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Feed efficiency plays a major role in the overall profitability and sustainability of the beef cattle industry, as it is directly related to the reduction of the animal demand for input and methane emissions. Traditionally, the average daily feed intake and weight gain are used to calculate feed efficiency traits. However, feed efficiency traits can be analysed longitudinally using random regression models (RRMs), which allow fitting random genetic and environmental effects over time by considering the covariance pattern between the daily records. Therefore, the objectives of this study were to: (1) propose genomic evaluations for dry matter intake (DMI), body weight gain (BWG), residual feed intake (RFI) and residual weight gain (RWG) data collected during an 84-day feedlot test period via RRMs; (2) compare the goodness-of-fit of RRM using Legendre polynomials (LP) and B-spline functions; (3) evaluate the genetic parameters behaviour for feed efficiency traits and their implication for new selection strategies. The datasets were provided by the EMBRAPA-GENEPLUS beef cattle breeding program and included 2920 records for DMI, 2696 records for BWG and 4675 genotyped animals. Genetic parameters and genomic breeding values (GEBVs) were estimated by RRMs under ssGBLUP for Nellore cattle using orthogonal LPs and B-spline. Models were compared based on the deviance information criterion (DIC). The ranking of the average GEBV of each test week and the overall GEBV average were compared by the percentage of individuals in common and the Spearman correlation coefficient (top 1%, 5%, 10% and 100%). The highest goodness-of-fit was obtained with linear B-Spline function considering heterogeneous residual variance. The heritability estimates across the test period for DMI, BWG, RFI and RWG ranged from 0.06 to 0.21, 0.11 to 0.30, 0.03 to 0.26 and 0.07 to 0.27, respectively. DMI and RFI presented within-trait genetic correlations ranging from low to high magnitude across different performance test-day. In contrast, BWG and RWG presented negative genetic correlations between the first 3 weeks and the other days of performance tests. DMI and RFI presented a high-ranking similarity between the GEBV average of week eight and the overall GEBV average, with Spearman correlations and percentages of individuals selected in common ranging from 0.95 to 1.00 and 93 to 100, respectively. Week 11 presented the highest Spearman correlations (ranging from 0.94 to 0.98) and percentages of individuals selected in common (ranging from 85 to 94) of BWG and RWG with the average GEBV of the entire period of the test. In conclusion, the RRM using linear B-splines is a feasible alternative for the genomic evaluation of feed efficiency. Heritability estimates of DMI, RFI, BWG and RWG indicate enough additive genetic variance to achieve a moderate response to selection. A new selection strategy can be adopted by reducing the performance test to 56 days for DMI and RFI selection and 77 days for BWG and RWG selection.

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Background: The Gyr breed is widely used in Colombian low tropic dairy production systems. During the last 10 years, the Asociación Colombiana de Criadores de Ganado Cebú† ASOCEBU, has been leading a dairy milk control program which led to the creation of a dataset that permits to carry out the first analysis of milk yield in Gyr cattle in the country using records from several herds. Objectives: To study milk production dynamics of Gyr cattle in the Colombian low tropic through the estimation of lactation curves and four derived production parameters: total milk yield between 5 and 305 days (TMY305), peak milk yield (PMY), days at peak (DP) and persistency (P). Methods: 13,798 daily milk yield records from 1,510 cows performing in 103 herds were used; the total number of lactations was 2,480. Four models were considered: Wood, Wiltmink, Papajcsik & Bordero, and a second-degree polynomial. Mean square error, mean absolute error, mean square error of prediction, Akaike and Bayesian information criteria were used to select the model better describing each lactation using the majority rule, that is, the model selected by most criteria was the chosen one. The shape of each fitted lactation curve was checked using basic results from calculus which permitted the classification of the estimated curves into two groups: typical and atypical; only typical functions were used to compute the four aforementioned production parameters. Results: The second-order polynomial was the model most frequently selected, while the Papajcsik & Bordero model had the lowest frequency. Average TMY305, PMY, DP and P were 3,489.86 kg, 17.28 kg, 57.17 days, and 0.83, respectively, with coefficients of variation: 0.27, 0.21, 0.41, and 0.16. Conclusions: This study permitted to identify individuals with outstanding phenotypic performance. To the best of our knowledge, this is the first study of this kind involving thousands of lactations from Gyr cows performing in several regions of Colombian low tropic.

Antecedentes: La raza Gyr es ampliamente utilizada en las lecherías de trópico bajo en Colombia. Durante los últimos 10 años, la Asociación Colombiana de Criadores de Ganado Cebú ASOCEBU, ha liderado un programa de control lechero que generó una base de datos que permite llevar a cabo el primer análisis de producción de leche de la raza Gyr en el país, considerando individuos de varias fincas. Objetivos: Caracterizar la dinámica de producción de leche de ganado Gyr en el trópico bajo colombiano mediante la estimación de curvas de lactancia y cuatro parámetros de producción derivados: producción total entre 5 y 305 días (TMY305), pico de lactancia (PMY), días al pico (DP) y persistencia (P) Métodos: Se utilizaron 13.798 registros de producción diaria de leche de 1.510 vacas provenientes de 103 fincas, el total de lactancias fue 2.480. Se consideraron cuatro modelos: Wood, Wiltmink, Papajcsik & Bordero, y un polinomio de segundo grado. Los criterios usados para elegir el modelo que mejor describió cada lactancia fueron: error cuadrático medio, error absoluto medio, error cuadrático medio de predicción, criterio de información de Akaike y criterio de información Bayesiano. Se utilizó el criterio de mayoría, esto es, el modelo seleccionado fue aquel elegido por más criterios. La forma de cada una de las curvas de lactancia estimadas fue chequeada utilizando resultados básicos del cálculo, esto permitió clasificar las curvas estimadas en dos grupos: típicas y atípicas; solamente las curvas típicas fueron empleadas para calcular los cuatro parámetros antes mencionados. Resultados: El polinomio de segundo grado fue el modelo que se seleccionó con mayor frecuencia, mientras que el modelo Papajcsik & Bordero tuvo la menor frecuencia. Los promedios para TMY305, PMY, DP y P fueron 3.489,86 kg, 17,28 kg, 57,17 días, y 0,83, respectivamente, con coeficientes de variación 0,27, 0,21, 0,41 y 0,16 Conclusiones: Este estudio permitió identificar individuos con desempeño fenotípico sobresaliente. De acuerdo al estado del arte, este es el primer estudio de este tipo que considera miles de lactancias de vacas Gyr provenientes de varias regiones del trópico bajo colombiano.

Antecedentes: Gir é uma raça Bos indicus amplamente utilizada em sistemas de produção leiteira no trópico baixo Colombiano. A Asociación Colombiana de Criadores de Ganado CebúASOCEBU, lidera um programa de controle de leite nos últimos 10 anos, o que permitiu à coleta de um conjunto de dados para realizar a primeira análise de produção de leite em bovinos Gir no país com informações de vários rebanhos. Objetivo: Estudar a dinâmica da produção de leite por meio da estimativa das curvas de lactação e quatro parâmetros de produção derivados: produção total de leite entre 5 e 305 dias (TMY305), produção de leite no pico de lactação (PMY), dias em produção de leite no pico de lactação (DP) e persistência (P). Métodos: Foram utilizados 13.798 registros de produção diária de leite de 1.510 vacas de 103 fazendas , totalizando 2.480 lactações. Foram considerados quatro modelos: Wood, Wiltmink, Papajcsik & Bordero e um polinômio de segundo grau. Para selecionar o modelo que melhor descreve cada lactação foram utilizados os seguintes critérios: erro quadrado médio, erro absoluto médio, e erro quadrado médio de predição. O modelo selecionado pela maioria dos parâmetros, de acordo com os critérios de informação de Akaike e Bayesiano, foi o escolhido. A forma de cada curva de lactação ajustada foi verificada utilizando os resultados básicos do cálculo, isso permitiu classificar as curvas estimadas em dois grupos: típico e atípico, e apenas funções típicas foram utilizadas para calcular os quatro parâmetros de produção acima mencionados Resultados: O polinômio de segunda ordem foi o modelo mais frequentemente selecionado, enquanto o modelo Papajcsik & Bordero apresentou a menor frequência. A média de TMY305, PMY, DP e P foram 3.489,86 kg, 17,28 kg, 57,17 d e 0,83, respectivamente, com coeficientes de variação de 0,27, 0,21, 0,41 e 0,16. Conclusões: Este estudo permitiu identificar indivíduos com excelente desempenho fenotípico. De acordo com a literatura atual, este é o primeiro estudo envolvendo milhares de lactações de vacas Gir em várias regiões do trópico baixo colombiano.

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Complex simulation models are a valuable tool to inform nutrient management decisions aimed at reducing hypoxia in the northern Gulf of Mexico, yet simulated hypoxia response to reduced nutrients varies greatly between models. We compared two biogeochemical models driven by the same hydrodynamics, the Coastal Generalized Ecosystem Model (CGEM) and Gulf of Mexico Dissolved Oxygen Model (GoMDOM), to investigate how they differ in simulating hypoxia and their response to reduced nutrients. Different phytoplankton nutrient kinetics produced 2-3 times more hypoxic area and volume on the western shelf in CGEM compared to GoMDOM. Reductions in hypoxic area were greatest in the western shelf, comprising 72% (~4,200 km2) of the total shelfwide hypoxia response. The range of hypoxia responses from multiple models suggests a 60% load reduction may result in a 33% reduction in hypoxic area, leaving an annual hypoxic area of ~9,000 km2 based on the latest 5-yr average (13,928 km2).

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The study of predator-prey interactions is commonly analyzed using functional responses to gain an understanding of predation patterns and the impact they have on prey populations. Despite this, little is known about predator-prey systems with multiple prey species in sites near the equator. Here we studied the functional response of cougars (Puma concolor) in relation to their main prey, armadillo (Dasypus novemcinctus), coati (Nasua narica) and white-tailed deer (Odocoileus virginianus). Between 2004 and 2010, cougar scats were collected along 5 transects to estimate the consumption of different prey species. A relative abundance index (RAI) was calculated for each prey species and cougar using 18 camera traps. We compared Holling type I, II and III functional response models to determine patterns in prey consumption based on the relative abundance and biomass of each prey species consumed. The 3 main prey species comprised 55% (armadillo), 17% (coati) and 8% (white-tailed deer) of the diet. Type I and II functional responses described consumption of the 2 most common prey species armadillos and coati similarly well, while a type I response best characterized consumption of white-tailed deer. A negative correlation between the proportions of armadillo versus coati and white-tailed deer biomass in cougar scats suggests switching to consume alternative prey, confirming high foraging plasticity of this carnivore. This work represents one of the few studies to compare functional responses across multiple prey species, combined with evidence for prey-switching at low densities of preferred prey.

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The aim of the present study was to evaluate the prediction ability of models that cope with longevity phenotypic expression as uncensored and censored in Nellore cattle. Longevity was defined as the difference between the dates of last weaned calf and cow birth. There were information of 77 353 females, being 61 097 cows with uncensored phenotypic information and 16 256 cows with censored records. These data were analyzed considering three different models: (1) Gaussian linear model (LM), in which only uncensored records were considered; and two models that consider both uncensored and censored records: (2) Censored Gaussian linear model (CLM); and (3) Weibull frailty hazard model (WM). For the model prediction ability comparisons, the data set was randomly divided into training and validation sets, containing 80% and 20% of the records, respectively. There were considered 10 repetitions applying the following restrictions: (a) at least three animals per contemporary group in the training set; and (b) sires with more than 10 progenies with uncensored records (352 sires) should have daughters in the training and validation sets. The variance components estimated using the whole data set in each model were used as true values in the prediction of breeding values of the animals in the training set. The WM model showed the best prediction ability, providing the lowest χ 2 average and the highest number of sets in which a model had the smallest value of χ 2 statistics. The CLM and LM models showed prediction abilities 2.6% and 3.7% less efficient than WM, respectively. In addition, the accuracies of sire breeding values for LM and CLM were lower than those obtained for WM. The percentages of bulls in common, considering only 10% of sires with the highest breeding values, were around 75% and 54%, respectively, between LM-CLM and LM-WM models, considering all sires, and 75% between LM-CLM and LM-WM, when only sires with more than 10 progenies with uncensored records were taken into account. These results are indicative of reranking of animals in terms of genetic merit between LM, CLM and WM. The model in which censored records of longevity were excluded from the analysis showed the lowest prediction ability. The WM provides the best predictive performance, therefore this model would be recommended to perform genetic evaluation of longevity in this population.

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We present a new Bayesian method for estimating demographic and phylogenetic history using population genomic data. Several key innovations are introduced that allow the study of diverse models within an Isolation-with-Migration framework. The new method implements a 2-step analysis, with an initial Markov chain Monte Carlo (MCMC) phase that samples simple coalescent trees, followed by the calculation of the joint posterior density for the parameters of a demographic model. In step 1, the MCMC sampling phase, the method uses a reduced state space, consisting of coalescent trees without migration paths, and a simple importance sampling distribution without the demography of interest. Once obtained, a single sample of trees can be used in step 2 to calculate the joint posterior density for model parameters under multiple diverse demographic models, without having to repeat MCMC runs. Because migration paths are not included in the state space of the MCMC phase, but rather are handled by analytic integration in step 2 of the analysis, the method is scalable to a large number of loci with excellent MCMC mixing properties. With an implementation of the new method in the computer program MIST, we demonstrate the method's accuracy, scalability, and other advantages using simulated data and DNA sequences of two common chimpanzee subspecies: Pan troglodytes (P. t.) troglodytes and P. t. verus.

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BACKGROUND: Brazil has the second highest annual number of new leprosy cases. The aim of this study is to formally compare predictions of future new case detection rate (NCDR) trends and the annual probability of NCDR falling below 10/100,000 of four different modelling approaches in four states of Brazil: Rio Grande do Norte, Amazonas, Ceará, Tocantins. METHODS: A linear mixed model, a back-calculation approach, a deterministic compartmental model and an individual-based model were used. All models were fitted to leprosy data obtained from the Brazilian national database (SINAN). First, models were fitted to the data up to 2011, and predictions were made for NCDR for 2012-2014. Second, data up to 2014 were considered and forecasts of NCDR were generated for each year from 2015 to 2040. The resulting distributions of NCDR and the probability of NCDR being below 10/100,000 of the population for each year were then compared between approaches. RESULTS: Each model performed well in model fitting and the short-term forecasting of future NCDR. Long-term forecasting of NCDR and the probability of NCDR falling below 10/100,000 differed between models. All agree that the trend of NCDR will continue to decrease in all states until 2040. Reaching a NCDR of less than 10/100,000 by 2020 was only likely in Rio Grande do Norte. Prediction until 2040 showed that the target was also achieved in Amazonas, while in Ceará and Tocantins the NCDR most likely remain (far) above 10/100,000. CONCLUSIONS: All models agree that, while incidence is likely to decline, achieving a NCDR below 10/100,000 by 2020 is unlikely in some states. Long-term prediction showed a downward trend with more variation between models, but highlights the need for further control measures to reduce the incidence of new infections if leprosy is to be eliminated.

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BACKGROUND: Mathematical models can help aid public health responses to Chagas disease. Models are typically developed to fulfill a particular need, and comparing outputs from different models addressing the same question can help identify the strengths and weaknesses of the models in answering particular questions, such as those for achieving the 2020 goals for Chagas disease. METHODS: Using two separately developed models (PHICOR/CIDMA model and Princeton model), we simulated dynamics for domestic transmission of Trypanosoma cruzi (T. cruzi). We compared how well the models targeted the last 9 years and last 19 years of the 1968-1998 historical seroprevalence data from Venezuela. RESULTS: Both models were able to generate the T. cruzi seroprevalence for the next time period within reason to the historical data. The PHICOR/CIDMA model estimates of the total population seroprevalence more closely followed the trends seen in the historic data, while the Princeton model estimates of the age-specific seroprevalence more closely followed historic trends when simulating over 9 years. Additionally, results from both models overestimated T. cruzi seroprevalence among younger age groups, while underestimating the seroprevalence of T. cruzi in older age groups. CONCLUSION: The PHICOR/CIDMA and Princeton models differ in level of detail and included features, yet both were able to generate the historical changes in T. cruzi seroprevalence in Venezuela over 9 and 19-year time periods. Our model comparison has demonstrated that different model structures can be useful in evaluating disease transmission dynamics and intervention strategies.

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Four models for in vitro embryo production traits in Guzerá cattle were compared: Gaussian (untransformed variable - LIN and transformed in logarithmic scale - LOG), Poisson (POI) and zero-inflated Poisson (ZIP). Data consisted of 5716 ovum pick-up and in vitro fertilization records performed in 1205 cows from distinct regions of Brazil. Analyzed count traits were the number of viable oocytes (NOV), number of grade I oocytes (NGI), number of degenerated oocytes (NDG), number of cleaved embryos (NCLV) and number of viable produced embryos (NEMB). Heritability varied from 0.17 (LIN) to 0.25 (POI) for NOV; 0.08 (LOG) to 0.18 (ZIP) for NGI; 0.12 (LIN) to 0.20 (POI) for NDG; 0.13 (LIN) to 0.19 (POI) for NCLV; 0.10 (LIN) to 0.20 (POI) for NEMB depending on the considered model. The estimated repeatability varied from 0.53 (LOG) to 0.63 (POI) for NOV; 0.22 (LOG) to 0.39 (ZIP) for NGI; 0.29 (LIN) to 0.42 (ZIP) for NDG; 0.42 (LIN) to 0.59 (POI) for NCLV; 0.36 (LIN) to 0.51 (POI) for NEMB. The goodness of fit, measured by deviance information criterion and mean squared residuals, suggested superiority of POI and ZIP over Gaussian models. Estimated breeding values (EBV) obtained by different models were highly correlated, varying from 0.92 for NOV (between LIN-POI) and 0.99 for NGI (between POI-ZIP). The number of coincident animals on the 10% top EBV showed lower similarities. We recommend POI and ZIP models as the most adequate for genetic analysis of in vitro embryo production traits in Guzerá cattle.

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Estimating daily solar radiation (Rs) provides an important alternative in situations where it cannot be measured by conventional pyranometers. This study used meteorological data from nine cities in the north of the Minas Gerais state, Brazil, for the period from 2008 to 2010 with the aim of evaluate the accuracy and applicability of some simple models to help regions where Rs is impossible to be measured. Five models were evaluated for their estimates of Rs based on simple available data. For each city studied, the equations were previously calibrated. Meteorologically based empirical models to estimate daily global solar radiation are an appropriate tool if the parameters can be calibrated for different locations. These models have the advantage of using meteorological data, which are commonly available. Based on the overall results, we conclude that the accuracy of estimation by available meteorological data is acceptable and comparable with the accuracy of classical models. Considering the greater availability of air temperature data and application in studies that do not require great accuracy in estimating Rs, all models were adequate for use. The accuracy of Rs was only slightly improved by adding rainfall records as input variable. Therefore, in the region studied, the choice of simpler models, having as input the daily maximum and minimum air temperature would not imply large error in the estimates. For most sites, Bristow and Campbell model had the best estimate of Rs with a RMSE of 2.69 MJ m-2 and R2= 0.69, with the possibility to calibrate with available temperature data, becoming a practical and reliable model. Hargraves model should be avoided due to its lower performance compared to the other models applied.

A estimativa da radiação solar diária (Rs) fornece uma alternativa importante em situações que não pode ser medida por piranômetros convencionais. O estudo utilizou dados meteorológicos de nove cidades do Norte do estado de Minas Gerais, Brasil, durante o período de 2008 a 2010, com o objetivo de mensurar a precisão e aplicabilidade de modelos empíricos simples nas regiões onde a Rs não pode ser medida . Cinco modelos foram avaliados para estimar Rs com base nos dados meteorológicos disponíveis. As equações foram previamente calibradas para cada município estudado. Modelos meteorológicos empíricos que estimam a radiação solar diária são ferramentas adequadas desde que os parâmetros sejam calibrados para os diferentes locais a serem utilizados. Estes modelos têm a vantagem de utilizar dados meteorológicos, que estão comumente disponíveis. Todos os modelos foram considerados adequados para o uso, considerando-se a maior disponibilidade de dados de temperatura do ar e aplicação em estudos que não exigem grande precisão na estimativa da Rs. A precisão da Rs apenas foi melhorada pela adição de registros de precipitação como variável de entrada. Assim, na região estudada, a escolha de um modelo mais simples, tendo como entrada a temperatura mínima e máxima do ar diária, não implica um grande erro na estimativa. Para a maioria das regiões, o modelo de Bristow e Campbell teve a melhor estimativa da Rs com um RMSE de 2.69 MJ m-2 e R2= 0.69, e a possibilidade de calibração com os dados de temperatura disponíveis, tornando-se um modelo prático e confiável. O modelo de Hargraves deve ser evitado devido seu pior desempenho comparado aos outros modelos propostos.

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