Extrusion treated canola meal (TCM) was produced in an attempt to increase the rumen-undegraded protein fraction of canola meal (CM). The objective of this study was to evaluate the effects of replacing soybean meal (SBM) with CM or TCM on ruminal digestion, omasal nutrient flow, and performance in lactating dairy cows. To assess performance, 30 multiparous Holstein cows averaging (mean ± SD) 119 ± 23 d in milk and 44 ± 7 kg of milk/d and 15 primiparous cows averaging 121 ± 19 d in milk and 34 ± 6 kg of milk/d were blocked in a randomized complete block design with a 2-wk covariate period and 12-wk experimental period (experiment 1). Dietary ingredients differed only in protein supplements, which were SBM, CM, or TCM. All diets were formulated to contain (dry matter basis) 30% alfalfa silage, 30% corn silage, 4% soy hulls, 2.4% mineral-vitamin premix, and 16% CP. The SBM diet contained 25% high-moisture shelled corn and 8.6% SBM; the canola diets contained 22% high-moisture shelled corn and either 11.2% CM or 11.4% TCM. To assess ruminal digestion and omasal nutrient flow, 6 rumen-cannulated cows were blocked into 2 squares of 3 cows and randomly assigned within blocks to the same 3 dietary treatments as in experiment 1 in a replicated 3 × 3 Latin square design (experiment 2). Data were analyzed using the MIXED procedure of SAS (SAS Institute, Cary, NC). Orthogonal contrasts were used to compare effects of different protein supplements: SBM versus CM + TCM and CM versus TCM. In experiment 1, compared with SBM, apparent total-tract digestibilities of dry matter and nutrients were greater in cows fed both CM diets, and there was a tendency for nutrient digestibilities to be higher in cows fed CM compared with TCM. Diets did not affect milk yield and milk components; however, both canola diets decreased urinary urea N (% of total urinary N), fecal N (% of total N intake), and milk urea N concentration. In experiment 2, compared with SBM, both canola diets increased N intake and tended to increase rumen-degraded protein supply (kg/d) and N truly digested in the rumen (kg/d). Diets did not affect ruminal digestibility, efficiency of microbial protein synthesis, and rumen-undegraded protein flow among diets. Results from this experiment indicate that replacing SBM with CM or TCM in diets of lactating cows improved digestibility and may reduce environmental impact. Moreover, under the conditions of the present study, treating CM by extrusion did not improve CM utilization.

The objectives of this study were to determine if midlactation dairy cows (Bos taurus L.) grazing intensively managed elephantgrass would have their protein requirement met exclusively with the pasture and an energy concentrate, making the use of protein ingredients unnecessary, as well as to determine the dietary crude protein (CP) content that would optimize the efficiency of N utilization (ENU). Thirty-three Holstein and crossbred (Holstein × Jersey) midlactation dairy cows, producing approximately 20 kg/d, were grouped within breed into 11 blocks according to milk yield and days in milk. Within blocks, cows were randomly assigned to 1 of 3 treatments and remained in the study for 11 wk. The control treatment contained only finely ground corn, minerals, and vitamins, and it was formulated to be 8.7% CP. Two higher levels of CP (formulated to be 13.4 and 18.1%) were achieved by replacing corn with solvent-extracted soybean meal (SSBM). Pasture was fertilized with 50 kg of N/ha after each grazing cycle and averaged 18.5% CP (dry matter basis). No differences were observed in milk yield or milk fat, protein, and casein content or casein yield. In addition, pasture intake was not different among treatments. Milk urea N increased linearly as the concentrate CP content increased. Cows fed the 8.7% CP concentrate had higher ENU. In another experiment, 4 ruminally cannulated Holstein dry cows were used in a metabolism trial designed in a 4×4 Latin square. Cows were fed the same treatments described as well as a fourth treatment with 13.4% CP in the concentrate, in which urea replaced SSBM as the main N source. Ruminal volatile fatty acid concentration and microbial synthesis were not affected by levels or sources of N in the concentrate. Ruminal NH(3)N content increased as the concentrate CP content increased. Inclusion of SSBM in the concentrate did not increase production and decreased the ENU of midlactation dairy cows grazing on tropical forage. Supplementation of an 8.7% CP concentrate, resulting in a diet with CP levels between 15.3 and 15.7% of dry matter, was sufficient to meet the protein requirements of such milk production, with the highest ENU (18.4%).

The objective of this study was to study how changing the ratio of Lys to Thr, Lys to His, and Lys to Val affects the expression of lipogenic genes and microRNA (miRNA) in bovine mammary epithelial cells. Triplicate cultures with the respective “optimal” amino acid (AA) ratio (OPAA = Lys:Met 2.9:1; Thr:Phe 1.05:1; Lys:Thr 1.8:1; Lys:His 2.38:1; Lys:Val 1.23:1) plus rapamycin (OPAARMC; positive control), OPAA, Lys:Thr 2.1:1 (LT2.1), Lys:Thr 1.3:1 (LT1.3), Lys:His 3.05:1 (LH3.0), or Lys:Val 1.62:1 (LV1.6) were incubated in lactogenic medium for 12 h. The expression of 15 lipogenic genes and 7 miRNA were evaluated. Responses to LT2.1, LT1.3, LH3.0, and LV1.6 relative to the control (OPAARMC) included up-regulated expression of ACSS2, FABP3, ACACA, FASN, SCD, LPIN1, INSIG1, SREBF1, PPARD, and NR1H3 (commonly known as LXR-α). Furthermore, LV1.6 up-regulated expression of ACSL1, DGAT1, and RXRA and down-regulated PPARG expression. Although no effect of OPAA on expression of PPARG was observed, compared with the control, OPAA up-regulated expression of the PPAR targets ACSS2, FABP3, ACACA, FASN, SCD, LPIN1, INSIG1, and SREBF1. Compared with the control, the expression of the anti-lipogenic MIR27AB was down-regulated by OPAA, LT2.1, LT1.3 and LH3.0. In contrast, compared with the control, the expression of the pro-lipogenic MIR21 was up-regulated by LT2.1, LT1.3, LH3.0, and LV1.6. The observed up-regulation of lipogenic gene networks and the changes in expression of key miRNA involved in the control of lipogenic balance are indicative of a potentially important role of EAA ratios and mTOR signaling in the regulation of milk fat synthesis.

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 d. 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 with 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.