Multi-site evaluation of stratified and balanced sampling of soil organic carbon stocks in agricultural fields

作者：Eric Potash, Kaiyu Guan, Andrew J. Margenot, DoKyoung Lee, Arvid Boe, Michael Douglass, Emily A. Heaton, Chunhwa Jang, Virginia L. Jin, Nan Li, Rob Mitchell, Nictor Namoi, Marty R. Schmer, Sheng Wang, Colleen Zumpf · 发表于：Geoderma · 年份：2023 · DOI：10.1016/j.geoderma.2023.116587 · 被引用次数：22 · 研究领域：Soil Geostatistics and Mapping、Soil and Water Nutrient Dynamics、Geochemistry and Geologic Mapping

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 i...