Long-term effects of conservation tillage on soil erosion in Central Europe: A random forest-based approach

Conservation tillage (CT) is of primary importance in food security, soil conservation, and sustainable development, even though its comprehensive effects on runoff (RO) and soil loss (SL) are still not fully understood. In 2004, a field-scale study was launched in southwest Hungary to investigate the long-term (16 years) effects of CT on RO, SL and soil, under a warm-summer humid continental climate. Four, especially large, 1200 m2 plots (2 ploughing tillage (PT) and 2 CT) were established, using a special, two-channel collection system. By the end of the study period, significantly higher water-stable aggregates (PT: 20.0 %, CT: 30.4 %), higher soil organic matter (PT: 1.4 %, CT: 1.9 %), greater earthworm abundance (4.9 times that in PT plots) was recorded on the CT plots. Conservation tillage decreased surface RO by 75 % and SL by 95 %. The difference between PT and CT was significant for mean annual soil erosion, with values of 2.8 t ha-1 and 0.2 t ha-1, respectively. The exceedance of extreme precipitation events was <2%, but their impact on soil erosion was extraordinarily high. Runoff and SL were predicted for the whole dataset, and for the sub-dataset of maize culture, in four separate Random Forest (RF) model developments. The often used linear models are not suitable for predicting soil erosion, hence a more robust, non-parametric, advanced method of classification tree analysis was used. The RF classification method was able to predict erosion risk. For the maize sub-dataset, the RF model best predicted the extreme events, followed by the no-runoff category. The sensitivity of the groups with the highest and lowest risk all exceeded 82 % for SL and 64 % for RO. Tillage type was the most important factor. This long-term study demonstrated that the use of CT enabled the maintenance of a major fraction of precipitation on arable land, and consequently, soil loss remained an order of magnitude lower than its tolerable value. The RF method is suitable for modelling RO and SL. In future, the integration of more datasets in modelling would considerably improve the precision and accuracy of prediction of RO and SL.