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.

This 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.

To 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.