A Bayesian approach to estimate the accuracy of in-house ELISA assay to measure rabies antibodies from compulsory vaccinated dogs and cattle

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Comparison of random regression models to estimate genetic parameters for milk production in Guzerat (Bos indicus) cows

Random regression models have been widely used to estimate genetic parameters that influence milk production in Bos taurus breeds, and more recently in B. indicus breeds. With the aim of finding appropriate random regression model to analyze milk yield, different parametric functions were compared, applied to 20,524 test-day milk yield records of 2816 first-lactation Guzerat (B. indicus) cows in Brazilian herds. The records were analyzed by random regression models whose random effects were additive genetic, permanent environmental and residual, and whose fixed effects were contemporary group, the covariable cow age at calving (linear and quadratic effects), and the herd lactation curve. The additive genetic and permanent environmental effects were modeled by the Wilmink function, a modified Wilmink function (with the second term divided by 100), a function that combined third-order Legendre polynomials with the last term of the Wilmink function, and the Ali and Schaeffer function. The residual variances were modeled by means of 1, 4, 6, or 10 heterogeneous classes, with the exception of the last term of the Wilmink function, for which there were 1, from 0.20 to 0.33. Genetic correlations between adjacent records were high values (0.83-0.99), but they declined when the interval between the test-day records increased, and were negative between the first and last records. The model employing the Ali and Schaeffer function with six residual variance classes was the most suitable for fitting the data. © FUNPEC-RP.

Characterization of the variable cow's age at last calving as a measurement of longevity by using the Kaplan-Meier estimator and the Cox model

In most studies on beef cattle longevity, only the cows reaching a given number of calvings by a specific age are considered in the analyses. With the aim of evaluating all cows with productive life in herds, taking into consideration the different forms of management on each farm, it was proposed to measure cow longevity from age at last calving (ALC), that is, the most recent calving registered in the files. The objective was to characterize this trait in order to study the longevity of Nellore cattle, using the Kaplan-Meier estimators and the Cox model. The covariables and class effects considered in the models were age at first calving (AFC), year and season of birth of the cow and farm. The variable studied (ALC) was classified as presenting complete information (uncensored = 1) or incomplete information (censored = 0), using the criterion of the difference between the date of each cow's last calving and the date of the latest calving at each farm. If this difference was >36 months, the cow was considered to have failed. If not, this cow was censored, thus indicating that future calving remained possible for this cow. The records of 11 791 animals from 22 farms within the Nellore Breed Genetic Improvement Program ('Nellore Brazil') were used. In the estimation process using the Kaplan-Meier model, the variable of AFC was classified into three age groups. In individual analyses, the log-rank test and the Wilcoxon test in the Kaplan-Meier model showed that all covariables and class effects had significant effects (P < 0.05) on ALC. In the analysis considering all covariables and class effects, using the Wald test in the Cox model, only the season of birth of the cow was not significant for ALC (P > 0.05). This analysis indicated that each month added to AFC diminished the risk of the cow's failure in the herd by 2%. Nonetheless, this does not imply that animals with younger AFC had less profitability. Cows with greater numbers of calvings were more precocious than those with fewer calvings. Copyright © The Animal Consortium 2012.

Development of equations to estimate microbial contamination in ruminal incubation residues of forage produced under tropical conditions using 15N as a label1

The objective of this study was to use 15N to label microbial cells to allow development of equations for estimating the microbial contamination in ruminal in situ incubation residues of forage produced under tropical conditions. A total of 24 tropical forages were ruminal incubated in 3 steers at 3 separate times. To determine microbial contamination of the incubated residues, ruminal bacteria were labeled with 15N by continuous intraruminal infusion 60 h before the first incubation and continued until the last day of incubation. Ruminal digesta was collected for the isolation of bacteria before the first infusion of 15N on adaptation period and after the infusion of 15N on collection period. To determine the microbial contamination of CP fractions, restricted models were compared with the full model using the model identity test. A value of the corrected fraction A was estimated from the corresponding noncorrected fraction by this equation: Corrected A fraction (ACPC) = 1.99286 + 0.98256 × A fraction without correction (ACPWC). The corrected fraction B was estimated from the corresponding noncorrected fraction and from CP, NDF, neutral detergent insoluble protein (NDIP), and indigestible NDF (iNDF) using the equation corrected B fraction (BCPC) = -17.2181 - 0.0344 × fraction B without correction (BCPWC) + 0.65433 × CP + 1.03787 × NDF + 2.66010 × NDIP - 0.85979 × iNDF. The corrected degradation rate of B fraction (kd)was estimated using the equation corrected degradation rate of B fraction (kdCPC) = 0.04667 + 0.35139 × degradation rate of B fraction without correction (kdCPWC) + 0.0020 × CP - 0.00055839 × NDF - 0.00336 × NDIP + 0.00075089 × iNDF. This equation was obtained to estimate the contamination using CP of the feeds: %C = 79.21 × (1 - e-0.0555t) × e-0.0874CP. It was concluded that A and B fractions and kd of CP could be highly biased by microbial CP contamination, and therefore these corrected values could be obtained mathematically, replacing the use of microbial markers. The percentage of contamination and the corrected apparent degradability of CP could be obtained from values of CP and time of incubation for each feed, which could reduce cost and labor involved when using 15N. © 2013 American Society of Animal Science. All rights reserved.

The employment situation in Latin America and the Caribbean: Recovery of employment: towards a sustainable model

The adverse effects on Latin America and the Caribbean of the global economic and financial crisis, the worst since the 1930s, have been considerably less than was once feared. Although a run of growth was cut short in 2009 and regional output shrank by 1.9%, the impact of the crisis was limited by the application of countercyclical fiscal and monetary policies by many of the region’s governments. The recovery in the economies, particularly in South America, has gone hand-in-hand with the rapid resurgence of the emerging economies of Asia, with all the favourable consequences this has had for global trade. A similar pattern may be observed regarding the impact of the crisis on labour markets in Latin America and the Caribbean. Although millions of people lost their jobs or had to trade down to lower-quality work, levels of employment (including formal employment) fell by less than originally foreseen. At the same time, real wages rose slightly in a context of falling inflation. The labour market thus stabilized domestic demand, and this contributed to the recovery that began in many countries in late 2009. Improved international trade and financing conditions, and the pick-up in domestic demand driven by macroeconomic policies, have led different commentators to estimate growth in the region’s economy at some 6% in 2010. As detailed in the first part of this edition of the Bulletin, the upturn has been manifested at the regional level by the creation of formal employment, a rise in the employment rate, a decline in joblessness and a moderate increase in real wages. Specifically, it is estimatedthat the regional unemployment rate will have dropped by 0.6 percentage points, from 8.1% in 2009 to 7.5% in 2010. The performance of different countries and subregions has been very uneven, however. On the one hand, there is Brazil, where high economic growth has been accompanied by vigorous creation of formal jobs and the unemployment rate has dropped to levels not seen in a long time. Other countries in South America have benefited from strong demand for natural resources from the Asian countries. Combined with higher domestic demand, this has raised their economic growth rates and had a positive impact on employment indicators. On the other hand, the recovery is still very weak in certain countries and subregions, particularly in the Caribbean, with employment indicators continuing to worsen.Thus, the recovery in the region’s economy in 2010 may be characterized as dynamic but uneven. Growth estimates for 2011 are less favourable. The risks associated with the imbalances in the world economy and the withdrawal of countercyclical fiscal packages are likely to cause the region to grow more slowly in 2011. Accordingly, a small further reduction of between 0.2 and 0.4 percentage points in the unemployment rate is projected for 2011. However, these indicators of recovery do not guarantee growth with decent work in the long term. To bolster the improvement in labour market indicators and generate more productive employment and decent work, the region’s countries need to strengthen their macroeconomic policies, improve regional and global policy coordination, identify and remove bottlenecks in the labour market itself and enhance instruments designed to promote greater equality. Like the rest of the world, the Latin American and Caribbean region is also confronted with the challenge of transforming the way it produces so that its economies can develop along tracks that are sustainable in the long term. Climate change and the consequent challenge of developing and strengthening low-carbon production and consumption patterns will also affect the way people work. A great challenge ahead is to create green jobs that combine decent work with environmentally sustainable production patterns. From this perspective, the second part of this Bulletin discusses the green jobs approach, offering some information on the challenges and opportunities involved in moving towards a sustainable economy in the region and presenting a set of options for addressing environmental issues and the repercussions of climate change in the world of work. Although the debate about the green jobs concept is fairly new in the region, examples already exist and a number of countries have moved ahead with the application of policies and programmes in this area. Costa Rica has formulated a National Climate Change Strategy, for example, whose foremost achievements include professional training in natural-resource management. In Brazil, fuel production from biomass has increased and social housing with solar panelling is being built. A number of other countries in the region are making progress in areas such as ecotourism, sustainable agriculture and infrastructure for climate change adaptation, and in formalizing the work of people who recycle household waste. The shift towards a more environmentally sustainable economy may cause jobs to be destroyed in some economic sectors and created in others. The working world will inevitably undergo major changes. If the issue is approached by way of social dialogue and appropriate public policies, there is a chance to use this shift to create more decent jobs, thereby contributing to growth in the economy, the construction of higher levels of equality and protection for the environment.

Climate estimates for Eastern Amazon with OLAM model

O modelo OLAM tem como característica a vantagem de representar simultaneamente os fenômenos de escala global e regional através de um esquema de refinamento de grades. Durante o projeto REMAM o modelo foi aplicado para alguns estudos de caso com objetivo de avaliar o desempenho do modelo na estimativa do clima da região leste da Amazônia em períodos de El Niño e La Niña. Estudos de caso foram feitos para os períodos chuvosos dos anos 2010 e 2011que apresentaram condições oceânicas distintas. Inicialmente, os resultados do modelo foram comparados com dados observados da região de estudo. Os resultados mostraram que o modelo consegue representar bem os principais centros convectivos da região e adjacências, da evolução local do ciclo diurno de temperatura, e da dinâmica dos ventos. Posteriormente, a análise dos resultados mostrou que, se tivermos bons dados de condição inicial e boa representação da evolução das condições de temperatura da superfície do mar, o modelo consegue prever com antecedência de dois e três meses se uma estação chuvosa será mais seca ou úmida.

3D stability orbits close to 433 Eros using an effective polyhedral model method

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A Spatial and Temporal Prediction Model of Corn Grain Yield as a Function of Soil Attributes

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Positioning and number of nutritional levels in dose-response trials to estimate the optimal-level and the adjustment of the models

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

Random regression models to estimate genetic parameters for milk production of Guzerat cows using orthogonal Legendre polynomials

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

A new probabilistic canopy dynamics model (SLCD) that is suitable for evergreen and deciduous forest ecosystems

There are strong uncertainties regarding LAI dynamics in forest ecosystems in response to climate change. While empirical growth & yield models (G&YMs) provide good estimations of tree growth at the stand level on a yearly to decennial scale, process-based models (PBMs) use LAI dynamics as a key variable for enabling the accurate prediction of tree growth over short time scales. Bridging the gap between PBMs and G&YMs could improve the prediction of forest growth and, therefore, carbon, water and nutrient fluxes by combining modeling approaches at the stand level.Our study aimed to estimate monthly changes of leaf area in response to climate variations from sparse measurements of foliage area and biomass. A leaf population probabilistic model (SLCD) was designed to simulate foliage renewal. The leaf population was distributed in monthly cohorts, and the total population size was limited depending on forest age and productivity. Foliage dynamics were driven by a foliation function and the probabilities ruling leaf aging or fall. Their formulation depends on the forest environment.The model was applied to three tree species growing under contrasting climates and soil types. In tropical Brazilian evergreen broadleaf eucalypt plantations, the phenology was described using 8 parameters. A multi-objective evolutionary algorithm method (MOEA) was used to fit the model parameters on litterfall and LAI data over an entire stand rotation. Field measurements from a second eucalypt stand were used to validate the model. Seasonal LAI changes were accurately rendered for both sites (R-2 = 0.898 adjustment, R-2 = 0.698 validation). Litterfall production was correctly simulated (R-2 = 0.562, R-2 = 0.4018 validation) and may be improved by using additional validation data in future work. In two French temperate deciduous forests (beech and oak), we adapted phenological sub-modules of the CASTANEA model to simulate canopy dynamics, and SLCD was validated using LAI measurements. The phenological patterns were simulated with good accuracy in the two cases studied. However, IA/max was not accurately simulated in the beech forest, and further improvement is required.Our probabilistic approach is expected to contribute to improving predictions of LAI dynamics. The model formalism is general and suitable to broadleaf forests for a large range of ecological conditions. (C) 2014 Elsevier B.V. All rights reserved.

Non-destructive linear model for leaf area estimation in Vernonia ferruginea Less

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

A Model-Based Approach for Making Ecological Inference from Distance Sampling Data

We consider a fully model-based approach for the analysis of distance sampling data. Distance sampling has been widely used to estimate abundance (or density) of animals or plants in a spatially explicit study area. There is, however, no readily available method of making statistical inference on the relationships between abundance and environmental covariates. Spatial Poisson process likelihoods can be used to simultaneously estimate detection and intensity parameters by modeling distance sampling data as a thinned spatial point process. A model-based spatial approach to distance sampling data has three main benefits: it allows complex and opportunistic transect designs to be employed, it allows estimation of abundance in small subregions, and it provides a framework to assess the effects of habitat or experimental manipulation on density. We demonstrate the model-based methodology with a small simulation study and analysis of the Dubbo weed data set. In addition, a simple ad hoc method for handling overdispersion is also proposed. The simulation study showed that the model-based approach compared favorably to conventional distance sampling methods for abundance estimation. In addition, the overdispersion correction performed adequately when the number of transects was high. Analysis of the Dubbo data set indicated a transect effect on abundance via Akaike’s information criterion model selection. Further goodness-of-fit analysis, however, indicated some potential confounding of intensity with the detection function.

The role of clouds in improving the regression model for hourly values of diffuse solar radiation

The study introduces a new regression model developed to estimate the hourly values of diffuse solar radiation at the surface. The model is based on the clearness index and diffuse fraction relationship, and includes the effects of cloud (cloudiness and cloud type), traditional meteorological variables (air temperature, relative humidity and atmospheric pressure observed at the surface) and air pollution (concentration of particulate matter observed at the surface). The new model is capable of predicting hourly values of diffuse solar radiation better than the previously developed ones (R-2 = 0.93 and RMSE = 0.085). A simple version with a large applicability is proposed that takes into consideration cloud effects only (cloudiness and cloud height) and shows a R-2 = 0.92. (C) 2011 Elsevier Ltd. All rights reserved.

Study of using marker assisted selection on a beef cattle breeding program by model comparison

A data set of a commercial Nellore beef cattle selection program was used to compare breeding models that assumed or not markers effects to estimate the breeding values, when a reduced number of animals have phenotypic, genotypic and pedigree information available. This herd complete data set was composed of 83,404 animals measured for weaning weight (WW), post-weaning gain (PWG), scrotal circumference (SC) and muscle score (MS), corresponding to 116,652 animals in the relationship matrix. Single trait analyses were performed by MTDFREML software to estimate fixed and random effects solutions using this complete data. The additive effects estimated were assumed as the reference breeding values for those animals. The individual observed phenotype of each trait was adjusted for fixed and random effects solutions, except for direct additive effects. The adjusted phenotype composed of the additive and residual parts of observed phenotype was used as dependent variable for models' comparison. Among all measured animals of this herd, only 3160 animals were genotyped for 106 SNP markers. Three models were compared in terms of changes on animals' rank, global fit and predictive ability. Model 1 included only polygenic effects, model 2 included only markers effects and model 3 included both polygenic and markers effects. Bayesian inference via Markov chain Monte Carlo methods performed by TM software was used to analyze the data for model comparison. Two different priors were adopted for markers effects in models 2 and 3, the first prior assumed was a uniform distribution (U) and, as a second prior, was assumed that markers effects were distributed as normal (N). Higher rank correlation coefficients were observed for models 3_U and 3_N, indicating a greater similarity of these models animals' rank and the rank based on the reference breeding values. Model 3_N presented a better global fit, as demonstrated by its low DIC. The best models in terms of predictive ability were models 1 and 3_N. Differences due prior assumed to markers effects in models 2 and 3 could be attributed to the better ability of normal prior in handle with collinear effects. The models 2_U and 2_N presented the worst performance, indicating that this small set of markers should not be used to genetically evaluate animals with no data, since its predictive ability is restricted. In conclusion, model 3_N presented a slight superiority when a reduce number of animals have phenotypic, genotypic and pedigree information. It could be attributed to the variation retained by markers and polygenic effects assumed together and the normal prior assumed to markers effects, that deals better with the collinearity between markers. (C) 2012 Elsevier B.V. All rights reserved.