Nutrient Requirements Of Dairy Cattle Research Articles

Evaluation of NASEM 2021 on predictions of milk protein yield on Quebec commercial dairy farms

A recent study assessed the ability of 4 feed evaluation models to predict milk protein yield (MPY) under commercial context, with data from of 541 cows from 23 dairy herds in the province of Québec, Canada. However, the recently published Nutrient Requirements of Dairy Cattle from the National Academies of Sciences, Engineering, and Medicine (NASEM, 2021) was not released at that time. Thus, the current study evaluated NASEM using the same data set. To be consistent with the previous study, predicted DMI was used. Therefore, MPY was predicted using the 2 estimations of DMI proposed by NASEM: one based on animal characteristics only (DMIAo) and one also including ration characteristics (DMIA&R). For each type of DMI estimates, 2 MPY predictions were made, using: 1) the multivariate equation directly published in NASEM and 2) a variable efficiency of utilization of MP predicted using inputs and outputs from NASEM, published a posteriori. With the 2 approaches, multivariate and variable efficiency, the DMIA&R yielded the best MPY predictions. The multivariate equation showed a regression bias between observed and predicted MPY with both DMI estimations. The estimated variable efficiency allowed for MPY predictions without mean and regression biases. With DMIA&R, concordance correlation coefficients (CCC) were 0.72 and 0.78 for MPY predicted using the multivariate and variable efficiency equations, respectively. In comparison, DMIAo CCC were 0.60 and 0.71, respectively. In conclusion, on commercial farms, where dairy rations are usually optimized for a group of cows, estimates of DMI based on animal and rations characteristics yielded the best MPY predictions. The multivariate equation from NASEM predicted MPY with a regression bias whereas the variable efficiency of utilization of MP based on MP and energy supplies resulted in no bias in MPY predictions.

The use of mix ration corn-silage based for dairy cattle: A systematic review on methane emission and milk quality

In subtropical countries, corn silage is the primary ration for dairy cattle. Corn silage is often chosen in mixed rations because of its higher biomass yield, superior palatability, homogeneous yield quality, and simple silage preparation due to its higher soluble sugar content. The review aimed to (i) compile a list of the different feed components that may be combined with corn silage and (ii) compare the results of their effects on methane gas emissions, milk quality, and feed efficiency as determined by an in vivo approach. Relevant papers indexed in the computerized Scopus database and published in a variety of scientific publications were found. This systematic review was based on the PRISMA. Records included in review from databases (n = 10). This method has been applied by the authors in the articles that have been reviewed. In general, the content of CP and EE in the study was almost the same. CH4 (g/d) is lowest at 315-329, and for CH4 (g/kg of DMI) is 15.7-15.9. Substituting ordinary corn silage with Enogen corn silage (ECS) in TMR can increase milk production (38.8-40.8 kg/d) and milk quality (fat 3.82-4%, protein 3.07-3.11% and lactose 4.86-4.92%). The present literature review confirms that all mixed feeds with corn silage base used have nutrient content in accordance with the daily nutrient requirements of dairy cattle. Mixed feed that produces the lowest CH4 emissions (g/kg of DMI) and good milk quality is by giving ECS (Enogen corn silage).

Effect of Metabolizable Protein Supply on Milk Performance, Ruminal Fermentation, Apparent Total-Tract Digestibility, Energy and Nitrogen Utilization, and Enteric Methane Production of Ayrshire and Holstein Cows

In North America, the nutrient requirements of dairy cattle are predicted using the Cornell Net Carbohydrate and Protein System (CNCPS) or the National Research Council (NRC). As Holstein is the most predominant dairy cattle breed, these models were developed based on the phenotypic, physiological, and genetic characteristics of this breed. However, these models may not be appropriate to predict the nutrient requirements of other breeds, such as Ayrshire, that are phenotypically and genetically different from Holstein. The objective of this study was to evaluate the effects of increasing the metabolizable protein (MP) supply using CNCPS on milk performance, ruminal fermentation, apparent total-tract digestibility, energy and N utilization, and enteric methane production in Ayrshire vs. Holstein lactating dairy cows. Eighteen (nine Ayrshire; nine Holstein) lactating cows were used in a replicated 3 × 3 Latin square design (35-d periods) and fed diets formulated to meet 85%, 100%, or 115% of MP daily requirement. Except for milk production, no breed × MP supply interaction was observed for the response variables. Dry matter intake (DMI) and the yields of energy-corrected milk (ECM), fat, and protein were less (p < 0.01) in Ayrshire vs. Holstein cows. However, feed efficiency and N use efficiency for milk production did not differ between the two breeds, averaging 1.75 kg ECM/kg DMI and 33.7 g milk N/100 g N intake, respectively. Methane yield and intensity and urinary N also did not differ between the two breeds, averaging 18.8 g CH4 /kg DMI, 10.8 g CH4 /kg ECM, and 27.6 g N/100 g N intake, respectively. Yields of ECM and milk protein increased (p ≤ 0.01) with increasing MP supply from 85% to 100% but no or small increases occurred when MP supply increased from 100 to 115%. Feed efficiency increased linearly with an increasing MP supply. Nitrogen use efficiency (g N milk/100g N intake) decreased linearly (by up to 5.4 percentage units, (p < 0.01) whereas urinary N excretion (g/d or g/100 g N intake) increased linearly (p < 0.01) with an increasing MP supply. Methane yield and emission intensity were not affected by MP supply. This study shows that feed efficiency, N use efficiency, CH4 (yield and intensity), and urinary N losses did not differ between Ayrshire and Holstein cows. Energy-corrected milk yield and feed efficiency increased, but N use efficiency decreased and urinary N losses increased with increasing dietary MP supply regardless of breed. Ayrshire and Holstein breeds responded similarly to increasing MP levels in the diet.

The application of nonlinear programming on ration formulation for dairy cattle

The objective of this study was to compare the application of iterative linear programming (iteLP), sequential quadratic programming (SQP), and mixed-integer nonlinear programming-based deterministic global optimization (MINLP_DGO) on ration formulation for dairy cattle based on Nutrient Requirements of Dairy Cattle (NRC, 2001). Least-cost diets were formulated for lactating cows, dry cows, and heifers. Nutrient requirements including energy, protein, and minerals, along with other limitations on dry matter intake, neutral detergent fiber, and fat were considered as constraints. Five hundred simulations were conducted, with each simulation randomly selecting 3 roughages and 5 concentrates from the feed table in NRC (2001) as the feed resource for each of 3 animal groups. Among the 500 simulations for lactating cows, 57, 45, and 21 simulations did not yield a feasible solution when using iteLP, SQP, and MINLP_DGO, respectively. All the simulations for dry cows and heifers were feasible when using SQP and MINLP_DGO, but 49 and 11 infeasible simulations occurred when using iteLP for dry cows and heifers, respectively. The average ration costs per animal per day of the feasible solutions obtained by iteLP, SQP, and MINLP_DGO were $4.78 (±0.71), $4.45 (±0.65), and $4.44 (±0.65) for lactating cows; $2.39 (±0.52), $1.48 (±0.26), and $1.48 (±0.26) for dry cows; and $0.98 (±0.72), $0.97 (±0.15), and $0.91 (±0.14) for heifers, respectively. The average computation time of iteLP, SQP, and MINLP_DGO were 0.59 (±1.87) s, 1.15 (±0.62) s, and 58.69 (±68.45) s for lactating cows; 0.041 (±0.070) s, 0.76 (±0.37) s, and 14.84 (±39.09) s for dry cows; and 1.60 (±2.90) s, 0.51 (±0.19) s, and 16.45 (±45.56) s for heifers, respectively. In conclusion, iteLP had limited capability of formulating least-cost diets when nonlinearity existed in the constraints. Both SQP and MINLP_DGO handled the nonlinear constraints well, with SQP being faster, whereas MINLP_DGO was able to return a feasible solution under some situations where SQP could not.

ADSA® Presidential Report

ADSA® Presidential Report

Predicting Daily Dry Matter Intake Using Feed Intake of First Two Hours after Feeding in Mid and Late Lactation Dairy Cows with Fed Ration Three Times per Day.

Simple SummaryIt is difficult to obtain feed intake of dairy cows in experiments since all cows are raised in a free-stall barn in commercial dairy farms nowadays. Therefore, it is necessary to develop a simple, accurate, and reliable feed intake prediction model to replace direct measurement. In this study, we generated a forecasting model to predict daily dry matter intake (DMI) of dairy cows in mid and late lactation based on the feed intake of first 2 h after feeding (DMI-2h). The proposed prediction equation was: DMI (kg/day) = 8.499 + 0.2725 × DMI-2h (kg/day) + 0.2132 × Milk yield (kg/day) + 0.0095 × Body weight (kg/day) (R2 = 0.46). Compared with NRC model (2001), our model shows higher accuracy and precision on predicting daily DMI of dairy cows with fed ration three times per day in mid and late lactation period. This prediction model could be used as an alternative approach for researchers who have difficulty in measuring DMI in dairy cows’ experiments.The objective of this study was to evaluate the feasibility of using the dry matter intake of first 2 h after feeding (DMI-2h), body weight (BW), and milk yield to estimate daily DMI in mid and late lactating dairy cows with fed ration three times per day. Our dataset included 2840 individual observations from 76 cows enrolled in two studies, of which 2259 observations served as development dataset (DDS) from 54 cows and 581 observations acted as the validation dataset (VDS) from 22 cows. The descriptive statistics of these variables were 26.0 ± 2.77 kg/day (mean ± standard deviation) of DMI, 14.9 ± 3.68 kg/day of DMI-2h, 35.0 ± 5.48 kg/day of milk yield, and 636 ± 82.6 kg/day of BW in DDS and 23.2 ± 4.72 kg/day of DMI, 12.6 ± 4.08 kg/day of DMI-2h, 30.4 ± 5.85 kg/day of milk yield, and 597 ± 63.7 kg/day of BW in VDS, respectively. A multiple regression analysis was conducted using the REG procedure of SAS to develop the forecasting models for DMI. The proposed prediction equation was: DMI (kg/day) = 8.499 + 0.2725 × DMI-2h (kg/day) + 0.2132 × Milk yield (kg/day) + 0.0095 × BW (kg/day) (R2 = 0.46, mean bias = 0 kg/day, RMSPE = 1.26 kg/day). Moreover, when compared with the prediction equation for DMI in Nutrient Requirements of Dairy Cattle (2001) using the independent dataset (VDS), our proposed model shows higher R2 (0.22 vs. 0.07) and smaller mean bias (−0.10 vs. 1.52 kg/day) and RMSPE (1.77 vs. 2.34 kg/day). Overall, we constructed a feasible forecasting model with better precision and accuracy in predicting daily DMI of dairy cows in mid and late lactation when fed ration three times per day.

Predicted Feed Efficiency index applied to Italian Holstein Friesian cattle population

Submitted 2020-08-02 | Accepted 2020-09-21 | Available 2020-12-01 https://doi.org/10.15414/afz.2020.23.mi-fpap.326-330 Feed efficiency has a major influence on farm profitability and environmental stewardship in the dairy industry. The aim of this study was to describe a new selection index adopted by the Italian Holstein and Jersey Association (ANAFIJ, Cremona, Italy) to improve feed efficiency using data recorded by the official dairy recording system. Predicted dry matter intake (pDMI) was derived from milk yield, fat content, and estimated cow body weight. Fat-protein corrected milk (FPCM) was derived from milk yield corrected for fat, protein, and a fixed coefficient for lactose content (4.80%). Therefore, the predicted feed efficiency (pFE) was estimated as ratio between FPCM and pDMI. Average pFE was 1.27±0.18 (kg.d -1 ) with heritability of 0.32. Predicted Feed Efficiency index (pFEi), traditional and genomic, has been implemented in the Italian Holstein Friesian evaluation system. Results suggest that pFEi may be a new breeding objective for Italian Friesians. The official selection index (PFT), in use since 2002, is positively correlated with pFEi. However, the introduction of pFEi will improve the positive feed efficiency trend. This approach will permit the Italian Holstein Friesian breeders to improve feed efficiency, without increasing costs of recording system. However, to avoid the risk of selecting animals with an excessive negative energy balance after calving, it would be useful to include in the pFE a correction for body condition score and reproductive performances. Meanwhile, in order to increase the accuracy of the predicted phenotype, an Italian consortium is creating a consistent phenotypic critical mass of individual data for dry matter intake in cows, heifers and young bulls. Keywords: feed efficiency, cattle breeding, dry matter intake, breeding value estimation References Cassandro, M. (2020). Animal breeding and climate change, mitigation and adaptation. Journal of Animal Breeding and Genetics, 137(2), 121-122. https://doi.org/10.1111/jbg.12469 Cassandro, M. et al. (2010). Genetic parameters of predicted methane production in Holstein Friesian cows. Proceedings of the 9th World Congress on Genetics Applied to Livestock Production, Leipzig, Germany. Cassandro, M., Mele, M. and Stefanon, B. (2013). Genetic aspects of enteric methane emission in livestock ruminants. Italian Journal of Animal Science, 12(3), 450-458. De Haas, Y. et al. (2012). Improved accuracy of genomic prediction for dry matter intake of dairy cattle from combined European and Australian data sets. Journal of Dairy Science, 95(10), 6103-6112. De Vries, M. and Veerkamp, R. (2000). Energy balance of dairy cattle in relation to milk production variables and fertility. Journal of Dairy Science, 83(1), 62-69. Finocchiaro, R. et al. (2017). Body weight prediction in Italian Holstein cows. ICAR Technical Series, 22, 95-98. Hegarty, R. et al. (2007). Cattle selected for lower residual feed intake have reduced daily methane production. Journal of Animal Science, 85(6), 1479-1486. Hurley, A. M. et al. (2018). Characteristics of feed efficiency within and across lactation in dairy cows and the effect of genetic selection. Journal of Dairy Science, 101(2), 1267-1280. https://doi.org/https://doi.org/10.3168/jds.2017-12841 Meuwissen, T. H., Hayes, B. J. and Goddard, M. E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157(4), 1819-1829. National Research Council. (2001). Nutrient Requirements of Dairy Cattle: Seventh Revised Edition, 2001. The National Academies Press. http://www.nap.edu/catalog.php?record_id=9825 Pryce, J. et al. (2014). Genomic selection for feed efficiency in dairy cattle. Animal, 8(1), 1-10. Sjaunja, L. et al. (1990). A Nordic proposal for an energy corrected milk formula. Proceedings of the 2nd Session of Committee for Recording and Productivity of Milk Animal, Paris, p. 156. Veerkamp, R. et al. (2000). Genetic correlation between days until start of luteal activity and milk yield, energy balance, and live weights. Journal of Dairy Science, 83(3), 577-583. Verbyla, K. et al. (2010). Predicting energy balance for dairy cows using high-density single nucleotide polymorphism information. Journal of Dairy Science, 93(6), 2757-2764. Wall, E., Coffey, M. and Brotherstone, S. (2007). The relationship between body energy traits and production and fitness traits in first-lactation dairy cows. Journal of Dairy Science, 90(3), 1527-1537. Wallace, R. J. et al. (2019). A heritable subset of the core rumen microbiome dictates dairy cow productivity and emissions. Science Advances, 5(7), eaav8391.

Relationship between feed protein content and faeces nitrogen content in early lactation dairy cows

Relationship between feed protein content and faeces nitrogen content in early lactation dairy cows

Low doses of lactoferrin supplementation in weaning calves

Low doses of lactoferrin supplementation in weaning calves

Nutritive value of ensiled Italian ryegrass and winter cereal mixture

Nutritive value of ensiled Italian ryegrass and winter cereal mixture

The effects of improved performance in the U.S. dairy cattle industry on environmental impacts between 2007 and 2017.

The U.S. dairy industry considerably reduced environmental impacts between 1944 and 2007, primarily through improved dairy cow productivity. However, although milk yield per cow has increased over the past decade, whole-system environmental impact analyses have not been conducted over this time period, during which environmental modeling science has improved considerably. The objective of this study was to compare the environmental impact of U.S. dairy cattle production in 2007–2017. A deterministic model based on population demographics, metabolism, and nutrient requirements of dairy cattle was used to estimate resource inputs, nutrient excretion, and greenhouse gas (GHG) emissions per 1.0 × 106 t (one million metric t or MMT) of energy-corrected milk (ECM) produced in 2007 and 2017. System boundaries extended from the manufacture and transport of cropping inputs to milk at the farm gate. Milk transport, processing, and retail were not included. Dairy systems were modeled using typical management practices, herd population dynamics, and production data from U.S. dairy farms. Cropping data were sourced from national databases. The resources required to produce 1.0 MMT ECM in 2017 were considerably reduced relative to those required in 2007, with 2017 production systems using 74.8% of the cattle, 82.7% of the feedstuffs, 79.2% of the land, and 69.5% of the water as compared to 2007. Waste outputs were similarly reduced, with the 2017 U.S. dairy industry producing 79.4%, 82.5%, and 85.7% of the manure, N, and P excretion, respectively. Dairy production in 2017 emitted 80.9% of the CH4 and 81.5% of the N2O per 1.0 MMT ECM compared to 2007. Enteric and manure emissions contributed the major proportion (80%) of GHG emissions per unit of milk, with lesser contributions from cropping (7.6%) and fertilizer application (5.3%). The GHG emissions per 1.0 MMT ECM produced in 2017 were 80.8% of equivalent milk production in 2007. Consequently, although total U.S. ECM production increased by 24.9% between 2007 and 2017, total GHG emissions from this milk production increased by only 1.0%. In line with previous historical analyses, the U.S. dairy industry has made remarkable productivity gains and environmental progress over time. To maintain this culture of continuous improvement, the dairy industry must build on gains made to date and demonstrate its commitment to reducing environmental impacts while improving both economic viability and social acceptability.

Meta-analysis of the effects of prepartum dietary protein on performance of dairy cows

The objectives were to review the published literature and use meta-analytic methods to determine the effects of dietary protein fed prepartum on productive performance of dairy cows. The hypothesis was that responses to dietary protein prepartum would differ between nulliparous and parous cows, and performance would be maximized at a greater protein supply in nulliparous than in parous cows. The literature was systematically reviewed, searching randomized experiments in which the prepartum dietary content or degradability of protein was manipulated. Twenty-seven experiments including 125 treatment means and 1,801 cows were included in the meta-analysis. Of those, 8 experiments with 27 treatment means reported responses for 510 nulliparous cows. Data collected included the ingredient composition and chemical analyses of prepartum diets, parity group, and means and respective measures of variance for productive responses. Mixed model meta-analysis was conducted and statistical models investigated the effects of dietary crude protein (CP) or supply of metabolizable protein (MP) prepartum on performance. Supply of MP was predicted using the National Research Council Nutrient Requirements of Dairy Cattle (2001) model. The mean ± standard deviation and median (range) concentrations of dietary CP fed to cows prepartum were 14.4 ± 2.2 and 14.4% (8.9 to 20.9%), resulting in mean and median (range) intakes of CP prepartum of 1,720 ± 432 and 1,734 g/d (745 to 2,482 g/d). Predicted prepartum supply of MP averaged 822 ± 157 in nulliparous cows, ranging from 517 to 1,094 g/d, and 1,146 ± 316 in parous cows, ranging from 463 to 1,733 g/d. Increasing prepartum CP content or predicted supply of MP improved postpartum dry matter intake in nulliparous cows, but increasing prepartum CP content reduced prepartum dry matter intake in parous cows. Yields of milk and fat-corrected milk increased with increasing prepartum supply of MP in nulliparous but not in parous cows. Yields of fat and protein increased in nulliparous cows with increased CP content or supply of MP. Alternatively, in parous cows, yield of milk fat was not influenced by supply of MP but responded quadratically to dietary CP content and was maximized at approximately 14% CP. Dietary CP had no effect on protein yield in parous cows, but increasing the supply of MP improved protein yield only in cows from experiments with >36 kg/d of milk production, whereas MP supply had no effect on protein yield of parous cows from experiments with <28 kg/d of milk production. Performance of dairy cows was responsive to prepartum supply of MP and nulliparous benefited from diets with increased MP intake. Based on current results, production responses for nulliparous cows increased linearly up to the maximum MP intake of 1,100 g/d observed in the study, whereas for parous cows, only yield of milk protein in cows producing more than 36 kg of milk/d was influenced by supplying more than 800 g/d of MP.

Updating predictions of dry matter intake of lactating dairy cows

Our objective was to model dry matter intake (DMI) by Holstein dairy cows based on milk energy (MilkE), body weight (BW), change in BW (ΔBW), body condition score (BCS), height, days in milk (DIM), and parity (primiparous and multiparous). Our database included 31,631 weekly observations on 2,791 cows enrolled in 52 studies from 8 states of the United States, mostly in the Upper Midwest. The means ± standard deviations of these variables were 24 ± 5 kg of DMI, 30 ± 6 Mcal of MilkE/d, 624 ± 83 kg of BW, 0.24 ± 1.50 kg of ΔBW/d, 3.0 ± 0.5 BCS, 149 ± 6 cm height, and 102 ± 45 DIM. Data analysis was performed using a mixed-effects model containing location, study within location, diet within study, and location and cow within study as random effects, whereas the fixed effects included the linear effects of the covariates described previously and all possible 2-way interactions between parity and the other covariates. A nonlinear (NLIN) mixed model analysis was developed using a 2-step approach for computational tractability. In the first step, we used a linear (LIN) model component of the NLIN model to predict DMI using only data from mid-lactation dairy cows (76-175 DIM) without including information on DIM. In the second step, a nonlinear adjustment for DIM using all data from 0 to 368 DIM was estimated. Additionally, this NLIN model was compared with an LIN model containing a fourth-order polynomial for DIM using data throughout the entire lactation (0-368 DIM) to assess the utility of an NLIN model for the prediction of DMI. In summary, a total of 8 candidate models were evaluated as follows: 4 ways to express energy required for maintenance (BW, BW0.75, BW adjusted for a BCS of 3, and BW0.75 adjusted for a BCS of 3) × 2 modeling strategies (LIN vs. NLIN). The candidate models were compared using a 5-fold across-studies cross-validation approach repeated 20 times with the best-fitting model chosen as the proposed model. The metrics used for evaluation were the mean bias, slope bias, concordance correlation coefficient (CCC), and root mean squared error of prediction (RMSEP). The proposed prediction equation was DMI (kg/d) = [(3.7 + parity × 5.7) + 0.305 × MilkE (Mcal/d) + 0.022 × BW (kg) + (-0.689 + parity × -1.87) × BCS] × [1 - (0.212 + parity × 0.136) × exp(-0.053 × DIM)] (mean bias = 0.021 kg, slope bias = 0.059, CCC = 0.72, and RMSEP = 2.89 kg), where parity is equal to 1 if the animal is multiparous and 0 otherwise. Finally, the proposed model was compared against the Nutrient Requirements of Dairy Cattle (2001) prediction equation for DMI using an independent data set of 9,050 weekly observations on 1,804 Holstein cows. The proposed model had smaller mean bias and RMSEP and higher CCC than the Nutrient Requirements of Dairy Cattle equation to predict DMI and has potential to improve diet formulation for lactating dairy cows.

Rumen-protected methionine a feed supplement to low dietary protein: effects on microbial population, gases production and fermentation characteristics

The present study was performed to evaluate the effects of different concentration of rumen-protected methionine (RPMet) with a low level of crude protein (CP) using rumen simulation technology on many parameters. The experiment was assigned randomly into four treatments: (1) high protein diet (163.39 g/kg CP) without RPMet (HP); (2) low protein diet (146.33 g/kg CP) without RPMet (LP); (3) low protein diet, supplement with low RPMet (RPMet: 0.11 g/kg) (LPLMet); and (4) low protein diet, supplement with high RPMet (RPMet: 0.81 g/kg) (LPHMet), mixed with 20 g basal diet in each fermenter. Based on National Research Council (NRC) (Nutrient requirements of dairy cattle, National Academies Press, Washington, DC, 2001) recommendation for dairy ruminants HP diet was formulated as positive normal control and LP as a negative control. Results demonstrated that CP disappearance was found significantly higher (P < 0.05) in supplement groups compared with HP and found similar (P > 0.05) with LP. However, neutral detergent fiber (NDF) and gross energy (GE) were found a parallel among supplement groups compared to HP and higher than LP. Furthermore, microbial crude protein, total and short chain fatty acids were found similar in LPHMet and HP and found significantly higher than LPLMet and LP. The R. albus population of LPHMet found parallel to HP and pointedly higher than LP in a solid and liquid fraction. Daily production of ammonia nitrogen, total gas, and methane were higher in HP than LP, LPLMet, and LPHMet. Overall, results concluded that values of digestibility, rumen fermentation, microbial crude protein, and R. albus population were similar of LPHMet to that of HP group. However, production of ammonia-N, total gas, and methane volume were significantly higher in the HP group than LPLMet, LPHMet, and LP groups. In conclusion, rumen-protected methionine is a good feed supplement to low dietary protein in the level of 0.81 g/kg.

EFFECT OF DIFFERENT FEEDING LEVELS ON THE PRODUCTIVE PERFORMANCE OF LACTATING EGYPTIAN BUFFALOES

Nutrient requirements of dairy animals are the main factor affecting on productive and reproductive performance. So, the objective of the study was to evaluate the nutrient requirements of dairy cattle performed by NRC could be to meet lactating Egyptian buffalo’s nutrient requirements. Twenty four lactating buffaloes at third to fifth seasons of lactation and weighed on average 514± 12.15 kg were used in the current study. The animals were divided randomly into three groups (eight animals each) to get three feeding levels of NRC requirements. Three feeding levels were performed to meet 80% (T1), 100% (T2) and 120% (T3) of NRC allowances for dairy cattle. The obtained results indicated that dry matter intake (DMI) (kg/d) has a gradual and significant increase (P < 0.05) by increasing feeding level. Apparent dry matter (DM), organic matter (OM), crude protein (CP) and crude fiber (CF) digestibility were not significantly affected by treatments. Milk yield, 4% fat corrected milk (4% FCM) and energy corrected milk (ECM) (kg/d) were having a gradual increase by increasing feeding level. Milk composition (%) was not significantly affected by treatments; however, milk composition yields (g/d) were significantly increased (P<0.05) by increasing feeding levels. Feed conversion for milk yield, 4% FCM and ECM (Kg/Kg DMI) were significantly (P<0.05) enhanced by increasing feeding level according to NRC requirements. It can be concluded that the NRC requirements for dairy cattle are not enough for Egyptian buffaloes under the conditions of this study. Also, increasing feeding level for buffaloes above NRC allowances for dairy cattle could enhance the productive performance for Egyptian buffaloes.

Liver mitochondrial oxygen consumption and efficiency of milk production in lactating Holstein cows supplemented with copper, manganese and zinc.

The objective of this study was to examine the relationship between mitochondrial proton leak and feed efficiency with supplementation of different levels of Cu, Mn and Zn (Bioplex, Alltech) at levels above Nutrient Requirements of Dairy Cattle (NRC, 2001). Milk yield and composition, mineral excretion in milk and faeces, feed efficiency and liver mitochondrial proton leak were measured in 60 Holstein dairy cows at approximately 70days in milk on a commercial dairy. Treatments reflect total Cu, Mn and Zn intake/day and are as follows: (i) High: 444mg/day Cu, 3492mg/day Mn, 2234mg/day Zn; (ii) Med: 436mg/day Cu, 3002mg/day Mn, 2047mg/day Zn; (iii) Low: 420mg/day Cu, 2764mg/day Mn, 2186mg/day Zn; (iv) LowMn: 391mg/day Cu, 2617mg/day Mn, 1849mg/day Zn; and (v) Control: 264mg/day Cu, 2850mg/day Mn, 1593mg/day Zn. Proton leak-dependent respiration was lowest in Control (p<.10). However, measures of efficiency were greatest in Med and least in High (p<.10). Therefore, measures of efficiency did not reflect efficiency due to low proton leak and there appears to be an upper limit to beneficial supplementation of Cu, Mn and Zn.

Drinking regime evaluation with continuous ruminal monitoring boluses

Drinking regime evaluation with continuous ruminal monitoring boluses

Short communication: Evaluation of the PREP10 energy-, protein-, and amino acid-allowable milk equations in comparison with the National Research Council model

The objective of this work was to evaluate the precision and accuracy of the milk yield predictions made by the PREP10 model in comparison to those from the National Research Council (NRC) Nutrient Requirements of Dairy Cattle. The PREP10 model is a ration-balancing system that allows protein use efficiency to vary with production level. The model also has advanced AA supply and requirement calculations that enable estimation of AA-allowable milk (MilkAA) based on 10 essential AA. A literature data set of 374 treatment means was collected and used to quantitatively evaluate the estimates of protein-allowable milk (MilkMP) and energy-allowable milk yields from the NRC and PREP10 models. The PREP10 MilkAA prediction was also evaluated, as were both models' estimates of milk based on the most-limiting nutrient or the mean of the estimated milk yields. For most milk estimates compared, the PREP10 model had reduced root mean squared prediction error (RMSPE), improved concordance correlation coefficient, and reduced mean and slope bias in comparison to the NRC model. In particular, utilizing the variable protein use efficiency for milk production notably improved the estimate of MilkMP when compared with NRC. The PREP10 MilkMP estimate had an RMSPE of 18.2% (NRC = 25.7%), concordance correlation coefficient of 0.82% (NRC = 0.64), slope bias of -0.14 kg/kg of predicted milk (NRC = -0.34 kg/kg), and mean bias of -0.63 kg (NRC = -2.85 kg). The PREP10 estimate of MilkAA had slightly elevated RMSPE and mean and slope bias when compared with MilkMP. The PREP10 estimate of MilkAA was not advantageous when compared with MilkMP, likely because AA use efficiency for milk was constant whereas MP use was variable. Future work evaluating variable AA use efficiencies for milk production is likely to improve accuracy and precision of models of allowable milk.

Calculating the energy value meadow hay - NEL, based solely on the NDF and compared with commonly used calculations NEL

DOI: 10.15414/afz.2015.18.04.95–98 Received 9. July 2015 ǀ Accepted 28. August 2015 ǀ Available online 7. December 2015 In our study was compare the accuracy of prediction of the energy value NEL of meadow hay. Predictive equations calculated from the results of our monitoring is NEL = 12.9085 - 0.1276x, where x = the NDF. Values of prediction equation which we calculated is comparable with the values indicated by the other authors. The great advantage of our prediction equation is the unpretentiousness of its calculation. Own calculation of NEL requires more analytically determined values. The number of analysis needed to calculate predictive equations and the time needed for all analysis is in our determination in the prediction of NEL, big advantage. It is further enhanced by lower finance costs. Keywords : meadow hay, prediction of the energy, NEL, NDF References HARRIS B. JR. (1993) Nonstructural and structural carbohydrates in dairy cattle rations. [Online] University of Florida, IFAS Extension. Available at http://ufdc.ufl.edu/U8566/00001/1j. [Accessed: 2015-06-10]. ISHLER V., HEINRICHS J. and VARGA G. (1996) From feed to milk: Understanding rumen fiction. [Online] Penn State College of Agricultural Sciences. Available at: http://extension.psu.edu/animals/dairy/nutrition/nutrition-and-feeding/rumen-function/from-feed-to-milk-understanding-rumen-function. [Accessed: 2015-06-10]. ISHLER, V. and VARGA, G. (2007) Carbohydrate nutrition for lactating dairy cattle: Information and tools to assist in managing dairy nutrition, including resources on forages, pasture, heifers, and calves. [Online] Penn State College of Agricultural Sciences. Available at: http://www.das.psu.edu/research-extension/dairy/nutrition/pdf/chonutrition.pdf. [Accessed: 2015-06-10]. LINN J. G. and MARTIN N. P., (1999) Forage Quality Test and Interpretations. In: Mendenhall, K. (ed.) 2009. The organic dairy handbook: a comprehensive guide for the transition and beyond. Cobleskill, NY: Northeast Organic Farming Association of New York. Available at: http://www.nofany.org/organic-farming/technical-assistance/organic-dairy . [Accessed: 2015-07-12]. MIKA V. et al. (1998) Forage quality. Praha: UZPI (in Czech). MUDŘIK, Z. et al. (2006) Foundations of modern cattle nutrition. Prague: CZU (in Czech). NRC (2001) Nutrient requirements of dairy cattle: Nutrient requirements of domestic animals. 7th revised edition. Washington: National Reserach Council. NOCEK, J. E. and RUSSELL, J. B. (1988) Protein and Energy as an Integrated System. Relationship of Ruminal Protein and Carbohydrate Availability to Microbial Synthesis and Milk Production. In J. Dairy Sci., vol. 71, pp. 2070-2107. DOI: http://dx.doi.org/10.3168/jds.S0022-0302(88)79782-9 POND, W. G. et al. (2005) Basic animal nutrition and feeding. 5th edition. New York: Wiley. SOMMER, A. et al. (1994) Nutrient requirements and nutritive value tables for ruminants. Pohořelice: VUVZ (in Czech). UNDERSANDER, D. et al. (1993) Forage analyses procedures. Madison: National Forage Testing Association. VAN SOEST, P. J., ROBERTSON, J.B. and LEWIS, B.A. (1991) Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. In J. Dairy Sci, vol. 74: 35 pp. 83-3597. DOI:http://dx.doi.org/10.3168/jds.S0022-0302(91)78551-2 VENCL, B. et al. (1991) The new evaluation systems for cattle feeds. Praha: AZV CSFR (in Czech). WEISS, P. H. (2010) Refining the net energy system WCDS. In Advances in Dairy Technology, vol. 22, pp. 191-202. ZEMAN, L. et al.(1995). Catalog of feeds (tables of feeds nutritional values). Pohořelice: VUVZ (in Czech)

Digestibility of NDF and its effect on the level of rumen fermentation of carbohydrates

DOI: 10.15414/afz.2015.18.04.110–113 Received 14. July 2015 ǀ Accepted 12. October 2015 ǀ Available online 7. December 2015 The objective of this study, was to determinate the effects of digestibility of NDF of TMR on rumen fermentation characteristics and nutrient digestion, dairy cows were fed total mixed ration (TMR). We are measured NDF digestibility of TMR using in situ methods.Digestibility of NDF of TMR in our experiment range 26.8 to 48.2%. Digestibility of NDF of TMR did not alter VFA production but had effects on A/P ratio and production of acetate and propionate. Our results indicate that TMR with more dNDF may provide more favourable condition for nutrient digestion both in the rumen and in the total tract of dairy cows. Keywords : rumen, fermentation, NDF, volatile fatty acids, digestibility of NDF References ALLEN, M. S., BRADFORD, B. J. and OBA, M. (2009) BOARD-INVITED REVIEW: The hepatic oxidation theory of the control of feed intake and its application to ruminants. In J. Anim. Sci., vol. 87, no. 10, pp. 3317–3334. doi:http://dx.doi.org/10.2527/jas.2009-1779 BEEVER, D. E. and MOULD, F. L. (2000) Forage evaluation for efficient ruminant livestock production. In: D. I. Givens et al. (eds.) Forage Evaluation in Ruminant Nutrition. Wallington: CAB International. HOFFMAN, P.C. and SHAVER, R.D. (2009) UW-Feed Grain Evaluation System Marshfield Soil and Forage Analysis Laboratory. [Online] Available at: http://www.uwex.edu/ces/dairynutrition/documents/ WisconsinFGES.pdf. [Accessed? 2015-06-04]. HUTJENS, M. F. (2002) Is your TMR as good as it can be? In Hoard´s Dairyman, vol. 147, no. 18, pp. 698. CHUMPAWADEE, S. and PIMPA, O. (2009) Effect of fodder tree as fiber sources in total mixed ration on feed intake, nutrient digestibility, chewing behaviour and ruminal fermentation in beef cattle. In J. Anim. And Vet. Adv, vol. 8, no. 7, pp. 1279:1284. doi: http://dx.doi.org/2009.1279.1284 KOLVER, E. S. and DE VETH, M. J. (2002) Prediction of ruminal pH from pasture based diets. In J. Dairy Sci., vol. 85, no. 5, pp. 1255–1266. doi:http://dx.doi.org/10.3168/jds.S0022-0302 (02)74190-8 LINN, J.G. et al. (1989) Feeding the dairy herd. Madison: North Central Regional Extension. LOPEZ, S., J. DIJKSTRA, and FRANCE, J. (2000) Prediction of energy supply in ruminants, with emphasis on forages. In: D. I. Givens, E. Owens, R. F. E., Axford, and H. M. Omed, (eds.) Forage Evaluation in Ruminant Nutrition. Wallingford: CAB International, pp. 63-94. NRC (2001) Nutrient requirements of dairy cattle: Nutrient requirements of domestic animals. 7th revised edition. Washington: National Reserach Council. OBA, M. and ALLEN, M. S. (1999) Evaluation of the importance of the digestibility of neutral detergent fiber from forage: effects on dry matter intake and milk yield of dairy cows. In J. Dairy Sci., vol. 82, no. 3, pp. 589-596. doi:http://dx.doi.org/10.3168/jds.S0022-0302(99)75271-9 OBA, M. and ALLEN, M. S. (2000) Effects of brown midrib 3 mutation in corn silage on productivity of dairy cows fed two concentrations of dietary neutral detergent fiber: 3. Digestibility and microbial efficiency. In J. Dairy Sci., vol. 83, no. 6, pp. 1342–1349. doi: http://dx.doi.org/10.3168/jds.S0022-0302(00)75001-6 ORSKOV, E.R. and MCDONALD, I. (1979) The estimation of protein degradability in the rumen from incubation measurements weighed according to rate of passage. In J. Agric. Sci., vol. 92, no. 3, pp. 499-503. doi:http://dx.doi.org/10.1017/S0021859600063048 VARGA G.A. (2006) In vivo digestibility of forage. In: Proc. Tri-State dairy nutrition conference, Fort Wayne. Columbus: The Ohio State University, pp 95-106. VOELKER LINTON., J. A. and ALLEN, M. S. (2009) Nutrient demand interacts with forage family to affect N digestion and utilization responses in dairy cows. In J. Dairy Sci., vol. 92, no. 4, pp.1594-1602. doi:http://dx.doi.org/10.3168/jds.2008-1327