Different seed priming treatments are widely used in order to improve the nutritional status of wheat, as well as to improve its grain yield and yield- related traits. The present study aimed to evaluate the impact of seed priming with zinc oxide nanoparticles (ZnO NPs) on the yield related traits, such as, field emergence, plant height, spike length and grain yield per plant of four winter wheat genotypes (Triticum aestivum L.) during two vegetation seasons of 2018/2019 and 2019/2020. The seeds of each wheat genotypes were primed with different concentrations of ZnO NPs (0 mg L⁻¹, 10 mg L⁻¹, 100 mg L⁻¹ and 1000 mg L⁻¹) for 48 h in a dark box by continuous aeration and were sown in soil pots with 60–70% moisture content until full maturity. The additive main effects and multiplicative interaction (AMMI) models were used to study the genotype environment effects. The results indicated that the plants response to ZnO nanoparticles significantly increased all of the observed traits of the wheat, while its maximum rates reduced the traits of the wheat. The AMMI analysis revealed the very complex nature of the variation observed in the trial and showed the significant effect of the G×E interaction, in which the first main component was significant for all components.

Biodiesel and biogas are especially important sources of renewable energy in the world and in Serbia. Biodiesel is used as transportation fuel; biogas is used for production of electricity and heat. Soybean (Glycine max L.) grain is the primary source of vegetable protein for food and feed supplements, also accounts for much of the world?s bio-oil supply. Due to the development of new technologies for processing agricultural waste into energy, the rate of increase in the use of alternative fuels is significantly increasing. The aim of this study was to determine the productivity of soybeans and the possibility of obtaining biogas from soybeans in divergent years, 2017-2019. Main effects were monitored, genotypes (G), years (Y) and genotype by years interaction effects (G x Y) and were used for evaluation of soybean genotypes in different environments. Soybean varieties were tested for mass of 1000 grain (MTG), plant height (PH), grain yields (GY) and biogas yield (BY). Stability of grain and biogas yield was determined to select best genotype. Results showed that genotype and years had a significant impact on all measured variables. In all tested years, the biggest GY and BY were at genotypes G1 (2.40-4.49 t ha-1, and 461.00-641.00 m3 ha-1, respectively) and G3 (1.97-4.30 t ha-1; i.e. 447.67-620.00 m3 ha-1). Genotype G2 had statistically significantly lower values for all tested parameters compared to other tested genotypes. Correlation analysis of some chosen traits showed different interdependence between measured variables depending on the year conditions. The results of this study pointed out that among best genotypes for production of grain and biogas were G1 and G3. From the results of this study it can be concluded that G x Y trials are important for evaluation of stability and choosing the most stable genotypes of soybean.

The less productive soils present one of the major problems in wheat production. Because of unfavorable conditions, halomorphic soils could be intensively utilized using ameliorative measures and by selecting suitable stress tolerant wheat genotypes. This study examined the responses of ten winter wheat cultivars on stressful conditions of halomorphic soil, solonetz type in Banat, Serbia. The wheat genotypes were grown in field trails of control and treatments with two soil amelioration levels using phosphor gypsum, in amounts of 25 and 50 tha−1. Across two vegetation seasons, phenotypic variability and genotype by environment interaction (GEI) for yield traits of wheat were studied. The additive main effects and multiplicative interaction (AMMI) models were used to study the GEI. AMMI analyses revealed significant genotype and environmental effects, as well as GEI effect. Analysis of GEI using the IPCA (Interaction Principal Components) analysis showed a statistical significance of the first two main components, IPCA1 and IPCA2 for yield, which jointly explained 70% of GEI variation. First source of variation IPCA1 explained 41.15% of the GEI for the grain weight per plant and 78.54% for the harvest index. The results revealed that wheat genotypes responded differently to stressful conditions and ameliorative measures.

The challenges of the global food supply and environment conservation require ongoing scientific observations of soil-to-plant and plant-to-environment interactions with the aim of improving agriculture resource management. This study included observations of winter wheat yield and biomass of four varieties over three consecutive growing seasons and four site-year cases to assess the effects of nitrogen (N) fertilization rate and time of application on grain yield and biomass. For different wheat varieties, the full factorial design was performed, where factorial combinations of year, location, fall and spring N applications were laid out in a randomized complete block design. The N rate significantly influenced grain yield and biomass production efficiency. The time of N application had a highly significant effect on grain yield, biomass and NUE traits. The N rate of 120 kg ha⁻¹ was recognized as a breakpoint over which the grain yield and biomass showed a downtrend. N application in the fall had a significantly higher impact on grain yield and biomass compared to spring N application. The major contribution of wheat variability production belongs to seasonal climate circumstances (<85%) and consequential intrinsic soil properties. The average difference of grain yield between varieties was 15.75%, and 12% of biomass, respectively.

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.