Meta-regression to develop predictive equations for urinary nitrogen excretion of lactating dairy cows

Authors: Beck, Matthew; MARSHALL, CAMERON - Lincoln University - New Zealand; GARRETT, K - Lincoln University - New Zealand; Thompson, Terra; FOOTE, ANDREW - Oklahoma State University; VIBART, RONALDO - Agresearch; PACHECO, DAVID - Agresearch; GREGORINI, P - Lincoln University - New Zealand

Submitted to: Animals
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/7/2023
Publication Date: 2/10/2023
Citation: Beck, M.R., Marshall, C.J., Garrett, K., Campbell, T.N., Foote, A.P., Vibart, R., Pacheco, D., Gregorini, P. 2023. Meta-regression to develop predictive equations for urinary nitrogen excretion of lactating dairy cows. Animals. 13(4). Article 620. https://doi.org/10.3390/ani13040620.
DOI: https://doi.org/10.3390/ani13040620

Interpretive Summary: Urinary nitrogen excretion is a significant environmental pollutant in both confined and pasture-based dairy systems. Urinary nitrogen excretion is difficult to measure as it requires significant labor, animal manipulation, and specialized facilities. Accordingly, there is a need for accurate and precise models to predict urinary nitrogen excretion. Providing accurate and precise models to predict urinary nitrogen from milk urinary nitrogen is especially needed because policy makers are interested in limiting nitrogen emissions from dairy farms and producers need tools to make informed management decisions. We demonstrated that previously established equations had significant biases and poor predictive capability for dairy cows fed fresh forages. We established a new set of predictive equations based on milk urea nitrogen concentration, dry matter intake, crude protein content, and body weight. The developed equations showed improved agreement and predictive capabilities relative to previously published equations for both total mixed ration and fresh forage fed cows.

Technical Abstract: 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 foraage (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.