Improving the prediction of methane production and representation of rumen fermentation for finishing beef cattle within a mechanistic model

Ellis, J.L.; Dijkstra, J.; Bannink, A.; Kebreab, E.; Archibeque, S. L.; Benchaar, C.; Beauchemin, K. A.; Nkrumah, J. D.

doi:10.4141/cjas2013-192

Cited by 12 publications

(8 citation statements)

References 72 publications

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“…The discrepancy illustrates that other mechanisms must override the effect of an increased starch inflow on rumen starch digestion, with turnover or outflow rate of rumen contents as the most likely candidate. The same was suggested by the findings of Ellis et al (2014) for representing the digestion of low-roughage diets in beef cattle. Also in contrast, Kreikemeier et al (1990) observed an increased in situ fractional rate of starch degradation with increased roughage in a wheat-based finishing steer diet, in spite of a reduced starch intake, a lower rumen starch pool, and a higher fractional outflow rate of labeled starch.…”

Section: Introductionsupporting

confidence: 60%

“…Predictive power for propionate and butyrate differed strongly among the alternative representations of VFA stoichiometry, and linear relationships between rumen pH and VFA production did not appear to improve prediction of VFA profile. Another evaluation study of enteric CH 4 in beef cattle by Ellis et al (2014) confirmed that inclusion of linear pH-dependency in VFA stoichiometry worsened CH 4 predictions (reflecting VFA profile) whereas non-linear dependency did not. The calculations on the VFA stoichiometry of Nozière et al (2011) were not available to Morvay et al (2011), but were used in a follow-up evaluation study on lactating cow data by Alemu et al (2011).…”

Section: Introductionmentioning

confidence: 89%

“…Although the rather theoretical model simulations require confirmation, these in vivo results illustrate the dynamic aspects of how substrate availability and activity of amylolytic and fibrolytic micro-organisms impact NDF digestion beyond the rumen, when feeding large amounts of starch relatively resistant to rumen degradation. Also, the importance of addressing these dynamic aspects was highlighted in a recent modeling study, where Ellis et al (2014) evaluated predictions of digestion and enteric methane (CH 4 ) emissions in beef cattle against in vivo observations. For realistic predictions they first had to make changes to the rumen and large intestinal model of Mills et al (2001), which was developed for lactating cows, to accommodate the specific enteric conditions observed in beef cattle fed low-roughage diets.…”

Section: Introductionmentioning

confidence: 99%

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The Contribution of Mathematical Modeling to Understanding Dynamic Aspects of Rumen Metabolism

et al. 2016

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All mechanistic rumen models cover the main drivers of variation in rumen function, which are feed intake, the differences between feedstuffs and feeds in their intrinsic rumen degradation characteristics, and fractional outflow rate of fluid and particulate matter. Dynamic modeling approaches are best suited to the prediction of more nuanced responses in rumen metabolism, and represent the dynamics of the interactions between substrates and micro-organisms and inter-microbial interactions. The concepts of dynamics are discussed for the case of rumen starch digestion as influenced by starch intake rate and frequency of feed intake, and for the case of fermentation of fiber in the large intestine. Adding representations of new functional classes of micro-organisms (i.e., with new characteristics from the perspective of whole rumen function) in rumen models only delivers new insights if complemented by the dynamics of their interactions with other functional classes. Rumen fermentation conditions have to be represented due to their profound impact on the dynamics of substrate degradation and microbial metabolism. Although the importance of rumen pH is generally acknowledged, more emphasis is needed on predicting its variation as well as variation in the processes that underlie rumen fluid dynamics. The rumen wall has an important role in adapting to rapid changes in the rumen environment, clearing of volatile fatty acids (VFA), and maintaining rumen pH within limits. Dynamics of rumen wall epithelia and their role in VFA absorption needs to be better represented in models that aim to predict rumen responses across nutritional or physiological states. For a detailed prediction of rumen N balance there is merit in a dynamic modeling approach compared to the static approaches adopted in current protein evaluation systems. Improvement is needed on previous attempts to predict rumen VFA profiles, and this should be pursued by introducing factors that relate more to microbial metabolism. For rumen model construction, data on rumen microbiomes are preferably coupled with knowledge consolidated in rumen models instead of relying on correlations with rather general aspects of treatment or animal. This helps to prevent the disregard of basic principles and underlying mechanisms of whole rumen function.

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Section: Introductionsupporting

confidence: 60%

Section: Introductionmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

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The Contribution of Mathematical Modeling to Understanding Dynamic Aspects of Rumen Metabolism

et al. 2016

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“…The RMSPE varied between 12.0% and 15.0% between different versions of the model with random error contributing from 27% to 91% of the total error. Recently, Ellis et al (2014) compared different VFA stoichiometry models in the modified version of the Dijkstra et al (1992) model with beef cattle data. The best model explained 52% of variation in total CH 4 emission with RMSPE as 29.8% of observed mean.…”

Section: Comparison To Mechanistic Modelsmentioning

confidence: 99%

Nordic dairy cow model Karoline in predicting methane emissions: 2. Model evaluation

Ramin

Huhtanen

2015

Livestock Science

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“…In their analysis, the inclusion of the molecular breeding value of ribeye area increased by 13% the precision to predict body weight at 28% empty body fat. Despite the development of new concepts for nutrition modeling, revisions of relationships established a long time ago are still needed for three reasons: collection of new data and incorporation with old data (Ellis et al, 2014;Galyean and Tedeschi, 2014); data used to develop past relationships were deemed deficient and/or were based on assumptions that do not hold anymore (Galyean et al, 2016); or more advanced mathematical and/or statistical analyses have been proposed (Moraes et al, 2014a;Moraes et al, 2014b).…”

Section: Integrating Genomics Into Nutrition Modelingmentioning

confidence: 99%

A glimpse of the future in animal nutrition science. 2. Current and future solutions

Tedeschi

Fonseca

Muir³

et al. 2017

R. Bras. Zootec.

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-Despite tremendous advancements in the livestock sector, additional opportunities exist to improve even further livestock production around the globe. Forecasting is not an exact science and it relies heavily on past and current knowledge. Improvements in the nutritional sciences (both human and animal) include a better understanding of agents that cause deterioration of human health, improving the quality of animal products, applying effective fetal programming, developing new feeds and feeding strategies, and revisiting longstanding technologies. Improvements in the understanding of the rumen microbiome will enable scientists to increase the fermentation efficiency and, hopefully, select microbial species of greater interest. Improvements in remote sensing and ground-based instrumentation, telecommunications, and weather forecasting technologies will aid in the continued improvements of early warning systems to assist livestock producers in reducing risk and adapting to the changing environment. Broad utilization of sensor technologies will allow scientists to collect real-time data and, when combined with mathematical modeling, decision support systems will become an indispensable managerial tool for livestock production with the possibility to automate low-level decisions on the farm, such as supplementation schedules, sorting of animals, and early detection of disease and outbreaks. The identification of feed efficient animals may be the single most impactful advancement towards long-term livestock sustainability and the promise of feeding the world animal products. We contend that education across societal levels is the first step to solve current and future challenges of the livestock industry. The dilemma has been who will take the first step forward.

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Improving the prediction of methane production and representation of rumen fermentation for finishing beef cattle within a mechanistic model

Cited by 12 publications

References 72 publications

The Contribution of Mathematical Modeling to Understanding Dynamic Aspects of Rumen Metabolism

The Contribution of Mathematical Modeling to Understanding Dynamic Aspects of Rumen Metabolism

Nordic dairy cow model Karoline in predicting methane emissions: 2. Model evaluation

A glimpse of the future in animal nutrition science. 2. Current and future solutions

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