Author
Listed:
- Zihao Wu
(Research Center for Transformation Development and Rural Revitalization of Resource-Based Cities in China, China University of Mining and Technology, Xuzhou 221116, China
School of Public Policy & Management, China University of Mining and Technology, Xuzhou 221116, China)
- Yaolin Liu
(School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China)
- Guie Li
(Research Center for Transformation Development and Rural Revitalization of Resource-Based Cities in China, China University of Mining and Technology, Xuzhou 221116, China
School of Public Policy & Management, China University of Mining and Technology, Xuzhou 221116, China)
- Yiran Han
(School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China)
- Xiaoshun Li
(Research Center for Transformation Development and Rural Revitalization of Resource-Based Cities in China, China University of Mining and Technology, Xuzhou 221116, China
School of Public Policy & Management, China University of Mining and Technology, Xuzhou 221116, China)
- Yiyun Chen
(School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China
State Key Laboratory of Soil and Sustainable Agriculture, Chinese Academy of Sciences, Nanjing 210008, China)
Abstract
Farmland is one of the most important and active components of the soil carbon pool. Exploring the controlling factors of farmland soil organic carbon density (SOCD) and its sequestration rate (SOCDSR) is vital for improving carbon sequestration and addressing climate change. Present studies provide considerable attention to the impacts of natural factors and agricultural management on SOCD and SOCDSR. However, few of them focus on the interaction effects of environmental variables on SOCD and SOCDSR. Therefore, using 64 samples collected from 19 agricultural stations in China, this study explored the effects of natural factors, human activities, and their interactions on farmland SOCD and SOCDSR by using geographical detector methods. Results of geographical detectors showed that SOCD was associated with natural factors, including groundwater depth, soil type, clay content, mean annual temperature (MAT), and mean annual precipitation. SOCDSR was related to natural factors and agricultural management, including MAT, groundwater depth, fertilization, and their interactions. Interaction effects existed in all environmental variable pairs, and the explanatory power of interaction effects was often greater than that of the sum of two single variables. Specifically, the interaction effect of soil type and MAT explained 74.8% of the variation in SOCD, and further investigation revealed that SOCD was highest in Luvisols and was under a low MAT (<6 °C). The interaction effect of groundwater depth and fertilization explained 40.4% of the variation in SOCDSR, and fertilization was conducive to SOCD increase at a high groundwater depth (<3 m). These findings suggest that low soil temperature, high soil moisture, and fertilization are conducive to soil carbon accumulation. These findings also highlight the importance of agricultural management and interaction effects in explaining SOCD and SOCDSR, which promote our knowledge to better understand the variation of SOCD and its dynamics.
Suggested Citation
Zihao Wu & Yaolin Liu & Guie Li & Yiran Han & Xiaoshun Li & Yiyun Chen, 2022.
"Influences of Environmental Variables and Their Interactions on Chinese Farmland Soil Organic Carbon Density and Its Dynamics,"
Land, MDPI, vol. 11(2), pages 1-16, January.
Handle:
RePEc:gam:jlands:v:11:y:2022:i:2:p:208-:d:737438
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Cited by:
- Artur Łopatka & Grzegorz Siebielec & Radosław Kaczyński & Tomasz Stuczyński, 2023.
"Analysis of Soil Carbon Stock Dynamics by Machine Learning—Polish Case Study,"
Land, MDPI, vol. 12(8), pages 1-14, August.
- Wirat Krasachat, 2023.
"The Effect of Good Agricultural Practices on the Technical Efficiency of Chili Production in Thailand,"
Sustainability, MDPI, vol. 15(1), pages 1-25, January.
- Fuat Kaya & Ali Keshavarzi & Rosa Francaviglia & Gordana Kaplan & Levent Başayiğit & Mert Dedeoğlu, 2022.
"Assessing Machine Learning-Based Prediction under Different Agricultural Practices for Digital Mapping of Soil Organic Carbon and Available Phosphorus,"
Agriculture, MDPI, vol. 12(7), pages 1-27, July.
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