Effects of farmland landscape pattern on spatial distribution of soil organic carbon in Lower Liaohe Plain of northeastern China

Knowledge of the factors affecting spatial distribution of farmland soil organic carbon (SOC) contribute to a better understanding of the impact of human activities on soil, which is important for improving soil quality and mitigating climate change. Intensive production has brought about significan...

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Bibliographic Details
Main Authors: Xiaochen Liu, Shuangyi Li, Shuai Wang, Zhenxing Bian, Wei Zhou, Chuqiao Wang
Format: Article
Language:English
Published: Elsevier 2022-12-01
Series:Ecological Indicators
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S1470160X22011256
Description
Summary:Knowledge of the factors affecting spatial distribution of farmland soil organic carbon (SOC) contribute to a better understanding of the impact of human activities on soil, which is important for improving soil quality and mitigating climate change. Intensive production has brought about significant changes in farmland landscape pattern. However, the consequences of this change on SOC remains unclear. In this study, using 307 sampling sites of SOC in Lower Liaohe Plain, we mapped the spatial distribution of SOC by Kriging method, and investigated the relationship between topographic and climate factors and SOC, then nine landscape indexes were used as the indicator of human activity and intensity to describe the farmland landscape pattern and analyzed the relationship between landscape pattern and SOC. We observed that SOC was positively related to mean annual precipitation (MAP), negatively related to mean annual temperature (MAT). Flat terrain weakened the relationship between topographic factors and SOC. SOC was positively related to the indexes that represent the complexity of patch shape, and negatively related to indexes that represent the contagion and connectivity degree of patches, indicated that farmland landscape with high connectivity, regular shape and high contagion were not conducive to carbon sequestration. Also, stepwise regression analysis showed that MAP, MAT and DEM contributed the most to SOC variation, these factors could explain 29.3% of the variation in SOC. While added Aggregation Index (AI) could improve the explanation degree by 3.1%, and AI became the strongest factor after natural factors. This study highlights the role of landscape pattern in influencing farmland SOC. The results are useful supplement to the study of farmland SOC and may provide support for SOC sequestration through regulating and controlling landscape pattern.
ISSN:1470-160X