Genetic parameter estimates for buffalo milk yield, milk quality and mozzarella production and Bayesian inference analysis of their relationships

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Evaluation of an average numerator relationship matrix model and a Bayesian hierarchical model for growth traits in Nellore cattle with uncertain paternity

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

Bayesian inference in genetic parameter estimation of visual scores in Nellore beef-cattle

The aim of this study was to estimate the components of variance and genetic parameters for the visual scores which constitute the Morphological Evaluation System (MES), such as body structure (S), precocity (P) and musculature (M) in Nellore beef-cattle at the weaning and yearling stages, by using threshold Bayesian models. The information used for this was gleaned from visual scores of 5,407 animals evaluated at the weaning and 2,649 at the yearling stages. The genetic parameters for visual score traits were estimated through two-trait analysis, using the threshold animal model, with Bayesian statistics methodology and MTGSAM (Multiple Trait Gibbs Sampler for Animal Models) threshold software. Heritability estimates for S, P and M were 0.68, 0.65 and 0.62 (at weaning) and 0.44, 0.38 and 0.32 (at the yearling stage), respectively. Heritability estimates for S, P and M were found to be high, and so it is expected that these traits should respond favorably to direct selection. The visual scores evaluated at the weaning and yearling stages might be used in the composition of new selection indexes, as they presented sufficient genetic variability to promote genetic progress in such morphological traits.

Bayesian analysis of random regression models using B-splines to model test-day milk yield of Holstein cattle in Brazil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Random regression analyses using B-splines functions to model growth from birth to adult age in Canchim cattle

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Random regression models to estimate test-day milk yield genetic parameters Holstein cows in Southeastern Brazil

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

Effect of lactation length adjustment procedures on genetic parameter estimates for buffalo milk yield

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

Milk fatty acid characterization and genetic parameter estimates for milk conjugated linoleic acid in buffaloes

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Genetic parameter estimates for live weight and daily live weight gain obtained for Nellore bulls in a test station using different models

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

Milk yield persistency in Brazilian Gyr cattle based on a random regression model

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Investigação dos processos hidrossedimentológicos em parcela experimental no semi-árido potiguar

In northeastern semiarid, seasonality on precipitation temporal distribution, high intensity storm events and inadequate management of native vegetation can promote soil erosion. Vegetation removal causes soil surface exposure, reduces soil water storage capacity and can be the source degradation processes. In this context, this approach aims to analyze water and soil erosion processes on a 250 m2 undisturbed experimental plot with native vegetation, slope 2.5% by using 2006 and 2007 monitoring data. The site was instrumented to monitor rainfall, overland flow runoff and erosion by using a 5 m³ tank downstream the plot. Soil erosion monitoring was made by transported sediment and organic matter collection after each event. Field infiltration experiments were made at 16 points randomly distributed within the plot area by using a constant head infiltrometer during drought and rainy seasons, respectively. Infiltration data revealed high spatial and temporal variability. It was observed that during the beginning of the rainy period, 77% of the events showed runoff coefficient less than 0.05. As the rainy season began, soil water increase produced annual species germination. High intensity storms resulted in runoff coefficients varying between 0.33 and 0.42. Once the annual species was established, it was observed that approximately 39% of the events produced no runoff, which reflects an increase on soil water retention capacity caused by the vegetation. A gradual runoff reduction during the rainy season emphasizes the effect of vegetative density increase. Soil erosion observed data allowed to fit an empirical relationship involving soil loss and precipitation height, which was used to analyze the plot installation impact on soil erosion. Observed soil loss in 2006 and 2007 was 230 Kg/ha and 54 Kg/ha, respectively

Avaliação das barragens subterrâneas no regime de um curso d água na região do semi-árido do RN

The Serido is a region of northeastern Brazil highlighted by its problems related to water scarcity because of its semi-arid region, large rates of evaporation and rainfall irregular and scarce. Thus, the underground dams, become a strategically important resource with a simple and inexpensive technique for storage of water. They act in a positive way in the development of inland cities that suffer from drought, because in addition to exercise a great improvement in the social role of these families, can enjoy the type of soil (silt) and provides water for both irrigation and for human consumption throughout the year. Is therefore essential to its monitoring and studies to assess its effectiveness in accordance with its purposes, along with their wells Amazons, as can occur in conditions of physical degradation, chemical and microbiological appropriate, according to the Ordinance No 518/04 Ministry of Health, however, the proposed work aims to analyze the underground dams in the municipality of Sierra Negra North-RN (semi-arid region) as to their uses and their influences on the quality and quantity of water in periods of drought and rain. Analyzing monthly these parameters: determination of pH, conductivity, calcium, magnesium, sodium, potassium, chloride, sulfate, RAS (sodium adsorption ratio), turbidity, total iron, nitrite, nitrate, total dissolved solids (STD), bicarbonate (HCO3), fecal coliform and pesticides compared to the standards allowed by the Ordinance in force for quality for human consumption. While at the risk of salinity and sodicity on the model proposed by the United Salinity Laboratory (USSL). Although efficient, it was found that results varied annually on water quality which may influence their specific uses, whether or irrigation water supply in the city

Extração semi-automática de rodovia baseada em um par estéreo de imagens aéreas e em otimização por programação dinâmica no espaço-objeto

This article proposes a method for 3D road extraction from a stereopair of aerial images. The dynamic programming (DP) algorithm is used to carry out the optimization process in the object-space, instead of usually doing it in the image-space such as the DP traditional methodologies. This means that road centerlines are directly traced in the object-space, implying that a mathematical relationship is necessary to connect road points in object and image-space. This allows the integration of radiometric information from images into the associate mathematical road model. As the approach depends on an initial approximation of each road, it is necessary a few seed points to coarsely describe the road. Usually, the proposed method allows good results to be obtained, but large anomalies along the road can disturb its performance. Therefore, the method can be used for practical application, although it is expected some kind of local manual edition of the extracted road centerline.

Excitations and finite-size properties of an integrable supersymmetric electronic model

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A novel neural model to electrical load forecasting in transformers

The paper describes a novel neural model to electrical load forecasting in transformers. The network acts as identifier of structural features to forecast process. So that output parameters can be estimated and generalized from an input parameter set. The model was trained and assessed through load data extracted from a Brazilian Electric Utility taking into account time, current, tension, active power in the three phases of the system. The results obtained in the simulations show that the developed technique can be used as an alternative tool to become more appropriate for planning of electric power systems.