Using a novel vector length stability index ( VLSI ) to evaluate soil aggregate stability: A preliminary study

Soil aggregate stability is an important soil characteristic which can represent the improvement of soil structure after revegetation. Due to the high heterogeneity of soil, large-scale sampling is usually necessary to estimate the spatial variation of aggregate stability across landscapes. Although there are many methods and corresponding indices for analysis of aggregate stability, many of them are not suitable for investigations with a large number of samples due to time-consuming experimental practices. To avoid unnecessary costs of time and labor in the longterm land degradation and restoration monitoring across large regional scales, simple indices of aggregate stability would help monitoring across large regional scales. For this reason, we introduced a new, simple, and normalized index, the vector length stability index (VLSI) and its weighted form (wVLSI) to describe soil aggregate stability. The VLSI shares key information of aggregate size distribution with mean weight diameter (MWD), but it is naturally normalized and dimensionless. This new index was tentatively tested to describe soil aggregate stability of the 0-30 cm soil layers under artificial plantations, natural grassland, abandoned farmland, and farmland in production in a small watershed located in the Loess Plateau of China. The results showed that the aggregate stability of the 0-20 cm layers in abandoned farmland could be recovered near to the condition under artificial plantations just after 2-year abandonment, but the aggregate stability hardly changed in the 20-30 cm layer. The aggregate stability of the 0-10 cm layer in natural recovered grassland is close to that in top soil layers under artificial plantations, but the stability in the 10-30 layers appeared as low as that in farmland. Because of its simple form and low data requirements, the VLSI could be an alternative efficient tool for soil aggregate stability evaluation when a large number of samples are needed.