What an animal eats affects how much greenhouse gases it emits. This makes nutrition strategies an important way of reducing livestock greenhouse gas emissions. 

Although the impact of feed and nutrition on greenhouse gases is a growing area of research, the available data is not well organised and would benefit from further analysis. GLOBAL NETWORK is a four-year collaborative project to address these issues, led by the Livestock Research Group’s Feed and Nutrition Network and funded by Alliance countries and the European Joint Programming Initiative on Agriculture, Food Security and Climate Change.

The project has developed two databases – one on mitigation and one on prediction.

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The mitigation database pulls together 1,800 experimental treatment means from over 400 publications. The database enables this data to be summarised and science-based enteric methane mitigation options to be recommended to stakeholders.

The prediction database collates individual animal data relating to dairy cattle diet, intake, emissions and performance. This data has been gathered from nearly 6,000 individual animal observations from 159 studies across North and South America, Europe and Oceania. This collation of data has supported the development of robust models for predicting methane emissions for ruminant livestock (dairy and beef cattle, small ruminants), based on different nutritional, animal and farm management scenarios. For a press release on this database, see here. For the full paper, see here: Niu, M., Kebreab, E., Hristov, A., Oh, J., Arndt, C., Bannink, A., … Yu, Z. (2018). Prediction of enteric methane production, yield and intensity in dairy cattle using an intercontinental database. Global Change Biology, 24(8), 3368-3389.

The project team is also working on developing a microbial database containing methane emissions and animal data, and the relative abundance of individual groups of ruminal bacteria, archaea, protozoa and fungi.

GLOBAL NETWORK is also helping improve research practices. Four papers have been published to date, in addition to the Niu et al. paper referenced above:

The Hristov et al. paper reviews the uncertainties and discrepancies in national greenhouse gas inventories for enteric methane, methane measurement methods, Dry Matter Intake (DMI) and methane prediction models. It finds that in situations where sufficient details or accuracy on dietary inputs is lacking, inventory compilers could still draw on simplified enteric methane prediction models based on DMI alone or DMI and limited feed- and animal-related inputs. To achieve high prediction accuracy, broadly applicable and robust prediction models must be developed from large data sets generated through international collaboration. These data sets should encompass a wide range of diets and production systems within regions and globally. Development of regionally specific Ym values and DMI prediction equations would also assist. The LRG has several research projects underway to help deliver on these recommendations.

The Hammond et al. paper compares the strengths and limitations of different in vivo techniques for measuring enteric methane.

The Yáñez-Ruiz et al. paper reviews the main factors to consider when using in vitro fermentation techniques to investigate the methane mitigation potential of animal nutrition.

Further papers are expected during 2019 and will be linked from this page as they are published.