Prediction of nitrogen excretion from data on dairy cows fed a wide range of diets compiled in an intercontinental database: A meta-analysis

A. Bougouin, A. Hristov, J. Dijkstra, M. J. Aguerre, S. Ahvenjärvi, C. Arndt, A. Bannink, A. R. Bayat, C. Benchaar, T. Boland, W. E. Brown, L. A. Crompton, F. Dehareng, I. Dufrasne, M. Eugène, E. Froidmont, S. van Gastelen, P. C. Garnsworthy, A. Halmemies-Beauchet-Filleau, S. HerremansP. Huhtanen, M. Johansen, A. Kidane, M. Kreuzer, B. Kuhla, F. Lessire, P. Lund, E. M.K. Minnée, C. Muñoz, M. Niu, P. Nozière, D. Pacheco, E. Prestløkken, C. K. Reynolds, A. Schwarm, J. W. Spek, M. Terranova, A. Vanhatalo, M. A. Wattiaux, M. R. Weisbjerg, D. R. Yáñez-Ruiz, Z. Yu, E. Kebreab

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Manure nitrogen (N) from cattle contributes to nitrous oxide and ammonia emissions and nitrate leaching. Measurement of manure N outputs on dairy farms is laborious, expensive, and impractical at large scales; therefore, models are needed to predict N excreted in urine and feces. Building robust prediction models requires extensive data from animals under different management systems worldwide. Thus, the study objectives were (1) to collate an international database of N excretion in feces and urine based on individual lactating dairy cow data from different continents; (2) to determine the suitability of key variables for predicting fecal, urinary, and total manure N excretion; and (3) to develop robust and reliable N excretion prediction models based on individual data from lactating dairy cows consuming various diets. A raw data set was created based on 5,483 individual cow observations, with 5,420 fecal N excretion and 3,621 urine N excretion measurements collected from 162 in vivo experiments conducted by 22 research institutes mostly located in Europe (n = 14) and North America (n = 5). A sequential approach was taken in developing models with increasing complexity by incrementally adding variables that had a significant individual effect on fecal, urinary, or total manure N excretion. Nitrogen excretion was predicted by fitting linear mixed models including experiment as a random effect. Simple models requiring dry matter intake (DMI) or N intake performed better for predicting fecal N excretion than simple models using diet nutrient composition or milk performance parameters. Simple models based on N intake performed better for urinary and total manure N excretion than those based on DMI, but simple models using milk urea N (MUN) and N intake performed even better for urinary N excretion. The full model predicting fecal N excretion had similar performance to simple models based on DMI but included several independent variables (DMI, diet crude protein content, diet neutral detergent fiber content, milk protein), depending on the location, and had root mean square prediction errors as a fraction of the observed mean values of 19.1% for intercontinental, 19.8% for European, and 17.7% for North American data sets. Complex total manure N excretion models based on N intake and MUN led to prediction errors of about 13.0% to 14.0%, which were comparable to models based on N intake alone. Intercepts and slopes of variables in optimal prediction equations developed on intercontinental, European, and North American bases differed from each other, and therefore region-specific models are preferred to predict N excretion. In conclusion, region-specific models that include information on DMI or N intake and MUN are required for good prediction of fecal, urinary, and total manure N excretion. In absence of intake data, region-specific complex equations using easily and routinely measured variables to predict fecal, urinary, or total manure N excretion may be used, but these equations have lower performance than equations based on intake.

Original languageEnglish (US)
Pages (from-to)7462-7481
Number of pages20
JournalJournal of dairy science
Volume105
Issue number9
DOIs
StatePublished - Sep 2022

All Science Journal Classification (ASJC) codes

  • Food Science
  • Animal Science and Zoology
  • Genetics

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