Soil erosion assessment and prioritization of affecting factors at plot scale using the Taguchi method

SummaryAssessment of factors affecting soil erosion processes is very important for managerial activities. However, full factorial design of an experiment is time consuming and expensive when there are a large number of factors and corresponding levels. Using the Taguchi method, this study assessed several factors affecting soil erosion. To the best of our knowledge, no study has been conducted to apply the Taguchi method in order to investigate the effects of different factors on soil erosion or sediment yield. The factors used in this study consisted of silt content, slope, aspect and vegetation cover, each of which had four levels in the study area in Lar Township, Iran. A L16 orthogonal array was selected according to the aforementioned factors and levels and experimental tests were performed according to the Taguchi’s orthogonal array. Analysis of the signal-to-noise (S/N) ratio was used to evaluate the experimental results. The optimum conditions for quantifying soil erosion were determined. A test experiment was also conducted in small-sized plots under the optimum conditions, and the prediction of other combinations of factors/levels was evaluated. The results showed that the Taguchi method could predict soil erosion with an average relative error of 12.4%. The percentage contribution of each factor was also determined. Silt and slope aspect made the greatest (49.4%) and the lowest (1.3%) contribution, respectively. The results showed that the Taguchi method is able to model the soil erosion process well (R2 > 0.95) and can therefore be applied in future studies conducted in various fields. Additionally, comprehensive conclusions can be drawn if this method is applied under different plot sizes and considering further potential affective factors with different agroclimatic circumstances.

► The Taguchi model is being used for the first time in soil erosion study.
► We examine application of Taguchi model in designating the no. of the samples.
► The model perfectly worked for the study subject.
► Increasing information will decrease sampling costs in the field.