Neutral detergent fiber concentration of corn silage and rumen inert bulk influences dry matter intake and ruminal digesta kinetics of growing steers

Dietary Neutral Detergent Fiber Levels Impacting Dairy Cows' Feeding Behavior, Rumen Fermentation, and Production Performance during the Period of Peak-Lactation

This study investigated the impact of dietary neutral detergent fiber (NDF) levels (25.49%, 28.65%, 31.66%, and 34.65%, respectively) on the feeding behavior, rumen fermentation, cellulolytic bacteria, and production performance of dairy cows during peak lactation. A feeding experiment was conducted using four fistulated Holstein dairy cows (600 ± 25 kg) with days in milk (50 ± 15 days), employing a 4 × 4 Latin square design to assign the cows to four groups. The results demonstrated that increasing NDF levels in the diet had the following effects: (1) A linear decrease in dry matter intake (DMI), NDF intake, and physically effective NDF8.0 (peNDF8.0) intake; a linear increase in the average time spent eating and ruminating, as well as the time spent eating and ruminating per kilogram of dry matter (DM); a quadratic response in the time spent ruminating per kilogram of NDF and peNDF8.0. (2) A linear increase in average pH value, acetate concentration, and the proportions of Fibrobacter succinogenes and Ruminococcus flavefaciens among total bacteria; a linear decrease in ammonia nitrogen (NH3-N) concentration, microbial crude protein (MCP), total volatile fatty acid (TVFA), propionate, butyrate, and lactate. (3) A linear decrease in milk yield, milk protein percentage, and nitrogen efficiency of dairy cows; a linear increase in milk fat percentage and milk urea nitrogen (MUN) concentration. Based on the combined results, it was found that diets with 25% and 34% NDF had detrimental effects on the feeding behavior, rumen fermentation, and production performance of dairy cows. However, the diet with 28% NDF showed superior outcomes in production performance compared to the one with 31% NDF. Therefore, it is strongly recommended to include a diet containing 28% NDF during the critical peak lactation period for dairy cows.

The use of artificial neural networks for modelling rumen fill

Artificial neural network (ANN) and random forest models for predicting rumen fill of cattle and sheep were developed. Data on rumen fill were collected from studies that reported body weights, measured rumen fill, and stated diets fed to animals. Animal and feed factors that affected rumen fill were identified from each study and used to create a dataset. These factors were used as input variables for predicting the weight of rumen fill. For ANN modelling, a three-layer Levenberg–Marquardt back-propagation neural network was adopted and achieved 96% accuracy in prediction of the weight of rumen fill. The precision of the ANN model’s prediction of rumen fill was higher for cattle (80%) than sheep (56%). On validation, the ANN model achieved 95% accuracy in prediction of the weight of rumen fill. A random forest model was trained using a binary tree-based machine-learning algorithm and achieved 87% accuracy in prediction of rumen fill. The random forest model achieved 16% (cattle) and 57% (sheep) accuracy in validation of the prediction of rumen fill. In conclusion, the ANN model gave better predictions of rumen fill compared with the random forest model and should be used in predicting rumen fill of cattle and sheep.

Effect of dietary forage sources on rumen microbiota, rumen fermentation and biogenic amines in dairy cows

BACKGROUND

Fifteen lactating Holstein dairy cows were assigned to three diets in a 3 × 3 Latin square design to evaluate the effects of dietary forage sources on rumen microbiota, rumen fermentation and biogenic amines. Diets were isonitrogenous and isocaloric, with a forage/concentrate ratio of 45:55 (dry matter basis) but different main forage sources, namely cornstalk (CS), Leymus chinensis (LC) or alfalfa hay (AH).

RESULTS

Pyrosequencing of the V3-V6 hypervariable coding region of 16S rRNA revealed that the rumen microbiota was significantly affected by forage sources. AH feeding increased the proportion of genera Prevotella and Selenomonas compared with the CS diet, while CS feeding increased the proportion of genera Anaerotruncus, Papillibacter, Thermoactimoyces, Bacillus and Streptomyces compared with the LC or AH diet. AH and LC feeding both increased the propionate concentration compared with the CS diet. AH feeding decreased the concentrations of tyramine, putrescine and histamine compared with the LC diet.

CONCLUSION

These results indicate that a high proportion of alfalfa hay in the ration is beneficial for milk yield and a healthy and balanced rumen microbiota in lactating cattle. This can be attributed to the higher degradation of rumen organic matter and the more balanced carbohydrates and proteins for optimal rumen microbial growth.

Supplementation of dried distillers grains with solubles to beef cows consuming low-quality forage during late gestation and early lactation

Three experiments were conducted to evaluate supplementation of dried distillers grains with solubles (DGS) to spring-calving beef cows (n = 120; 541 kg of initial BW; 5.1 initial BCS) consuming low-quality forage during late gestation and early lactation. Supplemental treatments included (DM basis) 1) 0.77 kg/d DGS (DGSL); 2) 1.54 kg/d DGS (DGSI); 3) 2.31 kg/d DGS (DGSH); 4) 1.54 kg/d of a blend of 49% wheat middlings and 51% cottonseed meal (POS); and 5) 0.23 kg/d of a cottonseed hull-based pellet (NEG). Feeding rate and CP intake were similar for DGSI and POS. In Exp. 1, cows were individually fed 3 d/wk until calving and 4 d/wk during lactation; total supplementation period was 119 d, encompassing 106 d of gestation and 13 d of lactation. Tall-grass prairie hay (5.6% CP, 50% TDN, 73% NDF; DM basis) was fed for ad libitum intake throughout the supplementation period. Change in cow BW and BCS during gestation was similar for DGSI and POS (-5.0 kg, P = 0.61 and -0.13, P = 0.25, respectively) and linearly increased with increasing DGS level (P < 0.01). Likewise, during the 119-d supplementation period, BW and BCS change were similar for DGSI and POS (-72 kg, P = 0.22 and -0.60, P = 0.10) and increased linearly with respect to increasing DGS (P < 0.01). The percentage of cows exhibiting luteal activity at the beginning of breeding season (56%, P = 0.31), AI conception rate (57%, P = 0.62), or pregnancy rate at weaning (88%, P = 0.74) were not influenced by supplementation. In Exp. 2, 30 cows from a separate herd were used to evaluate the effect of DGS on hay intake and digestion. Supplementation improved all digestibility measures compared with NEG. Hay intake was not influenced by DGS (P > 0.10); digestibility of NDF, ADF, CP, and fat linearly increased with increasing DGS. In Exp. 3, milk production and composition were determined for cows (n = 16/treatment) of similar days postpartum from Exp. 1. Daily milk production was not influenced by supplementation (6.3 kg/d, P = 0.25). Milk fat (2.1%) and lactose (5.0%) were not different (P > 0.10). Milk protein linearly increased as DGS increased (P < 0.05) and was greater for DGSI compared with POS. Similar cow performance was achieved when cows were fed DGS at the same rate and level of CP as a traditional cottonseed meal-based supplement. Increasing amounts of DGS did not negatively influence forage intake or diet digestibility.