How to estimate soil organic carbon stocks of agricultural fields? Perspectives using ex-ante evaluation

• Published in: Geoderma 2022-04, Vol.411, p.115693, Article 115693
• Main Authors: Potash, Eric, Guan, Kaiyu, Margenot, Andrew, Lee, DoKyoung, DeLucia, Evan, Wang, Sheng, Jang, Chunhwa
• Format: Article
• Language: English
• Subjects: Bayesian; ENVIRONMENTAL SCIENCES; Estimation; Evaluation; Geostatistics; Sampling; Soil carbon stocks
• ISSN: 0016-7061; 1872-6259
• DOI: 10.1016/j.geoderma.2021.115693

•Strategies to estimate soil organic carbon stocks in agricultural fields are reviewed.•Soil scientists face many strategy choices but limited guidance.•Strategies are evaluated for a representative North Central USA field using simulation.•Readily available auxiliary information substantially improves estimation performance.•Performance is somewhat uncertain but findings are not sensitive to simulation model. Estimating soil organic carbon (SOC) stocks of agricultural fields has a range of important applications from development of sustainable management practices to monitoring carbon stocks. There are many estimation strategies with the potential for more reliable estimates of SOC stock and more efficient use of soil sampling and analysis resources, especially by leveraging readily available auxiliary information such as remote sensing. However, concrete guidance for strategy selection is lacking. This study narrows this gap with a comparison of strategies for estimating deep SOC stock (0–60 cm) in a prototypical field. Using high density SOC stock measurements and simulation, we built on past studies by 1) ex-ante evaluating a large number of strategy options, 2) using a Bayesian approach to quantify the uncertainty of the comparison, and 3) considering multiple Bayesian models to assess sensitivity to this modeling choice. We found that, using readily available auxiliary information, both balanced and stratified sampling offer substantial improvements over simple random sampling. The auxiliary information most important for this improvement is a Sentinel-2 SOC index = blue / (green × red), followed by the topographic wetness index. We found that these results are robust to the choice of mapping method, but that there is uncertainty in the magnitude of improvement. We recommend future studies implement this Bayesian approach for simulated ex-ante evaluation of SOC stock estimation strategies across more fields to investigate the generalizability of these findings.