It is difficult to estimate the contribution of individual sources to the total CO₂ efflux from soil with vegetation. Long-term experiments with bare soil will provide useful conclusions. In this study, we aimed to mathematize the effect of soil temperature and soil moisture content on bare soil CO₂ efflux in a four-season semiarid region to assess the adequacy of different models and to enable future predictions by seasons. We proved that the exponential model adequately described the relationship between the CO₂ efflux and the soil temperature. The model calculations showed no significant relationship in the case of an additional quadratic exponential function, while, in the case of the linear model, the homoscedasticity criteria were not met, and the accuracy of the estimation was found to be dependent on the level of CO₂ efflux. When the soil moisture content with either an exponential function or power was added to the exponential formula, the models did not provide more accurate results. Our findings confirm that the best-fitting models are dependent on the local environmental conditions, and there are areas in which the moisture content does not significantly affect the CO₂ efflux of bare soil. Using trends in historical hourly temperature data in the exponential model, the CO₂ emission was estimated to be in the range 772–898 g m⁻² y⁻¹ in 2050 in the location we used. Trends in climate change are expected to have considerable effects on the processes that govern the CO₂ emissions of soil.

The research work was started on the preliminary knowledge that the risk of secondary salinization is high in the hobby gardens around Karcag as the water of the aquifers used for irrigation is saline, nevertheless irrigation in the drought periods is essential for vegetable production. A complex experiment was set up in 12 simple drainage lysimeters at the lysimeter station of the Research Institute of Karcag in 2012 in order to simulate the conditions of irrigation characteristic in the region with the goal of finding a solution to mitigate the harmful effects by means of optimization of irrigation. In 2017–2018 three approaches were applied for the scientific establishment of the problem studying the effect of different irrigation frequencies, different irrigation water qualities, and soil conditioning on the moisture content and the salt profile of the soil. The soil conditioner (Neosol) applied was found to have a positive effect on the water and salt regime of the soil, partly by creating a more favourable vertical distribution of the soil water, and partly preserving more moisture in the soil.

Soil compaction and degradation due to improper tillage are problems involving significant natural and economic damages. On compacted soils, suitable cultivation can be implemented only with higher energy and traction force input. In our study, the effect of a soil conditioner (Neosol) was examined on the penetration resistance of the soil and the traction power demand for ploughing in the experiment set up in the East-Slovak Plain in 2017 ‒ 2018 to justify several preliminary results showing that long-term soil conditioning results in enhanced root system, improved soil structure, cultivability, water- and salt regime. We found a positive effect of Neosol application with both investigated parameters and its long-term effect was also justified. The penetration resistance values of the soil of the untreated plot were 17 ‒ 23% higher, while the traction power demand values were 9 ‒ 32% lower in comparison with the Neosol treated plot in the first and the second year of the study, respectively. We assume the cumulative positive effect of soil conditioning on the physical soil properties in the study area, therefore the long-term application of Neosol is recommended for farms having similar soil properties.

Agricultural crop yield prediction is vital for ensuring global food security and optimizing resource management amid the increasing challenges posed by climate change and extreme weather variability. This study investigates the use of discrete-time, finite-state, time-homogeneous Markov chains to model crop failure and yield fluctuation probability. Maize yields in Hungary during 1921–1960 and 1980–2023 were analyzed. Yield distribution was assumed to depend only on the yield of the previous year. The Olympic average was computed for 5-year periods, excluding the highest and lowest values. Annual yield was divided by the value of the moving average and expressed as a percentage. According to our estimates, a higher degree of yield fluctuation is associated with an increased frequency of years with yields close to the long-term average. Considering the long-time trend during 1925–1960, the probability of having average maize yield, yield failure, and high yield would be 73.5%, 11.8%, and 14.7%, respectively. For the period of 1985–2023, the probability of failure was calculated to be at least 15% higher, while that of the high yield was found to be lower than for the first period. Taking the second period’s trend into account, the probabilities of average harvest, crop failure, and high harvest would be 66%, 21%, and 13%, respectively. Our findings confirm that the probability of yield variability can be modeled using the discrete-time Markov chain method, providing a new mathematical approach for crop yield prediction.