Mapping soil carbon stocks across Scotland using a neural network model

Abstract

A neural network model was trained to predict soil organic matter content, bulk density and soil organic matter density at different soil profile depths across Scotland. These predictions were then used to predict soil organic carbon content. The data used to train the model was developed from the National Soil Inventory of Scotland (NSIS) datasets, along with spatial datasets for topographic and climatic variables, and for geology, soil type and land cover. The trained network was tested and found to explain 79.8% of the variance in organic matter content, 77.9% of the variance in bulk density and 57.3% of the variance in profile depth. Various statistical measures were used to evaluate the predictive ability of the model, showing that it was suitable for predicting the carbon stocks of soils. The neural network model was used to make predictions from the surface to 1m in 1cm intervals, at 100m spatial resolution, across Scotland. This allowed us to make a prediction of the distribution, spatially and at depth, of carbon stocks to 1m across Scotland and to make estimates of the total carbon stock of Scottish soils (2954Tg) and the amount stored in different soil types across the country. We found that our estimate of the amount of carbon stored in Scottish soils was in agreement with previous estimates. Mineral and organo-mineral soils are predicted to hold a large amount of carbon in the upper portion, and in terms of carbon stock are almost as important as peat soils. At increased depth, a much smaller proportion of the total Scottish soil carbon stock is held in soils not classified as organic. We provide information about the distribution of carbon stocks with depth and soil type and under different land use/land cover types. Finally, we discuss the relevance of this information in relation to efforts to store carbon within Scottish soils in the medium to long term.