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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
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    References listed on IDEAS

    as
    1. 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).
    2. 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.
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