• Energy Research
• 13. Climate action close
• 12. Responsible consumption close
• 6. Clean water close
• DE close
• FR close
• UA close

The yield gap for maize across the Ethiopia has been estimated using crop model LINTUL5 embedded into the modeling framework SIMPLACE (Scientific Impact Assessment and Modelling Platform for Advanced Crop and Ecosystem Management. The yield gap of a crop grown in a certain location and cropping system is defined as the difference between the yield and biomass under optimum management and the average yield achieved by farmers. Yield under optimum management is labeled as potential yield (Yp) under irrigated conditions or water-limited potential yield (Yw) under rain-fed conditions.Yp is location specific because of the climate, and not dependent on soil properties assuming that the required water and nutrients are non-limiting and can be added through management. Thus, in areas without major soil constraints, Yp is the most relevant benchmark for irrigated systems. Whereas, for rain-fed crops, Yw, equivalent to water-limited potential yield, is the most relevant benchmark. Both Yp and Yw are calculated for optimum planting dates, planting density and region-specific crop variety which is critical in determining the feasible growth duration, particularly in tropical climatic conditions where two or even three crops are produced each year on the same field. Purpose: To increase food production, identifying the regions with untapped production capacity is of prime importance and can be achieved by quantitative and spatially explicit estimates of Yield gaps, thus considering the spatial variation in environment and the production system. This dataset was first published on the institutional Repository "Zentrum für Entwicklungsforschung: ZEF Data Portal" with ID={c2bbd5ed-fd4c-4a3f-b0b1-113a5d4f3ddf}. The yield gaps plotted in the map were calculated as the average values of 7 years (the year 2004 -2010). The unit is Megagram per hectare (Mg ha-1) which is equivalent to tons ha-1. The climate data at the national scale was made available from the National Aeronautics and Space Administration (NASA), Goddard Institute of Space Studies(https://data.giss.nasa.gov/impacts/agmipcf/agmerra/), AgMERRA.The dataset is stored at 0.25°×0.25° horizontal resolution (~25km). Soil parameter values were extracted from the soil property maps of Africa at 1 km x 1 km resolution (http://www.isric.org/data/soil-property-maps-africa-1-km). Maize yields (Mg ha-1) and fertilizer application (Nitrogen and Phosphorus) rates over seven years (2004 - 2010) at administrative zone level have been collected from the Central Statistical Agency, Ethiopia.

1. Aphids are abundant in natural and managed vegetation, supporting a diverse community of organisms and causing damage to agricultural crops. Due to a changing climate, periods of drought are anticipated to increase, and the potential consequences of this for aphid-plant interactions are unclear. 2. Using a meta-analysis and synthesis approach, we aimed to advance understanding of how increased drought incidence will affect this ecologically and economically important insect group, and to characterise any potential underlying mechanisms. We used qualitative and quantitative synthesis techniques to determine whether drought stress has a negative, positive, or null effect on aphid fitness and examined these effects in relation to 1) aphid biology, 2) geographical region, 3) host plant biology. 3. Across all studies, aphid fitness is typically reduced under drought. Subgroup analysis detected no difference in relation to aphid biology, geographical region, or the aphid-plant combination, indicating the negative effect of drought on aphids is potentially universal. Furthermore, drought stress had a negative impact on plant vigour and increased plant concentrations of defensive chemicals, suggesting the observed response of aphids is associated with reduced plant vigour and increased chemical defence in drought-stressed plants. 4. We propose a conceptual model to predict drought effects on aphid fitness in relation to plant vigour and defence to stimulate further research. Please check the ReadMe for an explanation of the values included in the dataset. Please note that n/a values are included in the Global_Dataset tab for plant meta-analysis data (_Plant_Vigour, _Plant_Defence, and _Plant_Nutrition), these indicate studies that did not report these parameters. Data was collected and curated using standard systematic literature synthesis approaches. The effect size (Hedges' g) reported in the dataset was calculated from extracted means and standard deviations.

Anthropogenic greenhouse gases contribute to global warming. Among those gases, perfluorocarbons (PFCs) are thousands to tens of thousands times more harmful to the environment than comparable amounts of carbon dioxide. Till date, materials that selectively adsorb perfluorocarbons in favor to other less harmful gases have not been reported. Here a series of porous organic cage compounds with alkyl-, fluoroalkyl, and partially fluorinated alkyl groups are presented. Their isomorphic crystalline states allowed us to study the property-relationship of the degree of fluorination of the alkyl chains on gas sorption properties for PFCs and their selective uptakes in comparison to other, non-fluorinated gases. By this approach, one compound was identified, having superior selectivities of PFCs vs N2 or CO2 under ambient conditions.

Optimisation results for the lowest lifetime cost system consisting of solar photovoltaic (PV), hybrid photovoltaic-thermal (PV-T) and solar-thermal collectors alongside battery and hot-water storage systems for meeting the electrical and thermal (hot-water) needs of three multi-effect distillation (MED) plants. The updated results are from optimisations runs carried out in response to peer-review comments.

We provide the storyline data (in NetCDF format) used in the paper: ���The July 2019 European heatwave in a warmer climate: Storyline scenarios with a coupled model using spectral nudging��� published in Journal of Climate. The data is structured in four .tar.gz files (Preindustrial, Present, 2 and 4 K warmer climates) containing all variables used in this each climate. The data from the five ensemble members (E1 to E5) have been included separately in 3-months files. Atmospheric variables (Files are named as: {variable}_E{ensemble member}_{starting month}{year}.nc: Latent heat flux (ahfl) Sensible heat flux (ahfs) Monthly Global Mean 2m Temperature (GMTT2mMonthly) Maximum 2m Temperature (t2max) Mean 2m Temperature (t2mean) Minimum 2m Temperature (t2max) Soil Wetness (ws) Only for present climate: 850 hPa Temperature (T850) Total Cloud Cover (TCC) 500 hPa Geopotential Height (Z500) Five layers soil moisture (Only for present climate, Files are named as: From20172019in2017Climatessp370{ensemble member}_{year}{starting month}.01_jsbid.nc) Oceanic variables (from FESOM, Files are named as: {variable}_E{ensemble member}_{year}{starting month}01.nc: Sea Ice Concentration (SIC) Sea Surface Temperature (SST) Please, note that FESOM uses an unstructured mesh. This work was supported by the Helmholtz-Climate-Initiative through the HI-CAM project (Drivers cluster). Author Goessling acknowledges the financial support by the Federal Ministry of Education and Research of Germany in the framework of SSIP (Grant 01LN1701A). The simulations were performed at the German Climate Computing Center (DKRZ) using the ESM-Tools (Barbi et al. 2021). We thank Sebastian Rast (MPI-M) for support with the spectral nudging in ECHAM, and the ESM-Tools staff for their assistance during the simulation.

There is limited attention to impacts of climate change on pigs in Uganda by stakeholders, despite the potential vulnerability of pigs to climate change. Pigs are sensitive to heat-stress, as they do not have functioning sweat glands as other livestock species do, and have small lungs which reduces their ability to disseminate heat by panting. The objectives of the study were to i) determine the heat-stress status in pigs, ii) analyze factors influencing heat-stress, and iii) explore the heat-stress adaptation options in Lira District, Uganda. Lira was selected because of presence of both rural & urban areas and expected heat stress throughout the year in the district. The data including household demographics, management systems, age, color, breeds, body/skin temperature, rectal temperature and others were collected from 104 households and 259 pigs during the hot months in Ojwina and Barr sub-counties- Lira district. We collected data on adaptation options during the four gender disaggregated focus group discussions. Weather data was collected during the time of administering the questionnaire, and it was complemented with data from Ngetta Meteorological Station, Lira. STATA, 14

Water resources index W quantifies the potential dependence of the world's lowland areas on water resources originating in mountain areas upstream. The data cover the timeframe from the 1960s (1961–1970) to the 2040s (2041–2050) in decadal steps. Data for projections from the 2010s onwards are available for three scenario pathways (SSP1-RCP4.5, SSP2-RCP6.0, SSP3-RCP6.0) and show median results from 5 CMIP5 GCMs (GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, NorESM1‑M). The files are GeoTIFF formatted and in a regular raster of 5’×5’ (arc minutes in WGS 1984 coordinate system) The values of W can be classified using the following ranges: W ≤ -2 → Essential but vastly insufficient -2 < W < -1 → Essential but insufficient -1 ≤ W < 0 → Essential and sufficient W = 0 → No surplus from mountains 0 < W ≤ 1 → Supportive 1 < W < 2 → Minor W ≥ 2 → Negligible The values of W are rounded to four decimal places and limited to a range of -1110 to 9998. Values falling outside of that range are set to the nearest limit. he following flag values apply to W: -5555 indicates that there is no water balance surplus from the mountain area upstream, but a lowland water balance surplus; -6666 indicates that there is no water balance surplus from the mountain area upstream, and a lowland water balance deficit. Mountain areas and oceans are NODATA, large ice shields are omitted (Greenland: NODATA, Antarctica: not covered in extent). Mountain areas provide disproportionally high runoff in many parts of the world, and here we quantify for the first time their importance for water resources and food production from the viewpoint of the lowland areas downstream. The dataset maps the degree to which lowland areas potentially depend on runoff contributions from mountain areas (39% of land mass) between the 1960s and the 2040s.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.IPSL.IPSL-CM6A-LR.ssp585' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The IPSL-CM6A-LR climate model, released in 2017, includes the following components: atmos: LMDZ (NPv6, N96; 144 x 143 longitude/latitude; 79 levels; top level 80000 m), land: ORCHIDEE (v2.0, Water/Carbon/Energy mode), ocean: NEMO-OPA (eORCA1.3, tripolar primarily 1deg; 362 x 332 longitude/latitude; 75 levels; top grid cell 0-2 m), ocnBgchem: NEMO-PISCES, seaIce: NEMO-LIM3. The model was run by the Institut Pierre Simon Laplace, Paris 75252, France (IPSL) in native nominal resolutions: atmos: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.