The determination of cropland suitability is a major step for adapting to the increased food demands caused by population growth, climate change and environmental contamination. This study presents a novel cropland suitability assessment approach based on machine learning, which overcomes the limitations of the conventional GIS-based multicriteria analysis by increasing computational efficiency, accuracy and objectivity of the prediction. The suitability assessment method was developed and evaluated for soybean cultivation within two 50 × 50 km subsets located in the continental biogeoregion of Croatia, in the four-year period during 2017–2020. Two biophysical vegetation properties, leaf area index (LAI) and a fraction of absorbed photosynthetically active radiation (FAPAR), were utilized to train and test machine learning models. The data derived from a medium-resolution satellite mission PROBA-V were prime indicators of cropland suitability, having a high correlation to crop health, yield and biomass in previous studies. A variety of climate, soil, topography and vegetation covariates were used to establish a relationship with the training samples, with a total of 119 covariates being utilized per yearly suitability assessment. Random forest (RF) produced a superior prediction accuracy compared to support vector machine (SVM), having the mean overall accuracy of 76.6% to 68.1% for Subset A and 80.6% to 79.5% for Subset B. The 6.1% of the highly suitable FAO suitability class for soybean cultivation was determined on the sparsely utilized Subset A, while the intensively cultivated agricultural land produced only 1.5% of the same suitability class in Subset B. The applicability of the proposed method for other crop types adjusted by their respective vegetation periods, as well as the upgrade to high-resolution Sentinel-2 images, will be a subject of future research.

Soybean is regarded as one of the most produced crops in the world, presenting a source of high-quality protein for human and animal diets. The general objective of the study was to determine the optimal soybean land suitability and conduct its mapping based on the multicriteria analysis. The multicriteria analysis was based on Geographic Information System (GIS) and Analytic Hierarchy Process (AHP) integration, using Sentinel-2 multitemporal images for suitability validation. The study area covered Osijek-Baranja County, a 4155 km2 area located in eastern Croatia. Three criteria standardization methods (fuzzy, stepwise and linear) were evaluated for soybean land suitability calculation. The delineation of soybean land suitability classes was performed by k-means unsupervised classification. An independent accuracy assessment of calculated suitability values was performed by a novel approach with peak Normalized Difference Vegetation Index (NDVI) values, derived from four Sentinel-2 multispectral satellite images. Fuzzy standardization with the combination of soil and climate criteria produced the most accurate suitability values, having the top coefficient of determination of 0.8438. A total of 14.5% of the study area (602 km2) was determined as the most suitable class for soybean cultivation based on k-means classification results, while 64.3% resulted in some degree of suitability.

Soil texture is a vital criterion in most cropland suitability analyses, so an accurate method for the delineation of soil texture suitability zones is necessary. In this study, an automated method was developed and evaluated for the delineation of these zones for soybean cultivation. A total of 255 soil samples were collected in the Continental biogeoregion of Croatia. Three methods for interpolation of clay, silt and sand soil content were evaluated using the split-sample method in five independent random repetitions. An automated algorithm for soil texture classification based on the United States Department of Agriculture (USDA) in 12 classes was performed using Python script. Suitability classes for soybean cultivation per soil texture class were determined according to previous agronomic and soybean land suitability studies. Ordinary kriging produced the highest accuracy of tested interpolation methods for clay, silt and sand. Highly suitable soil texture classes for soybean cultivation, loam and clay loam, were detected in the northern part of the study area, covering 5.73% of the study area. The analysis of classification results per interpolation method indicated a necessity of the evaluation of interpolation methods as their performance depended on the normality and stationarity of input samples.

The limitation of deformation, the desired level of bearing capacity of the structural elements and the dissipation of the energy produced by an earthquake ensure sufficient bearing capacity, stiffness and ductility of structures. These requirements are met by buildings which are designed and constructed in accordance with the latest technical regulations for buildings exposed to the earthquake. Most of the existing buildings in the city of Osijek do not meet these requirements. It is possible to use a capacity spectrum method for the purpose of considering possible responses of such structures to earthquakes and their estimates of seismic vulnerability. Calculation of seismic vulnerability by this method is based on the expected behavior of the building, obtained by overlapping the demand curve and capacity curve. The performance point is the point at which the capacity curve intersects the reduced response curve, at which capacity and demand are equal. The values of spectral displacement obtained for the performance point of a specific building class are used as the input parameters for the fragility curve for different levels of damage. This method will be applied to the collected database of traditional unreinforced masonry buildings of the city Osijek in Croatia. The database is prepared and the main characteristics of the buildings are processed using geographic information system.

Red spicy pepper is traditionally considered as the fundamental ingredient for multiple authentic products of Eastern Croatia. The objectives of this study were to: (1) evaluate the optimal interpolation method necessary for modeling of criteria layers; (2) calculate the sustainability and vulnerability of red spicy pepper cultivation using hybrid Geographic Information System (GIS)-based multicriteria analysis with the analytical hierarchy process (AHP) method; (3) determine the suitability classes for red spicy pepper cultivation using K-means unsupervised classification. The inverse distance weighted interpolation method was selected as optimal as it produced higher accuracies than ordinary kriging and natural neighbour. Sustainability and vulnerability represented the positive and negative influences on red spicy pepper production. These values served as the input in the K-means unsupervised classification of four classes. Classes were ranked by the average of mean class sustainability and vulnerability values. Top two ranked classes, highest suitability and moderate-high suitability, produced suitability values of 3.618 and 3.477 out of a possible 4.000, respectively. These classes were considered as the most suitable for red spicy pepper cultivation, covering an area of 2167.5 ha (6.9% of the total study area). A suitability map for red spicy pepper cultivation was created as a basis for the establishment of red spicy pepper plantations.