Dataset Information

Jugular arginine supplementation increases lactation performance and nitrogen utilization efficiency in lactating dairy cows.

ABSTRACT: Background:Enhancing the post-ruminal supply of arginine (Arg), a semi-essential amino acid (AA), elicits positive effects on milk production. Our objective was to determine the effects of Arg infusion on milk production parameters and aspects of nitrogen (N) absorption and utilization in lactating dairy cows. Six lactating Chinese Holstein cows of similar body weight (508?±?14?kg), body condition score (3.0?±?0), parity (4.0?±?0), milk yield (30.6?±?1.8?kg) and days in milk (20?±?2 d) were randomly assigned to 3 treatments in a replicated 3?×?3 Latin square design with 21 d for each period (1?week for infusion and 2?weeks for washout). Treatments were 1) Control: saline; 2) Arg group: saline +?9.42?g/L?L-Arg; 3) Alanine (Ala) group: saline +?19.31?g/L?L-Ala (iso-nitrogenous to the Arg group). Milk production and composition, dry matter intake, apparent absorption of N, profiles of amino acids (AA) in blood, urea N in urine, milk, and blood, and gene expression of AA transporters were determined. Results:Compared with the Control or Ala group, the infusion of Arg led to greater expression of AA transporters (SLC7A2 and SLC7A8) and apparent uptake of free AA in the mammary gland, and was accompanied by greater milk yield, milk protein yield and milk efficiency (calculated by dividing milk yield over feed intake), together with lower concentration of urea N [regarded as an indicator of N utilization efficiency (NUE)] in blood and milk. Furthermore, in the cows infused with Arg, the NUE was higher and the concentration of urea N in urine was lower than those in the Ala group, although no differences were detected in NUE and urea N in urine between the Control and Arg group. The infusion of Ala had no effect on those indices compared with the Control. Conclusions:Overall, enhancing the post-ruminal supply of Arg via the jugular vein had a positive effect on the synthesis of milk protein at least in part by increasing gene expression of some AA transporters and uptake of free AA by mammary gland.

SUBMITTER: Ding L

PROVIDER: S-EPMC6340174 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Jugular arginine supplementation increases lactation performance and nitrogen utilization efficiency in lactating dairy cows.

Ding Luoyang L Shen Yizhao Y Wang Yifan Y Zhou Gang G Zhang Xin X Wang Mengzhi M Loor Juan J JJ Chen Lianmin L Zhang Jun J

Journal of animal science and biotechnology 20190121

<h4>Background</h4>Enhancing the post-ruminal supply of arginine (Arg), a semi-essential amino acid (AA), elicits positive effects on milk production. Our objective was to determine the effects of Arg infusion on milk production parameters and aspects of nitrogen (N) absorption and utilization in lactating dairy cows. Six lactating Chinese Holstein cows of similar body weight (508 ± 14 kg), body condition score (3.0 ± 0), parity (4.0 ± 0), milk yield (30.6 ± 1.8 kg) and days in milk (20 ± 2 d) w ...[more]

PMID: 30680190

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Project description:Dairy cows' urinary nitrogen (N) excretion (UN; g/d) represents a significant environmental concern due to their contribution to nitrate leaching, nitrous oxide (a potent greenhouse gas), and ammonia emissions (contributor to N deposition). The first objective of the current study was to determine the adequacy of existing models to predict UN from total mixed ration (TMR)-fed and fresh forage (FF)-fed cows. Next, we aimed to develop equations to predict UN based on animal factors [milk urea nitrogen (MUN; mg/dL) and body weight (BW, kg)] and to explore how these equations are improved when dietary factors, such as diet type, dry matter intake (DMI), or dietary characteristics [neutral detergent fiber (NDF) and crude protein (CP) content], are considered. A dataset was obtained from 51 published experiments composed of 174 treatment means. The whole dataset was used to evaluate the mean and linear biases of three existing equations including diet type as an interaction term; all models had significant linear and mean biases and two of the three models had poor predictive capabilities as indicated by their large relative prediction error (RPE; root mean square error of prediction as a percent of the observed mean). Next, the complete data set was split into training and test sets, which were used to develop and to evaluate new models, respectively. The first model included MUN and BW, and there was a significant interaction between diet type and the coefficients. This model had the worst 1:1 agreement [Lin's concordance correlation coefficient (CCC) = 0.50] and largest RPE (24.7%). Models that included both animal and dietary factors performed the best, and when included in the model, the effect of diet type was no longer significant (p > 0.10). These models all had very good agreement (CCC ≥ 0.86) and relatively low RPE (≤13.1%). This meta-analysis developed precise and accurate equations to predict UN from dairy cows in both confined and pasture-based systems.

Project description:BackgroundNitrate leaching to groundwater and surface water and ammonia volatilization from dairy farms have negative impacts on the environment. Meanwhile, the increasing demand for dairy products will result in more pollution if N losses are not controlled. Therefore, a more efficient, and environmentally friendly production system is needed, in which nitrogen use efficiency (NUE) of dairy cows plays a key role. To genetically improve NUE, extensively recorded and cost-effective proxies are essential, which can be obtained by including mid-infrared (MIR) spectra of milk in prediction models for NUE. This study aimed to develop and validate the best prediction model of NUE, nitrogen loss (NL) and dry matter intake (DMI) for individual dairy cows in China.ResultsA total of 86 lactating Chinese Holstein cows were used in this study. After data editing, 704 records were obtained for calibration and validation. Six prediction models with three different machine learning algorithms and three kinds of pre-processed MIR spectra were developed for each trait. Results showed that the coefficient of determination (R2) of the best model in within-herd validation was 0.66 for NUE, 0.58 for NL and 0.63 for DMI. For external validation, reasonable prediction results were only observed for NUE, with R2 ranging from 0.58 to 0.63, while the R2 of the other two traits was below 0.50. The infrared waves from 973.54 to 988.46 cm-1 and daily milk yield were the most important variables for prediction.ConclusionThe results showed that individual NUE can be predicted with a moderate accuracy in both within-herd and external validations. The model of NUE could be used for the datasets that are similar to the calibration dataset. The prediction models for NL and 3-day moving average of DMI (DMI_a) generated lower accuracies in within-herd validation. Results also indicated that information of MIR spectra variables increased the predictive ability of models. Additionally, pre-processed MIR spectra do not result in higher accuracy than original MIR spectra in the external validation. These models will be applied to large-scale data to further investigate the genetic architecture of N efficiency and further reduce the adverse impacts on the environment after more data is collected.

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Dataset Information

Jugular arginine supplementation increases lactation performance and nitrogen utilization efficiency in lactating dairy cows.

Publications

Jugular arginine supplementation increases lactation performance and nitrogen utilization efficiency in lactating dairy cows.

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