This study aimed at evaluating the scale and costs of an environmentally and economically optimal set of Best Management Practices (BMPs) for agricultural pollution abatement in Lithuania in order to reach water protection goals in both inland and marine waters by distributing BMPs optimally in space, while taking climate change impacts into consideration. The assessment of BMPs impact involved the use of the SWAT model by applying two climate change representative concentration pathways (RCP4.5 and RCP8.5) and two time horizons (mid-century and end-century), as well as five BMPs (arable land conversion to grasslands, reduced fertilization, no-till farming, catch-crops, and stubble fields throughout winter). The optimization of the set of BMPs employed a genetic algorithm. The results suggest that the need for BMPs application will increase from 52% of agricultural areas in the historical period up to 65% by the end of century in the RCP8.5 scenario. This means less arable land could actually be used for crop production in the future if water protection targets are met. The high costs for reaching water targets would rise even more, i.e. by 173% for RCP4.5, and by 220% for the RCP8.5 scenario, reaching approximately 200 million euros/year. In such a context, the BMP optimization approach is essential for significant reduction of the costs. Winter cover crops and reduced fertilization show the best effectiveness and cost balance, and will therefore be essential in pursuing water protection targets.
Keywords: Best management practices; Genetic algorithm; Lithuania; Pollution abatement; SWAT model.
© 2022. The Author(s).