Buckwheat is an economically important gluten free cereal, and it is a good source of protein, fiber, and energy. The field experiments with buckwheat variety “Novosadska” were set in two experimental years in Pancevo, Serbia. The aim of this study was to determine the most optimal technology for buckwheat production. The study aims were to investigate the effects of (1) row spacing: RS₁-25 cm and RS₂-50 cm; (2) nutrition variants: (N₁) 0 kg ha⁻¹ NPK; (N₂) NPK 30 kg ha⁻¹; (N₃) NPK 60 kg ha⁻¹; and (N₄) NPK 90 kg ha⁻¹; (3) environment: Y₁-2018 and Y₂-2019; and their interactions on the plant height (PH), number of lateral branches (NoLB), mass of seeds per plant (MSP), number of seeds per plant (NoSP), buckwheat grain yield (GY); and (4) the correlations among these traits. All tested parameters of buckwheat significantly varied across row spacing, environment, and nutrition. The average grain yield in the row spacing, RD₁-25 cm, was significantly higher than in the wider spacing, RD₂-50 cm, and in 2018 than 2019 as well as with the nutrition NPK applied in the amount of 90 kg ha⁻¹ in relation with all the other nutrition variants. All the studied agronomic traits were positively and strongly correlated with GY.

The objective of this study was to estimate genotype by locality, by year, by treatments (G×LxYxT) interaction using AMMI model, to identify maize genotypes with stable number of rows of grains performance in different growing seasons. The trials conducted with seven maize lines/genotypes, four treatments, two years and at the two locations. The results showed that the influence of genotype (G), year (Y), locality (L), and G×L, G×T, G×L×T, G×Y×T, G×Y×L×T interaction on maize number of rows of grains were significant (p<0.01). The genotype share in the total phenotypic variance for the grains number rows of was 53.50%, and the interaction was 21.15%. The results also show that the sums of the squares of the first and second major components (PC1 and PC2) constitute 100% of the sum of the squares of the interaction G×L. The first PC1 axis belongs to all 100%, which points to the significance of the genotype in the total variation and significance of the genotype for overall interaction with other observed sources of variability. The highest stability in terms of expression of the grains number of rows had the genotype L-6, followed by the genotypes L-4, L-5 and L-3. The lowest stability was demonstrated by the genotypes L-2 and L-1, which confirmed that these genotypes are not important for further selection in terms of this trait.

Plant density, the number of plants per unit area, is an important factor in maize production. Plant density exhibits high variability and depends on a number of factors, i.e., the length of the growing period of the hybrid, the morphological characteristics of the plant, the amount and distribution of precipitation during the growing season, the reserve of winter moisture in the soil, the level of soil fertility, the time of sowing, agronomic management practices, and biomass and yield. The objective of this paper was to determine the agronomic optimal plant density for maize in calcareous soil in the semiarid conditions of the South Pannonian Basin. Field experiments were conducted at the experimental field—IFVCNS (two locations: Rimski Šančevi and Srbobran) to evaluate four plant densities (55,000; 65,000; 75,000; and 85,000 plants ha⁻¹). The experimental sites “Rimski Šančevi” and “Srbobran” are located in the typical chernozem zone of the southern part of the Pannonian Basin. On average for all hybrids, the grain yield followed a second-degree polynomial model in response to the increasing planting density, with the highest value at plant density (PD2: 65,000 plants ha⁻¹). To achieve maximum yield, the optimal planting density for corn hybrids of the FAO 200 group should be 57,600 plants ha⁻¹, for the FAO 300 group 64,300 plants ha⁻¹, for the FAO 400 group 68,700 plants ha⁻¹, for the FAO 500 group 66,800 plants ha⁻¹, and for the FAO 600 group 63,500 plants ha⁻¹. “Which–Won–Where” biplot showed that the hybrid H24 from FAO 600 group was the highest yielding in all of the environments. Hybrid H17 from the same FAO group was the most stable across all of the environments. Selected hybrids may further be studied for planting density and nutritional requirements for getting maximum yield. By introducing new maize hybrids with higher genetic yield potential and better agronomic management practices, modern mechanization and agricultural techniques allowed to increase planting densities.

In the face of increasing climate variability, understanding the dynamics of plant-to-plant interactions within crops is becoming increasingly important. This study aimed to examine plant responses to varying intensities of inter-plant competition, induced bz different planting densities, to enhance the accuracy of future yield prediction models. Six hybrids were grown at three planting densities (S1, S4, S7). Grain yield and yield components were estimated at four developmental points during grain filling (V1 to V4). These regression models and machine learning (ML) were applied to predict maize production under variable weather conditions. The factor year was the main source of variability, with less favourable conditions in the second year (G2) reducing yield by approximately 1–2%. Lower planting density (S1) improved individual plant development and yield components, while maximum density (S7) resulted in higher grain yield despite reduced individual performance. Hybrid H5 showed strong tolerance to high density, producing the highest yield under S7 conditions. Machine learning models accurately predicted key seed quality traits—moisture, oil, and protein—with performance metrics exceeding 80% accuracy. Specifically, R² values reached 0.82 for moisture content and 0.77 for oil concentration, indicating strong predictive capability. These findings support careful selection of hybrids and optimal planting density strategies in future cropping systems to increase yield and maintain seed quality in different environments.

Concern over the harmful impacts of heavy metal pollution in soil has increased dramatically on a global scale. For the sake of environmental preservation, accurate estimates of the heavy metal concentrations in soil are essential. This study provides valuable data regarding heavy metal concentrations in soil collected from field crops production area in Central and Western Serbia. Five wider localities in the zones of Central and Western Serbia were selected for the collection of soil samples. Based on our research, focused on determining the total contents of heavy metals in the soil and the degree of pollution in the environment caused by their behavior, distribution, and origin, it can be concluded that there is pronounced variability in relation to localities. Heavy metal contents were mostly within the same ranges as those in similar soils from Europe and around the world. Any pollution control system must include heavy metal monitoring, including the methodical collection of data on the concentrations of heavy metals in a particular environment. Before environmental degradation occurs, it is crucial to set pollution limits and implement efficient monitoring procedures.