The aim of the research was to examine the influence of different light treatments on the growth, phytochemicals and antioxidant potential of broccoli microgreens. Plants were grown in a growth chamber under LED (light-emitting diode) cold whi...

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.

This experimental trial was conducted during the 2022/23 growing season in the Bresje village area near Velika Plana, on anthropogenized soil, using a randomized block design with four replications. Sowing was performed in two date: the first in mid-May and the second in mid-July. Three sweet corn hybrids (Enterprise F1 , Union F1 , and Sweet Nugget F1 ) were evaluated. Two irrigation norms were applied: I - full irrigation (100%), II - 50% of full irrigation, and a control treatment under natural moisture conditions. The objective of this study was to examine the effects of sowing date, irrigation, and different sweet corn hybrids on total ear weight, kernel weight and shelling percentage. The best results were achieved by the hybrid Enterprise in the second sowing period, with significantly higher values than in the first period (total ear weight 392.47 g and kernel weight 272.13 g) under full irrigation, except for the shelling percentage, where the highest shelling percentage was observed in the 50% irrigation treatment at 70.44%. The control variants in both sowing periods across all three hybrids showed the lowest values for all parameters. Values shelling percentage did not significantly differ between sowing dates for any hybrid. Based on these findings, the hybrid Enterprise is an excellent choice for sweet corn cultivation in late sowing dates under full irrigation.

The aim of the research is to analyze the impact of the value of the production of permanent crops (fruit and viticulture sector) on the realized gross value added (GVA) in agriculture in Serbia and the EU from 2012 to 2022. The results were obtained by applying a multiple regression model where the dependent variable is the GVA in agriculture, and the independent variables represent the production values of the fruit and viticulture sector (in EUR mln). The coefficients of the model were tested using the t-test, and the model was verified using the F-test at a significance level of 0.05. The value of the standardized beta coefficient shows that the fruit-growing sector had a greater influence on the realized GVA of agriculture in Serbia and the EU (0.532 vs. 0.852), the t-values for Serbia belonged to the critical area in both sectors, while the t-values for the viticulture sector in the EU did not belong to the critical area. The F-test values show that the fitted model was significant at the 0.05 level for both observation areas. An analysis of the presence of multicollinearity in the independent variables was also conducted, and the results showed that there was a weak multicollinearity originating from the value of viticulture production.

Soybean (Glycine max L. Merr.) and winter wheat (Triticum aestivum L.) are among the key agricultural crops for which germination and early growth are crucial for achieving high yields. In this study, the biostimulant potential of Angelica archangelica hydrolate, a by-product obtained during essential oil distillation, was evaluated on soybean and winter wheat seeds. Seeds were treated with hydrolate solutions at different concentrations (0.25%, 0.5%, 1%, 5%, and 10%), and germination tests were conducted under controlled laboratory conditions. The results show that hydrolate treatments significantly increased seedling length in both crops compared to the control. The highest growth was recorded at the 10% concentration for both soybean (5.77 cm) and wheat (3.13 cm). Lower concentrations also improved growth, though with less pronounced effects. These findings confirm that A. archangelica hydrolate positively influences germination and early seedling development, suggesting its potential use as a natural biostimulant in sustainable agriculture. Since hydrolates are environmentally safe and underexplored in agricultural practice, further research under field conditions is necessary to optimize their application and fully exploit their potential in promoting plant growth.