Investigating potential causal relationships among carcass and meat quality traits using structural equation model in Nellore cattle.

The objective of this study was to investigate potential causal relationships among hot carcass weight (HCW), longissimus muscle area (LMA), backfat thickness (BF), Warner-Bratzler shear force (WBSF), and marbling score (MB) traits in Nellore cattle using structural equation models (SEM). The SEM fitted comprises the following links between traits: WBSF → LMA, WBSF → HCW, HCW → LMA, BF → HCW, and BF → MB, where the arrows indicate the causal direction between traits, with structural coefficients posterior means (posterior standard deviation) equal to -0.29 cm/kg (0.

The effect of life history events on carcass merit and price of cull dairy cows.

Cull dairy cows contribute almost 10% of national beef production in the United States. However, different factors throughout the life of dairy cows affect their weight and overall body condition as well as carcass traits, and consequently affect their market price. Therefore, the objectives of this study were: (1) to assess relationships between price ratio and carcass merit of cull dairy cows sold through several sites of an auction market and (2) to investigate the effect of animal life history events and live weight on sale barn price (BP) and price ratio (as a measure of relative price), as an indicator of carcass merit.

Would large dataset sample size unveil the potential of deep neural networks for improved genome-enabled prediction of complex traits? The case for body weight in broilers.

Background: Deep neural networks (DNN) are a particular case of artificial neural networks (ANN) composed by multiple hidden layers, and have recently gained attention in genome-enabled prediction of complex traits. Yet, few studies in genome-enabled prediction have assessed the performance of DNN compared to traditional regression models. Strikingly, no clear superiority of DNN has been reported so far, and results seem highly dependent on the species and traits of application.

Forecasting beef production and quality using large-scale integrated data from Brazil.

With agriculture rapidly becoming a data-driven field, it is imperative to extract useful information from large data collections to optimize the production systems. We compared the efficacy of regression (linear regression or generalized linear regression [GLR] for continuous or categorical outcomes, respectively), random forests (RF) and multilayer neural networks (NN) to predict beef carcass weight (CW), age when finished (AS), fat deposition (FD), and carcass quality (CQ). The data analyzed contained information on over 4 million beef cattle from 5,204 farms, corresponding to 4.

Improving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks.

The goal of this study was to compare the predictive performance of artificial neural networks (ANNs) with Bayesian ridge regression, Bayesian Lasso, Bayes A, Bayes B and Bayes Cπ in estimating genomic breeding values for meat tenderness in Nellore cattle. The animals were genotyped with the Illumina Bovine HD Bead Chip (HD, 777K from 90 samples) and the GeneSeek Genomic Profiler (GGP Indicus HD, 77K from 485 samples). The quality control for the genotypes was applied on each Chip and comprised removal of SNPs located on non-autosomal chromosomes, with minor allele frequency <5%, deviation from HWE (p < 10 ), and with linkage disequilibrium >0.

Network analysis of swine movements in a multi-site pig production system in Iowa, USA.

Pig production in the United States is based on multi-site systems in which pigs are transported between farms after the conclusion of each particular production phase. Although ground transportation is a critical component of the pork supply chain, it might constitute a potential route of infectious disease dissemination. Here, we used a time series network analysis to: (1) describe pig movement flow in a multi-site production system in Iowa, USA, (2) conduct percolation analysis to investigate network robustness to interventions for diseases with different transmissibility, and (3) assess the potential impact of each farm type on disease dissemination across the system.

Generalized additive mixed model on the analysis of total transport losses of market-weight pigs1.

Transportation losses of market-weight pigs are an animal welfare concern, and result in direct economic impact for producers and abattoirs. Such losses are related to multiple factors including pig genetics, human handling, management, and weather conditions. Understanding the factors associated with total transport losses (TTL) is important to the swine industry because it can aid decision-making, and help in the development of transportation strategies to minimize the risk of losses.

Genetic analysis of resistance to ticks, gastrointestinal nematodes and Eimeria spp. in Nellore cattle.

The aim of the present study was to obtain genetic parameters for resistance to ticks, gastrointestinal nematodes (worms) and Eimeria spp. in Nellore cattle, analyze the inclusion of resistance traits in Nellore breeding programs and evaluate genetic selection as a complementary tool in parasite control programs. Counting of ticks, gastrointestinal nematode eggs and Eimeria spp.