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Kebreab, E., Tedeschi, L., Dijkstra, J., Ellis, J. L., Bannink, A., & France, J. (2016). Modeling Greenhouse Gas Emissions from Enteric Fermentation. In E. Kebreab (Ed.), Advances in Agricultural Systems (Vol. 6, pp. 173–196). Synthesis and Modeling of Greenhouse Gas Emissions and Carbon Storage in Agricultural and Forest Systems to Guide Mitigation and Adaptation, Advances in Agricultural Systems (6).
Abstract: Livestock directly contribute to greenhouse gas (GHG) emissions mainly through methane (CH4) and nitrous oxide (N2O) emissions. For cost and practicality reasons, quantification of GHG has been through development of various types of mathematical models. This chapter addresses the utility and limitations of mathematical models used to estimate enteric CH4 emissions from livestock production. Models used in GHG quantification can be broadly classified into either empirical or mechanistic models. Empirical models might be easier to use because they require fewer input variables compared with mechanistic models. However, their applicability in assessing mitigation options such as dietary manipulation may be limited. The major driving variables identified for both types of models include feed intake, lipid and nonstructural carbohydrate content of the feed, and animal variables. Knowledge gaps identified in empirical modeling were that some of the assumptions might not be valid because of geographical location, health status of animals, genetic differences, or production type. In mechanistic modeling, errors related to estimating feed intake, stoichiometry of volatile fatty acid (VFA) production, and acidity of rumen contents are limitations that need further investigation. Model prediction uncertainty was also investigated, and, depending on the intensity and source of the prediction uncertainty, the mathematical model may inaccurately predict the observed values with more or less variability. In conclusion, although there are quantification tools available, global collaboration is required to come to a consensus on quantification protocols. This can be achieved through developing various types of models specific to region, animal, and production type using large global datasets developed through international collaboration.
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Bannink, A., & Dijkstra, J. (2016). Effects of roughage characteristics on enteric methane emission in dairy cows. Advances in Animal Biosciences, 7(03), 229–230.
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Kipling, R. P., Bannink, A., Özkan Gülzari, Ş., & Van Middelkoop, J. (2016). Editorial. Advances in Animal Biosciences, 7(03)(03), 223.
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Bannink, A., van Lingen, H. J., Ellis, J. L., France, J., & Dijkstra, J. (2016). The contribution of mathematical modeling to understanding dynamic aspects of rumen metabolism. Frontiers in Microbiology, 7, 1820.
Abstract: 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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Kipling, R., Scollan, N., Bannink, A., & van Middelkoop, J. (2016). From diversity to strategy: Livestock research for effective policy in a climate change world (Vol. 8).
Abstract: European livestock agriculture is extraordinarily diverse, and so are the challenges it faces. This diversity has contributed to the development of a fragmented set of research communities. As a result, livestock research is often under-represented at policy level, despite its high relevance for the environment and food security. Understanding livestock systems and how they can sustainably adapt to global change requires inputs across research areas, including grasslands, nutrition, health, welfare and ecology. It also requires experimental researchers, modellers and stakeholders to work closely together. Networks and capacity building structures are vital to enable livestock research to meet the challenges of climate change. They need to maintain shared resources and provide non-competitive arenas to share and synthesize results for policy support. ï‚· Long term strategic investment is needed to support such structures. Their leadership requires very different skills to those effective in scientific project coordination.
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