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.

In the summer of 2005 and 2010, moss samples were collected from 72 sampling sites evenly distributed all over the territory of Republic of North Macedonia. Kjeldahl method was used to determine the nitrogen content in the samples. Descriptive statistics and distribution maps were prepared. Data obtained from these two surveys were compared, and additional comparison was done with data obtained from similar studies in the South-Eastern European countries and Finland as a clean area. The median value of N content in the samples collected in 2005 is 1.21%, varies from 0.70% to 1.54%, while the content of N in samples collected in 2010 ranges between 0.68% and 1.75% with the median value of 1.06%. High contents of N were found in the Northern and Central parts of the country mainly as a result of agricultural activities, industry and traffic.

With the emergence of machine learning methods during the past decade, alternatives to conventional geostatistical methods for soil mapping are becoming increasingly more sophisticated. To provide a complete overview of their performance, this study performed cost–benefit analysis of four soil mapping methods based on five criteria: accuracy, processing time, robustness, scalability and applicability. The evaluated methods were ordinary kriging (OK), regression kriging (RK), random forest (RF) and ensemble machine learning (EML) for the prediction of total soil carbon and nitrogen. The results of these mechanisms were objectively standardized using the linear scaling method, and their relative importance was quantified using the analytic hierarchy process (AHP). EML resulted in the highest cost–benefit score of the tested methods, with maximum values of accuracy, robustness and scalability, achieving a 55.6% higher score than the second-ranked RF method. The two geostatistical methods ranked last in the cost–benefit analysis. Despite that, OK could retain its place as the most frequent method for soil mapping in recent studies due to its widespread, user-friendly implementation in GIS software and its univariate character. Further improvement of machine learning methods with regards to computational efficiency could additionally improve their cost–benefit advantage and establish them as the universal standard for soil mapping.

A 4-year monitoring of bat fauna at Rudine wind farm in Croatia aimed to produce mitigation measures to minimize both the number of bat fatalities and power loss in energy production. During the first 2 years, a high number of carcasses was found from mid-July to the end of October, indicating the need for some mitigation strategy. Based on the results of carcass searches, meteorological data and bat activity monitored at a weather mast and four wind turbine nacelles, mitigation measures were proposed. During the next 2 years, wind turbine curtailment was implemented in the high collision risk period based on critical wind speed thresholds varying from 5.0 to 6.5 ms⁻¹. Estimation of a total number of bat fatalities was conducted with the GenEst software for each monitoring year. A 78% reduction in estimated number of fatalities was recorded indicating the effectiveness of implemented measures.

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.

Study on fresh collections and type material of Hamatocanthoscypha rotundispora showed that the original description is incomplete and that the variation range in the essential characters is too narrowly defined. In this paper we therefore provide a detailed description, including ascospore morphometry using length, width, length/width ratio and volume. Non-parametric statistics showed significant deviation of normal distribution and high variability of all morphometrical variables, suggesting that use of living material for ascospore morphometrical analysis is more reliable than that of a traditional approach. Geometric criteria for defining the simple spore shape regarding the length/width ratio, polarity and symmetry in straight symmetric spores are proposed. Refractive cytoplasmic globules are described as a new type of inclusions in living cells. Species ecology and biogeography is also discussed.