Skip to main content
×
Home

The AusBeef model for beef production: I. Description and evaluation

  • H. C. DOUGHERTY (a1), E. KEBREAB (a1), M. EVERED (a2), B. A. LITTLE (a3), A. B. INGHAM (a3), R. S. HEGARTY (a4), D. PACHECO (a5) and M. J. MCPHEE (a2)...
Summary
SUMMARY

As demand for animal products, such as meat and milk, increases, and concern over environmental impact grows, mechanistic models can be useful tools to better represent and understand ruminant systems and evaluate mitigation options to reduce greenhouse gas emissions without compromising productivity. The objectives of the present study were to describe the representation of processes for growth and enteric methane (CH4) production in AusBeef, a whole-animal, dynamic, mechanistic model for beef production; evaluate AusBeef for its ability to predict daily methane production (DMP, g/day), gross energy intake (GEI, MJ/day) and methane yield (MJ CH4/MJ GEI) using an independent data set; and to compare AusBeef estimates to those from the empirical equations featured in the current National Academies of Sciences, Engineering and Medicine (NASEM, 2016) beef cattle requirements for growth and the Ruminant Nutrition System (RNS), a dynamic, mechanistic model of Tedeschi & Fox, 2016. AusBeef incorporates a unique fermentation stoichiometry that represents four microbial groups: protozoa, amylolytic bacteria, cellulolytic bacteria and lactate-utilizing bacteria. AusBeef also accounts for the effects of ruminal pH on microbial degradation of feed particles. Methane emissions are calculated from net ruminal hydrogen balance, which is defined as the difference between inputs from fermentation and outputs due to microbial use and biohydrogenation. AusBeef performed similarly to the NASEM empirical model in terms of prediction accuracy and error decomposition, and with less root mean square predicted error (RMSPE) than the RNS mechanistic model when expressed as a percentage of the observed mean (RMSPE, %), and the majority of error was non-systematic. For DMP, RMSPE for AusBeef, NASEM and RNS were 24·0, 19·8 and 50·0 g/day for the full data set (n = 35); 25·6, 18·2 and 56·2 g/day for forage diets (n = 19); and 21·8, 21·5 and 41·5 g/day for mixed diets (n = 16), respectively. Concordance correlation coefficients (CCC) were highest for GEI, with all models having CCC > 0·66, and higher CCC for forage diets than mixed, while CCC were lowest for MY, particularly forage diets. Systematic error increased for all models on forage diets, largely due to an increase in error due to mean bias, and while all models performed well for mixed diets, further refinements are required to improve the prediction of CH4 on forage diets.

Copyright
Corresponding author
*To whom all correspondence should be addressed. Email: malcolm.mcphee@dpi.nsw.gov.au
References
Hide All
Appuhamy J. A. D. R. N., France J. & Kebreab E. (2016). Models for predicting enteric methane emissions from dairy cows in North America, Europe, and Australia and New Zealand. Global Change Biology 22, 30393056.
Baldwin R. L. (1995). Modeling Ruminant Digestion and Metabolism. New York, NY: Chapman & Hall.
Baldwin R. L., France J. & Gill M. (1987 a). Metabolism of the lactating cow. I. Animal elements of a mechanistic model. Journal of Dairy Research 54, 77105.
Baldwin R. L., Thornley J. H. M. & Beever D. E. (1987 b). Metabolism of the lactating cow. II. Digestive elements of a mechanistic model. Journal of Dairy Research 54, 107131.
Baldwin R. L., France J., Beever D. E., Gill M. & Thornley J. H. M. (1987 c). Metabolism of the lactating cow. III. Properties of mechanistic models suitable for evaluation of energetic relationships and factors involved in the partition of nutrients. Journal of Dairy Research 54, 133145.
Beauchemin K. A., Kreuzer M., O'Mara F. & McAllister T. A. (2008). Nutritional management for enteric methane abatement: a review. Australian Journal of Experimental Agriculture 48, 2127.
Bibby J. H. & Toutenberg H. (1977). Prediction and Improved Estimation in Linear Models. Minneapolis: John Wiley & Sons.
Boland T. M., Quinlan C., Pierce K. M., Lynch M. B., Kenny D. A., Kelly A. K. & Purcell P. J. (2014). The effect of pasture pregrazing herbage mass on methane emissions, ruminal fermentation, and average daily gain of grazing beef heifers. Journal of Animal Science 91, 38673874.
Bruinsma J. (2003). World Agriculture: Towards 2015/2030 an FAO Perspective. Rome, Italy: FAO. Available from: http://www.fao.org/docrep/005/y4252e/y4252e00.htm (accessed 17 August 2016).
Chaves A. V., Thompson L. C., Iwaasa A. D., Scott S. L., Olson M. E., Benchaar C., Veira D. M. & McAllister T. A. (2006). Effect of pasture type (alfalfa vs grass) on methane and carbon dioxide production by yearling beef heifers. Canadian Journal of Animal Science 86, 409418.
Dijkstra J. (1993). Mathematical modelling and integration of rumen fermentation processes. PhD Thesis, Wageningen Agricultural University, Wageningen, The Netherlands.
Dijkstra J. (1994). Simulation of the dynamics of protozoa in the rumen. British Journal of Nutrition 72, 679699.
Dijkstra J., Neal H. D., Beever D. E. & France J. (1992). Simulation of nutrient digestion, absorption and outflow in the rumen: model description. Journal of Nutrition 122, 22392256.
Dijkstra J., Gerrits W. J. J., Bannink A. & France J. (2000). Modelling lipid metabolism in the rumen. In Modelling Nutrient Utilization in Farm Animals (Eds McNamara J. P., France J. & Beever D. E.), pp. 2536. New York: CAB International.
Dijkstra J., France J., Ellis J. L., Strathe A. B., Kebreab E. & Bannink A. (2013). Production efficiency of ruminants: feed, nitrogen, and methane. In Sustainable Animal Agriculture (Ed. Kebreab E.), pp. 1025. Wallingford, UK: CAB International.
Duthie C.-A., Rooke J. A., Hyslop J. J. & Waterhouse A. (2015). Methane emissions from two breeds of beef cows offered diets containing barley straw with either grass silage or brewers’ grains. Animal 9, 16801687.
Ehle F. R., Murphy M. R. & Clark J. H. (1982). In situ particle size reduction and the effect of particle size on degradation of crude protein and dry matter in the rumen of dairy steers. Journal of Dairy Science 65, 963971.
Ellis J. L., Kebreab E., Odongo N. E., McBride B. W., Okine E. K. & France J. (2007). Prediction of methane production from dairy and beef cattle. Journal of Dairy Science 90, 34563466.
Ellis J. L., Dijkstra J., Kebreab E., Bannink A., Odongo N. E., McBride B. W. & France J. (2008). Aspects of rumen microbiology central to mechanistic modelling of methane production in cattle. The Journal of Agricultural Science, Cambridge 146, 213233.
Ellis J. L., Kebreab E., Odongo N. E., Beauchemin K. A., McGinn S., Nkrumah J. D., Moore S. S., Christopherson R., Murdoch G. K., McBride B. W., Okine E. K. & France J. (2009). Modeling methane production from beef cattle using linear and nonlinear approaches. Journal of Animal Science 87, 13341345.
Escobar-Bahamondes P., Oba M. & Beauchemin K. A. (2017). Universally applicable methane prediction equations for beef cattle fed high- or low-forage diets. Canadian Journal of Animal Science 97, 8394.
Eshel G., Shepon A., Makov T. & Milo R. (2014). Land, irrigation water, greenhouse gas, and reactive nitrogen burdens of meat, eggs, and dairy production in the United States. Proceedings of the National Academy of Sciences USA 111, 1199612001.
FAO (2013). Tackling climate change through livestock – A global assessment of emissions and mitigation opportunities. Rome, Italy: FAO. Available from: http://www.fao.org/3/i3437e.pdf (accessed 17 August 2016).
FAO (2014). The State of Food and Agriculture: Innovation in Family Farming. Rome, Italy: FAO. Available from: http://www.fao.org/3/a-i4040e.pdf (accessed 17 August 2016).
FAO (2015). Towards a Water and Food Secure Future. Critical Perspectives for Policy-makers. Rome, Italy: FAO. Available from: http://www.fao.org/3/a-i4560e.pdf (accessed 31 May 2017).
Fox D. G., Sniffen C. J., O'Connor J. D., Russell J. B. & Van Soest P. J. (1992). A net carbohydrate and protein system for evaluating cattle diets: III. Cattle requirements and diet adequacy. Journal of Animal Science 70, 35783596.
Fox D. G., Tylutki T. P., Van Amburgh M. E., Chase L. E., Pell A. N., Overton T. R., Tedeschi L. O., Rasmussen C. N. & Durbal V. M. (2000). The Net Carbohydrate and Protein System for Evaluating Herd Nutrition and Nutrient Excretion. CNCPS Version 4.0: Model Documentation. Ithaca, NY: Department of Animal Science, Cornell University.
Fox D. G., Tedeschi L. O., Tylutki T. P., Russell J. B., Van Amburgh M. E., Chase L. E., Pell A. N. & Overton T. R. (2004). The Cornell Net Carbohydrate and Protein System model for evaluating herd nutrition and nutrient excretion. Animal Feed Science & Technology 112, 2978.
Freer M., Moore A. D. & Donnelly J. R. (1997). GRAZPLAN: decision support systems for Australian grazing enterprises – II. The animal biology model for feed intake, production and reproduction and the GrazFeed DSS. Agricultural Systems 54, 77126.
Gregorini P., Beukes P., Waghorn G., Pacheco D. & Hanigan M. (2015). Development of an improved representation of rumen digesta outflow in a mechanistic and dynamic model of a dairy cow, MOLLY. Ecological Modelling 313, 293306.
Hackmann T. J. & Firkins J. L. (2015). Maximizing efficiency of rumen microbial protein production. Frontiers in Microbiology 6, 465. doi: 10.3389/fmicb.2015.00465.
Haisan J., Sun Y., Guan L. L., Beauchemin K. A., Iwaasa A., Duval S., Barreda D. R. & Oba M. (2014). The effects of feeding 3-nitrooxypropanol on methane emissions and productivity of Holstein cows in mid lactation. Journal of Dairy Science 97, 31103119.
Hales K. E., Foote A. P., Brown-Brandl T. M. & Freetly H. C. (2015). Effects of dietary glycerin inclusion at 0, 5, 10, and 15 percent of dry matter on energy metabolism and nutrient balance in finishing beef steers. Journal of Animal Science 93, 348356.
Hegarty R. S. (2016). Impacts of CFI Methodologies on Whole-farm Systems. Final Report. Canberra, Australia: Department of Agriculture, University of New England, Filling the Research Gap Program. Available from: https://www.une.edu.au/__data/assets/pdf_file/0011/166808/Abridged_draft_Report_AusBeef.pdf (accessed 30 June 2017).
Herrmann N. (2013). AusFarm – A Tutorial Version 1.8. Canberra, Australia: CSIRO. Available from: http://www.grazplan.csiro.au/files/AusFarm20-20a%20tutorial.pdf (accessed 27 June 2017).
INRA (French National Institute for Agricultural Research), CIRAD (French Agricultural Research Center for International Development), AFZ (French Association for Animal Production) & FAO (Food and Agriculture Organization of the United Nations) (2016). Feedipedia: an On-Line Encyclopedia of Animal Feeds. Paris, France and Rome, Italy: INRA, CIRAD, AFZ & FAO. Available from: http://www.feedipedia.org (accessed 1 April 2017).
IPCC (Intergovernmental Panel On Climate Change) (2006). Emissions from Livestock and Manure Management. In 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Volume 4: Agriculture, Forestry and Other Land Use (Eds Eggleston H. S., Buendia L., Miwa K., Ngara T. & Tanabe K.). pp. 3032. Hayama, Japan: Institute for Global Environmental Strategies (IGES).
Jonker A., Muetzel S., Molano G. & Pacheco D. (2016). Effect of fresh pasture forage quality, feeding level and supplementation on methane emissions from growing beef cattle. Animal Production Science 56, 17141721.
Kennedy P. M. (1985). Effect of rumination on reduction of particle size of rumen digesta by cattle. Australian Journal of Agricultural Research 36, 819828.
Lin L. I.-K. (1989). A concordance correlation coefficient to evaluate reproducibility. Biometrics 45, 255268.
McDonnell R. P., Hart K. J., Boland T. M., Kelly A. K., McGee M. & Kenny D. A. (2016). Effect of divergence in phenotypic residual feed intake on methane emissions, ruminal fermentation, and apparent whole-tract digestibility of beef heifers across three contrasting diets. Journal of Animal Science 94, 11791193.
McGinn S. M., Chung Y.-H., Beauchemin K. A., Iwaasa A. D. & Grainger C. (2009). Use of corn distillers’ dried grains to reduce enteric methane loss from beef cattle. Canadian Journal of Animal Science 89, 409413.
McIntyre B. D., Herren H. R., Wakhungu J. & Watson R. T. (2009). International Assessment of Agricultural Knowledge, Science, and Technology for Development: Global Report. Washington, DC: IAASTD.
McNamara J. P., Hanigan M. D. & White R. R. (2016). Invited review: experimental design, data reporting, and sharing in support of animal systems modelling research. Journal of Dairy Science 99, 93559371.
McNamara J. P., Auldist M. J., Marett L. C., Moate P. J. & Wales W. J. (2017). Analysis of pasture supplementation strategies by means of a mechanistic model of ruminal digestion and metabolism in the dairy cow. Journal of Dairy Science 100, 10951106.
Mills J. A. N., Kebreab E., Yates C. M., Crompton L. A., Cammell S. B., Dhanoa M. S., Agnew R. E. & France J. (2003). Alternative approaches to predicting methane emissions from dairy cows. Journal of Animal Science 81, 31413150.
Moore A. D., Holzworth D. P., Herrmann N. I., Huth N. I., Keating B. A. & Robertson M. J. (2005). Specification of the CSIRO Common Modelling Protocol. Canberra, Australia: CSIRO. Available from: http://www.grazplan.csiro.au/files/Protocol%20Specification.pdf (accessed 1 June 2017).
Moraes L. E., Strathe A. B., Fadel J. G., Casper D. P. & Kebreab E. (2014). Prediction of enteric methane emissions from cattle. Global Change Biology 20, 21402148.
Moriasi D. N., Arnold J. G., Van Liew M. W., Bingner R. L., Harmel R. D. & Veith T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the American Society of Agricultural and Biological Engineers 50, 885900.
Murphy M. R., Baldwin R. L. & Koong L. J. (1982). Estimation of stoichiometric parameters for rumen fermentation of roughage and concentrate diets. Journal of Animal Science 55, 411421.
Nagaraja T. G. & Titgemeyer E. C. (2007). Ruminal acidosis in beef cattle: the current microbiological and nutritional outlook. Journal of Dairy Science 90 (Suppl. 1), E17E38.
Nagorcka B. N. (2004 a). AUSBEEF: A Decision Support System for Cattle Feedlots and the PGLP (Premium Grains for Livestock Program). Canadian Beef Research Center Seminar, July 2004, Lethbridge, Canada. Lethbridge, Canada: The Center. Available from: https://publications.csiro.au/rpr/search?q=AUSBEEF3A+a+decision+support+system+for+cattle+feedlots+and+the+PGLP+28Premium+Grains+for+Livestock+Program%29 (accessed 27 June 2017).
Nagorcka B. N. (2004 b). A Description of AUSBEEF Ruminant Model Highlighting the Differences with the Current Models CNCPS and MOLLY. Faculty of Animal Science, University of California Seminar, August, 2004. Davis, CA: University of California. Available from: https://publications.csiro.au/rpr/search?q=A20description20of20AUSBEEF20ruminant20model20highlighting20the20differences20with20the20current20models20CNCPS20and%20MOLLY.&p=1&rpp=25&sb=RECENT (accessed 27 June 2017).
Nagorcka B. N. & Zurcher E. J. (2002). The potential gains achievable through access to more advanced/mechanistic models of ruminants. Animal Production in Australia: Proceedings of the Australian Society of Animal Production 24, 455461.
Nagorcka B. N., Gordon G. L. R. & Dynes R. A. (2000). Towards a more accurate representation of fermentation in mathematical models of the rumen. In Modelling Nutrient Utilization in Farm Animals (Eds McNamara J. P., France J. & Beever D.), pp. 3748. New York: CAB International.
National Academies of Sciences, Engineering, and Medicine (NASEM) (2016). Nutrient Requirements of Beef Cattle, 8th revised edn. Washington, DC: The National Academies Press.
O'Connor J. D., Sniffen C. J., Fox D. G. & Chalupa W. (1993). A net carbohydrate and protein system for evaluating cattle diets: IV. Predicting amino acid adequacy. Journal of Animal Science 71, 12981311.
Owens F. N., Secrist D. S., Hill W. J. & Gill D. R. (1998). Acidosis in cattle: a review. Journal of Animal Science 76, 275286.
Pitt R. E., Van Kessel J. S., Fox D. G., Pell A. N., Barry M. C. & Van Soest P. J. (1996). Prediction of ruminal volatile fatty acids and pH within the net carbohydrate and protein system. Journal of Animal Science 74, 226244.
PTV Planning Transport Verkehr AG (2004). User's Manual, VISSIM 4.0. Karlsruhe, Germany: PTV.
Ricci P., Rooke J. A., Nevison I. & Waterhouse A. (2013). Methane emissions from beef and dairy cattle: quantifying the effect of physiological stage and diet characteristics. Journal of Animal Science 91, 53795389.
Romero-Perez A., Okine E. K., McGinn S. M., Guan L. L., Oba M., Duval S. M., Kindermann M. & Beauchemin K. A. (2014). The potential of 3-nitrooxypropanol to lower enteric methane emissions from beef cattle. Journal of Animal Science 92, 46824693.
Rooke J. A., Wallace R. J., Duthie C.-A., McKain N., de Souza S. M., Hyslop J. J., Ross D. W., Waterhouse T. & Roehe R. (2014). Hydrogen and methane emissions from beef cattle and their rumen microbial community vary with diet, time after feeding, and genotype. British Journal of Nutrition 112, 398407.
Russell J. B., O'Connor J. D., Fox D. G., Van Soest P. J. & Sniffen C. J. (1992). A net carbohydrate and protein system for evaluating cattle diets: I. Ruminal fermentation. Journal of Animal Science 70, 35513561.
Sniffen C. J., O'Connor J. D., Van Soest P. J., Fox D. G. & Russell J. B. (1992). A net carbohydrate and protein system for evaluating cattle diets: II. Carbohydrate and protein availability. Journal of Animal Science 70, 35623577.
Stackhouse K. R., Pan Y., Zhao Y. & Mitloehner F. M. (2011). Greenhouse gas and alcohol emissions from feedlot steers and calves. Journal of Environmental Quality 40, 899906.
Tedeschi L. O. (2006). Assessment of the adequacy of mathematical models. Agricultural Systems 89, 225247.
Tedeschi L. O. & Fox D. G. (2016). The Ruminant Nutrition System: An Applied Model for Predicting Nutrient Requirements and Feed Utilization in Ruminants. Acton, MA: XanEdu Publishing, Inc.
Tedeschi L. O., Chalupa W., Janczewski E., Fox D. G., Sniffen C. J., Munson R., Kononoff P. J. & Boston R. (2008). Evaluation and application of the CPM dairy nutrition model. Journal of Agricultural Science, Cambridge 146, 171182.
Thornton P. K. (2010). Livestock production: recent trends, future prospects. Philosophical Transactions of the Royal Society B: Biological Sciences 365, 28532867.
Tylutki T. P., Fox D. G., Durbal V. M., Tedeschi L. O., Russell J. B., Van Amburgh M. E., Overton T. R., Chase L. E. & Pell A. N. (2008). Cornell net carbohydrate and protein system; A model for precision feeding of dairy cattle. Animal Feed Science & Technology 143, 174202.
US EPA (United States Environmental Protection Agency) (2012). Global Anthropogenic non-CO 2 Greenhouse Gas Emissions: 1990–2030 . Washington, DC: US EPA. Available from: https://www.epa.gov/sites/production/files/2016-08/documents/epa_global_nonco2_projections_dec2012.pdf (accessed 31 May 2017).
Vetharaniam I., Vibart R. E., Hanigan M. D., Janssen P. H., Tavendale M. H. & Pacheco D. (2015). A modified version of the Molly rumen model to quantify methane emissions from sheep. Journal of Animal Science 93, 35513563.
Wang Y., Janssen P. H., Lynch T. A., van Brunt B. & Pacheco D. (2016). A mechanistic model of hydrogen-methanogen dynamics in the rumen. Journal of Theoretical Biology 393, 7581.
Wolin M. J. (1960). A theoretical rumen fermentation balance. Journal of Dairy Science 43, 14521459.
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

The Journal of Agricultural Science
  • ISSN: 0021-8596
  • EISSN: 1469-5146
  • URL: /core/journals/journal-of-agricultural-science
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×
Type Description Title
WORD
Supplementary Materials

Dougherty supplementary material
Appendix 1

 Word (125 KB)
125 KB

Metrics

Full text views

Total number of HTML views: 11
Total number of PDF views: 61 *
Loading metrics...

Abstract views

Total abstract views: 181 *
Loading metrics...

* Views captured on Cambridge Core between 3rd August 2017 - 12th December 2017. This data will be updated every 24 hours.