{"type": "FeatureCollection", "features": [{"id": "10.3390/land11122200", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:20:49Z", "type": "Journal Article", "created": "2022-12-05", "title": "Land Suitability Analysis as a Tool for Evaluating Soil-Improving Cropping Systems", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Agricultural land use planning is based on the capacity of the soil to support different types of crops and is a prerequisite for better use of cultivated land. Land Suitability Analysis (LSA) is used to measure the level of suitability of growing a specific crop in the area and can also be used to evaluate future scenarios as a means for sustainable agriculture. LSA was employed to calculate current land suitability, as well as four scenarios of Soil-Improving Cropping Systems (SICS): (a) Conservation Tillage (CT), (b) Cover Crop (CC), (c) Crop Residue Management (CRM), and (d) Manure Application (MA). The scenarios of SICS were derived by increasing soil organic matter and cation exchange capacity values depending on the SICS hypothetically applied for a period of 100 years in the future. LSA was evaluated for maize in three sites: (a) Flanders (BE), (b) Somogy (HU), and (c) Hengshui (CH). LSA was performed using the Agricultural Land Use Evaluation System (ALUES) considering soil and climatic and topographic parameters. Weighing factors of input parameters were assigned using the Analytical Hierarchy Process (AHP). The results show that in Flanders, the highly suitable (S2) class covered 3.3% of the total area, and the best scenario for improving current LS was CRM, in which S2 expanded to 9.1%. In Somogy, the S2 class covered 18.3% of the total area, and the best scenarios for improving current land suitability were CT and CC, in both of which the S2 class expanded to 70.5% of the total area. In Hengshui, the S2 class covered 64.7% of the total area, and all SICS scenarios performed extremely well, converting almost all moderately suitable (S3) areas to S2. The main limiting factor that was recognized from a limiting factor analysis in all cases was the climatic conditions. This work proves that LSA can evaluate scenarios of management practices and recognize limiting factors. The proposed methodology is a novel approach that can provide land suitability maps to efficiently evaluate SICS scenarios by projecting soil characteristics and LSA in the future, thus facilitating management decisions of regional policy makers.</p></article>", "keywords": ["IMPACT", "AHP", "Environmental Studies", "land use planning", "Environmental Sciences & Ecology", "3301 Architecture", "01 natural sciences", "4104 Environmental management", "CARBON SEQUESTRATION", "AGROECOLOGY", "MANAGEMENT", "BINH THUAN PROVINCE", "0502 Environmental Science and Management", "0105 earth and related environmental sciences", "2. Zero hunger", "Science & Technology", "DESERTIFICATION", "S", "Agricultural Land Use Evaluation System", "soil fertility", "3304 Urban and regional planning", "Agriculture", "sustainability; soil fertility; land use planning; AHP; ALUES", "ALUES", "04 agricultural and veterinary sciences", "15. Life on land", "sustainability", "Analytical Hierarchy Process", "0401 agriculture", " forestry", " and fisheries", "Life Sciences & Biomedicine"]}, "links": [{"href": "http://www.mdpi.com/2073-445X/11/12/2200/pdf"}, {"href": "https://www.mdpi.com/2073-445X/11/12/2200/pdf"}, {"href": "https://doi.org/10.3390/land11122200"}, {"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/land11122200", "name": "item", "description": "10.3390/land11122200", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/land11122200"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-12-04T00:00:00Z"}}, {"id": "10.5281/zenodo.15043864", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:22:50Z", "type": "Report", "title": "Post-processing & interactive visualisation of optimisation results. Deliverable D5.2 of the EU Horizon 2020 project OPTAIN", "description": "Deliverable report D5.2 of the EU Horizon 2020 Project OPTAIN (Grant agreement No. 862756)  Summary\u00a0Multi-objective optimisation is a powerful approach for generating a set of Pareto optimal design alternatives that decision-makers can evaluate in order to select the most-suitable configuration. In practice, however, selecting from a large number of Pareto optimal solutions can be daunting. The objective of this report is to enable researchers and stakeholders to assess the optimisation outputs produced in OPTAINs previous Task 5.2 in a structured manner, to render the results tangible and understandable, and to maximise their use for the subsequent stakeholder consultation.  This report describes the tool ParetoPick-R, including how to run it, its data input requirements and the processes it employs. ParetoPick-R allows (1) to make the complex optimisation outputs understandable through various intuitive visualisation techniques, including for the links between the objective space and the decision space of Natural/Small Water Retention Measures (NSWRM) implementation plans. (2) It implements a methodology for reducing the high number of solutions from the previous optimisation to a manageable number while reducing information loss, and (3) allows to perform an Analytical Hierarchy Process for stakeholders to assign priorities based on pairwise preferences in a structured manner.  This report is useful for researchers and stakeholders from OPTAIN and beyond working with complex optimisation problems who want to analyse their results in\u00a0a structured and meaningful way and render them actionable.", "keywords": ["CoMOLA", "combination", "SWAT+", "NSWRM", "post-processing", "H2020", "OPTAIN", "interactive visualisation", "stakeholder support", "R tool", "multi-objective optimization", "allocation", "Pareto solutions", "Analytical Hierarchy Process", "pareto pruning", "clustering"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15043864"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15043864", "name": "item", "description": "10.5281/zenodo.15043864", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15043864"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-03-18T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Analytical+Hierarchy+Process&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=Analytical+Hierarchy+Process&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=Analytical+Hierarchy+Process&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Analytical+Hierarchy+Process&offset=2", "hreflang": "en-US"}], "numberMatched": 2, "numberReturned": 2, "distributedFeatures": [], "timeStamp": "2026-07-27T06:57:46.985137Z"}