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Author Zhao, G.; Hoffmann, H.; van Bussel, L.G.J.; Enders, A.; Specka, X.; Sosa, C.; Yeluripati, J.; Tao, F.L.; Constantin, J.; Raynal, H.; Teixeira, E.; Grosz, B.; Doro, L.; Zhao, Z.G.; Nendel, C.; Kiese, R.; Eckersten, H.; Haas, E.; Vanuytrecht, E.; Wang, E.; Kuhnert, M.; Trombi, G.; Moriondo, M.; Bindi, M.; Lewan, E.; Bach, M.; Kersebaum, K.C.; Rotter, R.; Roggero, P.P.; Wallach, D.; Cammarano, D.; Asseng, S.; Krauss, G.; Siebert, S.; Gaiser, T.; Ewert, F. url  doi
openurl 
  Title Effect of weather data aggregation on regional crop simulation for different crops, production conditions, and response variables Type Journal Article
  Year 2015 Publication Climate Research Abbreviated Journal Clim. Res.  
  Volume 65 Issue Pages 141-157  
  Keywords crop model; model comparison; spatial resolution; data aggregation; spatial heterogeneity; scaling; climate-change scenarios; sub-saharan africa; winter-wheat; spatial-resolution; yield response; input data; systems simulation; large-scale; soil data; part i  
  Abstract We assessed the weather data aggregation effect (DAE) on the simulation of cropping systems for different crops, response variables, and production conditions. Using 13 process-based crop models and the ensemble mean, we simulated 30 yr continuous cropping systems for 2 crops (winter wheat and silage maize) under 3 production conditions for the state of North Rhine-Westphalia, Germany. The DAE was evaluated for 5 weather data resolutions (i.e. 1, 10, 25, 50, and 100 km) for 3 response variables including yield, growing season evapotranspiration, and water use efficiency. Five metrics, viz. the spatial bias (Delta), average absolute deviation (AAD), relative AAD, root mean squared error (RMSE), and relative RMSE, were used to evaluate the DAE on both the input weather data and simulated results. For weather data, we found that data aggregation narrowed the spatial variability but widened the., especially across mountainous areas. The DAE on loss of spatial heterogeneity and hotspots was stronger than on the average changes over the region. The DAE increased when coarsening the spatial resolution of the input weather data. The DAE varied considerably across different models, but changed only slightly for different production conditions and crops. We conclude that if spatially detailed information is essential for local management decision, higher resolution is desirable to adequately capture the spatial variability for heterogeneous regions. The required resolution depends on the choice of the model as well as the environmental condition of the study area.  
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  Language English Summary Language Original Title  
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  Series Volume Series Issue Edition  
  ISSN 0936-577x ISBN Medium Article  
  Area Expedition Conference  
  Notes CropM, ft_macsur Approved no  
  Call Number MA @ admin @ Serial 4754  
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Author Ingram, J.S.I.; Porter, J.R. doi  openurl
  Title Plant science and the food security agenda Type Journal Article
  Year 2015 Publication Nature Plants Abbreviated Journal Nature Plants  
  Volume 1 Issue 11 Pages 15173  
  Keywords africa; maize  
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  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2055-026x 2055-0278 ISBN Medium Editorial Material  
  Area Expedition Conference  
  Notes CropM, ftnotmacsur Approved no  
  Call Number MA @ admin @ Serial 4705  
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Author Nguyen, T.P.L.; Seddaiu, G.; Virdis, S.G.P.; Tidore, C.; Pasqui, M.; Roggero, P.P. url  doi
openurl 
  Title Perceiving to learn or learning to perceive? Understanding farmers’ perceptions and adaptation to climate uncertainties Type Journal Article
  Year 2016 Publication Agricultural Systems Abbreviated Journal Agricultural Systems  
  Volume 143 Issue Pages 205-216  
  Keywords climate variability; socio-cognitive learning process; adaptation strategies; mediterranean agricultural systems; agricultural land-use; adaptive capacity; farming systems; variability; knowledge; risk; drought; africa; future; rain  
  Abstract Perception not only shapes knowledge but knowledge also shapes perception. Humans adapt to the natural world through a process of learning in which they interpret their sensory impressions in order to give meaning to their environment and act accordingly. In this research, we examined how farmers’ decision making is shaped in the context of changing climate. Using empirical data (face-to-face semi-structured interviews and questionnaires) on four Mediterranean farming systems from a case study located in Oristano (Sardinia, Italy) we sought to understand farmers’ perception of climate change and their behaviors in adjustment of farming practices. We found different perceptions among farmer groups were mainly associated with the different socio-cultural and institutional settings and perceived relationships between climate factors and impacts on each farming systems. The research findings on different perceptions among farmer groups can help to understand farmers’ current choices and attitudes of adaptation for supporting the development of appropriate adaptation strategies. In addition, the knowledge of socio-cultural and economic factors that lead to biases in climate perceptions can help to integrate climate communication into adaptation research for making sense of climate impacts and responses at farm level.  
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  Language English Summary Language Original Title  
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  Series Volume Series Issue Edition  
  ISSN 0308-521x ISBN Medium Article  
  Area Expedition Conference  
  Notes CropM, ft_macsur Approved no  
  Call Number MA @ admin @ Serial 4707  
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Author Müller, C.; Waha, K.; Bondeau, A.; Heinke, J. doi  openurl
  Title Hotspots of climate change impacts in sub-Saharan Africa and implications for adaptation and development Type Journal Article
  Year 2014 Publication Global Change Biology Abbreviated Journal Glob. Chang. Biol.  
  Volume 20 Issue 8 Pages 2505-2517  
  Keywords Africa South of the Sahara; *Climate Change; Crops, Agricultural; Environment; Hydrology; *Models, Theoretical; Uncertainty; adaptation; climate change; development; impacts; modeling; sub-Saharan Africa  
  Abstract Development efforts for poverty reduction and food security in sub-Saharan Africa will have to consider future climate change impacts. Large uncertainties in climate change impact assessments do not necessarily complicate, but can inform development strategies. The design of development strategies will need to consider the likelihood, strength, and interaction of climate change impacts across biosphere properties. We here explore the spread of climate change impact projections and develop a composite impact measure to identify hotspots of climate change impacts, addressing likelihood and strength of impacts. Overlapping impacts in different biosphere properties (e.g. flooding, yields) will not only claim additional capacity to respond, but will also narrow the options to respond and develop. Regions with severest projected climate change impacts often coincide with regions of high population density and poverty rates. Science and policy need to propose ways of preparing these areas for development under climate change impacts.  
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  Language English Summary Language Original Title  
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  Series Volume Series Issue Edition  
  ISSN 1354-1013 ISBN Medium Article  
  Area Expedition Conference  
  Notes CropM, ft_macsur Approved no  
  Call Number MA @ admin @ Serial 4534  
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Author Müller, C.; Robertson, R.D. doi  openurl
  Title Projecting future crop productivity for global economic modeling Type Journal Article
  Year 2014 Publication Agricultural Economics Abbreviated Journal Agric. Econ.  
  Volume 45 Issue 1 Pages 37-50  
  Keywords climate change; crop modeling; agricultural productivity; land use; greenhouse-gas emissions; soil organic-carbon; sub-saharan africa; climate-change; elevated co2; land-use; system model; wheat yields; maize yields; agriculture  
  Abstract Assessments of climate change impacts on agricultural markets and land-use patterns rely on quantification of climate change impacts on the spatial patterns of land productivity. We supply a set of climate impact scenarios on agricultural land productivity derived from two climate models and two biophysical crop growth models to account for some of the uncertainty inherent in climate and impact models. Aggregation in space and time leads to information losses that can determine climate change impacts on agricultural markets and land-use patterns because often aggregation is across steep gradients from low to high impacts or from increases to decreases. The four climate change impact scenarios supplied here were designed to represent the most significant impacts (high emission scenario only, assumed ineffectiveness of carbon dioxide fertilization on agricultural yields, no adjustments in management) but are consistent with the assumption that changes in agricultural practices are covered in the economic models. Globally, production of individual crops decrease by 10-38% under these climate change scenarios, with large uncertainties in spatial patterns that are determined by both the uncertainty in climate projections and the choice of impact model. This uncertainty in climate impact on crop productivity needs to be considered by economic assessments of climate change.  
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  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0169-5150 ISBN Medium Article  
  Area Expedition Conference  
  Notes CropM, TradeM, ft_macsur Approved no  
  Call Number MA @ admin @ Serial 4533  
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