Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

Soil organic carbon (SOC) is used for soil health, indicating soils’ agricultural productivity potential, and correlating with other functions like water capacity and soil biodiversity. SOC stocks are increasingly recognized in climate change mitigation strategies. Soil carbon sequestration represents 25% of all natural climate solutions to carbon. Current SOC maps for Great Britain (GB) are limited due to their coarse resolution (0.5–1 km). High demand exists for fine resolution SOC maps to estimate stock baselines and inform field-scale sampling strategies and land management. We present SOC concentration and stock maps at 5 m resolution for GB, generated using machine learning and accounting for physical and chemical soil properties, weather, topography and land cover (LC). Our model explains 74% of SOC variability in the evaluation dataset, with a RMSE of 9.8 (tC ha− 1). Soil pH and LC are the most important SOC predictors. We estimate ~ 2704Tg SOC for GB in the soil’s top 30 cm: 1403Tg in England, 283Tg in Wales, and 1017Tg in Scotland. Neutral grasslands contribute the most SOC in England and Wales (37.2% and 50.7%). Dwarf heath shrubs, bogs and natural grasslands have higher contributions in Scotland (22–25%). Our SOC stock estimation compares with previous studies and our map reflects expected SOC distribution for different parts of GB under different LCs. Its high spatial resolution and accuracy enable SOC assessment at small scales (a single farm or a field) and can help to develop sustainable land management and guide soil sampling by optimizing sampling size and cost.

More information Original publication

DOI

10.1080/27658511.2024.2415166

Type

Journal article

Publication Date

2024-01-01T00:00:00+00:00

Volume

10