IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i13p6101-d1694037.html
   My bibliography  Save this article

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Anoop Valiya Veettil & Atikur Rahman & Ripendra Awal & Ali Fares & Timothy R. Green & Binita Thapa & Almoutaz Elhassan, 2025. "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," Sustainability, MDPI, vol. 17(13), pages 1-23, July.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:13:p:6101-:d:1694037
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/13/6101/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/13/6101/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xiaochen Liu & Shuai Wang & Qianlai Zhuang & Xinxin Jin & Zhenxing Bian & Mingyi Zhou & Zhuo Meng & Chunlan Han & Xiaoyu Guo & Wenjuan Jin & Yufei Zhang, 2022. "A Review on Carbon Source and Sink in Arable Land Ecosystems," Land, MDPI, vol. 11(4), pages 1-17, April.
    2. Tanha, Maryam & Mohtar, Rabi H. & Assi, Amjad T. & Awal, Ripendra & Fares, Ali, 2024. "Soil hydrostructural parameters under various soil management practices," Agricultural Water Management, Elsevier, vol. 292(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Prabhat Poudel & Jørgen Ødegaard & Siri Josefine Mo & Rebekka Kaald Andresen & Hans Andre Tandberg & Thomas Cottis & Harald Solberg & Kari Bysveen & Puspa Raj Dulal & Hesam Mousavi & Svein Øivind Solb, 2022. "Italian Ryegrass, Perennial Ryegrass, and Meadow Fescue as Undersown Cover Crops in Spring Wheat and Barley: Results from a Mixed Methods Study in Norway," Sustainability, MDPI, vol. 14(20), pages 1-17, October.
    2. Qiqi Wu & Jijun Meng & Cuiyutong Yang & Likai Zhu, 2025. "Quantifying the Provincial Carbon Emissions of China Embodied in Trade: The Perspective of Land Use," Land, MDPI, vol. 14(4), pages 1-16, April.
    3. Chaochao Du & Xiaoyong Bai & Yangbing Li & Qiu Tan & Cuiwei Zhao & Guangjie Luo & Luhua Wu & Fei Chen & Chaojun Li & Chen Ran & Xuling Luo & Huipeng Xi & Huan Chen & Sirui Zhang & Min Liu & Suhua Gong, 2022. "Inventory of China’s Net Biome Productivity since the 21st Century," Land, MDPI, vol. 11(8), pages 1-16, August.
    4. Cezary A. Kwiatkowski & Małgorzata Pawłowska & Elżbieta Harasim & Lucjan Pawłowski, 2023. "Strategies of Climate Change Mitigation in Agriculture Plant Production—A Critical Review," Energies, MDPI, vol. 16(10), pages 1-27, May.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:17:y:2025:i:13:p:6101-:d:1694037. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.