Agroforestry is defined as a multifunctional approach to land management that enhances biodiversity and soil health while mitigating environmental impacts compared to intensive agriculture. The efficacy of maize cultivation in agroforestry systems is significantly influenced by nutrient competition. The factors that influence this phenomenon include the dimensions and configuration of the tree rows, as well as the availability of nutrients. This study examined the effect of nitrogen fertilization, tree line spacing, and seasonal changes on the productivity and the leaf spectral characteristics of the intercropped maize (Zea mays L.) within a willow-based agroforestry system in eastern Hungary. The experiment involved the cultivation of maize with two spacings (narrow and wide field strips) and four nitrogen levels (0, 50, 100, and 150 kg N ha⁻¹) across two growing seasons (2023–2024). The results demonstrated that yield-related parameters, including biomass, cob size and weight, and grain weight, exhibited a strong response to nitrogen level and tree line spacing. The reduction in spacing resulted in a decline in maize productivity. However, a high nitrogen input (150 kg N ha⁻¹) partially mitigated this effect in the first growing season. Vegetation indices demonstrated a high degree of sensitivity to annual variations, particularly with regard to tree competition and weather conditions. Multispectral vegetation indices exhibited a heightened responsiveness to environmental and management factors when compared to indices based on visible light (RGB). The findings of this study demonstrate that a combination of optimized tree spacing and optimized nitrogen management fosters productivity while maintaining agroecological sustainability in temperate agroforestry systems.

The shallow groundwater has a direct and indirect effect on natural vegetation and agricultural production. The decline in groundwater level (GWL) can have negative impacts. In many areas in Hungary decreasing GWL trends in the last decades were found by earlier studies. In our research we studied the characteristics of groundwater level focusing on our study area, the subbasin of Szarvas-Békésszentandrás Oxbow. We analysed 20 years daily data of groundwater level of eight monitoring wells. Annual course and long term tendencies of groundwater level were examined. In average of 16 years the GWL reaches its maximum in April and its deepest level in autumn (September, October and November depending on the station). Four typical groups of groundwater level courses could be distinguished based on the average depth and seasonal variations of GWL. The year to year GWL variability is larger in January and April compared to July and especially to October. The trends of the middle months of the seasons are almost the same in significance and slope compared to the trends based on yearly mean time series. The differences in trends can be found between stations rather than between the months used for calculations. The larger part of the subbasin can be characterised by decreasing trend in groundwater levels (2002-2020). The change exceeds 1 m at station Szarvas 2832 (144 cm) and Szarvas 2778 (122 cm). However, there is a station with no significant trend, GWL at Csabacsűd 2779 station shows relative stability in yearly average, which is valid for some areas in the eastern part of the subbasin.

Soil temperature is the main factor in determining the germination of maize seeds and the emergence time of the crop. It controls the rate of phenological development while the meristem is underground, which is until the V6 (six leaf collar) stage of maize. The research performed by the authors aimed to model maize seedbed temperatures at sowing depth (soil temperature at 5 cm depth) during the sowing-emergence-early development period. The research is based on measurements in ploughed plots of the maize experiments at the Látókép Experiment Site of the University of Debrecen (Eastern Hungary) in two growing seasons of 2021–2022. Two types of empirical models were established, a multilinear regression model (M1) and a dynamic-empirical model (M2), where the daily increase and decrease of soil temperature are determined by multilinear regression. Candidates for input variables for both models were various, easily available daily meteorological parameters. M2 model performed better than M1 when applied to an independent database of 2022. This is particularly valid for the maximum and minimum soil temperatures. It was found that both M1 and M2 can be used to predict the soil temperature of the maize seedbed before shading by the plants. For daily mean temperature, M1 and M2 give a similarly good estimation, while the dynamic-empirical model has to be preferred for the maximum and minimum temperatures. M2, which is based on daily temperature, global radiation and wind speed data, predicts the daily mean (RMSE = 1.4 °C), maximum (RMSE = 2.2 °C), and minimum (RMSE = 1.6 °C) of seedbed temperature not worse than many earlier soil temperature models do, even hybrid or mechanistic ones with a large number of parameters.