Identification of stable sources for breeding for important agronomic traits is prerequisite for providing a continuous and long-term progress in breeding. In this study, thirty-one early-maturing genotypes from soybean collection of Maize Research Institute ?Zemun Polje? were evaluated across four environments (two years and two locations) according to randomized complete block design with three replications. The aim of research was to examine the value of the interaction of genotype and environment for three important agronomic traits (seed yield, protein content and oil content) using anadditive main effects and multiplicative interaction (AMMI) statistical model, and to identify stable sources for breeding for listed traits. The results of the research indicated that all traits were strongly influenced by environmental factors, while the influence of genotype and particularly interaction of genotype and environment was of less importance. AMMI analysis enabled identification of genotypes with above average value and high stability for seed yield (three genotypes), protein content (three genotypes) and oil content (two genotypes), which could be utilized as potential stable sources of variability in future soybean breeding programs.

The launch of a successful quality-oriented breeding program requires both mining the residual diversity in grain quality parameters contained in the elite, high-yielding breeding material with good agronomic performance and introgression of new germplasm, such as local landraces, with a high level of targeted quality parameters per se. This study analyzed the combining abilities of 31 maize landraces and two divergent inbred lines–testers (ZPL217 and ZPL-255/75-5) regarding the yield and protein, starch, and lipid content, assessed by Near Infrared Reflectance (NIR) spectroscopy as a fast, non-destructive, and cost-effective method. The general combining ability (GCA) defines the average behavior of genotype in hybrid combination, resulting from additive gene action, so positive GCA values of landraces AN13 and AN197 for protein, AN632 for lipids, and AN594 for starch content indicate that they can be valuable sources of the mentioned properties in quality-oriented maize breeding programs. The obtained correlation between starch content and protein and yield (−0.948 **; 0.587 **) pointed out that an increase in the protein content during breeding will be accompanied by a decrease in the starch content and yield. The specific combining ability (SCA) of the testers used, suggests their possible application in establishing and improving quality breeding programs’ initial material.

Considering the great potential of black soybean seed coat as a source of bioactive compounds, the objective of this study was to investigate the effect of anthocyanin-rich brine from the seed coat on functional properties of pickled baby corn, as well as its sensory properties. Given that the ears of sweet corn, popping corn and semi-flint corn were used for pickling in the pre-pollination phase, the effect of genotype and its growing stage on the chemical composition of Baby corn product was also taken into consideration. The brine of black soybean with a total anthocyanins content of 11,882.9 mg CGE/kg (cyanidin 3-glucoside equivalent) and an antioxidant capacity of 399.5 mmol Trolox Eq/kg determined by QUENCHER method had a positive impact on the functional potential of baby corn products. The content of total anthocyanins in the obtained products ranged from 748.6 to 881.2 mg CGE/kg, the predominant anthocyanin was cyanidin-3-glucoside (184.6 to 247.5 μg/g), while their colour was red. Compared to the commercial sample, baby corn products pickled in the enriched solution had a 26% to 46% and 17% to 26% higher content of total free phenolic compounds and antioxidant capacity, respectively. Contrarily, the control sample had higher sugar and fibre content. As established, pickled popping corn had the best sensory properties.

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.

Essential oils (EOs) are widely studied in agriculture. The study's objective was to examine the impact of EOs on alfalfa (Medicago sativa L.) seed dormancy. The four different varieties of alfalfa were used for the experiment (Zaječarka-83, Banatska-VS, K-28, Novosadska H-11). Two essential oils, lavender (Lavandula angustifolia Mill.) and peppermint (Mentha piperita L.) were applied at four concentrations:1%, 0.5%, 0.2%, and 0.02%, along with water as a control. Germination, dormant seeds and dead seeds were evaluated in a laboratory setting according to ISTA rules. The type of EOs had no discernible influence on germinated seeds, dormancy, and dead seeds. Both EOs in concentrations of 1% and 0.5% inhibited seed germination. The maximum germination of 91.66% was achieved with the Novosadska H-11 variety using lavender oil at a concentration of 0.02%, with reduced dormancy. Varieties Zaječarka-83 and Banatska-VS had the highest level of dead and dormant seeds when lavender and peppermint EOs were applied at a concentration of 0.2%. This study showed that both EOs at a concentration of 0.02% had a stimulatory effect on seed germination, simultaneously reducing seed dormancy, emphasizing their potential use for seed quality improvement in organic farming.

Colored grains are rich sources of anthocyanins that could play an important role in the prevention of various diseases associated with oxidative stress. Bearing in mind that cereals are widely grown crops, anthocyanins-rich colored grains could be used as a functional food ingredient that...