New estimation strategy improves soil carbon sampling in agricultural
fields

Date:
March 29, 2022
Source:
University of Illinois at Urbana-Champaign Institute for
Sustainability, Energy, and Environment
Summary:
Researchers have evaluated strategies for efficiently estimating
soil organic carbon in agricultural fields. Quantifying soil organic
carbon stocks in agricultural fields is essential for developing
sustainable management practices and monitoring. The research team
found that in a typical Midwestern agricultural field, public soil
surveys and satellite imagery can be leveraged to efficiently
select sample locations. This may reduce the number of samples
needed to achieve a given precision (compared to random sampling).



FULL STORY ========================================================================== There is much more carbon stored in Earth's soil than in its atmosphere. A significant portion of this soil carbon is in organic form (carbon
bound to carbon), called soil organic carbon (SOC). Notably, unlike
the inorganic carbon in soils, the amount of SOC, and how quickly it is
built up or lost, can be influenced by humans. Since its advent about
10,000 years ago, agriculture has caused a significant amount of SOC
to be released into the atmosphere as carbon dioxide, contributing to
climate change.


========================================================================== Quantifying the amount of SOC in agricultural fields is therefore
essential for monitoring the carbon cycle and developing sustainable
management practices that minimize carbon emissions and sequester carbon
from the atmosphere to the soil to reduce or reverse the climate effects
of agriculture.

"Accurate and efficient SOC estimation is essential," said Eric Potash,
a Research Scientist in the Agroecosystem Sustainability Center (ASC)
and Department of Natural Resource & Environmental Sciences (NRES) at the University of Illinois Urbana-Champaign. "Governments need to estimate SOC
in order to implement policies to minimize climate change. Researchers
need to estimate SOC to develop sustainable management practices. And
farmers need to estimate SOC to participate in emerging carbon credit
markets." The traditional and most reliable way to quantify SOC is by
soil sampling, with analyses in the lab ("wet chemical" measurement). But
which locations in the field should be sampled? And how many samples
should be taken for an accurate estimate? Each additional soil core adds significant labor and expense -- and uncertainties in how to optimize
sampling can lead to substantial extra costs.

In a new publication from the U.S. Department of Energy's (DOE) SMARTFARM Project, Potash and other SMARTFARM researchers evaluated strategies
for estimating SOC. Their goal was to develop an estimation strategy
that maximizes accuracy while minimizing the number of soil cores sampled.

The SMARTFARM Project, a program led by co-author and Blue Waters
Professor in NRES Kaiyu Guan and funded by the DOE's Advanced Research
Projects Agency- Energy (ARPA-E), endeavors to develop a precise solution
for measuring and quantifying greenhouse gas emissions and SOC change
during the production of crops.



==========================================================================
"We aim to collect gold-standard ground truth data and also to develop
new technology to quantify field-level carbon outcomes for bioenergy
crops, improving yield and also improving environmental sustainability,"
said Guan, ASC Founding Director.

This work is made possible with unprecedented data collection effort.

"We have collected 225 soil samples at 3 samples per acre at one of the SMARTFARM sites. The samples were collected up to 1 meter deep using a
Giddings probe. This level of dense sampling has never been done before,"
said co-author DoKyoung Lee, a Professor of Crop Sciences, a co-PI of
the SMARTFARM project, and also an ASC founding faculty member.

In this work, the researchers approached the problem by evaluating the two steps involved in estimating SOC: (1) deciding where in a field to take
soil samples; and (2) deciding on a statistical rule for calculating an estimate (called an estimator). By using a commercial field in central
Illinois that had been intensively sampled to measure SOC, a variety of strategies could be evaluated for their performance in estimating SOC
in the field.

The researchers found that in a typical Midwestern agricultural field,
they can leverage publicly available soil surveys and satellite imagery
to efficiently select sample locations. This should reduce the number
of samples needed to achieve a given accuracy of SOC quantification by
about 28% compared to selecting sampling locations at random.

"For researchers and agencies monitoring SOC stocks, this study offers a strategy to increase accuracy, supporting cost optimization of sampling methods," said co-author Andrew Margenot, Crop Sciences Assistant
Professor and ASC Associate Director.

"Future studies can use these findings both as a benchmark against which
to compare new SOC stock estimation strategies and as a demonstration
of how to evaluate those strategies," Potash said.

The research team is currently collecting data from many more fields to
test the ability to generalize their findings -- as well as to develop
further improvements to SOC estimation strategies. Team members are
also developing a software tool to make their improved sampling methods available to farmers and researchers.

In addition to Potash, Guan, Lee, and Margenot, co-authors on this
publication include Evan DeLucia, ASC and Professor Emeritus of Plant
Biology; Sheng Wang, ASC and NRES Research Assistant Professor; and
Chunhwa Jang, Crop Sciences Postdoctoral Researcher. Read the full
article in Geoderma >>>

========================================================================== Story Source: Materials provided by University_of_Illinois_at_Urbana-Champaign_Institute_for Sustainability,_Energy,_and_Environment. Original written by April
Wendling.

Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Eric Potash, Kaiyu Guan, Andrew Margenot, DoKyoung Lee, Evan
DeLucia,
Sheng Wang, Chunhwa Jang. How to estimate soil organic
carbon stocks of agricultural fields? Perspectives using
ex-ante evaluation. Geoderma, 2022; 411: 115693 DOI:
10.1016/j.geoderma.2021.115693 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/03/220329100007.htm

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