Avaliação biométrica e nutrientes das culturas de milho, milheto e sorgo

The objective of this work was to evaluate the biometric measurements and nutrient contents of the corn, sorghum, and pearl millet crops from 30 days after sowing up to ensiling time. The experiment was conducted in a randomized complete block design, in which the three crops were evaluated with eight replicates. Stem height and diameter and leaf length and width were measured to determine plant growth. In addition, samples were collected to evaluate plant chemical composition. For the characterization of nutrient accumulation and biometric evaluation, linear and nonlinear models were used. Dry matter accumulation did not differ between corn and sorghum, but decreased in pearl millet from the fiftieth day up to ensiling. Crude protein, ashes, and neutral detergent fiber tend to reduce over time. The biometric variables do not differ between corn, pearl millet, and sorghum from 30 days after sowing until ensiling time.O objetivo deste trabalho foi avaliar as medidas biométricas e o conteúdo de nutrientes das culturas de milho, sorgo e milheto desde 30 dias após a semeadura até o momento da ensilagem. O experimento foi conduzido em delineamento de blocos ao acaso, tendo-se avaliado as três culturas, com oito repetições. A altura e o diâmetro do caule e a largura e o comprimento das folhas foram medidos para determinar o crescimento das plantas. Além disso, foram coletadas amostras para avaliar a composição química das plantas. Para a caracterização do acúmulo de nutrientes e a avaliação biométrica, foram utilizados modelos lineares e não lineares. O acúmulo de matéria seca não diferiu entre o milho e o sorgo, mas diminuiu no milheto do quinquagésimo dia até a ensilagem. Proteína bruta, cinzas e fibras em detergente neutro tendem a diminuir com o tempo. As variáveis biométricas não diferem entre o milho, o milheto e o sorgo desde 30 dias após a semeadura até o momento da ensilagem

Alternative measures to evaluate the accuracy and bias of genomic predictions with censored records

This study aimed to propose and compare metrics of accuracy and bias of genomic prediction of breeding values for traits with censored data. Genotypic and censored-phenotypic information were simulated for four traits with QTL heritability and polygenic heritability, respectively: C1: 0.07-0.07, C2: 0.07-0.00, C3: 0.27-0.27, and C4: 0.27-0.00. Genomic breeding values were predicted using the Mixed Cox and Truncated Normal models. The accuracy of the models was estimated based on the Pearson (PC), maximal (MC), and Pearson correlation for censored data (PCC) while the genomic bias was calculated via simple linear regression (SLR) and Tobit (TB). MC and PCC were statistically superior to PC for the trait C3 with 10 and 40% censored information, for 70% censorship, PCC yielded better results than MC and PC. For the other traits, the proposed measures were superior or statistically equal to the PC. The coefficients associated with the marginal effects (TB) presented estimates close to those obtained for the SLR method, while the coefficient related to the latent variable showed almost unchanged pattern with the increase in censorship in most cases. From a statistical point of view, the use of methodologies for censored data should be prioritized, even for low censoring percentages

Avaliação de predições genômicas utilizando redes neurais com regularização Bayesiana

Recently there is an increase interest to use nonparametric methods, such as artificial neural networks (ANN). In animal breeding, an especial class of ANN called Bayesian Regularized Neural Network (BRNN) has been preferable since it not demands a priori knowledge of the genetic architecture of the characteristic as assumed by the most used parametric methods (RR-BLUP, Bayes A, B, Cπ, BLASSO). Although BRNN has been shown to be effective for genomic enable prediction. The aim of the present study was to apply the ANN based on Bayesian regularization to genome-enable prediction regarding simulated data sets, to select the most relevant SNP markers by using two proposed methods, to estimate heritabilities for the considered traits, and to compare the results with two traditional methods (RR-BLUP and BLASSO). The simplest Bayesian Regularized Neural Network (BRNN) model gave consistent predictions for both traits, which were similar to the results obtained from the traditional RR-BLUP and BLASSO methods. The SNP importance identification methods based on BRNN proposed here showed correlation values (0.61 and 0.81 for traits 1 and 2, respectively) between true and estimated marker effects higher than the traditional BLASSO (0.55 and 0.71, respectively for traits 1 and 2) method. With respect to h 2 estimates (assuming 0.35 as true value), the simplest BRNN recovered 0.33 for both traits, thus outperforming the RR-BLUP and BLASSO, that, in average, estimated h 2 equal to 0.215.Recentemente, há um aumento de interesse na utilização de métodos não paramétricos, tais como redes neurais artificiais (RNA), na área de seleção genômica ampla (SGA). Uma classe especial de RNA é aquela com regularização Bayesiana, a qual não exige um conhecimento a priori da arquitetura genética da característica, tais como outros métodos tradicionais de SGA (RR-BLUP, Bayes A, B, Cπ, BLASSO). O objetivo do presente estudo foi aplicar a RNA baseado em regularização Bayesiana na predição de valores genéticos genômicos utilizando conjuntos de dados simulados a fim de selecionar os marcadores SNP mais relevantes por meio de dois métodos diferentes. Objetivou-se ainda estimar herdabilidades para as características consideradas e comparar os resultados da RNA com dois métodos tradicionais (RR-BLUP e Lasso Bayesiano). A arquitetura mais simples da rede neural com regularização Bayesiana obteve os melhores resultados para as duas características avaliadas, os quais foram muito similares às metodologias tradicionais RR-BLUP e Lasso Bayesiano (BLASSO). A identificação de importância dos SNPs baseada nas RNA apresentaram correlações entre os efeitos verdadeiros e simulados de 0,61 e 0,81 para as características 1 e 2, respectivamente. Estas foram maiores do que aquelas produzidas pelo método tradicional BLASSO (0,55 e 0,71, para característica 1 e 2 respectivamente). Em relação a herdabilidade (assumindo o valor verdadeiro igual a 0,35), a RNA mais simples obteve valor de herdabilidade igual a 0,33, enquanto os métodos tradicionais a subestimaram (com média igual igual a 0,215).Coordenação de Aperfeiçoamento de Pessoal de Nível Superio

Nutritive value of Tanzania grass for dairy cows under rotational grazing

A nutritional analysis of Tanzania grass (Megathyrsus maximus Jacquin cv. Tanzânia was conducted. Pasture was managed in a rotational grazing system with a 30-day resting period, three days of paddock occupation and two grazing cycles. Ten Holstein × Zebu crossbred cows were kept within a 2-ha area divided into 11 paddocks ha-1. Cows were fed 2 kg of corn meal daily and performance was evaluated by weighing the animals every 14 days and by recording milk production twice a day. Nutritional composition of the Tanzania grass was determined from forage (extrusa) samples collected by esophageal fistulae from two animals. The nutritive value of Tanzania grass was estimated according to a modification of the CNCPS evaluation model. Tanzania grass supplemented with 2 kg of corn meal supplied 33.2% more net energy for lactation than required by the animals to produce 13.7 kg of milk day-1. Nevertheless, the amount of metabolizable protein met the daily protein requirement of the animals. Although the model used in the study requires adjustments, Tanzania grass has the potential to produce milk in a rotational grazing system

Polymorphism in the BIEC2-808543 locus and its association with growth curve in Brasileiro de Hipismo horse breed

Our objective was to evaluate whether the single nucleotide polymorphism (SNP) BIEC2-808543, identified in some horse breeds, also occurs in the Brasileiro de Hipismo (BH) breed. In addition, we verified if this SNP is related to the growth curve profile of these animals for the variables body mass, height at withers, and height at croup, using nonlinear mixed models. For the DNA isolation, we collected blood samples from 167 young BH horses. We obtained the genotypes of these animals using the polymerase chain reaction-restriction fragment length polymorphism technique. For the association studies of this polymorphism with the growth curve in foals, we selected three traits: body mass, height at withers, and height at croup. Polymorphism C/T exists in BH horses and is significantly associated with the evaluated traits. Animals that presented the TT genotype were smaller and lighter when compared with animals of the CT and CC genotypes. By the Akaike information criterion, the model that best described the growth curve for the body mass variable is the Brody model associated with the power of the mean variance function. For the height at withers variable, the best-fit model was von Bertalanffy, adjusted without polymorphism effect in parameter b, associated with the asymptotic variance. For the height at croup trait, the model that best described the growth curve was Brody model, associated with asymptotic variance. This polymorphism represents a good molecular marker. Nonlinear models are promising for describing growth curves in horses, particularly by the possibility of associating SNP effects to model parameter

Bayesian Models combining Legendre and B-spline polynomials for genetic analysis of multiple lactations in Gyr cattle

We aimed with this study to combine Legendre polynomials (LEG) and linear B-splines (BSP) to describe simultaneously the first and second lactation of Gyr dairy cattle under a multiple-trait random regression models (MTRRM) framework. Additionally we proposed the application of self-organizing map to define the classes of residual variances under these models. A total of 26,438 and 23,892 milk yield test-day records were used, respectively, for the first and second lactations of 3253 Gyr cows. Two preliminary MTRRM analyses considering 10 residual classes were performed: the first one was based on LEG for systematic and random effects for both lactations; and the second one was based on BSP. Three classes were defined by using a self-organizing map: from 6 to 35; 36–185 and 186–305 days in milk. After definition of residual variance classes, a total of 16 MTRRM combining LEG and BSP were compared. The MTRRM based on BSP to describe the systematic effects of the first and second lactation, BSP to describe the random effects of the first lactation and LEG to describe the random effects of the second lactation (BSP-BSP-BSP-LEG) outperformed all other models. From the BSP-BSP-BSP-LEG model, heritability estimates for milk yield over time ranged from 0.1107 to 0.2902, and from 0.2036 to 0.3967, for the first and second lactation, respectively. In general, additive genetic correlation estimates between days in milk within each lactation and between lactations had medium magnitude (mean of genetic correlations were 0.6630, 0.6226 and 0.4749 for the first, second and between both lactations, respectively). We concluded that combining different functions under a MTRRM framework is a feasible alternative for genetic modeling of lactation curves in Gyr dairy cattle

Genetic evaluation of lactation persistency and total milk yield in dairy goats

Lactation persistency (LP) has been neglected over time in genetic evaluations of dairy goats. The main reason for this is the difficulty to infer about the lactation curve shape. However, some lactations models such as Wood seem to be appropriate to provide persistency estimates under biological viewpoints. The aim of this study was to fit the Wood lactation model as well as to calculate and evaluate LP as selection criteria in dairy goat breeding programs through genetic parameters estimates. A total of 23,265 first lactation test day milk yield observations from 900 animals were used. The Wood random regression model was primarily fitted to estimate the lactation curve parameters (a, b and c), and then LP and total milk yield (TMY). Posteriorly, a multi-trait animal model was fitted considering simultaneously LP and TMY. The heritability estimates were 0.31 and 0.04 for TMY and LP, respectively. Based on the low LP heritability, selection based only on this trait might be inefficient. In conclusion, the results of this study suggests that selecting for high milk yields might result in high persistency since the genetic correlation between LP and TMY was moderate (0.39)

Performance and digestibility of steers fed by-product of fresh passion fruit or sorghum silage, with and without concentrate supplementation

ABSTRACTThe objective of this study was to evaluate the nutritive value of passion fruit by-product for cattle, contrasting the results with those found with sorghum silage. Four treatments were then constituted, comprising the combinations of the two roughages and the two levels of supplementation (with or without), in a completely randomized design with four animals per treatment. The considered variables included: feed intake, digestibility coefficients of the diets, and live weight gain of the animals. The experimental period lasted 70 days, preceded by a standardization period of 30 days. Chromium oxide was utilized to estimate the fecal output, in the digestibility trial. Treatments were compared by means of three orthogonal contrasts: between the two roughages and between the two concentrate levels within each roughage. Animals fed passion fruit by-product showed higher feed intake (total, per 100 kg of live weight (TLW), and per unit metabolic size) and had higher TLW gain than those fed sorghum silage (1.304 kg vs. 0.134 kg). The coefficients of apparent digestibility of dry matter (DM), organic matter (OM), and crude protein (CP) and the digestibility coefficient of neutral detergent fiber from passion fruit by-product were high, and much higher than those from sorghum silage. The concentrate supplement did not improve the TLW gain of animals fed passion fruit by-product and had a limiting effect on the digestibility coefficients of the diet. The concentrate supplement had a positive associative effect on intake and digestibility coefficients of DM, OM, and CP from sorghum silage. The by-product of fresh passion fruit is an excellent food for growing cattle as it provides high intake levels and weight gains, even when supplied as the only feed