**6. Conclusions**

*Hydrology*

droughts and climate change.

development agricultural sector.

not the best for water organisms.

vegetative buffer strips) can limit its scope.

Reviewing different modeling efforts in Slovenian agricultural areas is an excellent way to get insight into implementing hydrologic modeling in general. For this chapter, another Google Scholar search was conducted, this time with a query: (hydrologic OR water) AND model AND (agronomy OR agriculture OR farm) AND "Slovenia". The search was repeated in Slovene to find more studies that were not published in English. After a scan through the results, several interesting studies were selected, joined by some others we have known from previous work, and were for some reason not included in the search. Selected publications all fit into the category of hydrologic modeling in agricultural areas. In terms of scale, some of them feature large scale modeling of the whole country, some catchment scale, and another field-scale modeling. In terms of the type of model used, there are several of them, but SWAT model applications are the most frequent. Topics range from nitrate leaching and concentration in groundwater to sediment, phosphorus, and nitrate loads in surface waters, and even to weather extremes modeling, including

The whole country modeling effort to determine nitrogen reduction levels necessary to reach groundwater quality targets was a program led by Slovenian Environment Agency [21]. Hydrological model GROWA–DENUZ was coupled with agricultural N balances to simulate nitrate leaching for the whole country. Results indicate that stricter measures in vulnerable areas are crucial to meeting WFD

Several studies [22–24] were conducted in vulnerable areas where groundwater is not a good state. While studying nitrate leaching, just like the work above, they were limited to catchment scale, and the model used was SWAT. Several agricultural management scenarios were simulated to determine what type of management is the most effective at reducing nitrate leaching. Among many other findings, an important message is that careful placing of local measures based on soil characteristics can be just as effective at reducing nitrate leaching as applying more general limitations on a broader scale while allowing a much healthier socio-economic

One study [25] dealt with simulating the effect of different historical land-use scenarios on surface water quality. The SWAT model was used to determine how

would impact river quality. Interestingly, the authors found that historical land-use patterns generally caused more erosion than the present, but even the present one is

Another study [26] evaluated the effects of deforestation and increasing vineyard land use on surface water quality with the APEX model. Results show that though pollution increases with deforestation, proper protective measures (like

In one case [27], a new model was developed based on equations from existing ones to simulate the effects of wastewater treatment implementation in an agricultural catchment. Results suggest that applying the measure of wastewater treatment did reduce nitrogen concentrations in the stream and increase phosphorus concen-

Finally, there were two studies [28, 29] dealing with controlling erosion and

into differences between several agricultural management scenarios, which would be much more expensive and time-consuming if done with field trials. Interestingly, several studies also included some fieldwork, partially for input data acquisition, but mostly to collect reliable validation data like crop yields, nitrate concentration, soil properties, soil water showing that the "old" ways are

Most of the described case studies took advantage of modeling to gain insight

trations, which could worsen the situation in that specific catchment.

nutrient leaching in catchments with accumulation lakes.

and 21st centuries)

thresholds, while additional state-wide measures are not necessary.

the land use documented on historical maps (18th, 19th, 20th,

**34**

In this chapter, we have discussed the perspectives of hydrologic modeling in agricultural research. The most frequently used hydrologic models were identified and reviewed in terms of their suitability for different applications in agronomy. A section evaluated the strengths and weaknesses of hydrologic models for agricultural research and highlighted potential applications. The importance of modeling in light of agricultural pollution mitigation was also be presented. Furthermore, the importance of input data quality and uncertainty analysis was discussed to highlight the potential risks associated with modeling. Examples of different case studies in Slovenia were referenced to review the recent agricultural modeling work in this country.

Future development in the field should concentrate on strengthening the interaction between model developers and users on one side and field scientists and farmers on the other, to make models more adept to specific practices and applications in different areas. This would strengthen the trust in modeling among agricultural scientists while expanding the recognition of modeling among the public and policymakers.

Overall, through this chapter and with every single one of the highlighted case studies, we hope to have strengthened the importance of hydrologic modeling in the agricultural sector. While model results cannot foretell the future, they can give us a useful range of possibilities to consider and discuss further despite their shortcomings and uncertainties. In conclusion, modeling has enabled important advances in agricultural hydrology studies and sped up research that would otherwise take much longer to conduct.
