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Kersebaum, K. C., Kollas, C., Bindi, M., Palosuo, T., Wu, L., Sharif, B., et al. (2014). Model inter-comparison on crop rotation effects – an intermediate report. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: Data of diverse crop rotations from five locations across Europe were distributed to modelers to investigate the capability of models to handle complex crop rotations and management interactions. Crop rotations comprise various main crops (winter/spring wheat, winter/spring barley, rye, oat, maize, sugar beet, oil seed rape and potatoes) plus several catch crops. The experimental setup of the datasets included treatments such as modified soils, crops exchanged within the rotations, irrigation/rainfed, nitrogen fertilization, residue management, tillage and atmospheric CO2 concentration. 19 modeling teams registered to model either the whole rotation or single crops. Models which are capable to run the whole rotation should provide transient as well as single year simulations with a reset of initial conditions. In the first step only initial soil conditions (water and soil mineral N) of the first year and key phenological stages were provided to the modelers. For calibration, crop yields and biomass were provided for selected years but not for all seasons. In total the combination of treatments and seasons results in 301 years of simulation. Results were analyzed to evaluate the effect of transient simulation versus single-year simulation regarding crop yield, biomass, water and nitrogen balance components. Model results will be evaluated crop-specifically to identify crops with highest uncertainty and potential for model improvement. Full data will be provided to modelers for model-improvement and results will provide insights into model capabilities to reproduce treatments and crops. Further, the question of error propagation along the transient simulation of crop rotations will be addressed.
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Saetnan, E. R., & Veneman, J. B. (2014). MitiGate: an On-line Meta-Analysis Database of Mitigation Strategies for Enteric Methane Emissions. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: The animal science sector has seen a proliferation of potential mitigation strategies, aimed at tackling emissions from enteric fermentation in ruminant livestock production. By bringing together data from studies on the many mitigation options available through a structured meta-analytical approach, it is possible to evaluate the overall mitigation potential for these broad strategies as well as exploring the many factors influencing the potential of CH4 mitigation strategies. Such quantification of the different mitigation strategies will allow for better estimation of mitigation potential on different levels (animal, farm and sector scale) in modelling efforts. Also quantification is important to determine the strategies that show the best potential in lowering methane emissions and hence can be instrumental in policy recommendations. A database has been established through an initial extensive structured search of published literature on the topic. For each relevant paper identified, a range of meta-data have been extracted including information on the study design, mitigation strategy, animal husbandry, diet and methane emissions. By creating a database with multiple levels of moderator coding, we have provided a flexible platform for future meta-analyses at many levels of aggregation. Studies can then in future be aggregated at the level most appropriate for specific modelling or policy recommendations. This comprehensive database is being made available on-line through a user-friendly web interface. The web-site provides a facility for open access to the database, as well as future updates of the database as more research is published on the topic.
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Dalgaard, T., Hutchings, N., & Noe, E. (2014). Methods for regional scale farming systems modelling and uncertainty assessment – sustainability assessment case studies of production, nutrient losses and greenhouse gas emissions from grassland based systems. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: In the EU Joint-Programming-Initiative: Modelling European Agriculturewith Climate Change for Food Security (MACSUR, LiveM: http://www.macsur.eu/index.php/livestock-modelling) we develop a research frameworkfor the modelling and sustainability assessment of livestock and grasslandbased farming systems at farm and regional scales.Based on results from related research and model development in Denmark,methodologies used for regional scaling, the description of data requirementsand sources, and methods to predict the effect and effectiveness of climate-and environment related policy measures are developed. In this study we present results from farm modelling in a study areaaround Viborg, Western Denmark using the http://www.Farm-N.dk/ model (Env.Pol. 159 3183-3192), including thedistribution of N-surpluses into different types of losses, and a comparisonwith empirical studies of farm nitrogen balances in the Danish study and fiveadditional European landscapes (Biogeosciences 9, 5303–5321). Based on this,methods and development needs for the mapping and uncertainty assessment ofnutrient losses and greenhouse gas emissions are discussed, referring to the presentdevelopment of the Farm-AC model and ongoing scenario studies in e.g. the www.dNmark.org project. In these scenarios, regional-scale policy measures areimplemented via the responses of a range of stakeholders, such as farmers,public interest groups, regulators and politicians. When modelling the outcomeof the policy measures implementation, it is often assumed that stakeholdersrespond as economically rational entities. However, social and cultural factorsare also known to play a role and modelling methods that permit these factorsto be taken into account will also be discussed.
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Curnel, Y. R. W. (2014). Meteorological risks as drivers of environmental innovation in agro-ecosystem management. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: The extreme weather events, projected to increase both in frequency and magnitude with climate change have significant impact on agro-ecosystem services and pose severe limitations to sustainable agricultural land management. The proposed activities start from the hypothesis that these meteorological risks act as drivers of environmental innovation in agro-ecosystem management. These activities deal with risks associated with extreme weather phenomena and with risks of biological origin (e.g. pests and diseases). In order to reach this objective, the following elements of the chain of risk should be considered: • Hazard (assessment of the likely frequency and magnitude of extreme meteorological events)• Impact (analysis of the potential bio-physical and socio-economic impact of extreme weather events on agro-ecosystems)• Vulnerability (identification of the most vulnerable agro-ecosystems)• Risk Management (uncovering innovative risk management and adaptation options)These activities will concentrate on promoting a robust and flexible framework by demonstrating its performance across Belgian agro-ecosystems, and by ensuring its relevance to policy makers and practitioners. Impacts developed from physically based models will not only provide information on the state of the damage at any given time, but also assist in understanding the links between different factors causing damage and determining bio-physical vulnerability. Socio-economic impacts will enlarge the basis for vulnerability mapping, risk management and adaptation options. A strong expert and end-user network will be established to help exploiting project results to meet user needs
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Kyle, P., Müller, C., Calvin, K., & Thomson, A. (2014). Meeting the radiative forcing targets of the representative concentration pathways in a world with agricultural climate impacts. Earth’s Future, 2, 83–98.
Abstract: This study assesses how climate impacts on agriculture may change the evolution of the agricultural and energy systems in meeting the end-of-century radiative forcing targets of the representative concentration pathways (RCPs). We build on the recently completed Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) exercise that has produced global gridded estimates of future crop yields for major agricultural crops using climate model projections of the RCPs from the Coupled Model Intercomparison Project Phase 5 (CMIP5). For this study we use the bias-corrected outputs of the HadGEM2-ES climate model as inputs to the LPJmL crop growth model, and the outputs of LPJmL to modify inputs to the GCAM integrated assessment model. Our results indicate that agricultural climate impacts generally lead to an increase in global cropland, as compared with corresponding emissions scenarios that do not consider climate impacts on agricultural productivity. This is driven mostly by negative impacts on wheat, rice, other grains, and oil crops. Still, including agricultural climate impacts does not significantly increase the costs or change the technological strategies of global, whole-system emissions mitigation. In fact, to meet the most aggressive climate change mitigation target (2.6W/m(2) in 2100), the net mitigation costs are slightly lower when agricultural climate impacts are considered. Key contributing factors to these results are (a) low levels of climate change in the low-forcing scenarios, (b) adaptation to climate impacts simulated in GCAM through inter-regional shifting in the production of agricultural goods, and (c) positive average climate impacts on bioenergy crop yields.
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