Beef cattle responses to pre-grazing sward height and low level of energy supplementation on tropical pastures

A comparison of average daily gain, apparent digestibilities, energy balance, rumen fermentation parameters, and serum metabolites between yaks (Bos grunniens) and Qaidam cattle (Bos taurus) consuming diets differing in energy level

Yaks (Bos grunniens), indigenous to the harsh Qinghai-Tibetan Plateau, are well adapted to the severe conditions, and graze natural pasture without supplements all year round. Qaidam cattle (Bos taurus), introduced to the Qinghai-Tibetan Plateau 1,700 years ago, are raised at a lower altitude than yaks, provided with shelter at night and offered supplements in winter. Based on their different backgrounds, we hypothesized that yaks have lower energy requirements for maintenance than cattle. To test this hypothesis, we measured average daily gain (ADG), apparent digestibilities, energy balance, rumen fermentation parameters, and serum metabolites in growing yaks and cattle offered diets differing in metabolizable energy (ME) levels (6.62, 8.02, 9.42 and 10.80 MJ/kg), but with the same crude protein concentration. Six castrated yaks (155 ± 5.8 kg) and 6 castrated Qaidam cattle (154 ± 8.0 kg), all 2.5 years old, were used in 2 concurrent 4 × 4 Latin square designs. Neutral and acid detergent fiber digestibilities were greater (P < 0.05) in yaks than in cattle, and decreased linearly (P < 0.05) with increasing dietary energy level; whereas, digestibilities of dry matter, organic matter, crude protein and ether extract increased (P < 0.05) linearly with increasing energy level. The ADG was greater (P < 0.001) in yaks than in cattle, and increased (P < 0.05) linearly with increasing energy levels. From the regressions of ADG on ME intake, the estimated ME requirement for maintenance was lower (P < 0.05) in yaks than in cattle (0.43 vs. 0.57 MJ/kg BW^0.75). The ratios of digestible energy (DE):gross energy and ME:DE were higher (P < 0.05) in yaks than in cattle, and increased (P < 0.05) linearly with increasing dietary energy level. Ruminal pH decreased (P < 0.05), whereas concentrations of total volatile fatty acids (VFAs) and ammonia increased (P < 0.01) with increasing dietary energy level, and all were greater (P < 0.05) in yaks than in cattle. Concentrations of ruminal acetate and iso-VFAs were greater (P < 0.05), whereas propionate was lower (P < 0.05) in yaks than in cattle; acetate decreased (P < 0.001), whereas butyrate and propionate increased (P < 0.001) linearly with increasing dietary energy level. Serum concentrations of β-hydroxybutyrate were lower (interaction, P < 0.001) in yaks than in cattle fed diets of 9.42 and 10.80 MJ/kg, whereas non-esterified fatty acids were greater (interaction, P < 0.01) in yaks than in cattle fed diets of 6.62 and 8.02 MJ/kg. Concentrations of serum leptin and growth hormone were greater in yaks than in cattle and serum insulin and growth hormone increased (P < 0.01) linearly with increasing dietary energy level. Our hypothesis that yaks have lower energy requirements for maintenance than cattle was supported. This lower requirement confers an advantage to yaks over Qaidam cattle in consuming low energy diets during the long winter on the Qinghai-Tibetan Plateau.

Effects of Replacing Cottonseed Meal with Corn Dried Distillers' Grain on Ruminal Parameters, Performance, and Enteric Methane Emissions in Young Nellore Bulls Reared in Tropical Pastures

Two experiments were conducted to evaluate the effect of replacing cottonseed meal with DDG on ruminal parameters, methane (CH4) emissions (Experiment 1), and animal performance (Experiment 2) of young Nellore bulls grazing Marandu grass during the rainy season. Four supplementation strategies were used in both experiments: (1) Mineral supplementation (MS); (2) conventional multiple supplement (energy/protein) with cottonseed meal and citrus pulp (CMS); (3) CMS with 50% cottonseed meal replaced by DDG (50DDG); and (4) CMS with 100% cottonseed meal replaced by DDG (100DDG). The 50DDG condition resulted in greater intake of dry matter (p = 0.033), organic matter (OM) (p = 0.050), forage (p = 0.035), and digestible OM (p = 0.031) than 100DDG. The supplemented animals presented greater final body weight (BW) and average daily gain than the animals consuming MS (p = 0.011), and lower pH, acetate, and acetate:propionate (p < 0.05). However, the treatments had no influence on stocking rate, gain per area, and enteric CH4 emissions (p > 0.05). Replacing cottonseed meal with DDG does not result in great variations in ruminal parameters, animal performance, and enteric CH4 emissions of grazing Nellore cattle during the rearing phase in the wet season. Both protein sources in 0.3% BW supplementation can be used to intensify beef cattle production in pastures.

Disentangling data dependency using cross-validation strategies to evaluate prediction quality of cattle grazing activities using machine learning algorithms and wearable sensor data

Wearable sensors have been explored as an alternative for real-time monitoring of cattle feeding behavior in grazing systems. To evaluate the performance of predictive models such as machine learning (ML) techniques, data cross-validation (CV) approaches are often employed. However, due to data dependencies and confounding effects, poorly performed validation strategies may significantly inflate the prediction quality. In this context, our objective was to evaluate the effect of different CV strategies on the prediction of grazing activities in cattle using wearable sensor (accelerometer) data and ML algorithms. Six Nellore bulls (average live weight of 345 ± 21 kg) had their behavior visually classified as grazing or not-grazing for a period of 15 days. Elastic Net Generalized Linear Model (GLM), Random Forest (RF), and Artificial Neural Network (ANN) were employed to predict grazing activity (grazing or not-grazing) using 3-axis accelerometer data. For each analytical method, three CV strategies were evaluated: holdout, leave-one-animal-out (LOAO), and leave-one-day-out (LODO). Algorithms were trained using similar dataset sizes (holdout: n = 57,862; LOAO: n = 56,786; LODO: n = 56,672). Overall, GLM delivered the worst prediction accuracy (53%) compared to the ML techniques (65% for both RF and ANN), and ANN performed slightly better than RF for LOAO (73%) and LODO (64%) across CV strategies. The holdout yielded the highest nominal accuracy values for all three ML approaches (GLM: 59%, RF: 76%, and ANN: 74%), followed by LODO (GLM: 49%, RF: 61%, and ANN: 63%) and LOAO (GLM: 52%, RF: 57%, and ANN: 57%). With a larger dataset (i.e., more animals and grazing management scenarios), it is expected that accuracy could be increased. Most importantly, the greater prediction accuracy observed for holdout CV may simply indicate a lack of data independence and the presence of carry-over effects from animals and grazing management. Our results suggest that generalizing predictive models to unknown (not used for training) animals or grazing management may incur poor prediction quality. The results highlight the need for using management knowledge to define the validation strategy that is closer to the real-life situation, i.e., the intended application of the predictive model.