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  • 2020-2024  (11)
  • 1965-1969
  • 2021  (11)
  • 1
    Publication Date: 2022-04-28
    Description: People around the world recollect socially responsible and desirable values, new forms of living and coexistence as well as eco-social economic activities. This volume is dedicated to these real utopias –positive ideas aiming at a better future and practical approaches combining notions of a more livable society with concrete practical implementations.
    Description: Weltweit besinnen sich Menschen auf gesellschaftlich Verantwortbares und Wünschenswertes, auf neue Formen des Lebens und Zusammenlebens und des ökosozialen Wirtschaftens. Dieser Band widmet sich diesen realen Utopien, positiven, in eine bessere Zukunft gerichteten Vorstellungen und praktischen Ansätzen, die Ideen von einer lebenswerteren Gesellschaft mit konkreter Praxis verbinden.
    Keywords: social justice ; future ; zukunft ; life ; veränderungsprozess ; demokratie ; processi di cambiamento ; società ; process of change ; democrazia ; society ; giustizia sociale ; lebensqualität ; sozialstruktur ; sostenibilità ; leben ; futuro ; qualità di vita ; alternativen ; nachhaltigkeit ; democracy ; soziale gerechtigkeit ; alternative ; bevölkerung ; gesellschaft ; vita ; sustainability ; quality of life ; Soziale Arbeit ; bic Book Industry Communication::J Society & social sciences
    Language: German
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  • 2
    Publication Date: 2023-07-27
    Description: Potential climate-related impacts on future crop yield are a major societal concern. Previous projections of the Agricultural Model Intercomparison and Improvement Project’s Global Gridded Crop Model Intercomparison based on the Coupled Model Intercomparison Project Phase 5 identified substantial climate impacts on all major crops, but associated uncertainties were substantial. Here we report new twenty-first-century projections using ensembles of latest-generation crop and climate models. Results suggest markedly more pessimistic yield responses for maize, soybean and rice compared to the original ensemble. Mean end-of-century maize productivity is shifted from +5% to −6% (SSP126) and from +1% to −24% (SSP585)—explained by warmer climate projections and improved crop model sensitivities. In contrast, wheat shows stronger gains (+9% shifted to +18%, SSP585), linked to higher CO2 concentrations and expanded high-latitude gains. The ‘emergence’ of climate impacts consistently occurs earlier in the new projections—before 2040 for several main producing regions. While future yield estimates remain uncertain, these results suggest that major breadbasket regions will face distinct anthropogenic climatic risks sooner than previously anticipated.
    Language: English
    Type: info:eu-repo/semantics/article
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  • 3
    Publication Date: 2023-10-11
    Description: Renewable energy resources, which depend on climate, may be susceptible to future climate change. Here we use climate and integrated assessment models to estimate this effect on key renewables. Future potential and costs are quantified across two warming scenarios for eight technologies: utility-scale and rooftop photovoltaic, concentrated solar power, onshore and offshore wind energy, first-generation and lignocellulosic bioenergy, and hydropower. The generated cost–supply curves are then used to estimate energy system impacts. In a baseline warming scenario, the largest impact is increased availability of bioenergy, though this depends on the strength of CO2 fertilization. Impacts on hydropower and wind energy are uncertain, with declines in some regions and increases in others, and impacts on solar power are minor. In a future mitigation scenario, these impacts are smaller, but the energy system response is similar to that in the baseline scenario given a larger reliance of the mitigation scenario on renewables.
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  • 4
    Publication Date: 2023-10-11
    Description: We present results from the Agricultural Model Intercomparison and Improvement Project (AgMIP) Global Gridded Crop Model Intercomparison (GGCMI) Phase I, which aligned 14 global gridded crop models (GGCMs) and 11 climatic forcing datasets (CFDs) in order to understand how the selection of climate data affects simulated historical crop productivity of maize, wheat, rice and soybean. Results show that CFDs demonstrate mean biases and differences in the probability of extreme events, with larger uncertainty around extreme precipitation and in regions where observational data for climate and crop systems are scarce. Countries where simulations correlate highly with reported FAO national production anomalies tend to have high correlations across most CFDs, whose influence we isolate using multi-GGCM ensembles for each CFD. Correlations compare favorably with the climate signal detected in other studies, although production in many countries is not primarily climate-limited (particularly for rice). Bias-adjusted CFDs most often were among the highest model-observation correlations, although all CFDs produced the highest correlation in at least one top-producing country. Analysis of larger multi-CFD-multi-GGCM ensembles (up to 91 members) shows benefits over the use of smaller subset of models in some regions and farming systems, although bigger is not always better. Our analysis suggests that global assessments should prioritize ensembles based on multiple crop models over multiple CFDs as long as a top-performing CFD is utilized for the focus region.
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  • 5
    Publication Date: 2023-10-11
    Description: Concerns over climate change are motivated in large part because of their impact on human society. Assessing the effect of that uncertainty on specific potential impacts is demanding, since it requires a systematic survey over both climate and impacts models. We provide a comprehensive evaluation of uncertainty in projected crop yields for maize, spring and winter wheat, rice, and soybean, using a suite of 9 crop models and up to 45 CMIP5 and 34 CMIP6 climate projections for three different forcing scenarios. To make this task computationally tractable, we use a new set of statistical crop model emulators. We find that climate and crop models contribute about equally to overall uncertainty. While the ranges of yield uncertainties under CMIP5 and CMIP6 projections are similar, median impact in aggregate total caloric production is typically more negative for the CMIP6 projections (+1 to -19%) than for CMIP5 (+5 to -13%). In the first half of the 21st century and for individual crops is the spread across crop models typically wider than that across climate models, but we find distinct differences between crops: globally, wheat and maize uncertainties are dominated by the crop models, but soybean and rice are more sensitive to the climate projections. Climate models with very similar global mean warming can lead to very different aggregate impacts so that climate model uncertainties remain a significant contributor to agricultural impacts uncertainty. These results show the utility of large-ensemble methods that allow comprehensively evaluating factors affecting crop yields or other impacts under climate change. The crop model ensemble used here is unbalanced and pulls the assumption that all projections are equally plausible into question. Better methods for consistent model testing, also at the level of individual processes, will have to be developed and applied by the crop modeling community.
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  • 6
    Publication Date: 2023-10-11
    Description: How Global Gridded Crop Models (GGCMs) differ in their simulation of potential yield and reasons for those differences have never been assessed. The GGCM Inter-comparison (GGCMI) offers a good framework for this assessment. Here, we built an emulator (called SMM for Simple Mechanistic Model) of GGCMs based on generic and simplified formalism. The SMM equations describe crop phenology by a sum of growing degree days, canopy radiation absorption by the Beer-Lambert law, and its conversion into aboveground biomass by a radiation use efficiency (RUE). We fitted the parameters of this emulator against gridded aboveground maize biomass at the end of the growing season simulated by eight different GGCMs in a given year (2000). Our assumption is that the simple set of equations of SMM, after calibration, could reproduce the response of most GGCMs, so that differences between GGCMs can be attributed to the parameters related to processes captured by the emulator. Despite huge differences between GGCMs, we show that if we fit both a parameter describing the thermal requirement for leaf emergence by adjusting its value to each grid-point in space, as done by GGCM modellers following the GGCMI protocol, and a GGCM-dependent globally uniform RUE, then the simple set of equations of the SMM emulator is sufficient to reproduce the spatial distribution of the original aboveground biomass simulated by most GGCMs. The grain filling is simulated in SMM by considering a fixed in time fraction of net primary productivity allocated to the grain (frac) once a threshold in leaves number (nthresh) is reached. Once calibrated, these two parameters allow to capture the relationship between potential yield and final aboveground biomass of each GGCM. It is particularly important as the divergence among GGCMs is larger for yield than for aboveground biomass. Thus, we showed that the divergence between GGCMs can be summarized by the differences in few parameters. Our simple but mechanistic model could also be an interesting tool to test new developments in order to improve the simulation of potential yield at the global scale.
    Language: English
    Type: info:eu-repo/semantics/article
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  • 7
    Publication Date: 2023-10-11
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  • 8
    Publication Date: 2023-10-11
    Description: Irrigation is the largest sector of human water use and an important option for increasing crop production and reducing drought impacts. However, the potential for irrigation to contribute to global crop yields remains uncertain. Here, we quantify this contribution for wheat and maize at global scale by developing a Bayesian framework integrating empirical estimates and gridded global crop models on new maps of the relative difference between attainable rainfed and irrigated yield (ΔY). At global scale, ΔY is 34 ± 9% for wheat and 22 ± 13% for maize, with large spatial differences driven more by patterns of precipitation than that of evaporative demand. Comparing irrigation demands with renewable water supply, we find 30–47% of contemporary rainfed agriculture of wheat and maize cannot achieve yield gap closure utilizing current river discharge, unless more water diversion projects are set in place, putting into question the potential of irrigation to mitigate climate change impacts.
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  • 9
    Publication Date: 2023-10-04
    Description: Despite extensive research on the role of plant species richness for the regulation of ecosystem functions, the mechanistic understanding of the underlying processes, especially in species-rich communities, is still limited. Biogeochemical models of vegetation dynamics could potentially be used to complement empirical studies, but it is unclear how the particular process description within these models affects simulations of species performance and resulting ecosystem functions. We evaluate the models’ process descriptions to simulate the response of different species, their inter- actions and their joint performance to drought and mowing. Therefore, we compare simulations of two grassland models of different complexity for monocultures and two-species mixtures in a grassland experi- ment in Jena, Germany. Models’ process representations are crucial for species’ performance and interaction. We provide an in-depth analysis of the processes responsible for model behavior to identify potential fields of model devel- opment and discuss our findings in the context of other modeling approaches. Both models simulated similar average above-ground biomass (AGB) but showed different intra-annual variability. Generally, the models had difficulties representing a balanced species composition in multiple species mixtures and competition for space was the main driver of community composition in both models. The resulting communities were dominated by the more competitive species, while the weak competitor was only marginally present in most mixtures independent of drought and mowing. The competitive strength which we derived from the calibrated parameter sets of the species differed between the models and the agreement on which species dominate specific mixtures was mixed. While both models simulated reduced soil water content and above-ground biomass in response to drought, the strength and duration of these responses differed. Despite these differences, simulated species interactions were barely affected, and strong competitors remained dominant. In both models, the representation of competition for below-ground re- sources (water and nutrients) is less complex than that for above-ground resources (space and light). We found that in both models the transpiration reduction from water stress is too strong when soil water con- tent is close to field capacity, which weakened the drought effects. Mowing had opposing effects on the competition for space in the models, which could be attributed to the different representations of plants in the two models. Here, we demonstrated that process-based vegetation models in general allow for a detailed comparison of the modelled processes and their links to both – emerging vegetation responses and underlying plant parameters/traits. Such a model intercomparison supports the understanding of how much model complexity is necessary to simulate specific research questions and objectives. Concerning our example of drought effects on plant species competition, we conclude that a better representation of multi-species mixtures and their response to disturbances in grassland models is needed to allow for more robust future projections of grassland dynamics under future management and climate change.
    Type: info:eu-repo/semantics/article
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  • 10
    Publication Date: 2021-10-29
    Description: One of the main limitations in state-of-the art solid-state quantum processors is qubit decoherence and relaxation due to noise from adsorbates on surfaces, impurities at interfaces, and material defects. For the field to advance towards full fault-tolerant quantum computing, a better understanding of these microscopic noise sources is therefore needed. Here, we use an ultra-high vacuum package to study the impact of vacuum loading, UV-light exposure, and ion irradiation treatments on relaxation and coherence times, as well as slow parameter fluctuations of flux tunable superconducting transmon qubits. The treatments studied do not significantly impact the relaxation rate Γ1 and the echo decay rate $${{{Gamma }}}_{2,{{{ m{SS}}}}}^{{{{ m{e}}}}}$$ Γ 2 , SS e at the sweet spot, except for Ne ion bombardment which reduces Γ1. In contrast, flux noise parameters are improved by removing magnetic adsorbates from the chip surfaces with UV-light and NH3 treatments. Additionally, we demonstrate that SF6 ion bombardment can be used to adjust qubit frequencies in situ and post-fabrication without affecting qubit relaxation and coherence times at the sweet spot.
    Electronic ISSN: 2056-6387
    Topics: Physics
    Published by Springer Nature
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