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Evaluation of the yield and quality of maize hybrids (Zea mays L.) with different genotypes
Views:24The nutritional values of maize – protein, starch and oil content – play a decisive role in its use in industry, animal feed and the food industry. The development of these nutritional characteristics is influenced by numerous factors, including genetic background, crop year, agronomic factors and the ecological environment. By selecting the appropriate hybrid and applying appropriate cultivation techniques, these nutritional parameters can be tailored to different objectives, thereby ensuring economical and sustainable use. Precision technologies enable sustainable, climate-responsive farming and help farmers make faster, real-time, data-driven decisions. In these long-term field trials, smart machinery and tools are used and smart irrigation management ensures an optimal water supply, whilst sensor data supports the reliability of smart decisions. The analyses were aimed at reliably assessing the yield and yield quality of maize hybrids with different genotypes and growth periods (FAO 300, 400, 480) across two different growing seasons. In 2024 and 2025, in an experiment designed to assess field yield potential, the maize hybrids achieved varying but outstanding yields. Based on an analysis of the effects of the growing seasons, it was found that the average yield achieved in the more favourable year of 2024 (19.96 t/ha) significantly exceeded the yield achieved in 2025 (18.44 t/ha) by 1.52 tonnes per hectare. The genotypes studied achieved different yield and nutritional composition results in the years under investigation (2024, 2025). The greatest difference was observed in the FAO 300 hybrid, where, in the favourable growing season (2024), the yield was reliably 3.27 tonnes per hectare higher. The FAO 400 hybrid demonstrates good adaptability; its yield was reliably 1.07 tonnes per hectare higher in 2024 compared with the 2025 result. The FAO 480 hybrid produced an excellent yield, but this did not differ significantly between the two years studied. From a feed perspective, the nutritional values of maize kernels are very important. The starch content of the maize hybrids of different genotypes and maturity stages under investigation was very stable across the two crop years examined (62.18–63.42%). In the favourable growing season (2024), the starch content of the FAO 480 hybrid was favourable (63.15%), but did not differ significantly from that of the shorter-maturing hybrids. In the more favourable growing season (2025), a significant difference was observed only between the starch content of the FAO 300 and FAO 480 hybrids. The starch content of the FAO 300 hybrid was 1.24% higher than that of the FAO 480 hybrid. Protein content is a key characteristic of maize genotypes, and crop years also have a significant effect on it. Analysing the values for the two different crop years examined, it was found that the crop year has a significant effect on the protein content of maize kernels. The oil content of maize kernels is important in animal feed, but is of particular value in industrial processing. The oil content, averaged across the maize hybrids, showed a significant difference between the two crop years.
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Effects of solar radiation and night temperature on potential maize yield in two different crop years
Views:310Hungary's climate is undergoing change, and the heat unit (GDD) values have increased annually in the past nearly 50 years. In this study, we evaluate the performance of a maize hybrid in normal (2021) and drought (2022) crop years, along with the optimal agrotechnical factors (drip irrigation and high nutrient). In the potential experiment, we obtained a yield of 20.65t/ha in 2021 and 13.8t/ha in 2022. We examined the reasons for the large (33%) yield difference between the two years. By breaking down the weather data daily, it can be determined that the solar radiation (SR) and sunshine duration during the V6-V8 stage have an effect, and cloud cover affects the development of the reproductive organs of maize (ear differentiation). In the two years studied, we measured a significant difference in the SR value in the V6-V12 development stages (36% and 30% less SR was measured in 2022 vs 2021). The higher temperature (R1-R6) (2022) accelerated the phenological development of maize, so maize reached the black layer formation faster. The results indicate that in the future, we must also address the responses of maize to temperature changes with different levels of solar radiation and their dry matter incorporation dynamics.
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Stimulating maize growth under different water regimes through foliar application of micronutrients
Views:32Drought and nutrient stress constrain maize growth, production and productivity. Precision agricultural tools such as foliar fertilisation and irrigation enhance maize growth through mitigation of stresses at different growth stages. The study conducted at Látókép Crop Production Experimental Site, University of Debrecen, Hungary during year 2023, assessed the physiological and growth performance of two maize hybrids exposed to foliar fertilisation under precision drip irrigation and non-irrigated conditions. Foliar fertilizers composed of; nitrogen (10 g/l), zinc (8 g/l), K2O (8.5 g/l), P2O5 (0.83 g/l), and S (8.93 g/l). Data on plant height, leaf area index (LAI), normalized difference vegetation index (NDVI), and relative chlorophyll content (SPAD) were collected at the V12, R1, R4, and R6 analysed using a T-test in Genstat software (12th edition). Results showed significant increases in all parameters at the V12, R1, and R4, but NDVI, LAI, and SPAD declined at R6 under both irrigation and non-irrigated conditions. Foliar fertilisation of the two maize hybrids under precision drip irrigation conditions showed that NDVI and plant height had a significant difference (p < 0.05) while no significance difference was noted for SPAD and leaf area index. The percentage LAI due to foliar fertilisation under irrigated conditions was 16.86% and 18.39% for FAO490 and FAO290 respectively however the effect was slightly higher for FAO290 than FAO490. FAO490 and FAO290 hybrids recorded a 23.45% and 10.05% foliar fertilisation effect respectively over control under non-irrigated conditions. FAO490 hybrid consistently performed better under irrigated conditions compared to FAO290 which performed better under non-irrigated conditions. FAO490 hybrid consistently performed better under irrigated conditions compared to FAO290 which performed better under non-irrigated conditions.
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Evaluating relationships between multi-depth EM38-derived soil electrical conductivity and maize yield under contrasting tillage systems in irrigated conditions
Views:30Soil spatial variability influences crop productivity and is increasingly assessed using electromagnetic induction technologies. This study investigated the spatial and temporal variability of EM38-derived apparent soil electrical conductivity at 0.5 m and 1.0 m depths and its relationship with maize (Zea mays L.) grain yield under contrasting tillage systems in an irrigated production environment in eastern Hungary during the 2024 and 2025 growing seasons. Conductivity measurements were collected during vegetative and cob formation stages using an EM38 sensor operated in vertical dipole mode. Relationships between ECa and maize yield were examined using descriptive statistics, correlation analysis, and analysis of variance. Significant differences in maize yield were observed among tillage systems in both seasons, with strip tillage producing lower yields than winter ploughing and ripping tillage (p < 0.05). Strong positive correlations were found between conductivity measurements at 0.5 m and 1.0 m depths (r = 0.807–0.928, p < 0.001), indicating consistent spatial conductivity patterns across the soil profile. However, ECa was not significantly correlated with final maize grain yield in either season. These results demonstrate that EM38 sensing effectively characterises soil spatial variability but has limited value as a stand-alone predictor of maize yield under uniformly irrigated conditions.
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The effect of soil tillage and nutrient supply on maize yield based on multi-year experimental results
Views:217To optimize maize (Zea mays) yield, soil tillage and nutrient supply play a key role. The application of appropriate soil tillage techniques and the precise application of nutrients can contribute to increasing yield, maintaining plant health, and developing sustainable agricultural practices. The aim of the study was to analyse the long-term yield performance of maize hybrids under different nutrient supply levels and basic tillage methods. According to the repeated measurement model, soil tillage, fertilization, and crop year had a significant (p<0.001) effect on maize yield. The integrated approach allows for the optimization of yield and the development of sustainable agricultural practices. Reduced soil tillage methods reduce soil erosion and improve soil biological activity.
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Identifying maize yield drivers using statistical analysis and multilayer perceptron modelling
Views:22Maize yield formation is jointly determined by crop-year conditions, water availability, nutrient management, and their interactions, which may include complex nonlinear relationships. This study aimed to identify the principal agronomic and physiological variables associated with maize yield using classical statistical analyses complemented by multilayer perceptron modelling. Field data collected in Debrecen, Hungary, during 2024–2025 included crop year, irrigation, fertilizer treatment, phenological stage, SPAD chlorophyll readings, and grain yield. Pearson correlation and linear regression quantified individual relationships, while four MLP scenarios evaluated combined predictive effects. Fertilizer was positively associated with yield in both years and irrigation regimes, with the strongest relationship under irrigation in 2025 (r = 0.770; R² = 0.594). The SPAD–yield relationship generally strengthened during crop development, reaching its maximum at R3 under irrigation in 2025 (r = 0.916; R² = 0.839). The best-performing MLP scenario included year, fertilizer, and irrigation, confirming their central role in yield prediction and formation.
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Precision maize stand analysis using remote sensing methods: plant density measurement with spectral data integration
Views:212The aim of this study was to use remote sensing with a drone equipped with a multispectral camera to take a stand survey of maize after the phenological stage of emergence, and to count the number of emerged plants and determine its accuracy. Our investigations were carried out at the University of Debrecen, Látókép Production Experimental Station in a sowing date long-term experiment. In the 2024 growing season, Sowing Date I was on 4 April and Sowing Date II on 12 April. The same maize hybrids with 8-8 different genotypes were used for each sowing date. There is a strong correlation between number of plants/plot and number of plants/rowx2 for the two plant density measurements presented in this paper, with an r value of 0.977*** (p < 0.001). Among the plant density and NDVI values, the correlation between number of plants/rowx2 at the second measurement time (July 4) was significant at r=-0.418***. The analysis of the relationship between number of rows and yield showed that the hybrids included in the study compensated well for differences in number of rows due to sowing or emergence and this did not translate into an increase or decrease in yield. By using the plant density count method and results to identify emergence imbalances, farmers can correct their crop stand management strategies in a timely manner. Knowing the exact number of plants can also be important for subsequent agrotechnical decisions.
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Predicting maize yield with a multilayer perceptron (MLP) model using multivariate field data
Views:215This study presents the findings of a multi-year maize field trial conducted on experimental plots between 2017 and 2019, focusing on the application of machine learning techniques to enhance yield prediction accuracy. A multilayer perceptron (MLP) neural network was employed to model the effects of agronomic treatments, environmental variation, and compositional traits. Six distinct modeling scenarios were developed to explore different combinations of input variables, with the grain yield of maize serving as the sole output parameter. These scenarios range from treatment-only models to those incorporating detailed quality and compositional data. The primary objective was to evaluate how well MLP models can capture the complex, nonlinear relationships influencing yield under varying conditions. The findings provide valuable insight into the role of machine learning in supporting decision-making for sustainable crop production, especially under diverse technological and environmental settings. The approach demonstrated here offers a foundation for more adaptable, data-driven strategies in agronomic optimization.