Threshold Soil Moisture Levels Influence Soil CO 2 Emissions: A Machine Learning Approach to Predict Short-Term Soil CO 2 Emissions from Climate-Smart Fields

Anoop Valiya Veettil, Atikur Rahman, Ripendra Awal (), Ali Fares, Timothy R. Green, Binita Thapa and Almoutaz Elhassan
Additional contact information
Anoop Valiya Veettil: Cooperative Agricultural Research Center, College of Agriculture, Food, and Natural Resources, Prairie View A&M University, Prairie View, TX 77446, USA
Atikur Rahman: Cooperative Agricultural Research Center, College of Agriculture, Food, and Natural Resources, Prairie View A&M University, Prairie View, TX 77446, USA
Ripendra Awal: Cooperative Agricultural Research Center, College of Agriculture, Food, and Natural Resources, Prairie View A&M University, Prairie View, TX 77446, USA
Ali Fares: Cooperative Agricultural Research Center, College of Agriculture, Food, and Natural Resources, Prairie View A&M University, Prairie View, TX 77446, USA
Timothy R. Green: Water Management Systems Research Unit, Agricultural Research Service (ARS), United States Department of Agriculture (USDA), Fort Collins, CO 80526, USA
Binita Thapa: Cooperative Agricultural Research Center, College of Agriculture, Food, and Natural Resources, Prairie View A&M University, Prairie View, TX 77446, USA
Almoutaz Elhassan: Cooperative Agricultural Research Center, College of Agriculture, Food, and Natural Resources, Prairie View A&M University, Prairie View, TX 77446, USA

Sustainability, 2025, vol. 17, issue 13, 1-23

Abstract: Machine learning (ML) models are widely used to analyze the spatiotemporal impacts of agricultural practices on environmental sustainability, including the contribution to global greenhouse gas (GHG) emissions. Management practices, such as organic amendment applications, are critical pillars of Climate-smart agriculture (CSA) strategies that mitigate GHG emissions while maintaining adequate crop yields. This study investigated the critical threshold of soil moisture level associated with soil CO 2 emissions from organically amended plots using the classification and regression tree (CART) algorithm. Also, the study predicted the short-term soil CO 2 emissions from organically amended systems using soil moisture and weather variables (i.e., air temperature, relative humidity, and solar radiation) using multilinear regression (MLR) and generalized additive models (GAMs). The different organic amendments considered in this study are biochar (2268 and 4536 kg ha −1 ) and chicken and dairy manure (0, 224, and 448 kg N/ha) under a sweet corn crop in the greater Houston area, Texas. The results of the CART analysis indicated a direct link between soil moisture level and the magnitude of CO 2 flux emission from the amended plots. A threshold of 0.103 m 3 m −3 was calculated for treatment amended by biochar level I (2268 kg ha −1 ) and chicken manure at the N recommended rate (CXBX), indicating that if the soil moisture is less than the 0.103 m 3 m −3 threshold, then the median soil CO 2 emission is 142 kg ha −1 d −1 . Furthermore, applying biochar at a rate of 4536 kg ha −1 reduced the soil CO 2 emissions by 14.5% compared to the control plots. Additionally, the results demonstrate that GAMs outperformed MLR, exhibiting the highest performance under the combined effect of chicken and biochar. We conclude that quantifying soil moisture thresholds will provide valuable information for the sustainable mitigation of soil CO 2 emissions.

Keywords: soil CO 2 emissions; chicken and dairy manure; Biochar; CART; GAM (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/13/6101/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/13/6101/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:13:p:6101-:d:1694037

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().