{"type": "FeatureCollection", "features": [{"id": "10.5281/zenodo.5574882", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:23:30Z", "type": "Report", "created": "2020-03-09", "title": "Hyperspectral imaging for high resolution mapping of soil profile organic carbon distribution in an Austrian Alpine landscape", "description": "<p>         &amp;lt;p&amp;gt;Studies on soil organic carbon (SOC) stocks mostly focus on topsoils (&amp;lt; 30 cm). However, 30 to 63% of the SOC are stored in the subsoils (30 to 100 cm), and the factors controlling SOC storage in subsoils may be substantially different than in topsoils. The low mean SOC content in subsoils makes its quantification and characterization challenging. Thus, new approaches are required to depict the SOC stocks distribution in full soil profile. Hyperspectral imaging of soil core samples can provide high spatial resolution of the vertical distribution of SOC in a soil profile. The main objective of the ongoing study, within the Horizon 2020 European Project Circular Agronomics, is to apply laboratory hyperspectral imaging with a variety of machine learning approaches for the mapping of OC distribution in undisturbed soil cores. Soil cores were collected down to a depth of one meter in grasslands of 15 organic farms located in the Lungau Valley, in Austria. Some samples were divided into five depths in the field for classical bulk soil measurements (total carbon and nitrogen, texture, pH, EC and bulk density) on disturbed samples. Undisturbed soil cores were sliced vertically for laboratory hyperspectral imaging in the range of Vis-NIR (400-1000 nm). We were able to reveal the hotspots of OC and map the OC distribution in soil profile by applying a variety of machine learning approaches (i.e. partial least square and random forest regression) as a function of spectral responses. A digital elevation model was further exploited to investigate the effects of topographical factors such as elevation, aspect and slope on SOC profile distribution. Landsat 8 data were also used to depict the spatial variability of land insensitive cover/vegetation in study area.&amp;lt;/p&amp;gt;         </p>", "keywords": ["2. Zero hunger", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "Vis-NIR imaging spectroscopy", " Alpine grassland", " Digital elevation model", " Subsoils"], "contacts": [{"organization": "YASER OSTOVARI, K\u00f6ppend\u00f6rfer, Baptist, Guigue, Julien, Van Groenigen, Jan Willem, Creamer, Rachel, Guggenberger, Thomas, Grassauer, Florian, Hobley, Eleanor, Ferron, Laura, Martens, Henk, K\u00f6gel-Knabner, Ingrid, Vidal, Alix,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5574882"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5574882", "name": "item", "description": "10.5281/zenodo.5574882", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5574882"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-03-23T00:00:00Z"}}, {"id": "10.3390/land11050645", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:21:01Z", "type": "Journal Article", "created": "2022-04-27", "title": "Soil Compaction Prevention, Amelioration and Alleviation Measures Are Effective in Mechanized and Smallholder Agriculture: A Meta-Analysis", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Background: The compaction of subsoils in agriculture is a threat to soil functioning. Measures aimed at the prevention, amelioration, and/or impact alleviation of compacted subsoils have been studied for more than a century, but less in smallholder agriculture. Methods: A meta-analysis was conducted to quantitatively examine the effects of the prevention, amelioration, and impact alleviation measures in mechanized and small-holder agriculture countries, using studies published during 2000~2019/2020. Results: Mean effect sizes of crop yields were large for controlled traffic (+34%) and irrigation (+51%), modest for subsoiling, deep ploughing, and residue return (+10%), and negative for no-tillage (\u22126%). Mean effect sizes of soil bulk density were small (&lt;10%), suggesting bulk density is not a sensitive \u2018state\u2019 indicator. Mean effect sizes of penetration resistance were relatively large, with large variations. Controlled traffic had a larger effect in small-holder farming than mechanized agriculture. Conclusion: We found no fundamental differences between mechanized and smallholder agriculture in the mean effect sizes of the prevention, amelioration, and impact alleviation measures. Measures that prevent soil compaction are commonly preferred, but amelioration and alleviation are often equally needed and effective, depending on site-specific conditions. A toolbox of soil compaction prevention, amelioration, and alleviation measures is needed, for both mechanized and smallholder agriculture.</p></article>", "keywords": ["2. Zero hunger", "S", "tillage", "0401 agriculture", " forestry", " and fisheries", "Agriculture", "04 agricultural and veterinary sciences", "crop yield", "15. Life on land", "compacted subsoils", "mechanized agriculture", "smallholder agriculture", "soil bulk density", "soil penetration resistance"]}, "links": [{"href": "https://doi.org/10.3390/land11050645"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Land", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/land11050645", "name": "item", "description": "10.3390/land11050645", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/land11050645"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-04-27T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=+Subsoils&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=+Subsoils&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=+Subsoils&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=+Subsoils&offset=2", "hreflang": "en-US"}], "numberMatched": 2, "numberReturned": 2, "distributedFeatures": [], "timeStamp": "2026-07-26T09:18:22.935581Z"}