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Saetnan, E. (2015). Are we building a better connected community (Vol. 5).
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Kipling, R., Topp, K., & Don, A. (2014). Appropriate meta-data for modellers (Vol. 3).
Abstract: Report D-L1.4.1 provided an overview of the data and related resources available online and through EU funded projects, relating to soil organic carbon (SOC), and carbon sequestration in grasslands in particular. Building on D-L1.4.1, the report presented here discusses how meta-data describing these types of data (and experimental data more generally) can best be presented in an online resource useful to grassland modellers requiring data to use in their modelling work. Identifying the useful categories of meta-data is a necessary precursor to providing such a resource, which could facilitate better communication between modelling and experimental research groups, allowing researchers to more efficiently locate relevant data and to link up with other scientists working on similar topics. A survey among grassland modelling teams and an assessment of online meta-data resources was used to produce recommendations about the meta-data categories that should be included in an online resource. The categories are generic, so that the recommendations can be followed in the design of meta-data resources for the more general agricultural modelling community. No Label
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Ruiz-Ramos, M., Ferrise, R., Rodríguez, A., Lorite, I. J., Bindi, M., Carter, T. R., et al. (2017). Applying adaptation response surfaces for managing wheat under perturbed climate and elevated CO2 in a Mediterranean environment (Vol. 1ß).
Abstract: This study developed Adaptation Response Surfaces and applied them to a study case in North East Spain on winter crops adaptation, using rainfed winter wheat as reference crop. Crop responses to perturbed temperature, precipitation and CO2 were simulated by an ensemble of crop models. A set of combined changes on cultivars (on vernalisation requirements and phenology) and management (on sowing date and irrigation) were considered as adaptation options and simulated by the crop model ensemble. The discussion focused on two main issues: 1) the recommended adaptation options for different soil types and perturbation levels, and 2) the need of applying our current knowledge (AOCK) when building a crop model ensemble. The study has been published Agricultural Systems (Available online 25 January 2017, https://doi.org/10.1016/j.agsy.2017.01.009 ), and the text below consists on extracts from that paper.
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Bojar, W., Knopik, L., & Zarski, J. (2015). Application of Markov chains approach for expecting extreme precipitation changes having impact on food supply (Vol. 4).
Abstract: This work was co-financed by NCBiR, Contract no. FACCE JPI/04/2012 – P100 PARTNER No Label
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Schönhart, M. (2015). Analysis of climate change adaptation with bio-economic farm models: lessons from MACSUR regional pilot studies (Vol. 5).
Abstract: Integrated land use models (ILM) featuring agronomic and economic drivers of land use are frequently applied to serve the high information demand of stakeholders. This presentation results from collaboration among bio-economic farm modelers across the MACSUR regional pilot studies (www.macsur.eu) and shall compare and finally reveal good practice examples on the representation of climate change adaptation in bio-economic farm models. First results show a considerable diversity of approaches employed in the MACSUR regional pilot studies. All are programming models that optimize more or less elaborated forms of utility. All consider or plan to consider crop yield impacts from bio-physical crop models based on daily-resolution climate data. While some models include pest and diseases or livestock impacts, none take climate change impacts on market prices or interactions among farms into account so far. Clearly, adaptation options determine the solution space and are mainly expert-based in the regional case studies. Overall, the models are normative and analyze economically rational and optimal land use and management at the farm level, capable of showing the likely direction of differences in future management as a response to exogenous parameter changes (prices, yields, disease pressure, changed policy conditions, etc.). Such detailed models and their results may be applied in stakeholder interaction. Integrating the different direct and indirect effects of climate change, including the policy dimension, is the main contribution of farm level modelling of agricultural systems in the domain of climate change adaptation research. No Label
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