Maize yield forecasting is important for the organisation of harvesting and storage, for the estimation of the commodity base and for the provision of the country’s feed and food demand (export–import). To this end, a field experiment was conducted in dry (2021) and extreme dry (2022) years to track the development of the crop to determine the evolution of the relative chlorophyll content (SPAD) and leaf area index (LAI) for better yield estimation. The obtained results showed that SPAD and LAI decreased significantly under drought stress, and leaf senescence had already started in the early vegetative stage. The amount of top dressing applied at V6 and V12 phenophases did not increase yield due to the low amount of rainfall. The 120 kg N ha⁻¹ base fertiliser proved to be optimal. The suitability of SPAD and LAI for maize yield estimation was modelled by regression analysis. Results showed that the combined SPAD-LAI was suitable for yield prediction, and the correlation was strongest at the VT stage (R² = 0.762).

Nutrient stress has been known as the main limiting factor for maize growth and yield. Nitrapyrin, as a nitrification inhibitor—which reduces nitrogen loss—and foliar fertilizer treatments have been successfully used to enhance the efficiency of nutrient utilization, however, the impacts of these two technologies on physiological development, enzymatic responses, and productivity of maize are poorly studied. In this paper, the concentration of each stress indicator, such as contents of proline, malondialdehyde (MDA), relative chlorophyll, photosynthetic pigments, and the activity of superoxide dismutase (SOD) were measured in maize leaf tissues. In addition, biomass growth, as well as quantitative and qualitative parameters of yield production were examined. Results confirm the enhancing impact of nitrapyrin on the nitrogen use of maize. Furthermore, lower activity of proline, MDA, SOD, as well as higher photosynthetic activity were shown in maize with a more favorable nutrient supply due to nitrapyrin and foliar fertilizer treatments. The obtained findings draw attention to the future practical relevance of these technologies that can be implemented to enhance the physiological development and productivity of maize. However, this paper also highlights the importance of irrigation, as nutrient uptake from soil by the crops decreases during periods of drought.

The aim of this study was to determine the nitrogen requirement of maize, the optimal timing and amount of nutrient application, based on long time series data. An additional objective was to examine the response of the relative chlorophyll content of maize to nitrogen fertilisation. The examinations were carried out in a long-term field experiment at the University of Debrecen between 2016 and 2022, using two maize hybrids with different genotypes. Spatial and temporal changes in the N status of maize leaves were monitored using the Soil and Plant Analysis Development (SPAD) instrument. In addition to the non-fertilised (A₀) treatment, six fertiliser treatments were applied (spring basal fertilisation: 60 and 120 kg N ha⁻¹, A₆₀; A₁₂₀). Basal fertilisation was followed by two occasions of top-dressing at phenological stages V6 and V12, at rates of +30–30 kg N ha⁻¹ (V6₉₀ and V6₁₅₀, and V12₁₂₀ and V12₁₈₀). The CMR (Chlorophyll Meter Reading), averaged over the examined years, genotypes and fertiliser treatments, were lowest in the V6 phenological phase (40.23 ± 5.57, p < 0.05) and highest in R1 (49.91 ± 8.41, p < 0.05). A₁₂₀ fertiliser treatment increased the relative chlorophyll content by 5.11 compared to the non-fertilised treatment, 1.67 more than A60 treatment. The basal fertilisation treatment substantially increased the yield (A₆₀: +30.75%; A₁₂₀: +66.68%) compared to the A₀ treatment averaged over years and genotypes. Based on the obtained research results, a basal treatment of 120 kg N ha⁻¹ is recommended and it can be concluded that, under appropriate water supply conditions (rainfall, irrigation), nitrogen top-dressing applied in V6 phenophase results in a significant yield increase compared to basal fertilisation.

In 2021, correlation between relative chlorophyll content and yield in three maize hybrids of different genotypes was examined. The data were collected at the Látókép Experimental Station of the University of Debrecen located on the Hajdúság loess ridge in Hungary. The soil of the small plot field strip plot trial, which was set up in 2011, was calcareous chernozem. Apart from the control treatment (without fertilisation), N fertiliser is applied in the form of base and top dressing. The base fertiliser containing 60 and 120 kg ha-1 N of nutrient applied in spring was followed by top dressing containing +30–30 kg ha-1 N in V6 and V12 phenophases. SPAD values measured at different phenological stages of the growing season increased by an average of about 28% up to 10 leaf stage for all three hybrids. In the pre-silking period (Vn), the relative chlorophyll content decreased by 8% on average. After an average increase of 14% in the tasselling and silking period, SPAD decreased by an average of about 29% at full maturity (R6). For the different fertiliser treatments, higher N doses resulted in higher yields. In the basal fertiliser treatment, the A 60 N dose resulted in an average 34% increase in yield, and the A 120 N dose resulted in an average 94% increase in yield compared to the control. The 60 kg ha-1 N basal fertiliser (A60) increased in the V6 phenophase with an additional 30 kg ha-1 N resulted in an average yield increase of 26%. When 120 kg ha-1 N of basal fertiliser (A120) was increased by an additional 30 kg ha-1 N in the V6 phenophase, only the Merida hybrid showed a significant yield increase (7%). No further yield increase was observed when V690 and V6150 treatments were increased by an additional 30 kg ha-1 N in the V12 phenophase. The yield of the Armagnac hybrid decreased by almost 20%, the yield of Fornad by 3% and the yield of Merida by 1%.

Population growth, climate change, and increasing pressure on water and nitrogen resources pose major challenges for sustainable maize production. Maize yield is highly sensitive to inter-annual weather variability, yet many prediction approaches still rely on simple linear relationships and rarely integrate SPAD (Soil Plant Analysis Development)-based crop diagnostics with machine learning in multi-year nitrogen × irrigation experiments. In a three-year field experiment (2018–2020) in Hungary, we evaluated how basal and top-dressing fertilization and supplemental irrigation under contrasting water supply conditions affected the chlorophyll status and grain yield of a maize hybrid. Relative chlorophyll content was monitored using SPAD measurements at key phenological stages (V6, V12, and R1), and a multilayer perceptron (MLP) model was developed to improve yield prediction and to identify informative combinations of input variables. Five alternative scenarios (SC1–SC5) were tested by combining SPAD values with the fertilization rate, irrigation status, and crop year in different configurations, and model performance was assessed using root mean square deviation (RMSD), mean absolute error (MAE), normalized root mean square error (NRMSE), correlation (r, r²), Nash–Sutcliffe efficiency (NSE), Kling–Gupta efficiency (KGE), Kendall’s tau, and the index of agreement (d). Overall, SC4 (SPAD + fertilization + crop year + irrigation) achieved the best agreement with observed yields across most indices (e.g., r ≈ 0.93, NSE ≈ 0.86, KGE ≈ 0.90), whereas SC2 (SPAD + fertilization) produced the lowest prediction error on the independent test subset, indicating the most robust generalization. Basal fertilization with 60 and 120 kg N ha⁻¹ significantly increased yield in 2019 and 2020, while irrigation generally enhanced yield except for the 30 kg N ha⁻¹ top dressing applied at the V6–V12 stages. These results demonstrate that coupling SPAD measurements with MLP modeling and multi-criteria performance evaluation can support more efficient, site-specific nitrogen and irrigation decisions and help stabilize maize yields under variable climatic conditions.