Search

Soil organic carbon (SOC) sequestration assessment requires accurate and effective tools for measuring baseline SOC stocks. An emerging technique for estimating baseline SOC stocks is predictive soil mapping (PSM). A key challenge for PSM is determining sampling density requirements, specifically, determining the economically optimal number of samples for predictive soil mapping for SOC stocks. In an attempt to answer this question, data were used from 3861 soil organic carbon samples collected as part of routine agronomic soil testing from a 4702 ha farming operation in Saskatchewan, Canada. A predictive soil map was built using all the soil data to calculate the total carbon stock for the entire study area. The dataset was then subset using conditioned Latin hypercube sampling (cLHS), both conventional and stratified by slope position, to determine the total carbon stocks with the following sampling densities (points per ha): 0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, and 0.8. A nonlinear error function was then fit to the data, and the optimal number of samples was determined based on the number of samples that minimized soil data costs and the value of the soil carbon stock prediction error. The stratified cLHS required fewer samples to achieve the same level of accuracy compared to conventional cLHS, and the optimal number of samples was more sensitive to carbon price than sampling costs. Overall, the optimal sampling density ranged from 0.025 to 0.075 samples per hectare. [ABSTRACT FROM AUTHOR]

There is increasing demand for more detailed soil maps to support fine-scale land use planning, soil carbon management, and precision agriculture in Saskatchewan. Predictive soil mapping that incorporates a combination of environmental covariates provides a cost-effective tool for generating finer resolution soil maps. This study focused on mapping soil properties for multiple soil horizons in Saskatchewan using historical legacy soil data in combination with remote sensing band indices, bare soil composite imagery, climate data, and terrain attributes. Mapped soil properties included soil organic carbon content (SOC), total nitrogen, cation exchange capacity (CEC), electrical conductivity (EC), inorganic carbon (IOC), sand and clay content, and total profile soil organic carbon stocks. For each of these soil properties, a recursive feature elimination was undertaken to reduce the number of features in the overall model. This process involved iteratively removing features such that random forest out-of-bag error was minimized. Final random forest models were built for each property and evaluated using an independent test dataset. Overall, predictive models were successful for SOC (R2 = 0.71), total nitrogen (R2 = 0.65), CEC (R2 = 0.46), sand content (R2 = 0.44) and clay content (R2 = 0.55). The methods used in this study enable mapping of a greater geographic region of Saskatchewan compared to those previously established that relied solely on bare soil composite imagery. [ABSTRACT FROM AUTHOR]