2023-08-03 イリノイ大学アーバナ・シャンペーン校
◆Agroecosystem Sustainability Center（ASC）の研究者は、公共データベースの空間情報を利用してSOCサンプリングの効率を向上させる新しい方法を検証しました。この手法は、イリノイ州とネブラスカ州の8つの農地で試験されました。
◆SOC測定は変動が大きく、多くのサンプルが必要ですが、研究チームは「doubly balanced sampling」と呼ばれる方法を使用してSOCストックをより効率的に測定できることを見つけました。この方法により、必要なサンプル数が約30％減少することが示されました。
◆このアプローチは、土壌サンプリングの効率を向上させるための重要な進展であり、炭素プロジェクトの開発者や研究者によって将来的に広く活用されることが期待されています。研究チームはその方法とデータを公に共有し、SOCの理解を向上させるために科学コミュニティと協力してさらなる進展を図ることを目指しています。
＜関連情報＞
- https://sustainability.illinois.edu/new-method-shows-promise-for-accurate-efficient-soil-carbon-estimates/
- https://www.sciencedirect.com/science/article/pii/S0016706123002641?via%3Dihub
農地における土壌有機炭素蓄積の層別およびバランスサンプリングのマルチサイト評価 Multi-site evaluation of stratified and balanced sampling of soil organic carbon stocks in agricultural fields
Eric Potash, Kaiyu Guan, Andrew J. Margenot, DoKyoung Lee, Arvid Boe, Michael Douglass, Emily Heaton, Chunhwa Jang, Virginia Jin, Nan Li, Rob Mitchell, Nictor Namoi, Marty Schmer, Sheng Wang, Colleen Zumpf
Geoderma Available online: 28 July 2023
DOI:https://doi.org/10.1016/j.geoderma.2023.116587
Highlights
•Guidance for selecting SOC stock sampling strategies is currently lacking.
•Dense and deep measurements of SOC stocks conducted at eight US Midwest field sites.
•Simple random, stratified, and balanced sampling strategies were evaluated.
•SOC stock variability explained most by topography, SSURGO estimates, and remote sensing.
•Doubly balanced sampling significantly outperformed other sampling strategies.
Abstract
Estimating soil organic carbon (SOC) stocks in agricultural fields is essential for environmental and agronomic research, management, and policy. Stratified sampling is a classic strategy for estimating mean soil properties, and has recently been codified in SOC monitoring protocols. However, for the specific task of estimating the SOC stock of an agricultural field, concrete guidance is needed for which covariates to stratify on and how much stratification can improve estimation efficiency. It is also unknown how stratified sampling of SOC stocks compares to modern alternatives, notably doubly balanced sampling. To address these gaps, we collected high-density (average of 7 samples ha−1) and deep (average of 75 cm) measurements of SOC stocks at eight commercial fields under maize-soybean production in two US Midwestern states. We combined these measurements with a Bayesian geostatistical model to evaluate stratified and balanced sampling strategies that use a set of readily-available geographic, topographic, spectroscopic, and soil survey data. We examined the number of samples needed to achieve a given level of SOC stock estimation accuracy. While stratified sampling using these variables enables an average sample size reduction of 17% (95% CI, 11% to 23%) compared to simple random sampling, doubly balanced sampling is consistently more efficient, reducing sample sizes by 32% (95% CI, 25% to 37%). The data most important to these efficiency gains are a remotely-sensed SOC index, SSURGO estimates of SOC stocks, and the topographic wetness index. We conclude that in order to meet the urgent challenge of climate change, SOC stocks in agricultural fields could be more efficiently estimated by taking advantage of this readily-available data, especially with doubly balanced sampling.
