Tracing sediment loss from eroding farm tracks using a geochemical fingerprinting procedure combining local and genetic algorithm optimisation Original Research Article

Eroding farm tracks represent important spatially distributed features in many agricultural landscapes and there is concern over their role in catchment sediment problems. It is, however, important to place eroding farm tracks in the context of catchment sediment sources more generally, especially since the former afford potential for targeted sediment mitigation. A sediment source tracing procedure was therefore used to assess the importance of eroding farm track surfaces as a contemporary primary suspended sediment source relative to inputs from pasture or cultivated topsoils and channel banks/subsurface sources, in the upper River Piddle catchment (~ 100 km2), in southern England. The study provided a timely opportunity to assess the performance of both local and global (genetic algorithm; GA) optimisation techniques in the sediment geochemistry mass balance modelling used to apportion sources. Over the duration of the study, average median source contributions for individual time-integrated suspended sediment samples collected from three sub-catchments ranged between 1 ± 1 and 19 ± 3% for farm track surfaces, 31 ± 3 and 55 ± 2% for pasture topsoils, 1 ± 1 and 19 ± 1% for cultivated topsoils and 23 ± 2 and 49 ± 1% for channel banks/subsurface sources. Comparison of the local and GA optimisation techniques demonstrated that GA with random initial values improved the minimisation of the objective functions compared to local searching by 0.01–0.04% of 5000 repeat Monte Carlo iterations. GA informed by the outputs of the local optimisation as initial values improved corresponding performance by 0.05–0.20%. These findings increased confidence in the outputs from the local optimisation mass balance modelling, but fingerprint property datasets should be treated on an individual basis. Future sediment source tracing studies should always endeavour to combine local and global search tools to avoid the risk of using localised solutions for source apportionment estimates.