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Kipling, R. P., Saetnan, E. R., Van den Pol-van Dasselaar, A., & Scollan, N. G. (2014). Building modelling capacity for livestock systems: progress in LiveM. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: MACSUR provides an opportunity to connect disparate research groups and disciplines in livestock and grassland modelling. Within the livestock theme (LiveM) of MACSUR, grassland modelling capabilities have been significantly improved through joint modelling exercises, and grassland modellers have exploited their methodological overlaps with CropM to make important contributions to regional pilot studies. Animal health researchers have been contributing to the southern regional pilot, and modelling resources have been identified for livestock systems at the animal and farm-scales. Here, the priorities for the next steps for livestock and grassland modelling are discussed, and for the role of MACSUR in addressing the challenges facing the sector. While crop and grassland modelling deals with primary production, livestock modelling examines the complexity of secondary production. The unique position of livestock modelling presents challenges and opportunities. The diversity of livestock models (in scale and approach) makes model inter-comparisons and collaborative work challenging, while the range of variables involved in livestock systems provide many opportunities for increasing systemic efficiency and robustness to the impacts of climate change. Closer integration of experimental research and modelling teams also has the potential to increase the capability of livestock and grassland models to predict the impact of European adaptation strategies on livestock farming systems, and on the contribution of these systems to global food security.
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Kipling, R. P., Don, A., & Topp, K. (2014). Assessing the availability of data on grassland Carbon sequestration in Europe. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: The role of grasslands in the sequestration of atmospheric Carbon represents an important benefit of extensive livestock systems based on permanent grasslands. The accurate modelling of such systems is key to understanding their potential in mitigating GHG emissions, and this in turn relies on access to high quality data. Here, the availability of Carbon sequestration data for EU grasslands is investigated, using information gathered from reviews of journal papers and EU project outputs. The challenges involved in providing information on datasets to modellers are discussed, and the next steps in the gathering and sharing of meta-data are defined.
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Kipling, R. P., & Scollan, N. G. (2014). Livestock production and the feed challenge. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: It is predicted that world population will reach nine billion by 2050 (Godfray et al., 2010) with the biggest increases occurring in the developing world (Guyomard et al., 2013, Thornton, 2010). This growth is expected to dramatically increase the demand for meat and animal products (Tilman et al., 2002) with a requirement by 2050 for 73 % more meat and 58 % more milk than produced in 2010 (FAO, 2011). In order to meet this growing demand, the supply of livestock products must rise to an extent comparable with that of the ‘Green Revolution’ (Tilman et al., 2002). This must occur in the context of serious global challenges related to climate change, resource availability, inequality, and biodiversity loss. At present many European livestock production systems are heavily reliant on a small number of feed products to provide protein, with imported soya accounting for 55 % of the 2.6 million tonnes of plant-derived protein fed to animals in the UK. This reliance on a small number of imported products leaves supply vulnerable to economic and climatic change. There is increased research into alternatives to South American soybean in the European feed supply chain, including improving the nutritional and agronomic characteristics of such alternatives, and exploring the use of new sources of potential feed material. This presents an opportunity for collaboration between experimental researchers and modellers to investigate the potential impacts of alternative feeds on livestock system productivity, robustness to climate change and levels of GHG emissions.
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Kipling, R. P., Bannink, A., Bellocchi, G., Dalgaard, T., Fox, N. J., Hutchings, N. J., et al. (2016). Modeling European ruminant production systems: Facing the challenges of climate change. Agricultural Systems, 147, 24–37.
Abstract: Ruminant production systems are important producers of food, support rural communities and culture, and help to maintain a range of ecosystem services including the sequestering of carbon in grassland soils. However, these systems also contribute significantly to climate change through greenhouse gas (GHG) emissions, while intensi- fication of production has driven biodiversity and nutrient loss, and soil degradation. Modeling can offer insights into the complexity underlying the relationships between climate change, management and policy choices, food production, and the maintenance of ecosystem services. This paper 1) provides an overview of how ruminant systems modeling supports the efforts of stakeholders and policymakers to predict, mitigate and adapt to climate change and 2) provides ideas for enhancing modeling to fulfil this role. Many grassland models can predict plant growth, yield and GHG emissions from mono-specific swards, but modeling multi-species swards, grassland quality and the impact of management changes requires further development. Current livestock models provide a good basis for predicting animal production; linking these with models of animal health and disease is a prior- ity. Farm-scale modeling provides tools for policymakers to predict the emissions of GHG and other pollutants from livestock farms, and to support the management decisions of farmers from environmental and economic standpoints. Other models focus on how policy and associated management changes affect a range of economic and environmental variables at regional, national and European scales. Models at larger scales generally utilise more empirical approaches than those applied at animal, field and farm-scales and include assumptions which may not be valid under climate change conditions. It is therefore important to continue to develop more realistic representations of processes in regional and global models, using the understanding gained from finer-scale modeling. An iterative process of model development, in which lessons learnt from mechanistic models are ap- plied to develop ‘smart’ empirical modeling, may overcome the trade-off between complexity and usability. De- veloping the modeling capacity to tackle the complex challenges related to climate change, is reliant on closer links between modelers and experimental researchers, and also requires knowledge-sharing and increasing technical compatibility across modeling disciplines. Stakeholder engagement throughout the process of model development and application is vital for the creation of relevant models, and important in reducing problems re- lated to the interpretation of modeling outcomes. Enabling modeling to meet the demands of policymakers and other stakeholders under climate change will require collaboration within adequately-resourced, long-term inter-disciplinary research networks
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Saetnan, E. R., & Kipling, R. P. (2016). Evaluating a European knowledge hub on climate change in agriculture: Are we building a better connected community. Scientometrics, 109(2), 1057–1074.
Abstract: In order to maintain food security and sustainability of production under climate change, interdisciplinary and international collaboration in research is essential. In the EU, knowledge hubs are important funding instruments for the development of an interconnected European Research Area. Here, network analysis was used to assess whether the pilot knowledge hub MACSUR has affected interdisciplinary collaboration, using co-authorship of peer reviewed articles as a measure of collaboration. The broad community of all authors identified as active in the field of agriculture and climate change was increasingly well connected over the period studied. Between knowledge hub members, changes in network parameters suggest an increase in collaborative interaction beyond that expected due to network growth, and greater than that found in the broader community. Given that interdisciplinary networks often take several years to have an impact on research outputs, these changes within the relatively new MACSUR community provide evidence that the knowledge hub structure has been effective in stimulating collaboration. However, analysis showed that knowledge hub partners were initially well-connected, suggesting that the initiative may have gathered together researchers with particular resources or inclinations towards collaborative working. Long term, consistent funding and ongoing reflection to improve networking structures may be necessary to sustain the early positive signs from MACSUR, to extend its success to a wider community of researchers, or to repeat it in less connected fields of science. Tackling complex challenges such as climate change will require research structures that can effectively support and utilise the diversity of talents beyond the already well-connected core of scientists at major research institutes. But network research shows that this core, well-connected group are vital brokers in achieving wider integration.
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