• Energy Research
• 13. Climate action close
• Hyper Article en Ligne close

The Agricultural Model Intercomparison and Improvement Project (AgMIP) is a major international effort linking the climate, crop, and economic modeling communities with cutting-edge information technology to produce improved crop and economic models and the next generation of climate impact projections for the agricultural sector. The goals of AgMIP are to improve substantially the characterization of world food security due to climate change and to enhance adaptation capacity in both developing and developed countries. Analyses of the agricultural impacts of climate variability and change require a transdisciplinary effort to consistently link state-of-the-art climate scenarios to crop and economic models. Crop model outputs are aggregated as inputs to regional and global economic models to determine regional vulnerabilities, changes in comparative advantage, price effects, and potential adaptation strategies in the agricultural sector. Climate, Crop Modeling, Economics, and Information Technology Team Protocols are presented to guide coordinated climate, crop modeling, economics, and information technology research activities around the world, along with AgMIP Cross-Cutting Themes that address uncertainty, aggregation and scaling, and the development of Representative Agricultural Pathways (RAPs) to enable testing of climate change adaptations in the context of other regional and global trends. The organization of research activities by geographic region and specific crops is described, along with project milestones. Pilot results demonstrate AgMIP's role in assessing climate impacts with explicit representation of uncertainties in climate scenarios and simulations using crop and economic models. An intercomparison of wheat model simulations near Obregón, Mexico reveals inter-model differences in yield sensitivity to [CO2] with model uncertainty holding approximately steady as concentrations rise, while uncertainty related to choice of crop model increases with rising temperatures. Wheat model simulations with mid-century climate scenarios project a slight decline in absolute yields that is more sensitive to selection of crop model than to global climate model, emissions scenario, or climate scenario downscaling method. A comparison of regional and national-scale economic simulations finds a large sensitivity of projected yield changes to the simulations’ resolved scales. Finally, a global economic model intercomparison example demonstrates that improvements in the understanding of agriculture futures arise from integration of the range of uncertainty in crop, climate, and economic modeling results in multi-model assessments.

Abstract This paper investigates the impact of the oxidizing agent used in the physical activation of pyrolysis chars on activated carbons (AC), and their efficiency in tar cracking. The materials are produced from buckwheat husk, which is a French local biomass. Slow pyrolysis was chosen to favor the production of raw chars. Activated chars were then prepared by physical activation under steam or CO2. The materials produced were physically and chemically characterized before being implemented in a fixed bed. The efficiency of materials in removing tar from syngas was studied, and ethylbenzene (EB) was chosen as the tar surrogate. The purification efficiency of the chars and AC was deduced from the EB conversion profiles in comparison with thermal cracking. The nature of the oxidizing agent impacted the porosity and mineral composition of the AC produced: CO2 activation produced ACs with more carbon and less ash content. This is because the reduction of carbon with carbon dioxide presents a kinetic between 2 and 5 times slower than that conducted in the presence of steam. CO2 activation also resulted in a higher relative microporosity (89%) than steam activation (61%), which itself gives a higher surface area. Pyrolysis char did not generate any interest for EB catalytic cracking, whereas ACs demonstrated higher performances. This is due to their higher level of porosity and higher ash content. BH–H2O, which presents higher porosity, produced a better catalytic effect and was more resistant to deactivation.

This introduction sets out some of the key themes addressed by the papers in the special issue on ‘Confronting the Naturalness of Disaster in the Pacific’. Disasters are now widely understood not a...

Abstract Biogas is a valuable renewable energy that can be used as a fuel or as raw material for the production of hydrogen, synthesis gas and chemicals. Apart from its main constituent of CH 4 and CO 2 , it also contained various undesirable contaminants. The removal of these contaminants such as H 2 S will significantly improve the quality of the biogas for further use. This work involved the valorization of calcium carbonate (CaCO 3 )-based solid wastes for the removal of H 2 S from the biogas stream. The solid wastes were analyzed by different physicochemical methods CaCO 3 was found as the main component of both solid wastes, while trace amounts of other elements such as Mg, Al, etc. were also present. The solid wastes were dispersed in water and the resulted suspensions were tested for the removal of H 2 S from the gas phase, using a triphasic gas/liquid/solid glass reactor at room temperature and pressure. A commercial activated carbon (AC) and a pure commercial calcite were also used for comparison purpose. In addition, mixtures of AC with CaCO 3 waste in different ratios were also tested. The influence of the matrix of gas (air or biogas) on the removal of H 2 S was evaluated. Both solid wastes showed higher performance compared to the pure commercial calcite for H 2 S removal. The coupling of a CaCO 3 waste and AC allowed improving the sorption performance compared to pure commercial CaCO 3 and AC used alone. The results obtained open a new promising way for the valorization of CaCO 3 -based wastes for H 2 S removal from biogas.

Abstract The white‐clawed crayfish (Austropotamobius pallipes) is globally endangered due to the impacts of habitat modification and fragmentation, water pollution, climate change, and invasive species, particularly the signal crayfish (Pacifastacus leniusculus). These pressures have caused the decline of A. pallipes populations in Europe, demonstrating the importance of predicting the species' potential distribution under current and future conditions. Focusing on the watercourses of mainland France, we aimed to identify suitable areas for A. pallipes to guide the conservation of current populations and future introduction actions or protection measures. We applied ecological niche modelling to model the potential distribution of both A. pallipes and P. leniusculus and identified locations suitable for A. pallipes only. We also assessed the potential distribution of the species under two representative concentration pathway (RCP) scenarios: RCP 2.6 and RCP 8.5, respectively describing low‐warming and high‐warming conditions. We found that A. pallipes and P. leniusculus exploit equivalent niches in France. Despite this, under current conditions, about 5% of the study area simultaneously records a high suitability for A. pallipes and a low suitability for P. leniusculus and is therefore of significant conservation interest. This percentage remains relatively stable under RCP 2.6 for 2050 and 2100, but decreases to 2% under RCP 8.5 for 2100. Ecological niche modelling can supply crucial guidance for conservation actions aimed at protecting endangered species at a national scale by identifying sites most suitable for protection and sites where climate change and invasive species constitute a threat.

AbstractProcess‐based models are useful for assessing the impact of changing management practices and climate on yields and greenhouse gas (GHG) emissions from agricultural systems such as grasslands. They can be used to construct national GHG inventories using a Tier 3 approach. However, accurate simulations of nitrous oxide (N2O) fluxes remain challenging. Models are limited by our understanding of soil‐plant‐microbe interactions and the impact of uncertainty in measured input parameters on simulated outputs. To improve model performance, thorough evaluations against in situ measurements are needed. Experimental data of N2O emissions under two management practices (control with typical fertilization versus increased clover and no fertilization) were acquired in a Swiss field experiment. We conducted a multimodel evaluation with three commonly used biogeochemical models (DayCent in two variants, PaSim, APSIM in two variants) comparing four years of data. DayCent was the most accurate model for simulating N2O fluxes on annual timescales, while APSIM was most accurate for daily N2O fluxes. The multimodel ensemble average reduced the error in estimated annual fluxes by 41% compared to an estimate using the Intergovernmental Panel on Climate Change (IPCC)‐derived method for the Swiss agricultural GHG inventory (IPCC‐Swiss), but individual models were not systematically more accurate than IPCC‐Swiss. The model ensemble overestimated the N2O mitigation effect of the clover‐based treatment (measured: 39–45%; ensemble: 52–57%) but was more accurate than IPCC‐Swiss (IPCC‐Swiss: 72–81%). These results suggest that multimodel ensembles are valuable for estimating the impact of climate and management on N2O emissions.