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Author Schauberger, B.; Rolinski, S.; Müller, C. doi  openurl
  Title A network-based approach for semi-quantitative knowledge mining and its application to yield variability Type Journal Article
  Year 2016 Publication (up) Environmental Research Letters Abbreviated Journal Environ. Res. Lett.  
  Volume 11 Issue 12 Pages 123001  
  Keywords yield variability; crop models; interaction network; plant process; wheat; maize; rice; Global Food Security; Climate-Change; Crop Production; Stress Tolerance; Wheat Yields; Heat-Stress; Temperature Variability; Environmental-Factors; United-States; Elevated CO2  
  Abstract Variability of crop yields is detrimental for food security. Under climate change its amplitude is likely to increase, thus it is essential to understand the underlying causes and mechanisms. Crop models are the primary tool to project future changes in crop yields under climate change. Asystematic overview of drivers and mechanisms of crop yield variability (YV) can thus inform crop model development and facilitate improved understanding of climate change impacts on crop yields. Yet there is a vast body of literature on crop physiology and YV, which makes a prioritization of mechanisms for implementation in models challenging. Therefore this paper takes on a novel approach to systematically mine and organize existing knowledge from the literature. The aim is to identify important mechanisms lacking in models, which can help to set priorities in model improvement. We structure knowledge from the literature in a semi-quantitative network. This network consists of complex interactions between growing conditions, plant physiology and crop yield. We utilize the resulting network structure to assign relative importance to causes of YV and related plant physiological processes. As expected, our findings confirm existing knowledge, in particular on the dominant role of temperature and precipitation, but also highlight other important drivers of YV. More importantly, our method allows for identifying the relevant physiological processes that transmit variability in growing conditions to variability in yield. We can identify explicit targets for the improvement of crop models. The network can additionally guide model development by outlining complex interactions between processes and by easily retrieving quantitative information for each of the 350 interactions. We show the validity of our network method as a structured, consistent and scalable dictionary of literature. The method can easily be applied to many other research fields.  
  Address 2017-04-07  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1748-9326 ISBN Medium Review  
  Area Expedition Conference  
  Notes CropM, ft_macsur Approved no  
  Call Number MA @ admin @ Serial 4942  
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Author Humpenöder, F.; Popp, A.; Stevanovic, M.; Müller, C.; Bodirsky, B.L.; Bonsch, M.; Dietrich, J.P.; Lotze-Campen, H.; Weindl, I.; Biewald, A.; Rolinski, S. url  doi
openurl 
  Title Land-use and carbon cycle responses to moderate climate change: implications for land-based mitigation Type Journal Article
  Year 2015 Publication (up) Environmental Science and Technology Abbreviated Journal Environ Sci Technol  
  Volume 49 Issue 11 Pages 6731-6739  
  Keywords  
  Abstract Climate change has impacts on agricultural yields, which could alter cropland requirements and hence deforestation rates. Thus, land-use responses to climate change might influence terrestrial carbon stocks. Moreover, climate change could alter the carbon storage capacity of the terrestrial biosphere and hence the land-based mitigation potential. We use a global spatially explicit economic land-use optimization model to (a) estimate the mitigation potential of a climate policy that provides economic incentives for carbon stock conservation and enhancement, (b) simulate land-use and carbon cycle responses to moderate climate change (RCP2.6), and (c) investigate the combined effects throughout the 21st century. The climate policy immediately stops deforestation and strongly increases afforestation, resulting in a global mitigation potential of 191 GtC in 2100. Climate change increases terrestrial carbon stocks not only directly through enhanced carbon sequestration (62 GtC by 2100) but also indirectly through less deforestation due to higher crop yields (16 GtC by 2100). However, such beneficial climate impacts increase the potential of the climate policy only marginally, as the potential is already large under static climatic conditions. In the broader picture, this study highlights the importance of land-use dynamics for modeling carbon cycle responses to climate change in integrated assessment modeling.  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0013-936x ISBN Medium  
  Area Expedition Conference  
  Notes TradeM, ftnotmacsur Approved no  
  Call Number MA @ admin @ Serial 4998  
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Author Stevanović, M.; Popp, A.; Bodirsky, B.L.; Humpenöder, F.; Müller, C.; Weindl, I.; Dietrich, J.P.; Lotze-Campen, H.; Kreidenweis, U.; Rolinski, S.; Biewald, A.; Wang, X. url  doi
openurl 
  Title Mitigation Strategies for Greenhouse Gas Emissions from Agriculture and Land-Use Change: Consequences for Food Prices Type Journal Article
  Year 2017 Publication (up) Environmental Science and Technology Abbreviated Journal Environmental Science and Technology  
  Volume 51 Issue 1 Pages 365-374  
  Keywords  
  Abstract The land use sector of agriculture, forestry, and other land use (AFOLU) plays a central role in ambitious climate change mitigation efforts. Yet, mitigation policies in agriculture may be in conflict with food security related targets. Using a global agro-economic model, we analyze the impacts on food prices under mitigation policies targeting either incentives for producers (e.g., through taxes) or consumer preferences (e.g., through education programs). Despite having a similar reduction potential of 43-44% in 2100, the two types of policy instruments result in opposite outcomes for food prices. Incentive-based mitigation, such as protecting carbon-rich forests or adopting low-emission production techniques, increase land scarcity and production costs and thereby food prices. Preference-based mitigation, such as reduced household waste or lower consumption of animal-based products, decreases land scarcity, prevents emissions leakage, and concentrates production on the most productive sites and consequently lowers food prices. Whereas agricultural emissions are further abated in the combination of these mitigation measures, the synergy of strategies fails to substantially lower food prices. Additionally, we demonstrate that the efficiency of agricultural emission abatement is stable across a range of greenhouse-gas (GHG) tax levels, while resulting food prices exhibit a disproportionally larger spread.  
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  Corporate Author Thesis  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0013-936x ISBN Medium  
  Area Expedition Conference  
  Notes TradeM, ftnotmacsur Approved no  
  Call Number MA @ admin @ Serial 5007  
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Author Sándor, R.; Barcza, Z.; Acutis, M.; Doro, L.; Hidy, D.; Köchy, M.; Minet, J.; Lellei-Kovács, E.; Ma, S.; Perego, A.; Rolinski, S.; Ruget, F.; Sanna, M.; Seddaiu, G.; Wu, L.; Bellocchi, G. url  doi
openurl 
  Title Multi-model simulation of soil temperature, soil water content and biomass in Euro-Mediterranean grasslands: Uncertainties and ensemble performance Type Journal Article
  Year 2016 Publication (up) European Journal of Agronomy Abbreviated Journal European Journal of Agronomy  
  Volume Issue Pages  
  Keywords Biomass; Grasslands; Modelling; Multi-model ensemble; Soil processes  
  Abstract • We simulate biomass, soil water content (SWC) and temperature (ST) in grasslands. • We compare nine models to the multi-model median (MMM) at nine sites. • With model calibration, we obtain satisfactory estimates of ST, less of SWC and biomass. • We observe discrepancies across models in the simulation of grassland processes. • We improve performance with multi-model approach. This study presents results from a major grassland model intercomparison exercise, and highlights the main challenges faced in the implementation of a multi-model ensemble prediction system in grasslands. Nine, independently developed simulation models linking climate, soil, vegetation and management to grassland biogeochemical cycles and production were compared in a simulation of soil water content (SWC) and soil temperature (ST) in the topsoil, and of biomass production. The results were assessed against SWC and ST data from five observational grassland sites representing a range of conditions – Grillenburg in Germany, Laqueuille in France with both extensive and intensive management, Monte Bondone in Italy and Oensingen in Switzerland – and against yield measurements from the same sites and other experimental grassland sites in Europe and Israel. We present a comparison of model estimates from individual models to the multi-model ensemble (represented by multi-model median: MMM). With calibration (seven out of nine models), the performances were acceptable for weekly-aggregated ST (R² > 0.7 with individual models and >0.8–0.9 with MMM), but less satisfactory with SWC (R² < 0.6 with individual models and < ∼ 0.5 with MMM) and biomass (R² < ∼0.3 with both individual models and MMM). With individual models, maximum biases of about −5 °C for ST, −0.3 m3 m−3 for SWC and 360 g DM m−2 for yield, as well as negative modelling efficiencies and some high relative root mean square errors indicate low model performance, especially for biomass. We also found substantial discrepancies across different models, indicating considerable uncertainties regarding the simulation of grassland processes. The multi-model approach allowed for improved performance, but further progress is strongly needed in the way models represent processes in managed grassland systems.  
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  ISSN 1161-0301 ISBN Medium  
  Area LiveM Expedition Conference  
  Notes Approved no  
  Call Number MA @ admin @ Serial 4768  
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Author Rolinski, S.; Sætnan, E. url  openurl
  Title Uncertainties in climate change prediction and modelling Type Report
  Year 2013 Publication (up) FACCE MACSUR Reports Abbreviated Journal  
  Volume 1 Issue Pages D-L1.5  
  Keywords  
  Abstract As models become increasingly complex and integrated, uncertainty among model  parameters, variables and processes become critical for evaluating model outcomes and  predictions. A framework for understanding uncertainty in climate modelling has been  developed by the IPCC and EEA which provides a framework for discussion of uncertainty  in models in general. Here we report on a review of this framework along with the results  of a survey of sources of uncertainty in livestock and grassland models. Along with the  identification of key sources of uncertainty in livestock and grassland modelling, the  survey highlighted the need for a development of a common typology for uncertainty.  When collaborating across traditionally separate research fields, or when communicating  with stakeholders, differences in understanding, interpretation or emphasis can cause  confusion. Further work in MACSUR should focus on improving model intercomparison  methods to better understand model uncertainties, and improve availability of high  quality datasets which can reduce model uncertainties. No Label  
  Address  
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  Area Expedition Conference  
  Notes Approved no  
  Call Number MA @ admin @ Serial 2259  
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