Precision crop production requires accurate yield prediction and nitrogen management. Crop simulation models may assist in exploring alternative management systems for optimizing water, nutrient and microelements use efficiencies, increasing maize yields. Our objectives were: (i) to access the ability of the CERES-Maize model for predicting yields in long-term experiments in Hungary; (ii) to use the model to assess the effects of different nutrient management (different nitrogen rates—0, 30, 60, 90, 120, and 150 kg ha⁻¹). A long-term experiment conducted in Látókép (Hungary) with various N-fertilizer applications allowed us to predict maize yields under different conditions. The aim of the research is to explore and quantify the effects of ecological, biological, and agronomic factors affecting plant production, as well as to conduct basic science studies on stress factors on plant populations, which are made possible by the 30-year database of long-term experiments and the high level of instrumentation. The model was calibrated with data from a long-term experiment field trial. The purpose of this evaluation was to investigate how the CERES-Maize model simulated the effects of different N treatments in long-term field experiments. Sushi hybrid’s yields increased with elevated N concentrations. The observed yield ranged from 5016 to 14,920 kg ha⁻¹ during the 2016–2020 growing season. The range of simulated data of maize yield was between 6671 and 13,136 kg ha⁻¹. The highest yield was obtained at the 150 kg ha⁻¹ dose in each year studied. In several cases, the DSSAT-CERES Maize model accurately predicted yields, but it was sensitive to seasonal effects and estimated yields inaccurately. Based on the obtained results, the variance analysis significantly affected the year (2016–2020) and nitrogen doses. N fertilizer made a significant difference on yield, but the combination of both predicted and actual yield data did not show any significance.

In order to meet the requirements of sustainability and to determine yield drivers and limiting factors, it is now more likely that traditional yield modelling will be carried out using artificial intelligence (AI). The aim of this study was to predict maize yields using AI that uses spatio-temporal training data. The paper has advanced a new method of maize yield prediction, which is based on spatio-temporal data mining. To find the best solution, various models were used: counter-propagation artificial neural networks (CP-ANNs), XY-fused Querynetworks (XY-Fs), supervised Kohonen networks (SKNs), neural networks with Rectangular Linear Activations (ReLU), extreme gradient boosting (XGBoost), support-vector machine (SVM), and different subsets of the independent variables in five vegetation periods. Input variables for modelling included: soil parameters (pH, P2O5, K2O, Zn, clay content, ECa, draught force, Cone index), micro-relief averages, and meteorological parameters for the 63 treatment units in a 15.3 ha research field. The best performing method (XGBoost) reached 92.1% and 95.3% accuracy on the training and the test sets. Additionally, a novel method was introduced to treat individual units in a lattice system. The lattice-based smoothing performed an additional increase in Area under the curve (AUC) to 97.5% over the individual predictions of the XGBoost model. The models were developed using 48 different subsets of variables to determine which variables consistently contributed to prediction accuracy. By comparing the resulting models, it was shown that the best regression model was Extreme Gradient Boosting Trees, with 92.1% accuracy (on the training set). In addition, the method calculates the influence of the spatial distribution of site-specific soil fertility on maize grain yields. This paper provides a new method of spatio-temporal data analyses, taking the most important influencing factors on maize yields into account.

Biostimulants, when applied to plants in small amounts, increase crop yield and plant tolerance to abiotic and biotic stress. They play an important role in the development of new environmentally sustainable technologies. The aim of the current experiment was to investigate the potential of a cyanobacterium strain (Nostoc piscinale) to improve the growth, grain yield and stress tolerance of maize (Zea mays SY Zephir hybrid). Field trials were established at two sites. Freeze-dried biomass of N. piscinale resuspended in tap water (1g/L DW) was applied as a single foliar treatment (400 L/ha) at the V6-V7 phenological stage. Number of leaves, chlorophyll content, relative water content (RWC%) and free proline content were measured weekly. Grain yield, yield components and grain protein content were measured at harvest. N. piscinale treated maize had significantly earlier development in the vegetative growth stages with a higher number of leaves. Chlorophyll content (SPAD value) was significantly higher in the treated plants during the reproductive stages. There was little difference in the RWC and proline content compared to control plants. Faster vegetative growth and higher chlorophyll content in the cyanobacterium treated plants meant great photosynthetic light absorption over a longer period of time, resulting in significantly higher grain yield (6.5% and 11.5% at the two production sites) and increased grain protein content. Grain yield was significantly influenced by cob length and thousand grain weight. In conclusion, it was proved in field trials conducted in two different regions in Hungary that a single foliar application of a cyanobacterium-based biostimulant can contribute to crop production in a sustainable and environmentally friendly manner.