Water table forecasting plays an important role in the management of groundwater resources in agricultural regions where there are drainage systems in river valleys. The results presented in this paper pertain to an area along the left bank of the Danube River, in the Province of Vojvodina, which is the northern part of Serbia. Two soft computing techniques were used in this research: an adaptive neurofuzzy inference system (ANFIS) and an artificial neural network (ANN) model for one‐month water table forecasts at several wells located at different distances from the river. The results suggest that both these techniques represent useful tools for modeling hydrological processes in agriculture, with similar computing and memory capabilities, such that they constitute an exceptionally good numerical framework for generating high‐quality models.

The assessment of the suitability and status of irrigation water quality from the aspect of its potential negative impact on soil salinization and mapping of spatial distribution within the area of the three Morava rivers, which includes the South, West, and Great Morava basins, was the purpose of this research. A total of 215 samples of irrigation water were tested, and their quality was evaluated based on the analysis of the following parameters: pH, electrical conductivity (EC), total dissolved salt (TDS), sodium adsorption ratio (SAR), and content of SO₄²⁻, Cl⁻, HCO₃⁻, CO₃ ²⁻, Mg²⁺, Ca²⁺, Na⁺, and K⁺. The results showed that the average content of ions was as follows: Ca²⁺ > Mg²⁺ > Na⁺ > K⁺ and HCO₃⁻ > SO₄²⁻ > Cl⁻ > CO₃²⁻. The assessment of irrigation water suitability was determined by calculating the following indices: percentage sodium (Na %), residual sodium carbonate (RSC), permeability index (PI), magnesium hazard (MH), potential salinity (PS), Kelley’s index (KI), total hardness (TH), irrigation water quality index (IWQI). Based on Wilcox’s diagram, the USSL diagram, and the Doneen chart, it was concluded that most of the samples were suitable for irrigation. Using multivariate statistical techniques and correlation matrices in combination with other hydrogeochemical tools such as Piper’s, Chadha’s, and Gibbs diagrams, the main factors associated with hydrogeochemical variability were identified.

This study evaluated the effects of the application of microbial inoculants (N-fixing Klebsiella planticola and Enterobacter spp.), two rates of composite mineral fertilizers, and their combination on microbial biomass carbon (MBC), dehydrogenase (DHA), and proteinase activity (PTA) in Lessivated Cambisol and yield-related properties of maize and wheat grains in a two-year trial. Unfertilized soil was used as a control variant. MBC was measured using the chloroform fumigation-extraction method, DHA was determined spectrophotometrically by measuring the intensity of the formed red-colored triphenyl formazan, while PTA was determined using a titration method by measuring the degree of gelatine decomposition. In grain samples, P was determined spectrophotometrically, K—by flame emission photometry, N—on an elemental carbon/nitrogen/sulfur (CNS) analyzer, and crude proteins—by calculation of N content. Measuring both crops’ yield was carried out at the end of the vegetation. The results indicated that mineral fertilizers are not, in general, negative for soil microbiota when used in the context of sustainable agriculture without monoculture. There is a significant increase in the values of soil MBC, DHA, and PTA in the variants with combined application of bacterial inoculants and lower rates of mineral fertilizers. The highest values of these parameters were determined in the period with a better distribution of precipitation during the vegetation period of the year. The mentioned combination also resulted in a higher grain yield of maize and wheat comparing to the application of lower rates of the NPK nutrients solely. The combined application of high rates of mineral fertilizers and bacterial inoculants resulted in significantly increased N, P, K, and protein content in the grains of crops, and the same applied to yield. Concluding, studied bacterial inoculants can be used to specify the replacement of nitrogen fertilizers, stimulating the microbial biomass and enzyme activity in the soil, helping to ensure that the supply of nutrients contributing to an optimized yield of crops is maintained.

For the majority of the world population medicinal plants represent the primary source of the health care. therefore determining the content of heavy metals, accumulated in medicinal plants, is of high importance. thus, the purpose of this research was to evaluate the content ofheavy metals and two metalloids in selected medicinal plants(Matricaria chamomilla L., Melissa officinalis L., Mentha piperita L. and Foeniculum vulgare Mill.), that are traditionally used in alternative medicine.The plant materials were collected in a highly industrialized town (Pančevo). Plant analyses were done according to ICP methodology. the obtained results show that the contents of the potentially toxic elements in the investigated medicinal plant species were below the recommended limits and those medicinal plants from the studied growing site are suitable for preparation of teas and medicinal extracts.

To select isolates suitable for use in biological control of pathogenic fungi and as plant growth promoting (PGP) agents, Pseudomonas spp., indigenous to the rhizosphere of wild alfalfa and clover plants growing on the old magmatic hill Vagan (Serbia), were isolated. Isolates were selected on the basis of intrinsic antibiotic resistance (IAR) and PGP traits: phosphate solubilization, production of siderophores, hydrogen cyanide (HCN), acyl homoserine lactones (AHLs), indole acetic acid (IAA) and enzymatic activities. The results of the genotyping by repetitive sequence-based polymerase chain reaction, using ERIC and (GTG)₅ primers, and phenotyping by IAR clustered the isolates in five groups, represented by two isolates from alfalfa (Q1 and Q16) and three from white clover (B25, B28 and B29). Isolates Q16 and B25 showed production of HCN, IAA and AHLs. Good phosphate solubilization and production of siderophores were observed on Q1 and Q16. The Q16 isolate exhibited high enzymatic activity. The most promising PGP isolates, Q16 and B25, showed the best antifungal activity against Trichoderma viride, and good antifungal effect against Aspergillus fumigatus and Aspergillus niger, while Penicillium verrucosum was the most resistant fungus. Pseudomonas sp. B25 exhibited higher antifungal potential than Q16. The selected isolates could be further investigated as biological control agents and tested in field conditions to confirm their PGP efficacy.

The bioavailability of trace elements (TEs) is one of the major factors for successful plant production and environmental protection. The aim of this study was to determine the extent to which TEs are bioavailable and which of the basic soil parameters affect bioavailability. The survey included agricultural soil samples taken from 240 locations on the territory of the Republic of Serbia, where the soil analytics were carried out. On the basis of the analyzed data the prediction models were derived based on the Freundlich model, showing the dependence between trace elements (TEs) extracted using the DTPA buffer solution in relation to the trace elements extracted using an aqua regia, the organic matter content (SOM), the clay fractions content, and soil pH. On one part of the samples, the prediction models were separated on the basis of a suspension for determining the pH in H₂O and 1M KCl. The model was applied for the following TEs: Mn, Ni, Pb, Zn, and Cu. The content of the pseudo total forms of TEs statistically significantly influenced the bioavailability of TEs in all prediction models for all studied elements. The pH value statistically significantly affected the bioavailability of Ni, Mn, Pb, and Cu also in all prediction models. The impact of SOM and clay varied depending on the model and TEs. Multiple linear regression showed that the prediction models for TE Cu (R² = 0.763–0.848) were the most reliable and that the bioavailability of Cu was significantly influenced by all the studied soil parameters except clay. Reliable results were also shown by the prediction models for TE Pb, but the values of the determination coefficient and investigated parameters that influenced the bioavailability varied depending on the model. The derived models for TE Mn, Zn, and Ni were less reliable (R² is approximately 50% or less), and the effect of the tested parameters on bioavailability varied depending on the model and TEs.