scholarly journals Preweaning Calf Survival of a Nellore Beef Cattle Population

2017 ◽  
Vol 9 (8) ◽  
pp. 51
Author(s):  
Jairo Azevedo Junior ◽  
Juliana Petrini ◽  
Gerson Barreto Mourão ◽  
José Bento Sterman Ferraz
Keyword(s):  
Beef Cattle ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Estimation Methods ◽  
Linear Distribution ◽  
Continuous Trait ◽  
Categorical Trait ◽  
Calf Survival ◽  
Nellore Cattle ◽  
Cattle Rearing

The preweaning calf survival (SW) is one of the main economic bottlenecks of beef cattle rearing systems, however there is still few quantitative studies approaching this issue. Being a binary trait, genetic parameters for SW can be estimated considering continuous or categorical data under frequentist and Bayesian methods providing support for the selection and mating of animals in breeding programs. Therefore, the objectives in this study were to obtain and compare the variance component estimates for preweaning calf survival of calves in single-trait analyses and their correlations with a continuous trait in two-trait analyses. An amount of 25 218 data of the categorical trait of calf survival until weaning (SW) and the continuous trait of weaning weight (WW) were collected between the years of 2000 and 2012 in six herds of Nellore cattle. Methods III of Henderson, Maximum Restricted Likelihood (REML), Bayesian Inference and Generalized Linear Mixed Model (GLMM) were tested. Variance components obtained in one-trait analyses were similar to those obtained in two-trait analyses. Estimates of heritability (h2) obtained with different models for SW ranged from 0.0206 to 0.2644. The comparison between different estimation methods in single or two-trait analysis models allows the conclusion that the most appropriate method for SW analysis was the Bayesian estimation under an animal model and assuming linear distribution for phenotypes of SW trait.

scholarly journals Categorical Visual Score Traits of a Nellore Beef Cattle Population

2017 ◽  
Vol 9 (8) ◽  
pp. 63
Author(s):  
Jairo Azevedo Junior ◽  
Juliana Petrini ◽  
Gerson Barreto Mourão ◽  
José Bento Sterman Ferraz
Keyword(s):  
Variance Components ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Genetic Correlations ◽  
Estimation Method ◽  
Analysis Data ◽  
Suitable Model ◽  
Linear Distribution ◽  
Tropical Regions ◽  
Trait Analysis

Variance components and genetic parameters of economically relevant traits in livestock, whether continuous or categorical, can be estimated by methods computationally available providing support for the selection and mating of animals in breeding programs. The objectives of this paper were to obtain and compare the variance components estimates for visual traits under continuous or categorical distribution in single-trait analysis and their correlations with continuous productive traits in two-trait analysis. Data of conformation (CONF), precocity of fat deposition (PREC) and muscling (MUSC) visual scores evaluated at 18 months of age as well as the weight at 18 months of age (YW) were collected from animals born from 2000 to 2012, in Nellore cattle herds raised in Southeastern and Central Western tropical regions of Brazil. Methods III of Henderson, Restricted Maximum Likelihood (REML), Bayesian Inference and generalized linear mixed model (GLMM) were tested. Variance components obtained from single-trait analysis were similar to those obtained from two-trait analysis. The estimates of heritability (h2) for the visual scores ranged from 0.1081 to 0.2190. Heritability estimates for traits evaluated by visual scores have moderate to high magnitude justifying the inclusion of visual scores as selection criteria in animal breeding and the selection of animals with higher scores for mating. High genetic correlations between yearling weight and morphological traits were verified. For visual scores of conformation, precocity and muscling, the most suitable model based on one-trait or two-trait analyses considered an animal model, a linear distribution of the data and the estimation method of the components of (co)variance based on Bayesian methodology.

Empirical performance of estimation methods in Beta mixed models with application to ecological data

2020 ◽  
Vol 15 (2) ◽  
pp. 2279-2293
Author(s):  
Saliou Diouf ◽  
Bruno Enagnon Lokonon ◽  
Freedath Djibril Moussa ◽  
GLèLè KAKAï
Keyword(s):  
Mixed Models ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Real Life ◽  
Estimation Methods ◽  
Simulation Design ◽  
Ecological Data ◽  
Factors Affecting ◽  
Real Life Data ◽  
Empirical Performance

This study uses a Monte Carlo simulation design to assess the performance of Beta and linear mixed models on bounded response variables through comparison of four estimation methods. Four factors affecting the performance of the estimation methods were considered: the number of groups, the number of observations per group, the variance and distribution of the random effects. Our results showed that, for small number of groups (less than 30), the Beta mixed model outperformed the linear mixed model whatever the size of the groups. In the case of a large number of groups (superior or equal to 30), both approaches showed relatively close performance. The results from the simulation study have been illustrated with real life data.

scholarly journals Logit-normal mixed model for Indian monsoon precipitation

2014 ◽  
Vol 21 (5) ◽  
pp. 939-953
Author(s):  
L. R. Dietz ◽  
S. Chatterjee
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Indian Monsoon ◽  
Extreme Rainfall ◽  
Estimation Methods ◽  
Underlying Structure ◽  
Normal Model ◽  
Monsoon Precipitation ◽  
Tuning Parameters ◽  
Climatic Event

Abstract. Describing the nature and variability of Indian monsoon precipitation is a topic of much debate in the current literature. We suggest the use of a generalized linear mixed model (GLMM), specifically, the logit-normal mixed model, to describe the underlying structure of this complex climatic event. Four GLMM algorithms are described and simulations are performed to vet these algorithms before applying them to the Indian precipitation data. The logit-normal model was applied to light, moderate, and extreme rainfall. Findings indicated that physical constructs were preserved by the models, and random effects were significant in many cases. We also found GLMM estimation methods were sensitive to tuning parameters and assumptions and therefore, recommend use of multiple methods in applications. This work provides a novel use of GLMM and promotes its addition to the gamut of tools for analysis in studying climate phenomena.

scholarly journals Parametric and semi-parametric bootstrap-based confidence intervals for robust linear mixed models

Methodology ◽  
2021 ◽  
Vol 17 (4) ◽  
pp. 271-295
Author(s):  
Fabio Mason ◽  
Eva Cantoni ◽  
Paolo Ghisletta
Keyword(s):  
Longitudinal Data ◽  
Confidence Intervals ◽  
Robust Estimation ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Parametric Bootstrap ◽  
Estimation Methods ◽  
Low Probability ◽  
Coverage Rates ◽  
Comprehensive Study

The linear mixed model (LMM) is a popular statistical model for the analysis of longitudinal data. However, the robust estimation of and inferential conclusions for the LMM in the presence of outliers (i.e., observations with very low probability of occurrence under Normality) is not part of mainstream longitudinal data analysis. In this work, we compared the coverage rates of confidence intervals (CIs) based on two bootstrap methods, applied to three robust estimation methods. We carried out a simulation experiment to compare CIs under three different conditions: data 1) without contamination, 2) contaminated by within-, or 3) between-participant outliers. Results showed that the semi-parametric bootstrap associated to the composite tau-estimator leads to valid inferential decisions with both uncontaminated and contaminated data. This being the most comprehensive study of CIs applied to robust estimators of the LMM, we provide fully commented R code for all methods applied to a popular example.

Indirect estimation of poverty indicators at poviat level

2020 ◽  
Vol 65 (8) ◽  
pp. 7-26
Author(s):  
Łukasz Wawrowski
Keyword(s):  
Empirical Bayes ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Estimation Error ◽  
Local Level ◽  
Estimation Methods ◽  
Strongly Correlated ◽  
The European Union ◽  
Indirect Estimation ◽  
Poverty Indicators

The availability of detailed and precise data on poverty at a low level of spatial aggregation is important when pursuing an effective cohesion policy. In Poland, this type of information is gathered during household surveys conducted by Statistics Poland and is made available at country, region, and selected socio-economic group level. Direct estimates relating to domains not included in a survey are burdened with a serious estimation error. In a situation of a limited (or in extreme cases zero) sample size, an estimation becomes possible through the application of small area estimation methods – indirect estimation. These techniques use variables which are strongly correlated with the researched phenomenon and which come from a census or from an administrative register. The aim of the study discussed in the article is to estimate two indicators: the rate of poverty and the depth of poverty at a poviat level, with the application of the Empirical Bayes (EB) method. The first indicator provides information on the scale of the phenomenon and the other one on its intensity, and so they constitute complementary measures of poverty. The study used data from the European Union Statistics on Income and Living Conditions of 2011 and the National Census of Population and Housing 2011. Information about the scale and intensity of poverty at the poviat level was obtained through the adaptation of the EB method based on the linear mixed model and Monte Carlo simulations. The indicators estimated this way allow for an assessment of the diversity of poverty at a local level. In addition, they are more precise and consistent with administrative registers in comparison to direct estimation results.

scholarly journals Benefits of Silvopastoral Systems for Keeping Beef Cattle

Animals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 992
Author(s):  
Stella Maris Huertas ◽  
Pablo Ernesto Bobadilla ◽  
Ignacio Alcántara ◽  
Emilie Akkermans ◽  
Frank J. C. M. van Eerdenburg
Keyword(s):  
Beef Cattle ◽  
Fixed Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Temperate Climate ◽  
Zebu Cattle ◽  
Silvopastoral Systems ◽  
Native Grasses ◽  
Tropical Regions ◽  
Welfare Indicators

The potential benefits of keeping Zebu cattle in silvopastoral systems are well described in tropical regions. In order to obtain information on European breeds of beef cattle (Bos taurus taurus) in temperate climate zones, individual records of body weight and welfare indicators were obtained from 130 beef cattle. These belonged to four herds and were randomly allocated to two contiguous plots: Silvopastoral Systems (SPS) and Open Pastures Systems (OPS). The SPS in this study were areas with exotic trees of Eucalyptus globulus globulus for paper pulp production planted in a 2 × 2 design (two meters between each tree) over diverse, native grasses. The OPS were large open areas with a great diversity of native grasses, herbs, and small plots of trees where the animals could rest and shelter from extreme weather conditions. Over the course of one year, individual body weights and a number of specific animal welfare indicators were measured every 45 days. After a descriptive analysis, a generalized linear mixed model (GLMM) with a Gaussian distribution, with time and system (OPS or SPS) fitted as fixed effects and individuals nested by herd as random intercepts, was used. The results showed that weight gain did not differ between the two systems. None of the animals showed any sign of impaired welfare in either system over the study period. Silvopastoral systems offer animals a sustainable and richer environment that will improves their welfare. The additional income provided by the wood production allows the farmers to maintain their traditional cattle farming lifestyle.

Analyzing discontinuities in longitudinal count data: A multilevel generalized linear mixed model.

2020 ◽  
Author(s):  
James L. Peugh ◽  
Sarah J. Beal ◽  
Meghan E. McGrady ◽  
Michael D. Toland ◽  
Constance Mara
Keyword(s):  
Count Data ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Longitudinal Count Data

Predicting bycatch hotspots based on suitable habitat derived from fishery-independent data

2020 ◽  
Vol 641 ◽  
pp. 159-175
Author(s):  
J Runnebaum ◽  
KR Tanaka ◽  
L Guan ◽  
J Cao ◽  
L O’Brien ◽  
...  
Keyword(s):  
Habitat Suitability ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Gulf Of Maine ◽  
Homarus Americanus ◽  
Management Tool ◽  
American Lobster ◽  
Suitable Habitat ◽  
Climate Conditions ◽  
Suitability Index

Bycatch remains a global problem in managing sustainable fisheries. A critical aspect of management is understanding the timing and spatial extent of bycatch. Fisheries management often relies on observed bycatch data, which are not always available due to a lack of reporting or observer coverage. Alternatively, analyzing the overlap in suitable habitat for the target and non-target species can provide a spatial management tool to understand where bycatch interactions are likely to occur. Potential bycatch hotspots based on suitable habitat were predicted for cusk Brosme brosme incidentally caught in the Gulf of Maine American lobster Homarus americanus fishery. Data from multiple fisheries-independent surveys were combined in a delta-generalized linear mixed model to generate spatially explicit density estimates for use in an independent habitat suitability index. The habitat suitability indices for American lobster and cusk were then compared to predict potential bycatch hotspot locations. Suitable habitat for American lobster has increased between 1980 and 2013 while suitable habitat for cusk decreased throughout most of the Gulf of Maine, except for Georges Basin and the Great South Channel. The proportion of overlap in suitable habitat varied interannually but decreased slightly in the spring and remained relatively stable in the fall over the time series. As Gulf of Maine temperatures continue to increase, the interactions between American lobster and cusk are predicted to decline as cusk habitat continues to constrict. This framework can contribute to fisheries managers’ understanding of changes in habitat overlap as climate conditions continue to change and alter where bycatch interactions could occur.

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