Cost-effective sampling of 137Cs-derived net soil redistribution: part 2 – estimating the spatial mean change over time

• 137Cs-derived net soil redistribution is used with repeated sampling for short time periods.
• The aim is to understand how soil redistribution has responded to phases of land use change.
• We provide a rigorous statistical and pragmatic approach to estimate change over time.
• A case study identifies three phases of detectable change in net soil redistribution.
• Space-time sampling provides a cost-effective estimate of spatial mean change.

The caesium-137 (137Cs) technique for estimating net, time-integrated soil redistribution by the processes of wind, water and tillage is increasingly being used with repeated sampling to form a baseline to evaluate change over small (years to decades) timeframes. This interest stems from knowledge that since the 1950s soil redistribution has responded dynamically to different phases of land use change and management. Currently, there is no standard approach to detect change in 137Cs-derived net soil redistribution and thereby identify the driving forces responsible for change. We outline recent advances in space-time sampling in the soil monitoring literature which provide a rigorous statistical and pragmatic approach to estimating the change over time in the spatial mean of environmental properties. We apply the space-time sampling framework, estimate the minimum detectable change of net soil redistribution and consider the information content and cost implications of different sampling designs for a study area in the Chinese Loess Plateau. Three phases (1954–1996, 1954–2012 and 1996–2012) of net soil erosion were detectable and attributed to well-documented historical change in land use and management practices in the study area and across the region. We recommend that the design for space-time sampling is considered carefully alongside cost-effective use of the spatial mean to detect and correctly attribute cause of change over time particularly across spatial scales of variation.