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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.

This paper aims to test a new physically oriented approach to viticulture zoning at farm scale that is strongly rooted in hydropedology and aims to achieve a better use of environmental features with respect to plant requirements and wine production. The physics of our approach are defined by the use of soil–plant–atmosphere simulation models, applying physically based equations to describe the soil hydrological processes and solve soil–plant water status. This study (part of the ZOVISA project) was conducted on a farm devoted to production of high-quality wines (Aglianico DOC), located in southern Italy (Campania region, Mirabella Eclano, AV). The soil spatial distribution was obtained after standard soil survey informed by geophysical survey. Two homogeneous zones (HZs) were identified; in each one a physically based model was applied to solve the soil water balance and estimate the soil functional behaviour (crop water stress index, CWSI) defining the functional homogeneous zones (fHZs). For the second process, experimental plots were established and monitored for investigating soil–plant water status, crop development (biometric and physiological parameters) and daily climate variables (temperature, solar radiation, rainfall, wind). The effects of crop water status on crop response over must and wine quality were then evaluated in the fHZs. This was performed by comparing crop water stress with (i) crop physiological measurement (leaf gas exchange, chlorophyll a fluorescence, leaf water potential, chlorophyll content, leaf area index (LAI) measurement), (ii) grape bunches measurements (berry weight, sugar content, titratable acidity, etc.) and (iii) wine quality (aromatic response). This experiment proved the usefulness of the physically based approach, also in the case of mapping viticulture microzoning.

The quantification of the water balance terms within soil-crop-climate systems is required to derive proper management for plant growth and irrigation. A large number of available models use the well known Richards’ equation for the simulation of water redistribution at field scale. Despite their common basis of the representation of water flow in the unsaturated zone, apparently similar hydrological models give different answers if applied in the same pedological, climatic and agronomic scenarios. The objective of the present study was evaluating and comparing the performance of three well known models (SWAP, MACRO and CropSyst) based on the solution of the Richards’ equation: in a structured fine soil (Calciustepts located in Cerese, Mantova, Italy) and in a structured fine loamy over sandy soil (Hapludalf located in Caviaga, Lodi, Italy), both cropped with maize. The models were compared on the basis of their reliability to predict soil water content, measured by TDR, at 10 depths over 2 years. We compared the three models on the basis of difference-based indexes (CRM and RMSE) and correlation statistics (r and EF): at three depths (0–0.15, −0.4 and −1.0 m), in terms of soil water content profile following a drainage process on bare soil and on soil water content over the whole soil profiles. Although water retention and hydraulic conductivity curves were properly measured in laboratory on undisturbed soil samples, all three models required calibration and validation to obtain good quality simulations. The performances of the three models were quite similar: the average of all (models, sites and depths) root mean square error (RMSE) was 0.032 cm3 cm−3 (±0.007). Generally, SWAP had the best performance especially in simulating surface infiltration and drying processes, followed by CropSyst and then MACRO. The better performance of SWAP respect the other two models seemed rely on the hydraulic properties parameterization (van Genuchten-Mualem vs. Campbell equation), and to the different techniques used for the numerical solutions of Richards’ equation close to the bottom and upper boundaries. Moreover, despite its rather good performance, CropSyst, due to its internal numerical constraints in the parameterization of the retention and conductivity functions, needed a very strong calibration then loosing part of its “physical basis” towards an increasing of its empiricism.

The FAO framework for Land Evaluation (LE) has been the primary procedure used worldwide to address local, regional, and national land use planning. Despite its widespread and long-term application, the process has been criticised by the scientific community for its qualitative and empirical base, which is not effective to address many new agro-environmental challenges where the dynamic characterisation of the interrelated physical and chemical processes taking place in the soil landscape is a must. In today's environment, the LE expert is asked to choose the best LE methodology considering costs, complexity of the procedure and benefits in handling a specific land evaluation. Unfortunately very little scientific literature supports this choice especially in terms of the comparison of different LE approaches. In this scenario, we performed a forage maize land suitability study by comparing different methods having increasing complexity and costs. The study area was comprised of approximately 2000 ha and was located on the Lodi Plain (Po Valley) of North Italy. The primary land use in the region is forage maize and this study was designed to assess forage maize biomass. Methods were developed and applied to compare the efficacy of procedures of increasing complexity. The range of the 9 employed methods ranged from standard LE approaches to the extensive use of simulation modelling (SWAP and CropSyst), using as data input pre-existing soil information (soil map 1:50,000) and also hydraulic properties measured as well estimated by PTF(pedotransfer functions). The different methods were compared based on both predictive ability and cost. Independent estimates of forage maize biomass were obtained by locally tested remote sensing measurements, and predictive ability was estimated using statistical indexes including correlation, relative variance, and ANOVA to evaluate the mean differences in maize biomass. The level of expertise required to apply a specific methodology was a factor in the cost of the LE. In addition, an increase in method complexity corresponded to a higher quality/quantity of input parameters and as a consequence higher costs. Generally, more complex methods gave better results in terms of their predictive ability but this occurred in much discontinuous steps; this finding contradicts a simplified view on the better performance of more complex and mechanicistic methods. Methods operating on measurements resulted in increased performance relative to those operating on PTF. Furthermore, methods operating on “true” soils without aggregations (i.e. soil-mapping units) exhibited higher performance.