Glyphosate is one of the most widely used herbicides, but is still in the spotlight due to its controversial impact on the environment and human health. The main purpose of this study was to explore the effects of different glyphosate usages on harvested grain/seed contamination. Two field experiments of different glyphosate usage were carried out in Central Lithuania during 2015–2021. The first experiment was a pre-harvest application, with two timings, the first according to the label (14–10 days), and the other applied 4–2 days before harvest (off-label), performed in winter wheat and spring barley in 2015 and 2016. The second experiment consisted of glyphosate applications at label rate (1.44 kg ha⁻¹) and double dose rate (2.88 kg ha⁻¹) at two application timings (pre-emergence of crop and at pre-harvest), conducted in spring wheat and spring oilseed rape in 2019–2021. The results suggest that pre-emergence application at both dose rates did not affect the harvested spring wheat grain or spring oilseed rape seeds—no residues were found. The use of glyphosate at pre-harvest, despite the dosage and application timing, led to glyphosate’s, as well as its metabolite, aminomethosphonic acid’s, occurrence in grain/seeds, but the amounts did not reach the maximum residue levels according to Regulation (EC) No. 293/2013. The grain storage test showed that glyphosate residues remain in grain/seeds at steady concentrations for longer than one year. A one year study of glyphosate distribution within main and secondary products showed that glyphosate residues were mainly concentrated in wheat bran and oilseed rape meal, while no residues found in cold-pressed oil and wheat white flour, when glyphosate used at pre-harvest at the label rate.

The accurate, timely, and non-destructive estimation of maize total-above ground biomass (TAB) and theoretical biochemical methane potential (TBMP) under different phenological stages is a substantial part of agricultural remote sensing. The assimilation of UAV and machine learning (ML) data may be successfully applied in predicting maize TAB and TBMP; however, in the Nordic-Baltic region, these technologies are not fully exploited. Therefore, in this study, during the maize growing period, we tracked unmanned aerial vehicle (UAV) based multispectral bands (blue, red, green, red edge, and infrared) at the main phenological stages. In the next step, we calculated UAV-based vegetation indices, which were combined with field measurements and different ML models, including generalized linear, random forest, as well as support vector machines. The results showed that the best ML predictions were obtained during the maize blister (R2)–Dough (R4) growth period when the prediction models managed to explain 88–95% of TAB and 88–97% TBMP variation. However, for the practical usage of farmers, the earliest suitable timing for adequate TAB and TBMP prediction in the Nordic-Baltic area is stage V7–V10. We conclude that UAV techniques in combination with ML models were successfully applied for maize TAB and TBMP estimation, but similar research should be continued for further improvements.

Five different tillage systems were researched in a Cambisol of a loam texture in the long-term experiment: conventional ploughing at 22–24 cm (CT), shallow ploughing at 16–18 cm (ShT), harrowing at 8–10 cm (MT1), harrowing at 14–16 cm (MT2), and no tilling (NT). The aim of this study was to determine how different tillage and inter-cropping influence the accumulation and distribution of SOC (soil organic carbon) and its compounds in different soil layers. SOC content changed depending on the soil tillage system and inter-crops used. Stratification ratios (SR) of SOC in the surface soil (0–10 cm) to that in the 10–20 cm (SR1) and 20–30 cm (SR2) were calculated. In our research, SR for SOC varied in the range from 0.97 to 1.35 for SR1 and from 1.02 to 1.99 for SR2. The main conclusion was that inter-crops increased the SOC accumulation in the 0–10 cm layer of all investigated treatments. It was concluded that different soil tillage systems and inter-crops influenced processes of soil carbon changes and affected OM humification in the soil. The formation of humified carbon compounds should be considered not only as a preservation and improvement of the soil productivity, but also as an environmental assessment of their impact on the soil sustainability and reduction in carbon dioxide emissions into the atmosphere. Our results suggest that sustainable tillage and inter-cropping management may contribute to climate mitigation regarding SOC accumulation in soil.

Relationships between different microorganisms’ groups and the soil environment are reversible, and the state of the soil and its provided services can also change the structure and abundance of microorganisms as well as that microorganisms can affect soil conditions. The aim of our research was to analyze the physical and chemical properties of differently formed agroecosystems, which are affected by different anthropogenic pressures and to compare how bacterial composition differ in totally different environments. It was established that different soil microorganisms’ physiological groups significantly correlated with chemical and physical soil properties: atmospheric nitrogen-fixing bacteria showed a positive correlation with soil pH_KCl, N_sum, P₂O₅, and soil bulk density; meanwhile, soil porosity, and the K₂O amount in the soil negatively affected the population of atmospheric nitrogen-fixing bacteria. The same tendencies were inherent to actinomycetes and ammonifying bacteria. Micromycetes showed a negative trend with soil pH_KCl, showing that soils with lower pH_KCl are characterized by a higher abundance of micromycetes. Analysis of the taxonomic diversity of soil microbes reveals that the bacterial communities were dominated by two main species of bacteria: Betaproteobacterium and Candidatus Saccharibacteria. Bacterial identification shows that the main bacterial species were the same in all analyzed sampling places despite the different anthropogenic activities, parent material, and other abiotic conditions. Only a few species were identified in different soil groups, and it may be assumed that those groups could be potential bioindicators for specific soil types, but more in depth research is needed to confirm this hypothesis.