Kristóf Kozma-Bognár

Since grain sorghum ( Sorghum bicolor L. Moench ) is gaining recognition as a climate‐resilient alternative to maize in Central Europe, optimizing the cultivation technology of commercial hybrids has become a key area of research. A 2‐year study (2023–2024) was conducted in Keszthely, Hungary to assess the plant‐ and plot‐level responses of four grain sorghum hybrids of different maturity groups (C1: KWS Nemesis, C2: RTG Huggo, C3: GK Erzsében, C4: ES Foehn) to combinations of seeding rate (SR240–SR280: 240,000, 280,000 seeds ha ⁻¹ ) and nitrogen treatments (N0–N200: 0, 40, 80, 120, 160, 200 kg N ha ⁻¹ ). N optimums were determined by a novel approach using simplified N balance and relative grain yield loss (RYL) curves. Early‐season precipitation was found to be essential for successful sorghum production, as its absence in 2023 severely reduced the effectiveness of pre‐emergent weed control. Increasing the seeding rate from 240,000 to 280,000 plants ha ⁻¹ induced stem elongation, biomass loss, and reductions in tillering; however, yield gains from the increased plant number compensated for these adverse morphological changes. The optimal N rate varied widely among years, cultivars, and treatment combinations (40–160 kg N ha ⁻¹ ), but the highest yields were achieved under weed‐free conditions (in 2024) at a seeding rate of 280,000 plants ha ⁻¹ and N application rates of 120–160 kg N ha ⁻¹ . Mid‐early and medium‐maturing hybrids consistently outperformed the early maturing one; therefore, they should be prioritized under similar environmental conditions. Knowledge of optimal treatment combinations may assist producers in enhancing grain sorghum production across Central Europe.

Climate change is expected to increase the frequency and severity of drought events in Europe, necessitating the identification of more water-efficient cropping systems. This study compared the evapotranspiration dynamics, water-use efficiency, and yield performance of maize (Zea mays L.) and grain sorghum (Sorghum bicolor L. Moench) under controlled field conditions using a Thornthwaite–Mather-type compensation evapotranspirometer. Three water regimes (100%, 50%, and 30% of optimal water supply) were applied during the reproductive stage, combined with weed-free and weed-infested treatments. Under moderate water deficit (50% water supply), grain sorghum maintained stable grain yield, while maize grain yield decreased by 17.98%. Under severe water deficit (30% water supply), grain yield reductions reached 36.04% in maize and 42.80% in sorghum. Grain sorghum consistently required less water and used 2.87–38.17% less water to produce 1 kg of grain compared to maize across treatments. Weed interference was associated with a lower yield and water-use efficiency in both species, while severe water deficit (70%) caused substantial declines in all measured parameters. Evapotranspiration was primarily driven by solar radiation and temperature, with reduced sensitivity under increasing water limitation. Overall, the results suggest that grain sorghum may represent a viable alternative to maize under moderate drought conditions; however, both crops require supplemental irrigation under severe water scarcity. The study highlights the importance of integrated weed management and provides novel insights into crop water-use dynamics under combined abiotic and biotic stress conditions.

Due to the growing risk for water shortage during the summer season, grain sorghum has become a popular alternative to maize in most drought‐prone areas of Hungary. A three‐factorial model experiment was carried out between 28 May 2024 and 11 September 2024 in Keszthely (Hungary) in order to compare the responses of P8834 maize hybrid and RTG Huggo grain sorghum hybrid to weed infestation and different levels of reproductive stage water‐deficit stress (50% and 30% water supply relative to the individual controls' water consumption). The treatments were set in a Thornthwaite‐Mather type compensation evapotranspirometer, so water consumption could be controlled and monitored. Based on the results, weed interference affected all phenological and yield attributes of both species negatively, but the interaction of weed infestation and water treatment did not enhance relative grain yield loss neither in maize (p = 0.851) nor in grain sorghum (p = 0.28). Since temperature conditions during the reproductive stage were more favourable for grain sorghum, the general yield surplus for sorghum over maize (p < 0.001) was attributed to sorghum's better tolerance to temperature stress. The results also revealed that up to 50% water stress level, the yield stability and water use efficiency of grain sorghum were also better than those of maize, both in weed‐free and weed‐infested treatments. In conclusion, grain sorghum should be recommended as an alternative to maize in areas that are affected by extreme heat events and moderate water scarcity.

This research aimed to present the database construction process using some plant species found in the sample area. In addition to aerial photography, the characteristic plant species were determined through fieldwork, and their coordinates were recorded with the iPoint software. In the course of the research, DJI Phantom 4 Pro and Mavic Mini drones were used to take aerial photographs of Zimányi-island in Kis-Balaton. Several photographs were taken of the individual plants for identification and assessment of association characteristics, thereby broadening the spectrum of processed data. The aerial photos obtained during the recordings were initially processed with Agisoft PhotoScan 1.4.3 (Metashape after version 1.5), then for comparison with Agisoft Metashape 2.0.3 and Agisoft Cloud software. By combining the aerial photos, a high-resolution TIFF orthophoto was obtained for the entire area. After that, the visual data sets required for the 3D model-based vegetation map were created, pre-processed and processed. The integration of visual and non-visual data sets were integrated into the GIS system. In the research area, non-image data were also collected, which were entered into Microsoft Office Excel tables. QGIS software was used to insert these tables into the database. The completed project shows the coordinates of the 27 plant species recorded in 2019 and the 28 plant species recorded in 2020. The process of querying the developed database was also presented. Finally, a comparative analysis of two different versions of the software was presented and used for matching in the past period. The method can be widely used in precision agriculture as well, such as weed detection, which is pivotal to increasing crop yields.