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).

Maize is one of the most widely used plants in the agricultural industry, and the fields of application of this plant are broad. The experiment was conducted at the Látókép Crop Production Experimental Station of the University of Debrecen, Hungary. Three mid-ripening maize hybrids with different FAO numbers were used in the present study. The effects of different nitrogen supplies were examined as a variable rate of abiotic stress and the interrelationship among the essential nutrients through the nutrient acquisition and partitioning of the different vegetative and generative plant parts. The results showed that NPK application compared to the control treatment (no fertilizer application) increased DM in all tissues of maize, while increasing nitrogen application from 120 to 300 kg ha⁻¹ had no significant effect on this trait. The highest protein content was obtained with the nitrogen application of 120 kg ha⁻¹, and the higher nitrogen fertilizer application had no significant effect on this trait. Seeds and leaves had a maximum zinc and manganese value in terms of nitrogen content (protein). Dry matter was positively correlated with nitrogen, potassium, and manganese content, while the dry matter had a negative correlation with nickel content. In general, to achieve a maximum quantitative and qualitative yield, it is recommended to use NPK fertilizer with a rate of 120 kg ha⁻¹ N for maize cultivation.

One of the critical issues in using micronutrient fertilisers is the comparison of methods and amounts of fertiliser use, which is very important from the aspect of increasing production and economic viewpoint. The aim of this research was to analyse the nutrient composition of different parts of the maize (Zea mays L. FAO 490) during the growing season with six-level nitrogen fertilisation supplies at five phenological stages. The study included essential nutrients as nitrogen (N), phosphorus (P), potassium (K), and sulphur (S) (in first cluster) and calcium (Ca) and magnesium (Mg) (in second cluster) in treatments on different leaf stage. Growth stages have different nutrient demands based on their actual demand. The first cluster included nitrogen and sulphur and the second included calcium and zinc in the NPK treatments on the stalk of plants. Nitrogen and potassium had their maximum effect on the stalk of maize during the growing season. Magnesium and copper were the second most important and desirable factors during the different growth stages and treatments in relation to the stalk. Nitrogen and calcium had their maximum impact during the yield formation stage and nitrogen and phosphorus had their most desirable effect during the grain filling stage. The effect of nitrogen on the quantitative and qualitative properties of maize showed that nitrogen increases the production of dry matter, grain yield, and its components. The maximum amount of absorption in the plants occurs before the accumulation of applied fertiliser, which is the prelude to the production of maximum biomass.

Maize, regarded as a staple economic crop, attracts special global attention with the aim to enhance its production. Foliar fertilisation offers a complementary method to traditional soil fertilisation amongst resource-limited agricultural systems, providing a more efficient solution to nutrient deficiencies, especially in suboptimal soil conditions. This study aimed to analyse foliar fertiliser formulation research directions and their application in maize production. A literature search was conducted in the Web of Science (WoS) database. Bibliometric analyses were performed using the VOSviewer software (version 1.6.17). The changes in the publication trends of documents were tested using the Mann–Kendall test. The production effects of foliar fertilisation were independently synthesised. The results showed a strong positive increase in publication trends regarding maize foliar fertilisation (R² = 0.7842). The predominant nutrients that affected maize production were nitrogen, phosphorous, potassium, zinc, iron, and manganese. The timely foliar application of nutrients corrected deficiencies and/or sustained nutrient supply under several abiotic stresses. Foliar application at critical growth stages like flowering and grain filling boosted carbohydrate and protein content, lipid levels, kernel size, mineral content, and the weight of the maize grain. This review identified important research gaps, namely genotype-specific responses, interactions with other agronomic practices, and long-term environmental effects.

Artificial intelligence, specifically machine learning (ML), serves as a valuable tool for decision support in crop management under ongoing climate change. However, ML implementation to predict maize yield is still limited in Central Europe, especially in Hungary. In this context, we assessed the performance of four ML algorithms (Bagging (BG), Decision Table (DT), Random Forest (RF) and Artificial Neural Network-Multi Layer Perceptron (ANN-MLP)) in predicting maize yield based on four different input scenarios. The collected data included both agricultural data (production (PROD) (ton) and maize cropped area (AREA) (ha)) and climate data (annual mean temperature °C (Tmean), precipitation (PRCP) (mm), rainy days (RD), frosty days (FD) and hot days (HD)). This research adopted four scenarios, as follows: SC1: AREA+ PROD+ Tmean+ PRCP+ RD+ FD+ HD; SC2: AREA+ PROD; SC3: Tmean+ PRCP+ RD+ FD+ HD; and SC4: AREA+ PROD+ Tmean+ PRCP. In the training stage, ANN-MLP-_SC1 and ANN-MLP-_SC4 outperformed other ML algorithms; the correlation coefficient (r) was 0.99 for both, while the root mean squared errors (RMSEs) were 107.9 (ANN-MLP-_SC1) and 110.7 (ANN-MLP-_SC4). In the testing phase, the ANN-MLP-_SC4 had the highest r value (0.96), followed by ANN-MLP-_SC1 (0.94) and RF-_SC2 (0.94). The 10-fold cross validation also revealed that the ANN-MLP-_SC4 and ANN-MLP-_SC1 have the highest performance. We further evaluated the performance of the ANN-MLP-_SC4 in predicting maize yield on a regional scale (Budapest). The ANN-MLP-_SC4 succeeded in reaching a high-performance standard (r = 0.98, relative absolute error = 21.87%, root relative squared error = 20.4399% and RMSE = 423.23). This research promotes the use of ANN as an efficient tool for predicting maize yield, which could be highly beneficial for planners and decision makers in developing sustainable plans for crop management.