Invited Review: Limitations to current mineral requirement systems for cattle and potential improvements

The mineral requirements or recommendations generated by various NASEM committees are used by many ration formulation programs. The current NASEM dairy requirement system uses the factorial approach (requirements for maintenance, lactation, gestation, and growth) for most minerals but when data or equations were not available to estimate factorial requirements the committee used available data to estimate adequate intake values. The current beef NASEM uses the factorial method for Ca and P and recommendations for the other minerals. The factorial method works well for Ca and P because adequate data are available to estimate absorption coefficients (AC) and maintenance requirements. In addition, feeding Ca and P above requirements have few if any positive effects. For many other minerals the factorial method is problematic. Estimating both the maintenance requirement and AC can be extremely difficult and inaccuracies in those values have a major impact on accuracy of total dietary requirements. Some minerals have positive effects on health, production and reproduction when fed above factorially determined requirements. For those minerals response models rather than or in addition to requirement models are more appropriate. The AC is in the denominator of the factorial equation and converts absorbed requirements into dietary requirements. The AC for trace minerals is small, often <0.1, and small changes in a low AC can have substantial effects on dietary requirements. Although accurate AC are essential for the factorial method to work, woefully few data are available on the true absorption of trace minerals. Because of antagonism to absorption (e.g., negative effect of S on absorption of Cu, Mn, Se, and Zn) equations will be needed to estimate AC under different dietary conditions but current data is inaccurate to generate equations. The systems currently used will almost always prevent clinical mineral deficiencies, but because of uncertainties, most nutritionists formulate diets to exceed and often far exceed established recommendations. This leads to increased costs, potential antagonism, and increased manure excretion of environmentally important minerals. More accurate systems for estimating mineral requirements will optimize animal performance and health while keeping costs in check and reduce environmental damage.

Relative availability of five inorganic magnesium sources in non-pregnant non-lactating Holstein cows

Inorganic sources of Mg are commonly used in dairy cow diets, but their availability varies significantly. This study assessed the relative availability of 4 commonly used inorganic Mg sources and a novel alkalinizing proprietary mineral blend [PMB; Multesium (GLC Minerals, LLC, Green Bay, WI, USA)]. The study was a duplicated 6 × 6 Latin square, with 12 nonlactating, non-pregnant Holstein dairy cows assigned to a square based on BW and parity. Cows were fed 90% of their voluntary DMI (diet contained 0.21% Mg). Each experimental period lasted 7 d. On d 2 of each period, urinary catheters were fitted. Total urine collection started on d 3 for 48 h with samples collected and measured every 12 h. On d 4, 30 g of Mg were administered as boluses with gelatin capsules: negative control (one empty capsule), magnesium oxide (MgO), magnesium sulfate (MgSO4), calcium magnesium hydroxide [CaMg(OH)4], calcium magnesium carbonate [CaMg(CO3)2], and PMB [a blend of Ca and Mg sources that includes CaMg(CO3)2, CaMg(OH)4, and MgO]. Blood samples were collected at 0, 1, 2, 3, 12, and 24 h after treatment administration on d 4 of each treatment period. Urine and blood samples were analyzed for Mg and Ca concentration. Statistical analyses were conducted with PROC GLIMMIX including treatment, time, period, square, treatment × time, treatment × period, and time × period as fixed effects, and cow nested within square as a random effect in the model. Urinary Mg excretion for 4 of the Mg sources studied [PMB, MgO, CaMg(OH)4, and MgSO4] increased significantly, representing an increase of at least 40.8% relative to control. The supplementation of CaMg(CO3)2 did not significantly increase relative to control. There were no significant changes in blood Mg concentration with treatment; but, a significant treatment × time effect was observed. Calcium-rich sources [PMB, CaMg(OH)4, CaMg(CO3)2] had lower blood Mg concentrations at 12 or 24 h after treatment than control and CaMg(CO3)2. Based on urinary Mg excretion 24 h after treatment, 4 of the Mg sources evaluated (including PMB) showed a similar availability, however, the availability of the commercial CaMg(CO3)2 source included in our study was similar to the negative control (no-supplemented cows).

Urinary and fecal potassium excretion prediction in dairy cattle: A meta-analytic approach

Quantification of potassium (K) excretion in dairy cattle is important to understand the environmental impact of dairy farming. To improve and monitor the environmental impact of dairy cows, there is a need for a simple, inexpensive, and less laborious method to quantify K excretion on dairy farms. The adoption of empirical mathematical models has been shown to be a promising tool to address this issue. Thus, the current study aimed to develop empirical predictive models for K excretion in dairy cattle from urine and feces that can help evaluate efficiency and monitor the environmental impact of milk production. To develop urine K (KUr, g/d) and fecal K (KFa, g/d) excretion prediction models, published literature that involved 45 and 54 treatment means from 10 and 14 studies, respectively, were used. Some studies reported either urinary or fecal K excretion or both, but in total, treatment means used to develop the models were from 17 studies. The linear mixed models were fitted with the fixed effect of K intake, DMI, dietary K content, urine volume, milk yield, and water intake, and the random effect of study weighted according to the number of observations. Leave-one-study out cross-validation was used to evaluate the performance of the proposed models and the best model was based on the lowest root mean square prediction error as a percentage of the observed mean values (RMSPE%) and highest concordance correlation coefficient (CCC). As expected, most daily K excretion was through urine (202.5 ± 92.1 g/d) than through feces (43.5 ± 21.0 g/d), and among the proposed models, the model including dietary K concentration showed poor predictive ability for both KUr and KFa with the lowest CCC values (-0.15 and -0.02, respectively) and systematic bias. The model developed using DMI to predict KFa excretion showed reasonable accuracy, as indicated by RMSPE, CCC, and R2 marginal of 46.6%, 0.42, and 48%, respectively. Among the proposed models for KUr and KFa, the model with K intake demonstrated better predictive performance, showing minimal systematic bias and random errors due to data variability of >92%. While these proposed models suggested that reducing K intake can lead to a decrease in K excretion, it is important to ensure that dairy cows receive adequate amounts of this nutrient to maintain optimal health and productivity, especially during periods of heat stress.

Effects of cashew nut shell extract and monensin on in vitro ruminal fermentation, methane production, and ruminal bacterial community

The objective of this study was to evaluate the effects of cashew nut shell extract (CNSE) and monensin on ruminal in vitro fermentation, CH4 production, and ruminal bacterial community structure. Treatments were as follows: control (CON, basal diet without additives); 2.5 μM monensin (MON); 0.1 mg CNSE granule/g DM (CNSE100); and 0.2 mg CNSE granule/g DM (CNSE200). Each treatment was incubated with 52 mL of buffered ruminal content and 500 mg of total mixed ration for 24 h using serum vials. The experiment was performed as a complete randomized block design with 3 runs. Run was used as a blocking factor. Each treatment had 5 replicates, in which 2 were used to determine nutrient degradability, and 3 were used to determine pH, NH3-N, volatile fatty acids, lactate, total gas, CH4 production, and bacterial community composition. Treatment responses for all data, excluding bacterial abundance, were analyzed with the GLIMMIX procedure of SAS v9.4. Treatment responses for bacterial community structure were analyzed with a PERMANOVA test run with the R package vegan. Orthogonal contrasts were used to test the effects of (1) additive inclusion (ADD: CON vs. MON, CNSE100, and CNSE200); (2) additive type (MCN: MON vs. CNSE100 and CNSE200); and (3) CNSE dose (DOS: CNSE100 vs. CNSE200). We observed that pH, acetate, and acetate:propionate ratio in the CNSE100 treatment were lower compared with CNSE200, and propionate in the CNSE100 treatment was greater compared with CNSE200. Compared with MON, CNSE treatments tended to decrease total lactate concentration. Total gas production of CON was greater by 2.63% compared with all treatments, and total CH4 production was reduced by 10.64% in both CNSE treatments compared with MON. Also, compared with MON, in vitro dry matter degradabilities in CNSE treatments were lower. No effects were observed for NH3-N or in vitro neutral detergent fiber degradability. Finally, the relative abundances of Prevotella, Treponema, and Schwartzia were lower, whereas the relative abundances of Butyrivibrio and Succinivibrio were greater in all treatments compared with CON. Overall, the inclusion of CNSE decreased CH4 production compared with MON, making CNSE a possible CH4 mitigation additive in dairy cattle diets.

Milk as an indicator of dietary imbalance

BACKGROUND

Milk provides a readily available diagnostic fluid collected daily or more frequently on an individual animal or herd basis. Milk, as an aggregated sample in bulk tank milk (BTM) represents the status of a herd instead of a single animal. In this review, we examine the potential for milk to predict risks to efficient production, reproductive success, and health on the individual cow and herd level.

FINDINGS

For many conditions related to disorders of metabolism including hyperlipdaemia and ketonaemia, improved individual cow milk testing may allow a temporally useful detection of metabolic disorder that can target intervention. However, the extension of these tests to the BTM is made more difficult by the tight temporal clustering of disorder to early lactation and the consequent mixing of cows at even moderately different stages of lactation. Integrating herd recording demographic information with Fourier-transformed mid-infrared spectra (FT-MIR) can provide tests that are useful to identify cows with metabolic disorders. The interpretation of BTM urea and protein content provides useful indications of herd nutrition. These may provide indicators that encourage further investigations of nutritional influences on herd fertility but are unlikely to provide strong diagnostic value. The fat-to-protein ratio has a high specificity, but poor sensitivity for detection of fibre insufficiency and acidosis on an individual cow basis. Selenium, zinc, β-carotene, and vitamin E status of the herd can be determined using BTM.

CONCLUSIONS

There appears to be increasing potential for the use of milk as a diagnostic fluid as more in-parlour tests become available for individual cows. However, the BTM appears to have under-utilised potential for herd monitoring.

Magnesium in dairy cattle nutrition: A meta-analysis on magnesium absorption in dairy cattle and assessment of simple solubility tests to predict magnesium availability from supplemental sources

Supplemental Mg sources differ in bioavailability, and solubility is one of the determining factors. We explored whether and which in vitro solubility tests could reliably differentiate the quality of supplemental Mg sources. In experiment 1, we compared 3 chemical methods using an acetic acid solution (50 mL/L, termed vinegar test), a 1 M ammonium nitrate solution, and an artificial rumen buffer fluid without rumen microbiota. The Mg solubility results suggested the vinegar test was the best method due to its robustness, simplicity, and reproducibility. In experiment 2, we validated the reliability of the vinegar test using 4 MgO sources from experiment 1 and 12 new MgO sources plus a laboratory-grade MgO as a standard. Accordingly, we repeated the vinegar test with short (0.5 h) and long (3.0 h) incubation times on these sources and then conducted ruminal incubations in 24-h batch culture experiments. The repeated vinegar test resulted in similar results as in experiment 1. Linear regression across both experiments showed the soluble Mg content (g/kg) = 44.46 (±2.55) × pH - 142.9 (±14.9), root mean square error (RMSE) = 10.2, P slope <0.001, and concordance correlation coefficient (CCC) = 0.953. The predictable pH range was from 4 to 6. The equation cannot be applied to low-alkaline sources such as Mg sulfate, Mg acetate, or a group of MgO with exceptionally high alkaline properties showing a cluster of pH above 8.5. Solubility of the MgO sources in the vinegar test ranged from 5 to 35%, whereas the 24-h ruminal incubations led to more solubility (15-70%). Nevertheless, the differences among most MgO sources were parallel to the data from the in vitro rumen solubility. Next, we performed a meta-analysis of published studies (21 studies, 94 treatments) to assess the true Mg absorption in vivo and potential factors affecting Mg absorption in dairy cows. It appeared that on average dairy cows absorbed about 20% of the Mg intake (range 10-40%), regardless of their lactation status. We revealed a new strategy to predict Mg absorption relative to dietary K as follows: true Mg absorption (g/d) = 0.3395 (±0.025, P < 0.001) × Mg intake (g/d) - 1.9273 (±1.16, P = 0.11) when dietary K ≤20 g/kg DM, and 0.154 (±1.06, P = 0.05) + 0.209 (±0.026, P < 0.001) × Mg intake (g/d) when dietary K >20 g/kg DM (RMSE = 2.19). This strategy improved the accuracy of prediction as compared with the existing prediction (CCC = 0.922 vs. 0.845). Still, over- or underestimations inherent to individual studies were evident and might be related to unaccountable factors, especially the quality of supplemental Mg sources. In conclusion, the vinegar test is a useful tool to rank inorganic Mg sources with alkaline properties. Including in vitro solubility data in Mg nutrition research could help to refine the prediction of bioavailable Mg contents and increase precision in feed formulation.

Effects on rumen pH and feed intake of a dietary concentrate challenge in cows fed rations containing pH modulators with different neutralizing capacity

Forty-five Holstein lactating cows (41 ± 8.8 kg/d of milk yield, 96 ± 35.6 days in milk, and 607 ± 80.4 kg of body weight) were enrolled in this study to assess the effects of diets supplemented with sodium bicarbonate or a magnesium-based product and their corresponding differences in dietary cation-anion difference (DCAD) on rumen pH, rumen microbial population, and milk performance of dairy cattle exposed to an induced decrease in rumen pH through a dietary challenge. Cows were randomly allocated to 3 total mixed rations (TMR) differing in the type of supplement to modulate rumen pH: (1) control, no supplementation; (2) SB, supplemented with 0.82% of sodium bicarbonate with a neutralizing capacity (NC) of 12 mEq/g; and (3) MG, supplemented with 0.25% of magnesium oxide (pHix-Up, Timab Magnesium) with a NC of 39 mEq/g. Thus, SB and MG rations had, in theory, the same NC. The 3 TMR differed for control, SB, and MG in their DCAD-S (calculated considering Na, K, Cl, and S), which was on average 13.2, 21.2, and 13.7 mEq/100 g, respectively, or DCAD-Mg (calculated accounting for Mg, Ca, and P), which was 31.4, 41.2, and 35.2 mEq/100 g, respectively. The study lasted 63 d, with the first 7 d serving as a baseline, followed by a fortnightly progressive decrease of dietary forage-to-concentrate ratio (FCR) starting at 48:52, then 44:56, then 40:60, and finishing at 36:64. Individual dry matter intake (DMI) was recorded daily. Seven cows per treatment were equipped with electronic rumen boluses to monitor rumen pH. Control and SB cows consumed less dry matter (DM; 23.5 ± 0.31 kg/d) than MG cows (25.1 ± 0.31 kg/d) when fed dietary FCR of 44:56 and 40:60. Energy-corrected milk decreased from 40.8 ± 1.21 to 39.5 ± 1.21 kg/d as dietary FCR decreased, independently of dietary treatments. Rumen pH decreased and the proportion of the day with rumen pH <5.8 increased as dietary FCR decreased, and at low dietary FCR (i.e., 36:64) rumen pH was greater in MG cows than in control and SB cows. Reducing the DCAD-S from 28 to 18 mEq/100 g or the DCAD-Mg from 45 to 39 mEq/kg had no effects on DMI or milk yield. Cows supplemented with ∼62 g/d of magnesium oxide (pHix-Up) maintained a greater rumen pH and consumed more DM than cows supplemented with ∼200 g/d of sodium bicarbonate when fed a diet with low FCR.

Production, physiological response, and calcium and magnesium balance of lactating Holstein cows fed different sources of supplemental magnesium with or without ruminal buffer

The objective of this study was to evaluate the effects of dietary replacement of magnesium oxide (MgO) with calcium-magnesium hydroxide [CaMg(OH)₂] and its interaction with ruminal buffer (sodium sesquicarbonate) supplementation on production, Ca and Mg balance, and overall physiological response of mid-lactation Holstein dairy cows. Sixty cows averaging 40.5 ± 7.0 kg of milk/d were used. Treatments were assigned following a 2 × 2 factorial arrangement: (1) MgO, (2) MgO + buffer, (3) CaMg(OH)₂, or (4) CaMg(OH)₂ + buffer. Diets were formulated to have 16.5% of crude protein, 1.82 Mcal/kg of net energy for lactation, 0.67% Ca, 0.39% P, and 0.25% Mg, all on a dry matter (DM) basis. Treatments were individually top dressed. Milk production, composition, and DM intake were evaluated. A subsample of 20 cows were randomly selected for the evaluation of Ca and Mg balance, blood gases, and electrolytes. Ruminal fluid was also collected for evaluation of pH and Ca and Mg solubility. Effects of Mg source, buffer, and the interaction Mg source × buffer were analyzed through orthogonal contrasts. An interaction of Mg source × buffer was found for DM intake and feed efficiency, in which cows fed CaMg(OH)₂ had a similar feed efficiency regardless of ruminal buffer inclusion; however, when cows were fed MgO, the inclusion of buffer reduced feed efficiency. No effects on body weight and milk yield were observed. Buffer addition tended to increase the concentrations of fat, protein, and solids-not-fat, without affecting the yields of these milk components. Magnesium source and buffer did not affect ruminal fluid, blood, urine, or fecal pH; however, buffer supplementation increased urinary pH. Treatment with CaMg(OH)₂ increased blood concentration of HCO₃^-, total CO₂, and base excess compared with cows fed MgO. No differences were observed in the ruminal solubility of Ca and Mg or on milk or urinary Ca and Mg excretion. Greater plasma Mg concentration was observed for animals fed MgO compared with cows fed CaMg(OH)₂; however, both sources were above the threshold recommended in the literature for dairy cows. Also, a reduction in fecal Mg excretion was observed in animals fed CaMg(OH)₂. In summary, we provide evidence that CaMg(OH)₂ could replace MgO without affecting performance, overall physiological response, or Ca and Mg balance of mid-lactating dairy Holstein cows.

Concurrent and carryover effects of feeding blends of protein and amino acids in high-protein diets with different concentrations of forage fiber to fresh cows. 1. Production and blood metabolites

Because of low feed intake during the first weeks of lactation, dietary concentration of metabolizable protein (MP) must be elevated. We evaluated effects of providing additional rumen-undegradable protein (RUP) from a single source or a blend of protein and AA sources during the first 3 wk of lactation. We also evaluated whether replacing forage fiber (fNDF) or nonforage fiber with the blend affected responses. In a randomized block design, at approximately 2 wk prepartum, 40 primigravid (664 ± 44 kg of body weight) and 40 multigravid (797 ± 81 kg of body weight) Holsteins were blocked by calving date and fed a common diet (11.5% crude protein, CP). After calving to 25 d in milk (DIM), cows were fed 1 of 4 diets formulated to be (1) 20% deficient in metabolizable protein (MP) based on predicted milk production (17% CP, 24% fNDF), (2) adequate in MP using primarily RUP from soy to increase MP concentration (AMP; 20% CP, 24% fNDF), (3) adequate in MP using a blend of RUP and rumen-protected AA sources to increase MP concentration (Blend; 20% CP, 24% fNDF), or (4) similar to Blend but substituting fNDF with added RUP rather than nonforage neutral detergent fiber (Blend-fNDF; 20% CP, 19% fNDF). The blend was formulated to have a RUP supply with an AA profile similar to that of casein. A common diet (17% CP) was fed from 26 to 92 DIM, and milk production and composition were measured from 26 to 92 DIM, but individual dry matter intake (DMI) was measured only until 50 DIM. During the treatment period for both parities, AMP and Blend increased energy-corrected milk (ECM) yields compared with the diet deficient in MP based on predicted milk production (40.7 vs. 37.8 kg/d) and reduced concentrations of plasma 3-methyl-His (4.1 vs. 5.3 µmol/L) and growth hormone (9.0 vs. 11.9 ng/mL). Blend had greater DMI than AMP (17.4 vs. 16.1 kg/d), but ECM yields were similar. Blend had greater plasma Met (42.0 vs. 26.4 µmol/L) and altered metabolites associated with antioxidant production and methyl donation compared with AMP. Conversely, the concentration of total essential AA in plasma was less in Blend versus AMP (837 vs. 935 µmol/L). In multiparous cows, Blend-fNDF decreased DMI and ECM yield compared with Blend (19.2 vs. 20.1 kg/d of DMI, 45.3 vs. 51.1 kg/d of ECM), whereas primiparous cows showed the opposite response (15.3 vs. 14.6 kg/d of DMI, 32.9 vs. 31.4 kg/d of ECM). Greater DMI for multiparous cows fed Blend carried over from 26 to 50 DIM and was greater compared with AMP (23.1 vs. 21.2 kg /d) and Blend-fNDF (21.3 kg/d). Blend also increased ECM yield compared with AMP (49.2 vs. 43.5 kg/d) and Blend-fNDF (45.4 kg/d) from 26 to 92 DIM. Few carryover effects of fresh cow treatments on production were found in primiparous cows. Overall, feeding blends of RUP and AA may improve the balance of AA for fresh cows fed high MP diets and improve concurrent and longer-term milk production in multiparous cows. However, with high MP diets, multiparous fresh cows require greater concentrations of fNDF than primiparous cows.

Serum parameters related to mineral homeostasis and energy metabolism in ewes kept on different dietary magnesium supply during the transition period

The present study investigated the effects of dietary magnesium supplementation on mineral status and intermediary metabolism of ewes during the transition period (TP). For this purpose, 19 pregnant ewes (2nd and 3rd lactation) were divided into a control group (Con, n = 9) kept on a daily magnesium intake of approximately 3.0 g ante-partum (a.p.) and 5.0 g post-partum (p.p.), and a magnesium group (Mg, n = 10, 4.2 g a.p., 7.7 g p.p.). Blood was collected at day (d) 30 a.p., d 14 a.p., d 1 p.p., d 14 p.p. and d 30 p.p., respectively. Serum levels of total magnesium were not affected by the treatment. In Con animals, serum levels of total calcium and phosphate were lower at d 1 p.p. in comparison to d 30 a.p. (P ≤ 0.05). Only in Con ewes, serum glucose levels showed significant fluctuations throughout the entire observation period (P ˂ 0.05) and were correlated negatively with those of beta-hydroxybutyrate (r²: 0.70; P < 0.01, r²: 0.76; P < 0.01) and positively with insulin (r²: 0.46; P < 0.05, r²: 0.59; P < 0.05) on d 30 and 14 a.p., respectively. At d 30 p.p., serum insulin levels were lower in the Mg group compared to the Con group (P < 0.05). As serum concentrations of calcium, phosphate and glucose seemed more stable throughout the TP, magnesium supplementation might be an approach to promote metabolic health in ewes.

Effects of oscillating dietary crude protein concentrations on production, nutrient digestion, plasma metabolites, and body composition in lactating dairy cows

We hypothesized that dairy cows fed oscillating metabolizable protein (MP) and crude protein (CP) concentrations on a 24-h frequency for a diet formulated to be below MP requirements would use N more efficiently (i.e., increased milk protein yields and less manure N) without increasing mobilization of body protein stores than would cows fed the same deficient MP diet continuously, although both treatments would on average have equal MP concentrations. In a randomized block design, 30 Holstein cows (119 ± 21 d in milk; 667 ± 69 kg of body weight) were blocked according to milk yield within a parity (3 primiparous and 7 multiparous blocks) and fed 1 of 3 treatments: (1) diet with 16.2% CP (109% of MP requirements) fed continuously (109MP), (2) diet with 14.1% CP (95% of MP requirements) fed continuously (95MP), or (3) diets oscillating on a 24-h cycle from the 109MP diet and a diet with 11.9% CP (∼78% of MP requirements) such that average CP and MP concentration would be the same as 95MP (OSC). Dry matter intake was similar between 109MP and 95MP (22.9 vs. 23.2 kg/d) but tended to be lower for OSC (22.2 kg/d) compared with 95MP. Milk yield was greater for 109MP compared with 95MP (36.6 vs. 35.1 kg/d) and similar between 95MP and OSC (35.3 kg/d). Milk protein and energy-corrected milk yields were similar among treatments. Milk urea N (MUN) concentration was higher for 109MP compared with 95MP (12.8 vs. 10.2 mg/dL), and tended to be higher for OSC (10.9 mg/dL) compared with 95MP. Higher MUN concentration for OSC occurred despite lower N intake (474 vs. 512 g of N/d) and similar milk N outputs compared with 95MP (164 vs. 179 g/d). On days when cows on OSC were fed high versus low MP diets, yields of milk (34.8 vs. 36.3 kg/d) and milk protein (1.0 vs. 1.1 kg/d) and MUN concentration (9.3 vs. 12.5 mg/dL) followed the oscillation pattern but lagged the change in diet CP by 1 d, whereas dry matter intake, yields of milk fat, plasma energy metabolites, AA, and 3-methyl-His were similar between days. Nutrient digestibility was similar for major nutrients across treatments except for CP, which was greater for 109MP (65.2%) and OSC (65.3%) compared with 95MP (61.7%). Compared with 95MP, OSC did not increase milk N relative to N intake (averaged 0.35 g of milk N/g of N intake) or N balance; however, urinary N output was increased for OSC versus 95MP (0.32 vs. 0.24 g of urine N/g of N intake). Body composition estimated using urea dilution was similar across treatments, and all cows accreted lipid and energy during the trial. Empty body CP did not change over the 50-d treatment period. Overall, greater CP digestion, urinary N excretion, and MUN concentrations with lesser N intake and similar milk N outputs for OSC compared with 95MP suggests that the lower energy intake by OSC cows may have limited potential responses to altered N metabolism.

Impacts of feeding monensin sodium on production and the efficiency of milk production in dairy cows fed total mixed rations: evaluation of a confounded literature

The amount of milk Canadian dairy farmers produce is limited to a production quota expressed in milk fat. Because milk economic value is primarily based on fat and protein, it can be advantageous to decrease the milk fat to protein ratio. Monensin sodium has been suggested to reduce milk fat proportion and outputs, but not milk protein. Publications using lactating dairy cows were utilized to assess predictability of production responses to monensin feeding based upon their base production characteristics and diet composition. Predicted animal output changes due to monensin had poor fits with low r2 (0.31–0.44) and unevenly distributed residuals. Further assessment revealed that cow characteristics, and diets, were not independent of monensin feeding level. Thus, the 43 comparisons were clustered into levels of 10–12, 14–18, or 20–24 mg kg−1 of diet dry matter (DM). Milk fat yield reductions due to monensin differed (P < 0.05; 10–12 and 14–18 mg kg−1 DM), or tended to differ (P = 0.057; 20–24 mg kg−1 DM), from zero (i.e., no change). Monensin addition to total mixed rations of lactating dairy cows negatively impacted milk fat yield to a greater extent than milk protein.

Short communication: Effects of drying and analytical methods on nitrogen concentrations of feeds, feces, milk, and urine of dairy cows

Nitrogen concentrations in feeds, feces, milk, and urine samples were measured using 2 analytical methods following different drying procedures. Ten samples of corn silage, alfalfa silage, and concentrates collected from 2017 to 2018 at Krauss Dairy Research Center, The Ohio State University (Wooster), were used. A 4-d total collection digestion trial provided fecal samples from 10 cows (1 sample/cow), and another 10 cows were used to collect milk samples (1 sample/cow) and spot urine samples (1 sample/cow). Spot urine samples were acidified immediately to pH <3.0 when collected. Feed samples were oven dried (55°C) or lyophilized and analyzed using the Kjeldahl (KJ; copper sulfate as a catalyst) method and a combustion method (elemental analyzer; EA). Feces, urine, and milk samples were analyzed for N using the following methods: (1) fresh samples by KJ (referred to as wet KJ), (2) lyophilization (urine and milk for 8 h; feces for 120 h) followed by EA (LYO-EA), and (3) oven drying (milk and urine for 1 h; feces for 72 h at 55°C) followed by EA (OD-EA). Additionally, changes in N content of acidified urine at -20° over 180 d of storage were examined. Nitrogen concentrations in corn silage, alfalfa silage, and concentrates were greater for EA by 6.1, 4.8, and 8.3%, respectively, compared with KJ. Analysis of dried samples via EA compared with wet KJ resulted in lower fecal N content (27.8 vs. 29.3 g/kg of DM). Nitrogen concentration in fecal samples via KJ after lyophilization was lower by 5% compared with wet KJ but did not differ from LYO-EA, suggesting that N losses occurred during drying. Nitrogen determination with EA after drying of samples resulted in greater milk N (5.70 vs. 5.50 g/kg) and urinary N (9.16 vs. 9.06 g/kg) content compared with wet KJ. However, drying method (i.e., lyophilization vs. oven drying) did not affect N content of milk, urine, or feces. The use of EA resulted in lower percentage deviation of N content from duplicate sample assays for most samples (no difference was found for concentrate and fecal N), suggesting that EA was more precise than KJ. In conclusion, drying of feces caused N losses regardless of drying methods. For urine and milk samples, if drying is necessary (i.e., EA), oven drying at 55°C can be used rather than lyophilization. The N content was greater in feeds, milk, and urine when determined with EA versus KJ. In addition, N content in acidified and undiluted urine at -20° changed and should be analyzed within 90 d of storage. The results in the current study, however, did not account for laboratory-to-laboratory variation.

Evaluation of creatinine as a urine marker and factors affecting urinary excretion of magnesium by dairy cows

Nutrient balance studies require measuring urine volume, and urinary excretion can be used to assess Mg bioavailability. A less laborious method than total collection of urine could make balance studies more feasible and expand the utility of using urinary Mg as an index of bioavailability, but the method needs to be accurate and sensitive. Sampling interval can affect accuracy because excretion must be at steady state. Two experiments were conducted to (1) determine whether urinary creatinine could be used to accurately estimate urinary output of nutrients markedly excreted via urine (N, K, Na, S, and Mg; experiment 1) and (2) determine the appropriate sampling schedule to evaluate Mg excretion after abrupt diet changes (experiment 2). Experiment 1 was originally designed to evaluate the interaction of monensin [0 vs. 14 mg of monensin/kg of dry matter (DM)] and Mg source (MgO vs. MgSO₄; total diet Mg: 0.36% of DM) under antagonism from increased dietary K (2.11% of DM) on urinary Mg excretion. Experiment 2 evaluated the interaction of Mg concentration (basal vs. supplemental MgO; total diet Mg: 0.20 vs. 0.42% of DM) and K (basal vs. supplemental K₂CO₃; total diet K: 1.60 vs. 2.57% of DM) on urinary Mg excretion over time. Using 4-d composite samples from total collection of urine (n = 34 cow-periods), the average daily excretion of creatinine was similar to previous estimates (29.0 ± 1.16 mg of creatinine/kg of body weight) but was variable among cows (root mean squared error = 2,980 mg/d; 14% of mean). Treatment-average estimated excretion of urine and urinary N, K, Na, S, and Mg were similar to actual values; however, differences between actual and estimated values could be substantial for individual cows. Using the mean creatinine excretion per kilogram of body weight for all cows to estimate urine eliminates the lack of fit variance resulting in artificially low within-treatment variation for estimated urine volume. The standard error of the mean for estimated urine volume was 23% less (1.93 vs. 2.51) than that for actual urine production. This inflated the type I error rate, and, consequently, statistical inferences on N and K excretion differed when urine output was estimated rather than measured. The standard error of the mean for excretion of Mg calculated with actual or estimated urine production were almost identical (0.92 vs. 0.97); however, similar standard error of the mean was likely caused by differences in the covariance of urinary Mg concentration with estimated or actual urine output. Based on spot sampling (experiment 2), urinary Mg reached steady state by 2 d following an increase in dietary K regardless of Mg level, whereas excretion of urinary Mg following an increase in dietary Mg continued to increase through 7 d. Estimating nutrient excretion with urinary creatinine and body weight on average is accurate, but variance is likely underestimated. Knowing the time course of urinary Mg excretion will improve the value of using urinary Mg concentration to assess diet adequacy or Mg bioavailability.