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.

A methodology is presented for the quantitative assessment of soil biomass productivity at 100 m spatial resolution on a national scale. The traditional land evaluation approach—where crop yield is the dependent variable—was followed using measured yield and net primary productivity data derived from satellite images, together with digital soil and climate maps. In addition to characterizing of soil biomass productivity based on measured data, the weight of soil properties on productivity was also quantified to provide measured soil health and soil quality indicators as an information base for designing sustainable land management practices. To produce these results, we used only the Random Forest method for our calculations. The study considers high-input agriculture, which is predominant in the country. Biomass productivity indices for the main crops (wheat, maize and sunflowers) and general productivity indices were calculated for the whole agricultural area of Hungary. Results can be implemented in cadastral systems, in applied in agricultural and rural development programs. The assessment can be repeated for monitoring purposes to support general monitoring objectives as well as for reporting in relation to the United Nations Sustainable Development Goals. However, on the basis of the results, we also propose a method for periodically updating the assessment, which can also be used for monitoring biomass productivity in the context of climate change, land degradation and the development of cultivation technology.

Global climate change has discernible impacts on the quality of the landscapes of Hungary. Only a dynamic and spatially differentiated land evaluation methodology can properly reflect these changes. The provision level, rate oftransformation and spatial distribution of ecosystem services (ESs) are fundamental properties of landscapes and have to be integral parts of an up-to-date land evaluation. For agricultural land capability assessment soil fertility is a major supporting ES, directly associated with climate change through greenhouse gas emissions and carbon sequestration as regulationg services. Since for Hungary aridification is the most severe consequence of climate change, water-related ESs, such as water retention and storage on and below the surface as well as control of floods, water pollution and soil erosion, are of increasing importance. The productivity of agricultural crops is enhanced by more atmospheric CO2 but restricted by higher drought susceptibility. The value of floodplain landscapes, i.e. their agroecological, nature conservation, tourism (aesthetic) and other potentials, however, will be increasingly controlled by their water supply, which is characterized by hydrometeorological parameters. Case studies are presented for the estimation of the value of two water-related regulating ESs (water retention and groundwater recharge capacities) in the floodplains of the Kapos and Drava rivers, Southwest Hungary. It is predictable that in the future land evaluation techniques based on the FAO framework will be more dynamic and integrated with the monetary valuation of ESs. The latter task, however, still involves numerous methodological problems to solve.