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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.

Supplemental data and extended results associated with the article entitled 'Ambitious food system interventions required to mitigate the risk of exceeding Earth’s environmental limits' (see Hadjikakou et al., 2025, One Earth). This repository contains the following files: Systematic search results and strings used to identify studies (Systematic_search_details.xlsx) A harmonised input database assembled from systematically selected studies (Harmonised_input_database.xlsx) Mapping of all on-ground actions in the literature to food system interventions (Action-intervention mapping.xlsx) Source data for key figures in the article and SI (Source data for figures.xlsx) Linear mixed model (LMM) predictions in physical units across all environmental indicators for all intervention combinations (Extended_results - LMM_indicator_predictions.zip) Risk estimates across all environmental limits for all intervention combinations (Extended_results - Risk_estimates_across_environmental_limits.zip) For all code, see the Global Food System Intervention Meta-Regression Model (GFSI-MRM). 

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.

Agricultural production depends upon certain crucial inputs e.g., water, fertilizer etc. In the less developed regions of South Asia in general, and the indo-Pakistan sub-continent in particular, the use of these inputs depends not only upon the financial affordability but also upon the institutional accessibility of farmers to these inputs. Besides high economic costs, bureaucratic controls and corruption regarding the distribution of inputs have created problems of limited accessibility, especially to the small farmers. In the absence of any credit, information and/or input distribution networks, the use of these inputs, and related productivity gains, become confined to that class of farmers which not only has better access to these inputs but is capable of using them in the best possible way e.g. use of water and fertilizer in the appropriate amount and at the appropriate time. This paper attempts to study how input use and input productivity vary across farm sizes, with some reference to the infrastructural and institutional factors, whose development play an important role in improving the distribution and productivity of inputs. For such an analysis, a comparison of the two Punjabs i.e. Pakistani and Indian Punjabs, presents an ideal framework, Separated by a national boundary since 1947, the two Punjabs enjoy a common history and culture, similar agricultural practices and agro-climatic conditions, Government policies in the two Punjabs, however, have not only differed between the two provinces at the same time, but also over time in the same province. It may be noted that due to certain policy measures, land distribution, tenancy conditions, promotion of agricultural co-operatives and provision of infrastructural features, such as roads and electricity, are relatively more improved in Indian than Pakistani Punjab.

The global goal to mitigate climate change (CC) is to achieve net zero greenhouse gas emissions (GHGE) by 2050; the European Union (EU) aim is to cut GHGE at least by 55% already by 2030. These ambition targets require new GHGE mitigation measures across all land use sectors (LULUCF), where wetlands, as carbon (C) rich ecosystem, can effectively contribute to climate targets, biodiversity, and water-related ecosystem services. Natural peatlands accumulate C effectively due to water-logged conditions. However, they can turn into high GHG sources if they are drained, therefore there is still need to enhance knowledge regarding how and/or how much C is sequestered or released by peatlands after their restoration, as well as the socioeconomic effects.&#8220;ALFAwetlands - Restoration for the future&#8221; (www.alfawetlands.eu) is a Horizon Europe funded project (2022-2026), which is coordinated by Luke and carried out at local to EU levels with 15 partners across Europe. It&#8217;s main goal, in short, is to mitigate CC while supporting biodiversity and ecosystem services (BES) and being socially just and rewarding. This includes, e.g., increasing the knowledge about C storage and release in peatlands, specifically after restoration. While, in terms of C fluxes, focussing on peatlands, the project scope is larger and includes additionally floodplains, coastal wetlands and few artificial wetlands. ALFAwetlands will develop and indicate management alternatives for wetlands including such that have been or will be restored during this project. Measures under this project are not restricted to ecological restoration but include rehabilitation and re-vegetation action to improve ecosystem conditions (e.g., peatland forest: continuous-cover-forestry, cultivated peatlands: paludiculture). Studies are conducted in 9 Living Labs (LL&#8217;s) including 30 sites, which are located in wetlands in different parts of Europe (north-south gradient). At the local level, LL&#8217;s support and integrate interdisciplinary and multi-actor research on ecological, environmental, economic, and social issues. Experimental data from local sites are scaled-up and will be utilized e.g., by models to gain and understanding the potential impacts of upscaled wetland restoration measures. To achieve ALFAwetlands goals, 5 research workpackages are being implemented, namely: 1)improve geospatial knowledge base of wetlands, 2)co-create socially fair and rewarding pathways for wetland restoration, 3)estimate effects of restoration on GHGE and BES, with the data achieved from field experiments, 4)develop policy relevant scenarios for CC and BES, and 5)study societal impacts of wetland restoration. The project will also encourage stakeholders to utilise outputs and support their active participation in wetland management.

Governments prioritize global food insecurity. Food insecurity affects a billion people, with Asia and the Pacific Islands having the highest rates and Sub-Saharan Africa the lowest. Pakistan is one of the worst-hit countries due to a surge in chronically food insecure people. Pakistan's food crisis persists. Due to improved worldwide methods and statistics on population, food balance sheets, and consumption patterns, Pakistan's PoU for 2017-19 is 12.3%, up from 12.0% the year before. This is helping measure progress on SDG indicator 2.1.1. 26 million individuals cannot fulfill their basic calorie needs, and the number is rising