Topography-induced microclimate differences determine the local spatial variation of soil characteristics as topographic factors may play the most essential role in changing the climatic pattern. The aim of this study was to investigate the spatial distribution of soil organic carbon (SOC) with respect to the slope gradient and aspect, and to quantify their influence on SOC within different land use/cover classes. The study area is the Region of Niš in Serbia, which is characterized by complex topography with large variability in the spatial distribution of SOC. Soil samples at 0–30 cm and 30–60 cm were collected from different slope gradients and aspects in each of the three land use/cover classes. The results showed that the slope aspect significantly influenced the spatial distribution of SOC in the forest and vineyard soils, where N- and NW-facing soils had the highest level of organic carbon in the topsoil. There were no similar patterns in the uncultivated land. No significant differences were found in the subsoil. Organic carbon content was higher in the topsoil, regardless of the slope of the terrain. The mean SOC content in forest land decreased with increasing slope, but the difference was not statistically significant. In vineyards and uncultivated land, the SOC content was not predominantly determined by the slope gradient. No significant variations across slope gradients were found for all observed soil properties, except for available phosphorus and potassium. A positive correlation was observed between SOC and total nitrogen, clay, silt, and available phosphorus and potassium, while a negative correlation with coarse sand was detected. The slope aspect in relation to different land use/cover classes could provide an important reference for land management strategies in light of sustainable development.

Soybean plant density is an important factor of successful agricultural production. Due to the high number of plants per unit area, early plant overlapping and eventual plant loss, the estimation of soybean plant density in the later stages of development should enable the determination of the final plant number and reflect the state of the harvest. In order to assess soybean plant density in a digital, nondestructive, and less intense way, analysis was performed on RGB images (containing three channels: RED, GREEN, and BLUE) taken with a UAV (Unmanned Aerial Vehicle) on 66 experimental plots in 2018, and 200 experimental plots in 2019. Mean values of the R, G, and B channels were extracted for each plot, then vegetation indices (VIs) were calculated and used as predictors for the machine learning model (MLM). The model was calibrated in 2018 and validated in 2019. For validation purposes, the predicted values for the 200 experimental plots were compared with the real number of plants per unit area (m²). Model validation resulted in the correlation coefficient—R = 0.87, mean absolute error (MAE) = 6.24, and root mean square error (RMSE) = 7.47. The results of the research indicate the possibility of using the MLM, based on simple values of VIs, for the prediction of plant density in agriculture without using human labor.

The genus Brassica is recognized for including species with phytoaccumulation potential and a large amount of research has been carried out in this area under a variety of conditions, from laboratory experiments to field trials, with spiked or naturally contaminated soils, using one- or multi-element contaminated soil, generating various and sometimes contradictory results with limited practical applications. To date, the actual field potential of Brassica species and the feasibility of a complete phytoextraction process have not been fully evaluated. Therefore, the aim of this study was to summarize the results of the experiments that have been performed with a view to analyzing real potentials and limitations. The reduced biomass and low metal mobility in the soil have been addressed by the development of chemically or biologically assisted phytoremediation technologies, the use of soil amendments, and the application of crop management strategies. Certain issues, such as the fate of harvested biomass or the performance of species in multi-metal-contaminated soils, remain to be solved by future research. Potential improvements to current experimental settings include testing species grown to full maturity, using a greater amount of soil in experiments, conducting more trials under real field conditions, developing improved crop management systems, and optimizing solutions for harvested biomass disposal.

We investigated the concentration of Aluminium (Al), Cobalt (Co), Chromium (Cr), Copper (Cu), Iron (Fe), Manganese (Mn), Nickel (Ni) and Zinc (Zn) in the root and aboveground organs of four halophyte species (Salicornia europaea, Suaeda maritima, Salsola soda and Halimione portulacoides), as well as in the soil from maritime and inland saline areas. The aim of our research was to evaluate the capability of some halophyte species to absorb different heavy metals and to detect differentiation of heavy metal accumulation within populations from inland and maritime saline areas. Generally, the plant roots had significantly higher concentrations of metals when compared to stems and leaves. Zinc was the only metal with concentrations significantly higher in the leaves than in the root and stem. Populations from maritime saline areas had higher trace root and stem metal concentrations than populations from inland saline areas. Excepting zinc, populations from inland saline areas had higher heavy metal concentrations in the leaves. The factors that affected metal accumulation by halophytes included the percentage of salt in the soil. We also discuss the potential use of these halophytes in phytoremediation.