{"type": "FeatureCollection", "features": [{"id": "10.1111/sum.12198", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-25T16:22:24Z", "type": "Journal Article", "created": "2015-07-31", "title": "Long-Term Effects Of Tillage, Nutrient Application And Crop Rotation On Soil Organic Matter Quality Assessed By Nmr Spectroscopy", "description": "Abstract

Crop and land management practices affect both the quality and quantity of soil organic matter (SOM) and hence are driving forces for soil organic carbon (SOC) sequestration. The objective of this study was to assess the long\uffe2\uff80\uff90term effects of tillage, fertilizer application and crop rotation onSOCin an agricultural area of southern Norway, where a soil fertility and crop rotation experiment was initiated in 1953 and a second experiment on tillage practices was initiated in 1983. The first experiment comprised 6\uffe2\uff80\uff90yr crop rotations with cereals only and 2\uffe2\uff80\uff90yr cereal and 4\uffe2\uff80\uff90yr grass rotations with recommended (base) and more than the recommended (above base) fertilizer application rates; the second experiment dealt with autumn\uffe2\uff80\uff90ploughed (conventional\uffe2\uff80\uff90till) plots and direct\uffe2\uff80\uff90drilled plots (no\uffe2\uff80\uff90till). Soil samples at 0\uffe2\uff80\uff9310 and 10\uffe2\uff80\uff9330\uffc2\uffa0cm depths were collected in autumn 2009 and analysed for their C and N contents. The quality ofSOMin the top layer was determined by13C solid\uffe2\uff80\uff90stateNMRspectroscopy. TheSOCstock did not differ significantly because of rotation or fertilizer application types, even after 56\uffc2\uffa0yr. However, the no\uffe2\uff80\uff90till system showed a significantly higherSOCstock than the conventional\uffe2\uff80\uff90till system at the 0\uffe2\uff80\uff9310\uffc2\uffa0cm depth after the 26\uffc2\uffa0yr of experiment, but it was not significantly different at the 10\uffe2\uff80\uff9330\uffc2\uffa0cm depth. In terms of quality,SOMwas found to differ by tillage type, rate of fertilizer application and crop rotation. The no\uffe2\uff80\uff90till system showed an abundance of O\uffe2\uff80\uff90alkyl C, while conventional\uffe2\uff80\uff90till system indicated an apparently indirect enrichment in alkyl C, suggesting a more advanced stage ofSOMdecomposition. The long\uffe2\uff80\uff90term quantitative and qualitative effects onSOMsuggest that adopting a no\uffe2\uff80\uff90tillage system and including grass in crop rotation and farmyard manure in fertilizer application may contribute to preserve soil fertility and mitigate climate change.

", "keywords": ["Fertilizer application", "2. Zero hunger", "Crop rotation", " fertilizer application", " soil organic carbon (SOC)", " soil organic matter (SOM)", " tillage", " NMR spectroscopy.", "NMR spectroscopy", "Crop rotation", "Soil organic matter (SOM)", "13. Climate action", "Soil organic carbon (SOC)", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "Tillage"]}, "links": [{"href": "https://doi.org/10.1111/sum.12198"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20Use%20and%20Management", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/sum.12198", "name": "item", "description": "10.1111/sum.12198", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/sum.12198"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2015-07-31T00:00:00Z"}}, {"id": "10.17026/dans-z8z-5t4e", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-25T16:23:26Z", "type": "Dataset", "title": "Consumers' attitudes towards microbial applications in food production", "description": "The data was collected using an online survey as part of the research activities of the European Horizon 2020 project SIMBA (Sustainable Innovation of Microbiome Applications in the Food System). Online surveys were used to collect consumer respondent data for three food products (wheat bread, consumer potatoes and tomato sauce). These food products were selected as part of the EU Horizon 2020 SIMBA project for reflecting the diversity of food value chains in terms of organisation, technology, climatic conditions and consumption patterns across the EU. Three questionnaires corresponding to the three food products were prepared. The questionnaires consisted of four main parts: (1) socio-demographic characteristics (e.g. gender, education, income; Part One), (2) health and environmental concerns related to chemical use in farming, knowledge about microbial applications, perceived microbial health risks and attitude towards microbial applications in food production (Part Two), (3) questions for eliciting a consumer\u2019s willingness-to-pay (WTP) for a food product that had been obtained through a microbial-enhanced production system with reduced or no chemical use (Part Three), and (4) questions for eliciting a respondent\u2019s food choice motives (FCMs) using de Boer et al.\u2019s (2007) FCM questionnaire (Part Four). In addition, the questionnaire had an introduction section containing information sheet about the study and a consent form. The consent form and the information sheet for safeguarding the ethical aspects of this study (e.g. data handling, privacy and potential risks to respondents) were approved by the General Assembly of the SIMBA project as well as the Social Sciences Ethics Committee of Wageningen University prior to distributing the surveys. We collected data primarily from three countries: Germany, Italy and Netherlands.The data was collected to undertake a study to support the uptake of food products that are produced and/or processed using microbial applications. The aim of the survey was to get some insights about food choice motives of consumers, and the socio-demographic and behavioural factors influencing their food choices. The survey specifically aimed to assess the preferences and perceptions, and willingness-to-pay of consumers to wheat, potato and tomato-based food products that are produced and/or processed using microbial applications.", "keywords": ["2. Zero hunger", "13. Climate action", "Economics", "Social and Behavioural Sciences", "Business and Management", "Food economics", "12. Responsible consumption"], "contacts": [{"organization": "BM Ali", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.17026/dans-z8z-5t4e"}, {"rel": "self", "type": "application/geo+json", "title": "10.17026/dans-z8z-5t4e", "name": "item", "description": "10.17026/dans-z8z-5t4e", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.17026/dans-z8z-5t4e"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-01T00:00:00Z"}}, {"id": "10.23986/afsci.148486", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-25T16:24:30Z", "type": "Journal Article", "created": "2025-05-26", "title": "Defining critical SOC/clay thresholds for soil health in boreal croplands using satellite-based NDVI proxies for productivity and resilience", "description": "

The European Union\u2019s soil strategy underscores the necessity for establishing feasible criteria to assess the soil health condition. In this study, we developed a method to define a critical threshold value for SOC/clay ratio on the basis of crop productivity and resilience. The study integrated data from national soil monitoring (NSM) of Finnish cropland soils (n=505) with satellite-based normalized difference vegetation index (NDVI) obtained from the EcoDataCube (EDC) portal. The study area was confined to the boreal environmental zone to ensure consistent pedo-climatic conditions. The results show that the interannual variation in crop productivity increases rapidly below SOC/clay ratio of 0.09 (95% confidence intervals ranging from 0.07 to 0.16), whereas the corresponding threshold for mean productivity was 0.13 (0.09\u20130.16). The observed threshold values were found applicable for both cereals and temporary ley. The SOC/clay ratio of 1:13 (=0.08), regarded as a criterion for healthy soil in the current Soil Monitoring Law proposal, based on studies by Johannes et al. (2017) and Prout et al. (2021), is lower than the mean thresholds estimated in this study but aligns close to the lower bound of the 95% confidence intervals. In this research, Finnish agricultural land served as the case study area, but the method is easily applicable to various pedo-climatic regions and potentially to different land use types.

", "keywords": ["S", "Soil Monitoring Law", " SOC/clay ratio", " cropland", " NDVI", " satellite data", " national soil monitoring", "Agriculture (General)", "Agriculture", "S1-972"], "contacts": [{"organization": "Heikkinen, Jaakko, Keskinen, Riikka, Ylivainio, Kari,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.23986/afsci.148486"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agricultural%20and%20Food%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.23986/afsci.148486", "name": "item", "description": "10.23986/afsci.148486", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.23986/afsci.148486"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-05-26T00:00:00Z"}}, {"id": "10.5281/zenodo.8057232", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-25T16:29:39Z", "type": "Dataset", "title": "Upscaling soil organic carbon measurements at the continental scale using multivariate clustering analysis and machine learning", "description": "Data Description: To improve SOC estimation in the United States, we upscaled site-based SOC measurements to the continental scale using multivariate geographic clustering (MGC) approach coupled with machine learning models. First, we used the MGC approach to segment the United States at 30 arc second resolution based on principal component information from environmental covariates (gNATSGO soil properties, WorldClim bioclimatic variables, MODIS biological variables, and physiographic variables) to 20 SOC regions. We then trained separate random forest model ensembles for each of the SOC regions identified using environmental covariates and soil profile measurements from the International Soil Carbon Network (ISCN) and an Alaska soil profile data. We estimated United States SOC for 0-30 cm and 0-100 cm depths were 52.6 + 3.2 and 108.3 + 8.2 Pg C, respectively. Files in collection (32): Collection contains 22 soil properties geospatial rasters, 4 soil SOC geospatial rasters, 2 ISCN site SOC observations csv files, and 4 R scripts gNATSGO TIF files: \u251c\u2500\u2500 available_water_storage_30arc_30cm_us.tif [30 cm depth soil available water storage]
\u251c\u2500\u2500 available_water_storage_30arc_100cm_us.tif [100 cm depth soil available water storage]
\u251c\u2500\u2500 caco3_30arc_30cm_us.tif [30 cm depth soil CaCO3 content]
\u251c\u2500\u2500 caco3_30arc_100cm_us.tif [100 cm depth soil CaCO3 content]
\u251c\u2500\u2500 cec_30arc_30cm_us.tif [30 cm depth soil cation exchange capacity]
\u251c\u2500\u2500 cec_30arc_100cm_us.tif [100 cm depth soil cation exchange capacity]
\u251c\u2500\u2500 clay_30arc_30cm_us.tif [30 cm depth soil clay content]
\u251c\u2500\u2500 clay_30arc_100cm_us.tif [100 cm depth soil clay content]
\u251c\u2500\u2500 depthWT_30arc_us.tif [depth to water table]
\u251c\u2500\u2500 kfactor_30arc_30cm_us.tif [30 cm depth soil erosion factor]
\u251c\u2500\u2500 kfactor_30arc_100cm_us.tif [100 cm depth soil erosion factor]
\u251c\u2500\u2500 ph_30arc_100cm_us.tif [100 cm depth soil pH]
\u251c\u2500\u2500 ph_30arc_100cm_us.tif [30 cm depth soil pH]
\u251c\u2500\u2500 pondingFre_30arc_us.tif [ponding frequency]
\u251c\u2500\u2500 sand_30arc_30cm_us.tif [30 cm depth soil sand content]
\u251c\u2500\u2500 sand_30arc_100cm_us.tif [100 cm depth soil sand content]
\u251c\u2500\u2500 silt_30arc_30cm_us.tif [30 cm depth soil silt content]
\u251c\u2500\u2500 silt_30arc_100cm_us.tif [100 cm depth soil silt content]
\u251c\u2500\u2500 water_content_30arc_30cm_us.tif [30 cm depth soil water content]
\u2514\u2500\u2500 water_content_30arc_100cm_us.tif [100 cm depth soil water content] SOC TIF files: \u251c\u2500\u250030cm SOC mean.tif [30 cm depth soil SOC]
\u251c\u2500\u2500100cm SOC mean.tif [100 cm depth soil SOC]
\u251c\u2500\u250030cm SOC CV.tif [30 cm depth soil SOC coefficient of variation]
\u2514\u2500\u2500100cm SOC CV.tif [100 cm depth soil SOC coefficient of variation] site observations csv files: ISCN_rmNRCS_addNCSS_30cm.csv 30cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data ISCN_rmNRCS_addNCSS_100cm.csv 100cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data
Data format: Geospatial files are provided in Geotiff format in Lat/Lon WGS84 EPSG: 4326 projection at 30 arc second resolution. Geospatial projection: 
GEOGCS['GCS_WGS_1984', DATUM['D_WGS_1984', SPHEROID['WGS_1984',6378137,298.257223563]], PRIMEM['Greenwich',0], UNIT['Degree',0.017453292519943295]] (base) [jbk@theseus ltar_regionalization]$ g.proj -w GEOGCS['wgs84', DATUM['WGS_1984', SPHEROID['WGS_1984',6378137,298.257223563]], PRIMEM['Greenwich',0], UNIT['degree',0.0174532925199433]]
", "keywords": ["gNATSGO", "the United States SOC", "US soil properties", "15. Life on land", "Gridded National Soil Survey Geographic Database", "International Soil Carbon Network (ISCN)"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8057232"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8057232", "name": "item", "description": "10.5281/zenodo.8057232", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8057232"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-01-25T00:00:00Z"}}, {"id": "10.7910/DVN/M4ZGXP", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-25T16:30:53Z", "type": "Dataset", "title": "MSZSI: Multi-Scale Zonal Statistics [AgriClimate] Inventory", "description": "<b>MSZSI: Multi-Scale Zonal Statistics [AgriClimate] Inventory</b> <br><br> -------------------------------------------------------------------------------------- <br> MSZSI is a data extraction tool for Google Earth Engine that aggregates time-series remote sensing information to multiple administrative levels using the FAO GAUL data layers. The code at the bottom of this page (metadata) can be pasted into the Google Earth Engine JavaScript code editor and ran at https://code.earthengine.google.com/. <br><br> <i>Please refer to the associated publication</i>: <br> Peter, B.G., Messina, J.P., Breeze, V., Fung, C.Y., Kapoor, A. and Fan, P., 2024. Perspectives on modifiable spatiotemporal unit problems in remote sensing of agriculture: evaluating rice production in Vietnam and tools for analysis. <i>Frontiers in Remote Sensing</i>, 5, p.1042624. <br> <a href='https://www.frontiersin.org/journals/remote-sensing/articles/10.3389/frsen.2024.1042624'>https://www.frontiersin.org/journals/remote-sensing/articles/10.3389/frsen.2024.1042624</a> <br><br> <i>Input options:</i> <br> [1] Country of interest <br> [2] Start and end year <br> [3] Start and end month <br> [4] Option to mask data to a specific land-use/land-cover type <br> [5] Land-use/land-cover type code from CGLS LULC <br> [6] Image collection for data aggregation <br> [7] Desired band from the image collection <br> [8] Statistics type for the zonal aggregations <br> [9] Statistic to use for annual aggregation <br> [10] Scaling options <br> [11] Export folder and label suffix <br><br> <i>Output:</i> Two CSVs containing zonal statistics for each of the FAO GAUL administrative level boundaries <br> <i>Output fields:</i> system:index, 0-ADM0_CODE, 0-ADM0_NAME, 0-ADM1_CODE, 0-ADM1_NAME, 0-ADMN_CODE, 0-ADMN_NAME, 1-AREA_PERCENT_LULC, 1-AREA_SQM_LULC, 1-AREA_SQM_ZONE, 2-X_2001, 2-X_2002, 2-X_2003, ..., 2-X_2020, .geo <br><br> <img src ='https://github.com/cartoscience/seagul/blob/main/mszsi/mszsi_input_v5.PNG?raw=true' width='1000' height='auto'</img> <br><br> <b>PREPROCESSED DATA DOWNLOAD</b> <br><br> The datasets available for download contain zonal statistics at 2 administrative levels (FAO GAUL levels 1 and 2). Select countries from Southeast Asia and Sub-Saharan Africa <b>(Cambodia, Indonesia, Lao PDR, Myanmar, Philippines, Thailand, Vietnam, Burundi, Kenya, Malawi, Mozambique, Rwanda, Tanzania, Uganda, Zambia, Zimbabwe)</b> are included in the current version, with plans to extend the dataset to contain global metrics. Each zip file is described below and two example NDVI tables are available for preview. <br><br> <b>Key</b>: [source, data, units, temporal range, aggregation, masking, zonal statistic, notes] <br><br> Currently available: <br><b>MSZSI-V2_V-NDVI-MEAN.tar</b>: [NASA-MODIS, NDVI, index, 2001\u20132020, annual mean, agriculture, mean, n/a] <br><b>MSZSI-V2_T-LST-DAY-MEAN.tar</b>: [NASA-MODIS, LST Day, \u00b0C, 2001\u20132020, annual mean, agriculture, mean, n/a] <br><b>MSZSI-V2_T-LST-NIGHT-MEAN.tar</b>: [NASA-MODIS, LST Night, \u00b0C, 2001\u20132020, annual mean, agriculture, mean, n/a] <br><b>MSZSI-V2_R-PRECIP-SUM.tar</b>: [UCSB-CHG-CHIRPS, Precipitation, mm, 2001\u20132020, annual sum, agriculture, mean, n/a] <br><b>MSZSI-V2_S-BDENS-MEAN.tar</b>: [OpenLandMap, Bulk density, g/cm3, static, n/a, agriculture, mean, at depths 0-10-30-60-100-200] <br><b>MSZSI-V2_S-ORGC-MEAN.tar</b>: [OpenLandMap, Organic carbon, g/kg, static, n/a, agriculture, mean, at depths 0-10-30-60-100-200] <br><b>MSZSI-V2_S-PH-MEAN.tar</b>: [OpenLandMap, pH in H2O, pH, static, n/a, agriculture, mean, at depths 0-10-30-60-100-200] <br><b>MSZSI-V2_S-WATER-MEAN.tar</b>: [OpenLandMap, Soil water, % at 33kPa, static, n/a, agriculture, mean, at depths 0-10-30-60-100-200] <br><b>MSZSI-V2_S-SAND-MEAN.tar</b>: [OpenLandMap, Sand, %, static, n/a, agriculture, mean, at depths 0-10-30-60-100-200] <br><b>MSZSI-V2_S-SILT-MEAN.tar</b>: [OpenLandMap, Silt, %, static, n/a, agriculture, mean, at depths 0-10-30-60-100-200] <br><b>MSZSI-V2_S-CLAY-MEAN.tar</b>: [OpenLandMap, Clay, %, static, n/a, agriculture, mean, at depths 0-10-30-60-100-200] <br><b>MSZSI-V2_E-ELEV-MEAN.tar</b>: [MERIT, [elevation, slope, flowacc, HAND], [m, degrees, km<sup>2</sup>, m], static, n/a, agriculture, mean, n/a] <br><br><i>Coming soon</i> <br><b>MSZSI-V2_C-STAX-MEAN.tar</b>: [OpenLandMap, Soil taxonomy, category, static, n/a, agriculture, area sum, n/a] <br><b>MSZSI-V2_C-LULC-MEAN.tar</b>: [CGLS-LC100-V3, LULC, category, 2015\u20132019, mode, none, area sum, n/a] <br><br><br> <img src ='https://github.com/cartoscience/seagul/blob/main/mszsi/mszsi_diagram_v2.png?raw=true' width='1000' height='auto'</img> <br><br> <b>Data sources:</b> <br><li><a href='https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD13Q1'>https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD13Q1</a> <br><li><a href='https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD11A2'>https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD11A2</a> <br><li><a href='https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_PENTAD'>https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_PENTAD</a> <br><li><a href='https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_BULKDENS-FINEEARTH_USDA-4A1H_M_v02'>https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_BULKDENS-FINEEARTH_USDA-4A1H_M_v02</a> <br><li><a href='https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_ORGANIC-CARBON_USDA-6A1C_M_v02'>https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_ORGANIC-CARBON_USDA-6A1C_M_v02</a> <br><li><a href='https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_PH-H2O_USDA-4C1A2A_M_v02'>https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_PH-H2O_USDA-4C1A2A_M_v02</a> <br><li><a href='https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_WATERCONTENT-33KPA_USDA-4B1C_M_v01'>https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_WATERCONTENT-33KPA_USDA-4B1C_M_v01</a> <br><li><a href='https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_CLAY-WFRACTION_USDA-3A1A1A_M_v02'>https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_CLAY-WFRACTION_USDA-3A1A1A_M_v02</a> <br><li><a href='https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_SAND-WFRACTION_USDA-3A1A1A_M_v02'>https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_SAND-WFRACTION_USDA-3A1A1A_M_v02</a> <br><li><a href='https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_GRTGROUP_USDA-SOILTAX_C_v01'>https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_GRTGROUP_USDA-SOILTAX_C_v01</a> <br><li><a href='https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_Landcover_100m_Proba-V-C3_Global'>https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_Landcover_100m_Proba-V-C3_Global</a> <br><li><a href='https://developers.google.com/earth-engine/datasets/catalog/MERIT_Hydro_v1_0_1'>https://developers.google.com/earth-engine/datasets/catalog/MERIT_Hydro_v1_0_1</a> <br><li><a href='https://developers.google.com/earth-engine/datasets/catalog/FAO_GAUL_2015_level0'>https://developers.google.com/earth-engine/datasets/catalog/FAO_GAUL_2015_level0</a> <br><li><a href='https://developers.google.com/earth-engine/datasets/catalog/FAO_GAUL_2015_level1'>https://developers.google.com/earth-engine/datasets/catalog/FAO_GAUL_2015_level1</a> <br><li><a href='https://developers.google.com/earth-engine/datasets/catalog/FAO_GAUL_2015_level2'>https://developers.google.com/earth-engine/datasets/catalog/FAO_GAUL_2015_level2</a></li> <br> <b>Project information:</b> <br> SEAGUL: Southeast Asia Globalization, Urbanization, Land and Environment Changes <br> <a href='http://seagul.info/'>http://seagul.info/</a>; <a href='https://lcluc.umd.edu/projects/divergent-local-responses-globalization-urbanization-land-transition-and-environmental'>https://lcluc.umd.edu/projects/divergent-local-responses-globalization-urbanization-land-transition-and-environmental</a> <br> This project was made possible by the the NASA Land-Cover/Land-Use Change Program (Grant #: 80NSSC20K0740) <br><br> For an additional interactive visualization, visit: <a href='https://cartoscience.users.earthengine.app/view/maup-mapper-multi-scale-modis-ndvi'>https://cartoscience.users.earthengine.app/view/maup-mapper-multi-scale-modis-ndvi</a> <br><br> <img src ='https://github.com/cartoscience/seagul/blob/main/mszsi/mszsi_app.png?raw=true' width='1000' height='auto'</img> <br><br><br> <i> Google Earth Engine code</i> <pre> /*/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// MSZSI: Multi-Scale Zonal Statistics Inventory Authors: Brad G. Peter, Department of Geography, University of Alabama Joseph Messina, Department of Geography, University of Alabama Austin Raney, Department of Geography, University of Alabama Rodrigo E. Principe, AgriCircle AG Peilei Fan, Department of Geography, Environment, and Spatial Sciences, Michigan State University Citation: Peter, Brad; Messina, Joseph; Raney, Austin; Principe, Rodrigo; Fan, Peilei, 2021, 'MSZSI: Multi-Scale Zonal Statistics Inventory', https://doi.org/10.7910/DVN/YCUBXS, Harvard Dataverse, V# SEAGUL: Southeast Asia Globalization, Urbanization, Land and Environment Changes http://seagul.info/ https://lcluc.umd.edu/projects/divergent-local-responses-globalization-urbanization-land-transition-and-environmental This project was made possible by the the NASA Land-Cover/Land-Use Change Program (Grant #: 80NSSC20K0740) ///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////*/ /*----------------------------------------------------------------------------------------------------------------------------------- Description: MSZSI is a data extraction tool for aggregating time-series remote sensing information to multiple administrative levels using the FAO GAUL data layers. Input parameterization: [1] Enter the country code for the desired country [2] Select a start and end year. Be sure to check for data availability in the collection selected in input 6. [3] Select a start month and end month to specify a temporal range within each year. [4] Select an image collection for data aggregation. [5] Select the desired band from the image collection. [6] Option to mask data to a specific land-use/land-cover type. Enter 'TRUE' or 'FALSE'. [7] Enter a land-use/land-cover type code from CGLS LULC. Ignore this option if no masking is desired and set input 4 to 'FALSE'. [8] Select a statistics type for the zonal aggregations (defaults to mean) [9] Select a statistic for temporal aggregation (see available options in the parameterization below) [10] Scaling options [11] Export folder output file label suffix Check tasks tab for CSV exports. Select a point on the map to view timeseries statistics. Hover over the layers panel to turn layers on/off and set visualization parameters. For an additional interactive visualization, visit: https://cartoscience.users.earthengine.app/view/maup-mapper-multi-scale-modis-ndvi Boundary data Layers: https://developers.google.com/earth-engine/datasets/catalog/FAO_GAUL_2015_level0 https://developers.google.com/earth-engine/datasets/catalog/FAO_GAUL_2015_level1 https://developers.google.com/earth-engine/datasets/catalog/FAO_GAUL_2015_level2 -----------------------------------------------------------------------------------------------------------------------------------*/ // \u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022 USER PARAMETERIZATION \u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022 /*[1]*/ var countryCode = 264 // Refer to http://www.fao.org/in-action/countrystat/news-and-events/events/training-material/gaul-codes2014/en/ /*[2]*/ var startYear = 2001 // Check data availability for the collection selected in input 4 var endYear = 2020 /*[3]*/ var startMonth = 1 var endMonth = 12 /*[4]*/ var ic = ee.ImageCollection('MODIS/006/MOD13Q1') /*[5]*/ var band = 'NDVI' /*[6]*/ var maskToLULC = 'TRUE' // Set to 'TRUE' or 'FALSE' /*[7]*/ var lcType = 40 // Refer to https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_Landcover_100m_Proba-V-C3_Global /*[8]*/ var zonalStatType = ee.Reducer.mean() // examples: ee.Reducer.mean(), ee.Reducer.median(), ee.Reducer.stdDev(), // ee.Reducer.min(), ee.Reducer.max(), ee.Reducer.sum() /*[9]*/ var temporalAggregateType = 'mean' // available options: 'mean', 'median', 'stddev', 'min', 'max', 'sum' /*[10]*/ var nativeScale = 'TRUE' // Set to 'TRUE' or 'FALSE' var scale = 1000 // option to increase the scale to avoid memory crashes /*[11]*/ var exportFolder = 'GEE_Exports' var labelSuffix = 'NDVI-MEAN_VIETNAM' // sample export name: MSZSI-V2_2001-2020_1-12_LC40_GAUL-152-L1_NDVI-MEAN_VIETNAM, but can be changed during download prompt // note that some country names will need to be adjusted in the download prompt if they contain special characters ///////////////////////////////////////////////////// NO USER INPUT NEEDED BELOW //////////////////////////////////////////////////// // Administrative zones and preprocessing ------------------------------------------------------------------------------------------- ic = ic.select(band) var years = ee.List.sequence(startYear,endYear) var fc_L0 = ee.FeatureCollection('FAO/GAUL/2015/level0').filterMetadata('ADM0_CODE','equals',countryCode) var fc_L1 = ee.FeatureCollection('FAO/GAUL/2015/level1').filterMetadata('ADM0_CODE','equals',countryCode) .select(['ADM0_CODE','ADM0_NAME','ADM1_CODE','ADM1_NAME'], ['0-ADM0_CODE','0-ADM0_NAME','0-ADM1_CODE','0-ADM1_NAME']) fc_L1 = fc_L1.map(function(f) { return f.set('0-ADM2_CODE','NULL').set('0-ADM2_NAME','NULL') }) var fc_L2 = ee.FeatureCollection('FAO/GAUL/2015/level2').filterMetadata('ADM0_CODE','equals',countryCode) .select(['ADM0_CODE','ADM0_NAME','ADM1_CODE','ADM1_NAME','ADM2_CODE','ADM2_NAME'], ['0-ADM0_CODE','0-ADM0_NAME','0-ADM1_CODE','0-ADM1_NAME','0-ADM2_CODE','0-ADM2_NAME']) // LULC preprocessing --------------------------------------------------------------------------------------------------------------- var lulc = ee.ImageCollection('COPERNICUS/Landcover/100m/Proba-V-C3/Global').select('discrete_classification') var lulcMode = lulc.mode().eq(lcType) var lcLabel = '_LC'+lcType var lulcClip = lulcMode.clip(fc_L0) var lulcZone = lulcClip.remap([0,1],[1,1]).rename('zoneArea') var mask = lulcClip.updateMask(lulcClip.eq(1)).rename('mask') if(maskToLULC == 'FALSE') { lcLabel = '' mask = lulcZone } if(nativeScale == 'TRUE') { scale = lulc.first().projection().nominalScale() } // Add area fields ------------------------------------------------------------------------------------------------------------------ var temporal = ee.ImageCollection(years.map(function(y) { var filterYear = ic.filter(ee.Filter.calendarRange(y,y,'year')) .filter(ee.Filter.calendarRange(startMonth, endMonth, 'month')) var aggregate // the temporal aggregation type is set in input 9 if (temporalAggregateType == 'mean') { aggregate = filterYear.mean() } if (temporalAggregateType == 'median') { aggregate = filterYear.median() } if (temporalAggregateType == 'stddev') { aggregate = filterYear.stdDev() } if (temporalAggregateType == 'min') { aggregate = filterYear.min() } if (temporalAggregateType == 'max') { aggregate = filterYear.max() } if (temporalAggregateType == 'sum') { aggregate = filterYear.sum() } return aggregate.where(aggregate.eq(0),1e-10) // True zeroes are currently set to 1e-10 to avoid false no data flags .updateMask(mask) .set('extract',ee.String('2-'+labelSuffix+'_').cat(ee.Number(y).toInt())) .set('year',ee.Number(y).toInt()) .rename('band') })) // Run functions for each administrative level -------------------------------------------------------------------------------------- var zonal_L1 = zonalStat(fc_L1) var zonal_L2 = zonalStat(fc_L2) var merge = zonal_L1.combine(zonal_L2) var fcAreas_L1 = getAreas(fc_L1) var fcAreas_L2 = getAreas(fc_L2) var samples_L1 = createSamples(fc_L1) var samples_L2 = createSamples(fc_L2) var added_L1 = addFields(samples_L1,fcAreas_L1.select('zoneAreas'),fcAreas_L1.select('lulcAreas')) var added_L2 = addFields(samples_L2,fcAreas_L2.select('zoneAreas'),fcAreas_L2.select('lulcAreas')) exporter(added_L1,zonal_L1,1) exporter(added_L2,zonal_L2,2) // Calculate zonal statistics ------------------------------------------------------------------------------------------------------- function zonalStat(fc) { return temporal.map(function(i) { var year = i.get('year') return i.reduceRegions({ collection: fc, reducer: ee.Reducer.mean().setOutputs(['zStat']), scale: scale }).reduceToImage({ properties: ['zStat'], reducer: ee.Reducer.first() }).set('extract',i.get('extract')).set('year',year).rename('band') }) } // Calculate areas ------------------------------------------------------------------------------------------------------------------ function getAreas(fc) { var zoneAreas = ee.Image.pixelArea().updateMask(lulcZone).reduceRegions({ collection: fc, reducer: ee.Reducer.sum(), scale: scale }).reduceToImage({ properties: ['sum'], reducer: ee.Reducer.first() }).rename('zoneAreas') var lulcAreas = ee.Image.pixelArea().updateMask(mask).reduceRegions({ collection: fc, reducer: ee.Reducer.sum(), scale: scale }).reduceToImage({ properties: ['sum'], reducer: ee.Reducer.first() }).rename('lulcAreas') return zoneAreas.addBands(lulcAreas) } // Feature to points ---------------------------------------------------------------------------------------------------------------- function createSamples(fc) { return fc.map(function(g) { return ee.Feature(ee.FeatureCollection.randomPoints({ region: g.geometry(), points: 1, seed: 0 }).geometry()).copyProperties(g) }) } // Add area fields ------------------------------------------------------------------------------------------------------------------ function addFields(samples, areaGridZone, areaGridLULC) { return samples.map(function(p) { var point = p.geometry() var zoneArea = areaGridZone.rename('area').reduceRegion({ reducer: ee.Reducer.first(), geometry: point, scale: 1, maxPixels: 1e13 }).get('area') var lulcArea = areaGridLULC.rename('area').reduceRegion({ reducer: ee.Reducer.first(), geometry: point, scale: 1 }).get('area') var percLULC = ee.Number(lulcArea).divide(zoneArea).multiply(100) return ee.Feature(p).set('1-AREA_SQM_LULC',0) .set('1-AREA_SQM_ZONE',zoneArea).set('1-AREA_SQM_LULC',ee.Algorithms.If(lulcArea,lulcArea,0)) .set('1-AREA_PERCENT_LULC',ee.Algorithms.If(lulcArea,percLULC,0)) }) } // Export function ------------------------------------------------------------------------------------------------------------------ function exporter(e,zones,n) { var extracted = e.map(extractToPoints) function extractToPoints(feature) { var geom = feature.geometry() var addField = function(image, f) { var newFeature = ee.Feature(f) var getName = image.get('extract') var setValue = image.reduceRegion({ reducer: ee.Reducer.first(), geometry: geom, scale: 1, maxPixels: 1e13 }).get('band') return ee.Feature(ee.Algorithms.If(setValue, newFeature.set(getName, ee.String(setValue)), newFeature.set(getName, ee.String('No data')))) } var newFeature = ee.Feature(zones.iterate(addField, feature)) return newFeature } Export.table.toDrive({ collection: extracted, description: 'MSZSI-V2_'+startYear+'-'+endYear+'_'+startMonth+'-'+endMonth +lcLabel+'_GAUL-'+countryCode+'-L'+n+'_'+labelSuffix, folder: exportFolder }) } // Map display settings ------------------------------------------------------------------------------------------------------------- var leftMap = ui.Map() var rightMap = ui.Map() ui.Map.Linker([leftMap, rightMap]) ui.root.widgets().reset([leftMap,rightMap]) leftMap.centerObject(fc_L0) leftMap.setOptions('HYBRID').style().set('cursor', 'crosshair') rightMap.setOptions('HYBRID').style().set('cursor', 'crosshair') // Adds each image to the map displays var len = years.length().getInfo() for (var i = 0; i < len; i++) { var year = i+startYear var namer = 'ZSTATS_'+year var image_L1 = ee.Image(zonal_L1.toList(zonal_L1.size()).get(i)).rename(band) var image_L2 = ee.Image(zonal_L2.toList(zonal_L2.size()).get(i)).rename(band) leftMap.addLayer(image_L1,{},namer,false) rightMap.addLayer(image_L2,{},namer,false) } var hollow = {color: 'white', width: 0.3, fillColor: '00000000'} leftMap.addLayer(fc_L1.style(hollow),{},'FAO-GAUL-L1') rightMap.addLayer(fc_L2.style(hollow),{},'FAO-GAUL-L2') // Chart display settings ----------------------------------------------------------------------------------------------------------- var chartOptions = { fontSize: 11, width: '100px', curveType: 'function', format: 'short', margin: '0 0 0 0', hAxis: {format: '0000', textStyle: {fontSize: 10, color: '303030'}, gridlines: {color: 'transparent'}}, vAxis: {textStyle: {fontSize: 10, color: '303030'}, gridlines: {}}, trendlines: {0: {color: '303030', lineWidth: 0.5, visibleInLegend: false}}, series: {0: {color: '303030', lineWidth: 0.8}}, legend: {textStyle: {color: '303030'}}, } var panelStyle = { width: '235px', position: 'bottom-left', margin: '0 0 0 0', border: '1px solid #303030' } var leftChart = ui.Panel({ widgets: ui.Label('Select a point to chart regional time-series',{margin: '0 0 0 0', color:'303030'}), style: panelStyle }) leftMap.add(leftChart) // onClick function to query time-series -------------------------------------------------------------------------------------------- function pickLocation(location) { leftChart.widgets().set(0,ui.Label('Time-series',{fontSize: '14px', fontWeight: 'bold', color: '303030', margin: '7px 0 7px 10px'})) var chartOptions = { fontSize: 10, height: '200px', curveType: 'function', format: 'short', margin: '0 0 0 0', hAxis: {format: '0000', textStyle: {fontSize: 11, color: '303030'}, gridlines: {color: 'transparent'}}, vAxis: {textStyle: {fontSize: 10, color: '303030'}, gridlines: {}}, trendlines: {0: {color: 'blue', lineWidth: 0.5, visibleInLegend: false}, 1: {color: 'red', lineWidth: 0.5, visibleInLegend: false} }, series: {0: {color: 'blue', lineWidth: 0.8}, 1: {color: 'red', lineWidth: 0.8} }, legend: {position:'none'} } leftChart.widgets().set(1,ui.Label('Loading...',{fontSize: '13px',color:'9C9C9C', margin: '0 0 7px 10px'})) leftChart.widgets().set(2,ui.Label('',{fontSize: '13px',color:'9C9C9C', margin: '0 0 7px 10px'})) var pLat = location.lat var pLon = location.lon var point = ee.Geometry.Point([pLon,pLat]) var selection_L1 = fc_L1.filterBounds(point) var selection_L2 = fc_L2.filterBounds(point) var zone_L1_name = ee.Feature(selection_L1.first()).get('0-ADM1_NAME') var zone_L2_name = ee.Feature(selection_L2.first()).get('0-ADM2_NAME') leftChart.widgets().set(3,ui.Chart.image.series({ imageCollection: merge, region: point, scale: scale, xProperty: 'year' }).setOptions(chartOptions)) zone_L1_name.evaluate(function(result_L1) { zone_L2_name.evaluate(function(result_L2) { leftChart.widgets().set(1,ui.Label(result_L1+' (L1)',{fontSize: '13px', color: 'blue', margin: '0 0 7px 10px'})) leftChart.widgets().set(2,ui.Label(result_L2+' (L2)',{fontSize: '13px', color: 'red', margin: '0 0 0 10px'})) }) }) leftMap.layers().set(len+1, ui.Map.Layer(point,{color: 'blue', opacity: 0.6},'Selected point')) rightMap.layers().set(len+1, ui.Map.Layer(point,{color: 'red', opacity: 0.6},'Selected point')) } leftMap.onClick(pickLocation) rightMap.onClick(pickLocation) </pre>", "keywords": ["Computer and Information Science", "Agricultural Sciences", "Earth and Environmental Sciences", "Social Sciences"], "contacts": [{"organization": "Peter, Brad, Messina, Joseph, Raney, Austin, Principe, Rodrigo, Fan, Peilei,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.7910/DVN/M4ZGXP"}, {"rel": "self", "type": "application/geo+json", "title": "10.7910/DVN/M4ZGXP", "name": "item", "description": "10.7910/DVN/M4ZGXP", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.7910/DVN/M4ZGXP"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-01T00:00:00Z"}}, {"id": "10.1016/j.geoderma.2017.10.020", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-25T16:18:19Z", "type": "Journal Article", "created": "2017-11-06", "title": "Characterising and linking X-ray CT derived macroporosity parameters to infiltration in soils with contrasting structures", "description": "

Soils deliver the regulating ecosystem services of water infiltration and distribution, which can be controlled by macropores. Parameterizing macropore hydraulic properties is challenging due to the lack of direct measurement methods. With tension-disc infiltrometry hydraulic properties near saturation can be measured. Differentiating between hydrologically active and non-active pores, at a given water potential, indirectly assesses macropore continuity. Water flow through macropores is controlled by macropore size distribution, tortuosity, and connectivity, which can be directly derived by X-ray computed tomography (CT). Our objective was to parameterize macropore hydraulic properties based on the imaged macropore network of three horizons of an Andosol and a Gleysol. Hydraulic conductivity K unsat was derived from infiltration measurements. Soil cores from the infiltration areas were scanned with X-ray CT. K unsat was significantly higher in the Andosol than in the Gleysol at all water potentials, and decreased significantly with depth in both soils. The in situ measurements guided the definition of new macroporosity parameters from the X-ray CT reconstructions. For the Andosol, K unsat was best predicted using the imaged-limited macroporosity. A low total macroporosity, coupled with a high macropore density, indicated the abundance of smaller macropores, leading to homogeneous matrix flux. Imaged macropores were not well connected. In contrast, the Gleysol had a bi-modal macropore system with few very large, but well-connected macropores. K unsat was best predicted using the imaged macroporosity consisting only of macropores with diameters between 0.75 and 3 mm. Our research demonstrates that linking traditional soil physical measurements with soil-visualization techniques has a huge potential to improve parameterizing macropore hydraulic properties. The relevance of the relationships found in this study for larger scales and other soil types still needs to be tested, for example by a multi-scale investigation including a much wider range of different soils.

", "keywords": ["[SDE] Environmental Sciences", "Hydraulic parameters", "0207 environmental engineering", "04 agricultural and veterinary sciences", "02 engineering and technology", "15. Life on land", "6. Clean water", "Image analysis", "Tension disc in\ufb01ltrometr", "Tension disc infiltrometry", "Pore network", "13. Climate action", "Soil structure", "[SDE.ES] Environmental Sciences/Environment and Society", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "https://doi.org/10.1016/j.geoderma.2017.10.020"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoderma", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.geoderma.2017.10.020", "name": "item", "description": "10.1016/j.geoderma.2017.10.020", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.geoderma.2017.10.020"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-03-01T00:00:00Z"}}, {"id": "oai:DiVA.org:miun-38173", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-25T16:44:35Z", "type": "Other", "title": "M\u00e4ns v\u00e5ld mot kvinnor : Diskurser och kunskap i det sociala arbetets praktik", "description": "Open AccessI avhandlingen analyseras vilken kunskap och diskurser som v\u00e4gleder socialtj\u00e4nstens arbete med m\u00e4ns v\u00e5ld mot kvinnor. Avhandlingens \u00f6vergripande fr\u00e5gest\u00e4llningar fokuserar p\u00e5: diskurser om m\u00e4ns v\u00e5ld i Socialstyrelsens utbildningsmaterial, socialarbetares kunskapsbehov och hur dessa tillgodoses, samt socialarbetares uppfattningar om m\u00e4ns v\u00e5ld mot kvinnor. Avhandlingen har ett feministiskt och intersektionellt perspektiv och bygger p\u00e5 tv\u00e5 delstudier. I den f\u00f6rsta delstudien anv\u00e4nds kvalitativa metoder, och empirin best\u00e5r av fem utbildningsmaterial om m\u00e4ns v\u00e5ld mot kvinnor som publicerats av Socialstyrelsen. Empirin analyserats med hj\u00e4lp av Carol Bacchis policy approach What\u2019s the Problem Represented to be?, och Norman Faircloughs kritiska diskursanalys. Empirin i den andra delstudien utg\u00f6rs av enk\u00e4tdata som inh\u00e4mtats bland 153 socialarbetare verksamma i socialtj\u00e4nsten i tre svenska kommuner med olika demografi och geografisk placering. Avhandlingen visar tre centrala resultat. F\u00f6r det f\u00f6rsta framst\u00e4lls m\u00e4ns v\u00e5ld i k\u00f6nsneutrala termer och v\u00e5ldet beskrivs som ett relationsv\u00e5ld och/eller familjev\u00e5ld. F\u00f6r det andra problematiserar utbildningsmaterialens texter inte m\u00e4ns \u00f6verordning, de f\u00e4ster inte heller n\u00e5gon s\u00e4rskild vikt vid strukturella orsakssammanhang. F\u00f6r det tredje centreras kunskapen och diskurserna kring individuella behov och orsaker. Fr\u00e4mst fokuseras behoven hos de grupper av kvinnor som Regeringen menar \u00e4r s\u00e4rskilt s\u00e5rbara f\u00f6r v\u00e5ld. Det senare leder till att kvinnor kollektiviseras och att m\u00e4ns ansvar f\u00f6r v\u00e5ldet, i synnerhet svenska m\u00e4n, och sambandet mellan v\u00e5ld, j\u00e4mlikhet och andra sociala strukturer f\u00f6rbises. Professionell erfarenhet i \u00e4renden d\u00e4r v\u00e5ld f\u00f6rekommer och utbildning inom omr\u00e5det \u00e4r faktorer som socialarbetarna anser bidragit till att \u00f6ka deras kunskap. Trots detta uppger respondenter med l\u00e5ng arbetslivserfarenhet, h\u00f6g utbildning, och fortbildning att de saknar kunskap om m\u00e4ns v\u00e5ld mot kvinnor. \u00c4ven fast att socialarbetarna upplever kunskapsbrist, visar resultatet att socialarbetarna endast tagit del st\u00f6ddokument i begr\u00e4nsad omfattning. Den h\u00e4r avhandlingen bidrar med kunskap om vikten av spr\u00e5kanv\u00e4ndning och hur vi f\u00f6rst\u00e5r m\u00e4ns v\u00e5ld mot kvinnor. Resultatet kan anv\u00e4ndas f\u00f6r att ifr\u00e5gas\u00e4tta och utmana r\u00e5dande normer om m\u00e4ns v\u00e5ld i samh\u00e4llet.", "keywords": ["Socialt arbete", "knowledge", "education", "policys", "Social Work", "social work", "socialarbetare", "diskurs", "socialt arbete", "utbildning", "social workers", "socialtj\u00e4nst", "policies", "social service", "kunskap", "discourse", "m\u00e4ns v\u00e5ld mot kvinnor", "male violence against women"], "contacts": [{"organization": "Hoppstadius, Helena", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/oai:DiVA.org:miun-38173"}, {"rel": "self", "type": "application/geo+json", "title": "oai:DiVA.org:miun-38173", "name": "item", "description": "oai:DiVA.org:miun-38173", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/oai:DiVA.org:miun-38173"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-01-01T00:00:00Z"}}, {"id": "PMC9846495", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-25T16:37:27Z", "type": "Journal Article", "created": "2023-01-04", "title": "A genome wide association study to dissect the genetic architecture of agronomic traits in Andean lupin (Lupinus mutabilis)", "description": "

Establishing Lupinus mutabilis as a protein and oil crop requires improved varieties adapted to EU climates. The genetic regulation of strategic breeding traits, including plant architecture, growing cycle length and yield, is unknown. This study aimed to identify associations between 16 669 single nucleotide polymorphisms (SNPs) and 9 agronomic traits on a panel of 223 L. mutabilis accessions, grown in four environments, by applying a genome wide association study (GWAS). Seven environment-specific QTLs linked to vegetative yield, plant height, pods number and flowering time, were identified as major effect QTLs, being able to capture 6 to 20% of the phenotypic variation observed in these traits. Furthermore, two QTLs across environments were identified for flowering time on chromosome 8. The genes FAF, GAMYB and LNK, regulating major pathways involved in flowering and growth habit, as well as GA30X1, BIM1, Dr1, HDA15, HAT3, interacting with these pathways in response to hormonal and environmental cues, were prosed as candidate genes. These results are pivotal to accelerate the development of L. mutabilis varieties adapted to European cropping conditions by using marker-assisted selection (MAS), as well as to provide a framework for further functional studies on plant development and phenology in this species.Balancing economic, ecological, and social values has long been a challenge in the forests of the Pacific Northwest, where conflict over timber harvest and old\uffe2\uff80\uff90growth habitat on public lands has been contentious for the past several decades. The Northwest Forest Plan, adopted two decades ago to guide management on federal lands, is currently being revised as the region searches for a balance between sustainable timber yields and habitat for sensitive species. In addition, climate change imposes a high degree of uncertainty on future forest productivity, sustainability of timber harvest, wildfire risk, and species habitat. We evaluated the long\uffe2\uff80\uff90term, landscape\uffe2\uff80\uff90scale trade\uffe2\uff80\uff90offs among carbon (C) storage, timber yield, and old forest habitat given projected climate change and shifts in forest management policy across 2.1 million hectares of forests in the Oregon Coast Range. Projections highlight the divergence between private and public lands under business\uffe2\uff80\uff90as\uffe2\uff80\uff90usual forest management, where private industrial forests are heavily harvested and many public (especially federal) lands increase C and old forest over time but provide little timber. Three alternative management scenarios altering the amount and type of timber harvest show widely varying levels of ecosystem C and old\uffe2\uff80\uff90forest habitat. On federal lands, ecological forestry practices also allowed a simultaneous increase in old forest and natural early\uffe2\uff80\uff90seral habitat. The ecosystem C implications of shifts away from current practices were large, with current practices retaining up to 105\uffc2\uffa0Tg more C than the alternative scenarios by the end of the century. Our results suggest climate change is likely to increase forest productivity by 30\uffe2\uff80\uff9341% and total ecosystem C storage by 11\uffe2\uff80\uff9315% over the next century as warmer winter temperatures allow greater forest productivity in cooler months. These gains in C storage are unlikely to be offset by wildfire under climate change, due to the legacy of management and effective fire suppression. Our scenarios of future conditions can inform policy makers, land managers, and the public about the potential effects of land management alternatives, climate change, and the trade\uffe2\uff80\uff90offs that are inherent to management and policy in the region.

", "keywords": ["Carbon sequestration", "Forest management -- Economic aspects", "0106 biological sciences", "Climate Change", "Forestry", "Forest fires -- Effect of climate change on", "Forests", "15. Life on land", "Wood", "01 natural sciences", "Carbon", "Trees", "Oregon", "Forest management -- Social aspects", "13. Climate action", "Northwest Forest Plan (U.S.)", "Environmental Sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1002/eap.1460"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Ecological%20Applications", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1002/eap.1460", "name": "item", "description": "10.1002/eap.1460", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/eap.1460"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-02-17T00:00:00Z"}}, {"id": "10.1002/bse.2725", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-25T16:14:57Z", "type": "Journal Article", "created": "2021-01-15", "title": "How to innovate business models for a circular bio\u2010economy?", "description": "Abstract

Shifting from a linear to a circular bio\uffe2\uff80\uff90economy requires new business models. The objective was getting insights into the uncharted research field of business model innovation for a circular and sustainable bio\uffe2\uff80\uff90economy within the agrifood sector. Eight European cases valorising agricultural waste and by\uffe2\uff80\uff90products by closing loops or cascading were studied regarding their innovation drivers and elements, via interviews, on\uffe2\uff80\uff90site visits and secondary data. In this domain, the findings highlight that business model innovations are depending on the (i) macro\uffe2\uff80\uff90environmental institutional\uffe2\uff80\uff90legal conditions and market trends, (ii) driven by internal economic, environmental and/or social objectives, but especially strongly linked to (iii) other actors often from different sectors seeking synergies and (iv) value co\uffe2\uff80\uff90creation via combined organisational and technological innovations. Business models for a circular bio\uffe2\uff80\uff90economy thus depend on various action levels and need radical combined organisational and technological innovations for a most efficient usage of agricultural waste and by\uffe2\uff80\uff90products. This also means new business configurations instead of linear innovation strategies currently still being dominant due to economic viability.

", "keywords": ["330", "9. Industry and infrastructure", "circular economy", "bio-economy", "650", "sustainability", "7. Clean energy", "innovation", "[SHS]Humanities and Social Sciences", "12. Responsible consumption", "agricultural waste and by-products", "13. Climate action", "11. Sustainability", "business models", "[SHS] Humanities and Social Sciences", "co-creation"]}, "links": [{"href": "https://onlinelibrary.wiley.com/doi/pdf/10.1002/bse.2725"}, {"href": "https://doi.org/10.1002/bse.2725"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Business%20Strategy%20and%20the%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1002/bse.2725", "name": "item", "description": "10.1002/bse.2725", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/bse.2725"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-12T00:00:00Z"}}, {"id": "10.1002/cc.36819730306", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-25T16:14:57Z", "type": "Journal Article", "created": "2006-10-13", "title": "The eops story in california", "description": "Abstract

California has responded to the needs of disadvantaged young people with new community college programs and services that raise critical problems of evaluation.This 4\uffe2\uff80\uff90year on\uffe2\uff80\uff90farm study reports the effects of different agricultural practices on yield and soil organic carbon (SOC) in kiwifruit and apricot orchards grown in a Mediterranean area. Groups of plants under local orchard management (LOM,\uffc2\uffa7

Correction made here after initial publication.

) practices (i.e. soil tillage, removing of pruning residues and mineral fertilisers) were compared with plots under soil\uffe2\uff80\uff90protecting orchard management (SPOM) actions (i.e. cover crop, no\uffe2\uff80\uff90tillage, compost application and mulching of pruning residues). In the SPOM blocks fertilisation rate was based on plant demand and irrigation volumes calculated on the evapotranspiration values, while they were empirically calculated in the LOM plots. Results show that yield was 28\uffe2\uff80\uff9350 per cent enhanced by SPOM practices while SOC remained close to the initial values. In comparison with LOM plots, changed practices increased up to 28\uffe2\uff80\uff9390 per cent the amount of P and K, and 13 per cent that of N annually incorporated into soil increasing their reservoir in the soil. The study demonstrates that appropriate land management can increase the mean annual carbon soil inputs from about 1\uffc2\uffb75 to 9\uffc2\uffb70\uffe2\uff80\uff89t\uffe2\uff80\uff89ha\uffe2\uff88\uff921 per year. Copyright \uffc2\uffa9 2009 John Wiley & Sons, Ltd.

", "keywords": ["2. Zero hunger", "soil organic carbon", "Crop residues; land use; organic matter; soil carbon input; SOC; Mediterranean soil; soil organic carbon", "Crop residue", "land use", "0401 agriculture", " forestry", " and fisheries", "soil carbon input", "SOC", "04 agricultural and veterinary sciences", "15. Life on land", "Mediterranean soil", "organic matter"]}, "links": [{"href": "https://doi.org/10.1002/ldr.917"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Land%20Degradation%20%26amp%3B%20Development", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1002/ldr.917", "name": "item", "description": "10.1002/ldr.917", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/ldr.917"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2009-03-31T00:00:00Z"}}, {"id": "10.1016/j.agee.2013.01.012", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-25T16:16:52Z", "type": "Journal Article", "created": "2013-03-20", "title": "Changes In Soil Carbon And Nitrogen Following Tillage Conversion In A Long-Term Experiment In Northern France", "description": "Although continuous no-till (NT) is recommended for erosion control and carbon sequestration, it often has a limited duration since farmers alternate between NT and full inversion tillage (FIT) to control weed infestation and avoid soil compaction. In this paper, we evaluate the effect of continuous tillage and tillage conversion of NT to FIT and vice versa on SOC and SON stocks, in a long-term experiment at Boigneville in Northern France. Continuous NT (CNT) and FIT (CFIT) treatments were established in 1991 and maintained until 2011 while half of the plots were converted in 2005: from CNT to new FIT (NFIT) and CFIT to new NT (NNT). Bulk densities and organic C and N contents were determined in 2001 and 2011 down to the old ploughing depth (opd) which was also measured. SOC and SON stocks were calculated at equivalent soil mass by correcting either bulk densities or the opd. Both methods produced very close results and similar conclusions. A typical gradient of SOC and SON concentrations vs depth was observed in CNT as opposed to a rather uniform distribution in CFIT. CNT resulted in SOC concentration in the top soil (0-5 cm) higher by 38% in 2001 and 53% in 2011 compared to CFIT. Conversely, it led to a SOC reduction in the deeper layer (ca. 10-28 cm) by 14% in 2001 and 18% in 2011. The global effect was no significant change in SOC and SON stocks between treatments over the old ploughed layer (4060 t soil ha(-1)) in both years: 43.2 and 45.0 t C ha(-1) in 2001 and 44.7 and 45.8 t C ha(-1) in 2011, in CNT and CFIT, respectively. In 2011, six years after tillage conversion, the stratification of SOC and SON had disappeared in NFIT whereas a new one had appeared in NNT with a smaller gradient than in CNT. SOC or SON stocks over the old ploughed layer did not differ significantly between treatments after 6 years of conversion: SOC stocks were 45.8, 43.2, 44.7 and 43.1 t C ha(-1) in the CFIT, NFIT, CNT and NNT treatments, respectively. Furthermore, SOC stocks below the old ploughed layer (ca. 28-40 cm) were slightly greater in FIT than in NT treatment (10.9 vs 8.7 t C ha(-1)). In this experiment, continuous or conversion tillage did not result in any C sequestration benefit. (c) 2013 Elsevier B.V. All rights reserved.", "keywords": ["IMPACTS", "[SDE] Environmental Sciences", "Soil nitrogen", "[SDV]Life Sciences [q-bio]", "SEQUESTRATION", "630", "Tillage", "MOIST", "Long-term", "ORGANIC-CARBON", "[SDV.BV]Life Sciences [q-bio]/Vegetal Biology", "Full inversion tillage", "[SDV.BV] Life Sciences [q-bio]/Vegetal Biology", "SOC", "CONSERVATION TILLAGE", "2. Zero hunger", "GREAT-PLAINS", "Soil organic carbon", "TEMPERATE", "04 agricultural and veterinary sciences", "15. Life on land", "No till", "NO-TILL", "[SDV] Life Sciences [q-bio]", "[SDE]Environmental Sciences", "0401 agriculture", " forestry", " and fisheries", "MATTER", "SYSTEM"], "contacts": [{"organization": "Dimassi, Bassem, Cohan, Jean-Pierrre, Labreuche, Jerome, Mary, Bruno, B.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1016/j.agee.2013.01.012"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agriculture%2C%20Ecosystems%20%26amp%3B%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.agee.2013.01.012", "name": "item", "description": "10.1016/j.agee.2013.01.012", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.agee.2013.01.012"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2013-04-01T00:00:00Z"}}, {"id": "10.1007/978-3-031-50780-9", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-25T16:15:16Z", "type": "Journal Article", "created": "2024-03-19", "title": "Quantum Technology for Economists", "description": "Open Access106 pages, 13 figures", "keywords": ["FOS: Computer and information sciences", "Computational Economics", "Central Banks", "Quantum Physics", "Computer Science - Cryptography and Security", "General Economics (econ.GN)", "ddc:330", "05 social sciences", "Money", "FOS: Physical sciences", "C60", "FOS: Economics and business", "C50", "E50", "0502 economics and business", "Quantum Computing", "Econometrics", "E40", "Quantum Physics (quant-ph)", "Cryptography and Security (cs.CR)", "Economics - General Economics"]}, "links": [{"href": "https://doi.org/10.1007/978-3-031-50780-9"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/SSRN%20Electronic%20Journal", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/978-3-031-50780-9", "name": "item", "description": "10.1007/978-3-031-50780-9", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/978-3-031-50780-9"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-01-01T00:00:00Z"}}, {"id": "10.1016/j.agee.2014.04.006", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-25T16:16:54Z", "type": "Journal Article", "created": "2014-05-09", "title": "Comparative Analysis Of The Microbial Communities In Agricultural Soil Amended With Enhanced Biochars Or Traditional Fertilisers", "description": "(Uploaded by Plazi for the Bat Literature Project) No abstract provided.", "keywords": ["570", "anzsrc-for: 07 Agricultural and Veterinary Sciences", "bats", "Veterinary and Food Sciences", "anzsrc-for: 16 Studies in Human Society", "Carbon Sequestration Science", "bat", "30 Agricultural", "630", "anzsrc-for: 3004 Crop and Pasture Production", "anzsrc-for: 30 Agricultural", "Chiroptera", "Animalia", "2 Zero Hunger", "Chordata", "2. Zero hunger", "Soil Chemistry (excl. Carbon Sequestration Science)", "anzsrc-for: 44 Human society", "anzsrc-for: 05 Environmental Sciences", "Biodiversity", "04 agricultural and veterinary sciences", "15. Life on land", "3004 Crop and Pasture Production", "6. Clean water", "anzsrc-for: 41 Environmental sciences", "Soil chemistry and soil carbon sequestration (excl. carbon sequestration science)", "Mammalia", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "https://doi.org/10.1016/j.agee.2014.04.006"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agriculture%2C%20Ecosystems%20%26amp%3B%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.agee.2014.04.006", "name": "item", "description": "10.1016/j.agee.2014.04.006", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.agee.2014.04.006"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2014-06-01T00:00:00Z"}}, {"id": "10.1007/s00122-021-03815-0", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-25T16:15:28Z", "type": "Journal Article", "created": "2021-03-25", "title": "Genomic prediction models trained with historical records enable populating the German ex situ genebank bio-digital resource center of barley (Hordeum\u00a0sp.) with information on resistances to soilborne barley mosaic viruses", "description": "Abstract Key message

Genomic prediction with special weight of major genes is a valuable tool to populate bio-digital resource centers.

Abstract

Phenotypic information of crop genetic resources is a prerequisite for an informed selection that aims to broaden the genetic base of the elite breeding pools. We investigated the potential of genomic prediction based on historical screening data of plant responses against the Barley yellow mosaic viruses for populating the bio-digital resource center of barley. Our study includes dense marker data for 3838 accessions of winter barley, and historical screening data of 1751 accessions for Barley yellow mosaic virus (BaYMV) and of 1771 accessions for Barley mild mosaic virus (BaMMV). Linear mixed models were fitted by considering combinations for the effects of genotypes, years, and locations. The best linear unbiased estimations displayed a broad spectrum of plant responses against BaYMV and BaMMV. Prediction abilities, computed as correlations between predictions and observed phenotypes of accessions, were low for the marker-assisted selection approach amounting to 0.42. In contrast, prediction abilities of genomic best linear unbiased predictions were high, with values of 0.62 for BaYMV and 0.64 for BaMMV. Prediction abilities of genomic prediction were improved by up to\uffe2\uff80\uff89~\uffe2\uff80\uff895% using W-BLUP, in which more weight is given to markers with significant major effects found by association mapping. Our results outline the utility of historical screening data and W-BLUP model to predict the performance of the non-phenotyped individuals in genebank collections. The presented strategy can be considered as part of the different approaches used in genebank genomics to valorize genetic resources for their usage in disease resistance breeding and research.

", "keywords": ["Genetic Markers", "0301 basic medicine", "2. Zero hunger", "0303 health sciences", "Genotype", "Chromosome Mapping", "Genetic Variation", "Hordeum", "Genomics", "Potyviridae", "Linkage Disequilibrium", "Plant Breeding", "03 medical and health sciences", "Phenotype", "Databases", " Genetic", "Original Article", "Genetic Association Studies", "Disease Resistance", "Plant Diseases"]}, "links": [{"href": "https://link.springer.com/content/pdf/10.1007/s00122-021-03815-0.pdf"}, {"href": "https://doi.org/10.1007/s00122-021-03815-0"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Theoretical%20and%20Applied%20Genetics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/s00122-021-03815-0", "name": "item", "description": "10.1007/s00122-021-03815-0", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/s00122-021-03815-0"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-25T00:00:00Z"}}, {"id": "10.1007/s00267-021-01546-y", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-25T16:15:31Z", "type": "Journal Article", "created": "2021-10-11", "title": "Have farmers had enough of experts?", "description": "Abstract

The exponential rise of information available means we can now, in theory, access knowledge on almost any question we ask. However, as the amount of unverified information increases, so too does the challenge in deciding which information to trust. Farmers, when learning about agricultural innovations, have historically relied on in-person advice from traditional \uffe2\uff80\uff98experts\uffe2\uff80\uff99, such as agricultural advisers, to inform farm management. As more farmers go online for information, it is not clear whether they are now using digital information to corroborate in-person advice from traditional \uffe2\uff80\uff98experts\uffe2\uff80\uff99, or if they are foregoing \uffe2\uff80\uff98expert\uffe2\uff80\uff99 advice in preference for peer-generated information. To fill this knowledge gap, we sought to understand how farmers in two contrasting European countries (Hungary and the UK) learnt about sustainable soil innovations and who influenced them to innovate. Through interviews with 82 respondents, we found farmers in both countries regularly used online sources to access soil information; some were prompted to change their soil management by farmer social media \uffe2\uff80\uff98influencers\uffe2\uff80\uff99. However, online information and interactions were not usually the main factor influencing farmers to change their practices. Farmers placed most trust in other farmers to learn about new soil practices and were less trusting of traditional \uffe2\uff80\uff98experts\uffe2\uff80\uff99, particularly agricultural researchers from academic and government institutions, who they believed were not empathetic towards farmers\uffe2\uff80\uff99 needs. We suggest that some farmers may indeed have had enough of traditional \uffe2\uff80\uff98experts\uffe2\uff80\uff99, instead relying more on their own peer networks to learn and innovate. We discuss ways to improve trustworthy knowledge exchange between agricultural stakeholders to increase uptake of sustainable soil management practices, while acknowledging the value of peer influence and online interactions for innovation and trust building.Growing sustainability demands on land have a high knowledge requirement across multiple scientific domains. Exploring networks can expose opportunities for targeting. Using mixed-methods combining social network analysis (SNA) and surveys, networks for key soil functions in case studies in Germany, Ireland and the Netherlands are explored. We find a diversity of contrasting networks that reflect local conditions, sustainability challenges and governance structure. Farmers were found to occupy a central role in the agri-environmental governance network. A comparison of the SNA and survey results indicate low acceptance of messages from many central actors indicating scope to better harness the network for sustainable land management. The source of the messages was important when it came to the implementation of farm management actions. Two pathways for enhanced farmer uptake of multi-functionality are proposed that have wider application are; to increase trust between farmers and actors that are agents of multi-functional messages and/or to increase the bundling or multi-functionality of messages (mandate) of actors trusted by farmers.Quantifying the upper limit of stable soil carbon storage is essential for guiding policies to increase soil carbon storage. One pool of carbon considered particularly stable across climate zones and soil types is formed when dissolved organic carbon sorbs to minerals. We quantified, for the first time, the potential of mineral soils to sorb additional dissolved organic carbon (DOC) for six soil orders. We compiled 402 laboratory sorption experiments to estimate the additional DOC sorption potential, that is the potential of excess DOC sorption in addition to the existing background level already sorbed in each soil sample. We estimated this potential using gridded climate and soil geochemical variables within a machine learning model. We find that mid- and low-latitude soils and subsoils have a greater capacity to store DOC by sorption compared to high-latitude soils and topsoils. The global additional DOC sorption potential for six soil orders is estimated to be 107 $$ pm$$ \uffc2\uffb1 13 Pg C to 1\uffc2\uffa0m depth. If this potential was realized, it would represent a 7% increase in the existing total carbon stock.Accumulating evidence suggests that warming associated with climate change is decreasing the total amount of soil organic carbon (SOC) in drylands, although scientific research has not given enough emphasis to particulate (POC) and mineral-associated organic carbon (MAOC) pools. Biocrusts are a major biotic feature of drylands and have large impacts on the C cycle, yet it is largely unknown whether they modulate the responses of POC and MAOC to climate change. Here, we assessed the effects of simulated climate change (control, reduced rainfall (RE), warming (WA), and RE\uffe2\uff80\uff89+\uffe2\uff80\uff89WA) and initial biocrust cover (low (<\uffe2\uff80\uff8920%) versus high (>\uffe2\uff80\uff8950%)) on the mineral protection of soil C and soil organic matter quality in a dryland ecosystem in central Spain for 9\uffc2\uffa0years. At low initial biocrust cover levels, both WA and RE\uffe2\uff80\uff89+\uffe2\uff80\uff89WA increased SOC, especially POC but also MAOC, and promoted a higher contribution of carbohydrates, relative to aromatic compounds, to the POC fraction. These results suggest that the accumulation of soil C under warming treatments may be transitory in soils with low initial biocrust cover. In soils with high initial biocrust cover, climate change treatments did not affect SOC, neither POC nor MAOC fraction. Overall, our results indicate that biocrust communities modulate the negative effect of climate change on SOC, because no losses of soil C were observed with the climate manipulations under biocrusts. Future work should focus on determining the long-term persistence of the observed buffering effect by biocrust-forming lichens, as they are known to be negatively affected by warming.\u00a0SX67\u00a0>\u00a09882-41. Leaf input was higher in the intercropping system (1,961\u00a0kg\u00a0ha\u22121) than in the conventional system (1,673\u00a0kg\u00a0ha\u22121). Clonal differences in leaf inputs followed the same trends as those for root biomass and yield: SV1\u00a0>\u00a0SX67\u00a0>\u00a09882-41. Soil organic carbon was significantly higher in the agroforestry field (1.94\u00a0%) than in the conventional field (1.82\u00a0%). A significant difference in soil organic carbon was found between the three clones: soils under clone 9882-41 had the lowest soil organic carbon at 1.80\u00a0%.", "keywords": ["F08 - Syst\u00e8mes et modes de culture", "culture associ\u00e9e", "http://aims.fao.org/aos/agrovoc/c_28066", "production foresti\u00e8re", "Juglans nigra", "http://aims.fao.org/aos/agrovoc/c_24367", "rotation de coupe", "http://aims.fao.org/aos/agrovoc/c_6754", "http://aims.fao.org/aos/agrovoc/c_3086", "http://aims.fao.org/aos/agrovoc/c_33452", "http://aims.fao.org/aos/agrovoc/c_3061", "m\u00e9thode statistique", "biomasse", "http://aims.fao.org/aos/agrovoc/c_3048", "http://aims.fao.org/aos/agrovoc/c_4059", "agroforesterie", "clone", "2. Zero hunger", "http://aims.fao.org/aos/agrovoc/c_35927", "http://aims.fao.org/aos/agrovoc/c_24696", "http://aims.fao.org/aos/agrovoc/c_1678", "Salix", "04 agricultural and veterinary sciences", "15. Life on land", "plantation foresti\u00e8re", "Quercus rubra", "croissance", "http://aims.fao.org/aos/agrovoc/c_331583", "http://aims.fao.org/aos/agrovoc/c_207", "K10 - Production foresti\u00e8re", "s\u00e9questration du carbone", "http://aims.fao.org/aos/agrovoc/c_926", "http://aims.fao.org/aos/agrovoc/c_3394", "Fraxinus", "Robinia pseudoacacia", "culture en couloirs", "http://aims.fao.org/aos/agrovoc/c_6625", "http://aims.fao.org/aos/agrovoc/c_1236", "0401 agriculture", " forestry", " and fisheries", "Salix dasyclados", "http://aims.fao.org/aos/agrovoc/c_7377"]}, "links": [{"href": "https://doi.org/10.1007/s10457-012-9572-y"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agroforestry%20Systems", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/s10457-012-9572-y", "name": "item", "description": "10.1007/s10457-012-9572-y", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/s10457-012-9572-y"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2012-09-27T00:00:00Z"}}, {"id": "10.1007/s10460-020-10186-7", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-25T16:15:57Z", "type": "Journal Article", "created": "2021-02-21", "title": "Framing of sustainable agricultural practices by the farming press and its effect on adoption", "description": "Abstract

There is growing political pressure for farmers to use more sustainable agricultural practices to protect people and the planet. The farming press could encourage farmers to adopt sustainable practices through its ability to manipulate discourse and spread awareness by changing the salience of issues or framing topics in specific ways. We sought to understand how the UK farming press framed sustainable agricultural practices and how the salience of these practices changed over time. We combined a media content analysis of the farming press alongside 60 qualitative interviews with farmers and agricultural advisors to understand whether the farming press influenced farmers to try more sustainable practices. Salience of sustainable agricultural practices grew between 2009 and 2020. Many of the practices studied were framed by the press around economic and agronomic aspects, and farmer respondents said the most common reasons for trying sustainable agricultural practices were for economic and agronomic reasons. The farming press tended to use more positive rather than negative tones when covering sustainable agricultural practices. Respondents used the farming press as a source of information, though many did not fully trust these outlets as they believed the farming press were mouthpieces for agribusinesses. Whilst a minority of farmers stated they were motivated to try a new sustainable agricultural practice after learning about it in the farming press, this was rare. Instead, the farming press was used by respondents to raise their awareness about wider agricultural topics. We reflect on the role and power given to agribusinesses by the farming press and what this means for agricultural sustainability.This paper investigated occupational gender segregation and its vertical and horizontal dimensions in Turkey. In order to explore the extent of inequality entailed in occupational gender segregation (measured by the vertical dimension), average pay levels across occupations were used. In addition to the economic inequalities captured by pay, aiming to explore the social inequalities inherent in occupational segregation, Cambridge Social Interaction and Stratification Scale scores across occupations were used. The results showed that the extent of inequality associated with occupational gender segregation was substantial, operating to the detriment of women. Women were more likely to be employed in lower-paid jobs and in occupations that ranked lower across the overall stratification structure, while men remained at an advantaged position in terms of both the pay levels and the positions of the occupations they held in the social hierarchy.Over the last two decades, the design practice has been expanding to the public sphere to generate solutions for public challenges. In particular, the reflections on the design practice of public sector innovation (PSI) units, working in or with governments, are increasingly contributing to a growing body of literature attempting to characterise the practice in policy making. Although scholars conclude that design\uffe2\uff80\uff99s significant contribution in policy refers to the implementation of public services and programs, there is also an urgent advocacy for a deeper study of the nature of design practices within earlier stages of policy processes addressing more specifically policy proposals and reforms. As part of a broader investigation, this paper seeks to shed light to this matter by identifying design-led activities and methods of PSI units in the policy making process and positioning them in the stages of the policy cycle. This research examines academic, grey literature and web content to uncover and position design activities of 46 PSI units from different continents in a policy cycle model based on Howlett, Ramesh and Perl (2009). Our work confirms that most design activities develop in the implementation stage rather than in early stages of the policy process. While design interventions are growing within policy formulation and agenda-setting stages, few of them were identified in the stage of policy evaluation. Decision-making stage remains purely political. This research may serve to a further understanding of the design practice and its potential contribution to policy making in the future.The present study reports findings related to the treatment of polluted groundwater using macrophyte-assisted phytoremediation. The potential of three macrophyte species (Phragmites australis, Scirpus holoschoenus, and Typha angustifolia) to tolerate exposure to multi-metal(loid) polluted groundwater was first evaluated in mesocosms for 7- and 14-day batch testing. In the 7-day batch test, the polluted water was completely replaced\uffc2\uffa0and renewed after 7\uffc2\uffa0days, while for\uffc2\uffa014\uffc2\uffa0days exposure, the same polluted water, added in the first week, was maintained. The initial biochemical screening\uffc2\uffa0results of macrophytes indicated that the selected plants were more tolerant to the provided conditions with 14\uffc2\uffa0days of exposure. Based on these findings, the plants were exposed to HRT regimes of 15 and 30\uffc2\uffa0days. The results showed that P. australis and S. holoschoenus performed better than T. angustifolia, in terms of metal(loid) accumulation and removal, biomass production, and toxicity reduction. In addition, the translocation and compartmentalization of metal(loid)s were dose-dependent. At the 30-day loading rate (higher HRT), below-ground phytostabilization was greater than phytoaccumulation, whereas at the 15-day loading rate (lower HRT), below- and above-ground phytoaccumulation was the dominant metal(loid) removal mechanism. However, higher levels of toxicity were noted in the water at the 15-day loading rate. Overall, this\uffc2\uffa0study provides valuable insights for macrophyte-assisted phytoremediation of polluted (ground)water streams that can help to improve the design and implementation of phytoremediation systems.Transitions towards more sustainable agricultural systems are often characterised by \uffe2\uff80\uff98lock-ins\uffe2\uff80\uff99, understood as self-reinforcing mechanisms that reproduce the status quo and impede change. While socioeconomic, technological and institutional lock-ins have been widely used to understand processes of sustainable transitions in agri-food systems, the role of so-called cognitive lock-ins is still under-investigated. In this study, we focus on how institutional settings create cognitive lock-ins in farmers\uffe2\uff80\uff99 decision-making related to the adoption of sustainable agricultural practices. We apply goal framing for environmental behaviour and transition theory in explaining how socio-technical conditions may shape farmer\uffe2\uff80\uff99s decision-making. Empirically, we focus on the example of diversifying crop rotations with legumes as an established strategy to increase biodiversity and soil health, and reduce agrochemical use, emissions and pollution, which still remains rare in European agriculture. We use two cases in the Atlantic pedo-climatic region, Cornwall, UK, and Gelderland, Netherlands. Using in-depth interview data with farmers and extensive supplementary secondary data, we explore how context-specific socio-technical settings interact with farmers\uffe2\uff80\uff99 normative, gain-oriented and hedonic goal frames to shape the (un-)desirability of crop diversification with legumes. This creates conditions recognisable as cognitive lock-ins: the context of farmers\uffe2\uff80\uff99 decision-making creates cognitive processes that drastically reduce the perceived viability of alternative agricultural practices. Our findings in this case suggest the framework developed for this study may help to identify regionally specific, as well as common, barriers and solutions to crop diversification and comparable practices that are relevant to transitions towards sustainability in agri-food systems.

", "keywords": ["2. Zero hunger", "Goal framing", "330", "Lock-in", "05 social sciences", "0211 other engineering and technologies", "02 engineering and technology", "15. Life on land", "Legumes", "630", "12. Responsible consumption", "13. Climate action", "Crop diversification", "0502 economics and business", "Sustainability transition \u00b7 Legumes \u00b7 Crop diversification \u00b7 Lock-in \u00b7 Goal framing", "Sustainability transition"]}, "links": [{"href": "https://link.springer.com/content/pdf/10.1007/s11625-022-01156-5.pdf"}, {"href": "https://doi.org/10.1007/s11625-022-01156-5"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sustainability%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/s11625-022-01156-5", "name": "item", "description": "10.1007/s11625-022-01156-5", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/s11625-022-01156-5"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-05-19T00:00:00Z"}}, {"id": "10.1007/s43615-021-00011-6", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-25T16:16:42Z", "type": "Journal Article", "created": "2021-03-10", "title": "Urban Living Labs, Circular Economy and Nature-Based Solutions: Ideation and Testing of a New Soil in the City of Turin Using a Multi-stakeholder Perspective", "description": "Abstract

In the attempt to foster circular economy (CE), cities are increasingly adopting urban living labs (ULLs) as sites of co-production aimed at testing alternative solutions based on the reuse of products, reduction of consumption and recycling of materials. Taking this perspective, our study adopts an exploratory research design to discover the pragmatic implications emerging from a case study. The City of Turin joined proGIreg, a European project that entails the regeneration of former industrial districts by means of nature-based solutions (NBS). Ranging from aquaponics to green roofs, seven NBS have been experimented in Turin, which rely on the use of natural systems to tackle social, economic and environmental challenges efficiently and sustainably. Among them, the most promising is related to the production and test of the \uffe2\uff80\uff98new soil\uffe2\uff80\uff99, a blend obtained by mixing earth materials coming from construction sites with compost, zeolites and mycorrhizae. The case herein presented is interesting to analyse for the multi-stakeholder management setting used, where public institutions, private companies, research institutions, citizens and associations collaborated in the co-creation and testing phase of the NBS. Consequently, the data collected through participant observation and direct interviews allow researchers to describe multi-stakeholders\uffe2\uff80\uff99 dynamics and how they work. Thus, this paper narrates a micro-contextual experience while providing a critique. Results include an analysis of the unique combination of different stakeholders, which strongly impacted on the management and the effectiveness of the entire project. By consequence, the paper offers both theoretical contributions to the relational branch of stakeholder theory and practical evidence in demonstrating the importance of the relational branch of the theory over a more traditional transactional view.

", "keywords": ["Circular economy", "New soil", "Circular economy Urban living lab Nature-based solutions New soil Sustainable transition Turin", "Urban living lab", "9. Industry and infrastructure", "05 social sciences", "Nature-based solutions", "Turin", "01 natural sciences", "12. Responsible consumption", "13. Climate action", "11. Sustainability", "8. Economic growth", "0502 economics and business", "Sustainable transition", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://iris.unito.it/bitstream/2318/1805054/1/Ascione2021_Article_UrbanLivingLabsCircularEconomy.pdf"}, {"href": "https://link.springer.com/content/pdf/10.1007/s43615-021-00011-6.pdf"}, {"href": "https://doi.org/10.1007/s43615-021-00011-6"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Circular%20Economy%20and%20Sustainability", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/s43615-021-00011-6", "name": "item", "description": "10.1007/s43615-021-00011-6", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/s43615-021-00011-6"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-10T00:00:00Z"}}, {"id": "10.1016/j.agee.2013.05.001", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-25T16:16:53Z", "type": "Journal Article", "created": "2013-05-29", "title": "Earthworms Can Increase Nitrous Oxide Emissions From Managed Grassland: A Field Study", "description": "Earthworms are important in determining the greenhouse gas (GHG) balance of soils. In laboratory studies they have been shown to increase emissions of the potent GHG nitrous oxide (N2O). Here we test whether these earthworm-induced N2O emissions also occur in the field. We quantified N2O emissions in managed grassland in two different seasons (spring and autumn), applying two different types of fertilizer (organic and artificial fertilizer) and under two earthworm densities (175 individuals and 350 individuals m(-2)) of the species Lumbricus rubellus (Hoffmeister). We found an increase in earthworm-induced N2O emissions of 286 and 394% in autumn for low and high earthworm densities (P = 0.044 and P = 0.007, respectively). There were no effects of earthworms on N2O emissions in spring. Fertilizer additions significantly increased cumulative N2O emissions and grass N content in spring and autumn. For grass N content interactions between earthworm addition and fertilizer type existed in both seasons. Our results suggest that the pathways through which earthworms affect N cycling (and thereby N2O emission) differ with weather conditions. We postulate that in spring the dry weather conditions overruled any earthworm effects, whereas in autumn earthworms mainly improved soil aeration and thereby increased both plant N uptake and diffusion of N2O to the atmosphere. While we showed the presence of earthworm-induced N2O emissions in managed grassland under field conditions for the first time, the nature and intensity of the earthworm effect in the field is conditional on soil physicochemical parameters and thereby on meteorological and seasonal dynamics. (C) 2013 Elsevier B.V. All rights reserved.", "keywords": ["2. Zero hunger", "agroecosystem", "n2o emission", "04 agricultural and veterinary sciences", "15. Life on land", "carbon-dioxide", "fluxes", "soil", "crop residue", "13. Climate action", "peat", "gut", "0401 agriculture", " forestry", " and fisheries", "mesocosms", "nitrifier denitrification"]}, "links": [{"href": "https://doi.org/10.1016/j.agee.2013.05.001"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agriculture%2C%20Ecosystems%20%26amp%3B%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.agee.2013.05.001", "name": "item", "description": "10.1016/j.agee.2013.05.001", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.agee.2013.05.001"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2013-07-01T00:00:00Z"}}, {"id": "10.1016/j.agee.2013.10.027", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-25T16:16:53Z", "type": "Journal Article", "created": "2013-12-08", "title": "Effects Of Trees On Infiltrability And Preferential Flow In Two Contrasting Agroecosystems In Central America", "description": "Abstract We tested the hypothesis that trees have measurable effects on infiltrability, macroporosity, and preferential flows in agrosilvopastoral systems. Managing agricultural systems for water conservation is a critical component of sustainable systems. We investigated the relationship between infiltrability and the distance to the nearest tree, and whether differences in macroporosity can account for differences in infiltrability. In both systems, preferential soil water flows were dominant compared to matrix flow. Trees in the pasture landscape improved infiltrability and preferential flow but had no significant effect in the coffee agroforestry system. After comparing rainfall intensity and frequency data to the measured infiltrability values, we conclude that trees in the pasture system reduce surface runoff at the highest observed rainfall intensities (>50\u00a0mm\u00a0h \u22121 ). The volcanic soils of the coffee plantation are less degraded and their high natural permeability has been maintained. Since the coffee plants at this site are established (40 years) perennial vegetation with substantial residues and extensive root systems like trees, they improve soil physical properties similarly to trees. Trees increase hydrologic services in pasture lands, a rapidly expanding land use type across Latin America, and therefore may be a viable land management option for mitigating some of the negative environmental impacts associated with land clearing and animal husbandry. However, in land management practices where understorey perennial vegetation makes up a large proportion of the cover, such as for coffee agroforestry systems, the effect of trees on infiltration-related ecosystem services could be less pronounced", "keywords": ["P33 - Chimie et physique du sol", "F40 - \u00c9cologie v\u00e9g\u00e9tale", "F08 - Syst\u00e8mes et modes de culture", "culture associ\u00e9e", "structure agricole", "http://aims.fao.org/aos/agrovoc/c_1920", "01 natural sciences", "utilisation des terres", "\u00e9cologie", "p\u00e2turages", "http://aims.fao.org/aos/agrovoc/c_14398", "http://aims.fao.org/aos/agrovoc/c_16034", "K01 - Foresterie - Consid\u00e9rations g\u00e9n\u00e9rales", "http://aims.fao.org/aos/agrovoc/c_5626", "http://aims.fao.org/aos/agrovoc/c_7165", "\u00e9cosyst\u00e8me forestier", "0105 earth and related environmental sciences", "agroforesterie", "perm\u00e9abilit\u00e9 du sol", "2. Zero hunger", "http://aims.fao.org/aos/agrovoc/c_35927", "syst\u00e8me racinaire", "transport des substances nutritives", "04 agricultural and veterinary sciences", "http://aims.fao.org/aos/agrovoc/c_202", "15. Life on land", "ruissellement", "http://aims.fao.org/aos/agrovoc/c_207", "http://aims.fao.org/aos/agrovoc/c_1374842133961", "F61 - Physiologie v\u00e9g\u00e9tale - Nutrition", "conservation des sols", "http://aims.fao.org/aos/agrovoc/c_35388", "13. Climate action", "http://aims.fao.org/aos/agrovoc/c_4182", "0401 agriculture", " forestry", " and fisheries", "http://aims.fao.org/aos/agrovoc/c_5272", "http://aims.fao.org/aos/agrovoc/c_2467", "http://aims.fao.org/aos/agrovoc/c_3651"]}, "links": [{"href": "https://doi.org/10.1016/j.agee.2013.10.027"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agriculture%2C%20Ecosystems%20%26amp%3B%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.agee.2013.10.027", "name": "item", "description": "10.1016/j.agee.2013.10.027", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.agee.2013.10.027"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2014-01-01T00:00:00Z"}}, {"id": "10.1016/j.agee.2014.02.014", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-25T16:16:53Z", "type": "Journal Article", "created": "2014-03-22", "title": "Long-Term Effect Of Contrasted Tillage And Crop Management On Soil Carbon Dynamics During 41 Years", "description": "Although numerous studies have been conducted on the effect of tillage on soil organic carbon (SOC), there is still no consensus on the importance of sequestration which can be expected from reduced tillage. Most studies have used a synchronic approach in fields or long-term experiments which were often poorly characterized with respect to initial conditions. In this paper, we used a diachronic approach to quantify SOC changes in a 41 years experiment comparing no-till (NT), shallow till (ST) and full inversion tillage (FIT) combined with crop managements (residues removal, rotation and catch crops). It included SOC measurements at time 0 and every 4 years, calculations at equivalent soil mass within or below the old ploughed layer. Results show that tillage or crop management had no significant effect on SOC stocks after 41 years both in the old ploughed layer (ca. 0-28 cm) and deeper (ca. 0-58 cm). Tillage had no effect on crop yields and residues. In the reduced tillage treatments (ST and NT), SOC accumulated in the surface layer (0-10 cm), reaching a plateau after 24 years but declined continuously in the lower layer (10-28 cm) at a rate of 0.42-0.44% yr(-1). The difference in SOC stocks (ST or NT minus FIT) over the old ploughed layer followed a non-monotonic pattern over time. Reduced tillage caused a rapid SOC sequestration during the first 4 years which remained more or less constant (mean = 2.17 and 1.31 t ha(-1), resp.) during the next 24 years and disappeared after 28 years. The drop was attributed to the higher water balance recorded during years 24-28. In the reduced tillage treatments, the changes in SOC over time were negatively correlated with the water balance, indicating that sequestration rate was positive in dry periods and negative in wet conditions. This study highlights the interest of diachronic approaches to understand the effect of tillage and its interaction with environmental and management factors.", "keywords": ["[SDE] Environmental Sciences", "2. Zero hunger", "Soil organic carbon", "[SDV]Life Sciences [q-bio]", "04 agricultural and veterinary sciences", "15. Life on land", "630", "Tillage", "Dynamics", "[SDV] Life Sciences [q-bio]", "Long-term", "[SDE]Environmental Sciences", "[SDV.BV]Life Sciences [q-bio]/Vegetal Biology", "0401 agriculture", " forestry", " and fisheries", "[SDV.BV] Life Sciences [q-bio]/Vegetal Biology", "SOC", "Crop production", "Crop management"], "contacts": [{"organization": "Dimassi, Bassem, Mary, Bruno, B., Wylleman, Richard, Labreuche, Jerome, Couture, Daniel, Piraux, Fran\u00e7ois, Cohan, Jean-Pierre,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1016/j.agee.2014.02.014"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agriculture%2C%20Ecosystems%20%26amp%3B%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.agee.2014.02.014", "name": "item", "description": "10.1016/j.agee.2014.02.014", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.agee.2014.02.014"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2014-04-01T00:00:00Z"}}, {"id": "10.1016/j.agee.2014.02.021", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-25T16:16:54Z", "type": "Journal Article", "created": "2014-03-15", "title": "Biochar Does Not Affect Soil N-Transformations Or Microbial Community Structure Under Ruminant Urine Patches But Does Alter Relative Proportions Of Nitrogen Cycling Bacteria", "description": "Abstract Nitrogen (N) cycling, especially denitrification, can be significantly altered when biochar is used as a soil conditioner. These alterations in N-cycling have been attributed to a combination of physicochemical change, alterations in microbial community ecology and pervading climatic conditions. This study investigated seasonal bacterial community change over two years in combination with a short-term winter study of N-transformations under bovine urine patches. A silt-loam pastoral soil in Canterbury, New Zealand was amended with either 0, 15 or 30\u00a0t\u00a0ha \u22121 of Pinus radiata biochar (pyrolysed at \u223c450\u00a0\u00b0C) and bovine urine was added to patches within the 0 and 30\u00a0t\u00a0ha \u22121 biochar amended plots (designated as 0\u00a0U and 30\u00a0U treatments, where U indicates \u2018urine\u2019). No discernible differences in bacterial community structure were observed during the two year study or the short term N-transformation study when comparing non-amended and biochar-amended soil. Differences in bacterial community structure were only evident when comparing seasons, with data pertaining to each season from successive years clustering together. During the short-term N-transformation study, bacterial communities formed 3 distinct clusters corresponding to elevated levels of urine derived NH 4 + -N (days 0\u201310), increases in NO 3 \u2212 -N and N 2 O (days 10\u201322) and a decline in NO 3 \u2212 -N and N 2 O (day 20 onward). Biochar amendment did increase the relative abundance of up to 50% of individual operational taxonomic units (OTUs or \u2018species\u2019), including key nitrite oxidisers and nitrate reducers. Biochar amendment did not affect the concentrations of inorganic-N compounds. The nir S (nitrite reductase) gene became elevated in the 30\u00a0U treatment relative to the 0\u00a0U treatment \u223c10 days after the initial urine application. The nos Z (nitrous oxide reductase) gene became elevated in the 30\u00a0U plots during the latter part of the experiment. Conclusions: \u2022 Biochar did not have a significant impact on the microbial community structure in pastoral soil over the course of two years. \u2022 The relative proportion of nitrifiers and denitrifiers increased in biochar amended soils subjected to large influxes of urine derived N. \u2022 Differences in N-transformation dynamics in the presence of biochar during the winter months were not statistically significant.", "keywords": ["2. Zero hunger", "N\u2082O emissions", "570", "denitrification", "bovine urine", "silt-loam soil", "ANZSRC::30 Agricultural", "04 agricultural and veterinary sciences", "15. Life on land", "soil microbial ecology", "winter", "nitrification", "630", "6. Clean water", "veterinary and food sciences", "T-RFLP", "new generation sequencing", "13. Climate action", "ANZSRC::41 Environmental sciences", "XXXXXX - Unknown", "0401 agriculture", " forestry", " and fisheries", "biochar", "ANZSRC::44 Human society"], "contacts": [{"organization": "Timothy J. Clough, Kelly Hamonts, Leo M. Condron, Craig Anderson, Craig Anderson,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1016/j.agee.2014.02.021"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agriculture%2C%20Ecosystems%20%26amp%3B%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.agee.2014.02.021", "name": "item", "description": "10.1016/j.agee.2014.02.021", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.agee.2014.02.021"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2014-06-01T00:00:00Z"}}, {"id": "10.1016/j.agee.2016.01.026", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-25T16:16:56Z", "type": "Journal Article", "created": "2016-02-14", "title": "Overgrazing decreases soil organic carbon stocks the most under dry climates and low soil pH: A meta-analysis shows", "description": "Grasslands occupy about 40% of the world\u2019s land surface and store approximately 10% of the global soil organic carbon (SOC) stock. This SOC pool, in which a larger proportion is held in the topsoil (0\u20130.3 m), is strongly influenced by grassland management. Despite this, it is not yet fully understood how grassland SOC stocks respond to degradation, particularly for the different environmental conditions found globally. The objective of this review was to elucidate the impact of grassland degradation on changes in SOC stocks and the main environmental controls, worldwide, as a prerequisite for rehabilitation. A comprehensive meta-analysis was conducted using 55 studies with 628 soil profiles under temperate, humid, sub-humid, tropical and semi-arid conditions, to compare SOC stocks in the topsoil of non-degraded and degraded grassland soils. Grassland degradation significantly reduced SOC stocks by 16% in dry climates ( 1000 mm) and Asia was the most affected continent (\u221223.7%). Moreover, the depletion of SOC stock induced by degradation was more pronounced in sandy (<20% clay) soils with a high SOC depletion of 10% compared to 1% in clayey (\u226532% clay) soils. Furthermore, grassland degradation significantly reduced SOC by 14% in acidic soils (pH \u2264 5), while SOC changes were negligible for higher pH. Assuming that 30% of grasslands worldwide are degraded, the amount of SOC likely to be lost would be 4.05 Gt C, with a 95% confidence between 1.8 and 6.3 Gt C (i.e. from 1.2 to 4.2% of the whole grassland soil stock). These results by pointing to greater SOC losses from grasslands under dry climates and sandy acidic soils allow identification of grassland soils for which SOC stocks are the most vulnerable, while also informing on rehabilitation measures.", "keywords": ["580", "2. Zero hunger", "570", "Spatial variation", "[PHYS.PHYS.PHYS-GEO-PH] Physics [physics]/Physics [physics]/Geophysics [physics.geo-ph]", "Climate Change", "[PHYS.PHYS.PHYS-GEO-PH]Physics [physics]/Physics [physics]/Geophysics [physics.geo-ph]", "04 agricultural and veterinary sciences", "15. Life on land", "SOC stocks", "Grassland degradation", "630", "Soil", "Controlling factors", "13. Climate action", "Grasslands", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "https://doi.org/10.1016/j.agee.2016.01.026"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agriculture%2C%20Ecosystems%20%26amp%3B%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.agee.2016.01.026", "name": "item", "description": "10.1016/j.agee.2016.01.026", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.agee.2016.01.026"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-04-01T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=SOC&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=SOC&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "first", "title": "items (first)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=SOC&", "hreflang": "en-US"}, {"rel": "next", "type": "application/geo+json", "title": "items (next)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=SOC&offset=50", "hreflang": "en-US"}], "numberMatched": 687, "numberReturned": 50, "distributedFeatures": [], "timeStamp": "2026-06-25T21:25:56.284863Z"}