Effects of dietary nitrogen levels and carbohydrate sources on apparent ruminal synthesis of some B vitamins in dairy cows.

Effects of nitrogen level and carbohydrate source on apparent ruminal synthesis (ARS) of thiamin, riboflavin, niacin, vitamin B6, folates, and vitamin B12 were evaluated using 4 lactating Holstein cows distributed in a 4 × 4 Latin square design with treatments following a 2 × 2 factorial arrangement. Cows were fitted with cannulas in the rumen and proximal duodenum. The treatments were 2 N levels and 2 carbohydrate sources. The diet with the high N level provided 14% crude protein, calculated to meet 110% of the protein requirements and an adequate supply in rumen-degradable protein, whereas the diet with the low N level contained 11% crude protein, calculated to meet 80% of the protein requirements with a shortage in rumen-degradable protein. Carbohydrate source treatments differed by their nature (i.e., high in starch from barley, corn, and wheat, or high in fiber from soybean hulls and dehydrated beet pulp). All 4 diets were isoenergetic, based on corn silage, and had the same forage-to-concentrate ratio (60:40, dry matter basis). Duodenal flow was determined using YbCl3 as a marker. Each B-vitamin ARS was calculated as duodenal flow minus daily intake. The intake of several B vitamins varied among treatments, but because the animals consumed a similar amount of feed every day (average of 20 kg of dry matter/d) the difference was mostly due to vitamin content of each ingredient and their relative proportion in the diets. Decreasing N concentration in the diet reduced vitamin B6 duodenal flow and increased its apparent ruminal degradation. It also decreased duodenal flow and ARS of folates. The high-starch diets increased duodenal flow and ruminal balance of riboflavin, vitamin B6, and folates, whereas the high-fiber diets increased vitamin B12 ARS and duodenal flow. These effects on apparent synthesis are possibly due to changes in ruminal fermentation.

Relationship between efficiency of nitrogen utilization and isotopic nitrogen fractionation in dairy cows: contribution of digestion v. metabolism?

Animal tissues are naturally 15N enriched relative to their diet and the extent of this difference (Δ15Nanimal-diet) has been correlated to the efficiency of N assimilation in different species. The rationale is that transamination and deamination enzymes, involved in amino acid metabolism are likely to preferentially convert amino groups containing 14N over 15N. However, in ruminants the contribution of rumen bacterial metabolism relative to animal tissues metabolism to naturally enrich animal proteins in terms of 15N has been not assessed yet. The objective of this study was to assess the impact of rumen and digestion processes on the relationship between Δ15Nanimal-diet and efficiency of N utilization for milk protein yield (milk N efficiency (MNE); milk N yield/N intake) as well as the relationship between the 15N natural abundance of rumen bacteria and the efficiency of N use at the rumen level. Solid- and liquid-associated rumen bacteria, duodenal digesta, feces and plasma proteins were obtained (n=16) from four lactating Holstein cows fed four different diets formulated at two metabolizable protein supplies (80% v. 110% of protein requirements) crossed by two different dietary energy source (diets rich in starch v. fiber). We measured the isotopic N fractionation between animal and diet (Δ15Nanimal-diet) in these different body pools. The Δ15Nanimal-diet was negatively correlated with MNE when measured in solid-associated rumen bacteria, duodenal digesta, feces and plasma proteins, with the strongest correlation found for the latter. However, our results showed a very weak 15N enrichment of duodenal digesta (Δ15Nduodenal digesta-diet mean value=0.42) compared with that observed in plasma proteins (Δ15Nplasma protein-diet mean value=2.41). These data support the idea that most of the isotopic N fractionation observed in ruminant proteins (Δ15Nplasma protein-diet) has a metabolic origin with very little direct impact of the overall digestion process on the existing relationship between Δ15Nplasma protein-diet and MNE. The 15N natural abundance of rumen bacteria was not related to either rumen N efficiency (microbial N/available N) or digestive N efficiency (metabolizable protein supply/CP intake), but showing a modest positive correlation with rumen ammonia concentration. When using diets not exceeding recommended protein levels, the contribution of rumen bacteria and digestion to the isotopic N fractionation between animal proteins and diet is low. In our conditions, most of the isotopic N fractionation (Δ15Nplasma protein-diet) could have a metabolic origin, but more studies are warranted to confirm this point with different diets and approaches.

Effects of nitrogen underfeeding and energy source on nitrogen ruminal metabolism, digestion, and nitrogen partitioning in dairy cows.

This work aimed to investigate the effects of 2 levels of N (low or high) and 2 energy sources (starch or fiber) on N partitioning, N ruminal metabolism, and digestion in dairy cows. Four Holstein cows were used in a 4 × 4 Latin square design. The 4 cows (on average, 662 ± 62 kg and at 71 ± 10 d in milk at the beginning of the experiment) were fitted with rumen, proximal duodenum, and terminal ileum cannula. The cows received 4 diets having the same forage proportion on a DM basis. The high level of N supply met 110% of the protein requirements of cows with an adequate supply in rumen-degradable N. The low level covered 80% of these requirements with a shortage in rumen-degradable N. Energy sources differed by their nature (i.e., starch from barley, corn, and wheat or fiber from soybean hulls and dehydrated beet pulp). Duodenal digesta flow was determined using YbCl3 as a marker. Microbial duodenal N flow was determined using purine and pyrimidine bases as markers from liquid-associated bacteria and mixed bacteria samples. Microbial N flow and efficiency of microbial protein synthesis, calculated using mixed bacteria as a reference microbial sample, were not significantly modified by the N level (P = 0.19 and 0.29, respectively) and the energy source of the diet (P = 0.11 and 0.08, respectively). Total tract apparent digestibility of OM and total tact digestibility of NDF were lower at the low N level (P = 0.006 and 0.007, respectively). Total tract apparent digestibility of OM tended to be greater (P = 0.08) with high-starch diets than with high-fiber diets. Total tact digestibility of NDF was greater (P < 0.001) with high-fiber diets than with high-starch diets. Duodenal N flow was less (P = 0.001) at the low N level than high N level and tended to be greater (P = 0.06) with high-starch diets than with high-fiber diets. Daily output of N in urine was less (P < 0.001) at the low N level than at the high N level. Daily output of N in feces did not differ between low and high N levels (P = 0.24) and between high-starch and high-fiber diets (P = 0.17). Milk yield and protein yield were less (P = 0.002 and P = 0.013, respectively) at the low N level than at the high N level. Milk fat yield tended to be less (P = 0.09) at the low N level than at the high N level and with high-starch than with high-fiber diets (P = 0.06). In conclusion, a large reduction in dietary N led to reduced N excretion in urine and decreased milk production but did not affect N excretion in feces or microbial protein synthesis.

Comparison of intake and digestibility of fresh Digitaria decumbens grass fed to sheep, indoors or at pasture, at two different stages of regrowth.

The effect of two feeding systems (indoors and at pasture) on intake and digestion of fresh grass was studied at two stages of regrowth (21 and 35 days of regrowth) in two parallel experiments. In Experiment 1, 10 adult Martinik rams weighing, on average, 50.5 (± 0.9) kg, including four fitted with rumen cannula, were randomly allocated to two groups according to a 2 × 2 Latin Square design. These rams consumed a 21-day regrowth of Digitaria decumbens grass diet during two successive 28-day periods, indoors (five rams) or at pasture (five tethered rams). In Experiment 2, 10 other Martinik rams weighing, on average, 45.5 (± 0.9) kg, including four fitted with rumen cannula, were randomly allocated to two groups according to a 2 × 2 Latin Square design. These rams consumed a 35-day regrowth of D. decumbens grass diet during two successive 28-day periods, either indoors (five rams) or at pasture (five tethered rams). For the indoors groups, in vivo organic matter digestibility (OMD) was measured by total collection of feces. In addition, OMD was estimated indoors and at pasture using the fecal CP (CPf) method (OMDCPf). Organic matter intake (OMI) was then estimated using OMDCPf and fecal organic matter output (OMICPf). Correlations of 0.49 and 0.77 were found between in vivo OMD and OMDCPf (P < 0.05) and between OMI and OMICPf (P < 0.001), respectively. OMDCPf was 1.8% (P < 0.05) and 2.7% (P < 0.01) lower indoors than at pasture at 21 and 35 days of regrowth, respectively, whereas OMICPf indoors was 1.1 and 1.16 times that registered at pasture at 21 and 35 days of regrowth, respectively. The higher OMDCPf at pasture was linked to the higher selective behavior of rams at pasture, whereas the differences in OMICPf between the two feeding systems were linked to differences in the total bulk density of the grass. These studies show that differences in OMDCPf and OMICPf exist between animals fed indoors and at pasture with the same forage and that these differences may vary according to the stage of regrowth of the grass offered.

Comparison of fecal crude protein and fecal near-infrared reflectance spectroscopy to predict digestibility of fresh grass consumed by sheep.

Organic matter digestibility (OMD), an essential criterion for the evaluation of the nutrition of ruminants, cannot be measured easily at pasture. Therefore, the objective of this study was to test and compare 2 methods of OMD prediction based on the fecal CP content (CPf) or near infrared reflectance spectroscopy (NIRS) applied to feces. First, published equations derived from fecal N (Eq. 1(CP), n = 40) and from fecal NIRS (Eq. 1(NIRS), n = 84) were used to predict OMD of an independent validation data set from which in vivo OMD, ranging from 58 to 74%, was measured for 4 regrowth stages of Digitaria decumbens. Second, to establish equations usable in grazing situations and to improve the efficiency of the predictions, new equations were calculated from a large data set (n = 174) using CPf (Eq. 2(CP)) or fecal NIRS (Eq. 2(NIRS)). By applying the CPf method, Eq. 2(CPf) (OMD, % = 88.4 - 263.9/CPf, % of OM; residual SD = 2.92, r(2) = 0.63) showed similar statistical parameters (P < 0.01) when compared with Eq. 1(CP) (OMD, % = 86.6 - 266.2/CPf, % of OM; residual SD = 2.95, r(2) = 0.79). When using fecal NIRS, Eq. 2(NIRS) showed decreased SE of calibration (SEC = 1.48) and of cross-validation (SECV = 1.75) and greater coefficient of determination of cross-validation (R(2)(CV) = 0.85) than the previously published Eq. 1(NIRS) (SEC = 1.78, SECV = 2.02, R(2)(CV) = 0.77). The validation of the 4 equations on the validation data set was satisfactory overall with an average difference between the predicted and the observed OMD ranging from 0.98 to 2.79 percentage units. The Eq. 2(NIRS) was nevertheless the most precise with a decreased residual SD of 2.53 and also the most accurate, because the SD of the average difference between predicted and observed OMD was the lowest. Therefore, fecal NIRS provided the most reliable estimates of OMD and is thus a useful tool to predict OMD at pasture. However, an adequate number of reference data are required to establish good calibration. Indeed, better calibration statistics were obtained by increasing the data set from 84 (Eq. 1(NIRS)) to 174 (Eq. 2(NIRS)). In contrast, using fecal N on a set of 84 or 174 points did not improve the prediction. Both methods are useful for predicting OMD at pasture in certain circumstances, using fecal NIRS when a large data set (n = 84 and n = 174) is available and fecal CP with smaller data sets (n = 40).