• Energy Research
• 2025-2025close
• 6. Clean water close
• 15. Life on land close
• 16. Peace & justice close

In the steppes of the Volga region on the southern chernozem in a long stationary experiment, the influence of weather conditions and mineral fertilizers on the yield of grain crops was revealed.It is shown how weather and climatic conditions affect plant productivity, responsiveness to fertilizers and their payback by crop.The features of climate change in the Volga steppe at the end of the 20th and beginning of the 21st centuries are considered.The parameters of changing the productivity of crops of a sixfield graincrop rotation for eight rotations associated with climate transformation are established.The obtained information serves as a database for building predictive mathematical models. В степи Поволжья на черноземе южном в длительном стационарном опыте выявлено влияние погодных условий и минеральных удобрений на урожайность зерновых культур. Показано, как погодные и климатические условия влияют на продуктивность растений, отзывчивость на удобрения и их окупаемость урожаем. Рассмотрены особенности изменения климата в степном Поволжье в конце 20гоначале 21 веков. Установлены параметры изменения продуктивности культур шестипольного зернопарового севооборота за восемь ротаций, связанные с трансформацией климата. Полученные сведения служат базой данных для построения прогнозных математических моделей.

In the steppes of the Volga region on the southern chernozem in a long stationary experiment, the influence of weather conditions and mineral fertilizers on the yield of grain crops was revealed.It is shown how weather and climatic conditions affect plant productivity, responsiveness to fertilizers and their payback by crop.The features of climate change in the Volga steppe at the end of the 20th and beginning of the 21st centuries are considered.The parameters of changing the productivity of crops of a sixfield graincrop rotation for eight rotations associated with climate transformation are established.The obtained information serves as a database for building predictive mathematical models. В степи Поволжья на черноземе южном в длительном стационарном опыте выявлено влияние погодных условий и минеральных удобрений на урожайность зерновых культур. Показано, как погодные и климатические условия влияют на продуктивность растений, отзывчивость на удобрения и их окупаемость урожаем. Рассмотрены особенности изменения климата в степном Поволжье в конце 20гоначале 21 веков. Установлены параметры изменения продуктивности культур шестипольного зернопарового севооборота за восемь ротаций, связанные с трансформацией климата. Полученные сведения служат базой данных для построения прогнозных математических моделей.

Predicted changes in rainfall intensity due to climate change are likely to influence key soil health parameters, especially structural attributes and crop growth. Variations in rainfall intensity will impact crop production negatively. It is therefore imperative to investigate the interaction between predicted increases in rainfall intensity and key soil health parameters, particularly in relation to soil structural attributes and plant growth. The objectives of this study were to determine the effects of rainfall intensity on soil crust formation and mode of seedling emergence in soils dominated by primary minerals. Soil samples were collected from the top 200 mm, air dried and then packed uniformly into plastic pots, which were perforated at the bottom. Three maize seeds of equal size were planted in a triangular pattern in each pot at a depth of 30 mm, after which the pots were pre-wetted by capillary. The samples were then subjected to simulated rainfall at 3 intensities, i.e., 30, 45 and 60 mm/h, for 5 min. Rainfall intensity significantly (P < 0.05) affected crust strength and mean emergence day (MED), but not emergence percentage (EMP) and shoot length (P > 0.05). The 60 mm/h rainfall intensity resulted in the highest crust strength and MED. The strength of crust for all three rainfall intensities was influenced by quartz content, soil organic matter, clay and hematite. Most seedlings emerged through cracks, which resulted in rainfall intensity having no significant effects on seedling EMP and shoot length. We concluded that any increase in rainfall intensity is likely to increase the severity of crusting in these soils. However, soils with extensive cracking are likely to have higher EMP and lower MED and more vigorous seedlings despite the strength of the crust. As a result, post-planting tillage methods that enhance crust cracking may be employed to enhance seedling emergence and growth in these soils.

Predicted changes in rainfall intensity due to climate change are likely to influence key soil health parameters, especially structural attributes and crop growth. Variations in rainfall intensity will impact crop production negatively. It is therefore imperative to investigate the interaction between predicted increases in rainfall intensity and key soil health parameters, particularly in relation to soil structural attributes and plant growth. The objectives of this study were to determine the effects of rainfall intensity on soil crust formation and mode of seedling emergence in soils dominated by primary minerals. Soil samples were collected from the top 200 mm, air dried and then packed uniformly into plastic pots, which were perforated at the bottom. Three maize seeds of equal size were planted in a triangular pattern in each pot at a depth of 30 mm, after which the pots were pre-wetted by capillary. The samples were then subjected to simulated rainfall at 3 intensities, i.e., 30, 45 and 60 mm/h, for 5 min. Rainfall intensity significantly (P < 0.05) affected crust strength and mean emergence day (MED), but not emergence percentage (EMP) and shoot length (P > 0.05). The 60 mm/h rainfall intensity resulted in the highest crust strength and MED. The strength of crust for all three rainfall intensities was influenced by quartz content, soil organic matter, clay and hematite. Most seedlings emerged through cracks, which resulted in rainfall intensity having no significant effects on seedling EMP and shoot length. We concluded that any increase in rainfall intensity is likely to increase the severity of crusting in these soils. However, soils with extensive cracking are likely to have higher EMP and lower MED and more vigorous seedlings despite the strength of the crust. As a result, post-planting tillage methods that enhance crust cracking may be employed to enhance seedling emergence and growth in these soils.

Temperature data from the Centre for Marine Applied Research's (CMAR) Coastal Monitoring Program. The original dataset includes data measured on intervals of 1 minute to 1 hour. The figure shows the daily average values for the waterbody, station, depth, time period, and quality control flags selected from the dropdown filters. The filtered dataset can be exported using the "Export" button on the right. To see other variables that were measured in this county, explore the original dataset (https://data.novascotia.ca/d/wpsu-7fer). To learn more about CMAR's Coastal Monitoring Program, visit https://cmar.ca/coastal-monitoring-program/

Temperature data from the Centre for Marine Applied Research's (CMAR) Coastal Monitoring Program. The original dataset includes data measured on intervals of 1 minute to 1 hour. The figure shows the daily average values for the waterbody, station, depth, time period, and quality control flags selected from the dropdown filters. The filtered dataset can be exported using the "Export" button on the right. To see other variables that were measured in this county, explore the original dataset (https://data.novascotia.ca/d/wpsu-7fer). To learn more about CMAR's Coastal Monitoring Program, visit https://cmar.ca/coastal-monitoring-program/

This repository contains VBA and ArcObjects code used to analyze plant distributions in digitized quadrats near Flagstaff Arizona, over the years 2002 - 2024. This code was used to produce the data presented in the Data Paper "Cover and density of southwestern ponderosa pine understory plants in permanent chart quadrats (2002-2024)" (Moore et al. 2025). There are 262 VBA functions used in this project, distributed over 13 modules and comprising 25,566 lines of code. The primary analytical master function is "RunAsBatch" in the module "H_WB_Analysis". This function runs several other functions that do the various steps of the analysis. In particular: The function "OrganizeData" in module "H_WB_Analysis" assembles all original datasets into a single workspace with a common naming convention, and adds verbatim fields to keep track of edits made to data. The function "ReviseShapefiles" in module "H_WB_Analysis" corrects species misspellings and misidentifications. The function "ConvertPointShapefiles" in module "H_WB_Analysis" converts point features to small polygons, deletes a few extraneous objects, adds a few observations that were missed in the digitizing, switches species designations from Cover to Density or vice-versa if necessary, and rotates quadrats if they were mapped with the wrong orientation. The function "AddEmptyFeaturesAndFeatureClasses" in module "H_WB_Analysis" adds empty feature classes if a survey was done on that quadrat in that year but no features were found. These empty feature classes distinguish these cases from times when no survey was conducted. The function "RepairOverlappingPolygons" in the module "More_Margaret_Functions" fixes cases when polygons for a single observation are digitized twice, or when separate polygons for a single species overlap. The function "RecreateSubsetsOfConvertedDatasets" in the module "More_Margaret_Functions" combines all newly-corrected feature classes into a new workspace, and creates two global feature classes containing all cover and all density observations. The function "AddEmptyFeaturesAndFeatureClassesToCleaned" in module "H_WB_Analysis" adds empty feature classes to the newly corrected feature classes if a survey was done on that quadrat in that year but no features were found. These empty feature classes distinguish these cases from times when no survey was conducted. The function "ShiftFinishedShapefilesToCoordinateSystem" in module "H_WB_Analysis" correctly georeferences all feature classes and saves to a new workspace. Prior to this step all plant locations were in a local 1-square-meter coordinate system based on the 1-square-meter quadrat. The function "ExportFinalDataset" in module "H_WB_Analysis" removes extraneous and verbatim fields, and exports the final version of the dataset to a new workspace. The function "SummarizeSpeciesBySite" in module "More_Margaret_Functions" analyzes all the feature classes to determine which species were observed at each site. The function "SummarizeSpeciesByCorrectQuadrat" in module "More_Margaret_Functions" analyzes all the feature classes to determine which species were observed at each quadrat. The function "SummarizeYearByCorrectQuadratByYear" in module "More_Margaret_Functions" analyzes all the feature classes to determine which quadrats were surveyed each year. The function "ExportSubsetsOfSpeciesShapefiles" in module "Margaret_Functions_3" extracts each species individually from the full dataset, and saves them in a series of nested folders suitable for Integral Projection Model functions in R. The function "CreateFinalTables" in module "H_WB_Analysis" produces the final summary tables intended for distribution with the data, including a list of plant species observed, a summary of the basal area per species by quadrat and year, summary data describing all quadrats and overstory plots, and tabular versions of the global cover and density feature classes. The primary map export function is "ExportImages" in the module "Margaret", and is run separately from the 14 functions run in the batch file above. This map-making function creates common plant species symbology that can be applied to all 1,877 maps, and exports individual maps for each quadrat and for each year. This function is best run from an ArcMap document with no data in it, which is why it is run separately from the other functions. Note: These functions are written in ArcObjects and VBA, and therefore can only be run in ArcMap. ArcGIS Pro cannot run them. Moore, M. M., J. S. Jenness, D. C. Laughlin, R. T. Strahan, J. D. Bakker, H. E. Dowling, and J. D. Springer. 2022. Cover and density of southwestern ponderosa pine understory plants in permanent chart quadrats (2002-2020). Ecology 103(5): e3661. https://doi.org/10.1002/ ecy.3661 Moore, M. M., Jenness, J. S, Laughlin, D. C., Strahan, R. T., Bakker, J. D., Dowling, H. E., and Springer, J. D. 2025. Cover and density of southwestern ponderosa pine understory plants in permanent chart quadrats (2002-2020+). Fort Collins, CO: Forest Service Research Data Archive. Updated February 2025.

This repository contains VBA and ArcObjects code used to analyze plant distributions in digitized quadrats near Flagstaff Arizona, over the years 2002 - 2024. This code was used to produce the data presented in the Data Paper "Cover and density of southwestern ponderosa pine understory plants in permanent chart quadrats (2002-2024)" (Moore et al. 2025). There are 262 VBA functions used in this project, distributed over 13 modules and comprising 25,566 lines of code. The primary analytical master function is "RunAsBatch" in the module "H_WB_Analysis". This function runs several other functions that do the various steps of the analysis. In particular: The function "OrganizeData" in module "H_WB_Analysis" assembles all original datasets into a single workspace with a common naming convention, and adds verbatim fields to keep track of edits made to data. The function "ReviseShapefiles" in module "H_WB_Analysis" corrects species misspellings and misidentifications. The function "ConvertPointShapefiles" in module "H_WB_Analysis" converts point features to small polygons, deletes a few extraneous objects, adds a few observations that were missed in the digitizing, switches species designations from Cover to Density or vice-versa if necessary, and rotates quadrats if they were mapped with the wrong orientation. The function "AddEmptyFeaturesAndFeatureClasses" in module "H_WB_Analysis" adds empty feature classes if a survey was done on that quadrat in that year but no features were found. These empty feature classes distinguish these cases from times when no survey was conducted. The function "RepairOverlappingPolygons" in the module "More_Margaret_Functions" fixes cases when polygons for a single observation are digitized twice, or when separate polygons for a single species overlap. The function "RecreateSubsetsOfConvertedDatasets" in the module "More_Margaret_Functions" combines all newly-corrected feature classes into a new workspace, and creates two global feature classes containing all cover and all density observations. The function "AddEmptyFeaturesAndFeatureClassesToCleaned" in module "H_WB_Analysis" adds empty feature classes to the newly corrected feature classes if a survey was done on that quadrat in that year but no features were found. These empty feature classes distinguish these cases from times when no survey was conducted. The function "ShiftFinishedShapefilesToCoordinateSystem" in module "H_WB_Analysis" correctly georeferences all feature classes and saves to a new workspace. Prior to this step all plant locations were in a local 1-square-meter coordinate system based on the 1-square-meter quadrat. The function "ExportFinalDataset" in module "H_WB_Analysis" removes extraneous and verbatim fields, and exports the final version of the dataset to a new workspace. The function "SummarizeSpeciesBySite" in module "More_Margaret_Functions" analyzes all the feature classes to determine which species were observed at each site. The function "SummarizeSpeciesByCorrectQuadrat" in module "More_Margaret_Functions" analyzes all the feature classes to determine which species were observed at each quadrat. The function "SummarizeYearByCorrectQuadratByYear" in module "More_Margaret_Functions" analyzes all the feature classes to determine which quadrats were surveyed each year. The function "ExportSubsetsOfSpeciesShapefiles" in module "Margaret_Functions_3" extracts each species individually from the full dataset, and saves them in a series of nested folders suitable for Integral Projection Model functions in R. The function "CreateFinalTables" in module "H_WB_Analysis" produces the final summary tables intended for distribution with the data, including a list of plant species observed, a summary of the basal area per species by quadrat and year, summary data describing all quadrats and overstory plots, and tabular versions of the global cover and density feature classes. The primary map export function is "ExportImages" in the module "Margaret", and is run separately from the 14 functions run in the batch file above. This map-making function creates common plant species symbology that can be applied to all 1,877 maps, and exports individual maps for each quadrat and for each year. This function is best run from an ArcMap document with no data in it, which is why it is run separately from the other functions. Note: These functions are written in ArcObjects and VBA, and therefore can only be run in ArcMap. ArcGIS Pro cannot run them. Moore, M. M., J. S. Jenness, D. C. Laughlin, R. T. Strahan, J. D. Bakker, H. E. Dowling, and J. D. Springer. 2022. Cover and density of southwestern ponderosa pine understory plants in permanent chart quadrats (2002-2020). Ecology 103(5): e3661. https://doi.org/10.1002/ ecy.3661 Moore, M. M., Jenness, J. S, Laughlin, D. C., Strahan, R. T., Bakker, J. D., Dowling, H. E., and Springer, J. D. 2025. Cover and density of southwestern ponderosa pine understory plants in permanent chart quadrats (2002-2020+). Fort Collins, CO: Forest Service Research Data Archive. Updated February 2025.

PyPSA-Eur is an open model dataset of the European power system at the transmission network level that covers the full ENTSO-E area. It can be built using the code provided at https://github.com/PyPSA/PyPSA-eur. It contains alternating current lines at and above 220 kV voltage level and all high voltage direct current lines, substations, an open database of conventional power plants, time series for electrical demand and variable renewable generator availability, and geographic potentials for the expansion of wind and solar power. Not all data dependencies are shipped with the code repository, since git is not suited for handling large changing files. Instead we provide separate data bundles to be downloaded and extracted as noted in the documentation. This is the full data bundle to be used for rigorous research. It includes large bathymetry and natural protection area datasets. While the code in PyPSA-Eur is released as free software under the MIT, different licenses and terms of use apply to the various input data, which are summarised below: corine/* CORINE Land Cover (CLC) database Source: https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012/ Terms of Use: https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012?tab=metadata natura/* Natura 2000 natural protection areas Source: https://www.eea.europa.eu/data-and-maps/data/natura-10 Terms of Use: https://www.eea.europa.eu/data-and-maps/data/natura-10#tab-metadata gebco/GEBCO_2014_2D.nc GEBCO bathymetric dataset Source: https://www.gebco.net/data_and_products/gridded_bathymetry_data/version_20141103/ Terms of Use: https://www.gebco.net/data_and_products/gridded_bathymetry_data/documents/gebco_2014_historic.pdf je-e-21.03.02.xls Population and GDP data for Swiss Cantons Source: https://www.bfs.admin.ch/bfs/en/home/news/whats-new.assetdetail.7786557.html Terms of Use: https://www.bfs.admin.ch/bfs/en/home/fso/swiss-federal-statistical-office/terms-of-use.html https://www.bfs.admin.ch/bfs/de/home/bfs/oeffentliche-statistik/copyright.html nama_10r_3popgdp.tsv.gz Population by NUTS3 region Source: http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nama_10r_3popgdp&lang=en Terms of Use: https://ec.europa.eu/eurostat/about/policies/copyright GDP_per_capita_PPP_1990_2015_v2.nc Gross Domestic Product per capita (PPP) from years 1999 to 2015 Rectangular cutout for European countries in PyPSA-Eur, including a 10 km buffer Kummu et al. "Data from: Gridded global datasets for Gross Domestic Product and Human Development Index over 1990-2015" Source: https://doi.org/10.1038/sdata.2018.4 and associated dataset https://doi.org/10.1038/sdata.2018.4 ppp_2019_1km_Aggregated.tif The spatial distribution of population in 2020: Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 30 arc (approximately 1km at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution. Rectangular cutout for non-NUTS3 countries in PyPSA-Eur, i.e. MD and UA, including a 10 km buffer WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00647 Source: https://data.humdata.org/dataset/worldpop-population-counts-for-world and https://hub.worldpop.org/geodata/summary?id=24777 License: Creative Commons Attribution 4.0 International Licens data/bundle/era5-HDD-per-country.csv - Link: https://gist.github.com/fneum/d99e24e19da423038fd55fe3a4ddf875- License: CC-BY 4.0- Contains country-level heating degree days in Europe for 1941-2023. Used for rescaling heat demand in weather years not covered by energy balance statistics. data/bundle/era5-runoff-per-country.csv - Link: https://gist.github.com/fneum/d99e24e19da423038fd55fe3a4ddf875- License: CC-BY 4.0- Contains country-level daily sum of runoff in Europe for 1941-2023. Used for rescaling hydro-electricity availability in weather years not covered by EIA hydro-generation statistics. shipdensity_global.zip Global Shipping Traffic Density Creative Commons Attribution 4.0 https://datacatalog.worldbank.org/search/dataset/0037580/Global-Shipping-Traffic-Density seawater_temperature.nc Global Ocean Physics Reanalysis Link: https://data.marine.copernicus.eu/product/GLOBAL_MULTIYEAR_PHY_001_030/services License: https://marine.copernicus.eu/user-corner/service-commitments-and-licence