Modern livestock farming under tropical conditions using sensors in grazing systems.

The aim of this study was to evaluate a commercial sensor-a three-axis accelerometer-to predict animal behavior with a variety of conditions in tropical grazing systems. The sensor was positioned on the underjaw of young bulls to detect the animals' movements. A total of 22 animals were monitored in a grazing system, during both seasons (wet and dry), with different quality and quantity forage allowance. The machine learning (ML) methods used were random forest (RF), convolutional neural net and linear discriminant analysis; the metrics used to determine the best method were accuracy, Kappa coefficient, and a confusion matrix. After predicting animal behavior using the best ML method, a forecast for animal performance was developed using a mechanistic model: multiple linear regression to correlate intermediate average daily gain (iADG) observed versus iADG predicted. The best ML method yielded accuracy of 0.821 and Kappa coefficient of 0.704, was RF. From the forecast for animal performance, the Pearson correlation was 0.795 and the mean square error was 0.062. Hence, the commercial Ovi-bovi sensor, which is a three-axis accelerometer, can act as a powerful tool for predicting animal behavior in beef cattle production developed under a variety tropical grazing condition.

Genome-Wide Association Study Provides Insights into Important Genes for Reproductive Traits in Nelore Cattle.

The identification of genomic regions associated with reproductive traits as well as their biological processes allows a better understanding of the phenotypic variability of these traits. This information could be applied to animal breeding programs to accelerate genetic gain. The aim of this study was to evaluate the association between single nucleotide polymorphisms (SNP) with a scrotal circumference at 365 days of age (SC365) and at 450 days of age (SC450), gestation length (GL) as a calf trait, age at first calving (AFC), accumulated productivity (ACP), heifer early calving until 30 months (HC30), and stayability (STAY) traits, in order to identify candidate genes and biological pathways associated with reproductive traits in Nelore cattle. The data set consisted of pedigree, phenotypes, and genotypes of Nelore cattle from the "Associação Nacional de Criadores e Pesquisadores" (ANCP). The association analyses were performed using the Weighted Single-Step Genome-Wide Association method; the regions, consisting of 10 consecutive SNP, which explained more than 0.5% of additive genetic variance, were considered as a significant association. A total of 3, 6, 7, 5, 10, 25, and 12 windows were associated with SC355, SC450, GL, AFC, ACP, HC30, and STAY, respectively. The results revealed genes with important functions for reproductive traits, such as fertility and precocity. Some genes were associated with more than one trait, among them CAMK1D, TASP1, ACOXL, RAB11FIP5, and SFXN5. Moreover, the genes were enriched in functional terms, like negative regulation of fat cell differentiation, fatty acid alpha-oxidation, and sphingolipids signaling pathway. The identification of the genes associated with the traits, as well as genes enriched in the terms and pathway mentioned above, should contribute to future biological validation studies and may be used as candidate genes in Nelore breeding programs.

Evaluation of imputation accuracy using the combination of two high-density panels in Nelore beef cattle.

This study compared imputation from lower-density commercial and customized panels to high-density panels and a combined panel (Illumina and Affymetrix) in Nelore beef cattle. Additionally, linkage disequilibrium and haplotype block conformation were estimated in individual high-density panels and compared with corresponding values in the combined panel after imputation. Overall, 814 animals were genotyped using BovineHD BeadChip (IllumHD), and 93 of these animals were also genotyped using the Axion Genome-Wide BOS 1 Array Plate (AffyHD). In general, customization considering linkage disequilibrium and minor allele frequency had the highest accuracies. The IllumHD panel had higher values of linkage disequilibrium for short distances between SNPs than AffyHD and the combined panel. The combined panel had an increased number of small haplotype blocks. The use of a combined panel is recommended due to its increased density and number of haplotype blocks, which in turn increase the probability of a marker being close to a quantitative trait locus of interest. Considering common SNPs between IllumHD and AffyHD for the customization of a low-density panel increases the imputation accuracy for IllumHD, AffyHD and the combined panel.