Abstract. Today it is evident that there are many contrasting demands on our landscape (e.g. food security, more sustainable agriculture, higher income in rural areas, etc.) as well as many land degradation problems. It has been proved that providing operational answers to these demands and problems is extremely difficult. Here we aim to demonstrate that a spatial decision support system based on geospatial cyberinfrastructure (GCI) can address all of the above, so producing a smart system for supporting decision making for agriculture, forestry, and urban planning with respect to the landscape. In this paper, we discuss methods and results of a special kind of GCI architecture, one that is highly focused on land management and soil conservation. The system allows us to obtain dynamic, multidisciplinary, multiscale, and multifunctional answers to agriculture, forestry, and urban planning issues through the Web. The system has been applied to and tested in an area of about 20 000 ha in the south of Italy, within the framework of a European LIFE+ project (SOILCONSWEB). The paper reports – as a case study – results from two different applications dealing with agriculture (olive growth tool) and environmental protection (soil capability to protect groundwater). Developed with the help of end users, the system is starting to be adopted by local communities. The system indirectly explores a change of paradigm for soil and landscape scientists. Indeed, the potential benefit is shown of overcoming current disciplinary fragmentation over landscape issues by offering – through a smart Web-based system – truly integrated geospatial knowledge that may be directly and freely used by any end user (www.landconsultingweb.eu). This may help bridge the last very important divide between scientists working on the landscape and end users.

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Abstract. Today it is evident that there are many contrasting demands on our landscape (e.g. food security, more sustainable agriculture, higher income in rural areas, etc.) but also many land degradation problems. It has been proved that providing operational answers to these demands and problems is extremely difficult. Here we aim to demonstrate that a Spatial Decision Support System based on geospatial cyber-infrastructure (GCI) can embody all of the above, so producing a smart system for supporting decision making for agriculture, forestry and urban planning with respect to the landscape. In this paper, we discuss methods and results of a special kind of GCI architecture, one that is highly focused on soil and land conservation (SOILCONSWEB-LIFE+ project). The system allows us to obtain dynamic, multidisciplinary, multiscale, and multifunctional answers to agriculture, forestry and urban planning issues through the web. The system has been applied to and tested in an area of about 20 000 ha in the South of Italy, within the framework of a European LIFE+ project. The paper reports – as a case study – results from two different applications dealing with agriculture (olive growth tool) and environmental protection (soil capability to protect groundwater). Developed with the help of end users, the system is starting to be adopted by local communities. The system indirectly explores a change of paradigm for soil and landscape scientists. Indeed, the potential benefit is shown of overcoming current disciplinary fragmentation over landscape issues by offering – through a smart web based system – truly integrated geospatial knowledge that may be directly and freely used by any end user (http://www.landconsultingweb.eu). This may help bridge the last much important divide between scientists working on the landscape and end users.

One of the current priorities of the new Common Agriculture Policy (CAP) is to overcome the serious environmental prob- lems raised by intensive agriculture. Despite the steps for- ward guaranteed by new technologies and innovations (e.g., IoT, precision agriculture), the availability of real operational tools, helping the member states to fulfill the high require- ments and expectations of the new CAP, is still lacking. To fill this gap, in the H2020 LandSupport project, the web- based best practice tool was developed to identify, on-the- fly, optimized agronomic solutions. The core of the tool is the ARMOSA process-based model, which dynamically sim- ulates several combinations of cropping systems, crops, ni- trogen fertilization rates, tillage solutions and crop residues managements for a specific region of interest. To identify the optimized solutions, it provides a synthetic “Best Practice in- dex”, which combines the production, nitrate leaching and SOC_change, according to the end-user dynamic requests. The tool was implemented for three case studies: March- feld Region in Austria, Zala County in Hungary, Campania Region in Italy, which are representative of a variety of dif- ferent pedoclimatic conditions. In the present work, three possible uses are shown to i) maximize the crop production; ii) evaluate the use of different crops and related practices; iii) evaluate the best practices in the nitrate vulnerable zones. The tool offers a close representation of actual and optimized cropping systems, with the possibility of further applications in other regional case studies, and in tailored scenarios, in which users enter their own input data.