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Bougouin A, Hristov A, Dijkstra J, Aguerre MJ, Ahvenjärvi S, Arndt C, Bannink A, Bayat AR, Benchaar C, Boland T, Brown WE, Crompton LA, Dehareng F, Dufrasne I, Eugène M, Froidmont E, van Gastelen S, Garnsworthy PC, Halmemies-Beauchet-Filleau A, Herremans S, Huhtanen P, Johansen M, Kidane A, Kreuzer M, Kuhla B, Lessire F, Lund P, Minnée EMK, Muñoz C, Niu M, Nozière P, Pacheco D, Prestløkken E, Reynolds CK, Schwarm A, Spek JW, Terranova M, Vanhatalo A, Wattiaux MA, Weisbjerg MR, Yáñez-Ruiz DR, Yu Z, Kebreab E. Prediction of nitrogen excretion from data on dairy cows fed a wide range of diets compiled in an intercontinental database: A meta-analysis. J Dairy Sci 2022; 105:7462-7481. [PMID: 35931475 DOI: 10.3168/jds.2021-20885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 04/03/2022] [Indexed: 11/19/2022]
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.
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Affiliation(s)
- A Bougouin
- Department of Animal Science, University of California, Davis 95616.
| | - A Hristov
- Department of Animal Science, The Pennsylvania State University, University Park 16803
| | - J Dijkstra
- Animal Nutrition Group, Wageningen University and Research, 6700 AH Wageningen, the Netherlands
| | - M J Aguerre
- Department of Animal and Veterinary Sciences, Clemson University, Clemson, SC 29634
| | - S Ahvenjärvi
- Animal Nutrition, Production Systems, Natural Resources Institute Finland (Luke), FI-31600 Jokioinen, Finland
| | - C Arndt
- Mazingira Centre, International Livestock Research Institute (ILRI), 00100 Nairobi, Kenya
| | - A Bannink
- Wageningen Livestock Research, Wageningen University and Research, 6700 AH Wageningen, the Netherlands
| | - A R Bayat
- Animal Nutrition, Production Systems, Natural Resources Institute Finland (Luke), FI-31600 Jokioinen, Finland
| | - C Benchaar
- Sherbrooke Research and Development Centre, Agriculture and Agri-Food Canada, Sherbrooke, Quebec, Canada J1M 0C8
| | - T Boland
- School of Agriculture and Food Science, University College Dublin, Belfield, Dublin 4, Ireland
| | - W E Brown
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison 53706-1205; Department of Animal Sciences, The Ohio State University, Columbus 43210
| | - L A Crompton
- School of Agriculture, Policy and Development, University of Reading, Reading RG6 6AR, United Kingdom
| | - F Dehareng
- Department of Valorisation of Agricultural Products, Walloon Agricultural Research Centre, 5030 Gembloux, Belgium
| | - I Dufrasne
- Department of Veterinary Management of Animal Resources, Faculty of Veterinary Medicine, Fundamental and Applied Research for Animal and Health (FARAH), University of Liège, 4000 Liège, Belgium
| | - M Eugène
- INRAE - Université Clermont Auvergne - VetAgroSup UMR 1213 Unité Mixte de Recherche sur les Herbivores, Centre de recherche Auvergne-Rhône-Alpes, Theix, 63122 Saint-Genès-Champanelle, France
| | - E Froidmont
- Department of Valorisation of Agricultural Products, Walloon Agricultural Research Centre, 5030 Gembloux, Belgium
| | - S van Gastelen
- Wageningen Livestock Research, Wageningen University and Research, 6700 AH Wageningen, the Netherlands
| | - P C Garnsworthy
- School of Biosciences, University of Nottingham, Loughborough LE12 5RD, United Kingdom
| | - A Halmemies-Beauchet-Filleau
- Faculty of Agriculture and Forestry, Department of Agricultural Sciences, University of Helsinki, 00014 Helsinki, Finland
| | - S Herremans
- Department of Valorisation of Agricultural Products, Walloon Agricultural Research Centre, 5030 Gembloux, Belgium
| | - P Huhtanen
- Department of Agricultural Science for Northern Sweden, Swedish University of Agricultural Sciences, SE-901 83, Umeå, Sweden
| | - M Johansen
- Department of Animal Science, Aarhus University, AU Foulum, Dk-8830 Tjele, Denmark
| | - A Kidane
- Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, 1433 Ås, Norway
| | - M Kreuzer
- Institute of Agricultural Sciences, ETH Zurich, 8092 Zurich, Switzerland
| | - B Kuhla
- Institute for Farm Animal Biology (FBN), Institute of Nutritional Physiology "Oskar Kellner," Dummerstorf, Mecklenburg-Vorpommern, Germany
| | - F Lessire
- Department of Veterinary Management of Animal Resources, Faculty of Veterinary Medicine, Fundamental and Applied Research for Animal and Health (FARAH), University of Liège, 4000 Liège, Belgium
| | - P Lund
- Department of Animal Science, Aarhus University, AU Foulum, Dk-8830 Tjele, Denmark
| | - E M K Minnée
- DairyNZ Ltd., Private Bag 3221, Hamilton, New Zealand 3240
| | - C Muñoz
- Instituto de Investigaciones Agropecuarias, INIA Remehue, Ruta 5 S, Osorno, Chile
| | - M Niu
- Department of Animal Science, University of California, Davis 95616; Institute of Agricultural Sciences, ETH Zurich, 8092 Zurich, Switzerland
| | - P Nozière
- INRAE - Université Clermont Auvergne - VetAgroSup UMR 1213 Unité Mixte de Recherche sur les Herbivores, Centre de recherche Auvergne-Rhône-Alpes, Theix, 63122 Saint-Genès-Champanelle, France
| | - D Pacheco
- Ag Research, Palmerston North 4410, New Zealand
| | - E Prestløkken
- Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, 1433 Ås, Norway
| | - C K Reynolds
- School of Agriculture, Policy and Development, University of Reading, Reading RG6 6AR, United Kingdom
| | - A Schwarm
- Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, 1433 Ås, Norway
| | - J W Spek
- Wageningen Livestock Research, Wageningen University and Research, 6700 AH Wageningen, the Netherlands
| | - M Terranova
- AgroVet-Strickhof, ETH Zurich, 8315 Lindau, Switzerland
| | - A Vanhatalo
- Faculty of Agriculture and Forestry, Department of Agricultural Sciences, University of Helsinki, 00014 Helsinki, Finland
| | - M A Wattiaux
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison 53706-1205
| | - M R Weisbjerg
- Department of Animal Science, Aarhus University, AU Foulum, Dk-8830 Tjele, Denmark
| | - D R Yáñez-Ruiz
- Estación Experimental del Zaidin, CSIC, 1, 18008 Granada, Spain
| | - Z Yu
- Department of Animal Sciences, The Ohio State University, Columbus 43210
| | - E Kebreab
- Department of Animal Science, University of California, Davis 95616
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Heard JW, Hannah MC, Ho CKM, Wales WJ. Predicting Immediate Marginal Milk Responses and Evaluating the Economics of Two-Variable Input Tactical Feeding Decisions in Grazing Dairy Cows. Animals (Basel) 2021; 11:1920. [PMID: 34203434 PMCID: PMC8300297 DOI: 10.3390/ani11071920] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/24/2021] [Accepted: 06/24/2021] [Indexed: 11/24/2022] Open
Abstract
Feed is the largest variable cost for dairy farms in Australia, and dairy farmers are faced with the challenge of profitably feeding their cows in situations where there is significant variation in input costs and milk price. In theory, the addition of 5.2 MJ of metabolisable energy to a lactating cow's diet should be capable of supporting an increase in milk production of one litre of milk of 4.0% fat, 3.2% protein and 4.9% lactose. However, this is almost never seen in practice, due to competition for energy from other processes (e.g., body tissue gain), forage substitution, associative effects and imbalances in rumen fermentation. Pasture species, stage of maturity, pasture mass, allowance and intake, stage of lactation, cow body condition and type of supplement can all affect the milk protein plus fat production response to additional feed consumed by grazing dairy cows. We developed a model to predict marginal milk protein plus fat response/kg DM intake when lactating dairy cows consume concentrates and pasture + forages. Data from peer reviewed published experiments undertaken in Australia were collated into a database. Meta-analysis techniques were applied to the data and a two-variable quadratic polynomial production function was developed. Production economic theory was used to estimate the level of output for given quantities of input, the marginal physical productivity of each input, the isoquants for any specified level of output and the optimal input combination for given costs and prices of inputs and output. The application of the model and economic overlay was demonstrated using four scenarios based on a farm in Gippsland, Victoria. Given that feed accounts for the largest input cost in dairying, allocation of pasture and supplements that are based on better estimates of marginal milk responses to supplements should deliver increased profit from either savings in feed costs, or in some cases, increased output to approach the point where marginal revenue equals marginal costs. Such data are critical if the industry is to take advantage of the opportunities to use supplements to improve both productivity and profitability.
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Affiliation(s)
- Joanna W. Heard
- Department of Jobs, Precincts and Regions, Hamilton, VIC 3300, Australia
| | - Murray C. Hannah
- Department of Jobs, Precincts and Regions, Ellinbank, VIC 3820, Australia; (M.C.H.); (W.J.W.)
| | - Christie K. M. Ho
- Department of Jobs, Precincts and Regions, Bundoora, VIC 3083, Australia;
| | - William J. Wales
- Department of Jobs, Precincts and Regions, Ellinbank, VIC 3820, Australia; (M.C.H.); (W.J.W.)
- Centre for Agricultural Innovation, Faculty of Veterinary and Agricultural Sciences, School of Agriculture and Food, The University of Melbourne, Parkville, VIC 3010, Australia
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