice

Drug abuse is a concerning health problem in adults and has been recognized as a major problem in adolescents. induction of immediate-early genes (IEG), such as c-Fos or Egr-1, is used to identify brain areas that become activated in response to various stimuli, including addictive drugs. It is known that the environment can alter the response to drugs of abuse. Accordingly, environmental cues may trigger drug-seeking behavior when the drug is repeatedly administered in a given environment. The goal of this study was first to examine for age differences in context-dependent sensitization and then evaluate IEG expression in different brain regions. For this, groups of mice received i.p. ethanol (2.0 g/kg) or saline in the test apparatus, while other groups received the solutions in the home cage, for 15 days. One week after this treatment phase, mice were challenged with ethanol injection. Acutely, ethanol increased both locomotor activity and IEG expression in different brain regions, indistinctly, in adolescent and adult mice. However, adults exhibited a typical context-dependent behavioral sensitization following repeated ethanol treatment, while adolescent mice presented gradually smaller locomotion across treatment, when ethanol was administered in a paired regimen with environment. Conversely, ethanol-treated adolescents expressed context-independent behavioral sensitization. Overall, repeated ethanol administration desensitized IEG expression in both adolescent and adult mice, but this effect was greatest in the nucleus accumbens and prefrontal cortex of adolescents treated in the context-dependent paradigm. These results suggest developmental differences in the sensitivity to the conditioned and unconditioned locomotor effects of ethanol. (C) 2008 Elsevier B.V. All rights reserved.

Parametric correlation functions to model the structure of permanent environmental (co)variances in milk yield random regression models

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)