Effects of maternal nutrition regimen of ewes on performance, carcass, and meat traits of their feedlot-finished lambs.

The objective of this study was to evaluate the effects of diets with two energy levels fed to Ile de France ewes during the last third of gestation on the performance, carcass, and meat traits of their offspring. Treatments were: D0: maternal diet meeting the requirements for the last third of gestation, and D20: maternal diet containing an additional 20% energy requirements. Twenty single-born male lambs, ten from each group of ewes, were weaned at 60 d (18.3 ± 1.4 kg initial BW) and fed a common finishing diet. Animals were slaughtered when they reached 32 kg BW. Dry matter intake, average daily gain, feed conversion, and days on feed were unaffected by treatments (P≥0.09). No effects were observed on hot and cold carcass weights, dressing percentage, chilling loss, commercial cuts yields, and loin-eye area (P≥0.17). Meat pH, thawing loss, cooking loss, shear force, and water holding capacity were also not affected by treatments (P≥0.09). Temperature and meat color, as well as centesimal composition were similar between treatments (P≥0.27). Adding 20% energy on top of the requirements of Ile de France ewes during the last third of gestation does not influence the performance, carcass traits, nor meat traits of their offspring.

Strategy to Predict High and Low Frequency Behaviors Using Triaxial Accelerometers in Grazing of Beef Cattle.

Knowledge of animal behavior can be indicative of the well-being, health, productivity, and reproduction of animals. The use of accelerometers to classify and predict animal behavior can be a tool for continuous animal monitoring. Therefore, the aim of this study was to provide strategies for predicting more and less frequent beef cattle grazing behaviors. The behavior activities observed were grazing, ruminating, idle, water consumption frequency (WCF), feeding (supplementation) and walking. Three Machine Learning algorithms: Random Forest (RF), Support Vector Machine (SVM) and Naïve Bayes Classifier (NBC) and two resample methods: under and over-sampling, were tested. Overall accuracy was higher for RF models trained with the over-sampled dataset. The greatest sensitivity (0.808) for the less frequent behavior (WCF) was observed in the RF algorithm trained with the under-sampled data. The SVM models only performed efficiently when classifying the most frequent behavior (idle). The greatest predictor in the NBC algorithm was for ruminating behavior, with the over-sampled training dataset. The results showed that the behaviors of the studied animals were classified with high accuracy and specificity when the RF algorithm trained with the resampling methods was used. Resampling training datasets is a strategy to be considered, especially when less frequent behaviors are of interest.

Effect of nitrogen application rate on yield, forage quality, and animal performance in a tropical pasture.

A three-year-long field experiment was conducted in a continuous grazing system with a variable stocking rate to evaluate effects of increasing nitrogen levels in Marandu grass (Brachiaria brizantha Hochst ex A. Rich Stapf "marandu") on herbage mass, forage accumulation rate (FAR), forage quality, stocking rate (SR), average daily gain (ADG), gain per hectare (GPH), and gain per kg of applied N. The experimental design was completely randomized with four treatments (control without application of N, and 90, 180, and 270 kg N ha-1 year-1) and three replicates (paddocks per treatment); nitrogen was applied in the form of urea. Herbage mass, crude protein (CP), FAR, SR, GPH, and the nitrogen nutrition index increased with increasing nitrogen level (P < 0.05), whereas the neutral detergent fibre (NDF), acid detergent fibre, and nitrogen usage efficiency decreased with increasing nitrogen level (P < 0.01). Crude protein was higher than 12% and NDF lower than 60% in all treatments. Nitrogen application rate affected ADG (P < 0.05) but did not fit any equation. The highest ADG was 90 kg N ha-1 year-1 (985 g animal-1 day-1). Increasing the nitrogen level is a promising way to improve Marandu grass production, nutritive value, and animal production.