Search

The objective of this study was to investigate the effect of waste milk with antibiotic residue on rumen fermentation and rumen bacterial composition of dairy calves during pre-weaned and post-weaned periods. A total of 24 Holstein male calves (43.4 ± 0.93 kg body weight, mean ± standard error) were allocated into four blocks based on birth date. Dairy calves were supplied 100% milk replacer (MR, n = 8), 50% milk replacer mixed with 50% waste milk (MM, n = 8), or 100% waste milk (WM, n = 8). Ruminal samples were collected at 49 and 63 days of age and then subjected to determinations of pH value, volatile fatty acids (VFA), ammonia nitrogen (NH3–N) and 16S rRNA gene amplicon sequencing. The results showed that feeding WM had no effect on the pH value, the concentrations of VFA (acetic acid, propionic acid, butyric acid, isovaleric acid, valeric acid), and NH3–N in dairy calves compared to feeding MR. However, from 49 to 63 days of age, the pH value (p p = 0.004), acetic acid (p = 0.01), propionic acid (p = 0.003) and valeric acid (p Veillonellaceae was significantly lower (p = 0.05) in the calves fed WM than that from MR group at 49 days of age; however, no difference was detected at 63 days of age. Feeding WM to calves tended to reduce family Veillonellaceae and genus Olsenella in the rumen at 49 days of age (p = 0.049). Analysis of temporal changes in rumen bacteria based on alpha-diversity and beta-diversity as well as the microbial relative abundances did not exhibit any difference. In addition, relative abundances of Clostridia_UCG-014, Prevotella, Syntrophococcus, Eubacterium_nodatum_group, Pseudoramibacter and Solobacterium were correlated with rumen pH value and the concentrations of TVFA, propionic acid, isovaleric acid, valeric acid and NH3–N. In conclusion, compare to MR, calves supplied with WM had little changes on the rumen pH value, NH3–N or VFAs contents. Additionally, limited effects could be found on rumen microbiota in the calves fed WM. However, further studies needed to explore if there exist any long-term effects of early-life rumen microbiota modulation on dairy cows.

Oat hay and alfalfa hay are important roughage resources in livestock production. However, the effect of the mixture of oat hay and alfalfa hay on the meat quality of Albas goats is unclear. This study aimed to investigate the effects of feeding different proportions of oat hay and alfalfa hay on the growth performance and meat quality of Albas goats. Therefore, 32 goats were fed for 70 days and randomly divided into four treatment groups on the principle of similar weight: whole oat group (OAT), oat alfalfa ratio 3:7 group (OA73), oat alfalfa ratio 7:3 group (OA37) and whole alfalfa group (Alfalfa), with eight goats in each group. Daily feed intake records, feces, feed samples, and rumen fluid collection were made throughout the trial. The goats were weighed on the last day of the trial, and four goats per group were randomly selected for slaughter. Cecum contents, meat samples, and hot carcass weight were collected, and data were recorded. Furthermore, the relationship between the rumen and cecal microbes on performance and meat quality was clarified by analyzing the rumen and hindgut microbiomes. The results showed that feeding alfalfa could significantly reduce the daily weight gain of fattening goats. Compared with the highest group (OA37), the daily weight gain decreased by 19.21%. Although there was no significant change in feed intake in the four treatments, the feed conversion rate of the alfalfa group significantly decreased by 30.24–36.47% compared to the other groups. However, with the increased alfalfa content, MDA decreased significantly, T-AOC was up-regulated, and the antioxidant activity of the fattened goat meat fed with the high alfalfa group was significantly higher than that of the low alfalfa group. Notably, the abundance of Bacteroidales_unclassified and Clostridium were strongly correlated with T-AOC and MDA. Therefore, increasing the proportion of alfalfa in the diet can affect the antioxidant activity of goat meat by improving the gut microbiota, while an oat-hay mixture can improve the growth performance of livestock.

Volatile fatty acids (VFAs) and methane are the main products of rumen fermentation. Quantitative studies of rumen fermentation parameters can be performed using in vitro techniques and machine learning methods. The currently proposed models suffer from poor generalization ability due to the small number of samples. In this study, a prediction model for rumen fermentation parameters (methane, acetic acid (AA), and propionic acid (PA)) of dairy cows is established using the stacking ensemble learning method and in vitro techniques. Four factors related to the nutrient level of total mixed rations (TMRs) are selected as inputs to the model: neutral detergent fiber (NDF), acid detergent fiber (ADF), crude protein (CP), and dry matter (DM). The comparison of the prediction results of the stacking model and base learners shows that the stacking ensemble learning method has better prediction results for rumen methane (coefficient of determination (R2) = 0.928, root mean square error (RMSE) = 0.968 mL/g), AA (R2 = 0.888, RMSE = 1.975 mmol/L) and PA (R2 = 0.924, RMSE = 0.74 mmol/L). And the stacking model simulates the variation of methane and VFAs in relation to the dietary fiber content. To demonstrate the robustness of the model in the case of small samples, an independent validation experiment was conducted. The stacking model successfully simulated the transition of rumen fermentation type and the change of methane content under different concentrate-to-forage (C:F) ratios of TMR. These results suggest that the rumen fermentation parameter prediction model can be used as a decision-making basis for the optimization of dairy cow diet compositions, rapid screening of methane emission reduction, feed beneficial to dairy cow health, and improvement of feed utilization.

A correct quantification of mass and energy exchange processes among land surface and atmosphere requires an accurate description of unsaturated soil hydraulic properties. Soil pedotransfer functions (PTFs) have been widely used to predict soil hydraulic parameters. Here, 13 PTFs were grouped according to input data requirements and evaluated against a well-documented soil database with global coverage. Weighted ensembles (calibrated by four groups and the full 13-member set of PTFs) were shown to have improved performance over individual PTFs in terms of root mean square error and other model selection criteria. Global maps of soil water retention data from the ensemble models as well as their uncertainty were provided. These maps demonstrate that five PTF ensembles tend to have different estimates, especially in middle and high latitudes in the Northern Hemisphere. Our full 13-member ensemble model provides more accurate estimates than PTFs that are currently being used in earth system models.

Body condition score (BCS) is a common tool for indirectly estimating the mobilization of energy reserves in the fat and muscle of cattle that meets the requirements of animal welfare and precision livestock farming for the effective monitoring of individual animals. However, previous studies on automatic BCS systems have used manual scoring for data collection, and traditional image extraction methods have limited model performance accuracy. In addition, the radio frequency identification device system commonly used in ranching has the disadvantages of misreadings and damage to bovine bodies. Therefore, the aim of this research was to develop and validate an automatic system for identifying individuals and assessing BCS using a deep learning framework. This work developed a linear regression model of BCS using ultrasound backfat thickness to determine BCS for training sets and tested a system based on convolutional neural networks with 3 channels, including depth, gray, and phase congruency, to analyze the back images of 686 cows. After we performed an analysis of image model performance, online verification was used to evaluate the accuracy and precision of the system. The results showed that the selected linear regression model had a high coefficient of determination value (0.976), and the correlation coefficient between manual BCS and ultrasonic BCS was 0.94. Although the overall accuracy of the BCS estimations was high (0.45, 0.77, and 0.98 within 0, 0.25, and 0.5 unit, respectively), the validation for actual BCS ranging from 3.25 to 3.5 was weak (the F1 scores were only 0.6 and 0.57, respectively, within the 0.25-unit range). Overall, individual identification and BCS assessment performed well in the online measurement, with accuracies of 0.937 and 0.409, respectively. A system for individual identification and BCS assessment was developed, and a convolutional neural network using depth, gray, and phase congruency channels to interpret image features exhibited advantages for monitoring thin cows.