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

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

Authors :
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
Source :
Geoderma, Vol 438, Iss , Pp 116587- (2023)
Publication Year :
2023
Publisher :
Elsevier, 2023.

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.

Details

Language :
English
ISSN :
18726259
Volume :
438
Issue :
116587-
Database :
Directory of Open Access Journals
Journal :
Geoderma
Publication Type :
Academic Journal
Accession number :
edsdoj.582ddf86e02c443daa615b4b203f6a53
Document Type :
article
Full Text :
https://doi.org/10.1016/j.geoderma.2023.116587