Assessing the effects of good farming practices on water retention on a hilly catchment in Hungary using SWAT+

This study evaluates the water retention effectiveness of different good farming pactices on a hilly catchment (Felső-Válicka, Hungary) using the SWAT+ hydrological model. The Felső-Válicka case study area (124 km²), the focus of the research, is predominantly used for agricultural production (~35% of the total area). Farmers in the region face increasingly severe droughts, coupled with significant erosion damage to arable land due to intense rainfall events caused by unfavourable precipitation distribution. SWAT+ enables us to analyse management practices that potentially improve water management in agricultural fields. We followed the OPTAIN R workflow (www.optain.eu) to support the process of input data preparation, model setup, model calibration and scenario simulations. In our setup: i) individual fields/parcels can send surface runoff and lateral flow to neighbour objects (contiguous object connectivity approach), and ii) characteristic management practices can be assigned to each field annually, enabling us to analyse the effectiveness of NSWRMs with respect to their individual site-specific allocation within the catchment. The modelled good farming practices align with those defined in the agricultural subsidy framework (CAP) in Hungary.

Among the investigated practices, reducing soil tillage depth and minimizing soil disturbance through reduced tillage practices had the most significant positive impact on water retention. In contrast, linear structured measures (such as riparian buffers along streams and hedges between agricultural parcels) had a less pronounced effect. The periodic planting of perennial crops (e.g., alfalfa on a three-year cycle) or land use change from cropland to pasture yielded mixed results regarding the selected hydrological indicators.

These findings underline the potential of optimized tillage practices as a key strategy for improving water retention in agricultural landscapes.

 

This work was funded by the Széchenyi Plan Plus program, supported by the RRF-2.3.1-21-2022-00008 project and by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 862756, project OPTAIN.