Semenov, M. A., & Stratonovitch, P. (2016). Local-scale CMIP5-based climate scenarios for MACSUR2 (Vol. 8).
Abstract: Climate sensitivity of GCMs was used to select 5 GCMs from the CMIP5 ensemble for impact studies in MACSUR2. Selected GCMs for MACSUR2 are EC-EARTH (7), GFDL-CM3 (8) HadGEM2-ES (10), MIROC5 (13), and MPI-ESM-MR (15). These GCMs are evenly distributed among CMIP5 (Fig 1) and should capture, in principal, climate uncertainty of the CMIP5 ensemble. Using 5 GCMs will enable us to assess uncertainties in impacts related to uncertainty in climate projections. The selection of GCMs in MACSUR2 has a good overlap with selections of GCMs used in CORDEX and AgMIP projects. We used the LARS-WG generator to construct local-scale CMIP5-based climate scenarios for Europe (Semenov & Stratonovitch, 2015). Fifteen sites were selected in Europe for MACSUR2. For each site and each selected GCM, 100 yrs climate daily data were generated by LARS-WG for RCP4.5 and RCP8.5 emission scenarios and for baseline and 3 future periods: near-term (2021-2040), mid-term (2041-2060) and long-term (2081-2100).
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Dono, G. (2015). The economic impact of changes in climate variability on milk production in the area of Grana Padano (Vol. 5).
Abstract: Climate variability (CV) normally influences production and farm management, and climate change (CC) has precisely the effect of changing this variability. Thus, models that estimate the economic impact of CC, integrating with climatic models, agronomic, and livestock, must represent the implications of this variability on farm management. This study describes an economic model based on Discrete Stochastic Programming (DSP) which assesses the impact of CC on milk production in the Grana Padano area. The model is based on 23 farm typologies from FADN that represent 856 farms in Piacenza and Cremona, two of the most important provinces for Grana Padano production. The results of the model were projected at the regional scale. The climate scenarios, current and future, are generated with a Regional Atmospheric Modeling System. The forage production under these scenarios is estimated with the EPIC agronomic model. Estimates on milk production and livestock mortality are based on studies conducted in the Po valley. The nutritional needs of the cattle are estimated with the CNCPS model. Probability distribution functions (PDF) express the relations between the CV and the productive variables under both climate scenarios. These PDFs represent the expectations of farmers on the productive-climate variability in the DSP model, which is PMP calibrated based on land distribution observed in a reference year. Comparing the model results in the two scenarios indicates the effects of CC, given the opportunity to adapt the use of resources and techniques of cultivation. The structure of the model, and its economic results are presented and discussed, along with the strengths and weaknesses of this approach. No Label
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Lehtonen, H. (2016). Evaluating competitiveness of clover-grass as a resilient feed production option in Finland (Vol. 9 C6 -).
Abstract: Clover-grasses address the following objectives:– Decreased input use (N-fertilization), reduced dependency ofinorganic N => reduced GHG emissions– Possibility for increased protein content of silage, reduceddependency on purchased protein feed supplement (homegrown proteins, resilience)© Natural Resources Institute Finland– Better utilisation of farmland in the context of climate changein the north: Higher T – improved N fixation– Compatible with sustainable agriculture and sustainableintensification: more output with the same inputs / the sameoutput with reduced (non-renewable) inputs• In contrast: Shifting to silage maize increases N fertilisation– Major shift from grasslands to silage maize in e.g. Denmark 1. Small cost reductions in clover-grass cultivation, or clover-grasspremiums, may or may not increase clover cultivation- Their effectiveness is uncertain and subject to prices2. N tax is effective, but is not a suitable policy action in currentfinancial situation of farms (milk crisis 2015-2016)3. However, the results suggest that a 25% higher N price lead to© Natural Resources Institute Finlandsignificantly higher clover grass area and a small reduction ínmilk output – with no cost reductions or extra premiums!4. To increase clover cultivation, price ratios should be adjusted!5. If increasing clover -grass yield, a robust increase in clovergrass areas may realise, with small benefits for farm economyand overall production – How much more clover grass yieldcould be attained at low costs? A topic for further discussionand analysis
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Rivington, M., & Wallach, D. (2015). Communication strategy, including design of tools for more effective communication of uncertainty (Vol. 6).
Abstract: Communication is the key link between the generation of information by MACSUR about the uncertainty of climate change impacts on future food security and how information is used by decision makers. It is therefore important to make available the common tools for reporting uncertainty, with a discussion of the advantages or difficulties of each. That is the purpose of this report. No Label
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Schönhart, M., Schauppenlehner, T., Kuttner, M., & Schmid, E. (2015). Integrated Assessment of Climate Change Mitigation and Adaptation Impacts at Landscape level: Mostviertel, Austria. In FACCE MACSUR Reports (Vol. 6, SPp. 6). Brussels.
Abstract: ConclusionsIncreasing productivity can increase intensification pressuresThreatened permanent (extensive) grasslands and landscape elements, butsubject to resource constraints, costs and prices andfuture production potential to increase global food supplyFuture RDP and environmental policy design (e.g. WFD) should take changing productivity into accountHeterogeneity matters at farm and regional levelChanging relative competitiveness of farmsFuture research: analyze uncertainties No Label
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