Correction: Stanojković-Sebić et al. Response of Arugula to Integrated Use of Biological, Inorganic, and Organic Fertilization. 2024, , 1334.

In the original publication [...

Response of Arugula to Integrated Use of Biological, Inorganic, and Organic Fertilization.

This study evaluated the effects of solely and integrated application of inorganic (NPK), commercial organic (NC), and biological (MIX, mixed strains and ) fertilizers on the chemical characteristics of arugula biomass and its yield, as well as changes in soil microbiological parameters after the experiment in relation to the control treatment (Ø). The experiment was performed in semi-controlled greenhouse conditions, in pots, from the 4th decade of March to the 2nd decade of September, in 2023, at three cutting times/swaths, during one agricultural season, with Vertisol soil. For soil characterization, the following parameters were analysed: granulometric composition using sieving and sedimentation procedure; soil acidity-potentiometrically; SOM-soil organic matter by Kotzmann method; total N using CNS analyser; available P-spectrophotometrically; available K-flame photometrically; total number of microorganisms on an agarized soil extract medium; fungi on a solid Czapek agar; actinomycetes on a solid Krasiljnikov agar with saccharose; spp.

Case Study upon Foliar Application of Biofertilizers Affecting Microbial Biomass and Enzyme Activity in Soil and Yield Related Properties of Maize and Wheat Grains.

This study evaluated the effects of the application of microbial inoculants (N-fixing and 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.

Comparison of Groundwater Level Models Based on Artificial Neural Networks and ANFIS.

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.