Evaluating maize genotypes under different conditions is important for identifying which genotypes combine stability with high yield potential. The aim of this study was to assess stability and the effect of the genotype–environment interaction (GEI) on the grain yield traits of four maize genotypes grown in field trials; one control trial without nitrogen, and three applying different levels of nitrogen (0, 70, 140, and 210 kg ha⁻¹, respectively). Across two growing seasons, both the phenotypic variability and GEI for yield traits over four maize genotypes (P0725, P9889, P9757 and P9074) grown in four different fertilization treatments were studied. The additive main effects and multiplicative interaction (AMMI) models were used to estimate the GEI. The results revealed that genotype and environmental effects, such as the GEI effect, significantly influenced yield, as well as revealing that maize genotypes responded differently to different conditions and fertilization measures. An analysis of the GEI using the IPCA (interaction principal components) analysis method showed the statistical significance of the first source of variation, IPCA1. As the main component, IPCA1 explained 74.6% of GEI variation in maize yield. Genotype G3, with a mean grain yield of 10.6 t ha⁻¹, was found to be the most stable and adaptable to all environments in both seasons, while genotype G1 was found to be unstable, following its specific adaptation to the environments.

The aim of this research was to study the impact of application technique and rate of liquid starter fertilizer applied with a novel device on the production of corn. Starter fertilizer was applied in the root system range of freshly germinated plants in the ‘belt’ and ‘point’ forms at different quantities (35, 50, 70, and 100 L ha⁻¹), which led to intensive plant growth in the initial stages of development. This adapted system was used for sowing and for application of the liquid starter fertilizer at the same time. The field trial was set up at two sites (two different land types), in the conditions of the natural water regime of the soil during the three vegetation seasons in the period 2016–2018. For this purpose, a prototype of the electronic device EUKU-01 was designed. The starter fertilizer was applied at 5 cm laterally from the row where the sowing was performed and 5 cm below the depth at which the corn seeds were sown. Data were statistically analyzed by two-factor analysis of variance, where the influence of mineral fertilizer treatment and the influence of liquid starter fertilizer treatment were observed as factors. The results showed that the optimal choice of the technique of liquid starter fertilizer application can result in fertilizer savings by 30% without reducing yield.

The occurrence of late spring frosts due to climate change causes great damage to plantation production worldwide. The main objective of the paper is to provide a comprehensive overview of the problem and to outline effective protective measures against late spring frosts. The nature of frost depends on regional, altitudinal, and geographic differences, but they all share a common problem: they remove heat, resulting in the freezing of new plant growth and flowers. Tissue freezing is affected by critical temperatures and the frost type, intensity, and duration. Protection against late spring frosts can be broadly divided into three categories: active, passive, and chemical measures. In the field of agricultural engineering, various techniques have been thoroughly researched, and their effectiveness has been confirmed by research. These include various sprinkler systems, different heating devices, and large-diameter fans. Conclusive findings are being made on the performance of these systems in sub-zero temperatures and their cost-effectiveness. Climate change increases the importance of protecting permanent crops from late spring frosts and requires advances in agricultural technology to meet changing production demands and challenges.

Agriculture and forestry are among the labor-intensive industries where knowledge about health and safety at work is never enough. In recent years, the risk of accidents in the workplace is in forestry at the top, as well as farming is very high. Most accidents happen with agricultural forestry and techniques. In R. Slovenia, circumstances in non-professional workers sector are especially very critical, because working in agriculture and forestry are not only involved farmers, but also everyone who has an interest, time and agricultural land or forest. However, the situation is not problematic only for individual forest and agricultural workers, but on the national level. The fact that the exact data of accidents in the non-professional workers sector are not exactly known demands detailed analyses about the causes of accidents.

This study aimed to evaluate the complementary potential of UAV-based and proximal multispectral sensing using the Plant-O-Meter (POM) sensor for the assessment of wheat grain yield and quality across different phenological stages and two growing seasons. The analysis was based on three vegetation indices, the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), and Normalized Difference Red Edge Index (NDRE), and included two morphologically distinct genotype groups, awned and awnless. The study included nine awned and nine awnless genotypes. Vegetation indices showed pronounced seasonal dynamics, with higher values during intensive vegetative development and a decline during later growth stages. The strongest relationship with grain yield was observed for GNDVI-A during the heading to beginning of flowering stage (BBCH 51–61) in Season I (r = 0.85), whereas in Season II, the strongest relationship was observed for NDRE-A during the flag leaf stage (BBCH 37–39) (r = 0.80). Awned genotypes generally showed stronger VI–yield relationships, while associations with grain quality parameters varied among genotype groups and seasons. Linear mixed-effects models showed that both VI-related effects and genotype variability contributed to the variation in grain yield and quality. For yield, marginal R² was 0.43 in Season I and 0.50 in Season II, while conditional R² was 0.78 and 0.64, respectively. For protein and wet gluten content, model performance was more variable, with marginal R² values ranging from 0.30 to 0.39 for protein and from 0.31 to 0.33 for wet gluten. Genotype-level LOGO cross-validation further indicated variation in model performance when genotypes not included in model development were evaluated. Overall, the results indicate that relationships between multispectral vegetation indices and wheat grain yield and quality depend on sensing method, phenological stage, genotype characteristics, and growing season. The findings provide an exploratory basis for the application of multispectral sensing in wheat phenotyping and assessment of grain yield and quality, while further validation across broader genetic and environmental conditions is required.