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Hutchings, N. J., Özkan Gülzari, Ş., de Haan, M., & Sandars, D. (2018). How do farm models compare when estimating greenhouse gas emissions from dairy cattle production. Animal, 12(10), 2171–2180.
Abstract: The European Union Effort Sharing Regulation (ESR) will require a 30% reduction in greenhouse gas (GHG) emissions by 2030 compared with 2005 from the sectors not included in the European Emissions Trading Scheme, including agriculture. This will require the estimation of current and future emissions from agriculture, including dairy cattle production systems. Using a farm-scale model as part of a Tier 3 method for farm to national scales provides a more holistic and informative approach than IPCC (2006) Tier 2 but requires independent quality control. Comparing the results of using models to simulate a range of scenarios that explore an appropriate range of biophysical and management situations can support this process by providing a framework for placing model results in context. To assess the variation between models and the process of understanding differences, estimates of GHG emissions from four farm-scale models (DailyWise, FarmAC, HolosNor and SFARMMOD) were calculated for eight dairy farming scenarios within a factorial design consisting of two climates (cool/dry and warm/wet) x two soil types (sandy and clayey) x two feeding systems (grass only and grass/maize). The milk yield per cow, follower cow ratio, manure management system, nitrogen (N) fertilisation and land area were standardised for all scenarios in order to associate the differences in the results with the model structure and function. Potential yield and application of available N in fertiliser and manure were specified separately for grass and maize. Significant differences between models were found in GHG emissions at the farm-scale and for most contributory sources, although there was no difference in the ranking of source magnitudes. The farm-scale GHG emissions, averaged over the four models, was 10.6 t carbon dioxide equivalents (CO(2)e)/ha per year, with a range of 1.9 t CO(2)e/ha per year. Even though key production characteristics were specified in the scenarios, there were still significant differences between models in the annual milk production per ha and the amounts of N fertiliser and concentrate feed imported. This was because the models differed in their description of biophysical responses and feedback mechanisms, and in the extent to which management functions were internalised. We conclude that comparing the results of different farm-scale models when applied to a range of scenarios would build confidence in their use in achieving ESR targets, justifying further investment in the development of a wider range of scenarios and software tools.
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Kipling, R. P., Topp, C. F. E., Bannink, A., Bartley, D. J., Blanco-Penedo, I., Cortignani, R., et al. (2019). To what extent is climate change adaptation a novel challenge for agricultural modellers. Env. Model. Softw., 120, Unsp 104492.
Abstract: Modelling is key to adapting agriculture to climate change (CC), facilitating evaluation of the impacts and efficacy of adaptation measures, and the design of optimal strategies. Although there are many challenges to modelling agricultural CC adaptation, it is unclear whether these are novel or, whether adaptation merely adds new motivations to old challenges. Here, qualitative analysis of modellers’ views revealed three categories of challenge: Content, Use, and Capacity. Triangulation of findings with reviews of agricultural modelling and Climate Change Risk Assessment was then used to highlight challenges specific to modelling adaptation. These were refined through literature review, focussing attention on how the progressive nature of CC affects the role and impact of modelling. Specific challenges identified were: Scope of adaptations modelled, Information on future adaptation, Collaboration to tackle novel challenges, Optimisation under progressive change with thresholds, and Responsibility given the sensitivity of future outcomes to initial choices under progressive change.
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Sandars, D. (2015). Optimal Land-use Future Scenarios Nordic Area (Vol. 4).
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Sandars, D. (2015). Understanding Europe’s future ability to feed itself within an uncertain climate change and socio economic scenario space (Vol. 5).
Abstract: Europe’s ability to feed its population depends on the balance of agricultural productivity (yields and land suitability) and demand which are affected by future climate and socio-economic change (arising from changing food demand; prices; technology change etc). Land use under 2050 climate change and socio-economic scenarios can be rapidly and systematically quantified with a modelling system that has been developed from meta-models of optimal cropping and crop and forest yields derived from the outputs of the previously developed complex models (Audsley et al; 2015). Profitability of each possible land use is modelled for every soil in every grid across the EU. Land use in a grid is then allocated based on profit thresholds set for intensive agriculture extensive agriculture, managed forest and finally unmanaged forest or unmanaged land. The European demand for food as a function of population, imports, food preferences and bioenergy, is a production constraint, as is irrigation water available. The model iterates until demand is satisfied (or cannot be met at any price). Results are presented as contour plots of key variables. For example, given a 40% increase in population from the baseline socio-economic scenario, adapting by increasing crop yields by 40% will leave a 38% probability that the 2050 future climate will be such that we cannot feed ourselves – considering “all” the possible climate scenarios. No Label
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Del Prado, A., van den Pol-van Dasselaar, A., Chadwick, D., Misselbrook, T., Sandars, D. L., Audsley, E., et al. (2014). Synergies between mitigation and adaption to climate change in grassland-based farming systems..
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