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Research article - Peer-reviewed, 2022

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

Bougouin, A.; Hristov, A.; Dijkstra, J.; Aguerre, M. J.; Ahvenjarvi, S.; Arndt, C.; Bannink, A.; Bayat, A. R.; Benchaar, C.; Boland, T.; Brown, W. E.; Crompton, L. A.; Dehareng, F.; Dufrasne, I.; Eugene, M.; Froidmont, E.; van Gastelen, S.; Garnsworthy, P. C.; Halmemies-Beauchet-Filleau, A.; Herremans, S.;
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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 2manure 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.


manure nitrogen excretion; prediction model; dairy cow

Published in

Journal of Dairy Science

2022, volume: 105, number: 9, pages: 7462-7481

Authors' information

Bougouin, A.
University of California Davis
Hristov, A.
Pennsylvania State University - University Park
Dijkstra, J.
Wageningen University and Research
Aguerre, M. J.
Clemson University
Ahvenjarvi, S.
Natural Resources Institute Finland (Luke)
Arndt, C.
International Livestock Research Institute (ILRI)
Bannink, A.
Wageningen University and Research
Bayat, A. R.
Natural Resources Institute Finland (Luke)
Benchaar, C.
Agriculture and Agri Food Canada
Boland, T.
University College Dublin
Brown, W. E.
University of Wisconsin Madison
Brown, W. E.
Ohio State University
Crompton, L. A.
University of Reading
Dehareng, F.
Walloon Agricultural Research Centre
Dufrasne, I.
University of Liege
Eugene, M.
Froidmont, E.
Walloon Agricultural Research Centre
van Gastelen, S.
Wageningen University and Research
Garnsworthy, P. C.
University of Nottingham
Halmemies-Beauchet-Filleau, A.
University of Helsinki
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UKÄ Subject classification

Animal and Dairy Science

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