Climate change is an essential environmental challenge nowadays. Its effects are already being felt in multiple ways. In the future, we will also have to adapt to its effects because of our farming and our daily lives. In our research, we assessed the climate sensitivity of the lowland areas of Hungary through the changes in landscapes and the changes in groundwater resources that have the greatest impact on agriculture, using data from more than half of a century. We have quantified that at the mid-territory level (5-10 thousand km2) the groundwater resources show up to 3-5 km3/year changes in both positive and negative directions due to climatic effects. This significantly exceeds the anthropogenic water uses (the total water use of Hungary is about 5 km3 per year), so the effect of climate is the determining factor in the changes of regional water resources. Future changes in water circulation were modelled using the MIKE-SHE model in two micro-regions in Hungary. We have found that already at the level of the small catchments presented in our study, the water shortage increases by hundreds of millions of m3 per year due to the expected increase in temperature (mainly due to the increase in evapotranspiration), which cannot be compensated by current water supply solutions. Model simulations have confirmed previous results showing that groundwater movements play a very important role even in lowland landscapes. Based on our research, we would like to draw the attention of decision-makers and agricultural experts to the fact that current methods (irrigation, regional water transfers) are not sufficient for successful adaptation to climate change. So, it is not the limited precipitation but the inappropriate agricultural practices that cause a real threat in a changing climate. Based on our research, we have made a proposal for the adaptation of agriculture to climate change.

This study examines the impact of line spacing (X: 24 m, Y: 9 m, Z: 6.5 m) and orientation to tree lines on the growth, yield, and quality of red clover (Trifolium pratense L.) in a temperate, irrigated agroforestry system (2 ha) in Szarvas, Hungary. Three sampling locations were distinguished between the east and west oriented tree lines: the north (N) side, middle (M) strip, and south (S) side of the tree lines. The highest red clovers were observed in the 6.5 m spacing (mean height 69.3 ± 7.2 cm), although yields were similar across 24 m, 9 m, and 6.5 m spacings (2.9 t ha⁻¹, 2.3 t ha⁻¹, and 2.7 t ha⁻¹ dry matter, respectively). Orientation significantly influenced all forage quality parameters, with the north side showing earlier developmental stages and higher proportions of immature flowers (41–59%). Managing the spatial arrangement of red clover in agroforestry systems can help optimize forage quality by mitigating variations in plant maturity.

Due to extreme meteorological and soil hydrological situations the agricultural production security is highly unpredictable. To release the extent and duration of inland excess water (IEW) inundations or two-phase soil conditions during the period intended for cultivation, subsurface drainage (SD) has been used as a best practice in several countries. SD interventions took place between 1960’s and 1990 in Hungary. After 1989, land ownership conditions changed, thus professional operation and the necessary maintenance of the SD networks designed as a complex system became insignificant. In this paper, our aim was to present the IEW hazard in one of the most equipped areas by SD in Hungary. The occurrence frequency of IEW inundations in drained and non-drained (control) areas in different time intervals were compared. According to our results, we could state that the frequency of IEW on the subsurface drained areas was moderately lower in only a few periods compared to the control areas. IEW hazard of the arable areas at the Körös Interfluve was classified as nonhazarded in 52.7% of the area. Another 38.2% were moderately hazarded, 8.26% of the lands were meanly hazarded and less than 1% were highly hazarded area by IEW.

Regularly, large parts of the agricultural areas of the Great Hungarian Plain are inundated due to excessive rainfall and insufficient evaporation and infiltration. Climate change is expected to lead to increasingly extreme weather conditions, which may even increase the frequency and extent of these inundations. Shallow “floods”, also defined as inland excess water, are phenomena that occur due to a complex set of interrelated factors. Our research presents a workflow based on active and passive satellite data from Sentinel-1 and -2, combined with a large auxiliary data set to detect and predict these floods. The workflow uses convolutional neural networks to classify water bodies based on Sentinel-1 and Sentinel-2 satellite data. The inundation data were complimented with meteorological, soil, land use, and GIS data to form 24 features that were used to train an XGBoost model and a deep neural network to predict future inundations, with a daily interval. The best prediction was reached with the XGBoost model, with an overall accuracy of 86%, a Kappa value of 0.71, and an F1 score of 0.86. The SHAP explainable AI method showed that the most important input features were the amount of water detected in the satellite imagery during the week before the forecast and during the period two weeks earlier, the number of water pixels in the surroundings on the day before the forecast, and the potential evapotranspiration on the day of the forecast. The resulting inland excess water inundation time series can be used for operational action, planning, and prevention.

The extreme weather events highlight the need to develop action concepts to maintain agricultural production security in the future. Hydrological extremes can occur within a year in the form of surplus water (i.e. inland excess water), water scarcity or even drought. These adverse effects are influenced, inhibited and also facilitated by human activity. Previously, complex amelioration interventions, including subsurface drainage, aimed to improve the productivity of agricultural areas with unfavourable water management properties. The current efficiency of the subsurface drain networks in the regulation of groundwater level or soil moisture content can be questioned from several aspects. After the end of the socialist era (after 1990s), lack of maintenance and operation tasks have become typical, and are still a problem today in Hungary. Unfortunately, there is no exact national cadastre on the tile drained areas, and data is only available to a limited extent in the original amelioration plan documentations. In the present study, we aimed to reveal the possibilities of delineating the subsurface drained areas, and to develop a new method of condition assessment. Three tile drained study sites were selected on the Great Hungarian Plain in Central Europe. Our field investigations revealed the typical problems of the drained areas: (1) excessive vegetation of the receiving channels; (2) inadequate condition of the receiving main channel bed; (3) soil compaction in multiple layers above the drainage network; and (4) poor condition of outlets of the drain pipes. The developed methodology enabled us to evaluate the soil and the surface/subsurface water of the tile drained areas, and the technical condition of the drains. The necessary action plans or treatments were also outlined to replace the unused drain networks into use. Based on the scientific literature, we also sketched the target conditions and technological solutions that are required for the installation of new drains. The organization of the derived data into a GIS database could serve as a basis for the development of a cadastre of the tile drained areas based on a regional approach.

In Hungary, irrigation determines the success of water-intensive beet cultivation. Taking into account the guidelines of the circular economy, we investigated the effect of irrigation with pre-treated nutrient-rich effluent from an intensive catfish-farm on the growth stages of sugar beet and fodder beet. In the two-year-experiment (2020, 2021), two sugar beet (‘Helenika’, ‘Grandiosa’) and two fodder beet (‘Rózsaszín Béta’, ‘Béta Vöröshenger’) cultivars were grown. In addition to the effluent water of the fish farm, the water of the Körös oxbow lake and a mixed water type (1:3 effluent and Körös water, added gypsum) were used for irrigation (sprinkler irrigation methods, 4 replications). The experiment was performed in 64 lysimeter vessels/units (1 m2) in Szarvas. During the research we sought answers to the following questions: (1) whether the onset and length of sugar growth stage and accumulation stage differ depending on water quality, (2) which beet variety has the highest relative chlorophyll content, (3) whether irrigation water quality affected the relative chlorophyll content of beet cultivars. SPAD values measured with the SPAD-502 chlorophyll meter were used to estimate the relative chlorophyll content of beet leaves.