In this study, different land management scenarios were evaluated with the SWAT model in order to determine which are the most effective in reducing nitrate leaching on specific soil types in the Krška kotlina alluvial plain. The model was calibrated for three soil moisture field trial sites, each representing one major soil type of the area. Simulated soil moisture values were in good agreement with the observed values. Of the nine land management scenarios that were evaluated, vegetable rotation caused the most nitrate leaching on all soil types, but it fared better on Cambisol than on Fluvisol. Orchards on the other hand leached the least amount of nitrate, but also fared better on Cambisol. Results show that nitrate leaching varies for different land management scenarios on different soil types, so emphasis should be given to combining agricultural practices with the soil type that fares best in a certain combination.
COBISS.SI-ID: 36114179
Preservation of good groundwater quality is necessary for the safety of drinking water supply. Variations in water composition are essentially the result of a combination of both anthropogenic and natural contributions. The quality of groundwater is influenced by many factors, including the mineralogy of aquifers, the chemical composition of rainfall and surface water, climate, topography, and anthropogenic factors. Groundwater quality is intricately connected with the surface land use and is vulnerable to the effects of anthropogenic activities on the land surface. Shallow aquifers are particularly vulnerable to pollution from agriculture and other sources. We evaluated nitrate concentration in soil water, sampled with suction cups and lysimeter, under field with chili and peppers and under corn. All three experimental plots under chili and pepper field showed nitrate concentration decrease at the end of the season. On the other hand, nitrate concentration in corn field increased by the end of the season. Possibilities for reducing nitrate pollution are in improved irrigation technologies and fertilization supply in ratios
COBISS.SI-ID: 50100483
Based on the model scenario, we have shown that separation is an effective process by which we can make a nutrient-rich marketable organic fertilizer and thus sell off the excess nutrients from an intensive pig farm. 25-35% N, 55-65% P and 20-30% K pass into the solid part of pig manure. When regulating the water regime of the soil by irrigation, we must pay special attention to the dry spring periods at the beginning of the vegetation period, as was the case in 2019 after the modest winter period 2018/2019. Precise fertilization, where farmers would adjust the doses to the needs of the plants and rely less on generic guidelines, would be an interesting option for improvements in the nitrogen balance, given the results of the simulations. High concentrations of nitrates in groundwater occur in the area of the Drnovo pumping station due to two reasons: intensive agricultural use of space in the aquifer supply area and the absence of any impact of the Sava river component on groundwater. Due to the extremely unfavorable location of the pumping station, it would therefore be necessary to implement very rigorous protection measures, which would certainly have negative consequences for agricultural production in the supply hinterland of the pumping station.
COBISS.SI-ID: 30076419
In this paper, we focus on the analysis of groundwater’s physico-chemical field parameters and the major ions present in Slovenian aquifers. Several statistical methods were applied to outline the relevant criteria involved in determining the groundwater’s natural background. Additionally, a graphical method was applied to evaluate the source of major ions distribution in the groundwater. It turns out that the BRIDGE methodology might be relevant for chemical parameters mainly affected by geogenic sources, while the “anthropogenic” parameters have to be treated with a different approach e.g., probability plot method.
COBISS.SI-ID: 23645443
In the present study we focused on the isotope composition of oxygen (?18O) in groundwater, which is a natural tracer and provides a better understanding of the water cycle, in terms of origin, dynamics and interaction. Geostatistical tools, such as ordinary kriging, simple and multiple linear regression, and artificial neural networks were used and compared to select the best tool. Based on validation data sets, the artificial neural network model proved to be the most suitable method for predicting ?18O in the groundwater, since it produced the smallest deviations from the real/measured values in groundwater.
COBISS.SI-ID: 2705493